SpiNNaker/eg_small-world-network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1D small world network\n",
    "\n",
    "http://www.scholarpedia.org/article/Small-world_network <br>\n",
    "https://en.wikipedia.org/wiki/Network_science"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fileName\n",
    "\n",
    "fileName = 'eg_small-world_network'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "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",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-11 14:50:02 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",
      "2023-03-11 14:50:02 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",
      "2023-03-11 14:50:02 WARNING: /home/spinnaker/CNT-2023/SpiNNaker/reports has 31 old reports that have not been closed\n",
      "2023-03-11 14:50:02 WARNING: /home/spinnaker/CNT-2023/SpiNNaker/application_generated_data_files has 32 old reports that have not been closed\n",
      "2023-03-11 14:50:02 INFO: Setting time scale factor to 1.\n",
      "2023-03-11 14:50:02 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]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the network\n",
    "\n",
    "pops = {}\n",
    "n_pops = {}\n",
    "model_pops = {}\n",
    "\n",
    "model_pops['cx'] = sim.IF_cond_exp\n",
    "n_pops['cx'] = 100\n",
    "v_init = sim.RandomDistribution('normal', (-65, 2))\n",
    "\n",
    "pops['cx'] = sim.Population(\n",
    "                            n_pops['cx'],\n",
    "                            model_pops['cx'] ,\n",
    "                            cellparams=sim.IF_cond_exp.default_parameters,\n",
    "                            structure = space.Line(dx=1.0, x0=0.0, y=0.0, z=0.0),\n",
    "                            initial_values = {'v' : v_init},\n",
    "                            label=None,\n",
    "                            constraints=None,\n",
    "                            additional_parameters=None,\n",
    "                            )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the connections \n",
    "\n",
    "# the region length where nodes will be connected locally.\n",
    "degree = {'exc': 1.5,\n",
    "          'inh': 1.5         \n",
    "         }\n",
    "\n",
    "# the probability of rewiring each edge.\n",
    "rewiring = {'exc': 0.01,\n",
    "            'inh': 0.01         \n",
    "           }\n",
    " \n",
    "# if specified, the number of efferent synaptic connections per neuron.\n",
    "n_connections = {'exc': None,\n",
    "                 'inh': None} \n",
    "\n",
    "conns = {}\n",
    "for value in ['exc', 'inh']:\n",
    "    conns['cx', 'cx', value] = sim.SmallWorldConnector(\n",
    "                                                degree[value], \n",
    "                                                rewiring[value], \n",
    "                                                allow_self_connections=True,\n",
    "                                                safe=True,\n",
    "                                                verbose=True,\n",
    "                                                n_connections = n_connections[value], \n",
    "                                                        )\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the synapses\n",
    "\n",
    "synapses = {}\n",
    "\n",
    "delay = sim.RandomDistribution(\"normal_clipped\", mu=1.5, sigma=1.75, low=0.1, high=100)\n",
    "\n",
    "synapses['cx', 'cx', 'exc'] = sim.StaticSynapse(weight=0.1, delay=delay)\n",
    "synapses['cx', 'cx', 'inh'] = sim.StaticSynapse(weight=0.5, delay=delay)\n",
    "synapses['thal', 'cx'] = sim.StaticSynapse(weight=0.1, delay=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "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",
    "n_pops['thal'] = n_pops['cx']\n",
    "\n",
    "spike_times = [[]]*n_pops['thal'] #list of spike lists, where one spike list is related to one spike source\n",
    "\n",
    "random_sources_idx = np.arange(int(n_pops['thal']*0.40), int(n_pops['thal']*0.60)) #[0,1,2]\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",
    "model_pops['thal'] = sim.SpikeSourceArray(spike_times)\n",
    "\n",
    "pops['thal'] = sim.Population(n_pops['thal'], \n",
    "                              model_pops['thal'])\n",
    "\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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the projections\n",
    "\n",
    "projs = {}\n",
    "\n",
    "projs['thal', 'cx'] = sim.Projection(pops['thal'], \n",
    "                                    pops['cx'],\n",
    "                                    connector = sim.OneToOneConnector(),\n",
    "                                    synapse_type = synapses['thal', 'cx'],\n",
    "                                    space = sim.Space(axes='x', periodic_boundaries=((0,500), None, None)),\n",
    "                                    receptor_type = 'excitatory')\n",
    "\n",
    "projs['cx', 'cx', 'exc'] = sim.Projection(pops['cx'], \n",
    "                                         pops['cx'],\n",
    "                                         connector = conns['cx', 'cx', 'exc'],\n",
    "                                         synapse_type = synapses['cx', 'cx', 'exc'],\n",
