Neuron.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNjUjMnqkogEQlKCUlW570d",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mrezakhodashenas/PhD-NN/blob/main/Neuron.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install neuron"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GAhEp1aKeWBh",
        "outputId": "4eb31bfa-2212-450e-86d9-6cce00ca6ded"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting neuron\n",
            "  Downloading NEURON-8.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.9/14.9 MB\u001b[0m \u001b[31m29.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.9.3 in /usr/local/lib/python3.10/dist-packages (from neuron) (1.23.5)\n",
            "Installing collected packages: neuron\n",
            "Successfully installed neuron-8.2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install control"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3RRw2onZecKj",
        "outputId": "94d1021f-6787-405e-e16f-64d4a9db92a4"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting control\n",
            "  Downloading control-0.9.4-py3-none-any.whl (455 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m455.1/455.1 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from control) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3 in /usr/local/lib/python3.10/dist-packages (from control) (1.10.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from control) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (4.42.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (1.4.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->control) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->control) (1.16.0)\n",
            "Installing collected packages: control\n",
            "Successfully installed control-0.9.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pde"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tjwp_wBDehxz",
        "outputId": "d6027753-5ef5-4a92-ad09-a81100befc84"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pde\n",
            "  Downloading pde-1.3.0.tar.gz (7.0 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: pde\n",
            "  Building wheel for pde (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pde: filename=pde-1.3.0-py3-none-any.whl size=6856 sha256=54b64749ccb69ff0c738676ce943fa43af69a37b0dd8e785b7ce8854d6119374\n",
            "  Stored in directory: /root/.cache/pip/wheels/2c/d3/f7/141c1e5f70532b6665c4c59c847567ab12a521e8cb352a7c97\n",
            "Successfully built pde\n",
            "Installing collected packages: pde\n",
            "Successfully installed pde-1.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show pde"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PsDkQcLAfqOb",
        "outputId": "63703bfa-3248-4b73-8edc-5a4957c2ebf9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: pde\n",
            "Version: 1.3.0\n",
            "Summary: PDE is a simple development environment setup script for python projects.\n",
            "Home-page: http://github.com/Nekroze/Python-DevEnv\n",
            "Author: Taylor \"Nekroze\" Embrace\n",
            "Author-email: nekroze@eturnilnetwork.com\n",
            "License: MIT\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: \n",
            "Required-by: \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from neuron import h, gui\n",
        "# from neuron.units import mV, ms, um\n",
        "from matplotlib import cm\n",
        "import plotly\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.fftpack import fft\n",
        "from scipy import signal\n",
        "import scipy.signal as signal\n",
        "from numpy.fft import fft, fftfreq, rfft, ifft\n",
        "# import control\n",
        "from scipy.integrate import odeint\n",
        "# import pde\n",
        "from mpl_toolkits.mplot3d import axes3d\n",
        "import matplotlib.cm as cm\n",
        "# from control.matlab import *\n",
        "import math\n",
        "from matplotlib.widgets import Cursor\n",
        "from matplotlib.pyplot import semilogx\n",
        "from matplotlib import pyplot\n",
        "# from engineering_notation import EngNumber\n",
        "# import PySpice.Logging.Logging as Logging\n",
        "# logger = Logging.setup_logging()\n",
        "# from PySpice.Doc.ExampleTools import find_libraries\n",
        "# from PySpice.Probe.Plot import plot\n",
        "# from PySpice.Spice.Library import SpiceLibrary\n",
        "# from PySpice.Spice.Netlist import Circuit\n",
        "# from PySpice.Unit import *\n",
        "plt.close('all')\n",
        "import time\n",
        "start = time.time()\n"
      ],
      "metadata": {
        "id": "M_tpF-nRePZY"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# freq =  fs*np.arange(len(X))/len(X)\n",
        "\n",
        "t=np.arange(0,1000,0.025)\n",
        "fs=1000;\n",
        "n=np.size(t)\n",
        "fr=(fs/2)*np.linspace(0,1,n//2)\n",
        "freqs=fftfreq(n)\n",
        "mask=freqs>0"
      ],
      "metadata": {
        "id": "yZRqrtNMHvMd"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CA1Poz0=pd.read_table(\"0.12_CA1_inhTau12.dat\", sep=\"\\s+\", header = None)\n",
        "CA1Poz0=CA1Poz0.iloc[2:40002,1].values;\n",
        "\n",
        "CA1Poz0e=pd.read_table(\"0.25gNaCA1_inhTau12.dat\", sep=\"\\s+\", header = None)\n",
        "CA1Poz0e=CA1Poz0e.iloc[2:40002,1].values;\n",
