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   "source": [
    "# 3. Define calibration sections\n",
    "The goal of this notebook is to show how you can define calibration sections. That means that we define certain parts of the fiber to a timeseries of temperature measurements. Here, we assume the temperature timeseries is already part of the `xarray.Dataset` object."
   ]
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  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "import os\n",
    "from dtscalibration import read_silixa_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-06T08:09:09.855368Z",
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   "source": [
    "filepath = os.path.join(\"..\", \"..\", \"tests\", \"data\", \"double_ended2\")\n",
    "ds = read_silixa_files(directory=filepath, timezone_netcdf=\"UTC\", file_ext=\"*.xml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we have a look at which temperature timeseries are available for calibration. Therefore we access `ds.data_vars` and we find `probe1Temperature` and `probe2Temperature` that refer to the temperature measurement timeseries of the two probes attached to the Ultima.\n",
    "\n",
    "Alternatively, we can access the `ds.dts.get_timeseries_keys()` function to list all timeseries that can be used for calibration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-06T08:09:10.210852Z",
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   "source": [
    "# The following line introduces the .dts accessor for xarray datasets\n",
    "import dtscalibration  # noqa: E401  # noqa: E401\n",
    "\n",
    "print(ds.dts.get_timeseries_keys())  # list the available timeseeries\n",
    "ds.probe1Temperature.plot(figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A calibration is needed to estimate temperature from Stokes and anti-Stokes measurements. There are three unknowns for a single ended calibration procedure $\\gamma$, $C$, and $\\alpha$. The parameters $\\gamma$ and $\\alpha$ remain constant over time, while $C$ may vary. \n",
    "\n",
    "At least two calibration sections of different temperatures are needed to perform a decent calibration procedure.\n",
    "\n",
    "This setup has two baths, named 'cold' and 'warm'. Each bath has 2 sections. `probe1Temperature` is the temperature timeseries of the cold bath and `probe2Temperature` is the temperature timeseries of the warm bath."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Name section | Name reference temperature time series | Number of sections | Location of sections (m) |\n",
    "|--------------|:--------------------------------------:|:------------------:|:------------------------:|\n",
    "| Cold bath    |            probe1Temperature           |          2         |   7.5-17.0; 70.0-80.0  |\n",
    "| Warm bath    |            probe2Temperature           |          2         |  24.0-34.0; 85.0-95.0  |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sections are defined in a dictionary with its keywords of the names of the reference temperature time series. Its values are lists of slice objects, where each slice object is a section.\n",
    "\n",
    "Note that slice is part of the standard Python library and no import is required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-06T08:09:10.280352Z",
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     "shell.execute_reply": "2022-04-06T08:09:10.305366Z"
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   },
   "outputs": [],
   "source": [
    "sections = {\n",
    "    \"probe1Temperature\": [slice(7.5, 17.0), slice(70.0, 80.0)],  # cold bath\n",
    "    \"probe2Temperature\": [slice(24.0, 34.0), slice(85.0, 95.0)],  # warm bath\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NetCDF files do not support reading/writing python dictionaries. Internally the sections dictionary is stored in `ds._sections` as a string encoded with yaml, which can be saved to a netCDF file. Each time the sections dictionary is requested, yaml decodes the string and evaluates it to the Python dictionary. "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": []
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