Planetary_Ephemerides_Coordinates.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Planetary Ephemerides & Coordinates\n",
        "\n",
        "* The trajectory followed by an astronomical object is also called ephemeris. In former times, astronomers observed planetary positions on a daily basis and noted the positions in tables.\n",
        "* Planetary ephemerides (singular: ephemeris) are tables or datasets that provide the positions of astronomical objects in the sky at given times. These objects typically include planets, moons, asteroids, comets, and spacecraft. The term \"ephemeris\" comes from Latin and originally from the Greek word \"ephémeros,\" meaning \"diary\" or \"calendar.\"\n",
        "* Planetary ephemerides are specific to the positions of planets. They give detailed information about the location of planets in the sky at various times. This can include their coordinates in various celestial coordinate systems, such as right ascension and declination, or their distance from the Earth or the Sun.\n",
        "* While planetary ephemerides refer specifically to planets, the term \"ephemeris\" can be used more broadly for any celestial body. An ephemeris can provide data on stars, galaxies, artificial satellites, and other astronomical objects.\n",
        "*  Ephemerides are calculated using mathematical models of celestial mechanics, incorporating the laws of gravity and motion. These calculations take into account various factors such as gravitational interactions between bodies, perturbations, and relativistic effects.\n",
        "* Various institutions, like NASA's Jet Propulsion Laboratory (JPL) and the International Astronomical Union (IAU), publish ephemerides.\n",
        "* To compute such secular variations over many orbital periods with high accuracy, analytical and numerical calculations using perturbation techniques are applied. An example is the VSOP (**Variations Séculaires des Orbites Planétaires**). The solution known as VSOP87 represents the time-dependent heliocentric coordinates X,Y,Z of the planets in the solar system (including Pluto) by series expansions, which are available as source code in several programming languages.\n",
        "*VSOP87 Model:\n",
        "> $P = \\sum A \\cdot \\cos(B + C \\cdot t)$\n",
        "\n",
        "where:\n",
        "\n",
        "$P$: the planetary position in one of the heliocentric coordinates\n",
        "\n",
        "$A$, $B$, $C$: coefficients specific to each planet and coordinate\n",
        "\n",
        "$t$: time in Julian centuries from the J2000.0 epoch"
      ],
      "metadata": {
        "id": "f2BdVud9GAj_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install skyfield"
      ],
      "metadata": {
        "id": "O8rmENr2KE6K"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from skyfield.api import load,Topos,Star\n",
        "from skyfield.data import hipparcos\n",
        "from skyfield.positionlib import ITRF_to_GCRS"
      ],
      "metadata": {
        "id": "7TOlVteIKM5g"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import math,astropy\n",
        "from datetime import datetime,timedelta\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.patches import Circle\n",
        "import numpy as np\n",
        "plt.style.use(\"dark_background\")\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "bYuVLC1LI3mc"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "begZQ9NiF8tG"
      },
      "outputs": [],
      "source": [
        "def ConvertJulianDate(day:int,month:int,year:int,universelTime:int or float)->int or float:\n",
        "  if (month <= 2):\n",
        "    yearBase = year-1\n",
        "    monthBase = month+12\n",
        "  else:\n",
        "    yearBase = year\n",
        "    monthBase = month\n",
        "  if (year < 1582):\n",
        "    base = -2\n",
        "  elif (year == 1582):\n",
        "    if (month < 10):\n",
        "      base = -2\n",
        "    elif (month == 10):\n",
        "      if (day <= 4):\n",
        "        base = -2\n",
        "      else:\n",
        "        base = math.floor(year/400)-math.floor(year/100)\n",
        "    else:\n",
        "      base = math.floor(year/400)-math.floor(year/100)\n",
        "  else:\n",
        "    base = math.floor(year/400)-math.floor(year/100)\n",
        "  return math.floor(365.25*year)+math.floor(30.6001*(month+1))+base+1720996.5+day+universelTime/24\n",
        ""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "now = datetime.utcnow()\n",
        "print(f\"UTC - Now: {now}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_r9ToLnJWJq",
        "outputId": "9dfa35d6-dd55-40de-b752-26f8fefb5b5f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "UTC - Now: 2023-12-17 18:55:24.825877\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "julianDate = ConvertJulianDate(now.day,now.month,now.year,now.hour+now.minute/60+now.second/3600)\n",
        "print(f\"Julian Date: {julianDate}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6K_GFGbJd5A",
        "outputId": "9d8a8c23-e65a-4aea-c650-58b37fa9a45b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Julian Date: 2460296.288472222\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load ephemeris data (DE421 includes VSOP87 model data)\n",
