project/02_1_join_metrics.py.ipynb

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   "source": [
    "# Join aggregated metrics from each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d196e7af",
   "metadata": {},
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   "source": [
    "import pandas as pd\n",
    "\n",
    "N_HEADER_COLS = 3\n",
    "POS_INDEX_COL = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "metadata": {},
   "outputs": [],
   "source": [
    "filepaths_in = snakemake.input\n",
    "filepath_out = snakemake.output[0]\n",
    "filepaths_in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df472356",
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath_out"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "## Example\n",
    "\n",
    "- first file"
   ]
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   "execution_count": 5,
   "id": "6ce11572",
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    "\n",
    "\n",
    "def process(fpath: str) -> pd.DataFrame:\n",
    "    df = pd.read_csv(fpath, index_col=POS_INDEX_COL, header=list(range(N_HEADER_COLS)))\n",
    "    return df\n",
    "\n",
    "\n",
    "process(filepaths_in[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4395a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load all model metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0472f6ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = pd.concat([\n",
    "    process(fpath) for fpath in filepaths_in\n",
    "])\n",
    "metrics.stack('model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "103846cb-2e74-4727-b19d-f748613fcdc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b78be6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dump combined to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dfd243ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = metrics.to_csv(filepath_out)\n",
    "filepath_out"
   ]
  }
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