{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ND653384\\AppData\\Local\\Temp\\1\\ipykernel_20456\\4153960013.py:52: RuntimeWarning: overflow encountered in exp\n",
      "  sec.g_pas = 1e-5 * np.exp(distance / 100)                                                              # Set leak conductance (S/cm2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Soma mechanisms: ['pas']\n",
      "Section Apical 407: Distance from soma = 40.37 µm\n",
      "Section Apical 1967: Distance from soma = 92.14 µm\n",
      "Section Soma soma: Distance from soma = 52.81 µm\n",
      "Section Soma soma: Distance from soma = 33.56 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 58.40 µm\n",
      "Section Apical 410: Distance from soma = 44.34 µm\n",
      "Section Soma soma: Distance from soma = 31.07 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 7: Distance from soma = 40.97 µm\n",
      "Section Soma soma: Distance from soma = 23.85 µm\n",
      "Section Apical 17: Distance from soma = 55.11 µm\n",
      "Section Soma soma: Distance from soma = 20.08 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 6: Distance from soma = 39.48 µm\n",
      "Section Soma soma: Distance from soma = 51.28 µm\n",
      "Section Soma soma: Distance from soma = 48.26 µm\n",
      "Section Soma soma: Distance from soma = 29.89 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 20.08 µm\n",
      "Section Apical 1968: Distance from soma = 93.07 µm\n",
      "Section Soma soma: Distance from soma = 47.10 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1972: Distance from soma = 97.44 µm\n",
      "Section Apical 1972: Distance from soma = 97.44 µm\n",
      "Section Apical 1960: Distance from soma = 83.62 µm\n",
      "Section Apical 1968: Distance from soma = 93.07 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 21.28 µm\n",
      "Section Apical 1958: Distance from soma = 80.86 µm\n",
      "Section Apical 15: Distance from soma = 52.64 µm\n",
      "Section Apical 416: Distance from soma = 52.53 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 52.81 µm\n",
      "Section Soma soma: Distance from soma = 47.10 µm\n",
      "Section Soma soma: Distance from soma = 51.28 µm\n",
      "Section Apical 18: Distance from soma = 56.19 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 6: Distance from soma = 39.48 µm\n",
      "Section Soma soma: Distance from soma = 20.08 µm\n",
      "Section Soma soma: Distance from soma = 21.28 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 52.81 µm\n",
      "Section Apical 420: Distance from soma = 57.37 µm\n",
      "Section Apical 12: Distance from soma = 48.18 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1963: Distance from soma = 87.76 µm\n",
      "Section Apical 6: Distance from soma = 39.48 µm\n",
      "Section Apical 419: Distance from soma = 56.38 µm\n",
      "Section Apical 1973: Distance from soma = 98.76 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 31.07 µm\n",
      "Section Apical 1960: Distance from soma = 83.62 µm\n",
      "Section Apical 1972: Distance from soma = 97.44 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 43.02 µm\n",
      "Section Apical 1: Distance from soma = 34.04 µm\n",
      "Section Apical 431: Distance from soma = 66.97 µm\n",
      "Section Apical 1790: Distance from soma = 56.25 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1958: Distance from soma = 80.86 µm\n",
      "Section Apical 419: Distance from soma = 56.38 µm\n",
      "Section Soma soma: Distance from soma = 54.35 µm\n",
      "Section Apical 431: Distance from soma = 66.97 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 431: Distance from soma = 66.97 µm\n",
      "Section Apical 1798: Distance from soma = 61.66 µm\n",
      "Section Apical 1968: Distance from soma = 93.07 µm\n",
      "Section Soma soma: Distance from soma = 51.28 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1972: Distance from soma = 97.44 µm\n",
      "Section Apical 409: Distance from soma = 42.97 µm\n",
      "Section Apical 1962: Distance from soma = 86.39 µm\n",
      "Section Apical 1790: Distance from soma = 56.25 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1962: Distance from soma = 86.39 µm\n",
      "Section Apical 1798: Distance from soma = 61.66 µm\n",
      "Section Apical 1958: Distance from soma = 80.86 µm\n",
      "Section Apical 1960: Distance from soma = 83.62 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1958: Distance from soma = 80.86 µm\n",
      "Section Apical 1962: Distance from soma = 86.39 µm\n",
      "Section Soma soma: Distance from soma = 48.26 µm\n",
      "Section Apical 1968: Distance from soma = 93.07 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 20.08 µm\n",