    "                                         space = sim.Space(axes='x', periodic_boundaries=((0,500), None, None)),\n",
    "                                         receptor_type = 'excitatory')\n",
    "\n",
    "projs['cx', 'cx', 'inh'] = sim.Projection(pops['cx'], \n",
    "                                         pops['cx'],\n",
    "                                         connector = conns['cx', 'cx', 'inh'],\n",
    "                                         synapse_type = synapses['cx', 'cx', 'inh'],\n",
    "                                         space = sim.Space(axes='x', periodic_boundaries=((0,500), None, None)),\n",
    "                                         receptor_type = 'inhibitory')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# record state variables\n",
    "\n",
    "pops['cx'].record(['spikes','v','gsyn_exc','gsyn_inh'])   \n",
    "\n",
    "pops['thal'].record(['spikes'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-11 14:50:03 INFO: Simulating for 250 1.0ms timesteps using a hardware timestep of 1000us\n",
      "2023-03-11 14:50:03 INFO: Starting execution process\n",
      "2023-03-11 14:50:07 INFO: Time 0:00:03.136540 taken by SpallocMaxMachineGenerator\n",
      "Pre allocating resources for Extra Monitor support vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:15 INFO: Time 0:00:08.000705 taken by PreAllocateResourcesForExtraMonitorSupport\n",
      "Partitioning graph vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Partitioning graph edges\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:19 INFO: Time 0:00:04.891105 taken by PartitionAndPlacePartitioner\n",
      "Created spalloc job 7064703\n",
      "2023-03-11 14:50:19 INFO: Created spalloc job 7064703\n",
      "Waiting for board power commands to complete.\n",
      "2023-03-11 14:50:19 INFO: Waiting for board power commands to complete.\n",
      "2023-03-11 14:50:28 INFO: Time 0:00:08.941713 taken by SpallocAllocator\n",
      "2023-03-11 14:50:28 INFO: Creating transceiver for 10.11.193.1\n",
      "2023-03-11 14:50:28 INFO: Working out if machine is booted\n",
      "2023-03-11 14:50:32 INFO: Attempting to boot machine\n",
      "2023-03-11 14:50:38 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",
      "2023-03-11 14:50:38 INFO: Machine communication successful\n",
      "2023-03-11 14:50:38 INFO: Detected a machine on IP address 10.11.193.1 which has 856 cores and 120.0 links\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:09.634724 taken by MachineGenerator\n",
      "Generating partitioner report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.007053 taken by PartitionerReport\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.000348 taken by NetworkSpecificationReport\n",
      "Allocating virtual identifiers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.036955 taken by MallocBasedChipIDAllocator\n",
      "Inserting extra monitors into graphs\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.034982 taken by InsertExtraMonitorVerticesToGraphs\n",
      "Writing the board chip report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.004932 taken by BoardChipReport\n",
      "Filtering edges\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.030015 taken by GraphEdgeFilter\n",
      "Placing graph vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.033887 taken by OneToOnePlacer\n",
      "Inserting edges between vertices which require FR speed up functionality.\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.065327 taken by InsertEdgesToExtraMonitorFunctionality\n",
      "Generating routing tables for data in system processes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.013097 taken by DataInMulticastRoutingGenerator\n",
      "Generating fixed router routes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.009297 taken by FixedRouteRouter\n",
      "Generating placement report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Generating placement by core report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.050568 taken by PlacerReportWithApplicationGraph\n",
      "Routing\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.022956 taken by NerRoute\n",
      "Discovering tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Allocating tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.048022 taken by BasicTagAllocator\n",
      "Reporting Tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.008697 taken by TagReport\n",
      "Getting number of keys required by each edge using application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.021059 taken by EdgeToNKeysMapper\n",
      "Getting constraints for application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.022273 taken by ProcessPartitionConstraints\n",
      "Allocating routing keys\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.021218 taken by MallocBasedRoutingInfoAllocator\n",
      "Generating Routing info report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.022035 taken by routingInfoReports\n",
      "Generating routing tables\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:38 INFO: Time 0:00:00.022109 taken by BasicRoutingTableGenerator\n",
      "Finding executable start types\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:39 INFO: Time 0:00:00.036961 taken by LocateExecutableStartType\n",