        "planets = load(\"de421.bsp\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DLtM6tEzJvnM",
        "outputId": "89abb9e6-97d4-4f2e-a163-4b5134058b7f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[#################################] 100% de421.bsp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "planets.__dict__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dGJ2Uc-2KWjH",
        "outputId": "ad084f09-52a6-4100-8bf4-ffcb251bafd3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'path': 'de421.bsp',\n",
              " 'filename': 'de421.bsp',\n",
              " 'spk': <jplephem.spk.SPK at 0x7a708af6b6d0>,\n",
              " 'segments': [<ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 1 MERCURY BARYCENTER>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 2 VENUS BARYCENTER>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 3 EARTH BARYCENTER>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 4 MARS BARYCENTER>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 5 JUPITER BARYCENTER>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 6 SATURN BARYCENTER>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 7 URANUS BARYCENTER>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 8 NEPTUNE BARYCENTER>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 9 PLUTO BARYCENTER>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 10 SUN>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 3 EARTH BARYCENTER -> 301 MOON>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 3 EARTH BARYCENTER -> 399 EARTH>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 1 MERCURY BARYCENTER -> 199 MERCURY>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 2 VENUS BARYCENTER -> 299 VENUS>,\n",
              "  <ChebyshevPosition 'de421.bsp' segment 4 MARS BARYCENTER -> 499 MARS>],\n",
              " 'codes': {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 199, 299, 301, 399, 499}}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "earthData = planets[\"earth\"]\n",
        "print(f\"Earth Data:\\n{earthData}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqkkREd3KX7A",
        "outputId": "1becd371-d005-4177-a79f-5d659d0da763"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Earth Data:\n",
            "Sum of 2 vectors:\n",
            " 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 3 EARTH BARYCENTER\n",
            " 'de421.bsp' segment 3 EARTH BARYCENTER -> 399 EARTH\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "timeScale = load.timescale()\n",
        "print(f\"Time Scale - J2000:\\n{timeScale.J2000}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dm84-WV_K38c",
        "outputId": "d9468ee9-c14c-4b09-be67-36e46b696c32"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time Scale - J2000:\n",
            "<Time tt=2451545.0>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "UTCNow = timeScale.utc(now.year,now.month,now.day)\n",
        "print(f\"Now - UTC - Additional Method: {UTCNow}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "swLQDYRcLLrc",
        "outputId": "ccf61dc4-1d0c-4b68-8cdb-2a53e24fadf9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Now - UTC - Additional Method: <Time tt=2460295.500800741>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "observedPlanet = planets[\"mars\"]"
      ],
      "metadata": {
        "id": "BTMUUvS6L1Tm"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "earthAstrometric = earthData.at(UTCNow)\n",
        "print(f\"Astrometric Values for Earth:\\n{earthAstrometric}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIrEcxQnLjOJ",
        "outputId": "4a3ff089-2437-445d-dec6-21a27a06d9e8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Astrometric Values for Earth:\n",
            "<Barycentric BCRS position and velocity at date t center=0 target=399>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "positionObservedPlanet = earthData.at(UTCNow).observe(observedPlanet)\n",
        "print(f\"Right Ascension-Mars: {positionObservedPlanet.radec()[0]}\")\n",
        "print(f\"Declination-Mars: {positionObservedPlanet.radec()[1]}\")\n",
        "print(f\"Distance-Mars: {positionObservedPlanet.radec()[2]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nHinMQ-6L0pk",
        "outputId": "4818411c-f7d8-4c3a-d114-5fd612abc199"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Right Ascension-Mars: 16h 58m 46.17s\n",
            "Declination-Mars: -23deg 05' 42.9\"\n",
            "Distance-Mars: 2.46633 au\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def GetPlanetPosition(planetName:str,date:int or float)->tuple:\n",
        "  planet = planets[planetName]\n",
        "  timeUTC = timeScale.utc(date.year,date.month,date.day)\n",
        "  astrometric = earthData.at(timeUTC).observe(planet)\n",
        "  ra,dec,distance = astrometric.radec()\n",
        "  return ra,dec,distance"
      ],
      "metadata": {
        "id": "FZAqOCOGKlfA"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raVENUS,decVENUS,distanceVENUS = GetPlanetPosition(\"VENUS\",datetime(2023,12,15))\n",
