{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "anonymous-address",
   "metadata": {},
   "source": [
    "# Triplet STDP synapse tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cordless-convenience",
   "metadata": {},
   "source": [
    "In this tutorial, we will learn to formulate triplet rule (which considers sets of three spikes, i.e., two presynaptic and one postsynaptic spikes or two postsynaptic and one presynaptic spikes) for Spike Timing-Dependent Plasticity (STDP) learning model using NESTML and simulate it with NEST simulator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "orange-zambia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "              -- N E S T --\n",
      "  Copyright (C) 2004 The NEST Initiative\n",
      "\n",
      " Version: 3.6.0-post0.dev0\n",
      " Built: Mar 26 2024 08:52:51\n",
      "\n",
      " This program is provided AS IS and comes with\n",
      " NO WARRANTY. See the file LICENSE for details.\n",
      "\n",
      " Problems or suggestions?\n",
      "   Visit https://www.nest-simulator.org\n",
      "\n",
      " Type 'nest.help()' to find out more about NEST.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/charl/.local/lib/python3.11/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n",
      "  warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import nest\n",
    "import numpy as np\n",
    "import os\n",
    "import re\n",
    "\n",
    "from pynestml.codegeneration.nest_code_generator_utils import NESTCodeGeneratorUtils\n",
    "\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "understanding-commissioner",
   "metadata": {},
   "source": [
    "Early experiments in Bi and Poo (1998) [1] have shown that a sequence of $n$ pairs of \"pre then post\" spikes result in synaptic potentiation and $n$ pairs of \"post then pre\" result in synaptic depression. Later experiments have shown that these pairs of spikes do not necessarily describe the synaptic plasiticity behavior. Other variables like calcium concentration or postsynaptic membrane potential play an important role in potentiation or depression.\n",
    "\n",
    "Experiments conducted by Wang et al., [2] and Sjöström et al., [3] using triplet and quadruplets of spikes show that the classical spike-dependent synaptic plasticity alone cannot explain the results of the experiments. The triplet STDP model formulated by Pfister and Gerstner in [4] assume that a combination of pairs and triplets of spikes triggers the synaptic plasticity and thus reproduce the experimental results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "integrated-swimming",
   "metadata": {},
   "source": [
    "## Triplet STDP model"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "inside-south",
   "metadata": {},
   "source": [
    "The synaptic transmission is formulated by a set of four differential equations as defined below.\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{dr_1}{dt} = -\\frac{r_1(t)}{\\tau_+} \\quad\n",
    "\\text{if } t = t^{pre}, \\text{ then } r_1 \\rightarrow r_1+1\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{dr_2}{dt} = -\\frac{r_2(t)}{\\tau_x} \\quad\n",
    "\\text{if } t = t^{pre}, \\text{ then } r_2 \\rightarrow r_2+1\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{do_1}{dt} = -\\frac{o_1(t)}{\\tau_-} \\quad\n",
    "\\text{if } t = t^{post}, \\text{ then } o_1 \\rightarrow o_1+1\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{do_2}{dt} = -\\frac{o_2(t)}{\\tau_y} \\quad\n",
    "\\text{if } t = t^{post}, \\text{ then } o_2 \\rightarrow o_2+1\n",
    "\\end{equation}\n",
    "\n",
    "Here, $r_1$ and $r_2$ are the trace variables that detect the presynaptic events when a spike occurs at the presynaptic neuron at time $t^{pre}$. These variables increase when a presynaptic spike occurs and decrease back to 0 otherwise with time constants $\\tau_+$ and $\\tau_x$ respectively. Similarly, the variables $o_1$ and $o_2$ denote the trace variables that detect the postsynaptic events at a spike time $t^{post}$. In the absence of a postsynaptic spike, the vairables decrease their value with a time constant $\\tau_-$ and $\\tau_y$ respectively. Presynaptic variables, for example, can be the amount of glutamate bound and the postsynaptic receptors can determine the influx of calcium concentration through voltage-gated $Ca^{2+}$ channels.\n",
    "\n",
    "The weight change in the model is formulated as a function of the four trace variables defined above. The weight of the synapse decreases after the presynaptic spikes arrives at $t^{pre}$ by an amount proportional to the postsynaptic variable $o_1$ but also depends on the second presynaptic variable $r_2$.\n",
    "\n",
    "\\begin{equation}\n",
    "w(t) \\rightarrow w(t) - o_1(t)[A_2^- + A_3^- r_2(t - \\epsilon)] \\quad \\text{if } t = t^{pre}\n",
    "\\end{equation}\n",
    "\n",
    "Similarly, a postsynaptic spike at time $t^{post}$ increases the weight of the synapse and is dependent on the presynaptic variable $r_1$ and the postsynaptic variable $o_2$.\n",
    "\n",
    "\\begin{equation}\n",
    "w(t) \\rightarrow w(t) + r_1(t)[A_2^+ + A_3^+ o_2(t - \\epsilon)] \\quad \\text{if } t = t^{post}\n",
    "\\end{equation}\n",
    "\n",
    "Here, $A_2^+$ and $A_2^-$ denote the amplitude of the weight change whenever there is a pre-post pair or a post-pre pair. Similarly, $A_3^+$ and $A_3^-$ denote the amplitude of the triplet term for potentiation and depression, respectively.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collective-stuff",
   "metadata": {},
   "source": [
    "## Generating code with NESTML\n",
    "\n",
    "### Triplet STDP model in NESTML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "basic-given",
   "metadata": {},
   "source": [
    "In this tutorial, we will use a very simple integrate-and-fire model, where arriving spikes cause an instantaneous increment of the membrane potential, the \"iaf_psc_delta\" model. It is already provided with NESTML in the `models/neurons` directory."
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "ruled-myrtle",
   "metadata": {},
   "source": [
    "We will be formulating two types of interactions between the spikes, namely All-to-All interaction and Nearest-spike interaction.\n",
    "\n",
    "$\\textit{All-to-All Interaction}$ - Each postsynaptic spike interacts with all previous postsynaptic spikes and vice versa. All the internal variables $r_1, r_2, o_1,$ and $o_2$ accumulate over several postsynaptic spike timimgs.\n",
    "\n",
    "<div>\n",
    "<img src=\"attachment:image-2.png\" width=\"400\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "thorough-halifax",
   "metadata": {},
   "outputs": [],
   "source": [
    "nestml_triplet_stdp_model = '''\n",
    "model stdp_triplet_synapse:\n",
    "\n",
    "    state:\n",
    "        w nS = 1 nS\n",
    "\n",
    "        tr_r1 real = 0.\n",
    "        tr_r2 real = 0.\n",
    "        tr_o1 real = 0.\n",
    "        tr_o2 real = 0.\n",
    "\n",
    "    parameters:\n",
    "        d ms = 1 ms\n",
    "\n",
    "        tau_plus ms = 16.8 ms  # time constant for tr_r1\n",
    "        tau_x ms = 101 ms  # time constant for tr_r2\n",
    "        tau_minus ms = 33.7 ms  # time constant for tr_o1\n",
    "        tau_y ms = 125 ms  # time constant for tr_o2\n",
    "\n",
    "        A2_plus real = 7.5e-10\n",
    "        A3_plus real = 9.3e-3\n",
    "        A2_minus real = 7e-3\n",
    "        A3_minus real = 2.3e-4\n",
    "\n",
    "        Wmax nS = 100 nS\n",
    "        Wmin nS = 0 nS\n",
    "\n",
    "    equations:\n",
    "        tr_r1' = -tr_r1 / tau_plus\n",
    "        tr_r2' = -tr_r2 / tau_x\n",
    "        tr_o1' = -tr_o1 / tau_minus\n",
    "        tr_o2' = -tr_o2 / tau_y\n",
    "\n",
    "    input:\n",
    "        pre_spikes <- spike\n",
    "        post_spikes <- spike\n",
    "\n",
    "    output:\n",
    "        spike(weight real, delay ms)\n",
    "\n",
    "    onReceive(post_spikes):\n",
    "        # increment post trace values\n",
    "        tr_o1 += 1\n",
    "        tr_o2 += 1\n",
    "\n",
    "        # potentiate synapse\n",
    "        w_ nS = w + tr_r1 * ( A2_plus + A3_plus * tr_o2 )\n",
    "        w = min(Wmax, w_)\n",
    "\n",
    "    onReceive(pre_spikes):\n",
    "        # increment pre trace values\n",
    "        tr_r1 += 1\n",
    "        tr_r2 += 1\n",
    "\n",
    "        # depress synapse\n",
    "        w_ nS = w  -  tr_o1 * ( A2_minus + A3_minus * tr_r2 )\n",
    "        w = max(Wmin, w_)\n",
    "\n",
    "        # deliver spike to postsynaptic partner\n",
    "        emit_spike(w, d)\n",
    "        \n",
    "    update:\n",
    "        integrate_odes()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "colonial-serve",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,GLOBAL, INFO]: List of files that will be processed:\n",
      "[2,GLOBAL, INFO]: /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/iaf_psc_delta_neuron.nestml\n",
      "[3,GLOBAL, INFO]: /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/stdp_triplet_synapse.nestml\n",
      "[4,GLOBAL, INFO]: Creating target directory: '/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target'\n",
      "[5,GLOBAL, INFO]: Target platform code will be installed in directory: '/tmp/nestml_target_zo8wlcs5'\n",
      "\n",
      "              -- N E S T --\n",
      "  Copyright (C) 2004 The NEST Initiative\n",
      "\n",
      " Version: 3.6.0-post0.dev0\n",
      " Built: Mar 26 2024 08:52:51\n",
      "\n",
      " This program is provided AS IS and comes with\n",
      " NO WARRANTY. See the file LICENSE for details.\n",
      "\n",
      " Problems or suggestions?\n",
      "   Visit https://www.nest-simulator.org\n",
      "\n",
      " Type 'nest.help()' to find out more about NEST.\n",
      "\n",
      "[6,GLOBAL, INFO]: The NEST Simulator version was automatically detected as: master\n",
      "[7,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-nest-delay/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n",
      "[8,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-nest-delay/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n",
      "[9,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-nest-delay/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n",
      "[10,GLOBAL, INFO]: The NEST Simulator installation path was automatically detected as: /home/charl/julich/nest-simulator-install\n",
      "[11,GLOBAL, INFO]: Start processing '/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/iaf_psc_delta_neuron.nestml'!\n",
      "[12,iaf_psc_delta_neuron_nestml, DEBUG, [43:0;94:0]]: Start building symbol table!\n",
      "[13,iaf_psc_delta_neuron_nestml, INFO, [51:79;51:79]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n",
      "[14,iaf_psc_delta_neuron_nestml, INFO, [51:15;51:74]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n",
      "[15,GLOBAL, INFO]: Start processing '/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/stdp_triplet_synapse.nestml'!\n",
      "[16,stdp_triplet_synapse_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n",
      "[17,stdp_triplet_synapse_nestml, WARNING, [13:8;13:17]]: Variable 'd' has the same name as a physical unit!\n",
      "[18,stdp_triplet_synapse_nestml, INFO, [43:17;43:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[19,stdp_triplet_synapse_nestml, INFO, [44:17;44:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[20,stdp_triplet_synapse_nestml, WARNING, [47:16;47:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n",
      "[21,stdp_triplet_synapse_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[22,stdp_triplet_synapse_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[23,stdp_triplet_synapse_nestml, WARNING, [56:16;56:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n",
      "[24,iaf_psc_delta_neuron_nestml, DEBUG, [43:0;94:0]]: Start building symbol table!\n",
      "[25,stdp_triplet_synapse_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n",
      "[26,stdp_triplet_synapse_nestml, WARNING, [13:8;13:17]]: Variable 'd' has the same name as a physical unit!\n",
      "[27,stdp_triplet_synapse_nestml, INFO, [43:17;43:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[28,stdp_triplet_synapse_nestml, INFO, [44:17;44:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[29,stdp_triplet_synapse_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[30,stdp_triplet_synapse_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[31,iaf_psc_delta_neuron_nestml, DEBUG, [43:0;94:0]]: Start building symbol table!\n",
      "[32,iaf_psc_delta_neuron_nestml, INFO, [51:79;51:79]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n",
      "[33,iaf_psc_delta_neuron_nestml, INFO, [51:15;51:74]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n",
      "[34,stdp_triplet_synapse_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n",
      "[35,stdp_triplet_synapse_nestml, WARNING, [13:8;13:17]]: Variable 'd' has the same name as a physical unit!\n",
      "[36,stdp_triplet_synapse_nestml, INFO, [43:17;43:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[37,stdp_triplet_synapse_nestml, INFO, [44:17;44:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[38,stdp_triplet_synapse_nestml, WARNING, [47:16;47:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n",
      "[39,stdp_triplet_synapse_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[40,stdp_triplet_synapse_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[41,stdp_triplet_synapse_nestml, WARNING, [56:16;56:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n",
      "[42,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['tr_o1', 'tr_o2']\n",
      "[43,GLOBAL, INFO]: Parameters that will be copied from synapse to neuron: ['tau_minus', 'tau_y']\n",
      "[44,GLOBAL, INFO]: Moving state var defining equation(s) tr_o1\n",
      "[45,GLOBAL, INFO]: Moving state var defining equation(s) tr_o2\n",
      "[46,GLOBAL, INFO]: Moving state variables for equation(s) tr_o1\n",
      "[47,GLOBAL, INFO]: Moving definition of tr_o1 from synapse to neuron\n",
      "[48,GLOBAL, INFO]: Moving state variables for equation(s) tr_o2\n",
      "[49,GLOBAL, INFO]: Moving definition of tr_o2 from synapse to neuron\n",
      "[50,GLOBAL, INFO]: \tMoving statement # increment post trace values\n",
      "tr_o1 += 1\n",
      "[51,GLOBAL, INFO]: \tMoving statement tr_o2 += 1\n",
      "[52,GLOBAL, INFO]: In synapse: replacing ``continuous`` type input ports that are connected to postsynaptic neuron with suffixed external variable references\n",
      "[53,GLOBAL, INFO]: Copying parameters from synapse to neuron...\n",
      "[54,GLOBAL, INFO]: Copying definition of tau_minus from synapse to neuron\n",
      "[55,GLOBAL, INFO]: Copying definition of tau_y from synapse to neuron\n",
      "[56,GLOBAL, INFO]: Adding suffix to variables in spike updates\n",
      "[57,GLOBAL, INFO]: In synapse: replacing variables with suffixed external variable references\n",
      "[58,GLOBAL, INFO]: \t• Replacing variable tr_o1\n",
      "[59,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = tr_o1__for_stdp_triplet_synapse_nestml) in expression: tr_o1 * (A2_minus + A3_minus * tr_r2)\n",
      "[60,GLOBAL, INFO]: \t• Replacing variable tr_o2\n",
      "[61,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = tr_o2__for_stdp_triplet_synapse_nestml) in expression: A3_plus * tr_o2\n",
      "[62,iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml, DEBUG, [43:0;94:0]]: Start building symbol table!\n",
      "[63,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n",
      "[64,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [13:8;13:17]]: Variable 'd' has the same name as a physical unit!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Analysing input:\n",
      "INFO:{\n",
      "    \"dynamics\": [\n",
      "        {\n",
      "            \"expression\": \"V_m' = (-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\",\n",
      "            \"initial_values\": {\n",
      "                \"V_m\": \"E_L\"\n",
      "            }\n",
      "        }\n",
      "    ],\n",
      "    \"options\": {\n",
      "        \"output_timestep_symbol\": \"__h\"\n",
      "    },\n",
      "    \"parameters\": {\n",
      "        \"C_m\": \"250\",\n",
      "        \"E_L\": \"(-70)\",\n",
      "        \"I_e\": \"0\",\n",
      "        \"V_min\": \"(-oo) * 1\",\n",
      "        \"V_reset\": \"(-70)\",\n",
      "        \"V_th\": \"(-55)\",\n",
      "        \"refr_T\": \"2\",\n",
      "        \"tau_m\": \"10\",\n",
      "        \"tau_syn\": \"2\"\n",
      "    }\n",
      "}\n",
      "INFO:Processing global options...\n",
      "INFO:Processing input shapes...\n",
      "INFO:\n",
      "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\"\n",
      "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + I_stim)/C_m (symbols [V_m])\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_m]])\n",
      "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m\n",
      "DEBUG:\tnonlinear term: I_stim/C_m\n",
      "DEBUG:Created Shape with symbol V_m, derivative_factors = [-1/tau_m], inhom_term = E_L/tau_m + I_e/C_m, nonlin_term = I_stim/C_m\n",
      "INFO:\tReturning shape: Shape \"V_m\" of order 1\n",
      "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n",
      "INFO:All known variables: [V_m], all parameters used in ODEs: {E_L, I_e, I_stim, C_m, tau_m}\n",
      "INFO:No numerical value specified for parameter \"I_stim\"\n",
      "INFO:\n",
      "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\"\n",
      "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + I_stim)/C_m (symbols [V_m, V_m])\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [0]])\n",
      "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Created Shape with symbol V_m, derivative_factors = [-1/tau_m], inhom_term = E_L/tau_m + I_e/C_m + I_stim/C_m, nonlin_term = 0\n",
      "INFO:\tReturning shape: Shape \"V_m\" of order 1\n",
      "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n",
      "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m (symbols Matrix([[V_m]]))\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_m]])\n",
      "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Initializing system of shapes with x = Matrix([[V_m]]), A = Matrix([[-1/tau_m]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m]]), c = Matrix([[0]])\n",
      "INFO:Finding analytically solvable equations...\n",
      "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph.dot\n",
      "DEBUG:os.makedirs('/tmp')\n",
      "DEBUG:write lines to '/tmp/ode_dependency_graph.dot'\n",
      "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph.dot']\n",
      "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n",
      "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m (symbols [V_m])\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_m]])\n",
      "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot\n",
      "DEBUG:os.makedirs('/tmp')\n",
      "DEBUG:write lines to '/tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot'\n",
      "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph_analytically_solvable_before_propagated.dot']\n",
      "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable.dot\n",
      "DEBUG:os.makedirs('/tmp')\n",
      "DEBUG:write lines to '/tmp/ode_dependency_graph_analytically_solvable.dot'\n",
      "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph_analytically_solvable.dot']\n",
      "INFO:Generating propagators for the following symbols: V_m\n",
      "DEBUG:Initializing system of shapes with x = Matrix([[V_m]]), A = Matrix([[-1/tau_m]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m]]), c = Matrix([[0]])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[65,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[66,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[67,GLOBAL, INFO]: Successfully constructed neuron-synapse pair iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml, stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml\n",
      "[68,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_delta_neuron_nestml'\n",
      "[69,iaf_psc_delta_neuron_nestml, INFO, [43:0;94:0]]: Starts processing of the model 'iaf_psc_delta_neuron_nestml'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:System of equations:\n",
      "DEBUG:x = Matrix([[V_m]])\n",
      "DEBUG:A = Matrix([[-1/tau_m]])\n",
      "DEBUG:b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m]])\n",
      "DEBUG:c = Matrix([[0]])\n",
      "INFO:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\n",
      "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n",
      "INFO:In ode-toolbox: returning outdict = \n",
      "INFO:[\n",
      "    {\n",
      "        \"initial_values\": {\n",
      "            \"V_m\": \"E_L\"\n",
      "        },\n",
      "        \"parameters\": {\n",
      "            \"C_m\": \"250.000000000000\",\n",
      "            \"E_L\": \"-70.0000000000000\",\n",
      "            \"I_e\": \"0\",\n",
      "            \"tau_m\": \"10.0000000000000\"\n",
      "        },\n",
      "        \"propagators\": {\n",
      "            \"__P__V_m__V_m\": \"exp(-__h/tau_m)\"\n",
      "        },\n",
      "        \"solver\": \"analytical\",\n",
      "        \"state_variables\": [\n",
      "            \"V_m\"\n",
      "        ],\n",
      "        \"update_expressions\": {\n",
      "            \"V_m\": \"-E_L*__P__V_m__V_m + E_L + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\"\n",
      "        }\n",
      "    }\n",
      "]\n",
      "INFO:Analysing input:\n",
      "INFO:{\n",
      "    \"dynamics\": [\n",
      "        {\n",
      "            \"expression\": \"V_m' = (-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\",\n",
      "            \"initial_values\": {\n",
      "                \"V_m\": \"E_L\"\n",
      "            }\n",
      "        },\n",
      "        {\n",
      "            \"expression\": \"tr_o1__for_stdp_triplet_synapse_nestml' = (-tr_o1__for_stdp_triplet_synapse_nestml) / tau_minus__for_stdp_triplet_synapse_nestml\",\n",
      "            \"initial_values\": {\n",
      "                \"tr_o1__for_stdp_triplet_synapse_nestml\": \"0.0\"\n",
      "            }\n",
      "        },\n",
      "        {\n",
      "            \"expression\": \"tr_o2__for_stdp_triplet_synapse_nestml' = (-tr_o2__for_stdp_triplet_synapse_nestml) / tau_y__for_stdp_triplet_synapse_nestml\",\n",
      "            \"initial_values\": {\n",
      "                \"tr_o2__for_stdp_triplet_synapse_nestml\": \"0.0\"\n",
      "            }\n",
      "        }\n",
      "    ],\n",
      "    \"options\": {\n",
      "        \"output_timestep_symbol\": \"__h\"\n",
      "    },\n",
      "    \"parameters\": {\n",
      "        \"C_m\": \"250\",\n",
      "        \"E_L\": \"(-70)\",\n",
      "        \"I_e\": \"0\",\n",
      "        \"V_min\": \"(-oo) * 1\",\n",
      "        \"V_reset\": \"(-70)\",\n",
      "        \"V_th\": \"(-55)\",\n",
      "        \"refr_T\": \"2\",\n",
      "        \"tau_m\": \"10\",\n",
      "        \"tau_minus__for_stdp_triplet_synapse_nestml\": \"33.7\",\n",
      "        \"tau_syn\": \"2\",\n",
      "        \"tau_y__for_stdp_triplet_synapse_nestml\": \"125\"\n",
      "    }\n",
      "}\n",
      "INFO:Processing global options...\n",
      "INFO:Processing input shapes...\n",
      "INFO:\n",
      "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\"\n",
      "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + I_stim)/C_m (symbols [V_m])\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_m]])\n",
      "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m\n",
      "DEBUG:\tnonlinear term: I_stim/C_m\n",
      "DEBUG:Created Shape with symbol V_m, derivative_factors = [-1/tau_m], inhom_term = E_L/tau_m + I_e/C_m, nonlin_term = I_stim/C_m\n",
      "INFO:\tReturning shape: Shape \"V_m\" of order 1\n",
      "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n",
      "INFO:\n",
      "Processing differential-equation form shape tr_o1__for_stdp_triplet_synapse_nestml with defining expression = \"(-tr_o1__for_stdp_triplet_synapse_nestml) / tau_minus__for_stdp_triplet_synapse_nestml\"\n",
      "DEBUG:Splitting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml (symbols [tr_o1__for_stdp_triplet_synapse_nestml])\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_minus__for_stdp_triplet_synapse_nestml]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Created Shape with symbol tr_o1__for_stdp_triplet_synapse_nestml, derivative_factors = [-1/tau_minus__for_stdp_triplet_synapse_nestml], inhom_term = 0.0, nonlin_term = 0.0\n",
      "INFO:\tReturning shape: Shape \"tr_o1__for_stdp_triplet_synapse_nestml\" of order 1\n",
      "INFO:Shape tr_o1__for_stdp_triplet_synapse_nestml: reconstituting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml\n",
      "INFO:\n",
      "Processing differential-equation form shape tr_o2__for_stdp_triplet_synapse_nestml with defining expression = \"(-tr_o2__for_stdp_triplet_synapse_nestml) / tau_y__for_stdp_triplet_synapse_nestml\"\n",
      "DEBUG:Splitting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml (symbols [tr_o2__for_stdp_triplet_synapse_nestml])\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_y__for_stdp_triplet_synapse_nestml]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Created Shape with symbol tr_o2__for_stdp_triplet_synapse_nestml, derivative_factors = [-1/tau_y__for_stdp_triplet_synapse_nestml], inhom_term = 0.0, nonlin_term = 0.0\n",
      "INFO:\tReturning shape: Shape \"tr_o2__for_stdp_triplet_synapse_nestml\" of order 1\n",
      "INFO:Shape tr_o2__for_stdp_triplet_synapse_nestml: reconstituting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml\n",
      "INFO:All known variables: [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml], all parameters used in ODEs: {E_L, I_e, I_stim, tau_minus__for_stdp_triplet_synapse_nestml, tau_y__for_stdp_triplet_synapse_nestml, C_m, tau_m}\n",
      "INFO:No numerical value specified for parameter \"I_stim\"\n",
      "INFO:\n",
      "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\"\n",
      "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + I_stim)/C_m (symbols [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml, V_m])\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [0], [0], [0]])\n",
      "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Created Shape with symbol V_m, derivative_factors = [-1/tau_m], inhom_term = E_L/tau_m + I_e/C_m + I_stim/C_m, nonlin_term = 0\n",
      "INFO:\tReturning shape: Shape \"V_m\" of order 1\n",
      "INFO:\n",
      "Processing differential-equation form shape tr_o1__for_stdp_triplet_synapse_nestml with defining expression = \"(-tr_o1__for_stdp_triplet_synapse_nestml) / tau_minus__for_stdp_triplet_synapse_nestml\"\n",
      "DEBUG:Splitting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml (symbols [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml, V_m, tr_o1__for_stdp_triplet_synapse_nestml])\n",
      "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_minus__for_stdp_triplet_synapse_nestml], [0], [0], [0]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Created Shape with symbol tr_o1__for_stdp_triplet_synapse_nestml, derivative_factors = [-1/tau_minus__for_stdp_triplet_synapse_nestml], inhom_term = 0.0, nonlin_term = 0\n",
      "INFO:\tReturning shape: Shape \"tr_o1__for_stdp_triplet_synapse_nestml\" of order 1\n",
      "INFO:\n",
      "Processing differential-equation form shape tr_o2__for_stdp_triplet_synapse_nestml with defining expression = \"(-tr_o2__for_stdp_triplet_synapse_nestml) / tau_y__for_stdp_triplet_synapse_nestml\"\n",
      "DEBUG:Splitting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml (symbols [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml, V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml])\n",
      "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_y__for_stdp_triplet_synapse_nestml], [0], [0], [0]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Created Shape with symbol tr_o2__for_stdp_triplet_synapse_nestml, derivative_factors = [-1/tau_y__for_stdp_triplet_synapse_nestml], inhom_term = 0.0, nonlin_term = 0\n",
      "INFO:\tReturning shape: Shape \"tr_o2__for_stdp_triplet_synapse_nestml\" of order 1\n",
      "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n",
      "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m (symbols Matrix([[V_m], [tr_o1__for_stdp_triplet_synapse_nestml], [tr_o2__for_stdp_triplet_synapse_nestml]]))\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [0], [0]])\n",
      "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "INFO:Shape tr_o1__for_stdp_triplet_synapse_nestml: reconstituting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml\n",
      "DEBUG:Splitting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml (symbols Matrix([[V_m], [tr_o1__for_stdp_triplet_synapse_nestml], [tr_o2__for_stdp_triplet_synapse_nestml]]))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_minus__for_stdp_triplet_synapse_nestml], [0]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "INFO:Shape tr_o2__for_stdp_triplet_synapse_nestml: reconstituting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml\n",
      "DEBUG:Splitting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml (symbols Matrix([[V_m], [tr_o1__for_stdp_triplet_synapse_nestml], [tr_o2__for_stdp_triplet_synapse_nestml]]))\n",
      "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_y__for_stdp_triplet_synapse_nestml]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [tr_o1__for_stdp_triplet_synapse_nestml], [tr_o2__for_stdp_triplet_synapse_nestml]]), A = Matrix([[-1/tau_m, 0, 0], [0, -1/tau_minus__for_stdp_triplet_synapse_nestml, 0], [0, 0, -1/tau_y__for_stdp_triplet_synapse_nestml]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0], [0]]), c = Matrix([[0], [0], [0]])\n",
      "INFO:Finding analytically solvable equations...\n",
      "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph.dot\n",
      "DEBUG:os.makedirs('/tmp')\n",
      "DEBUG:write lines to '/tmp/ode_dependency_graph.dot'\n",
      "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph.dot']\n",
      "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n",
      "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m (symbols [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml])\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [0], [0]])\n",
      "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "INFO:Shape tr_o1__for_stdp_triplet_synapse_nestml: reconstituting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml\n",
      "DEBUG:Splitting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml (symbols [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml])\n",
      "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_minus__for_stdp_triplet_synapse_nestml], [0]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "INFO:Shape tr_o2__for_stdp_triplet_synapse_nestml: reconstituting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml\n",
      "DEBUG:Splitting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml (symbols [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml])\n",
      "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_y__for_stdp_triplet_synapse_nestml]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot\n",
      "DEBUG:os.makedirs('/tmp')\n",
      "DEBUG:write lines to '/tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot'\n",
      "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph_analytically_solvable_before_propagated.dot']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[70,iaf_psc_delta_neuron_nestml, DEBUG, [43:0;94:0]]: Start building symbol table!\n",
      "[71,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml'\n",
      "[72,iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml, INFO, [43:0;94:0]]: Starts processing of the model 'iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable.dot\n",
      "DEBUG:os.makedirs('/tmp')\n",
      "DEBUG:write lines to '/tmp/ode_dependency_graph_analytically_solvable.dot'\n",
      "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph_analytically_solvable.dot']\n",
      "INFO:Generating propagators for the following symbols: V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml\n",
      "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [tr_o1__for_stdp_triplet_synapse_nestml], [tr_o2__for_stdp_triplet_synapse_nestml]]), A = Matrix([[-1/tau_m, 0, 0], [0, -1/tau_minus__for_stdp_triplet_synapse_nestml, 0], [0, 0, -1/tau_y__for_stdp_triplet_synapse_nestml]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0], [0]]), c = Matrix([[0], [0], [0]])\n",
      "DEBUG:System of equations:\n",
      "DEBUG:x = Matrix([[V_m], [tr_o1__for_stdp_triplet_synapse_nestml], [tr_o2__for_stdp_triplet_synapse_nestml]])\n",
      "DEBUG:A = Matrix([\n",
      "[-1/tau_m,                                             0,                                         0],\n",
      "[       0, -1/tau_minus__for_stdp_triplet_synapse_nestml,                                         0],\n",
      "[       0,                                             0, -1/tau_y__for_stdp_triplet_synapse_nestml]])\n",
      "DEBUG:b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0], [0]])\n",
      "DEBUG:c = Matrix([[0], [0], [0]])\n",
      "INFO:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\n",
      "INFO:update_expr[tr_o1__for_stdp_triplet_synapse_nestml] = __P__tr_o1__for_stdp_triplet_synapse_nestml__tr_o1__for_stdp_triplet_synapse_nestml*tr_o1__for_stdp_triplet_synapse_nestml\n",
      "INFO:update_expr[tr_o2__for_stdp_triplet_synapse_nestml] = __P__tr_o2__for_stdp_triplet_synapse_nestml__tr_o2__for_stdp_triplet_synapse_nestml*tr_o2__for_stdp_triplet_synapse_nestml\n",
      "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n",
      "WARNING:Not preserving expression for variable \"tr_o1__for_stdp_triplet_synapse_nestml\" as it is solved by propagator solver\n",
      "WARNING:Not preserving expression for variable \"tr_o2__for_stdp_triplet_synapse_nestml\" as it is solved by propagator solver\n",
      "INFO:In ode-toolbox: returning outdict = \n",
      "INFO:[\n",
      "    {\n",
      "        \"initial_values\": {\n",
      "            \"V_m\": \"E_L\",\n",
      "            \"tr_o1__for_stdp_triplet_synapse_nestml\": \"0.0\",\n",
      "            \"tr_o2__for_stdp_triplet_synapse_nestml\": \"0.0\"\n",
      "        },\n",
      "        \"parameters\": {\n",
      "            \"C_m\": \"250.000000000000\",\n",
      "            \"E_L\": \"-70.0000000000000\",\n",
      "            \"I_e\": \"0\",\n",
      "            \"tau_m\": \"10.0000000000000\",\n",
      "            \"tau_minus__for_stdp_triplet_synapse_nestml\": \"33.7000000000000\",\n",
      "            \"tau_y__for_stdp_triplet_synapse_nestml\": \"125.000000000000\"\n",
      "        },\n",
      "        \"propagators\": {\n",
      "            \"__P__V_m__V_m\": \"exp(-__h/tau_m)\",\n",