        "\n",
        "CA1Poz0ee=pd.read_table(\"1gNaCA1_inhTau12.dat\", sep=\"\\s+\", header = None)\n",
        "CA1Poz0ee=CA1Poz0ee.iloc[2:40002,1].values;\n",
        "\n",
        "# # ================================================================== X (ECdend[65].v(1))\n",
        "# Sensinf=pd.read_table(\"ECdend[65].v(1).dat\", sep=\"\\s+\", header = None)\n",
        "# Sensinf=Sensinf.iloc[2:40002,1].values;\n",
        "# fSensinf= fft(Sensinf)\n",
        "# Sensinf_mag=(1/(60*fs))*abs(fSensinf[0:np.size(t)])\n",
        "# psdSensinf=np.square(np.abs(fft(Sensinf)/fs))\n",
        "# # ================================================================== X Epilepsy (ECdendE[65].v(1))\n",
        "# SensinfE=pd.read_table(\"ECdendE[65].dat\", sep=\"\\s+\", header = None)\n",
        "# SensinfE=SensinfE.iloc[2:40002,1].values;\n",
        "# fSensinfE=fft(SensinfE)\n",
        "# SensinfE_mag=(1/(60*n))*abs(fSensinfE[0:np.size(t)])\n",
        "# psdSensinfE=np.square(np.abs(fft(SensinfE)/n))\n",
        "# # ================================================================== A1\n",
        "# ECpyrBuz0=pd.read_table(\"EC.dat\", sep=\"\\s+\", header = None)\n",
        "# ECpyrBuz0=ECpyrBuz0.iloc[2:40002,1].values;\n",
        "# fECpyrBuz0= fft(ECpyrBuz0)\n",
        "# ECpyrBuz0_mag=(1/(60*n))*abs(fECpyrBuz0[0:np.size(t)])\n",
        "# psdECpyrBuz0=np.square(np.abs(fft(ECpyrBuz0)/n))\n",
        "# # ================================================================== A1e\n",
        "# ECpyrBuz0E=pd.read_table(\"ECe.dat\", sep=\"\\s+\", header = None)\n",
        "# ECpyrBuz0E=ECpyrBuz0E.iloc[2:40002,1].values;\n",
        "# fECpyrBuz0E= fft(ECpyrBuz0E)\n",
        "# ECpyrBuz0E_mag=(1/(60*n))*abs(fECpyrBuz0E[0:np.size(t)])\n",
        "# psdECpyrBuz0E=np.square(np.abs(fft(ECpyrBuz0E)/n))\n",
        "# # ================================================================== C1\n",
        "# CA3Am0=pd.read_table(\"CA3.dat\", sep=\"\\s+\", header = None)\n",
        "# CA3Am0=CA3Am0.iloc[2:40002,1].values;\n",
        "# fCA3Am0= fft(CA3Am0)\n",
        "# CA3Am0_mag=(1/(60*n))*abs(fCA3Am0[0:np.size(t)])\n",
        "# psdCA3Am0=np.square(np.abs(fft(CA3Am0)/n))\n",
        "# # ================================================================== C1e\n",
        "# CA3Am0E=pd.read_table(\"CA3e.dat\", sep=\"\\s+\", header = None)\n",
        "# CA3Am0E=CA3Am0E.iloc[2:40002,1].values;\n",
        "# fCA3Am0E= fft(CA3Am0E)\n",
        "# CA3Am0E_mag=(1/(60*n))*abs(fCA3Am0E[0:np.size(t)])\n",
        "# psdCA3Am0E=np.square(np.abs(fft(CA3Am0E)/n))\n",
        "# # ================================================================== G1\n",
        "# CA1Poz0=pd.read_table(\"CA1.dat\", sep=\"\\s+\", header = None)\n",
        "# CA1Poz0=CA1Poz0.iloc[2:40002,1].values;\n",
        "# fCA1Poz0= fft(CA1Poz0)\n",
        "# CA1Poz0_mag=(1/(60*n))*abs(fCA1Poz0[0:np.size(t)])\n",
        "# psdCA1Poz0=np.square(np.abs(fft(CA1Poz0)/n))\n",
        "# # ================================================================== G1e\n",
        "# CA1Poz0E=pd.read_table(\"CA1e.dat\", sep=\"\\s+\", header = None)\n",
        "# CA1Poz0E=CA1Poz0E.iloc[2:40002,1].values;\n",
        "# fCA1Poz0E= fft(CA1Poz0E)\n",
        "# CA1Poz0E_mag=(1/(60*n))*abs(fCA1Poz0E[0:np.size(t)])\n",
        "# psdCA1Poz0E=np.square(np.abs(fft(CA1Poz0E)/n))\n",
        "# # ================================================================== H1\n",
        "# CA1Basket0=pd.read_table(\"BasketCA1.dat\", sep=\"\\s+\", header = None)\n",
        "# CA1Basket0=CA1Basket0.iloc[2:40002,1].values;\n",
        "# fCA1Basket0= fft(CA1Basket0)\n",
        "# CA1Basket0_mag=(1/(60*n))*abs(fCA1Basket0[0:np.size(t)])\n",
        "# psdCA1Basket0=np.square(np.abs(fft(CA1Basket0)/n))\n",
        "# # ================================================================== H1e\n",
        "# CA1Basket0E=pd.read_table(\"BasketCA1e.dat\", sep=\"\\s+\", header = None)\n",
        "# CA1Basket0E=CA1Basket0E.iloc[2:40002,1].values;\n",
        "# fCA1Basket0E= fft(CA1Basket0E)\n",
        "# CA1Basket0E_mag=(1/(60*n))*abs(fCA1Basket0E[0:np.size(t)])\n",
        "# psdCA1Basket0E=np.square(np.abs(fft(CA1Basket0E)/n))\n"
      ],
      "metadata": {
        "id": "q36uSXLTHz-K"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# plt.figure(figsize=(10, 16))\n",
        "\n",
        "# Set font and figure size\n",
        "plt.rcParams.update({'font.size': 8})\n",
        "font = {'weight': 'bold', 'size': 18}\n",
        "\n",
        "# # # Plot the first subplot\n",
        "# # # plt.subplot(221)\n",
        "# plt.plot(t, CA1Poz0, 'green', label='gNa: 0.12mS', linewidth=3)\n",
        "# plt.ylim(-85, 60)\n",
        "# plt.xlabel('Time [ms]', fontdict=font)\n",
        "# plt.ylabel('Membrane potential', fontdict=font)\n",
        "# # plt.title('Oscillation sample of a biological neuron with Normal vs Mutated gNa\\n')\n",
        "# # # plt.grid(True)\n",
        "# ax = plt.gca()\n",
        "# ax.spines['top'].set_linewidth(2)\n",
        "# ax.spines['right'].set_linewidth(2)\n",
        "# ax.spines['bottom'].set_linewidth(2)\n",
        "# ax.spines['left'].set_linewidth(2)\n",
        "# legend = ax.legend(loc=\"upper left\", frameon=True, fancybox=True, shadow=True, fontsize=15)\n",
        "# frame = legend.get_frame()\n",