        "print(f\"Right Ascension-VENUS: {raVENUS}\")\n",
        "print(f\"Declination-VENUS: {decVENUS}\")\n",
        "print(f\"Distance-VENUS: {distanceVENUS}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Th_yaTrhMdzg",
        "outputId": "ac3130f0-bd20-4ee9-e379-e50e388e4513"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Right Ascension-VENUS: 14h 40m 13.86s\n",
            "Declination-VENUS: -13deg 13' 10.4\"\n",
            "Distance-VENUS: 1.07117 au\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* If ($x_1$, $y_1$, $z_1$) and ($x_2$, $y_2$, $z_2$) are the coordinates of two points in 3D space, the distance:\n",
        "> $d = \\sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2 + (z_2 - z_1)^2}$"
      ],
      "metadata": {
        "id": "F5cFawjNNo-3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sunData = planets[\"sun\"]\n",
        "timeNow = UTCNow = timeScale.now()\n",
        "print(f\"Current Time:\\n{timeNow}\")\n",
        "astrometricEarth = sunData.at(timeNow).observe(earthData)\n",
        "positionEarth = astrometricEarth.position\n",
        "print(f\"Position-Earth:\\n{positionEarth}\")\n",
        "print(f\"Position-Distance-Earth:\\n{positionEarth.au}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P74QdF1cORTv",
        "outputId": "d7793642-d0a0-4088-f83d-959394fe58dd"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current Time:\n",
            "<Time tt=2460296.2893171157>\n",
            "Position-Earth:\n",
            "[0.08178029 0.89978588 0.39004728] au\n",
            "Position-Distance-Earth:\n",
            "[0.08178029 0.89978588 0.39004728]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def DistanceCalculation(coordsOne:tuple or list,coordsTwo:tuple or list)->int or float:\n",
        "  x1,y1,z1 = coordsOne\n",
        "  x2,y2,z2 = coordsTwo\n",
        "  distance = math.sqrt((x2-x1)**2+(y2-y1)**2+(z2-z1)**2)\n",
        "  return distance"
      ],
      "metadata": {
        "id": "D8CXTJaqMumi"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def GetPlanetCoordinates(planetName:str,time:int or float)->tuple or list:\n",
        "  planet = planets[planetName]\n",
        "  astrometric = sunData.at(time).observe(planet)\n",
        "  return astrometric.position.au"
      ],
      "metadata": {
        "id": "BuBrmmIUPNMQ"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nowTimeBase = timeScale.now()\n",
        "print(f\"Time-Current:\\n{nowTimeBase}\")\n",
        "earthCoord = GetPlanetCoordinates(\"earth\",nowTimeBase)\n",
        "venusCoord = GetPlanetCoordinates(\"venus\",nowTimeBase)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01Hpqo2VPpna",
        "outputId": "5b0a4972-11af-463c-ecd9-feab1dc22c30"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time-Current:\n",
            "<Time tt=2460296.289317708>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Earth Coordinate: {earthCoord}\")\n",
        "print(f\"Venus Coordinate: {venusCoord}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1UhMGjL8wAOD",
        "outputId": "aa75e38a-b37b-414f-a9ac-e48a238b38b7"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Earth Coordinate: [0.08178028 0.89978588 0.39004728]\n",
            "Venus Coordinate: [-0.6867875   0.17486632  0.12213489]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "distanceBetweenVenusEarth = DistanceCalculation(earthCoord,venusCoord)\n",
        "print(f\"Distance From Earth to Venus: {distanceBetweenVenusEarth} AU\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tdgDgHFKvucC",
        "outputId": "a4b13252-492b-49a7-a850-a7cfc20d1e88"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distance From Earth to Venus: 1.089945804986788 AU\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def EarthVenusDistanceReturn(timeRange:list)->list:\n",
        "  distances = []\n",
        "  for date in timeRange:\n",
        "    earthPos = GetPlanetCoordinates(\"earth\",date)\n",
        "    venusPos = GetPlanetCoordinates(\"venus\",date)\n",
        "    distance = DistanceCalculation(earthPos,venusPos)\n",
        "    distances.append(distance)\n",
        "  return distances"
      ],
      "metadata": {
        "id": "wsKws30vxIsH"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "currentJulian = timeScale.now().tt\n",
        "timeRange = [timeScale.tt_jd(currentJulian+index) for index in range(1000)] # Over 1000 Days\n",
        "distances = EarthVenusDistanceReturn(timeRange)\n",
        "plt.figure(figsize=(11,5))\n",
        "plt.title(\"Distance between Earth and Venus Over 1000 Days\")\n",
        "plt.plot([date.utc_datetime() for date in timeRange],distances,label=\"Time-Distance\")\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Distance (AU)\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "jo7tH9mFwiTc",
        "outputId": "42d8cd77-35ae-480a-9497-83e0781096d4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example"
      ],
      "metadata": {
        "id": "sAEJzkTL0xEr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**To observe and visualize the apparent retrograde motion of Mars from Earth's perspective**"
