Lesson-7-12-Cell/D4.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cyneuro/Fear-Lesson-Plans/blob/master/Lesson-7-12-Cell/D4.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xioXUtIgDW0k"
      },
      "source": [
        "# 12 Cell Model of the Amygdala\n",
        "Now that you know feedback and feedforward inhibition circuits, it's time to put it to the test.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lLCtAE2JGBOT"
      },
      "source": [
        "# CHALLENGE\n",
        "---\n",
        "\n",
        "Sketch a 10 cell model of the amygdala with 8 PNs(as a triangle) and 2 ITNs(as a circle). Use T to indicate tone and S for shock. Assume both tone and shock make excitatory synapses. Try to implement feedback and feedforward inhibition within your model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjN9URBJCOKW"
      },
      "source": [
        "![12-cell-model-legend.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Phf21QNkGDss"
      },
      "source": [
        "# 12 Cell Model Diagram\n",
        "Our specific model has 12 cells with 8 PN, 2 PV, and 2 SOM cells. The network has background, tone, and shock connections. The diagram is given below."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-RKkmq_B6t8"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8R1nyJnOJWQ5"
      },
      "source": [
        "#The Model\n",
        "Each model run has 3 stages:\n",
        "*   Sensitization: Tone and shock is given 10 time each separately to get a baseline.\n",
        "*   Conditioning: Tone is played 10 times, with shock occurring in the last 20% of the tone occurrance.\n",
        "*   Early Extinction: Tone is played 30 times alone. The difference in the baseline and here will show how much has been learned due to conditioning.\n",
        "\n",
        "Run the cell below to view runs of the model with different parameters. First 'plot baseline' to see what a normal run looks like and make sure you understand why each neuron has the firing rate it does during each stage of the run."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/bin/nrnivmodl:10: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n",
            "  from pkg_resources import working_set\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/usr/bin/xcrun\n",
            "/Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms\n",
            "Mod files: \"modfiles/modfiles/CaDynamics.mod\" \"modfiles/modfiles/Ca_HVA.mod\" \"modfiles/modfiles/Ca_LVA.mod\" \"modfiles/modfiles/Gfluct_new_exc.mod\" \"modfiles/modfiles/Gfluct_new_inh.mod\" \"modfiles/modfiles/Ih.mod\" \"modfiles/modfiles/Im_v2.mod\" \"modfiles/modfiles/K_P.mod\" \"modfiles/modfiles/K_T.mod\" \"modfiles/modfiles/Kd.mod\" \"modfiles/modfiles/Kv2like.mod\" \"modfiles/modfiles/Kv3_1.mod\" \"modfiles/modfiles/MyExp2Sid.mod\" \"modfiles/modfiles/NaTa.mod\" \"modfiles/modfiles/NaTs.mod\" \"modfiles/modfiles/NaV.mod\" \"modfiles/modfiles/OLM_IA.mod\" \"modfiles/modfiles/OLM_Ih.mod\" \"modfiles/modfiles/OLM_Lca.mod\" \"modfiles/modfiles/OLM_cat.mod\" \"modfiles/modfiles/OLM_ccanl.mod\" \"modfiles/modfiles/OLM_ichan2.mod\" \"modfiles/modfiles/OLM_nap.mod\" \"modfiles/modfiles/OLM_sahp.mod\" \"modfiles/modfiles/SK.mod\" \"modfiles/modfiles/bg2pyr.mod\" \"modfiles/modfiles/bginh.mod\" \"modfiles/modfiles/ca.mod\" \"modfiles/modfiles/cad.mod\" \"modfiles/modfiles/cadyn.mod\" \"modfiles/modfiles/cal2.mod\" \"modfiles/modfiles/cal2CA3.mod\" \"modfiles/modfiles/capool.mod\" \"modfiles/modfiles/capoolCA3.mod\" \"modfiles/modfiles/cas.mod\" \"modfiles/modfiles/cat.mod\" \"modfiles/modfiles/cav31.mod\" \"modfiles/modfiles/currentclamp.mod\" \"modfiles/modfiles/function_TMonitor.mod\" \"modfiles/modfiles/gabaa.mod\" \"modfiles/modfiles/gap.mod\" \"modfiles/modfiles/h.mod\" \"modfiles/modfiles/hCA3.mod\" \"modfiles/modfiles/halfgap.mod\" \"modfiles/modfiles/im.mod\" \"modfiles/modfiles/imCA3.mod\" \"modfiles/modfiles/int2intFENG.mod\" \"modfiles/modfiles/int2pyrFENG.mod\" \"modfiles/modfiles/int2pyrKIM.mod\" \"modfiles/modfiles/interD2interD_SOMPV_STFD_new.mod\" \"modfiles/modfiles/interD2interD_STFD_new.mod\" \"modfiles/modfiles/interD2pyrD_CR2P_STFD_new.mod\" \"modfiles/modfiles/interD2pyrD_SOM2P_STFD_new.mod\" \"modfiles/modfiles/interD2pyrD_STFD_new.mod\" \"modfiles/modfiles/kadist.mod\" \"modfiles/modfiles/kaprox.mod\" \"modfiles/modfiles/kca.mod\" \"modfiles/modfiles/kdrCA3.mod\" \"modfiles/modfiles/kdrca1.mod\" \"modfiles/modfiles/kdrca1DA.mod\" \"modfiles/modfiles/kdrinter.mod\" \"modfiles/modfiles/kdrinter_WB.mod\" \"modfiles/modfiles/km.mod\" \"modfiles/modfiles/kv.mod\" \"modfiles/modfiles/leak.mod\" \"modfiles/modfiles/leakCA3.mod\" \"modfiles/modfiles/leakDA.mod\" \"modfiles/modfiles/leakinter.mod\" \"modfiles/modfiles/na.mod\" \"modfiles/modfiles/na12.mod\" \"modfiles/modfiles/na16.mod\" \"modfiles/modfiles/na3.mod\" \"modfiles/modfiles/na3DA.mod\" \"modfiles/modfiles/nainter.mod\" \"modfiles/modfiles/nainter_WB.mod\" \"modfiles/modfiles/nap.mod\" \"modfiles/modfiles/nap2.mod\" \"modfiles/modfiles/napCA3.mod\" \"modfiles/modfiles/nat.mod\" \"modfiles/modfiles/natCA3.mod\" \"modfiles/modfiles/pyr2intFENG.mod\" \"modfiles/modfiles/pyr2intKIM.mod\" \"modfiles/modfiles/pyr2pyrFENG.mod\" \"modfiles/modfiles/pyr2pyrKIM.mod\" \"modfiles/modfiles/pyrD2interD_P2CR_STFD.mod\" \"modfiles/modfiles/pyrD2interD_P2SOM_STFD.mod\" \"modfiles/modfiles/pyrD2interD_STFD.mod\" \"modfiles/modfiles/pyrD2pyrD_STFD_new.mod\" \"modfiles/modfiles/sahp.mod\" \"modfiles/modfiles/sahpCA3.mod\" \"modfiles/modfiles/sahpNE.mod\" \"modfiles/modfiles/shock2int.mod\" \"modfiles/modfiles/shock2pyr.mod\" \"modfiles/modfiles/synapse_inter2pyr.mod\" \"modfiles/modfiles/tone2pyr.mod\" \"modfiles/modfiles/vecevent.mod\" \"modfiles/modfiles/xtra.mod\" \"modfiles/modfiles/xtra_imemrec.mod\"\n",
            "\n",
            "Creating 'x86_64' directory for .o files.\n",
            "\n",
            " -> \u001b[32mCompiling\u001b[0m mod_func.cpp\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/CaDynamics.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/Ca_HVA.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/Ca_LVA.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/Gfluct_new_inh.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/Gfluct_new_exc.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/Ih.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/Im_v2.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/K_T.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/K_P.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/Kd.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/Kv2like.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/Kv3_1.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/MyExp2Sid.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/NaTs.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/NaTa.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/NaV.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/OLM_Ih.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/OLM_IA.mod\n",
            "Warning: Default -32.9 of PARAMETER eh will be ignored and set by NEURON.\n",
            "Warning: Default -90 of PARAMETER ek will be ignored and set by NEURON.\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/OLM_Lca.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/OLM_cat.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/OLM_ccanl.mod\n",
            "Warning: Default 6.3 of PARAMETER celsius will be ignored and set by NEURON.\n",
            "Warning: Default 6.3 of PARAMETER celsius will be ignored and set by NEURON.\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/OLM_ichan2.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/OLM_nap.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/OLM_sahp.mod\n",
            "Warning: Default 6.3 of PARAMETER celsius will be ignored and set by NEURON.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Translating Ca_HVA.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/Ca_HVA.c\n",
            "Translating Ca_LVA.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/Ca_LVA.c\n",
            "Translating CaDynamics.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/CaDynamics.c\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Translating Gfluct_new_inh.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/Gfluct_new_inh.c\n",
            "Translating Ih.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/Ih.c\n",
            "Translating Gfluct_new_exc.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/Gfluct_new_exc.c\n",
            "Notice: This mechanism cannot be used with CVODE\n",
            "Notice: This mechanism cannot be used with CVODE\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Translating Im_v2.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/Im_v2.c\n",
            "Translating K_T.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/K_T.c\n",
            "Translating K_P.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/K_P.c\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Translating Kd.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/Kd.c\n",
            "Translating Kv2like.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/Kv2like.c\n",
            "Translating Kv3_1.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/Kv3_1.c\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Translating MyExp2Sid.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/MyExp2Sid.c\n",
            "Translating NaTa.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/NaTa.c\n",
            "Translating NaTs.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/NaTs.c\n",
            "Notice: Assignment to the GLOBAL variable, \"total\", is not thread safe\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Translating NaV.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/NaV.c\n",
            "Translating OLM_Ih.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/OLM_Ih.c\n",
            "NEURON's CVode method ignores conservation\n",
            "Translating OLM_IA.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/OLM_IA.c\n",
            "Notice: LINEAR is not thread safe.\n",
            "Notice: Assignment to the GLOBAL variable, \"rexp\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"rinf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"tau_r\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"aexp\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"ainf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"bexp\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"binf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"tau_b\", is not thread safe\n",
            "Translating OLM_ccanl.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/OLM_ccanl.c\n",
            "Translating OLM_Lca.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/OLM_Lca.c\n",
            "Translating OLM_cat.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/OLM_cat.c\n",
            "Notice: VERBATIM blocks are not thread safe\n",
            "Notice: VERBATIM blocks are not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"matu\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"minf\", is not thread safe\n",
            "Notice: VERBATIM blocks are not thread safe\n",
            "Translating OLM_ichan2.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/OLM_ichan2.c\n",
            "Translating OLM_nap.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/OLM_nap.c\n",
            "Translating OLM_sahp.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/OLM_sahp.c\n",
            "Notice: VERBATIM blocks are not thread safe\n",
            "Notice: This mechanism cannot be used with CVODE\n",
            "Thread Safe\n",
            "Thread Safe\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/SK.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/bg2pyr.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/bginh.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/ca.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/cad.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/cadyn.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/cal2.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/cal2CA3.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/capool.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/capoolCA3.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/cas.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/cat.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/cav31.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/currentclamp.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/function_TMonitor.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/gabaa.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/gap.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/h.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/hCA3.mod\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Translating SK.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/SK.c\n",
