DP03_05_Near-Earth_Objects.ipynb

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   "source": [
    "#### <img align=\"left\" src = https://project.lsst.org/sites/default/files/Rubin-O-Logo_0.png width=250 style=\"padding: 10px\"> \n",
    "<br><b>Orbital Properties of Near-Earth Objects (NEOs)</b> <br>\n",
    "Contact author(s): Sarah Greenstreet <br>\n",
    "Last verified to run: 2024-04-24 <br>\n",
    "LSST Science Pipelines version: Weekly 2024_04 <br>\n",
    "Container Size: medium <br>\n",
    "Targeted learning level: beginner <br>"
   ]
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  {
   "cell_type": "markdown",
   "id": "9da1a210-d858-42fe-8591-570965b8be1a",
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   "source": [
    "**Description:** An introduction to near-Earth objects (NEOs) in the DP0.3 dataset, including illustrations of the four NEOs sub-populations: Amors, Apollos, Atens, and Atiras."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80a0baf5-51ad-40ec-8991-060a7b27c289",
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   "source": [
    "**Skills:** Use TAP queries to retrieve Solar System objects. Plot properties and orbits of near-Earth objects."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393da88f-7978-4920-aa4a-a9830df6eed9",
   "metadata": {},
   "source": [
    "**LSST Data Products:** TAP DP0.3 MPCORB (10-year) and SSObject tables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c67fab9-136a-4adc-bb42-142b91ab69dd",
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   "source": [
    "**Packages:** lsst.rsp.get_tap_service"
   ]
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  {
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   "source": [
    "**Credit:**\n",
    "Originally developed by Sarah Greenstreet and the Rubin Community Science Team in the context of the Rubin DP0."
   ]
  },
  {
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   "id": "28e91cbf-ab7f-4e26-9276-b00299d6065e",
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   "source": [
    "**Get Support:**\n",
    "Find DP0-related documentation and resources at <a href=\"https://dp0.lsst.io\">dp0.lsst.io</a>. Questions are welcome as new topics in the <a href=\"https://community.lsst.org/c/support/dp0\">Support - Data Preview 0 Category</a> of the Rubin Community Forum. Rubin staff will respond to all questions posted there."
   ]
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   "source": [
    "## 1. Introduction\n",
    "\n",
    "This tutorial demonstrates how to use TAP to query the MPCORB and SSObject tables. The tables are queried simultaneously and joined on their SSObjectId. The properties of near-Earth objects (NEOs; perihelia $q$<1.3 au) from the DP0.3 catalogs are examined. The NEOs in this sample are restricted to those with semimajor axes $a$<4.0 au, and eccentricities $e$<1.0. The orbital properties of NEOs are examined, including plotting the planet-crossing regions and boundaries that define the four NEO sub-populations: Amors, Apollos, Atens, and Atiras."
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    "### 1.1 NEO Dynamical Classes\n",
    "\n",
    "Near-Earth objects (NEOs) are traditionally divided into four dynamical sub-populations based on their orbital parameters compared to the orbit of the Earth. The Earth's orbit is nearly circular with a semimajor axis $a$ = 1.0 au and eccentricity $e$ = 0.017. This gives the Earth a perihelion distance $q$ = 0.983 au and an aphelion distance of $Q$ = 1.017 au. The four NEO sub-populations (Amors, Apollos, Atens, and Atiras) are divided using these orbital parameters of the Earth as follows:\n",
    "\n",
    "**Amors** have orbits exterior to Earth's orbit with perihelia larger than Earth's aphelion and less than 1.3 au (1.017 au < $q$ < 1.3 au). Amors are always farther from the Sun than Earth.\n",
    "\n",
    "**Apollos** are on Earth-crossing orbits with perihelia smaller than Earth's aphelion ($q$ < 1.017 au) and semimajor axes $a$ > 1.0 au. Apollos are Earth-crossers with semimajor axes larger than Earth's orbit.\n",
    "\n",
    "**Atens** are on Earth-crossing orbits with aphelia larger than Earth's perihelion ($Q$ > 0.983 au) and semimajor axes $a$ < 1.0 au. Atens are Earth-crossers with semimajor axes smaller than Earth's orbit.\n",
    "\n",
    "**Atiras** have orbits interior to Earth's orbit with aphelia smaller than Earth's perihelion ($Q$ < 0.983 au). Atiras are always closer to the Sun than Earth.\n",
    "\n",
    "The schematic diagram shown below illustrates examples of Amor, Apollos, Aten, and Atira orbits compared to Earth's orbit."
   ]
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pbWVuc2lvbj4KICAgICAgICAgPGV4aWY6UGl4ZWxYRGltZW5zaW9uPjEzNzY8L2V4aWY6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Qw0FQ43TsBf6zmKQlvOQZE0IACBVgjYJFr2IsK2UmvYCYE2CDBGa6Oe5ljp55m5vWB5IdecGiFO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KlbTYwJvI8m2FZ/aLAG7wtQRYoZcVHd8kVfQ/eQIsRNf0fK5iI+Z0le2YI13F/qxoV9nP/lAo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\nw/mQpkHoLMqJAMQ1p0ysxHx3iAWjA5t2+9AK8BIsmPtVtvlwSShwiZkABdi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RB73JCySRppCI7jyN4Q2ELAjpMxol8R77Z4Yv+25qv9W4bZgnNaGI491+LHbOhffYqrF18Z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\n"
    }
   },
   "cell_type": "markdown",
   "id": "397af528-eccf-4d47-873f-d5878fbdf643",
   "metadata": {},
   "source": [
    "![NEO_orbital_schematic.png](attachment:9e7ac462-1712-46dd-a398-7b92eb7f7a0d.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dbe2265-faf8-4bca-aa72-e7bddbc5ee6f",
   "metadata": {},
   "source": [
    "This notebook provides examples of plotting the boundaries for each NEO sub-population in semimajor axis $a$ versus eccentricity $e$ and how to divide the NEOs queried from the DP0.3 tables into these four sub-populations. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc36f107",
   "metadata": {},
   "source": [
    "### 1.2 Package Imports\n",
    "\n",
    "The `matplotlib` and `numpy` libraries are widely used Python libraries for plotting and scientific computing. We will use these packages below, including the matplotlib.pyplot plotting sublibrary.\n",
    "\n",
    "We also use the `lsst.rsp` package to access the TAP service and query the DP0 catalogs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cddc1458",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# general python packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# LSST package for TAP queries\n",
