SpiNNaker/eg_diverging-network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diverging network\n",
    "\n",
    "- A network of 100 excitatory cells without any inhibition\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fileName\n",
    "\n",
    "fileName = 'eg_diverging-network' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected PyNN version 0.9.4 and Neo version 0.6.1\n"
     ]
    }
   ],
   "source": [
    "# libs\n",
    "\n",
    "import pyNN.spiNNaker as sim\n",
    "from pyNN import space \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-17 10:11:08 INFO: Read cfg files: /home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spinn_front_end_common/interface/spinnaker.cfg, /home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spynnaker/pyNN/spynnaker.cfg, /home/spinnaker/.spynnaker.cfg\n",
      "2023-03-17 10:11:08 INFO: Will search these locations for binaries: /home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spinn_front_end_common/common_model_binaries : /home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spynnaker/pyNN/model_binaries\n",
      "2023-03-17 10:11:08 WARNING: /home/spinnaker/CNT-2023/SpiNNaker/reports has 38 old reports that have not been closed\n",
      "2023-03-17 10:11:08 WARNING: /home/spinnaker/CNT-2023/SpiNNaker/application_generated_data_files has 38 old reports that have not been closed\n",
      "2023-03-17 10:11:08 INFO: Setting time scale factor to 1.\n",
      "2023-03-17 10:11:08 INFO: Setting machine time step to 1000 micro-seconds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spinn_front_end_common/interface/spinnaker.cfg', '/home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spynnaker/pyNN/spynnaker.cfg', '/home/spinnaker/.spynnaker.cfg']\n"
     ]
    }
   ],
   "source": [
    "# simulation settings\n",
    "\n",
    "sim.setup(\n",
    "        timestep=1, # [ms]\n",
    "        min_delay=1, # [ms]\n",
    "        max_delay=100) # [ms]\n",
    "\n",
    "simtime = 250 #ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Exc': <spynnaker8.models.populations.population.Population at 0x7f60686a5798>}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defining the network\n",
    "\n",
    "pops = {}\n",
    "n_cells = {}\n",
    "neuron_Model = {}\n",
    "neuron_ModelPars = {}\n",
    "initial_Values = {}\n",
    "\n",
    "for popName in ['Exc']:\n",
    "    n_cells[popName] = 100\n",
    "    neuron_Model[popName] = sim.IF_cond_exp\n",
    "    neuron_ModelPars[popName] = sim.IF_cond_exp.default_parameters # or standard_pars or testing_pars\n",
    "    initial_Values[popName] = sim.IF_cond_exp.default_initial_values\n",
    "    pops[popName] = sim.Population(n_cells[popName], \n",
    "                                   neuron_Model[popName],\n",
    "                                   neuron_ModelPars[popName],\n",
    "                                   structure = space.Line(dx=1.0, x0=0.0, y=0.0, z=0.0),\n",
    "                                   label = popName)\n",
    "                                   # add spatial constraint\n",
    "\n",
    "    pops[popName].record(['spikes','v','gsyn_exc','gsyn_inh'])   \n",
    " \n",
    "pops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining the list of connections\n",
    "\n",
    "def compute_manual_list(n_cells_i, n_cells_j, weights, d_thresh, p_thresh, width): \n",
    "        v_c = 0.35 #m/s\n",
    "        d0 = 1 #ms\n",
    "        scale = 1\n",
    "\n",
    "        connections = {}\n",
    "        probabilities = {}\n",
    "        distances = {}\n",
    "\n",
    "        connections = []\n",
    "        probabilities = []\n",
    "        distances = []\n",
    "        for pre in range(n_cells_i):\n",
    "            for post in range(n_cells_j):\n",
    "                d_ij = np.sqrt((pre - post)**2)\n",
    "                delay = d0 + d_ij / v_c\n",
    "                if d_ij > d_thresh: \n",
    "                    distances.append(d_ij)\n",
    "                    p_ij = scale*np.exp(-0.5 * (d_ij**2/width**2))\n",
    "                    probabilities.append(p_ij)\n",
    "\n",
    "                    if p_ij > p_thresh:\n",
    "                        connections.append([pre, post, weights, delay])#, [d_ij, p_ij]])\n",
    "\n",
    "        return connections, distances, probabilities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute manual list of connections \n",
    "\n",
    "weight = {('Exc', 'Exc') : 0.08}\n",
    "          \n",
    "d_thresh = {('Exc', 'Exc'): 0}\n",
    "\n",
    "p_thresh = {('Exc', 'Exc'): 0.2}\n",
    "\n",
    "width = {('Exc', 'Exc'): 1}\n",
    "\n",
    "\n",
    "\n",
    "connections = {}\n",
    "distances = {}\n",
    "probabilities = {}\n",
    "\n",
    "connections['Exc', 'Exc'], distances['Exc', 'Exc'], probabilities['Exc', 'Exc'] = compute_manual_list(n_cells['Exc'], \n",
    "                                                                                                      n_cells['Exc'], \n",
    "                                                                                                      weight['Exc', 'Exc'], \n",
    "                                                                                                      d_thresh['Exc', 'Exc'], \n",
    "                                                                                                      p_thresh['Exc', 'Exc'], \n",
    "                                                                                                      width['Exc', 'Exc'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e89dbbf474d4bd3956114ec14bc8bd8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f60664e6c88>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visual check of the connections\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(9,5))\n",
    "fig.tight_layout(pad=3)\n",
    "axes_list = fig.axes\n",
    "\n",
    "axes_list[0].plot(np.asarray(connections['Exc', 'Exc']).T[0], np.asarray(connections['Exc', 'Exc']).T[1],'go', label='Exc (i) - Exc (j)')\n",
    "axes_list[0].grid()\n",
    "axes_list[0].legend()\n",
    "axes_list[0].set_title('scatter plot of connections')\n",
    "axes_list[0].set_xlabel('i cells')\n",
    "axes_list[0].set_ylabel('j cells')\n",
    "axes_list[0].set_xlim(20,40)\n",
    "axes_list[0].set_ylim(20,40)\n",
    "\n",
    "axes_list[1].plot(distances['Exc', 'Exc'],probabilities['Exc', 'Exc'],'g+')\n",
    "axes_list[1].plot(np.arange(0, 50), 1*np.exp(-0.5 * (np.arange(0, 50)**2/width['Exc', 'Exc']**2)), 'g:')\n",
    "axes_list[1].grid()\n",
    "axes_list[1].set_xlim(0,15)\n",
    "axes_list[1].set_ylim(0,1)\n",
    "axes_list[1].axhline(p_thresh['Exc', 'Exc'], color='k', label='p_threshold', )\n",
    "axes_list[1].set_title('connections as function of prob and dist')\n",
    "axes_list[1].set_xlabel('distance')\n",
    "axes_list[1].set_ylabel('probability')\n",
    "axes_list[1].legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the projections\n",
    "\n",
    "proj = {}\n",
    "\n",
    "\n",
    "\n",
    "proj['Exc', 'Exc'] = sim.Projection(pops['Exc'], \n",
    "                                    pops['Exc'],\n",
    "                                    connector = sim.FromListConnector(connections['Exc', 'Exc']),\n",
