{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5f752af3-8f33-447d-9160-e074acedbaf8",
   "metadata": {},
   "source": [
    "# Compute Latency\n",
    "\n",
    "This example demonstrates how to use direct function calls of the low-level TAT-C library to perform latency analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec9c6021-4d9a-4168-82e7-e5edc0f6799b",
   "metadata": {},
   "source": [
    "Similar to the _Collect Observations_ and _Compute Coverage_ examples, the first steps are to define the satellites for the mission. This example again uses the NOAA-20 satellite with a two-line elements model from July 2022 and a VIIRS instrument with field of regard computed based on a 834km altitude and 3000km swath width. Points are distributed over the globe with a 5000 km characteristic distance and observations are collected over a 30 day period starting on July 14, 2022 at noon UTC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a269529-21b1-4b1c-b56b-b336b437ed6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>point_id</th>\n",
       "      <th>geometry</th>\n",
       "      <th>satellite</th>\n",
       "      <th>instrument</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>epoch</th>\n",
       "      <th>sat_alt</th>\n",
       "      <th>sat_az</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>POINT Z (-157.51699 -67.51699 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>2022-07-26 23:05:45.258746+00:00</td>\n",
       "      <td>2022-07-26 23:08:44.580526+00:00</td>\n",
       "      <td>2022-07-26 23:07:14.919636+00:00</td>\n",
       "      <td>23.232376</td>\n",
       "      <td>96.383771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>POINT Z (-157.51699 -67.51699 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>2022-07-30 23:30:00.700453+00:00</td>\n",
       "      <td>2022-07-30 23:35:11.668269+00:00</td>\n",
       "      <td>2022-07-30 23:32:36.184361+00:00</td>\n",
       "      <td>29.980441</td>\n",
       "      <td>90.571477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>POINT Z (-157.51699 -67.51699 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>2022-07-31 01:09:44.380087+00:00</td>\n",
       "      <td>2022-07-31 01:17:23.069693+00:00</td>\n",
       "      <td>2022-07-31 01:13:33.724890+00:00</td>\n",
       "      <td>83.932517</td>\n",
       "      <td>71.659563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>POINT Z (-157.51699 -67.51699 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>2022-07-31 23:11:40.551119+00:00</td>\n",
       "      <td>2022-07-31 23:15:24.047070+00:00</td>\n",
       "      <td>2022-07-31 23:13:32.299094500+00:00</td>\n",
       "      <td>24.749767</td>\n",
       "      <td>94.883549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>POINT Z (-157.51699 -67.51699 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>2022-08-01 00:50:55.502645+00:00</td>\n",
       "      <td>2022-08-01 00:58:28.482647+00:00</td>\n",
       "      <td>2022-08-01 00:54:41.992646+00:00</td>\n",
       "      <td>70.126386</td>\n",
       "      <td>72.613305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2169</th>\n",
       "      <td>31</td>\n",
       "      <td>POINT Z (157.24514 67.38106 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>2022-08-12 18:28:51.557986+00:00</td>\n",
       "      <td>2022-08-12 18:34:04.353232+00:00</td>\n",
       "      <td>2022-08-12 18:31:27.955609+00:00</td>\n",
       "      <td>31.258697</td>\n",
       "      <td>324.067710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2170</th>\n",
       "      <td>31</td>\n",
       "      <td>POINT Z (157.24514 67.38106 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>2022-08-12 21:48:45.630367+00:00</td>\n",
       "      <td>2022-08-12 21:49:49.415577+00:00</td>\n",
       "      <td>2022-08-12 21:49:17.522972+00:00</td>\n",
       "      <td>21.115155</td>\n",
       "      <td>12.621024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2171</th>\n",
       "      <td>31</td>\n",
       "      <td>POINT Z (157.24514 67.38106 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>2022-08-12 23:25:37.442375+00:00</td>\n",
       "      <td>2022-08-12 23:31:05.304568+00:00</td>\n",
       "      <td>2022-08-12 23:28:21.373471500+00:00</td>\n",
       "      <td>32.779326</td>\n",
       "      <td>36.304442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>31</td>\n",
       "      <td>POINT Z (157.24514 67.38106 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>2022-08-13 01:04:22.215027+00:00</td>\n",
       "      <td>2022-08-13 01:11:48.221137+00:00</td>\n",
       "      <td>2022-08-13 01:08:05.218082+00:00</td>\n",
       "      <td>73.798507</td>\n",
       "      <td>55.756224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>31</td>\n",
       "      <td>POINT Z (157.24514 67.38106 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>2022-08-13 02:45:33.011228+00:00</td>\n",
       "      <td>2022-08-13 02:51:56.674083+00:00</td>\n",
       "      <td>2022-08-13 02:48:44.842655500+00:00</td>\n",
       "      <td>40.285445</td>\n",
       "      <td>264.921794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2174 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      point_id                          geometry satellite instrument  \\\n",
       "0            0  POINT Z (-157.51699 -67.51699 0)   NOAA 20      VIIRS   \n",
       "1            0  POINT Z (-157.51699 -67.51699 0)   NOAA 20      VIIRS   \n",
       "2            0  POINT Z (-157.51699 -67.51699 0)   NOAA 20      VIIRS   \n",