    "from lsst.rsp import get_tap_service"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c217adff-25ed-4fce-95e7-8aa04630f6cc",
   "metadata": {},
   "source": [
    "### 1.3 Define Functions and Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf067164-0780-4e77-99ed-0d04d1cee3de",
   "metadata": {},
   "source": [
    "#### 1.3.1 Define functions for computing orbital parameters not included in the MPCORB table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "148b1bea-ff1b-4ad7-9571-d97a26f7eeba",
   "metadata": {},
   "source": [
    "Define a function to convert a given perihelion distance ($q$) and eccentricity ($e$) to an orbital semimajor axis ($a$). Their relationship is defined by $q=a*(1−e)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06b02071-0f1c-4ea7-a7b1-790036927626",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def calc_semimajor_axis(q, e):\n",
    "    \"\"\"\n",
    "    Given a perihelion distance and orbital eccentricity,\n",
    "    calculate the semi-major axis of the orbit.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    q: ndarray\n",
    "        Distance at perihelion, in au.\n",
    "    e: ndarray\n",
    "        Orbital eccentricity.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    a: ndarray\n",
    "        Semi-major axis of the orbit, in au.\n",
    "        q = a(1-e), so a = q/(1-e)\n",
    "    \"\"\"\n",
    "\n",
    "    return q / (1.0 - e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e9c6374-e071-4e59-bc3b-4d95ad4da4ca",
   "metadata": {},
   "source": [
    "Define a function to convert a given perihelion distance ($q$) and eccentricity ($e$) to an aphelion distance ($Q$). Their relationships are defined by: $Q=q*(1+e)/(1-e)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee76da9f-8f8e-4333-a9a7-a0f55e326a18",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def calc_aphelion(q, e):\n",
    "    \"\"\"\n",
    "    Given a perihelion distance and orbital eccentricity,\n",
    "    calculate the semi-major axis of the orbit.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    q: ndarray\n",
    "        Distance at perihelion, in au.\n",
    "    e: ndarray\n",
    "        Orbital eccentricity.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    Q: ndarray\n",
    "        Distance at aphelion, in au.\n",
    "        Q = q*(1+e)/(1-e)\n",
    "    \"\"\"\n",
    "\n",
    "    return q * (1.0 + e) / (1.0 - e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ef34404-5cad-4433-897c-a2cc14e19727",
   "metadata": {},
   "source": [
    "#### 1.3.2 Define functions to return arrays needed for plotting planet-crossing curves on an ($a$, $e$) plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fdb40de-05eb-4af6-a36a-97d88f2a8f88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-27T22:43:10.968935Z",
     "iopub.status.busy": "2023-11-27T22:43:10.968648Z",
     "iopub.status.idle": "2023-11-27T22:43:10.972578Z",
     "shell.execute_reply": "2023-11-27T22:43:10.971800Z",
     "shell.execute_reply.started": "2023-11-27T22:43:10.968914Z"
    },
    "tags": []
   },
   "source": [
    "Define a function to return an array with ($a$, $e$) values needed to plot a curve showing the aphelion at a planet's perihelion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f4147a7-b832-459e-828a-c367db3aaf08",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_Q_at_planets_q_curve_values(q_planet):\n",
    "\n",
    "    a_Q = np.arange(0.001, q_planet, 0.001)\n",
    "    e_Q = (q_planet / a_Q) - 1.0\n",
    "\n",
    "    return (a_Q, e_Q)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1df6e3a-36d1-4609-af68-2f4c1f0ac29b",
   "metadata": {},
   "source": [
    "Define a function to return an array with ($a$, $e$) values needed to plot a curve showing the perihelion at a planet's aphelion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e508e8b-ca61-44e0-8a9c-0ca64b59c2f1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_q_at_planets_Q_curve_values(Q_planet):\n",
    "\n",
    "    a_q = np.arange(Q_planet, 4.0, 0.001)\n",
    "    e_q = (1.0 - (Q_planet / a_q))\n",
    "\n",
    "    return (a_q, e_q)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6159986f-064c-4042-a09a-7018c270158f",
   "metadata": {},
   "source": [
    "Define the perihelia and aphelia for the terrestrial planets and the perihelion boundary for NEOs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f35cdd2c-112a-4203-a333-d3355ed1336d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "q_Earth = 0.983\n",
    "Q_Earth = 1.017\n",
    "\n",
    "q_Mercury = 0.307\n",
    "Q_Mercury = 0.467\n",
    "\n",
    "q_Venus = 0.718\n",
    "Q_Venus = 0.728\n",
    "\n",
    "q_Mars = 1.381\n",
    "Q_Mars = 1.666\n",
    "\n",
    "q_NEOs = 1.3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8f4078-cad1-42fe-a72c-0ddf1a39da4c",
   "metadata": {},
   "source": [
    "Set some plotting defaults to make plots look nice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d5e15fd-cb86-40e3-8cdc-ca7e49023b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('tableau-colorblind10')\n",
    "%matplotlib inline\n",
    "\n",
    "params = {'axes.labelsize': 15,\n",
    "          'font.size': 15,\n",
    "          'legend.fontsize': 10,\n",
    "          'xtick.major.width': 2,\n",
    "          'xtick.minor.width': 1,\n",
    "          'xtick.major.size': 6,\n",
    "          'xtick.minor.size': 3,\n",
    "          'xtick.direction': 'in',\n",
    "          'xtick.top': True,\n",
    "          'lines.linewidth': 2,\n",
    "          'axes.linewidth': 2,\n",
    "          'axes.labelweight': 1,\n",
    "          'axes.titleweight': 1,\n",
    "          'ytick.major.width': 1,\n",
    "          'ytick.minor.width': 2,\n",
    "          'ytick.major.size': 6,\n",
    "          'ytick.minor.size': 3,\n",
    "          'ytick.direction': 'in',\n",
    "          'ytick.right': True,\n",
    "          'figure.figsize': [6, 6],\n",
    "          'figure.facecolor': 'White'\n",
    "          }\n",
    "plt.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cb24cb9-687f-46b2-aec6-b51fff5be345",
   "metadata": {},
   "source": [
    "## 2. Orbital Parameters of Near-Earth Objects (NEOs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff43554-b31a-4b1b-95a3-6951f46e6bc9",
   "metadata": {},
   "source": [
    "### 2.1 Query the DP0.3 catalogs to create a table of NEOs and their orbital parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f29090e8-de29-4d0d-949c-2276a8ba4cc8",
   "metadata": {},
   "source": [