    "                                    receptor_type = 'excitatory',\n",
    "                                    space = space.Space(axes = 'x'),\n",
    "                                    label = 'exc-exc connections'\n",
    "                                    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f66c274d2a9b4970855c9ef734a80dd2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,\n",
       "       57, 58, 59])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make the thalamic input sources\n",
    "\n",
    "popName = 'Thalamus'  \n",
    "\n",
    "n_cells[popName] = n_cells['Exc']\n",
    "spike_times = [[]]*n_cells[popName] #list of spike lists, where one spike list is related to one spike source\n",
    "#random_sources_idx = [np.random.randint(n_cells[popName]*0.40, n_cells[popName]*0.60) for i in range(n_cells[popName])]\n",
    "random_sources_idx = np.arange(int(n_cells[popName]*0.40), int(n_cells[popName]*0.60))\n",
    "\n",
    "for idx, sources in enumerate(random_sources_idx):\n",
    "    spike_times[sources] = np.sort([abs(np.random.normal(loc=100, scale=25)) for n in range(5)])\n",
    "\n",
    "\n",
    "neuron_Model[popName] = sim.SpikeSourceArray(spike_times)\n",
    "\n",
    "\n",
    "pops[popName] = sim.Population(n_cells[popName], \n",
    "                           neuron_Model[popName],\n",
    "                           label = popName)\n",
    "\n",
    "proj['Thalamus', 'Exc'] = sim.Projection(pops['Thalamus'], \n",
    "                                         pops['Exc'],\n",
    "                                         connector = sim.OneToOneConnector(),\n",
    "                                         synapse_type = sim.StaticSynapse(weight=0.08, delay=1),\n",
    "                                         receptor_type = 'excitatory',\n",
    "                                         label = 'thalamus-exc connections')\n",
    "\n",
    "fig, axes = plt.subplots(1,1)\n",
    "axes_list = fig.axes\n",
    "axes_list[0].eventplot(spike_times)\n",
    "axes_list[0].set_xlabel('[ms]')\n",
    "axes_list[0].set_ylabel('thalamic spike sources')\n",
    "axes_list[0].set_title('rasterplot of spike sources')\n",
    "axes_list[0].set_xlim(0, simtime)\n",
    "\n",
    "random_sources_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-17 10:11:10 INFO: Simulating for 250 1.0ms timesteps using a hardware timestep of 1000us\n",
      "2023-03-17 10:11:10 INFO: Starting execution process\n",
      "2023-03-17 10:11:13 INFO: Time 0:00:03.162726 taken by SpallocMaxMachineGenerator\n",
      "Pre allocating resources for Extra Monitor support vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:11:22 INFO: Time 0:00:09.040165 taken by PreAllocateResourcesForExtraMonitorSupport\n",
      "Partitioning graph vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Partitioning graph edges\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:11:27 INFO: Time 0:00:05.019021 taken by PartitionAndPlacePartitioner\n",
      "Created spalloc job 7076716\n",
      "2023-03-17 10:11:27 INFO: Created spalloc job 7076716\n",
      "Waiting for board power commands to complete.\n",
      "2023-03-17 10:11:27 INFO: Waiting for board power commands to complete.\n",
      "2023-03-17 10:11:32 INFO: Time 0:00:05.045653 taken by SpallocAllocator\n",
      "2023-03-17 10:11:32 INFO: Creating transceiver for 10.11.193.153\n",
      "2023-03-17 10:11:32 INFO: Working out if machine is booted\n",
      "2023-03-17 10:11:36 INFO: Attempting to boot machine\n",
      "2023-03-17 10:11:42 INFO: Found board with version [Version: SC&MP 3.2.5 at SpiNNaker:0:0:0 (built Thu Aug  1 08:15:06 2019)]\n",
      "2023-03-17 10:11:42 INFO: Machine communication successful\n",
      "2023-03-17 10:11:42 INFO: Detected a machine on IP address 10.11.193.153 which has 856 cores and 120.0 links\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:09.643453 taken by MachineGenerator\n",
      "Generating partitioner report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.009114 taken by PartitionerReport\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.000322 taken by NetworkSpecificationReport\n",
      "Allocating virtual identifiers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.053229 taken by MallocBasedChipIDAllocator\n",
      "Inserting extra monitors into graphs\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.032565 taken by InsertExtraMonitorVerticesToGraphs\n",
      "Writing the board chip report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.020357 taken by BoardChipReport\n",
      "Filtering edges\n",
      "|0%                          50%                         100%|\n",
      " =========================================================/home/spinnaker/sPyNNaker/lib/python3.6/site-packages/spynnaker/pyNN/models/neural_projections/connectors/from_list_connector.py:129: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  sort_indices, numpy.cumsum(index_count)))\n",
      "===\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.034680 taken by GraphEdgeFilter\n",
      "Placing graph vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.044294 taken by OneToOnePlacer\n",
      "Inserting edges between vertices which require FR speed up functionality.\n",
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      " ============================================================\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.071024 taken by InsertEdgesToExtraMonitorFunctionality\n",
      "Generating routing tables for data in system processes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.006338 taken by DataInMulticastRoutingGenerator\n",
      "Generating fixed router routes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.008194 taken by FixedRouteRouter\n",
      "Generating placement report\n",
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      " ============================================================\n",
      "Generating placement by core report\n",
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      "2023-03-17 10:11:42 INFO: Time 0:00:00.070391 taken by PlacerReportWithApplicationGraph\n",
      "Routing\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.028391 taken by NerRoute\n",
      "Discovering tags\n",
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      " ============================================================\n",
      "Allocating tags\n",
      "|0%                          50%                         100%|\n",
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      "2023-03-17 10:11:42 INFO: Time 0:00:00.043442 taken by BasicTagAllocator\n",
      "Reporting Tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.005531 taken by TagReport\n",
      "Getting number of keys required by each edge using application graph\n",
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      "2023-03-17 10:11:42 INFO: Time 0:00:00.033652 taken by EdgeToNKeysMapper\n",
      "Getting constraints for application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:11:42 INFO: Time 0:00:00.026772 taken by ProcessPartitionConstraints\n",
      "Allocating routing keys\n",
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      "2023-03-17 10:11:42 INFO: Time 0:00:00.011097 taken by MallocBasedRoutingInfoAllocator\n",
