{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) 2024 Massachusetts Institute of Technology\n",
    "\n",
    "SPDX-License-Identifier: MIT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observing a Satellite with a Sensor Network\n",
    "As we saw in the previous example notebook, a single sensor can't see everything on its own. That's why we in real life we use sensor networks, collections of different telescopes across the globe whose observations we can combine.\n",
    "\n",
    "`madlib` makes it easy to define several sensors and join them into one of these networks. Then, we can propagate a satellite, have all of these sensors observe it, and obtain a single easy-to-use data collection at the end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining a Sensor Network in Python\n",
    "We'll begin this example by defining 5 imaginary sensors directly in Python code. See the previous notebook for in-depth explanations of the various parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_limits = {\"el\": (15, 90), \"sun_el\": (-90, -18)}\n",
    "\n",
    "sensor_1_params = {}\n",
    "sensor_1_params[\"id\"] = \"Alpha\"\n",
    "sensor_1_params[\"lat\"] = 0.0\n",
    "sensor_1_params[\"lon\"] = 0.0\n",
    "sensor_1_params[\"alt\"] = 0.0\n",
    "sensor_1_params[\"dra\"] = 1.0\n",
    "sensor_1_params[\"ddec\"] = 1.0\n",
    "sensor_1_params[\"obs_per_collect\"] = 3\n",
    "sensor_1_params[\"obs_time_spacing\"] = 1\n",
    "sensor_1_params[\"collect_gap_mean\"] = 60\n",
    "sensor_1_params[\"collect_gap_std\"] = 5\n",
    "sensor_1_params[\"obs_limits\"] = obs_limits\n",
    "\n",
    "sensor_2_params = {}\n",
    "sensor_2_params[\"id\"] = \"Bravo\"\n",
    "sensor_2_params[\"lat\"] = 0.0\n",
    "sensor_2_params[\"lon\"] = 15.0\n",
    "sensor_2_params[\"alt\"] = 0.0\n",
    "sensor_2_params[\"dra\"] = 1.0\n",
    "sensor_2_params[\"ddec\"] = 1.0\n",
    "sensor_2_params[\"obs_per_collect\"] = 5\n",
    "sensor_2_params[\"obs_time_spacing\"] = 1\n",
    "sensor_2_params[\"collect_gap_mean\"] = 10\n",
    "sensor_2_params[\"collect_gap_std\"] = 0\n",
    "sensor_2_params[\"obs_limits\"] = obs_limits\n",
    "\n",
    "sensor_3_params = {}\n",
    "sensor_3_params[\"id\"] = \"Charlie\"\n",
    "sensor_3_params[\"lat\"] = 0.0\n",
    "sensor_3_params[\"lon\"] = 30.0\n",
    "sensor_3_params[\"alt\"] = 0.0\n",
    "sensor_3_params[\"dra\"] = 10.0\n",
    "sensor_3_params[\"ddec\"] = 10.0\n",
    "sensor_3_params[\"obs_per_collect\"] = 1\n",
    "sensor_3_params[\"obs_time_spacing\"] = 0\n",
    "sensor_3_params[\"collect_gap_mean\"] = 600\n",
    "sensor_3_params[\"collect_gap_std\"] = 0\n",
    "sensor_3_params[\"obs_limits\"] = obs_limits\n",
    "\n",
    "sensor_4_params = {}\n",
    "sensor_4_params[\"id\"] = \"Delta\"\n",
    "sensor_4_params[\"lat\"] = 0.0\n",
    "sensor_4_params[\"lon\"] = 45.0\n",
    "sensor_4_params[\"alt\"] = 0.0\n",
    "sensor_4_params[\"dra\"] = 1.0\n",
    "sensor_4_params[\"ddec\"] = 1.0\n",
    "sensor_4_params[\"obs_per_collect\"] = 3\n",
    "sensor_4_params[\"obs_time_spacing\"] = 1\n",
    "sensor_4_params[\"collect_gap_mean\"] = 120\n",
    "sensor_4_params[\"collect_gap_std\"] = 5\n",
    "sensor_4_params[\"obs_limits\"] = obs_limits\n",
    "\n",
    "sensor_5_params = {}\n",
    "sensor_5_params[\"id\"] = \"Echo\"\n",
    "sensor_5_params[\"lat\"] = 0.0\n",
    "sensor_5_params[\"lon\"] = 60.0\n",
    "sensor_5_params[\"alt\"] = 0.0\n",
    "sensor_5_params[\"dra\"] = 1.0\n",
    "sensor_5_params[\"ddec\"] = 1.0\n",
    "sensor_5_params[\"obs_per_collect\"] = 3\n",
    "sensor_5_params[\"obs_time_spacing\"] = 1\n",
    "sensor_5_params[\"collect_gap_mean\"] = 120\n",
    "sensor_5_params[\"collect_gap_std\"] = 5\n",
    "sensor_5_params[\"obs_limits\"] = obs_limits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a lot of information! For now, focus on the following:\n",
    "- We have five sensors\n",
    "- Their longitudes span 0 to 60 degrees in even intervals\n",