    "Get an instance of the Rubin TAP service client, and assert that it exists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d7d436c-ffc8-4e67-9662-36467e9c7480",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "service = get_tap_service(\"ssotap\")\n",
    "assert service is not None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "620679de-2f93-4227-8d06-2df6cdc49224",
   "metadata": {},
   "source": [
    "Query the DP0.3 catalogs, joining the MPCORB and SSObject tables on their ssObjectId. Limit the query to select sources in the MPCORB table with perihelia $q$ < 1.3 au, eccentricities $e$ < 1, and semimajor axes $a$ < 4 au to get only near-Earth objects, excluding interstellar objects with $e$ > 1 or $a$ < 0.\n",
    "\n",
    "Define the table returned by this query as \"uniqueNEOs\" since it contains the IDs of unique solar system objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8952dd2a-48dc-4ca6-b986-91b8a633acfc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "q_select = '1.3'\n",
    "e_select = '1.0'\n",
    "a_select = '4.0'\n",
    "\n",
    "query = \"\"\"\n",
    "        SELECT\n",
    "            mpc.ssObjectId, mpc.mpcDesignation,\n",
    "            mpc.e, mpc.q, mpc.incl\n",
    "        FROM\n",
    "            dp03_catalogs_10yr.MPCORB as mpc\n",
    "        INNER JOIN dp03_catalogs_10yr.SSObject as sso\n",
    "        ON mpc.ssObjectId = sso.ssObjectId\n",
    "        WHERE mpc.q < {} AND mpc.e < {}\n",
    "        AND mpc.q/(1.0-mpc.e) < {} ORDER by mpc.mpcDesignation\n",
    "        \"\"\".format(q_select, e_select, a_select)\n",
    "\n",
    "uniqueNEOs = service.search(query).to_table().to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0ee51f4-6d43-4de8-afc3-16891fe4e177",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-06T22:53:41.569250Z",
     "iopub.status.busy": "2024-03-06T22:53:41.568521Z",
     "iopub.status.idle": "2024-03-06T22:53:41.573077Z",
     "shell.execute_reply": "2024-03-06T22:53:41.572362Z",
     "shell.execute_reply.started": "2024-03-06T22:53:41.569225Z"
    }
   },
   "source": [
    "Calculate the semimajor axis $a$ of each object's orbit, using the function defined above, and add it as a column to the `uniqueNEOs` table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b84c014d-f490-4cfb-94c2-e020b1b715a7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "a = calc_semimajor_axis(uniqueNEOs['q'], uniqueNEOs['e'])\n",
    "uniqueNEOs['a'] = a"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e751eec9-6e7e-4264-91c2-80866ab4a644",
   "metadata": {},
   "source": [
    "Then use the function defined above to calculate the aphelion distance $Q$ of each object's orbit and add it as a column to the `uniqueNEOs` table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c4a7670-a5d7-4d8d-967e-16be47590daf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Q = calc_aphelion(uniqueNEOs['q'], uniqueNEOs['e'])\n",
    "uniqueNEOs['Q'] = Q"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "386c908e-0534-44c8-b4ac-8c96daae2cda",
   "metadata": {},
   "source": [
    "Print the `uniqueNEOs` table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f28a3fcc-8726-4fe2-af2d-bb66bbeceb43",
   "metadata": {},
   "outputs": [],
   "source": [
    "uniqueNEOs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae89d15-4340-4074-9332-c52149ac6a29",
   "metadata": {},
   "source": [
    "### 2.2 Create semimajor axis $a$ versus eccentricity $e$ plots of the four dynamical sub-populations of NEOs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "433b549a-0bf2-41ad-a74f-66381cedce4d",
   "metadata": {},
   "source": [
    "Plot the semimajor axis versus eccentricity of all NEOs in the `uniqueNEOs` table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b3abca3-2527-4ca9-9429-3412e4e1a058",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.xlim([0., 4.])\n",
    "plt.ylim([0., 1.])\n",
    "ax.scatter(uniqueNEOs.a, uniqueNEOs.e, s=0.1)\n",
    "ax.set_xlabel('semimajor axis (au)')\n",
    "ax.set_ylabel('eccentricity')\n",
    "ax.minorticks_on()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab3de02-8e98-4d1c-91ec-d1bb22d4b9b6",
   "metadata": {},
   "source": [
    "In order to show the boundaries for the four NEO sub-populations (Amors, Apollos, Atens, and Atiras), arrays of ($a$, $e$) values along the Earth's perihelion and aphelion distances are needed along with those for the $q$ < 1.3 au NEO boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29e3bb0c-de44-4d86-8917-fe9c404bd482",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "a_Q_Earth, e_Q_Earth = get_Q_at_planets_q_curve_values(q_Earth)\n",
    "\n",
    "a_q_Earth, e_q_Earth = get_q_at_planets_Q_curve_values(Q_Earth)\n",
    "\n",
    "a_q_NEO, e_q_NEO = get_q_at_planets_Q_curve_values(q_NEOs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db503365-4158-4d2a-ac0c-78a5c40c8b4c",
   "metadata": {},
   "source": [
    "Plot the Earth-crossing curves as a function of $a$ and $e$ and label the NEO class regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f153f27-4d75-423f-bc3a-6194e386dfdf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.xlim([0., 4.])\n",
    "plt.ylim([0., 1.])\n",
    "ax.axvline(x=1.0, color=\"blue\")\n",
    "ax.plot(a_Q_Earth, e_Q_Earth, \"blue\")\n",
    "ax.plot(a_q_Earth, e_q_Earth, \"blue\")\n",
    "ax.plot(a_q_NEO, e_q_NEO, \"black\", linewidth=3)\n",
    "ax.annotate('Amors', xy=(2.25, 0.5), xytext=(2.25, 0.5),\n",
    "            rotation=30.0, weight='bold')\n",
    "ax.annotate('Apollos', xy=(1.45, 0.75), xytext=(1.45, 0.75),\n",
    "            weight='bold')\n",
    "ax.annotate('Atens', xy=(0.7, 0.6), xytext=(0.7, 0.6),\n",
    "            rotation=270.0, weight='bold')\n",
    "ax.annotate('Atiras', xy=(0.3, 0.5), xytext=(0.3, 0.5),\n",
    "            rotation=280.0, weight='bold')\n",
    "ax.minorticks_on()\n",
    "plt.xlabel(\"semimajor axis (au)\")\n",
    "plt.ylabel(\"eccentricity\")\n",
    "plt.title(\"NEO Class Boundaries\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea0e5ba7-e504-4400-b002-348d601c3905",
   "metadata": {},
   "source": [