       "3            0  POINT Z (-157.51699 -67.51699 0)   NOAA 20      VIIRS   \n",
       "4            0  POINT Z (-157.51699 -67.51699 0)   NOAA 20      VIIRS   \n",
       "...        ...                               ...       ...        ...   \n",
       "2169        31    POINT Z (157.24514 67.38106 0)   NOAA 20      VIIRS   \n",
       "2170        31    POINT Z (157.24514 67.38106 0)   NOAA 20      VIIRS   \n",
       "2171        31    POINT Z (157.24514 67.38106 0)   NOAA 20      VIIRS   \n",
       "2172        31    POINT Z (157.24514 67.38106 0)   NOAA 20      VIIRS   \n",
       "2173        31    POINT Z (157.24514 67.38106 0)   NOAA 20      VIIRS   \n",
       "\n",
       "                                start                              end  \\\n",
       "0    2022-07-26 23:05:45.258746+00:00 2022-07-26 23:08:44.580526+00:00   \n",
       "1    2022-07-30 23:30:00.700453+00:00 2022-07-30 23:35:11.668269+00:00   \n",
       "2    2022-07-31 01:09:44.380087+00:00 2022-07-31 01:17:23.069693+00:00   \n",
       "3    2022-07-31 23:11:40.551119+00:00 2022-07-31 23:15:24.047070+00:00   \n",
       "4    2022-08-01 00:50:55.502645+00:00 2022-08-01 00:58:28.482647+00:00   \n",
       "...                               ...                              ...   \n",
       "2169 2022-08-12 18:28:51.557986+00:00 2022-08-12 18:34:04.353232+00:00   \n",
       "2170 2022-08-12 21:48:45.630367+00:00 2022-08-12 21:49:49.415577+00:00   \n",
       "2171 2022-08-12 23:25:37.442375+00:00 2022-08-12 23:31:05.304568+00:00   \n",
       "2172 2022-08-13 01:04:22.215027+00:00 2022-08-13 01:11:48.221137+00:00   \n",
       "2173 2022-08-13 02:45:33.011228+00:00 2022-08-13 02:51:56.674083+00:00   \n",
       "\n",
       "                                   epoch    sat_alt      sat_az  \n",
       "0       2022-07-26 23:07:14.919636+00:00  23.232376   96.383771  \n",
       "1       2022-07-30 23:32:36.184361+00:00  29.980441   90.571477  \n",
       "2       2022-07-31 01:13:33.724890+00:00  83.932517   71.659563  \n",
       "3    2022-07-31 23:13:32.299094500+00:00  24.749767   94.883549  \n",
       "4       2022-08-01 00:54:41.992646+00:00  70.126386   72.613305  \n",
       "...                                  ...        ...         ...  \n",
       "2169    2022-08-12 18:31:27.955609+00:00  31.258697  324.067710  \n",
       "2170    2022-08-12 21:49:17.522972+00:00  21.115155   12.621024  \n",
       "2171 2022-08-12 23:28:21.373471500+00:00  32.779326   36.304442  \n",
       "2172    2022-08-13 01:08:05.218082+00:00  73.798507   55.756224  \n",
       "2173 2022-08-13 02:48:44.842655500+00:00  40.285445  264.921794  \n",
       "\n",
       "[2174 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tatc import utils\n",
    "from tatc.schemas import Instrument, Satellite, TwoLineElements\n",
    "\n",
    "viirs = Instrument(\n",
    "    name=\"VIIRS\",\n",
    "    field_of_regard=utils.swath_width_to_field_of_regard(834000, 3000000),\n",
    "    req_target_sunlit=True,\n",
    ")\n",
    "noaa20 = Satellite(\n",
    "    name=\"NOAA 20\",\n",
    "    orbit=TwoLineElements(\n",
    "        tle=[\n",
    "            \"1 43013U 17073A   22195.78278435  .00000038  00000+0  38919-4 0  9996\",\n",
    "            \"2 43013  98.7169 133.9110 0001202  63.8768 296.2532 14.19561306241107\",\n",
    "        ]\n",
    "    ),\n",
    "    instruments=[viirs],\n",
    ")\n",
    "\n",
    "from tatc.generation import generate_equally_spaced_points\n",
    "\n",
    "points_df = generate_equally_spaced_points(5000e3)\n",
    "\n",
    "from tatc.schemas import Point\n",
    "\n",
    "points = points_df.apply(\n",
    "    lambda r: Point(id=r.point_id, latitude=r.geometry.y, longitude=r.geometry.x),\n",
    "    axis=1,\n",
    ")\n",
    "\n",
    "from datetime import datetime, timedelta, timezone\n",
    "\n",
    "start = datetime(year=2022, month=7, day=14, hour=12, tzinfo=timezone.utc)\n",
    "end = start + timedelta(days=30)\n",
    "\n",
    "from tatc.analysis import collect_observations\n",
    "from joblib import Parallel, delayed\n",
    "import pandas as pd\n",
    "\n",
    "observations_list = Parallel(n_jobs=-1)(\n",
    "    delayed(collect_observations)(point, noaa20, start, end) for point in points\n",
    ")\n",
    "observations = pd.concat(observations_list, ignore_index=True)\n",
    "display(observations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d2db42-188d-40e6-a968-78e587ebe4b3",
   "metadata": {},
   "source": [
    "Latency analysis measures the interval between observations and downlink to the first ground station. The following specifies a ground station at Hoboken with a 10-degree minimum elevaiton angle for downlink."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd8c9eea-4f5a-4efa-9c93-047e7a728284",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>geometry</th>\n",
       "      <th>satellite</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hoboken</td>\n",
       "      <td>POINT Z (-74.02686 40.74259 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>2022-07-14 17:12:43.763296+00:00</td>\n",
       "      <td>2022-07-14 17:22:57.456044+00:00</td>\n",
       "      <td>2022-07-14 17:17:50.609670+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hoboken</td>\n",
       "      <td>POINT Z (-74.02686 40.74259 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>2022-07-14 18:54:04.803537+00:00</td>\n",
       "      <td>2022-07-14 19:02:25.062501+00:00</td>\n",