    "- Their latitudes are all 0\n",
    "- Sensor \"Charlie\" is much noisier than the others, and it only collects data once every 10 minutes\n",
    "\n",
    "Now we just have to combine these sensors into a `SensorCollection` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import madlib\n",
    "\n",
    "sensor_network = madlib.SensorCollection(\n",
    "    [\n",
    "        madlib.GroundOpticalSensor(**sensor_1_params),\n",
    "        madlib.GroundOpticalSensor(**sensor_2_params),\n",
    "        madlib.GroundOpticalSensor(**sensor_3_params),\n",
    "        madlib.GroundOpticalSensor(**sensor_4_params),\n",
    "        madlib.GroundOpticalSensor(**sensor_5_params),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making Observations with a Sensor Network\n",
    "`SenorCollection` works a bit differently than `OpticalSensor`, but we still begin by defining our satellite and observation period. Let's use the same LEO object from previous examples, but restrict its motion to the equatorial plan and observe for 24 hours:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "epoch_mjd = 60197.5\n",
    "start_mjd = epoch_mjd\n",
    "end_mjd = start_mjd + 1\n",
    "\n",
    "leo_alt_km = 430.0\n",
    "leo_speed_kms = 27500.0 / 3600  # Converting from km/h to km/s\n",
    "earth_radius_km = 6378.0\n",
    "\n",
    "leo_pos_teted = np.array([leo_alt_km + earth_radius_km, 0, 0])\n",
    "leo_vel_teted = leo_speed_kms * np.array([0, 1, 0])\n",
    "\n",
    "leo_satellite = madlib.Satellite(epoch=epoch_mjd, pos=leo_pos_teted, vel=leo_vel_teted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to generate the schedule of observations. This process simply iterates over the sensors in the collection and determines the specific times at which they will attempt to collect data, based on the \"obs_per_collect\", \"obs_time_spacing\", \"collect_gap_mean\" and \"collect_gap_std\" parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There's no output, the schedule is saved internally\n",
    "sensor_network.generate_obs_timing(start=start_mjd, end=end_mjd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, all we have to do is run the `SensorCollection.observe()` function. Just like when we observe using a single sensor, we get an `ObservationCollection` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ObservationCollection(pos_observed=array([Observation(mjd=60197.804811895716, ra=-155.21005775050136, dec=-0.0018817143428408848, az=269.99791602336506, el=15.49170676336058, range_=None, range_rate=None, lat=None, lon=None, sun_el=-20.712844814126555, sun_separation=None, sensor_id='Alpha'),\n",
      "       Observation(mjd=60197.80482346979, ra=-155.0529433100505, dec=-0.002195432133841427, az=269.9975885370318, el=15.644643124520755, range_=None, range_rate=None, lat=None, lon=None, sun_el=-20.716994893751522, sun_separation=None, sensor_id='Alpha'),\n",
      "       Observation(mjd=60197.80483504386, ra=-154.89346873665775, dec=-0.0021193300793128987, az=269.9976656364844, el=15.799939625631518, range_=None, range_rate=None, lat=None, lon=None, sun_el=-20.72114496997639, sun_separation=None, sensor_id='Alpha'),\n",
      "       ...,\n",
      "       Observation(mjd=60198.02513733557, ra=119.01012884070508, dec=0.023250687823112872, az=89.97304354390796, el=29.82242564759488, range_=None, range_rate=None, lat=None, lon=None, sun_el=-19.8003466113589, sun_separation=None, sensor_id='Echo'),\n",
      "       Observation(mjd=60198.02514890965, ra=119.32386239463631, dec=0.02330856692913459, az=89.97305974743188, el=29.512870189685014, range_=None, range_rate=None, lat=None, lon=None, sun_el=-19.796196011021664, sun_separation=None, sensor_id='Echo'),\n",
      "       Observation(mjd=60198.02516048372, ra=119.63465898982892, dec=0.022636537303700226, az=89.97391052037683, el=29.20625185232571, range_=None, range_rate=None, lat=None, lon=None, sun_el=-19.79204541222008, sun_separation=None, sensor_id='Echo')],\n",