    "To better understand the above ($a$, $e$) plot, remember that the Sun is at the origin ($a$, $e$) = (0, 0) and the Earth has $a$ = 1.0 au and $e$ = 0.017, so it sits on the x-axis where the three blue curves converge. The space between the two outermost blue curves is Earth-crossing space where the Earth-crossing NEOs (Apollos and Atens) sit. A semimajor axis $a$ = 1.0 au divides the Earth-crossing NEOs into Apollos ($a$ > 1.0 au) and Atens ($a$ < 1.0 au). The black curve marks the $q$ < 1.3 au boundary for NEOs by definition. Amors sit between the $q$ < 1.3 au curve and the outer edge of Earth-crossing space (i.e., perihelia > Earth's aphelion = $q$ > 1.017 au); Amors thus always remain farther from the Sun than the Earth. Atiras are opposite to the Amors in that they always remain closer to the Sun than the Earth and live interior to Earth-crossing space; Atiras thus have aphelia < Earth's perihelion = $Q$ < 0.983 au."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4991320d-eab4-43f9-8fa2-8f27f2be3bf5",
   "metadata": {},
   "source": [
    "Next, in order to plot the four NEO sub-populations on an ($a$, $e$) plot like the one above, first define the parameters of each sub-population needed to extract them individually from the `uniqueNEOs` table.\n",
    "\n",
    "To ensure that we don't miss any NEOs in our table by our defined parameters, total the number of objects in each extracted sub-population and assert that it equals the number of objects in the `uniqueNEOs` table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08849b9f-d745-462c-bf85-ece622682d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "Amor_params = (uniqueNEOs.q < q_NEOs) & (uniqueNEOs.q > Q_Earth)\n",
    "\n",
    "Apollo_params = (uniqueNEOs.q < Q_Earth) & (uniqueNEOs.a > 1.0)\n",
    "\n",
    "Aten_params = (uniqueNEOs.Q > q_Earth) & (uniqueNEOs.a < 1.0)\n",
    "\n",
    "Atira_params = (uniqueNEOs.Q < q_Earth)\n",
    "\n",
    "total_NEOs = len(uniqueNEOs[Amor_params]\n",
    "                 + uniqueNEOs[Apollo_params]\n",
    "                 + uniqueNEOs[Aten_params]\n",
    "                 + uniqueNEOs[Atira_params])\n",
    "\n",
    "assert total_NEOs == len(uniqueNEOs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb761acf-bc98-4066-ac9b-460841acb1b4",
   "metadata": {},
   "source": [
    "Plot the semimajor axis versus eccentricity of all NEOs in the `uniqueNEOs` table divided into the four dynamical sub-populations, overplot the boundaries for each sub-population, and label each NEO class region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1068a14c-6807-4890-8a22-1890864824ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.xlim([0., 4.])\n",
    "plt.ylim([0., 1.])\n",
    "ax.scatter(uniqueNEOs[Amor_params].a, uniqueNEOs[Amor_params].e,\n",
    "           s=0.1, color='red')\n",
    "ax.scatter(uniqueNEOs[Apollo_params].a, uniqueNEOs[Apollo_params].e,\n",
    "           s=0.1, color='green')\n",
    "ax.scatter(uniqueNEOs[Aten_params].a, uniqueNEOs[Aten_params].e,\n",
    "           s=0.1, color='orange')\n",
    "ax.scatter(uniqueNEOs[Atira_params].a, uniqueNEOs[Atira_params].e,\n",
    "           s=0.25, color='purple')\n",
    "ax.plot(a_Q_Earth, e_Q_Earth, \"blue\")\n",
    "ax.plot(a_q_Earth, e_q_Earth, \"blue\")\n",
    "ax.axvline(x=1.0, color=\"blue\")\n",
    "ax.plot(a_q_NEO, e_q_NEO, \"black\", linewidth=5)\n",
    "ax.annotate('Amors', xy=(2.25, 0.5), xytext=(2.25, 0.5),\n",
    "            rotation=30.0, weight='bold')\n",
    "ax.annotate('Apollos', xy=(1.45, 0.75), xytext=(1.45, 0.75),\n",
    "            weight='bold')\n",
    "ax.annotate('Atens', xy=(0.7, 0.6), xytext=(0.7, 0.6),\n",
    "            rotation=270.0, weight='bold')\n",
    "ax.annotate('Atiras', xy=(0.3, 0.5), xytext=(0.3, 0.5),\n",
    "            rotation=280.0, weight='bold')\n",
    "ax.minorticks_on()\n",
    "plt.xlabel(\"semimajor axis (au)\")\n",
    "plt.ylabel(\"eccentricity\")\n",
    "plt.title(\"NEOs in DP0.3\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f76671d-5b9d-4561-83ff-d20837004fe2",
   "metadata": {},
   "source": [
    "To better see the density of points in the above ($a$, $e$) scatter plot, next use a 2-D histogram plot where the bins are shown as hexagons and the color represents the number of data points within each bin, from purple/blue as the fewest to red regions containing the most objects. Again, overplot the NEO class boundaries with labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e0406aa-d6b2-4c30-b9c0-7b9bbc48b893",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "plt.xlim([0., 4.])\n",
    "plt.ylim([0., 1.])\n",
    "\n",
    "ax.hexbin(uniqueNEOs.a, uniqueNEOs.e,\n",
    "          gridsize=(int(4./0.02), 50),\n",
    "          cmap='Spectral_r', bins='log',\n",
    "          extent=(0, 4., 0, 1))\n",
    "ax.plot(a_Q_Earth, e_Q_Earth, \"blue\")\n",
    "ax.plot(a_q_Earth, e_q_Earth, \"blue\")\n",
    "ax.axvline(x=1.0, color=\"blue\")\n",
    "ax.plot(a_q_NEO, e_q_NEO, \"black\", linewidth=5)\n",
    "ax.annotate('Amors', xy=(2.25, 0.5), xytext=(2.25, 0.5),\n",
    "            rotation=30.0, weight='bold')\n",
    "ax.annotate('Apollos', xy=(1.45, 0.75), xytext=(1.45, 0.75),\n",
    "            weight='bold')\n",
    "ax.annotate('Atens', xy=(0.7, 0.6), xytext=(0.7, 0.6),\n",
    "            rotation=270.0, weight='bold')\n",
    "ax.annotate('Atiras', xy=(0.3, 0.5), xytext=(0.3, 0.5),\n",
    "            rotation=280.0, weight='bold')\n",
    "ax.set_ylabel('eccentricity')\n",
    "ax.set_xlabel('semimajor axis (au)')\n",
    "ax.minorticks_on()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a9b5b46-9d9c-4e2b-9d93-cd8aa0360591",
   "metadata": {},
   "source": [
    "In the above plot, it is clear that in addition to other areas of overdensity, the Amors have an overdensity of points near 2.1 au < $a$ < 2.4 au and 0.4 < $e$ < 0.5 that is difficult to see in the scatter plot alone."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd237d00-1d40-4453-a88a-d0203f17f1a6",
   "metadata": {
    "tags": []
   },
   "source": [
    "The above density plot prompts the question of what fraction of NEOs are in each of the sub-populations, which can easily be calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0954cad-816c-4ba0-ad08-3b6ad697f791",
   "metadata": {},
   "outputs": [],
   "source": [
    "perc_Amors = len(uniqueNEOs[Amor_params]) / total_NEOs * 100.\n",
    "perc_Apollos = len(uniqueNEOs[Apollo_params]) / total_NEOs * 100.\n",
    "perc_Atens = len(uniqueNEOs[Aten_params]) / total_NEOs * 100.\n",
    "perc_Atiras = len(uniqueNEOs[Atira_params]) / total_NEOs * 100.\n",
    "\n",
    "print(\"Percentage of NEOs that are Amors: \", \"%4.1f\" % (perc_Amors), \"%\")\n",
    "print(\"Percentage of NEOs that are Apollos: \", \"%4.1f\" % (perc_Apollos), \"%\")\n",
    "print(\"Percentage of NEOs that are Atens: \", \"%4.1f\" % (perc_Atens), \"%\")\n",
    "print(\"Percentage of NEOs that are Atiras: \", \"%4.1f\" % (perc_Atiras), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd90bb35-1947-4de1-a3dd-91dcbca6c1ac",
   "metadata": {},