      "Section Soma soma: Distance from soma = 33.56 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 431: Distance from soma = 66.97 µm\n",
      "Section Soma soma: Distance from soma = 58.40 µm\n",
      "Section Apical 1966: Distance from soma = 91.15 µm\n",
      "Section Soma soma: Distance from soma = 51.28 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 31.07 µm\n",
      "Section Apical 1967: Distance from soma = 92.14 µm\n",
      "Section Apical 1960: Distance from soma = 83.62 µm\n",
      "Section Apical 15: Distance from soma = 52.64 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1967: Distance from soma = 92.14 µm\n",
      "Section Soma soma: Distance from soma = 57.45 µm\n",
      "Section Soma soma: Distance from soma = 23.85 µm\n",
      "Section Soma soma: Distance from soma = 37.49 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 29.89 µm\n",
      "Section Apical 411: Distance from soma = 45.71 µm\n",
      "Section Apical 413: Distance from soma = 48.43 µm\n",
      "Section Soma soma: Distance from soma = 20.08 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1: Distance from soma = 34.04 µm\n",
      "Section Soma soma: Distance from soma = 54.35 µm\n",
      "Section Soma soma: Distance from soma = 43.02 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1955: Distance from soma = 76.72 µm\n",
      "Section Apical 1798: Distance from soma = 61.66 µm\n",
      "Section Apical 18: Distance from soma = 56.19 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 58.40 µm\n",
      "Section Soma soma: Distance from soma = 43.02 µm\n",
      "Section Apical 1960: Distance from soma = 83.62 µm\n",
      "Section Apical 7: Distance from soma = 40.97 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 15: Distance from soma = 52.64 µm\n",
      "Section Soma soma: Distance from soma = 51.28 µm\n",
      "Section Apical 1960: Distance from soma = 83.62 µm\n",
      "Section Apical 6: Distance from soma = 39.48 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 7: Distance from soma = 40.97 µm\n",
      "Section Soma soma: Distance from soma = 23.85 µm\n",
      "Section Soma soma: Distance from soma = 20.08 µm\n",
      "Section Apical 1958: Distance from soma = 80.86 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 23.20 µm\n",
      "Section Apical 1955: Distance from soma = 76.72 µm\n",
      "Section Apical 12: Distance from soma = 48.18 µm\n",
      "Section Soma soma: Distance from soma = 52.81 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 23.85 µm\n",
      "Section Apical 411: Distance from soma = 45.71 µm\n",
      "Section Soma soma: Distance from soma = 20.08 µm\n",
      "Section Apical 3: Distance from soma = 35.42 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1955: Distance from soma = 76.72 µm\n",
      "Section Soma soma: Distance from soma = 31.07 µm\n",
      "Section Apical 1972: Distance from soma = 97.44 µm\n",
      "Section Soma soma: Distance from soma = 23.20 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 58.40 µm\n",
      "Section Apical 15: Distance from soma = 52.64 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 57.45 µm\n",
      "Section Soma soma: Distance from soma = 31.07 µm\n",
      "Section Apical 410: Distance from soma = 44.34 µm\n",
      "Section Apical 1968: Distance from soma = 93.07 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1966: Distance from soma = 91.15 µm\n",
      "Section Soma soma: Distance from soma = 23.20 µm\n",
      "Section Apical 17: Distance from soma = 55.11 µm\n",
      "Section Apical 1798: Distance from soma = 61.66 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 31.07 µm\n",
      "Section Soma soma: Distance from soma = 29.89 µm\n",
      "Section Apical 17: Distance from soma = 55.11 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 23.85 µm\n",
      "Section Soma soma: Distance from soma = 57.45 µm\n",
      "Section Apical 1955: Distance from soma = 76.72 µm\n",
      "Section Soma soma: Distance from soma = 23.20 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 23.20 µm\n",
      "Section Soma soma: Distance from soma = 23.85 µm\n",
      "Section Soma soma: Distance from soma = 43.02 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1790: Distance from soma = 56.25 µm\n",
      "Section Soma soma: Distance from soma = 48.26 µm\n",
      "Section Soma soma: Distance from soma = 58.40 µm\n",
      "Section Soma soma: Distance from soma = 57.45 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 33.56 µm\n",
      "Section Soma soma: Distance from soma = 51.28 µm\n",
      "Section Soma soma: Distance from soma = 33.56 µm\n",
