{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Create a multisubject egg\n\nAn egg is made up of two primary pieces of data: `pres`, which are the\nwords/stimuli that were presented to a subject and `rec`, which are the\nwords/stimuli that were recalled by the subject.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Code source: Contextual Dynamics Laboratory\n# License: MIT\n\nimport quail\nimport numpy as np\n\n# presented words\npresented_words = [[['cat', 'bat', 'hat', 'goat'],['zoo', 'animal', 'zebra', 'horse']],[['cat', 'bat', 'hat', 'goat'],['zoo', 'animal', 'zebra', 'horse']]]\n\n# recalled words\nrecalled_words = [[['bat', 'cat', 'goat', 'hat'],['animal', 'horse', 'zoo']],[['bat', 'cat', 'goat'],['animal', 'horse']]]\n\n# create egg\negg = quail.Egg(pres=presented_words, rec=recalled_words)\n\n# analyze and plot\nfegg = egg.analyze('accuracy')\n\nfegg.plot(plot_style='violin', title='Average Recall Accuracy')"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.11.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}