{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Search for scientific processes\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Here we demonstrate how to search for events of specific scientific processes using the **Event Search** subpackage included in **AIDApy**. First, we start with importing the modules required by **Event Search**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Python modules\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "\n",
    "#AIDApy modules\n",
    "from aidapy import event_search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking for specific EDR events\n",
    "In particular, here we want to look for Electron Diffusion Regions, which are tiny regions located at the very center of magnetic reconnection. To do so, we chose to use data from the MMS mission, which has been specifically designed to study the microphysics (down to electron scales) of magnetic reconnection. We also chose to look at data on 2017 July 20, during which a specific campaign was dedicated to the observation of reconnection in the magnetotail. The time interval is thus defined as: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = datetime(2017, 8, 20, 2, 0, 0)\n",
    "end_time = datetime(2017, 8, 20, 2, 10, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we need to set up the settings for the specific scientific process we want to study; i. e., here Electron Diffusion Region (EDR). The EDR is characterized by a very low magnetic field (< 5 nT), high-speed electron jets (> 4000 km/s) and a high currents (current density > 70 nA/m²). Thus we define these criteria as inputs to the Event Search tool, in a format compliant with the *event_search* function (see the Event Search section in the user guide). Additionally, we chose to download data from all probes on board MMS mission, in the default GSE coordinate system. Finally, since the EDR region size is of electron scale, we chose to use the high-resolution burst-mode data from MMS, and a time window of 60 s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "settings = {\"criteria\": lambda dc_mag_tot, j_curl_tot, e_bulkv_x: (np.any(dc_mag_tot < 5)) &\n",
    "            (np.any(j_curl_tot > 70)) & (np.any(np.abs(e_bulkv_x) > 4000)),\n",
    "            \"parameters\": {\"mission\": \"mms\",\n",
    "                           \"process\": \"edr\",\n",
    "                           \"probes\": ['1'],\n",
    "                           \"mode\": \"brst\",\n",
    "                           \"time_window\": 120,\n",
    "                           \"time_step\": 120}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the settings on the data and parameters are defined, we call *event_search* function as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\breuillard\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\heliopy-0+unknown-py3.7.egg\\heliopy\\data\\util.py:601: UserWarning: The CDF provided units ('0, 1') for key 'mms1_des_compressionloss_brst' are unknown\n",
      "  warnings.warn(message)\n",
      "C:\\Users\\breuillard\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\heliopy-0+unknown-py3.7.egg\\heliopy\\data\\util.py:601: UserWarning: The CDF provided units ('0, 1') for key 'mms1_des_steptable_parity_brst' are unknown\n",
      "  warnings.warn(message)\n",
      "C:\\Users\\breuillard\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\heliopy-0+unknown-py3.7.egg\\heliopy\\data\\util.py:601: UserWarning: The CDF provided units ('00-32') for key 'mms1_des_sector_despinp_brst' are unknown\n",
      "  warnings.warn(message)\n",
      "C:\\Users\\breuillard\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\heliopy-0+unknown-py3.7.egg\\heliopy\\data\\util.py:601: UserWarning: The CDF provided units ('hours') for key 'mms1_mec_mlt' are unknown\n",
      "  warnings.warn(message)\n",
      "C:\\Users\\breuillard\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\heliopy-0+unknown-py3.7.egg\\heliopy\\data\\util.py:601: UserWarning: The CDF provided units ('hours') for key 'mms1_mec_mlt' are unknown\n",
      "  warnings.warn(message)\n",
      "C:\\Users\\breuillard\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\heliopy-0+unknown-py3.7.egg\\heliopy\\data\\util.py:601: UserWarning: The CDF provided units ('hours') for key 'mms1_mec_mlt' are unknown\n",
      "  warnings.warn(message)\n",
      "C:\\Users\\breuillard\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\heliopy-0+unknown-py3.7.egg\\heliopy\\data\\util.py:601: UserWarning: The CDF provided units ('hours') for key 'mms2_mec_mlt' are unknown\n",
      "  warnings.warn(message)\n",
      "C:\\Users\\breuillard\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\heliopy-0+unknown-py3.7.egg\\heliopy\\data\\util.py:601: UserWarning: The CDF provided units ('hours') for key 'mms3_mec_mlt' are unknown\n",
      "  warnings.warn(message)\n",
      "C:\\Users\\breuillard\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\heliopy-0+unknown-py3.7.egg\\heliopy\\data\\util.py:601: UserWarning: The CDF provided units ('hours') for key 'mms4_mec_mlt' are unknown\n",
      "  warnings.warn(message)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[0, 3999, 8000, 11999]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_search(settings, start_time, end_time, plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen above, the function gives as ouput an overview plot with highlighted events for the user to eyeball the results, but if ``to_file`` parameter set to ``True``, also writes a simple text file with the events selected, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#events_list.txt\n",
    "\n",
    "'''\n",
    "List of events found:\n",
    "\n",
    "N°,       START TIME (UTC),              END TIME (UTC),                        S/C location (km)\n",
    "1, 2017-08-10T12:18:01.728287000, 2017-08-10T12:19:01.208817000          -96743.35/16839.555/30637.818\n",
    "''';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": 2
}