      "Section Soma soma: Distance from soma = 52.81 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 20.08 µm\n",
      "Section Apical 431: Distance from soma = 66.97 µm\n",
      "Section Apical 3: Distance from soma = 35.42 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 52.81 µm\n",
      "Section Apical 1968: Distance from soma = 93.07 µm\n",
      "Section Apical 1790: Distance from soma = 56.25 µm\n",
      "Section Soma soma: Distance from soma = 31.07 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 6: Distance from soma = 39.48 µm\n",
      "Section Soma soma: Distance from soma = 57.45 µm\n",
      "Section Apical 416: Distance from soma = 52.53 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1968: Distance from soma = 93.07 µm\n",
      "Section Soma soma: Distance from soma = 14.02 µm\n",
      "Section Apical 6: Distance from soma = 39.48 µm\n",
      "Section Soma soma: Distance from soma = 48.26 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1960: Distance from soma = 83.62 µm\n",
      "Section Apical 431: Distance from soma = 66.97 µm\n",
      "Section Apical 1960: Distance from soma = 83.62 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1958: Distance from soma = 80.86 µm\n",
      "Section Soma soma: Distance from soma = 31.07 µm\n",
      "Section Apical 1: Distance from soma = 34.04 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 52.81 µm\n",
      "Section Soma soma: Distance from soma = 54.35 µm\n",
      "Section Apical 18: Distance from soma = 56.19 µm\n",
      "Section Soma soma: Distance from soma = 48.26 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 411: Distance from soma = 45.71 µm\n",
      "Section Apical 1973: Distance from soma = 98.76 µm\n",
      "Section Soma soma: Distance from soma = 33.56 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 413: Distance from soma = 48.43 µm\n",
      "Section Apical 17: Distance from soma = 55.11 µm\n",
      "Section Soma soma: Distance from soma = 58.40 µm\n",
      "Section Soma soma: Distance from soma = 48.26 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Apical 1963: Distance from soma = 87.76 µm\n",
      "Section Soma soma: Distance from soma = 23.85 µm\n",
      "Section Soma soma: Distance from soma = 51.28 µm\n",
      "Section Apical 6: Distance from soma = 39.48 µm\n",
      "Soma mechanisms: ['pas']\n",
      "Section Soma soma: Distance from soma = 54.35 µm\n",
      "Section Soma soma: Distance from soma = 57.45 µm\n",
      "Section Soma soma: Distance from soma = 21.28 µm\n",
      "Section Apical 15: Distance from soma = 52.64 µm\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Created by Namit Dwivedi (Scimemi Lab) on 09/19/2024\n",
    "# Please contact Namit (namitdwivedi08@gmail.com) or Dr Annalisa Scimemi (scimemia@gmail.com) for questions\n",
    "# The goal of this code is to identify the number of active inhibitory synaptic inputs in ctrl that allow us to reproduce the amplitude of oIPSCs from PV-INs recorded experimentally\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from neuron import h\n",
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "# Save the average soma current to a CSV file\n",
    "output_file_path = 'C:/Users/ND653384/Documents/Namit/PV/PV_Ctrl107.csv'                                           # Change as needed\n",
    "\n",
    "# Function to calculate distance from soma\n",
    "def calculate_distance(section):\n",
    "    h.distance(sec=h.soma[0])  # Set soma as reference\n",
    "    return h.distance(0.5, sec=section)\n",
    "\n",
    "# Function to run a single simulation and return the somatic current\n",
    "def run_simulation():\n",
    "    # Load morphology and hoc files\n",
    "    h.load_file('C:/Users/ND653384/Documents/Namit/PV/nrnez_2024_09_12_16_49_35/run_1/CA1PC.nrn')               # Change as needed\n",
    "\n",
    "    # Define file path for the NRN-EZ location file\n",
    "    syn_loc_file = 'C:/Users/ND653384/Documents/Namit/PV/nrnez_2024_09_12_16_49_35/run_1/Inhibitory/syn_loc.dat'# Change as needed\n",
    "\n",
    "    # Read synapse location data\n",
    "    with open(syn_loc_file, 'r') as file:\n",
    "        synapse_data = [line.strip().split() for line in file.readlines()]\n",
    "\n",
    "    selected_synapses = random.sample(synapse_data, 4)\n",
    "\n",
    "    soma = h.soma[0]\n",
    "    axon = h.axon\n",
    "\n",
    "    # Create empty lists \n",
    "    recorders = []                                                                                              # Store the voltage recordings from the selected synapses\n",