      "Generating Routing info report\n",
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      "2023-03-17 10:11:42 INFO: Time 0:00:00.031024 taken by routingInfoReports\n",
      "Generating routing tables\n",
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      "2023-03-17 10:11:42 INFO: Time 0:00:00.025834 taken by BasicRoutingTableGenerator\n",
      "Finding executable start types\n",
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      "2023-03-17 10:11:42 INFO: Time 0:00:00.031221 taken by LocateExecutableStartType\n",
      "Initialising buffers\n",
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      "2023-03-17 10:11:42 INFO: Time 0:00:00.030607 taken by BufferManagerCreator\n",
      "Generating data specifications\n",
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      " ============================================================\n",
      "2023-03-17 10:11:43 INFO: Time 0:00:00.087166 taken by SpynnakerDataSpecificationWriter\n",
      "Preparing Routing Tables\n",
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      " ============================================================\n",
      "2023-03-17 10:11:43 INFO: Time 0:00:00.007266 taken by RoutingSetup\n",
      "Finding binaries\n",
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      "2023-03-17 10:11:43 INFO: Time 0:00:00.040211 taken by GraphBinaryGatherer\n",
      "Running routing table compression on chip\n",
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      "2023-03-17 10:11:47 INFO: Time 0:00:04.538062 taken by MundyOnChipRouterCompression\n",
      "Generating Router table report\n",
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      " ============================================================\n",
      "2023-03-17 10:11:47 INFO: Time 0:00:00.007178 taken by unCompressedRoutingTableReports\n",
      "loading fixed routes\n",
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      "2023-03-17 10:11:47 INFO: Time 0:00:00.045368 taken by LoadFixedRoutes\n",
      "Executing data specifications and loading data for system vertices\n",
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      "2023-03-17 10:11:47 INFO: Time 0:00:00.142311 taken by HostExecuteSystemDataSpecification\n",
      "Loading system executables onto the machine\n",
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      "2023-03-17 10:11:53 INFO: Time 0:00:05.709328 taken by LoadSystemExecutableImages\n",
      "Clearing tags\n",
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      " ============================================================\n",
      "Loading Tags\n",
      "|0%                          50%                         100%|\n",
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      "2023-03-17 10:11:53 INFO: Time 0:00:00.017258 taken by TagsLoader\n",
      "Writing data\n",
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      "2023-03-17 10:11:53 INFO: Time 0:00:00.053123 taken by WriteMemoryIOData\n",
      "Executing data specifications and loading data for application vertices\n",
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      "2023-03-17 10:11:53 INFO: Time 0:00:00.167695 taken by HostExecuteApplicationDataSpecification\n",
      "Expanding Synapses\n",
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      "2023-03-17 10:11:54 INFO: Time 0:00:00.540580 taken by SynapseExpander\n",
      "Writing fixed route report\n",
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      "2023-03-17 10:11:54 INFO: Time 0:00:00.046801 taken by FixedRouteFromMachineReport\n",
      "Loading executables onto the machine\n",
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      "2023-03-17 10:12:01 INFO: Time 0:00:06.766891 taken by LoadApplicationExecutableImages\n",
      "Reading Routing Tables from Machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:12:01 INFO: Time 0:00:00.010766 taken by RoutingTableFromMachineReport\n",
      "Generating compressed router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:12:01 INFO: Time 0:00:00.007653 taken by compressedRoutingTableReports\n",
      "Generating comparison of router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:12:01 INFO: Time 0:00:00.004957 taken by comparisonOfRoutingTablesReport\n",
      "2023-03-17 10:12:01 INFO: Running for 1 steps for a total of 250.0ms\n",
      "2023-03-17 10:12:01 INFO: Run 1 of 1\n",
      "Generating SDRAM usage report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:12:01 INFO: Time 0:00:00.074793 taken by SdramUsageReportPerChip\n",
      "Updating run time\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:12:01 INFO: Time 0:00:00.025404 taken by ChipRuntimeUpdater\n",
      "2023-03-17 10:12:01 INFO: creating live event connection database in /home/spinnaker/CNT-2023/SpiNNaker/application_generated_data_files/2023-03-17-10-11-08-684590/run_1/input_output_database.db\n",
      "Creating graph description database\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:12:01 INFO: Time 0:00:00.059448 taken by DatabaseInterface\n",
      "2023-03-17 10:12:01 INFO: ** Notifying external sources that the database is ready for reading **\n",
      "2023-03-17 10:12:01 INFO: Time 0:00:00.000976 taken by NotificationProtocol\n",
      "2023-03-17 10:12:01 INFO: *** Running simulation... *** \n",
      "Loading buffers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:12:01 INFO: ** Awaiting for a response from an external source to state its ready for the simulation to start **\n",
      "2023-03-17 10:12:01 INFO: ** Sending start / resume message to external sources to state the simulation has started or resumed. **\n",
      "2023-03-17 10:12:01 INFO: ** Awaiting for a response from an external source to state its ready for the simulation to start **\n",
      "2023-03-17 10:12:01 INFO: Application started; waiting 0.35s for it to stop\n",
      "2023-03-17 10:12:01 INFO: ** Sending pause / stop message to external sources to state the simulation has been paused or stopped. **\n",
      "2023-03-17 10:12:01 INFO: Time 0:00:00.444964 taken by ApplicationRunner\n",
      "Extracting buffers from the last run\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:12:01 INFO: Time 0:00:00.168778 taken by BufferExtractor\n",
      "Getting provenance data from machine graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting provenance data from application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:12:02 INFO: Time 0:00:00.089534 taken by GraphProvenanceGatherer\n",
      "Getting provenance data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:12:02 INFO: Time 0:00:00.027228 taken by PlacementsProvenanceGatherer\n",
      "Getting Router Provenance\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:12:02 INFO: Time 0:00:00.059629 taken by RouterProvenanceGatherer\n",
      "Getting profile data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 10:12:02 INFO: Time 0:00:00.027171 taken by ProfileDataGatherer\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "250.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run simulation\n",
    "\n",
    "sim.run(simtime) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting spikes for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for Exc\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n"