   "source": [
    "### 2.3 Create an ($a$, $e$) plot the terrestrial-planet-crossing regions and compare the the four NEO sub-populations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a883fdc0-0485-45c6-9b52-fd0eda1b78fb",
   "metadata": {},
   "source": [
    "In addition to plotting the Earth-crossing curves on an ($a$, $e$) plot, the planet-crossing curves for all the terrestrial planets can also be plotted.\n",
    "\n",
    "First, as with the Earth-crossing curves above, arrays of ($a$, $e$) values along the Mercury, Venus, and Mars' perihelion and aphelion distances are needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aec2b251-f678-483e-b507-a4b432425a37",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "a_Q_Mercury, e_Q_Mercury = get_Q_at_planets_q_curve_values(q_Mercury)\n",
    "a_q_Mercury, e_q_Mercury = get_q_at_planets_Q_curve_values(Q_Mercury)\n",
    "\n",
    "a_Q_Venus, e_Q_Venus = get_Q_at_planets_q_curve_values(q_Venus)\n",
    "a_q_Venus, e_q_Venus = get_q_at_planets_Q_curve_values(Q_Venus)\n",
    "\n",
    "a_Q_Mars, e_Q_Mars = get_Q_at_planets_q_curve_values(q_Mars)\n",
    "a_q_Mars, e_q_Mars = get_q_at_planets_Q_curve_values(Q_Mars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5d93c52-1033-46fd-b5c3-cef68232e878",
   "metadata": {},
   "source": [
    "Plot the terrestrial-planet-crossing curves as a function of $a$ and $e$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bf0c07b-5d47-47ca-babe-6a7c9fb0cb3e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.xlim([0., 4.])\n",
    "plt.ylim([0., 1.])\n",
    "ax.plot(a_Q_Mercury, e_Q_Mercury, \"green\", label=\"Mercury-crossing\")\n",
    "ax.plot(a_q_Mercury, e_q_Mercury, \"green\")\n",
    "ax.plot(a_Q_Venus, e_Q_Venus, \"orange\", label=\"Venus-crossing\")\n",
    "ax.plot(a_q_Venus, e_q_Venus, \"orange\")\n",
    "ax.plot(a_Q_Earth, e_Q_Earth, \"blue\", label=\"Earth-crossing\")\n",
    "ax.plot(a_q_Earth, e_q_Earth, \"blue\")\n",
    "ax.plot(a_Q_Mars, e_Q_Mars, \"red\", label=\"Mars-crossing\")\n",
    "ax.plot(a_q_Mars, e_q_Mars, \"red\")\n",
    "ax.minorticks_on()\n",
    "ax.legend()\n",
    "plt.xlabel(\"semimajor axis (au)\")\n",
    "plt.ylabel(\"eccentricity\")\n",
    "plt.title(\"Terrestrial-Planet-Crossing Regions for NEOs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09584c12-9601-4204-ae1a-6e4204147626",
   "metadata": {},
   "source": [
    "Each of the four NEO sub-populations can be plotted in addition to the terrestrial-planet-crossing curves in order to see which of the four inner planetary orbits each NEO class crosses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "322259b9-73f7-43e9-b240-0f04290b3fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.xlim([0., 4.])\n",
    "plt.ylim([0., 1.])\n",
    "ax.scatter(uniqueNEOs[Amor_params].a, uniqueNEOs[Amor_params].e,\n",
    "           s=0.1, color='red')\n",
    "ax.scatter(uniqueNEOs[Apollo_params].a, uniqueNEOs[Apollo_params].e,\n",
    "           s=0.1, color='green')\n",
    "ax.scatter(uniqueNEOs[Aten_params].a, uniqueNEOs[Aten_params].e,\n",
    "           s=0.1, color='orange')\n",
    "ax.scatter(uniqueNEOs[Atira_params].a, uniqueNEOs[Atira_params].e,\n",
    "           s=0.25, color='purple')\n",
    "ax.plot(a_Q_Mercury, e_Q_Mercury, \"green\", label=\"Mercury-crossing\")\n",
    "ax.plot(a_q_Mercury, e_q_Mercury, \"green\")\n",
    "ax.plot(a_Q_Venus, e_Q_Venus, \"orange\", label=\"Venus-crossing\")\n",
    "ax.plot(a_q_Venus, e_q_Venus, \"orange\")\n",
    "ax.plot(a_Q_Earth, e_Q_Earth, \"blue\", label=\"Earth-crossing\")\n",
    "ax.plot(a_q_Earth, e_q_Earth, \"blue\")\n",
    "ax.plot(a_Q_Mars, e_Q_Mars, \"red\", label=\"Mars-crossing\")\n",
    "ax.plot(a_q_Mars, e_q_Mars, \"red\")\n",
    "ax.plot(a_q_NEO, e_q_NEO, \"black\", linewidth=5)\n",
    "ax.annotate('Amors', xy=(2.25, 0.5), xytext=(2.25, 0.5),\n",
    "            rotation=30.0, weight='bold')\n",
    "ax.annotate('Apollos', xy=(1.45, 0.75), xytext=(1.45, 0.75),\n",
    "            weight='bold')\n",
    "ax.annotate('Atens', xy=(0.7, 0.6), xytext=(0.7, 0.6),\n",
    "            rotation=270.0, weight='bold')\n",
    "ax.annotate('Atiras', xy=(0.3, 0.5), xytext=(0.3, 0.5),\n",
    "            rotation=280.0, weight='bold')\n",
    "ax.minorticks_on()\n",
    "ax.legend()\n",
    "plt.xlabel(\"semimajor axis (au)\")\n",
    "plt.ylabel(\"eccentricity\")\n",
    "plt.title(\"Terrestrial-Planet-Crossing NEOs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fbb23cb-f899-40d2-ada4-0b16e61b65a4",
   "metadata": {},
   "source": [
    "Next, define the parameters of each planet-crossing regions for each of the terrestrial planets in order to extract the planet-crossing NEO populations individually from the `uniqueNEOs` table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5ac10aa-b6aa-4239-a0a0-1ed91b4137c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Earth_crossing = (uniqueNEOs.q < Q_Earth) & (uniqueNEOs.Q > q_Earth)\n",
    "\n",
    "Mercury_crossing = (uniqueNEOs.q < Q_Mercury) & (uniqueNEOs.Q > q_Mercury)\n",
    "\n",
    "Venus_crossing = (uniqueNEOs.q < Q_Venus) & (uniqueNEOs.Q > q_Venus)\n",
    "\n",
    "Mars_crossing = (uniqueNEOs.q < Q_Mars) & (uniqueNEOs.Q > q_Mars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7519d2b4-1928-4a01-888a-328a6c43454d",
   "metadata": {},
   "source": [
    "Make a 4-panel density plot of the planet-crossing NEO populations in the DP0.3 catalogs as a function of $a$ and $e$, with one panel for each of the four terrestrial planets; each density plot is individually normalized where the color represents the number of data points within each bin, from purple/blue as the fewest objects to red regions containing the most objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6902ab5c-ba5a-41df-966f-b51e5db1f2ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(2, 2)\n",
    "fig.suptitle('Planet-Crossing NEOs in DP0.3')\n",
    "\n",
    "axs[0, 0].set_xlim([0., 4.])\n",
    "axs[0, 0].set_ylim([0., 1.])\n",
    "axs[0, 0].hexbin(uniqueNEOs[Mercury_crossing].a,\n",
    "                 uniqueNEOs[Mercury_crossing].e,\n",
    "                 gridsize=(int(4./0.02), 50),\n",
    "                 cmap='Spectral_r', bins='log',\n",
    "                 extent=(0, 4., 0, 1))\n",
    "axs[0, 0].plot(a_Q_Mercury, e_Q_Mercury, \"green\")\n",
    "axs[0, 0].plot(a_q_Mercury, e_q_Mercury, \"green\")\n",
    "axs[0, 0].plot(a_q_NEO, e_q_NEO, \"black\", linewidth=2)\n",
    "axs[0, 0].set_title('Mercury-crossing')\n",
    "axs[0, 0].minorticks_on()\n",
    "\n",
    "axs[0, 1].set_xlim([0., 4.])\n",