      ],
      "metadata": {
        "id": "q9yFENCB05mJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def GetMarsPosition(startDate:int or float,endDate:int or float,observerLocation:tuple)->list:\n",
        "  ts = load.timescale()\n",
        "  planetsData = load(\"de421.bsp\")\n",
        "  mars = planetsData[\"mars\"]\n",
        "  earth = planetsData[\"earth\"] + observerLocation\n",
        "  currentDate = startDate\n",
        "  positions = []\n",
        "  while currentDate <= endDate:\n",
        "    time = ts.utc(currentDate.year,currentDate.month,currentDate.day)\n",
        "    astrometricValues = earth.at(time).observe(mars)\n",
        "    ra,dec,dist = astrometricValues.radec()\n",
        "    positions.append((ra.hours,dec.degrees))\n",
        "    currentDate += timedelta(days=1)\n",
        "  return positions"
      ],
      "metadata": {
        "id": "iacG6MdLyK2w"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def PlotMarsMotion(positions:list)->None:\n",
        "  raValues,decValues = zip(*positions)\n",
        "  plt.figure(figsize=(11,5))\n",
        "  plt.plot(raValues,decValues,\"o-\")\n",
        "  plt.xlabel(\"Right Ascension (hours)\")\n",
        "  plt.ylabel(\"Declination (degrees)\")\n",
        "  plt.title(\"Apparent Motion of Mars Over Several Months\")\n",
        "  plt.grid(True)\n",
        "  plt.tight_layout()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "ptGOKn0u171Z"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "observerLocation = Topos(\"40.7128 N\",\"74.0060 W\") # Example: New York City"
      ],
      "metadata": {
        "id": "GA4XBH_Z2UNS"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Observer:\\n{observerLocation}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pMjJkJcq2ZpV",
        "outputId": "714e34f4-706f-47a4-b854-2cb263b02af6"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observer:\n",
            "IERS2010 latitude +40.7128 N longitude -74.0060 E elevation 0.0 m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "startDate = datetime(2023,1,1)\n",
        "endDate = datetime(2023,6,30)"
      ],
      "metadata": {
        "id": "oROeQ7i62buA"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Start from: {startDate}\")\n",
        "print(f\"End to: {endDate}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJTB1BY-2jI2",
        "outputId": "c1771c23-34de-4c7b-a6ab-521bf4d5b247"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start from: 2023-01-01 00:00:00\n",
            "End to: 2023-06-30 00:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "marsPosition = GetMarsPosition(startDate,endDate,observerLocation)"
      ],
      "metadata": {
        "id": "ytaGPKNb2msg"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "PlotMarsMotion(marsPosition)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "rj1kN0il2suQ",
        "outputId": "b5e6b2f4-8574-4452-aa1b-359b9fa6571d"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example"
      ],
      "metadata": {
        "id": "8ZJhTxZAJLQa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The Two-Line Element set (TLE) is a data format encoding a list of orbital elements of an Earth-orbiting object for a given point in time.**\n",
        "\n",
        "**We can use it for tracking the International Space Station (ISS)**"
      ],
      "metadata": {
        "id": "3adBhE63JlfJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def FetchTLEData()->dict:\n",
        "  tleURL = \"http://celestrak.com/NORAD/elements/stations.txt\"\n",
        "  satellites = load.tle_file(tleURL)\n",
        "  iss = {sat.name: sat for sat in satellites}[\"ISS (ZARYA)\"]\n",
        "  return iss"
      ],
      "metadata": {
        "id": "gXyoYC_L2vNc"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "issDATA = FetchTLEData()\n",
        "print(f\"ISS:\\n{issDATA}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLWQrNAvJ9K4",
        "outputId": "78eefb1e-2bde-4c9a-b537-99a0a2dd4f0b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[#################################] 100% stations.txt\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ISS:\n",
            "ISS (ZARYA) catalog #25544 epoch 2023-12-17 13:50:26 UTC\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def CalculateISSPosition(issData:dict,time:int or float)->str or tuple:\n",
        "  return issData.at(time)"
      ],
      "metadata": {
        "id": "HcGfs4beKCPQ"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def PredictOverflights(issData:dict,observerLocation:tuple,startTime:int or float,endTime:int or float)->tuple:\n",
        "  timeScale = load.timescale()\n",
        "  time,events = issData.find_events(observerLocation,startTime,endTime,altitude_degrees=30)\n",
        "  return time,events"
      ],
      "metadata": {
        "id": "JGXu45NOKPwu"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def CalculateDistanceISS(issData:dict,observerLocation:tuple,timeBase:int or float)->int or float:\n",
        "  time = timeBase.now()\n",
        "  issPosition = issData.at(time).position.km\n",
        "  # Convert observer location to GCRS\n",