            "Translating bg2pyr.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/bg2pyr.c\n",
            "Thread Safe\n",
            "Translating bginh.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/bginh.c\n",
            "Thread Safe\n",
            "Notice: scop_random is not thread safe\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "Translating ca.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/ca.c\n",
            "Notice: VERBATIM blocks are not thread safe\n",
            "Notice: This mechanism cannot be used with CVODE\n",
            "Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n",
            "Translating cad.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/cad.c\n",
            "Thread Safe\n",
            "Translating cadyn.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/cadyn.c\n",
            "Thread Safe\n",
            "Translating cal2.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/cal2.c\n",
            "Notice: Assignment to the GLOBAL variable, \"minf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"tau\", is not thread safe\n",
            "Translating cal2CA3.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/cal2CA3.c\n",
            "Notice: Assignment to the GLOBAL variable, \"minf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"tau\", is not thread safe\n",
            "Translating capool.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/capool.c\n",
            "WARNING: xx (a STATE) is assigned a value in a DERIVATIVE block.\n",
            " Multistep integrators (such as Runge) may not work correctly.\n",
            "Thread Safe\n",
            "Translating capoolCA3.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/capoolCA3.c\n",
            "WARNING: xx (a STATE) is assigned a value in a DERIVATIVE block.\n",
            " Multistep integrators (such as Runge) may not work correctly.\n",
            "Translating cas.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/cas.c\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Translating cat.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/cat.c\n",
            "Thread Safe\n",
            "Translating cav31.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/cav31.c\n",
            "Notice: Assignment to the GLOBAL variable, \"linf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"ninf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"taun\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"taul\", is not thread safe\n",
            "Translating currentclamp.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/currentclamp.c\n",
            "Thread Safe\n",
            "Translating function_TMonitor.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/function_TMonitor.c\n",
            "Translating gabaa.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/gabaa.c\n",
            "Thread Safe\n",
            "Notice: VERBATIM blocks are not thread safe\n",
            "Notice: This mechanism cannot be used with CVODE\n",
            "Notice: Assignment to the GLOBAL variable, \"Rtau\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"Rinf\", is not thread safe\n",
            "Translating gap.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/gap.c\n",
            "Notice: Use of POINTER is not thread safe.\n",
            "Translating h.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/h.c\n",
            "Translating hCA3.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/hCA3.c\n",
            "Thread Safe\n",
            "Translating halfgap.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/halfgap.c\n",
            "Thread Safe\n",
            "Thread Safe\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/halfgap.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/im.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/imCA3.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/int2intFENG.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/int2pyrFENG.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/int2pyrKIM.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/interD2interD_SOMPV_STFD_new.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/interD2interD_STFD_new.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/interD2pyrD_CR2P_STFD_new.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/interD2pyrD_SOM2P_STFD_new.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/interD2pyrD_STFD_new.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/kadist.mod\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Translating im.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/im.c\n",
            "Translating imCA3.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/imCA3.c\n",
            "Translating int2intFENG.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/int2intFENG.c\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Notice: scop_random is not thread safe\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "Translating int2pyrFENG.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/int2pyrFENG.c\n",
            "Translating int2pyrKIM.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/int2pyrKIM.c\n",
            "Notice: scop_random is not thread safe\n",
            "Warning: dt undefined. (declared within VERBATIM?)\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "Notice: scop_random is not thread safe\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "Translating interD2interD_SOMPV_STFD_new.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/interD2interD_SOMPV_STFD_new.c\n",
            "Notice: scop_random is not thread safe\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "Translating interD2interD_STFD_new.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/interD2interD_STFD_new.c\n",
            "Translating interD2pyrD_CR2P_STFD_new.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/interD2pyrD_CR2P_STFD_new.c\n",
            "Notice: scop_random is not thread safe\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "Notice: scop_random is not thread safe\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "Translating interD2pyrD_SOM2P_STFD_new.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/interD2pyrD_SOM2P_STFD_new.c\n",
            "Notice: scop_random is not thread safe\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "Translating interD2pyrD_STFD_new.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/interD2pyrD_STFD_new.c\n",
            "Translating kadist.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/kadist.c\n",
            "Notice: Assignment to the GLOBAL variable, \"linf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"ninf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"taun\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"taul\", is not thread safe\n",
            "Notice: scop_random is not thread safe\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/kaprox.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/kca.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/kdrCA3.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/kdrca1.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/kdrca1DA.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/kdrinter.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/kdrinter_WB.mod\n",
            "Warning: Default -80 of PARAMETER ek will be ignored and set by NEURON.\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/km.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/kv.mod\n",
            "Warning: Default -90 of PARAMETER ek will be ignored and set by NEURON.\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/leak.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/leakCA3.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/leakDA.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/leakinter.mod\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Translating kaprox.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/kaprox.c\n",
            "Thread Safe\n",
            "Translating kca.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/kca.c\n",
            "Thread Safe\n",
            "Translating kdrCA3.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/kdrCA3.c\n",
            "Thread Safe\n",
            "Translating kdrca1.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/kdrca1.c\n",
            "Thread Safe\n",
            "Translating kdrca1DA.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/kdrca1DA.c\n",
            "Translating kdrinter.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/kdrinter.c\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Translating kdrinter_WB.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/kdrinter_WB.c\n",
            "Thread Safe\n",
            "Translating km.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/km.c\n",
            "Notice: VERBATIM blocks are not thread safe\n",
            "Notice: This mechanism cannot be used with CVODE\n",
            "Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n",
            "Translating kv.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/kv.c\n",
            "Notice: VERBATIM blocks are not thread safe\n",
            "Notice: This mechanism cannot be used with CVODE\n",
            "Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n",
            "Translating leak.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/leak.c\n",
            "Thread Safe\n",
            "Translating leakCA3.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/leakCA3.c\n",
            "Translating leakDA.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/leakDA.c\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Translating leakinter.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/leakinter.c\n",
            "Thread Safe\n",
            "Translating na.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/na.c\n",
            "Translating na12.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/na12.c\n",
            "Notice: VERBATIM blocks are not thread safe\n",
            "Notice: This mechanism cannot be used with CVODE\n",
            "Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n",
            "Notice: VERBATIM blocks are not thread safe\n",
            "Notice: This mechanism cannot be used with CVODE\n",
            "Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n",
            "Translating na16.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/na16.c\n",
            "Notice: VERBATIM blocks are not thread safe\n",
            "Notice: This mechanism cannot be used with CVODE\n",
            "Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n",
            "Translating na3.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/na3.c\n",
            "Translating na3DA.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/na3DA.c\n",
            "Notice: Assignment to the GLOBAL variable, \"htau\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"hinf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"mtau\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"minf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"sinf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"taus\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"htau\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"hinf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"mtau\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"minf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"sinf\", is not thread safe\n",
            "Notice: Assignment to the GLOBAL variable, \"taus\", is not thread safe\n",
            "Translating nainter.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/nainter.c\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/na.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/na12.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/na16.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/na3.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/na3DA.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/nainter.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/nainter_WB.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/nap.mod\n",
            "Warning: Default 45 of PARAMETER ena will be ignored and set by NEURON.\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/nap2.mod\n",
