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   "source": [
    "# How To: Fit Logistic Growth Models\n",
    "\n",
    "Fit logistic growth curves to time-series colony measurements and extract\n",
    "kinetic parameters (growth rate, carrying capacity, lag time) using\n",
    "`LogGrowthModel`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from phenotypic.data import load_meas\n",
    "from phenotypic.analysis import LogGrowthModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = load_meas()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the Model\n",
    "\n",
    "Specify the target measurement column, grouping structure, and time\n",
    "label. The model fits a logistic curve to each group independently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogGrowthModel(\n",
    "    on=\"Shape_Area\",\n",
    "    groupby=[\"Metadata_Strain\"],\n",
    "    time_label=\"Metadata_Time\",\n",
    "    agg_func=\"mean\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = model.analyze(df)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results DataFrame contains fitted parameters for each group:\n",
    "growth rate, carrying capacity (K), and lag time. Use these to\n",
    "compare strain fitness across experimental conditions."
   ]
  }
 ],
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