        "# frame.set_linewidth(2)\n",
        "# plt.tight_layout()\n",
        "# plt.xticks(fontsize=12)\n",
        "# plt.yticks(fontsize=12)\n",
        "# print('min:', min(CA1Poz0), 'max:', max(CA1Poz0))  # Adjust the limits based on your data\n",
        "\n",
        "\n",
        "# # Plot the second subplot\n",
        "# # # plt.subplot(222)\n",
        "# plt.plot(t, CA1Poz0e,'orange', label='gNa: 0.25mS', linewidth=3)\n",
        "# plt.ylim(-85, 60)\n",
        "# plt.xlabel('Time [ms]', fontdict=font)\n",
        "# plt.ylabel('Membrane potential', fontdict=font)\n",
        "# # plt.grid(True)\n",
        "# ax = plt.gca()\n",
        "# ax.spines['top'].set_linewidth(2)\n",
        "# ax.spines['right'].set_linewidth(2)\n",
        "# ax.spines['bottom'].set_linewidth(2)\n",
        "# ax.spines['left'].set_linewidth(2)\n",
        "# legend = ax.legend(loc=\"upper left\", frameon=True, fancybox=True, shadow=True, fontsize=15)\n",
        "# frame = legend.get_frame()\n",
        "# frame.set_linewidth(2)\n",
        "# plt.tight_layout()\n",
        "# plt.xticks(fontsize=12)\n",
        "# plt.yticks(fontsize=12)\n",
        "# print('min:', min(CA1Poz0e), 'max:', max(CA1Poz0e))  # Adjust the limits based on your data\n",
        "\n",
        "# # Plot the second subplot\n",
        "# # plt.subplot(223)\n",
        "plt.plot(t, CA1Poz0ee,'red', label='gNa: 1mS (over-firing)', linewidth=3)\n",
        "plt.legend(loc=\"center right\", facecolor='white')  # Move the legend to the upper-right corner and change the legend color to white\n",
        "plt.ylim(-85, 60)\n",
        "plt.xlabel('Time [ms]', fontdict=font)\n",
        "plt.ylabel('Membrane potential', fontdict=font)\n",
        "# plt.grid(True)\n",
        "ax = plt.gca()\n",
        "ax.spines['top'].set_linewidth(2)\n",
        "ax.spines['right'].set_linewidth(2)\n",
        "ax.spines['bottom'].set_linewidth(2)\n",
        "ax.spines['left'].set_linewidth(2)\n",
        "legend = ax.legend(loc=\"upper left\", frameon=True, fancybox=True, shadow=True, fontsize=15)\n",
        "frame = legend.get_frame()\n",
        "frame.set_linewidth(2)\n",
        "# Adjust spacing between subplots\n",
        "plt.tight_layout()\n",
        "plt.xticks(fontsize=12)\n",
        "plt.yticks(fontsize=12)\n",
        "print('min:', min(CA1Poz0ee), 'max:', max(CA1Poz0ee))\n",
        "\n",
        "\n",
        "# Save the figure in high-quality format\n",
        "# plt.savefig('OutputfromNeuron.pdf', dpi=300)\n",
        "# plt.savefig('Neuron_0.12.pdf', dpi=300)\n",
        "# plt.savefig('Neuron_0.25.pdf', dpi=300)\n",
        "# plt.savefig('Neuron_1.pdf', dpi=300)\n",
        "\n",
        "# Display the figure\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "GQfU2uE8H9j2",
        "outputId": "96720da5-5c03-4a87-a416-27bf23f2a66e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "min: -79.8259 max: 48.7876\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## one by one\n"
      ],
      "metadata": {
        "id": "rmtrX0Xothp_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Set font and figure size\n",
        "# plt.rcParams.update({'font.size': 8})\n",
        "# plt.figure(figsize=(10, 10))\n",
        "\n",
        "# # Plot the first subplot\n",
        "# # plt.subplot(311)\n",
        "# plt.plot(t, CA1Poz0, '#023e8a') # , label='with normal Na conductivity')\n",
        "# # plt.legend(loc=\"upper right\", fontsize=10)\n",
        "# plt.ylim(-85, 40)\n",
        "# plt.xlabel('Time [ms]', fontsize=18)\n",
        "# plt.ylabel('Membrane potential', fontsize=18)\n",
        "# # plt.title('Oscillation sample of a biological neuron with Normal vs Mutated gNa\\n')\n",
        "# plt.xticks(fontsize=15)\n",
        "# plt.yticks(fontsize=15)\n",
        "# # plt.gcf().set_size_inches(6, 4)  # Adjust width and height as needed\n",
        "# # plt.grid(True)\n",
        "\n",
        "# # # Plot the second subplot\n",
        "# # plt.subplot(312)\n",
        "# plt.plot(t, CA1Poz0e, '#023e8a') # 'black', label='with mutation in Na conductivity')\n",
        "# # plt.legend(loc=\"center right\")\n",
        "# plt.ylim(-85, 40)\n",
        "# plt.xlabel('Time [ms]', fontsize=18)\n",
        "# plt.ylabel('Membrane potential', fontsize=18)\n",
        "# plt.xticks(fontsize=15)\n",
        "# plt.yticks(fontsize=15)\n",
        "# # plt.grid(True)\n",
        "\n",
        "\n",
        "# Plot the second subplot\n",
        "# plt.subplot(313)\n",
        "plt.plot(t, CA1Poz0ee, '#fb8500') # 'black', label='with higher mutation in Na conductivity')\n",
        "# plt.legend(loc=\"center right\", facecolor='white')  # Move the legend to the upper-right corner and change the legend color to white\n",
        "plt.ylim(-85, 40)\n",
        "plt.xlabel('Time [ms]', fontsize=18)\n",
        "plt.ylabel('Membrane potential', fontsize=18)\n",
        "plt.xticks(fontsize=15)\n",
        "plt.yticks(fontsize=15)\n",
        "# plt.grid(True)\n",
        "\n",
        "plt.tight_layout()\n",
        "# plt.savefig('NeuronalOutput.png', dpi=300)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "0wbYI-jgtju2",
        "outputId": "34c9233a-03a4-4461-ddd8-7e4c07522155"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# ##############################======================================= Sensory Information Block\n",
