examples/simplecell/simplecell_arbor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Creating a simple cell optimisation with Arbor\n",
    "\n",
    "This notebook will explain how to set up an optimisation of simple single compartmental cell with two free parameters that need to be optimised using Arbor as the simulator.\n",
    "As this optimisation is for example purpose only, no real experimental data is used in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Install matplotlib if needed\n",
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "%load_ext autoreload\n",
    "%autoreload"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "First we need to import the module that contains all the functionality to create electrical cell models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import bluepyopt as bpop\n",
    "import bluepyopt.ephys as ephys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "If you want to see a lot of information about the internals, \n",
    "the verbose level can be set to 'debug' by commenting out\n",
    "the following lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# import logging\n",
    "# logger = logging.getLogger()\n",
    "# logger.setLevel(logging.DEBUG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Setting up a cell template\n",
    "-------------------------\n",
    "First a template that will describe the cell has to be defined. A template consists of:\n",
    "* a morphology\n",
    "* model mechanisms\n",
    "* model parameters\n",
    "\n",
    "### Creating a morphology\n",
    "A morphology can be loaded from a file (SWC or ASC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "morph = ephys.morphologies.NrnFileMorphology('simple.swc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "By default a Neuron morphology has the following sectionlists: somatic, axonal, apical and basal. Let's create an object that points to the soma using Arbor's S-expression language. This object will be used later to specify where mechanisms have to be added etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "somatic_loc = ephys.locations.ArbRegionLocation('somatic', region='(intersect (region \"all\") (region \"soma\"))')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Creating a mechanism\n",
    "\n",
    "Now we can add ion channels to this morphology. Let's add the default Neuron Hodgkin-Huxley mechanism to the soma. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "hh_mech = ephys.mechanisms.NrnMODMechanism(\n",
    "        name='hh',\n",
    "        suffix='hh',\n",
    "        locations=[somatic_loc])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The 'name' field can be chosen by the user, this name should be unique. The 'suffix' points to the same field in the NMODL file of the channel. 'locations' specifies which sections the mechanism will be added to.\n",
    "\n",
    "### Creating parameters\n",
    "\n",
    "Next we need to specify the parameters of the model. A parameter can be in two states: frozen and not-frozen. When a parameter is frozen it has an exact value, otherwise it only has some bounds but the exact value is not known yet.\n",
    "Let's define first a parameter that sets the capacitance of the soma to a frozen value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "cm_param = ephys.parameters.NrnSectionParameter(\n",
    "        name='cm',\n",
    "        param_name='cm',\n",
    "        value=1.0,\n",
    "        locations=[somatic_loc],\n",
    "        frozen=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "And parameters that represent the maximal conductance of the sodium and potassium channels. These two parameters will be optimised later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "gnabar_param = ephys.parameters.NrnSectionParameter(                                    \n",
    "        name='gnabar_hh',\n",
    "        param_name='gnabar_hh',\n",
    "        locations=[somatic_loc],\n",
    "        bounds=[0.05, 0.125],\n",
    "        frozen=False)     \n",
    "gkbar_param = ephys.parameters.NrnSectionParameter(\n",
    "        name='gkbar_hh',\n",
    "        param_name='gkbar_hh',\n",
    "        bounds=[0.01, 0.075],\n",
    "        locations=[somatic_loc],\n",
    "        frozen=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Creating the template\n",
    "\n",
    "To create the cell template, we pass all these objects to the constructor of the template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "simple_cell = ephys.models.CellModel(\n",
    "        name='simple_cell',\n",
    "        morph=morph,\n",
    "        mechs=[hh_mech],\n",
    "        params=[cm_param, gnabar_param, gkbar_param])  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now we can print out a description of the cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simple_cell:\n",
      "  morphology:\n",
      "    simple.swc\n",
      "  mechanisms:\n",
      "    hh: hh at ['ArbRegionLocation (region-def \"somatic\" (intersect (region \"all\") (region \"soma\")))']\n",
      "  params:\n",
      "    cm: ['ArbRegionLocation (region-def \"somatic\" (intersect (region \"all\") (region \"soma\")))'] cm = 1.0\n",
