examples/example-1.3-aln-bifurcation-diagram.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bifurcation diagram of the `aln` model\n",
    "\n",
    "In this notebook, we will discover how easy it is to draw bifurcation diagrams in `neurolib` using its powerful `BoxSearch` class.\n",
    "\n",
    "Bifurcation diagrams are an important tool to understand a dynamical system, may it be a single neuron model or a whole-brain network. They show how a system behaves when certain parameters of the model are changed: whether the system transitions into an oscillation for example, or whethter the system remains in a fixed point (of sustained constant activity).\n",
    "\n",
    "We will use this to draw a map of the `aln` model: Since the `aln` model consists of two populations of Adex neurons, we will change its inputs to the excitatory and to the inhibitory population independently and do so for two different values of spike-frequency adaptation strength $b$. We will measure the activity of the system and identify regions of oscillatory activity and discover bistable states, in which the system can be in two different stable states for the same set of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change into the root directory of the project\n",
    "import os\n",
    "if os.getcwd().split(\"/\")[-1] == \"examples\":\n",
    "    os.chdir('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logger = logging.getLogger()\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "#logger.setLevel(logging.DEBUG)\n",
    "#logging.disable(logging.WARNING)\n",
    "#logging.disable(logging.WARN)\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from neurolib.models.aln import ALNModel\n",
    "from neurolib.utils.parameterSpace import ParameterSpace\n",
    "from neurolib.optimize.exploration import BoxSearch\n",
    "import neurolib.utils.functions as func\n",
    "import neurolib.utils.stimulus as stim\n",
    "import neurolib.optimize.exploration.explorationUtils as eu\n",
    "import neurolib.utils.devutils as du\n",
    "from neurolib.utils.loadData import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"seaborn-white\")\n",
    "plt.rcParams['image.cmap'] = 'plasma'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ALNModel()\n",
    "model.params['dt'] = 0.1 # Integration time step, ms\n",
    "model.params['duration'] = 20 * 1000 # Simulation time, ms\n",
    "\n",
    "model.params['save_dt'] = 10.0 # 10 ms sampling steps for saving data, should be multiple of dt\n",
    "model.params[\"tauA\"] = 600.0 # Adaptation timescale, ms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measuring bistability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `aln` model has a region of bistability, in which two states are stable at the same time: the low-activity *down-state*, and the high-activity *up-state*. We can find these states by constructing a stimulus, which uncovers the bistable nature of the system: Initially, we apply a negative push to the system, to make sure that it is in the *down-state*. We then relax this stimulus slowly and wait for the system to settle. We then apply a sharp push in order to reach the *up-state* and release the stimulus slowly back again. The difference of the two states after the stimulus has relaxed back to zero is a sign for bistability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we place the system in the bistable region\n",
    "model.params['mue_ext_mean'] = 2.5\n",
    "model.params['mui_ext_mean'] = 2.5\n",
    "\n",
    "# construct a stimulus\n",
    "rect_stimulus = stim.RectifiedInput(amplitude=0.2).to_model(model)\n",
    "model.params['ext_exc_current'] = rect_stimulus * 5.0    \n",
    "    \n",
    "model.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Activity [Hz]')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 3), dpi=150)\n",
    "plt.plot(model.t, model.output.T, lw = 3, c='k', label='rate')\n",
    "plt.plot(model.t, (rect_stimulus * 100).squeeze(), lw = 3, c='r', label=\"stimulus\")\n",
    "plt.text(3000, 7, 'down-state', fontsize=16)\n",
    "plt.text(15000, 35, 'up-state', fontsize=16)\n",
    "plt.legend(fontsize=14)\n",
    "plt.xlim(1, model.t[-1])\n",
    "plt.xlabel(\"Time [ms]\")\n",
    "plt.ylabel(\"Activity [Hz]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define evaluation function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's construct a rather lengthy evaluation function which does exactly that, for every parameter configuration that we want to explore. We will also measure other things like the *dominant frequency* and amplitude of oscillations and the maximum rate of the excitatory population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluateSimulation(traj):\n",
    "    # get the model from the trajectory using `search.getModelFromTraj(traj)`\n",
    "    model = search.getModelFromTraj(traj)\n",
    "    # initiate the model with random initial contitions\n",
    "    model.randomICs()\n",
    "    defaultDuration = model.params['duration']\n",
    "    \n",
    "    # -------- stage wise simulation --------\n",
