SpiNNaker/eg_decaying-network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decaying network (parrot neurons)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- a network of 100 excitatory cells without any recurrent connections and inhibitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fileName\n",
    "\n",
    "fileName = 'eg_decaying-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",
    "%matplotlib inline\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 09:57:58 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 09:57:58 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 09:57:58 WARNING: /home/spinnaker/CNT-2023/SpiNNaker/reports has 37 old reports that have not been closed\n",
      "2023-03-17 09:57:58 WARNING: /home/spinnaker/CNT-2023/SpiNNaker/application_generated_data_files has 38 old reports that have not been closed\n",
      "2023-03-17 09:57:58 INFO: Setting time scale factor to 1.\n",
      "2023-03-17 09:57:58 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": [],
   "source": [
    "# defining the network\n",
    "\n",
    "pops = {}\n",
    "n_cells = {}\n",
    "neuron_Model = {}\n",
    "neuron_ModelPars = {}\n",
    "initial_Values = {}\n",
    "\n",
    "for popName in ['network']:\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",
    "\n",
    "    pops[popName].record(['spikes','v','gsyn_exc','gsyn_inh'])   \n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make the thalamic input sources\n",
    "\n",
    "popName = 'Thalamus'  \n",
    "\n",
    "n_cells[popName] = n_cells['network']\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=50, scale=10)) for n in range(5)])\n",
    "\n",
    "neuron_Model[popName] = sim.SpikeSourceArray(spike_times)\n",
    "\n",
    "\n",
    "pops[popName] = sim.Population(n_cells[popName], \n",
    "                               neuron_Model[popName],\n",
    "                               structure = space.Line(dx=1.0, x0=0.0, y=0.0, z=0.0),\n",
    "                               label = popName)\n",
    "\n",
    "proj = {}\n",
    "proj['Thalamus', 'network'] = sim.Projection(pops['Thalamus'], \n",
    "                                         pops['network'],\n",
    "                                         connector = sim.OneToOneConnector(),\n",
    "                                         synapse_type = sim.StaticSynapse(weight=0.08, delay=1),\n",
    "                                         space = space.Space(axes = 'x'),\n",
    "                                         receptor_type = 'excitatory',\n",
    "                                         label = 'thalamus-network 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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-17 09:58:42 INFO: Simulating for 250 1.0ms timesteps using a hardware timestep of 1000us\n",
      "2023-03-17 09:58:42 INFO: Starting execution process\n",
      "2023-03-17 09:58:45 INFO: Time 0:00:03.220681 taken by SpallocMaxMachineGenerator\n",
      "Pre allocating resources for Extra Monitor support vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:58:54 INFO: Time 0:00:09.084090 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 09:58:59 INFO: Time 0:00:05.152755 taken by PartitionAndPlacePartitioner\n",
      "Created spalloc job 7076699\n",
      "2023-03-17 09:58:59 INFO: Created spalloc job 7076699\n",
      "Waiting for board power commands to complete.\n",
      "2023-03-17 09:58:59 INFO: Waiting for board power commands to complete.\n",
      "2023-03-17 09:59:08 INFO: Time 0:00:08.938656 taken by SpallocAllocator\n",
      "2023-03-17 09:59:08 INFO: Creating transceiver for 10.11.194.1\n",
      "2023-03-17 09:59:08 INFO: Working out if machine is booted\n",
      "2023-03-17 09:59:12 INFO: Attempting to boot machine\n",
      "2023-03-17 09:59:18 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 09:59:18 INFO: Machine communication successful\n",
      "2023-03-17 09:59:18 INFO: Detected a machine on IP address 10.11.194.1 which has 837 cores and 114.0 links\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:09.631610 taken by MachineGenerator\n",
      "Generating partitioner report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.009225 taken by PartitionerReport\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.000399 taken by NetworkSpecificationReport\n",
      "Allocating virtual identifiers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.024809 taken by MallocBasedChipIDAllocator\n",
      "Inserting extra monitors into graphs\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.034881 taken by InsertExtraMonitorVerticesToGraphs\n",
      "Writing the board chip report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.014694 taken by BoardChipReport\n",
      "Filtering edges\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.027892 taken by GraphEdgeFilter\n",
      "Placing graph vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.033712 taken by OneToOnePlacer\n",
      "Inserting edges between vertices which require FR speed up functionality.\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.059476 taken by InsertEdgesToExtraMonitorFunctionality\n",
      "Generating routing tables for data in system processes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.007030 taken by DataInMulticastRoutingGenerator\n",
      "Generating fixed router routes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.005945 taken by FixedRouteRouter\n",
      "Generating placement report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Generating placement by core report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.069434 taken by PlacerReportWithApplicationGraph\n",
      "Routing\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.034995 taken by NerRoute\n",
      "Discovering tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Allocating tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.050951 taken by BasicTagAllocator\n",
      "Reporting Tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.005485 taken by TagReport\n",
      "Getting number of keys required by each edge using application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.025503 taken by EdgeToNKeysMapper\n",
      "Getting constraints for application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.024377 taken by ProcessPartitionConstraints\n",
      "Allocating routing keys\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.009269 taken by MallocBasedRoutingInfoAllocator\n",
      "Generating Routing info report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.042028 taken by routingInfoReports\n",
      "Generating routing tables\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.023231 taken by BasicRoutingTableGenerator\n",
      "Finding executable start types\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.026787 taken by LocateExecutableStartType\n",
      "Initialising buffers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.032657 taken by BufferManagerCreator\n",
      "Generating data specifications\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.084648 taken by SpynnakerDataSpecificationWriter\n",