       "      <td>2022-07-14 18:58:14.933019+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hoboken</td>\n",
       "      <td>POINT Z (-74.02686 40.74259 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>2022-07-15 05:32:06.654997+00:00</td>\n",
       "      <td>2022-07-15 05:39:35.858756+00:00</td>\n",
       "      <td>2022-07-15 05:35:51.256876500+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hoboken</td>\n",
       "      <td>POINT Z (-74.02686 40.74259 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>2022-07-15 07:11:08.223574+00:00</td>\n",
       "      <td>2022-07-15 07:21:37.089606+00:00</td>\n",
       "      <td>2022-07-15 07:16:22.656590+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hoboken</td>\n",
       "      <td>POINT Z (-74.02686 40.74259 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>2022-07-15 16:54:27.877548+00:00</td>\n",
       "      <td>2022-07-15 17:03:52.170605+00:00</td>\n",
       "      <td>2022-07-15 16:59:10.024076500+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Hoboken</td>\n",
       "      <td>POINT Z (-74.02686 40.74259 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>2022-08-12 08:28:09.529139+00:00</td>\n",
       "      <td>2022-08-12 08:32:42.603014+00:00</td>\n",
       "      <td>2022-08-12 08:30:26.066076500+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Hoboken</td>\n",
       "      <td>POINT Z (-74.02686 40.74259 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>2022-08-12 16:30:12.255865+00:00</td>\n",
       "      <td>2022-08-12 16:37:46.514194+00:00</td>\n",
       "      <td>2022-08-12 16:33:59.385029500+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Hoboken</td>\n",
       "      <td>POINT Z (-74.02686 40.74259 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>2022-08-12 18:08:28.080213+00:00</td>\n",
       "      <td>2022-08-12 18:19:02.956149+00:00</td>\n",
       "      <td>2022-08-12 18:13:45.518181+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Hoboken</td>\n",
       "      <td>POINT Z (-74.02686 40.74259 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>2022-08-13 06:26:51.204464+00:00</td>\n",
       "      <td>2022-08-13 06:37:31.285805+00:00</td>\n",
       "      <td>2022-08-13 06:32:11.245134500+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Hoboken</td>\n",
       "      <td>POINT Z (-74.02686 40.74259 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>2022-08-13 08:08:18.072998+00:00</td>\n",
       "      <td>2022-08-13 08:15:29.539014+00:00</td>\n",
       "      <td>2022-08-13 08:11:53.806006+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     station                        geometry satellite  \\\n",
       "0    Hoboken  POINT Z (-74.02686 40.74259 0)   NOAA 20   \n",
       "1    Hoboken  POINT Z (-74.02686 40.74259 0)   NOAA 20   \n",
       "2    Hoboken  POINT Z (-74.02686 40.74259 0)   NOAA 20   \n",
       "3    Hoboken  POINT Z (-74.02686 40.74259 0)   NOAA 20   \n",
       "4    Hoboken  POINT Z (-74.02686 40.74259 0)   NOAA 20   \n",
       "..       ...                             ...       ...   \n",
       "115  Hoboken  POINT Z (-74.02686 40.74259 0)   NOAA 20   \n",
       "116  Hoboken  POINT Z (-74.02686 40.74259 0)   NOAA 20   \n",
       "117  Hoboken  POINT Z (-74.02686 40.74259 0)   NOAA 20   \n",
       "118  Hoboken  POINT Z (-74.02686 40.74259 0)   NOAA 20   \n",
       "119  Hoboken  POINT Z (-74.02686 40.74259 0)   NOAA 20   \n",
       "\n",
       "                               start                              end  \\\n",
       "0   2022-07-14 17:12:43.763296+00:00 2022-07-14 17:22:57.456044+00:00   \n",
       "1   2022-07-14 18:54:04.803537+00:00 2022-07-14 19:02:25.062501+00:00   \n",
       "2   2022-07-15 05:32:06.654997+00:00 2022-07-15 05:39:35.858756+00:00   \n",
       "3   2022-07-15 07:11:08.223574+00:00 2022-07-15 07:21:37.089606+00:00   \n",
       "4   2022-07-15 16:54:27.877548+00:00 2022-07-15 17:03:52.170605+00:00   \n",
       "..                               ...                              ...   \n",
       "115 2022-08-12 08:28:09.529139+00:00 2022-08-12 08:32:42.603014+00:00   \n",
       "116 2022-08-12 16:30:12.255865+00:00 2022-08-12 16:37:46.514194+00:00   \n",
       "117 2022-08-12 18:08:28.080213+00:00 2022-08-12 18:19:02.956149+00:00   \n",
       "118 2022-08-13 06:26:51.204464+00:00 2022-08-13 06:37:31.285805+00:00   \n",
       "119 2022-08-13 08:08:18.072998+00:00 2022-08-13 08:15:29.539014+00:00   \n",
       "\n",
       "                                  epoch  \n",
       "0      2022-07-14 17:17:50.609670+00:00  \n",
       "1      2022-07-14 18:58:14.933019+00:00  \n",
       "2   2022-07-15 05:35:51.256876500+00:00  \n",
       "3      2022-07-15 07:16:22.656590+00:00  \n",
       "4   2022-07-15 16:59:10.024076500+00:00  \n",
       "..                                  ...  \n",
       "115 2022-08-12 08:30:26.066076500+00:00  \n",
       "116 2022-08-12 16:33:59.385029500+00:00  \n",
       "117    2022-08-12 18:13:45.518181+00:00  \n",
       "118 2022-08-13 06:32:11.245134500+00:00  \n",
       "119    2022-08-13 08:11:53.806006+00:00  \n",
       "\n",
       "[120 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tatc.schemas import GroundStation\n",
    "\n",
    "hoboken = GroundStation(\n",
    "    name=\"Hoboken\", latitude=40.74259, longitude=-74.02686, min_elevation_angle=10\n",
    ")\n",
    "\n",
    "from tatc.analysis import collect_downlinks\n",
    "\n",
    "downlinks = collect_downlinks(hoboken, noaa20, start, end)\n",
