Kv13_parameter_optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c0aebb47-7e1a-44b2-8735-1e2ddb60f150",
   "metadata": {},
   "source": [
    "# Ion channel simulation #"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3414b4b-128e-4fab-bf53-cb9c62071bcc",
   "metadata": {},
   "source": [
    "#### Importing and preparing data ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "930d060d-7b66-4a96-88c6-5e3e9ce6b24a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyswarms as ps\n",
    "from pyswarms.backend.topology import Star\n",
    "from scipy.integrate import odeint\n",
    "import matplotlib.pyplot as plt\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f297d6-f5dd-4826-9a7f-dfbf44777d22",
   "metadata": {},
   "source": [
    "### 1. Defining the model ### "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "804de525-6bcd-4089-834b-766f05992b35",
   "metadata": {},
   "source": [
    "#### Define time and voltage protocol ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e8822262-d5cc-45e4-969e-1af2e04140ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define basic parameters\n",
    "t = np.arange(0,523,1)\n",
    "v_hold = -60\n",
    "v_test = np.arange(-80,70,10)\n",
    "rev_potential = -90.783\n",
    "\n",
    "# Function to obtain each individual voltage trace\n",
    "def v_trace(t, v_hold, v_step):\n",
    "    v_trace = []\n",
    "    for ms in t:\n",
    "        if ms < 25:\n",
    "            v_trace.append(v_hold)\n",
    "        elif ms < 275:\n",
    "            v_trace.append(v_step)\n",
    "        else:\n",
    "            v_trace.append(-40)\n",
    "    return v_trace\n",
    "\n",
    "# Loop to get all voltage traces together in a single protocol\n",
    "v_protocol = []\n",
    "for v_step in v_test:\n",
    "    v_step_trace = v_trace(t, v_hold, v_step)\n",
    "    v_protocol.append(v_step_trace)\n",
    "v_protocol = np.array(v_protocol)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5cd650b-5b84-4554-bbc4-a60160ffb703",
   "metadata": {},
   "source": [
    "#### Define model functions ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "776e28fa-df46-4960-ae42-fb41ea7bd614",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate voltage-dependent rate constants\n",
    "def v_dep_rate_constants(v, rates):\n",
    "    #Explain how to unravel the given rates\n",
    "    k0_RA, k0_AR, k0_CO, k0_OC, k0_OI, k0_IO, z_RA, z_AR, z_CO, z_OC = rates\n",
    "    \n",
    "    # Define biophysical constants\n",
    "    F = 9.648  # C / mol\n",
    "    R = 8.314  # J / mol·K\n",
    "    T = 293.15 # Degrees Kelvin\n",
    "    \n",
    "    # Define voltage-dependent parameters\n",
    "    # The 1000 is to convert F/RT from V to mV to match the input voltage\n",
    "    k_RA = k0_RA * np.exp((v / 1000) * z_RA * (F / (R * T))) \n",
    "    k_AR = k0_AR * np.exp((v / 1000) * z_AR * (F / (R * T)))\n",
    "    k_CO = k0_CO * np.exp((v / 1000) * z_CO * (F / (R * T)))\n",
    "    k_OC = k0_OC * np.exp((v / 1000) * z_OC * (F / (R * T)))\n",
    "    k_OI = k0_OI \n",
    "    k_IO = k0_IO \n",
    "\n",
    "    k = [k_RA, k_AR, k_CO, k_OC, k_OI, k_IO]\n",
    "    \n",
    "    return k\n",
    "\n",
    "# Define the transition matrix\n",
    "def transition_matrix(k):\n",
    "    T = np.array([\n",
    "        [-4*k[0], k[1], 0, 0, 0, 0, 0],   #c0\n",
    "        [4*k[0], -k[1] -3*k[0], 2*k[1], 0, 0, 0, 0],   #c1\n",
    "        [0, 3*k[0], -2*k[1] -2*k[0], 3*k[1], 0, 0, 0],   #c2\n",
    "        [0, 0, 2*k[0], -3*k[1] -k[0], 4*k[1], 0, 0],   #c3\n",
    "        [0, 0, 0, k[0], -4*k[1] -k[2], k[3], 0],   #c4\n",
    "        [0, 0, 0, 0, k[2], -k[3] -k[4], k[5]],   #o5\n",
    "        [0, 0, 0, 0, 0, k[4], -k[5]],   #i6\n",
