{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "62b53cfa-59f2-44ac-93d6-dc10c75ee4c1",
   "metadata": {},
   "source": [
    "# PRISM products in VirES - visualization of subauroral boundaries\n",
    "\n",
    "This notebook demonstrates of the subaroral boundaries from the `MITx_LP_2F`, `MITxTEC_2F` and `PPIxFAC_2F` Swarm products retrieved by means of the vires client.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85981ae6-3803-4011-99dc-4fbb91246e6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from viresclient import SwarmRequest\n",
    "\n",
    "SERVER_URL = \"https://vires.services/ows\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "217f7988-9eb2-49b7-9835-633fcf422279",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "bea618e7efee42ccb771b9c830676255",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:    0%|          |  [ Elapsed: 00:00, Remaining: ? ] [1/1] "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a05cd57268f04ba8b6aed639ce85ae71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |  [ Elapsed: 00:00, Remaining: ? ] (1.483MB)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:     (Timestamp: 25920)\n",
      "Coordinates:\n",
      "  * Timestamp   (Timestamp) datetime64[ns] 2015-12-30 ... 2016-01-01T23:59:50\n",
      "Data variables:\n",
      "    Spacecraft  (Timestamp) object 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A' 'A'\n",
      "    Longitude   (Timestamp) float64 -94.26 -94.26 -94.27 ... -95.37 -95.37\n",
      "    Latitude    (Timestamp) float64 -25.88 -25.24 -24.6 ... 29.82 30.46 31.1\n",
      "    Kp          (Timestamp) float64 2.3 0.0 0.0 0.0 0.0 ... 1.7 1.7 1.7 1.7 1.7\n",
      "    QDLon       (Timestamp) float64 -18.99 -19.1 -19.22 ... -24.87 -24.9 -24.92\n",
      "    QDLat       (Timestamp) float64 -15.46 -14.85 -14.24 ... 39.53 40.17 40.8\n",
      "    Radius      (Timestamp) float64 6.832e+06 6.832e+06 ... 6.823e+06 6.823e+06\n",
      "Attributes:\n",
      "    Sources:         ['SW_OPER_AUX_KP__2__19980101T013000_20220407T193000_000...\n",
      "    MagneticModels:  []\n",
      "    RangeFilters:    []\n"
     ]
    },
    {
     "data": {
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       "model_id": "4873dc5e5db049349d45fb0aa379a92a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |  [ Elapsed: 00:00, Remaining: ? ] (0.054MB)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:     (Timestamp: 68)\n",
      "Coordinates:\n",
      "  * Timestamp   (Timestamp) datetime64[ns] 2015-12-30T00:37:35.196999936 ... ...\n",
      "Data variables:\n",
      "    Spacecraft  (Timestamp) object 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A' 'A'\n",
      "    QDLat       (Timestamp) float64 57.14 58.0 66.07 59.68 ... 64.2 49.69 53.02\n",
      "    Longitude   (Timestamp) float64 72.8 49.22 -140.3 ... -23.28 143.5 96.42\n",
      "    Radius      (Timestamp) float64 6.818e+06 6.818e+06 ... 6.818e+06 6.818e+06\n",
      "    Latitude    (Timestamp) float64 61.01 61.7 64.41 63.0 ... 63.66 55.69 57.22\n",
      "Attributes:\n",
      "    Sources:         ['SW_OPER_MITA_LP_2F_20150101T000000_20151231T235959_010...\n",
      "    MagneticModels:  []\n",
      "    RangeFilters:    []\n"
     ]
    },
    {
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       "model_id": "ed710f0c8b2b466d9de09329e0cf6d6d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |  [ Elapsed: 00:00, Remaining: ? ] (0.063MB)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:     (Timestamp: 46)\n",
      "Coordinates:\n",
      "  * Timestamp   (Timestamp) datetime64[ns] 2015-12-30T00:37:41.196999936 ... ...\n",
      "Data variables:\n",
      "    Spacecraft  (Timestamp) object 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A' 'A'\n",
      "    Longitude   (Timestamp) float64 72.85 49.28 25.7 2.2 ... -169.9 166.4 143.5\n",
      "    PointType   (Timestamp) uint8 6 6 6 6 6 6 6 6 6 6 6 ... 6 6 6 6 6 6 6 6 6 6\n",