        "  observerPosition = observerLocation.at(time).position.km\n",
        "  distance = np.linalg.norm(issPosition-observerPosition)\n",
        "  return distance"
      ],
      "metadata": {
        "id": "9WgnV1roL9LP"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "timeScaleBase = load.timescale()\n",
        "observerLocation = Topos(\"40.7128 N\",\"74.0060 W\") # Example: New York City\n",
        "startTime = timeScaleBase.now()\n",
        "endTime = timeScaleBase.utc(startTime.utc_datetime()+timedelta(days=1))"
      ],
      "metadata": {
        "id": "Q1UhDMG_KmcF"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "issData = FetchTLEData()\n",
        "issPosition = CalculateISSPosition(issData,startTime)"
      ],
      "metadata": {
        "id": "1AX7OVuBK5tT"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"ISS position at {startTime.utc_datetime()}: {issPosition.position.km} [KM BASE]\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YmLLo7_fLCVM",
        "outputId": "537821b4-f9f2-41ca-d542-b20e55bb2d29"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ISS position at 2023-12-17 19:05:51.547633+00:00: [ 5243.69534976   -60.43093489 -4327.48630063] [KM BASE]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "times,events = PredictOverflights(issData,observerLocation,startTime,endTime)\n",
        "for time,event in zip(times,events):\n",
        "  print(event)\n",
        "  name = (\"rise\",\"culminate\",\"set\")[event]\n",
        "  print(f\"{time.utc_datetime()} {name}\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HmXdJpnGLHQO",
        "outputId": "d5c8a3b8-9842-4b24-b261-94081b8a062f"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "2023-12-18 11:20:17.074456+00:00 rise\n",
            "\n",
            "1\n",
            "2023-12-18 11:21:41.428897+00:00 culminate\n",
            "\n",
            "2\n",
            "2023-12-18 11:23:06.309949+00:00 set\n",
            "\n",
            "0\n",
            "2023-12-18 17:50:01.634722+00:00 rise\n",
            "\n",
            "1\n",
            "2023-12-18 17:51:00.902677+00:00 culminate\n",
            "\n",
            "2\n",
            "2023-12-18 17:52:00.027683+00:00 set\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "distanceISS = CalculateDistanceISS(issData,observerLocation,timeScaleBase)\n",
        "print(f\"Distance from ISS to New York City: {distanceISS:.2f} km\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4j4IHRWyLilN",
        "outputId": "52944df4-a847-4e66-c317-43831f44825f"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distance from ISS to New York City: 12562.82 km\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example"
      ],
      "metadata": {
        "id": "S7ahywfDOaog"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create a sky chart showing the positions of stars, planets, and major constellations from a given location and time.**"
      ],
      "metadata": {
        "id": "DKiPNHdFOeEN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "planets = load(\"de421.bsp\") # ephemeris DE421"
      ],
      "metadata": {
        "id": "cP1uaXxCbNH2"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "planets.segments"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LfPHn1t8eoYP",
        "outputId": "c6700852-c2af-41e7-bdee-9b3fe71b3e8a"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 1 MERCURY BARYCENTER>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 2 VENUS BARYCENTER>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 3 EARTH BARYCENTER>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 4 MARS BARYCENTER>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 5 JUPITER BARYCENTER>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 6 SATURN BARYCENTER>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 7 URANUS BARYCENTER>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 8 NEPTUNE BARYCENTER>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 9 PLUTO BARYCENTER>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 10 SUN>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 3 EARTH BARYCENTER -> 301 MOON>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 3 EARTH BARYCENTER -> 399 EARTH>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 1 MERCURY BARYCENTER -> 199 MERCURY>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 2 VENUS BARYCENTER -> 299 VENUS>,\n",
              " <ChebyshevPosition 'de421.bsp' segment 4 MARS BARYCENTER -> 499 MARS>]"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "planets[\"MOON\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtDAnSAZevu-",
        "outputId": "0b52c1c9-fe27-4551-86d8-840b0def36d3"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<VectorSum of 2 vectors:\n",
              " 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 3 EARTH BARYCENTER\n",
              " 'de421.bsp' segment 3 EARTH BARYCENTER -> 301 MOON>"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "planets[\"JUPITER BARYCENTER\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8f74Kbb-e7JO",