            "Warning: Default 55 of PARAMETER ena will be ignored and set by NEURON.\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/napCA3.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/nat.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/natCA3.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/pyr2intFENG.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/pyr2intKIM.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/pyr2pyrFENG.mod\n",
            "Warning: Default 120 of PARAMETER eca will be ignored and set by NEURON.\n",
            "Warning: Default 120 of PARAMETER eca will be ignored and set by NEURON.\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/pyr2pyrKIM.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/pyrD2interD_P2CR_STFD.mod\n",
            "Warning: Default 120 of PARAMETER eca will be ignored and set by NEURON.\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/pyrD2interD_P2SOM_STFD.mod\n",
            "Warning: Default 120 of PARAMETER eca will be ignored and set by NEURON.\n",
            "Warning: Default 120 of PARAMETER eca will be ignored and set by NEURON.\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/pyrD2interD_STFD.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/pyrD2pyrD_STFD_new.mod\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Thread Safe\n",
            "Translating nainter_WB.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/nainter_WB.c\n",
            "Translating nap.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/nap.c\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Translating nap2.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/nap2.c\n",
            "Thread Safe\n",
            "Translating napCA3.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/napCA3.c\n",
            "Translating nat.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/nat.c\n",
            "Thread Safe\n",
            "Thread Safe\n",
            "Translating natCA3.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/natCA3.c\n",
            "Thread Safe\n",
            "Translating pyr2intFENG.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/pyr2intFENG.c\n",
            "Translating pyr2intKIM.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/pyr2intKIM.c\n",
            "Notice: scop_random is not thread safe\n",
            "Notice: scop_random is not thread safe\n",
            "Warning: dt undefined. (declared within VERBATIM?)\n",
            "Translating pyr2pyrFENG.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/pyr2pyrFENG.c\n",
            "Notice: scop_random is not thread safe\n",
            "Translating pyr2pyrKIM.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/pyr2pyrKIM.c\n",
            "Translating pyrD2interD_P2CR_STFD.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/pyrD2interD_P2CR_STFD.c\n",
            "Notice: scop_random is not thread safe\n",
            "Warning: dt undefined. (declared within VERBATIM?)\n",
            "Notice: scop_random is not thread safe\n",
            "Translating pyrD2interD_P2SOM_STFD.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/pyrD2interD_P2SOM_STFD.c\n",
            "Notice: scop_random is not thread safe\n",
            "Translating pyrD2interD_STFD.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/pyrD2interD_STFD.c\n",
            "Translating pyrD2pyrD_STFD_new.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/pyrD2pyrD_STFD_new.c\n",
            "Notice: scop_random is not thread safe\n",
            "Notice: scop_random is not thread safe\n",
            "Translating sahp.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/sahp.c\n",
            "Thread Safe\n",
            "Translating sahpCA3.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/sahpCA3.c\n",
            "Translating sahpNE.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/sahpNE.c\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: Default 120 of PARAMETER eca will be ignored and set by NEURON.\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/sahp.mod\n",
            "Warning: Default 120 of PARAMETER eca will be ignored and set by NEURON.\n",
            "Warning: Default 120 of PARAMETER eca will be ignored and set by NEURON.\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/sahpCA3.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/sahpNE.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/shock2int.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/shock2pyr.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/synapse_inter2pyr.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/tone2pyr.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/vecevent.mod\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/xtra.mod\n",
            "Warning: Default 120 of PARAMETER eca will be ignored and set by NEURON.\n",
            " -> \u001b[32mNMODL\u001b[0m ../modfiles/xtra_imemrec.mod\n",
            " -> \u001b[32mCompiling\u001b[0m CaDynamics.c\n",
            " -> \u001b[32mCompiling\u001b[0m Ca_HVA.c\n",
            " -> \u001b[32mCompiling\u001b[0m Ca_LVA.c\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Thread Safe\n",
            "Thread Safe\n",
            "Translating shock2int.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/shock2int.c\n",
            "Notice: scop_random is not thread safe\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "Translating shock2pyr.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/shock2pyr.c\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "WARNING: Dimensions may be wrong for READ ica with POINT_PROCESS\n",
            "Translating synapse_inter2pyr.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/synapse_inter2pyr.c\n",
            "Thread Safe\n",
            "Warning: dt undefined. (declared within VERBATIM?)\n",
            "Thread Safe\n",
            "Translating tone2pyr.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/tone2pyr.c\n",
            "Translating vecevent.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/vecevent.c\n",
            "Notice: VERBATIM blocks are not thread safe\n",
            "Warning: dt undefined. (declared within VERBATIM?)\n",
            "Thread Safe\n",
            "Translating xtra.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/xtra.c\n",
            "Notice: Use of POINTER is not thread safe.\n",
            "Notice: Assignment to the GLOBAL variable, \"LFP\", is not thread safe\n",
            "Translating xtra_imemrec.mod into /Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell/biophys_components/mechanisms/x86_64/xtra_imemrec.c\n",
            "Notice: Use of POINTER is not thread safe.\n",
            "Notice: Assignment to the GLOBAL variable, \"LFP\", is not thread safe\n",
            "CaDynamics.cCa_HVA.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            ":42:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "Ca_LVA.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17         extern double *getarg();:\n",
            " note:                         ^expanded from macro 'getarg'\n",
            "\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            ":15#define getarg  hoc_getarg\n",
            "                ^\n",
            ":17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49::49::161616:::  note: note: conflicting prototype is hereconflicting prototype is here\n",
            " \n",
            "note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "extern double* getarg(int);\n",
            "extern double* getarg(int);               ^\n",
            "\n",
            "               ^/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h\n",
            ":15:/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h17:/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            ": note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "1 warning generated.\n",
            "1 warning generated.\n",
            "1 warning generated.\n",
            "Gfluct_new_exc.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "Gfluct_new_inh.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "Ih.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "Im_v2.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " -> \u001b[32mCompiling\u001b[0m Gfluct_new_exc.c\n",
            " -> \u001b[32mCompiling\u001b[0m Gfluct_new_inh.c\n",
            " -> \u001b[32mCompiling\u001b[0m Ih.c\n",
            " -> \u001b[32mCompiling\u001b[0m Im_v2.c\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "1 warning generated.\n",
            "1 warning generated.\n",
            "1 warning generated.\n",
            "1 warning generated.\n",
            "Kd.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "K_P.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "K_T.c:43         extern double *getarg();\n",
            "                        ^\n",
            ":18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15         extern double *getarg();:\n",
            "17                        ^:\n",
            " note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: /Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:note: 49expanded from macro 'getarg'\n",
            ":16#define getarg  hoc_getarg:\n",
            "                 ^note: \n",
            "conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17:/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h :note: 49expanded from macro 'getarg':\n",
            "16: note: conflicting prototype is here\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "Kv2like.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " -> \u001b[32mCompiling\u001b[0m K_P.c\n",
            " -> \u001b[32mCompiling\u001b[0m K_T.c\n",
            " -> \u001b[32mCompiling\u001b[0m Kd.c\n",
            " -> \u001b[32mCompiling\u001b[0m Kv2like.c\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "1 warning generated.\n",
            "1 warning generated.\n",
            "1 warning generated.\n",
            "1 warning generated.\n",
            "MyExp2Sid.c:42:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "Kv3_1.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "MyExp2Sid.c:313:32: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "  if (_tsav > t){ extern char* hoc_object_name(); hoc_execerror(hoc_object_name(_pnt->ob), \":Event arrived out of order. Must call ParallelContext.set_maxstep AFTER assigning minimum NetCon.delay\");}\n",
            "                               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:46:14: note: conflicting prototype is here\n",
            "extern char* hoc_object_name(Object*);\n",
            "             ^\n",
            "NaTa.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'NaTs.c:43:18:\n",
            " warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h15::1517::17 :note:  expanded from macro 'getarg'note: \n",
            "expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg#define getarg  hoc_getarg\n",
            "\n",
            "                ^                ^\n",
            "\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " -> \u001b[32mCompiling\u001b[0m Kv3_1.c\n",
            " -> \u001b[32mCompiling\u001b[0m MyExp2Sid.c\n",
            " -> \u001b[32mCompiling\u001b[0m NaTa.c\n",
            " -> \u001b[32mCompiling\u001b[0m NaTs.c\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "1 warning generated.\n",
            "2 warnings generated.\n",
            "1 warning generated.\n",
            "1 warning generated.\n",
            "NaV.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "NaV.c:404:17: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            " extern double *_getelm();\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/nrniv_mf.h:26:16: note: conflicting prototype is here\n",
            "extern double* _getelm(int, int);\n",
            "               ^\n",
            "OLM_Ih.c:44:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "OLM_IA.c:44:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "OLM_Lca.c:43:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " -> \u001b[32mCompiling\u001b[0m NaV.c\n",
            " -> \u001b[32mCompiling\u001b[0m OLM_IA.c\n",
            " -> \u001b[32mCompiling\u001b[0m OLM_Ih.c\n",
            " -> \u001b[32mCompiling\u001b[0m OLM_Lca.c\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "1 warning generated.\n",
            "1 warning generated.\n",
            "1 warning generated.\n",
            "2 warnings generated.\n",
            "OLM_cat.c:42:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "OLM_ccanl.c:42:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "OLM_ichan2.c:45:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "OLM_ichan2.c:597:2: error: void function 'initmodel' should not return a value [-Wreturn-type]\n",
            "        return 0;\n",
            "        ^      ~\n",
            "1 warning and 1 error generated.\n",