    "display(downlinks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f3c6b1-71c0-44ac-80d4-4e7f7d9a5ded",
   "metadata": {},
   "source": [
    "Finally, latencies are computed by comparing the observations and downlink opportunities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5446b79a-2c9d-430d-87c8-71a665b4e41a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>point_id</th>\n",
       "      <th>geometry</th>\n",
       "      <th>satellite</th>\n",
       "      <th>instrument</th>\n",
       "      <th>sat_alt</th>\n",
       "      <th>sat_az</th>\n",
       "      <th>station</th>\n",
       "      <th>downlinked</th>\n",
       "      <th>latency</th>\n",
       "      <th>observed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>POINT Z (22.34708 22.41505 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>46.686116</td>\n",
       "      <td>260.777813</td>\n",
       "      <td>Hoboken</td>\n",
       "      <td>2022-07-14 17:17:50.609670+00:00</td>\n",
       "      <td>0 days 05:10:34.144994500</td>\n",
       "      <td>2022-07-14 12:07:16.464675500+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>POINT Z (22.34708 67.38106 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>29.024717</td>\n",
       "      <td>271.880297</td>\n",
       "      <td>Hoboken</td>\n",
       "      <td>2022-07-14 17:17:50.609670+00:00</td>\n",
       "      <td>0 days 04:57:46.494320</td>\n",
       "      <td>2022-07-14 12:20:04.115350+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>POINT Z (-22.61894 67.38106 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>48.704552</td>\n",
       "      <td>49.346995</td>\n",
       "      <td>Hoboken</td>\n",
       "      <td>2022-07-14 17:17:50.609670+00:00</td>\n",
       "      <td>0 days 04:56:13.129769</td>\n",
       "      <td>2022-07-14 12:21:37.479901+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>POINT Z (-112.55097 67.38106 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>31.323022</td>\n",
       "      <td>324.299192</td>\n",
       "      <td>Hoboken</td>\n",
       "      <td>2022-07-14 17:17:50.609670+00:00</td>\n",
       "      <td>0 days 04:47:32.626919500</td>\n",
       "      <td>2022-07-14 12:30:17.982750500+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>POINT Z (-157.51699 67.38106 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>55.694847</td>\n",
       "      <td>103.325667</td>\n",
       "      <td>Hoboken</td>\n",
       "      <td>2022-07-14 17:17:50.609670+00:00</td>\n",
       "      <td>0 days 04:44:55.809464</td>\n",
       "      <td>2022-07-14 12:32:54.800206+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2169</th>\n",
       "      <td>26</td>\n",
       "      <td>POINT Z (-67.58495 67.38106 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>28.848897</td>\n",
       "      <td>327.376449</td>\n",
       "      <td>None</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2022-08-13 09:44:37.403851500+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2170</th>\n",
       "      <td>5</td>\n",
       "      <td>POINT Z (67.3131 -67.51699 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>78.725002</td>\n",
       "      <td>245.320118</td>\n",
       "      <td>None</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2022-08-13 10:37:41.608547500+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2171</th>\n",
       "      <td>20</td>\n",
       "      <td>POINT Z (22.34708 22.41505 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>35.605265</td>\n",
       "      <td>70.967792</td>\n",
       "      <td>None</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2022-08-13 11:04:24.936363+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>28</td>\n",
       "      <td>POINT Z (22.34708 67.38106 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>56.654314</td>\n",
       "      <td>258.438321</td>\n",
       "      <td>None</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2022-08-13 11:16:22.849990500+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2173</th>\n",
       "      <td>27</td>\n",
       "      <td>POINT Z (-22.61894 67.38106 0)</td>\n",
       "      <td>NOAA 20</td>\n",
       "      <td>VIIRS</td>\n",
       "      <td>31.043210</td>\n",
       "      <td>33.884307</td>\n",
       "      <td>None</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2022-08-13 11:19:02.608528500+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2174 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      point_id                         geometry satellite instrument  \\\n",
       "0           20    POINT Z (22.34708 22.41505 0)   NOAA 20      VIIRS   \n",
       "1           28    POINT Z (22.34708 67.38106 0)   NOAA 20      VIIRS   \n",
       "2           27   POINT Z (-22.61894 67.38106 0)   NOAA 20      VIIRS   \n",
       "3           25  POINT Z (-112.55097 67.38106 0)   NOAA 20      VIIRS   \n",
       "4           24  POINT Z (-157.51699 67.38106 0)   NOAA 20      VIIRS   \n",
       "...        ...                              ...       ...        ...   \n",
       "2169        26   POINT Z (-67.58495 67.38106 0)   NOAA 20      VIIRS   \n",
       "2170         5    POINT Z (67.3131 -67.51699 0)   NOAA 20      VIIRS   \n",
       "2171        20    POINT Z (22.34708 22.41505 0)   NOAA 20      VIIRS   \n",
       "2172        28    POINT Z (22.34708 67.38106 0)   NOAA 20      VIIRS   \n",
       "2173        27   POINT Z (-22.61894 67.38106 0)   NOAA 20      VIIRS   \n",
       "\n",
       "        sat_alt      sat_az  station                       downlinked  \\\n",