     ]
    }
   ],
   "source": [
    "# save the results\n",
    "outputs = {}\n",
    "\n",
    "for layer in ['Exc']:\n",
    "    outputs[layer] = pops[layer].get_data()\n",
    "    for recording in ['v', 'gsyn_inh', 'gsyn_exc', 'spikes']:\n",
    "        pops[layer].write_data(fileName + '_' + str(layer) + '_' + str(recording) + '.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v\n",
      "gsyn_exc\n",
      "gsyn_inh\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys([('Exc', 'v'), ('Exc', 'gsyn_exc'), ('Exc', 'gsyn_inh'), ('Exc', 'spikes')])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check results\n",
    "%matplotlib inline\n",
    "def recover_results(outputs):\n",
    "    results = {}\n",
    "    for key in outputs.keys(): # to extract the name of the layer, e.g., Exc, Inh, Thalamus, etc  \n",
    "        \n",
    "        # to get voltage and conductances\n",
    "        for analogsignal in outputs[key].segments[0].analogsignals:\n",
    "            print(analogsignal.name)\n",
    "            results[key, analogsignal.name] = analogsignal\n",
    "\n",
    "        # to get spikes\n",
    "        results[key, 'spikes'] = outputs[key].segments[0].spiketrains\n",
    "    return results\n",
    "\n",
    "results = recover_results(outputs)\n",
    "results.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the spikes\n",
    "\n",
    "fig, axes = plt.subplots(1, 1, figsize=(9,9))\n",
    "fig.tight_layout(pad=5)\n",
    "axes_list = fig.axes\n",
    "\n",
    "for idx, value in enumerate(['Exc']):\n",
    "    axes_list[idx].eventplot(results[value, 'spikes'])\n",
    "    axes_list[idx].eventplot(spike_times, color='r')\n",
    "    axes_list[idx].set_title('rasterplot of ' + str(value) + ' layer')\n",
    "    axes_list[idx].set_xlabel('[ms]')\n",
    "    axes_list[idx].set_ylabel('cells')\n",
    "    axes_list[idx].set_xlim(0, simtime)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rkt5Ck8OLl6/iruEelv7wLWQeEozrTu2PbBph8qetdO5xMZUDGjGPGQxVc+qNotUwp5RSSj0GEnAoLEyTa3eYWARu2iPZNklTPM+i5BDNwRx9oSFKJsC1G55H4v7pcytdGXLdIbK9FuVGn4sveoiXNd3B+259A7Hr7yRWP675vBVM9sXY7LSyMDGCXZ49Z36+xcEfKPDy5XdzdfMaFq5axsJ/vn1q/54nO6x/yZdY9Is30vyVRwjfb2g7xL2UmoR/ab6L87acxsBVqzHl8qyKm7dhM40bNgOzK3HznQ+UjFbmlFJKqXnFCodhUS9+PEixNYwXFMpJq74qgWDsWkUtUICu67bh7twFgDgOVt8C/HiESnOEStJm91OBdJnUXWEiqy0qyTQj4Qa2LejES7ucv3wTneFx7Op0BBLbJtcdYmyp4Jw+wdkte/n92iXctud0eu4vAOBe+iTGFwVJbndJrctirCR3rFtJ+4OFWffS+FAOMXGuf+AS/rv9KXTcOvte2293GUi8mbY7eNTlslruLbHiurfT8JBgqu5hjzvVGAzVUyqe1miYU0opdUqTWJSJZSmKjRYTiw1+wqO5Y5TGcJFl6b00OAX6Qvu4O9fHutuXwYwwV+xvpNDikF0olBs9LnvqH2kPjnPtj55P+Ma7po5zLz6DXGeQ+1Kd+B2CNSPMIRa5LguzJMd7l9/M38V3c+H176Tts7fV9wt7zw/R+LQ9lL7dSvBXa0neB8lD3ItZ9SANq6DhMPcaumkVAzcd+Tuxb7mXRbc89u/wVGGMUDKHmzVt/tIwp5RS6qRkJ5OYvk68WJBScwgvZFFOCMYBNyK1OdQCEBkytPxoDV42C9QqcdLXjR8NUm6OUmy0GXyqB/g0rnJwijaVZBN7osKG3k5IVXnusjXsLSUQz0zVbSQYZKLXId8NqTOH6I/l+PbtTyE06LBw8zAmGmXwspXkO6HtDo/0IzncUIJH0kvo2jwy3c3eEvLdHi9a9BCfePA5fHhPlIX3TY8IxRha7q0wPtlO6/qJU7BudPIwQFXDnFJKKXViSEOK0dNTFJssJhd5mHiVjvYxGsJFliVrFbWFoX18edslyM1JqIc5iUWZXJqh1GAxsQiqLVX+6aIbWZ3rYdOX+vAfqE2RYSUSFC9eymRXiNUtnQgQd2d0jg8GyPVCaMkEn1n2Q2JS5e2feRuRn92GR20JrN6XbeSTPT/llWuuJHzjQzTeXTt1Zq8sEaGhd4x/bv0zv/vmBSz40m0H3Wvw5rtpufnU6p92MqqFOWuum3HMaZhTSil1TNnNzZiOJtxEqDYFRsSiEhP8AHihWmXND0Bqs0fsJ3eDX4s+djIJXW34sRDlTIiRrgAjF1Wxsg5Nd1sgQfLJNsbjsKani3CmxIsH7mOiGCbmZ6c+XyIRJvpsim2GzjNrqwx8/HcvILzbpm9oM5JOkX3mUtyQkNxaIpOtMhRuwwikhraxvweZKRRpvcsnvy3FZZvfjLFg8aZx/Kn9BTbcuJJndV7J4g25wwYx47r4Nzdx5o530L+mdJij1IngI5TMqRd9Tr07UkopNbdaMoycmabYKuR7PCRVprN5nKZIjoHEEE2BSXqCw7z/9hez5MYAfqkW5qQhxfjpGYoZYbIXpKfAF8/9IR9d/3wyHxvDGx4BwGlrJfvkXib6EjzU0kEuHwZ/fOrjTTTM5IBLR98w31pyDTfmTuMXV16I/8AjuIDT18Pu/1PFcnwyV+fw1m2k9U+1c2cOBfALBaI/uZMo0Lx/28z9+Twd/16rsj1aRc24Li1fvI2Wv+ArVceGQagYe66bccxpmFNKKQXUVhfwm1K46TCVRAA3alGNCH4QvOB0H7WW1eVZqxLYTY3Q2oSXCFFJBRlbEiR7dhlnd5DmOyy8UJjxZDv70obVnT1kWrK8uOd+TGn2/1T9RIzxfotim8/A6TsZLkR52y8uI7HJxhR2Yy/uZ9uLW3GK0HpXnkAuwEann+SYweSnR336sTC9iwZpj2Z55q1vw94RZmBoy3RFLTtJ5g8dYAHju0/AN6tOFr4RSiYw18045jTMKaWUAqC6oJnxJVFy3UKx3SWQKdCRydIRm6A3OkJLMEtHYIwP/ejl9M5YlYDWJsbOaKDQYlHoNKSXD/PNZdfz2t9eTsP3V09NRmsv6mPi7FZGTm/kkaZWrKKNmTF9htsQobi0xOm9u/le/w28fNMLaf3ICN7wCD5QOK2Ja6/4DF8YfAZ7r0siu3bT+b+1c2f2UfPiQa7su5mHit2M/1sL3prVsypu3sgojd+4/aDz1KnPAFW0MqeUUupkIoLT042fjFJtjOJGbKpxGy8IbqjWT80PCAi03zqOf9/aqVOd9jZMQxI3FcGNOew7J0R+cYXI5iAtd9hU4nFGYnF2trRzR3OF/u59PKd1DVZl9mjAaibKxEKLYrfLksW72Dme5nW/fANN91gYz8Ne3M/YOc0E8j6JLXksN8oq93SaN/ngTcepwN4JMn9qZdMjC1mx6K1ENwZZULhvan9sa44X/epthPc69OXXHPYrCeyd4O03vga7JAyMbjmGX7aa7wxCVfvMKaWUOpmIbVNc1Mxkd5BsP1QyHtHWSZoTefqSI7SEJlkQGiFmlflC6cU0TWcj3J4Wsn1Rct0WxWbD0nO38N4Fv+RNm95M+rv1VQVEsFYuY2Jpkk1P7mBdYhi7PLsNhbYg1dMKPGfgEb7QcRuLf3c5i995z1RFLntGE7xqiKHbW+m56WGiq1wW/FTAmEOvOiDTYdGfUbkzq9ew+M21fd6jTIjrbdjMon+shTj3UY5TTzwGoeTrY1allFLHgDgOVm83Jham0hjFC1lUEza+A254f/80wXINbTfvwt26vX6i4HR1YhJR3IYolbjDnosClNtc4hsCJDfZVJIphiMptne2IukKT+rbzpnJnVgHTPRfbA2T7bMoLinR3zXE+r3NXLb2CrrX1A6sPvscBp8UJLnVJ7kpjxeI84fxM2l7qDr7Oo0Wly5ax8PjrSxedzmpP4cxMypu8a15dt3SStNGD+PXw9WjhazHu+/xHKeeUHwdAKGUUupYkVCI3OnNFJptJgbATXqk28dJR4ssTu2jMZCnNzzMnkqaW7ZfRKge5sS2KQ20km8PkO2rrfP5nKfcy9NTD/Pvf3wF6WvumDrOu+B0cgvC3POcBcQGKtiVmasSCJOdDtUVef7vij/xzob1nH7bW1n8z3dMBaGtz7d5+MWf47Tvv43k9x8kvUpIw0FBqdAufLHzTyxZewWLX39/LcjNrKjd/RAd94gGLDXnjEEfsyql1BOdFQ4jCzoxkSCVxih+0KIat/AdqVXU7NqIz2DOkLlpHd7YGFCrxNldHZhoGLchSjEdYM+FNl7cJ7nOxinalNMZBsOwtbuFQLLMs/vX1T7TmxGCbHsqyLmn5+jOZPnV+uXcNLGSgc1FAMyTV5LrCBEedUlsKVK6PcZtW0+nZ0tx+jrG0LCxgnFifHnPs/hq28W0PGhmBa6mey1Ob3rD9KPZw4Sxxoc8lv/hctJ3BqcerR5Eg5w6CRh0NKtSSj3hSSrJ+FnNFBstsgM+fry2zmcqXGJJch/pQIG+0BA3j5xGbnUL7A9zkQj501opNDlk+6Hc5PGmi39D1dj84QcXYv3pPhALKxKm9JRl5Dpj/PkFfXSmJrCq07ObieOQXWjhr5jk38/6KeeE9vKiH7yHzLfvAt9DAkG2PTtK8pwh7K824Pz2HlruEBBranLe/QK/vofWX0OrWIglsx6NAjR85w4y19gHbT9Q/Pq7SPz0yMcpNddqAyD0MatSSs1LdjIJ7S340RDVhnCtoharVdS80HRFLbbPI/rL+zHlWi9/KxzG6mjDREJUGyLk20IMXgj4PskNFmKE/OZmJmKGTV0txFJFXtD3INlKGGtGNUqCASa7HArtQmDFOA3hMl+97ykwHGLJ4BAmGmX0b8+g3CA0rKuQ3lRi758zbIpk6BscnppCQ2ybYqfLxd3b+Pi65zI2EaN3c3kqqBnPI/Owz0SpmczO+jqfxoA5RNDa3z7jYfyDd2PM4Sttj+c4peaYj1DWARBKKTVPtTQyem5TbZ3PhR4m4dLWNkoqVGJpapCUU6QvNMSn1j6L+B9jePUwJ6kk2TNbKWYssguh2l7h6gt/xvWD5+D+ZwRvc60vm92YIXdRH9nuNKte2MNQPkbbjIAj4TDjSwzJ/jG+fsZ32esl+fQnX0Hg1rvxXBenp5snvX01L86s4qNvfx3WrffR8cdaxcybGZQCDsuW7OTqjl/wD19+D4u+dw/GnTEgwfdI/PBOEmJhDpnQlHriMkYrc0opdcLZjRloyuDHQ7iJEF7Yxo1Y+E5tVQJj10Z+pjaXsX9/79R5ViKBtDZhwiG8VJjRvjD7zjc4OUN6rYUfDDGebmUoYdjY2UxzwyQv7KpQrc7+D71EI2S7bYqthvjpo4gYrl71Nzjbw/RPbMROJSlcsAhjC6HhCumyYfsfF2CVQSY3T13HFIpkHhIKQxleMvlG/IrNsr3ZqaBm8gX+99Yz+VXTchbvLtQm0z1ExcyUymz+0xL+au9b6NlawlQrB39ph6vEKfUEZ0DDnFJKnWimu43RM1Lk24VCl4c0VGhrmqA5kqc3PkJTIEdXcJSP/O+LGLjVnnrcKK1NTJzdSqHJIrfA4PTn+MJZ1/Gue15C2ye24uXyiCXYXR2Mn9fB2ECU+5/bRSUXBH/68aifipE9rUpP7xDXLL2Gzww9lYff3Ye/+UE818U+bQk7X1XFywdY/rERrO076fl17X8W7oyKmjc2Rubbd9Fo1fuvAd6MIOYNj7DofRMg1uxK2wH8fJ6+q1cd8Til1MF0njmllDoKTlsrJhnHT0Xxog5uxMELSX2Nz9ofY0PjfROzViWwGzNIKokfD+PFQ+xbEWVshU9o2Ca9xqIajzCSiLCnwePh1lZ6m0Z5TttDiDd7VQI/ESW7wKLY7pNZOkK56vD2O15G9IEIfrmM3d/D4KW1dT6TW0qIH2RVYimpXTLVXw7AiwXp6R0iHSrygvtfx9jmDEvHNk31EZNsnsjqRuxyrbr2qP3H/MP0Tat7rP3OtH+aUo+PQXC1MqeUUo+BCJWBDrJ9YbK9QrnZw2ku0pjK0xmfoCMyQVswS2tggs9+82/pmLEqgelqZXxpiny7RaHN0HLmIF9e9HPefMPl9Hz97vr1LWTJQsbPSLPprBhrnjqGXZwd5sotEQorizypbztf672RZ93/ajrfsQ1/IotxXUYuaOW7V32at254KYGXTmL9eYL+n9lgfPwZYancEOT/Lfox3x25iMob4jRvWz3Vnw7A3bGTzs/sBZjdt00pddIxBqq+NdfNOOY0zCmlpongdLRjomH8dAwvZOPGHIwjeCGrXlEDDGTu2IO7ZdvUqXZzMxKP4iejeNEg+86JMNnrExkUUo/YVHbHGY3F2NuSYU1DiZUdu7goXUIOqFRVMxEmF1jkezwae8eYKER466qXk35EMK6LvaiPwuImrIpPfHsJNxThd84KGtfBzLJXaF+R6AMp7t03wKVjl1G4p4lMdttUVSu+u8JLV19O+ZEU/aUH6lWzg/uZRQaLXLbqtVT3Rlk6umFW1W4/rZQpNT/URrOeetHn1LsjpdTjJsEg+TM6ybfV5kKrpn3CbXlSsSJ9yVGaQjm6w6P4xuJnH3sGif1hTgR3cSe57jDZHotyxrDyyet5ddttfOiLr6H1K3fV+olZgv+kpWT7Ytx5ST+pM4vYpdltmOwKUTyzyIuW3s9HW+/gtP95K0uvfAhTqWCA0Qta8V82Qul3zXR87i4a74BG2wbPqw0aqDP3raXzoQBYgtg2prp11mAB55b76Lo9hPE8/EMEtKnr3P0QCy8LYYyZVZFTSs1H+phVKXWSEsfB7mzHhIJ46SgmYFONOxi7VlEzNviOYFcMiVs34g2P1E8UnLZWCIfwUzHceJChswKUG30igxaRQZvyviQj0QSD7SliyRKXdG2iOTiJzJzQXyyKLSEmuy3yAxUyrVke3tfKlbv+jrZtLsZ18S85i7FFYaLDHontZUoPRfh16Qy6t82uapUywtk921kz0c6zhl5Keo2DXyhM7Y/tqbLzviaat3nTFU9EBUIAACAASURBVLFDVcaMmQpvh1x7wPdmXfewjMEvlY58nFLqpOcboeydetHn1LsjpZ6ArHiM7JM6KDZaTCwCN+ERb8+RjJToTw2TDhTpDY+wJtfBjj39SD3MSTBI8bROii0BJhZalDM+f3PJXfSHh/jRP/01sRtX1ypbwSDlC5eQ60jxm+cu5uzOndiVGasSWMJEn03prALvWnkLL0uu5elfeA/9X6pNvmssm42vtvnjsz7Js7/yXro/cQ9td9i02xbGdWeFrVyPz7d7f8npN7yNJVetJVHaOWu/c8t9LPxz4KDzlFLqSAzgGu0zp5Q6hqxwGKu1GRMK4qei+AELN+pgHAsvJBir1kctNOER+tPaqUqSOA52awuEgvjJKOVMhKGVNl7IENknMOhQHk0xGEky1BknFS+R7CrhM3uQgDgOhbYAuU6L0uISiWSR32xbwo3FFSzcW8J4HnLGUsoNYayqT2JHhdz9Ce7Ys4RFg9PrfBrfkNjh4UaifNa/lOuazya12cPP5+sfJMQ2BHll9ytJbDdT/dPMIWbWSG60eN7Df0/qYRt/cvLgA3wPv6RzqCmlHgcjuL4+ZlVKHUNWUyNjF3ZSzFhM9vt4cY902ySJcJmB1BCJQIne8DA/2HYOkXWN+NtqYc5KJMid1UUpYzPRL5SbXd781JtZn29jx1v64MENiAiSSJC7qI9ce4xbXjBA0PYIVb2pipYEg4wtEaylk3zhrOtotid551Vvo/vnD+IXS1ixKJv+PklgIEvbf4YJ/OF+ev7sgGXhl2b0H/M9EjesJmlbYNuIbeMXdkxXzoyh+7P3Iv8ZJFzc/agVtdZv3IN1TYhQea9W3pRSx5QPVDTMHT0RsYG7gV3GmOeLSB/wA6ARuAd4lTHmEFOYK3Xi2ekUkk7VRnPGQvhBGy9s10ZxBqb7niFgLMEITFXs5YCLGYjtqWDfev/0RLahEHZDGgIBTDxCsTPJ2FKrduxOCy9okRtrYCLmM9weIxMv0NSRwzcHVNRCQfLtNoUWwRsoEI9U+O+N5zG5L87y0SH8YJD8pcuoRi0iIy7JbYbd97ViLBiYGJxa5xPHodLmsqJliC9sfwZD+RiZnaXpilq1Snwb5L0kwZEJfNc97MhNU60cstK2n18qwWPoe2bKZR1ooJQ6LgyCq1OTPC7vAB4GkvX3/w/4jDHmByLyZeBy4L9OQDuUOiLT08HoivocZ50+Jl2loXGSTLTIgtgYmWCeztAYCatEo5MjIC4Jq4SNT0BmP/rzjcVr73kNvavCU+HISqcontFNOe2Q67TId/k84+LV/HbjEha+Zx/+yCjYNlZTholzOsj2JPnD86qMjsdp9qY765t4lLHTDPHecb55xjX8sbCYX7/xYjofeAQ3l8NeNsC5/3w33eFRfnHF07DuWkv/H2qznnszOv1LOMTTVjzC61v+wHuuejOtNz+Mn8tPVcT8UonWb60G28Yv6iAApdT8ZsyxrcyJyIeBNwBD9U0fMMb8QkR6qWWfdfXtdxhj3nTMPvgAxzXMiUgX8Dzg48C7RESAS4GX1w/5DvBhNMw9sYhgtzQjkTB+PIoJ2XiRAMax8IMWvi0YRzAWGLtW/UJgqjglM6pUM6aiEMPUsMX9r8WY2k+/9j62fgRv/aapc6xoFInFkHAIEw5S6IxTarCoxsEP+zhBj6DjEbA8rPrwzbIfoM2ZYCAwxO8Li7lu55MAsC2f9miW81NbaHayLA3uxbZnT6Im4RCFZodSo8Vkr48J+fz6kWWENoUxkzkklSR3QS8IhEeqiIEt93USHLUwM6paUiwT32pRKKV5d/jvGRpLsGhwAq/ex0wKJX72yEpC4Qo940W8auWQa3iaUpnfP7CUNZ1tpHcU8cYnDjpGR3IqpU4Vx6ky9xljzKcOsX2TMebMY/1hh3K8K3OfBd4LJOrvG4FxY8z+5zQ7gc7j3AZ1kpFgkOJZPeRba3OZVVKGQFuBeLRAT2qMhmCRrsgYKbtIk5MlZlWIWWXCUiUgLvaMnlQegodF1dh4xqJkAlSNU/9pk/dDVI1NwQtRNg43fOsS2maEOelopdyTodgcoNhkUUlCJW3woj4SrlXacqUQ5arDRDlMwPIJ2S4XNAW5JLKDzz/4dPrfvndqaoxd5w7wmecvIdGV5e1LbqGYD84KnH46zvgSodxZ4XlnPMiv1i9nyQeG8YdH8fJ5qhcs5m8+/lt+tfc0Qq8H5897Gfh1CDwPrzg94MDdvYeOr47X+qc5DikzgjeRnd6/bQeL31ILZl42d9i/C294mGVXlmuVt8lJ7aOmlDrleTqa9bETkecD+4wx94jI0x7H+VcAVwCEiR7j1qk5Ua/IkYjVq1NCpcGDVJXmVI6mSJ7++DBNgRw9wWESdpFGK0/UqhITl4BAALBnVOYqxuADVQMVY/H1kadw51AvnhE83yIaqBJ2qgRtD0c83AhYZyzF2DY4FrnOCPkWm0paqKQMlUaPcGMRdyhCZGMI40A5GKEQNozFPCTk44RcXGPR4OTxPIvCk3qI7MrhP/AI4d2TJNdnyHop7mjvx2SDs7+CfInYLrAqQX4ZXI4ZDTH65E5ie5tx/vQQgWyt71suG6FrpUO0OYW5b91UVc1OJqmcvQhxfax71x96tCeAMYessh3yuGz2yMcppdQpwBih6h30mLVJRO6e8f6rxpivHsVl3yoir6Y2PuDdxpix+vY+EVkNZIF/Msb88XE3/AiOZ2XuIuD/iMhzgTC1PnOfA9Ii4tSrc13ArkOdXP8ivwqQlIwWDE4BVihE4eweCi0O40vATXnEOiZpiBZZkt5Hc3CS3vAwaTtPiz1JwiqRsKoEMAQEgjNCnFevdlVNbXRSydjkjcOvrr+Anq+tmzrOXdzFZGeYQrNNuQG8qGHjKxrwYj4m7GFHqoTCeUIBl2SwyjPb1/Ga9J1cetO76PncgwCICHS1kV+YppwOUGwOsSce4wsNHdjtRZZf/SD/+4czWfR+G/+hDbRvDJF58jJ+E15OYpON8ab70nmbt9Oye3Cqojb210t430eu4Uvbn4718jT+3WvpujxJ+cw+xt4xwY59CZb/UwPu3kEA/P5uvA+OMJKP0v2PzfgzltNSSin16AzgHfyYddgYc87hzhGR3wBth9j1QWrdxD5av/RHgf8AXgfsARYYY0ZE5EnADSJymjHmuPz2fNzCnDHmKuAqgHpl7kpjzCtE5Drg76iNaL0M+NnxaoM6uRjPJ5it4geF2G6HatamlE+xM5JgZyKDFfSIRCoEbI9osErA9ghaHrbl41i1vmdW/UGgj+Cb6T9V36bq2aS2+NOrGwCBRJyYSeMUQ5QnLAqtFuVGgwdI0CcQdImFK8SCFVLBEg1OHlsg0lKgcMmy2kUEihmbYqtQjRsqKZ/ghJDcZFHIR/mttZjApIUsX4Q9Oom7cxehfXniGzOExgwy0Ic9kcPduWtq1QErFoPmDABf3nEJGze3sayyEauhgcK5veTaHcZ2pQjtczDV6SGiVqHM1i0tSNGGymOovCmllJpmwDMHTj1whFOMeeZjOU5EvgbcWD+nDJTrr+8RkU3AYmrVu2NuLuaZex/wAxH5GLAa+MYctEHNAVOtYN21lqglxJzaXGViW1NrdgK1wQ1S/63Jmv4X7uDJMKaLtRYQokrIN5jJPcwccuBu24m1YxcR2yYqQuLcZYwtDpPvtCnZBj/kEg1UaY1O0hMdBeDechsfPuPnlD4VIGxVp/rrxaSCJT5BfF70+zez8FMbah8SDFBe2cvmlzSQXtdA+tq9mLUb6doSwTt9IVv/NkNiewOZa/dNPS6Vnk62vbCJ0KjBfp3NsomNeOPjVJ79JF7+Hzfxi30raHlzGnbsmRrUAOBt2MKy942A8XEn9PGoUkodDYPgeseuz5yItBtj9tTfvgh4qL69GRg1xngishAYADYfsw8+wAkJc8aY3wO/r7/eDJx3Ij5XnXym1so8UfOI+R7GB+pLPwVGC8QGA4CNXXEolqLs9CwK1UBtzT7fwTcWTU6WFmeShFUkbZUJiE9YDH8s9vDz4ZWEtofwcnmsYKA2Kjdg4Qeg3CDIWUvxQw6VdJBKwiY0BqEJH4yP3dxM5fRuiimH4DhEh328PYNT30cg5/L9neeydzxJ64IwEcdC1m6aHonqe3hjY4e7W6WUUo/CGPCP7WjWfxeRM6lVGLYCb6xvfyrwERGpUusN9CZjzOix/OCZdAUI9YTir9tEdJNDtL5KQeXsRYwtiZDtiLKqPU0kU6S7YZzWaJb+6DBNgUk6A2MkrCKNVoEP3f5Cln1okIWTa/B8D6u9i8kzWim02IgHk70+EyuitHWN8k8DN3Hl6r9n4dv24Y/XJtwtnNfLkn95iN+sX8rS9+3EHxnFnxFsrbvWEn11hvYVIQbfWKCwL8GyD6fxBvfN4bemlFKnjmNZmTPGvOow238M/PiYfdARaJhTTyjmgBUMgoOTxBMO4trYpQDVEYf1qSjrI63cFeshFHSJh8sEbY+IUyWyPoS7cxdWJILT1kq1LV0fXCG4MR/xIDDssNdq4NrkhZQmQ5SXdRIcTMHDG3AKHrfv7sWMhDDpBJbv4+8dnJq+xFQruHv2EmpMU9ydIjRmTU17opRS6i9j6v2tTzUa5tQTmr9hC9EtO4gGArX+e7Y91W9P7PpvbyJg1V735NfgGYN0tTN2djP5Votcn4+fqtDaOsH43c30/+dmjOczEYrSdrGD+eBu1t/fwcCHIshta+h+OI27wGHXsxuJ7Gug4cfjB03Max7ZyNKrU+D5eOPjJ/prUUqpU5MB42uYU/OAhEJY6RQSCGCiYXBsTNCpraYQsGtritaDirHr64zun/bjEOuLzrp2fUWFqdf7RxsYU191wUydV9tfey++mToOv75v//sZ15H97/fv92e8nrF91vVm7t9/zsz3U9v9We/NjMl8p84BMLX+baY6PaWIhMOYWAS/PUM1vYhywMIp+oQmBHefRbkaZDQcw3YFk0lhgg7lTIRKQhgZaiA4boHvI8EgpiFJsT3C5CIPL2TTYE/PeWQlEpjFC7BKLv66TVNVRDuZpLqyH3F97NXrdFUGpZR6HAzgHcPHrCcLDXOnILuthfHzOihlLCb7wI37BFsKREJVOlOjxANlOsPjROwqTYFJAuKRsIrYYg5aYaFqakGjZAJTKyz4xqLgB+srK9R+Fr0gVWORd0O4xqLkBnCNRdEN4PkWJdfB8y3Kro3vW1SrDsYXPM/C+IKpWuALeFILe64gviAutZ9ebUmu2s/ae/z6tqntM376tcAnXm1pr5nba8t7mdnHmUPtm14KbLLbYaIfEstH+eRp3+f1t7yWZe9aR8wYmhwHf1EXQ2fHqSRhw6sbsfryfHDlz/j4/c9l0QcnYWwHXqmErBhg3StjpHrH+d6Ka3n/xhcjXw9Dfe1W/7Q+9l1VYXxPimUfasAbqi33Vz1jIed/4W7WZtsov6YNf/PWE/sPlVJKnQpO0sqciHz+MRyWNcb806F2HHWYExELiB+vie/UMeB6OEWDU4BAVhDPouxEqISDVD2boOMyGEkQsl2SwRKO+ETsKpYYApZH0HJZEBolZFVptHMExCNtF7DwsWV64g/PWFSxZ4W8vB/CYzrs5bwwVd+m4AdxfZu8F6y9dwNUfIey51D17IPCnuta+J6F51n4Rxv2PJkOffsDnA/WzADoyYzQJjOC3ewQt/+9GxaMbfB8i13VBvAEiUXBrVfvfB+nAF5ourhpiyEcquK2pmr/oo2OIRWXQFaYmIiyqrgQ17conrOQ8M5mzCMboewxPhpDKkJp5QJCexsx6zZjlz3uHl3A7mySbu/wy3MppZR6NILxTr4wB7wA+OcjHPN+4JBhTmY9ajoMEfke8CbAA1ZRX83BGPPJo2rq45SUjDlfnnEiPurUYNlY4VBtlYFgACy7Pp+bTPcJs2R6wfqZC9eL4GXi7Ds3QTktFBZWcWJVeltHSAWLLI7vI+UU6QkOE7XKNNo5wuISFbdW2ZtR1fOpr51qhLKx8RDyJlgLfSZI1ThkvTBV4zDph2eFv5wXouw7U5W+ghuk4tm1ip9vUXYdXM+i6tl4noXnWvi+he8KxrNqoc8TZFb4q//cX6nbH/wOrOh50wFw/3ssMBYYG/wAODkIj/lTYa/26BqMAAL5Dov86SWC4SrJWImJ+5pY+NHVGM/HSiXw+zrY9bQE+S6fZ114P79Zv5Ql79iGn81hN2XIn92NvHMf2wczLH3PHrzRMez2VnA93N17wfdQSqlTwW/M9fc82goMx1Kor8u0X/3WWdu2XXbVCfv8wxGRdxpjPvt4j3mslbnlxpisiLwC+CW1dHgPcELCnDpK9VUGHi97IkOyuZ9y2gYCuDGHjdl2JOyxLtVC0HHJRIu1yl6gRMh2a5U9DI7lYc+YttfDwjeCa2w8I7i+TdVYVHwH3wglrx7OvHplzrPxfIuqWz/etfF9wXNtjJFaWPMFXKueFvdX6KRefZMDgtkBVTvzaMHNHFChm/EY9sBHtPXKXTUqlNM2gZwhOuTW+wOCFxAKw0EqGSHTNMpQo4d/xgDO8CTulm044TDJrVG8kE3ErmI7HohMjWYNZNtpjmYZiUfBsjDlMu7W7