{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IPDxIRR_2F (Ionospheric plasma densities)\n",
    "\n",
    "> Abstract: Access to the derived plasma characteristics at 1Hz (level 2 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",
    "from matplotlib.dates import DateFormatter\n",
    "\n",
    "request = SwarmRequest()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IPDxIRR_2F product information\n",
    "\n",
    "Derived plasma characteristics at 1Hz, for each Swarm spacecraft.\n",
    "\n",
    "Documentation:\n",
    "- https://earth.esa.int/web/guest/missions/esa-eo-missions/swarm/data-handbook/level-2-product-definitions#IPDxIRR_2F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check what \"IPD\" data variables are available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "request.available_collections(\"IPD\", details=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "request.available_measurements(\"IPD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetch three hours of IPD data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "request.set_collection(\"SW_OPER_IPDAIRR_2F\")\n",
    "request.set_products(measurements=request.available_measurements(\"IPD\"))\n",
    "data = request.get_between(\n",
    "    dt.datetime(2014,12,21, 0),\n",
    "    dt.datetime(2014,12,21, 3)\n",
    ")"
   ]
  },
  {
   "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.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(nrows=7, ncols=1, figsize=(20,18), sharex=True)\n",
    "df.plot(ax=axes[0], y=['Background_Ne', 'Foreground_Ne', 'Ne'], alpha=0.8)\n",
    "df.plot(ax=axes[1], y=['Grad_Ne_at_100km', 'Grad_Ne_at_50km', 'Grad_Ne_at_20km'])\n",
    "df.plot(ax=axes[2], y=['RODI10s', 'RODI20s'])\n",
    "df.plot(ax=axes[3], y=['ROD'])\n",
    "df.plot(ax=axes[4], y=['mROT'])\n",
    "df.plot(ax=axes[5], y=['delta_Ne10s', 'delta_Ne20s', 'delta_Ne40s'])\n",
    "df.plot(ax=axes[6], y=['mROTI20s', 'mROTI10s'])\n",
    "axes[0].set_ylabel(\"[cm$^{-3}$]\")\n",
    "axes[1].set_ylabel(\"[cm$^{-3}$m$^{-1}$]\")\n",
    "axes[2].set_ylabel(\"[cm$^{-3}$s$^{-1}$]\")\n",
    "axes[3].set_ylabel(\"[cm$^{-3}$m$^{-1}$]\")\n",
    "axes[4].set_ylabel(\"[TECU s$^{-1}$]\")\n",
    "axes[5].set_ylabel(\"[cm$^{-3}$m$^{-1}$]\")\n",
    "axes[6].set_ylabel(\"[TECU s$^{-1}$]\")\n",
    "axes[6].set_xlabel(\"Timestamp\")\n",
    "\n",
    "for ax in axes:\n",
    "    # Reformat time axis\n",
    "    # https://www.earthdatascience.org/courses/earth-analytics-python/use-time-series-data-in-python/customize-dates--matplotlib-plots-python/\n",
    "    ax.xaxis.set_major_formatter(DateFormatter(\"%Y-%m-%d\\n%H:%M:%S\"))\n",
    "    ax.legend(loc=\"upper right\")\n",
    "    ax.grid()\n",
    "fig.subplots_adjust(hspace=0)"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "### Alternative plot setup\n",
    "\n",
    "To plot the data from xarray, we need a different plotting setup. This does however give us more control over the plot. The units are extracted directly from the xarray object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(nrows=7, ncols=1, figsize=(20,18), sharex=True)\n",
    "def subplot(ax=None, y=None, **kwargs):\n",
    "    \"\"\"Plot combination of variables onto a given axis\"\"\"\n",
    "    units = ds[y[0]].units\n",
    "    for var in y:\n",
    "        ax.plot(ds[\"Timestamp\"], ds[var], label=var, **kwargs)\n",
    "        if units != ds[var].units:\n",
    "            raise ValueError(f\"Units mismatch for {var}\")\n",
    "    ax.set_ylabel(f\"[{units}]\")\n",
    "    # Reformat time axis\n",
    "    # https://www.earthdatascience.org/courses/earth-analytics-python/use-time-series-data-in-python/customize-dates--matplotlib-plots-python/\n",
    "    ax.xaxis.set_major_formatter(DateFormatter(\"%Y-%m-%d\\n%H:%M:%S\"))\n",
    "    ax.legend(loc=\"upper right\")\n",
    "    ax.grid()\n",
    "subplot(ax=axes[0], y=['Background_Ne', 'Foreground_Ne', 'Ne'])\n",
    "subplot(ax=axes[1], y=['Grad_Ne_at_100km', 'Grad_Ne_at_50km', 'Grad_Ne_at_20km'])\n",
    "subplot(ax=axes[2], y=['RODI10s', 'RODI20s'])\n",
    "subplot(ax=axes[3], y=['ROD'])\n",
    "subplot(ax=axes[4], y=['mROT'])\n",
    "subplot(ax=axes[5], y=['delta_Ne10s', 'delta_Ne20s', 'delta_Ne40s'])\n",
    "subplot(ax=axes[6], y=['mROTI20s', 'mROTI10s'])\n",
    "fig.subplots_adjust(hspace=0)"
   ]
  }
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