    "    Rec_id = []                                                                                                 # Store the identifiers of the sections (e.g., soma, apical) where the recorders are placed\n",
    "    sections = []                                                                                               # Store the actual sections (compartments) of the selected synapses\n",
    "    distances = []                                                                                              # Store the calculated distances of the synapses from the soma\n",
    "    h.dt= 0.025\n",
    "\n",
    "    # Set axial resistance for all sections \n",
    "    for sec in h.allsec():\n",
    "        sec.Ra = 150                                                                                            # ohm.cm\n",
    "\n",
    "    # Introduce leak channels to all sections \n",
    "    for sec in h.allsec():\n",
    "        sec.insert('pas')                                                                                      # Insert leak channel\n",
    "        distance = calculate_distance(sec)\n",
    "        sec.g_pas = 1e-5 * np.exp(distance / 100)                                                              # Set leak conductance (S/cm2)\n",
    "        sec.e_pas = 0                                                                                          # Set leak reversal potential\n",
    "\n",
    "    soma_mechs = [mech.name() for seg in soma for mech in seg]\n",
    "    print(f\"Soma mechanisms: {soma_mechs}\")\n",
    "    \n",
    "    # Iterate through the selected synapse data\n",
    "    for section_type, section_number in selected_synapses:\n",
    "        section_type = int(section_type)\n",
    "        section_number = int(section_number)\n",
    "\n",
    "        if section_type == 0:\n",
    "            section = h.soma[section_number]\n",
    "        elif section_type == 3:\n",
    "            section = h.apical[section_number]\n",
    "        else:\n",
    "            continue\n",
    "\n",
    "        # Calculate and store distance\n",
    "        distance = calculate_distance(section)\n",
    "        if distance <= 100:                                                                                    # Only include sections at <50 um in radial distance from the soma and <100 um in geodesic distance from the soma\n",
    "            distances.append(distance)\n",
    "            \n",
    "            # Record membrane potential\n",
    "            rec = h.Vector()\n",
    "            rec.record(section(0.5)._ref_v)\n",
    "            recorders.append(rec)\n",
    "            sections.append(section)\n",
    "            Rec_id.append(section_number if section_type == 3 else \"soma\")\n",
    "\n",
    "    # Print distances for each selected section\n",
    "    for idx, distance in enumerate(distances):\n",
    "        section_type = \"Soma\" if Rec_id[idx] == \"soma\" else \"Apical\"\n",
    "        print(f\"Section {section_type} {Rec_id[idx]}: Distance from soma = {distance:.2f} µm\")\n",
    "    \n",
    "    # Access the soma\n",
    "    soma_rec = h.Vector()\n",
    "    soma_rec.record(h.soma[0](0.5)._ref_v)\n",
    "\n",
    "    # Create SEClamp\n",
    "    seclamp1 = h.SEClamp(0.5, sec=soma)\n",
    "    seclamp1.dur1 = 1e9\n",
    "    seclamp1.amp1 = 0\n",
    "    seclamp1.rs = 12\n",
    "\n",
    "    t = h.Vector().record(h._ref_t)\n",
    "\n",
    "    # Run simulation\n",
    "    h.load_file(\"stdrun.hoc\")\n",
    "    h.finitialize(0)\n",
    "    h.continuerun(300)\n",
    "\n",
    "    # Parameters for the inhibitory synapses\n",
    "    syn_tau1 =3                                                                                                  # Rise time constant in ms\n",
    "    syn_tau2 = 23                                                                                                # Decay time constant in ms\n",
    "    syn_e = -70                                                                                                  # GABA reversal potential\n",
    "\n",
    "    # List to hold synapses and netstims\n",
    "    synapses = []                                                                                                # Store the inhibitory synapses created for each selected section\n",
    "    netstims = []                                                                                                # Store the NetStims used to trigger the synaptic events\n",
    "    netcons = []                                                                                                 # Store the NetCons (connections between NetStim and synapse)\n",
