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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Introduction to the JABS downstream analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Importing necessary libraries\n",
    "\n",
    "\n",
    "###### We are just going to import basic python libraries to help us analyze predicted behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# auto reload\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stuff import *\n",
    "from ipywidgets import FloatSlider, interactive, fixed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Visualizing the Ethogram for a sample prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20328a1857e5430c9bd1027fcfa7dc7e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=1.0, description='i', min=1.0, step=1.0), Checkbox(value=False, descri…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reading the sample file\n",
    "df = load_file('data/sample_file.h5')\n",
    "int_plot = interactive(draw_ethogram, df=fixed(df['Class'].values.astype(int)),i=FloatSlider(min=1, max=100, step=1), save_fig=False);\n",
    "int_plot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Frame counting statistics"
   ]
  }
 ],
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