      "    Latitude    (Timestamp) float64 60.63 61.26 61.94 62.09 ... 60.06 61.7 55.63\n",
      "    QDLat       (Timestamp) float64 56.76 57.55 58.57 ... 56.88 56.46 49.63\n",
      "    Radius      (Timestamp) float64 6.818e+06 6.818e+06 ... 6.818e+06 6.818e+06\n",
      "Attributes:\n",
      "    Sources:         ['SW_OPER_MITA_LP_2F_20150101T000000_20151231T235959_010...\n",
      "    MagneticModels:  []\n",
      "    RangeFilters:    ['PointType:6,6']\n"
     ]
    },
    {
     "data": {
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       "model_id": "f06e3411c8b64294a69d9b287c48e7d6",
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       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |  [ Elapsed: 00:00, Remaining: ? ] (0.054MB)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:     (Timestamp: 39)\n",
      "Coordinates:\n",
      "  * Timestamp   (Timestamp) datetime64[ns] 2015-12-30T03:42:15 ... 2016-01-01...\n",
      "Data variables:\n",
      "    Spacecraft  (Timestamp) object 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A' 'A'\n",
      "    QDLat       (Timestamp) float64 67.61 67.48 67.74 ... 57.92 58.67 58.04\n",
      "    Longitude   (Timestamp) float64 23.69 0.7023 151.7 ... 164.5 141.2 119.2\n",
      "    Radius      (Timestamp) float64 6.817e+06 6.817e+06 ... 6.818e+06 6.817e+06\n",
      "    Latitude    (Timestamp) float64 70.64 69.12 72.71 ... 63.13 64.15 62.94\n",
      "Attributes:\n",
      "    Sources:         ['SW_OPER_MITATEC_2F_20150101T000000_20151231T235959_010...\n",
      "    MagneticModels:  []\n",
      "    RangeFilters:    []\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7338cea393048b1a3cf16a1e7623590",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          |  [ Elapsed: 00:00, Remaining: ? ] (0.054MB)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:     (Timestamp: 144)\n",
      "Coordinates:\n",
      "  * Timestamp   (Timestamp) datetime64[ns] 2015-12-30T00:18:40.500000 ... 201...\n",
      "Data variables:\n",
      "    Spacecraft  (Timestamp) object 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A' 'A'\n",
      "    QDLat       (Timestamp) float64 55.39 63.47 59.65 ... 63.77 67.42 60.38\n",
      "    Longitude   (Timestamp) float64 -94.91 -94.5 -118.0 ... -71.22 93.33 95.42\n",
      "    Radius      (Timestamp) float64 6.82e+06 6.819e+06 ... 6.817e+06 6.817e+06\n",
      "    Latitude    (Timestamp) float64 45.9 54.3 53.64 59.97 ... 55.43 71.98 64.65\n",
      "Attributes:\n",
      "    Sources:         ['SW_OPER_PPIAFAC_2F_20150101T000000_20151231T235959_010...\n",
      "    MagneticModels:  []\n",
      "    RangeFilters:    []\n"
     ]
    }
   ],
   "source": [
    "SPACECRAFT = 'A'\n",
    "START_TIME = \"2015-12-30T00:00:00Z\"\n",
    "END_TIME = \"2016-01-02T00:00:00Z\"\n",
    "MIT_LP_TE_PEAK = 0x6\n",
    "\n",
    "def _insert(list_, value):\n",
    "    if value not in list_:\n",
    "        list_.apped(value)\n",
    "\n",
    "request = SwarmRequest(SERVER_URL)\n",
    "request.set_collection(f\"SW_OPER_MOD{SPACECRAFT}_SC_1B\")\n",
    "request.set_products(\n",
    "    measurements=[],\n",
    "    auxiliaries=['QDLat', 'QDLon', 'Kp'],\n",
    "    sampling_step=\"PT10S\"\n",
    ")\n",
    "data_track = request.get_between(\n",
    "    start_time=START_TIME,\n",
    "    end_time=END_TIME,\n",
    "    asynchronous=True,\n",
    ").as_xarray()\n",
    "\n",
    "print(data_track)\n",
    "\n",
    "request = SwarmRequest(SERVER_URL)\n",
    "request.set_collection(f\"SW_OPER_MIT{SPACECRAFT}_LP_2F\")\n",
    "request.set_products(\n",
    "    measurements=[],\n",
    "    auxiliaries=['QDLat'],\n",
    ")\n",
    "data_ne_min = request.get_between(\n",
    "    start_time=START_TIME,\n",
    "    end_time=END_TIME,\n",
    "    asynchronous=False,\n",
    ").as_xarray()\n",
    "print(data_ne_min)\n",
    "\n",
    "request = SwarmRequest(SERVER_URL)\n",
    "request.set_collection(f\"SW_OPER_MIT{SPACECRAFT}_LP_2F:ID\")\n",
    "request.set_products(\n",