            "make: *** [OLM_ichan2.o] Error 1\n",
            "make: *** Waiting for unfinished jobs....\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " -> \u001b[32mCompiling\u001b[0m OLM_cat.c\n",
            " -> \u001b[32mCompiling\u001b[0m OLM_ccanl.c\n",
            " -> \u001b[32mCompiling\u001b[0m OLM_ichan2.c\n",
            " -> \u001b[32mCompiling\u001b[0m OLM_nap.c\n",
            "/Users/gregglickert/Documents/GitHub/Fear-Lesson-Plans/Lesson-7-12-Cell\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "OLM_nap.c:44:18: warning: a function declaration without a prototype is deprecated in all versions of C and is treated as a zero-parameter prototype in C2x, conflicting with a previous declaration [-Wdeprecated-non-prototype]\n",
            "         extern double *getarg();\n",
            "                        ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/oc_ansi.h:49:16: note: conflicting prototype is here\n",
            "extern double* getarg(int);\n",
            "               ^\n",
            "/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/include/hocdec.h:15:17: note: expanded from macro 'getarg'\n",
            "#define getarg  hoc_getarg\n",
            "                ^\n",
            "1 warning generated.\n",
            "1 warning generated.\n",
            "1 warning generated.\n",
            "Traceback (most recent call last):\n",
            "  File \"/Users/gregglickert/opt/anaconda3/envs/bmtk/bin/nrnivmodl\", line 94, in <module>\n",
            "    subprocess.check_call([exe, *sys.argv[1:]])\n",
            "  File \"/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/subprocess.py\", line 364, in check_call\n",
            "    raise CalledProcessError(retcode, cmd)\n",
            "subprocess.CalledProcessError: Command '['/Users/gregglickert/opt/anaconda3/envs/bmtk/lib/python3.8/site-packages/neuron/.data/bin/nrnivmodl', 'modfiles']' returned non-zero exit status 2.\n"
          ]
        }
      ],
      "source": [
        "#@title setup\n",
        "import os\n",
        "RunningInCOLAB = 'google.colab' in str(get_ipython())  # checks to see if we are in google colab\n",
        "if RunningInCOLAB:                                     # installs packages and repo if in colab\n",
        "    !pip install ipywidgets &> /dev/null\n",
        "    !pip install neuron &> /dev/null\n",
        "    !pip install bmtk &> /dev/null\n",
        "\n",
        "    !git clone https://github.com/cyneuro/Fear-Lesson-Plans &> /dev/null\n",
        "    %cd Fear-Lesson-Plans/Lesson-7-12-Cell\n",
        "\n",
        "if(os.path.exists('biophys_components/mechanisms/x86_64')==False):\n",
        "    %cd biophys_components/mechanisms \n",
        "    os.system('nrnivmodl modfiles')\n",
        "    %cd ../.."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194,
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        },
        "id": "o_J2l--l8iWg",
        "outputId": "3fefd42f-23e3-4c1d-db7f-38de056af3e5",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 12 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x600 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 12 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1f1d713c575744498766d7fe0d30c2cb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HBox(children=(IntSlider(value=5, description='PN_input', max=20), Button(description='Generate…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title Run Cell to activate model\n",
        "import os, sys\n",
        "from io import StringIO\n",
        "\n",
        "print(\"Downloading model this will only take a min!\")\n",
        "class Capturing(list):\n",
        "    def __enter__(self):\n",
        "        self._stdout = sys.stdout\n",
        "        sys.stdout = self._stringio = StringIO()\n",
        "        return self\n",
        "    def __exit__(self, *args):\n",
        "        self.extend(self._stringio.getvalue().splitlines())\n",
        "        del self._stringio    # free up some memory\n",
        "        sys.stdout = self._stdout\n",
        "\n",
        "\n",
        "from bmtk.builder import NetworkBuilder\n",
        "from bmtk.utils.reports.spike_trains import PoissonSpikeGenerator\n",
        "from bmtk.utils.sim_setup import build_env_bionet\n",
        "import numpy as np\n",
        "import sys\n",
        "import synapses\n",
        "import random\n",
        "import warnings\n",
        "\n",
        "warnings.simplefilter(action='ignore', category=DeprecationWarning)\n",
        "\n",
        "seed = 967\n",
        "random.seed(seed)\n",
        "np.random.seed(seed)\n",
        "\n",
        "synapses.load()\n",
        "syn = synapses.syn_params_dicts()\n",
        "\n",
        "# Initialize our network\n",
        "\n",
        "net = NetworkBuilder(\"biophysical\")\n",
        "\n",
        "num_inh = [1]\n",
        "\n",
        "num_exc = [1]\n",
        "\n",
        "##################################################################################\n",
        "###################################BIOPHY#########################################\n",
        "\n",
        "net.add_nodes(N=5, pop_name='PyrA',\n",
        "              mem_potential='e',\n",
        "              model_type='biophysical',\n",
        "              model_template='hoc:Cell_A',\n",
        "              morphology=None)\n",
        "\n",
        "net.add_nodes(N=3, pop_name='PyrC',\n",
        "              mem_potential='e',\n",
        "              model_type='biophysical',\n",
        "              model_template='hoc:Cell_C',\n",
        "              morphology=None)\n",
        "net.add_nodes(N=2, pop_name='OLM',\n",
        "              mem_potential='e',\n",
        "              model_type='biophysical',\n",
        "              model_template='hoc:SOM_Cell_old',\n",
        "              morphology=None)\n",
        "\n",
        "net.add_nodes(N=2, pop_name='PV',\n",
        "              mem_potential='e',\n",
        "              model_type='biophysical',\n",
        "              model_template='hoc:basket',\n",
        "              morphology=None)\n",
        "\n",
        "##################################################################################\n",
        "###################################External Networks##############################\n",
        "\n",
        "# print(\"Internal nodes built\")\n",
        "\n",
        "#print(\"making {} exc_stim nodes\".format(np.sum(num_exc)))\n",
        "\n",
        "# External excitatory inputs\n",
        "tone = NetworkBuilder('tone')\n",
        "tone.add_nodes(N=1,\n",
        "               pop_name='tone',\n",
        "               potential='exc',\n",
        "               model_type='virtual')\n",
        "\n",
        "#print(\"making {} inh_stim nodes\".format(np.sum(num_inh)))\n",
        "# External inhibitory inputs\n",
        "shock = NetworkBuilder('shock')\n",
        "shock.add_nodes(N=1,\n",
        "                pop_name='shock',\n",
        "                potential='exc',\n",
        "                model_type='virtual')\n",
        "#backgrounds\n",
        "backgroundPN_A = NetworkBuilder('bg_pn_a')\n",
        "backgroundPN_A.add_nodes(N=5,\n",
        "                   pop_name='tON',\n",
        "                   potential='exc',\n",
        "                   model_type='virtual')\n",
        "\n",
        "backgroundPN_C = NetworkBuilder('bg_pn_c')\n",
        "backgroundPN_C.add_nodes(N=3,\n",
        "                   pop_name='tON',\n",
        "                   potential='exc',\n",
        "                   model_type='virtual')\n",
        "\n",
        "backgroundPV = NetworkBuilder('bg_pv')\n",
        "backgroundPV.add_nodes(N=2,\n",
        "                   pop_name='tON',\n",
        "                   potential='exc',\n",
        "                   model_type='virtual')\n",
        "\n",
        "backgroundOLM = NetworkBuilder('bg_olm')\n",
        "backgroundOLM.add_nodes(N=2,\n",
        "                   pop_name='tON',\n",
        "                   potential='exc',\n",
        "                   model_type='virtual')\n",
        "\n",
        "\n",
        "\n",
        "##################################################################################\n",
        "###################################Edges##########################################\n",
        "\n",
        "def one_to_all_shock2OLM(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    #print(\"connecting shock cell {} to OLM cell {}\".format(sid, tid))\n",
        "    return 1\n",
        "\n",
        "def one_to_all_shock2PV(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    #print(\"connecting shock cell {} to PV cell {}\".format(sid, tid))\n",
        "    return 1\n",
        "\n",
        "def shock2PN(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    if (tid == 0 or tid == 3 or tid == 4 or tid == 6 or tid==7):\n",
        "        #print(\"connecting shock cell {} to PN {}\".format(sid, tid))\n",
        "        return 1\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "def tone2PN(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    if (tid == 2 or tid == 4 or tid == 6 or tid == 7):\n",
        "        #print(\"connecting tone cell {} to PN {}\".format(sid, tid))\n",
        "        tmp_nsyn = 1\n",
        "    else:\n",
        "        #print(\"not connecting{}\".format(tid))\n",
        "        tmp_nsyn = 0\n",
        "    return tmp_nsyn\n",
        "\n",
        "def tone2PV(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    #print(\"connecting tone cell {} to PV cell {}\".format(sid, tid))\n",
        "    return 1\n",
        "\n",
        "def tone2OLM(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    #print(\"connecting tone cell {} to OLM cell {}\".format(sid, tid))\n",
        "    return 1\n",
        "\n",
        "def PN2OLM(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    #print(\"connecting PN cell {} to OLM cell {}\".format(sid, tid))\n",
        "    return 1\n",
        "\n",
        "def PN2PV(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    #print(\"connecting PN cell {} to PV cell {}\".format(sid, tid))\n",
        "    return 1\n",
        "\n",
        "def PV2OLM(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    #print(\"connecting PV cell {} to OLM cell {}\".format(sid, tid))\n",
        "    return 1\n",
        "\n",
        "def one_to_one(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    if sid == tid:\n",
        "        #print(\"connecting cell {} to {}\".format(sid, tid))\n",
        "        tmp_nsyn = 1\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "    return tmp_nsyn\n",
        "\n",
        "def pyr_connection(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    if (sid != tid):\n",
        "        #print(\"connecting PN cells {} and {}\".format(sid, tid))\n",
        "        return 1\n",
        "\n",
        "def PV2PV(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    if (sid != tid):\n",
        "        #print(\"connecting PV cell {} to PV {}\".format(sid, tid))\n",
        "        tmp_nsyn = 1\n",
        "    else:\n",
        "        return 0\n",
        "    return tmp_nsyn\n",
        "\n",
        "def PV2PN(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    #print(\"connecting PV cells {} to PN cell {}\".format(sid, tid))\n",
        "    return 3\n",
        "\n",
        "def OLM2PN(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    #print(\"connecting OLM cells {} to PN cell {}\".format(sid, tid))\n",
        "    return 2\n",
        "\n",
        "def BG_to_PN_A(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    if sid == tid:\n",
        "        #print(\"connecting BG {} to PN_A{}\".format(sid,tid))\n",
        "        tmp_nsyn = 1\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "    return tmp_nsyn\n",
        "\n",
        "def BG_to_PN_C(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    tid = (tid-5)\n",
        "    if sid == tid:\n",
        "        #print(\"connecting BG {} to PN_C{}\".format(sid,tid))\n",
        "        tmp_nsyn = 1\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "    return tmp_nsyn\n",
        "\n",
        "def BG_to_PV(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    sid = sid + 10\n",
        "    if sid == tid:\n",
        "        #print(\"connecting BG {} to pv{}\".format(sid,tid))\n",
        "        tmp_nsyn = 1\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "    return tmp_nsyn\n",
        "\n",
        "def BG_to_OLM(source, target):\n",
        "    sid = source.node_id\n",
        "    tid = target.node_id\n",
        "    sid = sid + 8\n",
        "    if sid == tid:\n",