      "            \"__P__tr_o1__for_stdp_triplet_synapse_nestml__tr_o1__for_stdp_triplet_synapse_nestml\": \"exp(-__h/tau_minus__for_stdp_triplet_synapse_nestml)\",\n",
      "            \"__P__tr_o2__for_stdp_triplet_synapse_nestml__tr_o2__for_stdp_triplet_synapse_nestml\": \"exp(-__h/tau_y__for_stdp_triplet_synapse_nestml)\"\n",
      "        },\n",
      "        \"solver\": \"analytical\",\n",
      "        \"state_variables\": [\n",
      "            \"V_m\",\n",
      "            \"tr_o1__for_stdp_triplet_synapse_nestml\",\n",
      "            \"tr_o2__for_stdp_triplet_synapse_nestml\"\n",
      "        ],\n",
      "        \"update_expressions\": {\n",
      "            \"V_m\": \"-E_L*__P__V_m__V_m + E_L + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\",\n",
      "            \"tr_o1__for_stdp_triplet_synapse_nestml\": \"__P__tr_o1__for_stdp_triplet_synapse_nestml__tr_o1__for_stdp_triplet_synapse_nestml*tr_o1__for_stdp_triplet_synapse_nestml\",\n",
      "            \"tr_o2__for_stdp_triplet_synapse_nestml\": \"__P__tr_o2__for_stdp_triplet_synapse_nestml__tr_o2__for_stdp_triplet_synapse_nestml*tr_o2__for_stdp_triplet_synapse_nestml\"\n",
      "        }\n",
      "    }\n",
      "]\n",
      "INFO:Analysing input:\n",
      "INFO:{\n",
      "    \"dynamics\": [\n",
      "        {\n",
      "            \"expression\": \"tr_r1' = (-tr_r1) / tau_plus\",\n",
      "            \"initial_values\": {\n",
      "                \"tr_r1\": \"0.0\"\n",
      "            }\n",
      "        },\n",
      "        {\n",
      "            \"expression\": \"tr_r2' = (-tr_r2) / tau_x\",\n",
      "            \"initial_values\": {\n",
      "                \"tr_r2\": \"0.0\"\n",
      "            }\n",
      "        }\n",
      "    ],\n",
      "    \"options\": {\n",
      "        \"output_timestep_symbol\": \"__h\"\n",
      "    },\n",
      "    \"parameters\": {\n",
      "        \"A2_minus\": \"0.007\",\n",
      "        \"A2_plus\": \"7.5e-10\",\n",
      "        \"A3_minus\": \"0.00023\",\n",
      "        \"A3_plus\": \"0.0093\",\n",
      "        \"Wmax\": \"100\",\n",
      "        \"Wmin\": \"0\",\n",
      "        \"d\": \"1\",\n",
      "        \"tau_minus\": \"33.7\",\n",
      "        \"tau_plus\": \"16.8\",\n",
      "        \"tau_x\": \"101\",\n",
      "        \"tau_y\": \"125\"\n",
      "    }\n",
      "}\n",
      "INFO:Processing global options...\n",
      "INFO:Processing input shapes...\n",
      "INFO:\n",
      "Processing differential-equation form shape tr_r1 with defining expression = \"(-tr_r1) / tau_plus\"\n",
      "DEBUG:Splitting expression -tr_r1/tau_plus (symbols [tr_r1])\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_plus]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Created Shape with symbol tr_r1, derivative_factors = [-1/tau_plus], inhom_term = 0.0, nonlin_term = 0.0\n",
      "INFO:\tReturning shape: Shape \"tr_r1\" of order 1\n",
      "INFO:Shape tr_r1: reconstituting expression -tr_r1/tau_plus\n",
      "INFO:\n",
      "Processing differential-equation form shape tr_r2 with defining expression = \"(-tr_r2) / tau_x\"\n",
      "DEBUG:Splitting expression -tr_r2/tau_x (symbols [tr_r2])\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_x]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Created Shape with symbol tr_r2, derivative_factors = [-1/tau_x], inhom_term = 0.0, nonlin_term = 0.0\n",
      "INFO:\tReturning shape: Shape \"tr_r2\" of order 1\n",
      "INFO:Shape tr_r2: reconstituting expression -tr_r2/tau_x\n",
      "INFO:All known variables: [tr_r1, tr_r2], all parameters used in ODEs: {tau_plus, tau_x}\n",
      "INFO:\n",
      "Processing differential-equation form shape tr_r1 with defining expression = \"(-tr_r1) / tau_plus\"\n",
      "DEBUG:Splitting expression -tr_r1/tau_plus (symbols [tr_r1, tr_r2, tr_r1])\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_plus], [0], [0]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Created Shape with symbol tr_r1, derivative_factors = [-1/tau_plus], inhom_term = 0.0, nonlin_term = 0\n",
      "INFO:\tReturning shape: Shape \"tr_r1\" of order 1\n",
      "INFO:\n",
      "Processing differential-equation form shape tr_r2 with defining expression = \"(-tr_r2) / tau_x\"\n",
      "DEBUG:Splitting expression -tr_r2/tau_x (symbols [tr_r1, tr_r2, tr_r1, tr_r2])\n",
      "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_x], [0], [0]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Created Shape with symbol tr_r2, derivative_factors = [-1/tau_x], inhom_term = 0.0, nonlin_term = 0\n",
      "INFO:\tReturning shape: Shape \"tr_r2\" of order 1\n",
      "INFO:Shape tr_r1: reconstituting expression -tr_r1/tau_plus\n",
      "DEBUG:Splitting expression -tr_r1/tau_plus (symbols Matrix([[tr_r1], [tr_r2]]))\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_plus], [0]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "INFO:Shape tr_r2: reconstituting expression -tr_r2/tau_x\n",
      "DEBUG:Splitting expression -tr_r2/tau_x (symbols Matrix([[tr_r1], [tr_r2]]))\n",
      "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_x]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "DEBUG:Initializing system of shapes with x = Matrix([[tr_r1], [tr_r2]]), A = Matrix([[-1/tau_plus, 0], [0, -1/tau_x]]), b = Matrix([[0], [0]]), c = Matrix([[0], [0]])\n",
      "INFO:Finding analytically solvable equations...\n",
      "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph.dot\n",
      "DEBUG:os.makedirs('/tmp')\n",
      "DEBUG:write lines to '/tmp/ode_dependency_graph.dot'\n",
      "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph.dot']\n",
      "INFO:Shape tr_r1: reconstituting expression -tr_r1/tau_plus\n",
      "DEBUG:Splitting expression -tr_r1/tau_plus (symbols [tr_r1, tr_r2])\n",
      "DEBUG:\tlinear factors: Matrix([[-1/tau_plus], [0]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "INFO:Shape tr_r2: reconstituting expression -tr_r2/tau_x\n",
      "DEBUG:Splitting expression -tr_r2/tau_x (symbols [tr_r1, tr_r2])\n",
      "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_x]])\n",
      "DEBUG:\tinhomogeneous term: 0.0\n",
      "DEBUG:\tnonlinear term: 0.0\n",
      "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:os.makedirs('/tmp')\n",
      "DEBUG:write lines to '/tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot'\n",
      "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph_analytically_solvable_before_propagated.dot']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[73,iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml, DEBUG, [43:0;94:0]]: Start building symbol table!\n",
      "[74,GLOBAL, INFO]: Analysing/transforming synapse stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.\n",
      "[75,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [2:0;63:0]]: Starts processing of the model 'stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable.dot\n",
      "DEBUG:os.makedirs('/tmp')\n",
      "DEBUG:write lines to '/tmp/ode_dependency_graph_analytically_solvable.dot'\n",
      "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph_analytically_solvable.dot']\n",
      "INFO:Generating propagators for the following symbols: tr_r1, tr_r2\n",
      "DEBUG:Initializing system of shapes with x = Matrix([[tr_r1], [tr_r2]]), A = Matrix([[-1/tau_plus, 0], [0, -1/tau_x]]), b = Matrix([[0], [0]]), c = Matrix([[0], [0]])\n",
      "DEBUG:System of equations:\n",
      "DEBUG:x = Matrix([[tr_r1], [tr_r2]])\n",
      "DEBUG:A = Matrix([\n",
      "[-1/tau_plus,        0],\n",
      "[          0, -1/tau_x]])\n",
      "DEBUG:b = Matrix([[0], [0]])\n",
      "DEBUG:c = Matrix([[0], [0]])\n",
      "INFO:update_expr[tr_r1] = __P__tr_r1__tr_r1*tr_r1\n",
      "INFO:update_expr[tr_r2] = __P__tr_r2__tr_r2*tr_r2\n",
      "WARNING:Not preserving expression for variable \"tr_r1\" as it is solved by propagator solver\n",
      "WARNING:Not preserving expression for variable \"tr_r2\" as it is solved by propagator solver\n",
      "INFO:In ode-toolbox: returning outdict = \n",
      "INFO:[\n",
      "    {\n",
      "        \"initial_values\": {\n",
      "            \"tr_r1\": \"0.0\",\n",
      "            \"tr_r2\": \"0.0\"\n",
      "        },\n",
      "        \"parameters\": {\n",
      "            \"tau_plus\": \"16.8000000000000\",\n",
      "            \"tau_x\": \"101.000000000000\"\n",
      "        },\n",
      "        \"propagators\": {\n",
      "            \"__P__tr_r1__tr_r1\": \"exp(-__h/tau_plus)\",\n",
      "            \"__P__tr_r2__tr_r2\": \"exp(-__h/tau_x)\"\n",
      "        },\n",
      "        \"solver\": \"analytical\",\n",
      "        \"state_variables\": [\n",
      "            \"tr_r1\",\n",
      "            \"tr_r2\"\n",
      "        ],\n",
      "        \"update_expressions\": {\n",
      "            \"tr_r1\": \"__P__tr_r1__tr_r1*tr_r1\",\n",
      "            \"tr_r2\": \"__P__tr_r2__tr_r2*tr_r2\"\n",
      "        }\n",
      "    }\n",
      "]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[76,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n",
      "[77,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [13:8;13:17]]: Variable 'd' has the same name as a physical unit!\n",
      "[78,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[79,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[80,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n",
      "[81,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [13:8;13:17]]: Variable 'd' has the same name as a physical unit!\n",
      "[82,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[83,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n",
      "[84,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp\n",
      "[85,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.h\n",
      "[86,iaf_psc_delta_neuron_nestml, INFO, [43:0;94:0]]: Successfully generated code for the model: 'iaf_psc_delta_neuron_nestml' in: '/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target' !\n",
      "[87,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.cpp\n",
      "[88,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.h\n",
      "[89,iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml, INFO, [43:0;94:0]]: Successfully generated code for the model: 'iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml' in: '/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target' !\n",
      "[90,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h\n",
      "[91,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [2:0;63:0]]: Successfully generated code for the model: 'stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml' in: '/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target' !\n",
      "[92,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_82fabdb7b0ca411190789324e0b5cb66_module.cpp\n",
      "[93,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_82fabdb7b0ca411190789324e0b5cb66_module.h\n",
      "[94,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/CMakeLists.txt\n",
      "[95,GLOBAL, INFO]: Successfully generated NEST module code in '/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target' !\n",
      "CMake Warning (dev) at CMakeLists.txt:95 (project):\n",
      "  cmake_minimum_required() should be called prior to this top-level project()\n",
      "  call.  Please see the cmake-commands(7) manual for usage documentation of\n",
      "  both commands.\n",
      "This warning is for project developers.  Use -Wno-dev to suppress it.\n",
      "\n",
      "-- The CXX compiler identification is GNU 12.3.0\n",
      "-- Detecting CXX compiler ABI info\n",
      "-- Detecting CXX compiler ABI info - done\n",
      "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n",
      "-- Detecting CXX compile features\n",
      "-- Detecting CXX compile features - done\n",
      "\n",
      "-------------------------------------------------------\n",
      "nestml_82fabdb7b0ca411190789324e0b5cb66_module Configuration Summary\n",
      "-------------------------------------------------------\n",
      "\n",
      "C++ compiler         : /usr/bin/c++\n",
      "Build static libs    : OFF\n",
      "C++ compiler flags   : \n",
      "NEST compiler flags  :  -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n",
      "NEST include dirs    :  -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n",
      "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so  /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so    /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "You can now build and install 'nestml_82fabdb7b0ca411190789324e0b5cb66_module' using\n",
      "  make\n",
      "  make install\n",
      "\n",
      "The library file libnestml_82fabdb7b0ca411190789324e0b5cb66_module.so will be installed to\n",
      "  /tmp/nestml_target_zo8wlcs5\n",
      "The module can be loaded into NEST using\n",
      "  (nestml_82fabdb7b0ca411190789324e0b5cb66_module) Install       (in SLI)\n",
      "  nest.Install(nestml_82fabdb7b0ca411190789324e0b5cb66_module)   (in PyNEST)\n",
      "\n",
      "CMake Warning (dev) in CMakeLists.txt:\n",
      "  No cmake_minimum_required command is present.  A line of code such as\n",
      "\n",
      "    cmake_minimum_required(VERSION 3.26)\n",
      "\n",
      "  should be added at the top of the file.  The version specified may be lower\n",
      "  if you wish to support older CMake versions for this project.  For more\n",
      "  information run \"cmake --help-policy CMP0000\".\n",
      "This warning is for project developers.  Use -Wno-dev to suppress it.\n",
      "\n",
      "-- Configuring done (0.1s)\n",
      "-- Generating done (0.0s)\n",
      "-- Build files have been written to: /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target\n",
      "[ 25%] Building CXX object CMakeFiles/nestml_82fabdb7b0ca411190789324e0b5cb66_module_module.dir/nestml_82fabdb7b0ca411190789324e0b5cb66_module.o\n",
      "[ 50%] Building CXX object CMakeFiles/nestml_82fabdb7b0ca411190789324e0b5cb66_module_module.dir/iaf_psc_delta_neuron_nestml.o\n",
      "[ 75%] Building CXX object CMakeFiles/nestml_82fabdb7b0ca411190789324e0b5cb66_module_module.dir/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.o\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml::init_state_internal_()’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.cpp:188:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  188 |   const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml::update(const nest::Time&, long int, long int)’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.cpp:293:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n",
      "  293 |     for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n",
      "      |                      ~~^~~~~~~~~~~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.cpp:288:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  288 |     auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n",
      "      |          ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml::init_state_internal_()’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp:173:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  173 |   const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml::update(const nest::Time&, long int, long int)’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp:262:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n",
      "  262 |     for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n",
      "      |                      ~~^~~~~~~~~~~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp:257:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  257 |     auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n",
      "      |          ^~~~~\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In file included from /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_82fabdb7b0ca411190789324e0b5cb66_module.cpp:36:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25:   required from ‘nest::GenericConnectorModel<ConnectionT>::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierPtrRport>; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5:   required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70:   required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:590:99:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:729:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  729 |   const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:744:3:   required from ‘nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25:   required from ‘nest::GenericConnectorModel<ConnectionT>::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierPtrRport>; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5:   required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70:   required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:590:99:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:716:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  716 |   const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25:   required from ‘nest::GenericConnectorModel<ConnectionT>::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierIndex>; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34:   required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierIndex>; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80:   required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70:   required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:590:99:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:729:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  729 |   const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:744:3:   required from ‘nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25:   required from ‘nest::GenericConnectorModel<ConnectionT>::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierIndex>; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34:   required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierIndex>; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80:   required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70:   required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:590:99:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:716:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  716 |   const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::send(nest::Event&, size_t, const nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22:   required from ‘void nest::Connector<ConnectionT>::send_to_all(size_t, const std::vector<nest::ConnectorModel*>&, nest::Event&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierPtrRport>; size_t = long unsigned int]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:505:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  505 |         auto get_t = [_tr_t](){ return _tr_t; };   // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |              ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:530:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  530 |         auto get_t = [__t_spike](){ return __t_spike; };    // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |              ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:567:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  567 |         auto get_t = [__t_spike](){ return __t_spike; };    // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |              ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:438:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  438 |     const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                  ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:440:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n",
      "  440 |     auto get_thread = [tid]()\n",
      "      |          ^~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::send(nest::Event&, size_t, const nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22:   required from ‘void nest::Connector<ConnectionT>::send_to_all(size_t, const std::vector<nest::ConnectorModel*>&, nest::Event&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierIndex>; size_t = long unsigned int]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:505:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  505 |         auto get_t = [_tr_t](){ return _tr_t; };   // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |              ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:530:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  530 |         auto get_t = [__t_spike](){ return __t_spike; };    // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |              ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:567:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  567 |         auto get_t = [__t_spike](){ return __t_spike; };    // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |              ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:438:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  438 |     const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                  ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:440:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n",
      "  440 |     auto get_thread = [tid]()\n",
      "      |          ^~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::update_internal_state_(double, double, const nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:500:9:   required from ‘bool nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::send(nest::Event&, size_t, const nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22:   required from ‘void nest::Connector<ConnectionT>::send_to_all(size_t, const std::vector<nest::ConnectorModel*>&, nest::Event&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierPtrRport>; size_t = long unsigned int]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:795:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  795 |     const double __resolution = timestep;  // do not remove, this is necessary for the resolution() function\n",
      "      |                  ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:796:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  796 |     auto get_t = [t_start](){ return t_start; };   // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |          ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::update_internal_state_(double, double, const nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:500:9:   required from ‘bool nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::send(nest::Event&, size_t, const nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22:   required from ‘void nest::Connector<ConnectionT>::send_to_all(size_t, const std::vector<nest::ConnectorModel*>&, nest::Event&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierIndex>; size_t = long unsigned int]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:795:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  795 |     const double __resolution = timestep;  // do not remove, this is necessary for the resolution() function\n",
      "      |                  ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:796:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  796 |     auto get_t = [t_start](){ return t_start; };   // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |          ^~~~~\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100%] Linking CXX shared module nestml_82fabdb7b0ca411190789324e0b5cb66_module.so\n",
      "[100%] Built target nestml_82fabdb7b0ca411190789324e0b5cb66_module_module\n",
      "[100%] Built target nestml_82fabdb7b0ca411190789324e0b5cb66_module_module\n",
      "Install the project...\n",
      "-- Install configuration: \"\"\n",
      "-- Installing: /tmp/nestml_target_zo8wlcs5/nestml_82fabdb7b0ca411190789324e0b5cb66_module.so\n"
     ]
    }
   ],
   "source": [
    "# Generate code for All-to-All spike interaction\n",
    "module_name, neuron_model_name, synapse_model_name = \\\n",
    "    NESTCodeGeneratorUtils.generate_code_for(\"../../../models/neurons/iaf_psc_delta_neuron.nestml\",\n",
    "                                             nestml_triplet_stdp_model,\n",
    "                                             post_ports=[\"post_spikes\"],\n",
    "                                             logging_level=\"DEBUG\",\n",
    "                                             codegen_opts={\"delay_variable\": {\"stdp_triplet_synapse\": \"d\"},\n",
    "                                                           \"weight_variable\": {\"stdp_triplet_synapse\": \"w\"}})"
   ]
  },
  {
   "attachments": {
    "image-3.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "settled-keeping",
   "metadata": {},
   "source": [
    "$\\textit{Nearest spike Interation}$ - Each postsynaptic spike interacts with only the last spike. Hence no accumulation of the trace variables occurs. The variables saturate at 1. This is achieved by updating the variables to the value of 1 instead of updating by at step of 1. In this way, the synapse forgets all other previous spikes and keeps only the memory of the last one.\n",
    "\n",
    "<div>\n",
    "<img src=\"attachment:image-3.png\" width=\"400\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "addressed-roads",
   "metadata": {},
   "outputs": [],
   "source": [
    "nestml_triplet_stdp_nn_model = '''\n",
    "model stdp_triplet_nn_synapse:\n",
    "\n",
    "    state:\n",
    "        w nS = 1 nS\n",
    "\n",
    "        tr_r1 real = 0.\n",
    "        tr_r2 real = 0.\n",
    "        tr_o1 real = 0.\n",
    "        tr_o2 real = 0.\n",
    "\n",
    "    parameters:\n",
    "        d ms = 1 ms\n",
    "\n",
    "        tau_plus ms = 16.8 ms  # time constant for tr_r1\n",
    "        tau_x ms = 101 ms  # time constant for tr_r2\n",
    "        tau_minus ms = 33.7 ms  # time constant for tr_o1\n",
    "        tau_y ms = 125 ms  # time constant for tr_o2\n",
    "\n",
    "        A2_plus real = 7.5e-10\n",
    "        A3_plus real = 9.3e-3\n",
    "        A2_minus real = 7e-3\n",
    "        A3_minus real = 2.3e-4\n",
    "\n",
    "        Wmax nS = 100 nS\n",
    "        Wmin nS = 0 nS\n",
    "\n",
    "    equations:\n",
    "        tr_r1' = -tr_r1 / tau_plus\n",
    "        tr_r2' = -tr_r2 / tau_x\n",
    "        tr_o1' = -tr_o1 / tau_minus\n",
    "        tr_o2' = -tr_o2 / tau_y\n",
    "\n",
    "    input:\n",
    "        pre_spikes <- spike\n",
    "        post_spikes <- spike\n",
    "\n",
    "    output:\n",
    "        spike(weight real, delay ms)\n",
    "\n",
    "    onReceive(post_spikes):\n",
    "        # increment post trace values\n",
    "        tr_o1 = 1\n",
    "        tr_o2 = 1\n",
    "\n",
    "        # potentiate synapse\n",
    "        #w_ nS = Wmax * ( w / Wmax + tr_r1 * ( A2_plus + A3_plus * tr_o2 ) )\n",
    "        w_ nS = w + tr_r1 * ( A2_plus + A3_plus * tr_o2 )\n",
    "        w = min(Wmax, w_)\n",
    "\n",
    "    onReceive(pre_spikes):\n",
    "        # increment pre trace values\n",
    "        tr_r1 = 1\n",
    "        tr_r2 = 1\n",
    "\n",
    "        # depress synapse\n",
    "        #w_ nS = Wmax * ( w / Wmax  -  tr_o1 * ( A2_minus + A3_minus * tr_r2 ) )\n",
    "        w_ nS = w  -  tr_o1 * ( A2_minus + A3_minus * tr_r2 )\n",
    "        w = max(Wmin, w_)\n",
    "\n",
    "        # deliver spike to postsynaptic partner\n",
    "        emit_spike(w, d)\n",
    "        \n",
    "    update:\n",
    "        integrate_odes()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "civic-xerox",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "              -- N E S T --\n",
      "  Copyright (C) 2004 The NEST Initiative\n",
      "\n",
      " Version: 3.6.0-post0.dev0\n",
      " Built: Mar 26 2024 08:52:51\n",
      "\n",
      " This program is provided AS IS and comes with\n",
      " NO WARRANTY. See the file LICENSE for details.\n",
      "\n",
      " Problems or suggestions?\n",
      "   Visit https://www.nest-simulator.org\n",
      "\n",
      " Type 'nest.help()' to find out more about NEST.\n",
      "\n",
      "[17,stdp_triplet_nn_synapse_nestml, WARNING, [13:8;13:17]]: Variable 'd' has the same name as a physical unit!\n",
      "[20,stdp_triplet_nn_synapse_nestml, WARNING, [48:16;48:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n",
      "[23,stdp_triplet_nn_synapse_nestml, WARNING, [58:16;58:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n",
      "[26,stdp_triplet_nn_synapse_nestml, WARNING, [13:8;13:17]]: Variable 'd' has the same name as a physical unit!\n",
      "[35,stdp_triplet_nn_synapse_nestml, WARNING, [13:8;13:17]]: Variable 'd' has the same name as a physical unit!\n",
      "[38,stdp_triplet_nn_synapse_nestml, WARNING, [48:16;48:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n",
      "[41,stdp_triplet_nn_synapse_nestml, WARNING, [58:16;58:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[64,stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [13:8;13:17]]: Variable 'd' has the same name as a physical unit!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n",
      "WARNING:Not preserving expression for variable \"tr_o1__for_stdp_triplet_nn_synapse_nestml\" as it is solved by propagator solver\n",
      "WARNING:Not preserving expression for variable \"tr_o2__for_stdp_triplet_nn_synapse_nestml\" as it is solved by propagator solver\n",
      "WARNING:Not preserving expression for variable \"tr_r1\" as it is solved by propagator solver\n",
      "WARNING:Not preserving expression for variable \"tr_r2\" as it is solved by propagator solver\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[77,stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [13:8;13:17]]: Variable 'd' has the same name as a physical unit!\n",
      "[81,stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [13:8;13:17]]: Variable 'd' has the same name as a physical unit!\n",
      "CMake Warning (dev) at CMakeLists.txt:95 (project):\n",
      "  cmake_minimum_required() should be called prior to this top-level project()\n",
      "  call.  Please see the cmake-commands(7) manual for usage documentation of\n",
      "  both commands.\n",
      "This warning is for project developers.  Use -Wno-dev to suppress it.\n",
      "\n",
      "-- The CXX compiler identification is GNU 12.3.0\n",
      "-- Detecting CXX compiler ABI info\n",
      "-- Detecting CXX compiler ABI info - done\n",
      "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n",
      "-- Detecting CXX compile features\n",
      "-- Detecting CXX compile features - done\n",
      "\n",
      "-------------------------------------------------------\n",
      "nestml_d21f76a04f014810b633adbbfabc1701_module Configuration Summary\n",
      "-------------------------------------------------------\n",
      "\n",
      "C++ compiler         : /usr/bin/c++\n",
      "Build static libs    : OFF\n",
      "C++ compiler flags   : \n",
      "NEST compiler flags  :  -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n",
      "NEST include dirs    :  -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n",
      "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so  /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so    /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "You can now build and install 'nestml_d21f76a04f014810b633adbbfabc1701_module' using\n",
      "  make\n",
      "  make install\n",
      "\n",
      "The library file libnestml_d21f76a04f014810b633adbbfabc1701_module.so will be installed to\n",
      "  /tmp/nestml_target_4f42fhl3\n",
      "The module can be loaded into NEST using\n",
      "  (nestml_d21f76a04f014810b633adbbfabc1701_module) Install       (in SLI)\n",
      "  nest.Install(nestml_d21f76a04f014810b633adbbfabc1701_module)   (in PyNEST)\n",
      "\n",
      "CMake Warning (dev) in CMakeLists.txt:\n",
      "  No cmake_minimum_required command is present.  A line of code such as\n",
      "\n",
      "    cmake_minimum_required(VERSION 3.26)\n",
      "\n",
      "  should be added at the top of the file.  The version specified may be lower\n",
      "  if you wish to support older CMake versions for this project.  For more\n",
      "  information run \"cmake --help-policy CMP0000\".\n",
      "This warning is for project developers.  Use -Wno-dev to suppress it.\n",
      "\n",
      "-- Configuring done (0.1s)\n",
      "-- Generating done (0.0s)\n",
      "-- Build files have been written to: /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target\n",
      "[ 25%] Building CXX object CMakeFiles/nestml_d21f76a04f014810b633adbbfabc1701_module_module.dir/nestml_d21f76a04f014810b633adbbfabc1701_module.o\n",
      "[ 50%] Building CXX object CMakeFiles/nestml_d21f76a04f014810b633adbbfabc1701_module_module.dir/iaf_psc_delta_neuron_nestml.o\n",
      "[ 75%] Building CXX object CMakeFiles/nestml_d21f76a04f014810b633adbbfabc1701_module_module.dir/iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml.o\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml::init_state_internal_()’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp:173:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  173 |   const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml::update(const nest::Time&, long int, long int)’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp:262:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n",
      "  262 |     for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n",
      "      |                      ~~^~~~~~~~~~~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp:257:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  257 |     auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n",
      "      |          ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml::init_state_internal_()’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml.cpp:188:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  188 |   const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml::update(const nest::Time&, long int, long int)’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml.cpp:293:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n",
      "  293 |     for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n",
      "      |                      ~~^~~~~~~~~~~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml.cpp:288:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  288 |     auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n",
      "      |          ^~~~~\n",