        "# libraries_path = find_libraries()\n",
        "# spice_library = SpiceLibrary(libraries_path)\n",
        "\n",
        "# circuit = Circuit('VCO with BJTn')\n",
        "# circuit.model('2N3904', 'npn', IS=1E-14, VAF=100, Bf=300, IKF=0.4, XTB=1.5, BR=4,\n",
        "#               CJC=4E-12, CJE=8E-12, RB=20, RC=0.1, RE=0.1, TR=250E-9,\n",
        "#               TF=350E-12, ITF=1, VTF=2, XTF=3, VCEO=40)\n",
        "# R=15\n",
        "# pulse_width=0.002\n",
        "# period=0.050\n",
        "# delay=0.005\n",
        "# rise=0.0001\n",
        "# fall=0.010\n",
        "\n",
        "# circuit.PulseVoltageSource(2, 'cn', circuit.gnd,initial_value=0    , pulsed_value=0.1,\n",
        "#                             pulse_width=pulse_width, period=period, delay_time=delay, rise_time=rise, fall_time=fall)\n",
        "# # making another input\n",
        "# pulse_widthe=0.010\n",
        "# periode=0.10\n",
        "# delaye=0.005\n",
        "# risee=0.0001\n",
        "# falle=0.010\n",
        "# circuit.PulseVoltageSource(1, 'c', circuit.gnd,initial_value=0    , pulsed_value=0.1,\n",
        "#                             pulse_width=pulse_widthe, period=periode, delay_time=delaye, rise_time=risee, fall_time=falle)\n",
        "# # R=1\n",
        "# # pulse_width=0.1\n",
        "# # period=0.20\n",
        "# # delay=0.005\n",
        "# # rise=1\n",
        "# # fall=0.0001\n",
        "# # circuit.PulseVoltageSource(2, 'cn', circuit.gnd,initial_value=0, pulsed_value=10,\n",
        "# #                             pulse_width=pulse_width, period=period, delay_time=delay, rise_time=rise, fall_time=fall)\n",
        "# circuit.BJT(3, 'out2n', 'b2n', circuit.gnd, model='2N3904')\n",
        "# circuit.BJT(4, 'outn', 'b1n', circuit.gnd, model='2N3904')\n",
        "# circuit.C(3,'out2n','b1n', 100@u_nF)\n",
        "# circuit.C(4,'b2n','outn', 100@u_nF)\n",
        "# circuit.R(5, 'b1n', 'cn', R@u_kΩ)\n",
        "# circuit.R(6, 'cn', 'b2n', R@u_kΩ)\n",
        "# circuit.R(7, 'outn', 'ccn', 1@u_kΩ)\n",
        "# circuit.R(8, 'out2n', 'ccn', 1@u_kΩ)\n",
        "# circuit.V('Vccn', 'ccn', circuit.gnd, 6@u_V)\n",
        "\n",
        "# steptime = 1@u_us\n",
        "# switchingtime = 50@u_ms\n",
        "# finaltime = 1000@u_ms\n",
        "# simulator = circuit.simulator(temperature=25, nominal_temperature=25)\n",
        "# analysis = simulator.transient(step_time=steptime, end_time=finaltime)\n",
        "# time=np.array(analysis.time)\n",
        "\n",
        "\n",
        "\n",
        "# #  -----------------------------------------------------Generate Freq Axis\n",
        "# # ts=np.arange(0,np.size(time),10)\n",
        "# # ns=np.size(ts)\n",
        "# frs=(fs/2)*np.linspace(0,1,n)\n",
        "\n",
        "# fout= np.fft.rfft(analysis['cn'])\n",
        "# X1=(1/(60*n))*abs(fout[0:np.size(fr)])\n",
        "# freqss=fftfreq(n)\n",
        "# masks=freqss>0\n",
        "\n",
        "# foute= np.fft.rfft(analysis['c'])\n",
        "# X2=(1/(60*n))*abs(foute[0:np.size(fr)])\n",
        "# # ====================================================================================== Blocks\n",
        "# Xdend=Sensinf_mag\n",
        "# Y=CA1Poz0_mag\n",
        "# A=ECpyrBuz0_mag/(Xdend+Y)\n",
        "# C=CA3Am0_mag/ECpyrBuz0_mag;\n",
        "# F=CA1Basket0_mag/Y;\n",
        "# D=Y/(CA3Am0_mag-CA1Basket0_mag);\n",
        "# K=D/(1+(D*F));\n",
        "\n",
        "# TF=(A*C*K)/(1-(A*C*K))\n",
        "# # Y1=X1*(TF)\n",
        "# # ============================================================== Epil Blocks\n",
        "# Xdende=SensinfE_mag\n",
        "# Ye=CA1Poz0E_mag\n",
        "# Ae=ECpyrBuz0E_mag/(Xdende+Ye)\n",
        "# Ce=CA3Am0E_mag/ECpyrBuz0E_mag;\n",
        "# Fe=CA1Basket0E_mag/Ye;\n",
        "# De=Ye/(CA3Am0E_mag-CA1Basket0E_mag);\n",
        "# Ke=De/(1+(De*Fe));\n",
        "# # Y2=(X2*((Ae*Ce*Ke)/(1-(Ae*Ce*Ke))))\n",
        "\n",
        "\n",
        "\n",
        "# # --------------------------------------------------------- plotting Simulated Sens Infor\n",
        "# # plt.figure()\n",
        "# # plt.subplot(321)\n",
        "# # plot(analysis['cn'], label='Period=%.1fms,  pulse_width=%.fms' % (period*1000,pulse_width*1000))\n",
        "# # plt.legend()\n",
        "# # plt.xlabel('Time [s]')\n",
        "# # plt.title('X1 Input from Circuit + FFT')\n",
        "# # plt.ylabel('Amplitude')\n",
        "# # plt.show()\n",
        "\n",
        "# # plt.subplot(323)\n",
        "# # plt.xlim([0, 600])\n",
        "# # # plt.ylim([0, 20])\n",
        "# # plt.plot(X1,label='FFT of the simulated SensInf,  period=%.1fms,  pulse_width=%.fms' % (period*1000,pulse_width*1000))\n",
        "# # # plt.plot(outf_mag,label='FFT of the VCO Output, R=%.1fk, Sine F=%.fHz, Ampl=%.f, delay=%.fms, Df=%.f' % (R,freq,amp, Td*1000, Df))\n",
        "# # plt.xlabel('Freq [Hz]')\n",
        "# # plt.ylabel('Ampl [db]')\n",
        "# # plt.legend()\n",
        "# # plt.grid()\n",
        "# # plt.show()\n",
        "\n",
        "\n",
        "# # plt.subplot(322)\n",
        "# # plot(analysis['c'],'r', label='Period=%.1fms,  pulse_width=%.fms' % (periode*1000,pulse_widthe*1000))\n",
        "# # plt.legend()\n",
        "# # plt.title('X2 Input from Circuit + FFT')\n",
        "# # plt.xlabel('Time [s]')\n",
        "# # plt.ylabel('Amplitude')\n",