        "        #print(\"connecting BG {} to olm{}\".format(sid,tid))\n",
        "        tmp_nsyn = 1\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "    return tmp_nsyn\n",
        "\n",
        "net.add_edges(source=shock.nodes(), target=net.nodes(pop_name='OLM'),\n",
        "              connection_rule=one_to_all_shock2OLM,\n",
        "              syn_weight=1.0,\n",
        "              target_sections=['basal'],\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='shock2INT12.json',\n",
        "              model_template=syn['shock2INT12.json']['level_of_detail'])\n",
        "\n",
        "net.add_edges(source=shock.nodes(), target=net.nodes(pop_name='PV'),\n",
        "              connection_rule=one_to_all_shock2PV,\n",
        "              syn_weight=1.0,\n",
        "              target_sections=['basal'],\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='shock2INT12.json',\n",
        "              model_template=syn['shock2INT12.json']['level_of_detail'])\n",
        "\n",
        "\n",
        "# Create connections between Tone --> Pyr cells\n",
        "conn = net.add_edges(source=tone.nodes(), target=net.nodes(pop_name=['PyrA', 'PyrC']),\n",
        "              connection_rule=tone2PN,\n",
        "              syn_weight=1.0,\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='tone2PN.json',\n",
        "              model_template=syn['tone2PN.json']['level_of_detail'])\n",
        "conn.add_properties(['sec_id', 'sec_x'], rule=(2, 0.9), dtypes=[np.int32, np.float64]) # places syn on apic at 0.9\n",
        "\n",
        "#net.add_edges(source=tone.nodes(), target=net.nodes(pop_name='OLM'),\n",
        "#              connection_rule=tone2OLM,\n",
        "#              syn_weight=3.0,\n",
        "#              target_sections=['somatic'],\n",
        "#              delay=0.1,\n",
        "#              distance_range=[10.0, 11.0],\n",
        "#              dynamics_params='tone2INT.json',\n",
        "#              model_template=syn['tone2INT.json']['level_of_detail'])\n",
        "\n",
        "conn = net.add_edges(source=tone.nodes(), target=net.nodes(pop_name='PV'),\n",
        "              connection_rule=tone2PV,\n",
        "              syn_weight=1.0,\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='tone2INT.json',\n",
        "              model_template=syn['tone2INT.json']['level_of_detail'])\n",
        "conn.add_properties(['sec_id', 'sec_x'], rule=(1, 0.9), dtypes=[np.int32, np.float64]) # places syn on basal at 0.9\n",
        "\n",
        "# Create connections between Pyr --> Pyr cells\n",
        "conn = net.add_edges(source=net.nodes(pop_name='PyrA'), target=net.nodes(pop_name=['PyrA', 'PyrC']),\n",
        "              connection_rule=pyr_connection,\n",
        "              syn_weight=1.0,\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='PN2PN.json',\n",
        "              model_template=syn['PN2PN.json']['level_of_detail'])\n",
        "conn.add_properties(['sec_id', 'sec_x'], rule=(2, 0.9), dtypes=[np.int32, np.float64]) # places syn on apic at 0.9\n",
        "\n",
        "conn = net.add_edges(source=net.nodes(pop_name='PyrC'), target=net.nodes(pop_name=['PyrA', 'PyrC']),\n",
        "              connection_rule=pyr_connection,\n",
        "              syn_weight=1.0,\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='PN2PN.json',\n",
        "              model_template=syn['PN2PN.json']['level_of_detail'])\n",
        "conn.add_properties(['sec_id', 'sec_x'], rule=(2, 0.9), dtypes=[np.int32, np.float64]) # places syn on apic at 0.9\n",
        "\n",
        "\n",
        "conn = net.add_edges(source=net.nodes(pop_name=['PyrA', 'PyrC']), target=net.nodes(pop_name='OLM'),\n",
        "              connection_rule=PN2OLM,\n",
        "              syn_weight=1,\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='PN2SOM.json',\n",
        "              model_template=syn['PN2SOM.json']['level_of_detail'])\n",
        "conn.add_properties(['sec_id', 'sec_x'], rule=(1, 0.9), dtypes=[np.int32, np.float64]) # places syn on basal at 0.9\n",
        "\n",
        "# Create connections between Pyr --> PV cells\n",
        "conn = net.add_edges(source=net.nodes(pop_name=['PyrA', 'PyrC']), target=net.nodes(pop_name='PV'),\n",
        "              connection_rule=PN2PV,\n",
        "              syn_weight=1.0,\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='PN2PV.json',\n",
        "              model_template=syn['PN2PV.json']['level_of_detail'])\n",
        "conn.add_properties(['sec_id', 'sec_x'], rule=(1, 0.9), dtypes=[np.int32, np.float64]) # places syn on basal at 0.9\n",
        "\n",
        "\n",
        "# Create connections Int --> Int cells\n",
        "\n",
        "net.add_edges(source=net.nodes(pop_name='PV'), target=net.nodes(pop_name='PV'),\n",
        "              connection_rule=PV2PV,\n",
        "              syn_weight=1.0,\n",
        "              target_sections=['somatic'],\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='PV2PV.json',\n",
        "              model_template=syn['PV2PV.json']['level_of_detail'])\n",
        "\n",
        "net.add_edges(source=net.nodes(pop_name='PV'), target=net.nodes(pop_name='OLM'),\n",
        "              connection_rule=PV2OLM,\n",
        "              syn_weight=1.0,\n",
        "              target_sections=['somatic'],\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='PV2SOM.json',\n",
        "              model_template=syn['PV2SOM.json']['level_of_detail'])\n",
        "\n",
        "# Create connections Int --> Pyr cells\n",
        "net.add_edges(source=net.nodes(pop_name='PV'), target=net.nodes(pop_name=['PyrA', 'PyrC']),\n",
        "              connection_rule=PV2PN,\n",
        "              syn_weight=1.0,\n",
        "              target_sections=['somatic'],\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='PV2PN.json',\n",
        "              model_template=syn['PV2PN.json']['level_of_detail'])\n",
        "\n",
        "conn = net.add_edges(source=net.nodes(pop_name='OLM'), target=net.nodes(pop_name=['PyrA', 'PyrC']),\n",
        "              connection_rule=OLM2PN,\n",
        "              syn_weight=1.0,\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='SOM2PN.json',\n",
        "              model_template=syn['SOM2PN.json']['level_of_detail'])\n",
        "conn.add_properties(['sec_id', 'sec_x'], rule=(2, 0.6), dtypes=[np.int32, np.float64]) # places syn on apic at 0.6\n",
        "\n",
        "conn = net.add_edges(source=backgroundPN_A.nodes(), target=net.nodes(pop_name='PyrA'),\n",
        "              connection_rule=BG_to_PN_A,\n",
        "              syn_weight=1.0,\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='BG2PNA.json',\n",
        "              model_template=syn['BG2PNA.json']['level_of_detail'])\n",
        "conn.add_properties(['sec_id', 'sec_x'], rule=(2, 0.9), dtypes=[np.int32, np.float64]) # places syn on apic at 0.9\n",
        "\n",
        "conn = net.add_edges(source=backgroundPN_C.nodes(), target=net.nodes(pop_name='PyrC'),\n",
        "              connection_rule=BG_to_PN_C,\n",
        "              syn_weight=1.0,\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='BG2PNC.json',\n",
        "              model_template=syn['BG2PNC.json']['level_of_detail'])\n",
        "conn.add_properties(['sec_id', 'sec_x'], rule=(2, 0.9), dtypes=[np.int32, np.float64]) # places syn on apic at 0.9\n",
        "\n",
        "conn = net.add_edges(source=backgroundOLM.nodes(), target=net.nodes(pop_name='OLM'),\n",
        "              connection_rule=BG_to_OLM,\n",
        "              syn_weight=1.0,\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='BG2OLM.json',\n",
        "              model_template=syn['BG2OLM.json']['level_of_detail'])\n",
        "conn.add_properties(['sec_id', 'sec_x'], rule=(1, 0.9), dtypes=[np.int32, np.float64]) # places syn on basal at 0.9\n",
        "\n",
        "conn = net.add_edges(source=backgroundPV.nodes(), target=net.nodes(pop_name='PV'),\n",
        "              connection_rule=BG_to_PV,\n",
        "              syn_weight=1.0,\n",
        "              delay=0.1,\n",
        "              distance_range=[-10000, 10000],\n",
        "              dynamics_params='BG2PV.json',\n",
        "              model_template=syn['BG2PV.json']['level_of_detail'])\n",
        "conn.add_properties(['sec_id', 'sec_x'], rule=(1, 0.9), dtypes=[np.int32, np.float64]) # places syn on basal at 0.9\n",
        "\n",
        "# Build and save our networks\n",
        "\n",
        "net.build()\n",
        "net.save(output_dir='network')\n",
        "\n",
        "tone.build()\n",
        "tone.save_nodes(output_dir='network')\n",
        "\n",
        "shock.build()\n",
        "shock.save_nodes(output_dir='network')\n",
        "\n",
        "backgroundPN_A.build()\n",
        "backgroundPN_A.save_nodes(output_dir='network')\n",
        "\n",
        "backgroundPN_C.build()\n",
        "backgroundPN_C.save_nodes(output_dir='network')\n",
        "\n",
        "backgroundPV.build()\n",
        "backgroundPV.save_nodes(output_dir='network')\n",
        "\n",
        "backgroundOLM.build()\n",
        "backgroundOLM.save_nodes(output_dir='network')\n",
        "\n",
        "t_sim = 232500  # early extinction time is 232500 sensitization time is 40000\n",
        "#print(\"stim time is set to %s\" % t_sim)\n",
        "\n",
        "\n",
        "#build_env_bionet(base_dir='./',\n",
        "#                 network_dir='./network',\n",
        "#                 tstop=t_sim, dt=0.1,\n",
        "#                 report_vars=['v'],\n",
        "#                 spikes_inputs=[('tone', './12_cell_inputs/tone_spikes.csv'),\n",
        "#                                ('shock', './12_cell_inputs/shock_spikes.csv'),\n",
        "#                                ('bg_pn', '12_cell_inputs/bg_pn_spikes.h5'),\n",
        "#                                ('bg_pv', '12_cell_inputs/bg_pv_spikes.h5'),\n",
        "#                                ('bg_olm', '12_cell_inputs/bg_olm_spikes.h5')],\n",
        "#                 components_dir='biophys_components',\n",
        "#                 config_file='config.json',\n",
        "#                 compile_mechanisms=False)\n",
        "\n",
        "psg = PoissonSpikeGenerator(population='tone')\n",
        "psg.add(node_ids=range(1),  # need same number as cells\n",
        "        firing_rate=2,    # 1 spike every 1 second Hz\n",
        "        times=(0.0, t_sim/1000))  # time is in seconds for some reason\n",
        "psg.to_sonata('12_cell_inputs/tone_background.h5')\n",
        "\n",
        "#print('Number of background spikes for tone: {}'.format(psg.n_spikes()))\n",
        "\n",
        "psg = PoissonSpikeGenerator(population='bg_pn_a')\n",
        "psg.add(node_ids=range(5),  # need same number as cells\n",
        "        firing_rate=6,    # 1 spike every 1 second Hz\n",
        "        times=(0.0, t_sim/1000))  # time is in seconds for some reason\n",
        "psg.to_sonata('12_cell_inputs/bg_pn_a_spikes.h5')\n",
        "\n",
        "#print('Number of background spikes for PN_A: {}'.format(psg.n_spikes()))\n",
        "\n",
        "psg = PoissonSpikeGenerator(population='bg_pn_c')\n",
        "psg.add(node_ids=range(3),  # need same number as cells\n",
        "        firing_rate=2,    # 1 spike every 1 second Hz\n",
        "        times=(0.0, t_sim/1000))  # time is in seconds for some reason\n",
        "psg.to_sonata('12_cell_inputs/bg_pn_c_spikes.h5')\n",
        "\n",
        "#print('Number of background spikes for PN_C: {}'.format(psg.n_spikes()))\n",
        "\n",
        "\n",
        "psg = PoissonSpikeGenerator(population='bg_pv')\n",
        "psg.add(node_ids=range(2),  # need same number as cells\n",
        "        firing_rate=7.7,    # 8 spikes every 1 second Hz\n",
        "        times=(0.0, t_sim/1000))  # time is in seconds for some reason\n",
        "psg.to_sonata('12_cell_inputs/bg_pv_spikes.h5')\n",
        "\n",
        "#print('Number of background spikes for PV: {}'.format(psg.n_spikes()))\n",
        "\n",
        "psg = PoissonSpikeGenerator(population='bg_olm')\n",
        "psg.add(node_ids=range(2),  # need same number as cells\n",
        "        firing_rate=8.5,    # 8 spikes every 1 second Hz\n",
        "        times=(0.0, t_sim/1000))  # time is in seconds for some reason\n",
        "psg.to_sonata('12_cell_inputs/bg_olm_spikes.h5')\n",
        "\n",
        "#print('Number of background spikes for OLMM: {}'.format(psg.n_spikes()))\n",
        "\n",
        "from bmtk.analyzer.spike_trains import to_dataframe\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "import h5py\n",
        "import warnings\n",
        "\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "warnings.simplefilter(action='ignore', category=DeprecationWarning)\n",