      "    gnabar_hh: ['ArbRegionLocation (region-def \"somatic\" (intersect (region \"all\") (region \"soma\")))'] gnabar_hh = [0.05, 0.125]\n",
      "    gkbar_hh: ['ArbRegionLocation (region-def \"somatic\" (intersect (region \"all\") (region \"soma\")))'] gkbar_hh = [0.01, 0.075]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(simple_cell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "With this cell we can build a cell evaluator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Setting up a cell evaluator\n",
    "\n",
    "To optimise the parameters of the cell we need to create cell evaluator object. \n",
    "This object will need to know which protocols to inject, which parameters to optimise, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Creating the protocols\n",
    "\n",
    "A protocol consists of a set of stimuli, and a set of responses (i.e. recordings). These responses will later be used to calculate\n",
    "the score of the parameter values.\n",
    "Let's create two protocols, two square current pulses at the relative position 0.5 of branch 0 with different amplitudes.\n",
    "We first need to create a location object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "soma_loc = ephys.locations.ArbLocsetLocation(\n",
    "        name='soma_center',\n",
    "        locset='(location 0 0.5)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "and then the stimuli, recordings and protocols. For each protocol we add a recording and a stimulus in the soma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "sweep_protocols = []\n",
    "for protocol_name, amplitude in [('step1', 0.01), ('step2', 0.05)]:\n",
    "    stim = ephys.stimuli.NrnSquarePulse(\n",
    "                step_amplitude=amplitude,\n",
    "                step_delay=100,\n",
    "                step_duration=50,\n",
    "                location=soma_loc,\n",
    "                total_duration=200)\n",
    "    rec = ephys.recordings.CompRecording(\n",
    "            name='%s.soma.v' % protocol_name,\n",
    "            location=soma_loc,\n",
    "            variable='v')\n",
    "    protocol = ephys.protocols.ArbSweepProtocol(protocol_name, [stim], [rec])\n",
    "    sweep_protocols.append(protocol)\n",
    "twostep_protocol = ephys.protocols.SequenceProtocol('twostep', protocols=sweep_protocols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Running a protocol on a cell\n",
    "\n",
    "Now we're at a stage where we can actually run a protocol on the cell. We first need to create a Simulator object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "sim = ephys.simulators.ArbSimulator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The run() method of a protocol accepts a cell model, a set of parameter values and a simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "default_params = {'gnabar_hh': 0.1, 'gkbar_hh': 0.03}\n",
    "responses = twostep_protocol.run(cell_model=simple_cell, param_values=default_params, sim=sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Plotting the response traces is now easy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_responses(responses):\n",
    "    plt.subplot(2,1,1)\n",
    "    plt.plot(responses['step1.soma.v']['time'], responses['step1.soma.v']['voltage'], label='step1')\n",
    "    plt.legend()\n",
    "    plt.subplot(2,1,2)\n",
    "    plt.plot(responses['step2.soma.v']['time'], responses['step2.soma.v']['voltage'], label='step2')\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "\n",
    "plot_responses(responses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "As you can see, when we use different parameter values, the response looks different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "other_params = {'gnabar_hh': 0.05, 'gkbar_hh': 0.05}\n",
    "plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=other_params, sim=sim))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Defining eFeatures and objectives\n",
    "\n",
    "For every response we need to define a set of eFeatures we will use for the fitness calculation later. We have to combine features together into objectives that will be used by the optimisation algorithm. In this case we will create one objective per feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "efel_feature_means = {'step1': {'Spikecount': 1}, 'step2': {'Spikecount': 5}}\n",
    "\n",
    "objectives = []\n",
    "\n",
    "for protocol in sweep_protocols:\n",
    "    stim_start = protocol.stimuli[0].step_delay\n",
    "    stim_end = stim_start + protocol.stimuli[0].step_duration\n",
    "    for efel_feature_name, mean in efel_feature_means[protocol.name].items():\n",
    "        feature_name = '%s.%s' % (protocol.name, efel_feature_name)\n",
    "        feature = ephys.efeatures.eFELFeature(\n",
    "                    feature_name,\n",
    "                    efel_feature_name=efel_feature_name,\n",
    "                    recording_names={'': '%s.soma.v' % protocol.name},\n",
    "                    stim_start=stim_start,\n",
    "                    stim_end=stim_end,\n",
    "                    exp_mean=mean,\n",
    "                    exp_std=0.05 * mean)\n",
    "        objective = ephys.objectives.SingletonObjective(\n",
    "            feature_name,\n",
    "            feature)\n",