      "Initialising buffers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:39 INFO: Time 0:00:00.024681 taken by BufferManagerCreator\n",
      "Generating data specifications\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:39 INFO: Time 0:00:00.093323 taken by SpynnakerDataSpecificationWriter\n",
      "Preparing Routing Tables\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:39 INFO: Time 0:00:00.005851 taken by RoutingSetup\n",
      "Finding binaries\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:39 INFO: Time 0:00:00.028363 taken by GraphBinaryGatherer\n",
      "Running routing table compression on chip\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:43 INFO: Time 0:00:04.539492 taken by MundyOnChipRouterCompression\n",
      "Generating Router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:43 INFO: Time 0:00:00.005455 taken by unCompressedRoutingTableReports\n",
      "loading fixed routes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:43 INFO: Time 0:00:00.043275 taken by LoadFixedRoutes\n",
      "Executing data specifications and loading data for system vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:43 INFO: Time 0:00:00.158897 taken by HostExecuteSystemDataSpecification\n",
      "Loading system executables onto the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:49 INFO: Time 0:00:05.706100 taken by LoadSystemExecutableImages\n",
      "Clearing tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Loading Tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:49 INFO: Time 0:00:00.016105 taken by TagsLoader\n",
      "Writing data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:49 INFO: Time 0:00:00.032324 taken by WriteMemoryIOData\n",
      "Executing data specifications and loading data for application vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:49 INFO: Time 0:00:00.148078 taken by HostExecuteApplicationDataSpecification\n",
      "Expanding Synapses\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:50 INFO: Time 0:00:00.535543 taken by SynapseExpander\n",
      "Writing fixed route report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:50 INFO: Time 0:00:00.042212 taken by FixedRouteFromMachineReport\n",
      "Loading executables onto the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:57 INFO: Time 0:00:06.769907 taken by LoadApplicationExecutableImages\n",
      "Reading Routing Tables from Machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:57 INFO: Time 0:00:00.014715 taken by RoutingTableFromMachineReport\n",
      "Generating compressed router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:57 INFO: Time 0:00:00.008809 taken by compressedRoutingTableReports\n",
      "Generating comparison of router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:57 INFO: Time 0:00:00.007858 taken by comparisonOfRoutingTablesReport\n",
      "2023-03-11 14:50:57 INFO: Running for 1 steps for a total of 250.0ms\n",
      "2023-03-11 14:50:57 INFO: Run 1 of 1\n",
      "Generating SDRAM usage report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:57 INFO: Time 0:00:00.126733 taken by SdramUsageReportPerChip\n",
      "Updating run time\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:57 INFO: Time 0:00:00.027930 taken by ChipRuntimeUpdater\n",
      "2023-03-11 14:50:57 INFO: creating live event connection database in /home/spinnaker/CNT-2023/SpiNNaker/application_generated_data_files/2023-03-11-14-50-02-700895/run_1/input_output_database.db\n",
      "Creating graph description database\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:57 INFO: Time 0:00:00.055313 taken by DatabaseInterface\n",
      "2023-03-11 14:50:57 INFO: ** Notifying external sources that the database is ready for reading **\n",
      "2023-03-11 14:50:57 INFO: Time 0:00:00.001316 taken by NotificationProtocol\n",
      "2023-03-11 14:50:57 INFO: *** Running simulation... *** \n",
      "Loading buffers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:57 INFO: ** Awaiting for a response from an external source to state its ready for the simulation to start **\n",
      "2023-03-11 14:50:57 INFO: ** Sending start / resume message to external sources to state the simulation has started or resumed. **\n",
      "2023-03-11 14:50:57 INFO: ** Awaiting for a response from an external source to state its ready for the simulation to start **\n",
      "2023-03-11 14:50:57 INFO: Application started; waiting 0.35s for it to stop\n",
      "2023-03-11 14:50:57 INFO: ** Sending pause / stop message to external sources to state the simulation has been paused or stopped. **\n",
      "2023-03-11 14:50:57 INFO: Time 0:00:00.442044 taken by ApplicationRunner\n",
      "Extracting buffers from the last run\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:58 INFO: Time 0:00:00.173547 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",
      "2023-03-11 14:50:58 INFO: Time 0:00:00.114159 taken by GraphProvenanceGatherer\n",
      "Getting provenance data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:58 INFO: Time 0:00:00.026843 taken by PlacementsProvenanceGatherer\n",
      "Getting Router Provenance\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:58 INFO: Time 0:00:00.066084 taken by RouterProvenanceGatherer\n",