    "    syn_voltage = []                                                                                             # Store the voltage recordings of the synapse for each section\n",
    "    syn_currents = []                                                                                            # Store the synaptic current values recorded for each synapse\n",
    "\n",
    "    # Release probability\n",
    "    release_probability = 0.1\n",
    "\n",
    "    for section in sections:\n",
    "        syn = h.Exp2Syn(0.5, sec=section)\n",
    "        syn.tau1 = syn_tau1\n",
    "        syn.tau2 = syn_tau2\n",
    "        syn.e = syn_e\n",
    "\n",
    "        # Create a NetStim to activate the synapse\n",
    "        stim = h.NetStim()\n",
    "        stim.number = 1                                                                                          # Number of spikes\n",
    "        stim.start = 100                                                                                         # Start time in ms\n",
    "        stim.interval = 0                                                                                        # Interval between spikes in ms\n",
    "\n",
    "        # Connect the NetStim to the synapse\n",
    "        nc = h.NetCon(stim, syn)\n",
    "        nc.weight[0] = 0.485e-3                                                                                  # Synaptic weight in uS\n",
    "\n",
    "        # Record the synaptic current\n",
    "        syn_current = h.Vector()\n",
    "        syn_current.record(syn._ref_i)\n",
    "        \n",
    "        synapses.append(syn)\n",
    "        netstims.append(stim)\n",
    "        netcons.append(nc)\n",
    "        syn_currents.append(syn_current)\n",
    "        syn_voltage.append(rec)\n",
    "\n",
    "    h.finitialize(0)\n",
    "    h.continuerun(300)\n",
    "\n",
    "    # Record the current at soma\n",
    "    soma_current = h.Vector()\n",
    "    soma_current.record(seclamp1._ref_i)\n",
    "\n",
    "    h.finitialize(0)\n",
    "    h.continuerun(300)\n",
    "\n",
    "    return soma_current, t\n",
    "\n",
    "# Run simulation 100 times and collect results\n",
    "soma_currents = []\n",
    "times = None\n",
    "\n",
    "for i in range(50):\n",
    "    soma_current, t = run_simulation()\n",
    "    soma_currents.append(np.array(soma_current))\n",
    "    if times is None:\n",
    "        times = np.array(t)\n",
    "\n",
    "# Calculate average soma current\n",
    "average_soma_current = np.mean(soma_currents, axis=0) * 1000                                                      # Convert to pA\n",
    "\n",
    "# Calculate the rise time of the soma current recorded wave\n",
    "peak_index = np.argmax(average_soma_current)\n",
    "peak_value = average_soma_current[peak_index]\n",
    "\n",
    "# Find 20% and 80% of the peak value\n",
    "threshold_20 = 0.2 * peak_value\n",
    "threshold_80 = 0.8 * peak_value\n",
    "\n",
    "# Find indices where the current crosses these thresholds\n",
    "crossing_20 = np.where(average_soma_current[:peak_index] >= threshold_20)[0][0]\n",
    "crossing_80 = np.where(average_soma_current[:peak_index] >= threshold_80)[0][0]\n",
    "\n",
    "rise_time_20_80 = times[crossing_80] - times[crossing_20]\n",
    "\n",
    "# Calculate half-decay time (t50)\n",
    "half_value = 0.5 * peak_value\n",
    "crossing_50 = np.where(average_soma_current[peak_index:] <= half_value)[0][0]\n",
    "half_decay_time = times[peak_index + crossing_50] - times[peak_index]\n",
    "\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'Time (ms)': times,\n",
    "    'Average Somatic Current (pA)': average_soma_current\n",
    "})\n",
    "df.to_csv(output_file_path, index=False)\n",
    "\n",
    "# Plot average somatic current\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(times, average_soma_current, label='Average Somatic Current', color='red')\n",
    "plt.xlabel('Time (ms)')\n",
    "plt.ylabel('Current (pA)')\n",
    "plt.title('Average Somatic Inhibitory Postsynaptic Current')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67.2212293840857\n",
      "4.125000000000938\n",
      "20.54999999999994\n"
     ]
    }
   ],
   "source": [
    "print(max(average_soma_current))\n",
    "print(rise_time_20_80)\n",
    "print(half_decay_time)\n",
    "\n",
    "#Release probability= No.of active synapses/Total SST synapses\n",
    "# Release_probability= 6/60= 0.1\n",
    "\n",
    "#Values for oIPSC recorded experimentally at soma (Figure 5)\n",
    "#Amplitude:              64.5                              \n",
    "#Rise_time:              3.5                               \n",
    "#Half_decay_time:        19.6                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}