        "outputId": "49c3cb00-26d4-4675-d6e4-4195bb70e991"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ChebyshevPosition 'de421.bsp' segment 0 SOLAR SYSTEM BARYCENTER -> 5 JUPITER BARYCENTER>"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "earthData = planets[\"earth\"]"
      ],
      "metadata": {
        "id": "GQh8fdl6bPXK"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "observerLocation = Topos(\"40.7128 N\",\"74.0060 W\") # NW\n",
        "timeBase = load.timescale()\n",
        "observingTime = timeBase.utc(2018,9, 3)"
      ],
      "metadata": {
        "id": "TogwBvthbIEh"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with load.open(hipparcos.URL) as hFile:\n",
        "    stars = hipparcos.load_dataframe(hFile)"
      ],
      "metadata": {
        "id": "J3n2j0TBa-gQ"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stars = stars[stars[\"magnitude\"] <= 2.5]\n",
        "print(f\"Found {len(stars)} - Depends on Magnitude <= 2.5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdpTAHA3dk9e",
        "outputId": "93e6e857-9c25-4bb6-9832-47e6d3c9040b"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 93 - Depends on Magnitude <= 2.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stars"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "zCJ6P_hiddaV",
        "outputId": "c690577d-6c17-435f-f708-733186095a57"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        magnitude  ra_degrees  dec_degrees  parallax_mas  ra_mas_per_year  \\\n",
              "hip                                                                         \n",
              "677          2.07    2.096533    29.090828         33.60           135.68   \n",
              "746          2.28    2.292040    59.150218         59.89           523.39   \n",
              "2081         2.40    6.570281   -42.305122         42.14           232.76   \n",
              "3179         2.24   10.126613    56.537409         14.27            50.36   \n",
              "3419         2.04   10.896785   -17.986684         34.04           232.79   \n",
              "...           ...         ...          ...           ...              ...   \n",
              "109268       1.73  332.057818   -46.960616         32.16           127.60   \n",
              "112122       2.07  340.666395   -46.884566         19.17           135.68   \n",
              "113368       1.17  344.411773   -29.621837        130.08           329.22   \n",
              "113881       2.44  345.943056    28.082455         16.37           187.76   \n",
              "113963       2.49  346.190070    15.205368         23.36            61.10   \n",
              "\n",
              "        dec_mas_per_year   ra_hours  epoch_year  \n",
              "hip                                              \n",
              "677              -162.95   0.139769     1991.25  \n",
              "746              -180.42   0.152803     1991.25  \n",
              "2081             -353.64   0.438019     1991.25  \n",
              "3179              -32.17   0.675108     1991.25  \n",
              "3419               32.71   0.726452     1991.25  \n",
              "...                  ...        ...         ...  \n",
              "109268           -147.91  22.137188     1991.25  \n",
              "112122             -4.51  22.711093     1991.25  \n",
              "113368           -164.22  22.960785     1991.25  \n",
              "113881            137.61  23.062870     1991.25  \n",
              "113963            -42.56  23.079338     1991.25  \n",
              "\n",
              "[93 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-10d21a93-ea44-43fc-ac45-cdf4f891e2ff\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        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>magnitude</th>\n",
              "      <th>ra_degrees</th>\n",
              "      <th>dec_degrees</th>\n",
              "      <th>parallax_mas</th>\n",
              "      <th>ra_mas_per_year</th>\n",
              "      <th>dec_mas_per_year</th>\n",
              "      <th>ra_hours</th>\n",
              "      <th>epoch_year</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hip</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>677</th>\n",
              "      <td>2.07</td>\n",
              "      <td>2.096533</td>\n",
              "      <td>29.090828</td>\n",
              "      <td>33.60</td>\n",
              "      <td>135.68</td>\n",
              "      <td>-162.95</td>\n",
              "      <td>0.139769</td>\n",
              "      <td>1991.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>746</th>\n",
              "      <td>2.28</td>\n",
              "      <td>2.292040</td>\n",
              "      <td>59.150218</td>\n",
              "      <td>59.89</td>\n",
              "      <td>523.39</td>\n",
              "      <td>-180.42</td>\n",
              "      <td>0.152803</td>\n",
              "      <td>1991.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2081</th>\n",
              "      <td>2.40</td>\n",
              "      <td>6.570281</td>\n",
              "      <td>-42.305122</td>\n",
              "      <td>42.14</td>\n",
              "      <td>232.76</td>\n",
