Raster_for_Spike_Train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spynnaker8 as p\n",
    "import pyNN.spiNNaker as sim\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-12 09:20:56 INFO: Read cfg files: /home/spinnaker/sPyNNakerGit/SpiNNFrontEndCommon/spinn_front_end_common/interface/spinnaker.cfg, /home/spinnaker/sPyNNakerGit/sPyNNaker/spynnaker/pyNN/spynnaker.cfg, /home/spinnaker/.spynnaker.cfg\n",
      "2021-06-12 09:20:56 INFO: Will search these locations for binaries: /home/spinnaker/sPyNNakerGit/SpiNNFrontEndCommon/spinn_front_end_common/common_model_binaries : /home/spinnaker/sPyNNakerGit/sPyNNaker/spynnaker/pyNN/model_binaries\n",
      "2021-06-12 09:20:56 WARNING: /home/spinnaker/work/reports has 45 old reports that have not been closed\n",
      "2021-06-12 09:20:56 WARNING: A timestep was entered that has forced sPyNNaker to automatically slow the simulation down from real time by a factor of 10. To remove this automatic behaviour, please enter a timescaleFactor value in your .spynnaker.cfg\n",
      "2021-06-12 09:20:56 INFO: Setting time scale factor to 10.\n",
      "2021-06-12 09:20:56 INFO: Setting machine time step to 100 micro-seconds.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/spinnaker/sPyNNakerGit/SpiNNFrontEndCommon/spinn_front_end_common/interface/spinnaker.cfg', '/home/spinnaker/sPyNNakerGit/sPyNNaker/spynnaker/pyNN/spynnaker.cfg', '/home/spinnaker/.spynnaker.cfg']\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "sim.setup(timestep = 0.1)\n",
    "TotalDuration = 1000.0\n",
    "time_resol = 0.1\n",
    "TimeInt = 1.0 / time_resol\n",
    "TotalDataPoints = int(TotalDuration * TimeInt)\n",
    "rate_Poisson_Inp_base = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NumCells_exc = 80\n",
    "NumCells_inh = 20\n",
    "NumCells_extSource = 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Synaptic Conn params\n",
    "conn_delay = 1.0\n",
    "conn_weights = 5.0\n",
    "conn_prob = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# excitatory cell params\n",
    "exc_a_tonic = 0.02\n",
    "exc_b_tonic = 0.2\n",
    "exc_c_tonic = -65.0\n",
    "exc_d_tonic = 6.0\n",
    "exc_v_init_tonic = -65.0\n",
    "exc_u_init_tonic = exc_b_tonic * exc_v_init_tonic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inhibitory cell params\n",
    "inh_a_tonic = 0.1\n",
    "inh_b_tonic = 0.2\n",
    "inh_c_tonic = -65.0\n",
    "inh_d_tonic = 6.0\n",
    "inh_v_init_tonic = -70.0\n",
    "inh_u_init = inh_b_tonic * inh_v_init_tonic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time constants\n",
    "tau_exc = 1.7\n",
    "\n",
    "tau_inh = 2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DC bias current\n",
    "current_Pulse = 0.0\n",
    "exc_cell_params = {\n",
    "'a': exc_a_tonic, 'b': exc_b_tonic, \n",
    "'c': exc_c_tonic, 'd': exc_d_tonic,\n",
    "#'v_init': exc_v_init_tonic, 'u_init': exc_u_init_tonic,\n",
    "'tau_syn_E': tau_exc, 'tau_syn_I': tau_inh,\n",
    "'i_offset': current_Pulse\n",
    "               }\n",
    "\n",
    "inh_cell_params = {\n",
    "    'a' : inh_a_tonic, 'b': inh_b_tonic,\n",
    "    \n",
    "    'c': inh_c_tonic, 'd': inh_d_tonic,\n",
    "    #'v_init' : inh_v_init_tonic,'u_init' : inh_u_init,\n",
    "    'tau_syn_E' : tau_exc, 'tau_syn_I':tau_inh,\n",
    "    'i_offset' : current_Pulse\n",
    "               }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "external_spike_source_pop = p.Population(NumCells_extSource,p.SpikeSourcePoisson,{'rate': rate_Poisson_Inp_base,'duration': 1000,'start': 100},label='spike_source_poisson')\n",
    "exc_pop = p.Population(NumCells_exc, p.Izhikevich(**exc_cell_params), label='exc_pop')\n",
    "inh_pop = p.Population(NumCells_inh, p.Izhikevich(**inh_cell_params), label='inh_pop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#external to excitatory\n",
    "Proj00 = p.Projection(external_spike_source_pop, exc_pop, p.FixedProbabilityConnector(p_connect=0.5),p.StaticSynapse(weight=conn_weights, delay=conn_delay), receptor_type='excitatory')\n",
    "\n",
    "#external to inhibitory\n",
    "Proj01 = p.Projection(external_spike_source_pop, inh_pop, p.FixedProbabilityConnector(p_connect=0.5),p.StaticSynapse(weight=conn_weights, delay=conn_delay), receptor_type='excitatory')\n",
    "\n",
    "#excitatory to inhibitory\n",
    "Proj02 = p.Projection(exc_pop, inh_pop, p.FixedProbabilityConnector(p_connect=conn_prob),p.StaticSynapse(weight=conn_weights, delay=conn_delay), receptor_type='excitatory')\n",
    "\n",