      "In file included from /home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_d21f76a04f014810b633adbbfabc1701_module.cpp:36:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25:   required from ‘nest::GenericConnectorModel<ConnectionT>::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierPtrRport>; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5:   required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70:   required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:590:102:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:729:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  729 |   const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:744:3:   required from ‘nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25:   required from ‘nest::GenericConnectorModel<ConnectionT>::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierPtrRport>; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5:   required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70:   required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:590:102:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:716:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  716 |   const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25:   required from ‘nest::GenericConnectorModel<ConnectionT>::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierIndex>; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34:   required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierIndex>; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80:   required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70:   required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:590:102:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:729:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  729 |   const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:744:3:   required from ‘nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25:   required from ‘nest::GenericConnectorModel<ConnectionT>::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierIndex>; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34:   required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierIndex>; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80:   required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70:   required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string<char>]’\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:590:102:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:716:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  716 |   const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::send(nest::Event&, size_t, const nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22:   required from ‘void nest::Connector<ConnectionT>::send_to_all(size_t, const std::vector<nest::ConnectorModel*>&, nest::Event&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierPtrRport>; size_t = long unsigned int]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:505:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  505 |         auto get_t = [_tr_t](){ return _tr_t; };   // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |              ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:530:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  530 |         auto get_t = [__t_spike](){ return __t_spike; };    // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |              ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:567:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  567 |         auto get_t = [__t_spike](){ return __t_spike; };    // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |              ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:438:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  438 |     const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                  ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:440:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n",
      "  440 |     auto get_thread = [tid]()\n",
      "      |          ^~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::send(nest::Event&, size_t, const nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22:   required from ‘void nest::Connector<ConnectionT>::send_to_all(size_t, const std::vector<nest::ConnectorModel*>&, nest::Event&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierIndex>; size_t = long unsigned int]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:505:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  505 |         auto get_t = [_tr_t](){ return _tr_t; };   // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |              ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:530:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  530 |         auto get_t = [__t_spike](){ return __t_spike; };    // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |              ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:567:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  567 |         auto get_t = [__t_spike](){ return __t_spike; };    // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |              ^~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:438:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  438 |     const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function\n",
      "      |                  ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:440:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n",
      "  440 |     auto get_thread = [tid]()\n",
      "      |          ^~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::update_internal_state_(double, double, const nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:500:9:   required from ‘bool nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::send(nest::Event&, size_t, const nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22:   required from ‘void nest::Connector<ConnectionT>::send_to_all(size_t, const std::vector<nest::ConnectorModel*>&, nest::Event&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierPtrRport>; size_t = long unsigned int]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:795:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  795 |     const double __resolution = timestep;  // do not remove, this is necessary for the resolution() function\n",
      "      |                  ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:796:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  796 |     auto get_t = [t_start](){ return t_start; };   // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |          ^~~~~\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::update_internal_state_(double, double, const nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:500:9:   required from ‘bool nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<targetidentifierT>::send(nest::Event&, size_t, const nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22:   required from ‘void nest::Connector<ConnectionT>::send_to_all(size_t, const std::vector<nest::ConnectorModel*>&, nest::Event&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml<nest::TargetIdentifierIndex>; size_t = long unsigned int]’\n",
      "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3:   required from here\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:795:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n",
      "  795 |     const double __resolution = timestep;  // do not remove, this is necessary for the resolution() function\n",
      "      |                  ^~~~~~~~~~~~\n",
      "/home/charl/julich/nestml-fork-nest-delay/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:796:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n",
      "  796 |     auto get_t = [t_start](){ return t_start; };   // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n",
      "      |          ^~~~~\n",
      "[100%] Linking CXX shared module nestml_d21f76a04f014810b633adbbfabc1701_module.so\n",
      "[100%] Built target nestml_d21f76a04f014810b633adbbfabc1701_module_module\n",
      "[100%] Built target nestml_d21f76a04f014810b633adbbfabc1701_module_module\n",
      "Install the project...\n",
      "-- Install configuration: \"\"\n",
      "-- Installing: /tmp/nestml_target_4f42fhl3/nestml_d21f76a04f014810b633adbbfabc1701_module.so\n"
     ]
    }
   ],
   "source": [
    "# Generate code for nearest spike interaction model\n",
    "module_name_nn, neuron_model_name_nn, synapse_model_name_nn = \\\n",
    "    NESTCodeGeneratorUtils.generate_code_for(\"../../../models/neurons/iaf_psc_delta_neuron.nestml\",\n",
    "                                             nestml_triplet_stdp_nn_model,\n",
    "                                             post_ports=[\"post_spikes\"],\n",
    "                                             codegen_opts={\"delay_variable\": {\"stdp_triplet_nn_synapse\": \"d\"},\n",
    "                                                           \"weight_variable\": {\"stdp_triplet_nn_synapse\": \"w\"}})"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAJqCAYAAADXOGGHAAAgAElEQVR4AeydBbhdxdWGkUBwhzRoKAkSICEEAkmQFlrci7sXKUWSQIBS+HGX4lagUCgUGqQ4FAsECe4SHAIEl3o7//Ou8h1WNsfukXvPPXvN85wzs0fWzHwze+bba2bPniKFCQQCgUAgg8B///vf9J///Md8H3/88TT11FOn+eefP40dO9b8/v3vf0+WgvgY0vzzn/+cLKzUhdIonOt//etfugw7EAgEAoGaEZii5pSRMBAIBNoegU8++STtscceaYoppkhTTjllGjBgQLrvvvus3iI4kBK5CRApqgYc0kKG9KsmTcQJBAKBQKASAkFuKiEU4YFAjhEYN25cWmaZZVL//v2N4Ew77bRp3333NUT+8Y9/TEZqaoXp1ltvTYssskhaY4010ptvvmliQoNTK5qRLhAIBEAgyE30g0AgECiKwLPPPptWX331dM4556SXX37ZCAganNlmm838SITGRctL2NVqbaTpeeutt9LQoUONOKEV+vTTT01etXKKFjw8A4FAIPcIBLnJfRcIAAKB4ghcccUVRmjef/99i/Dggw+mGWaYIfXo0SMNHz7ciA0kxZObajUuSvPee++ZxqZnz57p/PPPt3y+/PLLgsziJQvfQCAQCATKIxDkpjw+ERoI5AoBkY7PPvssXXDBBen2228v1P+1115LvXv3Ni3LwIEDE2QHA8GRJqajGheWveacc87Uq1evdNNNNxXkFTINRyAQCAQCNSAQ5KYG0CJJINBuCEBK/v73v1u1/va3v6Vf//rXaZ111rFrvf309ddfpwMOOMDIDctTQ4YMSe+8845pWbQkpbi6Fk7+WgQK++yzzzZ5W265ZSJfyqFwpQ07EAgEAoGOIhDkpqOIRfxAoA0Q8GQDQiLNC1V75plnbB/M4YcfbjX95ptvzCYNYeuvv36aY4450owzzpjOOOMMC4OYyEBQpMHxpIkNyGh7Lr/88nTDDTek66+/Pu28885GbjbccMP0wAMPJJbC0OAQFxNER6iGHQgEAh1BIMhNR9CKuIFAmyAAafBaFpGbd999N6211lppv/32s5pKm6NwPCdMmJBWXXVVIyVobz7++GOLy34bkRzia/+NCMqkSZMSGpqppprKlqLYmDz99NOnmWaayX5cs/eGjcUvvPCCyRRJsov4CwQCgUCgSgSC3FQJVEQLBNoZAZGIhx56KPXp0yc9+eSTVl0RFggKP8VDuwJJmXXWWdOhhx5qcUWEiCcyJGIjDQ4Eh9e9X3/99XTiiScaQRo0aFB67rnn7Pfqq6+mN954IzQ27dzZom6BQCcgEOSmE0COLAKBVkQA4gF5EWH54osv0mWXXZZuueUWKy4ERWEqv8jKo48+mmaZZRYjOCussEJ66aWXLArLSVpSIm1WhkgP5GbppZc2crPmmmtaPDRJSiutj/INOxAIBAKBjiAQ5KYjaEXcQKDNEBBZgYiceuqpabnllrMa/vWvfzViI5JBuJaxiMA+nIMOOsjICZuL1157bQsnvmR6qLLpWdqC3Mw+++xpzJgxFlV5iXT59OEOBAKBQKAjCAS56QhaETcQaBMEIBBoUUREeOtpgw02SLvvvrvVUCRF4ZCTrHniiScSWhdOLeZ17j/84Q8WJau90T4c5UkkXitfYIEF0g9/+EPbpIyf8szmE9eBQCAQCHQUgSA3HUUs4gcCbYgAe2jWW289qxlExpMaERvZRFL4K6+8YiQF7Q1vPGHQ+pQzyLnkkktM67Pwwgun8ePHW3RPbiS/nJwICwQCgUCgFAJBbkohE/6BQDdHIEsQuC5GUCAjaF14RRujfTHlqi85EydONO0N5GbmmWdOo0aNsmTaO0OeWm6SXE4gHjFihJGbvn37pscee8zSaNlLssvlH2GBQCAQCJRDIMhNOXQiLBDoxghkyU2xqkA4OIl4o402smBt6i2XljAfzp4ZyA2/+eefP/HGFQZSI2LDtdKw+ZgTjom/+eab2+vj0tpAbILcGHzxFwgEAnUgEOSmDvAiaSDQXRAQscCGPOj6jjvuMJKx2267WVUgNwrD1o9AubGRIU0L357aYYcdTM7UU0+ddtxxR5PliQ1u7b25+eabLS5n3LApGYOmB7kY5H7++efmlp9dxF8gEAgEAlUiEOSmSqAiWiDQDghAFrRkhNbmvPPOs0P7cPMT8cHdEcPJwtNNN519VLNfv36F70SJ4IikfPXVV2nfffc1crPkkkume+65x7JRPE5A3nXXXQv7cDpajo6UOeIGAoFA+yIQ5KZ92zZqFgh8DwHICz/M22+/nY4//vj04osv2jV7YdCuaGmKvThcF/txYB/hfG8Kw6vdvEbOUtOUU06ZpAkSkVKekBf25hBv4403trT6vAMaoK222iptuummCRIEIRIpsojxFwgEAoFAlQgEuakSqIgWCHRXBDxB8O7bbrstzTXXXAkNCht7+ewBby9xQvHiiy+eFllkEfMnDG2M/+FHvAUXXND82WuD5obPJ0wzzTRp7rnntgMBwUwaIdwvv/xymmeeeYzcQGQUjo0Wh9fRr7rqKvOXNscu4i8QCAQCgQ4gEOSmA2BF1ECguyEAmSlGEv7yl7+kXr162TISmhY0KfzYM8O3nnRdyiZNjx49isbjswykW2KJJdJnn31m+Utz8+GHH6ZddtnFwiFIbD5GczN69Oi07bbbmgaIMnsS1t0wj/IGAoFA1yMQ5Kbr2yBKEAg0BQGRhCy5QZPCOTMLLbSQLSUtu+yyaZlllkmDBw82e6mllkrLL7+8ufnuEz/iZH+88URclqNIj40fdv/+/e1NqI8++siICuRG+2f40CYf5kQ7tPrqq6ett97a8jv33HMNh2x5mwJOCA0EAoG2RiDITVs3b1QuECiOAMSH/TDsr2nGD4ICoSmlgSFMe3bYX6O9OZSWNNL0FC99+AYCgUAgUB6BIDfl8YnQQCAQqAGBjhCUYgSomF8NxYgkgUAgkFMEgtzktOGj2oFAIBAIBAKBQLsiEOSmXVs26hUItDAC0ux4DQ1uf93CxY+iBQKBQIsjEOSmxRsoihcIBAKBQCAQCAQCHUMgyE3H8IrYgUAgEAgEAoFAINDiCAS5afEGiuIFAoFAIBAIBAKBQMcQCHLTMbwidiAQCAQCgUAgEAi0OAJBblq8gaJ4gUBXIpA9byZ7XalsfpMwZ99wpo4MYTqwD7cO+ZOteGEHAoFAINBRBILcdBSxiB8I5BQBvcmEXYnkEM7HNTEiND69DvnLQunjZMPiOhAIBAKBahEIclMtUhEvEMg5AiI01ZAboFJ8TiK+8sor06mnnpomTpxoKHpygxttDXJlS6OTc8ij+oFAIFAjAkFuagQukgUC7YyAJxqqJ2RFmhX5lbK95uaNN95ISy+9dOJjm3yB/LDDDkvvvPNOeuWVV9KkSZNMBHL1w6PafErlH/6BQCCQbwSC3OS7/aP2gUCHEOgI6ZDmBiIzfPhw+xI4XxLny+OzzDJLmmeeedIWW2yRHnnkESuDvi/VkTw6VPiIHAgEArlBIMhNbpo6KhoIVEZAxIKveR9xxBGmZRk5cmQ6+OCD7Td69Oh00EEH2Q93qd8hhxxi8Ym7//77pz59+hi5gdj07NkzQXKmmGIK+/Xq1Svtscce6eyzz06ffvqpaW20T6dyiSNGIBAIBALfRyDIzfcxCZ9AIPcIQHK++OKL9N5776W33norvfvuu+bmGvfbb79duMYv+0NbQzxkPPjgg2nYsGEFciNiA9FZeOGF06hRo9KECRPSxx9/XPZL4rlvlAAgEAgEqkYgyE3VUEXEQCBfCEBwpMmppeZK++abb6ZBgwYVyA0am7nmmsu0PhAfDBuJ4xXwWlCONIFAIFAMgSA3xVAJv0CgzRGAeGhPjGyqLEJSb/WRI7Ly4Ycfpu222y6tssoq6eqrr07PP/98evXVVy0L5XfNNdekVVddNX355ZeT+ddbjnZO79tQ9Szmp7CwA4E8IRDkJk+tHXUNBBwCTITaxAvBkbtRE6SXg2x/7o3C5PfYY4+lESNGFDQ4nnC5Ioczg4BwlDe4BXZCI+w8IxDkJs+tH3UPBL5FgEkS40lOveB4mdLieJnkpY3DjczX59GubpEa7GLYCvt2rX/UKxCohECQm0oIRXgg0IYIMPnpoDz/pN/ISTEri2smYmwO9lP+XPsy4FZYG0LfsCqBpde2ieRk8WxYhiEoEOhGCAS56UaNFUUNBJqFAHtgeGX7oosusiw82ag1T2Sw7MRky0+TbzF5hP3tb38rFhR+DgFwxHDS81FHHZXOOusseyPNExrvdknDGQjkCoEgN7lq7qhsIFAcgZtuusneZlphhRUK+140kRZP0TFfZBXTxuDP7/LLL09Dhw5NnK/DdTki1LGc2yu2SOf48ePTtNNOayc+v/TSS1ZJhYFfmEAg7wgEucl7D4j65xKBLIG4++67jdysvPLKhkcxItJRoMgDOZ6oKF+FSSYbik844YSC9kYTtcLD/h8CwuWZZ55Jc845p5EbPm+BIQxcFScwCwTyjECQmzy3ftQ9EPgWgTvuuMPIDa9jY7TRtx6AmGhlcLM/RH7ejslYKFW2hdVTTz1ln7BYaqmlksiN3jyrLCViBALtj0CQm/Zv4y6roQZi/+QuPwrl3Vz7p/xiaYr5dVnl2iRjkYwsuQFrhTWzquQhLZHyw/Zt3cz8u5ts3TOQm9lmmy15ctNZbdbdMIvy5hOBIDf5bPdOq7UGYyYsrw3QRFZsYvN+cqvAyNMbIvILu3YE1A5dQW6UN6WnXXmDKkhN+bbU/RTkpjxOERoIBLmJPtAUBERmNBhDbPxkpkwhL88++2x67rnnzEtPnxAYpVVc2TEBCon6bbVJV5EbteXvfve7NGTIkALBUbnqr2F7SdA9EeSmvdo1atN4BILcNB7TkFgEAU1iBOG+//7707HHHpuOPvro1LdvX5vYzjjjjHTKKaekhx9+2CRoIGeikwYHd0x8RQCu0UtYdgW58UV+/PHHbUMx2huMyuXjhPu7pdwgN9EbAoHyCAS5KY9PhNaBgMiJiMlXX32VDjjggNS/f/8099xz2wbWGWaYweypppqqYPfu3dvIzuGHH5746CIGWX7ik8w6ihdJHYnoanLjGwNi48mwD8u7W/dUkJu894SofyUEgtxUQijC60JAkxTE5Be/+IURGM7n4MvQ00wzjdlzzDFHWnvttQt+008/vbmJ06dPn/TAAw9YGRjYJa+uQkXiAgLSkHQFuSFvkVTaVm/7RBsXmud7jiA334MkPAKBoggEuSkKS3g2CgEmr+uuuy4NHz7cCMuMM86Yll9++cS5KjfeeKOFjR07NqHVueqqq9KYMWPSpZdeal+I7tGjR+rZs6dpeThkDqPBvVHly7ucriQ3wp4ysCeLvsJPZVJ42N8hoP4fmpvvMAlXIFAMgSA3xVAJv4Yh8NBDD6Xpppsusew09dRTG8m58MILTX72CV0DN4GvvfZaWm655QoanEUWWSR9+OGHheP8fdyGFTaHgkQkukpzI8j57MPiiy+evv76ayOwKpfCw/4fAur3QW6iRwQC5REIclMenwgtg0A1E5BOvmVvDdobCIqWIsqItqAnn3wyDRw40AgO6U866STzj28QVUKu+nC1YVeQG19K3pa7+OKLbV8VE7jK5eOE+zvNJeRm1llnLXnOTeAXvSXvCAS5yXsPaED99TQpURpYX3nlFdsYzN4ZlqPOOecci4LGRnGUppTNhuIBAwYYweFV4UmTJsWTfSmwavBXO3Q2uSHfrOYOv2xfqqFKbZ1E+AS5aetmjso1AIEgNw0AMUR8h4C0Mn4D8SyzzGKvfDMwE16N5sVPfn/+858L+3V+/etfW2bafPpdzuGqBYGuIjcqK30i+1kG/LLER/HzbntyU+6EYrVr3vGK+ucXgSA3+W37htecAVXk5uOPP0577LGHkRJe+37xxRctPw26GqQrFYKNpiI3M800UzrmmGMsSZxSXAm56sLVHp2tuaF09AGdWv3NN98UXvtXmaqrQb5i6b6ptOcmMMxXv4jafh+BIDffxyR86kSAAViEhNe911xzTdsoykRWLSlhcNZA/sQTT6TFFlvMiNKmm26aPv/881iaqrONlFyTYFeQG5UB+7LLLkuLLrpo+vTTT61t1fY+Tri/23Pz9NNPx7elokMEAmUQCHJTBpwIqh4BTZKkYEnh97//vZERlqTYKIrxS0nVTF6SSTqWo9i706tXr/TII4/Y0pbCqy9lxMwiIAy7mtyg2eMoAO3HUrmy5c37te6bIDd57wlR/0oIBLmphFCEV4UAk5GfkP74xz8aGeGAvhNPPNFkMDD7OJUEKy57dA488ECTN88883zv8wyV5ER4aQSEcWeTG/Itta+GfqLlqtIlz2eIJzezzz57ybel8olO1DoQ+A6BIDffYRGuGhDQ5OiT4ifNzcwzz5zOOussC9ZTufbl+DTF3JLNAX98igHNDft3OPQPo4G+WNrwqw4BYdzZ5IbSkbfvC1yrPNWVPn+x1OfR3AS5yV/7R42rRyDITfVYRcwqENDkxIbiDTfc0AgJr3C/9dZbtt9GkxnxNFBLrNJyjVvX9913X5p33nkTn23YfffdLXqpp37JagW7WP1Uf19H1bUrbJXxzjvvtLZaZZVVrF0oJ2H1lCnbBllZwgCbw/smTJhgSchX5aokIyvTX2fTcq1w3OSh9vD54S6lOVJ67KzxYcjwfZQwf+3L4tN5d1Y+15LBGVBoRflO28svv2xRVWZkYLysrnBbIdyfL4PKJ9wJkzuLnRMRzkCgagSC3FQNVUTsCAK8/bLXXnsVlpJeeuklS87ApVfBGdA0uWRlaxDHH00NWhu+OcXyFAb5rW40YGc3UePv69cK9fDkply7NKus5557burXr1/64IMPLAtNdM3KD7nUE0LADzd9KttWjcof+aoTNte1GMmA3KAV5QwoHhww6lM+r1ry6Mw0Kiu2fp2Zf+TVvggEuWnftu3ymt18881prrnmsu9DbbPNNunaa6+1MmmAZjDmp2sV2A9yX3zxReINKcgNT6mPP/74ZDKUplVtP5Fp8lFZVc+usCmXnvQpz+23324Y//jHP7Y2UbvUUzbklkpPGGUQmXj99dcT+7TkXyodafQrFSfrb0K//VMYMnx74C+DmzDFlT921k/XWZv0Iu6EkR8G2/tn0/lrn6/yVpkhNyxLQW7ATrIVT7aX19nuasqgONgylDNMIFAvAkFu6kUw0pdEAGKy7rrr2qTZs2fPNHr0aIvrJ1Ulxk8DtwY3NDyjRo2y9Hyb6ic/+YlF51Xw7mBUH+rx8MMPp/Hjxxs5w/3YY4+ZG7LWFT9er+f36KOP2hlE7IuCQPI9L95Go4zNLhd5jxs3zvJ69tlnE59gACPyJ2+V0duNKhPkgB95IZ9yIJsy8eMcGbWXyuLz9mHeX27CaWPlgXzc+He0/VV/ZCMTLeiVV16ZOPfphz/8oR27AHZ8x41ykw9u0qk8rWYLc/YOieCKAHaHezvK2PoIBLlp/Tbq1iW89957bdJkSWno0KHpzDPPTCyBYPQEW2xQgxBccMEFhY9nMpD/5je/sXTF4rcySA888IDtj6AO/PgUBUsKYNJVPz5myo9X9fmgKeWA3EAiKRtlbGbZILvkgWaP/HHzrSSVCZvviflfo8tDHuzj4qRf6iwcqqk7acuVBxm0NfGQzU9uNC7Uv1z6SmGUe8oppzQ5YMR5UtQDudSDdq1Uxkp5NDMcfOhvgwcPNmLJ/evvaz3gtPJ9HWVrbQSC3LR2+3T70vEZhoMPPtgGMgZeBrS11167cFibtDhoOaTpePXVV9NWW21lcRmomQxOPvlkw4I4DHx+IGxFkHwZH3zwQatLjx49jDxgg0Mr/Zgowbqzy6avxYMFZSB/7GZgk5VL3pAC8sKGjOAGh2z+2bTZ8GLXyFGfV3rsYvKLpa/k58tPXGSrj1VK29XhwoDDOdEyYbrLvd2K402U6fsIBLn5Pibh0yAEREDYqLnbbrvZhKHBvm/fvvaKOCcZ33LLLXbA35gxY+wbVD/4wQ/siVoD8Oabb24lKqfpaVCRmyKGt1mo/4477ph23XXXtMsuu6Qddtgh7bzzzl36owyUibJtv/32k/3w22mnneoqX7n0YOAxQauHpmHLLbdMP//5z+2tOMqGjFK/SvgpXal4lIH2IL9f/OIXaZ999rG22XbbbcvWW3Kxi8nGH5nYLBv16dOnIFtpyLtY2mr9SM+bg8ij7cCS9mRvG9et0L9U12J1AnfKygsCettL40VTbsIQmjsEgtzkrsmbV2G0FaXUyRCTa665Jg0bNqzwdKynZtTpq622mvnzNMqPJ1C0N7fddpvtUaDUDH4aAGU3rzaNkSxtVGOkNV5KqfYq5d/4EvxP4vvvv5/uuusuu6BtRWSblZ+Xi/bw6quvTieddJJ5k3c99Vda9rn07t07bbzxxiaXvSWE8WtEv0BO9j5Q3r5+rexWeX1dcMu/lcseZWttBILctHb7dOvSMUBp2Qk3k8ZHH32UNtpoIzu3Bs2M1NNSqUtbAwni7BMGbw122Np8iDtMeyGgdqZW2Um7npoi15MJ33dwn3feeUas119/fcumGnLjy+rdCJB8vpdFf4akY9BgqhyKYwHuz/t7t4ti8huJj5cd7kCgXRAIctMuLdmC9dAArAFdRWTy4I2RU045JXG+yemnn25uNhvrbJz5558/3XPPPQVyo7TYyEVGmPZBgPbU+UeNqJXvI+qHn332mREZ9oDxrbKjjz46jRgxwjbiQkI22WQTyxpCLlJebVkgIspHadgQj9zLL7/cvNRni8XN+onY+HoghHupo2VTecIOBPKEQJCbPLV2J9fVkxoGaQZsHb6ngT5bJE4jRpvDmxrsg8CwKVkGGdLeyC/s7o/AJ598kp5//nmrSKMmbxEDEQUOu1tvvfXSwgsvnAYNGpRGjhyZbrzxRnuzCBLCXhuM72+VkJVsxRPBee211ywPzg3S0QW6BxQ3a3tZWbfkZtPEdSAQCBRHIMhNcVzCt4EIMFD7CStLenxWfB16qaWWsifen/3sZwVVPmmQE4O8R6v7umlLCC42P96YQaMi8otfPYZ+IhKMW30OP75VxpInhjNh0BJCbtZZZx3zo6+WIt+lyqR6qNxnn312GjhwYOJNOYzqmk2v+KX8JdfXh7il0mXlxHUgkFcEgtzkteU7sd4aiJk0NFgzycifooi0MAkwMTDZsOGYN2Ywmpxwk86TJYsQf90eAdq1FAmotXL0K/WtYv2NQ+QWWmgh629bbLGFZdORMiCTn8g3At544w0702m77bYzeX//+98LxVdcefgyZcOI48O5pt9H3xd6YQcCpREIclMamwipEwEmiazRROPJSjbOe++9Z5MDBIevgHMQIIZBvSMTT1ZuXLcWApq4sdUvGl1C5HrigXyfHycjS3PDa+iYavf+INtrh1Sfiy++2MjS3nvvbfKkjVLeiqdri1Tmj/jl7pcySSMoEMgtAkFuctv0nVdxBmc/wPuJjEFbT6LEg7zgxxss7L3hlXDO9MCI2GQnq86rSeTULAT8BE7/8H2k3jyRlSXaks+yFKckQ6RZBsWon1XKlzJLjsjHu+++m1ZdddU055xzFk7iFilXXOSStpTxso444gjbZL/ffvulE044IUH8CfeySskJ/0AgzwgEuclz63dB3bOTDEXQYO6L88ILL9jR+xzspqUpnpKLxfXpwt09EeCIAPaneMJQb03KyRI5YFlqvvnmM3Kj82iqJQ8iKMhSv77jjjtM1iqrrPK9owyIRxrF9fWTLGyWsfjmFZuR+cwC9oILLmhyeaOLt74wSuPlhDsQCAT+h0CQm+gJLYGABmrZkBtOM+b8G06vnTBhgpVT4S1R6ChEwxB45pln0rHHHmtaPE8WGpZBRpDIDfnOO++8Rhz4+jymWs2NL6fk3X333fYdqeybfvRb9V1sxVexdM2SGK+p8y0qTuaGLGF4kwySj5bpRz/6kX3FXZuiJVeysIv5+fBwBwLtjkCQm3Zv4W5avy+//DIddNBBNunwkT0mPkyxp95uWsUotkOgsydjkYlayY0nKFpWRfvEvp3NNtvMauY3EuPh6yg3trSRvMU1atQoI/RHHXWUyeC1dO3rwYOTlFlCGzJkSJo0aZKllSzqFPeHwRZ/gUAKchOdoMsQ0ARTqgAc4seGYgZznq5vvvlmi8pk4Af8UunDv3sgwOSsvoCbX7MnaeVXD7nxpAKkb7/9djvGAAKC8XVQvdQi5C9ShB8k5rjjjrO+zivpECPtUyOtCJA+wjp8+PDC6+zIUlkkP+xAIO8IBLnJew/owvprglERig3SEJoZZpjBBn0GdAbxat9mkdywWx8B2lVkAHe2bzS6BpJfK7mhPMiQdgZNIx+KpI9iICOqh/LK1oE46st8h4olJ0g8H5KVDGzSiwiNHTvW7oUVV1zRPmWicEsQf4FAIFBAIMhNAYpwtBICmhA++OADG/DZe+P3RGgibKUyR1lqR4B25sOZtDukoNlG/aseckMZpVG5//77jXSstdZa5leuHqofttx//vOfLT0nJ7/zzjtWffo44eSh8j7wwAMFcvPxxx9bPIU1G7OQHwh0JwSC3HSn1spRWTXoo5rX96YWX3zxNG7cOEMhBvT26gxsmD3//PMLmgy1f7Nqqf5TD7mhjJJz3XXX2bEFfCcNU82yqeoIsYO4s/zKKc2QGuQqXDZLV7wOTjxeN0frozKoHM3CK+QGAt0NgSA33a3Fclhevv/DgM6Jxdtvv70hIDV9DuFoqyozOfPTMg6V02TezIqKDNRDbigfciAdp512mm3ynThxohXbk5NidVK9CbvmmmvszcAf/OAH6ZxzzrH0IjjS3uDJ96pWXnlluxewIUVZ3KRJMiHxFwjkGIEgNzlu/O5SdU6R7du3rw3qfPTwscces6JrgjIi23gAACAASURBVOou9YhyVoeAyE51sWuLpb5TK7nxZeQgQL6Hpjf6tA+nVMl8WuJwYCXkffDgwYnyYCDvKqOI/MMPP5wWWGABi/vLX/7Swv2Slc+PPMIEAnlGIMhNnlu/xeuuwZ3J4sQTT7RBnUPNOO+DAZ+BPfukyqBezZJAi1c9F8WjrdTGVFgTsuxGgYA89RPyo+8oj6eeeqrwRp5OKBZh8GWjLFyjSfFlxY/XtldaaaXC4XrZdMXqofyfe+65NGzYMOvbHH2A4ZVwjC83hw0utthiFm/RRRctfJBT9bIE8RcIBAIFBILcFKAIR6shwOCup1bO9OD8ED3hKgxbkwluGe+WX9itiQATNEsuHIAHeVCb11vabB/gWj+WkjCQC7SB9Ct96NKTC8UXieAaNzaGwyWXXnrptO2229o1chVmHiX+FIfjDiDsbJg/4IADLDbkXPnhAblHK0QZ55hjjnTGGWdYPPX7ElmEdyCQawSC3OS6+Vu78kwAfqLj6ZXvTbFE9ac//ckK7yca1YZ0esKWX9ithUC2jTiRWvtNmLQ1+Tey1F6miAF9ir0uEIcNN9zQsqPvEM7PkwzS8/N+l19+uaXdfffdLS3ERLLLlV1xeMtKRx2wSRgcyJ98FIdlWJajZplllvTb3/62UMZy8iMsEMg7AkFu8t4DWrT+DO5aPtAeBr63wyQEwVlvvfVsAvDkh6po4iF9mNZGwE/gmsixs21aSy2Q4wmu+sMnn3xi58hAGDh0T28psVkdkoH2ZPz48fbRS04cxiitbJUV+Swl8VVx7ZUR2VY/LFV2hbOPZtZZZ02zzz67LU+xMRnC9cQTT9iP64EDB9pm5YsuusjEQaBUllLywz8QyDsCQW7y3gNavP6aBCjmq6++mvggISp81PPS3miyIQ6Dfgz8Ld6o3xavM9opS5Zeeukl+8TB/vvvnw499NA0evTo9Ktf/Srx1e2RI0da2IgRI9IhhxyS7r33XiupLydu9TcICP1w1113tXj4Q3h0MF+pVpA8SIr2kq255prp/ffft29JQdz55Ai/AQMGpDPPPDPpLSzuB39PlMoj/AOBvCMQ5CbvPaCF688kIK0NxeT6kksuKWhvODCNCYXBnjBNGpp8WrhqUbQmI0BfKEYC1EfUn6Qlos9ANooZCIvikV7964YbbjDywdIShjiKV0yO/FQGyDpf/EYbCWlHq4T59NNPE3txkPv666+bH2l83pIVdiAQCBRHIMhNcVzCtwURYFLhJFf2HjAhYF977bVWUk04XHh3C1YjivRtG3ky8dZbb9l5L4CjibwRQHlCIDf9Q3lDXGQI99ee1KhcIttobfr375923nlnSy5/xUNWKaMwlqQWWWQR68v6ijjkSGUjHj/fnyFsCi8lP/wDgUAgxYczoxO0PgIM6Brgsa+66qo03XTT2aSA9gbjJyVNHq1fs3yXkHZSu73yyivWrt6vUejQZ6TFwa081afUX/y14lMGwrmWH+mPPPJI6396w4mlKMktV27lhSxeIYek9+vXr7AEpjx8vrh9uZGhspbLK8ICgTwj0OWaG93sNIJ357lRou6TI6CBXP2D/QdswJxqqqnSFltsYZH1NEtcxZtcSly1EgK0UZYMqN2w/SRfqdxKVyleLeHIVp9SeT///HM7KZv+d/HFF5vY7D4bpVOeulZZWXrivCbIjc63kfZH/V1pm2WrLKXkq8yE46ZNfNl8uOJUklkqr/APBBqNQJeTGyqkm1oDmr+BGl3hkNc9EGCQ1GSSLfGLL76YevXqZRMDb5LoTRXSKJ36UjZtXLcWApBS3f/c99XsWemMGlAW9T+VS32KjcYzzzyzHdynfTLE4VfJSMb1119v/Rc5fE8KQ919vpVkNTNc95LyoNzCw7eZwrGrxcCnCXcg0CwEupTc6EbPVk43UdY/rvOLgB84GVx/85vfmPaGA9B44wWjySG/KHWfmnPve23H119/ndh3gyGs1NjQFTVkovflYVM75JpXyVVebOJVMiJAY8eOteWozTffPOmVc4UhR+5K8poVniU3KpP8KR/3G9eyVRbCurr8KkvY+UWgS8kNsOukUBEabhY/kOS3aaLm9AX1C6GBH4ZTZPmIJmp9DmHTGSDE1xKV0oTdugjoXv/DH/5gnxf44osvChNmpVKrL1SKV0s4sv0ErXLyKvmQIUMKhFrjl8oiu1Ke9FGWprL9m3x8vpXkNCvc10NuykX5uBYeKqvCKI/iN6tsITcQqAaBLiU3ujFUUJ7eZLI3vfzDzg8C2f5BzRk48eepH3U+5IbfkksuaROFniKxw7Q2ArrH33vvPfvadY8ePdIJJ5xghe5o+2Un1Ox1vUhoMudzDZxGzMdcMZRTZFp9s1hehElGsXDvRzzJ9P6d6S6Gn+5H9ryxoZoTnU855ZRCWUlTDoPOLH/kFQh0KbkBfg1w3DhvvvlmuvXWWwvnPUTzBAIgwIBJP9GAK/uDDz5Ia6+9tpGbH/3oRwaWJkXFCQRbFwG10eOPP55mm222BLnRxyvVjrWWXrJrTU86ZGQJCeMUfRFbe4WUF7YIQLl8fTzJIz7ueutdLt9awyivLzN73oYOHWr3HZ+MYFlNcWrNI9IFAo1GoMvJjSrEevvyyy9vR+vzhV3eSOCGCZNfBIq1P35+wnnooYdskOXjhxBjjCad/CLX+jWnHUUE+BTC3HPPbe241VZbWeG/+eabspXwpKBsxBoDfT/DDaFRv+Ma48mI/GQr2+y1/EmLPB+O2+ejuJ1lUyZfHvL116r/yy+/bIcOojHlEEK/qbqzyhr5BAKVEOhScsPNhOEGOvnkk21w44bhDBNO/1SYOeIvtwionxTrD/fdd5/1G74NpC8z84Xp559/3vDyaXMLYItWXJMlSz1zzTWXtSP7qDBeU1es+ISzP0ftyxgid7H4tfhlZXKtMkteMT+FZW3ieuPlezne38dvppt6oTXK4q4y+/JxJhEaG8bqn/zkJ4UN0aRX/GaWNWQHAtUg0HRyU6mzc1NxyuxMM82UePOFG4YfJ3dygifGD1rZwaWaSkac9kVgwoQJaYUVVrA+wxssO+ywg31BmS+Hv/3229Z36D/qQ36QFiq+jxYLV7ywG4uA2uSpp55Kc845p7WhCKpfnqFNFBetHKdU//KXv0xXXHGFFchPyL4tG1va9pQmXNnvyP4ZTkpmDxRGYaq59gHx2YiVV17Z2gtyk9XcRBsIsbC7EoGmkxtfOX+zaMCCrKyxxhp2o/BBRJ7AITd8+Xmbbbax5P5mQYZuMi873PlEgLemjjjiCOsz7NlQ/5l33nnTO++8Y0+S9B9+TIxMhFmjfunDfJ/Lxo/rxiAg3J988snCstRmm21mwqVFoM2YeJlAedhhE+v000+f5plnnsLnGhhD+PHmktrat2VjStteUsDJk0LaQvcRXzk/7LDDEg8OEJ2PP/7YKq8HS8iN19wEuWmvvtEutelUcuMnDLl562X99dc3rY2O1IfkQHDQ5mgfBfG5uZROA2O7NETUo2MI0P6oxznvhn4CGcaW5m+hhRaycOJlyTB9yPupL6lvYcfk2LH2qCW28H700UcLmpvtttvORHGvQ2ouvPBC+zglEy6EVe3LHp1rrrmmEJc2jDarpRX+lwa8jznmGMMX8sj9BN7gjpaGcVh72dhzs9pqq1ncn/70p6G5qR32SNlEBDqV3Ph6MLDx46ltvvnmKzxxM3hpeYobjIOy/GFfkqGBUddh5wsBNpzuvffeNsDST/hBirHpQ5x9o0Ph1NeyCOlJlHA9xTKAi+xk48d14xDwpJOPUPK2FO22xx57mAbm7LPPLix9oJFTu/bs2dPcPAjxptyoUaNsKYW+MGLECFuu2nPPPdO+++6b9tlnn/iVwIDlp7322suwAzeuBw0aZG0gDSiflxDujMkQGYgo99Xw4cMtbixLNe6eCEmNRaDTyY3XvqB6Zu2cQU0Tk24m/LjJTjvtNKuxJzP+qbuxcIS07oTAu+++aweq0VfQ2swwwwzWl7ju3bu3DcLa5Oj7DxOrrmV3p3q3S1lFIp9++umEpo1207IUbcu9z4F5TKosQxHO+MCPiZc35FZZZZW03HLLpWHDhtneq2WWWcZs0rEXK36lMQCjwYMHpxVXXNFe7dYbaxAZ8AVv7inOkNpkk03SBRdcYG+xvv7664W3pYLctMvd2H71aDq5KTd58MTGt1XY/MlTmCc27J2A3LBJVGvpGgzbrxmiRrUiwJtRDNIMxPQXaQBQp7PnxpMbaWp8Xt5P/Ys+6/19/HA3DgGNDY888oh9SoM25GOSGLS1tAcaNbRp99xzT9p4442tjYkHeeXr8Bi0eMRFnpamsGnD+BXHIIsX9wn7bMAWcsP9g+brrLPOKuxd07LUCy+8YKSRuEFurAvGXwsi0HRyU6rO3Cj6Ki5ruvfff78tTaHB4alBTw+4jzrqKBOjwZALTUSl5Id/eyNAX2BAxnCYH0+gDLbad8NSJ4Mwhk3HvDb84Ycf2uZINkhOmjTJ3IRxDD4ywnQeAr79eMjR21L6yjttq/alVIrPuUYc2IjW5o9//KMVmHh+PMDtrzuvVt0rJ4ifMMZ95pln2mbtJZZYokAcRRbVBthsKNZLILHnpnu1eZ5K21Ry48kIbl1zQ3EaMWdb8LR93XXX2RPWLrvsUngCh9RAdFhj58mcCajY3ps8NVbU9fsIMChjONdmqaWWKqjT2XPz/vvv29seaAXRDi6++OJp6aWXtiWQxRZbzL5lxEC+wAIL2BMo8ZFHP5Xc7+cYPo1CQASEr7rPMcccdu/7V8HVDho3ZPN2Dq/500aafJGl8EaVLy9yhBvjK29I8RAgPNVGYCEixIZilrJCc5OXHtI969lUcgMkmixw60bBPvDAA+3mYGc+J5Ti5/ffsGEQgqPfeeedZwhzI2pA656QR6kbhYAICH2CHwMzey4YdNnDcdddd6X11lvPrulHLHvSryDUM844o2kHtdcLfybWu+++24qnvsqFBn+fnw9vVH3yJke48vkFaW50QjETqcLzhkur1ld9nrcUdc4NGpx4FbxVWyzf5Wo6uRHb9zAzaB133HE26ey8886FNXPekGA5igmHCYofExKT0SGHHGIiNODpRvNyw50vBOgLbC73ZJfNqWht6EcsXdCHINC8CcJAzPIncfi6M5Mq+wrYv0E8CBB7DW677TYDUl98FqrkV6w/KzzsjiGgeznITcdw66rYGnM9uYk9N13VGpFvJQSaSm40eFEIPfXi5mCo1Vdf3TYR6hRi/Jk4OJmYSQZCI3LDNU/WLGVlJ7NKFYzw9kfA9y1U6pAaaWToQ8suu6y95UE89UmfhuMIBg4caP2N+P369Ut33HGHAQdx0tt5pBWRkt3+6DavhmqLIDfNw7iRkoPcNBLNkNVsBJpKbnQz+Ergd+mll9pEwmufUj8zgTDYcSgbE4w2FOsNKt6C4YlBxk9O8gs7PwjQVzzp0JscaGQ4fAxyAyHm3BQ0Nepnvt8gQ30UgsOmZGl7FlxwwXTzzTcboKSRxoY0XkZ+EG98TcESE+Sm8dg2Q6LuldDcNAPdkNloBJpKbigsm9T0lMukwP4a9kOgmWFjGgOcBjnC8RswYICdr4DGRsfpc94CG4vZV0E8pWk0ICGv+yBAHxDBodRoBLXHho3oLDkxIKu/YGNkk159E39OxNXrsBBsNh9/+eWXJgM5Pi8TFH91IaB7GHKjD2duvfXWJlNksq4MInFDEQhy01A4Q1iTEWgqudHNQB14smZS4dswEBv22uj8GoVjM+Cdfvrppr0RsWHPBJMNWpxDDz3UINHAaBfxl0sEfP+CeLBni35Cf1l00UWN7NBPfF9RGkiN3ITLzWuuvFWFHEg4y1zIloysnUvgG1RpYRnkpkGANlmM7pHQ3DQZ6BDfEASaSm4ooW4IuTkmnSWD888/3yqgp2jF4/rEE0+0yYV4aG/4MdmwVMXH3RgUNTA2BIUQ0m0RUP/hRFte66afcJrqxIkTi2pc6GfSwNCHpLmRHM7DOeCAA0wOS1Pjx483bNDqYEij9N0WtBYpuO7hIDct0iAViqExOshNBaAiuCUQaDq5oZbaD/Hcc8/ZBARZ4eRLDAOcBjld8/0SJhZpbkRssDlunTNvfBoTFH+5QoD2FzGh4pyVAvlFK6iPL3oSQnwtdeAWmdGAjQz1KYjS7LPPbgSHZS4MsvgpvuJaYPzVhIAwDHJTE3ydnkh9P8hNp0MfGdaAQKeQG8rFZKLXv7fccsv02WefFSanYhMNmzkhM2wKxRbRYfIaM2ZMgRSJOJFHdjKrAY9I0o0QYHIUwWHA5VMe7LXhDCUMYZpAsbPkRmEatNUPITezzjqraQy33357k0Va4iluJZh82Xxc5en9OttNGZpZjkrylbdsNnMXO+emHlwkuxYZlcpfSWa96SvJrxReKf9K4cj3cejz3Evq+yzd6pwbDsjkxG+M7sVK5VM4eUgmfrh1DypO2IFArQg0ndyowzNhcAopGhm/yZiCF+vQkJ911lnHiI3emILkQG54bRfDzYHJnlzsSY5FiL+2Q4C2V9+ickyQHMzXp08fG2wZKNU/ZJcDgTiKx1ePdfYNfQ3ihNHgK7ucPMIkj/hKL/+O9lGVT7bqp+titmVa5K9Y3GJ+RZJW5SVZpSIrXLigdeNsIu5vHeLnH1pKycn6e7mSnY1TzbXkVBO3WJx605eTKdnYpYziVApXvGK20hImLDVO89KHvgrOkR7ZQ/yIp7iSU872eSgefmECgXoQaDq5oXB09JEjRxoxQRPDZxZ23XVX+7bUbrvtlnbYYYe00047mT9LCoSxN4cDohjwtO9G2hu+XnvRRRdN9mROPh2dMOoBLtK2BgIaBPmAJpqbWWaZxb5eTOk0wGpwJq78CFdawj1REhGn7/HFab6CTBzJ6UjNycNrjGqR0ZH8ulNcYQG5QVMG3owDmLiXu7YlaRt/T1Aa3Tvca3zfi/YqRm6qKbnkc3/oV026iBMIVItAU8mNNCo8helYfBEUbgx+aGWkmWEDMT+FYWtZSpuKFXfddde1k439G1e+0ppQvF+42wcBBkQmQD/gclwA/UuvExd7+icdRrZHRLL4bhFn5dD/NthgA4vi+xMDsyZmnz7rljzuA758zTEIDz74oP3GjRtXcOPHByH5Kdz7KayjtmQpna6xx44dW8hT4Vnbx1eY95O7WJiXr3jeJg0Y8NFMzr3iHCvu/VVXXTVxsCfhxJft08qtfLO2wr2tOJX8fHgldzGZldKUCi8mS36V7FIy5a/0uqZtfPso3Nu0Af0VP071fuCBBywNGtJrr73WDsbk/hg2bFi65ZZbrM1oT+KTj2zl6W3yZl8l8dmwr/sUMlXNfZW9z+I6ECiGQFPJjTKkw6LGhKDsvvvu6eKLL05nnHGGHdjHoX28wnvaaafZ75xzzjH/U0891V4J5+ZhwmIvhTaMclNxzaCIYaJi8tGElX3iUDnCbl8EXnzxRdu3wQSppQ1PSNRPPAL0l6yGgL7D15HpY35zMv7E1+CrvubllXIzOdB3IeYi6ciP3/8woM3AhfsczLMPQIFT1/UV2oWjFRhvaQfahuVf/Gkr+jTXeuistq10H3DsAkQIo4cBf5+VuqfCPxCohEDTyc0333xjRIb9NuxjgLF78qEOTUE1cfhC87VntDfcXF6rw431u9/9zqKSR6n0Xla42w8BDYSQlMMPP9wGYD7hccUVV1g/832qFCHBXyTn888/t1fJGaTnm2++9Je//MVAkxzfX8uhiUzF5SlYEwN92fdj3MV+xC/mX01apaskQ/FK2ZXSVwovJRd/peU+xs1kh78mSexS6Ynvf9l45cIUlzhyd5VdrgyVwsqFqz7l4lQKy5JMZIqQ4CY9bQRpx81PhEfXslUeb6udPbnx80K5eyvCAoFqEGg6uUHtSEfmt+eee1qZNCFxoUlDhaWDyw+bH/twuFG4kfQUkZXnn9JFdiQz7PZDgD7k2xwi8fvf/74w0PIq9zXXXGMVV3/KokCa7ICK2p2TielvfIqB822II/JDvphSMrN5cM030Q4++GB7i2vEiBGJ36hRo2wfGnvRcPsffsX8fZxq3KXklPLPyiwVT/6yS6UrFU58MNh///0Nl4MOOsjc+I8ePXoyLLxsyZOtMN6O8z/5Z+Nl/UuFK14lu1npq5VbKV6p8FL+vr6KQ9vwI4w+zAeMCdt3333TfvvtZ360JXFoO9ycE6X03vbyiY+ME044IbGBv6P3VLH7LPwCAY9AU8kNkwcHqsHY+doySwfeaKIgXqnJAn/21WyzzTY24finBp4UjjzySEsrWV5+uPOBAP0HwwF8l112WeEofz7XgSYGYkL/8H0k29+++uorOyByySWXtH7G5wCOPfZYk4t80pJG6bKkqBqkkaGyVhM/D3F074ONMBXGeah/q9dR/b5cOYnDT8a75Rd2INDZCNRNbvxApMFJAzib0HgLAkLCXhsMHV/7Y3xa3P4mkQzS4NaSg9SZsldccUV7m0XxLJP4ywUCvv9oQP3ggw8KrxVDfgcNGpQuueQSw0N9zPctAuiP1113XSEdWpuNN97Y0mizYy4AjUoGAoFAINAmCNRNbpgoRGqYJORmsmFjJxMFkwyv1+In9b7HTxMTfn7ikT826ktkabOhyA32SSedZOKI5yc8n0e42xMB9Rf1FbR8qLzRvGg/B6+HswcHw4cwWWriDSbS8rFNlkvpW+pTaG/uu+8+ix/9qT37TdQqEAgE2huBusiNJhQgYqJg2YkDz/hy98knn1w4wh5CcvXVV9tBay+88ELiMww6+CkLr2Qy+RCPSYZzFdhRzymmyNLGNG164+vPzz77rL1W+NJLL6WnnnqqcGpmVn5ctx8C9BmRamrHNa+e6hBI9mrxqjFv3rFUxZt7aPyGDh2all9++QKxmWmmmdLxxx+f3n///cm0iO2HWNQoEAgEAoH2RqAuciNoIDZMLv379y9oangSltYGm7dEeDKGmKDJOffccy25yAwX3g2h4RVD7bEhjbQ2/ikbf+Wj123Jx3+7SuUMu70QkNbG14p+qPOVIClrrLFGgbz4foPb/9iAfMwxx5ioYnJ9HuEOBAKBQCAQaG0EGkJumFDQxPA0DMHRD/U+7r59+6Z+/frZD7/BgwfbQVBA4wmNd3MqLF95XnbZZc0eMGBA4hVfbF4f5E0WwhdbbLG06KKLWh7kJbeWIbzM1m6KKF0tCPhlo2JtzTLVnXfeaYeM8b0yPry60EILpfXXXz/ddddd6d577zXtIG7tyUGOfxOrlnJFmkAgEAgEAoGuQ6AucsMk4JcDeDOFj6jx1go2X+/G5tXsSZMmJd5I+fDDD81m3wOm2ISEP3tzkDNx4sT00UcfmSzkIYe0hJEf1zyp8yMucQiP18G7rlN1Zs5ZciNSgr/C1EfpI/QLvlvG3htpaGRn+3Nn1iPyCgQCgUAgEGgcAnWRGxWDCUUTCX6aLHB78uLjZMMky8dRWsnTxMU1P4XjrwlMfpIXdvsjQF9Qv1FfyfaDbLiuQYe46j/0Ja6z6dsfxahhIBAIBALtg0Bd5EYThCYCXZeCR/H0xpSPrzCl9dfeTbi/1mSGv3f7OJIZdnshQHuLlNDe2muTrSX9jHCRY9/vFEYa3398HNLql5Ud14FAIBAIBAKth0Bd5Kb1qhMlCgQCgUAgEAgEAoG8IxDkJu89IOofCAQCgUAgEAi0GQJBbtqsQaM6gUAgEAgEAoFA3hEIcpP3HhD1DwQCgUAgEAgE2gyBIDdt1qBRnUAgEAgEAoFAIO8IBLnJew+I+gcCgUAgEAgEAm2GQJCbNmvQqE4gEAgEAoFAIJB3BILc5L0HRP0DgUAgEAgEAoE2QyDITZs1aFQnEAgEAoFAIBDIOwJBbvLeA6L+gUAgEAgEAoFAmyEQ5KbNGjSqEwgEAoFAIBAI5B2BIDd57wFR/0AgEAgEAoFAoM0QCHLTZg0a1QkEAoFAIBAIBPKOQJCbvPeAqH8gEAgEAoFAINBmCAS5abMGjeoEAoFAIBAIBAJ5RyDITd57QNQ/EAgEAoFAIBBoMwSC3DS4Qf/zn/80WGKICwQCgUAgEAgEAoGOIBDkpiNoFYn773//+3u+xfy+Fyk8AoFAIBAIBAKBQKApCAS5qQPWf/7zn5YaMvOPf/yjDkmRNBAIBAKBQCAQCAQahUCQmwYh+a9//atBkkJMIBAIBAKBQCAQCNSDQJCbOtD773//mz777LOCBK4xQXQKkIQjEAgEAoFAIBDodASC3DQAcpEabSbWdQNEh4hAIBAIBAKBQCAQ6CACQW46CFhEDwQCgUAgEAgEAoHWRiDITR3t89Zbb6VzzjnHJKCtCY1NHWBG0kAgEAgEAoFAoEEIBLkpA6SWmfyr3XpD6umnn04jR45M+++/v0kgLnttPMHx6YiUvS6TdQQFAoFAIBAIBAKBQI0IBLnpAHAiO3feeWfq3bt36tGjRzrooINMgsKwRXBk+9fEccu/A1lH1EAgEAgEAoFAIBCoEoEgN2WAkqblb3/7m8Xi+ve//33aYost0uqrr55mnnnmdNRRRxXCvCiRHflBaERqsCVb4WEHAoFAIBAIBAKBQGMQCHJTI45ff/11GjRokC1NIQIyA2ERgZFYESOuRWi++eYbBYcdCAQCgUAgEAgEAg1GIMhNGUDZXyNCQjTv/vLLL9OAAQPSfvvtZxIUBrnR0tOYMWPSSiutlIYOHWr2CiuskE488USL75eqyhQhggKBQCAQCAQCgUCggwgEuakAGJuEtYnYR0VzM2TIkHTYYYeZ91//+lcfnCZNmpTmnXfeNNVUU6VpppkmTTHFFObefvvtLV522WqyxHERCAQCgUAgEAgEAjUjEOSmDHTSxigKWhktO3311Vdp6aWXTqNHj7ZgbSSWRuajjz5Kc889d5p22mmN3Pzwhz9MCy64YBo1fXnnxAAAIABJREFUapTF//vf/y6xYQcCgUAgEAgEAoFAAxEIclMFmMU+p8CyFHtuDj30UJOQ1cR8+umnRmamnHLKtNpqq9lnGj7++OMUpKYKwCNKIBAIBAKBQCBQBwJBbmoED83N4MGDjdyg4ZFGR+JYlppvvvkS5Gbttdc2b09ssmRI6cIOBAKBQKAaBBhzpF3GHWNKNahFnLwgEOSmxpaG3Cy55JLpwAMPNAkMLOzN0QDDBzV/8IMfGLlBc4MhDI3PQw89lMaPH58effTRyX6PPPJI8r+HH344tfKP8vvy4vbl9WHev7Pd2XKNGzcu8VP5suVReNa/o9fNlt/R8nQ0vspfypY8wuXGFn5K58N8eNa/1HUp+aXiV+vfqPKVyk/ys+UvFb8Wf7BmPLnnnnvSk08+aeMMS+Mah8wj/gKBHCIQ5KbGRmdD8fLLL1/YQ6O9NhL3wQcfFDQ3a665pnmT5pJLLkmbbbZZOvbYY9MxxxxjNm798OsuP9UhW96jjz468VOdSsXLpmvWtcoh+dnyyd/bxPHXtbiz+XoZjZDv5XWlO1vPRuNbSn69dc7K9fIa0T6Sj+1lN8KNTN68PP744032EUccka6++mp7wJI2R2NR2IFAHhEIclNjq6O5YZPwL3/5S5OQfVL68MMP0/zzz2+amx/96EcWBwJ01VVXpbPPPrtomhqLEskCgUAgZwiwDOU1xbj1VidjUbF9gjmDKKqbcwSC3NTYAdg/w9Pdn/70J5OgNW9sDORmnnnmST179rQNxfiR5ne/+126+OKLLU6WEJln/AUCgUAgUAMCjD2htakBuEjSYQQ0z3U4YScmCHJTJ9h+MPFkRctSnHOz6qqrWi6Qm8suuyxdeumldt0dOkid8ETyQCAQaCICjDnS2JBNjClNBDtEdysEgtzU0VzZr4B7UZ988klaYIEF7PC+H//4xxYEuUFrc9FFF9m1J0M+bbgDgUAgEKgGAchMjCPVIBVx8oZAkJsGtbjX4CCSM230tpReBWdDMR/evPDCCy3XGJQaBH6ICQRyikBWU8OYkh2LcgpNVLsMAiLF9JdyvzIiWj4oyE2Tmohzbnr37m2fXPjJT35iuWjPTUc0N54A+U2C2UGtnmp4WR0dGElLud5///304osvprfeeiu9/fbb6bXXXktvvPGGuVmie+WVVxLaLIxuLF9m5St5hPn6ehyUDj/i6+OkuKWiV3yfF26fj+Sw0ZvykkbhhBG/mCnnrzL7fFVOySQPfl4ObqVVvGJ5Z/2y6QhX3bNxi12rDNg+HdcKIx1uYePDcPtyI0P1VXylz8ovVp5Sfr4sxeKoHCqb8uZabtKpbLgJy5ZJ19hyKz+uSeON8pM8nx/x1R8Jz157OeEOBDoDAfXpiRMnphEjRiS+d8i2iZVXXjkxT2HzAgw/rv/85z9bsbjH1fd9n++MMteaR5CbWpGrkK4R5CbbifwgXSH7moLV8Uns3eWEMXhfccUVtvw222yzpemmm87eEMPm0xMcYsh3tWacccb061//2kTpO1zZ1+ez9SWyrzPhEEQZf42/n2SJ4+tAXD/REFeyr732WiOi7777rqVR+bx8ZHkS5ctGGLJ9foSTXmXybsXDT2VQfK6zAwnxfXrJ9PUhHDyxJcscZf58XLkVXWWULMJ9WXGr/Qjz6bNxkaW4kl+N7WVm45O/2sOHgY3HhbBsebwfcnx7Z/PMpvV1ER4+jdyyie/Lk5Xnyx7uQKDZCOi+fvbZZ9OwYcNsbJ5++ultrGZ/aI8ePcyPMZvxWy+/fPPNN80uWsPlB7lpOKT/E9gIcqOiaaDk2rsV3ihbHV+Ddjm5lIP4/NBEcTPMPPPM9vr7QgstZG+K8eFQXpfnpGaW6I488kgTiXzl5UkB/qofbpUDP+IpDCGkV7hkKZ78iYefnwQVx9ftpptuSossskiC3Ei2t0kjQqF0ytPnRZjiYssQV4MDbsqDLRnYii8/8vOyOaiNYwd4Cw+jMNLhxuYHyZMs5V/OVlxs8tQ1aZ5//vl0yy23WHKRSj9RS67KPGHChLTxxhun//u//zM5qpdkE9+7lR4/xc366bqYrbKiKdx///3Tueeea9EoozBROrSGnAmzzjrr2IZ+xVF41iZ9MUKWLb/KQPnBD1vpcMuojv5a7rADgc5CQH2SsW6rrbZKM800k43RvXr1KhCbOeaYw8bsfv36peuuu86K5vu0ZHRWmWvNJ8hNrchVSNdIcuM7kx9MKxShqmAvG3d2EK4khAEddg+5YeM06k4mEurPRIybj4hynZ1wfN7KR/XTNXbWT+UkDJmKowlY1xbw7Z/iyVYYsrhxKaM3yhNbboWrHiJcuvHBwmsBik2g2TrrmjxIj1F+2AofM2ZM4gmLU2jJH39fF659/VXWUjay+VFGDOlVH645SZvvpu29994WronbpyGAa9X/gQcesH4wdOhQI3DIE6kzId/+kUb18v61uCnX6aefbvnqtHC1geRxXAOEe/jw4TagQ2Tpqy+99JJFAUfkUC4Z78aPunisCFc9sNV2Sk/9sn4+TTZM6cIOBDoDAfo8YzMPBpjf/va39oFnxnE+Bk34O++8kzjPDePvV/VjC2jhvyA3TWqcRpKbJhWxpNhig3WpyMTl7B5uCm2c1kThbwjS6xqbyVNG/lwzgSgMDYcmcOIgl89ayJC3T8s1m7aRwYSrNMRXnWRrQvOyvIaHcE3MyOEmz06ayNLELjnY5K9yU2biEFcGNz/kKk9NnOQpP+IrHeRmzjnntE9G4E9awpQPaSif/DsyeSoPbLlffvnlNMsss5hG6/XXX7eiI1tlytYJ/9tuu836Aev4n3/+ueEgedjUUdeSYwJdPeWveik8a9MPOFJh1113TUOGDLF8GZQxanvct956q9WDT6AwWGPQ4NBfR40aZddqZ7twfQUM6U9qD8ovAgkW/IQD9VKZ8Vd74qc02B1pF5Un7ECgWQjQfzE8AMw666x2Xxx11FHmp/tI/Rpb7maVp5Fyg9w0Ek0nq1HkRp0P0QygfnJw2dXk1GRF4lLuUoJVDmxpbjbYYAOLnr0BsgP6U089ldZaay37Ho7kf/HFF2nPPfe0TWyPPfaYaVLY6MYyAtcHHXSQ7YvBjzfOmHRUBmSgMWJZgqdy1KmsJ99xxx02mQhD4vDpixtuuCFRBuKtscYadmw9xExLPmyARvuw7rrrpjPPPDMtscQSadFFF7Unf950I979999vhzPiT5w333wzkU6G+vCpjb59+6ZlllnG9hsRhw3XGCY/wpdeemk7Rv/pp5+2zXzkxdfmTznlFItHWRdeeGErKxMyboXRZo8//riRStINGDAg7bHHHvamHok9Piaswp/iQwK22247W1qcYYYZ0vbbb28pwVFYelG8GcgnBeaee+7CfisIDt87wvi+xXU9g6RkocFCE8MnUMi3FFkBK8L4VIEMm9t/+tOfGjHCT/0T2aofROTkk0+2j+PyaYNddtkl9enTx9pht912S6+++mqC9J111lkJlf5+++1ny3j4qYzgwqZNlmZJu/nmmyeW7iDKiqMyhR0IdDYC3O/q+9dcc03iXudeOfzww60ohOnny0Y6jRXev9XcQW6a1CL1khs6FU/rJ5xwgqndTz31VBtsGawZdLnGXc/vpJNOMjmSjc0PNT9EAFOpEzMJMElzU6y33nqWRjcMafUE62WhYWCy4MOjGOL/8Y9/NBmQHswTTzxha8BscJtmmmnShhtuaFisvvrqFo9lBcgC5s4777S9Pj179kxbbrmlTbQQpbnmmssICupXJtT33nvPSMpyyy1n5IqJB60T3+RhyQdtBYa3vvQaP5MSTzJshuYbYdSTJxwGArQG7C+BVJAePDFMvJCeqaeeOkH40BSsssoqlpZJH60D2gDSIo/N15yJxJ4atAnsXUIeJ2AzibI8BCljwzbk5a677rJ8WA8n/YILLmj7mYiHhgJ51AuTJZrm+e0f7VMs/De/+Y2Vn0+FIB+SR1y0MTJMzmpnbIjMpptuam8H8tkRCMFzzz1n0Ymrydz3J9z4yw93sfIoT9nkJxJCGmliDj74YIsiDRtyGaghmPRDrtUf7733XtOEXX/99ZZGdVFZaCOwpv6cNL7jjjsaQYLE0K9oH/olfRjitNFGG1ncn/3sZyYPYgNBpJ+wtMd9utNOO1nbQNQx1FX5mUf8BQKdhAD9jv6n+0FjIP1dmhuFUSTuM37dyQS5aVJr1UtuUJUzgbODXW8c0fG4xm70z+fBJjO9AlipQ3OTsDxAeVg2gZzwVAwJYaJFI4K2hbrofB8gf+aZZ9JKK62Uzj//fGuBCy64wDQk+rIxu/mZ5CEsW2yxRWE5Cq0J8tnJTxry/8UvfmH5a8+FJkieRigXT8wYNtEx0fGkr/oRF5Usm59Fbog3++yz2xM5X2qWQTNDXcgbEoiBOO2zzz5GxERujjvuOJvEIE9a0mBPD2nB9sorr7S0hx12WKF8ECoZygZBGThwYGEpDAIHvpAdGQghRMuXEZIBbtoro/yVxk+mcmPLTXw0cWeccYYty9GeTOD0Rw1wvk/4tGx6Bm80KhgIg+T6NAya4EFbQqKxIQP48UPr5QdWZElOth6QHJFEvyylNJCbpZZaypIRV33j7rvvNkJMH8EQJoLDNUTu5z//udUHQoNRWvYnUE8Iq/oMG6/xg4Rj7rvvPiNBaLRk6PNgySu21BnjcVG8sAOBzkJAfZ6xhGVo+jAPVRjuQd3zXPt7vbPKV08+QW7qQa9M2kaQGwgCpIOfSI1/VY+OWM/PExovhwmT16MxpQZfTTbYfFKC9Gg7eDrlKXfrrbc2UoKGZocddjCCwdMBhjTIvf32200bccABB6Rf/epXNnloAmGZhqdetBKo8bnRlCcTPBM4EwXLNtrxj6aBPPjxRI7Wi3KxZIPh3B00Krp5lRfxIQ6cdYNhkx2vrqO1YRISBhAZNCg6t4jyUDbqS7sw6UGMkLX77rsbhgwaLKPdfPPNCTKD9kXaArQblE8aB03oDDhM2NRdfrzRhVyW02SYmCknH2J94YUXzBtNGvWCpAgvbNVVaRXGtXePGzcuiSRSDrRyYH3aaadZUmlMlAZsVEY0StQH0goZIk+F+XwgAuBFfcAD+fQ5BleII2SE5RuMsLcL96f8KSMExuMIBkoLjiz9UQ5kCQfKylt8agv5Sy4EizZEO3POOeeYPBFF2oKlKLQ/MhBkyiDNDUtfvDnGEhZvnWHIH62S1ywpP8nBLubnw/Pipk00+YKd3L7+YKU+Infg5xEq79b9yTjFOEofLqa5KS+lNUOD3DSpXeolN9zYDz74oC0v8KTPxH355ZenP/zhD+bHdT0/ZCKPiR2Z/JiE+RHmN+6Wg4iBBY0MN4WeWjUJMMgwGWrwkRxNJBADljKYQFDhY7jZSMdkzbIOZER7FDRoQVKYDNHCQMJYIiAu2haWDNDCMOnzSiNaGg6kIk/ejmHiPPbYYwt54YB4MNFqQmVfDMQKcgMOGlR5mmfZCy2UDGWDvDFZgx1lI2/KR1koA8sa2MhcbLHFLD/SS+OAhgcj4kBZIWC84YMBP0gcEyp7bGTQevA6J+QAIjxy5EhrOxEb0glrsNNAhpsfYZ4IIJc6ZvNAQ4EWSeSPdMgSLmoX+iv9YMUVV7QyE09hypM8IAdopsAcbGgvsGFpDps9Ssor23dUd8klD7Qj5KsNwsKRuCwZsQ8LQ1yVGWICnl5zo75HXPZ07bXXXiZXWjphdeONN1q/QlMlw8ZlysAeMZWNNqNfsBTJkiMklHHB46L0YU+OABgWa3thiy335CnjqiMIaEwIctMR1HIet15yI/iK3cAMjs0wxfIqlY8GFwag8847zwZ2kRsmkOzEyrXSIJNrCAybLZngtGlVkw8aCp7i0brw9hT+eqtF2hEmLe076d+/v23wJAySAsmApODWJl40MosvvnhBc0MZMBxCyGnSIjdsCkWjALlhn47iffrpp6aNWn/99S0d/pSNZSk0ayzPMSlDatjzQlpkkS9P8mzUpVzUBQKiZSlpSjTQIJdJmUlfEyqDD5oc5AgH8GQiRz5YoiEADzbZsqSCUVvYRZE/ZChf9jmxtEP+niBARpi4RRKzk7P6o8gNy1KQA2QgP2soP0t8Wpqi/HKDETgL82za7DX5QAQpn3AUZuSNdoy2pV0op+SibYQMQ2wx6ncqL6SVpT3ksgcNI0wg1GzsfvTRR82fPx40iCvNDflQT/oUP9qNJUPuETaxSwOn8iBDeReE5tgBLupX1cBAXMUHx8CyGtT+9zBJzCA31eEVsVKyJ7R6P7/gB74sqI28eZWPZGJrsM/mm71mwEcDxMCuzcBMlgw0kqdBh7Rya3Jnw+348eNtSYu9NTLsueEpHpKBdokyqpwsWaEdQXPDsgIkCG0BholNZc/abECG3LABFaPJiiUFNCvaz0I8VLRMYGhHVGYmXZal2GArwwSmSRCSx0TNPiFNtB4H0ggTMJLGQXFFMrBZaoHMyKBhAAtwwVB2P4lLLpomlsl4WwwDBoTxAz+fh+pPPJZhUEezRKh0wpu31WhftF7IQ4bXzglntCHEY9mOcNKrXOCg5RgIH5tqOdQR7RWbtbEhIocccoi9fSbNodJbodyf/MlbOGrPDbgQzo+9Q5QJAioD0aEu7AmjTYUJ8dXWkBttKGYzMEZ4oZGhbR555BGJLJBsyAt1R5bqjy0seaOP8khTqTYsCPrWQfow/3sIAnfaxeMobLI4qQ2z/oof9uQIqO8HuZkcl7gqg0CjNDc+C25Y3bQahH14I9ySX60sJhdpblga4SkV7QR7VdCiMNmjwZCf5LL0wavYqOox7LlheUlv2KCFYDKHuLCBmP0uyED+NttsY/sz2A/Czbnvvvva8hUaGp78ISlsUOVVcF7p1qRHOPt0dFKyJhyW5NgvxKSLIR/IDRMYGgbFo03ZRyENFVihuWGiYlmMDc6qC1of6kgaJlPKxSZWyBwkANyYyJno9PaMJjry4404NFMa0NmfhIaLN+gw4MEbYcgjH+rG973Q4FBGlpIwyGRyIH8IDIa+o3aWzRILb/wQD0O+CmM5D6IOIdDr7sgQqRE+OsSPt9G0V0l1MqHf/rHnhrqw5MgyHXKpK25w4/s2aDpUDp9WbpWNutF3wDG7d4m4epMPDSDLhpAmkSGWijDIQJ6/p4jHfjHkZpelmAjAg/1JMizlEhfNDbhAiNBg0bcgdfQp2ggCSTzaXnlLhrdVP++XN3cpDOhvaivi+L6Kv8Lyhlct9Q1yUwtqOU/TKHLjb9RG37ialGgqn4/3L9WMDCoaWHTODRoL9lLwY7MyNpM+e2rQtPB0jkHbwgAPaZGBzLBEpSUf3ixhLwn7MnjCZg8LhAN5bDzVW1akZ/JA08H+BsqAxgf5/Fii0Um0TC5MqCI3qjOaG5aS9MYSE7zIDW4mPwxP+Ztssontq1C5WRZh0yjLUrxCjYFYsTeH/KkD5UE+1+TPRIdMbYTNam4gBGDFHhQN8FoaQp7Kr7d2qDP+YEweEActpVBHtY8mcyZf2lj1oswQDvYSsaxFfDbRQlpZagNrverMvh4MMigbMqTF4Q0h2pofSy/cAxjVwS6+/cOPslEOESBsfmoXHz/rlkziggf15lV4jOThpn20cRt89EYI+5m0JOXzk1yIINoe5GoDuuTSX8B47NixhWLpbSn23GB4iwpNJvcB7cOPPsU9wRECkGLyVX4FQeH4HgL0EX7gpclY19nIgWcWkfLXwjM0N+VxilCHQKPIjZ6OEc0N3UijgYBBww/wHcmDZRm0CUwaPH3zBgqTMjbnnfCEy94RrkVu2HxJXCYX6qQ68uTPkgbqfiYHyApkgKUh9uQwOfF0r28IkU4TNBMsT8PaKyNShCZDBs0G+0a0LKX0bBDljRqdmwOhoQ4sdfEEL9zZF8IGXt4Iw+BPOBoD6inCRZkgSiy3UB4mQuqLNkYaCSY39tV40qeBhnKhLUADonaBWLHcBMnT2wxs/mXvCBtWWVYjD5b29BaP2pdlPSZp0mM0SSsc2UzktBP1RpbajLLzY/MtNm9CacOx8JMcsOC1fCZytDF63Z78iOPzVb3wF7649VO4FbjIH/EwpOUQRUgDJAYjHBWHvkFf4xwmSCbLiNrzovyQIzcyIDeQQTR6EDyMltUgN/QXHVtAGFpC6s0bVqonmkvag3uD/k/fYxKh7TEqn13E32QI0B70L4z6B+MC2jT68bbbbms24wI/3s7k+2LsG8NU6j8WKf4K90qQm+gMVSPQKHKjDHWDc+3dCq/F1uCqyaBW2QxCEBCWLKg3mguWJfhBFHTN5IchvpY3fLmpF2nY7wAJQAMAuWESp4xoPAjHEFcDGPVAJn5MKCyDIZ98McRTfNJrslV6bXAljmSTlknfG+JTFupBnorPZEU9pcEQrsQnP8oCPpItmUyWpCN/jG8H/CBTGA3ykCaW+UToyIcf+ZMPdfcTp8pH/mjEOIQQQ3rCVAds6gu5o/1Y8oIIgiNlwE04ZcUPbQhG6e3i2z/KqnS+nCqL0ikNMlQ/hfm4ilfKJj1tAr4iFfh5o2vaB4x8O6kP+Pi4SUM9FV8yCCMfMFJ7EYabeuOv9NhgAH781J4+Ty/XEsbfZAiAj/DSpm0eenQ0BqRdbog5Gk6M71OTCYyLyRBQHw5yMxkscVEOgUaTm3J5decwP7j7SY2nYp7GWT5gMmGA04TcnevbmWUXtrzFxAGCaKgwmiw6syyRVyDQUQTUfzUu0H/RyKK9ZH8eS3780H5CeNhPx742TJCb6tAOclMdThHLIRDkxoFRxqkBjCgaxHDzRhRPZWw6BktPbHyaMqIj6FtM0bqwDCMMY+CPrtFdEKDPagJmqZAlUV4Y0D4qllA5twqtIi8jSDscY0R1LSxsQ3NTHV4Rq0GvgucBSD8IeXKDCp89OnyYkBuQsNA41NYjPMZI4DqwrA3LSNV1CNBvNUaw4Z2HH8gNG98xflky2+e7rtStnXOQm9Zun5YsnTQ3qE11XD83H4e7XXTRRVZm3agtWYFOKlSpQQh/ftqP4ouDf2DnESntFk5gJpJYOnaEBAKtgwAaRk9Y6Mv0Y4wnNzqCgf5NuOK0Tk1atyRBblq3bVq2ZEFu6m+a7CDlJ+r6pbe/BPALDU37t3O715A+zMZsT1w4dgHNDW8jitywUVyb2Nsdk0bVL8hNo5DMkZwgN/U1NgOZbjwGt5ioO46nJ4PZfTYK67jUSBEINB8BT2Rwy8jN6/+QG17tlyZc4wV9O0i9ECtvC7PYc1Mepwh1CAS5cWB00KkBrFyyGLzKofNdGFh6rLiuBt/vJIQrEOg6BHx/hbRoMkZbI82NP18q+nbH2kp4BrnpGG65jh3kpmPNX0qT4CdmJJaK17Hc8hNbeGFr/4L88oNC1LQ7IgBRUV/FzVgg8sJnW6S50bKUDlnsjnXtqjIHuekq5OvMVxOjbgh/c8ivWBYKw/Y3jG400qDm99f4IV9rvpAbTjf1G4qRxfdtODYfo3zsosIfeWlpQTZJ6JySo/p62YSpAyueL7f8SKP0+Pk8ShVN8akznzbQAXrYvJrJD7f8iVcMN5UBW/gpT5WVMH7KU+FcKw1uH+7TKj5+yk9xwYfyY5QHdikMlJ74ykPyvY184gp/XSsfn9bnK3+fD275K73Pi7BsfB+edXt5wsH7+fjyx4YgVYOLT4+btD6fbHhnXFMGfhjwEp5Z/2xZfBq1JX5KT3zFkZswX1+uiSM/5aF0Xh5xsxh7Px9XcvJg+3rzFiXkhrelRG4YBzyeecCk3jpq7OTDvPpsjU5AV18nD/ofPwwYC+d6829m+imaKbxVZDOgqGEokxpGDasnWtkKV+PKLlUf5Hj5xGNi5/MDkBu+s4NBPjfihRdeaNfZNOaZ+cuSAZUFWdn0fuCkDtlwRKtusvHTQCrbh2WK871L6s6NwUAz99xz2w3CTaLv6HD0PIfx8dOHDX05cZMfxE/5ylZm1ENthVtvUHk5lF3lVzps4gsHxUe+8iCMj1JyWCAfzgRfhZFeaVROyeZaYV6e/IiHv/qUrn14OT/VlzgqP26ML1/2mrBiOPwv5eT/xFU+5KGTe3HLP5uX8gMn1UW24mLzQw62L4/8Jy9Jc67IX/cLbl+ObI6USwY37aa2y4YhS4a6Sy62MFQ4aYWPL4NkEI5b/U5xlZ4wtQV+KotsxcuDLayoa5Cb+locLPmp7zKGcxgi47jIDf2Ofqn+1936XC7IDd1AgxyNybd3+A4J347hLBU1tLoLg1p2kFEY9rPPPmvfkOE7OiNGjEh89BHjOwFH9/MtHk7O5GvZGORyhLg0NxrgLLDMn+JRTtVD0eWnTip/yk861QM3aSULf8lSp5VNWFae5MomLvXF1ocZITWQmJ49e9oHLjlFlPrzGQX89fVr5JNOZZFMbMklzIfjz8SB7Q1xNAkpPXGKlV/yCBcupOHLzRBRPm1AmORlZft8iefxU9v7OHL78uAmrsoim/Ly8/nj5ofhED6+VaUj/H39kEF9VCfS+HCVo5Tt4ys/4kL2/vKXv1gy8iBM5VU8bOGA9ov7itd0KYv8EeDT6toEd/Ifh0PedtttlqvKJ/zw5NMffK+Lb0QxgepL6r6+wllYYBfDG3/ywNa9onj4eUwknzJ4t4fHx/f+eXH7+ge5qa/VwZKf+qPfc3PCCSeY8GJjmvptfbl3Tuq2Jjc0njdc89Xj2Wef3b5AzcfvYKt83I7v09Bw3jCIqfFJywcI+XL1kCFD7McXp9FWsGOfiQejZSy+58PH8tDc8LVlDJPm5ZdfXrXmRoMoaSmbyqJJPlte4hEnW28vR7KwZYgv2fKr1qYMvK0A4+fDgJwSygTCKaJ8/JKve/PxSr7MXer0UPLX4E++vvy4RTYIU51lq5yk12Sl+pJWfpLrb1jJAE990FLxVaZsOnCSDOIIN9nI9OW3AnhFAAAgAElEQVRVevyzT/XKX3VQXMmiHuSBkdpYJw0TpnIoHbbqbomq/CMPlUX58V2l4447zj6EiRhp1oSP8vJ14uh7+sGwYcOMJFAW1QX5Sqs8qixeTdHIQ/ipjI899lhaYoklCl8PpzyUS2VkDOBhhTr079/fjvjnSH/IL0bf7VIabMkmP9XLh/vCKxybPNVWxBc2xCdM14qDv9Jn3T6PdncLgyA39bU0fYyP+95///32EMO9zgMofX+vvfayB3Y+ZMw30TDZsUbtUF8pmpu6rcmNHxhwjxs3zjQJkA0GbwYVNCloF7bZZhsbvHwa34DEv+SSS6zxt9xyS2sV0j/66KP2pWqI0oQJE2wAIh0fMtSem3XXXdfiM+mxJNWRZSlfBt8VyBujcGwNiPgTrjBdK40Gc/ljYxSelfVtcFFLhI2bYq211rI4Xr5PpPJ4jAnP+lMOyVCZdI1NfN1sspWPZHsslEc2rvIlnHx0jQzJwV9khXCVQ/lJti+TwsrFz5ZPdSZNsXR81RqyzICEES4+L8kkvcqv8FJ2Vo6u6ct8w6dv3772QU2VCxs8lBdy8cOMHTvW7o9VVlnF8ueeyRriKo9sWCOvVSbKwNfJf/WrX6XBgwfbAH700UdbVvQH4YSm9cgjj0yDBg2yL3cTQff7xhtvbCSGclPvbPnJSz/JIz1+XGMX6zfEQZbCJB9/0vn+KvnY2fyJnxdD/TFBbmprcfUd7m8+pMu4jYa9Z8+e5uYaTTsP5Wwt4GEco/ud9GqD2krQeanamtwIRgYKBpBtt93WiAwaGA0cNNaJJ55oyyiQHzWebGTIzX4Z2C3qeg1iNDRP03QInbdAGp7y+EotnUWaG57yrrjiiqqWpZhAGJA33HDDtN5669lvgw02SPvuu69VizKpDLhvuOGGBIni68/rrLNOIi5unkSpO3FkcN96663WuTfZZBOTTdwdd9xxMoKm+MVs6s0PWeBCPSknhnIpP9y6MSQH7c7PfvazdOCBB8rLvqbM5HLYYYeZH5of6k59mdh/+tOf2vIe+5f23HNP0ySo/iRAM0S9SQPexD/kkEMmm4zY5L3ffvvZUgvaJMjYrrvuassUO+ywQ+HL3XyLaeuttzYMkUm9hg4danjyNMMkzqnTpF9ppZUMRyZMcKZMaDnOOOOMtPbaa1ubrLbaalamPfbYY7JlJVTBaAHBAu0hgw0yWcbcaaedbJMz+aEJWWaZZayPkt+5555rGDEh4yadykNanrykaalmIFIctRk4sbQ0evRoG+S0nKh7hsxJo0kZjQd9AG0H9wGDIrg99NBDVk7fH5SH8rQITfy76667TLPKnip+0047rX2LiCw9ueEeRiurPikSc8oppxippK0w6svUQ27877jjDuuXjC2QIQge/fCAAw6wD5byQAQm9AU0nCeddJLdI8KQ/rjZZptZO/KRU+Kw90GaYOEl2wqTwz/VP8hNbY2v+4/+xpgmcgOZ4Tf11FPbPSySowdxf++Ts9qhtlJ0Tqq2JzeaANHQ0HgMPBgaWQ3ERE9jam+MD1Mz4HfppZeajDvvvLMgAwfqeJ5ytZcGuajzGEyRqw3FbISFCfNEiFFHs4vMH3GZsEhPh5tqqqnMjVodo3rhZpBlnVQdEhttFLaPr/wYUBUfsgYuxOW0T5aUCBc2mWJ97xKZ1Jv0wtYP+j6B8udpmsl8qaWWsmDqAjlDBhMAhuUs6sBkRBlZAtx7773T4osvbvEgAZAQZLLnaeGFF7YnELCGGDCZgBn5yPDWFngwcVBWfmxypl1nnnlmW0YjLk81LF1SHpYdkQehWnbZZe3GpzxM4pAeiMS8885ry5sMuBi+aD5gwABLTxr2ZnENzhAZlueYtFjKJA/8WR7daqutrLzUm43YHFSGLIgWfYE9TZBRTbTqt5R1++23T7vssksaOHCgvUFCf8f4fmIe7o821uQKjmpz+uciiyySrrzySiuf7gvalfg+LuIg7SybrbzyygXMdt55Z1N5qww+Dflw3RmGfDQw89HFRRdd1JbbVG7w4Qeppi34CCNGpII6szePfocs1V+4CrM//elPlr53796F/rLiiiuaH30YwgexYS8P/YWnZQgkZvz48UYMe/XqZX1lt912sw/GEo/9gRjlZxc5/hPeQW5q6wTCj20TEHfGDfo3D0j0Ue6B5ZZbzvrhPvvsk3i4wuj+Vfracu/cVLkhNxp8aEQ1FjaDFxsMaVQGI0xWna6BhT0kPI0vtthi1iGYtNAsoKFBLm9IadBGc8PgxATLRMFTOwM+EyKTBp2kXEdhwmAiZGBk4tMAyRMhhjKRnvz46Vsr7P9h4GSyZhJWnVQH0jJAo2VigiYOxAybwRVGX6lsVoBv/5DLBkzwI2+IAIMzmhDcTLhMzthMgMqfJ2WeXiEX99xzj2GD5ujhhx+2OEzq3GxM/DwBv/fee+bPhlqIGfXbdNNNbWLlaZtJZcyYMfYFcSIyoXG4F+X6+c9/bmkhN3369LH6imASwD4sSMxrr71m8dhYDNGbc845bUkDTzBBm8SND0HiDSvqzgZUtD/kc+qpp1p6SAmYUl/6BIZ4PMXTV8gPI3KDxo0nfwyTKrLRpDERq91uueUWW+ZkgpZBPm0IMcQQF1JNvZEhP3OU+dPARRT2HlEetGhocMAebNGeqb9JFOm0ZIcfbQcOTOKE0c/4YZQWW3WSnGbY5KOfygBx595FI4oRgeFeox9Sdl4wwIgQIYN2QqvDPU3Z8cPgJi0GjSxtce2119o1f2+++WYaPny4PeTQfsKK78uxZH366adbXL5wTX/jRQUM2DHWMIGz7I1RHYSreebwT9gHuWlM46tfMT/yQMU9IILPPSC8G5Nb50ppa3IjDQINxPkyNBxP/BgGCTUcEwthIgKEyWiQ45r4aBRY3iK+NB5MZEzGhGsAY2LgDRxpUIgL0enZs2dhAFT+ysvbhH3yyScJhs2kzCTJBM8SQHZy4JrJk7jE40ca3l4ptlGa+lEvJnFk8iM+ZWbA9/X3ZSrmJq42FFNfVJ1MbqjWeZJH48CTP9c6SVQYPfLII6aN4MkBokiddbMxEUG4+vXrZ8tFflKhnkwOaMZuvPFGIxJs/GRZEaKEJo23fFiyoZ0Iw4AHhEHLLMKRpQTkvfHGGxYPzQ3Eirx5W0bxWAZibxZLXzLgDpmDfEJqwH7BBRc0IkP9KAfLWEz8TGq8QQcZoa7SFrB8hvH9lWUtiJD6H4QIskU/k4Ek058gl0yGGOTyw/h2pA7CVvEsUubvwQcfNFKKN/FFmpmAMSoj8iRf+bE5Ebxpd8pNXMKIp7jIQOOEton6gQdaN/oO9YMgs1RHG2B8OmSRr/KzCEX+lKeCJAPsyEtLn8KW/kifoOyHH364JRNpIe3IkSOt3xCfH/JVBtmQZYgzBFhGfYMHIoxkPvHEE0aQNYmg+kczRF/mPsRIrl24P8qj+jjv3DiFS5CbxjS57mf/tpReBVdYY3LqfCltTW6Ak5uBQZonTwavjTbayFD2A4SWRJiYMRr0NKmpkdE28CTGvg12l6PWY68Bg+UKK6xg+z6UlkmOJ17IDQMqT/Xsy2DpQUsGulEt0xJ/1cRRUurkJx2VX+FZWf6atP5aaSrZpBO5QduA8ZMo18JaOOqaQR58eOKVdkVx2JdDGGSEZRxfPvY8QSgJ44mDCZ64LN+xdIObpxAIB23O0hCGiYcn92OOOcauhQ8aECZZvTHFBIN8yBMEU/EoB/sitEEcvNDyUXaIK8tzECQ0Z2jbKAeTOCQNN2WhrpAhjLQFfp8H/uDHAMPkLyzRSlFGcJGBnEr7hY1GApmQSGklwI0+qTpQZn5cZ9sbIgcBIy8MbcGEzIQPOWWTfLF0knPfffdZHdH2SMuh9lQ5kEs7MLGz141JCm0JbQKRws1HEZVesmVTRojEoYceamnAifuKHzKwwYETbCGjGNWd5Uv2BZEWo3uVvNQWpMeIiJAW4gP5EPH3dVK5WNr0fQgZ9A3aWst6IvX0e5ZkTz75ZMuLOqHxoR9QH16nx402CEMf0D1jHjn+E95BbhrTCdSXg9w0Bs9Ol8IAxUDB5OI1NxSEgYNlKSYklhwwfgDXzcT+DlTTeuVbky2dg/iQHSYjntZIg402gMkM7QWGwY3XSnnSxki2XXTwz6f17g6KKRmdOlVjwE/n3LCJF6NJA3e5QZklFggJy2Es7WA0qTARQVrAHD/fJpAb1PhoVtBeoBVj0kJzw9M568S4UelzzSvAGDRUvA2jpQfVEYJEW4k4sJxAf4DcTJw4saCtYLLcfPPNCxunqRt7o1hyhNxADNCgQLAYfJnEyJtNrWyupb5oN9BK0T+Y4OmT0iSJkKg/UXcZCDg4kRYD7rS7JkxNkCwnIVMbz4nnjdqDtPy4FubUn+VEkQLF1XITm9Qx6vMKl623pSA3TOzKm3ANor4s3q0+rDIpzPvjB/FEPnWk72D7H+3ANWQEEkV6tbOWpXQPC2+vuRG5IYy0tC/7EnhrDBJEXVQmyiM3e+lYhmZjuwzx6deUF6O2ol+gTTz++OPN32ND/VhSRJOJPB6KyNPnS338Pab88mAL7yA3jWlt9b0gN43Bs1OlaGDjyYhBj42mGixUEGluUIdjSEOjcyPpZuKJnMkWlTrpJZf4xEEbw4QodTpLIKjayRM1PYZJhAlQGwmRU8loUCMPn2c16VRP1cGnUZj8lI+uq7WRzZM09fSaG+WJXBnKrwmPgZ6JhA3EDOZouXizS4blP7QfTOg6SE71B1vIDeQDrQt5S+uGXPLWTYtbZUHTwVtHmsAkj75BPpAmDFo3kRu0FaoDmhs2KOutMOQygbHZmEkVHND+oKXRU7kmNOSqHLjpC1oKEblRmZm4KCP9R+amm26yfUEiYJRd5cKturAMCUFB04IR3tgiMcqHcJUJskWebCLEUAbJhyiCMZuVpb1QOi9D5EYPCeRDniqb5EEymbS5D9AyYaNpgSSfd955hiP1wKj8yo82QLPDUiZvc6Hh0A8c+RGGVogHEozqC9GF9IjcUkfkUi4eftCwaVlKxIelIvzR7Kg8qo95fPtH2VmO1L4ZvMGUvWYiN8KfDcRoENVHRFSQ63Gn36uPI4+wYnn7crS7W/0gyE1jWlr3RpCbxuDZaVJ0I5AhEyIkg0mUfRkYDZzsZ2DwhniQxqdTYXmiYnmJQQzj4zA4siTBplQmRgwDKxMmk57W3JnoeMJjAMdosLeLDv75/DuYtCHRlT82KnnwY1M1kz1LOWDLjzozKIt0kDkTB5MZAzznthDOUh8y0IBhIDd6Y4lJgzfUGNg5s4Q3othQTFz2CbEPBkLBEgBaBvJFkzFq1CjbP6KzYegDtKEmN00U0orQxhg0N+TNHgrakfJRTyZWNnnrjS7aj3x4yqbs7LnBMLnS7jzxs1TDAHL99dcbsWayww95kAnSsQyB0eQFVmh1mNiEM0tFfEeHPTEYSBf1g8iwPIqGgbjgw94VLZ0hk/7OPjG/zKG6m7BvT91mwsAoT9kQDTaFo22gvr4twUCy0JhRH15dpw+QXjKwFU9LiSzjsPwHicUNYSU959FoiVBpSE9e2LpvVfZiti+XZLCsTPv/P3t3Avd9NeeP3zKjP5UtKVuoRJSSUPYlimJkLYUWTNlHxVjSGMOMlCVDi72G5C+FZEkjlAY/SxPhNxgRP/xqWu6o+76W83s8z3W/rvvcn77XdV/3tV/39Tnfx+d7zufs53221+d93ucc3C1KOTK4o58lIdxAdNCOycKQw1O/AWnipaKnfGT6tNOAT37EQaieDBolPQq4IZQO2KHn85///PphEI6uutVe7LADcHH+KHVJhRb1ZZn9hd49uJmdik/778HN7NBzQWIxMPgKM3iaYLIjytKFgdWXv504Bi2DkknNhGKypAyQhDz5tcTAn45mILI0AsjkS4x/9gYmk26WpQygwE3Ow8kAORlB0pm7fuZygFufuJUhW8HJuACPJiwyL5ZnTMgmLZyQLAdkC3NAgrL5quYn2+Yt5wAYQAx5JnGJ1xIUMxmTKHQNwPCVLVwk/y3t2AFGATcmr8jcZLLA/SFAnN1SwA1wAlwAPKkDEzauTYADe5OT9qRdZZlBO+KPnfxkt5t3B/H5ujeo4Dyw63JutDWcB34zIeJu8YsG4SKYHNmhm/YH1FgGlWYEgAEBYJo/AIXKgCb/KRtQJM9k07RRk7UlVLTBlcyOsKSNdvLZtmGC3Oimzdtmjw78eFoljD4GLOqHOBxAqsc7++RLuInMbZyTmZNHy0EAa8qADuKOO0DlME60ItiMU6uthZuT9tKmlbCAEHCTZUN+lNEOQGCPCh2MH4C9OqZworQ//UdbkbY8aMc50yhh6VMBdzXiDfAvbaEHN7NTuRkLenAzO/Sct1h0hHZAMigY2EwAthGbEE1gvqLydS9zwmV3VbZes+PH0hXQIh7scF+ZBiL+2p0SBmqcHJNx2NImKvHmYKR01KkSJAPpVP2Lf33TWJ+4Q1tclne/+911EjVQ4+QAepYbDOC4Xbga5E0oyz/cs21WuXBz7C7yVU+RXTFRqhtm279xe8ST4/Az8fNvUpSO9E36uBkAEGCTiQEQAUSznTq0sZQIAJhkdXZgSVw4edJgJ4/Ka/JPHls76ab+hcctsfTC3kBs2y9wjGtDaYtADqAVu+RTvGjhSz5LGYACThWacqOkJ17tic4NfXBoxJ/4lJdQNS4P1Z3U2QF+8ioNXCP5kn95x+mgSyf3MsljVOhoFxvgLh/oH3rw2+Yn/ultPOKLW+KeiS6uFgwAp4Bbe7ggd20v6eK8KD9QAaxqFxkblEF98OvxnnCAMb+AGaVc6IzjmK3/3iljg3EA2KK0O+BVexWHutSfcp5W6iv1Kc0u3WpEy+Av9O7BzexUdtpkD25mh57zHouBIIOBAcKEC5QY8MkmkMXgnsGD2eTEjyUGKu5Y9CYPky7hVCfGGqgMnPFH9w4EAT4OjaNMlOQyTP5U8lRfpvCXjs3rROYpRDOrXtqBNnky6EexCwhi1y0z93Qw5oS1hIADYRlBePbqp62jpEFP2szxw26QuZuHNp6YEy7v9DZc0ovOjbn1M1HYQX6ETdmFa9+TRvQ23vht7fiT//jHQbBEFg4CesaNHrN8teVmnzzRY05arXvsuvlp/Yg/dR3/3D1U6BI9frp6/Hftu+/8iYs+WbrcPSl7wokveeHWusccP/HXzUPeubf9IPb0pJ18TBRX3Nuwy82MBlQPbman5tMvenAzO/RcFrH4OrNEg/uQZSngBjdgfZalFjOx2gE++TQwTzQ4x08GqPjznk4WP8CN3TAOQDQ588tfGyZ+F5Pelg19vHcntZRhvvKNQ4DLSLaIGlRv85WXPp2eAjOhQPpXD27WpmI7NsaFXehlzGnHUGOAJ+MubjQRAh/jkUmLW+JbavoGf87NQlXIcgA3oa0OlIk8dq3efvF3J9bWTTzpUCZkHY2MhGWDdMZu+DadhTInb9I3gLRlavOkfBO5tf5m0wxYWdLSHqW9EHmYzfL0cS1vCmi/VA9uxjh+3Q8l9AmIYc6DZuxb/8YDYyvlIFPyjcbc7CbNWFw9LMG/HtzMUaUtB3AzEdDQoXSMidyRnBt/EynyGwR17QjSCdtOKUz3faJ4FsK+HUSUMYBmsvLORT6l52k5R8lD9LlIt4+zp8BcUSDtdrmDG3QILSaitXEoY3DrnzljkrGBzKR7+7IJI7tJe3AzEWWXuf1yADfrU8XpXG2HTMdrO6FON2gyltagONYnD7PtV/6TV2VoBZzbAUS6XcAz3wNHm/5s06GPr6fAfFEg40cPbtaAGzTJWJp6YKfPU9wCZqLbzWvzgWMI7CDFsbHrku5IBCpjW31Zgn8952aOKm05gZt0IqTMpN7adUmcAaprn/e464jMbSdL/PG7GPSUVd4CWlKG6PwwK0vs5jrv0slg1qaV/LZ2vbmnwFKgQPrOcgc3k9UVMBOw0/b12DnmxBEFgIxjG3LSNzM7uwWpQWPHZOkuNrce3MxRjSwncNOSMINP7DKpp8NxT4djboFLwrAPSIjf1i3mhdTbvMtjBg55GpTn2Am3EIPGuvK4kLTs0+4pMFUK6D9UD27W5gajiT6eccZ7xlxmY044y442sHMSkHHApHPKnIEVkOPYDSpjcH1Zgn89uJmjSltu4KbtVC04aSfzrjmkZ5/zXJijxOnMmK5q4+m6zed7m492YAktWnfmFgDNZz6TH2m25vnMQ59WT4HZoIB+RPXgZm0wY2zJR5M+zoxW5iGcGqrt++RqHJiaA06dOZYTwnuB4kqu/m8iCswOuNGRu083xa77WOcf8xW3bhjv3Jwc67E224bzHrvWflA8Y0tRcXGYnsP6HLhHAj+dTMfKwOQySu4OLqNs9XbOQo7cT+dMnAlHXyxfE8CY7ZMODHROEmWAkccMMk46duBgDnZzIaUzknKHVcqVcs6VLp3kTRrzle5claePd/lSIG23Bzdrgxt0CXghT+MUeKeUOwk7J7Ibs7ofWBlPjVNOx8bNCbgZxFVfSi2v59zMUW2NgZu7lpvf/BblCU9Ycyu4U2ends7NaCmjw2VolRNRoXDI3N0yBMkAhYAP5oCUFqjw177XWc3MVks8PLKyrBpeUUbKn8tIWVmGvNcTVE2Etl7jmDiML+kMJlQGm+gOOXSBpk7iEEOHGbbARiyADPec/4NN6jTn3K0jH4lvcKprZHta93Turp24phJnG45ZuACV1sxN/T7iEY+ox+YrD9UdOJyGbAeCS1cpV3k4Wj/l7Pqvnvq/ngI9BSakQMaFDR3cKGfKGmIY37pjBjtjVMY+l60+4QlPqNe0GGM9WWby4Unxm7gDbvpD/ELlXl8nBdaAm1uWJzzhidW/Nc/1AjcVWIxUkAOoADTDwwAHgJIH+OiCm7HsDQQ3qyft0QpcbnCNZRmt4GbsNu0yqrMANi75mxq4CXdGqrZw77DDDrVTbbnlluOXlKbz8XPuuedW99wj5XoFdyPlYsduBx4rzeB/Xxe+TFxwiL4R2G3jSNo6NPt07MEx3tSW/3zFJC53YeUeLVcNJO64093L5GCscHZcmCjMdMp501z1Nj0Flh8F0nc3dHCjZo2r+bjKuJLye2/HOPannHJKvV8OoMnOJ+MqDjqVsS9xsOvBTSVN/7c+FJgI3Ex+QvEYVybAZWjlqjI6PFLoI0MjZXR4tAytHKp2o7b54cJUDg6AM/ZUjg7UPzzGocn7WN7HAJHGPTIyVEZH3NZ93Ri4Gb6hxie90ZFVZWQYl2hyrk06XOiC7eluorZzuYyRijCbtHOBacCN5SiTvmssqLbTVosBf2NlGDvO/+ijj653hPEmD8kXP+m89Hy5DIhuoJXwGVx4SLzkgNwtBrjsv//+9YJDrGB++UmawA1OTW6KdxkobhaODjWVcg7MWG/ZU2CZUkCfpJYDuFFOY4QxxdiSDyzjS8aYjCHuNsMJN/YSDMZVdjfcBRdcUOklvDE49KuWPbgJGXp9fSiwBtysvSw1MbhpOTHYhmO4ZWS4lJGhyryxSlUf7x5MHBcuDzWP9647f+KJ31VDvgqgdqBjtKxcNWZOOjXNyXHNOCl0vAAXHciFf74WXJ7oVuP99tuv+o0fL74kdMInPnGMowXc3PWud10vcJMM4Pocfvjh9awGF1G6rDEdWMd3KabTjnX+5IF7BofEM5He+g24cRGlsyEuvPDCOnhstNFG5dWvfnWNQhpJxzq25baAGzfQ9+BmIkr39j0F1k2B9O0NHdwYu4ynUcrtMQZFZ/YE9Jx99tnlUY96VAV++ZALvRIPvY0742C/LNVSqDdPSoExcHOXeiu4NVDKpLc2uLGcFFBDj/yMW7KHy5e//INy8innlQ99+OJyyoe+X0486fvlgx+6tHzgpB+WE0/+UTnxlEvKiafQm+fkH5WTTr6kPieefEnJc1Lj96QPCvfD8q8nfruceMrF5f0nXVTef+LF5V/f/73yr/96cTnlg98qv/rvoTIyWuozUUEz2cedoPDTnva0eo+RTuUsha222qq4aZlip8MSJAZuInMDoNztbncbBzdtp07crZ4Oi1uy3XbbFeDC7evi3HHHHauAMgBy2GGHVYDF3SWcgJYLUZOXNs6uWRoZNFr/uDZ/+7d/W0EK+SIyRS6ldLs82aF8SQljwLjzne9crrjiiho9zg1/udW89dtNv3/vKdBT4KYUSN/f0MFNW/KMs65KAGCOOeaYemO8cRI9AnIGjSf8BMAwt2OaNOLWg5uW4r15UgqsG9xolBH4XbOsZLkIyPnDH1eUvZ7ysnKnLfcpd9ziuWWzuxxabrfFoeX2Wx5WNt3ib8vt7/rSsuldDh97tjy8bLrl4eW2Wx5ebrflYeV2VWdmN+af26bMHuH4u8uh5Q53PbTc4S7ifVG5011eWjbd7MCy+V2eWc4866dleGSMwzNpQSPDMzpaZWlsJ7zkkktqkG984xt1crdsRGVgyrJUODfAzXQ4GjoroPTMZz6zXrB5zjnnFAJ1Lt0EIkj/v/CFLyzf/va3y5ve9Ka6jHSPe9yj5lN+MmjUzA34k1+d36CRQQCX6a//+q9r/L/73e8qkAKaHIBlBxiVQcb2SuAmnBsyNz24GUDo3qqnwBQpkDFkuYAb5fUY6/7lX/6lfsj5iCNj+JOf/KRSLWNUS8KEY9eavRufMkZlXOvBTUu93jwpBQaDmxvKqafaLfVBTa7KvURWZkwfLmOCviPl2utGyz8fe2458ODTygEvPKu84NCvlf1feF553sFfL896/vll/0MvLM89+JvlOasf5ue+8Btl/4O/VZ530Niz30HfKJ7nHvyNst8hngvKcw6+oDz7hd8szz3owup2wKEXlgMOubAceMhF5eAXfac8/6Dzykte+tnyo0uHKrhZfYL3wLKmg3DExXAw1O677z6+1XnFihXlWc96VtAMciEAACAASURBVAUVuCzpVF/84hcrlyUyNwE3p59+ek2njXdgwg1Q0jl9ydz73veuXrFjARkDwOte97pqZ2Cg7Foi5AyMCDcZuMmAIhwzZcu6+67Evddee9Ut3uI20N7udrcrJ5xwQvWX/BswNttss3L55ZdXe+AGuFqfctaA/V9PgZ4ClQLpixs6uEk5Lasfe+yx5UEPelAdd25xi1vUjzZj53nnnVdpYhyLf+aMP/S4xa7bjHpw06VI/75OCkwN3LTLUrg3a8ANrsmNK0u5/i+j5fo/j1b92utHy3V/Hi3X/nm0/M+K0XLNn9d+2I/7We33ur+s7Wc8zPWj5errxvxffe1ouW7FaLn26tGy4rqx9P5y42gFN6vn9UnLq+NYbrP0A1hQ6Uznn39+/doI0NAJncEAIOy5557V789+9rO6LBVB24CRSRNd7YhV60CqbbfdttrgppD5scwFUOncHsr71ltvXQHHxRdfXO3iVl8G/MlvBg5LUE7yxLmxMyEKaCKDY5nNmTbxj3PTg5tQqdd7CsycAulbGzq4yfj5ne98Z3z3KWCz66671o8jH5MUfxkvjWUJF0q341do19r14CaU6vUpU2AM3Ny13OIWtyx77LFmK/jaMjdjk+5YpGMyN+HkABUEgAn/Evwl9Hvj6ueGVaVM9MRPq0/klz0AlWfVylLqQ+B49WasqRSYHMohhxxSAcumm25aJ3oggjwMkAHIPPKRj6xR6Yhf+MIXqh3uB0VWpV2WsozzrW99qwAggx7LTDr9tddeWzkwb3zjG8fBjW3X0gZwcr5MOjyZoHvd616Ve0P42ADhwEHbui+66KJ6Do2zaDyEhWMnHCW9lNNyE0DjwTVSRkLRWZLjn2xNmw/poEfPuank7P96CqwXBUzK+RgJuPHxkHPDIk+SSXy9Ip8Dz/KavARQtGWQJD8pU5uF2PlYesYznlFuc5vb1GsSfMxRibcNMx1zD26mQ7VlHmYNuPmrssceT6rUuKlAcQtueFkDcKqJQO/Imgc3Z9ADiHgGuQ2yW8v/6l1UdZeVnVbSWy1IPLYYs+6KtKz0wAc+sIKKN7/5zfXQKGfP2BZOHoawr63eEebF0QAG3GtCCW/LdARt3UrLXYe+1a1udZNHfAY1JwQDLpaltt9++xoXYCTcNttsU8/cAaYyUIRzY2mK7Ixlo6QjDDPuE/mZnBOxySabjC83AVry/IpXvKKuf//jP/5jZRnjVhEqFsc73/nOcYLlSPOccwMwkbmRLhXQNR6gN/QU6CkwToHuBN4CA/Jt+utCgRt56eZvPOOrwUfyq593/RuT0v8dX2EHqROFcbGpcGOAD+diGUtin/GsWszwrwc3MyTgcgw+U3CDZsBF3RJuW/h8PqvTnkq96YQf/vCH60CTXWHpmMLj6hxwwAHV/e///u9rlAEVkbkhGIdzk8OmgB2CwR7LWt3na1/7WgVKwIq0ACoHB1K4LICWsx5e+9rXVrt80f3pT3+qHB4cFtwiy0zkf3CJAC9cHOdCfPWrX606O+9kZgwCBh9hKe/tAEUeRzmBKmU2mAFftoL//ve/r2EIOts9luW3DG7Vsf/rKdBTYC0KdMFDwAJPg8BNJupuuLUinaUXaUyWTtzpLRjxbsxi5zFWkKnxwebojJz15UM440PiyvssFaFGE5oZk33IAYw+Lqm4zWZ68xlXf/3CHFF75uBmqnyTOSrAOqJNh9MJX/nKV9bOedxxx9VQbWfWkS1DuYYgYOOss86qnSjLUpG5ScfWiVuANFlWsGnf8IY3FMthp512WnF/k+UfS1M5DdiXj7jtNnjYwx42fqdV8qkTBwClQ3tXRkpexI2r89znPrcOSvGXvFmyEr/BIXezWIK0LEUOiCJQDNx89KMfre9zMVglP73eU2BDo4D+mD7bLkv5uKK6fXKhyy8/xkfKGBKzMcoHFM72LrvsUscM44ZDQbNkHb/GCI9yZ0wUb8armZYxNOvBzUwpuYzCb+jgJlVpeUfHtAsIh0IHNAi1EzfZlsc+9rGVo2LZyJZtYdhRwA0OBzBAZQALgKqWzV9rLz1LQeJzns2zn/3s6jMAhz1BPLrBgzAzJQ1PO0iIN52dH+8UbtDOO+9cv6zaHQqtH/lwgKF0LJHhKgFx8hTZH+BGHnrOTSVr/9dTYL0o0Pb7973vfbWvWZZarOCmLVw71pDpu//971/zb9ndmIHzTHYoOyv5z/hjbMm4xH42VeLtwc1sUnUDj2tDBzcZaACa5z//+eNyJDpfgA0/6YwO1XvOc55THGQHEOHaWE6iTP5kVpyLQwmTcNViHX9ofdRRRxXLXAceeGDBzZEHQsf77rtv3Y5uS7rzdRJ/okw5AnK8G0yETzksObluAXAyMFG55oFbFODmpGLltARFuFi5wrlxSvJLXvKSKqwsjLR61VOgp8DUKZA+s9g5N/KZMawdz2KHk2wpfqeddqoyfMZHypgjrIc54EO4jEdTp9a6fSb+Htysm1a9j9UU2NDBTVvR6bDs0gF1TiCBas3d9/iJfcLXgOv5J52omOlh8cpnd7cB9+Rf2m36CdvGFRCUdFo9Yflvy8UPu7hHb8P25p4CPQXWTYH0xcXOuUkf9+Fm16V3T8YPoCLXxSi193bMUM6UtUsV41XGrK7b+r734GZ9Kdb7L2vAzVS3giOaybl9lgYh09Git7mOXTpq24Hjlg5fKdC59qCNq2tOnPR00jZ+9kmDuXUTVwagpJv4Yx899tGTbuKO3qYXv/R24Irf1r039xToKbBuCuhf6XuLFdzo3/KIs2u5zC5Q3GObFyhjSsYVfrvjgfe4p6wpd/R1U2rqPjJu9pybqdNsUfhM40hjkalUZtccP5kAo2fiFVfLARA+8aewicP7H/7whyrQSgo+u4iEn/icm8SydPSUvy03u3RwJRnUeQfZL9ZSt2Vpy9nmN2Vu7YRLG4o9fxPRLH7o8dPaTWTu0lfYNl3vKUPsYzdRnL19T4HFQAHt1EOlzaa9O0STrIodRieffHL1Y3ydqI9OpzxJU1jpJm5m80jykjw6k4Y8oV2VOVFYHh0RcdJJJ9UsCMd/wkwnX7MZJvNhD25mk6rzHJfGlMaZCk0WAlq8mwAma3iJg99uPLHT6CF1u3U2ZHAT+q2P3tJvfcIthN+0g+4glvdBZeGWcPLctrtuGbilDQHR4mvtuv4HvUuvbb9Je6J0k/dBcfV2PQUWCwW0027/0kdsTmCfnYk2InzgAx+o2eaWdp9+Nd3yiCd9SRzM8pQndrH37kiLRzziERV02UgA2Nzvfver4OvXv/51zUqWxevLIvgLnXpwswgqY32yoCGm8rrh0nEyMWikMfML5MSP99bsPQ0/X8PSapVlKWeibMjgJmVGC3TIe+gQGsU+ddGldfwvNl2+5TX5Tv5SLu+tOe7RB7mx05bEnbbDztO+h2OYuAbpib8N2/pj37bb1n+3TG243txTYKEpoK169JOMy7ZPO0oBoHG0hGtQnGdlB6JdUw996EPHhfXbdj/dsnT7qHj00eTHuzymLwE3jpqwOzOHfebgUn7ERzFPpX9Xz3P8l7z34GaOCT0X0WdAF3dQc9s42acjtH6TFw1ykD33NFZmfhIPe1uBB4Eb56XkuPA2fNJbanrK3OY7dl26pbxxb8MsdrOyGJBShm5+DXptufhv/bbmblh+A2wm89cNx29oTE/60fnvxtfNVzfO/r2nwGKhgLarLaeNW9LHDXHeFN3TniZu99HnPve5mv1M2jMti7STfleXN2NC7HGOnLL+sY99rI7/0paP+JtpXuYifOjUg5u5oO4cxpmK00lilpxDlL773e/WbcnOQ6EyCWiohMFalQ7GTxqy9VXb9zyXXXZZ9c4tiDzgBopvZW42NHATeiAAOnVV6Bqde2vu+l9s78lrt2zsA0iS55YWsYveujELG7uk4d3TTStxDNLbsG1+kkbCeI/f9Yk/4Xu9p8BCUKBtx8bsxz3uceUhD3lIPWkcsPE4FZyd4x4c/0DNVhuXPmVct7TkkL199tmn5sMJ6kmLP2nqY8lz+lv1tHrcS3zR47ZQeubFHtwsVA3MIF3HW0elAb73ve8d/wLYeOON6zH5/LSTg3f+uw2UPWFhLFBLTlij4iBIRsX/lVdeWTk3g8BNDp2K3xpwif2hTR7nuTiV9x3veEcFjIqSwSU0RQ90v+KKK2pJAwKXQrHVU8qD+xcOYPIeN+/8ogtFb93iXh1XcxINlmj3rne9q7znPe8Zv3gzg078TqS3abheQhw//vGPq3dfkk5n/spXvlLfQ/M2zETx9vY9BRaSAtpo+pI+ZBxp+8Sg6xe0b+ESdqb5Fw8l/eOPP74KBuMaGe9xjZ7ylKfUXbHSzTjXjhUJ244Byd9M8zZb4UPTHtzMFkUXKB4NyyFyTtMl7Gubnkaak3I1zDRSDbJtlNwoYMm187e73e3qOq+lJ18PT3va06p7wkTmZkMFN6lC5bUTAB1dNukSSQodQ0vv6G5gaE8IThyLUddW0vG7+VNm9RuQw18GwrSThEl74N7SQzsCRMJap3vIE7iQ09JpN67EGV2cnvhzcODtb3/74oAz6pe//GXZcccdy0EHHVTfxdnmIfH0ek+BxUiB9Kk2b2m/dkjpL2Rtslsq4L31P11z+pQDR1254noX6fmgNaY7sfzss8+u0evj6YvRjQnp+20exDvIvvUzn+aMcT24mU+qz2JamQA0VJ3BxYouTtRRnJfwqEc9qvzd3/1dncz4bRtf2xidxrvHHnvU+4IySefiM1v/Dj/88PGJxlc04DMTcOO7YewZLX55W1tf8zZGstbf+hOxDc28LoVW7krS8XGxnMhLtTT0jl18t7vdrV5E6T2DR/W8yP8yYKVMBgK3eztxmOIevTvAdsspDm0Pl8sAuf/++9cTjffbb7/yjGc8o+y99971ygYAh8pgXl8m+Ev6Dgz727/923oBKK+Ocvd1eeihh9aQGcjif4LoeuueAouCAvqOR3tN30vbPfHEE+uYQ5jYtnCqBe/xN92CtOGdTm58e8ADHlA/Yl2zQulPbX8XptvfvSfvKcsgf9PN50zDZUzowc1MKTnP4dsGqoEddthhFXlbAqDS6LDtgRCTTtsA2/Dsc7aCNdeEZ0/913/9V3n7298+Lklv6WrLLbesXJ1W5sax2wAVlbD1pfM3NDJcVo4MlZWjQ2XlyMoyUobK8CiJ+1VldGRVKaO+FobL8MhwGR4dqc9I8RsuI6PkObiPDQwtYBnRAd0wLn1rxEPDZRg7d2RsvXjVsPSGy6pRcY3562RtrVc0dFdSvmo22mijktu/W8Ft4AZXwt1M6BratmYRtzRpzfEvvdQb95jbTLH3JEzrFnP85J3fNr/sB8XN3kCA85ftnQCI+AYp8XJPXoDhl770pbUdhsPSApjrr7++Xkth2RNApoTtxpP0Em/SbsvtOojdd9+9vPjFL67OBuLWP3Piid7atXEmH8lr/MdPr/cUmGsKtG0zy1LATTZodNu3Ntr2Ye9pv+LyDFJJJ238y1/+ct399JnPfKZ6DyAYFHap2aUsPbhZajXXTJbnn39+3T5oq54JJJWqSJ/97Gfr5OyOJKp1866DaOi+Fmw99FUtDgJuBJLbDqRjeCdjQnofG/PRj350laX4z//8z3qdPKFiaqLOlTRXrbyxDK+8sYwO3VjKyMqxZ9j7DWV01V9KGb6xlGH2q1Y/q/2MsF/tNryqlPYZGSplZKiMDq0so6vG4klcoytvKKM33lCGbryhjNSrEwZ3/uQ9g0BYxEDca17zmkpnXATuWbohl2MguuCCC2rZ0RiYIB9iOcUDILpckjB2aFANpRQC3D/4wQ+q8DYdWAJGcdMiAM7Pz3/+8xqEvIn6cd6QtAx84iYA7thz9cbvihUrxutbfuUJMHWHlK2d/MmjOvUANG9729vqkia2NDfppy7pATPJlwzF3e3huDXAdEC2NNs2h1Y4hLnHSpqW9eRXmdFKvpRD2Aziysg99CPfZMnVfVaUulAGF5WioTYsX8KLR5wRkOfuwtDkPflP+apD/9dTYB4poO2lHQbc4MR/8IMfrLlowU38atvM6SPJbuLR7i3f6sc+NlzC2yr+xJv+mbhaP0vZnHL14GaJ1qKGnW2ET33qU2spDPIUHTcB54HEfeyEicpkBdyQaTAJm8jJ2pC98WUcoc3Ea6LFuTGJOaFyk002qTIp9E9/+tM16nSwpEOP3Q3XrSjnfOrT5fT3n1jOO/1T5bxPfqJ89ZOnlvNOP7Vc8NlPlq9+6mP1Oe+MU8tXP9U+Hy/nnTH28HP+p08r/37G2HP+GaeV8z71sfK1M4T9SPnCv51czjnjQ+X8L5xevvzpj5ezP/iBcub7/7Wc+/GPl2suvxxxxp42gx0zOoVFbG3auQ5ka175yldWn6GHyRnnxlkVUSbTO9/5zlVAz0mjZHbQx2Ss06G7+AEYuyQse6G/AY3uRt1nPvOZNToDkC2YLqIDrB7+8IdX/7hp1Mtf/vJaD1tssUVNg1Cg+JwmSqWTm/jd2CsNYAw4tRsDaAAGLD1mCU451fGZZ55Z45DfxFMtVv+p09CBmZCvtJP3tK+0OX5ilqa8SxP9tCeyYuiES3bIIYeMJwWQuF04t45bNrPkGj/itMND2YCncIYAPJeYoon2LG7pCPurX/1qHDyOJ9QbegosAAXaftFevxCZm0F9T5tv+55ssxMXRV5Qm9en9En9OdvJ0y+F579NfwGKPydJhmY9uJkT8s5PpFiKJggyDVQmD2a3RXN7zGMecxO3dAJfvSZPk/CDH/zgKj9yr3vdqwonm6Cf97znrRXWVy93nB5x5zEh2SFDJe76svov+bry8t+Utxx0aHnBA3cpr9r94eWI3XcrR+y2a3nlLjuUI3bbqRy5+871OWL3ncsRuz9o7HnYzuXI3Zh3Lq/Zbaf6VH8P26kc6dmN+87lqN12KkfstmP5u0c8sPzdY3YuL3/kA8srdt+pvHa3XcvLd9yhvOoRjyi/+NKXyuiNN5bRVZbAJubgoEtkbp785CfXUpx77rlV6PprX/tafVemb3/72/UAroAbEypBV5MozhbWL/92JaDRwQcfPB4WcET3Jz7xiVWeRBy4Z2gagOALLILN1sZN8jhkuDCWZwxcDgDTiXHx1AGZqHvf+97jW/mBga233rpsv/32RRkANTu8+Hnd615X84OrcuSRR1aAYNfbhRdeWDkl0qcyANJjzgCS+gbWlFn7IJxO/ga35Te/+U3llgDGUW4df8UrXlHLigZoLE0Aadttt60Am5wNhTOojB//+Mfru3Lvueee5UUvelF9V24Acrfddhvnyhi4w3kDDLH4cbic1yEu+UNDeVfGTAyZMGrE/V9PgXmiQPrQCSecUPuED5AW3HCPH1lqzdqujyBKXzF24K4bR3yoGnf0jYxb3T4tnDgCdmpES/wvY1MPbpZwRfq61ogHgRtInVsX3KTi06gzeZpgNPwseWBp+ho26Wn8lLN0dDzcBV8DuDsHHnhgXbvNslT8tmTNpHHNL35ZPnLwoeWt939Aef+OO5SP7Hj/8rEd7lNO3WHr8m87bF0+QX/A1uW0B2xdPv6Ae48/3E/bYczuYw/Yuvr/5I7blk/ssG01cz99x23KJ3a4d/nkTtuU0x64dTn1gduW03e6bzlzpx3KR+53n/L+XXcu1332/y8jK66rAGcycKMMJsSWtuhmwHGKqBtxKUs8QGDADeCw3XbbjXM9lFtcBh8yO44tp8K9AaK4e6dM+sAR7gKlPoAs3JRjjz12rQEIsCW8a9KmQmMgAihVLxSAZVdEy4UzOOKekJXJQKm9ABrAA8Xeo9yp0+jsY6ZbqpK+8uCc2F1GCFv7uM997lPTB3za5bVwi+yuouSHQlsAR5nVAS4LwIQOFLBEoPiII46oS3TyTGhZulkyw5In35O2j/5xO+uss2q9BkDKd2hQE+j/egrMMwXS/tplqYAbY4A+mP6drAmTJ+OHD1rcX+OWfuc6BxtO+BNP22cTRrwxJ+6lrmeO68HNEq1JDTZCr1mWSqVqrFmWIo9DDWrEJiNf6jgAmfzES4mLkKhJxgRD4dz44se5aSdgwAYHiEoHqi+r/xLn8JVXlWs/98Vy1XveV659/4nlmne/p1zzruPKNe85rlxzwvHlmuPfUa5517Grn3eUa97NzK6xr3bs3zn2vPudY/6O+5dyjecd/1SuOfafyrXvO75cc8K7y9X/fGy55tjjy7Unn1hW/uyyMXADTAzg3Min/HuUxyARQWuToy9/dg7Wosi/AHvf/OY3K70sW3mnW4aydIID8epXv7oCG0tDGaikYdKVJpDB3vKKr66nP/3pNX71444Zu5ikn0GI7NNee+1VgdRRRx1VOULSki7ukDgs0VAmfXJX/OOMiANIpdoB06TvOIEAkEH1yC5tTL6TnxpZ85f6Vi4cF/RCN4LA5IZwuHBe2AXcJF7lBKi5+ZK1O8qyW5Y9gS/HwGuHu+yyS/X37Gc/u6aOnuIJgAGKXvWqV9V2/LKXvax4ACNx56iETB4iGFTmpli9safAnFAg/UVf1zYB9oCbjBFtwtpp2/fSj3ExjT84vDmMT39o/YpHekmzNbdpLGVzxpIe3CzBWkxj/sY3vlE7AgDjqz+VqkgZ4HMeSAZuYROeHc6NDiGsd4097l/84hcr98YEqZMRHCUjAdxYGqBMwCbNqRziN3rjqjJ89XVl6A9XlaHf/6kM/f6PZeh3vyurfvvbsec3v6n60BVXlKErfluGfnfFmsd7fS4vQ1dcXlb99jer38f8rVodlv2qX/+qrLr8V9XPqt9eUVb99vdl6I9/KCPXXD3GtcGJGgBu0hTQgUCfgcayUZSJ2fZ6ckzohXODu4XlawABYtCGLI2J1ySMe8A/TkJ2XAnrQV9nCQFAOBl/8zd/U7+86BSaG/CADkAh9aKuDzjggMpyBmIss7jczoMFDchELkcYYAb4AYLJugBP8prDB/nBCfLVR7aHUq+4KHm+9a1vVQFgAsHAHN1DJkZ4ICTLWcl7jWg1UHbMgPNpcGDQF1eH7E922WUAli7AhvaW83Bh7OLSxqjf/e53la7cLbWJU77DducnoB/d0ET/sOwKXD3ykY8sT3rSk8ob3/jGGh+69qqnwEJSIEAjnBvgJlvBjROU/qGf+TDwUUWOjtKX0ndwP3FqcN+pxMvdWEIxJ85q0SxL5X2p6ylfD26WcE1q7NmCe9xxx9WSsKMsR5D9cI0CuzT0trjsgBuTDJYmpbNQOoPlDZwbEwwVzo2JJVvBTUa+zqcEboaGy8iKv5SR6/5cRq69frW+ooxcc20Zufa6see6Fat1dqsft+ZefXUFJyMrri0j13n/n/Fn5JprahzDVzf+rr26jNTn2jIsfrtoyJCgzyTAJmX/yEc+UidYEyGVAQTQI4xr2efiiy+uYM9uKRwAZ690BbgTThypAyCRzAlgYiJmj94mWhyfcNvYmfwJBqJz6lYeAKecv5M01J24sgRTM776jz1/4jAA4mgAXllSw5Wy1BjODQ6Js5PUv8GWTjiXILmlOTqOHyBnMFF27Q23hUqeUmb0sbR23/vet/oHbgJg+M+AJO9AIDc7ryxDATetzA3AAiyS6dG+xZn2KB6cROGf85zn1LxIm0peYk5br479X0+BBaJA2mXAjQ/IbAXXdrkbC2xgeOELX1jbtmUnnBoq40I3+8Lph9q5J30y/mKf9w1Fz1jSg5slXKMap3VV3IJ8ifvyNuCblMlktIeyBdk7iZbSKWwZB25wKIAX/m3TNXFbDjDBpFMQwjTR8J/JxGRk4snWRXmaUFnxciDN8EgZHRpe/djCPdVnVRkdap8m3KqhMuqpcd3UTwU16+DYZJBRhsgi4XJ4zwDCDHCY6AnNWvrAwdChjjnmmAoCcFvIvoQW6AcgAiUULgduWYBAwIgda+RHAqjEidOBO4RrlDyoR+BAXRASpqTFPzAQuRnv4szXHD8pozYCIERoHGfEgYQJC2jxY0C1RR2HRjm1C+++HunKJR3tD6AgpOzrUjpJS/l8aeJ6SZOyfMY/moV7In84jkCW3U0EpOWdkHTAjbaJ+4LTReFi6QOWqSKXIxygBXhlC7m8oB/BZ/QPLdK2a2T9X0+BOaaAdpd+nDaZJANujLvODqP0Df3JeBR5GoLCPnjaJd30tcQ1XV088jhVpSyeblkGheenm09pddNr7RImdvqr8YZq3Vqapk9byjaOGGdygGjCtuFjrpEu8r+bLfL8zTh7bQNRkbYr+4o2CeoAhFwzQap0qJ/6whe+UCs7Mh3i0Wh0nHQYnUZ4UvaZ+NJYsDsDbiLT4cvClvR8aXQb6lQLC/e0z1TDTeQvcU3kPpm98kbmhowGFRowk0GyFKTToHdOdjbRWv5hj5sgDqABJwKnI3QnM0NORFhASSfE1bCMoh4AHAqgcEYFzolOqR5T94CHjqu+0F467nMi7G0JxoAIBNkODYxah8eNwiniTzpAQ7aIWpYieAzMRkhZetKVZtqQfHkPPbQvfnCjyPYoO9oAu8qlbSmr9gkQA0/yFpkbHCEnYeO2GKwN7HY/vfWtb600cP4N4JLBHjAhnJydZ+HKSAvgtIWfsqQlL+SVLEE5v8dSnT6CFll+SznSF2rg/q+nwBxQIG2Mrs8YK/WftGF9RZs1/kbmxseKcZi9x/hrPLHhgxJefFNR6/IXACGu1pz39BXv4pL2VNPvxpc4kqfEk3FOWvnoa8Pynydx0KO4ZazCuckVExlPxCnu+GnDxbyY9Q0a3GgElAqnNAbo3pesScFXr85BLoJq/ZvITCaZPMXBnW6SBWhwFLBFHepmayGVBmhS1rl8FbfgxsQ0lWWpGtkS+EMTHQOt9t133/FBqM26w+bIe6C1LeFROA04IIR6TaTOm1AvgEQ4C+gNEAEtlnhMyszACsBDlsSyf0iV7gAAIABJREFUEaFwwJEgsk6Z+lIfBkRfI0CTNISXjs6MOwJoUeRtLBmpW2kBQ9KQt3BDxIcDJH1+yO20a/ri4UdbozLIyU/c6ICLpTLxoEHO2hAn0ILDQuFCKZ/Bml9tTrvlz3P00UdXf/4skwEjySvOIjklBytSyRMukjKSq6HQS5tGH7RHF+VGpze84Q3jftK2q0X/11Ngnimg/aUNAvf6RD5YkhXAXdu12zLLyOmD/AifvpgwM9XFn77VjUt63DK3dP22ZRoUNuXN3OM9cbX+40/Z4ifl9h53eQlAZPahRRk7A24i72hcmCytNv3FaN6gwQ2CpyJb4qtoy0a+1tslkTSANAZs+lR+wnMDkAiYEtg0+aTxJjy/3EwUXXAzG5yb5GUx6DqQThAugzx1kT47tLbMQaGTTohu7NQBMIjbpV5aIT/+POjJTX1IS3yWDHEnLL9Q8pJlxGrR/GkH6hOA4Ud6lr7olPgoaXG37NimGbfkRzh551d5UmbmdlCpka6O10DR+uWvbUfy5p1ObohCG7JiBvJcSqq8aKTtoX2UvCknulLelUvepJu+IB8JKw+eDHTK7ZEGP+3g1pYtafZ6T4G5oIC2lkc7prTFtEccWH3CB1PAvHasjWdsFyb9lVkbTx+oEc7RnzTlXV7bvihteZxMJb/CU9ETJu/ibuNKH+avNRvzgDwyR+QdbSawZE5u0EYXH0rOBTNXEdnw+JjDxcVRzwGloVvyl/wsZn2DBzeIn4kiFdE2jDSW6PwwmxDSkRIuFZx3etvAWvssSw0CN1OSuWkjW0JmjX8yhbaD6CgcWrbug/yJmx9P6idpsot7BpVuXoQZ1B74U+fd+hRnN1/skmYbP/vkIe7ek8+Eo8tD/LRxxJx4AGmC05bLyCdRlqq6KnGzZ06eJ/IXP6170qTLMz15ZA5Nmbt0auPpzT0F5oIC2h3lgwZHHbgxEYcTrk91+1re2/y07bq1Z+aWdLpuk73rD+0HTvpP4ow+nbj1wTzi9eiL6ZuW3f75n/+5cqcto7/lLW+pZnKOLuTFkbG0brnbfMSMdsYUZnbe44fZY9k+IhQp22Q0WGxuGzy4aRvToAFfQ0kjmagCM6jHn0rsDu5xi44rMFcyN4ulEaWsyU/7ns6N/gETzOyjvAsTWrZ+44+7CX4ioNP6Ez51lTTo8RM7/tq20HWPv0Fh4yYOT9pM4mxpwG/3PeGjJ1zy470tK85LZG7IxlDSTDkTnn1r9i5tdvFbA6/215aZv/Y9YeO/fRffusrUhuvNPQVmgwJpc2T4cv6SydhSbWRu2r6onUalD6SNt27xMx1dnpKvycJLLw9/uK02FwAlrnUhE8hs+T6PpW4Pe8vLxB7Iwzk2g/A/UQfLynaCWooPWKEDK4ALHScmdnQAx5IdmT5xWda3oxKXJiCH3F6OgrBpgUK7tgyTlXexuG3Q4KYd1NPwEZ69BtY2zEywqchUUGsfu8TFrY2De94tWQTcZLeUSWtDkrnR2AfRJ3TinjpgzqQdO/7YRw8ICg3jlvjo3EL/Nv7WD/s8yZ/3DG6xSxj2bdrJZ9yFTRhpJ/3E18YtjHdlpMcce2ETV8Innegpv3dLQwSvDeS+xKiET/x5HxSeHX9UaBf6t/YJG7uEa9+FT55b+4Tt9Z4Cs0UB7cujzaU/GlOdlp1Jmk7mJpxw/UD7TPtu+9FU22vSXJ9ySE+/FpY5fYQMnMs4CfjbMIELYpMCkAJg4KhElpD8nHcyheQXgRBycXnnTk4w3BZlbx/2gIxw7B1N8frXv74exukoDbJzDua069SyHWUOzLI2WSVAUdhWzi60R8vQUzmnSs/1oeNs+92gwU2I1Tb2dVVKKjBh6cKkwWZia92FSbzxR26CYKhG14IbO3VaYdk2nqVqDs1aOjG39ulEoVPK6j31E7vQMO8Jk/hiT2/tBvmLO7e27rx7usCgjTvxtXYxxy1x0KWVciZvAU3cU67WnPzRPfGTdLzbrWTdnCxM4u36i33C0dt0mENn5rbc3uM/+Uk8cYu9dFOm+On1ngJzQYG0veh2MBpPPZnECQ9nPNW/006TH+952jYf9+jSMJHbXGDsJlYw2UPWjj9LZIT/Xeuz//7718dOSDsUneMFLCSv8u09OrPHh4sNBeRfbK4g/8Ls8YHc6sw4K3QAya5Gh4Y6NZ88jTPbzjnnnPEdjqGH8oWOyhxzaJIdoPKT3VIBNqFR9ITN+2LVlwW4WQjia/waqYac3VI6HwSv8UPzuDjADnP7sNsQHl8pnonKNpG9sqNN6x5z6DLIPW5T1RNnqyds6ibv09HXp/zJQ9JJ+bz7qnLGEp29eO1QE2aq9B0Uf9Lq9aXb35xTRDjUBG/HINWdyBZi/Es+MrkmD/IWu0yS0fnh1gL3+LVckyMlHAPhSAQTMc6HMZUyGbcAJ2G78VoSIijr1HPLLo79cIK9Xa/itnvRmVzM0ZldqgtYMAMd/NHJpshLuwSUJR7jf+wBF1vVHWrqNHw7M3GinHvlLCx5B65wa+keGyjysLfBAOii+8hoaTWdes8HT3+IX21C/d9UKBBwA7XnWgIN0fqq81EIhzqIysPcPhPZt36WgjnlaHVs2jyxz3t025I9cW/11k9rzxy36F337vsgGrZ+Es9E+qDwrV0bV8xtXLFr9bin/HTn0ZAroGMr8+OcjzZczAlPb/MSc/zRW7+DzAnT62v3z8VCD3WY9qCdOL7ChNiChamMVXPhRx5acGHsC5cg6fFjcg2gaTkF/Gfi5Z/smatGAASAXn8AKByNEJmbcE3J6JmsLcNYmnGPnOMQHJtgm3OAkWUgj3jCTcFFCVhhZ/y2TGR5yMPNclGOkhDeu2UjbpZ2cG3kEeAk7EzXX3FWkkdl9rR08q7MU6m/0Cy0nK4eGvfgZroUXIbhgBvLUlB7wI0GSXU7+TIkz6RF1rnX1cHX5T5pAlNwnOv415WFidKfyH5d8XXdZyuebrz9+/xRQB1mTGHOV3xrP3+5mTilQW3NGDhoHGQXkBOAk/CWZXFYgAXbl4ESwML1Cq5hIViLu0PepAUtxuBwTwJkAJcAGtwXd8y5IDaP+HDY3ReHY+KAQBwf8ipkfxzZYNOIPDmaIg+3LNsCDqmTFowoT9ctZYw+MTVn16UHN7NLz2URG3Cj0+hAkbnRcdPIMygtC2L0hewp0FNgziiQyTJjSvQ5S3CaEcunyb6dwL3LrydghztzuBzegQxLOD4YcVKAFWNrQEu7MwiA8XDjlxlnxen0hPJtlcbRwcnxkFOhko8AEnqb3+Qr9PUeM39ZEmvtxdu+M3smU+vy04Zfl9/J0uHWg5t1Uah3vwkFgJutttqqsjV9DVBpSDfx3FuMUyCdte3A446NYV3ujdclaeyWb13vS7KQfaZnRIFum0hkE9nHfT70FqwMSq+bx7wDCA7IJA/jLBsHzeF8k18EUrL9G2cGuHGqtiWnSy65pFx44YVVdsUhdT/84Q/r4zRu9q4myUGdxuGkB5R0x2V5Zx/Fb0AMu/adfevWvieNxJOwrT1z3tuwbZi5NKfs/bLUXFJ5A4sby9IBUzqjDkqlw7SdYQMrdl+ceaJABsR5Sq5PZhFTQFvImNJOloshy/KVcQ83JpOpvLHPEpR3QsOEbMnV4LSEAxPhXMK9bvo+5ZRT6o4e7raCR6AYp0ca6Rvi98hDyxWKe+gTmrX+2nzHvg2XMOLgDpSlnIk3OnfPVFUb91TDTNdf6qMHN9Ol4DIMh3NDql4HfMxjHlMpkMafBrUMydIXuadAT4FZoIAJMBNmO+nOQtSzGsWgiRrQwEUJ4CDLQug342VkYcjT4NBYinr5y19eD7NLWe3wC7jJCcWJLwUYlDY7/oARYKilYfyzS1yxa+MEyLgbz8XBT6uEz1jf2sfMP/fWDzvh6OJOvhJmrvTMRT24mSsKb4DxAjfu59ABI1CsQ/Wqp8BMKJABcCZx9GE3HApkUlSi+ZoQ15d6JnGTqMe5MG9+85vLtttuW7bbbrsq/GtLtXEycjPh1BAOdoaL3U8ZO5WXsjtMGOfc4ORQrYyO9y5tgIaEj7u8xS56jazzx80TGuedtxaoSMMTFX/RYx+9jZPdRP7iv6uvr/9u+B7cdCnSv6+TAhEo1lEjUKwTpHOsM4LeQ0+BngI9BeaQAibGrjI+tZNz1z3v3UnVe8a2xNv6cQntMcccU0FNgAwOjWX7CAezD9eG7r4kKsCGOXE7uoB/W8EDbviLe/Tkt9cHU6AHN4Pp0ttOQoEe3ExCnN6pp0BPgUVBgYAZegBBQAqQE26IzLL3gdZVCRd7h8y9+93vrncXubfIXVAPfvCDKxgBZAAS27Wd4PvOd76zAD7O/sqyFDcHEtqCTUk3aSctZ/z04CYUn77eg5vp027ZhuzBzbKt+r7gPQUWPQUCFqaSUYCiBTXehY8ig8LddQTHHXdc2WmnnSo3xgF4ONfhxtiWbYneicAU8NTG5ZqRY489tl41wt3E2+UiJR8OxevBTWpg+noPbqZPu2Ubsgc3y7bq+4L3FFhyFAAyAImAFiCiCywUiju/loCcwrvPPvvUHUwud91tt90q4AA6AJnNN9+8Hodx5JFHlq9//evlggsuqCcoi0f8JtakJ05P0nAQXpaj5CPm+InMTb8sVUk27b8e3EybdMs3YA9ulm/d9yXvKbBUKNCCCnnOe/RwSgIqVqxYUU499dTy+Mc/vl5HgCuTg/SAGhdAPvrRj65345k43YsUAEMPYEp8SbNNJ/5Dw/hNnti7dqLn3IRC09d7cDN92i3bkD24WbZV3xe8p8CSpABQAWQAEQEbKYit22effXYFLllmslXbbiX3LDlMz3IU4PPHP/6xBguQ8SI+8bcAJ+/Sizlh4q99T97E536vHtykdqav9+Bm+rRbtiF7cLNsq74veE+BJUGBliPSZtiE5xZ6J6u7w8lyk8fBeoCNs2e23377KjRsnHNOzf/+3/+73lQtHsCEAkxySF/AC3sHnL7qVa+qt2o/5znPKT/4wQ/G/QMwmXDb/CXO6OsSKK4R9n/rpEBo3Z9zs05S9R5CgR7chBK93lOgp8BipkBAxNVXX12FfV17YKkp583gkOQBdiwJuTCSCmelLV84Ma0dUBK5mU984hPj8bn00mWYbVwJHz3xePdQ65K5SZhen5wCPbiZnD6L1jUdIRnsdpbYT1fvxi+e2GHNujjTALHHHnvUJMKabf1NN+0+XE+B2aJAvobTduntE3d6/MxW2n08k1Mg9KanHhKia9dyPfjNMg6dav1z//a3v12e97zn1XO4XCppJxOZGUBmk002qZwVt2O73sAdTV/5ylfKr371qxqXOBNv8jORHgBkTHzTm95UD+8zLlrWcokllUl2ojhiH3r059yEIjPTQ/eeczMzOi6r0C3npgc3y6rql1xhM2G0k1+3EPHDnnmqE1s3nv596hRA55buCRm7VjdJqROgJUtB3ANCUl9O+7Vz6UUvelGVlYlAsG3bQI33+9///pUz4vwZAsTTUdLLxJnwtolLA6ixmyqH9HX9KYNnkEqZe3AziDrrbxfa9+Bm/Wm3aEOkk8xGBgfF1YKbQScUDwozG3np4+gpMBUKaH+Z8Nq2yGxiyRKC967fhJtKOr2fmVOgrR91k/e2XqTCLZNVwMF//dd/laOOOqq84hWvKLZkH3DAAVVmBsi41a1uVQWBd95550KG5YwzzqgCwT/5yU9qplPP4op5fUqT/CW/DvaT7qMe9agKsAAtinub98nSSFw9uJmMSlN3S3vpwc3UabaofaYzpaPMRWYJzWVZqgc3c0HhPs7ZooB+kGdQnG0/Yc4ywyC/vd3cUADdA1jaFFI33AJI6ZaRXvziF1fwAlBYBmplaO5617uWf/iHfyi//vWvy5VXXlnjTr0mrS7g4B6Qk3Sjt3liZp94MoH+/ve/ryDrPe95T/We04/FG3PCDipr3Og9uKkknPFf6qYHNzMm5fKJYF2cm+VDib6kS4ECJiITFz2TnHx7NwDSo5gnmnzip9dnToGWzszqhe5JXUnFO2UZyT1LdiJtttlmFcy4u8kSUM6icUnl61//+nohpTAtYMl7gMZ06jh5qRla/Zf8ynPynbjjlrQD0NrwrTnx9+Cmpcr0zT24mT7tlm3IgBtr2IM4N8uWMH3BFy0FuhOOiSR2Ms2cySeTzKItzAaQsS792yJxw3VxGeUuu+xShYEtL7UcGstOBx98cPne975XLrnkkvL973+//PznPx+PRhxtPcYcPR69px3E3PUTv60OyJD/id+u3vpNvOzir3WPOW7r2goefwnX64Mp0IObwXRZsrbpSPmKmGlB8kXVxjMI3Bgg+k7XUqk3LxQF0gfa9LVPx95H8aOPUNzyRd/ax2+vzy4F0DsctIAEKfzmN7+pZ9C8/e1vL9tss804mAFqcGgsQd3pTncqhx12WJVrEUZ48eXJhMaNXd7pEUZu/WbMip6S5p0ec9y0o29+85vlwgsvrFYRTE68/HfHX3bcqeiJL3rS6cFNKDIzPXXfL0vNjI6LJrQKJczm0ZkzgE8ngwag3IPSdkjbHrfccsu6+yCcG+7pnNNJqw/TU2C2KZD2qA988YtfLPvuu2859NBD6xe/bcIEUOnPfvazy9ve9raa/Ez6y2znf0OPz0eSM10cdgewPOlJTyqWmnCEARnLTW7dPuuss8qZZ55ZTj/99Lrk1NZr6otdnnasQkPv8dfSNPGwm8jcuiUOl2M6ufje9773OMjKRJo8JJx34dr42zy05vhZF7hpw/TmiSmQOunBzcQ0WpQu+fKJLpM6saO7Xbhm/fl+97tfPe+Bn3S6dPx0pLzTY+af2WCyxRZblCOOOKLSII3lqquuKne/+93rIOT8iKQd92rR//UUWGAKpI0D+m9961vrhNluCzZ5mkxNpE6l1e7TVxY46wuaPLplLMi4Qc/knsyFvu07+lEJ1zW7XRvH4+STTy7bbrttHUOAGU+Egp0SDPCce+6540s/bdrddJP+XOjSCi3Eb6LEPdJmXMvwrne9qybrQ7L1N9W8tPFnWZRskfjvcIc7VDkjcWVslcZ8ln+q5ViM/kKzHtwsxtqZJE9tR9LYcVgMGI4Vf+lLX1qOP/74Yp16q622KpdddlntEC1bVtTpTK197K655prymMc8pg46vnapDDB2INz5zneubo973OPWcpOv+KsO/V9PgQWgQLd/kMl45zvfWT7ykY9UWQ5CqQE1J554YvnUpz5VJ5AMiAuQ5UWZpL5sTAg98y6zATKtnbGIfcYASzb/9m//VgV9nQWDS4buQCVwQCc/A8zw53wYnBrhk+Z8E0YZPNJvwe6Xv/zlcq973avmH9dGezK2Umk3wqTsE+U78XPn3zsVPScUA1Ef/OAHq5tl07hHrw7934QUSJ304GZCEi1eh8gQ6CDOWTBQOMCK0in/8Ic/VO4NgPOLX/yidg4D1USdI/bidaqnQcjXLZYxlcZiKziOjvSwkqmAImb56VVPgcVEAW0yk7G7gkxO2veee+5Zs8mt9bOY8r4QeUGL9HfjQsaGQXnhV/+P3BJOmTuZzjnnnHpv08Ybbzy+owl3xpjC7hGPeET5+Mc/Xrdr+2Cikg5d+snDoHTnwy5AxZhHgFmbcU2DE42pgDA0kOf4n0reQjd+XdPgjisC1Lju0iFntMMOO5SHP/zh5WlPe1rlwifNqcS/3P2k7fTgZom1hICJDMq2QGLt+jqidDSDzec///n6ZXT55ZdXuwwebSeMXUjg6HKdzHq3TvaSl7ykOqWxuHvFWRIGqoCbNr7E0+s9BRaaAmnbmXzkxzH7BFa17b/5m7+pWdS2+fXw26s1FGjpwZz3Lq3++7//uzjnxaF6wAvODBobJywHAjUma9u5LZ+7jZsSn/EjdbUm5bVN/K3Lz9ohZvYmrQBiH4ePfOQja3lcs0BlPGzpkLJMljL/KUdo6RoItAoni248p7O/5z3vWb70pS/VaDP2T5ZG77amfnpws4Rbw0UXXVQ233zzCkjIw6TjKNLXvva12jkOOuigWsJ2gGg7Z8DJD3/4w3KPe9yjCvCddtppNewhhxxSw8aPrxjgRqfT4YEoh1hJO36WMDn7rG8gFGj7AXMmBSfbbr311rX9PvWpT639Bbeyb7tjFY9W+WhKU2CXiZidPu8yShwHH0E+cnbcccdK08jOmJwtjQMyF198cZ2cL7300hplG1ebhjrouknbs1Aqaf/2t78tlqbs6qLQKOAneZtqXhNn2pxrI7RJHHGyNgE6xlmcHFyd8847ryaTMEmz1wdTIPNbD24G02dR22YQ+MxnPlM7g/VsCluY4q5D6ChYwJSOkUpP+Oqw+kvENkxfV76qTjrppBr2ZS97WfUS/8CNDmfHwEYbbVQ7pE6Zi+LiL/H2ek+B+aZAJo823UwKlkyyLPXkJz+5egF84t633zGqoUMAYeiIrji3+rpJOMK1AE24DMaCxz/+8eUNb3hDIbsnTMYcNAYkQ2Pv0sh70plMH1S3k/mfjpv8BLgwJ3+tfeJNGfI+FV0ZxJWyoAn5JO/veMc76rh729vetoobiA+QDJ3STqeSznL2kzbXg5sl1grS8TR0lQfA7LPPPrUU6Yg6ih0H4bBw1Im6it1Pf/rTej/Lgx/84CokJ950ssMPP3yteHFpgBsDWs6fMLDtv//+tXMm/W46/XtPgYWgQCaQ6JYYsPn1i8jcGAi5e/rJY+3dTuh19NFHl/e+973ln/7pn8p973vfSjsfNmiIa/zqV7+6TsRk/8jaUGhpLEBP5kw27DJ+dceKhEk78d7GFfu51OW3C+qSB/nxpI3QW3PKOJX8tWVNvMLlhGLA8UMf+lCNan3inUray8FPaNaDmyVY2xkYPv3pT9dBhtAZFXsdxjqtAWj33Xcfd4t7isyf7YfYyO5koQw+dl8Ja/cVlQ7vfArghv9NNtmkAETYzzkrpBt/Ddz/9RSYRwpo05l0JOs97ZLMTXa95INA2+6GmcfsLqqk0MGW7Q9/+MOV40uA1jjQbp3HVdhpp52K5W4C2iYS4Tyh9yB6xo+6yOQT2guXOko8XcKwj5+u22y+W2479dRT68dgW45B6bPLM5W8tXFoo13aDdotZUdr6CV8r9ZNgdCrBzfrptWi8pEGTncejcHn6U9/eu1kbQfzFdWCm3bAT4FwbhxohvtifdyWzE984hPl5S9/eQ3rpltbZb/61a/WIHY25Jwb28XDUp3uWQ/JR6/3FJhLCqTPtOBm7733rkkC822/mct8zCTulEEczOGAtHGyjz9lavt86yZMymxnpUnV7dmEWx/60IfWvm9MCHdWn3/Na15TxwVjDlCSJfCkL/42vdgvZl2eQwflOf/88wtZLOMmbrRDS9E5NJ2LsrR5COfGcQXZCt4CwLnMx1yUbaHi7MHNQlF+FtLNboPPfvazFZgAGlQGPJ0gnBtCf1Q6cX1Z/edSOh05OxqYIxTILmvpu+66aw0Rzg0//W6plpK9eTFTIJPCUgE3mfD02TyhLwCRJeaUix+TIMUcjoB3/rNVm3/v1113Xf1o8VGUnUC4sfo/Lg2ZGhOsYyFsTLCLkpJGzsZqxxppJi/V4xL5k+eUwzKQ8hvz0MD5O2QMKeWbKyUPib8HN7ND5R7czA4d5z2W9uvIQLPffvvVLd84LFTcCRQbsOyoolLh6Uh0u5ywl22LFY/B7lnPelbdfaWjO0mUfQSGydzc5S53qfHutddeNd7E13bS6tD/9RRYJBTIxLtUwM0gsimDPpzJOP08fvVD40HKyp45/gimOiEYh8YmA1u2248Z5tvd7nb1QlzXAOBkWKJKPOJP3OnzSTt63PO+GHX0CE2SX3T9+7//+/EPvRe84AVVIFr+Q8PQfbbLJP7Qswc3s0PdzHX9stTs0HPeYtExU3kS/e53v1t3LllCuuKKK2rHtZUbexUw0Xl0TB3IVlidmDuVzt2a+SNAaLDLIX7pfMCQLYpAU3v9QsK38dUE+r+eAouAAmmXSwXcJL/6XSZiZGTffWfPXyZf7szGiLPPPru89rWvrWdg4cK43kDfxZWlEwy2u8mHkT5P1gZ3NnEmzWqxOn3xJq3W3PWbMItVx4VCN9yZY489dvyDznKUnV4tTee6DKnvgBvX6JCFpPplqfWnfubHHtysP+0WRYh0CAMNLgowYpsrIV/Hmj/kIQ+pg6FOSqlwgxh/gy69DLvZ4Kiz8+cLhmLnMfBF5qYFN8lL9dz/9RRYZBRI+1wq4GZd5FOe9El+mVNGZmeyWDa+/e1vX/uxpRbHN+jTllyMETYNOL8FN5ZK+K457+JtFf8J09Vbf4vNHNCSPP/85z+vJwGjjUuBXdNBGS/5SbnpMc92mZKXQRdnZqKe7TQ35PhCsx7cLMFazvq6rDMbtN/4xjfWiy5tzXzd615XhY25p6KZHdR31FFH1UvfdNR0qnyJ6fj8//u//3v94iM8SAUg4dxkt1QX3CSuGqD/6ymwiCiQtrnYwY18pq8lzyFj3ls/+vB//Md/lBNOOKHu8MF9efGLXzy+5GTCxp3B1T3mmGOKc6tc+JjlJmmJz9OOB9L0blzgljGkTbvNJ79LUaUMdn298pWvrFdCKAd742rKP9flQ1dqEOcmtF+K9F2oPIdmPbhZqBqYhXTTKUSlQ6azRk8SqWz2bRjmCBvyGzedOfbMAVO5fgFLe4899qjRJ85umkm713sKLDQF0q4XO7gJneRXv0u+9a30QX68f/Ob3yzPfOYzx7kzODKecGfcdu5G9P/8z/8srkegxJkJO2NC0hRnHunmgyfuyVP7zk/CxH6x62ig7HRKuZQhijll77rFz2zr0qF6cDM7lE3b7sHN7NBz3mNJ5+wmnI7CPpWsw7b23NpBqY0rHTt+2vhbzk0X3HTjb8P15p4CC0mBtM3FDm7kMxNtdKDmRz/6Ub0x231xDufEZbBqf40jAAAgAElEQVRNO7sZ7V584AMfWO+C23fffatcneUm/VqcedSBeLt9PONE6ij0av22du140YYZZB/3xaArQ/LIHBqnbMlj64+d9zzxM5t60u/BzexQNe25BzezQ89lEUu2gk8kULwsiNAXcslRIJPHTMGNeEyI9HaSDEGSTusvQKIbLuETdpB+2WWX1YM0cx+WfufJcQ12Llpq+uIXv1hPFxdHJuxB8W3oduieJ/RNnYQu3/ve9+r1ED/72c8qOfiLn+jzTaekOwjcALdxjz7f+Vtq6fXgZqnV2CLIbw9uFkEl9FlYbwpkUpgpuEnC4gtoYTaY5p2frnsm1oSPH7rJVVh+mJ09RebNieFbbbVVXWYCZgJoLDs5np9sHWFYalD81WGZ/qH/oHq54IILxu8WIzNI1mYx0E9eqR7cVDLM+K8HNzMm4fKLoAc3y6/ON4QSZ/KYKbgRDyCR+FraxC56AEfCtH6ZARmPU3CdDk7Q9/jjj6/Cv7ZqZ3eTHU9ODOfnX/7lX6o/k3TiSDr0DOrdtJbTe7d+QhN0ts0bOMTxetGLXlRvLEeb0HCh6JQ204Ob2amB1Hm/LDU79FwWsQA3E51zsywI0BdySVIgk8dMwU238Dgu4dgMAjGtHXMeu5UuvfTSerbMdtttN86dybKToxycEuyU3B//+Mfj15xI30QMFFGJP4N5N3/L8T00busGvU488cRx4esdd9yx/K//9b8qebpgaCFoJs9UD25mh/rpDz24mR16LotYenCzLKp5gytkJo/ZADfiCqDpEopbgEfr5k42A+2ZZ55Z729yQvCd73zncpvb3KYCG5fQPuMZz6iCwoceemg5/PDDy+c+97kaRc6f8mIiTtrSajkO3fc2/eVk7tIB/XDEwglzK7yDTB1oSqEnuZZMiAtBK3mmenAzO9RPXfbgZnbouSxi6cHNsqjmDa6QmTxmA9xMRBxAI0BEekCOQ/JwDHJ/E44M7owt22RoXHdg16ELGx29IFzCJs8BNImffbaFM7OnZ0CfKH/LzT70o7sQ1HLU/e9///L973+/kgK9AhRZxP9C0Clp9+BmdqifvtCDm9mh57KIpQc3y6KaN7hCZvKYDXCTuIAKT5Slpq985SsVqBAIxonBndl8880roMGledzjHlfvbnOXmyWnU089tYIa8QSwDOL8JA1pB8xkYm7DJm/xv1x1NAl90ODaa68tn/70p2vdeG/dvKO5MAulUm89uJmdGujBzezQcVnF0oObZVXdG0xhM3nMBrhBFPFlgnQPkZO899577/ElJlwZHJqcQ7PTTjvVZancWZT8RA+h2/cWtMS91fkNEGrNrZ/laEa30IWeekKj0LdrXmg6JV89uJmdmujBzSR0TOfgZRCiZxd7esuObjvOJEksSSfgxk4Dg3fup0Kr0GJJFqrP9AZFgUwUbaHSPoEb969Zothnn32ql+uvv77qwsVfGzbmxIvDYru2aw9cdbDDDjtU4V9xBszY7bTNNtvUy2U/9KEPleuuu65GM1n8SafXJ6cAGraApUvTrju/7NZVv5OnOneubb56cDM7dA64wa0jz6ZvHn300TVybunLLfidnZTnPpabzSSJFFwcWQdPfK1bOlj0+JlM73bEyfwuRrce3CzGWunzFAq0E0Xs6Om3v/jFL8q2225bB7snP/nJ1YvBzpO+Sc8SUcIZB770pS+VD37wg/Uep3vd617jN2sbOHFpHvSgB5U3v/nNddu2aw9ySFzibfPTm2dGATQ17qqf9kMUZ+y8884rLqB0sGEmuaSW+sz7YtDlKW2kBzczrxG0zNVBwM3GG29c+/vb3va2Gnnbt9MehEkdzDwHcxvDjMGNQqfgsoog3c7UBTX86GgJG31uizq/sffgZn7p3ac2PQroe+2kFzPOzT3ucY862JF7ofTbtq8y69tkaGzDPvLII+uSk63Z4c7YeYNLg4vphu13vvOd4/c3tYOkuPLO3KuZUyB0TD3RU7+XX3552XPPPWs9OR/IhaJU6njmqc9+DPKfNtKDm+nRN23C/OQsKH0bV3X33XevHyEE+H3UsHfW0be+9a2aUOZwehcITy8ncx9qxuBGFv/85z/XnOoYrUpDZBfitO6DzCH+ILelZNeDm6VUW8szr/pnBirmv/zlL+OT3y9/+cu6XASkPOUpT6kEil/93aD35S9/ue5wsrPmbne7W+XK8O+xpGWrNq4P3aWUuAUZK6SXiVa8ibudwJZnrcxOqVv6JkZ26KsOzjjjjLLbbrvVugI6XV9hjO6O4Qm7WPTMDz24mV6NaAOU7f2E9vMRkn5Lt1PRBwmggwNLaTOhfbVYAn8zAjcZnJQzRMPm6g5iXToIN6jztf6WGiHbvDP34KZLkf59sVIgfVf+Ynbcfu5pevzjH1+zjpuDZe1smRxQSaaM3Ey2bbPPDdttfPpzxgvmpNPShH0+gpZ6/2/LtRjMof2KFSvqdRSZzOxMU19U/KD9YqV/8tWDm+m1qvS7//7v/67cGf0XV9U1JS6YxcXz4LjawWiXIpWPD2ZxJJ7p5WJ+Qs0I3CSLtg9eeOGFxSDo0K2HPvShlQVNQPBrX/taueiii+rjpMsMXgkbPR0r70td78HNUq/BDTv/BiecGioTRmsmc+O+JoPcZpttVq86cJu2SRGQoW+xxRZVWB4HYL/99ivf/va3y5/+9KcaZwbD9GvpxVw9NH/GBP6TD/pSGDybIixqI1qmPnDQ7nvf+9Yv84c85CHjy1HqoKV56mKxFSz56sHNzGoGh46wv48YXLuf/OQn5Te/+U1dMiYDB/z4mLn66qtnltAChp4xuNHYPvOZz1RWVr4G6Nhaz33uc+ux6S628xV429vetp5Zce6559YiL3YW6EzqpQc3M6FeH3Y+KKDvBnC0ZgObk2m33HLLCmQ22mijtUCNA/Xc4fT1r3+9ZlMcJsaAlOQ9E1EmTe8xm2wHfeiIKzsqE0+vT58CqdfQ3YfoIYccUu5+97uXr371qzVibvzF72KmvzxSPbiZfptISPWedsEObfOe9uA9YwR9UJ9NfItNnzG4UaCPfvSjdfADarCoffG99rWvrWVNY4QOrdP7wnvYwx5WDjrooCqt/9Of/nT8C1KA+F9shFrf/PTgZn0p1vufbwpkAIvuRNpXvepV47ukgBqsav3a8sVTn/rUyoklPCyMgY4ergAz1Q6QGQzZtWb+EkfCxD1u9F7NHgVSP1dddVW9Lypb+1OP6mixf3CmDD24mXm7SP8LePHu0Q6ix61NLf5au8VonjG4MbB95zvfqZLVBkMsaiwuChFanZl/E7+1vMMOO6zeEePI77POOquywWqAZoDM+1LTe3Cz1GpsceY3A81UcpcBqfXb9sFMXLEjG0ew9Atf+EKVu8Cp0YfDgc3yk3No+DPQyc/65KnNS2+ePQq0dc3sCUiJuZta6o49P0tNybMyUC24idBr2vdSK1ef37mhwIzATRqaQU/Dcz8FlnZU3Ae952uPm4GT8JK7ZTTQbriEX0p6D26WUm0tzrx2+0H3Pbk2qeULi5/46+r866dk33xYPO1pT6tnWxAqDKixdPySl7yknHbaaeOHej396U+vSeUsq8Sb9Ht9YSnQTvraQs4uYSZXxZ2K3jUvbO6nnnpbzoAb8mBkO6ke3EydlsvB56yAG4RanwFPp+uq3//+9/XSNvI7LUDq+lsq7z24WSo1tbjzaUAPcGlzqr9FIJi998hKCNP2MW2RfIxl4SOOOKLuhgh3xrZPW4FtBnCHk7ueKFtFc0IxEESJf6L8VA/937xRQH23dSzhtJN8OBIQfdaznlVlbP74xz+Ob+fthpu3TM9CQtofNRm4iZ9ZSK6PYglTYEbgZrJy62jpbDpTGhxdx/QEacfN+6tf/eryute9rkYd+8nSWaxuPbhZrDWz9PKVPkPXl9Kv2pLEjV36jbMpLP+SlQFmAJmAGrqzaY4//vjy61//uoZp+6lzbu55z3tW/0984hPHL63Ub6WfNNo89Ob5pUDqgJ6nrUMfirb4qmu7WF1twR8/S1WlzAE3d7zjHW/CuYmfpVrGPt+zQ4FZATcZbDWqmKebvYsvvrjebbHUB9Ae3Ey3BfThUEBfCvgfRBHumaTS77zbxnnJJZeUc845p+yyyy7jk5sJDgt/1113rZyaN77xjeX//J//c5P+CrxQ7d1SwBGFIyCt+KmW/d+CU6BbJ96JCNjYod4Jhb/iFa8YBzfqb6nWobJRATfa9Ic//OFql/4SPwteMX0GFpQCMwI3LZBpO0trzgBMj33Yphpha0YJ7w4Ky7HPC0qdGSTeg5sZEK8POpAC+lDb51pPlnUtORH+zYFcThg1uTlB+PWvf31d9tUHPemX+iBzJoT0UUvD4hI+F2f68p8o/TYvvXnuKaC+Ulepu6T62c9+tm7jV3eu0CDTSKXeM+bG/1LSU9ZB4Cblip+lVK4+r7NPgRmBm2QHYnZ4lwZnUv/tb39bD//5wx/+UL10B8R0yoSPzp7fY489dvwMhqXaUHtwk1rt9elSQNvv9h1xsQNm9DlLSq985SvL/e53vwpEnC9FON+kZvfTHnvsUa644oqaBeHEmUlAf0v/orfvDvfKl39kbrqAaLrl6sPNDgXaOsuY6tA17QGwcUeQSzGpbt3F/+zkZP5iSXs96aSTahkB+XBu0q7jZ/5y1ae0GCkwK+BGY3rpS19aD/zytefAPs/OO+9czjzzzOLQvrAM08miI0o7gLMnBHfBBRcsRnpNOU89uJkyqXqPE1AgE5D+lT7ibBLH5QMuD3jAA+phbM6WsuPJhOYWbnc+4bIQKHVoGyWuts8lye5EkDRbzk3ulmoFmLvhEl+vLwwFtI/skvrmN79ZnD7spmdKvauv1Fn0hcnp9FJN/tM+TznllB7cTI+UyybUjMBNBlyd6itf+cr4dtKcj2Gw3XTTTSvocWjfG97whnLppZeOA51QOfHQDzzwwMoGdwbHTDth4k069EEDPPugfuY2XMzCxcxPm7fY63ixx7VyZwdaPOEJTxBkrS/jatH/zYgCJnC3TBNA9zg48uijj65mJ+y+5jWvqRN9S/vUf+ppRhmYQuAMxrxKM20leqKI20T58nHgRNl99923XmgHzOQhKPzsZz+7yll84xvfqFG26SaNqehJn8wNoKQP77333jUo2k033qmk3ftZmwJonXYSnV07VqXdCJm6szPqhz/8YY0s4daOeWm8pfwpl/PT9HPziDFV23R0AfDN/u1vf3txbQjVt9OlUcdzmcsZgZs2YxoTwcPHPOYx5eEPf3h59KMfXU811QCz9u8L08V6Dvo77rjj6uF/wI4TTwlBOh2V/3e84x016jTqNp3pmBNPd1CIfeL0nluL2WUijB5/g/Rsw41bD25CidnXU28GcUsnAKS2pe20j0n/TW96U82AuhduKnU5+zleM9gOygO7lClpA27Oo/nBD35QQdr2228/DjYsPbkfSB/bc889i5OFcXS67TtxrY+efPTgZn2oNjd+UxdiB1LC/fbOrZ3Avccu7WApA5tQVBlTnk9+8pO1f5tPwqnU32O2anDeeefVoBtC2UODXp8eBWYF3GiAaUw6YCZ6WxGPPPLIcswxx4wPzO3k444aE5DGGQ6P5SxcIKrt3NMrXqns+YniyuCQ/KYTtWmtazJMHPKKBnm/8sorK+dG2XrOTUvR2TNb+rM0o025HsCuEPTeeOONq522hYNDpY6Z1dWgup69nI3FlLaQePOunXjSvtN2+LP0o984OA9o00da4IYbCLD5QhWuLVc3nbyvj5489eBmfag2d37VR+pEKszabsYlH1FAMMXOw0/c5y5ncxuz/pE+ykyZFzbZZJPaL/QND6Cf/kHGyEXNVMLUl/5vWVJgRuCm7UBZ7+1SMY3MoWBY5i960YvKdtttV3yJEnpk1ihNSs7dCDu17dDdOKf6njhMKvIR/eSTTy6PfexjK+h4xjOeUYWg27Lw2y1POhqdDJHwvpyf/OQn1+WC7iTTytwQ6qSknzxNtQy9v4kpoI5wNrQZp5Qa7DbffPM6wP3Hf/xHvame4C2F9uqOPl9KXWtLbdvqpi1PtmQTwteu9tprrzpg5+vUycGEhXfYYYfKeneTL6UcibdtV9IUZ/pdN711vad99uBmXZSaW3f10LbVtKXWTg60mQc96EHl/e9/f81Qxrm5zd38xp4yE5a+8MILqzymI0PcQk++6Lvf/W7V7bD1UUlNt/3Pb8n61OaSAjMCNzJmgA27NDr71uw9g6YvU5OSSUdjXbFiRT2R+MUvfnFdjvre975Xy5sGXV+m+ZcBIcG9v+c976lf9b6MX/jCFxaHQD3ucY+rAIe7p027Ndt1AszgEBCWPvjgg+u5ISYip7vqWIkDuLEEB7T14CY1MHe63T23v/3tK1gGNAM21Uc70Hmn2nqdq1xJKwBEHoCONm3LamSGgBfAPgeuaTOWnbQvoE1/0Vfkuc03c8op3pRtJuVJHD24mQkVZy+s+mjrXBvyGDf/4R/+oWyxxRZ1PNNe8mHYtonZy8n8x6Tc6T/oEFqkP6etdnMWf137/n15UWDG4CbkSoNLY2Qfu/iZyC72DhbDzQlrcaLG28a3Pma7ByxhvOAFL6jgS1594d/hDneouwvILWQgoUvfA5DRs/0QGALO2F1++eXlfe97XxWmfu973zueHUCnBzfj5JgTQ9u+yG7d+ta3rlxA8iqpvzlJeD0ilY/IcdG1OW2D8LPzZ7JMi72uveA8kUkjh0alPaYtmtiYW5V3OprQu5zH1v9k5sTVg5vJqDQ/bqkLunZDT3u46KKLxpf6gXonu+eW74Sbn1zOfSoT9eWUk3vmHe2/HRfmPnd9CouVAjMCNxpU25Bac75SFbztlBoiNzr/0fnTObFX7fxgn8Y7XeKlwQtvwrMEBch8/vOfHx8kpOOIepOLUz2lmSd5j+5iT/6cLZK8cxOHm2nvfve714nLe8ANrk7PuUGl2VfqKZO4k3kBBUKFwKgnir+2LcR+PnRt4ac//WndxUX4GaCxFGsJTX7JB9kB5URhS1NkaSxTUSmbOJQh3FBm7c/DTM1WGRNfD24qWRf8L3UtI+qbMn5FrgzHxnKMNuKhhIm5WizBP2VIWzRfpP/GfqLybQhlX4LVtSizPCNw0y1RGmPXPp2yBTyx4zeDNjMBOTuu7ACZLaUjOG/HZOKeHKrtHM4F6bp1Own/P/rRj8oJJ5xQO11bVm6f+9znKucgl35acvAl3oOb2arFwfGkHi+77LI64OP8AcnaV9pYW1exGxzb7Nt+4QtfqNwYsjPaWEANIUhLnAC1ZQTlaPOWPMcug7scpszM/HX7VcJMpzRJtwc306He7IdRH+o79QIAOzFaW9KmDj/88AqCtQ9+Jqr7hJ/9HM5djMlzq3fbft7p6SP8J8zc5a6PebFTYMbgpm1cCusrlfyDL9B0tDS6rt/Yt0QCdP7xH/+xnH/++a31jMwmDztQDAg5kCx5MTF86Utfqm4ul6OS7ySafLaTSNzouDQOVrPTi5lyeixOjjQf9rCH1eUr4AjoofrOV8kwzT/cirWfyy77SbnVrf66bL31vcuKFZal1nA1bprIWNjRUQMiOZix97Ewvn5NEmOTRepqbMBUb+TMxr6gh3BOgI0yWvUbVt5Yfvrzn5Wf/PQn5cjXHlmPRCAsrw0AM27Z3nHHHesN3AQhtcu2rWmTC90ukj5wk1vB+3NubtqCZttmrH1pTTflvLRu2owDTp/73OfWg1O1mbQb/tr2NNt57OPrKbCUKDAr4CadDyix7GNSt4WVwFtXxS975ugBEDrnW97ylgo44lY9zfDv7LPPrpOMr2WqHQTCuQm4yWDBX/KV5OU5ywXsTAJ2TdnN0t6HRQDUtl2cG9uUPYSXneUzKN7E3+tTocAYGAEr8lx22Y/LrW71V2Xrre9VVqy4dkrgZmTE7cgAzsrxeEZHh8uNN1rSGvv6S/2PtQlbyvnFbVxVRiwVDa0qQyPD5dwvf6ns/dR9ymZ32qzcafPNys1uvoZL43ZtW9KBW/E4oJJK3PVlkfylT/bgZu4rJLROSt61j6i8t3bGLe3GR+BE7Yj/dnxLfL3eU2A5UWDG4AaxdCbLNXZ5kEnxperBgn/mM59Zd304XTKClcK0A3vbeQlSWkfO2Q2zURk4Lp/4xCdqnnLDcdL0JWQ7pfw+6lGPqslxM7BkuYy5za+BQ5y4U0CNUzIPO+yw8bAMltfIWAA3aOIcH8DP7bxU0q8v/d80KLA2wFk/cCM5EwkOzBhAAnQCdsbsbsraTjugk+n585+vLxd/5+LypL32LLf8678qt7jlLcrNbnHzcquNblU23nSTupPlqKOOqu2kptgRBJ6s0NJYCJV0e3CzENRf88En9YwR6iRghXki+/hZmJz3qfYUWFwUmDG4MelbQiLgRlgSQLDbw6TuAXgAB1unH/nIR5aPfOQj9QqGliWfzkpexaFsL3nJS2pnzkA7XZKls4vn9NNPr/mwBZxqwQoBPXm0fEQlP8yJozqsdjvrrLMqACMY6oDCn//853X3FD9ZunKnT2RunNj8y1/+ssoRWTPv1WxRIABntLTLUtdfv2IdnJukP7b8lGWp7jIVX9qCNpD2AvA6J2nXXXctj3jEw8s222xduTRAzR03u2PZ7eG7lze9+ejy2yuuKIScbdltlbboSXxx67b17nv8zbWedHtwM9eUXhM/mrfjTOqArp20H1kZm+gxi0l4jzAZg9ak0Jt6Ciw/CswY3CDZxz/+8XqRH24FZWeSJSrnd9ieG4ADQHiAHmvGbhE3UeD62CUF/OD2HH/88TWedPL6MoM/QCrLUk960pNqTBkYpOHkS/mSPmUwiXubLL/kc5xzA8QBTBR7T7tc5dJC4EZ5IsTcDmCD4m/T6s3rRwFAwonE5J6ypV+dTKS4mTjaekudRA90+suNN5SvfPWrtV3usssuta20bXrzze9UXve619ZDxaQnvHjbSSZ5Sdz8xW6iPC6EffLUg5uFoP5YmzBOpJ04tM7RE7n5OmNI6om/9kNxYXLdp9pTYPFRYMbgBhDA+bBLJeAmxcSJIfzmvg/bXS03mYDInwATlmtM/sCOd0s3DsPD5ZgtlUHiqquuqqe/brbZZsUOligT0Kc+9akq8Am45Osn7u1gohwOWyNgaVkiX9/tJJZw1sOdWaJsTp2l2rgTNv57fWYUIMiubdkKPhVwM1FqJg31dOWVV5X/vPQn5fCXvrw85rGPL7e+zSblZje7Rbn5zV3vsEnZfffdK7i1fPqjH7mkcO1lrEw+0mnN3rXJFghPlJeFsE9ee3AzP9QPvaXG3I4RDuXD8TY2GjsJoVMZ07o5FNZ4LJ5+fOlSp39fbhSYEbjJAE1/2cteVnAr0vHSufKOPW8nkQPvcG0e+MAHrvUFTOjS8laAQtvpp1spbSdntu3WrpX999+/ghN5+853vlOX1Cyl/c///M/4ROSY+zPOOGOtr/GPfvSjFZBl8kzZBuXVWSUEq4E3W9spNOF3kP/plrEPN0aBMXCzUdlmm23L9df/uQwPr3vnET9RqRPMns997vNlu+22L/fYaptys5sB43cst/yrW5f/79ablkc+6rHlX99/Yrn+z3+pbV047YCe5QOTTKu4te6t22IzyyfVg5v5qRn0zjhCj9khfQE2Dulz0TCFS5MznFr/85PbPpWeAkuHAjMCN4qpgxnMnfT7gQ98oOCQkDeJajtgAA83wrjAA8Bgm7ZzStKxu5ND4pqOLs5MLoDTQx7ykPqF76yIAw88cPz6BZyWFnycdtppFXxhCVPKRXbGktQBBxxQAZKTjj2HHHJItQPwXLtA0bfYcsty81vcojz2sWNxjIysWSbJJFI9d/5sLB7bXJyFkegdj0v8Vamy32mmJbQstdFGwM02VXBdG5qMxiHdqqGh8qcr/1Su/J+ryj++9Z/KfvsfUO5+963KzW52y/LXf71x2XTTzctd7nLPsuuuu5ezzj6nXHnV1WV4vB6BFodSju2gatMba3cmqzGgEzd6APxYHlLyifTkdF36TMOvzs1a4OZetQ/svfdTKmdK/0gfXTvv3by1eem6Laf30OGmZdYOtJ0xjt+YnrYCJB999NGV9rg2bn7P9RvCqYMAaTHHjpnb2nV007Q3TJvQeqr6hkmFvlRrKDAjcJMOpnNRlqAI5boF27boFswkyakAl6n4SXyT6W08MbsfylcQTo2t35aYcFnGBpuxL2xml3xaejjiiCNqEsAN2RkXZpLbEVZZvTuBWHy2meNeUX/6v/+3bHm3u5ab3/KW5Yl77FVlVoeHSjGeja7+sB8GvAxIuAzDYzQcHhkuQyOryvDoKpuULWbVxxblGtjEGkSwDj31UtOwO8hjcBTFWHJrka/6a9wNkmvRRV5b99XxVLvV8Tv7xeRf0xixq2xs4k9CNb7V8d44NFRuHBkuNwIITf4y4CfthF2jy+loCf28Xfazy6pg7z3vvVW5/i8rysqhG8vQyFClr3NpAEs0Hl41RnwTybf/46Jy0CEvLFtvd+9yn+3vU25Rd/oRgL9l2fg2ty37739AufTSn5Rf//ryem6RyWhkeFV9aiWOqlAgSpxjcLTSeDU4HRl1gnfO0Vl74hmrG+3NWTnyiQL01Dnai5s+8eM8Hmnb6TWW1lh8a3Z+jdGKn1ZJvzsJ6sYB4L/4xX+Vu93jrpWmT3zS42sbHFq1sozidtU27E/5k/5IGbWNuTbwVaWMKvfYpD2WdpuPqZrbHA8yryueQWHmyy51qz7WfKrkTCQ22sfQ8I3j/XvsSILR8u///vWy2WZ3quDG/Xc+FgeNpesuybroM9fu687hZD7kbg29BrSi1W14DCSu7i+r+9LI6Mrx/pR+NaYPl5HiGWubtU/XD5Cxsb/bJybLX++2uCkwI3CjaFmaykREbsUSE0Fi8jM4IPHTNpyADaqp3dMAACAASURBVHbMY4P9GLG8t37nkoTJhzzG3E2vm5c2v8LFvf0iv+rq/yl3ucfdKrjZ44lj4GbVKl9VqzHKyGi5geByZTWvLCNDY19cN6y8oawcvqECG+Amk10m0DpHZUyaBNyYyE02dRIzCKwGD3XAGC1jAAQIWf2lN66vBiwAirApK/e/oNFq4LJqaKTUea76XxP/cAN+Vg05C2akDAMFo6PlxpUrK9di1Y0ra7xDFdSMFFRYOTpUVg2vLMMG/BE2w6uB1Fg+ajlWtxXApA5KqFQBzEj58WWXlo1ufaty3+3vU6659soyNALcrCw3rBw7s0b+0WRo5VAF4QceeED1f/Nb3qzcYiNbuG9Wbnbzm/8/9q4DvopieyehShGkpAACKkXSSSWhS7EhYKVLLyJNQHoTRIoUqSEkEBKCgPTeBBRRbPSqICVAgPTk3uT2+/1/38xOskTx6fP934P3vPmdzOzs7OzszOzMt+ecOQdlyz+Bvv3ewReHvxLDQLYLd0tZJCol4HBYYbOatMVdghtVH06a6o9pkmQ7qvFR0K6qA7kAFoAb2efaZK2BJyfuBzpsA/mlzz7k9RIEO5xWWKx8Zpl2/3UFbck6qDalqIOghmS1EqxIzuoztZ4R4OaFF2nV2w6rxZzfhiKTADDMrz0nFwkB9AhsSKyTesZ/JlQD/UGhKlM+u/oQUPcsaH/5Hqhn/veEfJ9tsFrN4Hvwm0/gZB8yj0kDsnwOCDDdqFETVK9eA19//Y1I44fkn6+3Gn//qfDX4+2PPgPni99sMwHlfwXVxTjj2JfvDsckqfC4kMcS3DCuxo8K5R3VuBEN//e/R7YF/hK40X9NcNCqHxXh6N2b9mPo0I0/LhLqp65TA12lq8mfxw8CGirvHwkLl6+uUemqToXvxfPqHK/R11fJu5le+DqmqeuS796B55PV4Fq0CFq0aiVeVAt9ahEwOJwgo8bChV9c44SN3n4Ft8ECq9MKG2xikSRs4KvMF+63fupZCociL/tE6xfBXcnntBDcyFOKo5H/nutuwkVTz3lRxYk1TLtedbt6LsUVEuCIbeR0IM9iFkutmEI4DuwO2DhZg1wqC2xOAW/gAAFJHsw2I6xOs2Ygj8BIcprkM8i+ke3h1L58rfj55/MoWqQInnn6KTjsJhiN6bA78wibYDBmYs2aBDRr2gTNmzYTDge528nV1QVFixeFf30/fDR7Bk6eOYPjJ0+JvuI/6uRI20wSQBAEiPYU3AnZL7/+niSM5LZcG3JzCYB0DcqeFICxoC/VRCrBKxchCVpoOVlybgq+NAtzAGg8kMXzjmarWcBXgkOCxAK4Ka+S0/avayuu5zDRFg3FDfv58hVU8XoSri5F0Oall8VZU55JPAzHDJ+PX8d2jQQQzwdiXGTIeZTcR577Z0h+aUuA//txeT/WRfIA5TXyngXtp0Dn/3eY3+LinWXLyrFktbJv5Xjg2FKWrtV7xrGg5pSUFCq0XxD9YiNnl/OQwy7eCdFnuh5WHy7/TaEcj/J/weiUbaf+q7lHHmutIsC1AjXyfdKDGPWeCa6qg1xlvkMFY0wBJJmPZf79e1Rb4C+BG/XQYtLXFnsu7mqhVaCAoIUvLdP5Uy+w/nr9OT3IUXn+mVABDV7Luujvq86p+6o8isuk7qfyqWdR6QzVc6uy1bPz3L2UFE0s5YYWrVrIxcGSB4vNArOFVm2dyDVbcGD/YWz5dDv2bjyIbZ/uxebEPdiy9iC2f/Yldqz/AtvXHsb2Tw9ja+LB+2jL6s+xbc0hQTy3/dND99G2NQexY+1hQdvXHcK2tSTe6xA2rzmIrZ8ewpbEz7El8SBEWSx/9efYKtL2Y+uaA9iUsBfbWJe1B7F59T7s3vAFNq/eg4PbvoI10wlHrhNOrvfaR7rT5gAJVKglILFKEYbdTnCgfdTzlCbNEPMQG4vcF5sFTptZcEXsTrv47rI6ARbBIkWxdH3AuE2SxUKRnuyRi+d/QtlSHqj9dCBMRquYz3LzLIhfvQ7BIY3g4lIKxYo+gcdKusPFpQw8vWpjyNCx+GjWAtxLywbvZWHVeQ87QSpAJoaaXgsAloZvBPdLgsQCDpYCYVqoAKQGzjj8FYeEcfWeFIT82ucCKN8hNSnrgYp+YWY7kdRiynyEywylaNMmzotFT8e94zFBJ60rMxTLAtvaTq6ZBExXr15DnVr14OpSFK+2e1U0BLk6XJT5DARfEjxIECM5RwK+igVDfTkLzpGei/Qn4g/68v51uvxSV4uUOn//vf89S79qf8GBLCSSlSNVjh+lzC7HhOTa6s+zTziu8kwWWKwEh7LPVMh++vc8UcHo+9P3E1xcJQr/nVADahyLJN5HvHca57PgLVQtpN6dgmOZR44/AhOKaOXHAd8lpqs3WYX6sSrBkNPJDR+KW66u0d/j7/ij1AJ/CdxwUlaLPx9aDxQe1Ah6kKC/lvn1AOiPlPWge/yjdLWYMJ+6jwI+6pjn9PXT17uwCEt/jbp3amoKqlTxhJubC55v+ZyQRdm5gIsXli8YkJ1lRP9ug9Cy7svoUL8P3gwcjLeCx+CFusPxsvdovBYwAa8HjMervqPR3mcU2nm/j/a+o9De532005FI830f7XxHop3viHx6qe5gvFRvCF70HowXvAej1bOD0JphvUEi/oL3ELzsPRSveA9DW0FD0dZ3GNr6DsXL3oPRzn8E2geMRHv/4XjFZzA6hIzAK9590CGsP+YPjca89xZhwej5mDtqBqYMeh+j+r6D4b17Y2C3LujTqSP6dOyBob2HYFDvwRjcZzD6du0rqHeXfujz9kD07DYQfXoMQr8eA9C7Sy/07tQLfbr2R7cu/dGhU3+83WsE3u41El26D0PnboPRudsgvN1zOLr3GIdub49Fzx5j0LPHSPTuORzt2/ZC5QpheLp6C3R6Yxg6dxiGFi26o1RZP5R/IgwVKkWiskdTlH0iAtWffgEe1Zqi78CZ6PvOh+gzYCJ69h2Ft3sNQ8KabcgxOnE7ORupaSZk5ziQfNeA9EwrUtJMSM0w4U5KDlLSjUhJNyA1w4i0jNx8SmV6qgHpGbzWhqSbacjKoXhMTtj8Cuev8JjhMbldPE8gJadg9QVaEN4PIrgMSL0NtbDbHVLXwO7g5K4+NHg/eU/eR6bLSV/lYZ3IpUtLS4XJlIfvvj2GmtVrwtXFFc+3ao20lNR8ZXnFceC1EoxJbpMqV+jd8AnyFye1kMhQgQ4F3n4dagtQPofs/uulXo8+raB9WJas04PFEgXg5x/nUXn/TMi+kM90v1hGcrzY7gV9wXbnLs5x48YJkS3nIY4BAho5S8i8ai5i/6m4GEgP6T85ztS4e3D4Z6vPcvl2cKypt0S29f0cmMKcF3Fd/pCRYzN/HKlxpoUUpfPv79+j3QJ/Cdzw0dWLxsGjAALTFRhQ5ws3kzqvv+ZfxbH5rXvJl6LwmYJjfT0VR4ZnC9epcDmF66/Op6TcQ9UqnihRzA2tWzQXbAs7xTP8WuYEZrHjzs17GN93LIY16oeJ4e9hfPBoTAz/AKNCpmJcg1kYHz4DE8KmYyIp9ENMCJmGiWEfYmLYNEz4DRof9gHGhX6AsSFTMCZkMsaHT8O4BlMxNmIqRjf4AKMaTMX7DaZiZNgUQaPCpmB06BSRf3zoFIwPnYxxIRMxLow0CRMbfIjRQRMxNmQSxodOxJRI0hi8H9QPo0J6YGjg6xga3AZDwp7HO2FN0C8kAn1CQ9EzNBg9QoLRKzgSfYIbY0B4M/QNYdgEgxq2Qr/wVnjLpzFeqROJ9t5N8IZvE7zp3Rhv1muCtrWaoHmNhghyD0ft8iGoUzECdSpF4pkK4XimQhieqdgQT1dsgacqtEKNJ5qjZoWmqFGhIWpUeA5PVX4DtT27oJbna6he6RXUqNIR7pU7okb1fqj6ZH9UrtIX5d17wr16f5T36oGy7h1RzvNNVKzyOsq5v4xylVvDo/oriGg8CP71u8M/6G0Eh/VCeOQABIX1Rf3QvmjYdBhCGryD0Mh3EdaQNAjhDYcgLHKwoNCIwSAFhb2D+iH90KzFe4ho2B8DBi7Et9+nYu++C9i46Ri++OIn7Nt3Gnv3nsKePSexf/9ZHDz8Ew5/cQWHDl/G5wd/wsFDl/D5wYv5dODzC9h/4LyggwfpoNYgmGRWK8ep5GZRv5hE7pZKf1BIzpfJxPeWhiuB2NgdKF+hHp6p1RBPPxMJV7eqcHOrihLFn4ZXlfpo166XKDcvzwmTWXJ4FDjiuFfgRn4xE3DkryZ/Mq4vi2CA99JIib5EWoE4QQIKBXLUfXksuTr/7lAqk5NbXTDH6GMq/YcffkT5JyqgVu06+OHH4wLQ2KgHR9iq43ara9nOnD/084469zCFcjw8GNRIgKevsQZoNeBCgCrL+K0GlONDOLdV40IvWhIKwwUc0PxyCm6hw0VaogI4kkcmeEkyk76Of8cfpRb4S+BGAQI9AOBAKvxTQIbp+rg+nxqAx48fz3eaqT//z8TVbi79tawziZODuqc6/6AJQ19nda3Kq392lqOen1s3q1bxghu3crZsIb9iaedG3NsBh82JrHtZWDh8KqYGtcfKkA5YE9YJiaEdsTLkTcQ36Iz4kI5I0Gh1SEeQEoLfQkLwm0gMeQtrmBb0FhKDO2A1KUSjYObthPjgTlgV1BFxwR0RF9IZK0O7YGVoV6wI6YKVwV2QEPY2Vod2w+qQrkgM7ow1wZ1EmSx3TUgnrAnujITADlgb2hWJQR0RH/gq1oW9hQ2RbyI+6AV8Gvki1kS2xJqGzyGxcTMkNmmGhKZNsappEyQ0aYLEhg2x1Kce1jcIQUKANxL9ffBpQCA+rR+JxOCWWNvkDayIeBXLw9piZYPXERfeAUuDO2GmXxeMe7YbBj3THYOfHYiB9Qahb52B6FH7HXR6egBeqz4YL3m9i1Ze76BltUFoWXMYnqv5PkI9RiPIYxzCqo1DnYrDULPyONSqPgs+daNQu/YyeNZYhLr+iaj01FJUejoKnnViUKLKbFStF40KT82DR60FKFd9Fop7ToNH7fkoV20qynpNhvtTs1Gh+scoX30+XCvPgJvHPLh5LICL52K4ekYJcvGMwn3ksRgulefCrdIMlHSfBrfyo1D8iSFwK9MTRct0QbHSb6J42TdQvOyrKFbmVTxWviPKVOqBMpV6oXj5HihevidKVuiN4hV6o0SFPoIYL/5EL0GPVeiN4AazMXLUUYydcBSjxx7GxMlfYdz4wxg77guMGfcFxk44gtETvsSYiUcwduJRjJnw1X3EtElTvxPnRow+jJYvfAKXYq+gbMUuKFrmDZQs3wmPPdEFRUu/BZeiL6F+yBBc/MmObANFqk4YcnPFzjT62CIZcnNgzMuG0ZQFY16miOeacpBrMgjKMxtBUscPCo15LMcgyzYV5C+4Pgd5Zu6Iy4XJwng2ck1ZIs5jk8WAPDPTeW9JzF+Yfu9c4bx/5pjl5plzhZNVi4XuE2zIyzPlG/5U+miZWTmYMHEKXFyKwq1oSYwaM17AwTyzBVZuqtD+yPMTolrNg70Cker8wxpyJaCenJpnC0I911CBV+pwWaE4j4U5L2qOFnOsgMpKXEZRlhTPijbirkHBedGLiKVIrOD+sk5yvpb3l7JyucvPQVl7vkL8r9czfV3+jj+8LfCXwE3hx1IgQC38aqFXIKhwfh7zGrpriImJEVuraaeELFr+1PW/dd2fSVP1+b1rVN1VnsL35jMUTlPlMp2kL4N2bqp4ecHN1RWtW7UULFS7AjdiW7LUVbFcuYPc/adg3Hsaxt0/wrj3B+R+cQK5+79B7v7vYdyr6AcY93wP4+5vdXQMxj3fSWI64+I8076Hcc8PMO49DuPeEzLcdxJGHeUeOAPjvlMw7jkO464fYNzxHYw7WQbv8wOMOxmegHGnFue5Hd9o9ziK3P1HYdx1EMbdB5C773PkHvwCuYeOiNC4bScyJk/C4TbP4+SbbfFj2+dx9tVXcLptO5xq2wFn3+yLr9v3xWcR7RDr0wRx3s2Q4NMaq7zbYIXvm1jm1x2LAgZgYeAQLPQfgvk+g7DAbyiWBo3BktAp+CR0KuZHfoTpIZMxI+IjjPaZiIkh8zC1YTTGhMzH5OZxGNlkNZb2PY2vFpuxfaYZh5aZcTDOgoNrLTj5hRWnv7Ph7Ekbvj1mw/ETNhz9xoYjX1tx/LQNXx2z4fvjNhz7zoavjloFRS03IyByH4KafInI1sfRoOUphLc8g/CWZxHe6hzCWp5FmDg+g/AWJxHR6gdEtjyKpq2/RKPndqJpy014rvVaPP/SajRuvgSNmi/A821i0Oql5QiKmAPPp8ahROURKOU5AaW8pqCYx2QU95yC4h4FVMyD6TyejJJeU1Cu2hSUcB+O0p7vobTHEJSqPAiPe7wnqKzHMJTxeA9lPEeitMcYlPKYiMc8PkBJzw9R3HMGinvNRjHP2Sju9TEeqzoXxb3moLjnHJT0moey1RejhNd8FPWajxJVFqLck5+glMd41AuYhtDI0QgO64PQBt0Q1qATwhp0QViDrgiL6IqwyM4IjeyMkIhOCIvshvDI7giL7IHwhr0Q3rCnoAYNe0JSLzRoqCeVzpAcs54Ii+ghKJxl5FN3RDbuhUbN+qBBI5bNe7wtQh5HNO4pqVFPROipcS9E6KhBo54gsaxIdY0WinLEtb0Q0ejPEZ8tomFvhEX0QUhoLzRp9i4aNu4PX/8OaP3Ce0hIPI6fr9gx9L0YuLjWhWvxANT1eR0bt17E6fM2/HjaiFPnTTh9zoQfT2bj1DmjoDPnjDh9JhunTmfh3AUjTp/LxqmzpBxBJ89kgXTiTLak01k4cZpp2YVI5lP5VSjLYnlZOHUuCyfPZuYTj/8I5V9zJiu/XqyfrGsWTp7OxKkzmThzLhtnzxtw/GQaTp/NxNnzrOs9nDqbguu3zDCaCF5+zfsjL476cQwNZid+/iUdx0/dwcnTGTh7MQ9nLuSJ8PT5XNEG8tkzcfKMvO9p1ottcioD585n4c5dKuDL8qgvZYcFdpg1ZXjCxl/X4Y+mERb9Nmk6RQ88/8/fU1+3wnxMVZd/lEetgY96+C8FN2wMLvIKzKhQNRK5HHqAQHBAVwv0B1SsWDFh14EuGT766CNxiT6vKuNRCQluvAhu3NyE3R/Wm8+b3yYcaVQ4pVggwwLbPSNsd7Nhu5sBW0oa7KlpsKWkw3ZPo7vpsP1hypDl3M2E7W5WAd3Lgk1P+nN3smATlAnbHUUqTYUqneWzPmmw3UuF7V6KRowzLQ22uymw3bkDW/Jt2JJvwnrtF1gu/wzr1euwXr0Dyy/3YPnpDgwbD+DE6I+QNHURboybi5tj5+PWuCW4OnIhfhryCXa06If1/u2xK/B1HAhojy+DXsf+wFex0/8VbPZ/CVsDX8FW/5ex3b8dtvq9hq0Bb2FHSBes9nkDcX7dERfQHwn1hyAhZCTiI8djuu9ATGs0BrunHsC+6B+xPe40tqy5hM3rryEl2QELRS4mJ/IU5TmRZ3TClOtEXq4TBqMTmdlOZBudyDI4kWlwIj3HiUymM57tFDo7mTlOZBicyM6Vx8ZcJ3KNThgMshyWb8hzIjvPCYPFiZv3HJi94AyGjTmK8dOuYPjEyxjxwXWMnXEHY6fdwfuTb2HEpBsYPjkJwz9IwuAJVzB04mWM/OAyxnx0BWNn/oyxMy5i9LSzeH/iaYyZeAYTp17A++PPYvSEX9C971mUrUZu0lIUr74RLtW3waXqDrjU2A+XJ/fCpeouuHhug0uVnXCpsgsuT+6TVP0AXJ7cD5cq2+HisQYu7svgUnkO3CpNhVulSShSaQKKVpqMYpU/gFulKSjqPhVFPaahiPtHKOIxF0U9FwguFzldbh6fCCri8QlIMq3gvEr/Y+F8uHk8mIp4fiLuXcRzIYpXWSLuxbg4rroEruJa1mE+injMQ1FPArl5KOI5V5Cbx1yRXkScZ54/R5K79wmKeS2Ea+V5cHPXqPIclPCahzLVFqCox2yUrDIHRT1moKjndBTxmIoi7gSvk1CSoNZdgdpJKO4xHsUrj0GJyqPwmPv7KOXxPkp7jUZprzEowWPP0YLKV5+IMtXG44maU1Ch5hQ8UWMyylefhHJPTkSZquNRttp4lHtyAso9yXDcfVS22lhIGoOy1f4alXtyLMpXnyBI3mcMHn9ylKRqo8HzZarKe1SsORYVqo+A+9PvoXilt+EdOhYTPtqDJSu3YWFMHBbHJmDpykQsWZmARSviMS96BaLiN2HC9FWoE9AN5by6w6PmaJTnc1WdhAp8tmpjUL46n5H1eB/lnxyB8tVGolyVUShfdSwqVH0f5b36o0GTsfho7lasWLMTi2JXYPGKpYhatRwxiXFYunIFlqxYiWWrEjBncRQ+WRaDeVExok5RqxKxeEU8lsatxtK4BETFJYh8y1bFIzo+HotjYrA4ehmili/HkmXLER27ElGxsViyfBmiYknRiF65AkuXr8SSZQlYGv0pli5LxLLoBCxZFoOlMbFYuiJe3GvB8mjEJK5CdHwcFkRHY9HylVi4PB4Llydi2apNWBSzDktXrsWS2FWIWhmPuMT1Ir40YTWWxCdgYWwcFkXHY1nMWiyNXoflKzYhOm4TFsaswSex8ZgXE4u5y5Zh7ZYtuH0vRep6EQE94r+/BG7UQq1CPRhRaWwfbp9W5xjSaB6dZtKAXr169fItcRLgEAzMmDFDNKu65lFs438IbvhQ1GvjjhyTDY5cMxzGPDhyjXDk5sCZZ4AjV0f0dP2nyQiHUUeibJavkf7cX47nwmEk5UkyGGHPzIIjm5QJR04W7JkZsKelw56RDXuGAfY0A+ypBgGqLL/cgTUpHdarqbBcSob1WhYsVzNg+v4KLr87GSMrlcUsLw/M9qqMyR6PY7aXO+Lr+uJoy1dgWLcXmR8uxtwqlTGvige+atwGnzd6GfuavIZ9TTpgZ1h7rK7TGJ/5tsTawBcw37sxpvo3xWDfCPSt3xg9gpqja1BLzOwxGgsHfYS578zAkuELsGT4J4ga8Qmi3puPqGGfYOGQT7Bw2GJsX7Ifl4+m4NqPuUg6Z8edn+1i55jDKHeQ2Y1OWAmICIwsThgZmuieAbBQN8bOnUmA0eyE2QHk2oE8BwTIyTA6kW1yIsvsRKrJiXSzE2kGJ7JyncjIdSLV6EQaz5mdSMlzIt0iicck5uf1/PLNIRgjqOJ1KQ589ZUVO3dZsGOXBdt2WrBrnxW791uxbY8VO/dZsX23FXsPWLFzjxU7dss825hfO96+ywISz+/aa8XuvVbs2WfF3v0yZNrOvVZRLssW1/HaPbJslq/KYMh7FCZ1num8zz8i/T0Kx3ftsWL/51Zs2moRdU1cZ0HocydQ9umNqBd+BF7eO1DNdxee9N2F6n67UCNgF2oG7kIN/50y7r8bNfz3PJBqBuyBnlTe6n7yumq+O1HVZwfqBB8E48/U34fqvjtRJ2gfildbCZfy8+DmFQWPumtQw289ngpYj6re8XjSJx41fONRwy8RNf3XoKZ/Imr6xaOm3yo85ReHOoFxqB0Qi5o+UagVGItnAlfimYA4PO0fB1f3mXCtsgQuVaLhUiUGLlVj4eIVU4iWw8UrWqNlcPFaViBS9YqCC6nKsr9O4h5aOV5L4eK1WKOlBWVXjUaRarz/fLhVmYMiXtPh6jEBxT3fQSmP1ijrEY6yHhGCSrs3QCn3MEkeTVDKqz1KevZDqapTUbTKIrh48plXokjV5XDxZBvIe7t6LYIbQSZDz6Vw9YhC8SpLUdJrJkq6D8fjXp3xWKVmKO0ehHJeAXjcIwClKvvjiSrhqPhkI5SqFIrS7hEo69kYpdwbopQ7w8Yo5dEUpT2aopR7E5R2b4yyHo1R2j1SUHnPhqjoFYEn3MNRrlI4nnBvgPLuwSjnHoAKnoEo7xGICl4NUKlqM5T3aI6yFZuhXKXmKFexMR6vGImylSLxWKXGKFmxMcp6NUT5qg3B53+iWjM8UbU5Srk3RVnPlihVuSVKubdGCV7v1QzlPBujTKWGKOvZFCU9G6KkV1OU8WyN0pVfQMnyL6JspXYoW7kdSlR4GSUqv4QSHq1QqkpzuJX1hXfQS/j8y9NiQ4OVW0cf8d9fAjf6Z6dIhmBEiWrUOb2ohufoy4mcmscffzwf1JQoUUL4bKKpcTqanDJlirj8vx7c8CnJweH+Y9qy4KqnNEILhU6271+kwmX/1fLuv94u6s9nEMTdHrRlQ24dLdsKYtwmyWSF0+LQgJ0FDoMZjhwT7Fm5sGeZYLuTgUtbduLbZSvRrqIXwoq5oUnxImhcvAieL1kUh94fA/O5C7Bc+hm2W3eRe/goXihZFL3LV4Tp+CWYTlyE+exPMJ04h9x9XyJj1if4qc9AXO/3Lm4MHIRfBg/Glx3fwgI/byzx98NS/wAs8w/EIm9fLPELxCIfPxFfHhCEVYHBiPMPxuqQxlgZ2hzT6gRhok8EJkW8hIF+zTC1TQ+cjNuJa7u+xfUDP+Dnz79F6rmrcOSYJXjl9nUN0HDK4HZ2M0GOE8jJs4ot6BYnYOLWXztgskugY7A6BejJswEmK8CQXB6jDcjMk2GuTQIjox3IIWDSyiC4IcfJSlBlkpynXIMW5jqRneMEuUkEPwRB5ESR28SdYtSpITFOEvkYGiX3iRwocrEUETyRqGjMvPn5GdeuU+kiVGm/E5JD9ldJ1MNQUIfsbCdu3XLg9m0Hbic7cD3JgRu3JCXddoB0M7mAeHzjD1BS8oPzXb8py2e5125Iun3HAYLG6nXHo3u/vYKDdzXJARLLun5Lxm/cceA6jwuVzzx3Uh24m+bAtZvyGoZffm3F0FHn0KXXZfQcmIO3B+Sh24A8dO2fhy798tCpr6TO/fLwIOrCvP0ffP5B1z0wvX8uug4wokv/HHQZkIXO/TPRuV8OOvfPRYfeBnTsY0S3fyW8qAAAIABJREFUd3i/LHQflIPeQ7LRqW8SuvY7j35Dv0OfQZ+j5zsH0fOdw+g58Av0fPcwegz8HD3f/RLdB36Pbv0vomu/O+jUOxMde+ehS988dOidgbd6pWnlGtClfza6DchG13456NrHgG5989ClZza69r6H/oOT0W/wWfQa8CUGDT+CgcMOoWf/vejRdx/6DfkKL72+ESU9R8LliWFwrTQKLpXGwKXSWLhUGg+XypPgUnmyINfKk0FyqzwJbpUnwq3SOLhVHImiFYfjscrvo1HzeAwb+SMGD/8az7ddhcerDEXRSu+haOXRcHliJIq7j0PRSmPhVnEsirlPQpHKU1Cq2scoUXWWuJeb+3gU95oiyIVlu4+HS8Wxog7FvKajbA3mnQq3yuNQxGM83DwmwMV9EtyqzkbJGtEoWm0FXDxj4OIRi2JPrkaxJ+MliPVaAFfP6XApNwi1/EZi9/67+fMTl6ZH+feXwU1hMKMag+lK2ZbKtdOmTcM777yDp556Kt8LOLk09OSsQA2BTenSpUVelvM/AW5Ug/0Xh+xHjgUCXXL0hOVkbonWAF0+SLJacf677zC8b190frkNSrm6oLyrCyq6uuJpt+J4MygMuxYuxoWdO+EwZMORkylDQzaMx3+AdxE3tHT3gPWX67ClpMKekQJ76m3YU+/AdisJ1l+uwPLzJVh+ugDLzxdgPv0jspcvQU78KuSsWYPspUuRvegTZMdGI3vpImQvWYjsqCXIHDsKx55vgd3hIdjTpBHWRUYgLjIS84KCMK9BBOZENMK0oFBMDQ3HtIgGmBoZhjEN6mNR97ewfOgAzB/YFx/2ehsz+/fF0vdHIuGDyVgxYRw2zJ4B04ULQrRnvXEbtttpcOTYYEvJy7cf5LAJM0A0BSTs7pDjQ1J2fwQupo0a2ukBDUNK0CSal/qRvM5MtxMAjSybzU7BPeLWdCF+I1fJrBHTrIDZppEVsNgk0eYPiTqt95HamcV8zK8RwZuK/7OhmfX9J4n3NPG5CO74TFo5TCPl5jnFs/F5CxOf32LXtYNqjz8RqjJZFuMEm/lpViA904lLV+xITnEgI1sqaDMvOX2i/cnN47FGIk27P8uhQjfLZD1FubzOAgEss7OcyMl2IjPTiQwd8Z5pGZIY//+mjCwn0jVK4711xGfm/VPSHCKd+e6lOZCa7hAgm+ezciQIzyHQ1gC3At4MKRYWZfIarTyWyfvyWNwjSx6L9AwnMtKdyNRRdqYTbC/RZryO5zKcMORIEfKVq3YkfGpBbLwZq9dbBK1aa0FsgjmfVqyW8ZUJZqyINyN2lVlcs3qdBdErzFi5yozTp21CJE3AfvOWA5u3W7Awyow1n1nw2RYLlq0w49MNFqzdaEH0SjNWr7UgbrUZi5eZERVrRswqM2LizYhbY0HiZxas+tSCqJVmzFtiFuUsX2nGsjgzlsebEZNgxpIVZqzdYsHAkXlw8doMl6qH4FL9O7g8fRIuVQ/CpcbnGDbOhE83WrBspRkLlpqxbqMFyXdoI0vqIP3PgxuuyXoRFBcyxa2hM0o6xqQHcLpjIIghUa+GDigJZkgvvfQSoqOj80HOzJkzxVL/N7h5tBEPrd3mG+Qq9CjsW7UdlCHB8MGDnyMoqD6KuLoK4k6zx4qVQKsmzREfHQtDSgacViecFnKFyPmxCFEdwdEvp08JMBRSuw7yku/AaTLBaTbCaaJYLkeIxAh4BOhJTYE95R5s9+7CdvcObHfvwZZ8F7ZbybDevA3brVuw3pQk4teuw3TiOEzfHYPp26+Re2gvjNs+w7FhA7Cny+v4vNNr+PKtdvj85RaIq1cTmwPrYmNgHcT51UKMf20s8XkGS/3qYCm5RL4kXyzw9hbcog0tnse2Nq9iS9s3sPH1Dvi0QxdsHzYCN7fvwbWjJ3D6qxO4cuay0M1ymAGbGYITQ41KZQyRhgy5nVtt+Sb44DmlDcldruQcKe4RwREBDe3pCIOIaiu5ZizRzAWToEU7Zqg3YvigOAGV/pr/dJzPSfDHUHDKNI4ZQcBv1lUDhuTIEyyq+isAqQ95Tn+sj+uv470JENV5VRd1rEKmE3CyjwjGWAbTSKI8xjUgqfqSz6DSaHSS/W4miCJw00IhGiWA/QOkOHCCC6fjzCkO3V8NRV20chkXx4W4gLwHOYMkc54k6rs9kJRuHENyDsltzJLXi2dmOxQiVS45myS2lUqjzp2e08l01oXl6utm1HTnVF3JFTWSG5ojy2ReIRI2Sa6meF6tjvkfFXkShBJIsTxyUwm6KcbO1a5TfUJdPcYJWI+fsuHotzZM//gewpptR5MXLuLZ0O/Qa2AGzl6yI4fi6mwJftest6D805vgUuUAXLyOwaXmSVQLOotOfbPwzXGb0AnMypZAjm3MMcTxqubtQtP2I3X4lzk3CtgoLo16+h9//BHe3t75YIYghpwaiqBIpUqVElycqVOn4u7du2L7t+Lg0LElf3+DG9Waj17IviO4UVZH1VeA4NxolkuZRv9aW7ZtQ4cunYV/J9fiReFaxBUly5RCkxbPYeXq1TCazLDYqYBLc/+c/CUHiOVLi7pO3LhxQ4ypWrVqw2g0Cj9IdiIBsixoM8NslXpNBhMc2QY4cqjDpOkJ5RjhIGWTDLBnZcOeniF0huzp6XBkUV+IukIpsKffhT2d3KCbsN25BmvSZVivX4L53HEYNn2KzMmjkTl+JDLHj0LGe+/i23YvYn1gPWwO9seuBiHYVD8AGwMDsTk4GBsDArHexxfb6odgg58fNgT4Y5VvPUQH+GBWgC+G+fhhQP0wjH+xLTZP+RCHFkfhm5XxOBgdi1NbtwvFbXt6Fhy5VjhNfEZajNaIINBqFyCP5pYpDrTRfYRCJprivwSZNBxYsDOkYJeF2mQsXRgo+zViV4mTVnNl+oPD37FKq42BfJcahY+FpRF1/382lPdXxhE53kiCq0VlfnHMnSvcmaP+pEUdig95Xmxl1uLq+t8LuSiouYslsmw6If3666PaveWuS74HHL9mq0WMYfpt4x1VG//eVuj7ZgOtMpRiC7coGmC16Lh1NJT8h0iBXIvk8pHT968ixTlkeeRCKgDBONMUd1G8rjpupQLwDBUXk3HmU0QQw4WfQF3YeNLukQ/2FXdR1w5cxFWdeG9F6nnFOe1DQt2HediuvFbVmSHP8xynG0HUrdP06wgU1AcErxX9QBBKfTsTfd3JvhHAVOMqknsnTC0QqBkk55EcH+bZvuMXVPZ6Ca4lX0TJSoNR3ONjuFZZBxf3eLiUHoPRE06LDxOKmgmSE9ck440ORzFjlgnvjzcJTtCV63a52YFASgOHov7as3CKkMZC5Vi+b7w9Qgd/CdyQQ6PEUgQ33NI9b948NG/eHHXq1BFgxtXVVXBtlLIw0+k9/OjRo7h06ZJoKk6wGzduFECIwGfChAn56Y9QW95X1T+kUHzfFf+dB78FUPnKnD57Bj1790LjZk1RqmwZuBZ1E/TY42UwffZMfP39t7h07Yp0w6CZ1OKCw8VG+F3itk163Rbm0u04c+403Iq6oXrN6kjLoONMG+jLi+PTwU9dXkxuB/n4YoHnp7cdVqNBcnmoz2QurCOk6QqZzXCa8uA05QpukMOQBXs6gU6qpNR7sKeSC0TRUhKsSdcl3bgB03ffwbh1O4x79iH3wCEYd+yBcfseGLbsQM7K1cheEoOMUeNw4uVXcP6F1jjXshlOtWyMLyKDsdanDlb7Pos4n7qID/BBQn0/xAT6Yml9f8wLC8GiNm0w+9XXMe2tzpjwZldM6tgTM3oNwvx3R2Lu0JH4cNAgTB8+DJ+vX4u82zeRc/smUq9dQ9btZG2Fl7v3aDTOQsOSdG5qs8Fkob0RCU6VTRW52IoeKGAJKdZQvuEz2o6S1ooliOUHyp8jWvH9s9c8OL800aCsKPNZGFcfYvnPphmCU/CGkERSQcqfiXHMq4++n376CS1btsQTTzyBiRMnCpctPK+fO1VeBW7obJOOWmVbakDeTttc0kaXbFvp4d6cR4eucu4QLkOsBE9yQWXItIeVyG0kqfrxNSVXjdwowVnTzos8heIEcuo6FRJEEDQQBOjP68cH83LYKsCSP4TZVhq3k5xOiWwLRLCirryOHDKNCyruwXoUIl7P+whQowEGBc4keJJuaehrjHX77LP9iGz4Bl57YxSef/k9PP/KMDRu0QsTp8YiLUv6ISTnkWVmZTnRu88CuBRvh8im2xHR/BRq+n+HMRNMOHXGhqvX6a9PcvyY/+4dB25ctwvOF7lDQg+NYJDgiu2llct6mXOl+NrJj0EHfer8D4Mb/XJ87NgxVK1aVQAU6s0oERQ5NoyTU9O7d2+cOHEi/zK18PFlXbduncjH/P8zu6XyW+K/L8K+1ZN6QoPBgA+nT0fpsmXg4uYKtyJucC3iBrdiRREaEY5lK2JgpqdkzQUiXSGabAVft9KRJ5ceAhv6RqLTRDvOXTiDIsXcUKtOLWH8Lc+cJ8zYK4NpkoWveTIXOtxqAbs/lK8zFyfp/JIgSXEyRFz4wKJiNLklFkGOXO4Q03alcXeY2BmWDnJ9JBcoU2zvF1vnuX3+bipst+/CmnQb1LWxXLkB89mLyPv6O5hOnoLph++QkxCHlAG9kTF0oKBfunfGsReewzr/ulgb7I+YIH/MDg7E5AZhGBHeAEPCG2JIg6YYFtEcIyKfw7CIxhjSsCEGRYRjZNPGmNamDca0bIlJbV7G/O49sPvjOdgfFYV9MTH4PH4VLn91RIjvHAZytIxwcodjntq9x2O5w86ZR4CXBye9otvNmvNM6WeqwO0AF1jpCV58BWoAR8c0EpO6fmHRL0AirsNROuOxYmESx7oyf3WtOiccfHIBkaCGFm0lgJDgQ4IbCcgIJARp3CiCZgVo7h8hKpWhPKN4PzxSXCymGXKNGD5iRP5cGBERIRyxcr5TgIbviPpAVGOO9ZILi6qb5Pbo3yfGRftpz1rgq4ojWFLh/A/bMZ9bvs+cK1S7cjFXfrTYB9IpKh2xKt9P/Eph3OEgCJck24wAgH0rAbVam+X4KJiPxFykmol5BaqRcl4Hnc46LMKgID22iz4h51NgIvawbHbR+6qfeFIrVI4FKUrknEOO0uHDl7F40TYcO5YkAJTZZEVeXib51rh69TI6dOiDMmXrodqTL8Dd60VU8moBF7dn0ObVfriTSue/LM8pQB+fbcfOi5g79wRuJzmQnOTA3dsOUHeI4iUh2hTYiWNM2I4V91RcJ3JzlNiT5RJIWsUHn/Zg1INUKE81oHq8Ryy8j3OjXjg+g9Kb0aepLx6eZ3p6erqwSdO5c2dUr149/yVWwIaApl27dli/fj0OHTokmua3yuVLR/8qSiz1P2Hn5hEYKGIC5VTJ2UELOQZ4zP5X6TynxonqX55Tk3ZycjImT56Mjh07ivFA7hz7mty8Hj164PAXX2Dn7l24c++ueJHpwLFgEsmfw7UWk9OMmmJUeP7CWRQrVhRPP/0UDIYc4bWadWBuPYnnKJSmzms3uC8ofDdRmlhYqCikSO50y1eMpkdvsUtM0w0iRyjPLMVi3PKfa5Lb5YUojPpAWWKLvNT9SYbtNvV9rguy3b4B6/UrsPx0HnlHv4Rx6yYY1q2BcctGGLdtgnHbRqRtXo/UzRuQtnkzrsbEYkf/AVjUtCnin2+N+GZNsOm55xAX6I84P28kBgUgzt8Xi73rYkbtpzDdpzY+8K2N2Beew5H3BuHr0cNx6L1B+GLkMHw1eiS+HDUCX4waiYPvj8DekSOwY/RoXN22GbbkG8hOvoZ7qTeQnZeODGO69AROz9xOglF6eKcYkc442Z9OSLcCsj8cdCFAvSl+BhN0CI4PwacTTjpa5VYyDVXSwSrJbiUbX371spO4CHKciAWFfqwIRG28J7l6Dli5c0/M2VwYCGzkWFZcE4edFsMLlJMIzkh0CEpP9VZ6PIcVVptJfMnya1aCagJefiETYGsLHx1ZCkBEo3AmAXL2HzognOe6FS2Crm93w+VfLsMqHDpKsCXBFZ9A/lRdJQhUo1I+ocrzqIVynpC1lnBQAUK+uRRb22B30KM9P1TYdgTK4oxmSI++0jSAw3Ek+kwDIk5ex/N2cR0tQfMngJLdBruFOzclCOQry/EgrReTC2aDXTj2JRfNCIfDCLvdBIvFJOukE7uq8WQhkAIdzlqQnpGK9NS7MBlzpLNgAT5kvyenpmHOgk/RvV8c3np7A6rXGQeX4p1Rs/YYfLbhnuCs5JpyhRXkO8mpGDhgOjw9XsCAPmuxY2sedu7MxqYtv+DY9zelbhzXYzs5qRK0kVNE7pQS7VFXhiIuoX/FPLIVZKNr4Jc4jGAn/wNDmwNlpsL/1dgrnP5oHd8Hblh1tUgxrhYnvRsDBXAoRvLx8RH6MxQ9KUCjQhqwO3DgAFJSUvJbhGWT9L6beJKL0N/gJr+ZHpqIAi8MVb/rx0fhiqrxwvzMx+NVq1YJ3SoCGY4TRbREPXLkSNH3ubl80eVLqV4rhgrgyJdVfzd9Lhk/f/6cAEs0M2A00ueSNE2gL1dfwp+JF75b/rX6E/mJhSLMo2YUAYY03jbdf1AURhDELfMmExy51AEywmHIkbaBsjLgyE6HIzsN9gwqQCujiLdFnLvA7KnJckdYyi0pGrt3B5bLV2D6/nuYfjyBvKPfwrh9NzInT8WFLl1w+e1uuNi1C0537YQvX38FiQ2DscjvGSytXxfLgp7FYv86WBJQB9EhvlgUUAfLgryxPNgX0fV9kdggDFFBAZgXHoIJofUxrEk4RrV/Ab1bNMLcwf1x/dB+3D5yCLeOHMLNo18i+9JFYVPJaTbBydlYJ1+Qx0pRhLxxC6zkBnFqFrvoqCfENqKrEgfsjHNy5sRucwiuHMGS9GTOL2wCFfl1T4/oXIDE+BEASHKSxKKnuCYsTHx5S/BArg1FQTa7WXADCXAIbgTZTAXa25oolH7rCXGo82UymzVOn+Q38tptO7egavVqcHFxRbVqT2Lr1q1iYNBNhYBjmlNRxXHgSTFUdCLYgjei0Jh6ZA7VCyIrbCc3VAOehIT8ExwyJ/f5EcBI+KPAjZ1AmTxcnme7E7hSkYYfLQ6mm+AU1oQJSKXDWfrjYh8KkMpxQnMTQqRFZW0Jhi02M0wWjjUOSYKrXDggwYYE44QwzEuw6xBuRn44cQH7D53D9ycz8PG87ajn3RY+9V5A7LLNAoRbTJxvZP0vX0vCG11GwaV0ezxWZQxKeE1AKc/JcHlsEMZN+E4oiefxnRdyciAtxYGfztuRcdcBK801GDQdIL4WBCfaFML6cvwrUR6/CfKJorECZp42mmS7/y/+5xqUD27U4iUaUFuY1GLFNAIcghJu6R46dCjKlSsnAI0CNly8uK27W7duWL16Nb799tt87o8q80ELIyvyN7h5eIegHtyovqRhRvXjeRJ/HCM3b97ErFmzMHDgQGHPiDvlKG6k7kF8fDyWLl0qLFMzP69T40I/BsW5P/GKnj9/Xgdu+CV2P2dJVO4/9U/N8SJUn1JaKD6npP4PZ6187o+wC2QW4h8nOQcWbg83FYiKjDlSjJSbI4w+0vAjxWFii3xWJuwZ6dLKNS1F05r07XuwXr8B223uBrsJW3ISLJdOI2PdStxcMhvp0fOQvmAG0uZMRepHk5D64XhkzZ6E9A/ex8XenbA7oj4OhQZgf4A3DoUFY1t9fyT6eyM+0A8JIUFYHuCHZYF+WBrgjZiw+pjj7405EY2xvu+7SBw5GmumT8eKWTOwdOZ0JC5eiM0rY7Hyk3lYuyIaiXHLEBu9EFs3r0dW+j3Yco1wEjCYzbCa8gRwsNisyLNZYCFHhl/JgriYEAtJTgjhMIGNmRwjjStAwELxBTk0kuywWS0wU4eKHDZydNgHYglRHSXLIYBRC63gClnMcFA5QZSuzjGv5P4QYJFbZbLlYnlsNNxc3FCjSg1sWrtJLIBW6scIrpQddn5mC/RVMCjlvaQSvqpRwdlHI6bmAYpzKN4BxcZckbWffN/J1ZUfuhLcEZio9qQIxi4Urakzx34ht8Zmpb8nAlKWJXpdgB4CH6ngLrk3VgddJxCgknNI8EnFWimOJudDgAUh9rILAEwwY7HnYf3GNXjn3T6Iil6Mi5ouqMXMOc6J60nJeL3DCLiVeQ3lq00Rxv8e85gOt1ID8eHU74X+jsFArpJ8yMzsXGzd+QM+mvctpn/yC+Yvu4vYBCMWLk7BN99wjEiuI8eqTeNQUsWFzaUUklkWsTeBjMVCPTY5v3KafSD9iflS9cd/c5gPbtSgLPywBDi3b99GVFQUgoKC8jk03PlEQENODQGOn5+fUBKmToUeFKnylLhCndMvZLy3Htz8rXOjWu0/GyrQ8aCxwdrxnOpTjpPly5fj2WefFdv9FZemTJkyQvzEXXHMr/qecT1IYnlKZPBnn/yhBjd/9mG0SYpfjxSsiEVPfbIRAJHbI3RfzAIEOK0EQWY4LSRugdf0ZbgbLDsHDkMeHDm5EPo0tA9Ei9EGWo2mQvRt2DOSYU9Jgu3WFViTSJdhvXYR1mvnYb1+QewEyz24H8a9+2DYuBWGrbuQvTQGJ15ug6+fb40f2rXDkReex7G2bbCnSSTW+dXB2gBvRHnXw/zAIIwL8MdAfx/0DQ5A3wZB6FnfF31DA9E3vD56hgWge0QgukXWR9/nIjGrz9uYP6AP5vTpgVk9u2HNBxNw4+ghpJw7jmvHv0HKT+eQfPY00n66JNqAzySUvc0U9RlEO1AXiOS05gFi1SDXSG0F4Seu2nqjaYJqSue0vyQXZS4m8qtdtr5aUCXrSAAd6mWIP23HGPMLrgT5OQ7k5OYgIS4BsUtiJLAxSIOOVrMVRoNBjAilPM0DCau0vhY8jX9i0DxUl/CJKG4kQJHyFL7vkogn+VGjjiWU42YBcnbYCoq4ecBCUCna1yJFR2ZyWaRoiiG5L1JEqDhnZk2sKG0+CQVg7loSu5wkAjl27CRee+MdPP/SO2j10kB4B7RHyTLeqFmrITZuPigAhIU6dZq+zYpVR1C+ykjhNsQn/BAaNDuK4PB1iIvLkorD+Tu1OA6kQi8NbuaQaDVc53qFW8FFfws9L4nXpNhVDk3Wk1vJGf7+T44aNXoKwt+/6n/lbD64UQ+sREZqwfrmm2/yt3QTyNBGjRI9MaTy8KhRo0Bv3grAqLJ4rBZIpvG4cB6mc8D/DW5Uqz18oRoL7Cc1PvR9SU7NwoULUbt27fwdcuTUkKiLRSVy5uf1v/Vj+arc3zr/R9IuXLggwDbFXdwK/lBxbv7IAxTKwzahjoBiS/O0aD8hTlFcH/KhKcbRPvMEAJKWrPkZKUCQsBitWYsmt43iOnKFzFSCJtdHJwbLzoAji1vf0+DISoU98y7smdwJdge2O8nSDtCN27Beuw3r9TsyvJoE640kWH7+CZYLZ2BYG49bo99D5uSxyJw0BmlTx+PnccOx+oWmmB1YBzFNQrA4xBvLw3wRE+qDmBAfxIb6CpHYfJ+nEB3sjdhQHyzxfQZxwfWwLKguljSrjw/Cn8WE8Hr4oEkQJoQGYH6bF3Fs7ixcWf8prm7ZiJ83b8TF9etxet06XN6/D5Yb18RONnKzrFkZsGRniWe25hphyc2B3ZIHO0UTXDS5inARo96MxudnGrk0DrtVcGPEYut0wE5uj/hEd8JqN8NCkZWAoBLcUOSRS9EW+4usAkY03Swec1ySo0DdD+5OU8wbsbBrIjNyNR7wqhQaJQ/voQSJ5GhJYKjEb2IMaw9N3SohbmT7aD/CeamzlQur3UgNFyEmpB6TzcHjXKlnQ7EUdfP4jlBkyd1kFG3a2R82JN9Nx9oNh7Fx6w9IT6cOl9yuzV1LRBabNn2LqjX6oJwXfVJNR+mq0+FSdhhq1h2H7XuShZ0jQy5FVgRawLXrdkz54CRmzMpGUpJDGP2jsT/awaGCrslMThXfQwme2PXcjURdXUHcmUSji7QvJbCefGdlG1DPTFP+JX7medUg2nuvnzs5htScrMv2d7RQC/wK3PB8RkYGFixYgDfeeEM4f1RWhFVIjk3r1q2xf/9+HDly5L4i9WBGndB3jErTLz48/ze4US3zcIXqJWIfkRQIoZiSO9xefPFFhISE5IMaGmckyHn77bfF2Lh48aKY0FU5imvDp+QYIKdP/dSYUHlV+h8JFbipVauWADcs47fG3R8p62HJo9jPbBdOslwQFQmujtiufb89IStZ+tzuLBRcOUVyAeWf5AJx6qVyrY3KvFwUuHXYzJ1f5AJR7MWdUQZpAJGhice5EDvCsnNgzzLAnsmQvsNyYE/PhD0zE45s2gWi41Q6Sb0C6y8/w/rLRVh/OQ/LlfPI3b8Nho2JyD20G4YNq2FYuwqGzetg2LgWOatXImd1LAzr4pETF4W0sUNwoHVDHGjZANuaBmHDCw0Q1dgX80PrYGG4DxYGe2NefW/Mj6iPGWH+mB7qj5nhQZgTFoIFjSIx77mmmPPi8/iobRtMe609Jrdvi486dcC8Xj0wq3tXTO/SEbN7vI2Z3bth0ltvYN6A/ji7ZROsN67Dduc2rDeTYL12Vex2k/pQuQIQEqSQEeE0Ux9I4zYI8ZcVVrsFBDZmhxVSE0Tu+BHAUyivmoXtpTxTnuAyUMxGcCMBjvyS57gT75kmgnhYxuE/Vw+OPb0YiYBNcXKIMKQSuShbZCVg5+gkx4y6TwQ3BjicFDFRtwpITTUgOTlT2JBRKlNC8dwJnD55CV26DEabtgPw5luj0fL5kXCv2glFy7yIESPjhfItmXdCqugEDh36GQFBk+FR82P0eidJ+FrbvM2CI19bcfseRZ/ajieCUO7e1BR4jXSMmyUNAyqbN0rRV9afzyANZBKzkWtECSTfZd6bdm44N3G7v5A7ac2k0IzVaoOZ76bWMASJfP///v35FsgHN2pBoZhg+/bt+dwZAhlqoYq9AAAgAElEQVTlJoEhFYX79u0LLlpqoRKTL78etR/t3fDHdNUxKlT34Xm1+DD8G9xojfcQBewXvTK56rMrV67g448/FpwZjgmld1W2bFkkJiaKXXRK3KT6vXBZqu/V46p8KlTpfzT8bwM3bB+hSyK+7hWTXobyq58TsB1Wp55ssHDHidjLQ0BELgO3ylJ5kcBI6hmIfiQXgSsGv55tmg5Evs4PdVEkCU6P8mmmfIXRMnQOuT5c9K1SNCaAEYEPOT8EPNwKT2/xtwToEdyf5CTY7klbQLbkW7AmJUmv8bQPdIdGEXl8DdbrF2G5fFaQ+cz3MH3/Je4lLMPBgT1xZMDbuPD+uzg7tA9OD+qBE307YWfzcKwPqIO9IX7Y6V8X2/zqYINfHcTUrYlY39qIC3gWy31qIdavDuIDvREf4I0VPnWxKsAH0T51scS7NtY1jcSGVs2wIjIUCc81RnTL5ojt3BUnVibg3K59+GbrLhzcsBPf7jmCo7u+wJ0rt8Tntc3pwL2sNHxx7Cucv3IJubAjzWqGQaijUpdE7vShwizj1C2hnodQrFWgVdMPY59z/CtuwR8d+w9nPn4McU2QxB1PDkcurLYMWG2psNnT4XRKJV4u4Fah4EvbPiZYbdR1sSPp+hVs3rARhw8ew8rYnXiuyRA8XeMNvNPvE+EmgUuOicY5AWze8i0qV+mIUpUG4zGPySjqPgvFvZbApdR7eP6lGLFVmgw6sTGPYiMrcO+eA7duS9cNtAqcQ3cP2dKNhQAlwkK0FE8yP3clESBxl1K+QT4aDBTcFio5S8DL/qDIzWpjmsA6Qh9HMHYEo1Vy+YTpAZGhoAc5BmS78cKC8phDnpOwp+AKfYzn9KQ/978XF+BGDzjYBNSdaNGiRT7A4eLVvn17sXBRUbjwj43On77xVVrhvPpjlYfh3+BG3zIPR1z1D8fHnj170LNnTwwaNEjo1ChdKxone+WVV5CQkICDBw+KiqvreMC4AsEPeqrfAjT6Mh50nT6d4IacRSWWYp3/bBn68h6GuJqmBNdFx7UhuGEapz5OvYoIaghuSFSyFJMnwY3dIgCOtMHigNiGrSm08tXlF6ZwN0CuDncgaeJj5mPfyC20khPB3UncrmvnlmaKV2xW2JVSLkVhBEVU1qX+D/WCTLlw5NAPmJ64Gywbjizq/1ARmnEColQ4csgBugdHVooMuVssSwNKd2/Bdvs6bLevwnrzZ1ivXYD5/HEYd2xE9qK50i/Y4kXIjopC9tIlyGactHQxshbOR/biBSL9Vt+eWOtbB3siw3CgaQQ2BftgW3gAtob5Y51fbWwKqItE/3pYHBiAST5+6PNMLfSo54d+QRHoH9IYnevWx9TXu2LDtBnYNHs2lo5+H238fTC6w5vYtmQREj6ehXWfzMXamR9i69wZ2L1wLrbNnYmt82bh+43rhbFHeyZFf9wJly5cg1AR3GnOlTpT/MTnKszVMJ/4+a+4HQWcHjVO5Vhhujynxo4KZT51xFD/U+kqjcdycZVw+tf3kznVdfeHsg68XoqYCOxI3GptMufAbM3GuYvnMWN2DAYNmYVRoxfj8OELYlk2W4zgrir+5sz+BK4u5eDhHomKFV5BFa+hKP5YD7RtuxAZFAkJX1t8FyCcoK7bkIPVayxIWGPB8hVmxGj01dfU25FuBYQyrwCQ0oqw8Dmm+f8Sbjm03UgETmSgUVwo9X0K/LMRj7B7hDRT66J8HKLTQeQz6HWL+C6JHVU6BXQCO8HpYZ3EnMU8TJPtz3Ocx+S1bOf711mRkP/v/n7IT/4fjeRzbvQLDEUPdHZJsZS7u7sAOTTAR90K/a/w4qHK+EeLGcvQAyqWowc3D4udG/3z6ePqOfXPoG8XxmnbpUqVKoKr0apVK3FaXacvq/B1/x/Hv1VPpfvEOunro8/LdHLhPvvsM8Gxow6N4tKUKFFCiCa3bdsmlILVs7H++vL+P55HX6aqb1JSktAH41ZwpnEM/lY99PXUl/MoxdUUpqa/3w5VLob6HDy+/6c/y/ivc8i0gnP6svVlqa0cFEdI5VuxOHOlyOcKad7tlXd4hoIzRE6RBU4bFW+5ddwkd4pZuVPMJACT0BHijjChG5QGe6bmDiMlWbMOTftANwvRLaETRL0gcS4pCZYL52D67mvkHfsCeV/tR96RvTDu+AxZ82fi+jt98fMb7XH51bY491p7nOrwJo68/ipWhgZhbp1aiA2qjyifeojyfhax/j6ID/LHqvq+gmL8n8Uy/3qICvTB0kBvLK//LFYE1kViiB9i/Z/FIp/aWNwgGHNbN8fEVk0xs1NbTO/2Ksa0fxGjXn8FG+ZMR+rpH5B6/gxunjqBuxfO4u6FM0j76QLSL1+A8c41wJEHm5X2VKRrCbY+F2Bufc+zkm9EYEubQrQ4LRd+2b/kIkkbRBwPXGQFZ1BTiiaI4S4ykth2LXRccmEjJ0UYyyQXnror5PhRe5ZKIgRb5MhIrhRhNneLUReJ26jFNm6HCXYbt7/b8WniZ6hTNxhNW72FOv5vorRHH1R4cgJcygzAmPFfCdGT0WQTIiHWefbsGLi41oFHtYGoHxaPl9v+iLCG2zFhyk/CfQCditIPmvAbRv0Wui/QPNPT6SZdEBjoU0tzCEtRk4QNUmxERgtFSsJ3lxZnGoeu9r0uBjfbSj+X8JxQhqaYUux+o7kCDQ0JQKN2vLH99X9S+VxuN9cUrbW+EDpJ5NqoCmgwVf+m6ftc/9apuD7vb73DKt//SpgPbgo/sAIodJUwf/58tGnTRrhRoC+oxYsX36crQTslSnyhFhuWpx8QPOY5/Xl1T+Z7GMEN6/d7i6F6PhUSMCiwQF0SivAIBv5T4IZ1Uf3IOipAo293FdeHJpMJu3btEpaiqVNDLg3FT6RGjRph9uzZWLNmjbhEtY8KmVj4Pvqy/5VxgvDNmzeL8TlixAjBualQoYKoN+vIcas4japObAfSb43Df2Xd/h1lFZ7MCh8X1OHBZ5jn98/+upSCKwrO6WOqPH1aflydVGH+CUZUoi7kSiLYSzSUqClDi11h3BpPzhAtJ9OqMv2FkSMkiXpA+RaiNQ6RMJaYSc4QgdE9OHJS4Mi8DXv6Tdju3RBGEy1XfoH5wk8wn7kA85nzMF+4ANN33yJnZSyyZs9E1ifzkTljOjLHj0GCdy3MqVIeMU9XweeNGiDzgw+QMXyY8CKfOW4UrvTqgm2hftgWWA97QgKwOdgPK/yfxdyQAEwO88OUJoEY19APYyMCMDYyCGObhmB4o/oY0SQcY1s2xfgWzTCmSUNMaNoYk1s1x+KenbBhylhs/PgjJE6fhrUffYitcz7GZzM+wpZ5H2PjvFnYumgeNs2bgw1z52B39DJBWxcuwN7ly3D9yGHx7I7cbKFXJXyrGahQTr0q7rKzwZlHZXMqWlOUSYN2eUK8KfqAXSFsD8mt7EeOfIWPpn+M1QnbsSpuF+bNX4dZs9dg1pxPceDQSQGpbXYTnHYTrGYLFs5fiZKP1Yb7k61QrmpXPFZlMop5fYyilT/A6HGnhOJtHnc0adzE777/BRMnb8LHc5Pw/Q825NAKb4a284iAxSnBTa7JLjgzxFvEXQQ7es/p5PDkOyAlQCs0zgqsSmsiRII2ghJyIrX5Quq+kJMpSYARbY3j3EciMOGcZBY2bDia9X8Cmgh+q0jV2TmS4ElDVJJdJMqizR5epXsb8uP3vTa//eYUzvI/d/xAcKNagqCFCwMXyb179+Z/wffp00c4K1T5VKgW099bPDhg9Ishjx9WcMO6/aOfWjiZT+V/WDk3qn4qZH+pvmCcu97atm2LSpUqCTCjdsYRNNAgX1pammgOXq/KUNerdip8rNL/1eG9e/dABWICSCoyKxDGHXyMk7tEy8j86fvoX12Pv8v7N7SAAjn8shWkyQPydzcV4ggp/SBhJJF6QZZ8T/KSQ0SOEMGRQdoJEmKxTNjTMmC7kwZbcgpsd+7Cdode43VEO0HJt3F1yWK0KFEULUsUxfMli2Jr126wJt2C9ZdrwqM8vclbLl1A7t49MGzaCuO2nTDu2AHjzu3IPfw5kpYvxqLnG2JVuxbY9OYrWN+2Nda1aYb1LzfGxpcaY4l/LcQH+mBDcH3haPWzAD/E1KuFBXVrYp5vbcz1q43FfnWx1K8uonzrYJF3Lcz3qY053rUw89lnMNu7DqbXrYWZ3nXwsW9dzPapg/iWLbCvVw/s6NMVG3p2xMbefbBz8Ais7tkf8f2H4Pzarci7eA13Ll1G0pXLuJV0FdeuXELSz5eRlnQbmbeTYcrKlFw4AHGrNqB48WdQsXIblKvUFSWf6IMy7oPgWroHRozeLTgiRiqoC0dOQOKqXahTqw1avzwT86Nu4oczNuw/YsX23Vacu6h8IpHrQQ6QdDKpuDH03k1lXuF1m44lLU4oY9Viw6DmT4mghmCGAIkGiwl2KDXVS/ko9SEHSgI2ycVS6vZKD4YgRhoc5NZ0+SfBt/yvFPsZmqw0GknhsMoh3wfOg4oowiVR1Cu2l2tcH4EAda+Pfl7VJedH1T0eFOZn/DtSYMSPjcqOUAsW20YfVwsWFz+6W+CW8IoVK0J58GZ+tZNG364sg0BHXa8/p+LMowc3D4udG32d9W3BevOZ1LPpj9UzEQT8J8VSrJsCmvp6Mq76Wb/gU/xE0SNt0ijRE/u4SZMmYucTRVP8qWsKg1eW+XvtpdrlXxHyGfgjuOFWc71Ss6q7AjeTJk0SeVlv1k/VW5Xxr6jP32U8fC3A/hVbc8WSw10rXDy584ViAn69O4SpfQF0hMFEgh9aijZrrjE0z/EaJ0hyhKg3RB2hHHwdFwffIm54xs0Vk9q9Buv1W7BnZMGeQd0hepHPlN7k09MLjCjeTIKNekN3b8GeQhHaZVgunoLl8iWYzxyH5dIpWH46AePO9bg8pC9uDx6EjKHDcatbL6S/OwwZw0bg5rsDcbJHR+xsGYnVvrWwKbAeNvjWwmc+z2BjgDc+C/TFGr+6WFvfB2vq++CzUH+sY9yvHrZHNMKngUGIfvYZrAkJQEL9IMT6BiImMBiLAkPxUWA4RvmHYEBwKPpENEDP8FD0bxyJQQ0bYVBkI7zbMBKLBvTFscRVuHzgII59ugPDXhmOVjX7o73fHLSsuwgh1efC22sa5k8+CafJqZkbyBa2luz0fZTigCnTCUOmBCpiK7XRCXueE8JQsUAhNLBIUaRdpNG4nZXuBWj7hZ66eaw5r7TSerXYOi97le4ybDTg5zSLHWyC+yKAhGDG3C+hzR+2EiooDooYO0KhvwDgOHSiKeam2I873ig+E3pq2hZ/ppGE3pq27kkwQ1AuTTcoUZaSFjuFzR85f/6jtZJV/hvY5Hfc70Z+l3OjFjJVglq82PncAs6twFz8Xn/9dWGNlrtoCl+jrv29kOU9jOBGidr0dVdtoE9Tz8znUD8arPtPghtVD1Vf9cKqdIbc8k8OXPfu3fHyyy8LbgdBweOPPw6CArrPuHr1qriE1yuwpNqFaXx2/XOruAr19/tXxyk+2717tyCat9+yZYvY6UcgRl2gTZs24dy5c/n1/1ff/+/yHo0W4Fsplz792iY1UYTeCHeV0TaNJQ82s1Ea/6MDRbrEMFI52g5nnlX4BhNiG5MVjmwDznzxFX7YcwD2tCy5ENN/mIFKwTqlalEGlaoJlmg8MR2O3Ew4jFJxWugNcWdZWgrsQncoCfY0coqSYUu+A+vNu7D8kgzLlduwXqXfsbuwXvsZeV8dgGHzeuR8uhrZsdHISYhH9opVyF4ShZy4lchZuQzZy6lE/TGy5s7FrffGYrVfOKJq1MZmnyAcDArHd+ENcbbJ/7F3HeBVFGsbsVz1Wrgq0sQCClITEjoEQlVRsaCC2MVCUYoIYkFRUAHpHenSFSmCNOm9SUdaQkhIIL2cvrvnnPd/3m92kkME9d77o/L/58Bm2+zs7OzszDtfeb9Y7KlXD9vq1cGa2tFYGFkNsyOriFfZrKjKmF2jMuZEVMG8GtUwvWoFzIyugmm1q2JYpfIYF1kbk6Mexze1emBSRD+MjBiKobUmoHf1wRjYeiRm9BmCKR/1xvR+PfD1B90xqseHGNVzCEb0noyv3puOge9NxNAPJ2HCgBkY9dFIfNW7L0Z+8iHGfP4BJgz6BAc3r4ORkQkrzwe/OwjD4RfA5HY4kZOVCY8zDz47nAVBgQq5ooyYyVCsbIxoe8dJuxLUWIwvZZvHaINhDRZ0a6Y9jTAo06Mw389bTfZ1v8a15Q+I2zbDOjAP8WK0aRjy7Z0KhoT8SaXcRzXK0AZpl9OWJLHMlFBe4KelRrrcen2BpP+vD/0muNE1owdvvR+6TkxMFK4TqgVKliyJfv36yWk9qOq0v4VI2VD+juAmtOx6W685sPKnB3w+AyVX+rkJHP4O4IbvLtQtm9ssIzmKCGoIZrQahxIQslDTVoU/vjN9LZ+X1+nnkwSF/jC9ro9Cpy7Jru5oQtuWdDp2kETelPu6zNxmWv5+q01fksKGM/3Ta0DGD0pw8sGNMsIVDxiZiBS43TK6PINleunRw1m/BMwkuZ9SYZhe+vzKCParddCgLRANbDmCMpFi2qVXGblvQg2l4adUggCKXELkEQrhFPLRfsgOo8H4aLQbyiQIcsCfycUJf67H5hhyw0r1wErxwnc0Hc7v1sG9Zh/MpDyYSWdhJiXASo2H8/t56FejCXrd0wgdilVF79vr4d3bKmJ4mRrI+WIqPJsPwLXsJ3g2b4Vjxjf4rmpFjL2rFPY+1hoHHn8EJ9u1QcJzbRH37NM4+MQjOPB0a+xr9xg2PfIAfmzUFHOrNsL3VZ7AoqqvYm7VtzClaneMiuiOT6o+h24RMehSqzq61quMLrUr482omuhUqwk61nsYL0W1xGv1HsLrdVqgc91m6FavCd6uXRdd69XCW/Vr4M1aVfDxw80x8qXnhaPo8/bP4dOnn0b/tm3x5QvtMeD5thj40vNYMPBzHF27CvHbNuH4hnWI37wBCVvW4/TW9UjcvhGnt2/CkTWrkbRrF6z0dBW01u1D0GtzO3kpIVJhTYIem9mbgW3dPEaPP55nejud2HvZjOD0aqO3k4An2seof8psWAWKDQ0zoSVDbB/si6S5aKNkkd4oex6JdyUSLJuk0/5yeA1/GswUXv/pH9jf/Ia/C250hfI5OCDogUIqOWSw2L59u3CfxMbG4plnnsGnn34q8YPIFnuhX2i+3L4cwI0u84kTJzBgwACx5yCYS0hIyH9EpiEQyMjI+EvBDcsRqibkPqUYNLTt2LGjcNQQ2JCbhoR7DJvAmE95eXnnPcuFQADz0iCJ24V/PPZnghzdJrnW4EWXicf0eX0svP6/XwPC+Etga2sjZLZrq92lyYrHC9uGGpRoX8HACXSnVzxB9hBFt10x0qDHV0GkQr/BEA8h7trCXuyDx0NSSqrADBgBehrRc4jMfxaCJEmRcBD0BKMRb54COUYO/J5MgIEgLS8sGkjTVoVhNOgtRmkSPcbMAFJOpGDYwGkY9OlsjOj/I8b2W4Pe7Sei1JWR6NisB/zZQfizTVGPBZy5OLJ0A+rc1gJ1b3sZjUr0RNXrX8IrFQYgbvwZ+A77YZzKhXkqCVbKGfh+OYL0aTPhWrYRZmIuzMRUmIlnYMSdgnHyJMxTcTBPJ8A8FQ/j2Em4lqxC2qAJyBu/ELlD5iPny3nIHbIQucN/QN74H5A6aRbix41BwrjhODliMOJGDMHJ4SNxfPgYHBo0Egc/H4HFz7+OEVG1MLFmTUyJjsKc+vUwvXY0ZtSJxjc1a2BW7Zr4pl4dfB0ZiW+ia2J69QhMrU7vtOoYX7kihlS+D/1qVkLPyHvxYXQlfBJVBZ9FVsaAGlXxaY1K6F+zOnpXuQ+DGzfEqm7dcWjocOwZOhw7vxqKn4ePwJ7hw7F35Ej8PHKkWo9Qx3aPHIHdY0djz4Sx2DdhHPaNH4e948Ziz/gx2DlxHDZOHocjq3+Q8CVB0wHDlwvDcovHmhEwhPiPnmc0giddAnl8yHZNZBzKc6SBNtcarPDrFNsc2wNLB44Vj6wwuPnDnde/BW50rnrg0gNb6IBCjpynn35abDcouejRowcOHDigL73gmvlocEM7j4EDB0o6nf8FL/qTDhYuA/enTp2ab7xKgBARESGu86EDaVZW1l8KbkKrh2CMwU4rVqyYr3piPdNtunv37jL4h75Dfa0+Fgpq+fy/BRYK15fO61KtdXkKl0mXnfdlmguBtEtVpnC+F6sB3X0XXstbsmGIknycPz8NzU9d+2sTz4I8ucVcbLYfpZKyZ8r5aEfsSamsOn/GzWu4KDWWYtLNyUpDr3e64+3OHQXo+BmzShh2OdnjgEVViFkQjdqOZk2YZILBPk2lspDwC4wZ5EOAsa8YAytgICs7DcdOJODnfWfxbp+vUbP+y6gT8zJ6vDsUbi+ljSRaZOBIYNVPO3Brqfq4ueSjuKX0qyhWoiNuK9EZV1/9KB5q1BvOcwEEaL/iYbR5C76sIPZttbB1tYmf15s4sN5Eym4LVkYQfoYlcARFxaZUZmSe9sBKt+DPIUjyKKkRJUiyZMOfzQj1DMzqhJWWByslG2ZCGsyEVJin0mDEczsH5ukcmGdolJ1uB2tNhpVMd/xE0NjaSqbKLQO+gyfg+nEl3KvXwLVyDZwLf4BzMY2vV8C1bCWcCxcjd9BX2Nz6MWxo8QB2tmqNPa0fw67Wj2LXY49h/ROtMb/Ng5j6RDPMfuIBTIypjZHVKuHr6AiJbM/o9mMiq2BsVHWMiY7CkIgIfBUVja9q18bAqCgMio7Cl9E1ZPkiKhJcPq8RgX7VK6NPtYoCjoZERWBkVHWMiqyM4dUrYlh0VXwaUQH9G9fCqPaPod/jD6Dfs49jcKdX0Kftk3i3TWv0e/5ZDHjxeQx+9RV80Ibu/o9i1scfImndahjHj8L3y2G4DuyD6+B+uA7ug/vIfniPHIDn8AG4Dx+E8/BB+E6egD8jQ0mQGN7BsBmybcqj/NAeIgJig9YnbAZom0BROcDzGEF2oYXt2HaQL/jmQr+3y3f7d8HNv/NoeoAh+NmyZYsMqAQ6jCBO7hp62yQnJ0uWelDS+dNOgkCBXCqU+vD3Zw+UuiwXW588eVLCUtStWxfvvPMOevXqJXG17rnnHlBiRR4gPahSckNXcBLLNWnSRLLkOQINRsYm6R3ZfLmeO3cuJk2aJNHUGVFdx0YqPCDr+tXlC62fwmlp0Dxt2jQMGzbsPM8nehCRjI/xwEh8xzIVfhf6GfR9wutwDRSuAdVmFAFZQadI4KtsH+SY2A3Y6hoa8dp8KCqKM1l6aZCpoIR4yNBtGCqSNDtqptc09WKUKW64JBFk1G/yoRCYMMaTIilUPCuMBeWH12/CS8kJVIBLEhFahh9+I2jH0qTnCuMWMS9VDqqlfBY5ZNTsWsIFwMLmDWtxd9kyKHHrLZg/hxQIFrw+J3wmQymY8Amvi1wFS0CIhaycdMxfNB9TZk7F9NmzMHXGLEydPhuzZ3+HlOR0qR6WyWcGMXXGQpQo0wwl7n4P15T8AteUHooi13dHbItxcDgVYy7Zbvk7nZiJAQPnodu7C9Dz/S3o8d5evNllA97ptQazZx+UWEcUEOWPV7QvYdgBGuR6grDcQQTsJcgIACJUolRKBRYNmkHIcZ6TV8EQHVS7MVyHvdAGyedHwONHwGUgkOeCP5fAyA7LIaozLwIOj5yjfZIyyGaYDmVozX1/jhP+bIeKXp/O6PUZsM5xyVTrs2mwzqWpmGZUtZ1KgRFHSdJpGHGUIJ2GmZAAMyke5pk4ePfsRNLkydjZ+wPsf/c9nOzTByf79EJcn/cQJ9sf4OT7H+H4B31x7MO+ONK7Dw727IVDvd/D7m7dsb1zZ+x95x3s69kTO7u/hZ/7dMfGV9phYe0I/FD1Pqyufi/WR9yLNZH34sdq5bAw4l7MqVYOsyIqYFpkRUyJroKJNSrh6+oVMZlLtQr4pgbtlyphStUK+CaqKubVi8bc+rUwo2YE5tSNwryGNTG9fnXMbhyF2TE1MCsmClPqR2J0nQiMb9EIy3q8je1fT8LSkeOwdOQULBkxFUvGzMCySXOx4OuZWDJzPr6dOh0Lpk/F2iXfYuX3s7B80SysWbEAq5YuwNoVi7Fp/XJs3bQCm35ahE2rvsfG5d9i/bJ5WP3DXGxa8wMy05IE+FimRxnZi2q18Bd/+e3/r4IbDrB6kKXaQm8vXbpUooYTuHCgp3RHD8x6PX/+/Hxw83eS3PAZCCq4kN+HAKxt27byprVhLQ1veZyR0/ljWkpuihcvLp48zZs3z0+vJVSsC16jF71frFgxEESxXnTd8GK9rwEOAaQ+HwpGaMjMuGD169cXYEUJDe9BI2ECzd27dwsnEa/VdkPc1nlwW783KXT4T7gGLlIDtCFQwEYrf9Sa7rRkdQ0FOEokT+kGuVMoySAI8dn8yrYEJeiFYTqVVMMOG0GAQ7E+RfIm7R0k8rZJujjlpUJDUhsIURpi0TCYsYgEshjwWAy2yHKp4vhNUugrwBUM+uAzHYpsjiErbLBFsES1Eme627dtQtXKVVC0yFW4q2xFrFy+UWwlklOykJKai4xsl+AII0CwpticV6xYjQYNW6LUnTVx4201cdNtDXB7qea4/sbaKFOmMb77bqN4s3sNAjNgwqQluPL6WFSqMREVon9E8QrzUeye8XjljY3IyFRxlZiO9hs0J3N7guC1JKhjtGmHJwinNwiXL5gfE0m/Mk7qaZeaH05Au0bbRrWh8i69na8fUXgqv+7OO85zrFZ6Kgno0VHpbZd7cubQFokLtxm/zGfHL5M1DbYNkUpI3DJGsHdy8SDg9Npr7ruUMXYe7Y1oh6SAkvZIE680AiaSO+bmwUrLhJVMaVGmsj1KTBZPNpMBXxPPqoVA6QwDvybDPHXGtlGinVKKWq3OheoAACAASURBVBLPiOrNPH0c3p+3wfndfDhmTIdj2hQ4pnwNx7Sv4Zg6EY6Z0+CcO1POOefORt6ksfi57WNYVLsaNjRviPVN6mNdbF2sb1wXm5vFYGOzGKyNrY+fGtXDxqaNsKV5IyyrWQXfVy+HxRHlsCziHqyILI8VURWwJPp+zKxxP4ZWqYDu996HzlUi0aNGDLpHxuCtqvXQuWodvFG9Dl6tFoUOkZHoUrcWutSPwktRFfFSnWp4oV51vNygBjo0jEIHrutWxet1KuHN2hXRsWYFdKp1P16odjc6x0RhROdXMOG9Hpj91UCkHz8mdkQSQFY3ost0/V+DGz3A6ufXg6Te14ER4+PjBRSQL4WU/e+9955OIvYZCxYskEGYgzztQvgrnHf+BX/BBoEMJS1abcZ9/awkMWzcuDHeffddKRmP06C4bNmy4lZNIjz+CBroYUawcfXVV8vCbUp3dFBS2sDQQ4nPzoUgRgOa33ps3pPhD6pUqSL1q0ET11Q/bdy4UcDMhYBL4WP6uX7rfuFz4RpQNcB2Slsngu0C2ngeoxRHgR8a2hLsEDAosYKOuUSPFhX7igRqJI4zlIRHwAJBkJX/nalvgu7cJnxU7ZDsLaBcvimFETWPACfypPBaSm2UZMgw+B3ZFDlCv89yUL3ktqNQE2xZwvRLYEN4tHHrGtxfpQKKFOH3WQqtH3sLO3ZlYvTYFSh37wO4r+KDGDR4lozxbjvGEelT5sxbidtLN8WNJd7A9aU+wTUlB+KG0kNQ9Kb3ENNoDPbuY/mUOzOdcRYv3onuPabh+Ak/TiX4EZ/gx/F4P+JP0924wO2Z6iniSZr60B3a6wuK2oq8LiS/40K+Fb4LJbpR6ES0FrZtKutM74dil/+oNTMDjZ4KKteu5JAbiluSrnyiKnubDy8Lw3ZcYMlnr6bkyF4EMNm8RTTyZUwz2iPJYqgArw4bJAkgooTIXrLz4NdL6DFu83gWXfh1eqriSPbIYLCpokoThuvERGV3dDoBRlwcjJPxit/oTDKsswRFx+A7she+Azvg3bkRvgO74d33M3yHD8N38CC8O3eJ8bZnyxZ492yHc95MnOrYAYkvPYusTq8i8+V2SGv/JDJffBbpr72KpI4dcbhTV+zv8g62vfQmptVuiJH3MUZaDcyIjMA3EVUxP6o6vqsViRmRZMiugNERlTE8ohKGRtyLCbUqYVpURcysUQHzoytiTkR5zIssjwXRFTC3xr2YHV0Rs+pUx8BK5TGkUSNkLF+hjKcZ2FPQ7H/UMv4WF/3X4OZiT6GlGjyvvYg4iJIxluoSDvgM79ClSxcJwhkquSELMn9/NbgJBRXcHjt2rBjgFvYcIgAhUy6BGaUu/FEtRe8x8q7cddddYsTbtWtXAW58fqroaMRbuXJlATv0WBoxYgTmzJkjz02AEfr8oWXh/fjjMfLTkGuIRsIEMRrUECS1a9dO3KH37dsn6XV+XGtwFpqvJLL/6LShx8Lb4RrQNcD2oUFxgQRHqaVMUvjb03x9jgE8g2S9lcVCMEAKAUuABddUKUlwRUV2Iqy4BrlOqHiy72VwABNjXUXvz2/Ez0GR0gPmRxsWUTEp42DyjYiAwQZBhkl1EgEUcOjwcbzTox/efqs/evUejXd6jkbXbsOwZMkuAU1r1q1Dxcr3oujVV6F4iQq4487GKHHH4yh553MoVrItbr+jPW65/Ql06z5Zxmey5BKwUJjhcAWxdkM2Ro/Pw4zZBiZM8mHkaB9GjfJh80YalwJOVxAGAzmSr8WOVu3zBuU4wwYwZIBwuXD8t42fLdMUpl/ehLiRGIFpZGE6qS3Ws61vktLoN3Z5rdkvURIm+EkXXUCSQmd894xzJsIj1UokLf/kAyVKjMQrip5Rv7OQmVkvkpb7pAFQnlQBp6MgICxd94XpOhf+XBsUkQXbSXCUBn8WOY/OIpCbpkKEpKeJ6k2lzYY/I0tsaSSI7NkkkBXbOHocxlHa4hwREOQ7eBi+A4fhO3ISvqMJMI4nwTiZJHZIuQMHIXfgl8j5+CNk9+mFrPZPYXtkBcS3bIycj/sib8o0OKZNQ96k0cgd0h/7H22BFVXLYUeNCthe7R7sjbwX26uXQ1LLhsju+TZyPvkAuUMGwzl/AYwTcRLqRNSUqkZ17V926/8K3IQOgIUH44vVhL6GEh2GdCARYJkyZUTiQGkG3ZHpihyqdrlYXn/GcT4Xfyz35MmTQckT427x49PPwjS0m6FtEZmJ+aOLPCU3lMjo5+KzPf/883Ke11Pi07RpUwEk9913n8xQmZcGHPreHER4L30/ZkACO0qSoqOj5XpNXsd7kGF4586dkn/oNbyO+7xv4R+P68Gq8Lnw/v/XGtBDi15fvB50O+NatV8l0RHpDQcpfkfibaRm6EQQAduTRAXzUcMU1VaG5VFAh8a6QU2WSUmMF6bfCa+RKyBGARo73pHwnBhITE7A9r278PPhg9i2ex+OHIuH20sQpTjU+CRp6Tno02cUrrnuEdxWqieKlfwQ19zyLq65+W28+spCARuLF63DNVddixLF70C3bl/i6bZfosg1bRBRdxwefHwxajeahGYPTMWUqQkCUNwexmpSYEU7Vfl8QbhcQTgdQYkoTZZdRpS2q0HjP1CDR7DCQIxUNxEk0cSGoEVUVwRwrAcBhhq4qH6JJHaU7rD2lG8YgaOKD3U5z7zFi8jWfEnrY/9nu03rtfDRkJPGXmTf9EvsKwVwgggyMnehRaHFwpIle5/3YHqT6jbb3ig/7Ae91wh47IXhP+gRRfJHqtro2UZATuZrSedWHm88R7dyupSTC4mu6OJi7hHAZKVnwJ+VDX9mpkiJvLt3wbX0B7jXroXvyDFYaTnKBiktHWbiaWROGouVrR/A8cdbIfOJhxDfIBJ7I8pjT0QF5Az4AkZcojBlW8mJ8O7egSNvvYW5FcthW7XyOBFVEaeiK2HT/WWQ+eQT8GzcruyXkkgumSFgjc8hBvP/n8HNxbo63dHxvB6oeSz0uL5279696Nu3Lx588EEZpDk4/10kN4XBBW1qKGEh8NL2KhoQUK12/fXXgwEceR3VUpTcENwQeFCqw2cjaZ7+0XC4Xr168tz0LGNevFbnyfrS9+E1vC8lXHTZrlGjhnikUVJDjiHGr/r888+xatUqyT40D30/vWa+Wvqjj4XX4Rr4dQ1wWOGwqYbOCw2W+pvmWn/rbMOq3XIaTS8OpYagW6xSSagstYe1UIWIdIIDNo2EaeBLNRJtY4D4uByMG7cY4yYsxZy565GRyWCMgI96GBn5gHNnMzFs2DQ8/FhH3Fy8DoqVjMW1xRrirvIPYePmYwJufDQoZnEArP7pGEqX7Yxrb+2Da4p/gsef3orhw30YNDAeiadpfAws/n4pPujTD1u3nMSy5cno/+UBHPrFLwEZc51BOPPUIt7itjSFAIULy0cBq5vkcz56SNnHKKWhho5ghMbXdnR2wxeAx80AlGS/pRSrgPZfeYZxgkPJlFbtKdUTVVAWI7NTfUfpmPaYERUhn/Ty/F205emmyLVM1grWSvvFdhjy3Do9D9kqOXWFvq4gLduwtGdeY79PqgIlX5EtUlLEejbEGF3AtU0VQJsyvjcuptcDv21DRJ6is/sOYsvcBdj0zTxkHzwuBtsBt7JNop1Qxq492DZxEnaMGY31Awbgy5bN8db9FdGzanXsGDQU/lw3rHQtLcpGwndzMbJJDCZWLId595bFqmr3Yun9d2J3w3pwfrcI1lmq0jKEGNJMSoJr6UpkfjEYOR9/gpy+fZHzwUfIfv8jOGbMhXEiUUKNUCUnZSIAI0gTL6qCugmts8tl+7+S3PAhNQAIfWAeC+3s9LnQtOwI9QDM49x//fXXUaJECXz00UdyiTQ0ffFfsA69P8s4ZcoU0OCXRrs8p8+z7PocwQ1/GtwQ1NC4l5KUzZs349ChQ/lPQglQTEyM2PHQ44oqplB1nk7IY7yeAIaGwQRL2lCYKi8y8VKSwx/LouuZ5QvNj/u6znXeuu71fngdroGCGmDnZvf05wEce6QoSPirLbY1ejjRGFg4XmRA8ME0vRKNOivbifj4dJw960ZqmlMABwclZYisPK4WL/kRTZu2R72GvXDDrW/gxts/ROm7+mL+vETQrISSELeTs8wANm7Yh5tvaYnbS3fFLXcMxD/LjEKRYp+jWvQE7D9oKYNacvLZA19eHlVHWVi20o01m02cSPDD4yM3lJKsiNpHaP2VWQhVSGLn4g3C41XRp70ELvRAsrVwNHqmIaY2Q9GSHGI7qqI8bvYZ7DNZHq92VRKAY5AkkPNl0wfDR64cqtQ4mCpXdYIXqu24+ANM44HFQsmrUO68It2hrZNE/FaSnV+9mMvogICKQuOrtKtCfS+jm0sUdIkHRQJFvVC1VRBCofC2BiMEjzQ65z8BkpK/qiiRChG4890IeCGoDMCRk43M5BS4s7LhcTqU3IzsxD4DualZmDt2Mno91R4T3uyKka2fQf/oRhgUEYP4Ud/AnxdAwEVj6wBObd+Jr9o8hXfK3YOva0VhSvXKmFr1fsypEYmJFSoh/qMv4c8iw7Unn1jQSj6LRX0/xdpOPeBa8hOc8xfDMftbeLbsUp5m6dlKxSbhROix5gClQ1YKjatPwYyPh5lIl/wUUZExgKrEXqM6ji7ntD7/P/D7r8GNroMLDZz6nJYS6IYZOsByMNY/2uasW7cOpNIPPa7P/5VrloeSGxpD83kKl0/b3Jw+fVqKSbBxxx1lceWVVyE2VrmCq2v0wECRtQt0K6f0hSosqurY8bHHsiwD+/btxQ8/LBEOoPLl70WRIlegaNErUb58eYkD9cKLL+CntT/JB6k8Qgq8nghqdBlZXtY9f/pdnF+X7HT5Hgr1JGrICRng/sj5wmnOv5PaY5rfWi50jT52sev0+YK1ShmavuDcxbculj70OLdDf9xn/SnJg5pph6bXafUxpr1Qfet0eq3Th671ucLrP5Km8DUX29flK1xG3qPgGJtUTq6JVav3YN68DVi4iPZ0y7F+434BE2zKikKeUgg/Tp9JxrffL8fMOWvQ7rmPcX/VF3DnPU+iaYvO2H8wXaQqpLTnPTiojB0/BUWuuBMlynTEzaUGoMito3B98SF4p8daARQEF37qbmDh+PE0vPH6dLzwwlq88WYinn0+GU8/exqzZxugLQuBibJjJYDigGerfaj6CQLpeUEci6OXlEqnAIofhqmOUcBCSYxIZBjbiNIY5T0tQItu5mzTNGzmtytAwwYiPp9i91Z58tmUbZAAGLFFou2QInpT/DkELcpGybRoX6T+UTJD8KdADEVd1NqpexJAsu/gZEXeknzv/22bCL1evXuxgdL9BA/Zv9CU+ljBN15wJHQr5PLQwxfcVmOH3fwkhW1ILu9SARiGPODCN6Faka45XWr9ZfLOVJOSiJGeWTk4s283Nn47HwfWr4cnM1syIFhlP0pQ48jJwtaVy/HdhHFYMHokRvfsiW4tWqHfcy/h6Nat0jCE2DEApJw4jYFv9cLrEXXRu0pN9K8YiYlRMRhevjqOfTQU/hxTgRtTcQwlLViAnzp1wtFuXXH2ne5IfqsLEt7ohJS3e8O5cDX8OWRMJvCgUXUAQa8lHETmmTyYyU5Y5xyw0uhSnysgiOFCpAIoNMhXmzEPBwJ52QjkZimXfBeDxpIt2xCAHaA6TrTHCoRf8EVcRgf/18DNZfTMf7ioGhAQJHCb4IZSE6qG+NNAgW7vrVq1EpWTviYjIxNlypRF0SuuRssWyluKHZg2rOTH5XQ6UKdOHRS98krcU74cGEGXbqhp6Sno2q0zyt5VGkWKFhF1VpEiRXHtP/6JPu99hIOHDgugEZfVoCG08YqrwyYks2ennNFQdy2dLjlBGGTOpCicRphKhy2sl2zRIsam1EfPPHktPVk4My4wBLUMS7nSyhin47iQG4SGmnw+CwEZBWhnUbBwyiodr3jR8B6WDARqVm/P7sWlV0ZE+1pVRtY134F4yNBlN2DAtDw2aRVtEexr+J5ssMzn5kxNzXyVa7KwfnIwEFUAO0eWSc3upBsUNQjrSc+q+ZA06KTXik88cJQXjvLAkZPyJpRhrD/oBhfypSgvHQ5GdGW2QGNQ9jjKaJYu0JyFq/clXW2IDRfzJdjkICZcLwa9epTRrJKEqGGBgzTTsfPlc0p+WkfC2SbF8HbdCcBle9AGuCSUswLg++Sa7UHaBduJ3wuf4YKXsZb8QRw/loD1a7dIZnTHZqZUtezYcRQvd+iH20o9jn8UewpX39QORa5sjloNOiE5leVSLsh8atqQTJy6EEWuKI+bS7bHtcW74Tpyutz+qUSRfqv7fHFjdvvU7JnGpIePnsaosSswbnwSxo/zYdxoH6ZM8uHsGbZjBTaUMbKpDHN9EC4XL7lhXEF4uTDYokhsWKfqjfEBpF5sOMrpxNSZ36LcfdWxZOlqOacmGdLIZZ/pZbFVGxfGDjpXnVoNsepKfW+ueVwBYbVdOH3oOVUGdbVOx3Xoj/uh6fi98D2qNsdz0lbYvvns0raUulBHqxbVof2k7MNU2yLoMhAgWCPnkN8jASkpRaJxNjmDeIkKSmrB0hITAk75tqheZBumJI59RUFN8BqRsNjfIIvPpyCoZdBJfr+6nPIt0w3e4GBNV34bZYpNFlV5ynVfMUwT4ChJi7jmE/Dxa/NbMAgOlKYTKafjsejr0Vg0+FOs6v8+xrZ5BF3KlcMHMU2wf8lykO8n4OO3S4maid1rV+DVutF4/q7S6FmhPAZFRmJQhSoYWqcRUlasRJD2N163PLfjXCa+fq8f+kTUxaGeHyN39FTkDh6L3BGT4V61BQEHXeJJsqhCQDAQK13arTRy+iSLO7qVnCYEiVY6AQifV4FZ3XRodx9wBRBwkmtIcQ6J1IV1z7T5TUQ3WL4T5kPWZII6my1bKsROzFX+daHt6/LcDoOb33hvGqjoJCtWrBC7Grp986clIww9QcI+xmvSUql8cFP0KrRs+aBKT3dZ22uEMzS324HYpo1R5IoiKHtXWRw8fBAn40+gabMYOVb0yiK45tqrRVoUERGJQQO/kj6IA5VB8b7wcij7BD8MeEjTLp2X6siVWJcDOMGE3eBltgdQDK6I0TjbM2CQkIwdmQyy6nqqEJSO3wc/ZzgcRGWAtEGL6PnJK+JBALaninQ4ahAV4MTOi26ctrqM11t+Az4a3YmqgkBCEbdxwCdPCUXz0unaA7BBgzwbrNDYFOC+GwGStBk0juZDKYNA3pPPa8918+tIwJSAHxqgEhARPPAydoQKBKkZMYGSmjVz36CUTv4pFloCK83TImoDAYA+AYH+oAtcWBe0GSGPiwBExheSWbcPhslwJMp2gtdzoCBoY5+i55kFYEWpIdRsXnkYCUi0GLSPkocgDMsPr+kX8OBhPYlqQxl1cJucKEmJPqSlBnAyzo9jx/04edIPR65Sv7Bv44yNF+Zk5eG9Xv1Qv97jaNrsVTRq8ibqNXwdEdXb4sMPJkrb83lZP8CRI3GIafQ4bihWG7eXfQE3l+qCYqV74frb3sCNxR/F19M2iCTGZ/CpACMArFy3H/WbdkHtmC9Rs+F01Ki3GJVrzsPzr67FL8f9Am7o1kwvJwJzbpOhl0a4HkcQbkaSzgmK1MTGzwISVf0ofE6TE47pHJO0ZIV4V8CI/pDtAV4GUAAJiQl46ZWX5Jvr+zGjyNPWje2MNchF/wrv6+N6Xfh84f3C6Qqf1/uF1/q6i611+tDzfKdq0iLSHgLhkIXqM+EysW2iFADh981rWGE0GyDdhdK5BYLkFfLAa7nFXV76FjUnscfbIL9KeLRBkSAZ+5vhvQiILQsur1fAkGERkLDdEngo4OMz6YqvgA37CEq4ggE3gpYDQSMXQYNr8t5kS0wu8PuSyZcPnuxUpB89DMfRX+Cky/WRw9i/8Du89+zTmPjFZ/DnZou1twAjBLF99UqJeP5udDWMalgbo6Mj0L9cOYxo1BwpazaKyogAhADRNBw4snUNpnTriCntnsR3bZ/Ej0+1wcpHnsSG17rAs307gl7GBaMUhME9A3AdiYd77Tb49sfBTEhXS2IGrFRy8hAI0bZFgTQxSGbMP7cLASd5fLjwORm4lcSJCizmN0e+br4i1VXJOl9cdYGmoA9puZVu1wX7oe3m/852GNz8G++SRpI0CKb0hoR4X331lcTRolqpQ4cOkpMGN7S5KVW6NIpeeQWatySJX8AetNki1cwsNy8TjWLr4ZrriuKKq4rg2uuvxfX/vF462auuuRLPvfA8+n32Kfbv3yct2U/2VL8Xps8ltKMym7Jow6AGU4ID3sfkrIoSigDVWxzs9UBOY0bO4jhDIjkYAZIXZoCSF3Ut03pcpIa3VQripcHBxoDPZFoDdNGlZEIN7mrmxHtTymOYlF6ozor393gIkBQQJCMrOznOqDhzDTKWjoAUp6zFjdcGC0pkrw1Qlf2GAkZemFYGgsgSjhIxsmQZmbfMWvjVc65G4z7FVyIeOHa5eG9KXwhpFJCjRKQAzCgafdYLQQxnl0qyw46ehoIcOfkOLMsFK0AgQ2BnG8BSYhMkkZwbppDHKeDnDzgQDNKOggMmjfX4zvgc9AqyoVOQg7tPFh/fi+RJ0OlSUioZQoJISEjEkGFjMO2bbzFy3BQMGzMZXwwdj3YvdsTS5eslFftMGZ8A7NyZiLvLv44bb38HN5Xpj+tLD0Kp8mPw2YB45Z3jtaVjgSAS4pPR5ol3cPOtr+HGEv3xz1IjcG2Jwbjx9vfR9+PtoMCLYIMSGa8JbN1xDG91H4a+ny7FBx+vQ+8+G/BR3/UYN36LeAgRXBBUcNZOJl62eq8f4ubsotuzWxHR0dZF87l4qPMX2zBeoyahtFchwNH2LVQNEdyx0xZgIzmrZ+ZBBVrtOvgVsFGAlleyXBMnTsQ/b7hBpKfXXXddfuBfLZWVW1yGf2QSIvxDFrwetwB6Siy11FIeSSQ0yuWa8jL+BPSwAdlSTAn+SamHHXuL5InyTYikT6kvDL8Ft9+AI+AX2E7Qwmjdlk9JHOW7ZX/DiQ7rPai+TcZg8gZ8cq2XEwmR8NFFnn2Rhfj4fZg5dRAWTvsS88d+iBUzv8CUgd0wY+B78CYfF7BD0kcEnNj6/Sz0bxKD4TUjMalmBObWj8bMOtXxSflSWNDpJVjJJyUSu+ll7DwT+1etwICmLTHngUdx5M2uON65B4527YNTQ8fDOJmYb3siE7ogpR2UoDD0BO1UzsBKJa8NXbyzEXA5EeQERXh3aEsDCYERyPPCT5LHHLe9VoBFXM7Z4OlBKMg7gCCl+pwEMZ4YySqF44f9GvWkavJxXjPUiEUL7PT+eYn+f++Ewc2/+f737NmDO++8UwCO5pQhV402FBYJCSBhJhS4KYrmLZvJYK4lEjLy0CjNlY2WDz2Ia667DdfeUA7X3VAZ/7juflx1zZ14tn0XkS5KJ07mU0o6gj7k5GTibHI6zibn4swZB/KcQaSmu5CWkSvdkxqwVcfOSVRaugfJyS6cOePCubM+pKcZyMhwy+CgBn+fiJopvXC4fOKJQtfVlGQDKcke5GTT84uDsBKksxMksKFKjOYRLB+X7Bwfzp7LRcq5bDWTU4IUqV1+vx5PAD7Opv0Qfg8X7SV4rei1XTIDE5AmAIF2Cz54PZbMwkW6wOt8BGyEbyxPHgJwy8yd3iJMw8GUWhk+NyXXSi3CzltR/SuJCK8nJwnVbqpv0X2MeCvzmTjDZSoBSYDLbSIlJQd5OUEkJzmQes6FtLQ8ZGTminieIm+mJfjjkOt2e5ByNgPpGW5k51pIScnGjOlz0aljNzEspWSJEip29RTvr127Fe2e7YxXXvsEL7/WHy92GIA3u3yBrdsOqnoXN2eCySC279yP20vWwL+K18NNxWNwU4nGKHp9NMrd/zB+Wn9UcaNQqkNVE4Ct21JQrERXXF96LK68Yw6KlPoORW6fg5oNluHESQJf5d3DxGnnsvFah0EodfeHuLvybDzS5iSWLDFkOXiIs2qINMXUrsoBgOp9Ah0Syvk8QfhoZEsjFo5i7HjtnwK1EOkMOVx4HflciEd5vZvvlpdQzSGs4JROKQkV3yffO8tI0MZQBKovVyBWSdzUDZWaSgFvDX90GQqvly9fLh6H9GLkt0wm8bi4OLm//o4LX3O57LMfUO3XRnvyApTElZJUqkpZP34CERox8+NhCyOokQ/BktAX/FbIvuyxTBj8juTL4HfMiRPfAiWIpqgw+YqYr5JIEJkqyQTfJ9VNbJGGx42cpNPwpiTDnZyInMQEBJx58HsogVVpWPd0pf5+9kS0qlUer9Urh7fr3YVuje7G89Vvw6S3n4M/gwDEAZPgJujC7vnfYFT92pgTUQXLIytjc/0aWN0gAlOjK2J7n66wUk8j6MmB3zbWNs8kIW/xKrjX7YZxMg3GiVQYcekwzzB+lkNctaWDE5WWDwEGOqXkyJONgDsbAUeWuHEH3DTGpQs4Ox/pnOBnaApOEBmmQiKLk43Z5tnhxyMqJvu9qGat2r48Pyc76ltQtXu5tLi/XznD4OY33glndnoJTcYQBk8++aRw1jBOEz2Z5IOk6sT2FHM4nChT+i6xuaGXE3t6LQYWNlf4kePMQavWL+KmWx/EbWU646bbe+Aft3TGLaVexoAvVyngoPogmcnk5GXiiafa4557Y3FnuUdwX+XnUPKux1D23ocxaOh8+WaoBuDMNSfHja+GfoM772mGsnc/gnL3PoOydz6O24o3Rps2XWWQ8BnKa4NSDo/PxJBhU1Ds1kjccfejKH1XG5Qq2xp33v0Q2j/3rny3MriZVLdwUA7gl2NJWPrjDixY9DPav/AZSpVtjsrVHsfGLXFSFg5c/EB37voF9es/jo6dBmPlqkT8sCwei5ccw8KFB7Fq1WGZ5ctgJXp2NWDl5LqwWdf/tAAAIABJREFUbt1+LF9+ECtWnsTSpXGYP/cQVq2KQ1YW1U4KpLAb8Hi92LZtHxYu3I5ly45g2fLjWLr8MFb9RJ6TJEnr9VGErboLn4/eZwewfPk+LFt+FMtXn8TiZYfx7fc7cOSXDKk/g+JlBOHyWPhq6HSUKFUf91V8GneX49IGxUs0RcsHOsPrCQpgYZ2zPr+ePAN33R2Ncvc2R5k7W6Fk6dYoV/5Z3HhTUzSN7SoePpREiCdNgF51XowevQjXXBeLUnf2wY0lB6DorZ/g+uLd0bHjFAFUHIfEDgEBbNl+FGXubCveQxE1x+Ll13bi+Vc24q3ua7BjD+2CKF1R3iOsIzLeduqyFa+8noL2r7rwWDsX2jzrwcsdzmDHDkr41FjG9k3gQE+gxDMBnDrtR0Z6AC5HUBYCFpZDqf6ZPyVbBVIcvms+Fw1thYyY7176b7ZHLsoGiM/NtPZYK21c77NT54As5Gy2zYVqQQXSFhkARR0q83zb9VmpUeQBODToAkjrC/1yz9+eMGGCgJqrr74aLVu2RG4u+XM4Rqnynp/68tpjnRcYIId6UBYIAgQUikeWUn9SgimIlHUvAD90UqAGXEppDL53KlfJ+kypnNeFuCMHcXTrNpzcsg0/r1qF7IRTyvpapNTydSMr4xx++nYevmj3LDpHROLD2Fj0ahSDd5u1wPop08Sw1fC4xAjWdDiwcOJEvBhRDV9GV8G4Gvdh4yuPYUOnZ3F48AAJxhn0UGWlgo+mbtuCvb3fR3KX7sju2h15Y0bDd2gXjCO7YSachD8nCwGXS8rE6Owk6AvkeeDPcIJ2Lf4MF/xZHvhzSdrnVbMkghW7HQYoVfG61CLR2rnvAY2ICe7Eukikwn5Ru4l6Tz4uEZPLBy/G37QpIt0HJ1eqtmXNGhKJGEGpTLxUX3V5tbq/V2nD4OYPvA89i2Onxx+9un7vl5yckg9uWmpwI+JWduAq8k16ThYef6obbin5Jm4uTYr2cbjitoG4qVQf9P1sq5JAMF4M+Tn8JlLTU9Hsgba4vcyjuLXMKyhWpiOuue1F3FjiaXzUf5EMbAQg7NMdDgufDZiB64s1Qck7X8Wtpd5E8VIdcfOtbVC/4WvKHkKkG8qwNc/pwxeDpuPam+qi1D2v4uaSr+FfpV7Djbc+habNu+dLXDgo8bPLdXjRr/8Y/LNYFMrc0xbFSrbDdcWewV33voJd+12KhMxWH2zYuA/VIlqheMmHcFupZ3BbqWdxe5nncdOtbXBP+XbYtfucqCDEzteu4927f0GJ0jEoUaY1biv1NEre8RJuLd4e5co/jw0bT4mKhIMgpR9pGWlo/fgLKF6iMUqUfhK3lHwW/7ztMdxSIhY93xshQgRynNCAmIU/k3gGrR5qh1uKx6B4mSdxa5ln8K9SbXDjrc3R95OZAuToEsznzMrx4ON+U3DtjY1R4s4OKFbqDdxS6jX8s9jTqN/gXaSnBuB2qnwJoMaMm4zSZWvh/qpPoXyll3BPhTdR5u43UbrsK2jVagBysqiqC4rbMYEIpVnTpq7HbcWfxqOtV2DcOB/Gf+3D9G98SEqi7ZIaI1h0Dv5ZuUHMmp2IyVM8WLvGhDM3iKyMANwuAjGVrwIg7ED9QgJHgMJ0vHdudhB5WUE4sguMbXUfTHBGVRClKpSu0F5H7F2Yt4MDnq3qEYmWUkcSjIiEzCawE40GJTsMVElLXum6FWBV21RlUAKnQI+AFRqr2qpOSgXzjbYZf4qDihiaK08hZQPC9852y8bObzJ0IGD+WmwUevzXXyyD1lJi06hRIwl6yxRaray/9V9fdbkcUfUgEylbPWWJhFGroRT4Yb1TBUTbMMbmosgtYPpgeV1iCxdw58GfmaHckMWQN5APbg4fPIzBn/TDF127oXfrx9C7QSP0rlsfz1WqgnlffCHkdhZVYgREXhemTxqHh6pXRt9GjTCwTm18USsaA+rUxMcNG2DDiFGikrHo0WOjf2fcaRyYPguZY8fCMWcWfPt2wEw4Lka3/swcBFweBLyKRZgxqBhTyjiRBN/RUzDiTsFKTUQgNxUBR54qv6BoqqEVohdCPbpYM1wD7Vuchmz73VT7K5skektxwqrsBxVQoQqcXn1GMABPwII7YImVneqNCALVP07BuBh+vyw2hFQtlxPhfHCj+prLpWVdLuUMg5vfeFNs1PqnOzu9Lgx4mI7HNPBJT09H6dJlcMUVRdGyBdVSAeVFJDNY2oL4kZ6dgxYPdcR1t3RG0VsHoVrNlXiw9U5E1pmAUeNPCOdGvn1fkDYKbnR++3PUb/QZWj22EA2azUPNmClo1moSps6MUwaZtrqcEoWJk9eiRavP8ciT36HZg4tQJ2YmYltMQPdes0FHB3KgUYfOT5FqgUnTVqNG7U5o1GIcWjz6PZo8+B0at5iEt7ovlBm99G22NIY2E6PHL0HNBl3QsOlIxLSYhXpNZuKhx77B3sOWGJASrHD2nufwo1ef0Yhp+iFaPDwBjR74Gg1bTEXd2Kl44NFJOHScom+lsuAgzt/Ro2l44OFP0eShcajfdBJiW8xC/ZiJePSR8di71yMSB4+Xdixe5Diy8Fa3T9CwSU80aj4aDVrMRb1m09HogS8xZdYemJSq0A6FozMC4nLff8B4xMT2xgOtv0bdpuNRv/l41IkZgCHDN0veMkgHqZIKYvac3Wj54Jdo0WoGmj30nZQlqvYQvPHGAlHDUFph0OtIAjH6cORYGo6cMLHroIV9v/ix74gf+w9bSEwOCABhH6sAiJKanDsbwJ6dbqQmB8TLh+RwdF8WLx/Wt/SptEvhwG/zrdBexWkDJYIQSk1EwqPsqWhczvfK9BwrCMCoNuLamROEYauPmDdVhTosAbeZj+Rn27qwF6YwRFR+AhKV5xa91MRYXWadBEXKK8rMn5VyllrgcccB1Ec7DM6GabMhbt8F74XvhrZdyujatla1Z/7y7vjtsID2e1TARs9/2XC46P3CoEe1K/7V3y7ZxF999VX0799fTupvtyDlZb5FiZkYERMEEmASbJpwuXNxJvm0AErWl5Z0WaYXCXHHsHvbRpzYtxN71/yIOZ99jOFPPIlZr72Js6vXI+gx4XC4JKfjR46hZ9v26FE9GpMbNML0atUwuXIljKldB4nTv1Hgg2ob8dqysGfLGvR//QXM6vAClrzwDBa1fwLHBn0K3+G9sFJOI0iHCDL8suHTTtBrimGtPyMTVuo5MFyBP5MhDejy7FIsv/xQ6ahh+CQquZWeDSs1W+JE0fVZsQUrwjzx/OJXEaJ6k2sJoGkzY7vVsc7YrilJodG5SHwJ7G3vQpG60kg+SIDjF5CibJL4xVEySRUu1Xg63jx7e34bylVdSShVWt1qL/OW9rcsfhjc/M5rYUeoO0O91pdooMN9rY7SaUj0x7ASV15ZAG78HDHEQFV54DjcHjRs/ByKXPUQSt75ORIT/MjhzNqpZuFUA/AS/jjo0zuGAyOBiyMviNzcIBzOYD5jKr1L5MOzZ/ukbudsnhTwJC3Lzg4iJy8ItxGEl4OezNmU/Qzz5fUELbSFIAMr1zT6pKqC4EAPerwH7SQISAiQGJmYZWbejFTMY3SqYTohRdOcIqbKP8cVRI59j2x6wViQ8vD+8rGLEaqyw8izIx8zFo8M+lQD0T6DAIFkZhZtb6gqU9IG3j/bGUS2K4hcRks2gvDRrkSkCco1m90KwQUBHdNLeSidoFcOJWUEiDbfCddUp7POWY8OSkFYZleBfQndjeUdwWO7yyrDWbcfyPUF4fEDLiMo0ZupchebFaWelzolcOA9aDBrepWai2XQiyoL7SNUuYi5laqqwIapANiwXti+FGgVEEVwxDp2ExzYphB8+TZOYOkpCtdtON8AndIVeqzRY4yL7TLOY0rnpAZPuvVKhy3tiYCZs1KqrPiXZ3zKwFriRqlrmRcHDYJfDjaMRyVebvRUswNoqgIqQCN8NiwoL9f3V63FBjXScmRbgVj1Tvhe9Bl5SSF/9LetJzHc5za/64tdoy5XZznfDp1za1il76zz0Pty7Xk7obmFFCw/oc6hIF0BgOPEi6DFdlAg4BM9YMEDkxE6+eQJrJs/D6smjceKMcOwfeZkTPvgXfRu1wZxe3eKZ6BJN2a/idPxJ9Hj9Q6ILX83OtStjQ5V7seHNSLxVeUqmFy/EVKmzwFVOZR48M6Gy4sTazbiyJAxyB0yGrmDhiHni0FwfDNXIm6LV5DY95BHhaELbKNcGuSeS4B1Ll4Wf2YyAs4ssbMRd2X7WSSEAjm6qEJy0RvJiaCHqiGvHUjTnskR3GhDXIk0bikSO1ORRlKCyDeq5OVKosk2Qgkf+xB6NYpzBVVcth0S0+v3ye/BpFs4QzuwPxBbMzpkqLwkbz/7UQWIRK0kckXlaqC+RJ2f+iJU/lTVnj++6Dapx5HCrSK8/8drIAxu/nhd/VspGTiTIRXIUNy8OSU3HJDZIbE75PAahMPlQpNmT6LIFffh9hKPISszIKoAUQ2EGFHqG7OrI8iRgY2Dob1wEOSEVksDmJ79HPcJWihxofqDiza0LRCJ6s/MzpsfLg1H7WuYXiQNdt8ZWhbmLx860xfO31ZfSVl0uVkW2yiV4IcAiYBGbOzUuKWzt8GcOq/LItJquz+T0UfmTGqUZkfEsjA/MXC11Sv59nuSM1OpAfdC6TWAYwemf2rwVeCA9awlGjK5tMvCNGrQUYJndRcFHgVAsp7s96HrXQ1bcqG6lPWrF7vu7LO6KPlrGcNETaXkFHq/ID1LwPeqht7888xX3Vjds+CC/Lz/0w1mpe5asFbZ8y+HQhtNSarfugtzods7vUcs5S3ChqYbN0/zZ0t/VDvQh9Qd+Y2IAavNXEuDWL51r+ETFvD8+giJVSRgy1KehgRlMmunSsEyhV9FmprclrN5U+JfGQEfWZdElUUDbi+DObKFiXuzBS8HPBveETRxgAzYBGtUkQp/i0x2CgY+GRiF/ZBeYwWtRSQIIokz4HXlwJmTCnfeOZiuDLgyz8GXmw1Pbo4M+gFGzibdgeHD9oXf4/0GdTG0Vg1MiK6CGdFVMLbS3RgXUxtpG5bbbtZuESGejTuFWZ9/iTGtHsfs2JZY1vQBbGj2IDbGNMXOx9vCuWAZrHTS9CvDeX6klK74s0kklwUrhdwsqbBSs+DPdQnpnHzI8pLYuPlOCY4YZNI2ynWRTVdJWATYSPvge2RjDUHz7Ds5oykQZYc0Zrvl5aen3Re5qUx4LEPeA6UsfBcFNcp78DoeUZKt0Jab/+2opnXeX/tu8lR2Sc/bluZ5ke9BtdDzsgvvXKIaCIObS1SxVEsxKCg9MRgckz/p4DgTsf9RzVSzdgMUKXIz7rqrlthicIClvQP78gt9COyYZSYf8q3LfqEBUX+6qqNX6g/2DzL7twdG9WGqvyyf5C0Dg0rHtPnpC5Xn9/IvXH5Jb+ctUhR7BsRtPW4xjf6Fppdy6Oc9D2Sxm2HHxIFcgTOm1fkz3/PzllQ2uFTndBqupSzsT6UgKi3LI4Oh3c/q/pV1bk8w7SIzPctCuUVBN6k7wtA1z3O5MMIoyEflolLaNwm5l25FOrfQdPrY+Vdd6j19V71W9+Ne4af/rZKEpA/Z1FkoYGLPdtkm+eLsH2e7CjBoUEdJJ+fNwIlTcWjcJFbCnSQkKBZxDWgkD7uIlCYR3Jh0Vxa3Z0V+yTFQJFeiqiBxpg8GOHCSAyog3mleXitPS3UgwY0JDwEOCfXkI+X4TkmUklix5FJmGq97TGGXPbl5O74fORaLx03E0c1bEaQkgmRvIk0KYOeWDeja7gm826YV3nusOXq3isUHjzyATk1iMLDb28KV4nc4EKD3UcDEibWrMahJY3xZ/k7MrV4Rhx9vhVMvtsWxnl1gHP0ZQTNXqYPoeuyzhIfFOHYGvl/OwLsvHt7dx+Ddcxy+A3Ewk9IFuJB0ju9USEkJpKg+cnlBmn9ZXB4pt3Qe+a+HGxQV2i7PjF8k0hyvTShHCRRfQv4Fv/o+pL5+dZQvX53R53mE4JQ9Lc3OFb0o4TXfjwKS6i78G9o29f3VOjQ/3cYK7hZ65MLb+nq9vnCq8NFLVQNhcHOJapaSG6qlrr76ajRposIvENyEir1dHjfqN4hBkSJX4Y47yiM3j2oWJSnhQMuP4kI/1cHbgy47+ELAQ1+jP6pfpS/Uhej0XMs1Ok87X52PSqf37AGk8DV6PzRTe/tXeev7XKQ8+XfS6ezyFJQjtGPSXVxIvVywLCrX/LxD0uhj53d4PKoSFa7HX7+gghz01oXWKsPf+ht61W+lu1zO/bvPw8Ff8RARFIiayAYuWoKhWx/TSeBIIYrjpEABC8JdepjxzglJSYht1kyMh//xj3/g/fffl4rTcdeYf77hqAx+VLNZMIX5lrIYG9gQSImHmKJP8IO2ZYbiciKQJrQV8MNh1FTgSKS0lPgZMNz07KG6UbHzMgaU4XJj74YtmPxRf3zfsy9GtHgcve+thrfuqoiRT70M62wuAk4TAYk95ceB9avxbp1oDImqgmm1qmBm1P2YUrUCvqhwDxZ27wJ/VioCzlyl4mH5U5ORvPh75M2cCeeiRfDu3g0zMQFWSgICOecAPz2IDASELVex3YoHUWqeuEWbSVkwk7NhnVMu0mLrQiNbWyoolUP7GHEBt4MuCgOuGLnJ6YI/fBv2rCB/lqB1o7qNFKT+b7aYG8uoVKS//qIL1D76vhda/zclCF/7V9dAGNxcojdAcEPWYhL+acmNzN5C7udwOhHTqJF0ugyAmUcabuqCLzLYh1z6J23qkvyn6/+2mP/pffV1v3d/nY5rDZQ4RGnhtZ7J/V4+4fOXogbkrWhpjO0iK7ZBIglRtP7KdkJ5XilbKtpBKCsHvr2jx46jYQy/sStww403onFsLBYsWCBfmIeuxLYcj9fq6zXJnI+SBUoFWQZKV2hzRJ0wQ2qYHvi8TiW9sMjCHYSXYtdgALmpKdi3YQ0Stm5EyuZNOLhoEYa91Q1jen0AMs+S+ZZBLy2fD57MHKycPhfPVo5G/5qNMDq6ISZE1cPH5e/HvFffkphB9OKRwIYBE2mH92FFrx5Y3qY1Dr7wDOJebIuTL7ZH7tCvYJw4ikBeFujhRMNckZIYbvhzaGCbLSoj83QS/BlpiuXXILAh4OIzKRf+IO3ZGJ3cZdreQz7lSeSgV5JSd/Ed0GhcqABodyK2J8rLjRQBij+LMiy+wb/md94XTcBpgzEe5/ssADcXKp+kutCJ8LHLqAbC4OYSvSyCm5IlSwq4Ydwp/ii14U+DHLfbjdjYWAE3JAJkjCohvLLTSeLwnz+pBnSHVnj9J90+fJsL1oAACw68ZNymEXK+UbOyYVN2bMoo2WuQWVrF6+JgRpuXQUOGQOKyXXsdOnbuJBCWwTAZToCUDIbfg3zbGYIWCVVCNnFDFtPrg+khQRuQeCIe44YMw6wJEzBl5AhMGTESezdtzafBZxraoyybMg3PVqyIEfXrYFJUdcyqWQsjKlfFnCfbwYw/o+j3PT6YDjLXOvHTpFno3aQVfhk0HGe+GoaU4V/h9IRRSFv1ozKiJTM2QxUwiKYEP8yBPz0N1tkUmElJMM+QMTcV/mxFLCeARbh+lIsbgRHLJZGlnaT5d4s9jibZEz0sBSoUooielvprqqlsUCWhAhQ5HwGZJuWjJFP/WN+0bPL6yTpMWyMCCmKmkEQ68Z+0Dn/Jf1JF/01vEwY3l+jFENxoJmNF4qdADYHN8ePHQW+q+Ph4VK9eXcANgRDJAHNycvJB0CUqWjjbcA387WuAoIYSgvwZNkcqLVyTgViBG04YONgapKwXRUSBIe6hvXtRo3JVXFWkCMrfVQ4bNm6U+bvLmYfEk8eQGvcLHMlxyDh1BAkHdyPh551wnkkUAzXS3yuDMyD9zDnMGDUW7z7dDh3qNUTnhjHoUKsW2laPwMzPyedCIEBbEko9fNjy7UJ89vCj+Kl9O2x+pBV+im2KZU1aIO7TL2EmporLstjRMBCkJwAzOQu+g8dhpVG6kgwr9RT8mYkIONIR9DoUUR3BAiVWXreK6OzIgT87E1Z6mkhlxD3aRRZdujQzuKmKRUaOGaUyIjihkbHat90NVZ1qOz7bFo9yLxpKC8OxGJYpCaYCmuqdaNCpG5LwHdG42nY4V7ZHf53shuXTiy5jeP3/qwbC4OYSve9Qyc1DD6mo4C6XCyQOu+mmm0SiQ2Pjf/3rXwJurrnmGlx37bW48YYbkUiDx/DHeYneTDjby6EGlGGwGmDFe0m8ZWyjK6qJaCdju/iKQokGwDRSZUBGnwfutFTMHz4SkbfejmY1amLOlGky0hIgJCecwiddO+P5WtXw0SON8XaDqmhfoRR61K6BTaNGCPMs2W9V9Gng3KkkDH27B/pUj8KoyGh8dV95fF0nChu6vomMpd8KJX+QtP4EEmYAQY8lUhIrPV2YdM2kFFjnMuHPyBVqf/E2IigzGFmbwIjX0KDYiUCuTe/vzETAlSf8K8yXUeVF4st+gbws5IOh2szwIMiAqbSZoYcUg45qlVSAAEcFzBSxDPlmRAWlAkIyPQGjGOwTTArzMMO+WqBVDSUx9DQz6dFlY0tiTIZYICeQeG4Kp5Eo9AQSmUFTrteSm7+2rZ0vuzl/r8BGL7SMBETh3/+NGgiDm0v0HgluaFBMm5tmzegKrtRSixcvxo033oiyZcvKUrx4cbHNoZSn2M03o2rlKkhPTbPdxpV++BIVMZzt79TAhTrD0EtCz4ce//1tfaWaEf/R+a2+Sq0L9vTWb91Xp+H6j//0VRe6Qp8ryFEdoRVL6L+QawtdUmg3JCENI5SehNwm/sx0UcEo1UuOSEdkIJeAnEpik3D0MD55802M6/UuhnV8Ex898jA+rt8Q/Zu2xO7Z80WyEvAqF+YzcSfxeY+30a1ZfQx+sgUGPxGLLx9uiBGPPIBtw4cpLhWSDRJQEU8ZFtyHfoFj9gI4Zn6LvClT4Zj9DXyHf4Y/4zQCjnMSWFFcmelezuCfXktxs5AdV0d5ZqRnlzdfesK3T0AhvD0M28IYReScobs0wYvhQ4AcCyyD8BDReNoS8j0Gg6VNDd3ShV+FvDM23xCNoakkUt5HVA3RyJqAhqRZtFWi91WBZEo8Km33dYIZX0DFkSJA4UJgQ0kM70P7GvHcssEQy09ow/xNPzljDAFF2sbl/Jf6Z+/ZLVI4bWxCPmVFdZ4NTmipwuAmtDYu7+0wuLlE7y9UcsO4Nfwx0jAji9NN/Ny5c6Ka4nZmZiaYntuMb3O5RyS+RFV6ybOlikN3btqlmIaT5MjgcCFaEZEaqDhJjA+jDCvZvdshBejyy8FdvH2YH2OKaVsrEgSScJBGl4wYzoWOqkrlIER1HNMZaNAw8mfdykNIzaw5mNBNWRQHHNwkXIEiEJMy2J03wx/4aCvCybnEs1EuybyeYRYUmZ/SQ3CwEiI9RjynbQfPk3zNJJuwMpKVNEwuDkNMb5PHsezC1UJSM1Oixuvo5or9WjER+xlCxKcWy2uJbQcZlykVkMHRMmFyILe5B0y3B9vXrsPY3r0woGkjzH7kQUxvHIM5jz2BrJVrlcGr2wvTIGAxcfCn5ehVowZGRNXE17WiMKlqJcyufD8WNW4K14pVID2/kMEJ/4uJ5KNHsH3BLFjnTsJK+QVWygmYCXEqZhHzFFZdDo5iOCLgSPhcUrNgpaaJzYs/J10iTQdNhipQ9ZTvDSCIgZIUWy0kRFQ2z4AtHWDuGt7aCCYf1ClwdzGbXIFFAlSYg7TL8/LSR+zyy0Pou+lzbGgq/wucOS9PfUVobswy9DoFZ1ULVH/Vebl1+E+4Bv6CGgiDm0tU6QQr2luK0Yb504bEFzMaDuuIL9HL+I1s9TspnESDG3bshCbCkSEeO7QFUeBGHaf7r3JBFhBDIGEaEmnZNBmsU8WoEaZf+vfLoEB1i2Lf4NyY3jkCqshA7WUEYqoY1MJBTgASeX4ZfkHm5KRx53VqYVRnNQApXhchoGOZtHeL7c0iz0LVA8vB/8JWzGjtDIKpVEDMy+ehzQYLygGbM34rv+3SDobeSIoOjRwwPlg+ggGCIYINFyy/G36yEsNQQEwGeojq5dyRY1g8cjQWDxmG7wYPwsIRI7B62nTknDipVDo2yd2+rdvxbGwz9IppjIk1a+D7iMqYX7EcFtWtD9eyNQjk0YvHDYMSFvgQv2kNxrV4EN/UqoUlsfWR0fMt5I0eg7yJk2AcP4GAy6HUN7Tjoe2JzyvRnYO+bAS9mQh66XJNcjmnqLZUUEk9nNPPm1w0BgI0yHVSAuNUQRQNGvtSusLWoNOf35qkKs8/ZLcDu5ovduXFLtSoxF7rZHpdkGvhm+oUeq3O673fW/9+bioHnU/h9OH9cA38mTUQBjeXqLa1Wqowid8fvd3FBt0/en043R+rAdYzwSbXoZIbXs1OWoEGtSao4TFFFBiU+FxkQWEaiusNepWQ0I18RiLNYL7K7kF7pDCxuC8LZPIrrx2h/LftSUQ04hc6/KCoAJTLM8GHil9D9YByv6WkRMk9VBmENM4yRK1ANUGQg26AEiBlY0E+GD6rfigKmQwrALdJOjoyszA3PqH6sewSG8oGPgLWguoYJUZ0hU5NSkBmwimkx5/EuRPHcDbuODLPkiTPgMfvUWwvlh8Bt4HtS3/EG3XrYWRMDCZEVsP0WpEYVbUS+t53P3YPHqX4XJw+YVfMijuFlYNHYc5Tz2Nz84cQ/0ArxLd8CIfbPAvPxh0I5Hlh5TqQnZst0DPlwB6MadsWKzp3hPunxTDj98NMOgHrbBICjhyJMUQ0JyCSkhNhuqUNCrlZCFBMBAVUqvd1fk2wMSgGSmXXYhvlkklYM+aG1Juuv/A6XAPhGvjraiAMbi5R3RPcMPwCwY22uZGBhQOhPZjy1uxsObBKp3uJyhLO9sI1ULjOQ/cJdEgCp6XhxgRaAAAgAElEQVQgfEc6FpIlMb4C8DFQpA2C5J2aVPcE4DUMeAwSMmrJjTLcFGATCMIwyQmiaNCUON8uH9sCjVJtlQ8ZYBVDIzlF/MJ0S/mAFVDSI5aNdhAen09FHpZAfQwBQHdoJR2SsAdUYzF2k3jSUFzDwZlcK5TYFLC48h40yCUPys7FCzHxo/exYPhQrJw+Haf37ZXrqK5h3hlpKZg3eSKeax6LTs1j0blxA1nebBKDz954FeknjgjoYB0I8V2eS1ykn7nnHkysUxOLa0VifpXymB9ZGYe6dFPSGIdPbFWoygk43fBnuWGezoTvcCK8e07Au/s4fEfPwEzOhD8zV9muANiwfg1KXX8t7i56BSoULYq+bVsh6D4Hf9YZofoPep1iy0LpCqVOEmso4IdheAV88l0rfRsFMJRMqVhZBTDvwu0nfDRcA+Ea+PvWQBjcXKJ3Q3CjeW5CXcF5Ow6U4d/frwZCwY0CnMoWRbYJQMm1Yo+DWqLDmEIWI2WTg8VQ8YOUxIeqKjVICqU/bT0Y4sGW7BgBP3wcaG1piZDT2TY/6jq7jQjg8cM0DNCOhnmb/kA+mKE0h4tiY1WqJWXcSdWYFw6XA1mZWUhNPoszJ08gOzEBqUcPwcw8p8joJNgfI46bSElMwHdjRmLam69gWGw9fHZ/OfSrWB69K1XCio8/E2NYMydXgMC5kycw4LWX8XadCMx8uhXmPhqLaU3rYXRMHSzo3BHmqQTQ44igioCBqiDfuTRkbNoK58JFcC5aCOf8WXD9sFCMhf0ZGWKAy+jMlp/SFHKzmPDneGCdzYGZlAEzkdGh8+DPIlhRgDEvx4Geb3XH9UWKoNgVRdC4UhUc3bVNjHIDDoYWsKM9813YajxCUlE7iSeR4s5R9k5iUJSvoguDm7/fNxouUbgG/mgNhMHNH62pfzOdtrmh5KYwuNFZhQ6m+lh4/efWAGftFwKbfDfaKFiDG8YQIi6leorSEwIbUunr+IYMjOh1ewSIiGEuvU4k0rDy/KE+SEtgaKSs1ExKyqLtW+D3ImC56aIDP7dtYjkpYwDIOZuOvavW4NDyVTi8bDkOL/sRR35ciaNr18NMT1e2IZT4IIDUtLPo0bkTGlSohJcbNUWnRo3xamQk3oiKxJrRI8SN2PS4VEgQy4/E4yfw+Ruv48OoahgbVQXjq5THqErlMKVpE2QvWKjsTRg3iGompxNWShKM44dg/LIbnq2r4dm6Fr59u2EcOw5/Th6CHq8AIbox062aHkT+jGxY6Zk2+Vwi/Oln4c9Ohz8nU3kI0euGqiKRIimvI3+uG1zI1CuLS4UxcDnc+PSTz3DzNTfgH0WKokntBoj/5bhIipQ0hnZFtDGiBI71TJWaD35tI0PgqOOcEHlysT29qJ4Lg5s/91sM3y1cA/+bNRAGN/+btRmSV6haShsUa7WUHkzD4Cakwv4GmwrQqCGN70h7KXFNVQW9o2g8TJmKuMDSPkbsbSj5UB5LVHs4JTqzF6bXAWduGkxvDgJ+J5zODLFs0QCH+VDK4nZnI2Dm4NSxXRg3tC/6f9AJ0ycMQnLiIQThgeEn866SMOz6YQU+adAMo2s1xJToOphSIxpjq0ZhTMPmSFm8AgGXYpUl4nLkZGDljKkY8cQzGFc/FtOj6mBuzTpY0LgZTg8dCQZAJOOtMl72w52Zi58X/YDDw0fBMW0qnPOmI+eb8XCvW45AbhYCjrwCPhXDEKNa2rP4M8/Cn5EMKy1FUfszgKKDLLimqFsJNMRWhRwwdJF2eJR7dC7dpHPFMFex6tJbiy7Fhhhli8cQ7ZzEAFiT0NFOJiBgafTIMfjHldfimiJXo2Sx2zCg78diA2MaBDABaPWhQemUqOqoDiRoVIEoiV50oExlY0OgSzspSnnC0OZv8EmGixCugf+4BsLg5j+uut++UCQ3JUqi6BVF0Lw5eW4406fPDSeIIR0n+TzY2ZKAzLAXky6xmqdCzSDzr6B6hPmYJO5SXBgyiJjabkDuoMxc6e3iN1RaiucZYViYVEMNITnE8hpbuiBEZIxEzEWpB8QtVrjZ80thX8Kyk/VUGWOqdAUuwiqRXU98ZhnkTLvcHvXMFo1eC3uacBbNvDmghTyrkLRx1k7+kZCysPwsn9Qj64ULn5vPX1CP+W9MHpd8JOoZGXsn+cB+xO3eBcfZszJ40gZDAI5pIislBUd27MD+9evwC2MFrV+Pn1evRtKBwyrKM92cgwFRA/2yfy/6vPwKBnTogP4vtsdXr7+Iz198Bp++1A7jPnoP3tRkcVOy+GwM6hh/AoM/+RDvvtIOfdo+jJfrVcar9SrhpZiq+KzbK/Bkn0Eg4BM3a16QtHkHBlSvg9kRdbCoagRW1qiFbc0fxbqnX4Jnyx6RbATp7SSEbT5hs/X+fAjuDbvgXrcDruUb4Vq5Bb6jp0FW26DwudAGhx5EDJzIyNC5EofIn56MQE4yArnnlMuzj5GmVfBXkYywvmlX5KKXUa7ND0OuFta9ypMgUHOjMEQ7owII0R1VedIeFaOuuJ0LB4uyiVFeWUqtJ6o80wJBS0BctAMwfF5kZ2Rj9ow5eLL1k9izfSd8Toe0d4ZOIO4U8BgETNra2ApA2gspYjvauSlTG4JNHU+K4EaFYAhtX/ktJ7wRroFwDVwmNRAGN5foRWWkpeOOEiVxtYCbWHHgpYqCM3vxuvEH4XE6sWXFj1gyZhi2Th6NTSMGYtOIQVg/YTRSdu+QwV9cbcXAEXA5nFizaCFWjBuNdSOHYuOIwdg2diRWDx6CzB27EfRZCLjpGhuA5XXi1P7t+GnaeGwYNQybBg3ErqEjsH3UGBxY+L0iCzPcCFhe+AN053Xh0LZNWDhmBFYPH4oNQ7/CpuFDsHH8KOxf+YNNKuaVYH8cNSyPF4e2b8W84cOwYfRY7Jo6BUbCcQR9jEjsUu6xtCkhwACQl5aG+WNGY/no4dg8agh2jhmCTaMHY/2UcSBfiAyS8pyM6+NBTkoSlk4YjxUjhuKnYYOxY/xIbBw1CMkbVtlkZwRFCph53XnYvnYlfvx6NH4aNQRbR43A5pHDsXnyRGQe3GcTo5k2WVoQrlwndqz8n/bOAkyuImvDuC++uFuAkJBAIMDCLrrAIosv+sMu7r4QYNnFneABggV3C0GCu6zg7gnEiDuThPM/7+n5OpVL93jPdPecmudO3VteX9W99fWpU1X97fleveyVyy+1u484xE7svq7ttcpK1q8n0zU1NnUSJOoXGz90mD15/Y22z1qd7JA1VrdjO65pR6+xuu255FJ28e772tTh43wlECPl+HGj7d5rrraDOnS0KzfaxK7r2tnu2mg9691lLevZeS27/k/bW803A23amLG+fJr9YcYPH2H9b7ndDuqyrl2wQXe7fJ3OdnGnVeySjde1/hei5wJp4IBG9nOZYjXffW8Drr3VRp13tY068zIbfWUfm/j6R36C89ThY325spf/59ot+JFijB5vU4ZCWDjhebjrsLjeCidNiyg64cut/vaDE8dN9Hx/mYxkB1KTS4++RdDc0F8biXaY4dIUj8IlL5kiO/tQODnKzv0AgODgguEHQY50QIRrCbmCu11LcH0VWi4W/wmZu6S6nfOrTTaXttcnDf3rMGn4uA8EAoHyRyDITYnaKEdulrTZIDdbbep7faJL4HP/037xvUSGDfjBzjr8MDui65p23nod7bKuHeyyddaw07qsYa/0vMTF9VMnjvcpkZopU23IgIF21iGHWo+NutvF63exS9dZwy7psqZduM669sX1N+dO753AzqVTbdywQfbE9T3t8HXXtPPW7WzXd1vXbuy6rl3caW27ab99bdroUb5EduqkcU5wfh49wh6+qqf9be2OduGG69uV63W1izp1sH90Ws2u3X8vmzryJ98EjV/l6E9MHjXKHrvuWtu/4xp28UYb2WXd17ee229rtxx3hI3+5P3cr/kJE3zd9NBvB9gVp5xmh67Tzc7qvp5dsX5n69m1g5231sp20eYb2cg3X8lJc9hO35fX1tjnb7xmJ/7+93behhtazw27Wc9une38zqtar522scmffGLTxk82l1D8Yjb0h4F22kF/tUO7drSLN17feq67tl3atZOdsc7a9vylF9kvkyZYzfixvocMI92QgYPs/ONPtP07dbR/dF/Hzt+wi52x9mp26jqd7M2rr/J0nST+8otNHDbM+l19rR3RrZv13m1n67Xtlnb7rjvbDTvvYg8df6rVfDvYt8+HCE0aP9b++2Q/u2Xfv9qjO+1ur+y6i722/Z/sua22sKf+tL29cvQJ9vMX3/lKoGk1tdIYVucMHW1jnnnZJrzwuo1//Ckbd9+9NuHF52zK4EG5qSAkVuiK1OT2WOEsoppvR9jPXwyzyZ8Osik/omSLTsqknDTk59zUTV76NYkpJPaFmZi7xoFdbrv+GSRgteM7ZyShIO1Sr2mQU9aETScVBMtSgewzr5XCNeUVy04R5tJAmsYqp8m51GfI4NclkLfsYuWY7j/9rljYcA8EAoHKQCDITYnaadiwn2zJJZa2WWedzTbbEnIztXbjNcT602zyuIk29Otv7cpDDrMru3W1B7usbv3XXs1eWK+j9em8qn1+3pk5AjJhvIvRx48bZ99//IlduM8+dlWXtezhLh3sqc4r2zNdVrfHuna1URf1tKk/QVjG+ZTM+EEDre85Z9q5Xdeyh7utY8916Wwvd+5sj3TsaH132sVqvmHb+DG+moTTicd9P8CeuuACO71jB7ura0fr22kV6991dXtknbWs7y472c+foiSKZGCyn5Y85ofv7eF//sPO7drZHuzWxfquvZY92KWz3bbR72zEQ4/WKpROtikjRtsb9z9uJ2y4uV2/3sb22Hrr21Nd17TnulL+Va3vxhvYhGeesakjR9VuTc/0yM/2Rf/n7aqNN7MHunW3fl06Wf8ua9rja61i93bvZhNeeNWmjp5k08ZNtqnjamzYtz/aRQceZhd37WoPr9vFnuq8mvXr3MH6dOlk7//zzNxBgxOYsmCQNpswZpw9c/fddu8pJ9mL/zjJXjv9GHvl1CPtxR4n2LB+j+dI3M9szDbN9Uo+fK6/vXpbb5s6aqhN/WmAMV0z5YfvbcrgoU4WfvGN59iJODeFNnX4CJvywyA/rbnm++/9fsqPg31nW/RcmE5kK3y0d3zaDT0U9m0ZNtamDB6RmxL66SebOnpMbuoIKZIvVyY8y6Qn2dSRnCg90aaOZuqJqSGm+2q31EeIUSvVcvLCaiVO02bqCf0VJy6+4c6ve7/Gd+Zs/KqVkhSgKgqqRLLPcm+uLR21QYMG2d133+07epPmDNO7nkmpStDcGkT8QCAQaG0EgtyUCHHIzRJLLGUzzzKrbbbV5r7gF0VJpqWQHvgJwuMm2ui33rFxDz5kY2+71cb06mVjb73Jxt55q0165w0/9ZelrD9Pzu0a+8ukGhvz2ps27sEHbexdd9mYG3rZ2NvvsDG9b7eJL7/jB/PlDuWb5sRl0n/esfGPPmpj77rfxvS61cZcd5uNveVeG//EszZlyHCbOnJkrd7JNJs6YoxNevcDG//Y4zbu3nts7G0325gbr7exd95u4x993Kb8MNimjUFJlMJPsymjRtikt9+y8Y/3tbG33W5jbupjY/vc69ekf7+fkxCMZxfYn30Qnvjauza2z8M25qa7bEzvm2zMTb1tzI29bewtfeznz7+qJUMM0AzAU2zK4GE2vu+zNvbOh2zMdTfbmN632Zjre9u4e+63mu8G27SxP9svk35hUZFNmzDNRr79vo175Akbe9sdubN/brnFxva5wya+/qZLqFCcRaLlAyKD/eTJNnXUCJv600CbMvRrPyNoypAB7pY744cpuNxBjL9MGm/Txo60XyaOtmkTRti0cSjXQiTHumIsuiPs7Od6NOwWPGG8TRs3NkdOkZCNHl2rvDvOd8FlY738gQ5MvaBoOxkihbLtJJvGGURMC03KERbICMch+JJqtvNnF+PxE2vJIKQlJ2mBvMwgiSlR327NZEVsOKJkp512Ms5ie/bZZ70IUtBvzfJEXoFAIFAZCAS5KVE7SaF41llmts233MwlNym5QcrPoAYZ8dODBw0yfuXXfPedTRkyyKaO4Nya3EoW36ht0s82hamFcZOdmNR8N9D1L2q+HWA13w+xKYORfKBIi0LmFJvMwDtuvE0dOdqmDP7JagYMsZpvBlvNd0NtytBROaIyEYVklD9RJJ3kkp+pw0fZFMoyYIBLHqb8wCqYYTZ1NCtrUMBl6fMvuQF2zHjz8D8OsZoBlH+ITRk03CU8bMLGgO91HPezTR01yaYMHWs1A3+ymu9+sJrvf7CabwbYlB+H2tSRSCiQCuVWxEzhpOOfJ7kb6dUMHGo13/9oNQN+sClDWDbM9vcM/AzsZtMmgeOUXFkGUZYBVvMtOA5ziRDTUr4ax3fqZUO7Gic3TkCcpIx08sK+KBATFJL9bAIkK34MAkQKMsGW+7my5ZS6kdTUKnK7bgerqnIb5FF+8uXi8EePV8PS6NrDC1m/U7vPSo7coGCbIzn+XHu+kzYOVDdl2o7N+FxXBgXtqbmdlX2fnNoVXs2bEFJObW9LMsN5XOedd57NNNNMNt9889nxxx/vZ3RBfBSm7UsbJQgEAoFyQiDITYlaYzingi+xhM0680y2xRYoFLOUmLOAWG76i/3iV06CAImZNmZUTpIwaoTfs4qFi8FSSpEu8Zk0dfqv+zHjfPCeOhqJAESFkZ5FrTnD87QJuTN4CINiaU7vgkE3tyIKHQZmHHzaAj2MiRCocS75Ydpq6ihOYZYUAakKUgKW5+ZOP4bw+KnH0ucYS7nR56idIkHQw0GJEBCmkUZS1/E+5ZI7pwfF1VrSNIUVVSh+Ehe9kAmufMuyYsiVl8VJGyutprnib+6gRYji1FzYcWAywqVSKO6y1wo4opitZb5+mCEEh+MSJk3K5QOp4ZlyI+FB8bv22AGOQQBXx9PrxsqsnDRFq6qcXPghkLXnTDmohKMu6MDU2FS2+K89MAHcNTAz++ObyLlObO0qLQ+Z03QBFtqUVUO55em1++Vw5lOtwqxv4ld7gGWuj6EjUx3mrbfesk6dOjm5WW211eyzzz7zikmqUx21jFoEAoFASyIQ5KYl0UzSgtwsufjiOXKz+aY+BPlKqfwAlDv88GdWwaB/MQWpC9M+E23qzxNdJwOaMollrVrxIRKC/gTTFCzvZuqEi51gp9T4ihIPD3kiHIcnchGewbuGwRa9CzaYq8kdCFk7ePsA7svMSQ+SkpM85JZjT7EpE2pcSjJlAtIJ9pnL6XA4+WE5OEuAmRqhvAzYtfvA5MkTxMAlRZSDskNSaqUkEAPS8A3X2I8EPRPCUZZaIuJlq5WWTP3Fpk6ZbJN/npBDHR2T7FJwP0eJlTy12+5Pm+rnMnkExwAcqIswQSrC3iy5Yw04q2kyS599Q79aHVafAsoRPCdilAP8/MwpDs2cYmzmh3TLT9f2/VaoT+5U7nzbsNvxlKm5U8I56mEq++RAfjneIHfl4uT21snxvtweO36OFfvM0B8gPZTXlzv7QufaPY/xqUwj0jdy5Eg78cQT/QDa2WabzeaZZx775z//mT++ROEqs5ZR6kAgECglAkFuSoSuk5vFFrNZZprJttxicx8Z82cVuRyHgSi3O23NLzW+Cwe/wxnWJvuZyrUHJbo0AKIwLX8atcTx/PqXdCEn4cntq8ovfL8gNQyyEIZaiQ6b0RGH8OySO5mdcmsPVHQpju+iW1O72RmHLrJVfe4UaZ9KSxbOMLj6IY6e/i82aRpl50DJX6ymNr/JP9fkic7kSRNdKuK77vogntt/5Gf2unEGhEQLTHLYTPmlVjfFT8bOLQX2MrraD2cAcTYSG79BLthRGGXtHDkBI+EEGYA8sAEf18+QJV+67AzMaiBlbM5XezxCbsM+zmjyk5ny64SQuPmOtlNrRSnwBzgVB1kyFTgZJWSENFP9cj/i+Fqj6Zi45M7TyBEj/NlmbmLNRJti6GWxCV3ttgGQ4dq1Sm5r3xjEPbWEij12uOg/XPxV8v66Ii2DBw+2HXfc0SU2iyyyiPXq1StX51oyWaJXN5INBAKBKkCg6slNIdG1Pp60X7F7ta1+ladh0zQVP6vciALkkkssYbPNOmv+4Ex0B3LjYX5yIj8Q1Y6TtQOh9ubI/TLPDWMqUc5Wvl6uHDXwtGcMNeMT6SitND8GV/nldgNhcHQZQ63MqDZWGqlWalDrU1uv6QuGleaMJVBpc2mTRy6/JP3asuR8VAblkk0t9c8ULhO0bt9cYMKkl1LHxr3BRonURuAxTSubTs5/ep9gCjMXI0dR0riFyqHsFK5QmGye5fJMP07fnbRfU8Ynn3zSmIq69NJLvchp2HKpQ5QjEAgEyg+BqiY3IiaQCgwfRojJ119/bffff7/de++99thjj7kfH1X8FFZxcU8/uNxPmDDB7rrrLrvjjjvsqaeeMn5hYvBTPNwWZ1pq1llt002ZljL7meXFtQMlv7RnGNiTgbUhg9QMZWrg4KtBUGVI85Ffzs6VbPpQ78We/lg7eipOsfRqYxWwFFP2jEGmu06/mzGEnrL+2WeFy9l1+06vHqEJm8VnxtQa/qR8ZWdjyn16f5BLLqSeZGfj67k+f4UrF5t3ZeJENp1EpQm0c4Z7vSu8syNGjMgToDScwocdCAQCgUAWgaomN9nK8jx58mSft2flxbzzzmtzzjmnXXHFFU5KUsJAWBEVpQPx4WPMklTiL7jggm7/7ne/sxtvvHGGNPggL7nkkk5uttiC4xemp5fNR+nLbq1BqlA+csNurFHcxsYr1/DVVp9ywpl3QFKYlMykZUzDpPfFwqdx4z4QCATaNwJVTW6yv/IGDhxoHGK5zjrruLj7k08+sRNOOMFmn312u/jii23SpEl5yY26hYgIvySfeOIJW2WVVZzU3HzzzTZgwAC75pprbO2117ZFF13Unn76aY9GHMjNUkstZShCkidulMenpmr1JZRHudkxqJdbi1RveXgneO8wrILifRzG6eZIz2rfE2zdZ99pDxj/AoFAIBDIIFDV5CatKx/Qk046yWaeeWYnMvKDbIjgQHYwfEg1haVwxL/11ltdUrP33nu7s0TqY8aMcYK0xx572FiWKk+bZuxzs9hiizm5keQm/TCn98oj7ECgvSDAO5a+Azz37t3b3y+UiJGw4o/0VOEIw7Ou9oJV1DMQCAQaj0BVkxs+hhgIzIsvvujTRN27d3epij6YSGTeeOMN/6gecMAB+fCCUqJz0kLPpnPnzvbFF1/MIOFBB+eiiy6yOeaYw7799luP6pv4LbmkzTLLLC65wTFNS+mHHQi0VwREVqg/um9zzz23v4dIVr/88kuHRe8pYXXfXvGKegcCgUDDEahqcpPCgAIxejLbbbedO/Oh1Mfy0UcfdT8p/spdZETKjbiPY4O7RPlReZA++3CgrIyB3Cy77LIuKVprrbVc+fiGG26wF154wf1FvBS/FLaml2SXIo9IMxBoDgK8Y/xo4FgF3s+55prLzjzzTE+SHyUYJDV6B0VyWuP9aU69Im4gEAi0LQJVT24gInwIWRnFxxP9F4yIC379+vVzvw033ND9UlF42jz62KZuxB8yZIhttdVWPr2FH27oDSyzzDJObtC7IW+kOAcffLBHL5RWmm5L3IvUyG6JNCONQKAlEOAdwSDp7NatW/79+NOf/pT/gaAfEQrLs0hOS5Qh0ggEAoHqRaCiyY0+fpCR1OhjKDeeH374Yf+Abrvttu6sOKTx6quvut/666+f95N/SoKUnj6wEJRTTz3VlYl33nln91aZIDdLLLGET1VBbLhQXD7wwAPz4RRW6abPyld+YQcClYgA757eJcpPH0/d+GGh6Sh+eEiZOH0XKrHeUeZAIBBoWwQqmtwAHYqHspkykhk/frx/VPmQcvXp08cJxm677eZB0o8ne9VAPjbbbDP3E7FQXKUJqcGPuExl7bnnnv5hPu644+yVV17xYFr5wbTU8ssv70vBO3To4EvFr732Wrv88st9S3m2lT/55JONuMcee6z9/e9/d8kPSs+jR4/2MivfsAOBSkOAd0fvkcqOG0SH94h7DCsYN9lkE9t8881tFOeYJaunFC/sQCAQCAQai0DFkhs+gpdccokv62YpNnotKAujjMhz165dXdyNyPvDDz/0TfuYHtp+++0dI/2a5EP7zDPPOLlBWRiTfpT5CGtVFH4s8WYKCn0afml++umnBUXlnIsDuWF11jbbbJPP88EHH/S82NwPQoW/JDvY888/v/3www950uYR418gUKEI8J5K0kkVRHqw8eNdQ3n4q6++8hqK9FRodaPYgUAgUCYIVDS5efzxx13acfrpp7vdo0ePGaQixx9/vOHHbsGQkDXWWMNPF/7vf//r8OtDyv40EIsLL7wwLzbHT/4E5iP83nvvGVNXq6++unFSMUYfbj7UaRwkN9K5+eMf/+hhIUm3336758XKKvJkE0EuEZyFF17YV2OlaXnk+BcIVBgC9GH9UEjfpQqrRhQ3EAgEKhCBiiU3wlofTz1nbSQ0XJCPc88910kEqzFEHtijhh2Gmff/8ccf84QGfRrE5JpmIjzEBInL//73v/yUF/mlysH6iLPBH9IdwkvPhzJAetgM8Pnnn3dFZqRMEBs2/Lvnnnvs5ZdfdsKUppmtUzwHApWCAO8D/R5D3z/nnHPsrLPO8mfeXb2/Cqf3p1LqF+UMBAKB8kSg4skNsEp6wj1EJvuBFFFgPxpOF15ooYXspptustdee82PUuAYhb59+3o80uKDCwHBfb/99nN30kBHBhIEMXnzzTftpZdeyt/379/f9W5UFvRmICwQlz/84Q/e+iqHugLlQd+AMOx8jJ4QA4EGA4ULOxCoZATUn1kZhfR0gQUW8H2hqJOmh1U/3l3ek/xLARAAACAASURBVKy7/MMOBAKBQKAhCFQ0ucmShWyF9aGUO2TiX//6l0tT0G1hTw1WMLE8GwmNSBEfY5aO4yd9GaQ6fJTnm28+Jz3YkCTOp4IEMbUEmUFigxk6dKhLbiAuUlSmvMoDGzKz0UYbOblBykP5JGVSOJU97ECg0hCgD4ukILVhB2/eB3TfDjnkEK8OPwYUjsUBkpRWWl2jvIFAIFBeCFQ0uQFKiAgfR5EBiblTmPXxxA3/d955x9C7+c9//uPXN99848H50OpXJpIXJDtSdCQecT744APXveGe6Sl2N8b+97//7ekq/vDhw53soDC89dZbe/r60KuskJnf//73eckNRzdo9Vda/rgPBCoRAfVzFOSZmoXY8IPimGOOcdID2ed9yf5IIZ7eo0qsd5Q5EAgE2h6Biic3bQ9h4RLwS5VTwdm4T2dLSSqjGJAb9H346K+wwgouueGjrkFB4cIOBFoDARTmL730UrvtttuMg2GZumVXbc58wmavKPVhyL76KX1WxF22/JB4olBPH+diFSMrDoPAtEaLRh6BQPtFIMhNido+yE2JgI1kWxQBSUggK6wWlHRFZARyrh222WYBiaSkixAU4mG4T4mNyA3vwV/+8hff8mDXXXd1yaemolq0IpFYIBAIBAIJAkFuEjBa8rY55KYlyxFpBQJ1ISASwtTQBRdc4OQGXTPIDVOqbFmgvZg6derkZAYSo4u0RWyUltxEnJj2vfHGG/1cNsLIva5yhV8gEAgEAs1BIMhNc9CrI26QmzrACa+yQUCEBJtpqZ49e/qWBEhxUJSH5HTp0sXYXRsle6Q2EBsRFOJJekOlcE/90vs0nPItGyCiIIFAIFBVCAS5KVFzBrkpEbCRbIsiIPKhREU6UKRnewLIzS677OLeSHfSqSfd46l42HLnXulzD7khDW2XoDzDDgQCgUCgpREIctPSiNamF+SmRMBGsi2KgEiHEuUZ8/XXX/vxIZAbTuqGpEjXBn/CceGeLt/WKkOtgMI/ldgorgiQZxb/AoFAIBBoYQSC3LQwoEouyI2QCLucEUjJDfciJejJrLTSSi650XlskBTICmFEbFIpDFsrsDKKPZt22mkn30qBukNkCI+RXc6YRNkCgUCg8hEIclOiNgxyUyJgI9kWQ0AEhQQhLTyLfHCYJQe/IrnRRpbZKaU0/nfffecH1xKei52IP/vss3y6IkXklZXktFiFIqFAIBAIBGoRCHJToq7QEHLDDsWcZM5gsOKKK/pqkqy4nkFBhkEhOzAwwKRxsv6KW422BmJsSRxStxQ71T/1l1vY0xEQPkhu2HuJvrnjjjt6APqrDOHU15jCYrn3oosu6uE7duxob7/9tgdN+6bihh0IBAKBQKkRCHJTIoQbQm7YzIyjGRhAll56addd0C9oDdYaoDXoUFzuuRQmrYLCKV7qV0331FODq3DALZ0mUX2LYSX/sKcjoP6TTkttueWWHgCchbtIC3hzGCZ9mGvVVVe1Dz/80MNXex+cjlrcBQKBQLkhEOSmRC3SEHLDL2E2RmNQWGaZZYzjFxgQNGhroJEiJ34aXNJi46awuHNfaJBP41TTvequAVdkJ62jwqRucf9rBIQT0hhJbrbaaisPSJ9iV22Mwo0cOdIOO+ww78PsPswxJBi1hT/Ev0AgEAgEWhmBIDclArwh5IaBgYEDcsNp45wz1adPHxszZoyXSgMID+mvYO5ZoZK6qRoiRnquVhtsqKsGUZ6zpCb1A4dCYaoVn6bWS30OyQ1TpfRNTUtBsulz2GCvvsbZUaeccor169fPs039mlqOiBcIBAKBQHMQCHLTHPTqiNsQcpPq3LDNPbvBMpisueaafr4P01YyDDpacqtBuxC5IZwuxa1Wm0H2kUceseuuu87PQuIMJM5Duuuuu/wsJM5Iev/99736GrRlVysmza2X8EnJzQ477ODJgrf6YJqP4uBG3ywUJg0f94FAIBAIlBqBIDclQrgh5AYR/yabbOKEZt5557UFFljA5pxzTj/LZ5555rGXX37ZS8eggmHgEKHRr2bcGVzSZ7l5pCr+N3jwYCeCnH3ElZ6DBEnkGIHTTjvNERCGVQxHi1RNREXTUhy9oNVS9DH86YP0RYWVmwrAswi43MIOBAKBQKA1EQhyUyK0G0pudCo4UwAsp+X0ZfRw2GNk4MCBMwwikKGjjz7aHnvsMdfP0QCTTsdo4ClRtcoiWQ2qQ4YM8T1VIDWQQggN91zcIwk79dRTvcwifyKHSqMsKlRGhRAuqeSGTfxk6JNHHnmkH8WAm3AlHhf4huRGaIUdCAQCbYVAkJsSId8QcpNOS6G8iXQBwkLcd955x0umwZiHcePGuaQHicRuu+1mZ599th133HF25pln+oZp6QBTomqVVbITJ060O++806elwACCw6qzSy65xN2YrnrrrbfyOIKlBuOyqkgZFUbk5ttvv80fv7Dddtt5CSGTO++8sxNH9sB56qmn3D2V0ii+7DKqWhQlEAgE2hECQW5K1NgNITfptBSSGwZrkRkRlbR4/CLee++9fXDRSc2aitH0i341a3BJB550YFc+Sl/Psomv8Glaqb/iloP9xRdf2FxzzeUDMjhKqlUOZSuXMqjtKA/3atesO/5MSy233HLe13bffXeXFLKXDRIxrs6dOxsb/Skt7LTPuEf8CwQCgUCgjRAIclMi4FuC3FA0CEY67cRy8TPOOMPOO+88+9e//mVrr722Lbzwwvbcc895TRioWLLLQMN2+BdddNEM0gsRFgJDAEQCCA8pwHCvPLP3HqAM/3366ac+DbXyyisbEjHVqwyL2qpFSolLoYzBSfpIaVsjuenQoYMTmfXWWy9PqiGQHLFw3333ed8kPn2FuJisXSjPcAsEAoFAoNQIBLkpEcItQW4YOLImJScMJAxCb775pgdL/RjgTzjhBEPCw1k/BxxwgA0dOtQHH+JpECIi99oXJ3VX3vUNkArXlvbHH3/sSsXoKlF3ylyoLm1ZxrbIuxgGuIvAUi76Gv1H4T///PP8tJT0mZDYcGYU+Cou4dN+yrPSaIv6Rp6BQCAQCIBAkJsS9YOGkpuNN97Yfx0XmpaiaAwiDB4aQNKBQ/eS1KTk5vvvv/ejHVhFpCmshRZayAmP0tMApbT1TDq4aYqrEETlRng++eQTJzfgiG5SkJtft5r6Cz7cc6ntuQcztetXX33lpBhJDYSRaShW8PXq1csTpq+ovyinNH25hR0IBAKBQFsgEOSmRKi3BLlh4JFEhWJqMMnuEptWQYMU9kcffeRTWEsssUT+3B+Wm7MqC38NRuSjQQ23QqRGgyB5KV6ab1vfc0gjq6OC3ExviWw7qY3VhvhDZHFX+yoOOkzaoRiFYiRj99xzjydOWIVXblk3pSP/sAOBQCAQaE0EgtyUCO3GkhsGEkhLdtCgeAw+0oso5K8wGlCwRYqIy34wSDb+/Oc/2/bbb2+sekmnIIj/v//9z3r06OGEaPjw4Y4KaWhATPNN7z1gGfxjGoXpE6QMIbmpv0FoV/URQqNvxdEJ9IPRo0f7dKfIzdZbb+0JMh2lPqYcss9yDzsQCAQCgbZEIMhNidBvLLlhaS2DR0ocChELipsOSungwr1IEPfyw07jqMoKg6Tm2GOP9emx+eabzzbffHNfTs2S6nfffdeDKy0eVC6lUw62JDehczO9NdTu6XQlvmrLUaNG2bXXXmtnnXWW/e1vf/NpPfRq2GbgpZdeyuvcbLvttp6opDzYuldaSpe+kbpNL03cBQKBQCDQeggEuSkR1i1BbrJFy5IKPafEhYFF7sTnWdNZSi87+HCWFaeTs3+OjoBgkEPfQjoWkK6UeCmtcrGRTFF+pqVCoTjXKhBdpHQc46E2T/sH05ZsJUCbo5elzQ9ZcXbHHXfkD3XVJn4QGqWjdld6WXf5hx0IBAKBQFsgUPXkJh2Q03vA1gdZNm4iCgqb+hVroJRMMABgmNpZaqmlfNBAEoLR4KA0Wdb9hz/8wSUmSG4gGcrXI5T4H+XgIk/q0L9/fzv00EN9F+R99913BgVS1YtwTF1whhN6GRjVR3ZabLkpfupX7J44KabFwqXupZDcZMvAc9aNsqqODSm3wqZp4SZ8su2v8NQ1DVfsnkMs2byQ87X22msvl8awVcBll13mcJGP8oCAsz8SbX7UUUf56d6c8N23b18nRayyg+RqEz/iZuuftkHcBwKBQCBQLghUPbkB6PSDrL1c9IHPNgRhs5IOhUnTSQcd+WuA4nnYsGG25JJLOrmRWD+bZ7qJH+SG+OSdDkBKuxS2yqN68SxlYmwGuexGbbgffPDBPuitu+66dvjhh/tJ5uBBfKXJs6607LipDRQn9W/qfSnIjcpCObNG9ZQ7YdT+6b36k9qW8MKbcCLT3OOOTR8g/Ww/VBpIZOSnPFUOTpWHkCCNmXXWWf2eZ0g0Ei3CZ/NS3LRsHL8gnZsgNylCcR8IBAKVgEBVkxsNInzQs4OAGocPPZd0VYqFIzzhZHTPIKN7+ZEvUwHs8Iq4f8stt3QvDWQamJDcMB3E4LPGGmv4QKcyy1aarW2ng3d6Dz5XXXVVfgCl7CwRRtcFEscSYgyYpJfcVHfql+LGfYp96teQurc2uaF8qoPaNVvOutpQ9cMGXy65pekgXXnhhRfs9ddft2OOOcYWWWQRJx1gjc6M4ivOq6++6gdd7rjjji5xQYH88ssvz0/VUSa1QZof97pISwdn0r5BboRu2IFAIFApCFQ1uck2QvqLN/2wK5zc0sFcflmbsOngpThKgw3zfvvb3zq5YUdXDGE+/PBDX5XE7sIc6oiOCAPI/PPP73vQnH/++U6MsvmV4pmyitSl6WuglRSHcCnxYHDs3bu3SwMgNlzUAUkVK24w6YAvnJSGsErzzN4rTta92HMpyY3ypExpudTWqT/3uKcEIsVZdceNK8WJZ4zCcM/0EtiyEiy7ZxG7BKfhiU/5wDmbtsqNu9JXfp5I7T+Fg6SG5CZFJu4DgUCgkhCoanKT/XjrmQEdfRH2e8HWB52GU5hijcjAkIZh2TFpoGOTurP8Gp0bBqQtttjCk2Mgu/32232wQnlTm+ul0wcLLrigTwWRVppesfK0pHuanzARISEfDYrKc+TIkX7EwxVXXGGrrrqqT1EpHGkRF6VVdI44ZJETpeWPrbRl46Z8PWAj/pWS3BTChXKm7iqq3PBP6yV/bPoM+8aA2/rrr28bbbSRcTo8q9MwKeFhbxnIDavYFltsMVfy7d69uyv8sjs1JpsXZUjbKosp/iqnJ5D8k3uxaSn5J1HiNhAIBAKBskOg3ZAb6XkgUUFkD6GYd955bfHFF8+fbpwOKmqpuj7m6DBwkCVEBUVMjPJhWgpyg+5DqlB89913+2DFyh4uBi7CaKUK0w6QgHRwUlla2i6WB3WWRIeBUVII8sevEE5p2Qgj3J5//nmvI5sH/uY3v3HF1VtuucVuvfVWx0rhCg3AaZr13bcWuVE5Uuyog/CSO31DJ7urjsRlKhLlXdod4outa4899vDkwRuMwYR6HXHEEb5cm+kpDHll8cKdfFJCxbPKgz/36XM2vMJgQ5wkVUynpdK6EC5MIBAIBALliEC7ITeAz4d/11139UGFaZUnn3zSdthhB5t77rntscce8/bRoJEOEoUajkGCwYuBCZKCYi0Gd9JAV0KrpTbddNN82pCeBx980PNGYffxxx/3VUpPP/209evXzx555BEP21qDCPkoL8ouIoOb7qmPwqVuFDTr7oVP/r3yyiu29NJLu5QKEqeBHBLH1Afpkb5wV5pJEg26LSW5UQEoY7ZfgJmICDowZ555pu2yyy621VZbuR4SK9AwhMMgtYEIgwOED4nXmmuu6VIbpizTsP6QSLPSNpEfNu6ys+VTOMKIlFIW7XKd+qstcUNyI3LDO4IhbeWleGEHAoFAIFCOCFQ1uQFwfbAZmNBzYVBh0zIZfkmzcy86L3zQCa+BNv2QKx3iMSCziogVKEwRkOaBBx7oSWoQY6Dj2AMkMlotJakOAQmnfHhO74sNUJ5BC/9L821M0sXi4Z7iRppIy8AeYskSc1ZZMRUDRml48EHSA8GT7k42rWJlLAW5SfOmzZl65ErbhzKffvrpvieQyKymG5EMsiwbo7bHZu8gJH6QW6QwSHkw5JfFVfiIVBImvfeItf+ycZWmwhA3Lbvc03jqv0FuhE7YgUAgUIkIVDS5SQcfKb/SCPqFqgbhg80mb2xOxqokBkLFZaDQ1MkZZ5zhUfSxl610sImHyJ5f3KyC0tJbTUtp4GEp+KKLLurTX6lCsdJQ/mna1XZfCD8G1x9//NE4LiGLBdIulFiRhKGMfdJJJ9kHH3zg4QqlpYGYAEg9kMARX/sFaSBP46b3yt8zqG3btO8gZTvnnHN89+bjjz/eVyHdf//9HlztTF6SxKBYjc4UZT/uuOPszjvvnGEvoDTvcm1/lSslN+m0VFoH4RZ2IBAIBALlhkBFkxvATD+2fJizEhHC4CZdF32oiacP+TPPPOPSlw022MDbh4FL6RKXcFwMfAyYV155pQ9gSBf4FY7k5pBDDvG4GnAhN/ySZ6DeZJNNfJAmvJRAPXA7+iccqTLYSkdFBAR3dtPlHCPwRCcKCcgyyyzj5EEK22pjQac2hLASb7XVVnO9FrVv2o6KIzcIsaaC1MaEGTRokG244YZOYJlKm3POOT1tNsNj9RJG7YwkBgK03nrr2dVXX+1khmXUGOXjD7XkSfflagvPIDfl2kJRrkAgEGgIAhVPbqikBprsYIKf3B599FEfoJiCwkjngI/5Qw895H4iN4rjATP/nnjiCR9AGUzJ98ILL/S4nM2DUVzIDTu8MkgzQDJQM/WFcmgazh/awT8Nmtmq4g6ZlD9SnYsvvthxAi8IS8eOHX3VFXEVTm2u9GgPFJbZDBEJEO1AWK5UGsMU2UUXXWQ9e/b0/VtWX331/PEEShsCysokiCnthuIvUj/aOiUulEFxVA7sbNnUJxQm+yz3crBVnyA35dAaUYZAIBBoKgJVQW6ofHZA4SMtN+61X8g222zjWGnwY+B78cUXfRBFDwSDVEFx3cHMd+5FOZbzlnbeeWd35pe/NrRjBQyGgRppBMuk2cRPq7IYpNG/YUt8TDkPcF7AEv2jLag7l6Q3ykoDK8/4sXx8n332yStrp1IesEeCxnQRbfXyyy87vkwX4pe2IYSGwyBZJbfWWmt5W2t1mo69IG3l//3337suFdIYFL5fe+21vA4QeWX7BuVVndK6pOXFnfSVh8KVm63yBbkpt5aJ8gQCgUBjEKhYcsNHmNVKt912mz3wwANOXtgTBBKDrgMXU1FIWlBcZYUSBEMrP1JygWInfii6YrKDF3lBWph6YhUM6WIYvNirhLiallK66T43KBYfeeSRdsABB3hZFdcTaef/wFYkgPtChEeY4i8pDBIU9oYBe/BFasM9hBI9mJRsMKWF3hP+kE1sJDMo9Z588slOOJQHzaH21r3Kp2ds9RHiER7DPcSqPkN4xa8vbGv7qy6Qm9jEr7XRj/wCgUCgpRCoWHLD4CBFTqYOGLQ0cPHMAMbFlBCSGZbk8syvd0w6uGTJjQY6PvT62LPhH/E5LuGEE07wVT+csdStWzfXyejQoYPtt99++Y3YkNywlJayQIiUp9Jzh3byr1idaYO0HbLhaAdIJQY/rTji+dNPP3VdJvRhkMIwhUT7MM0EmSEul9KE3LKDMlsAsLMvS+8x5C9CRXiVR/E8UCJpI4zCEyYNn95T7jQ9pVPutupdjNzIv9zrEeULBAKB9o1AxZIbBo6bb77ZVyyxAzArl/h1js3UEzbuEAt2g33vvfd8XxHICNMU6cDElAYDI2f3YNLBS7/E0aEhTcgNabIEfOONN3ZdDAZWlId5ZpoEwwCrpeBaLUWZZSSB0HM12ml9s/WTXyoVwY12Ef5pnJQ4pHEhrUjjmFKkDSGUat90ICZNFJZTo3RSt/Q+jU/YtM3w0zP3qkfqnqbFPWnUl2c2Tms/q85Bblob+cgvEAgEWhKBiiU3AkGDhQY/ucvWxxp/iAe/9M877zx5u2Rg9913d+LDMQqSFBAgvedZeSlNnrOrpTTIMcCyeR1SBW3ip/QUP1+IdnBDnYWfqpu2WSFM5EY87rOkBzfhzdJyFH85wBNCijv+wpw8eRYhUVkUH3+5ZculOGl+iqc4aXzlhY0hXmrSdFL3crhXWYPclENrRBkCgUCgqQhUPLlpTMVRFGWpMLoEKImyQy46F0wdXXPNNfmBkA88U1nodEiao8EyzY9B79prr3WJwaGHHupeGhhHjx7t0yCQG50thV86GKZpxX3zEGC1FFOQkBs2xQNnDdTNS7n9xE4xY8UY7wrSMCnhi+SBSGDbfvpF1DQQqEQEqp7c8MEWoYBccG4ThxWy4Zo22UMpGKNf+Xy477jjDv+wo6MDsVEa2ApHeiwrZgBA/ybNSwrFQW5a57UIctM0nOmzIirs+cPqP5TxOZ2e94P+27lzZ9/Dhw0M03ehaTlGrEAgEAgESo9A1ZMbIJQ0hY84F8SDDzV737z00kuOsj7wCs8KK86bevPNN92fj3qhDzu/cFFQTTeDIwLSAxRYg9w4fCX/F+SmaRCr30PYITQQde3vgySMqT4d8MmPApS6U0LUtFwjViAQCAQCpUWg6smNPt6CUQrCEJXU8CwShLukMwrHBz01uMsP92w+AwYMcCXjIDcpaqW7D3LTOGzpr2mfzpIbSA4X/RcpJ/ccXcLUFP0+naJqXM4ROhAIBAKB0iNQ9eRGEIp8YIvg4JcSFIXVR19x5I4t0iM3hc36cS5RSG6EUuntIDfNw5h+/Prrr1uPHj18qvXcc8/1naI5b+3ss8/2g085XoL3hfei0LvRvBJE7EAgEAgEWg6BdkFu+JUpqUx2xY2gFEnho617+aUf8qwfZEdpKzx26NykaJT+PshN4zFWX1b/5jl9P+SepowbBKdQn0/DxX0gEAgEAm2JQFWTG328BXD6QeZem8LxwVZY7ot91Ekn9ctKfUhTeaCzE5IbIV96O8hN4zFWn1fMtG9zr77MvSQ2Wcml4oYdCAQCgUA5IVDV5AagUwKij3f6UU8/4JquUjjiyz+No3uFSz/4cmPTvyA3rdfVg9w0DWv6svp4oRToz7rqClcobrgFAoFAINBWCFQ9uWkrYDmZOshN66Ef5Kb1sI6cAoFAIBAodwSC3JSohYLclAjYIskGuSkCTDgHAoFAINAOEQhyU6JGD3JTImCLJBvkpggw4RwIBAKBQDtEIMhNiRo9yE2JgC2SbJCbIsCEcyAQCAQC7RCBIDclavQgNyUCtkiyQW6KABPOgUAgEAi0QwSC3JSo0YPclAjYIskGuSkCTDgHAoFAINAOEQhyU6JGD3JTImCLJBvkpggw4RwIBAKBQDtEIMhNiRo9yE2JgC2SbJCbIsCEcyAQCAQC7RCBIDclavQgNyUCNklWmyni9MUXX9jcc89tyy23nA0fPvxXJ1drIzrsdGPHJLm4DQQCgUAgEKgSBILclKghg9yUCNgkWYiKzkL65JNP/OTqFVdc0Q9G1XEBBC9EaGK33QTIuA0EAoFAoMoQCHJTogYNclMiYJNkU4Ly8ccf23zzzWcrrbSSgb1ITxLcbyE6mDRuNkw8BwKBQCAQCFQ2AkFuStR+QW5KBGyRZL/55hubZZZZbNVVVzWdAg+RSUmM7kVwiiQVzoFAIBAIBAIVjkCQmxI1YJCbEgGbJMuBpW+//ba9+eabdtddd9kcc8xhyy+/vPXv399efPFFe+211+yrr77yGCI02KFzk4AYt4FAIBAIVCECQW5K1KhBbkoEbJIsGHfp0sVmm202m2eeeWzmmWd26c28885rs846qy244IJ2+umne4wJEya4LZKTJBO3gUAgEAgEAlWGQJCbEjVokJsSAVurIEzqQ4YMsWWXXdYJDVKbmWaayS9IDtfss8+eJzeS1miFVZCc0rVPpBwIBAKBQFsjEOSmRC0Q5KZEwCbJIo25+OKL7bDDDrOjjz7ajjjiCDvllFPs4IMPtmOPPdaOOeYYe/zxxz0G+jZMY4UJBAKBQCAQqH4E2i25qeuXuwZB/cpPu0EaL70njOJxP2LECFtyySVdgrDFFlt4EgywSjMbN80j7gOBQCAQCAQCgUCg6Qi0K3ID+YBgiFjIFnxMXWj6Qm7YIiRyI17qVuhekhumR4LcCLmwA4FAIBAIBAKB0iNQ9eQG4qElwI2BM0tyIDQiQ7JTSU027aFDh4bkJgtKPAcCgUAgEAgEAq2AQNWTGzBMJS0QkpSU6J4wIkGSxMgmjXRKKXVX+tjsryIzcuRIJzfsvRKSG6ESdiAQCAQCgUAgUHoEqprciKwUghGCIglM1r/Y7rYKRzyRokJ5IPWR5KYYuVFaYQcCgUAgEAgEAoFAyyJQ1eQGqFIpC6QkS2h41h4ohM/6Z92yZCYNn95zeCMKxZCbLbfc0lstlf64Q/wLBAKBQCAQCAQCgRZHoOrJjSQsIJclOrhlpTRZ8pLGw09TT+PHj883huKkCsmp5Ebkpi5pUT6xuAkEAoFAIBAIBAKBZiFQ9eRG0pTUfvnll+3www+3Qw891E499VQbM2bMDKukUhIEuiI0QhrC9NRTT9mBBx7oafTo0cPDKA/CabVUKrkJciMEww4EAoFAIBAIBEqHQNWRm5RgSGqTddtvv/3yu9lCPo477jhHOJXiSBqDrfgiJ3fffbfvfktcdsVlq/8zzjjD0xAxYlpqmWWW8X1utt1221+lT9mUbtZW3k1tdtJTmoXSqM+/UJzUrb749fmnaRW6b0z8LFZ6lt3c9EsRv1CaqVtj6p/G03198RvqTziMbN2rjyu/rN3Q9LPxWuq51PnXlz71IIxw0j12dhVmoTo3JP1C8Rrq1tz0iV+XaW76SjubD+90ip/yAWe5yy0bve7GeQAAIABJREFUV2m2hK08WiKtQmnUl35z65ZNX+mBY9pn5U4ZdU8b1PVtLVSftnKreHIj0IsBmPrTcH//+9+dmCC5Oeuss2z99de3lVZayT788ENPIg2Pg14a7on/7LPPOmHZbrvt7KKLLvLt/ddaay1be+217dNPP813gsGDB9tSSy01wyZ+pDVp0iRPh6MDzjvvPE/j+eef97zpNORPOHUy98j8I0y2nGmQ5vqnaRW6L5f005cMSdnll19u1113nWNcSnzKpf6F2ga35pRPuLEJ5TXXXOP986GHHvKsRP4J0xL9U3kVqkd9fuXur3agj/I+c8ArP4CuvfZa+/zzz73KxTCkbm1Zv0LtkbrVVTbVu74wCpemm94Xiy/39957z7/fl112mf373//2qEjY9U0gnMKm6eq+Pr9S+qsMxezmlJ006yq7/BVGeH388cd26aWX2rnnnmtvvPGGF43+STjC6FLZFL9YHcrBveLJTUNA5ONCY9x0000uaWFrfjUcEhYOV1xttdV8V2HcCZ8aSYCefPJJ+81vfmO/+93vXFdH4QYOHOidYo899vApLuJCXrRD8R//+EdPTh2Jh2eeecbLwplIJ5xwgvsX+9i5Z/yrE4GPPvrI8URaNmrUqHz71hkpPH+FgD5anKYOOUcyqa0M9B4RSeF+lUA45BEQRtgcEwKWyy23nIks6vuRjxA3eQTATN9ocOLbiZu+oVdddZXjyeG4EEYM5EaY5xOKmzoR0Nh25513+gHE9NF//etfHif9MZNNpBJwrhpyw4ugl4GG0LMa5bvvvrMNN9zQfvvb39prr70mZ1/S/eqrr/qLopckbTjSwUycONHuuOMOD7fnnnu6W5oHLxZ722BwHz16tOfFDsV/+tOf3F0diQfKQEfiNGukSZjUPy2De8a/gggIJ6RmTBOuscYahrI3bSC/ghHDsSACwuzbb7+1VVdd1fuo+m9KbgpGDsc8AuCogRhHJAwiNw8//LCHC3KTh6vBN/oe9+rVy/FcfPHF7cYbb/T4DMb6Jge2DYNUON1///0GUaSPIr3BZMkNYdMxqmE5tF2oqiE3xSDkI0Oj0HgMfrvttpsH1UvAwxNPPOGN2q1bN/fjo0QcNbw++OjaIGlhGunpp5+2XXfd1Q9tfP/99z0e4bSKatiwYbbiiis6eeEX8N/+9jf761//agcddJDbO+ywg09ZUSamtTj8EX+u/fff3w444ABXWEZpmbjNvUinOWnVFV9+TU2/WHy5F6s7/uCJDhXtOvfcc9uiiy7q2IGj4ikdbLlhyz11K3SvcE2NXyjNrJvySN0LuaX+Db1XOtnyK37qD26HHHKI7b777jb//PP7e7H88su72//93/95/1Q82Wl8uTXWVhppvEJuqb/uFQ5bbllbYVL3Qm6pf333dcUHR95hbPoo0l4GjnnnndeQ5HLIK/7koXSw0zzlnrql9/X5K6zCZdOXf0NspVEsbH3+xeLJPRsfbOhvfAv1PdQ9YTfYYAPHc/bZZ7dNNtnEjjrqqHz49N1P09e9bNLRfX12tnyEL+RWXzrF/JuSVkPjKBx2mj947rvvvj720D/RHaWP8gOR/gmOfFsJwzcB+4EHHsiPn35Txv+qntyAPWSFX0s0nKaI0H3BQEj69u3rfhtvvLG76dcBD9wTn3A333yzf5wuueQSD6/OgPTlH//4xwxxf/zxR1tsscWcUEGIyFuX4vGseyQ8EB1shQt7OmYNwQL8hGdDwkeY+vENTOvHqCH9CBwZiBU23vOG46pvI9iBI5fceN9nm202x1WYyk9Yh/1rrMGIC+yEH1jKHRvcFGauuebyZ0gkRj/8/aFM/1UFuUnFv5AQkRPuuTAPPvigNxQSEwxhFBYlYRpys802cz+J4/yh9h9hITeE6969uytb9uzZ06688krX2enUqZN98cUXebEo4rvjjz/eFlhgAe88EBzEfnQSyBAfOiQN6PDMOeecHoZn/HjGbu6l9JqaTnPiNycu5a0vPr+A55tvPscRjIkDxuAJxvjVVe/60q8rbkPKV1/8lkqjIfkUClNf/fHnEo70SXDGrSH9s770C5WpJd1U/pZMszFpkb/edfol7ztY4gZ+8iuWZqnxa0j69YWpz79Y3RriTtrCinved3DELcWR9530GKAJgz/huG9IPnWFKXX9Spl+XfXCjz6oMQi8hC228AZb4QjmjH0nn3yyj4jpmJuOk+V0X7HkBrLxww8/2GeffWZozn/wwQeGxjernrjQwdA9UhqmpWgcVjlhRIC4f/zxx93v97///Qx+5KFGhPCgkMyvhpNOOmmGcOxwjH4Cq65YtaO0sSnfl19+aV9//bWXiXvKyTNKm6ycIMw333zj4SBIPGM35yIf0m5OGs2J35y4lLmh8cGSuoLlJ5984vfCt666NzT9Ymk0N35j6lisDHW5g0ld/g0pP5iShvojdkPiEae+cPWVr66yN8SP/OsrQ0PSKRamMeVX/wQ/3QvbYumXsuzk2ZD06wtTn3+xujXUPcVY30psYYetd17+fPfBme9rc8vX3Ph11ZO0m5o+uKTY1JVPMT8wAjPSoRxpmnKTOxiDJzYLZTCMjeVuKpbcQDpOO+00Z+oLLbSQ/6rklyVMk+eFF17YdQYWWWQR15HR6qStt97a20SKUTSSyA3zjpiUnKgBcUOJjfRRFuZZ4Ujjtttu818KgwYNcnfclAdpSIyHO8rJxYzIFOHCTEdAeGALy+m+0+/ATxjKVe2ErbhyU5hqtIUVNpfqDD48p4bnFDfCKp7cFV+24ist3LMbXipMtdnUWThksaOuwkT1Jmz6PVCYbFsUSktptCcbvNTvhFGKDf56l4VlFnPc03CkozbAvZoNWAgP2Vmc5C4bTLK4yK8SsapYcgPY33//vT322GP24osv+v4zL7zwgvXv399eeuklV/hFl4Z7JDdIVLbZZhtXOIXMpIbdhhFnSlmKBk0bmWeuW265xYkT7FVuvDCEveeee1w0ijQJPxmF4zk73VVsIMiGU1rtyQa39OOV1h28dR5YinU2DH6F2hHcU/c0XrXcU3cNDtQpiyfPuoQzmGgfJsURTmlY7gmHLaP3ADelJ79qtql3Wl/wSjFL20A44Ja+44TnGbu94SdMCtkQkUI/BIUxNlimmKk9sm4iNfIvlF+1uaV1BQ9M6pbe46cw2GCbNcI9DZsNU07PFU1u1GEBlIbCqFHUUAIbf3YiZm72wgsvzIfF/eyzz/b5R1Y4ET/9WCk+7ldffbVPX2WXbo8bN853I0byoz1W9PHKEhjSSd1UXsqh+7Qeyr8927Sl2gSMdC9MUtxwS8MrDDbxsnFT/2q8L4QFeKl/UmeeISup0fuDrXdL/vLjWemrDQqFV7xqtal7oX6VYsE94bBleE6/YXIPO/eupn0UTMAuxQ+3FHv5KRw27UIYxfebdvJPOFB/YSAc6Hd6rxWuobBk02tovNYOV9HkhkaR0X3aiNmX480333TJzTrrrGMnnniiHXPMMXbkkUcaz3369PFl3Ernf//7ny+H6927t7KwAQMG2EYbbeSb80GUUK5iA76//OUvvkMxEh1eJqUhmzLpXomlH8PsBy4lPwrfHm1hhq17cOBeGKXuadsLr/QFVlz5VbsNNupn3KdY8AxeckuxUH9VGPxwE+bEwY9+m2KOm/IrlG6aRzXcU3fVP8UqWzf8uGTATVjKjXQUpq60FL692OBCX8LWd1L3KV7gUR9uCk989dNqx5G6cmHS+7TeKW5gnb67xNGz4qfh03TK7b6iyY3AVKfVsxoz+0wjvfLKK05wUAxGwRhbOwTrg0N8yA7+0sMhLi8Xxyp06dLFJUD4c3Xs2NG3/SeMOoBsyqDyqVOknUVlxMa9vbx0ab3ruldbCjMwLCSqJg3hTDuqLbNpk157wlj4YacY6l64gUmKTYoR7ukPBfVjxRXGqbvc2ost7MBA77H6YIoL9xi5YQtrubUXzOqrJ3hwCS+Fz7qlfRvshXvqno2r52q16+pL9Df1OTDiwgjXFJPUjfv0OQ1XjvcVT27USIUaBz81nMCncdgdmJO82Waa8zT4cKdiecKgZX7mmWe6ng1x03TY6p9zYtjJkTCs1CqUP24aRNL4lOuuu+7yXUs5X4ol5azEwhCO/IsN4B6oHf7TSyWbVREoeIMXl3DGXx83jtZgKvH888+3c845xwmoMG5PEIJJio9+AYPPDTfc4EcDsHcTZ6Xdd999Do36q2zeDza7BEvC0mfRccOQttLnWW3knlX6L8VU3yAWGrDRJxiBJd8WdiaWjp+wFCR8h1gUwRlenHPHlhSYFEuFba82OAtfVu+wcAO9Rgx+6svoX4I1x1yAJ20gPMGdsFztzagvsdCFd51+yU78jDu8xxdccIFjikoGRuH5NoC1zj+84oorfEUVYcC8ErCseHIjkOnA2Y8HDYF/+qtTjYe7Xgxv1dp/Cqu0CCM3guhFUzqpG/caWFUu3JSW3Ji+YjUXK6+WXnppt9lTgOXgxcpFOu3NgBt4SHlY2HPMRdeuXf14C7AknPyEES8rxwawCRXL9NnbgbO+3nnnnYp5OVWXptrqb4oPTiLxYHjooYe65JGdiNnGgE286Id89NQP6fvc82FkXwx23eaac845bfXVV7fXX3/dk1e/54Hw6vPKu1ptYcyPkX/+858u0eX8KDACSyS77PaMoY+qn/7nP/+xJZZYwjGlf3KMwCqrrOIEkrDpN6dasauvXvQhfaNZnswhx3wvdbAjfupnkHKwZi+xZZdd1u/ZsBUdSIzCYasN6su/Gvw1TrGJ7QorrODnxbFjPmfw0U/po3wndXA02EDS2aGY7wFnLnJkEdjutddexjFGKZ7ljFHFkxuBq4+MbLmntjq1Ojp+hFcHKOSv+HrJeM6Gl1uartKWHzYGBk1n4mPGKi0My9R53nzzzX3aq646eIR29E9YCFt2fuZXGS8bL2iq56Sw6EbttNNOvj0Aq+kwrKjjw8i27aSBUXh/qNJ/1FH9WvWF2HAkABh27tzZGGgZTMGK89fYPgG8ZNA/40MHmSEu6XAECfEZcCCftA/u2Om7ojSq1Va/5DgW8Nhqq63yZPy5557zKXAw5qwuGbA+5ZRTPLx2NmdVJySTPkpaGLWb4rU3W/0VKTorXcEXFQCwwqifvfzyy/79pC8KZySLkHEGaeGo9NoLjhqnICvs77b33nt71TkiiHdWFz9MwEY4oWcK1hy/gOH9Z4Nb3JipwCisP5Tpv6ohN+WIrzqXXio+hBy+yYotlqlj5Mdgwq9hTrvFVELn8YKW8J8GDmXBrwb2KdJ24UgbGGzBWTgSVvsW6cBHSSv4CPJLhdVxmPaAcYqLcGRw4EPFmVFvv/22O2uggOggUdThsHgy/cQOpYj7ZcaMGeNnzXTo0ME/kmCZ5pXeK0612dRRfYitIBhM7733Xq+mJFn0RX75Mi0qw2CBZOfYY4+Vk7HiUoRnn332cfdsv84HrpIb9RHec30rqRruPOv9Z3UrpI8fLJzDJ+KNPzgjgUQqweadio/NqlbiqY/TVsRRvh64Sv8JQ6rHjznICThh9K5nq64+i7oF7zvkXAbVC35McuYXJm0vhSk3O8hNiVuETqCOgJSBl5MBFvaMu8TPDBZIbzjGYeDAgf4CtoeXsCHwCz9ePnajRsTKRo0MEJqWEo4jRoxwDDUFRVx9JFEGR/TPrz/m6DHtEWMGV/qhfpnpow8efMQgPmuuuaaNHTvWJY3bb7+9H7iHP0RRmLFrKasHWXmIoX34cBb7eHqgKvoHDupb7K+144475vVBRHrY1ZUpVO1qTv88/PDDbcstt/T3H8yEJ30SrPHD0HflV0Ww5asCdsKJe73nBBCuDLAs6kCaxQ9CyIokN4TjBwv9FewVT4M0kkj8kFJCHjHCU7Y7Vuk/4ckeb0zRsTIY89///telMdKzAS/peLLgZt111/VpaMLyLisdiDrnL0oaLvdyhS/ITQlbRi+osmBKCt0P5jzpcOkgALnBnTljwvHytYcXUNgUsoWBcBIekBSkC+ClvYmE9dChQ5344McvFuLoJSSs5pCRUGAUr1D+1eBG/dMBhHu5YWsg4B7Dh48+KBE2SpxMq2j6JCU3tAOStD//+c8eV+1UDbg1pg5gKr0w4r3//vuuqHn77bf7wgMGBKaeMWDGCfZcGEi52ocfPJzazPQ0Rm3lD1X4j3dP7yb9j2cuDbRs3YH+DOQEwwpWTqx+66238mig9AqB2XXXXb1fp4MxhBM/prSUJumrr+cTqdIb6ooBBySs/BDZdNNNfYUwswRgyUIWfsSoD/br189//KEYj9GPcO4hmsS5/vrr3a/c3/cgN95Mpfmnl0gdB4VhHQ3BR06dj9yRQKDrwBES6IwQV/FLU7ryTzX98KWlZQ4eksiHD9zASVim5Ib5d9xTP8TXnNbOijeM/NL0q+EeTNTvqE/al7hPBwF9pJAqcLAsv45RvMagyJlKHhiM1S6sWuEXocgNxEd+1Yqr+oYwVD3BUAMoK3YYVLmYkmJlJIY49E8UjLfddlt3o42UBgTpwAMPtD/84Q/uB5Zpu7ljFf0rVDe5gQmShpVXXtnPiqLat956qz+j7yWjMwP32GMPJ4pgxoCMYXUfbQDWYEva6p/KR+lUoy1pNtPw6otMjyKdhSgiIcSd/sh3VJitvfbarj7BM/1TmCEl44cOq1Qx+m74Qxn+C3JT4kZJXyIGW1ZJ8csYtswLrA8bUgWkDZAfwhEvjVviYpZt8nqxKKCwQvcGDNEZyZIbcGQVChcHw4Gh4jGwoKfD9B9LydM0yxaAZhSMuusDlOKQJgm+EBim+piTR+GaY0ZkmCLlQFmmUjB8MCXtAXt+FaMLgYHcpITKHav8X/Ydpa8xfQIm++23nw+s6NBolQlLbNn0U+QGPNU/+Sbsv//+eckNbZNNv5rgzNYNHOSG7iFKsPoRQr3vvPNOlxwwyMpoldTOO+/s/Y801AdZsCFyg1IthvS5hLnSqUZbdXz00Udtiy228KXdqrv82BIFRXZNNT344IMuuYE0YninJZVEcgO5YeUkRjj7Qxn+C3JTwkbRi6os+KVLR0JCw8BAB1MnY+BFTwTJTUxLCbGcLYyEJwOFpvfALcURcoNkBiw1oIgg4YeeDr+mmTrAKO0Zc6yOJ+FFHUVIsjVDOZNfxwwCDKwY8BIuEG1WoUivBrKkdJE+oiPChZGf/N2xSv+BkX4Zp79uqS71V5+DsBx22GEujSEc5IYplFTapUGCsExL6XBf3KsdS9VPNvixWmeOOebIK2GDGebGG2/0vpoquj700EPed5nmo8+Sjvo6Uyz0a1awSecm7dueaBX/o67qhym+4KS+qyOFIOIYdJvQx2O6D8M7rTSQmLGB7c033+x+6rf+UIb/gtyUsFHoUOoAdBCmm5DMoFDMLwm9jBSBZ4gP5CYUinMDhKQOwlAvKBIZJDdMS0EEwVFTAjyj6c/0Ezjix4WBCKFQDPlhagujNP2hiv5RZ33kVS3c+CUmPNgvBBLIhU6N/OirigvWfOxOPfVUTwZ3YQZBZyCW5EbTAfJXvu3Fpt5gp/qr3zI9Ql9lqg/dul122cUlXuBCH1d7gD9ECL0ITJpWNWMo3KgjmGnVGNJXdEXYgoCFFvwoYUUa06ZIE/mBIgLDtBT4pX1XfkiAUnKj9qlmTNV/1AfpZ/qeUn/1ucsvv9wJoFZHPvLIIy4dk+SG+GCKQdmYdmATQIzS84cy/BfkphUbhReMX2xIbtBvoHPoReOZ6RL86UzqfK1YvLLMSvhQOL1kSGQggkzjgRtYCS9IDtMrHKuhpcsadFEwZloKSQPxSDtNvywBaEahqJs+buk9G5ux3BOJDfPrrHrCpDgKF7CD2KBbwz3u8nv11VdtvfXWm2F+XsVVGD1Xo60+R19iB1eUM/lFrI++sEfkD6FGQRbDgAJuWrqs8GykhsK7JGHEbw84pnXkHUeaCNlm91xs7STP1g4QHKb5wBtJ+GeffeY/WLTtA2kJT6Zj5pprrrwEKB2o0zyrsW9SP/VPFk+wMzGLBTBgLMkNGCPduu6669wPSS0/Vjg7EUMawgrdHb4DlTKlH+TGm7A0/9QplDodhVUTc889tysO6tcxHe2QQw7xqRZ+5WGycZVGe7VFbKg/LyAYZhWK9TIjkeCFRRFWHzp+1bEcF3G3fpWkaVY7rulHSstnWfnAVB04ZPsbz1k8tcJHbcAvaJaME5+L8O0JU2GGzTEK9EntwSIc2MEZfYeDDz4438WQhqG7wK9gBmcMBBNpBP32qKOOcjeRo3zEdnST9iW9w+iDsJuzjv3Q95NNPdlkUrogwrNbt27eP8Ebo3TaC4zqgxztQb9iQ0MMPwDps+BHv2Q1H1JuGYgj4dmpHENboANVaX0zyI1atER2+pKqoyBRYJBlZQR7BzDPzocRd4hOOrCUqFgVmawGEyQw/CJDciPdJflRMQYFlivywdt33319Hvnoo4/2l5MXnZdeL35FAtGEQoOPBkskLnyokNygPIzewgMPPOASGBQ02WhSS5fJisEBEoNkkVUWbFTHYM5AwwZr6Ioo7SYUrSKj8F5TZ/U79l9iyhOdBJSzudikj+M/kBZmdx1mqS1twADMGUgc3UA4JBXag4k8wkxXXKVf8oOGM7kwIitIzljxw+aeSCj4gciADfl+8sknPaywpL10X+3Y6hsHGWTncVQeOCSaHzecewa53mSTTfL6h5LmoIjNeIR0UX2TuExPC89KeN+D3JSwh+vDl80C7XPmLRGxon+DzgPiQQwvXnt5+bK4FHoGQ7108ufXLgMrUypSKBbWwo5nBmpeSqawGJi1S6xeeqVXzXah/sTHDvw4W2bRRRf1Dxm6YEyXspqPKb3u3bvnj6kAH6ayrrzySieU9FmWK2slC3mAt7AnvNqjmrFV3VRXFKzRFwFT9L7AF4LDajQM+GhQ4NczRBFlbfCG5EA6Me2pf3qFi/wDBwiM8IUQgpP2uQFLYYUEkh8wDMpcDMRMS2FIQ+GUldLUc7XaeifB6vTTT3edJaZIWZDBfkrvvvuuVx1/MOEiDvqhJ598sn8PCMtuz0i/8VcfLnfMgty0QgvxYqnjKDs6EPveIA5k4Mh2Gp7bu9GLCQ760Omel0+roVIlV3CTuJqPGh89JD0MPMI0DVOtGFPH9CPEs37pYiMdUP/jly8SMHQYwAkbNwwfNAz4kx54Mi0IqZS78FY/d4928E8Y00+5p/7cgx3vNViCGW7CXrAQHnfefcg6NgY3rvZuwAA8MfQvLgghm5/KqE+CJYYfjfouKJzaRO2juO3FVj+jvrzLLLjg3eXbybssIwzT8OiI8u2kLyOdxSic4pWzHeSmhK2TlTjwgeNlSztQNoyKEx+4HBL6wAkX7BSbYi9binE2Lh/J9mLAodDAmuLGPYOH3NI43Kd4F8KNMGon3WNXu1Fd03rilpLKrF8xLMGv2LcgTSPuf41Atq/xDM6yicF9ev06lepyoT8V6oe46ceIakxYvb8pVuk9/nxHwLBSTJCbVmipbGdSp9HyRDqMfiHjV+wD2ApFLbssUiyKvbDgB8YKy7MGdO71kqfx5V92FW7hAlF/XekHLM0Gfwy2wnCvAUIfNbkprHDVM7bagPv2ZMANnIQfdU/x0rMwwU8mG07uYc8osaVv6VLf0zNYcQ/+4KkL9/RemOLWHozwyWIATuqrwgwbgk0cTCGM8BP25Y5fkJsSt5CIDR2l2C+zdKCtpM5TYuh+lTxY6sVKX04FFMZ6acFSL24x7BW32m2wAAN9sHjmkuFe/ZAwPAtHnnVPeJ7VDnpWOkpfttyr1QYzveOqo7AUvtgp9oQDn9RfbsK5veAnzIrZKU7CBFuXMKQdCr3j+AtTbN0Xy6+a3Kl7+p5SN+HGPf7CT/WWGzgpruLwXEn4BblRq5bAVufIvnRpB9GAQvapewmKU5FJChPhxIsmXPXSaXBJ3RWPShNOLzF2e5iWos7CQLY6AH4YsFDfTDFSeNzAXbhiK67SytppOlm/antOseAe3LBTdzBW31UfVNgUD+IKd4VL/dvjPTgJC2GadeM57ZcKn8WLcGkaus+Gq5Zn1Q88VHdhI7+sDY7qq+CAv/p0iovSS93K8T7ITTm2SpQpEAgEAoFAIBAIBJqMQJCbJkMXEQOBQCAQCAQCgUCgHBEIclOOrRJlCgQCgUAgEAgEAoEmIxDkpsnQRcRAIBAIBAKBQCAQKEcEgtyUY6tEmQKBQCAQCAQCgUCgyQgEuWkydBExEAgEAoFAIBAIBMoRgSA35dgqUaZAIBAIBAKBQCAQaDICQW6aDF1EDAQCgUAgEAgEAoFyRCDITTm2SpQpEKgABNgUTJsApsXVJl/aNEx+PMtNG4jJrxJtbXJG2amXNuFTXbI4pOEVJuxAIBAoDQJBbkqDa6QaCFQ1Agzm2s2UQZtdotPz0ah8OpinpEY7HlcDQFkcRGiyRKca6hp1CAQqCYEgN5XUWlHWQKCMEGAgx4i4qGi4p8RHAz3hCkl6FK+S7JTUqNwiNlk8Uv9C93ILOxAIBFoOgSA3LYdlpBQItCsEGMR1ro8IjYgMz/jxzL2M3PVc6Tb1yZIZ4ULd5FcoXKXXPcofCJQzAkFuyrl1omyBQAUgkCUsGshTdwZ5DfQVUKUGFTElMaozEbkPEwgEAm2LQJCbtsU/cg8EKhKBdDAXacENvRtNSeGuE9uppOJgS8JTkZVPCk090qk21Zl6C4ckeBCfFIy4DwRKiECQmxKCG0kHAtWMACSFCzNkyBA74IADbI011rD11lvPVlttNVtppZWsR48e7i8yQ/hqUiju16+fLbfccta1a1fr1KmTrbvuul7/Aw880OsJ2RFG2MKhmvtF1C0QKAcEgtyUQytEGQKBCkSAwVqSmS+//NJWXXVVW2eddezcc8+1Y445xo499li7/fak/+a9AAAXX0lEQVTbvWYa1IlTDYM8dcC8++67dsQRR9hpp51mp59+uu2yyy4200wz2SabbGLDhw+foa7VUO8K7KZR5HaKQJCbdtrwUe1AoCUQ0CD/+eefu9RGkhpIj/ywuXD7+uuvbcCAATZo0CD78ccfXeIzcOBA4/r+++/zfpAl3DS1o7RI44cffvD4hB86dGj+HukRaX/zzTeeNvF5JgwmVX7WVFpzMVC5lN4bb7zh5GajjTayMWPGePLya25eET8QCAQajkCQm4ZjFSEDgUAgg4AGd8jN6quvbqeeeqqHEClJg999990+8C+11FL229/+1rCXXXZZn75afvnljWvxxRf3a+GFF7aOHTva888/70lI8gOx2XvvvW2eeebx+AsuuKAts8wyHneFFVbwKSKel1hiCVtsscVs7rnnts0339x1gdIporRczblX/UVgXn/9da/jhhtuGOSmOcBG3ECgmQgEuWkmgBE9EGjPCGhwr4vcIDHh+uqrr+zqq6+2Z555xnr16uWkZuaZZzYupnKwN910U3vsscfsxhtvtAceeMCGDRvm8CqfiRMn2rPPPmu9e/e2+++/33V65phjDuOaZZZZbLbZZrN5553X/vrXv1rfvn19Wqx///6ehnR9WpLkqFwiN6+88orXZYMNNghy055fjKh7myMQ5KbNmyAKEAhULgIa3EVuTjnlFK9MOi2V1g4JDAQF8+ijjzoRQLoCuVlllVXs448/dj+RBdJXHti4p1KhcePG2UknnTRDOnvuuaenQRm0kkkEyz1a8J/KpvKK3HTv3t3JjcrcgllGUoFAINAABILcNACkCBIIBAKFEdDgLnLz97//3QOKgMhfsSEBmmJ68cUXXeKCtAVyw4ojJDXEIUw2LmnIT+kR7uKLL/b4SG9I5//+7//cWwQrm05aBqXTVFtpZ8lNSG6aimjECwRaBoEgNy2DY6QSCLRLBDS4i9ycfPLJjgPEQoYwmhLCTXFeeOEFn0ISKVl//fVdiTgbXumktsgEJOqss85yUgNJYmrrqKOO8qBpGSBBPCtemlZz7lUXpfvqq696WSA3o0eP9qTl15x8Im4gEAg0DoEgN43DK0IHAoFAgoAGd8hNhw4dfIoIb0hHlqTwzCXJDbozs88+u80555xOCNZcc01fPk18CEGWFCgvbPlBms477zyPP9dcc7m9//77ewmZ/lJeKjJxiaP4cm+qrTIpvSA3TUUy4gUCLYtAkJuWxTNSCwTaFQIa3LPkJpWaCBCIRnpy+EsvveRkBILDdNJaa601w7QU8Uhfeeg5tSFR7KtDfE1vHXTQQZ4lfpAObIwIiGx3bOY/lU1ppjo3IblpJrgRPRBoBgJBbpoBXkQNBMoBAQ2wlCW955lBV27YGoRlN7f8SvuLL77wfW5OPPFET7IQucnm9dxzzzkhYVl3ltyk01jZeDyr/BCXs88+ewZy87e//c2jiNSk8VXeFAv5y49n7nWlZYGgpdIgxVF5JLlhKfjYsWM9Dfkpn7ADgUCg9AgEuSk9xpFDINDiCDCoMmhiaxBnENZAjHs6CGuAxV8riFqiUBrcG0NuFIc9YZiSksRl7bXXzi/9VnnrKyN1yU5LHXrooR5twoQJbpNfQ9JLwxEe/IQVfimeKpfqovRTchOb+AmlsAOB1kcgyE3rYx45BgItjgCDq6QlDLhcIj3poKzBuKUKoPQaQ26UNzo3SG1mnXVWl7x06dIlPy0lkqawxWz0as4//3yPr+mtVHIDLpRR6aWERWkqTBpOfsXcUn/ug9wIkbADgfJAIMhNebRDlCIQaBQCWTIjIgPBefnll+2yyy6z66+/3q677jp76623PG0NwAzYXC1hlE5TyA27D6MErH1uOnfu7EcpUC6VtVgZlS+kRZIbVl2xWkrkBqmLSI3SUTxskT65KQx5EzctA2EULuueljckN0Ix7ECgbREIctO2+EfugUCLIfDf//7X9ttvP2MZMscbLLnkkrbQQgsZG8qxp4yUedOBurmZa8BvDLlRHMqErg07CkvnhvOmIGoiHsXKB8HgSsmNSJJWS0H0FE7p8HzffffZxhtv7Idc7rTTTrb77rv7IZ9HHnmk28cdd5wdf/zxvvILe9999/WwhNt5553tpptu8uTIW3UhXUyQGyEddiDQtggEuWlb/CP3QKDJCEAAIAKQFvaXQX/lwgsv9P1VOCxy5MiRNmLECLvoooucPGgPGkl5mpxxElGDe1PIDSuL0LfRdBKrpVhhRJoiC0lWM9wqXzC44IILZpiWks6N9GWIKJID4bnkkks8POdSkSdSrp9++sm+/fZbP9iTAzk54JNzrAYPHuynfUO+uDbbbDM/jFNpqhwqb5CbGZopHgKBNkMgyE2bQR8ZBwLNQwDJAVNQO+ywg5+rxPQMRhIapX777bf7wIxE58MPP3Tn+iQjilufrcG9MeRGaSK5QdoinZv11lvPSQX1ypaPfNJL/hy/cOaZZ3r9lM5hhx3mWUDiIB2KhyPxIIAQle22287JH+7kiSG8dJd4/uyzz2zLLbf08Bz0idQHQzppukFuHJb4FwiUDQJBbsqmKaIggUDjEHjnnXeM07M5MFIDeiqtYPDFMNWzzTbb+AANgWCJsgb9xuX469DKozHkRnHY5wZpk0gJ+jccwbDRRhvZuuuua5Q1e3Xr1s241llnHff73e9+56eDQ1bAAfvAAw/0gkJSREBEPiB+nFy+0korubSGgFkypbBIb5i+Ik1IGIdxShdHdZCtOCG5+XUfCZdAoC0QCHLTFqhHnoFAAxBg4NRgSnAN1NyzzJlznBh4V111VevXr5+nqEGWB90z1cN5S4RFF6dPnz4eVtIPf2jiPw3uTSE3SG4gNtKVWXTRRe3ggw82dGa4UAzmglToOuCAAyx7sacMdRNJQu8Ik5IbVQ9yw+GeKC9jUv0e8FJ9kNhsvfXWni5pQ6iYosKkuCm8sE7JTWzi53DFv0CgTRAIctMmsEemgUDdCDBoproxGjyJheSFAZpBd7XVVrNnnnnGE2PQJZymVRR/1KhReXKDjsvmm2/u4TUVU3dJ6vbV4N4YcqMUWQqOMrH2uUFaIwKR1lfhU1v5UlfpFIncSHIDMRQWafgePXq4RIb0hJn8ceP+hhtucHznm28+J1rffPPNr6b7FBZb5Q1yAxphAoG2RyDITdu3QZQgECiIAAMmA212gH7ggQd84F1ggQXs0ksv9bipBEKJacBFsXi33XbLSyG22GKLfByFbaotUtAUcoO+0Pzzz5+XuHTs2NF1YEgzlY4UKpvyJZwUinUAp6boUt0jwhGHjfVuvfVWe/jhhz1ZTePhr/u+ffvaEkss4XihdMw+Ohi1A/fKX7awDnLjUMW/QKDNEQhy0+ZNEAUIBAojwMCpQV73DKL33HOPD7yLL754flmyBm8NsqlUBsnNPvvs43GQkmy77bZ5PRMNzoVLUL+r4jeG3CgOp4KzUkqkpFOnTq4HQ11Uj2IlUBqQOp0KroMzWbqNwY9wYKH0hg8f7uQGEoLJ5sWREOgxIRXjQv/ngw8++FV67pCQHKUf5EbIhB0ItC0CQW7aFv/IPRAoiIAG5awn0gVtWsceLQzODN4iM8TDLTWPP/64S0c0bYNyMYMx4UQS0vCNuVf8ppAbJDcoFGtailPBhw0b5tmLLBQri/KFwJxzzjlORLSkXJv4IWlROOrKM+kivQFH1V9h2O0YZWMRG3R5WB6OIYzS4D7FG3+VN8hNsRYL90CgdREIctO6eEdugUCDEWDA1NSKBk+kCOipQFRQvsUwwMsonAZsnq+88kofsJFssKmfprI0QCtuU2zl0xhyo3zYoRipTSq5GTJkSF7aonB12eCjpd0iSYVwIQ2VNU1PbhAelpTrEE+IzUcffeRBIUXCmPDCOE1Tbim5ibOlUqTjPhBoXQSC3LQu3pFbINBgBDTwymawPOSQQ/JSir322svTQgohfRHCKjyeDMromCCNgBCxBHrAgAEeJg3X4EJlAiqNxpAbxWG1FIREpIQN9QYNGuQ5iCxksss/Kg2Ix7nnnpvHhHoedNBBHo66k05KTJC+cMmQDgSpV69engbxkSZx0jiG9DGE4wJnNvxT/rJV3iA3Dlf8CwTaHIEgN23eBFGAQKA4Aho0CTFw4EBDGZgB+De/+U1eAqMBmLAagJUiz9rEj3islIIk4a6BWWGbYiuNxpAb5QO5kRSKsjEthU4MaaYEROFTW7ggfcqSG01LidQQj/CSVJG+4uMHYTnjjDMcV6RbrLZiqT1lIKzqCG6HH364H8FAPNKXn9ILcpO2UtwHAm2HQJCbtsM+cg4E6kSAgVMDMoPnXXfdZayQggigfMtgi392gE0T/fLLL/3IAOJwdABHCmDqIw9pGnXdK+9C5EZ+ip/WBzfITSGFYuokskC4bDy5YVMPHacgnRtJbiB92XqSFm5KHynWEUcc4fvegFG6R47CyEYxe4899sgvI5dkiHIoTJAb0AgTCLQ9AkFu2r4NogSBQEEENGDiyd42LElmAGYn3j//+c9ObBiodTFwS0eHOEyfsHKIOExJSaIhSU/BTBvpSJ6YLLmR1IQ66FLSisNqKSRQmpZiKTjnOmGIQziRuzQu9VUaSF2yxy9AVjCSrBCWK42HP+Tw2GOPzWO62GKL+RQefgqrcuD21FNPeVgUuTEpCSMcJsiNwxD/AoE2RyDITZs3QRQgECiMQDqwQkg0/bLccssZG+BhNKgqBQZxuXF4po5dgNxoczv2vWkpI5KRJTeUV37kJXLBPcQBg+QG4iWFYu1zQ/mzBCytl0eu/UdaktxIqsWydwzEJ8WQsKyIkuH4ilVWWcXLsMgii9jNN9/sXiItqoOI2vXXX+9EjKX4GMKpjsI8yI3QDTsQaFsEgty0Lf6ReyBQFAEGTkkuGJSlF7Lyyisb5zJh8GcQFxnQYMsBmaz4QYeEDeluvPFGPyWcOApTNONGeCitQuRGyUAqUomS3NlZmU3ydCZUly5d8sq6IgukLzIk0kF8+UM8RPogcJClQpIb5al4n3/+uZEf4VkhpUNHwVH5EBZsZXr37m3LLLOMn9WFG+VS/ZVuSm7i+AUhF3Yg0PoIBLlpfcwjx0CgwQho8GTA7dmzpw/G7OrL3i4Y3GU0wPLMMmtJRY488kgPAkFKwyhec2yVrxi5IT+RE8JCRvT89ttvO7HQtFSqR0SYtG7ZMipfwmmHYi3j1lJwSW4UV3UnzjXXXGMzzzyzY8TU2MUXX+zBJK3hgfCKwzOrzjp06JCfOiMdlUPhgtwI7bADgbZFIMhN2+IfuQcCdSKgwRNJDAdkMoBvv/32fqgjO/NiIAEKh4TklltuMaZ4OIgSaQPnImEIwyAsKY87NvOf8i1GbvAXScFmNRQkC0Xn3Xff3cmFDs5EisMSbFYqsd9NuqoLIiFiNGLECJfwIBl5/fXXbfXVV58hnTXWWMMeeughlxaxKeC4ceNmqCXTShAbJEacLs5mgigLCx/y0vQVed55552ujA2mLFen7JggNzPAGg+BQFkhEOSmrJojChMI5BBgoGXwlHn66ad9AIcAnHjiia5nwrTKe++9pyA++COBQGIDsbntttvcD1KhtGRL0pCP3MSbYuRGRCSdjrrvvvucnDG1o7ObIBlIbnR0AlNLKPYuvfTSvoLplVdeydeBG/SIUJJGekU6LCWHpGilFHXnYrNC9Gk4+HLPPffMkxXIDvsDEQaJDVi+++679thjj/kBpEi8OIIBnSYuDuWkfKRDHJarf/31116mIDdN7DQRLRBoBQSC3LQCyJFFINAcBFAAZn8VBleUbzkRHMPJ1SxNRoH2pJNO8pO///jHP/q+NqlOjggNcSAjIiTNKZPiKq1ikptUUoTEg92RkSZdd911LmHingudIOw+ffq4pOSKK66wO+6441eb+rFqjEMvUe7lIg44XH311T5tRBw25MMdf/LjIExJj1j6jeIwF9NMd999t4cjDmXgIj1sJGDK56abbvJ78kaylMVSZDGmpdQzwg4E2haBIDdti3/kHggURUDEgfONVlxxxby0QccnQFqYunnzzTd9CfK///1v3+iPBBW3aOIt5KFBXeTmhBNO8JRbcuqrhYpakmSEs3BIyQ1tg5FfSQoQiQYCgUBBBILcFIQlHAOB8kHgiSee8CkYJDecUg1xgNhoWoQpoNQw4EpSkbqX4l4Dt8gN0zyYIDcbOvEEC2FUCvwjzUAgECiMQJCbwriEayDQpghIIsCv/wMOOMClNuijoD+CYSWQwqQFlVtrDajKJ8hNbOKX9sO4DwTaGoEgN23dApF/IFAHAl999ZWfZYTUBgXat956y0OLVKRRRWxSt1LfqxxBboLclLqvRfqBQGMQCHLTGLQibCDQygig8Aqx4WL1z2effeYlgFQwLZWdfoLgZN1KWeQgN7njJ4RD6NyUsrdF2oFAwxEIctNwrCJkINDqCPTr18+JDUuee/To4fm3Jnmpr8Ia1JHcsEw6dG5e9fZid+hQKK6v94R/IFA6BILclA7bSDkQaDYCktwsvPDCdtlll3l6IjciFoUyqcuvUPimuikfkRuWpGNCoTjITVP7VMQLBFoCgSA3LYFipBEIlAiBRx55xCUBbDjHGUoYnXckYqGsmZJqbb0blSHITejcqB+GHQiUAwJBbsqhFaIMgUABBCAODzzwgJObFVZYwc+LIhi6NiIVBaK1mlNKpj755BPjbKhUckM5MYTTsvVWK1yJM1KdlI3ag/Oy0I9Kp6WEEzZGtuKGHQgEAi2PQJCblsc0UgwEWgwBzlHi2IKPPvrIB8XsoNpiGTUhobQsrOpabbXVjHOdDjvsMF+yzjEHl19+uafMVBrhIQFpvCZkW1ZRXnjhBdtuu+28vvvvv7+f+QW5YT+in376ycsq4kO9wwQCgUDrIBDkpnVwjlwCgUYhIALAwJgOjmzYp+dGJViiwBqwOW8JYsPAzn48XNxvttlmnrPIjcKXqDitkmxaB04Xp54c/jnnnHP6OVQ8d+/e3TgqgrZSeNmtUsjIJBBo5wgEuWnnHSCqX/4IMCgW2oW4rUtOuaTczMogptA4M4pznjib6dprr7Unn3zSi5lOUbV1uVsifxEVJFYXXnih15d69+zZ06666io//0oniys/xdFz2IFAIFA6BILclA7bSDkQaBYCGgxTSQ0kQYSiWYm3YGTKKfJCsjynZaT88scvrU8LFqNVk8rWuVDmhClkirkXChtugUAg0DQEgtw0DbeIFQi0CgIpGYAwlAsxoFwiMI0drKlDudSjOY0IYdPKtRSDrHvq15z8Im4gEAg0HIEgNw3HKkIGAm2CAIMjl6amIAYaVNukQEmmlEskR84iL/ilEhvdK1yl29QzrTv1S+uY1r/S6xrlDwQqDYEgN5XWYlHedokAA6VMuUg9ssQmLaPKilvWXeRHYarBTuuoe2y1FfdZ4lMN9Y46BALlikCQm3JtmShXIBAIBAKBQCAQCDQJgSA3TYItIgUCgUAgEAgEAoFAuSIQ5KZcWybKFQgEAoFAIBAIBAJNQiDITZNgi0iBQCAQCAQCgUAgUK4IBLkp15aJcgUCgUAgEAgEAoFAkxAIctMk2CJSIBAIBAKBQCAQCJQrAkFuyrVlolyBQCAQCAQCgUAg0CQEgtw0CbaIFAgEAoFAIBAIBALlikCQm3JtmShXIBAIBAKBQCAQCDQJgSA3TYItIgUCgUAgEAgEAoFAuSIQ5KZcWybKFQgEAoFAIBAIBAJNQiDITZNgi0iBQCAQCAQCgUAgUK4IBLkp15aJcgUCgUAgEAgEAoFAkxAIctMk2CJSIBAIBAKBQCAQCJQrAkFuyrVlolyBQCAQCAQCgUAg0CQEgtw0CbaIFAgEAoFAIBAIBALlikCQm3JtmShXIBAIBAKBQCAQCDQJgSA3TYItIgUCgUAgEAgEAoFAuSIQ5KZcWybKFQgEAoFAIBAIBAJNQiDITZNgi0iBQCAQCAQCgUAgUK4I/D+BVPW5v//SdAAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "id": "expensive-snapshot",