    "    \n",
    "    # Stage 3: full and final simulation\n",
    "    # ---------------------------------------    \n",
    "    model.params['duration'] = defaultDuration\n",
    "    \n",
    "    rect_stimulus = stim.RectifiedInput(amplitude=0.2).to_model(model)\n",
    "    model.params['ext_exc_current'] = rect_stimulus * 5.0    \n",
    "    \n",
    "    model.run()\n",
    "    \n",
    "    # up down difference    \n",
    "    state_length = 2000\n",
    "    last_state = (model.t > defaultDuration - state_length)\n",
    "    down_window = (defaultDuration/2-state_length<model.t) & (model.t<defaultDuration/2) # time period in ms where we expect the down-state\n",
    "    up_window = (defaultDuration-state_length<model.t) & (model.t<defaultDuration) # and up state\n",
    "    up_state_rate = np.mean(model.output[:, up_window], axis=1)\n",
    "    down_state_rate = np.mean(model.output[:, down_window], axis=1)\n",
    "    up_down_difference = np.max(up_state_rate - down_state_rate)\n",
    "        \n",
    "    # check rates!\n",
    "    max_amp_output = np.max(\n",
    "          np.max(model.output[:, up_window], axis=1) \n",
    "        - np.min(model.output[:, up_window], axis=1)\n",
    "    )\n",
    "    max_output = np.max(model.output[:, up_window])\n",
    "     \n",
    "    model_frs, model_pwrs = func.getMeanPowerSpectrum(model.output, \n",
    "                                                      dt=model.params.dt, \n",
    "                                                      maxfr=40, \n",
    "                                                      spectrum_windowsize=10)\n",
    "    max_power = np.max(model_pwrs)     \n",
    "    \n",
    "    model_frs, model_pwrs = func.getMeanPowerSpectrum(model.output[:, up_window], dt=model.params.dt, maxfr=40, spectrum_windowsize=5)\n",
    "    domfr = model_frs[np.argmax(model_pwrs)]    \n",
    "\n",
    "    result = {\n",
    "        \"end\" : 3,\n",
    "        \"max_output\": max_output, \n",
    "        \"max_amp_output\" : max_amp_output,\n",
    "        \"max_power\" : max_power,\n",
    "        #\"model_pwrs\" : model_pwrs,\n",
    "        #\"output\": model.output[:, ::int(model.params['save_dt']/model.params['dt'])],\n",
    "        \"domfr\" : domfr,\n",
    "        \"up_down_difference\" : up_down_difference\n",
    "    }\n",
    "    \n",
    "    search.saveToPypet(result, traj)\n",
    "    return    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now define the parameter space over which we want to serach. We apply a grid search over the mean external input parameters to the excitatory and the inhibitory population `mue_ext_mean`/`mui_ext_mean` and do this for two values of spike-frequency adapation strength $b$, once without and once with adaptation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploration parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MainProcess pypet.storageservice.HDF5StorageService INFO     I will use the hdf5 file `./data/hdf/example-1.3-aln-bifurcation-diagram.hdf`.\n",
      "MainProcess pypet.environment.Environment INFO     Environment initialized.\n",
      "MainProcess root INFO     Number of parameter configurations: 3362\n",
      "MainProcess root INFO     BoxSearch: Environment initialized.\n"
     ]
    }
   ],
   "source": [
    "# low number of parameters for testing:\n",
    "parameters = ParameterSpace({\"mue_ext_mean\": np.linspace(0.0, 4, 2), \n",
    "                             \"mui_ext_mean\": np.linspace(0.0, 4, 2),\n",
    "                              \"b\": [0.0, 20.0]\n",
    "                             }, kind=\"grid\")\n",
    "# real: \n",
    "# parameters = ParameterSpace({\"mue_ext_mean\": np.linspace(0.0, 4, 41), \n",
    "#                              \"mui_ext_mean\": np.linspace(0.0, 4, 41),\n",
    "#                               \"b\": [0.0, 20.0]\n",
    "#                              })\n",
    "search = BoxSearch(evalFunction = evaluateSimulation, model=model, parameterSpace=parameters, filename='example-1.3-aln-bifurcation-diagram.hdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MainProcess pypet.environment.Environment INFO     I am preparing the Trajectory for the experiment and initialise the store.\n",
      "MainProcess pypet.environment.Environment INFO     Initialising the storage for the trajectory.\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Initialising storage or updating meta data of Trajectory `results-2021-06-19-01H-23M-48S`.\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Finished init or meta data update for `results-2021-06-19-01H-23M-48S`.\n",
      "MainProcess pypet.environment.Environment INFO     \n",
      "************************************************************\n",
      "STARTING runs of trajectory\n",
      "`results-2021-06-19-01H-23M-48S`.\n",
      "************************************************************\n",
      "\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Initialising storage or updating meta data of Trajectory `results-2021-06-19-01H-23M-48S`.\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Finished init or meta data update for `results-2021-06-19-01H-23M-48S`.\n",
