Olimaol develop

notebooks/experiment_example.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true,
+      "authorship_tag": "ABX9TyMvW7lhxBXG2gi+iXDgU0PT",
+      "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/Olimaol/CompNeuroPy/blob/olimaol_develop/notebooks/experiment_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#Install CompNeuroPy and ANNarchy"
+      ],
+      "metadata": {
+        "id": "MQwRmyFdmUuD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install CompNeuroPy\n",
+        "!git clone https://github.com/ANNarchy/ANNarchy && cd ANNarchy && git checkout develop && pip install .\n",
+        "!rm -rf ANNarchy"
+      ],
+      "metadata": {
+        "collapsed": true,
+        "id": "VGC0ujzTm-z3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#Imports and setup ANNarchy timestep"
+      ],
+      "metadata": {
+        "id": "Mcoz2oq76flU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from IPython.display import Image, display\n",
+        "from CompNeuroPy import (\n",
+        "    CompNeuroExp,\n",
+        "    CompNeuroMonitors,\n",
+        "    CompNeuroModel,\n",
+        "    current_step,\n",
+        "    current_ramp,\n",
+        "    PlotRecordings,\n",
+        ")\n",
+        "from CompNeuroPy.full_models import HHmodelBischop\n",
+        "from ANNarchy import dt, setup, get_population\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "setup(dt=0.01)"
+      ],
+      "metadata": {
+        "id": "bMhApHy-6iJ3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Define the Experiment\n",
+        "A single run resets the model, sets the leakage potential of population 0 and runs a current ramp and current step."
+      ],
+      "metadata": {
+        "id": "pyf2gSTyN80I"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class MyExp(CompNeuroExp):\n",
+        "\n",
+        "    def run(self, model: CompNeuroModel, E_L: float):\n",
+        "        # PREPARE RUN\n",
+        "        self.reset()\n",
+        "        self.monitors.start()\n",
+        "        # SET E_L PARAMETER\n",
+        "        get_population(model.populations[0]).E_L = E_L\n",
+        "        # SIMULATION\n",
+        "        ret_current_ramp = current_ramp(pop=model.populations[0], a0=0, a1=100, dur=1000, n=50)\n",
+        "        self.reset(parameters=False)\n",
+        "        ret_current_step = current_step(pop=model.populations[0], t1=500, t2=500, a1=0, a2=50)\n",
+        "        # OPTIONAL DATA OF RUN\n",
+        "        self.data[\"population_name\"] = model.populations[0]\n",
+        "        self.data[\"time_step\"] = dt()\n",
+        "        self.data[\"current_arr\"] = np.concatenate([ret_current_ramp[\"current_arr\"], ret_current_step[\"current_arr\"]])\n",
+        "        # RETURN RESULTS\n",
+        "        return self.results()"
+      ],
+      "metadata": {
+        "id": "8coZOl3oOc1B"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Create/Compile Model\n",
+        "The model is a single population (consisting of 1 neuron) of a Hodgkin & Huxley neuron. The `HHmodelBischop` class is a child of the `CompNeuroModel` class with a predefined model creation function."
+      ],
+      "metadata": {
+        "id": "XCbqszREO0U2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = HHmodelBischop()\n",
+        "model.populations"
+      ],
+      "metadata": {
+        "id": "gHmfSnNlO2WP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Initialize the experiment\n",
+        "Recording the membrane potential of the models first population."
+      ],
+      "metadata": {
+        "id": "EQL2vn40PA_O"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "my_exp = MyExp(monitors=CompNeuroMonitors({model.populations[0]: [\"v\"]}))"
+      ],
+      "metadata": {
+        "id": "I6SphV4qPHTU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Set the \"reset-state\" for the experiment\n",
+        "Set the membrane potential of the model to -90 mV."