   "metadata": {},
   "source": [
    "## Results \n",
    "\n",
    "###  Spike pairing protocol\n",
    "\n",
    "If we set the values of $A_3^+$ and $A_3^-$ to 0 in the above equations, the model becomes a classical STDP model. The authors in [4] tested this model to check if they could reproduce the experimental results of [2] and [3]. They tested this with a pairing protocol which is a classic STDP protocol where the pairs of presynaptic and postsynaptic spikes shifted by $\\Delta{t}$ are repeated at regular intervals of $(1/\\rho)$. They showed that spike pairs repeated at high frequencies did not have considerable effect on the synaptic weights as shown by the experimental data.\n",
    "\n",
    "\n",
    "<div>\n",
    "<img src=\"attachment:image.png\" width=\"400\">\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "secure-north",
   "metadata": {},
   "source": [
    "### Triplet STDP model with spike pairs\n",
    "\n",
    "Let us simulate the pairing protocol in the above formulated model to see if we can reproduce the frequency dependence of spikes on the synaptic weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "conscious-trailer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create pre and post spike arrays\n",
    "def create_spike_pairs(freq=1., delta_t=0., size=10):\n",
    "    \"\"\"\n",
    "    Creates the spike pairs given the frequency and the time difference between the pre and post spikes.\n",
    "    :param: freq: frequency of the spike pairs\n",
    "    :param: delta_t: time difference or delay between the spikes\n",
    "    :param: size: number of spike pairs to be generated.\n",
    "    :return: pre and post spike arrays\n",
    "    \"\"\"\n",
    "    pre_spike_times = 1 + abs(delta_t) + freq * np.arange(size).astype(float)\n",
    "    post_spike_times = 1 + abs(delta_t) + delta_t + freq * np.arange(size).astype(float)\n",
    "    \n",
    "    return pre_spike_times, post_spike_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "similar-command",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_triplet_stdp_network(module_name, neuron_model_name, synapse_model_name, neuron_opts,\n",
    "                             nest_syn_opts, pre_spike_times, post_spike_times, \n",
    "                             resolution=1., delay=1., sim_time=None):\n",
    "    \"\"\"\n",
    "    Runs the triplet stdp synapse model\n",
    "    \"\"\"\n",
    "    nest.set_verbosity(\"M_ALL\")\n",
    "    nest.ResetKernel()\n",
    "    nest.Install(module_name)\n",
    "    nest.SetKernelStatus({'resolution': resolution})\n",
    "    \n",
    "    # Set defaults for neuron\n",
    "    nest.SetDefaults(neuron_model_name, neuron_opts)\n",
    "\n",
    "    # Create neurons\n",
    "    neurons = nest.Create(neuron_model_name, 2)\n",
    "\n",
    "    pre_sg = nest.Create('spike_generator', params={'spike_times': pre_spike_times})\n",
    "    post_sg = nest.Create('spike_generator', params={'spike_times': post_spike_times})\n",
    "\n",
    "    spikes = nest.Create('spike_recorder')\n",
    "    weight_recorder = nest.Create('weight_recorder')\n",
    "\n",
    "    # Set defaults for synapse\n",
    "    nest.CopyModel('static_synapse',\n",
    "                   'excitatory_noise',\n",
    "                   {'weight': 9999.,\n",
    "                    'delay' : syn_opts[\"delay\"]})\n",
    "\n",
    "    _syn_opts = nest_syn_opts.copy()\n",
    "    _syn_opts.pop('delay')\n",
    "\n",
    "    nest.CopyModel(synapse_model_name,\n",
    "                       synapse_model_name + \"_rec\",\n",
    "                       {'weight_recorder'   : weight_recorder[0]})\n",
    "    nest.SetDefaults(synapse_model_name + \"_rec\", _syn_opts)\n",
    "\n",
    "    # Connect nodes\n",
    "    nest.Connect(neurons[0], neurons[1], syn_spec={'synapse_model': synapse_model_name + \"_rec\"})\n",
    "    nest.Connect(pre_sg, neurons[0], syn_spec='excitatory_noise')\n",
    "    nest.Connect(post_sg, neurons[1], syn_spec='excitatory_noise')\n",
    "    nest.Connect(neurons, spikes)\n",
    "    \n",
    "    # Run simulation\n",
    "    syn = nest.GetConnections(source=neurons[0], synapse_model=synapse_model_name + \"_rec\")\n",
    "    initial_weight = nest.GetStatus(syn)[0]['w']\n",
    "    \n",
    "    nest.Simulate(sim_time)\n",
    "    \n",
    "    updated_weight = nest.GetStatus(syn)[0]['w']\n",
    "    dw = updated_weight - initial_weight\n",
    "    print('Initial weight: {}, Updated weight: {}'.format(initial_weight, updated_weight))\n",
    "\n",
    "    connections = nest.GetConnections(neurons, neurons)\n",
    "    gid_pre = nest.GetStatus(connections,'source')[0]\n",
    "    gid_post = nest.GetStatus(connections,'target')[0]\n",
    "\n",
    "    # From the spike recorder\n",
    "    events = nest.GetStatus(spikes, 'events')[0]\n",
    "    times_spikes = np.array(events['times'])\n",
    "    senders_spikes = events['senders']\n",
    "    # print('times_spikes: ', times_spikes)\n",
    "    # print('senders_spikes: ', senders_spikes)\n",
    "\n",
    "    # From the weight recorder\n",
    "    events = nest.GetStatus(weight_recorder, 'events')[0]\n",
    "    times_weights = events['times']\n",
    "    weights = events['weights']\n",
    "    # print('times_weights: ', times_weights)\n",
    "    # print('weights: ', weights)\n",
    "    \n",
    "    return dw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cosmetic-rachel",
   "metadata": {},
   "source": [
    "### Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "passing-character",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulate the network\n",
    "def run_frequency_simulation(module_name, neuron_model_name, synapse_model_name,\n",
    "                             neuron_opts, nest_syn_opts,\n",
    "                             freqs, delta_t, n_spikes):\n",
    "    \"\"\"\n",
    "    Runs the spike pair simulation for given frequencies and given time difference between spikes.\n",
    "    \"\"\"\n",
    "    dw_dict = dict.fromkeys(delta_t)\n",
    "    for _t in delta_t:\n",
    "        dw_vec = []\n",
    "        for _freq in freqs:\n",
    "            spike_interval = (1/_freq)*1000 # in ms\n",
    "\n",
    "            pre_spike_times, post_spike_times = create_spike_pairs(freq=spike_interval,\n",
    "                                                                   delta_t=_t,\n",
    "                                                                   size=n_spikes)\n",
    "\n",
    "            sim_time = max(np.amax(pre_spike_times), np.amax(post_spike_times)) + 10. + 3 * syn_opts[\"delay\"]\n",
    "\n",
    "            dw = run_triplet_stdp_network(module_name, neuron_model_name, synapse_model_name, \n",
    "                                          neuron_opts, nest_syn_opts,\n",
    "                                          pre_spike_times=pre_spike_times,\n",
    "                                          post_spike_times=post_spike_times,\n",
    "                                          sim_time=sim_time)\n",
    "            dw_vec.append(dw)\n",
    "\n",
    "        dw_dict[_t] = dw_vec\n",
    "        \n",
    "    return dw_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "respective-police",
   "metadata": {},
   "source": [
    "### All-to-all spike interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "active-pipeline",
   "metadata": {},
   "outputs": [],
   "source": [
    "freqs = [1., 5., 10., 20., 40., 50.] # frequency of spikes in Hz\n",
    "delta_t = [10, -10] # delay between t_post and t_pre in ms\n",
    "n_spikes = 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "psychological-reflection",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial weight: 1.0, Updated weight: 1.000062712440608\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 59034\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 11834\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "Initial weight: 1.0, Updated weight: 1.045481723674705\n",
      "Initial weight: 1.0, Updated weight: 1.1180707933363045\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "Initial weight: 1.0, Updated weight: 1.205329009261286\n",
      "Initial weight: 1.0, Updated weight: 1.4186655196495506\n",
      "Initial weight: 1.0, Updated weight: 1.5813821544865971\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 5934\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 2984\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 1509\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 1214\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 59024\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "Initial weight: 1.0, Updated weight: 0.6678711978627694\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial weight: 1.0, Updated weight: 0.6653426131462727\n",
      "Initial weight: 1.0, Updated weight: 0.6450780469148971\n",
      "Initial weight: 1.0, Updated weight: 0.6180411107607721\n",
      "Initial weight: 1.0, Updated weight: 1.068737821702289\n",
      "Initial weight: 1.0, Updated weight: 1.5937453662768748\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 11824\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 5924\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 2974\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 1499\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 1204\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n"
     ]
    }
   ],
   "source": [
    "syn_opts = {\n",
    "        'delay': 1.,\n",
    "        'tau_minus': 33.7,\n",
    "        'tau_plus': 16.8,\n",
    "        'tau_x': 101.,\n",
    "        'tau_y': 125.,\n",
    "        'A2_plus': 5e-10,\n",
    "        'A3_plus': 6.2e-3,\n",
    "        'A2_minus': 7e-3,\n",
    "        'A3_minus': 2.3e-4,\n",
    "        'Wmax':  50.,\n",
    "        'Wmin' : 0.,\n",
    "        'w': 1.\n",
    "}\n",
    "\n",
    "synapse_suffix = neuron_model_name[neuron_model_name.find(\"_with_\")+6:]\n",
    "neuron_opts = {'tau_minus__for_' + synapse_suffix: syn_opts['tau_minus'],\n",
    "                'tau_y__for_' + synapse_suffix: syn_opts['tau_y']}\n",
    "\n",
    "nest_syn_opts = syn_opts.copy()\n",
    "nest_syn_opts.pop('tau_minus')\n",
    "nest_syn_opts.pop('tau_y')\n",
    "\n",
    "dw_dict = run_frequency_simulation(module_name, neuron_model_name, synapse_model_name, \n",
    "                                   neuron_opts, nest_syn_opts,\n",
    "                                   freqs, delta_t, n_spikes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "piano-limit",
   "metadata": {},
   "source": [
    "### Nearest spike interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "inclusive-galaxy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 59034\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "Initial weight: 1.0, Updated weight: 1.0000000027196625\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "Initial weight: 1.0, Updated weight: 1.0086627050654013\n",
      "Initial weight: 1.0, Updated weight: 1.0903003652138468\n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of loInitial weight: 1.0, Updated weight: 1.2776911537160713\n",
      "Initial weight: 1.0, Updated weight: 1.4771400111243256\n",
      "Initial weight: 1.0, Updated weight: 1.530550096954562\n",
      "cal nodes: 6\n",
      "    Simulation time (ms): 11834\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 5934\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 2984\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 1509\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrInitial weight: 1.0, Updated weight: 0.554406040254968\n",
      "ix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 1214\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 InstalInitial weight: 1.0, Updated weight: 0.5544062835543123\n",
      "Initial weight: 1.0, Updated weight: 0.5555461935366892\n",
      "Initial weight: 1.0, Updated weight: 0.632456315445355\n",
      "Initial weight: 1.0, Updated weight: 1.2001792723059206\n",
      "Initial weight: 1.0, Updated weight: 1.5398255566140917\n",
      "l [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 59024\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 11824\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 5924\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 2974\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 1499\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:10 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:10 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:10 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:10 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 1204\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:10 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n"
     ]
    }
   ],
   "source": [
    "syn_opts_nn = {\n",
    "        'delay': 1.,\n",
    "        'tau_minus': 33.7,\n",
    "        'tau_plus': 16.8,\n",
    "        'tau_x': 714.,\n",
    "        'tau_y': 40.,\n",
    "        'A2_plus': 8.8e-11,\n",
    "        'A3_plus': 5.3e-2,\n",
    "        'A2_minus': 6.6e-3,\n",
    "        'A3_minus': 3.1e-3,\n",
    "        'Wmax':  50.,\n",
    "        'Wmin' : 0.,\n",
    "        'w': 1.\n",
    "}\n",
    "\n",
    "synapse_suffix_nn = neuron_model_name_nn[neuron_model_name_nn.find(\"_with_\")+6:]\n",
    "neuron_opts_nn = {'tau_minus__for_' + synapse_suffix_nn: syn_opts_nn['tau_minus'],\n",
    "                  'tau_y__for_' + synapse_suffix_nn: syn_opts_nn['tau_y']}\n",
    "\n",
    "nest_syn_opts_nn = syn_opts_nn.copy()\n",
    "nest_syn_opts_nn.pop('tau_minus')\n",
    "nest_syn_opts_nn.pop('tau_y')\n",
    "\n",
    "dw_dict_nn = run_frequency_simulation(module_name_nn, neuron_model_name_nn, synapse_model_name_nn,\n",
    "                                      neuron_opts_nn, nest_syn_opts_nn,\n",
    "                                      freqs, delta_t, n_spikes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "successful-capitol",
   "metadata": {},
   "source": [
    "### Plot: Weight change as a function of frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "biological-drunk",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f93d1e86310>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, axes = plt.subplots()\n",
    "ls1 = axes.plot(freqs, dw_dict_nn[delta_t[0]], color='r')\n",
    "ls2 = axes.plot(freqs, dw_dict_nn[delta_t[1]], linestyle='--', color='r')\n",
    "axes.plot(freqs, dw_dict[delta_t[0]], color='b')\n",
    "axes.plot(freqs, dw_dict[delta_t[1]], linestyle='--', color='b')\n",
    "\n",
    "axes.set_xlabel(r'${\\rho}(Hz)$')\n",
    "axes.set_ylabel(r'${\\Delta}w$')\n",
    "\n",
    "lines = axes.get_lines()\n",
    "legend = plt.legend([lines[i] for i in [0,2]], ['Nearest spike', 'All-to-all'])\n",
    "legend2 = plt.legend([ls1[0], ls2[0]], [r'${\\Delta}t = 10ms$', r'${\\Delta}t = -10ms$'], loc = 4)\n",
    "axes.add_artist(legend)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interior-parliament",
   "metadata": {},
   "source": [
    "### Triplet protocol\n",
    "\n",
    "In the first triplet protocol, each triplet consists of two presynaptic spikes and one postsynaptic spike separated by $\\Delta{t_1} = t^{post} - t_1^{pre}$ and $\\Delta{t_2} = t^{post} - t_2^{pre}$ where $t_1^{pre}$ and $t_2^{pre}$ are the presynaptic spikes of the triplet. \n",
    "\n",
    "The second triplet protocol is similar to the first with the difference that it contains two postsynaptic spikes and one presynaptic spike. In this case, $\\Delta{t_1} = t_1^{post} - t^{pre}$ and $\\Delta{t_2} = t_2^{post} - t^{pre}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "adequate-diesel",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_triplet_spikes(_delays, n, trip_delay=1):\n",
    "    _dt1 = abs(_delays[0])\n",
    "    _dt2 = abs(_delays[1])\n",
    "    _interval = _dt1 + _dt2\n",
    "    \n",
    "    # pre_spikes\n",
    "    start = 1\n",
    "    stop = 0\n",
    "    pre_spike_times = []\n",
    "    for i in range(n):\n",
    "        start = stop + 1 if i == 0 else stop + trip_delay\n",
    "        stop = start + _interval\n",
    "        pre_spike_times = np.hstack([pre_spike_times, [start, stop]])\n",
    "    \n",
    "    # post_spikes\n",
    "    start = 1 + _dt1\n",
    "    step = _interval + trip_delay\n",
    "    stop = start + n * step\n",
    "    post_spike_times = np.arange(start, stop, step).astype(float)\n",
    "    \n",
    "    return pre_spike_times, post_spike_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "certified-bubble",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pre_spike_times, post_spike_times = create_triplet_spikes((5, -5), 2, 10)\n",
    "l = []\n",
    "l.append(post_spike_times)\n",
    "l.append(pre_spike_times)\n",
    "colors1 = ['C{}'.format(i) for i in range(2)]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3))\n",
    "ax1.eventplot(l, colors=colors1)\n",
    "ax1.legend(['post', 'pre'])\n",
    "\n",
    "l = []\n",
    "l.append(pre_spike_times)\n",
    "l.append(post_spike_times)\n",
    "ax2.eventplot(l, colors=colors1)\n",
    "ax2.legend(['post', 'pre'])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "treated-jerusalem",
   "metadata": {},
   "source": [
    "### STDP model with triplets\n",
    "\n",
    "The standard STDP model fails to reproduce the triplet experiments shown in [2] and [3]. The experments show a clear asymmetry between the pre-post-pre and post-pre-post protocols, but the standard STDP rule fails to reproduce it.\n",
    "\n",
    "\n",
    "<div>\n",
    "<img src=\"attachment:image.png\" width=\"600\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adult-observation",
   "metadata": {},
   "source": [
    "### Triplet model with triplet protocol\n",
    "\n",
    "Let us try to formulate the two triplet protocols using the triplet model and check if we can reproduce the asymmetry in the change in weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "liquid-offset",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulation\n",
    "def run_triplet_protocol_simulation(module_name, neuron_model_name, synapse_model_name,\n",
    "                                    neuron_opts, nest_syn_opts,\n",
    "                                    spike_delays, n_triplets = 1, triplet_delay = 1000,\n",
    "                                    pre_post_pre=True):\n",
    "    dw_vec= []\n",
    "\n",
    "    # For pre-post-pre triplets\n",
    "    for _delays in spike_delays:\n",
    "        pre_spike_times, post_spike_times = create_triplet_spikes(_delays, n_triplets, triplet_delay)\n",
    "        if not pre_post_pre: # swap the spike arrays\n",
    "            post_spike_times, pre_spike_times = pre_spike_times, post_spike_times\n",
    "\n",
    "        sim_time = max(np.amax(pre_spike_times), np.amax(post_spike_times)) + 10. + 3 * syn_opts[\"delay\"]\n",
    "\n",
    "        print('Simulating for (delta_t1, delta_t2) = ({}, {})'.format(_delays[0], _delays[1]))\n",
    "        dw = run_triplet_stdp_network(module_name, neuron_model_name, synapse_model_name,\n",
    "                                      neuron_opts, nest_syn_opts,\n",
    "                                      pre_spike_times=pre_spike_times,\n",
    "                                      post_spike_times=post_spike_times,\n",
    "                                      sim_time=sim_time)\n",
    "        dw_vec.append(dw)\n",
    "        \n",
    "    return dw_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "moderate-surfing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All to all - Parameters are taken from [4]\n",
    "syn_opts = {\n",
    "        'delay': 1.,\n",
    "        'tau_minus': 33.7,\n",
    "        'tau_plus': 16.8,\n",
    "        'tau_x': 946.,\n",
    "        'tau_y': 27.,\n",
    "        'A2_plus': 6.1e-3,\n",
    "        'A3_plus': 6.7e-3,\n",
    "        'A2_minus': 1.6e-3,\n",
    "        'A3_minus': 1.4e-3,\n",
    "        'Wmax':  50.,\n",
    "        'Wmin' : 0.,\n",
    "        'w': 1.\n",
    "}\n",
    "\n",
    "synapse_suffix = neuron_model_name[neuron_model_name.find(\"_with_\")+6:]\n",
    "neuron_opts_nn = {'tau_minus__for_' + synapse_suffix: syn_opts['tau_minus'],\n",
    "                  'tau_y__for_' + synapse_suffix: syn_opts['tau_y']}\n",
    "\n",
    "nest_syn_opts = syn_opts.copy()\n",
    "nest_syn_opts.pop('tau_minus')\n",
    "nest_syn_opts.pop('tau_y')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "revolutionary-coffee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nearest spike - Parameters are taken from [4]\n",
    "syn_opts_nn = {\n",
    "        'delay': 1.,\n",
    "        'tau_minus': 33.7,\n",
    "        'tau_plus': 16.8,\n",
    "        'tau_x': 575.,\n",
    "        'tau_y': 47.,\n",
    "        'A2_plus': 4.6e-3,\n",
    "        'A3_plus': 9.1e-3,\n",
    "        'A2_minus': 3e-3,\n",
    "        'A3_minus': 7.5e-9,\n",
    "        'Wmax':  50.,\n",
    "        'Wmin' : 0.,\n",
    "        'w': 1.\n",
    "}\n",
    "\n",
    "synapse_suffix_nn = neuron_model_name_nn[neuron_model_name_nn.find(\"_with_\")+6:]\n",
    "neuron_opts_nn = {'tau_minus__for_' + synapse_suffix_nn: syn_opts_nn['tau_minus'],\n",
    "                  'tau_y__for_' + synapse_suffix_nn: syn_opts_nn['tau_y']}\n",
    "\n",
    "nest_syn_opts_nn = syn_opts_nn.copy()\n",
    "nest_syn_opts_nn.pop('tau_minus')\n",
    "nest_syn_opts_nn.pop('tau_y')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extra-minnesota",
   "metadata": {},
   "source": [
    "### pre-post-pre triplet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "close-seating",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating for (delta_t1, delta_t2) = (5, -5)\n",
      "Initial weight: 1.0, Updated weight: 1.0003735276417982\n",
      "Simulating for (delta_t1, delta_t2) = (10, -10)\n",
      "Initial weight: 1.0, Updated weight: 0.9998230228609227\n",
      "Simulating for (delta_t1, delta_t2) = (15, -5)\n",
      "Initial weight: 1.0, Updated weight: 0.9984719712644969\n",
      "Simulating for (delta_t1, delta_t2) = (5, -15)\n",
      "Initial weight: 1.0, Updated weight: 1.001383086591746\n",
      "Simulating for (delta_t1, delta_t2) = (5, -5)\n",
      "Initial weight: 1.0, Updated weight: 1.0005542494412774\n",
      "Simulating for (delta_t1, delta_t2) = (10, -10)\n",
      "Initial weight: 1.0, Updated weight: 1.0000931206450185\n",
      "Simulating for (delta_t1, delta_t2) = (15, -5)\n",
      "Initial weight: 1.0, Updated weight: 0.9991105337807658\n",
      "Simulating for (delta_t1, delta_t2) = (5, -15)\n",
      "Initial weight: 1.0, Updated weight: 1.0012383200640604\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 24\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 34\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 34\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 34\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 24\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 34\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 34\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 34\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n"
     ]
    }
   ],
   "source": [
    "pre_post_pre_delays = [(5, -5), (10, -10), (15, -5), (5, -15)]\n",
    "\n",
    "# All-to-All interation\n",
    "dw_vec = run_triplet_protocol_simulation(module_name, neuron_model_name, synapse_model_name,\n",
    "                                         neuron_opts, nest_syn_opts,\n",
    "                                         pre_post_pre_delays,\n",
    "                                         n_triplets=1,\n",
    "                                         triplet_delay=1000)\n",
    "\n",
    "# Nearest spike interaction\n",
    "dw_vec_nn = run_triplet_protocol_simulation(module_name_nn, neuron_model_name_nn, synapse_model_name_nn,\n",
    "                                            neuron_opts_nn, nest_syn_opts_nn,\n",
    "                                            pre_post_pre_delays,\n",
    "                                            n_triplets=1,\n",
    "                                            triplet_delay=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "alpine-consultancy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "bar_width = 0.25\n",
    "fig, axes = plt.subplots()\n",
    "br1 = np.arange(len(pre_post_pre_delays))\n",
    "br2 = [x + bar_width for x in br1]\n",
    "axes.bar(br1, dw_vec, width=bar_width, color='b')\n",
    "axes.bar(br2, dw_vec_nn, width=bar_width, color='r')\n",
    "axes.set_xticks([r + bar_width for r in range(len(br1))])\n",
    "axes.set_xticklabels(pre_post_pre_delays)\n",
    "axes.set_xlabel(r'${(\\Delta{t_1}, \\Delta{t_2})} \\: [ms]$')\n",
    "axes.set_ylabel(r'${\\Delta}w$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alert-provision",
   "metadata": {},
   "source": [
    "### post-pre-post triplet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "proved-broadcasting",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating for (delta_t1, delta_t2) = (-5, 5)\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "Initial weight: 1.0, Updated weight: 1.0452168105331474\n",
      "Simulating for (delta_t1, delta_t2) = (-10, 10)\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 9114\n",
      "    Number of OpenMP thrInitial weight: 1.0, Updated weight: 1.0275785817728278\n",
      "Simulating for (delta_t1, delta_t2) = (-5, 15)\n",
      "Initial weight: 1.0, Updated weight: 1.008936270857372\n",
      "eads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepareSimulating for (delta_t1, delta_t2) = (-15, 5)\n",
      "Initial weight: 1.0, Updated weight: 1.050539844879153\n",
      "Simulating for (delta_t1, delta_t2) = (-5, 5)\n",
      "_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 9214\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal Initial weight: 1.0, Updated weight: 1.048644757755009\n",
      "Simulating for (delta_t1, delta_t2) = (-10, 10)\n",
      "state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 9214\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_82fabdb7b0ca411190789324e0b5cb66_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. InterInitial weight: 1.0, Updated weight: 1.026345906763637\n",
      "Simulating for (delta_t1, delta_t2) = (-5, 15)\n",
      "nal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 9214\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 3Initial weight: 1.0, Updated weight: 1.0099778920748412\n",
      "0 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 9114\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 9214\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::preSimulating for (delta_t1, delta_t2) = (-15, 5)\n",
      "Initial weight: 1.0, Updated weight: 1.0466078732990223\n",
      "pare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 9214\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n",
      "\n",
      "Apr 30 14:28:11 Install [Info]: \n",
      "    loaded module nestml_d21f76a04f014810b633adbbfabc1701_module\n",
      "\n",
      "Apr 30 14:28:11 correlation_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlomatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.5 to 5 ms\n",
      "\n",
      "Apr 30 14:28:11 correlospinmatrix_detector [Info]: \n",
      "    Default for delta_tau changed from 0.1 to 1 ms\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n",
      "    Simulation resolution has changed. Internal state and parameters of the \n",
      "    model have been reset!\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::set_status [Info]: \n",
      "    Temporal resolution changed from 0.1 to 1 ms.\n",
      "\n",
      "Apr 30 14:28:11 NodeManager::prepare_nodes [Info]: \n",
      "    Preparing 6 nodes for simulation.\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::start_updating_ [Info]: \n",
      "    Number of local nodes: 6\n",
      "    Simulation time (ms): 9214\n",
      "    Number of OpenMP threads: 1\n",
      "    Not using MPI\n",
      "\n",
      "Apr 30 14:28:11 SimulationManager::run [Info]: \n",
      "    Simulation finished.\n"
     ]
    }
   ],
   "source": [
    "post_pre_post_delays = [(-5, 5), (-10, 10), (-5, 15), (-15, 5)]\n",
    "\n",
    "# All-to-All interaction\n",
    "dw_vec = run_triplet_protocol_simulation(module_name, neuron_model_name, synapse_model_name,\n",
    "                                         neuron_opts, nest_syn_opts,\n",
    "                                         post_pre_post_delays,\n",
    "                                         n_triplets=10,\n",
    "                                         triplet_delay=1000,\n",
    "                                         pre_post_pre=False)\n",
    "\n",
    "# Nearest spike interaction\n",
    "dw_vec_nn = run_triplet_protocol_simulation(module_name_nn, neuron_model_name_nn, synapse_model_name_nn,\n",
    "                                            neuron_opts_nn, nest_syn_opts_nn,\n",
    "                                            post_pre_post_delays,\n",
    "                                            n_triplets=10,\n",
    "                                            triplet_delay=1000,\n",
    "                                            pre_post_pre=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "plastic-stewart",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "bar_width = 0.25\n",
    "fig, axes = plt.subplots()\n",
    "br1 = np.arange(len(pre_post_pre_delays))\n",
    "br2 = [x + bar_width for x in br1]\n",
    "axes.bar(br1, dw_vec, width=bar_width, color='b')\n",
    "axes.bar(br2, dw_vec_nn, width=bar_width, color='r')\n",
    "axes.set_xticks([r + bar_width for r in range(len(br1))])\n",
    "axes.set_xticklabels(post_pre_post_delays)\n",
    "axes.set_xlabel(r'${(\\Delta{t_1}, \\Delta{t_2})} \\: [ms]$')\n",
    "axes.set_ylabel(r'${\\Delta}w$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "initial-interval",
   "metadata": {},
   "source": [
    "### Quadruplet protocol\n",
    "\n",
    "This protocol is with a set of four spikes and defined as follows: a post-pre pair with a delay of $\\Delta{t_1} = t_1^{post} - t_1^{pre}< 0$ is followed after a time $T$ with a pre-post pair with a delat of $\\Delta{t_2} = t_2^{post} - t_2^{pre} > 0$. When $T$ is negative, a pre-post pair with a delay of $\\Delta{t_2} = t_2^{post} - t_2^{pre} > 0$ is  followed by a post-pre pair with delay $\\Delta{t_1} = t_1^{post} - t_1^{pre}< 0$.\n",
    "\n",
    "Try to formulate this protocol using the defined model and reproduce the following weight change window graph."
   ]
  },
  {
   "attachments": {
    "image-3.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "lined-voluntary",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"attachment:image-3.png\" width=\"400\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "primary-spending",
   "metadata": {},
   "source": [
    "References\n",
    "----------\n",
    "\n",
    "[1] Bi G, Poo M (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18:10464–10472.\n",
    "\n",
    "[2] Wang HX, Gerkin RC, Nauen DW, Bi GQ (2005) Coactivation and timing-dependent integration of synaptic potentiation and depression. Nat Neurosci 8:187–193.\n",
    "\n",
    "[3] Sjöström P, Turrigiano G, Nelson S (2001) Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron 32:1149–1164.CrossRefPubMedGoogle Scholar\n",
    "\n",
    "[4] J.P. Pfister, W. Gerstner Triplets of spikes in a model of spike-timing-dependent plasticity\n",
    "J. Neurosci., 26 (2006), pp. 9673-9682"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "shared-spice",
   "metadata": {},
   "source": [
    "Acknowledgements\n",
    "----------------\n",
    "\n",
    "This software was developed in part or in whole in the Human Brain Project, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270 and No. 785907 (Human Brain Project SGA1 and SGA2).\n",
    "\n",
    "License\n",
    "-------\n",
    "\n",
    "This notebook (and associated files) is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 2 of the License, or (at your option) any later version.\n",
    "\n",
    "This notebook (and associated files) is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}