{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\nSolar Orbiter low latency data\n==============================\n\nDownloading and plotting low latency data from Solar Orbiter.\nNote that this data is not suitable for publication.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Import the required modules\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from datetime import datetime\nimport warnings\n\nimport astropy.units as u\nimport matplotlib.pyplot as plt\nfrom matplotlib import dates as mdates\nimport numpy as np\n\nfrom heliopy.data.solo import download"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Download some magnetic field data\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "data = download(datetime(2020, 7, 10), datetime(2020, 8, 3), 'MAG', 'LL02')\nprint(data.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Calculate the magnetic field mangitude\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "modB = np.sqrt(data.quantity('B_RTN_0')**2 +\n               data.quantity('B_RTN_1')**2 +\n               data.quantity('B_RTN_2')**2)\ndata = data.add_column('modB', modB)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot the data\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "fig, axs = plt.subplots(nrows=2, sharex=True)\n\nax = axs[0]\nax.plot(data.index, data.quantity('modB'))\nax.set_ylabel('nT')\nax.set_title(r'$|B|$')\nax.set_ylim(bottom=0)\n\nax = axs[1]\nax.plot(data.index, data.quantity('B_RTN_0'))\nax.set_ylabel('nT')\nax.set_title(r'$B_{r}$')\nax.axhline(0, color='black', linewidth=1, linestyle='--')\n\nfig.suptitle('Solar Orbiter MAG low latency (not for science use)')\nax.xaxis.set_major_formatter(\n    mdates.ConciseDateFormatter(mdates.AutoDateLocator()))\nplt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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",
      "version": "3.7.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}