      "Preparing Routing Tables\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.014016 taken by RoutingSetup\n",
      "Finding binaries\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:18 INFO: Time 0:00:00.035977 taken by GraphBinaryGatherer\n",
      "Running routing table compression on chip\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:23 INFO: Time 0:00:04.520818 taken by MundyOnChipRouterCompression\n",
      "Generating Router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:23 INFO: Time 0:00:00.006382 taken by unCompressedRoutingTableReports\n",
      "loading fixed routes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:23 INFO: Time 0:00:00.049398 taken by LoadFixedRoutes\n",
      "Executing data specifications and loading data for system vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:23 INFO: Time 0:00:00.151222 taken by HostExecuteSystemDataSpecification\n",
      "Loading system executables onto the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:29 INFO: Time 0:00:05.721667 taken by LoadSystemExecutableImages\n",
      "Clearing tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Loading Tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:29 INFO: Time 0:00:00.021001 taken by TagsLoader\n",
      "Writing data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:29 INFO: Time 0:00:00.038856 taken by WriteMemoryIOData\n",
      "Executing data specifications and loading data for application vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:29 INFO: Time 0:00:00.150938 taken by HostExecuteApplicationDataSpecification\n",
      "Expanding Synapses\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:30 INFO: Time 0:00:00.541108 taken by SynapseExpander\n",
      "Writing fixed route report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:30 INFO: Time 0:00:00.046549 taken by FixedRouteFromMachineReport\n",
      "Loading executables onto the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:36 INFO: Time 0:00:06.774829 taken by LoadApplicationExecutableImages\n",
      "Reading Routing Tables from Machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:37 INFO: Time 0:00:00.016527 taken by RoutingTableFromMachineReport\n",
      "Generating compressed router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:37 INFO: Time 0:00:00.014052 taken by compressedRoutingTableReports\n",
      "Generating comparison of router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:37 INFO: Time 0:00:00.012777 taken by comparisonOfRoutingTablesReport\n",
      "2023-03-17 09:59:37 INFO: Running for 1 steps for a total of 250.0ms\n",
      "2023-03-17 09:59:37 INFO: Run 1 of 1\n",
      "Generating SDRAM usage report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:37 INFO: Time 0:00:00.062647 taken by SdramUsageReportPerChip\n",
      "Updating run time\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:37 INFO: Time 0:00:00.027781 taken by ChipRuntimeUpdater\n",
      "2023-03-17 09:59:37 INFO: creating live event connection database in /home/spinnaker/CNT-2023/SpiNNaker/application_generated_data_files/2023-03-17-09-57-58-882124/run_1/input_output_database.db\n",
      "Creating graph description database\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:37 INFO: Time 0:00:00.057501 taken by DatabaseInterface\n",
      "2023-03-17 09:59:37 INFO: ** Notifying external sources that the database is ready for reading **\n",
      "2023-03-17 09:59:37 INFO: Time 0:00:00.000872 taken by NotificationProtocol\n",
      "2023-03-17 09:59:37 INFO: *** Running simulation... *** \n",
      "Loading buffers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:37 INFO: ** Awaiting for a response from an external source to state its ready for the simulation to start **\n",
      "2023-03-17 09:59:37 INFO: ** Sending start / resume message to external sources to state the simulation has started or resumed. **\n",
      "2023-03-17 09:59:37 INFO: ** Awaiting for a response from an external source to state its ready for the simulation to start **\n",
      "2023-03-17 09:59:37 INFO: Application started; waiting 0.35s for it to stop\n",
      "2023-03-17 09:59:37 INFO: ** Sending pause / stop message to external sources to state the simulation has been paused or stopped. **\n",
      "2023-03-17 09:59:37 INFO: Time 0:00:00.447975 taken by ApplicationRunner\n",
      "Extracting buffers from the last run\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:37 INFO: Time 0:00:00.169276 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 09:59:37 INFO: Time 0:00:00.067308 taken by GraphProvenanceGatherer\n",
      "Getting provenance data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:37 INFO: Time 0:00:00.025975 taken by PlacementsProvenanceGatherer\n",
      "Getting Router Provenance\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:38 INFO: Time 0:00:00.298956 taken by RouterProvenanceGatherer\n",
      "Getting profile data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-17 09:59:38 INFO: Time 0:00:00.029282 taken by ProfileDataGatherer\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "250.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run simulation\n",
    "\n",
    "sim.run(simtime) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting spikes for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for network\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n"
     ]
    }
   ],
   "source": [
    "# save the results\n",
    "outputs = {}\n",
    "\n",
    "for layer in ['network']:\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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v\n",
      "gsyn_exc\n",
      "gsyn_inh\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys([('network', 'v'), ('network', 'gsyn_exc'), ('network', 'gsyn_inh'), ('network', 'spikes')])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check results\n",
    "\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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the spikes\n",
    "\n",
    "fig, axes = plt.subplots(1, 1)\n",
    "fig.tight_layout(pad=5)\n",
    "axes_list = fig.axes\n",
    "\n",
    "for idx, value in enumerate(['network']):\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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\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(['network']):\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",
    "    axes_list[idx].set_xlim(0, simtime)\n",
    "    fig.colorbar(im, ax=axes_list[idx], fraction=0.010, label='[mV]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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(['network']):\n",
    "    for idx, gsyn in enumerate(['gsyn_exc', 'gsyn_inh']):\n",
    "        im = axes_list[idx].imshow(results['network', 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",
    "        axes_list[idx].set_xlim(0, simtime)\n",
    "        fig.colorbar(im, ax=axes_list[idx], fraction=0.010, label='[uS]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# end simulation\n",
    "\n",
    "sim.end()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}