    "axs[0, 1].set_ylim([0., 1.])\n",
    "axs[0, 1].hexbin(uniqueNEOs[Venus_crossing].a,\n",
    "                 uniqueNEOs[Venus_crossing].e,\n",
    "                 gridsize=(int(4./0.02), 50),\n",
    "                 cmap='Spectral_r', bins='log',\n",
    "                 extent=(0, 4., 0, 1))\n",
    "axs[0, 1].plot(a_Q_Venus, e_Q_Venus, \"orange\")\n",
    "axs[0, 1].plot(a_q_Venus, e_q_Venus, \"orange\")\n",
    "axs[0, 1].plot(a_q_NEO, e_q_NEO, \"black\", linewidth=2)\n",
    "axs[0, 1].set_title('Venus-crossing')\n",
    "axs[0, 1].minorticks_on()\n",
    "\n",
    "axs[1, 0].set_xlim([0., 4.])\n",
    "axs[1, 0].set_ylim([0., 1.])\n",
    "axs[1, 0].hexbin(uniqueNEOs[Earth_crossing].a,\n",
    "                 uniqueNEOs[Earth_crossing].e,\n",
    "                 gridsize=(int(4./0.02), 50),\n",
    "                 cmap='Spectral_r', bins='log',\n",
    "                 extent=(0, 4., 0, 1))\n",
    "axs[1, 0].plot(a_Q_Earth, e_Q_Earth, \"blue\")\n",
    "axs[1, 0].plot(a_q_Earth, e_q_Earth, \"blue\")\n",
    "axs[1, 0].plot(a_q_NEO, e_q_NEO, \"black\", linewidth=2)\n",
    "axs[1, 0].set_title('Earth-crossing')\n",
    "axs[1, 0].minorticks_on()\n",
    "axs[1, 0].xaxis.set_tick_params(labelbottom=False)\n",
    "\n",
    "axs[1, 1].set_xlim([0., 4.])\n",
    "axs[1, 1].set_ylim([0., 1.])\n",
    "axs[1, 1].hexbin(uniqueNEOs[Mars_crossing].a,\n",
    "                 uniqueNEOs[Mars_crossing].e,\n",
    "                 gridsize=(int(4./0.02), 50),\n",
    "                 cmap='Spectral_r', bins='log',\n",
    "                 extent=(0, 4., 0, 1))\n",
    "axs[1, 1].plot(a_Q_Mars, e_Q_Mars, \"red\")\n",
    "axs[1, 1].plot(a_q_Mars, e_q_Mars, \"red\")\n",
    "axs[1, 1].plot(a_q_NEO, e_q_NEO, \"black\", linewidth=2)\n",
    "axs[1, 1].set_title('Mars-crossing')\n",
    "axs[1, 1].minorticks_on()\n",
    "axs[1, 1].xaxis.set_tick_params(labelbottom=False)\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(xlabel='semimajor axis (au)', ylabel='eccentricity')\n",
    "for ax in axs.flat:\n",
    "    ax.label_outer()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "9d5f36f5-2042-4e22-899a-c6a767cc8a0c",
   "metadata": {},
   "source": [
    "The percentage of NEOs that are Earth-crossing or Mars-crossing or Venus-crossing or Mercury-crossing (as shown above) can also easily be calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9d681b5-ddc4-4e34-ad68-924476f19167",
   "metadata": {},
   "outputs": [],
   "source": [
    "perc_Earth_crossing_NEOs = (\n",
    "    len(uniqueNEOs[Earth_crossing]) / total_NEOs * 100.)\n",
    "perc_Venus_crossing_NEOs = (\n",
    "    len(uniqueNEOs[Venus_crossing]) / total_NEOs * 100.)\n",
    "perc_Mercury_crossing_NEOs = (\n",
    "    len(uniqueNEOs[Mercury_crossing]) / total_NEOs * 100.)\n",
    "perc_Mars_crossing_NEOs = (\n",
    "    len(uniqueNEOs[Mars_crossing]) / total_NEOs * 100.)\n",
    "\n",
    "print(\"Percentage of NEOs that are Mercury-crossing: \", \"%4.1f\" %\n",
    "      (perc_Mercury_crossing_NEOs), \"%\")\n",
    "print(\"Percentage of NEOs that are Venus-crossing: \", \"%4.1f\" %\n",
    "      (perc_Venus_crossing_NEOs), \"%\")\n",
    "print(\"Percentage of NEOs that are Earth-crossing: \", \"%4.1f\" %\n",
    "      (perc_Earth_crossing_NEOs), \"%\")\n",
    "print(\"Percentage of NEOs that are Mars-crossing: \", \"%4.1f\" %\n",
    "      (perc_Mars_crossing_NEOs), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1206c04e-567d-406f-98e1-9964c329bcc6",
   "metadata": {},
   "source": [
    "Note that the Mars-crossing NEOs include all of the Amors, nearly all of the Apollos, and some of the Atens, covering the vast majority of the NEO population density at >90%. The Earth-crossing NEOs, Apollos and Atens, constitute 61.8% of the NEO population, consistent with their NEO percentages calculated above (Apollos = 55.3% of NEOs and Atens = 6.5% of NEOs). Venus- and Mercury-crossing NEOs include respectively fewer Apollos, Atens, and Atiras, where the Atens + Atiras are only 6.6% of the NEO population, resulting in respectively smaller percentages of the NEOs crossing the orbits of these planets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f376e60b-c6fd-4f49-8cb7-bb30efede72e",
   "metadata": {},
   "source": [
    "It is also possible to extract the planet-crossing NEOs for multiple (or all) planets together as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dd34144-6845-42b3-8a07-717291bf4159",
   "metadata": {},
   "outputs": [],
   "source": [
    "V_AND_E_crossing = (\n",
    "    (uniqueNEOs.q < Q_Venus) & (uniqueNEOs.Q > q_Earth))\n",
    "\n",
    "Me_AND_V_AND_E_AND_Ma_crossing = (\n",
    "    (uniqueNEOs.q < Q_Mercury) & (uniqueNEOs.Q > q_Mars))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "595136cc-aea8-42ea-badb-a1787dd82483",
   "metadata": {},
   "source": [
    "The percentage of NEOs that are both Venus- and Earth-crossing can also easily be calculated, as can the percentage of NEOs that cross the orbits of Mercury, Venus, Earth, and Mars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50374ad9-4edd-4820-afd4-5a2cf50808b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "perc_V_AND_E_crossing_NEOs = (\n",
    "    len(uniqueNEOs[V_AND_E_crossing]) / total_NEOs * 100.)\n",
    "perc_Me_AND_V_AND_E_AND_Ma_crossing_NEOs = (\n",
    "    len(uniqueNEOs[Me_AND_V_AND_E_AND_Ma_crossing]) / total_NEOs * 100.)\n",
    "\n",
    "print(\"Percentage of NEOs that are both \\\n",
    "Venus- and Earth-crossing: \", \"%4.1f\" %\n",
    "      (perc_V_AND_E_crossing_NEOs), \"%\")\n",
    "print(\"Percentage of NEOs that are \\\n",
    "Mercury- and Venus- and Earth- and Mars-crossing: \", \"%4.1f\" %\n",
    "      (perc_Me_AND_V_AND_E_AND_Ma_crossing_NEOs), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbeddc2e-845d-45f7-8326-9373357efa83",
   "metadata": {},
   "source": [
    "NEOs on orbits that cross both Venus' and Earth's orbit can be additionally be plotted, as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21d3c566-d4f4-4ea4-a737-040b0c626893",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.xlim([0., 4.])\n",
    "plt.ylim([0., 1.])\n",
    "ax.scatter(uniqueNEOs[V_AND_E_crossing].a,\n",
    "           uniqueNEOs[V_AND_E_crossing].e,\n",
    "           s=0.1)\n",
    "ax.plot(a_Q_Mercury, e_Q_Mercury, \"green\", label=\"Mercury-crossing\")\n",
    "ax.plot(a_q_Mercury, e_q_Mercury, \"green\")\n",
    "ax.plot(a_Q_Venus, e_Q_Venus, \"orange\", label=\"Venus-crossing\")\n",
    "ax.plot(a_q_Venus, e_q_Venus, \"orange\")\n",