      "MainProcess pypet.environment.Environment INFO     Starting multiprocessing with at most 8 processes running at the same time.\n",
      "MainProcess pypet INFO     PROGRESS: Finished    0/3362 runs [                    ]  0.0%\n",
      "MainProcess pypet INFO     PROGRESS: Finished  169/3362 runs [=                   ]  5.0%, remaining: 0:01:27\n",
      "MainProcess pypet INFO     PROGRESS: Finished  337/3362 runs [==                  ] 10.0%, remaining: 0:01:24\n",
      "MainProcess pypet INFO     PROGRESS: Finished  505/3362 runs [===                 ] 15.0%, remaining: 0:01:27\n",
      "MainProcess pypet INFO     PROGRESS: Finished  673/3362 runs [====                ] 20.0%, remaining: 0:01:26\n",
      "MainProcess pypet INFO     PROGRESS: Finished  841/3362 runs [=====               ] 25.0%, remaining: 0:01:26\n",
      "MainProcess pypet INFO     PROGRESS: Finished 1009/3362 runs [======              ] 30.0%, remaining: 0:01:24\n",
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      "MainProcess pypet INFO     PROGRESS: Finished 3362/3362 runs [====================]100.0%\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Initialising storage or updating meta data of Trajectory `results-2021-06-19-01H-23M-48S`.\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Finished init or meta data update for `results-2021-06-19-01H-23M-48S`.\n",
      "MainProcess pypet.environment.Environment INFO     \n",
      "************************************************************\n",
      "FINISHED all runs of trajectory\n",
      "`results-2021-06-19-01H-23M-48S`.\n",
      "************************************************************\n",
      "\n",
      "MainProcess pypet.environment.Environment INFO     \n",
      "************************************************************\n",
      "STARTING FINAL STORING of trajectory\n",
      "`results-2021-06-19-01H-23M-48S`\n",
      "************************************************************\n",
      "\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Start storing Trajectory `results-2021-06-19-01H-23M-48S`.\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Storing branch `config`.\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Storing branch `parameters`.\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Finished storing Trajectory `results-2021-06-19-01H-23M-48S`.\n",
      "MainProcess pypet.environment.Environment INFO     \n",
      "************************************************************\n",
      "FINISHED FINAL STORING of trajectory\n",
      "`results-2021-06-19-01H-23M-48S`.\n",
      "************************************************************\n",
      "\n",
      "MainProcess pypet.environment.Environment INFO     All runs of trajectory `results-2021-06-19-01H-23M-48S` were completed successfully.\n"
     ]
    }
   ],
   "source": [
    "search.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MainProcess root INFO     Loading results from ./data/hdf/example-1.3-aln-bifurcation-diagram.hdf\n",
      "MainProcess root INFO     Analyzing trajectory results-2021-06-19-01H-23M-48S\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     I will use the hdf5 file `./data/hdf/example-1.3-aln-bifurcation-diagram.hdf`.\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Loading trajectory `results-2021-06-19-01H-23M-48S`.\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Loading branch `config` in mode `2`.\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Loading branch `parameters` in mode `2`.\n",
      "MainProcess pypet.storageservice.HDF5StorageService INFO     Loading branch `results` in mode `1`.\n",
      "MainProcess root INFO     Creating `dfResults` dataframe ...\n",
      "MainProcess root INFO     Aggregating results to `dfResults` ...\n",
      "100%|██████████| 3362/3362 [00:22<00:00, 152.47it/s]\n",
      "MainProcess root INFO     All results loaded.\n"
     ]
    }
   ],
   "source": [
    "search.loadResults(all=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The results dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mue_ext_mean</th>\n",
       "      <th>mui_ext_mean</th>\n",
       "      <th>b</th>\n",
       "      <th>up_down_difference</th>\n",
       "      <th>max_power</th>\n",
       "      <th>max_output</th>\n",
       "      <th>max_amp_output</th>\n",
       "      <th>end</th>\n",
       "      <th>domfr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>17.665778</td>\n",
       "      <td>0.038741</td>\n",
       "      <td>3.747003e-16</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.500025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>5.151548</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>2.042323e-05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.500025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>15.441657</td>\n",
       "      <td>0.024629</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.500025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>4.140089</td>\n",