        "\n",
        "\n",
        "# # plt.subplot(324)\n",
        "# # plt.xlim([0, 600])\n",
        "# # # plt.ylim([0, 20])\n",
        "# # plt.plot(X2,'r',label='FFT of the simulated SensInf,  period=%.1fms,  pulse_width=%.fms' % (periode*1000,pulse_widthe*1000))\n",
        "# # # plt.plot(outf_mag,label='FFT of the VCO Output, R=%.1fk, Sine F=%.fHz, Ampl=%.f, delay=%.fms, Df=%.f' % (R,freq,amp, Td*1000, Df))\n",
        "# # plt.xlabel('Freq [Hz]')\n",
        "# # plt.ylabel('Ampl [db]')\n",
        "# # plt.legend()\n",
        "# # plt.grid()\n",
        "# # plt.show()\n",
        "# # # ======================================================================================= inverse FFT\n",
        "\n",
        "# # plt.subplot(325)\n",
        "# # # plt.plot(Y1,'b',label='X(sim)1*(A.C.K/(1-A.C.K))')\n",
        "# # plt.plot(t, ifft(X1), 'b',label='inverse (X1)')\n",
        "# # # plt.xlim([0, 1000])\n",
        "# # # plt.ylim([-.0010, 0.2])\n",
        "# # # plt.xlabel('Freq [Hz]')\n",
        "# # plt.xlabel('Time [s]')\n",
        "# # plt.ylabel('Amplitude')\n",
        "# # plt.legend()\n",
        "# # plt.tight_layout()\n",
        "# # plt.show()\n",
        "# # plt.subplot(326)\n",
        "# # plt.plot(t, ifft(X2), 'r',label='inverse (X2)')  # ifft inverses the fft\n",
        "# # plt.xlabel('Time [s]')\n",
        "# # plt.ylabel('Amplitude')\n",
        "# # plt.tight_layout()   # automatically adjusts subplot params so that the subplot(s) fits in to the figure area.\n",
        "# # plt.show()\n",
        "# # # --------------------------------------------------------- plotting ECdend[65] input\n",
        "# # plt.figure()\n",
        "# # plt.subplot(311)\n",
        "# # plt.plot(t, Sensinf, label='dend[65].v(1)')\n",
        "# # # plt.plot(t, SensinfE, label='Epil dend[65].v(1)')\n",
        "# # plt.title(\"Voltage of dend[65] + FFT\")\n",
        "# # plt.legend()\n",
        "# # plt.subplot(312)\n",
        "# # plt.plot(Sensinf_mag,label='FFT of EC.dend[65].v(1)= Sensory Information')\n",
        "# # # plt.plot(SensinfE_mag,label='FFT of Epil EC.dend[65].v(1)= Sensory Information')\n",
        "# # # plt.plot(outf_mag,label='FFT of the VCO Output, R=%.1fk, Sine F=%.fHz, Ampl=%.f, delay=%.fms, Df=%.f' % (R,freq,amp, Td*1000, Df))\n",
        "# # plt.xlim([0, 600])\n",
        "# # plt.ylim([0, 0.20])\n",
        "# # plt.title(\"Sens inf FFT (X)\")\n",
        "# # plt.xlabel('Freq [Hz]')\n",
        "# # plt.ylabel('Ampl [db]')\n",
        "# # plt.legend()\n",
        "# # plt.grid()\n",
        "# # plt.tight_layout()\n",
        "\n",
        "# # # # ======================================================================================= inverse FFT\n",
        "# # plt.subplot(313)\n",
        "# # plt.plot(t, ifft(Sensinf_mag), 'b',label='inverse of (Sensinf_mag)= Sensinf(t)')\n",
        "# # plt.xlabel('Time [s]')\n",
        "# # plt.ylabel('Amplitude')\n",
        "# # plt.legend()\n",
        "# # plt.tight_layout()\n",
        "# # plt.show()\n",
        "\n",
        "# ##############################=================================================== CA1 Time domain\n",
        "# plt.figure()\n",
        "# plt.subplot(211)\n",
        "# plt.plot(t,CA1Poz0, 'black',label='Normal CA1 Output')\n",
        "# plt.legend()\n",
        "# plt.xlabel('Time [ms]');\n",
        "# plt.ylabel('CA1 Output potential');\n",
        "# plt.title('CA1 Output with healthy and weak Interneuron')\n",
        "# plt.grid()\n",
        "# plt.show()\n",
        "\n",
        "# plt.subplot(212)\n",
        "# plt.plot(t,CA1Poz0E,label='CA1 with Weak interneurons')\n",
        "# plt.legend()\n",
        "# plt.xlabel('Time [ms]');\n",
        "# plt.ylabel('Epil CA1 Output');\n",
        "# plt.grid()\n",
        "# plt.show()\n",
        "# # ##############################=================================================== BasketCa1 Time domain\n",
        "# # plt.figure()\n",
        "# # plt.subplot(211)\n",
        "# # plt.plot(t,CA1Basket0, 'black',label='Interneuron (normal)')\n",
        "# # plt.legend()\n",
        "# # plt.xlabel('Time [ms]');\n",
        "# # plt.ylabel('BasketCA1 Output potential');\n",
        "# # plt.title('BasketCA1 Output w ith healthy and weak Interneuron')\n",
        "# # plt.ylim([-80, 35])\n",
        "# # plt.grid()\n",
        "# # plt.show()\n",
        "\n",
        "# # plt.subplot(212)\n",
        "# # plt.plot(t,CA1Basket0E,label='Epil Interneuron (weak)')\n",
        "# # plt.legend()\n",
        "# # plt.xlabel('Time [ms]');\n",
        "# # plt.ylabel('Epil BasketCA1 Output');\n",
        "# # plt.ylim([-80, 35])\n",
        "# # plt.grid()\n",
        "# # plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# # # --------------------------------------------------------- plotting each Block Transfer Function\n",
        "# # # --------------------------------------------------------- EC\n",
        "# plt.figure()\n",
        "# plt.subplot(211)\n",
        "# # plt.plot((((A*C*K)/(1-(A*C*K)))),'b',label='(A.C.K/(1-A.C.K))')\n",
        "# plt.plot(A,'b',label='TF of EC block (A)')\n",
        "# plt.xlim([0, 1000])\n",
        "# plt.title(\"'A & Ae' Transfer Function (EC)\")\n",
        "# plt.legend()\n",
        "# plt.tight_layout()\n",
        "# plt.show()\n",
        "\n",
        "# plt.subplot(212)\n",