X/pPxlKKaVOztGsfz7SAY8W9h5rmAuISAB4IfBFY0xVRHS91FOUNzpG5Na1RG2bhmBgah3RWmXPmhrtaSTEuBWunSSP8V+OAyrB4hsECBsDGGK4Bw9cgCMPapix7VEHNhyqHYc75sB9B14byF+6jH3PrBJ/IETTDesx1Vr7w4t7caNpStkQGwMthBuLjP1LifEH2+n/6BDu7r0kf5Uluqefn3WfRXhXACozlu2qemwcbyI3HgXvuM0zqZRSTyyGk/Ux61dFJE5tqdPvG2PWHs3JjzXMfYXazMb3A7eKSA+gfeZOVcbg1zvknyqsaBSJRpBQCIIBjGNDwMFYFjhWbTSvXXv0bASwBCNCYO847ozF7CUQxIpFIBBEQkGqUQsnVKbcEMQ9rQ9nXxZv4xaciTzRoSSIRTkTpBR28H3BLgoYgxUOYTU2gC1EdgSIDppZ899ZhQpD2xuw8xbV/nacRAx/y/apY6xwGBb1Ir6Pv2nbiVtRQyml5ruTcACEMeYsEVkCvBS4vr5yxPeBHxhjth7p/McU5owxnwdmjrTYJiJPfxztVWpOmGV9ZPsT5Dosii2GaoOHk6qQiBdpjE2SChZpDOWJ22XiTpmQuETtMv91w3Po/eB0mLO72skvbaGUscm3W5QzBrdq037uIM944YN8+U9PZ8k7d+Ht2EVqZIxUVxuR4QzBLETXTyK5QdxiEWvFEja+pIHwsNB3zU7MxCResTjd3nVbWP7xJopL29j6Np/qaBPLP5LH3TtYO2BRLzs/alEuBxm4qm1W4FRKKXUY+2cuOAkZY9YBVwNXi8hKasHutyKy1xhz0aOd+6hhTkTedYTP/vRRtVSpOSKeQTxTG8AQMkjMpSGVpyOepTc+QntwgvbAGI1OjkYrT9SqEhOXL/Y+HTl3Bfa+cdxtO8D3sTwDUhsI4QfACdQGI+yrJJDK/omGrdogkzo/IPjpGJYIDFLr31et9eczjg3O7H8VRQQTDmIcC69qY1Vmz4skvk+5HMStOAc9ulZKKXU49VkRTmL1WUNagFYgBhxxPccjVeYSx6BdSs058/AmkptDhM4ZYDwXJJ8PMVS2yWeCFN0AE7EIpUiAgh+iGnBImwK+VeRT513Hhm+18rUbn03fVTvxdu0hNDxKqL8by00zEhX624ZYt3oBa78TYtnIDtxyGae7i9yZHRSabLILodLh8zcr1/Hz+1ey9J1DmA1bWPiVMdzeVnb8bTuRfYbGH+WnB64s7uXhNyexJ20GPl3AHtqHu2946n78TdsYuKoNjMHbuXuOvlWllJpnDCflY1YAEbkYeBm18QkPUus/94/GmIkjnfuoYc4Yc/UxaaFSc8yUy3jlMsGRApGMQzVu48ZsqnEHEcPWyQzrx5uJByo0hAu0hCbpDY+QsgssD++i2uhiDyycGgRSaotTzFhUUx6d0QnWe4JZswG33qfNhIMUmmxKzUKl2SWcLFP2HXBr/xER28bEo/gBe2oKlFnqI3HtEljb9+COjB50P/poVSmljt7J+JhVRHYA26gFuA8bY45YjZvpSI9ZH3VGYmPM24/mw5Sac+u2kNwWInxWP6P5EOONDudktvOjXz6FRd8eAjtKVmLsWjzALQM2pSaD31HCiris+1CSUKRKJl6gKbKDM+IjdIbGWBHeyW8al9UerdZ5jXHGl4BZUOA1p93FtQ+fw9a3LmL50D7cXA7OX8H6K4IEdwfo//YeGJuY3WduwxaWfbI+r9zYEX8pU0op9RjJyVmZe4ox5nH/hn6kx6z3PN4LK3Uy8kslKJUIjBaJDgfIDgVZNdJDdLfgrds4dVzCLMbYDVhl+/+3d99xcp3Vwcd/55bpszPbV7va1aqsumxZso1tjINtSAAHnPglhDRMCS2GgE0KIQkmhbwOJCROIICJHUxoL8WAIXSwQzW2JRfJktW7treZ3an33uf9445Wu7Lkhtbalc7389FnZu7cufeZuZ7Z4/OUw4QThZYyy9oH8Uy4UHElsBkqpwBwawvYyYol2KM5vCNHkaqPUxRKRYecF6uNeysfrxhxjFBb6mXmj4vYdlhXt+ohljxhxRSllFLPggHm4O/psUBORC4E/hJYRBijSfi0Oe/JXv9U3ax3Tn8sIgljzLNfjVapuWLHPuoOxsk8kMKkkrQP7WZ6mBXsOUBd7yCJ1Yuwy3FGEy4bGw7y+Z9cyoqPj4OJMGzXcWhRHd9f7SDtAcUPlej7eTfdfz+EbN/Hkv56Kt3NfO95lyBthvwt4/RtXUjPzaPw6G5W/UMrlYX1HPitNuIDrTR/9pGpMXPehuXU33KI7YOtdL2tBe+IjotTSqnTQeZ2AZ3PAH9KOGbuaYedT2tpEhG5FLgdSAFdtSmzbzbG/NGzaKhSz5oViyGRCESjyLFZoLYVVkmYWtB4Wrmyp1jMeGoeaGMWuzFb23hsQWITrh+XCCtMbB7tJN5rEzy2A8TCikWJR5bgdtVhIgG/0fEwt7a2gCUEhQJBoYAbixAdiVOpE6KOh7Fr56uVVjN2rQyYdULDbCHpVHBtf8Z7sGIxpKsDgoDgwBFMtRIeLhrFWrQwbPaBw7runFJKnURYvWdOdrMeM2iMufuZvujpLhr8r8CvAXcDGGMeEZErnunJlPqliOBfsIJCe4x8l005C5VGH1Ie8XSJZKxCJlYi7ZZIuWXidpW4XcUVH1d8rFrRElsCfBOWGfOx8AKLqrHxjE01sCkHNl4QdqVmrWGW2VX+96drkZsydA8dwDMGp62Z6pI2hs6LM3n5JM/rOsTLU1v5aP0V4bIitSabWJRSk5A6aHDvTrNypBevUKB69QZif9nLvoN1rPzAMAyNzhgz52zeTe9bu2irePjH1pYDzKqllD4wwfBkgs6b2vH27gfAWrqI8r+VsMTg/nE3/mM7nquropRS88pcnAAxzc0i8p/AD4Cp/ys3xtz1ZC96usEcxphDMjPLMbcTlersZAmBI+EabxGDiQa4UY9krEJ9rEhjbJJspEjaKZGwKiTsMjHxcMWbCuYAklaZpFWmwZ4ga5XYVWlhe6mdqrEpBe5UAAhh8HevgDWaB9vCWbyIans9ucUxJtuhs3mUtFPmx8UlVHJRjDFhpqyujkpzgnKDITIOPLoLr5ZJ81I2r2h9hP6JFPQP4w8Nh28vFsNqb4Oqh/fI4/hB2AaJRrE625lYlKQreYSqb2OmrWNnLIvG2CSWGMbt5HN0MZRSap4xc76b9XXASsDleDerAU5LMHdIRC4DTK1G6zuA7c+yoUo9O8ZgP7qH7I4I9bFYWI7LdcCxw4V3rTpG7AwjIscDnVpZrhkExpclyHcJ9sYxbljxv3zgey+n53OFqVqs+cVJxpeEFR5MWwnTWGHbza04MY+6dIFsfIRVqVE6YmNckDjAXz1yLfs/vJxVvSP4lQqyfjUHXpqh0F3luo2/4K5NG2n5jDvVLWpVDNsK7eTyCVr9wamm+RtWMHlzjsO9raz88wp+f212+nnL8W8ZpVoZ5tA/LCd9KE9waO/x97R7P7m3LwULZOeeWbsESik1383xzNxFxpgVz/RFTzeYewtwK9ABHAG+C+h4ubOVCFY8DraNRCMgVjg+zbJq/yS8re0LzMgSzRindpLqBDKjsL154n7T7wfm5PsZA6UyUiqDMUyd0XXBtgiGRwny+fB8AJaN2DZiW2Db1Fk9BE6coUVJHu9aQGzQwt59pNZAi2R0IeVMDGMLxZSLmy2zuGUAEYMlhpRbJu2UAOjzMpRGYrhb90DVw87UUWyJU+jyiDcUKQfu1CKV4jhY6TTGEjYPdeKPRMEESDSK3dxEri3GovQBBnMppmfCA8diUWqU3bkmEo/1T60xJ24Ee+GCcPHgrbsw1QpaD0IppU5hDpfzqvmZiKw2xmx7Ji96usHcPwNvM8aMAohIfW3b659ZG9V8YGezFC5ZRjlrk+u28BKGSrMPUZ9kpkTE8ahPFInaHnWREo4EJJ0ythicWv76WJdmYITAWFSNRWAsyoFNYCwqgY0XWFQCBy+wKPsOfmBR8e3w1rMJjOB5NkEg+L6FCQTjhbd4AoEgvoQL7HoCBpyOAq3ZPNU711D32fum3pOzoJWgKUO1Pk417VBstKlkhEifw933bcRJGg68cQXGAmODHzf4cUMQ8cENaMhMclnTXj7588tZcXuRHDBud1BuiDLR7tBchfGrevCjQiUt5BfB+av28sjOLnb8+2pW9Y3gFwrYPUs48tJW3AlD4/tSrBwZws9NIBtX8/g7HMwYuO9dSvfA5FTXK4C9dS8H37GUaNXHP3p8CRVZsQTv1gnKvkPiHYvwt+96bv4jUUqp+WpurIgHuQAAIABJREFUB3OXAA+LyD7CMXO//NIk05x3LJAjPOqoiFzwrJuq5jbbIogKXkzwkgYvaXDqKkRjVToy4yTdMu3xHHG7QpM7QdSqkrAqRMTDFR972jfFx8I3QtU4+FiUA3dqXFrV2BSCCF5gUQwiVAObou9SCWxKvhtu91z8wKLkOfiBUPVtfN8KgzzfIvAFEwiBZ0EgNGcmWJEd4OFY28z3ZFkY18aPWngxIXDACNhlweQsgoih2BFgHANugNgGcQJsARFDNlZkQWQsPNToBIxP4A8OkmhtITrSRrk+yuQCh0paKDUbvKxPvhrFGXFwfrGFwA+wGxuodGTILwlIHbCwHt2Nf2wpkpTL+V2HeFQWEnvkMP5g2PUq0Sh2SzN4HsH9j2GCmYM9jGuzKttH0Xc5aC+axf8olFJq/hPmfGbuJc/mRU83mLNEpH5aZq7hGbxWzTPB2Dipn+wh7bo0J+NhIBRzawFRPZMCO512mDE2jSeOTZtGZnSdMtVdOjUnwZgw/T1tWRAxEK09ThgTZuCMAQyYaviaYNrxjSFIJDjgLqNl/z68aef3+waQkVHikQhxx6GytpOxpVG8hOAlA8hWyWYnqU8UWZDIkXWLNEfypO0SGbtAs5Ojyxnl76/8Mj/d2MP3v3cBi/9qhGB4BKtQJNG5AC9RT6HVJnX+MOM7G7DfW0/PYD9+uYysX82e6+rwl5T4uwu/yC3bfg35ryjUgjnxDKPlBH7JZsYKwet66Lu5wuhIilXvsZ6w3pzs3M+2t68BA/ZezcoppdSTmqMTIERkszFmw5NVgTi2z8meeybdrD8XkS/WHv8W8P5n1lQ1XxjPm9HFdzJywu2stONpbjuRwIxADgjHklUrMDkJgDPWgjsZwSkIdsnCK9uUKi4F16PguUQtj0IQwZaAqFWlz8uSD+KkrSKvbryPb7WtxW5sgMAHyyaIu3jxMJO5KDPKo0Ej1qbH8WvrvflJl0p7lSWtw1wWO0BbXT4ce3iszYFhvBgDX6CxHtsY/JFRgojNqsYB9jg+fks9dqmEPzI6FQybchlnTy8EBr+ka8sppdRTmaOZuVUi8uiTPC9A5lRPPq1gzhjzKRF5ELiqtum6Zzo4T6m5RHbup6E3RUMsiolGMK6DidoYO8GEkyJvCQftcCbsVPZRIL8wQm4pxHzhwBt7qKYM1QYfK1WlrWmAC9JjPD+7h4fqF81Yb84u+9hjMY6kM3xncgWHR7Is9nNT7XHGikxuaUBShu1/liV2qIXFt26HrfsY+NMlWO1Rdr4e3LEVLP3wnqlZrlZ3J9v/tAkCYfU/xvH2H3yOP0mllJpH5mhmjnA5kqdyypY/k3XmtgEawKnnlLiRcAaq607NRD1WPUGmV3g4YXbt02JMWPu06oUZxiB44vNTd8P7VrkdLx6n1ASFLg+nrkJn4zgNsQJLU4PUOSUSVhmJBFj12XC2LVCNOYgnVMsOu4qtVCsONDXgxGJg2/iJCLGBcBJHbNEk+aKNZNKYsXGcnUeIRzppWpJnZDwZVsA41q5YlEWLByn7NiYaQSml1JObi5m5J+tefTp03Juauywbc8EKCgvi5Bc6VOqg3BjgJwOsVBU36pGIVYhHqiTcKlHbI2JVcawAR4IZiwQHJgzyvFrlBy+w8UxY/cE3FlU/nF3rBRZV3yIIwm3HZtIGvkUQCLbjE42O44oh5YYLEftGGCwmGSomwxrORrCcgN1vW4z44UzbIGrwkwFm0uUrW9cjlmHH+7JEY0mWNA2zY2+UlbeOIqUqQV2cpjqP/hd3UK5fyMTKCvVNed67/Ft8YeAixqLZqfcllSoHexswvkVDZfDET1AppdQ0YqaN1T5dxxR5O3ADYebsf4wxf1bb/hfAG2rb/9gY853Te+bjNJhTc5qfdKmkLcr1UKkPMC1l0ukSC9J5GmOTtMTy1DsFGpxJ0naRhFUmIj6ueLjTMtIBFj61WbUmLN9VMi4V41AOXErHbgOXou9Snrp1KPkOlcCh7DlUApuKb1P1bapeLQA8tnyKZ0+dzxiodpbDuRqBhFNn/TDzZooOTqbMryzZzeL4EC9LP8p75VqqfhaGRjA7R4l1dzF4fgelJkN35yCrsv0scYdoiBQYk/qp84jnQ95FDJhYBLuuDqLh2nX+yFg4pg9ABDubDStojOcwnnd8e2MD4jj4w6NTixorpdTZ6nR2s4rIlcC1wPnGmLKItNS2rwZeDawB2oHvi8hyY8ysdPJqMKfmrsAn8uh+GnfGaEjGMREXE3MwjkvgNjNgt9BvS1is3hKMUJt3/hTHNcdvp8+mlTCtFt5On1l77H4AjjG4hpmzbwNTm01bpdiWYKwnzCKWFnhIwqO+cYJEpEpbMkfCqdAQKdDkTrA2fpi0VSRhefxRxz18+rZL+em2Zax69z6C3n46vyp4LXUM7O/gB+3tjF0ZZ8vAAroq+eNvJT9J42aLako4+uJmqslmrIvHKBYjLP+bevwd4Zp0dkszu961FK/OZ9Wto1Pr0dktzTz+wYUs7RiEm1dj/eTh03LplFJqTjr9iwa/FbjFGFMGMMbUyvZwLfD52vZ9IrIbuBj4+Wk9e82sBnMikgX+E1hL+Cf09cAO4P8B3cB+4FXT17BTajp/eOSk263TdQLLRlwHse3j4/AsAalVupDafZgx+/RUYk4bdmcSSQjYBsf1aUwWqI8WWJ4aCNfhk4CEVWHMT1AxYTYvYZW5ccF3GavECbJ1UCwRDA7j+D6p3jjGsTmQr6dYjBBkU9gTDfij41CtkOzzKDXYjC+1KLf4vLXnFxwp17Nl4flEhxvxR8YQ18UsLNKcmcRPxxA3glWXwrQ1smHxQX6z5SE+kb6OKGCl04jjEOTzxzN4Sil1ljhJMNdUm+R5zG3GmNue5uGWAy8QkfcDJeBPjDEPEFbMum/afodr22bFbGfmbgW+bYx5pYhEgATwHuAHxphbROTdwLuBP5/ldih1UnbPYgrL6ik0OxRbhEraUM0EmFiAnariuj7xaIWI4xNzPFzbx7V8rFpZL6uW