+      ],
+      "metadata": {
+        "id": "bJn_adfcPRz5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(f\"Compilation state v = {get_population(model.populations[0]).v}\")\n",
+        "get_population(model.populations[0]).v = -90.0\n",
+        "print(f\"Changed state v = {get_population(model.populations[0]).v}\")\n",
+        "my_exp.store_model_state(compartment_list=model.populations)"
+      ],
+      "metadata": {
+        "id": "wyE8p0SmPZ0c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Run the experiment twice with different leakage potentials"
+      ],
+      "metadata": {
+        "id": "QOuRAfGhPeyT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results_run1: CompNeuroExp._ResultsCl = my_exp.run(model=model, E_L=-68.0)\n",
+        "results_run2: CompNeuroExp._ResultsCl = my_exp.run(model=model, E_L=-90.0)"
+      ],
+      "metadata": {
+        "id": "PKqJScoJPf7q"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# PlotRecordings\n",
+        "This allows to easily get overview plots of the recordings of a single recording chunk."
+      ],
+      "metadata": {
+        "id": "Xg_52O9uPkgd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for chunk in [0,1]:\n",
+        "    PlotRecordings(\n",
+        "        figname=f\"example_experiment_chunk_{chunk}.png\",\n",
+        "        recordings=results_run1.recordings,\n",
+        "        recording_times=results_run1.recording_times,\n",
+        "        chunk=chunk,\n",
+        "        shape=(1, 1),\n",
+        "        plan={\n",
+        "            \"position\": [1],\n",
+        "            \"compartment\": [results_run1.data[\"population_name\"]],\n",
+        "            \"variable\": [\"v\"],\n",
+        "            \"format\": [\"line\"],\n",
+        "        },\n",
+        "    )"
+      ],
+      "metadata": {
+        "id": "ftBPfNZgPmok"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "display(Image(filename='example_experiment_chunk_0.png', height=700))"
+      ],
+      "metadata": {
+        "id": "o8Lvap-8QFIL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "display(Image(filename='example_experiment_chunk_1.png', height=700))"
+      ],
+      "metadata": {
+        "id": "p4-rWESeQJkm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Get Data and Time Arrays\n",
+        "Each experiment run created 2 recording chunks. They all start at time 0 (because of resetting the model, see above). The function combine_chunks() can be used to combine the chunks into a single recording time and value array."
+      ],
+      "metadata": {
+        "id": "OkEO6pyePzmT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "time_arr1, data_arr1 = results_run1.recording_times.combine_chunks(\n",
+        "    recordings=results_run1.recordings,\n",
+        "    recording_data_str=f\"{results_run1.data['population_name']};v\",\n",
+        "    mode=\"consecutive\",\n",
+        ")\n",
+        "time_arr2, data_arr2 = results_run2.recording_times.combine_chunks(\n",
+        "    recordings=results_run2.recordings,\n",
+        "    recording_data_str=f\"{results_run2.data['population_name']};v\",\n",
+        "    mode=\"consecutive\",\n",
+        ")\n",
+        "current_arr = results_run1.data[\"current_arr\"]"
+      ],
+      "metadata": {
+        "id": "SoiA-7cMP2bj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Plot Data of Both Runs"
+      ],
+      "metadata": {
+        "id": "mUso4pycQTLf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure()\n",
+        "plt.subplot(211)\n",
+        "plt.plot(time_arr1, data_arr1, label=\"E_L = -68.0\")\n",
+        "plt.plot(time_arr2, data_arr2, label=\"E_L = -90.0\")\n",
+        "plt.plot(\n",
+        "    [time_arr1[0], time_arr1[-1]], [-90, -90], ls=\"dotted\", label=\"initial v = -90.0\"\n",
+        ")\n",
+        "plt.legend()\n",
+        "plt.ylabel(\"Membrane potential [mV]\")\n",
+        "plt.subplot(212)\n",
+        "plt.plot(time_arr1, current_arr, \"k--\")\n",
+        "plt.ylabel(\"Input current\")\n",
+        "plt.xlabel(\"Time [ms]\")\n",
+        "plt.tight_layout()\n",
+        "plt.savefig(\"example_experiment_combined.png\")"
+      ],
+      "metadata": {
+        "id": "61hXVuUDQYJ2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "display(Image(filename='example_experiment_chunk_1.png', height=700))"
+      ],
+      "metadata": {
+        "id": "aMVQLAi-QcFG"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}