    "        ])\n",
    "    return T\n",
    "\n",
    "# Define how to calculate the open probability\n",
    "def open_prob_model(P0, t, voltage, rates):\n",
    "    #Unpack the state variables\n",
    "    states = P0\n",
    "    # Get the current voltage-dependent constants\n",
    "    index = np.clip(int(t), 0, len(voltage) - 1)\n",
    "    v = voltage[index]\n",
    "    k = v_dep_rate_constants(v, rates)  # Example: get the constant based on time index\n",
    "    markov_matrix = transition_matrix(k)\n",
    "    \n",
    "    # Define the rate of change based on the Markov process\n",
    "    d_states = np.dot(markov_matrix, states)\n",
    "\n",
    "    return d_states\n",
    "    \n",
    "# Define how to calculate current\n",
    "def current_model(P0, t, voltage, rates, n):\n",
    "    g = (13 / 1000) # pA per mV\n",
    "    i_protocol = []\n",
    "    for v_step in voltage:\n",
    "        v_step_trace = v_trace(t, v_hold, v_step)\n",
    "        results, info = odeint(open_prob_model, P0, t, args=(v_step_trace, rates), atol=1e-5, rtol=1e-5, mxstep=15000, full_output=True)\n",
    "        open_prob = results[:,5]\n",
    "        current = open_prob * n * g * (np.array(v_step_trace) - rev_potential)\n",
    "        i_protocol.append(current)\n",
    "    current_df = pd.DataFrame(i_protocol).transpose().set_index(t)\n",
    "    current_df.columns = v_test\n",
    "    return current_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cba5f3e3-e948-4a81-8110-114cba1e6c7a",
   "metadata": {},
   "source": [
    "#### Solve odeint ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "88129ef8-5e46-4782-aec1-86527026e95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial values\n",
    "P0 = [1, 0, 0, 0, 0, 0, 0]\n",
    "rates = (0.448, 0.043, 280.035, 1.648, 0.003, 0.00008, 9176.06, -14412.199, 1443.847, -248.558)\n",
    "n = 1000\n",
    "\n",
    "# Execute the model\n",
    "current_df = current_model(P0, t, v_test, rates, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4cfc3b4-0230-493c-a735-52f9bb685634",
   "metadata": {},
   "source": [
    "#### Plot the results ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c8da95fb-1188-4c5c-a8c4-8ba51e05b28e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "for column in current_df.columns:\n",
    "    ax.plot(t, current_df[column], label=f\"V = {column} mV\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0e5de97-0087-4503-b037-423aa4287794",
   "metadata": {},
   "source": [
    "### 2. Optimization algorithms ###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f61e21-6677-455d-86c7-16aacbf5befc",
   "metadata": {},
   "source": [
    "Open experimental file to compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "928a7740-c9b2-47a5-be77-b41fcfb6eec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Open experimental file\n",
    "exp = pd.read_csv('Kv13_IV.csv', sep=';')\n",
    "experimental_current = np.array(exp.iloc[::2, :].set_index(t).values, dtype=float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5af772f0-6352-4b6e-bbe8-6a459d675409",
   "metadata": {},
   "source": [
    "#### Define objective function to minimize ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "81182777-173d-4161-b278-94820bc85808",
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective_function(parameters, voltage, experimental_current):\n",
    "    parameters = np.array(parameters)\n",
    "    errors = []  # Store the error for each particle\n",
    "    if parameters.ndim == 1:  # Single particle\n",
    "        parameters = np.expand_dims(parameters, axis=0)  # Convert to 2D for consistent handling\n",
    "    for parameters_set in parameters:  # Iterate over each particle's parameters\n",