5gsQAiNk3UOsiuVJWBXq3Ulc8UlYFWwCbAn4+L4X4NzWhF0KwIJK2ibfaeEloNzsY5WExLUWsAAvAX7UUGnwIV4mboRFLSPYt1bZfngRK/9qGP/wURIP7CeRSRGZaGay1eazCy+iMztG/K+Psm+4jUXvbcLfvZ+l/9qAn0xjbJ/qledx8LUebQ05er+wnE/sXUziwf2YdJqDb1tHcaHP8jsmYNNjZ/YCKaXU6XTy2axDxpgLT/USEfk+0HaSp/6SMI5qIKzccBHwBRFZcnoa+/TNWjAnIhngCuC1AMaYClARkWuBF9Z2uxO4Fw3m1BkSJKKUsjbFZqHQFhDUV8k2TNKQLNCRHKMhUmBBZJyUXaLRniBmVUlIGVsC3Novgk2AT1iu7Ni4vEptTF7VOFPVLsb9BANDdaz4wXb8XLgsSaKpkcTaRVQyDvkOBy8BxWZDEDMEmSp2JCCVKOPaPrYYGmOTvKfjm3whcxGb2tZj5yfC2q7lKnbJYJchX4xQTjn8Xsd9bKnrZEtiLaZchge34SYTeOuXUWh2uHbFZjakDvDRo68k8bPdBPk8VjpNscOnqXuEaqYO140gERexbYJCYUamTqJRJBJBHCesGpKfCM9TY6XTSDSCmZgkKJVOeQ2sZBJJJDCTkwS1RZSVUmo2PJsKEMaYF53yeCJvBe4y4ZIH94tIADQR1rHvnLbrwtq2WTGbmbnFwCDwXyJyPrAJeAfQaozpre3TB7TOYhuUelLWnkM0DtXREI9iElGCiE0QjWOcBIfdFg7awkO2gIQ1W43I1Jg8c5JeV5kxHm/amLsgvF02VMKfmJzaPxjPE9l6gKjrko7H8JrSDG5IUclYTEZsAoF4pEo6WqY7NUJDZJKDXj0r40fZ9P4uxqtZIrZPxPLpTOwg6xZ5Xnov24vtfOgjryK7u0pi104CN4KsWUa1PsbY0ijlrPC1x8/nR5ll8LpR9ryqk+5/NgSbt7P8vyaoZuqwqgHe5Ws5elmMSkPAss9NwINbw/fpOIy98gLGeyzM6jydjWOUPryMxFd+Uftgbfb9yTqW/co+hj7RQ+Yz03sbZjr81vPpecUu9n1+PS3/8bNf8ooqpdSTMCD+aZ3O+lXgSuAeEVkORIAh4G7gsyLyIcIJED3A/afzxNPNZjDnABuAtxtjfiEitxJ2qU4xxhiRk08SFpE3AW8CiJGYxWaqc5mfy0EuN2PbsfF49hN3P+1MtTKj2oZTaCXenQSgPGHjCRQrLrYVUA4cJv0o20sdJKwKr1v406lM4bEZvHat2/exQgdtP89hHtyKD1ixGF46SqXOwYsLgQtmIMpQyeY1F/6clF3i6wuuJhmPI7sOEY24lDYsZrLVpbKmQGfzKJXvNOMea6hYFNosKkuLvGnVfbw0vYU/6LqJdDZDMFkEE1DpLvEP3V/h99tuIus4U1m8oFiaMWt2stvnv5bczYZlN7KgsQEzWZiRyRM3gsSimEplRuZPKaWejdM8AeIO4A4R2QpUgOtrWbrHROQLhOvzesANszWTFUCMOa0R6vEDi7QB9xljumuPX0AYzC0DXmiM6RWRBcC9xpgVT3asOmkwz5OrZ6WdSs0lEo1iNzdhYhGCdALjWvgJF2MJftQCCwL3WGUKwoxhbWLG9EyhWwhI/ngH/th47cC1JUiiUUwihknGGNyYodQkFFaXiKfKFPtSWCXBT/tI1KexcYKGeIEXNO8malW5+70vInFXmHkTx6FwzQbGux3yy33cpiK2HRAEQtudMaLffBBz6XnkF8WJjvs4BZ/+i2NMLPZY9A1D9H8emGpr77suo+vl+4Bwjb7eL3bPyNCVr7mIw1fatN1nSH7pF7N7AZRSz7nvmy9terIxa6dTsqnTrLnmxhnbHvjUu56z88+WWcvMGWP6ROSQiKwwxuwAriaMULcB1wO31G6/NlttUGq+MeUy3uGZwyqOZQif6Zd1xv8CGjMjA2jFYqQ61iGBQ7UuSrHOJdpWIJ0o0Z0ZoT5SoDM2SsYu0uzkGPZTM4JFExhig+UwsLRtyiNJui8/wDWtW7ij49eJR6Pw8C6yjwjehcvJdcWYWFnhqtWP88jmdUSnNS06Yth+uI3rVj/Me5p/yoU9N7Ggro6gXMaUyxSaHTIrhpk40ETKccJZx46D8byZmTrLxoq44fOug6lUZ4zBE8fBSiWfsP1E4jhYiQTG9wkmJ0+5n1Jq/hED1tws5/VLme3ZrG8HPlObyboXeB1hL9YXROQNwAHgVbPcBqXUCYJKleTmQyRjUZoeShJE7DADaEcYiGbpt4Rtbpj9M1bYLZH5xWGmpj8EPs6OQ2QOxql7LEEQi9Db382/di4i0igcfcsGKlnwEga/tUw6M8ZVbYdYlz7Mpth5M9pSd6BCZVOcL49czHe6VpJcPM7hT3UQ+UaWxv/8Oc3/e5TSoSYkKOFdfh4jq6KMnufTfJ9N/Z3Tlmy6eA0Hrk5R7PBYtqKXQz/ppPvv7p+atFF8yQb8tw3R/3Ary9730CknZfiXrmP4zycZ3l/Pyr/eiT+qKycpdTaRYHZ6JM+kWQ3mjDEPAydLXWqfqVJnUuDj9fbN2PRUGcATV5ybsQagCI2ZC4jkouS6Id8WYDeXyNYV6EiP0xrP0R4dP+lx3dESqSMuGJviZJaWC/r5i2Xf5MZtr6MpGiXo7SfSP4hZtZixVWnGVxh+deMWftR/AfUiUzV0y41RyiuLXLpkPx/p+h+uyP0hVjYzNZt2stXmjpWf5XXV1yCxKOIHYQbP92dk+IotET625uP8S8OvMtJYj1Uuz8zkiWBFoxhjdAyfUvONATkLl8/UChBKqV+eMUS2H6bpYIKGRxMEMQc/7uBH6hh1swy7wmN2OL6vfcvgjC5ga/9RsiMpMsk4QSLC0J5W/nj1a4mUhL43b6SagmqdoVrvkWzOc37TAGuTR/lhfP2MJsT6CiQfynB/30peONiGiGH/xxZgPVDHwg89SMuP+nnje28kVjJU1qeY6IgwcDGk91q0f3zzVKYuNlzlxp2/TTZWpPjRgAM71rHy5l1TwauzeBH7frcddxI6PrldM3dKzSdGM3NKKXVKfv/AjMc2J58RfOJwFX90FKYFRE1mDXa5jskOyC3zkfoKjQ0TtCQn6EqO0hzJ44r3hLJtVq5I+nAau2Qx6WVxevLccv5dvLPwaqx4jGD/YeoPHsHq6mD0whbGeywuv3grP46upN11oRbMOZNV9hxqxF40yH+v+Aw3p19Cb30zVm2WbVCXoLq6QHEswsJkAsnnwzF8J8nUiRsBS8K1+IwhKBanMokQjl3Esp6w/URWLAa2TVAsHa+3q5R6Vk7zbNY5QYM5pdScYu3vpWl0koZUDD8VJYjaeLEseaeeRyNdGFsIbFiyZ2JGAGT6Bsne75GJRwlSMcZ7Utx48DW4k8LR69fix6CSNVTTAdEFk7Rlc6xPH+Zn6aVhGbdj5x+dIPNomqN97bx04k1YYij/g4O/9wJ6PrgDDvTS9Z9LCNyAytIWyhd20HuJjTshdH9i91RQazc30/fKZRTaBGfdOMVChBV/l5+ql+ssaGPbzV24mTJL/2+Z4NHHT/p52NkMO967inh3ngUfcrV+rlK/BDEG6/SuMzcnaDCnlJpT/OERqHVpCqfO8J0oyOcJ8vmpx/XjSzFWM8VmIbcswKQ9mlvHqY8VWVo3RNYpkLDKnLjUpUwWSR3xcQoWeclQ7ajwvku+xp0NlyHpFP6RPtyfbsVuamT8kk7yXTYrLtnHvuEGJBE/fpxEjPEVhsySEe5Y9ykeq7TzybZXYO+vrZmXSnDtRZu5LL2Ljze9EseywxrBlmAqlalAVWIxNl68i79a+D+8ueudZBMJglJ5RoZOHAfEwnjVJ83wKaU0M6eUUvOG6R2g4b4Ak4ji1cXCGbvxLEW3nkciCzEW+K7Q2T9zqZJgdIzMpgh10QiNj8Yptsf528HfQgJIvlww1kIq9VBNGqSjSLauwCUN+5ioRDG2dfz8hRLZbUJhpJHfrbyBeLRC/q0+3qvWs/JfBzF9g9z/gQv5Sfoi6pwq1avW03tZlCBqWPqpwakMnilXeGDbMv6ea7BfM8CB65bQ8e8u1v8+BIRduSO/t5H8ImHR3eOYh7SerlKnZADNzCml1PwQ5PNQy9RZtX+n+sGb/tMelEoE+w9OPU4vaKMl1k2xwSK3LCBI+TQsGCcbL7EiM0DaKdHk5InaHkjk+IHKZVJHfeyyTc6uY6TZ44+u+AEDlToe+fR5WPsOUvflB7FSSQrPX85Eu0v6eYPUx4p432xCdtSO4/vEjrpsqW/nH9d/mR53iNd2vYv6aJjhE9tivAcSa0co/yJNRCTM8J0sU2fZiCVgh7lOU/WemOGz7SdsfwIrXMsP359Rr1ep+UC7WZVS6hwTjI5Rf38f2XiUpkfjBBEbL5ml4ggPR9sxFtzjCtF8QKpv2/HXFQqktvSSjEao356gUh/lE2O/ih83pJ5nYW24mHI9+HFDdWGZRLrAr7XtYcKPssdtmepaNpUKmd0B+XKam6qvoik7wdivTzLwwnX03F4luG8r3V+fpHRfHXbZI7h8Pf0Xxym2GBZ/rYD87JHwQCKUrtnIyEqHiaUe6bY86c/Ukfri8aoa/W++mODFo8S+nCX739PW8DvB6B9cTPk3x7C+W6/1dNW8IjqbVSmlzj1BqUSwdz/w1Bm+6UNxjOfhHTg09TheV0drYhXFJovxHoNX51PXlqclUaQnM0jWLdAdG2JvsXnGTF3jByR7q2BcjJ1gsDHGNS/YxIWpfXzsu68kfR/Ig9uI2zbeZWvIdccobixw6eK97Nm0mtSxA4nF2BKH0gUF3nH+vbwps5MLH3gnaTcylcEbW+fx8IV3cPHWm8iKgFiIJRjfn5HhG1sFj130aZb3v4XWaPSJmbxadtAERmffqrnFgHgazCmllHoWgkKBuof7SCdi1G+PE0Rtqqk0nlPH1mgbgS3c64JVhaadB6YWaTZeldiOPqKHYmR2JfBSEb43ehHfaN1Assui/MaLKWcFPwal9ipuZpLLu/exNDHIzsjq4w0wAZkDHn48wa3lq/nSgg0E5+fZcds6Or9mE//q/Sz5os8V299F8xEfc+l5jKxJML4M2n/sE/vG/VOHSh4W3nDwSpYs7efQZ5cR+UFmRobOXHY+h65K0LDNJ3nX/TopQ80hRjNzSimlnh3jeXj7DgBPneGbMQrNmBn1el03Qpt7HhPtLrllhonFPonWSZpTBbrrhmmN5umKjmBJgLFmLsYXGyiTjltgovQPtrLukt28pf1e3rXtjcQtG+eeh2m7B2TjasaXJRne4POrG7dwX98FtE0/znDAz/Yt4ddXbOXbq37B8tE30+pGwgxe4DPRGSNzWT+jXisp2w63iwUmeGJgZ4UdymLJEzN5zyDDJ44z9TkrdUoGRMfMKaWUOpOMVyWxrY/4oQT1OxP4MZtqKkngptgZbeVxRwjccN+WB4aOL9JsDO7ePuqHUmR2xfGTLrsGe3jz0sXEIzD8+oup1AleAkotPlZTiXUdfaxK9vKT2AUz2pDsrZB/NMHXxjeweUknifoiOz52Ho0/c2m8/edkNw2QqzbTXKgSXLSGfHec4XVC/XbIfub+qcDMXrOCo1c1Umo2BMsncbamWPRPx6txmEvPY9dbbNwDUZZ8YOuMpWems9au5PF3prDHHJZ/cO8TFrBWajrxz761STSYU0qp+cQYvEOHgXBoncOpf8hPzGV5ff3Q1w+EmcEF3nrGBuLkF8HIBQFOY5GW7AQL02N0xkdZEBmn2cljTljozx0pULc/gl1y6M230bRugI+s/xyvL7yRRsvG372P5O592MuXMra+ieHzhBdcuYUfOeuotwRT+1tabk2Ru6jE+u5DfGrJ13l5428jH4lDuQzGMNEZ5+tX/At/ffAVlD4ch4mJk2b4ym1J/uvKO/hubi0Pf2I5DA4/MZN3bGFo7fI9t+nSJEoppc4m7t4+msYyZHfF8FIuXiKGF02wL9LK7ggYBwJbWPjw5IzXyZEBsmWPulQUry7K8IFWXnf+G4iMWYy89mK8hFBJhxU3gvYSC5rHWZnq5Z7kmjAYq4kMF0g8Vs8jw0t5WfF3yJei7P/HFtI7XDo+spnsQ4P8zr+9C3fS0LisSPmibgY2OsQHDK2fPp6pc3MV/mbvy2mMTbL37+J4By5mxT/vDYNXwGlr5ej/WQoGFnxxF/7g4HP3Ias5RTCIf/ZNytFgTimlzlFebx/09iGAW/v3dEyv0mEDbcMrcSfqKbQJwxt9rGyFtsZxWhJ5lqaGaHInaHfHMO7M7i0rVyCzL0MkJ/SW27AWT/Lhq/6b97T8JnJHHH/XXhb8y16c9gWMX9rJ2FKbNS/ayeZ9XbR9KTa1jqA9WWH/3hb87iG+e8lH+dTKC/nZHeugfwCMwdSlyF1axPjCgu+mYHDw1Jk6OaHo78meF+upZ+k+3f3Uc0vHzCmllFJPJEcHafINfjpKJRvFj7lU4y0ccVs5GF1GYEPgCov2VMNlUGrM8CiZR1zSiSgNj0fJLUrytrE/wM45DFyXxThQyQjVlKHcWaGuPsf6zGEeT7XCtMkdMlGg7vFGekfb+O3gegD6bswSO3IpSz6+BzMwRMeXGvFdwWvN4HdtpP+iKOJD1+f24x05CoCVTjP262sotFnkVnhgYNWHx/EfC1dwtrMZ9r1zDaUFVVbcVsBsOnm1DSsW49A7NjC5osKyO/2pah1qLjAQ6Jg5pZRSagZ/aBiGhgE4VgMjfurdj78ul4NcDggzfM2LF2F5Cyg2CSPrfUh7LGgbpT5WZFVdHxmnyOLoIIloBZmWQTOFEnUHfNycxXDQQrWtyt9c8RW+O7KWkc834ff1E//q/djZDJOXryDfadN09VEKVRfzrTTUJgtLIs7gBiHZM8r/O/9TlIzLzV97I5FtAsYg8TgX/dpWbmr7Hm//zh+T2HTy9yWRCA1X9fLD1Z/mZT/7Exp/JDpWb64wIJ4Gc0oppdSsMMOjNDwUw09GqN8RI4jaVFItDDjC0Vg3xg5n6iYGAoLc8eVaTD5PeusQqUSUzL44xRaXm0vXIYFQ/ysW1mXNVLKCF4fConAtvisbD7JrooWqU3+8AeUydXtgspTlLdbvk4mX2P9Kg/vCS1h25xDBwaNsu/08fq95Lc2lKuay8xnckKSShkVfG8LftjNsj+9zaE8z72u4mvI14+x83oUs/YyPfe/m8DwiFF9xEWPLHNrvHT9lhk/NAgPobFallFJqdvi5HDwWZuqitW2nyvBN/3MclEqwcw8Q/lHLNjUiQQ/FRmFslSFI+TS3j9EWK7Ii00+9W2BptJ+xaoIjVuPUcUylSt1BD7fgMB