       "0     46.686116  260.777813  Hoboken 2022-07-14 17:17:50.609670+00:00   \n",
       "1     29.024717  271.880297  Hoboken 2022-07-14 17:17:50.609670+00:00   \n",
       "2     48.704552   49.346995  Hoboken 2022-07-14 17:17:50.609670+00:00   \n",
       "3     31.323022  324.299192  Hoboken 2022-07-14 17:17:50.609670+00:00   \n",
       "4     55.694847  103.325667  Hoboken 2022-07-14 17:17:50.609670+00:00   \n",
       "...         ...         ...      ...                              ...   \n",
       "2169  28.848897  327.376449     None                              NaT   \n",
       "2170  78.725002  245.320118     None                              NaT   \n",
       "2171  35.605265   70.967792     None                              NaT   \n",
       "2172  56.654314  258.438321     None                              NaT   \n",
       "2173  31.043210   33.884307     None                              NaT   \n",
       "\n",
       "                       latency                            observed  \n",
       "0    0 days 05:10:34.144994500 2022-07-14 12:07:16.464675500+00:00  \n",
       "1       0 days 04:57:46.494320    2022-07-14 12:20:04.115350+00:00  \n",
       "2       0 days 04:56:13.129769    2022-07-14 12:21:37.479901+00:00  \n",
       "3    0 days 04:47:32.626919500 2022-07-14 12:30:17.982750500+00:00  \n",
       "4       0 days 04:44:55.809464    2022-07-14 12:32:54.800206+00:00  \n",
       "...                        ...                                 ...  \n",
       "2169                       NaT 2022-08-13 09:44:37.403851500+00:00  \n",
       "2170                       NaT 2022-08-13 10:37:41.608547500+00:00  \n",
       "2171                       NaT    2022-08-13 11:04:24.936363+00:00  \n",
       "2172                       NaT 2022-08-13 11:16:22.849990500+00:00  \n",
       "2173                       NaT 2022-08-13 11:19:02.608528500+00:00  \n",
       "\n",
       "[2174 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tatc.analysis import compute_latencies\n",
    "\n",
    "latencies = compute_latencies(observations, downlinks)\n",
    "display(latencies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c86bf341-7071-465f-b7a1-7e5e2d68cc26",
   "metadata": {},
   "source": [
    "Similar to _Coverage Analysis_, latency analysis can also be reduced to compute descriptive statistics for each observation point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f2306ed-e78e-4401-9124-a0655c8ec299",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>point_id</th>\n",
       "      <th>geometry</th>\n",
       "      <th>latency</th>\n",
       "      <th>samples</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>POINT Z (-157.51699 -67.51699 0)</td>\n",
       "      <td>0 days 05:10:59.268689</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>POINT Z (-112.55097 -67.51699 0)</td>\n",
       "      <td>0 days 08:14:18.836280</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>POINT Z (-67.58495 -67.51699 0)</td>\n",
       "      <td>0 days 04:21:48.654757</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>POINT Z (-22.61894 -67.51699 0)</td>\n",
       "      <td>0 days 01:17:44.394353</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>POINT Z (22.34708 -67.51699 0)</td>\n",
       "      <td>0 days 04:08:15.422021</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>POINT Z (67.3131 -67.51699 0)</td>\n",
       "      <td>0 days 08:42:06.877307</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>POINT Z (112.27912 -67.51699 0)</td>\n",
       "      <td>0 days 02:43:07.838991</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>POINT Z (157.24514 -67.51699 0)</td>\n",
       "      <td>0 days 02:19:26.312887</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>POINT Z (-157.51699 -22.55097 0)</td>\n",
       "      <td>0 days 05:44:31.317841</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>POINT Z (-112.55097 -22.55097 0)</td>\n",
       "      <td>0 days 08:37:46.021444</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>POINT Z (-67.58495 -22.55097 0)</td>\n",
       "      <td>0 days 00:17:46.170450</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>POINT Z (-22.61894 -22.55097 0)</td>\n",
       "      <td>0 days 01:35:26.242661</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>POINT Z (22.34708 -22.55097 0)</td>\n",
       "      <td>0 days 04:33:05.882867</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>POINT Z (67.3131 -22.55097 0)</td>\n",
       "      <td>0 days 09:05:11.024889</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>POINT Z (112.27912 -22.55097 0)</td>\n",
       "      <td>0 days 00:45:24.121858</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>POINT Z (157.24514 -22.55097 0)</td>\n",
       "      <td>0 days 02:52:47.346982</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>POINT Z (-157.51699 22.41505 0)</td>\n",
       "      <td>0 days 06:21:54.644226</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>POINT Z (-112.55097 22.41505 0)</td>\n",
       "      <td>0 days 09:24:22.656684</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>POINT Z (-67.58495 22.41505 0)</td>\n",
       "      <td>0 days 06:12:15.863428</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>POINT Z (-22.61894 22.41505 0)</td>\n",
       "      <td>0 days 02:11:37.441686</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>POINT Z (22.34708 22.41505 0)</td>\n",
       "      <td>0 days 06:27:50.003063</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>POINT Z (67.3131 22.41505 0)</td>\n",