    "        rates = parameters_set[:-1]  # All except the last one are rates\n",
    "        rates = rates.flatten()\n",
    "        n = parameters_set[-1]       # The last parameter is the number of channels\n",
    "    \n",
    "        # Compute model current for this set of parameters\n",
    "        model_current = current_model(P0, t, voltage, rates, n)\n",
    "        model_current = model_current.to_numpy()\n",
    "\n",
    "        # Compute the error (sum of squared differences)\n",
    "        error = np.sum((experimental_current - model_current) ** 2)\n",
    "        errors.append(error)  # Append the error for this particle\n",
    "    return np.array(errors)  # Return the array of errors for all particles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c6a17a-d3fe-4684-901b-3ce012140fbc",
   "metadata": {},
   "source": [
    "#### Golden Section Search (GSS) ####\n",
    "A technique for finding the extremum (minimum or maximum) of a unimodal function by successively narrowing the range of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c0250b00-2f91-44e7-8e52-3f21b8ccb564",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definition of the GSS function\n",
    "def golden_section_search(f, a, b, tol=1e-5):\n",
    "    invphi = (np.sqrt(5) - 1) / 2  # 1/phi\n",
    "    invphi2 = (3 - np.sqrt(5)) / 2  # 1/phi^2\n",
    "    h = b - a\n",
    "    if h <= tol:\n",
    "        return (a + b) / 2\n",
    "\n",
    "    n = int(np.ceil(np.log(tol / h) / np.log(invphi)))\n",
    "\n",
    "    c = a + invphi2 * h\n",
    "    d = a + invphi * h\n",
    "    yc = f(c)\n",
    "    yd = f(d)\n",
    "\n",
    "    for _ in range(n - 1):\n",
    "        if yc < yd:\n",
    "            b = d\n",
    "            d = c\n",
    "            yd = yc\n",
    "            h = invphi * h\n",
    "            c = a + invphi2 * h\n",
    "            yc = f(c)\n",
    "        else:\n",
    "            a = c\n",
    "            c = d\n",
    "            yc = yd\n",
    "            h = invphi * h\n",
    "            d = a + invphi * h\n",
    "            yd = f(d)\n",
    "\n",
    "    if yc < yd:\n",
    "        return (a + d) / 2\n",
    "    else:\n",
    "        return (c + b) / 2\n",
    "\n",
    "# GSS implementation\n",
    "def optimize_parameters_gss(best_params, lb, ub, voltage, experimental_current, tol=1e-5):\n",
    "    refined_params = best_params.copy()\n",
    "\n",
    "    for i in range(len(best_params)):\n",
    "        def f(x):\n",
    "            params = refined_params.copy()\n",
    "            params[i] = x\n",
    "            return objective_function(params, voltage, experimental_current)\n",
    "\n",
    "        # Use GSS to optimize the i-th parameter\n",
    "        refined_params[i] = golden_section_search(f, lb[i], ub[i], tol)\n",
    "\n",
    "    return refined_params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a198d26-7040-4698-9201-05439bae5fba",
   "metadata": {},
   "source": [
    "#### Particle Swarm Optimization (PSO) ####\n",
    "A global optimization algorithm inspired by the social behavior of birds flocking or fish schooling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "21b3a732-075a-4fec-823d-ed5e84f5e8bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define iterative PSO + GSS optimization\n",
    "def pso_gss_optimization(initial_positions, lb, ub, iterations, generations, voltage, experimental_current):\n",
    "    bounds = (lb, ub)\n",
    "    options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n",
    "    error_history = []\n",
    "\n",
    "    # Initialize optimizer\n",
    "    optimizer = ps.single.general_optimizer.GeneralOptimizerPSO(n_particles=n_particles, dimensions=len(lb), options=options, topology=Star(), bounds=bounds, init_pos=initial_positions)\n",
