02b_anisotropic-network-output-learning.ipynb

{
 "cells": [
  {
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   "source": [
    "\"\"\"\n",
    "A network with an inhomogeneous weight structure (anisotropic network)\n",
    "\n",
    "This example contains a reservoir network of 4500 neurons, distributed over 2 Loihi chips.\n",
    "3600 neurons are excitatory and 900 neurons are inhibitory.\n",
    "The neurons are locally connected with a gaussian distribution on a 2-dimensional grid of neurons.\n",
    "Edges are connected to form a torus shape.\n",
    "The gaussian distribution is shifted, where the shift direction is drawn by perlin noise.\n",
    "\n",
    "The network activity is started with a short cue input and then maintained.\n",
    "After some time the network activity is stopped using a snip.\n",
    "The next trial is started with a short cue input again,\n",
    "which is slightly different (noisy) than the one before.\n",
    "\n",
    "Using linear regression, a function is trained from the trials (using all but the last).\n",
    "Finally the spiking activity of the last trial is used to predict the function,\n",
    "using the weights trained with the other trials.\n",
    "\n",
    "The experiment is defined in 'pelenet/experiments/readoutanisotropic.py' file.\n",
    "A log file, parameters, and plot figures are stored in the 'log' folder for every run of the simulation.\n",
    "\n",
    "NOTE: The main README file contains some more information about the structure of pelenet\n",
    "\"\"\"\n",
    "\n",
    "# Load pelenet modules\n",
    "from pelenet.utils import Utils\n",
    "from pelenet.experiments.readoutanisotropic import AnisotropicReadoutExperiment\n",
    "\n",
    "# Official modules\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Overwrite default parameters (pelenet/parameters/ and pelenet/experiments/readoutanisotropic.py)\n",
    "parameters = {\n",
    "    # Experiment\n",
    "    'seed': 3,  # Random seed\n",
    "    'trials': 25,  # Number of trials\n",
    "    'stepsPerTrial': 110,  # Number of simulation steps for every trial\n",
    "    'isReset': True,  # Activate reset after every trial\n",
    "    # Network\n",
    "    'refractoryDelay': 2, # Refactory period\n",
    "    'voltageTau': 10.24,  # Voltage time constant\n",
    "    'currentTau': 10.78,  # Current time constant\n",
    "    'thresholdMant': 1000,  # Spiking threshold for membrane potential\n",
    "    'reservoirConnProb': 0.05,\n",
    "    # Anisotropic\n",
    "    'anisoStdE': 12,  # Space constant, std of gaussian for excitatory neurons\n",
    "    'anisoStdI': 9,  # Space constant, std of gaussian for inhibitory neurons (range 9 - 11)\n",
    "    'anisoShift': 1,  # Intensity of the shift of the connectivity distribution for a neuron\n",
    "    #'percShift': 1,  # Percentage of shift (default 1)\n",
    "    'anisoPerlinScale': 4,  # Perlin noise scale, high value => dense valleys, low value => broad valleys\n",
    "    'weightExCoefficient': 12,  # Coefficient for excitatory anisotropic weight\n",
    "    'weightInCoefficient': 48,  # Coefficient for inhibitory anisotropic weight\n",
    "    # Input\n",
    "    'inputIsTopology': True,  # Activate a 2D input area\n",
    "    'inputIsLeaveOut': True,  # Leaves one target neuron out per trial\n",
    "    'patchNeuronsShiftX': 44,  # x-position of the input area\n",
    "    'patchNeuronsShiftY': 24,  # y-position of the input area\n",
    "    'inputNumTargetNeurons': 25,  # Number of target neurons for the input\n",
    "    'inputSteps': 5,  # Number of steps the network is activated by the input\n",
    "    'inputWeightExponent': 0,    # The weight exponent of the weights from the generator to the target neurons\n",
    "    'inputGenSpikeProb': 1.0,  # Spiking probability of the spike generators\n",
    "    # Output\n",
    "    'partitioningClusterSize': 10, # Size of clusters connected to an output neuron (6|10)\n",
    "    # Probes\n",
    "    'isExSpikeProbe': True,  # Probe excitatory spikes\n",
    "    'isInSpikeProbe': True,   # Probe inhibitory spikes\n",
    "    'isOutSpikeProbe': True   # Probe output spikes\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initilizes the experiment, also initializes the log\n",
    "# Creating a new object results in a new log entry in the 'log' folder\n",
    "# The name is optional, it is extended to the folder in the log directory\n",
    "exp = AnisotropicReadoutExperiment(name='anisotropic-network-output-learning', parameters=parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the utils singleton\n",
    "utils = Utils.instance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the network, in this function the weight matrix, inputs, probes, etc. are defined and created\n",
    "exp.build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;30m\u001b[1;30mWARNING\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:  \u001b[33melementType would be deprecated in 0.9 in favor of messageSize, which provides more flexibility\u001b[0m\n",
      "\u001b[1;30m\u001b[1;30mWARNING\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:  \u001b[33melementType would be deprecated in 0.9 in favor of messageSize, which provides more flexibility\u001b[0m\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:  Connecting to 127.0.0.1:42441\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Host server up..............Done 0.05s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Encoding axons/synapses.....Done 15.66s\n",
      "\u001b[1;30m\u001b[1;30mWARNING\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:  \u001b[33m~/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages ~/pelenet\n",
      "Compiling in directory : /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104\n",
      "Include dir is : /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/pelenet/pelenet/snips/\n",
      "Compiling -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/initnetwork_0_0.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/pelenet/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/initnetwork_0_0.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/initnetwork_0_0.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/0_spiking_packed.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/pelenet/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/0_spiking_packed.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/0_spiking_packed.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/1_reset_init.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/pelenet/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/1_reset_init.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/1_reset_init.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/2_reset.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/pelenet/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/2_reset.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/2_reset.c\n",
      "/home/seq/pelenet/pelenet/snips//reset.c: In function ‘reset’:\n",
      "/home/seq/pelenet/pelenet/snips//reset.c:45:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n",
      "         nx_fast_init64(nc->cx_state, neuronsPerCore, *(uint64_t*)&cxs);\n",
      "         ^~~~~~~~~~~~~~\n",
      "/home/seq/pelenet/pelenet/snips//reset.c:60:13: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n",
      "             nx_fast_init32(nc->cx_meta_state, neuronsPerCore/4, *(uint32_t*)&ms);\n",