       "      <td>0 days 07:34:34.545599</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>POINT Z (112.27912 22.41505 0)</td>\n",
       "      <td>0 days 00:32:56.099903</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>POINT Z (157.24514 22.41505 0)</td>\n",
       "      <td>0 days 03:11:43.033986</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>POINT Z (-157.51699 67.38106 0)</td>\n",
       "      <td>0 days 05:48:32.308626</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>POINT Z (-112.55097 67.38106 0)</td>\n",
       "      <td>0 days 06:31:10.233941</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>POINT Z (-67.58495 67.38106 0)</td>\n",
       "      <td>0 days 04:58:06.133920</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>POINT Z (-22.61894 67.38106 0)</td>\n",
       "      <td>0 days 03:01:50.017806</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>POINT Z (22.34708 67.38106 0)</td>\n",
       "      <td>0 days 04:34:58.875476</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>POINT Z (67.3131 67.38106 0)</td>\n",
       "      <td>0 days 04:11:56.901332</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>POINT Z (112.27912 67.38106 0)</td>\n",
       "      <td>0 days 04:35:16.325513</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>POINT Z (157.24514 67.38106 0)</td>\n",
       "      <td>0 days 05:24:20.336656</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    point_id                          geometry                latency  samples\n",
       "0          0  POINT Z (-157.51699 -67.51699 0) 0 days 05:10:59.268689       31\n",
       "1          1  POINT Z (-112.55097 -67.51699 0) 0 days 08:14:18.836280       31\n",
       "2          2   POINT Z (-67.58495 -67.51699 0) 0 days 04:21:48.654757       30\n",
       "3          3   POINT Z (-22.61894 -67.51699 0) 0 days 01:17:44.394353       31\n",
       "4          4    POINT Z (22.34708 -67.51699 0) 0 days 04:08:15.422021       31\n",
       "5          5     POINT Z (67.3131 -67.51699 0) 0 days 08:42:06.877307       31\n",
       "6          6   POINT Z (112.27912 -67.51699 0) 0 days 02:43:07.838991       33\n",
       "7          7   POINT Z (157.24514 -67.51699 0) 0 days 02:19:26.312887       34\n",
       "8          8  POINT Z (-157.51699 -22.55097 0) 0 days 05:44:31.317841       34\n",
       "9          9  POINT Z (-112.55097 -22.55097 0) 0 days 08:37:46.021444       34\n",
       "10        10   POINT Z (-67.58495 -22.55097 0) 0 days 00:17:46.170450       32\n",
       "11        11   POINT Z (-22.61894 -22.55097 0) 0 days 01:35:26.242661       32\n",
       "12        12    POINT Z (22.34708 -22.55097 0) 0 days 04:33:05.882867       32\n",
       "13        13     POINT Z (67.3131 -22.55097 0) 0 days 09:05:11.024889       34\n",
       "14        14   POINT Z (112.27912 -22.55097 0) 0 days 00:45:24.121858       32\n",
       "15        15   POINT Z (157.24514 -22.55097 0) 0 days 02:52:47.346982       32\n",
       "16        16   POINT Z (-157.51699 22.41505 0) 0 days 06:21:54.644226       32\n",
       "17        17   POINT Z (-112.55097 22.41505 0) 0 days 09:24:22.656684       34\n",
       "18        18    POINT Z (-67.58495 22.41505 0) 0 days 06:12:15.863428       32\n",
       "19        19    POINT Z (-22.61894 22.41505 0) 0 days 02:11:37.441686       34\n",
       "20        20     POINT Z (22.34708 22.41505 0) 0 days 06:27:50.003063       34\n",
       "21        21      POINT Z (67.3131 22.41505 0) 0 days 07:34:34.545599       32\n",
       "22        22    POINT Z (112.27912 22.41505 0) 0 days 00:32:56.099903       34\n",
       "23        23    POINT Z (157.24514 22.41505 0) 0 days 03:11:43.033986       32\n",
       "24        24   POINT Z (-157.51699 67.38106 0) 0 days 05:48:32.308626      173\n",
       "25        25   POINT Z (-112.55097 67.38106 0) 0 days 06:31:10.233941      173\n",
       "26        26    POINT Z (-67.58495 67.38106 0) 0 days 04:58:06.133920      171\n",
       "27        27    POINT Z (-22.61894 67.38106 0) 0 days 03:01:50.017806      176\n",
       "28        28     POINT Z (22.34708 67.38106 0) 0 days 04:34:58.875476      174\n",
       "29        29      POINT Z (67.3131 67.38106 0) 0 days 04:11:56.901332      174\n",
       "30        30    POINT Z (112.27912 67.38106 0) 0 days 04:35:16.325513      177\n",
       "31        31    POINT Z (157.24514 67.38106 0) 0 days 05:24:20.336656      178"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tatc.analysis import reduce_latencies\n",
    "\n",
    "reduced_results = reduce_latencies(latencies)\n",
    "display(reduced_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "998b8c5a-1e44-4a66-88ed-37629ede6d4c",
   "metadata": {},
   "source": [
    "Finally, GeoPlot can visualize the geospatial data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea8c0dd6-64df-490c-9c87-1b8946177ce0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "reduced_results[\"latency_hr\"] = reduced_results.latency / timedelta(hours=1)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
    "reduced_results.plot(\n",
    "    column=\"latency_hr\",\n",
    "    legend=True,\n",
    "    legend_kwds={\"label\": \"Latency (hr)\", \"orientation\": \"horizontal\"},\n",
    "    ax=ax,\n",
    "    transform=ccrs.PlateCarree()\n",
    ")\n",
    "ax.coastlines()\n",
    "ax.set_global()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1677267f-7502-434f-99a7-4e821af577ed",
   "metadata": {},
   "source": [