        "# plt.plot(Ae,'r',label='Epil EC block TF (Ae)')\n",
        "# plt.xlim([0, 1000])\n",
        "# # plt.xlim([0, 600])\n",
        "# # plt.ylim([-.0010, 0.1])\n",
        "# plt.ylabel('Ampl [db]')\n",
        "# plt.legend()\n",
        "# plt.tight_layout()\n",
        "# plt.subplots_adjust(left= 0.086, bottom= 0.156, right= 0.966, top= 0.937, wspace=200, hspace= 0.200 )\n",
        "# plt.show()\n",
        "\n",
        "# # --------------------------------------------------------- CA3\n",
        "\n",
        "# plt.figure()\n",
        "# plt.subplot(211)\n",
        "# plt.plot(C,'b',label='CA3 block TF (C)')\n",
        "# plt.title(\"'C & Ce' Transfer Function (CA3)\")\n",
        "# plt.legend()\n",
        "# plt.xlim([0, 1000])\n",
        "# plt.tight_layout()\n",
        "# plt.show()\n",
        "# # plt.xlim([0, 600])\n",
        "# # plt.ylim([-.0010, 0.1])\n",
        "\n",
        "# plt.subplot(212)\n",
        "# plt.plot((Ce),'r',label='Epil CA3 block TF (Ce)')\n",
        "# plt.legend()\n",
        "# plt.xlim([0, 1000])\n",
        "# # plt.xlim([0, 600])\n",
        "# # plt.ylim([0, 0.1])\n",
        "# plt.tight_layout()\n",
        "# plt.subplots_adjust(left= 0.086, bottom= 0.156, right= 0.966, top= 0.937, wspace=200, hspace= 0.200 )\n",
        "# plt.show()\n",
        "\n",
        "\n",
        "# # --------------------------------------------------------- CA1\n",
        "# plt.figure()\n",
        "# plt.subplot(211)\n",
        "# plt.plot(D,'b',label='CA1 block TF (D)')\n",
        "# plt.xlim([0, 1000])\n",
        "# # plt.xlim([0, 600])\n",
        "# # plt.ylim([-.0010, 0.1])\n",
        "# plt.title(\"'D & De' Transfer Function (CA1)\")\n",
        "# plt.legend()\n",
        "# plt.tight_layout()\n",
        "# plt.show()\n",
        "\n",
        "\n",
        "# plt.subplot(212)\n",
        "# plt.plot(De,'r',label='Epil CA1 block TF (De)')\n",
        "# plt.xlim([0, 1000])\n",
        "# # plt.xlim([0, 600])\n",
        "# # plt.ylim([-.0010, 0.1])\n",
        "# plt.legend()\n",
        "# plt.tight_layout()\n",
        "# plt.show()\n",
        "\n",
        "# # --------------------------------------------------------- BasketCA1\n",
        "# plt.figure()\n",
        "# plt.subplot(211)\n",
        "# plt.plot((F),'b',label='BasketCA1 block TF (F)')\n",
        "# plt.legend()\n",
        "# plt.xlim([0, 1000])\n",
        "# # plt.xlim([0, 600])\n",
        "# # plt.ylim([0, 0.1])\n",
        "# plt.tight_layout()\n",
        "# plt.title(\"'F' Transfer Function (BasketCA1)\")\n",
        "# plt.show()\n",
        "\n",
        "# plt.subplot(212)\n",
        "# plt.plot((Fe),'r',label='Epil BasketCA1 block TF (Fe)')\n",
        "# plt.legend()\n",
        "# plt.xlim([0, 1000])\n",
        "# # plt.xlim([0, 600])\n",
        "# # plt.ylim([0, 0.1])\n",
        "# plt.tight_layout()\n",
        "# plt.show()\n",
        "# # --------------------------------------------------------==============================- Total TF\n",
        "# plt.figure()\n",
        "# plt.subplot(211)\n",
        "# plt.plot(((A*C*K)/(1-(A*C*K))),'b',label='(A.C.K/(1-A.C.K)) TF')\n",
        "# plt.legend()\n",
        "# plt.xlim([0, 1000])\n",
        "# # plt.xlim([0, 600])\n",
        "# # plt.ylim([0, 0.1])\n",
        "# plt.tight_layout()\n",
        "# plt.title(\"Total Transfer Function\")\n",
        "# plt.show()\n",
        "\n",
        "# plt.subplot(212)\n",
        "# plt.plot((Ae*Ce*Ke)/(1-(Ae*Ce*Ke)),'r',label='Epil Total TF')\n",
        "# plt.xlim([0, 1000])\n",
        "# plt.legend()\n",
        "# # plt.xlim([0, 600])\n",
        "# # plt.ylim([0, 0.1])\n",
        "# plt.tight_layout()\n",
        "# plt.show()\n",
        "# # =====================================================================   Test for outputs\n",
        "# plt.figure()\n",
        "# plt.subplot(221)\n",
        "# plt.plot((Xdend*((A*C*K)/(1-(A*C*K)))),'b',label='X*(A.C.K/(1-A.C.K))')\n",
        "# plt.xlim([0, 600])\n",
        "# plt.ylim([-.0010, 0.1])\n",
        "# plt.title(\"Convolution of Normal Cells Vs CA1 output\")\n",
        "# # plt.xlabel('Freq [Hz]')\n",
        "# plt.ylabel('Ampl [db]')\n",
        "# plt.legend()\n",
        "# plt.tight_layout()\n",
        "# plt.show()\n",
        "# plt.subplot(223)\n",
        "# plt.plot((Y),'b',label='FFT for Normal [CA1]= Y')\n",
        "# plt.legend()\n",
        "# plt.xlim([0, 600])\n",
        "# plt.ylim([0, 0.1])\n",
        "# plt.xlabel('Freq [Hz]')\n",
        "# plt.ylabel('Ampl [db]')\n",
        "# plt.tight_layout()\n",
        "# # plt.subplots_adjust(left= 0.086, bottom= 0.156, right= 0.966, top= 0.937, wspace=200, hspace= 0.200 )\n",
        "\n",
        "# plt.subplot(222)\n",
        "# plt.plot((Xdende*((Ae*Ce*Ke)/(1-(Ae*Ce*Ke)))),'r',label='Epil X*(A.C.K/(1-A.C.K))')\n",
        "# plt.xlim([0, 600])\n",
        "# plt.ylim([-.0010, 0.1])\n",
        "# plt.title(\"Convolution of Epil Cells Vs CA1 output\")\n",
        "# # plt.xlabel('Freq [Hz]')\n",
        "# plt.ylabel('Ampl [db]')\n",
        "# plt.legend()\n",
        "# plt.tight_layout()\n",
        "# plt.show()\n",
        "# plt.subplot(224)\n",
        "# plt.plot((Ye),'r',label='FFT for Epil [CA1]= Ye')\n",
        "# plt.legend()\n",
        "# plt.xlim([0, 600])\n",
        "# plt.ylim([0, 0.1])\n",
