{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EFIx_LP_1B (Langmuir probe 2Hz)\n",
    "\n",
    "> Abstract: Access to the electric field instrument langmuir probe data (2Hz) (level 1b product)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext watermark\n",
    "%watermark -i -v -p viresclient,pandas,xarray,matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from viresclient import SwarmRequest\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "request = SwarmRequest()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EFIx_LP_1B product information\n",
    "\n",
    "Measurements from the Langmuir Probe (LP) of the Electric Field Instrument (EFI) at 2Hz, for each Swarm spacecraft.\n",
    "\n",
    "Documentation:\n",
    "- https://earth.esa.int/web/guest/missions/esa-eo-missions/swarm/data-handbook/level-1b-product-definitions#EFIX_LP_1B_Product"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check what \"EFI\" data variables are available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "request.available_collections(\"EFI\", details=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "request.available_measurements(\"EFI\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetch one day of EFI data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "request.set_collection(\"SW_OPER_EFIA_LP_1B\")\n",
    "request.set_products(\n",
    "    measurements=['U_orbit',\n",
    "                 'Ne',\n",
    "                 'Ne_error',\n",
    "                 'Te',\n",
    "                 'Te_error',\n",
    "                 'Vs',\n",
    "                 'Vs_error',\n",
    "                 'Flags_LP',\n",
    "                 'Flags_Ne',\n",
    "                 'Flags_Te',\n",
    "                 'Flags_Vs']\n",
    ")\n",
    "data = request.get_between(\n",
    "    dt.datetime(2016,1,1),\n",
    "    dt.datetime(2016,1,2)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and plot using pandas/matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.as_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(y=[\"Ne\", \"Te\", \"Vs\"], subplots=True, figsize=(20,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(x=\"Latitude\", y=\"Ne\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load as xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = data.as_xarray()\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(figsize=(10, 5), nrows=2, sharex=True)\n",
    "def subplot(_ax, da):\n",
    "    \"\"\"Plot a given DataArray on a given axes\"\"\"\n",
    "    _ax.plot(da)\n",
    "    _ax.set_ylabel(f\"{da.name} [{da.units}]\")\n",
    "for var, ax in zip((\"Ne\", \"Te\"), (ax1, ax2)):\n",
    "    subplot(ax, ds[var])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}