              "      <td>-353.64</td>\n",
              "      <td>0.438019</td>\n",
              "      <td>1991.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3179</th>\n",
              "      <td>2.24</td>\n",
              "      <td>10.126613</td>\n",
              "      <td>56.537409</td>\n",
              "      <td>14.27</td>\n",
              "      <td>50.36</td>\n",
              "      <td>-32.17</td>\n",
              "      <td>0.675108</td>\n",
              "      <td>1991.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3419</th>\n",
              "      <td>2.04</td>\n",
              "      <td>10.896785</td>\n",
              "      <td>-17.986684</td>\n",
              "      <td>34.04</td>\n",
              "      <td>232.79</td>\n",
              "      <td>32.71</td>\n",
              "      <td>0.726452</td>\n",
              "      <td>1991.25</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109268</th>\n",
              "      <td>1.73</td>\n",
              "      <td>332.057818</td>\n",
              "      <td>-46.960616</td>\n",
              "      <td>32.16</td>\n",
              "      <td>127.60</td>\n",
              "      <td>-147.91</td>\n",
              "      <td>22.137188</td>\n",
              "      <td>1991.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112122</th>\n",
              "      <td>2.07</td>\n",
              "      <td>340.666395</td>\n",
              "      <td>-46.884566</td>\n",
              "      <td>19.17</td>\n",
              "      <td>135.68</td>\n",
              "      <td>-4.51</td>\n",
              "      <td>22.711093</td>\n",
              "      <td>1991.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113368</th>\n",
              "      <td>1.17</td>\n",
              "      <td>344.411773</td>\n",
              "      <td>-29.621837</td>\n",
              "      <td>130.08</td>\n",
              "      <td>329.22</td>\n",
              "      <td>-164.22</td>\n",
              "      <td>22.960785</td>\n",
              "      <td>1991.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113881</th>\n",
              "      <td>2.44</td>\n",
              "      <td>345.943056</td>\n",
              "      <td>28.082455</td>\n",
              "      <td>16.37</td>\n",
              "      <td>187.76</td>\n",
              "      <td>137.61</td>\n",
              "      <td>23.062870</td>\n",
              "      <td>1991.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113963</th>\n",
              "      <td>2.49</td>\n",
              "      <td>346.190070</td>\n",
              "      <td>15.205368</td>\n",
              "      <td>23.36</td>\n",
              "      <td>61.10</td>\n",
              "      <td>-42.56</td>\n",
              "      <td>23.079338</td>\n",
              "      <td>1991.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>93 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10d21a93-ea44-43fc-ac45-cdf4f891e2ff')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-10d21a93-ea44-43fc-ac45-cdf4f891e2ff button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-10d21a93-ea44-43fc-ac45-cdf4f891e2ff');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4bd37fcb-fd19-4978-bb2f-da7ed5812a8b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4bd37fcb-fd19-4978-bb2f-da7ed5812a8b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4bd37fcb-fd19-4978-bb2f-da7ed5812a8b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "starFrame = Star.from_dataframe(stars)\n",
        "observer = earthData + observerLocation\n",
        "astrometric = observer.at(observingTime).observe(starFrame)"
      ],
      "metadata": {
        "id": "OHpGMrH-bAp7"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "starFrame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zpmPIfzvdudc",
        "outputId": "3e33e305-446a-456f-c857-283b3341245c"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Star(ra shape=93, dec shape=93, ra_mas_per_year shape=93, dec_mas_per_year shape=93, parallax_mas shape=93, epoch shape=93)"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alt,az,d = astrometric.apparent().altaz()"
      ],
      "metadata": {
        "id": "-I_PV2DVcCOT"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ZsCACTacT8F",
        "outputId": "e0a1a144-fff6-4576-9e42-4dee5c613bee"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Angle 93 values from 15deg 35' 54.3\" to 18deg 39' 03.0\">"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "az"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aUs7s_-ScVIb",
        "outputId": "76045ff8-97b6-4c95-b41b-f1ed7b977fb6"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Angle 93 values from 64deg 40' 08.8\" to 85deg 34' 35.8\">"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OwydUhShcWFW",
        "outputId": "b492b635-8f92-4252-bc76-05e3d0893d77"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Distance [6.13883277e+06 3.44406053e+06 4.89475023e+06 1.44544359e+07\n",
              " 6.05948228e+06 3.87715798e+07 1.26078727e+07 9.09456767e+06\n",
              " 2.24444834e+07 4.16864955e+06 2.72837047e+07 5.86980074e+06\n",