       "      <td>0.024062</td>\n",
       "      <td>8.806806e-06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.500025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>12.466037</td>\n",
       "      <td>0.012277</td>\n",
       "      <td>7.112366e-17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.500025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>14.426393</td>\n",
       "      <td>29.965853</td>\n",
       "      <td>1.263433e-06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>58.447442</td>\n",
       "      <td>93.451476</td>\n",
       "      <td>2.842171e-14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3359</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>14.482713</td>\n",
       "      <td>29.928097</td>\n",
       "      <td>1.243205e-06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3360</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>58.494615</td>\n",
       "      <td>93.380582</td>\n",
       "      <td>1.421085e-14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3361</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>14.534853</td>\n",
       "      <td>29.891972</td>\n",
       "      <td>1.224562e-06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3362 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mue_ext_mean  mui_ext_mean     b  up_down_difference  max_power  \\\n",
       "0              0.0           0.0   0.0            0.000019  17.665778   \n",
       "1              0.0           0.0  20.0            0.000009   5.151548   \n",
       "2              0.0           0.1   0.0            0.000010  15.441657   \n",
       "3              0.0           0.1  20.0            0.000006   4.140089   \n",
       "4              0.0           0.2   0.0            0.000006  12.466037   \n",
       "...            ...           ...   ...                 ...        ...   \n",
       "3357           4.0           3.8  20.0            0.000038  14.426393   \n",
       "3358           4.0           3.9   0.0            0.000674  58.447442   \n",
       "3359           4.0           3.9  20.0            0.000038  14.482713   \n",
       "3360           4.0           4.0   0.0            0.000674  58.494615   \n",
       "3361           4.0           4.0  20.0            0.000038  14.534853   \n",
       "\n",
       "      max_output  max_amp_output  end     domfr  \n",
       "0       0.038741    3.747003e-16  3.0  0.500025  \n",
       "1       0.037037    2.042323e-05  3.0  0.500025  \n",
       "2       0.024629    1.110223e-16  3.0  0.500025  \n",
       "3       0.024062    8.806806e-06  3.0  0.500025  \n",
       "4       0.012277    7.112366e-17  3.0  0.500025  \n",
       "...          ...             ...  ...       ...  \n",
       "3357   29.965853    1.263433e-06  3.0  0.000000  \n",
       "3358   93.451476    2.842171e-14  3.0  0.000000  \n",
       "3359   29.928097    1.243205e-06  3.0  0.000000  \n",
       "3360   93.380582    1.421085e-14  3.0  0.000000  \n",
       "3361   29.891972    1.224562e-06  3.0  0.000000  \n",
       "\n",
       "[3362 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.dfResults"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting 2D bifurcation diagrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's draw the bifurcation diagrams. We will use a white contour for oscillatory areas (measured by `max_amp_output`) and a green dashed lined for the bistable region (measured by `up_down_difference`). We can use the function `explorationUtils.plotExplorationResults()` for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_key_label = \"Max. $r_E$\"\n",
    "eu.plotExplorationResults(search.dfResults, \n",
    "                          par1=['mue_ext_mean', '$\\mu_e$'], \n",
    "                          par2=['mui_ext_mean', '$\\mu_i$'], \n",
    "                          by=['b'], \n",
    "                          plot_key='max_output',\n",
    "                          plot_clim=[0.0, 80.0],\n",
    "                          nan_to_zero=False,\n",
    "                          plot_key_label=plot_key_label, \n",
    "                          one_figure=False,\n",
    "                          contour=[\"max_amp_output\", \"up_down_difference\"],\n",
    "                          contour_color=[['white'], ['springgreen']],\n",
    "                          contour_levels=[[10], [10]],\n",
    "                          contour_alpha=[1.0, 1.0],\n",
    "                          contour_kwargs={0 : {\"linewidths\" : (5,)}, 1 : {\"linestyles\" : \"--\", \"linewidths\" : (5,)}},\n",
    "                          #alpha_mask=\"relative_amplitude_BOLD\",\n",
    "                          mask_threshold=0.1,\n",
    "                          mask_alpha=0.2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.10 64-bit ('neurolib')",
   "language": "python",
   "name": "python3710jvsc74a57bd040c7a7921e4911bf33743545bc23717ac3cf742f9e21e92f1609fffc896830ac"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}