        "# plt.xlabel('Freq [Hz]')\n",
        "# plt.ylabel('Ampl [db]')\n",
        "# plt.tight_layout()\n",
        "# # plt.subplots_adjust(left= 0.086, bottom= 0.156, right= 0.966, top= 0.937, wspace=200, hspace= 0.200 )\n",
        "\n",
        "# # ====================================================-----------------========= Simulated Inputs from Circuit\n",
        "# # plt.figure()\n",
        "# # plt.subplot(221)\n",
        "# # plt.plot(Y1,'b',label='X(sim)1*(A.C.K/(1-A.C.K))')\n",
        "# # plt.xlim([0, 1000])\n",
        "# # plt.ylim([-.0010, 0.2])\n",
        "# # # plt.xlabel('Freq [Hz]')\n",
        "# # plt.ylabel('Ampl [db]')\n",
        "# # plt.title(\"Circuit Simulated Input Vs CA1 outputs (Y1 & Y)\")\n",
        "# # plt.legend()\n",
        "# # plt.tight_layout()\n",
        "# # plt.show()\n",
        "\n",
        "# # plt.subplot(223)\n",
        "# # plt.plot((Y),'b',label='FFT for Normal [CA1]= Y')\n",
        "# # plt.legend()\n",
        "# # plt.xlim([0, 1000])\n",
        "# # plt.ylim([-.0010, 0.2])\n",
        "# # plt.xlabel('Freq [Hz]')\n",
        "# # plt.ylabel('Ampl [db]')\n",
        "# # plt.tight_layout()\n",
        "# # # plt.subplots_adjust(left= 0.086, bottom= 0.156, right= 0.966, top= 0.937, wspace=200, hspace= 0.200 )\n",
        "\n",
        "# # plt.subplot(222)\n",
        "# # # plt.plot((Xe*((Ae*Ce*Ke)/(1-(Ae*Ce*Ke)))),'r',label='Epil X(sim)*(A.C.K/(1-A.C.K))')\n",
        "# # plt.plot(Y2,'r',label='X(sim)2*(Ae.Ce.Ke/(1-Ae.Ce.Ke))')\n",
        "# # plt.xlim([0, 1000])\n",
        "# # plt.ylim([-.0010, 0.2])\n",
        "# # # plt.xlabel('Freq [Hz]')\n",
        "# # plt.ylabel('Ampl [db]')\n",
        "# # plt.title(\"Circuit Simulated Input Vs CA1 outputs (Y2 & Ye)\")\n",
        "# # plt.legend()\n",
        "# # plt.tight_layout()\n",
        "# # plt.show()\n",
        "\n",
        "# # plt.subplot(224)\n",
        "# # plt.plot((Ye),'r',label='FFT for Epil [CA1]= Ye')\n",
        "# # plt.legend()\n",
        "# # plt.xlim([0, 1000])\n",
        "# # plt.ylim([-.0010, 0.2])\n",
        "# # plt.xlabel('Freq [Hz]')\n",
        "# # plt.ylabel('Ampl [db]')\n",
        "# # plt.tight_layout()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# # ==================================================================== Differences between Y and cell vonvolution\n",
        "# # plt.figure(4)\n",
        "# # plt.plot(Y-(X*((A*C*K)/(1-(A*C*K)))),'r',label='Y - (A.C.K/(1+A.C.K))X')\n",
        "\n",
        "# # ==================================================================== Epileptic mode\n",
        "# # plt.figure(4)\n",
        "# # plt.subplot(211)\n",
        "# # Ge=ECpyrBuz0E_mag*CA3Am0E_mag;\n",
        "# # He=CA1Basket0E_mag;\n",
        "\n",
        "# # # plt.plot((ECpyrBuz0_mag*Gran0_mag*100),label='Entorhinal*Granuale')\n",
        "# # # plt.plot((ECpyrBuz0_mag*Gran0_mag*CA3Am1_mag*1000),'green',label='Entorhinal*Granuale*CA3')\n",
        "# # # plt.plot((ECpyrBuz1_mag*Gran0_mag*CA3Am0_mag*1000),'green',label='2nd Entorhinal*Granuale*CA3')\n",
        "# # # plt.plot((ECpyrBuz1_mag*Gran0_mag*CA3Am1_mag*1000),'green',label='3rd Entorhinal*Granuale*CA3')\n",
        "# # plt.plot(15*Ge/(1+(Ge*He)),'red',label='Ge/1+Ge.He')\n",
        "# # # plt.plot((G),'green',label='Entorhinal*CA3')\n",
        "# # # plt.plot((ECpyrBuz1_mag*Gran0_mag*CA3Am1_mag*100),label='FFT for Normal [A*B*C]')\n",
        "# # # plt.plot((CA1Poz0_mag),label='FFT for Normal [D]')\n",
        "# # # plt.plot(40*(Gran0_mag*CA3Am1_mag),label='FFT for Normal [A*B] with \"1s\" Stimulation')\n",
        "# # # plt.plot(Subic1E_mag,'--', label='Less gNa in All interneurons')\n",
        "# # plt.xlim([0, 600])\n",
        "# # plt.ylim([0, 0.1])\n",
        "# # plt.title(\"Convolution of Cells\")\n",
        "# # # plt.xlabel('Freq [Hz]')\n",
        "# # plt.ylabel('Ampl [db]')\n",
        "# # plt.legend()\n",
        "# # plt.tight_layout()\n",
        "# # plt.show()\n",
        "# # plt.subplot(212)\n",
        "# # plt.plot((CA1Poz0E_mag),label='FFT for Epileptic Final Output (CA1e)')\n",
        "# # plt.plot((CA1Basket0E_mag),label='FFT for Epileptic Basket cell (BasketCA1e)')\n",
        "# # plt.legend()\n",
        "# # plt.xlim([0, 600])\n",
        "# # plt.ylim([0, 0.1])\n",
        "# # plt.xlabel('Freq [Hz]')\n",
        "# # plt.ylabel('Ampl [db]')\n",
        "# # plt.tight_layout()\n",
        "# # plt.subplots_adjust(left= 0.086, bottom= 0.106, right= 0.966, top= 0.937, wspace=200, hspace= 0.100 )\n",
        "\n",
        "# # =============================================================== Printing Cross correlation of CA1poz0 and A*B*C blocks\n",
        "# # maxlags=50\n",
        "\n",
        "# # plt.xcorr((X*((A*C*K)/(1-(A*C*K)))), Y, usevlines=False, color='black', maxlags=maxlags, normed=True, lw=2,label='Cross-Correlation Coeff of  D & A*B*C with 0-lag' )\n",
        "# # coords=plt.gca().lines[0].get_xydata() # Get coords for most recently plotted lineplt.grid()\n",
        "# # plt.legend()\n",
        "# # print(\"Cross-correlation coefficient between  D & A*B*C with 0-lag is: (%% %.3f)\" %(coords[maxlags,1]*100))\n",
        "# # # =============================================================== Cross correlation of CA1poz0 and A*B*C blocks\n",
        "# # # plt.subplot(212)\n",
        "# # plt.figure(4)\n",