    "Similar to _Coverage Analysis_, cells can also be used to aggregate descriptive statistics.  Note that, when working on the reduced results, the revisit aggregation function must perform a weighted average based on the number of samples for each point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc1d306c-61f6-4f48-ae7b-53fc0b4dc4dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_id</th>\n",
       "      <th>geometry</th>\n",
       "      <th>samples</th>\n",
       "      <th>latency_hr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON Z ((-135.03398 -90 0, -135.03398 -45.0...</td>\n",
       "      <td>31</td>\n",
       "      <td>5.183130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON Z ((-90.06796 -90 0, -90.06796 -45.033...</td>\n",
       "      <td>31</td>\n",
       "      <td>8.238566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON Z ((-45.10194 -90 0, -45.10194 -45.033...</td>\n",
       "      <td>30</td>\n",
       "      <td>4.363515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON Z ((-0.13593 -90 0, -0.13593 -45.03398...</td>\n",
       "      <td>31</td>\n",
       "      <td>1.295665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON Z ((44.83009 -90 0, 44.83009 -45.03398...</td>\n",
       "      <td>31</td>\n",
       "      <td>4.137617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON Z ((89.79611 -90 0, 89.79611 -45.03398...</td>\n",
       "      <td>31</td>\n",
       "      <td>8.701910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON Z ((134.76213 -90 0, 134.76213 -45.033...</td>\n",
       "      <td>33</td>\n",
       "      <td>2.718844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON Z ((179.72815 -90 0, 179.72815 -45.033...</td>\n",
       "      <td>34</td>\n",
       "      <td>2.323976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>POLYGON Z ((-135.03398 -45.03398 0, -135.03398...</td>\n",
       "      <td>34</td>\n",
       "      <td>5.742033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON Z ((-90.06796 -45.03398 0, -90.06796 -...</td>\n",
       "      <td>34</td>\n",
       "      <td>8.629450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>POLYGON Z ((-45.10194 -45.03398 0, -45.10194 -...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.296158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>POLYGON Z ((-0.13593 -45.03398 0, -0.13593 -0....</td>\n",
       "      <td>32</td>\n",
       "      <td>1.590623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>POLYGON Z ((44.83009 -45.03398 0, 44.83009 -0....</td>\n",
       "      <td>32</td>\n",
       "      <td>4.551634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>POLYGON Z ((89.79611 -45.03398 0, 89.79611 -0....</td>\n",
       "      <td>34</td>\n",
       "      <td>9.086396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>POLYGON Z ((134.76213 -45.03398 0, 134.76213 -...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.756701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>POLYGON Z ((179.72815 -45.03398 0, 179.72815 -...</td>\n",
       "      <td>32</td>\n",
       "      <td>2.879819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>POLYGON Z ((-135.03398 -0.06796 0, -135.03398 ...</td>\n",
       "      <td>32</td>\n",
       "      <td>6.365179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>POLYGON Z ((-90.06796 -0.06796 0, -90.06796 44...</td>\n",
       "      <td>34</td>\n",
       "      <td>9.406294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>POLYGON Z ((-45.10194 -0.06796 0, -45.10194 44...</td>\n",
       "      <td>32</td>\n",
       "      <td>6.204407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>POLYGON Z ((-0.13593 -0.06796 0, -0.13593 44.8...</td>\n",
       "      <td>34</td>\n",
       "      <td>2.193734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>POLYGON Z ((44.83009 -0.06796 0, 44.83009 44.8...</td>\n",
       "      <td>34</td>\n",
       "      <td>6.463890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>POLYGON Z ((89.79611 -0.06796 0, 89.79611 44.8...</td>\n",
       "      <td>32</td>\n",
       "      <td>7.576263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>POLYGON Z ((134.76213 -0.06796 0, 134.76213 44...</td>\n",
       "      <td>34</td>\n",
       "      <td>0.548917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>POLYGON Z ((179.72815 -0.06796 0, 179.72815 44...</td>\n",
       "      <td>32</td>\n",
       "      <td>3.195287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>POLYGON Z ((-135.03398 44.89806 0, -135.03398 ...</td>\n",
       "      <td>173</td>\n",
       "      <td>5.808975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>POLYGON Z ((-90.06796 44.89806 0, -90.06796 89...</td>\n",
       "      <td>173</td>\n",
       "      <td>6.519509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>POLYGON Z ((-45.10194 44.89806 0, -45.10194 89...</td>\n",
       "      <td>171</td>\n",
       "      <td>4.968371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>POLYGON Z ((-0.13593 44.89806 0, -0.13593 89.8...</td>\n",
       "      <td>176</td>\n",
       "      <td>3.030561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>POLYGON Z ((44.83009 44.89806 0, 44.83009 89.8...</td>\n",
       "      <td>174</td>\n",
       "      <td>4.583021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>POLYGON Z ((89.79611 44.89806 0, 89.79611 89.8...</td>\n",
       "      <td>174</td>\n",
       "      <td>4.199139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>POLYGON Z ((134.76213 44.89806 0, 134.76213 89...</td>\n",