    "        objectives.append(objective)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Creating the cell evaluator\n",
    "\n",
    "We will need an object that can use these objective definitions to calculate the scores from a protocol response. This is called a ScoreCalculator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Combining everything together we have a CellEvaluator. The CellEvaluator constructor has a field 'parameter_names' which contains the (ordered) list of names of the parameters that are used as input (and will be fitted later on)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "cell_evaluator = ephys.evaluators.CellEvaluator(\n",
    "        cell_model=simple_cell,\n",
    "        param_names=['gnabar_hh', 'gkbar_hh'],\n",
    "        fitness_protocols={twostep_protocol.name: twostep_protocol},\n",
    "        fitness_calculator=score_calc,\n",
    "        sim=sim)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Evaluating the cell\n",
    "\n",
    "The cell can now be evaluate for a certain set of parameter values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'step1.Spikecount': 0.0, 'step2.Spikecount': 0.0}\n"
     ]
    }
   ],
   "source": [
    "print(cell_evaluator.evaluate_with_dicts(default_params))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Setting up and running an optimisation\n",
    "\n",
    "Now that we have a cell template and an evaluator for this cell, we can set up an optimisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "optimisation = bpop.optimisations.DEAPOptimisation(\n",
    "        evaluator=cell_evaluator,\n",
    "        offspring_size = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "And this optimisation can be run for a certain number of generations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The optimisation has return us 4 objects: final population, hall of fame, statistical logs and history. \n",
    "\n",
    "The final population contains a list of tuples, with each tuple representing the two parameters of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final population:  [[0.10724858027620049, 0.030384510486737674], [0.10724858027620049, 0.030384510486737674], [0.10724858027620049, 0.030384510486737674], [0.10724858027620049, 0.030384510486737674], [0.10724858027620049, 0.030384510486737674], [0.08869789340527853, 0.02498056382291062], [0.09897779748630668, 0.02669588721168879], [0.1057605282594995, 0.030384510486737674], [0.1076294309890433, 0.030384510486737674], [0.09181168257196179, 0.02498056382291062], [0.09319078965060591, 0.03367988191444934], [0.09315627382586422, 0.030727169741078426], [0.09319078965060591, 0.03367988191444934], [0.09319078965060591, 0.03367988191444934], [0.10887316587433475, 0.03367988191444934], [0.09058616618750419, 0.033619209959752636], [0.09450057314084677, 0.030406932859913336], [0.09315627382586422, 0.030704747367902765], [0.09161282398694515, 0.029920395084114818], [0.09319078965060591, 0.03457319907176254]]\n"
     ]
    }
   ],
   "source": [
    "print('Final population: ', final_pop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The best individual found during the optimisation is the first individual of the hall of fame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best individual:  [0.09181168257196179, 0.02498056382291062]\n",
      "Fitness values:  (0.0, 0.0)\n"
     ]
    }
   ],
   "source": [
    "best_ind = hall_of_fame[0]\n",
    "print('Best individual: ', best_ind)\n",
    "print('Fitness values: ', best_ind.fitness.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We can evaluate this individual and make use of a convenience function of the cell evaluator to return us a dict of the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'step1.Spikecount': 0.0, 'step2.Spikecount': 0.0}\n"
     ]
    }
   ],
   "source": [
    "best_ind_dict = cell_evaluator.param_dict(best_ind)\n",
    "print(cell_evaluator.evaluate_with_dicts(best_ind_dict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "As you can see the evaluation returns the same values as the fitness values provided by the optimisation output. \n",
    "We can have a look at the responses now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=best_ind_dict, sim=sim))\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let's have a look at the optimisation statistics.\n",
    "We can plot the minimal score (sum of all objective scores) found in every optimisation. \n",
    "The optimisation algorithm uses negative fitness scores, so we actually have to look at the maximum values log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 4.4)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy\n",
    "gen_numbers = logs.select('gen')\n",
    "min_fitness = logs.select('min')\n",
    "max_fitness = logs.select('max')\n",
    "plt.plot(gen_numbers, min_fitness, label='min fitness')\n",
    "plt.xlabel('generation #')\n",
    "plt.ylabel('score (# std)')\n",
    "plt.legend()\n",
    "plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1) \n",
    "plt.ylim(0.9*min(min_fitness), 1.1 * max(min_fitness)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "581988038cf9ce8838e7faf3da7c29f4ff88d898cd43cb17e0086e389d8deda2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}