    "#inhibitory to excitatory\n",
    "Proj03 = p.Projection(inh_pop, exc_pop, p.FixedProbabilityConnector(p_connect=conn_prob),p.StaticSynapse(weight=conn_weights, delay=conn_delay), receptor_type='inhibitory')\n",
    "\n",
    "#inhibitory to inhibitory\n",
    "Proj04 = p.Projection(inh_pop, inh_pop, p.FixedProbabilityConnector(p_connect=conn_prob),p.StaticSynapse(weight=conn_weights, delay=conn_delay), receptor_type='inhibitory')\n",
    "\n",
    "#excitatory to excitatory\n",
    "\n",
    "Proj05 = p.Projection(exc_pop, exc_pop, p.FixedProbabilityConnector(p_connect=conn_prob),p.StaticSynapse(weight=conn_weights, delay=conn_delay), receptor_type='excitatory')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-12 09:20:56 INFO: Starting execution process\n",
      "2021-06-12 09:20:56 INFO: Simulating for 10000 0.1ms timesteps using a hardware timestep of 10us\n",
      "2021-06-12 09:20:59 INFO: Time 0:00:03.074053 taken by SpallocMaxMachineGenerator\n",
      "Pre allocating resources for Extra Monitor support vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:07 INFO: Time 0:00:08.253836 taken by PreAllocateResourcesForExtraMonitorSupport\n",
      "Partitioning graph vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Partitioning graph edges\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:12 INFO: Time 0:00:04.809523 taken by PartitionAndPlacePartitioner\n",
      "Created spalloc job 6012030\n",
      "2021-06-12 09:21:12 INFO: Created spalloc job 6012030\n",
      "Waiting for board power commands to complete.\n",
      "2021-06-12 09:21:12 INFO: Waiting for board power commands to complete.\n",
      "2021-06-12 09:21:17 INFO: Time 0:00:05.046062 taken by SpallocAllocator\n",
      "2021-06-12 09:21:17 INFO: Creating transceiver for 10.11.195.65\n",
      "2021-06-12 09:21:17 INFO: Working out if machine is booted\n",
      "2021-06-12 09:21:21 INFO: Attempting to boot machine\n",
      "2021-06-12 09:21:26 INFO: Found board with version [Version: SC&MP 3.3.0 at SpiNNaker:0:0:0 (built Mon May 11 13:46:47 2020)]\n",
      "2021-06-12 09:21:26 INFO: Machine communication successful\n",
      "2021-06-12 09:21:26 INFO: Detected a machine on IP address 10.11.195.65 which has 838 cores and 117.0 links\n",
      "2021-06-12 09:21:26 INFO: Time 0:00:09.633671 taken by MachineGenerator\n",
      "Generating partitioner report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:26 INFO: Time 0:00:00.006904 taken by PartitionerReport\n",
      "2021-06-12 09:21:26 INFO: Time 0:00:00.000480 taken by NetworkSpecificationReport\n",
      "Allocating virtual identifiers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.019943 taken by MallocBasedChipIDAllocator\n",
      "Inserting extra monitors into graphs\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.031744 taken by InsertExtraMonitorVerticesToGraphs\n",
      "Writing the board chip report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.006050 taken by BoardChipReport\n",
      "Filtering edges\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.023705 taken by GraphEdgeFilter\n",
      "Getting number of keys required by each edge using application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.019734 taken by EdgeToNKeysMapper\n",
      "Placing graph vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.030588 taken by OneToOnePlacer\n",
      "Inserting edges between vertices which require FR speed up functionality.\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.063669 taken by InsertEdgesToExtraMonitorFunctionality\n",
      "Generating routing tables for data in system processes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.014366 taken by SystemMulticastRoutingGenerator\n",
      "Generating fixed router routes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.007675 taken by FixedRouteRouter\n",
      "Generating placement report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Generating placement by core report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.052664 taken by PlacerReportWithApplicationGraph\n",
      "Routing\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.028728 taken by NerRoute\n",
      "Discovering tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Allocating tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.037945 taken by BasicTagAllocator\n",
      "Reporting Tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.008333 taken by TagReport\n",