    "ax.plot(a_Q_Earth, e_Q_Earth, \"blue\", label=\"Earth-crossing\")\n",
    "ax.plot(a_q_Earth, e_q_Earth, \"blue\")\n",
    "ax.plot(a_Q_Mars, e_Q_Mars, \"red\", label=\"Mars-crossing\")\n",
    "ax.plot(a_q_Mars, e_q_Mars, \"red\")\n",
    "ax.minorticks_on()\n",
    "ax.legend()\n",
    "plt.xlabel(\"semimajor axis (au)\")\n",
    "plt.ylabel(\"eccentricity\")\n",
    "plt.title(\"Venus- and Earth-Crossing NEOs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6a742bf-a0a1-41c4-8b83-f6b2d945b190",
   "metadata": {},
   "source": [
    "### 2.4 Explore the inclination distribution of NEOs in DP0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ecdfe9-d21a-4a7b-8039-0ffc2b3e4eb6",
   "metadata": {},
   "source": [
    "First add a plot of the semimajor axis $a$ versus inclination $i$ of all NEOs in the `uniqueNEOs` table to the ($a$, $e$) plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7f284c8-d75c-4f59-b565-844457a4c542",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1)\n",
    "\n",
    "ax1.set_xlim([0., 4.])\n",
    "ax1.set_ylim([0., 90.])\n",
    "ax1.scatter(uniqueNEOs.a, uniqueNEOs.incl, s=0.1)\n",
    "ax1.set_ylabel('inclination (deg)')\n",
    "ax1.set_title('NEOs in DP0.3')\n",
    "ax1.minorticks_on()\n",
    "\n",
    "ax2.set_xlim([0., 4.])\n",
    "ax2.set_ylim([0., 1.])\n",
    "ax2.scatter(uniqueNEOs.a, uniqueNEOs.e, s=0.1)\n",
    "ax2.set_xlabel('semimajor axis (au)')\n",
    "ax2.set_ylabel('eccentricity')\n",
    "ax2.minorticks_on()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63a1af26-deef-41a8-8134-bc861fb04f02",
   "metadata": {},
   "source": [
    "To better see the density of points in the above ($a$, $e$) and ($a$, $i$) scatter plot, use a 2-panel 2-D histogram plot where the bins are shown as hexagons and the color represents the number of data points within each bin, from purple/blue as the fewest to red regions containing the most objects. Again, overplot the NEO class boundaries with labels in the $a$ versus $e$ plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fe66cb7-40a5-4e43-9bd8-fc38a928db00",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(6, 6))\n",
    "\n",
    "axs[0].hexbin(uniqueNEOs.a,\n",
    "              uniqueNEOs.incl,\n",
    "              gridsize=(int(4.2/0.02), 50),\n",
    "              cmap='Spectral_r', bins='log',\n",
    "              extent=(0, 4.2, 0, 90))\n",
    "axs[0].set_ylabel('inclination (deg)')\n",
    "axs[0].minorticks_on()\n",
    "axs[0].set_xlim(0., 4.)\n",
    "axs[0].set_ylim(0., 90.)\n",
    "\n",
    "axs[1].hexbin(uniqueNEOs.a,\n",
    "              uniqueNEOs.e,\n",
    "              gridsize=(int(4.2/0.02), 50),\n",
    "              cmap='Spectral_r', bins='log',\n",
    "              extent=(0, 4.2, 0, 1))\n",
    "axs[1].plot(a_Q_Earth, e_Q_Earth, \"blue\")\n",
    "axs[1].plot(a_q_Earth, e_q_Earth, \"blue\")\n",
    "axs[1].axvline(x=1.0, color=\"blue\")\n",
    "axs[1].plot(a_q_NEO, e_q_NEO, \"black\", linewidth=5)\n",
    "axs[1].set_ylabel('eccentricity')\n",
    "axs[1].set_xlabel('semi-major axis (au)')\n",
    "\n",
    "axs[1].set_xlim(0., 4.)\n",
    "axs[1].set_ylim(0., 1.)\n",
    "axs[1].minorticks_on()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10b6c08a-cdd8-4c29-8a37-5fc16e129dfc",
   "metadata": {},
   "source": [
    "A 2-panel scatter plot of ($a$, $i$; top) and ($a$, $e$; bottom) with all NEOs in the `uniqueNEOs` table split into the 4 sub-populations can also be plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "525fd8ef-fc73-4194-bf66-9fd1f2534d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1)\n",
    "\n",
    "ax1.set_xlim([0., 4.])\n",
    "ax1.set_ylim([0., 90.])\n",
    "ax1.scatter(uniqueNEOs[Amor_params].a,\n",
    "            uniqueNEOs[Amor_params].incl,\n",
    "            s=0.1, color='red',\n",
    "            label=\"Amors\")\n",
    "ax1.scatter(uniqueNEOs[Apollo_params].a,\n",
    "            uniqueNEOs[Apollo_params].incl,\n",
    "            s=0.1, color='green',\n",
    "            label=\"Apollos\")\n",
    "ax1.scatter(uniqueNEOs[Aten_params].a,\n",
    "            uniqueNEOs[Aten_params].incl,\n",
    "            s=0.1, color='orange',\n",
    "            label=\"Atens\")\n",
    "ax1.scatter(uniqueNEOs[Atira_params].a,\n",
    "            uniqueNEOs[Atira_params].incl,\n",
    "            s=0.1, color='purple',\n",
    "            label=\"Atiras\")\n",
    "ax1.minorticks_on()\n",
    "ax1.set_ylabel(\"inclination (deg)\")\n",
    "ax1.set_title(\"NEOs in DP0.3\")\n",
    "\n",
    "ax2.set_xlim([0., 4.])\n",
    "ax2.set_ylim([0., 1.])\n",
    "ax2.scatter(uniqueNEOs[Amor_params].a,\n",
    "            uniqueNEOs[Amor_params].e,\n",
    "            s=0.1, color='red',\n",
    "            label=\"Amors\")\n",
    "ax2.scatter(uniqueNEOs[Apollo_params].a,\n",
    "            uniqueNEOs[Apollo_params].e,\n",
    "            s=0.1, color='green',\n",
    "            label=\"Apollos\")\n",
    "ax2.scatter(uniqueNEOs[Aten_params].a,\n",
    "            uniqueNEOs[Aten_params].e,\n",
    "            s=0.1, color='orange',\n",
    "            label=\"Atens\")\n",
    "ax2.scatter(uniqueNEOs[Atira_params].a,\n",
    "            uniqueNEOs[Atira_params].e,\n",
    "            s=0.1, color='purple',\n",
    "            label=\"Atiras\")\n",
    "ax2.plot(a_Q_Earth, e_Q_Earth, \"blue\")\n",
    "ax2.plot(a_q_Earth, e_q_Earth, \"blue\")\n",
    "ax2.axvline(x=1.0, color=\"blue\")\n",
    "ax2.plot(a_q_NEO, e_q_NEO, \"black\", linewidth=5)\n",
    "ax2.annotate('Amors',\n",
    "             xy=(2.4, 0.5), xytext=(2.4, 0.5),\n",
    "             rotation=20.0, weight='bold', fontsize=10)\n",
    "ax2.annotate('Apollos',\n",
    "             xy=(1.45, 0.75), xytext=(1.45, 0.75),\n",
    "             weight='bold', fontsize=10)\n",
    "ax2.annotate('Atens',\n",
    "             xy=(0.7, 0.6), xytext=(0.7, 0.6),\n",
    "             rotation=270.0, weight='bold', fontsize=10)\n",
    "ax2.annotate('Atiras',\n",
    "             xy=(0.4, 0.5), xytext=(0.4, 0.5),\n",
    "             rotation=280.0, weight='bold', fontsize=10)\n",
    "ax2.minorticks_on()\n",
    "ax2.set_xlabel(\"semimajor axis (au)\")\n",
    "ax2.set_ylabel(\"eccentricity\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3885cac-df4a-4b27-8f0f-6c48e6ee3596",
   "metadata": {},
   "source": [
    "It can also be helpful to make a 4-panel scatter plot showing the semimajor axes $a$ versus inclinations $i$ of each NEO sub-population individually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bed4829d-066c-4ac7-91b2-a4165493bf13",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(2, 2)\n",