        "warnings.simplefilter(action='ignore', category=UserWarning)\n",
        "\n",
        "\n",
        "def generate_plots(path):\n",
        "    f = h5py.File(path)\n",
        "    df = pd.DataFrame(\n",
        "        {'node_ids': f['spikes']['biophysical']['node_ids'], 'timestamps': f['spikes']['biophysical']['timestamps']})\n",
        "    #df0 = df.loc[df['node_ids'] == 0]\n",
        "    #df0.to_csv(\"spikes.csv\")\n",
        "    node_spike_array = []\n",
        "\n",
        "    ##tone sense\n",
        "    def tone_response(node_spike_array):\n",
        "        node_num = 0\n",
        "\n",
        "        while(node_num <= 11):\n",
        "            df0 = df.loc[df['node_ids'] == node_num]\n",
        "\n",
        "            x0 = df0['timestamps'].tolist()\n",
        "            first_trial = []\n",
        "            second_trial = []\n",
        "            third_trial = []\n",
        "            fourth_trial = []\n",
        "            fifth_trial = []\n",
        "            sixth_trial = []\n",
        "            seventh_trial = []\n",
        "            eighth_trial = []\n",
        "            ninth_trial = []\n",
        "            tenth_trial = []\n",
        "\n",
        "            for i in range(len(x0)):\n",
        "                if (x0[i] >= 0 and x0[i] <= 400):\n",
        "                    first_trial.append(x0[i])\n",
        "                if (x0[i] >= 4000 and x0[i] <= 4400):\n",
        "                    second_trial.append(x0[i])\n",
        "                if (x0[i] >= 8000 and x0[i] <= 8400):\n",
        "                    third_trial.append(x0[i])\n",
        "                if (x0[i] >= 12000 and x0[i] <= 12400):\n",
        "                    fourth_trial.append(x0[i])\n",
        "                if (x0[i] >= 16000 and x0[i] <= 16400):\n",
        "                    fifth_trial.append(x0[i])\n",
        "                if (x0[i] >= 20000 and x0[i] <= 20400):\n",
        "                    sixth_trial.append(x0[i])\n",
        "                if (x0[i] >= 24000 and x0[i] <= 24400):\n",
        "                    seventh_trial.append(x0[i])\n",
        "                if (x0[i] >= 28000 and x0[i] <= 28400):\n",
        "                    eighth_trial.append(x0[i])\n",
        "                if (x0[i] >= 32000 and x0[i] <= 32400):\n",
        "                    ninth_trial.append(x0[i])\n",
        "                if (x0[i] >= 36000 and x0[i] <= 36400):\n",
        "                    tenth_trial.append(x0[i])\n",
        "\n",
        "            for i in range(len(second_trial)):\n",
        "                second_trial[i] -= 4000\n",
        "            for i in range(len(third_trial)):\n",
        "                third_trial[i] -= 8000\n",
        "            for i in range(len(fourth_trial)):\n",
        "                fourth_trial[i] -= 12000\n",
        "            for i in range(len(fifth_trial)):\n",
        "                fifth_trial[i] -= 16000\n",
        "            for i in range(len(sixth_trial)):\n",
        "                sixth_trial[i] -= 20000\n",
        "            for i in range(len(seventh_trial)):\n",
        "                seventh_trial[i] -= 24000\n",
        "            for i in range(len(eighth_trial)):\n",
        "                eighth_trial[i] -= 28000\n",
        "            for i in range(len(ninth_trial)):\n",
        "                ninth_trial[i] -= 32000\n",
        "            for i in range(len(tenth_trial)):\n",
        "                tenth_trial[i] -= 36000\n",
        "\n",
        "            final_array = np.concatenate((first_trial, second_trial, third_trial, fourth_trial,\n",
        "                                          fifth_trial, sixth_trial, seventh_trial, eighth_trial,\n",
        "                                          ninth_trial, tenth_trial))\n",
        "\n",
        "            node_spike_array.append(final_array)\n",
        "\n",
        "            node_num = node_num+1\n",
        "\n",
        "    def find_bins(array, width):\n",
        "        try:\n",
        "            minimmum = np.min(array)\n",
        "            maximmum = np.max(array)\n",
        "            bound_min = -1.0 * (minimmum % width - minimmum)\n",
        "            bound_max = maximmum - maximmum % width + width\n",
        "            n = int((bound_max - bound_min) / width) + 1\n",
        "            bins = np.linspace(bound_min, bound_max, n)\n",
        "        except:\n",
        "            bins = 10\n",
        "        return bins\n",
        "\n",
        "    def set_up_graphs_sense():\n",
        "        fig, axs = plt.subplots(4, 3, sharey=True, tight_layout=True, sharex=True)\n",
        "        fig.suptitle('Spike histogram sensitization', y=1)\n",
        "\n",
        "        i = 0\n",
        "        column_cnt = 0\n",
        "        row_cnt = 0\n",
        "\n",
        "        while (i < 12):\n",
        "            bins = find_bins(node_spike_array[i], 10)\n",
        "\n",
        "            axs[row_cnt, column_cnt].hist(x=node_spike_array[i], bins=bins)\n",
        "            axs[row_cnt, column_cnt].set_title('PN' + str(i + 1))\n",
        "            if (i == 8):\n",
        "                axs[row_cnt, column_cnt].set_title('OLM 1 ')\n",
        "            if (i == 9):\n",
        "                axs[row_cnt, column_cnt].set_title('OLM 2 ')\n",
        "            if (i == 10):\n",
        "                axs[row_cnt, column_cnt].set_title('PV 1 ')\n",
        "            if (i == 11):\n",
        "                axs[row_cnt, column_cnt].set_title('PV 2 ')\n",
        "            # axs[row_cnt, column_cnt].ylabel(\"spikes\")\n",
        "            # axs[row_cnt, column_cnt].xlabel('ms')\n",
        "\n",
        "            axs[row_cnt, column_cnt].set_xlim([0, 400])\n",
        "\n",
        "            column_cnt = column_cnt + 1\n",
        "            if (column_cnt > 2):\n",
        "                column_cnt = 0\n",
        "                row_cnt = row_cnt + 1\n",
        "\n",
        "            i = i + 1\n",
        "\n",
        "        plt.setp(axs[-1, :], xlabel='time (ms)')\n",
        "        plt.setp(axs[:, 0], ylabel='spike count')\n",
        "        plt.show()\n",
        "\n",
        "    def spike_freq_bar_tone(axs):\n",
        "        hz = []\n",
        "        for i in range(12):\n",
        "            hz.append(((len(node_spike_array[i]))/0.4)/10) # calculates firing rate in Hz\n",
        "        x = [0,1,2,3,4,5,6,7,8,9,10,11]\n",
        "        cells = [\"PN1\", \"PN2\", \"PN3\", \"PN4\", \"PN5\", \"PN6\", \"PN7\", \"PN8\", \"SOM1\", \"SOM2\", \"PV1\", \"PV2\"]\n",
        "        axs.set_xticklabels(cells)\n",
        "        axs.bar(height=hz,x=cells,color=['black', 'black', 'black', 'black', 'black', 'black','black', 'black',\n",
        "                                          'red', 'red', 'green', 'green'])\n",
        "        axs.set_title(\"firing rates during tone during sensitization\")\n",
        "        axs.set_xlabel(\"cells\")\n",
        "        axs.set_ylabel(\"firing rate (Hz)\")\n",
        "        axs.grid()\n",
        "        return axs\n",
        "        plt.show()\n",
        "\n",
        "    #shock sense\n",
        "    def shock_response(node_spike_array):\n",
        "        node_num = 0\n",
        "\n",
        "        while(node_num <= 11):\n",
        "            df0 = df.loc[df['node_ids'] == node_num]\n",
        "\n",
        "            x0 = df0['timestamps'].tolist()\n",
        "            first_trial = []\n",
        "            second_trial = []\n",
        "            third_trial = []\n",
        "            fourth_trial = []\n",
        "            fifth_trial = []\n",
        "            sixth_trial = []\n",
        "            seventh_trial = []\n",
        "            eighth_trial = []\n",
        "            ninth_trial = []\n",
        "            tenth_trial = []\n",
        "\n",
        "            for i in range(len(x0)):\n",
        "                if (x0[i] >= 1750 and x0[i] <= 1850):\n",
        "                    first_trial.append(x0[i])\n",
        "                if (x0[i] >= 5750 and x0[i] <= 5850):\n",
        "                    second_trial.append(x0[i])\n",
        "                if (x0[i] >= 9750 and x0[i] <= 9850):\n",
        "                    third_trial.append(x0[i])\n",
        "                if (x0[i] >= 13750 and x0[i] <= 13850):\n",
        "                    fourth_trial.append(x0[i])\n",
        "                if (x0[i] >= 17750 and x0[i] <= 17850):\n",
        "                    fifth_trial.append(x0[i])\n",
        "                if (x0[i] >= 21750 and x0[i] <= 21850):\n",
        "                    sixth_trial.append(x0[i])\n",
        "                if (x0[i] >= 25750 and x0[i] <= 25850):\n",
        "                    seventh_trial.append(x0[i])\n",
        "                if (x0[i] >= 29750 and x0[i] <= 29850):\n",
        "                    eighth_trial.append(x0[i])\n",
        "                if (x0[i] >= 33750 and x0[i] <= 33850):\n",
        "                    ninth_trial.append(x0[i])\n",
        "                if (x0[i] >= 37750 and x0[i] <= 37850):\n",
        "                    tenth_trial.append(x0[i])\n",
        "            \"\"\"\n",
        "            for i in range(len(second_trial)):\n",
        "                second_trial[i] -= 5750\n",
        "            for i in range(len(third_trial)):\n",
        "                third_trial[i] -= 9750\n",
        "            for i in range(len(fourth_trial)):\n",
        "                fourth_trial[i] -= 13750\n",
        "            for i in range(len(fifth_trial)):\n",
        "                fifth_trial[i] -=\n",
        "            for i in range(len(sixth_trial)):\n",
        "                sixth_trial[i] -= 25750\n",
        "            for i in range(len(seventh_trial)):\n",
        "                seventh_trial[i] -= 29750\n",
        "            for i in range(len(eighth_trial)):\n",
        "                eighth_trial[i] -= 33750\n",
        "            for i in range(len(ninth_trial)):\n",
        "                ninth_trial[i] -= 32000\n",
        "            for i in range(len(tenth_trial)):\n",
        "                tenth_trial[i] -= 36000\n",
        "            \"\"\"\n",
        "\n",
        "            final_array = np.concatenate((first_trial, second_trial, third_trial, fourth_trial,\n",
        "                                          fifth_trial, sixth_trial, seventh_trial, eighth_trial,\n",
        "                                          ninth_trial, tenth_trial))\n",
        "\n",
        "            node_spike_array.append(final_array)\n",
        "\n",
        "            node_num = node_num+1\n",
        "\n",
        "    def spike_freq_bar_shock(shock_response_arr, axes):\n",
        "        hz = []\n",
        "        for i in range(12):\n",
        "            hz.append(((len(shock_response_arr[i]))/0.1)/10) # calculates firing rate in Hz\n",
        "\n",
        "        #print(shock_response_arr[9])\n",
        "        x = [0,1,2,3,4,5,6,7,8,9,10,11]\n",
        "        cells = [\"PN1\", \"PN2\", \"PN3\", \"PN4\", \"PN5\", \"PN6\", \"PN7\", \"PN8\", \"SOM1\", \"SOM2\", \"PV1\", \"PV2\"]\n",
        "        axes.set_xticklabels(cells)\n",
        "        axes.bar(height=hz,x=cells,color=['black', 'black', 'black', 'black', 'black', 'black','black', 'black',\n",
        "                                          'red', 'red', 'green', 'green'])\n",
        "        axes.set_title(\"firing rates during shock during sensitization\")\n",
        "        axes.set_xlabel(\"cells\")\n",
        "        axes.set_ylabel(\"firing rate (Hz)\")\n",
        "        axes.grid()\n",
        "        return axes\n",
        "\n",
        "\n",
        "    ## tone during cond\n",
        "    def tone_during_cond(node_spike_array, axes):\n",
        "        total_spikes = []\n",
        "        timestamp = 40000\n",
        "        node_num = 0\n",
        "        while(node_num <= 12):\n",
        "            df0 = df.loc[df['node_ids'] == node_num]\n",
        "            df0.sort_values(by=['timestamps'])\n",
        "            x0 = df0['timestamps'].tolist()\n",
        "            trial_spikes = []\n",
        "            i = 0\n",
        "            while(i < 10):\n",
        "                for j in range(len(x0)):\n",
        "                    if(x0[j] >= timestamp+(i*4000) and x0[j] <= timestamp+(i*4000+400)):\n",
        "                        value = (x0[j]-(i*4000) - timestamp)\n",
        "                        trial_spikes.append(value)\n",