      "Getting profile data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2023-03-11 14:50:58 INFO: Time 0:00:00.028592 taken by ProfileDataGatherer\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "250.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run simulation\n",
    "\n",
    "sim.run(simtime) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting spikes for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_exc for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting gsyn_inh for AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for SpikeSourceArrayVertex_2\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for SpikeSourceArrayVertex_2\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n"
     ]
    }
   ],
   "source": [
    "# save the results\n",
    "\n",
    "outputs = {}\n",
    "\n",
    "outputs['cx'] = pops['cx'].get_data()\n",
    "for recording in ['v', 'gsyn_inh', 'gsyn_exc', 'spikes']:\n",
    "    pops['cx'].write_data(fileName + '_' + str(recording) + '.pkl')\n",
    "\n",
    "outputs['thal'] = pops['thal'].get_data()\n",
    "for recording in ['spikes']:\n",
    "    pops['thal'].write_data(fileName + '_' + str(recording) + '.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recover results function\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v\n",
      "gsyn_exc\n",
      "gsyn_inh\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys([('cx', 'v'), ('cx', 'gsyn_exc'), ('cx', 'gsyn_inh'), ('cx', 'spikes'), ('thal', 'spikes')])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# recover results\n",
    "\n",
    "results = recover_results(outputs)\n",
    "results.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 spikes\n",
    "\n",
    "fig, axes = plt.subplots(2,1, figsize=(9,9))\n",
    "fig.tight_layout(pad=5)\n",
    "axes_list = fig.axes\n",
    "\n",
    "for idx, value in enumerate(['thal', 'cx']):\n",
    "    axes_list[idx].eventplot(results[value, 'spikes'])\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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the voltage\n",
    "\n",
    "fig, axes = plt.subplots(1, 1, figsize=(9,5))\n",
    "fig.tight_layout(pad=4)\n",
    "\n",
    "axes_list = fig.axes\n",
    "\n",
    "for idx, value in enumerate(['cx']):\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]')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
    "for layer in ['cx']:\n",
    "    fig, axes = plt.subplots(2, 1, figsize=(9,9))\n",
    "    fig.tight_layout(pad=4)\n",
    "    fig.suptitle(str(layer) + ' layer')\n",
    "    axes_list = fig.axes\n",
    "    \n",
    "    for idx, gsyn in enumerate(['gsyn_exc', 'gsyn_inh']):\n",
    "        im = axes_list[idx].imshow(results[layer, 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]')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check adjacent matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([('thal', 'cx'), ('cx', 'cx', 'exc'), ('cx', 'cx', 'inh')])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the small world projections\n",
    "\n",
    "projs.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting ['source']s for projection between AbstractPopulationVertex_1 and AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting ['source']s for projection between AbstractPopulationVertex_1 and AbstractPopulationVertex_1\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n"
     ]
    }
   ],
   "source": [
    "syn = {}\n",
    "\n",
    "syn['exc'] = projs['cx', 'cx', 'exc'].get('source', 'target', 'list')\n",
    "syn['inh'] = projs['cx', 'cx', 'inh'].get('source', 'target', 'list')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 1.,  1.,  1., ..., nan, nan, nan],\n",
       "       [nan,  2.,  2., ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., 97., 97., nan],\n",
       "       [nan, nan, nan, ..., 98., 98., 98.],\n",
       "       [nan, nan, nan, ..., nan, 99., 99.]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syn['exc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 1.,  1.,  1., ..., nan, nan, nan],\n",
       "       [nan,  2.,  2., ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., 97., 97., nan],\n",
       "       [nan, nan, nan, ..., 98., 98., 98.],\n",
       "       [nan, nan, nan, ..., nan, 99., 99.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "syn['inh']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-11 14:51:00 WARNING: The delays in the connector SmallWorldConnector from AbstractPopulationVertex_1 to AbstractPopulationVertex_1 was clipped to 1.0 a total of 65 times.  This can be avoided by reducing the timestep or increasing the minimum delay to one timestep\n",
      "2023-03-11 14:51:00 WARNING: The delays in the connector SmallWorldConnector from AbstractPopulationVertex_1 to AbstractPopulationVertex_1 was clipped to 1.0 a total of 61 times.  This can be avoided by reducing the timestep or increasing the minimum delay to one timestep\n"
     ]
    }
   ],
   "source": [
    "# end simulation\n",
    "\n",
    "sim.end()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task1: on the small-world network\n",
    "- try to find in literature a 1D small-world network and use its parameter \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
}