      "             ^~~~~~~~~~~~~~\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/main_0_0.c\n",
      "gcc -Wall -I/home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/graph/nxinputgen/../../snips/ -I/home/seq/pelenet/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/main_0_0.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/main_0_0.c\n",
      "OBJFILES :  /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/initnetwork_0_0.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/0_spiking_packed.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/1_reset_init.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/2_reset.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/main_0_0.c.o\n",
      "gcc -Wall -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/initnetwork_0_0.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/0_spiking_packed.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/1_reset_init.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/2_reset.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/main_0_0.c.o -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/launcher.link nxsdk/lib/embedded/libboot.a nxsdk/lib/embedded/libembedded_nx.a nxsdk/lib/embedded/liblmt.a nxsdk/lib/embedded/libnx.a -Wl,--script=nxsdk/utils/linker.scr -Wl,--build-id=none -Wl,-N -nostdlib\n",
      "\u001b[0m\n",
      "\u001b[1;30m\u001b[1;30mWARNING\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:  \u001b[33m~/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages ~/pelenet\n",
      "Compiling in directory : /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104\n",
      "Include dir is : /home/seq/pelenet/pelenet/snips/\n",
      "Compiling -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/initnetwork_1_0.c\n",
      "gcc -Wall -I/home/seq/pelenet/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/initnetwork_1_0.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/initnetwork_1_0.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/0_reset_init.c\n",
      "gcc -Wall -I/home/seq/pelenet/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/0_reset_init.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/0_reset_init.c\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/1_reset.c\n",
      "gcc -Wall -I/home/seq/pelenet/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/1_reset.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/1_reset.c\n",
      "/home/seq/pelenet/pelenet/snips//reset.c: In function ‘reset’:\n",
      "/home/seq/pelenet/pelenet/snips//reset.c:45:9: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n",
      "         nx_fast_init64(nc->cx_state, neuronsPerCore, *(uint64_t*)&cxs);\n",
      "         ^~~~~~~~~~~~~~\n",
      "/home/seq/pelenet/pelenet/snips//reset.c:60:13: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]\n",
      "             nx_fast_init32(nc->cx_meta_state, neuronsPerCore/4, *(uint32_t*)&ms);\n",
      "             ^~~~~~~~~~~~~~\n",
      "Compiling without -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/main_1_0.c\n",
      "gcc -Wall -I/home/seq/pelenet/pelenet/snips/ -Inxsdk/include -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/main_1_0.c.o -c /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/main_1_0.c\n",
      "OBJFILES :  /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/initnetwork_1_0.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/0_reset_init.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/1_reset.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/main_1_0.c.o\n",
      "gcc -Wall -fno-stack-protector -msoft-float -m32 -march=i586 -fno-builtin-printf -fno-builtin-sprintf -fno-builtin-fprintf -fno-builtin-memcpy -fstrict-volatile-bitfields -Os -DNDEBUG -std=c99 /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/initnetwork_1_0.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/0_reset_init.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/1_reset.c.o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/main_1_0.c.o -o /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/launcher.link nxsdk/lib/embedded/libboot.a nxsdk/lib/embedded/libembedded_nx.a nxsdk/lib/embedded/liblmt.a nxsdk/lib/embedded/libnx.a -Wl,--script=nxsdk/utils/linker.scr -Wl,--build-id=none -Wl,-N -nostdlib\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Compiling Embedded snips....Done 0.59s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  Using Kapoho Bay serial number 308\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  Args chip=0 cpu=0 /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/launcher_chip0_lmt0.bin --chips=2 --remote-relay=0 --epoch=0 \n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  Args chip=1 cpu=0 /home/seq/.local/share/virtualenvs/seq-r0_WvjRo/lib/python3.5/site-packages/nxsdk/driver/compilers/../../../temp/1599822461.4436104/launcher_chip1_lmt0.bin --chips=2 --remote-relay=0 --epoch=0 \n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  Lakemont_driver...\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Booting up..................Done 1.56s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Encoding probes.............Done 0.01s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Configuring registers.......Done 15.19s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Transferring spikes.........Done 0.04s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Transferring probes.........Done 0.04s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Transfered values 20, 130, 10 \n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Transfered values 20, 130, 10 \n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 0 to 10\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 0 to 10\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 10\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 10\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 130 to 140\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 130 to 140\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 140\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 140\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 260 to 270\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 260 to 270\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 270\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 270\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 390 to 400\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 390 to 400\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 400\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 400\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 520 to 530\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 520 to 530\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 530\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 530\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 650 to 660\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 650 to 660\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 660\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 660\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 780 to 790\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 780 to 790\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 790\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 790\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 910 to 920\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 