              " 3.74346290e+07 4.11788392e+06 4.88779163e+07 2.66871291e+06\n",
              " 1.53699559e+07 8.28705550e+06 5.79395525e+07 8.48826365e+07\n",
              " 5.16954404e+07 4.56338069e+07 2.70333956e+07 5.19297126e+06\n",
              " 3.15872601e+07 1.97761081e+07 6.62804695e+06 5.43933366e+05\n",
              " 2.72476631e+07 1.13332312e+08 2.02220399e+08 3.26007344e+06\n",
              " 7.21382875e+05 2.13215704e+06 8.85256684e+07 5.31610330e+07\n",
              " 3.99738000e+07 5.04314968e+06 3.62504059e+07 7.03015719e+06\n",
              " 4.37929528e+07 3.40933568e+07 1.12100447e+07 4.90056659e+06\n",
              " 7.94548659e+06 5.02227498e+06 7.81898491e+06 2.28776502e+06\n",
              " 5.29019832e+06 2.02816922e+07 5.56119764e+06 8.24729381e+06\n",
              " 2.22988983e+07 5.11823399e+06 4.94284279e+06 1.65807729e+07\n",
              " 2.37632269e+07 6.36816377e+06 3.32149449e+07 3.85397666e+06\n",
              " 2.32149546e+06 1.95141731e+07 2.77940141e+05 2.77940141e+05\n",
              " 3.46663542e+07 1.32646183e+07 7.99786014e+06 4.72542546e+06\n",
              " 2.54020699e+07 3.81971864e+07 2.62757714e+07 4.13770919e+06\n",
              " 5.32021670e+06 4.44536218e+07 2.95339053e+06 1.72030695e+07\n",
              " 2.93406550e+07 9.33324907e+06 9.14699765e+06 1.59981984e+06\n",
              " 1.41860247e+07 1.06081400e+06 9.63854230e+07 1.15879099e+07\n",
              " 2.04222580e+08 4.55733049e+06 3.08594832e+06 4.25288251e+07\n",
              " 6.41370590e+06 1.07597700e+07 1.58567562e+06 1.26001706e+07\n",
              " 8.82982811e+06] au>"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def PlotNightSky(observerLocation:tuple,observingTime:int or float or tuple,titleCoord:str)->None:\n",
        "  timeBase = load.timescale()\n",
        "  timeUTC = timeBase.utc(\n",
        "      observingTime.year,\n",
        "      observingTime.month,\n",
        "      observingTime.day,\n",
        "      observingTime.hour,\n",
        "      observingTime.minute\n",
        "  )\n",
        "  planets = load(\"de421.bsp\")\n",
        "  earthData = planets[\"earth\"]\n",
        "  with load.open(hipparcos.URL) as hFile:\n",
        "    stars = hipparcos.load_dataframe(hFile)\n",
        "  stars = stars[stars[\"magnitude\"] <= 2.5]\n",
        "  print(f\"Found {len(stars)} - Depends on Magnitude <= 2.5\")\n",
        "  figure = plt.figure(figsize=(11,11))\n",
        "  ax = plt.subplot(111,polar=True)\n",
        "  for planetName in ['mercury','venus','mars','moon','JUPITER BARYCENTER','SATURN BARYCENTER','URANUS BARYCENTER','NEPTUNE BARYCENTER']:\n",
        "    planetData = planets[planetName]\n",
        "    observer = earthData + observerLocation\n",
        "    astrometricPlanet = observer.at(timeUTC).observe(planetData)\n",
        "    alt,az,d = astrometricPlanet.apparent().altaz()\n",
        "    if alt.degrees > 0: # Only plot if above the horizon\n",
        "      ax.annotate(\n",
        "          planetName.upper(),\n",
        "          xy=(np.radians(az.degrees),90-alt.degrees),\n",
        "          xytext=(5,5),\n",
        "          textcoords=\"offset points\"\n",
        "      )\n",
        "      ax.scatter(\n",
        "          np.radians(az.degrees),\n",
        "          90-alt.degrees,\n",
        "          color=\"white\",\n",
        "          label=planetName.capitalize())\n",
        "  observer = earthData + observerLocation\n",
        "  for starID in stars.index[:200]:\n",
        "    star = Star.from_dataframe(stars.loc[starID])\n",
        "    astrometricStar = observer.at(timeUTC).observe(star)\n",
        "    alt,az,d = astrometricStar.apparent().altaz()\n",
        "    if alt.degrees > 0: # Only plot if above the horizon\n",
        "      ax.scatter(\n",
        "          np.radians(az.degrees),\n",
        "          90-alt.degrees,\n",
        "          color=\"yellow\",\n",
        "          s=0.7\n",
        "          )\n",
        "  ax.set_title(f\"From: {observerLocation} / {titleCoord}\")\n",
        "  ax.set_theta_zero_location(\"N\")\n",
        "  ax.set_theta_direction(-1)\n",
        "  ax.set_ylim(0,90)\n",
        "  ax.set_yticks(range(0,91,30))\n",
        "  ax.set_yticklabels(['90°','60°','30°','Horizon'])\n",
        "  ax.legend()\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n"
      ],
      "metadata": {
        "id": "hd5FpsTKM0qT"
      },
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "observerLocation = Topos(\"55.9533 N\",\"3.1883 W\") # Edinburgh / Coordinates\n",
        "observingLocal = datetime(2023,4,1,22,0)  # 10:00 PM local time\n",
        "PlotNightSky(observerLocation,observingLocal,titleCoord=\"Edinburgh\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 982
        },
        "id": "hs9xGLKGQHr0",
        "outputId": "ecb1a0f4-35ca-45f2-ece5-dbd44296d8a4"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 93 - Depends on Magnitude <= 2.5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x1100 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fk6zWbi0QepQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}