40Mtbk88YX3EvCLnP3D1+Eu30Xjf/5cyQapXr5WsZ6ElSvGqencYjxzZ1EjlVz830Shxx+2NTD366/mxfHe7l687tovjd8WmybofMc6p/fx8SBZpKnyPCp2aDdrEoppdScZyYmyWwdIZ2IULc/ThAVKqlGRlzhJ7FOjC34EXAnDa1H90wt4WIqFRI7h4gfjpI+lKCScbi9fCVB2iezwiXaeulUtY3JLh8yZX6l/SAZt8hD7qLj5/cD0gcDxiNpbpZX8MmmYcYuKZNbdglL7irBzx6h/ccl8gdaSeaqyIVrGV6XZrJdWHjP5PF6ukDwggsYWRVjohMqbR7t37Vm1NOdeNUlHH2xT+u9NpnP3HfKz6T8sos48BvQ8KBD0yfuO3e7fY2Bs3BhaQ3mlFJKnVWCUglqXZ5PleGbPtfUeB7+7n1AOIYvmUiw0F/LZKvDeI9hfI1PdkGOlmSBFZkBGtxJFkcHGfGTbJ7219T4PqkjFSBCzkuzozHJiy9/hFc0PMTN215H408N9r2byYpgLjmP3LIUQy+octXqx3lvU7H1AAAPPklEQVT06DoajldGY3RljPyVBV675j7e07SDntG3kvri8ef7LxJ2v+zj9JTfSuYzp/5MBs93efyaW1mX/kOa77DP3UoZ2s2qlFJKnTtMpUJyxzDxozHShxN4cYtKqp7xSD0/jy8MZ+lGQHxYuH1gWrWNgOj+IdyRBKkjCaoph3tK6/newlUkG4Tc715CJS14caHQbvAaqqxZcoSliUEeis5sQ7LXZ3J7ktv9y3iwqwvpLLDng5fS8SOf2NfvZ+EPPVZVb6BpJ8gFa8gvSzG2zKb5kSrRbz4wdZz4oOHP+y6lvXGcHR/eQOMmm8bbj1fjsNaupO+FDaQPecS/vuksXlLFgK4zp5RSSp0bjOfh18biRWr/EqfYd0Z4YAze/oNAmOGzLZvO4npyXVFySwx9SwISLZO01E2wIT3Cgug4XdFh0naJwJ25zl28r0B2V4qJYpxHe5exbONB/vp5X+cNxRtY9HWIfusBFn8rDMbG12Tpfx5c9fxH+Kl9Pp3fPH6cxJDPN3as5cqlu/jBy+9iTcP1NH3SxtSCtsmldcSu6ad/UwuLv+1gymdfwAPUMnNn33vTYE4ppZSaTSYgtncIdyxF+kicatKmkqojH63j4VgHmyIQuGAs6HhwZrUN+8gQ2YpH6nCcap3DwbFFXL/m9bi+kH/1JVQTQjUpFFsMlVaP9q5hliUG+FFs5pi46FAF9/EE359czW9X4riuz573b6RhK2Q/fT+px0cY/GILzbkA1i6j3JJgeI1L+lBA3V2bMdUKAM6iToauWEipUcgvDkgetlj4sS1T1TjsVT3sur6JyLiw6GPb8UdHT/qROIs62f3GhVSzAdzwpVn40E/FaDerUkoppZ4hY/D2HYB9xyttnCrDd6JjVToswvF/nYPrGD2SYqIDeq8IcBtKtDfkWJ0cpzsxTEskR7s7ShAhrEJRm+jgDk9SvzNGYczl4bEe6laM8O//5+O8vvW11H/Oxt+xm4Ydu3G6uxi7aAEjq2w2vmQbP3toBZmvO1PBXLW9gcGrK6xc1Mvner7MDQdfyuh/J6aCuUJ3lk++6iPcNXohj3++E04RzHmtWf7mtz7Pq9Oj2Df8Mh/uM2Q4K8cLajCnlFJKzRPO0REaAkPqcJRK1qEaT5JPJNkWbWdLFAInzPK1P+TNnLE6OEJmu0PycJTMvgijvY28ceI12P1RctdtIHChkhbKWaHQ5ZFoGacnOcBP48tmlDhzxovEdzbx+EQn11u/QX8hxdCNLaQOLKXtv7eQ2DPCGz59A3ZJ6FgwielqYPC8OO6Eofkr2/DHxgGwClU+fvAKtjbvBXY/dx+gOf1j5kTk7cANhL3t/2OM+TMR6Qa2Aztqu91njHnLaT3xNBrMKaWUUvOEd/gIHD7yjGrpwvEqHRYQAxau6mF4qIlCi9B3RYDUVehoGaM7PsHKdH+t2sYAbrwKljV1HMlNkt1VT2zY4rGJJZiuIh+67k5uPfAirK8k8XbuYdF792A3NTJx+VLGux06rt3PvqFGWn5YB1PBXIkDO9o4MpQFvnIaP6GnYjCnMZgTkSuBa4HzjTFlEWmZ9vQeY8z603ayJ6HBnFJKKXWuGRqjfluU1OEopQMOXixGPr2AcRd2JHowVjhTN7vXYErlqZeZXJ66nTmSyQjJvigT++PcZH4bf9yl7UoL8bup1AnVlJDvDpDGsBTbUCE5I8NHsURyv0115Ol2OJ8exoCpntZu1rcCtxhjyuHxzcDpPPjTpcGcUkopdY7xBwdhcBAHSD3FvtOnUvi5HDySQwjX7ksv7CCa66TYaNF/iY9J+bS3j9ASLbEm00vGKbIs2s/2RBtYkePHLBap3+VRTVgnnm52mdObmQOWAy8QkfcDJeBPjDHH1oRZLCIPATngr4wxPz6dJ55OgzmllFJKPSsmlyezI08qFSHZF8GPOhQybUy4cCDejbHBjxpiw0LH+OPHX1cqk9yTw0Tt57S9eUa/8/3gC00nbI6JyIPTHt9mjLnt2AMR+T7QdpLD/SVhHNUAXAJcBHxBRJYAvUCXMWZYRDYCXxWRNcaY3Ol8P8doMKeUUkqpZ8XP5WDTY1g8dYZvej4sKJVg6+On3He2GGNe8ixe86JTPScibwXuMsYY4H4RCYAmY8wgcKzrdZOI7CHM4j14qmP9Mp7j/KZSSiml1Fnjq8CVACKynHBt6SERaRYRu7Z9CdAD7J2tRmhmTimllFLq2bkDuENEtgIV4HpjjBGRK4C/FZEqEABvMcaMzFYjZjWYE5EbgT8kHD+5BXgdsAD4PNAIbAL+wBhTmc12KKWUUkqdbrX45fdPsv3LwJefq3bMWjeriHQAfwxcaIxZS1ii7tXAPwL/YoxZBowCb5itNiillFJKne1me8ycA8RFxCGsXtILXAUcK8R2J/Abs9wGpZRSSqmz1qwFc8aYI8A/AQcJg7hxwm7VMWPMsRX7DgMdJ3u9iLxJRB4UkQerlE+2i1JKKaXUOW82u1nrCUtcLAbagSTwtKcEG2NuM8ZcaIy50CU6S61USimllJrfZrOb9UXAPmPMoDGmCtwFPB/I1rpdARYCR2axDUoppZRSZ7XZDOYOApeISEJEBLga2AbcA7yyts/1wNdmsQ1KKaWUUme12Rwz9wvCiQ6bCZclsYDbgD8HbhKR3YTLk9w+W21QSimllDrbzeo6c8aYm4GbT9i8F7h4Ns+rlFJKKXWu0HJeSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmAZzSimllFLzmBhjznQbnpKIDAKTwNCZbouaoQm9JnONXpO5R6/J3KPXZG5ZZIxpPtONmM/mRTAHICIPGmMuPNPtUMfpNZl79JrMPXpN5h69Jupso92sSimllFLzmAZzSimllFLz2HwK5m470w1QT6DXZO7RazL36DWZe/SaqLPKvBkzp5RSSimlnmg+ZeaUUkoppdQJ5nwwJyIvEZEdIrJbRN59pttzrhKR/SKyRUQeFpEHa9saROR7IrKrdlt/ptt5thORO0RkQES2Ttt20usgoX+rfXceFZENZ67lZ6dTXI/3iciR2nflYRF52bTn/qJ2PXaIyK+dmVaf3USkU0TuEZFtIvKYiLyjtl2/J+qsNaeDORGxgY8ALwVWA78jIqvPbKvOaVcaY9ZPm9L/buAHxpge4Ae1x2p2fRJ4yQnbTnUdXgr01P69Cfjoc9TGc8kneeL1APiX2ndlvTHmmwC1365XA2tqr/mP2m+cOr084F3GmNXAJcANtc9evyfqrDWngzngYmC3MWavMaYCfB649gy3SR13LXBn7f6dwG+cwbacE4wxPwJGTth8qutwLfApE7oPyIrIguempeeGU1yPU7kW+LwxpmyM2QfsJvyNU6eRMabXGLO5dj8PbAc60O+JOovN9WCuAzg07fHh2jb13DPAd0Vkk4i8qbat1RjTW7vfB7Semaad8051HfT7c+a8rdZld8e04Qd6PZ5jItINXAD8Av2eqLPYXA/m1NxxuTFmA2GXxA0icsX0J004LVqnRp9heh3mhI8CS4H1QC/wz2e2OecmEUkBXwbeaYzJTX9OvyfqbDPXg7kjQOe0xwtr29RzzBhzpHY7AHyFsHuo/1h3RO124My18Jx2quug358zwBjTb4zxjTEB8AmOd6Xq9XiOiIhLGMh9xhhzV22zfk/UWWuuB3MPAD0islhEIoSDh+8+w20654hIUkTSx+4DvwpsJbwW19d2ux742plp4TnvVNfhbuA1tdl6lwDj07qZ1Cw5YbzVbxJ+VyC8Hq8WkaiILCYccH//c92+s52ICHA7sN0Y86FpT+n3RJ21nDPdgCdjjPFE5G3AdwAbuMMY89gZbta5qBX4SvgbiQN81hjzbRF5APiCiLwBOAC86gy28ZwgIp8DXgg0ichh4GbgFk5+Hb4JvIxwoH0BeN1z3uCz3CmuxwtFZD1hN95+4M0AxpjHROQLwDbCGZc3GGP8M9Hus9zzgT8AtojIw7Vt70G/J+osphUglFJKKaXmsbnezaqUUkoppZ6EBnNKKaWUUvOYBnNKKaWUUvOYBnNKKaWUUvOYBnNKKaWUUvOYBnNKKaWUUvOYBnNKqWdERLIi8kfTHreLyJdm4TzvE5EjIvK3p+FYN4rIQRH58Olom1JKzSW6zpxS6hmpFS//hjFm7Syf533AhDHmn07T8V4LXGiMedvpOJ5SSs0VmplTSj1TtwBLReRhEfmgiHSLyFYIAyYR+aqIfE9E9ovI20TkJhF5SETuE5GG2n5LReTbIrJJRH4sIiuf6qS1TN2dtf0PiMh1IvIBEdlSO5Zb2+8WEdkmIo+KyGkJBJVSai7TYE4p9Uy9G9hjjFlvjPnTkzy/FrgOuAh4P1AwxlwA/Bx4TW2f24C3G2M2An8C/MfTPPdS4CrgFcCngXuMMeuAInCNiDQS1kNdY4w5D/j7Z/MGlVJqPpnTtVmVUvPSPcaYPJAXkXHg67XtW4DzRCQFXAZ8sVbvFyD6NI/9LWNMVUS2ENZr/va0Y3cD3wBKwO0i8o3aY6WUOqtpMKeUOt3K0+4H0x4HhL85FjBmjFn/bI9tjAlEpGqOD/oNAMcY44nIxcDVwCuBtxFm8pRS6qyl3axKqWcqD6Sf7YuNMTlgn4j8FoCEzj8dDatl/TLGmG8CNwKn5bhKKTWXaTCnlHpGjDHDwE9FZKuIfPBZHub3gDeIyCPAY8C1p6l5aeAbIvIo8BPgptN0XKWUmrN0aRKl1JykS5MopdTTo5k5pdRcNQG86XQtGgz8BZD7pVullFJzjGbmlFJKKaXmMc3MKaWUUkrNYxrMKaWUUkrNYxrMKaWUUkrNYxrMKaWUUkrNYxrMKaWUUkrNY/8fgBdq14TkhnAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the voltage\n",
    "\n",
    "fig, axes = plt.subplots(1, 1, figsize=(9,9))\n",
    "fig.tight_layout(pad=4)\n",
    "\n",
    "axes_list = fig.axes\n",
    "\n",
    "for idx, value in enumerate(['Exc']):\n",
    "    im = axes_list[idx].imshow(results[value, 'v'].T)\n",
    "    axes_list[idx].set_title('voltage of ' + str(value) + ' layer')\n",
    "    axes_list[idx].set_xlabel('time [ms]')\n",
    "    axes_list[idx].set_ylabel('cells')\n",
    "    fig.colorbar(im, ax=axes_list[idx], fraction=0.010, label='[mV]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the conductances\n",
    "\n",
    "# check the voltage\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(9,9))\n",
    "fig.tight_layout(pad=4)\n",
    "\n",
    "axes_list = fig.axes\n",
    "\n",
    "for idx, value in enumerate(['Exc']):\n",
    "    for idx, gsyn in enumerate(['gsyn_exc', 'gsyn_inh']):\n",
    "        im = axes_list[idx].imshow(results['Exc', gsyn].T)\n",
    "        axes_list[idx].set_title(str(gsyn))\n",
    "        axes_list[idx].set_xlabel('time [ms]')\n",
    "        axes_list[idx].set_ylabel('cells')\n",
    "        fig.colorbar(im, ax=axes_list[idx], fraction=0.010, label='[uS]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# end simulation\n",
    "\n",
    "sim.end()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# task 1: on the stimulus\n",
    "\n",
    "- go to the notebook cell '# make the thalamic input sources' and design and test a new type of thalamic input (e.g., more/sparser input cells, etc)\n",
    "- add amounts of inhbitory cells by changing their number and their recurrent or feedback connectivity in order to restore the persistent behavior \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sPyNNaker",
   "language": "python",
   "name": "spynnaker"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}