      "Getting constraints for application graph\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.020447 taken by ProcessPartitionConstraints\n",
      "Allocating routing keys\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.020608 taken by MallocBasedRoutingInfoAllocator\n",
      "Generating Routing info report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.024399 taken by routingInfoReports\n",
      "Generating routing tables\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.018196 taken by BasicRoutingTableGenerator\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.000408 taken by RouterCollisionPotentialReport\n",
      "Finding executable start types\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.030662 taken by LocateExecutableStartType\n",
      "Initialising buffers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.028308 taken by BufferManagerCreator\n",
      "Generating data specifications\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.085104 taken by SpynnakerDataSpecificationWriter\n",
      "Preparing Routing Tables\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.013285 taken by RoutingSetup\n",
      "Finding binaries\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.029775 taken by GraphBinaryGatherer\n",
      "Generating Router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.013707 taken by unCompressedRoutingTableReports\n",
      "loading fixed routes\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.038018 taken by LoadFixedRoutes\n",
      "Executing data specifications and loading data for system vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:27 INFO: Time 0:00:00.152900 taken by HostExecuteSystemDataSpecification\n",
      "Loading system executables onto the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:33 INFO: Time 0:00:05.728072 taken by LoadSystemExecutableImages\n",
      "Clearing tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Loading Tags\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:33 INFO: Time 0:00:00.012490 taken by TagsLoader\n",
      "Writing data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:33 INFO: Time 0:00:00.029844 taken by WriteMemoryIOData\n",
      "Executing data specifications and loading data for application vertices\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:33 INFO: Time 0:00:00.103695 taken by HostExecuteApplicationDataSpecification\n",
      "Expanding Synapses\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:37 INFO: Time 0:00:03.946396 taken by SynapseExpander\n",
      "Running bitfield generation on chip\n",
      "|0%                          50%                         100%|\n",
      " ==========================================================Extracting IOBUF from the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "==\n",
      "2021-06-12 09:21:41 INFO: Time 0:00:03.407891 taken by OnChipBitFieldGenerator\n",
      "on chip compressing routing tables and merging in bitfields as appropriate\n",
      "|0%                          50%                         100%|\n",
      " ==========================================================Extracting IOBUF from the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "==\n",
      "2021-06-12 09:21:48 INFO: rerunning synaptic expander\n",
      "Extracting IOBUF from the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:52 INFO: Time 0:00:11.234400 taken by SpynnakerMachineBitFieldRouterCompressor\n",
      "2021-06-12 09:21:52 INFO: Time 0:00:00.001521 taken by BitFieldCompressorReport\n",
      "Reading Routing Tables from Machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:52 INFO: Time 0:00:00.013430 taken by ReadRoutingTablesFromMachine\n",
      "Generating compressed router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:52 INFO: Time 0:00:00.007898 taken by compressedRoutingTableReports\n",
      "Generating comparison of router table report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:52 INFO: Time 0:00:00.005137 taken by comparisonOfRoutingTablesReport\n",
      "Generating Routing summary report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:52 INFO: Time 0:00:00.031913 taken by CompressedRouterSummaryReport\n",
      "Reading Routing Tables from Machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:52 INFO: Time 0:00:00.005029 taken by RoutingTableFromMachineReport\n",
      "Writing fixed route report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:52 INFO: Time 0:00:00.044883 taken by FixedRouteFromMachineReport\n",