      "      dtype=object), pos_truth=array([Observation(mjd=60197.804811895716, ra=-155.2104622064897, dec=-0.002256093648601913, az=269.9975275349775, el=15.49130230304489, range_=None, range_rate=None, lat=None, lon=None, sun_el=-20.712844814126555, sun_separation=None, sensor_id='Alpha'),\n",
      "       Observation(mjd=60197.80482346979, ra=-155.05271736271766, dec=-0.002251872619237827, az=269.9975299221894, el=15.644869071152364, range_=None, range_rate=None, lat=None, lon=None, sun_el=-20.716994893751522, sun_separation=None, sensor_id='Alpha'),\n",
      "       Observation(mjd=60197.80483504386, ra=-154.89355925099872, dec=-0.0022475888638940544, az=269.99753234277983, el=15.799849109708324, range_=None, range_rate=None, lat=None, lon=None, sun_el=-20.72114496997639, sun_separation=None, sensor_id='Alpha'),\n",
      "       ...,\n",
      "       Observation(mjd=60198.02513733557, ra=119.00968511868683, dec=0.023199823255768543, az=89.97310205339448, el=29.822869381434998, range_=None, range_rate=None, lat=None, lon=None, sun_el=-19.8003466113589, sun_separation=None, sensor_id='Echo'),\n",
      "       Observation(mjd=60198.02514890965, ra=119.32411846162333, dec=0.02301141984816988, az=89.97340126680339, el=29.51261419101619, range_=None, range_rate=None, lat=None, lon=None, sun_el=-19.796196011021664, sun_separation=None, sensor_id='Echo'),\n",
      "       Observation(mjd=60198.02516048372, ra=119.63435554797195, dec=0.0228252768596681, az=89.97369421361361, el=29.20655525210278, range_=None, range_rate=None, lat=None, lon=None, sun_el=-19.79204541222008, sun_separation=None, sensor_id='Echo')],\n",
      "      dtype=object), pos_expected=array([Observation(mjd=60197.804811895716, ra=-155.2104622064897, dec=-0.002256093648601913, az=269.9975275349775, el=15.49130230304489, range_=None, range_rate=None, lat=None, lon=None, sun_el=-20.712844814126555, sun_separation=None, sensor_id='Alpha'),\n",
      "       Observation(mjd=60197.80482346979, ra=-155.05271736271766, dec=-0.002251872619237827, az=269.9975299221894, el=15.644869071152364, range_=None, range_rate=None, lat=None, lon=None, sun_el=-20.716994893751522, sun_separation=None, sensor_id='Alpha'),\n",
      "       Observation(mjd=60197.80483504386, ra=-154.89355925099872, dec=-0.0022475888638940544, az=269.99753234277983, el=15.799849109708324, range_=None, range_rate=None, lat=None, lon=None, sun_el=-20.72114496997639, sun_separation=None, sensor_id='Alpha'),\n",
      "       ...,\n",
      "       Observation(mjd=60198.02513733557, ra=119.00968511868683, dec=0.023199823255768543, az=89.97310205339448, el=29.822869381434998, range_=None, range_rate=None, lat=None, lon=None, sun_el=-19.8003466113589, sun_separation=None, sensor_id='Echo'),\n",
      "       Observation(mjd=60198.02514890965, ra=119.32411846162333, dec=0.02301141984816988, az=89.97340126680339, el=29.51261419101619, range_=None, range_rate=None, lat=None, lon=None, sun_el=-19.796196011021664, sun_separation=None, sensor_id='Echo'),\n",
      "       Observation(mjd=60198.02516048372, ra=119.63435554797195, dec=0.0228252768596681, az=89.97369421361361, el=29.20655525210278, range_=None, range_rate=None, lat=None, lon=None, sun_el=-19.79204541222008, sun_separation=None, sensor_id='Echo')],\n",
      "      dtype=object))\n"
     ]
    }
   ],
   "source": [
    "network_observations = sensor_network.observe(target_satellite=leo_satellite)\n",
    "print(network_observations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot these results exactly like we did in the previous notebook. Let's also add an extra step and color the data points according to the sensor used to collect them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "times = np.array([o.mjd for o in network_observations.pos_observed])\n",
    "times = 24 * (times - min(times))  # Convert the times from days to hours\n",
    "ra = np.array([o.ra for o in network_observations.pos_observed])\n",
    "dec = np.array([o.dec for o in network_observations.pos_observed])\n",