       "      <td>177</td>\n",
       "      <td>4.587868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>POLYGON Z ((179.72815 44.89806 0, 179.72815 89...</td>\n",
       "      <td>178</td>\n",
       "      <td>5.405649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cell_id                                           geometry  samples  \\\n",
       "0         0  POLYGON Z ((-135.03398 -90 0, -135.03398 -45.0...       31   \n",
       "1         1  POLYGON Z ((-90.06796 -90 0, -90.06796 -45.033...       31   \n",
       "2         2  POLYGON Z ((-45.10194 -90 0, -45.10194 -45.033...       30   \n",
       "3         3  POLYGON Z ((-0.13593 -90 0, -0.13593 -45.03398...       31   \n",
       "4         4  POLYGON Z ((44.83009 -90 0, 44.83009 -45.03398...       31   \n",
       "5         5  POLYGON Z ((89.79611 -90 0, 89.79611 -45.03398...       31   \n",
       "6         6  POLYGON Z ((134.76213 -90 0, 134.76213 -45.033...       33   \n",
       "7         7  POLYGON Z ((179.72815 -90 0, 179.72815 -45.033...       34   \n",
       "8         8  POLYGON Z ((-135.03398 -45.03398 0, -135.03398...       34   \n",
       "9         9  POLYGON Z ((-90.06796 -45.03398 0, -90.06796 -...       34   \n",
       "10       10  POLYGON Z ((-45.10194 -45.03398 0, -45.10194 -...       32   \n",
       "11       11  POLYGON Z ((-0.13593 -45.03398 0, -0.13593 -0....       32   \n",
       "12       12  POLYGON Z ((44.83009 -45.03398 0, 44.83009 -0....       32   \n",
       "13       13  POLYGON Z ((89.79611 -45.03398 0, 89.79611 -0....       34   \n",
       "14       14  POLYGON Z ((134.76213 -45.03398 0, 134.76213 -...       32   \n",
       "15       15  POLYGON Z ((179.72815 -45.03398 0, 179.72815 -...       32   \n",
       "16       16  POLYGON Z ((-135.03398 -0.06796 0, -135.03398 ...       32   \n",
       "17       17  POLYGON Z ((-90.06796 -0.06796 0, -90.06796 44...       34   \n",
       "18       18  POLYGON Z ((-45.10194 -0.06796 0, -45.10194 44...       32   \n",
       "19       19  POLYGON Z ((-0.13593 -0.06796 0, -0.13593 44.8...       34   \n",
       "20       20  POLYGON Z ((44.83009 -0.06796 0, 44.83009 44.8...       34   \n",
       "21       21  POLYGON Z ((89.79611 -0.06796 0, 89.79611 44.8...       32   \n",
       "22       22  POLYGON Z ((134.76213 -0.06796 0, 134.76213 44...       34   \n",
       "23       23  POLYGON Z ((179.72815 -0.06796 0, 179.72815 44...       32   \n",
       "24       24  POLYGON Z ((-135.03398 44.89806 0, -135.03398 ...      173   \n",
       "25       25  POLYGON Z ((-90.06796 44.89806 0, -90.06796 89...      173   \n",
       "26       26  POLYGON Z ((-45.10194 44.89806 0, -45.10194 89...      171   \n",
       "27       27  POLYGON Z ((-0.13593 44.89806 0, -0.13593 89.8...      176   \n",
       "28       28  POLYGON Z ((44.83009 44.89806 0, 44.83009 89.8...      174   \n",
       "29       29  POLYGON Z ((89.79611 44.89806 0, 89.79611 89.8...      174   \n",
       "30       30  POLYGON Z ((134.76213 44.89806 0, 134.76213 89...      177   \n",
       "31       31  POLYGON Z ((179.72815 44.89806 0, 179.72815 89...      178   \n",
       "\n",
       "    latency_hr  \n",
       "0     5.183130  \n",
       "1     8.238566  \n",
       "2     4.363515  \n",
       "3     1.295665  \n",
       "4     4.137617  \n",
       "5     8.701910  \n",
       "6     2.718844  \n",
       "7     2.323976  \n",
       "8     5.742033  \n",
       "9     8.629450  \n",
       "10    0.296158  \n",
       "11    1.590623  \n",
       "12    4.551634  \n",
       "13    9.086396  \n",
       "14    0.756701  \n",
       "15    2.879819  \n",
       "16    6.365179  \n",
       "17    9.406294  \n",
       "18    6.204407  \n",
       "19    2.193734  \n",
       "20    6.463890  \n",
       "21    7.576263  \n",
       "22    0.548917  \n",
       "23    3.195287  \n",
       "24    5.808975  \n",
       "25    6.519509  \n",
       "26    4.968371  \n",
       "27    3.030561  \n",
       "28    4.583021  \n",
       "29    4.199139  \n",
       "30    4.587868  \n",
       "31    5.405649  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tatc.generation.cells import generate_equally_spaced_cells\n",
    "\n",
    "cells_df = generate_equally_spaced_cells(5000e3)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "grid_results = (\n",
    "    cells_df.sjoin(reduced_results, how=\"inner\", predicate=\"contains\")\n",
    "    .dissolve(\n",
    "        by=\"cell_id\",\n",
    "        aggfunc={\n",
    "            \"samples\": \"sum\",\n",
    "            \"latency_hr\": lambda r: np.average(\n",
    "                r, weights=reduced_results.loc[r.index, \"samples\"]\n",
    "            ),\n",
    "        },\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "display(grid_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d3f24a7-d9b5-4872-8255-85dd23a64614",
   "metadata": {},
   "source": [
    "Finally, plotting can illustrate the latency over global regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "acd11c1f-b20d-4a3f-912a-3b922a3fac09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
    "grid_results.plot(\n",
    "    column=\"latency_hr\",\n",
    "    cmap=\"viridis\",\n",
    "    edgecolor=\"k\",\n",
    "    legend=True,\n",
    "    legend_kwds={\"label\": \"Latency (hr)\", \"orientation\": \"horizontal\"},\n",
    "    ax=ax,\n",
    ")\n",
    "ax.coastlines()\n",
    "ax.set_global()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tatc",
   "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}