      "Loading executables onto the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:59 INFO: Time 0:00:07.165899 taken by LoadApplicationExecutableImages\n",
      "2021-06-12 09:21:59 INFO: Running for 1 steps for a total of 1000.0ms\n",
      "2021-06-12 09:21:59 INFO: Run 1 of 1\n",
      "Generating SDRAM usage report\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:59 INFO: Time 0:00:00.061686 taken by SdramUsageReportPerChip\n",
      "2021-06-12 09:21:59 INFO: creating live event connection database in /home/spinnaker/work/reports/2021-06-12-09-20-56-50742/run_1/input_output_database.db\n",
      "Creating graph description database\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:59 INFO: Time 0:00:00.048997 taken by DatabaseInterface\n",
      "2021-06-12 09:21:59 INFO: ** Notifying external sources that the database is ready for reading **\n",
      "2021-06-12 09:21:59 INFO: Time 0:00:00.000897 taken by CreateNotificationProtocol\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",
      "2021-06-12 09:21:59 INFO: Time 0:00:00.072266 taken by GraphProvenanceGatherer\n",
      "Waiting for cores to be either in PAUSED or READY state\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Updating run time\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:59 INFO: Time 0:00:00.037469 taken by ChipRuntimeUpdater\n",
      "2021-06-12 09:21:59 INFO: *** Running simulation... *** \n",
      "Loading buffers\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:21:59 INFO: ** Awaiting for a response from an external source to state its ready for the simulation to start **\n",
      "2021-06-12 09:21:59 INFO: ** Sending start / resume message to external sources to state the simulation has started or resumed. **\n",
      "2021-06-12 09:21:59 INFO: ** Awaiting for a response from an external source to state its ready for the simulation to start **\n",
      "2021-06-12 09:21:59 INFO: Application started; waiting 10.1s for it to stop\n",
      "2021-06-12 09:22:10 INFO: ** Sending pause / stop message to external sources to state the simulation has been paused or stopped. **\n",
      "2021-06-12 09:22:10 INFO: Time 0:00:10.195924 taken by ApplicationRunner\n",
      "Extracting buffers from the last run\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:22:10 INFO: Time 0:00:00.770792 taken by BufferExtractor\n",
      "Extracting IOBUF from the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:22:10 INFO: Time 0:00:00.045906 taken by ChipIOBufExtractor\n",
      "clearing IOBUF from the machine\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:22:10 INFO: Time 0:00:00.007849 taken by ChipIOBufClearer\n",
      "2021-06-12 09:22:10 INFO: Time 0:00:00.000056 taken by FinaliseTimingData\n",
      "Getting provenance data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:22:10 INFO: Time 0:00:00.032537 taken by PlacementsProvenanceGatherer\n",
      "2021-06-12 09:22:10 INFO: Time 0:00:00.000729 taken by RedundantPacketCountReport\n",
      "Getting Router Provenance\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:22:10 INFO: Time 0:00:00.049378 taken by RouterProvenanceGatherer\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting profile data\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "2021-06-12 09:22:11 INFO: Time 0:00:00.030294 taken by ProfileDataGatherer\n",
      "2021-06-12 09:22:11 WARNING: spinnaker_get_data is non-standard PyNN and therefore may not be portable to other simulators. Nor do we guarantee that this function will exist in future releases.\n",
      "Getting spikes for spike_source_poisson\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for exc_pop\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting spikes for inh_pop\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for exc_pop\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n",
      "Getting v for inh_pop\n",
      "|0%                          50%                         100%|\n",
      " ============================================================\n"
     ]
    }
   ],
   "source": [
    "external_spike_source_pop.record(\"spikes\") #need to store external spike source records and need to print them\n",
    "exc_pop.record((\"spikes\", \"v\"))\n",
    "inh_pop.record((\"spikes\", \"v\"))\n",
    "p.run(TotalDuration)\n",
    "external_spike_source = external_spike_source_pop.spinnaker_get_data(\"spikes\")\n",
    "exc_spikes = exc_pop.spinnaker_get_data(\"spikes\")\n",
    "inh_spikes = inh_pop.spinnaker_get_data(\"spikes\")\n",