910 to 920\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 920\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 920\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 1040 to 1050\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 1040 to 1050\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 1050\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 1050\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 1170 to 1180\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 1170 to 1180\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 1180\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 1180\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 1300 to 1310\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 1300 to 1310\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 1310\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 1310\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 1430 to 1440\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 1430 to 1440\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 1440\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 1440\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 1560 to 1570\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 1560 to 1570\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 1570\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 1570\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 1690 to 1700\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 1690 to 1700\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 1700\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 1700\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 1820 to 1830\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 1820 to 1830\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 1830\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 1830\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 1950 to 1960\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 1950 to 1960\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 1960\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 1960\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 2080 to 2090\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 2080 to 2090\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 2090\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 2090\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 2210 to 2220\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 2210 to 2220\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 2220\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 2220\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 2340 to 2350\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 2340 to 2350\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 2350\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 2350\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 2470 to 2480\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 2470 to 2480\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 2480\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 2480\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 2600 to 2610\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 2600 to 2610\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 2610\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 2610\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 2730 to 2740\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 2730 to 2740\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 2740\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 2740\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 2860 to 2870\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 2860 to 2870\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 2870\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 2870\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 2990 to 3000\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 2990 to 3000\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 3000\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 3000\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset current/voltage from time step 3120 to 3130\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset current/voltage from time step 3120 to 3130\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 Reset functional state at 3130\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 Reset functional state at 3130\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Executing...................Done 260.58s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:      Processing timeseries.......Done 1.57s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mDRV\u001b[0m\u001b[0m:  Executor: 3250 timesteps........Done 277.44s\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=0 cpu=0 halted, status=0x0\n",
      "\u001b[1;30m\u001b[1;30mINFO\u001b[0m\u001b[0m:\u001b[34m\u001b[34mHST\u001b[0m\u001b[0m:  chip=1 cpu=0 halted, status=0x0\n"
     ]
    }
   ],
   "source": [
    "# Run the network simulation, afterwards the probes are postprocessed to nice arrays\n",
    "exp.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot spike trains of the excitatory (red) and inhibitory (blue) neurons\n",
    "exp.net.plot.reservoirSpikeTrain(fr=0, to=400, figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot firing rates of the excitatory (red) and inhibitory (blue) neurons\n",
    "exp.net.plot.reservoirRates(fr=0, to=400, ylim=(0.0,0.3), figsize=(10,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot spike trains of the pool neurons\n",
    "exp.net.plot.outputSpikeTrain(fr=0, to=400, color=exp.p.pltColor3, figsize=(12,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot firing rates of the pool neurons\n",
    "exp.net.plot.outputRates(fr=0, to=400, color=exp.p.pltColor3, ylim=(0,0.5), figsize=(10,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get spike data from excitatory neurons and pool neurons\n",
    "dataEx = exp.net.condenseSpikeProbes(exp.net.exSpikeTrains)\n",
    "dataOut = exp.net.condenseSpikeProbes(exp.net.outSpikeTrains)\n",
    "\n",
    "# Define target\n",
    "target = np.sin(np.arange(0,2,0.02))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare excitatory spiking data\n",
    "(x, xe, y) = utils.prepareDataset(dataEx, target, binSize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate parameters based all but last trial and predict target based on last trial\n",
    "# Apply elastic net estimation approach to reduce parameters\n",
    "predEx = utils.estimateMovement(x, xe, y, alpha=0.001, L1_wt=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "# Plot target, prediction and smoothed prediction\n",
    "exp.net.plot.movement1D(predEx, target, legend=True, suffix='reservoir', ylim=(-0.5,1.5), precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare pool spiking data\n",
    "(x, xe, y) = utils.prepareDataset(dataOut, target, binSize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate parameters based all but last trial and predict target based on last trial\n",
    "predPool = utils.estimateMovement(x, xe, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "# Plot target, prediction and smoothed prediction\n",
    "exp.net.plot.movement1D(predPool, target, legend=True, suffix='pooling', ylim=(-0.5,1.5), precision=2)"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}