    "fig.suptitle('NEO Inclinations in DP0.3')\n",
    "\n",
    "axs[0, 0].set_xlim([0., 4.])\n",
    "axs[0, 0].set_ylim([0., 90.])\n",
    "axs[0, 0].scatter(uniqueNEOs[Atira_params].a,\n",
    "                  uniqueNEOs[Atira_params].incl,\n",
    "                  s=0.25, color='purple')\n",
    "axs[0, 0].set_title('Atiras')\n",
    "axs[0, 0].minorticks_on()\n",
    "\n",
    "axs[0, 1].set_xlim([0., 4.])\n",
    "axs[0, 1].set_ylim([0., 90.])\n",
    "axs[0, 1].scatter(uniqueNEOs[Aten_params].a,\n",
    "                  uniqueNEOs[Aten_params].incl,\n",
    "                  s=0.1, color='orange')\n",
    "axs[0, 1].set_title('Atens')\n",
    "axs[0, 1].minorticks_on()\n",
    "\n",
    "axs[1, 0].set_xlim([0., 4.])\n",
    "axs[1, 0].set_ylim([0., 90.])\n",
    "axs[1, 0].scatter(uniqueNEOs[Apollo_params].a,\n",
    "                  uniqueNEOs[Apollo_params].incl,\n",
    "                  s=0.1, color='green')\n",
    "axs[1, 0].set_title('Apollos')\n",
    "axs[1, 0].minorticks_on()\n",
    "axs[1, 0].xaxis.set_tick_params(labelbottom=False)\n",
    "\n",
    "axs[1, 1].set_xlim([0., 4.])\n",
    "axs[1, 1].set_ylim([0., 90.])\n",
    "axs[1, 1].scatter(uniqueNEOs[Amor_params].a,\n",
    "                  uniqueNEOs[Amor_params].incl,\n",
    "                  s=0.1, color='red')\n",
    "axs[1, 1].set_title('Amors')\n",
    "axs[1, 1].minorticks_on()\n",
    "axs[1, 1].xaxis.set_tick_params(labelbottom=False)\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(xlabel='semimajor axis (au)', ylabel='inclination (deg)')\n",
    "for ax in axs.flat:\n",
    "    ax.label_outer()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f5f879-110d-46e3-b1ed-abe51e923bd1",
   "metadata": {},
   "source": [
    "So far the above plots showing the inclination distribution of NEOs in the DP0.3 catalogs has been limited to inclinations $i<90$ deg. Now extend the inclination range in a ($a$, $i$) scatter plot beyond 90 deg to the retrograde ($90<i<180$ deg) region and label the prograde ($0<i<90$ deg) and retrograde regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21171d45-4563-40a5-8c6d-6d6261746a35",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.xlim([0., 4.])\n",
    "plt.ylim([0., 180.])\n",
    "ax.scatter(uniqueNEOs.a, uniqueNEOs.incl, s=0.1)\n",
    "ax.axhline(y=90.0, color=\"black\", linestyle=\"dotted\")\n",
    "ax.annotate('prograde', xy=(0.05, 80.0), xytext=(0.05, 80.0))\n",
    "ax.annotate('retrograde', xy=(0.05, 95.0), xytext=(0.05, 95.0))\n",
    "plt.xlabel(\"semimajor axis (au)\")\n",
    "plt.ylabel(\"inclination (deg)\")\n",
    "plt.title(\"NEOs in DP0.3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d150c3ac-e906-440d-b18d-5105c642a702",
   "metadata": {},
   "source": [
    "The retrograde (i>90 deg) NEOs in the DP0.3 catalogs can be extracted and plotted with their object designations labeled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4be9f24-da4d-4e97-b302-85e2b97e7b3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_i = (uniqueNEOs.incl > 90.0) & (uniqueNEOs.q < 1.3)\n",
    "print(len(uniqueNEOs[high_i]))\n",
    "print(uniqueNEOs[high_i][['mpcDesignation', 'q', 'a', 'e', 'incl']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c4561e5-2447-43ca-baf9-5582cf0cff86",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.xlim([0., 4.])\n",
    "plt.ylim([90., 180.])\n",
    "ax.scatter(uniqueNEOs[high_i].a, uniqueNEOs[high_i].incl)\n",
    "ax.annotate(uniqueNEOs[high_i].iloc[0].mpcDesignation,\n",
    "            xy=(uniqueNEOs[high_i].iloc[0].a,\n",
    "                uniqueNEOs[high_i].iloc[0].incl),\n",
    "            xytext=(uniqueNEOs[high_i].iloc[0].a+0.1,\n",
    "                    uniqueNEOs[high_i].iloc[0].incl-2.),\n",
    "            fontsize=10)\n",
    "ax.annotate(uniqueNEOs[high_i].iloc[1].mpcDesignation,\n",
    "            xy=(uniqueNEOs[high_i].iloc[1].a,\n",
    "                uniqueNEOs[high_i].iloc[1].incl),\n",
    "            xytext=(uniqueNEOs[high_i].iloc[1].a-0.65,\n",
    "                    uniqueNEOs[high_i].iloc[1].incl-2.),\n",
    "            fontsize=10)\n",
    "ax.annotate(uniqueNEOs[high_i].iloc[2].mpcDesignation,\n",
    "            xy=(uniqueNEOs[high_i].iloc[2].a,\n",
    "                uniqueNEOs[high_i].iloc[2].incl),\n",
    "            xytext=(uniqueNEOs[high_i].iloc[2].a+0.1,\n",
    "                    uniqueNEOs[high_i].iloc[2].incl-2.),\n",
    "            fontsize=10)\n",
    "ax.annotate(uniqueNEOs[high_i].iloc[3].mpcDesignation,\n",
    "            xy=(uniqueNEOs[high_i].iloc[3].a,\n",
    "                uniqueNEOs[high_i].iloc[3].incl),\n",
    "            xytext=(uniqueNEOs[high_i].iloc[3].a+0.1,\n",
    "                    uniqueNEOs[high_i].iloc[3].incl-2.),\n",
    "            fontsize=10)\n",
    "ax.annotate(uniqueNEOs[high_i].iloc[4].mpcDesignation,\n",
    "            xy=(uniqueNEOs[high_i].iloc[4].a,\n",
    "                uniqueNEOs[high_i].iloc[4].incl),\n",
    "            xytext=(uniqueNEOs[high_i].iloc[4].a+0.1,\n",
    "                    uniqueNEOs[high_i].iloc[4].incl-2.),\n",
    "            fontsize=10)\n",
    "ax.annotate(uniqueNEOs[high_i].iloc[5].mpcDesignation,\n",
    "            xy=(uniqueNEOs[high_i].iloc[5].a,\n",
    "                uniqueNEOs[high_i].iloc[5].incl),\n",
    "            xytext=(uniqueNEOs[high_i].iloc[5].a+0.1,\n",
    "                    uniqueNEOs[high_i].iloc[5].incl-2.),\n",
    "            fontsize=10)\n",
    "plt.xlabel(\"semimajor axis (au)\")\n",
    "plt.ylabel(\"inclination (deg)\")\n",
    "plt.title(\"Retrograde NEOs in DP0.3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a54f3e2-8c81-482d-a023-faa4eb895fda",
   "metadata": {},
   "source": [
    "### Exercises for the learner:\n",
    "\n",
    "1. Plot the subset of Apollos that are only Venus-crossing (i.e., exclude the Earth-crossing Apollos).\n",
    "\n",
    "2. Compute the percentage of NEOs on retrograde (incl > 90 deg) orbits.\n",
    "\n",
    "3. Make a 4-panel plot showing the 1-D inclination distributions for each of the four NEO sub-populations individually.\n",
    "\n",
    "4. How many objects in the `uniqueNEOs` table are interstellar objects (with prefix \"iso\" in their mpcDesignation)? Query the DP0.3 tables to include the interstellar objects and plot their semimajor axes, eccentricities, and inclinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "697dc5df-3fce-4cdb-af61-cfaa3783bd30",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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