    "exc_voltage = exc_pop.spinnaker_get_data(\"v\")\n",
    "inh_voltage = inh_pop.spinnaker_get_data(\"v\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4233\n",
      "2181\n",
      "1769\n",
      "[[  0.  106.2]\n",
      " [  0.  107.6]\n",
      " [  0.  108.6]\n",
      " ...\n",
      " [ 19.  953.6]\n",
      " [ 19.  977.6]\n",
      " [ 19.  997.9]]\n"
     ]
    }
   ],
   "source": [
    "#print(exc_spikes) #print length of source spike data\n",
    "print(len(exc_spikes))\n",
    "#print(inh_spikes)\n",
    "print(len(inh_spikes))\n",
    "print(len(external_spike_source))\n",
    "print(inh_spikes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f80eed87668>]"
      ]
     },
     "execution_count": 13,
     "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": [
    "#print(exc_spikes)\n",
    "plt.figure(1)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Neuron number')\n",
    "plt.title('Excitatory Spike Train graph')\n",
    "plt.plot(exc_spikes[:, 1], exc_spikes[:, 0], '.',color ='red')\n",
    "#need to put x and y axis names \n",
    "#need to plot graphs for inhibitory and external source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f81b08bbb38>]"
      ]
     },
     "execution_count": 14,
     "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": [
    "plt.figure(2)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Neuron number')\n",
    "plt.title('Inhibitory Spike Train graph')\n",
    "plt.plot(inh_spikes[:, 1], inh_spikes[:, 0], '.',color = 'blue')\n",
    "#plt.savefig('inhibitory.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f81b0820908>]"
      ]
     },
     "execution_count": 15,
     "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": [
    "plt.figure(3)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Neuron Number')\n",
    "plt.title('External Spike Train graph')\n",
    "plt.plot(external_spike_source[:, 1], external_spike_source[:, 0], '.',color = 'green')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-70.         -69.99578857 -69.99194336 ... -76.39483643 -76.16244507\n",
      "  -75.99551392]\n",
      " [-70.         -69.99578857 -69.99194336 ... -64.92431641 -64.24450684\n",
      "  -63.65356445]\n",
      " [-70.         -69.99578857 -69.99194336 ... -65.33190918 -65.54998779\n",
      "  -65.82266235]\n",
      " ...\n",
      " [-70.         -69.99578857 -69.99194336 ... -59.24987793 -58.93179321\n",
      "  -58.65847778]\n",
      " [-70.         -69.99578857 -69.99194336 ... -62.58065796 -62.37390137\n",
      "  -62.23291016]\n",
      " [-70.         -69.99578857 -69.99194336 ... -60.34494019 -60.26992798\n",
      "  -60.26602173]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print(exc_voltage)\n",
    "#print(len(exc_voltage))\n",
    "exc_mem_voltage = np.reshape(exc_voltage[:, 2], [NumCells_exc, TotalDataPoints])\n",
    "print(exc_mem_voltage)\n",
    "avgsignaltcr = np.mean(exc_mem_voltage, axis=0)\n",
    "plt.figure(5)\n",
    "plt.title('Excitatory Membrane Potential')\n",
    "plt.xlabel('Time in Milli seconds')\n",
    "plt.ylabel('voltage in Milli volts')\n",
    "plt.plot([x for x in range(TotalDataPoints)], avgsignaltcr,color = 'red')\n",
    "#plt.plot(exc_mem_voltage[:, 1], exc_mem_voltage[:, 2],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-70.         -69.99578857 -69.99194336 ... -59.52389526 -58.80383301\n",
      "  -58.13330078]\n",
      " [-70.         -69.99578857 -69.99194336 ... -61.63946533 -61.25073242\n",
      "  -60.90908813]\n",
      " [-70.         -69.99578857 -69.99194336 ... -19.93463135 -11.00796509\n",
      "    2.23388672]\n",
      " ...\n",
      " [-70.         -69.99578857 -69.99194336 ... -65.         -64.41720581\n",
      "  -64.18084717]\n",
      " [-70.         -69.99578857 -69.99194336 ... -60.08612061 -59.74108887\n",
      "  -59.42886353]\n",
      " [-70.         -69.99578857 -69.99194336 ... -63.21514893 -63.4624939\n",
      "  -63.74349976]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inh_mem_voltage = np.reshape(inh_voltage[:, 2], [NumCells_inh, TotalDataPoints])\n",
    "print(inh_mem_voltage)\n",
    "avgsignaltcr1 = np.mean(inh_mem_voltage, axis=0)\n",
    "plt.figure(6)\n",
    "plt.title('Inhibitory Membrane Potential')\n",
    "plt.xlabel('Time in Milli seconds')\n",
    "plt.ylabel('voltage in Milli volts')\n",
    "plt.plot([x for x in range(TotalDataPoints)], avgsignaltcr1,color = 'blue')\n",
    "plt.savefig('inhibitory_voltage.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}