    "    \n",
    "    for gen in range(generations):\n",
    "        print(f\"Generation {gen + 1}\")\n",
    "\n",
    "        # Perform PSO\n",
    "        cost, best_pos = optimizer.optimize(lambda parameters: objective_function(parameters, voltage, experimental_current), iters=iterations)\n",
    "        error = objective_function(best_pos, voltage, experimental_current)[0]  # Best position is a single particle\n",
    "        error_history.append(error)\n",
    "        \n",
    "        # Refine with GSS\n",
    "        refined_params = optimize_parameters_gss(best_pos, lb, ub, voltage, experimental_current)\n",
    "\n",
    "        # Update the initial positions with the refined parameters\n",
    "        optimizer.swarm.position = np.random.uniform(low=lb, high=ub, size=(n_particles, len(lb)))\n",
    "        optimizer.swarm.position[:, :] = refined_params  # Set all particles to refined parameters\n",
    "\n",
    "        \n",
    "    # Return the final optimized parameters and error\n",
    "    \n",
    "    final_cost = objective_function([refined_params], voltage, experimental_current)[0]\n",
    "    return refined_params, final_cost, error_history"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ed0f0dc-71ac-4ddb-8ae9-4b55bf66caef",
   "metadata": {},
   "source": [
    "#### Execute the optimization protocol ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "138b5169-8376-4927-a95d-133b77e4d011",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-11 09:40:22,345 - pyswarms.single.general_optimizer - INFO - Optimize for 1 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pyswarms.single.general_optimizer: 100%|██████████|1/1, best_cost=4.45e+9\n",
      "2024-09-11 09:40:26,437 - pyswarms.single.general_optimizer - INFO - Optimization finished | best cost: 4452331935.779963, best pos: [ 4.67974948e-01  4.19631477e-02  2.80039023e+02  1.75351004e+00\n",
      "  2.87956327e-03  8.42395138e-05  8.54458445e+03 -1.63642434e+04\n",
      "  1.27240349e+03 -2.57180040e+02  5.17152875e+03]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[59], line 13\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;66;03m# Run PSO + GSS optimization\u001b[39;00m\n\u001b[0;32m     12\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m---> 13\u001b[0m final_params, final_error, error_history \u001b[38;5;241m=\u001b[39m pso_gss_optimization(initial_positions, lb, ub, iterations, generations, voltage\u001b[38;5;241m=\u001b[39mv_test, experimental_current\u001b[38;5;241m=\u001b[39mexperimental_current)\n\u001b[0;32m     14\u001b[0m end_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m     16\u001b[0m \u001b[38;5;66;03m#Output\u001b[39;00m\n",
      "Cell \u001b[1;32mIn[54], line 19\u001b[0m, in \u001b[0;36mpso_gss_optimization\u001b[1;34m(initial_positions, lb, ub, iterations, generations, voltage, experimental_current)\u001b[0m\n\u001b[0;32m     16\u001b[0m error_history\u001b[38;5;241m.\u001b[39mappend(error)\n\u001b[0;32m     18\u001b[0m \u001b[38;5;66;03m# Refine with GSS\u001b[39;00m\n\u001b[1;32m---> 19\u001b[0m refined_params \u001b[38;5;241m=\u001b[39m optimize_parameters_gss(best_pos, lb, ub, voltage, experimental_current)\n\u001b[0;32m     21\u001b[0m \u001b[38;5;66;03m# Update the initial positions with the refined parameters\u001b[39;00m\n\u001b[0;32m     22\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mswarm\u001b[38;5;241m.\u001b[39mposition \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39muniform(low\u001b[38;5;241m=\u001b[39mlb, high\u001b[38;5;241m=\u001b[39mub, size\u001b[38;5;241m=\u001b[39m(n_particles, \u001b[38;5;28mlen\u001b[39m(lb)))\n",