    "\n",
    "# Color by sensor\n",
    "sensors = np.array([o.sensor_id for o in network_observations.pos_observed])\n",
    "color_map = {\"Alpha\":\"r\", \"Bravo\":\"b\", \"Charlie\":\"c\", \"Delta\":\"g\", \"Echo\":\"k\"}\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, figsize=(9, 9))\n",
    "\n",
    "for sensor_id, color in color_map.items():\n",
    "    index = np.where(sensors == sensor_id)[0]\n",
    "    t = times[index]\n",
    "    x = ra[index]\n",
    "    y = dec[index]\n",
    "    _ = ax1.plot(t, x, f\"o{color}\", ms=5, lw=2, label=sensor_id)\n",
    "    _ = ax2.plot(t, y, f\"o{color}\", ms=5, lw=2, label=sensor_id)\n",
    "ax1.set_ylabel(\"Right Ascension (degrees)\", fontsize=16)\n",
    "ax1.set_xlabel(\"Time (Hours)\", fontsize=16)\n",
    "ax2.set_ylabel(\"Declination (degrees)\", fontsize=16)\n",
    "ax2.set_xlabel(\"Time (Hours)\", fontsize=16)\n",
    "ax1.grid()\n",
    "ax2.grid()\n",
    "_ = ax1.legend()\n",
    "_ = ax2.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, you might immediately notice that the measurements across different sensors don't exactly match up. This is due to parallax, since we have sensors in very different geographic positions observing a low-flying object. This is a true-to-life effect that must be considered when working with RA/DEC measurements from an optical sensor array.\n",
    "\n",
    "With our sensor network, we have a much more complete picture of the satellite's motion. Notice also how the cyan-colored points for sensor \"Charlie\" are significantly noisier than the others, just as expected based on the sensor configurations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining a Sensor Network with a YAML File\n",
    "What if we wanted to reuse this sensor network in a different simulation? Or a colleague wanted to replicate our experiment? Defining a sensor network in a standalone Python file will work, but you can also define the network in a YAML file if you prefer to keep your configurations separate from your code.\n",
    "\n",
    "The format for `SensorCollection` YAML files is below. Any key that can be set to an arbitrary value is wrapped in \\<angle brackets\\>. You can also find an example in this repository at `examples/example_inputs/sample_sensor_network.yaml`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### YAML Syntax\n",
    "\n",
    "    sensor_list:\n",
    "      <Sensor_1>:\n",
    "        id: Sensor 1\n",
    "        lat: 0.0\n",
    "        lon: 0.0\n",
    "        alt: 0.0\n",
    "        dra: 0.0\n",
    "        ddec: 0.0\n",
    "        obs_per_collect: 1\n",
    "        obs_time_spacing: 0.0\n",
    "        collect_gap_mean: 0.0\n",
    "        collect_gap_std: 0.0\n",
    "        obs_limits:\n",
    "          el: [0.0, 90.0]\n",
    "          sun_el: [0.0, 90.0]\n",
    "\n",
    "      <Sensor 2>:\n",
    "        ...\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The YAML format even lets you define constants for commonly reused settings within a file. Below, we define a constant \"twilight_sun_el\" for the limits on the sun's elevation during astronomical twilight. If we decide to adjust those values for any reason, we just have to change the constant to affect every sensor that uses it.\n",
    "\n",
    "    twilight_sun_el: &twilight_sun_el [-90, -18.0]\n",
    "\n",
    "    sensor_list:\n",
    "      Sensor_1:\n",
    "        id: Sensor 1\n",
    "        ...\n",
    "        obs_limits:\n",
    "          el: [15.0, 90]\n",
    "          sun_el: *twilight_sun_el\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And of course, `madlib` makes it easy to turn a properly-formatted YAML file into a fully functioning `SensorCollection` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "yamlSensor = madlib.SensorCollection.fromYAML(\n",
    "    \"example_inputs/sample_sensor_network.yaml\"\n",
    ")"
   ]
  }
 ],
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