        "# # plt.xcorr((X*((A*C*K)/(1-(A*C*K)))), Y, usevlines=True, color='blue', maxlags=maxlags, normed=True, lw=2,label='Correlation Coefficient: (%% %.3f)' % (coords[maxlags,1]*100))\n",
        "# # plt.grid()\n",
        "# # plt.legend()\n",
        "# # plt.ylabel('Correlation Coeff')\n",
        "# # plt.title('Cross-correlation between  D & A*B*C with 0-lag')\n",
        "\n",
        "# # ========================================== PSD for Normal block with \"1s\" Stimulation\n",
        "# # plt.figure()\n",
        "# # plt.plot(psdCA1Poz0[mask], label='PSD for Normal [CA1] with \"1s\" Stimulation')\n",
        "# # plt.plot(psdCA1Poz0E[mask],'--', label='Less gNa in All interneurons')\n",
        "# # # plt.xlim([0, 405])\n",
        "# # # plt.ylim([-3, 85])\n",
        "# # plt.legend()\n",
        "# # plt.tight_layout()\n",
        "# # plt.grid()\n",
        "# # plt.show()\n",
        "\n",
        "# # psdSensinf\n",
        "# # psdSensinfE\n",
        "# # psdECpyrBuz0\n",
        "# # psdECpyrBuz0E\n",
        "# # psdCA3Am0\n",
        "# # psdCA3Am0E\n",
        "# # psdCA1Poz0\n",
        "# # psdCA1Poz0E\n",
        "# # psdCA1Basket0\n",
        "# # psdCA1Basket0E\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# # ==================================================================================== Just the Block FFT not CA1\n"
      ],
      "metadata": {
        "id": "F4ziR8ZoDD3g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LafisqeEDW0S"
      },
      "outputs": [],
      "source": [
        "\n",
        "# # h.load_file('JustThree.ses')\n",
        "# # h.load_file('May3aftCreateCA1Epil.hoc')\n",
        "# # h.load_file('stdrun.hoc')\n",
        "# # -------------------------------------------------------------------\n",
        "# x=[0, 0.1, 0.2, 0.5, 0.7, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59, 1.592, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "# vdend=[-64.97, -63.97, -62.98, -59.94, -57.87, -54.52, -53.32, -52.03, -50.60, -48.92, -46.61, -46.28, -45.93, -45.55, -45.10, -44.59, -43.95, -43.08, -41.68, -37.51, -33.42, 3.46, 11.22, 14.02, 15.69, 17.11,\n",
        "#        26.10, 32.90, 38.81, 44.97, 50.17, 56.06, 60.97, 66.23]\n",
        "# vsoma=[-64.97, -64.78, -64.6, -64.03, -63.62, -62.88, -62.60, -62.29, -61.91, -61.45, -60.74, -60.63, -60.50, -60.37, -60.22, -60.04, -59.83, -59.52, -59.05, -57.49, -53.2, 25.96, 29.4, 30.26, 30.94, 30.10,\n",
        "#        31.93, 31.30, 31.06, 32.64, 32.02, 33.10, 32.52, 31.32 ]\n",
        "# # -------------------------------------------------------------------\n",
        "# # h.topology()  # shows the topology\n",
        "\n",
        "# # axon = h.Section()\n",
        "# # axon.nseg = 75\n",
        "# # axon.diam = 100\n",
        "# # axon.L = 20000\n",
        "# # axon.insert('hh')\n",
        "\n",
        "# # #=================================== connect the cells\n",
        "\n",
        "# # PostSyn = h.ExpSyn(PostCell.dend[0](0.5))   # ExpSyn: g changes by a weight\n",
        "# # PreSyn = PreCell.axon(1)._ref_v    # g decays exponentially with time constant\n",
        "\n",
        "# # pc = h.ParallelContext()    # to connect to cells in different machines\n",
        "# # nc = h.NetCon(PreSyn, PostSyn)\n",
        "# # nc.delay = 1.0\n",
        "# # nc.weight = 0.001\n",
        "# # =========================================================\n",
        "# # h.hh.insert(h.allsec())\n",
        "\n",
        "# # soma = h.Section(name='soma')\n",
        "# # soma.L = soma.diam = 10 *um\n",
        "# # soma.insert(h.hh)\n",
        "\n",
        "# # ic = h.IClamp(soma(0.5))\n",
        "# # ic.delay = 1*ms\n",
        "# # ic.dur = 1*ms\n",
        "# # ic.amp = 1\n",
        "\n",
        "# # t = h.Vector().record(h._ref_t)\n",
        "# # v = h.Vector().record(soma(0.5)._ref_v)\n",
        "\n",
        "# # h.finitialize(-65*mV)\n",
        "# # h.continuerun(20*ms)\n",
        "\n",
        "# # ========================================== plot\n",
        "# # ps = h.PlotShape(False)\n",
        "# # ps.variable('v')\n",
        "# # ps.plot(plotly, cmap=cm.cool).show()\n",
        "# # =======================================\n",
        "# plt.plot(x,vdend, '--', color='black',label='EC Dendrite')\n",
        "# plt.plot(x,vsoma, '--', color='red',label='EC Soma')\n",
        "\n",
        "# plt.title('Action Potential Probability Distribution')\n",
        "# # plt.ylim(-90, 50)\n",
        "# plt.xlabel('Current Amplitute (nA)')\n",
        "# plt.ylabel('Action Potential prob')\n",
        "# plt.legend()\n",
        "# plt.show()\n",
        "\n",
        "# # begintemplate SenInf_NetStim\n",
        "# # public pp, connect2target, x, y, z, position, is_art\n",
        "# # objref pp\n",
        "# # proc init() {\n",
        "# #   pp = new NetStim()\n",
        "# #     pp.interval = 50\n",
        "# #     pp.number = 20\n",
        "# #     pp.start = 5\n",
        "# # }\n",
        "# # func is_art() { return 1 }\n",
        "# # obfunc connect2target() { localobj nc\n",
        "# #   nc = new NetCon(pp, $o1)\n",
        "# #   if (numarg() == 2) { $o2 = nc }\n",
        "# #   return nc\n",
        "# # }\n",
        "# # proc position(){x=$1  y=$2  z=$3}\n",
        "# # endtemplate SenInf_NetStim\n"
      ]
    }
  ]
}