      "Cell \u001b[1;32mIn[51], line 48\u001b[0m, in \u001b[0;36moptimize_parameters_gss\u001b[1;34m(best_params, lb, ub, voltage, experimental_current, tol)\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m objective_function(params, voltage, experimental_current)\n\u001b[0;32m     47\u001b[0m     \u001b[38;5;66;03m# Use GSS to optimize the i-th parameter\u001b[39;00m\n\u001b[1;32m---> 48\u001b[0m     refined_params[i] \u001b[38;5;241m=\u001b[39m golden_section_search(f, lb[i], ub[i], tol)\n\u001b[0;32m     50\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m refined_params\n",
      "Cell \u001b[1;32mIn[51], line 30\u001b[0m, in \u001b[0;36mgolden_section_search\u001b[1;34m(f, a, b, tol)\u001b[0m\n\u001b[0;32m     28\u001b[0m         h \u001b[38;5;241m=\u001b[39m invphi \u001b[38;5;241m*\u001b[39m h\n\u001b[0;32m     29\u001b[0m         d \u001b[38;5;241m=\u001b[39m a \u001b[38;5;241m+\u001b[39m invphi \u001b[38;5;241m*\u001b[39m h\n\u001b[1;32m---> 30\u001b[0m         yd \u001b[38;5;241m=\u001b[39m f(d)\n\u001b[0;32m     32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m yc \u001b[38;5;241m<\u001b[39m yd:\n\u001b[0;32m     33\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (a \u001b[38;5;241m+\u001b[39m d) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n",
      "Cell \u001b[1;32mIn[51], line 45\u001b[0m, in \u001b[0;36moptimize_parameters_gss.<locals>.f\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m     43\u001b[0m params \u001b[38;5;241m=\u001b[39m refined_params\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m     44\u001b[0m params[i] \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m---> 45\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m objective_function(params, voltage, experimental_current)\n",
      "Cell \u001b[1;32mIn[48], line 12\u001b[0m, in \u001b[0;36mobjective_function\u001b[1;34m(parameters, voltage, experimental_current)\u001b[0m\n\u001b[0;32m      9\u001b[0m n \u001b[38;5;241m=\u001b[39m parameters_set[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]       \u001b[38;5;66;03m# The last parameter is the number of channels\u001b[39;00m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;66;03m# Compute model current for this set of parameters\u001b[39;00m\n\u001b[1;32m---> 12\u001b[0m model_current \u001b[38;5;241m=\u001b[39m current_model(P0, t, voltage, rates, n)\n\u001b[0;32m     13\u001b[0m model_current \u001b[38;5;241m=\u001b[39m model_current\u001b[38;5;241m.\u001b[39mto_numpy()\n\u001b[0;32m     15\u001b[0m \u001b[38;5;66;03m# Compute the error (sum of squared differences)\u001b[39;00m\n",
      "Cell \u001b[1;32mIn[37], line 58\u001b[0m, in \u001b[0;36mcurrent_model\u001b[1;34m(P0, t, voltage, rates, n)\u001b[0m\n\u001b[0;32m     56\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m v_step \u001b[38;5;129;01min\u001b[39;00m voltage:\n\u001b[0;32m     57\u001b[0m     v_step_trace \u001b[38;5;241m=\u001b[39m v_trace(t, v_hold, v_step)\n\u001b[1;32m---> 58\u001b[0m     results, info \u001b[38;5;241m=\u001b[39m odeint(open_prob_model, P0, t, args\u001b[38;5;241m=\u001b[39m(v_step_trace, rates), atol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-5\u001b[39m, rtol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-5\u001b[39m, mxstep\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m15000\u001b[39m, full_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m     59\u001b[0m     open_prob \u001b[38;5;241m=\u001b[39m results[:,\u001b[38;5;241m5\u001b[39m]\n\u001b[0;32m     60\u001b[0m     current \u001b[38;5;241m=\u001b[39m open_prob \u001b[38;5;241m*\u001b[39m n \u001b[38;5;241m*\u001b[39m g \u001b[38;5;241m*\u001b[39m (np\u001b[38;5;241m.