Custom Export.ipynb

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  {
   "cell_type": "markdown",
   "id": "f492d104",
   "metadata": {},
   "source": [
    "# Custom Export\n",
    "\n",
    "In this example we take metadata and data from BMM and export it in the [XDI](https://github.com/XraySpectroscopy/XAS-Data-Interchange/blob/master/specification/spec.md) format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b5c9221",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tiled.client import from_uri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb93ebec",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = from_uri(\"https://tiled-demo.blueskyproject.io/api\")\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b904f32",
   "metadata": {},
   "outputs": [],
   "source": [
    "run = c['bmm']['raw'][22521]\n",
    "run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29dbb2c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = run['primary']['data'].read().to_dataframe()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80900a92",
   "metadata": {},
   "outputs": [],
   "source": [
    "xdi_metadata = run.metadata['start']['XDI']\n",
    "xdi_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5d04ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "\n",
    "\n",
    "def write_xdi(output, df, metadata):\n",
    "\n",
    "    xdi_version = metadata.pop(\"xdi_version\", \"1.0\")\n",
    "    comments = metadata.pop(\"comments\", \"\")\n",
    "    extra_version = metadata.pop(\"extra_version\", \"\")\n",
    "\n",
    "    output.write(f\"# XDI/{xdi_version} {extra_version}\\n\")\n",
    "\n",
    "    for namespace, namespace_dict in metadata.items():\n",
    "        if namespace.startswith(\"_\"):\n",
    "            continue\n",
    "        for tag, value in namespace_dict.items():\n",
    "            output.write(f\"# {namespace}.{tag}: {value}\\n\")\n",
    "\n",
    "    output.write(\"# /////////////\\n\")\n",
    "    output.write(\"# generated by tiled\\n\")\n",
    "    output.write(comments)\n",
    "    output.write(\"# -------------\\n\")\n",
    "\n",
    "    # write column labels\n",
    "    curated_df = pandas.DataFrame()\n",
    "    curated_df[\"energy\"] = df[\"dcm_energy\"]\n",
    "    curated_df[\"i0\"] = df[\"I0\"]\n",
    "    curated_df[\"itrans\"] = df[\"It\"]\n",
    "    curated_df[\"irefer\"] = df[\"Ir\"]\n",
    "    output.write(\"# \")\n",
    "    output.write(\" \".join(curated_df.columns))\n",
    "    output.write(\"\\n\")\n",
    "\n",
    "    # write data\n",
    "    df.to_csv(output, header=False, index=False, sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78f57d90",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"my_data.xdi\", \"w\") as file:\n",
    "    write_xdi(file, df, xdi_metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a68ace1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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