    "    measurements=['PointType'],\n",
    "    auxiliaries=['QDLat'],\n",
    ")\n",
    "request.set_range_filter('PointType', MIT_LP_TE_PEAK, MIT_LP_TE_PEAK)\n",
    "data_te_peak = request.get_between(\n",
    "    start_time=START_TIME,\n",
    "    end_time=END_TIME,\n",
    "    asynchronous=False,\n",
    ").as_xarray()\n",
    "print(data_te_peak)\n",
    "assert (data_te_peak['PointType'].values == MIT_LP_TE_PEAK).all()\n",
    "\n",
    "request = SwarmRequest(SERVER_URL)\n",
    "request.set_collection(f\"SW_OPER_MIT{SPACECRAFT}TEC_2F\")\n",
    "request.set_products(\n",
    "    measurements=[],\n",
    "    auxiliaries=['QDLat'],\n",
    ")\n",
    "data_tec_min = request.get_between(\n",
    "    start_time=START_TIME,\n",
    "    end_time=END_TIME,\n",
    "    asynchronous=False,\n",
    ").as_xarray()\n",
    "print(data_tec_min)\n",
    "\n",
    "request = SwarmRequest(SERVER_URL)\n",
    "request.set_collection(f\"SW_OPER_PPI{SPACECRAFT}FAC_2F:ID\")\n",
    "request.set_products(\n",
    "    measurements=[],\n",
    "    auxiliaries=['QDLat'],\n",
    ")\n",
    "data_ssfac = request.get_between(\n",
    "    start_time=START_TIME,\n",
    "    end_time=END_TIME,\n",
    "    asynchronous=False,\n",
    ").as_xarray()\n",
    "print(data_ssfac)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8065ce9c-3dac-48bd-a2f3-cbae50d6aef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc9a40156d0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1500x1200 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure, subplot, show\n",
    "%matplotlib inline\n",
    "\n",
    "MIT_LP_TE_PEAK = 0x6\n",
    "\n",
    "fig = figure(figsize=(15, 12), dpi=100)\n",
    "\n",
    "ax = subplot(3, 1, 1)\n",
    "ax.fill_between(data_track['Timestamp'].values, data_track['Kp'].values, color='tab:red', alpha=0.25)\n",
    "h_kp, = ax.plot(data_track['Timestamp'].values, data_track['Kp'].values, '-', color='tab:red')\n",
    "ax.set_ylim([-0.5, 8.5])\n",
    "ax.set_ylabel(\"Kp index\")\n",
    "ax.grid()\n",
    "\n",
    "ax = subplot(3, 1, 2)\n",
    "h_tec_min, = ax.plot(data_tec_min['Timestamp'].values, data_tec_min['QDLat'].values, 'v', color='tab:cyan')\n",
    "h_ne_min, = ax.plot(data_ne_min['Timestamp'].values, data_ne_min['QDLat'].values, 'v', color='tab:blue')\n",
    "h_te_peak, = ax.plot(data_te_peak['Timestamp'].values, data_te_peak['QDLat'].values, '^', color='tab:red')\n",
    "h_ssfac, = ax.plot(data_ssfac['Timestamp'].values, data_ssfac['QDLat'].values, 'o', color='tab:green', ms=3)\n",
    "ax.set_ylim([30, 80])\n",
    "ax.set_ylabel(\"QD Latitude / deg\")\n",
    "ax.grid()\n",
    "ax.legend(\n",
    "    [\n",
    "        h_ne_min,\n",
    "        h_te_peak,\n",
    "        h_tec_min,\n",
    "        h_ssfac,\n",
    "    ],\n",
    "    [\n",
    "        'Ne minimum',\n",
    "        'Te peak',\n",
    "        'TEC minimum',\n",
    "        'SSFAC boundary'\n",
    "    ],\n",
    ")\n",
    "\n",
    "\n",
    "ax = subplot(3, 1, 3)\n",
    "h_tec_min, = ax.plot(data_tec_min['Timestamp'].values, data_tec_min['QDLat'].values, 'v', color='tab:cyan')\n",
    "h_ne_min, = ax.plot(data_ne_min['Timestamp'].values, data_ne_min['QDLat'].values, 'v', color='tab:blue')\n",
    "h_te_peak, = ax.plot(data_te_peak['Timestamp'].values, data_te_peak['QDLat'].values, '^', color='tab:red')\n",
    "h_ssfac, = ax.plot(data_ssfac['Timestamp'].values, data_ssfac['QDLat'].values, 'o', color='tab:green', ms=3)\n",
    "ax.set_ylabel(\"QD Latitude / deg\")\n",
    "ax.set_ylim([-80, -30])\n",
    "ax.grid()\n",
    "ax.legend(\n",
    "    [\n",
    "        h_ne_min,\n",
    "        h_te_peak,\n",
    "        h_tec_min,\n",
    "        h_ssfac,\n",
    "    ],\n",
    "    [\n",
    "        'Ne minimum',\n",
    "        'Te peak',\n",
    "        'TEC minimum',\n",
    "        'SSFAC boundary'\n",
    "    ],\n",
    ")\n"
   ]
  }
 ],
 "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",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}