\u001b[39marray(v_step_trace) \u001b[38;5;241m-\u001b[39m rev_potential)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\scipy\\integrate\\_odepack_py.py:243\u001b[0m, in \u001b[0;36modeint\u001b[1;34m(func, y0, t, args, Dfun, col_deriv, full_output, ml, mu, rtol, atol, tcrit, h0, hmax, hmin, ixpr, mxstep, mxhnil, mxordn, mxords, printmessg, tfirst)\u001b[0m\n\u001b[0;32m    241\u001b[0m t \u001b[38;5;241m=\u001b[39m copy(t)\n\u001b[0;32m    242\u001b[0m y0 \u001b[38;5;241m=\u001b[39m copy(y0)\n\u001b[1;32m--> 243\u001b[0m output \u001b[38;5;241m=\u001b[39m _odepack\u001b[38;5;241m.\u001b[39modeint(func, y0, t, args, Dfun, col_deriv, ml, mu,\n\u001b[0;32m    244\u001b[0m                          full_output, rtol, atol, tcrit, h0, hmax, hmin,\n\u001b[0;32m    245\u001b[0m                          ixpr, mxstep, mxhnil, mxordn, mxords,\n\u001b[0;32m    246\u001b[0m                          \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mbool\u001b[39m(tfirst)))\n\u001b[0;32m    247\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    248\u001b[0m     warning_msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_msgs[output[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Run with full_output = 1 to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    249\u001b[0m                    \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget quantitative information.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[1;32mIn[37], line 48\u001b[0m, in \u001b[0;36mopen_prob_model\u001b[1;34m(P0, t, voltage, rates)\u001b[0m\n\u001b[0;32m     45\u001b[0m markov_matrix \u001b[38;5;241m=\u001b[39m transition_matrix(k)\n\u001b[0;32m     47\u001b[0m \u001b[38;5;66;03m# Define the rate of change based on the Markov process\u001b[39;00m\n\u001b[1;32m---> 48\u001b[0m d_states \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdot(markov_matrix, states)\n\u001b[0;32m     50\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m d_states\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Define optimization parameters\n",
    "generations = 100  # Number of generations\n",
    "iterations = 1  # Iterations per generation\n",
    "n_particles = 10\n",
    "lb = np.array([0.01, 0.001, 1,  0.01, 0.00001, 1e-8,     10, -25000,    10, -25000, 100])\n",
    "ub = np.array([2,    2,   500, 5,     1,       0.001,  25000,   -10, 25000,    -10, 10000])\n",
    "estimated_params = np.array([0.448, 0.043, 280.035, 1.648, 0.003, 0.00008, 9176.06, -14412.199, 1443.847, -248.558, 5200]) #Input initial values for PSO\n",
    "initial_positions = np.random.normal(loc=estimated_params, scale=(0.3*np.abs(estimated_params)), size=(n_particles, len(estimated_params)))\n",
    "initial_positions = np.clip(initial_positions, lb, ub)\n",
    "\n",
    "# Run PSO + GSS optimization\n",
    "start_time = time.time()\n",
    "final_params, final_error, error_history = pso_gss_optimization(initial_positions, lb, ub, iterations, generations, voltage=v_test, experimental_current=experimental_current)\n",
    "end_time = time.time()\n",
    "\n",
    "#Output\n",
    "elapsed_time = end_time - start_time\n",
    "print(f\"Total time taken for optimization: {elapsed_time:.2f} seconds\")\n",
    "print(\"Final Parameters:\", final_params)\n",
    "print(\"Final Error:\", final_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "785c48a6-9967-4084-974d-07d21249da48",
   "metadata": {},
   "source": [
    "#### Plot results ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "81162dea-21d5-4224-ae95-9d465cada1e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(error_history)\n",
    "plt.xlabel('Iteration Number')\n",
    "plt.ylabel('Error')\n",
    "plt.title('Error in Each Iteration of PSO-GSS')\n",
    "plt.grid()\n",
    "plt.savefig('Kv13_error.png', format='png')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}