        "\n",
        "            #should go through all 30 trials and get every value and condense them\n",
        "                i = i+1\n",
        "\n",
        "            # should create a list of lists 12 of them each one being a node\n",
        "            node_spike_array.append(trial_spikes)\n",
        "            node_num = node_num+1\n",
        "\n",
        "        hz = []\n",
        "        for i in range(12):\n",
        "            hz.append(((len(node_spike_array[i]))/0.4)/10) # calculates firing rate in Hz\n",
        "\n",
        "        x = [0,1,2,3,4,5,6,7,8,9,10,11]\n",
        "        cells = [\"PN1\", \"PN2\", \"PN3\", \"PN4\", \"PN5\", \"PN6\", \"PN7\", \"PN8\", \"SOM1\", \"SOM2\", \"PV1\", \"PV2\"]\n",
        "        axes.set_xticklabels(cells)\n",
        "        axes.bar(height=hz, x=cells, color=['black', 'black', 'black', 'black', 'black', 'black','black', 'black',\n",
        "                                          'red', 'red', 'green', 'green'])\n",
        "        axes.set_title(\"firing rates during tone during conditioning\")\n",
        "        axes.set_xlabel(\"cells\")\n",
        "        axes.set_ylabel(\"firing rate (Hz)\")\n",
        "        axes.grid()\n",
        "        return axes\n",
        "\n",
        "    def shock_during_cond(node_spike_array, axes):\n",
        "        total_spikes = []\n",
        "        timestamp = 40000\n",
        "        node_num = 0\n",
        "        while(node_num <= 12):\n",
        "            df0 = df.loc[df['node_ids'] == node_num]\n",
        "            df0.sort_values(by=['timestamps'])\n",
        "            x0 = df0['timestamps'].tolist()\n",
        "            trial_spikes = []\n",
        "            i = 0\n",
        "            while(i < 10):\n",
        "                for j in range(len(x0)):\n",
        "                    if(x0[j] >= timestamp+(i*4000+400) and x0[j] <= timestamp+(i*4000+500)):\n",
        "                        value = (x0[j]-(i*4000) - timestamp)\n",
        "                        trial_spikes.append(value)\n",
        "\n",
        "            #should go through all 30 trials and get every value and condense them\n",
        "                i = i+1\n",
        "\n",
        "            # should create a list of lists 12 of them each one being a node\n",
        "            node_spike_array.append(trial_spikes)\n",
        "            node_num = node_num+1\n",
        "\n",
        "        hz = []\n",
        "        for i in range(12):\n",
        "            hz.append(((len(node_spike_array[i]))/0.1)/10) # calculates firing rate in Hz\n",
        "\n",
        "        x = [0,1,2,3,4,5,6,7,8,9,10,11]\n",
        "        cells = [\"PN1\", \"PN2\", \"PN3\", \"PN4\", \"PN5\", \"PN6\", \"PN7\", \"PN8\", \"SOM1\", \"SOM2\", \"PV1\", \"PV2\"]\n",
        "        axes.set_xticklabels(cells)\n",
        "        axes.bar(height=hz, x=cells, color=['black', 'black', 'black', 'black', 'black', 'black','black', 'black',\n",
        "                                          'red', 'red', 'green', 'green'])\n",
        "        axes.set_title(\"firing rates during tone + shock during conditioning\")\n",
        "        axes.set_xlabel(\"cells\")\n",
        "        axes.set_ylabel(\"firing rate (Hz)\")\n",
        "        axes.grid()\n",
        "        return axes\n",
        "\n",
        "\n",
        "    def create_arrays_extinction_early(node_spike_array):\n",
        "        total_spikes = []\n",
        "        timestamp = 116000\n",
        "        node_num = 0\n",
        "        while(node_num <= 12):\n",
        "            df0 = df.loc[df['node_ids'] == node_num]\n",
        "            df0.sort_values(by=['timestamps'])\n",
        "            x0 = df0['timestamps'].tolist()\n",
        "            trial_spikes = []\n",
        "            i = 0\n",
        "            while(i < 10):\n",
        "                for j in range(len(x0)):\n",
        "                    if(x0[j] >= timestamp+(i*4000) and x0[j] <= timestamp+(i*4000+400)):\n",
        "                        value = (x0[j]-(i*4000) - timestamp)\n",
        "                        trial_spikes.append(value)\n",
        "\n",
        "            #should go through all 30 trials and get every value and condense them\n",
        "                i = i+1\n",
        "\n",
        "            # should create a list of lists 12 of them each one being a node\n",
        "            node_spike_array.append(trial_spikes)\n",
        "            node_num = node_num+1\n",
        "        #print(total_spikes)\n",
        "        #node_spike_array = total_spikes\n",
        "        #print(node_spike_array)\n",
        "\n",
        "    def set_up_graphs_EE():\n",
        "        fig, axs = plt.subplots(4, 3, sharey=True, tight_layout=True, sharex=True)\n",
        "        fig.suptitle('Spike histogram for early extinction', y=1)\n",
        "\n",
        "        i = 0\n",
        "        column_cnt = 0\n",
        "        row_cnt = 0\n",
        "\n",
        "        while (i < 12):\n",
        "            bins = find_bins(extinction_array[i], 10)\n",
        "\n",
        "            axs[row_cnt, column_cnt].hist(x=extinction_array[i], bins=bins)\n",
        "            axs[row_cnt, column_cnt].set_title('PN ' + str(i + 1))\n",
        "            if (i == 8):\n",
        "                axs[row_cnt, column_cnt].set_title('OLM 1 ')\n",
        "            if (i == 9):\n",
        "                axs[row_cnt, column_cnt].set_title('OLM 2 ')\n",
        "            if (i == 10):\n",
        "                axs[row_cnt, column_cnt].set_title('PV 1 ')\n",
        "            if (i == 11):\n",
        "                axs[row_cnt, column_cnt].set_title('PV 1 ')\n",
        "            # axs[row_cnt, column_cnt].ylabel(\"spikes\")\n",
        "            # axs[row_cnt, column_cnt].xlabel('ms')\n",
        "\n",
        "            # axs[row_cnt, column_cnt].set_xlim([0,400])\n",
        "\n",
        "            column_cnt = column_cnt + 1\n",
        "            if (column_cnt > 2):\n",
        "                column_cnt = 0\n",
        "                row_cnt = row_cnt + 1\n",
        "\n",
        "            i = i + 1\n",
        "\n",
        "        plt.setp(axs[-1, :], xlabel='time (ms)')\n",
        "        plt.setp(axs[:, 0], ylabel='spike count')\n",
        "\n",
        "    def tone_during_EE(spike_array, axs):\n",
        "        hz = []\n",
        "        #print(spike_array)\n",
        "        for i in range(12):\n",
        "            hz.append(((len(spike_array[i])) / 0.4) / 10)  # calculates firing rate in Hz\n",
        "        x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n",
        "        cells = [\"PN1\", \"PN2\", \"PN3\", \"PN4\", \"PN5\", \"PN6\", \"PN7\", \"PN8\", \"SOM1\", \"SOM2\", \"PV1\", \"PV2\"]\n",
        "        axs.set_xticklabels(cells)\n",
        "        axs.bar(height=hz, x=cells,color=['black', 'black', 'black', 'black', 'black', 'black','black', 'black',\n",
        "                                          'red', 'red', 'green', 'green'])\n",
        "        axs.set_title(\"firing rates during tone during early extinction\")\n",
        "        axs.set_xlabel(\"cells\")\n",
        "        axs.set_ylabel(\"firing rate (Hz)\")\n",
        "        axs.grid()\n",
        "        return axs\n",
        "\n",
        "\n",
        "    tone_response(node_spike_array)\n",
        "\n",
        "    set_up_graphs_sense()\n",
        "    fig, axs = plt.subplots(3,2, sharex=False,sharey=True, tight_layout=True, figsize=(12,6))\n",
        "    spike_freq_bar_tone(axs[0,0])\n",
        "    shock_arr = []\n",
        "    shock_response(shock_arr)\n",
        "    spike_freq_bar_shock(shock_arr, axs[0,1])\n",
        "    tone_cond = []\n",
        "    tone_during_cond(tone_cond, axs[1,0])\n",
        "    shock_cond = []\n",
        "    shock_during_cond(shock_cond, axs[1,1])\n",
        "    fig.delaxes(axs[2][1])\n",
        "\n",
        "\n",
        "    #EE stuff\n",
        "    extinction_array = []\n",
        "    create_arrays_extinction_early(extinction_array)\n",
        "    tone_during_EE(extinction_array,axs[2,0])\n",
        "    plt.show()\n",
        "    set_up_graphs_EE()\n",
        "    plt.show()\n",
        "\n",
        "from ipywidgets import widgets, HBox, VBox\n",
        "from ipywidgets import interactive_output,HBox,VBox,Label,Layout\n",
        "from bmtk.utils.reports.spike_trains import PoissonSpikeGenerator\n",
        "from IPython.display import clear_output\n",
        "from IPython.utils import io\n",
        "import os, sys\n",
        "import json\n",
        "from bmtk.simulator import bionet\n",
        "import numpy as np\n",
        "import warnings\n",
        "import random\n",
        "from io import StringIO\n",
        "import synapses\n",
        "from neuron import h\n",
        "\n",
        "class Capturing(list):\n",
        "    def __enter__(self):\n",
        "        self._stdout = sys.stdout\n",
        "        sys.stdout = self._stringio = StringIO()\n",
        "        return self\n",
        "    def __exit__(self, *args):\n",
        "        self.extend(self._stringio.getvalue().splitlines())\n",
        "        del self._stringio    # free up some memory\n",
        "        sys.stdout = self._stdout\n",
        "\n",
        "slider1 = widgets.IntSlider(value=5, min=0, max=20, step=1,description=\"PN_input\")\n",
        "#Create your button.\n",
        "button1 = widgets.Button(description=\"Generate\")\n",
        "output1 = widgets.Output()\n",
        "slider2 = widgets.IntSlider(value=5, min=0, max=20, step=1,description=\"PV_input\")\n",
        "#Create your button.\n",
        "button2 = widgets.Button(description=\"Generate\")\n",
        "output2 = widgets.Output()\n",
        "button3 = widgets.Button(description=\"run_model\")\n",
        "\n",
        "slider4 = widgets.IntSlider(value=5, min=0, max=20, step=1,description=\"PN2PV\")\n",
        "#Create your button.\n",
        "button4 = widgets.Button(description=\"Edit\")\n",
        "output4 = widgets.Output()\n",
        "\n",
        "slider5 = widgets.IntSlider(value=5, min=0, max=20, step=1,description=\"PV2PN\")\n",
        "#Create your button.\n",
        "button5 = widgets.Button(description=\"Edit\")\n",
        "output5 = widgets.Output()\n",
        "\n",
        "slider6 = widgets.IntSlider(value=5, min=0, max=20, step=1,description=\"SOM_input\")\n",
        "#Create your button.\n",
        "button6 = widgets.Button(description=\"Generate\")\n",
        "output6 = widgets.Output()\n",
        "\n",
        "slider7 = widgets.IntSlider(value=5, min=0, max=20, step=1,description=\"SOM2PN\")\n",
        "#Create your button.\n",
        "button7 = widgets.Button(description=\"Edit\")\n",
        "output7 = widgets.Output()\n",
        "\n",
        "\n",
        "#function to handle input.\n",
        "def gen_bg_pn(btn):\n",
        "    clear_output()\n",
        "    output1.clear_output()\n",
        "    return_value = slider1.value\n",
        "    os.remove('12_cell_inputs/bg_pn_c_spikes.h5')\n",
        "    if return_value != 0:\n",
        "        psg = PoissonSpikeGenerator(population='bg_pn_c')\n",
        "        psg.add(node_ids=range(1),  # need same number as cells\n",
        "            firing_rate=return_value,    # 1 spike every 1 second Hz\n",
        "            times=(0.0, 40000/1000))  # time is in seconds for some reason\n",
        "        psg.to_sonata('12_cell_inputs/bg_pn_c_spikes.h5')\n",
        "    else:\n",
        "        psg = PoissonSpikeGenerator(population='bg_pn_c')\n",
        "        psg.add(node_ids=range(1),  # need same number as cells\n",
        "            firing_rate=0.00001,    # 1 spike every 1 second Hz\n",
        "            times=(0.0, 40000/1000))  # time is in seconds for some reason\n",
        "        psg.to_sonata('12_cell_inputs/bg_pn_c_spikes.h5')\n",
        "    print('Generated background of PN at : {} Hz'.format(return_value))\n",
        "    display(ui)\n",
        "    #with output1:\n",
        "        #print(return_value)\n",
        "def gen_bg_pv(btn):\n",
        "    clear_output()\n",
        "    output2.clear_output()\n",
        "    return_value = slider2.value\n",
        "    os.remove('12_cell_inputs/bg_pv_spikes.h5')\n",
        "    if return_value != 0:\n",
        "        psg = PoissonSpikeGenerator(population='bg_pv')\n",
        "        psg.add(node_ids=range(1),  # need same number as cells\n",
        "            firing_rate=return_value,    # 1 spike every 1 second Hz\n",
        "            times=(0.0, 40000/1000))  # time is in seconds for some reason\n",
        "        psg.to_sonata('12_cell_inputs/bg_pv_spikes.h5')\n",
        "    else:\n",
        "        psg = PoissonSpikeGenerator(population='bg_pv')\n",
        "        psg.add(node_ids=range(1),  # need same number as cells\n",
        "            firing_rate=0.00001,    # 1 spike every 1 second Hz\n",
        "            times=(0.0, 40000/1000))  # time is in seconds for some reason\n",
        "        psg.to_sonata('12_cell_inputs/bg_pv_spikes.h5')\n",
        "    print('Generated background of PV at : {} Hz'.format(return_value))\n",
        "    display(ui)\n",
        "\n",
        "def gen_bg_olm(btn):\n",
        "    clear_output()\n",
        "    output6.clear_output()\n",
        "    return_value = slider6.value\n",
        "    os.remove('12_cell_inputs/bg_olm_spikes.h5')\n",
        "    if return_value != 0:\n",
        "        psg = PoissonSpikeGenerator(population='bg_olm')\n",
        "        psg.add(node_ids=range(1),  # need same number as cells\n",
        "            firing_rate=return_value,    # 1 spike every 1 second Hz\n",
        "            times=(0.0, 40000/1000))  # time is in seconds for some reason\n",
        "        psg.to_sonata('12_cell_inputs/bg_olm_spikes.h5')\n",
        "    else:\n",
        "        psg = PoissonSpikeGenerator(population='bg_olm')\n",
        "        psg.add(node_ids=range(1),  # need same number as cells\n",
        "            firing_rate=0.000001,    # 1 spike every 1 second Hz\n",
        "            times=(0.0, 40000/1000))  # time is in seconds for some reason\n",
        "        psg.to_sonata('12_cell_inputs/bg_olm_spikes.h5')\n",
        "    print('Generated background of OLM at : {} Hz'.format(return_value))\n",
        "    display(ui)\n",
        "\n",
        "def change_PN2PV(btn):\n",
        "    clear_output()\n",
        "    output4.clear_output()\n",
        "    return_value = slider4.value\n",
        "    with open('biophys_components/synaptic_models/PN2PV.json', 'r+') as f:\n",
        "        data = json.load(f)\n",
        "        data['initW'] = return_value # <--- edit `initW` value.\n",
        "        f.seek(0)        # <--- should reset file position to the beginning.\n",
        "        json.dump(data, f, indent=4)\n",
        "        f.truncate()     # remove remaining part\n",
        "        print(\"Changed PN2PV synaptic weight to {}\".format(return_value))\n",
        "    display(ui)\n",
        "\n",
        "\n",
        "def change_PV2PN(btn):\n",
        "    clear_output()\n",
        "    output5.clear_output()\n",
        "    return_value = slider5.value\n",
        "    with open('biophys_components/synaptic_models/PV2PN.json', 'r+') as f:\n",
        "        data = json.load(f)\n",
        "        data['initW'] = return_value # <--- edit `initW` value.\n",
        "        f.seek(0)        # <--- should reset file position to the beginning.\n",
        "        json.dump(data, f, indent=4)\n",
        "        f.truncate()     # remove remaining part\n",
        "        print(\"Changed PV2PN synaptic weight to {}\".format(return_value))\n",
        "    display(ui)\n",
        "\n",
        "def change_OLM2PN(btn):\n",
        "    clear_output()\n",
        "    output7.clear_output()\n",
        "    return_value = slider7.value\n",
        "    with open('biophys_components/synaptic_models/SOM2PN.json', 'r+') as f:\n",
        "        data = json.load(f)\n",
        "        data['initW'] = return_value # <--- edit `initW` value.\n",
        "        f.seek(0)        # <--- should reset file position to the beginning.\n",
        "        json.dump(data, f, indent=4)\n",
        "        f.truncate()     # remove remaining part\n",
        "        print(\"Changed SOM2PN synaptic weight to {}\".format(return_value))\n",
        "    display(ui)\n",
        "\n",
        "button1.on_click(gen_bg_pn)\n",
        "button2.on_click(gen_bg_pv)\n",
        "button4.on_click(change_PN2PV)\n",
        "button5.on_click(change_PV2PN)\n",
        "button6.on_click(gen_bg_olm)\n",
        "button7.on_click(change_OLM2PN)\n",
        "##do the same thing but change the weight in the json file but opening it and changing it might be a bitch but idk how else\n",
        "model_ran = False\n",
        "def run_model(*args):\n",
        "    clear_output()\n",
        "    with Capturing() as output:\n",
        "        synapses.load()\n",
        "        h.nrnmpi_init()\n",
        "        pc = h.ParallelContext()  # object to access MPI methods\n",
        "        MPI_size = int(pc.nhost())\n",
        "        MPI_rank = int(pc.id())\n",
        "\n",
        "\n",
        "        config_file = 'simulation_config.json'\n",
        "\n",
        "\n",
        "\n",
        "        conf = bionet.Config.from_json(config_file, validate=True);\n",
        "        conf.build_env();\n",
        "\n",
        "        graph = bionet.BioNetwork.from_config(conf);\n",
        "        sim = bionet.BioSimulator.from_config(conf, network=graph);\n",
        "\n",
        "        cells = graph.get_local_cells();\n",
        "\n",
        "        sim.run();\n",
        "\n",
        "        pc.barrier();\n",
        "        pc.gid_clear()\n",
        "        pc.done()\n",
        "    model_ran = True\n",
        "    display(updated_ui)\n",
        "button3.on_click(run_model)\n",
        "\n",
        "def change_tone2PN(btn):\n",
        "    clear_output()\n",
        "    output8.clear_output()\n",
        "    return_value = slider8.value\n",
        "    with open('biophys_components/synaptic_models/tone2PN.json', 'r+') as f:\n",
        "        data = json.load(f)\n",
        "        data['initW'] = return_value # <--- edit `initW` value.\n",
        "        f.seek(0)        # <--- should reset file position to the beginning.\n",
        "        json.dump(data, f, indent=4)\n",
        "        f.truncate()     # remove remaining part\n",
        "        print(\"Changed tone2PN synaptic weight to {}\".format(return_value))\n",
        "    if(model_ran == False):\n",
        "      display(ui)\n",
        "    else:\n",
        "      display(updated_ui)\n",
        "\n",
        "\n",
        "def change_tone2INT(btn):\n",
        "    clear_output()\n",
        "    output9.clear_output()\n",
        "    return_value = slider9.value\n",
        "    with open('biophys_components/synaptic_models/tone2INT.json', 'r+') as f:\n",
        "        data = json.load(f)\n",
        "        data['initW'] = return_value # <--- edit `initW` value.\n",
        "        f.seek(0)        # <--- should reset file position to the beginning.\n",
        "        json.dump(data, f, indent=4)\n",
        "        f.truncate()     # remove remaining part\n",
        "        print(\"Changed tone2PV synaptic weight to {}\".format(return_value))\n",
        "    if(model_ran == False):\n",
        "      display(ui)\n",
        "    else:\n",
        "      display(updated_ui)\n",
        "\n",
        "def change_Shock2INT(btn):\n",
        "    clear_output()\n",
        "    output9.clear_output()\n",
        "    return_value = slider9.value\n",
        "    with open('biophys_components/synaptic_models/shock2INT12.json', 'r+') as f:\n",
        "        data = json.load(f)\n",
        "        data['initW'] = (return_value * 10) # <--- edit `initW` value.\n",
        "        f.seek(0)        # <--- should reset file position to the beginning.\n",
        "        json.dump(data, f, indent=4)\n",
        "        f.truncate()     # remove remaining part\n",
        "        print(\"Changed shock2PV synaptic weight to {}\".format(return_value))\n",
        "    if(model_ran == False):\n",
        "      display(ui)\n",
        "    else:\n",
        "      display(updated_ui)\n",
        "\n",
        "slider8 = widgets.IntSlider(value=5, min=0, max=20, step=1,description=\"TONE2PN\")\n",
        "#Create your button.\n",
        "button8 = widgets.Button(description=\"Edit\")\n",
        "output8 = widgets.Output()\n",
        "button8.on_click(change_tone2PN)\n",
        "\n",
        "slider9 = widgets.IntSlider(value=5, min=0, max=20, step=1,description=\"TONE2PV\")\n",
        "#Create your button.\n",
        "button9 = widgets.Button(description=\"Edit\")\n",
        "output9 = widgets.Output()\n",
        "button9.on_click(change_tone2INT)\n",
        "\n",
        "slider10 = widgets.IntSlider(value=5, min=0, max=20, step=1,description=\"Shock\")\n",
        "#Create your button.\n",
        "button10 = widgets.Button(description=\"Edit\")\n",
        "output10 = widgets.Output()\n",
        "button10.on_click(change_Shock2INT)\n",
        "\n",
        "from ipywidgets import widgets, HBox, VBox\n",
        "from ipywidgets import interactive_output,HBox,VBox,Label,Layout\n",
        "from IPython.display import clear_output\n",
        "from IPython.utils import io\n",
        "import os, sys\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "button11 = widgets.Button(description=\"plot baseline\")\n",
        "button12 = widgets.Button(description=\"increased INT2PN\")\n",
        "button13 = widgets.Button(description=\"increased PN2PN\")\n",
        "button14 = widgets.Button(description=\"decreased PV2PV\")\n",
        "button15 = widgets.Button(description=\"increased shock2INT\")\n",
        "button16 = widgets.Button(description=\"increased tone2PV\")\n",
        "outputbutton = widgets.Button(description = \"results\")\n",
        "ui =  VBox([ HBox([slider1, button1, slider2, button2, slider6, button6]),\n",
        "            HBox([slider4,button4,slider5,button5, slider7, button7]),\n",
        "            HBox([slider8,button8,slider9,button9,slider10,button10]),\n",
        "           HBox([button11,button12,button13,button14,button15,button16]),\n",
        "             button3])\n",
        "display(ui)\n",
        "\n",
        "\n",
        "def plotnewplot(bnt):\n",
        "    clear_output()\n",
        "    generate_plots('output/spikes.h5')\n",
        "    if(model_ran == False):\n",
        "      display(ui)\n",
        "    else:\n",
        "      display(updated_ui)\n",
        "outputbutton.on_click(plotnewplot)\n",
        "def plot1(bnt):\n",
        "    clear_output()\n",
        "    generate_plots('baseline/spikes.h5')\n",
        "    if(model_ran == False):\n",
        "      display(ui)\n",
        "    else:\n",
        "      display(updated_ui)\n",
        "\n",
        "def plot2(bnt):\n",
        "    clear_output()\n",
        "    generate_plots('INT2PN5/spikes.h5')\n",
        "    if(model_ran == False):\n",
        "      display(ui)\n",
        "    else:\n",
        "      display(updated_ui)\n",
        "\n",
        "def plot3(bnt):\n",
        "    clear_output()\n",
        "    generate_plots('PN2PN5/spikes.h5')\n",
        "    if(model_ran == False):\n",
        "      display(ui)\n",
        "    else:\n",
        "      display(updated_ui)\n",
        "\n",
        "def plot4(bnt):\n",
        "    clear_output()\n",
        "    generate_plots('PV2PV1/spikes.h5')\n",
        "    if(model_ran == False):\n",
        "      display(ui)\n",
        "    else:\n",
        "      display(updated_ui)\n",
        "\n",
        "def plot5(bnt):\n",
        "    clear_output()\n",
        "    generate_plots('shockdoubled/spikes.h5')\n",
        "    if(model_ran == False):\n",
        "      display(ui)\n",
        "    else:\n",
        "      display(updated_ui)\n",
        "\n",
        "def plot6(bnt):\n",
        "    clear_output()\n",
        "    generate_plots('toneweightdouble/spikes.h5')\n",
        "    if(model_ran == False):\n",
        "      display(ui)\n",
        "    else:\n",
        "      display(updated_ui)\n",
        "\n",
        "button11.on_click(plot1)\n",
        "button12.on_click(plot2)\n",
        "button13.on_click(plot3)\n",
        "button14.on_click(plot4)\n",
        "button15.on_click(plot5)\n",
        "button16.on_click(plot6)\n",
        "\n",
        "\n",
        "\n",
        "updated_ui =  VBox([ HBox([slider1, button1, slider2, button2, slider6, button6]),\n",
        "            HBox([slider4,button4,slider5,button5, slider7, button7]),\n",
        "            HBox([slider8,button8,slider9,button9,slider10,button10]),\n",
        "           HBox([button11,button12,button13,button14,button15,button16]),\n",
        "                     HBox([button3,outputbutton])])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oh518t8TGu80"
      },
      "source": [
        "# Making Predictions\n",
        "Now that you understand what the baseline run looks like, make predictions for the other runs available. If you want, you can run the model with your own parameters, but note that it takes ~20 minutes to run."
      ]
    }
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