Spectral_Analysis_FITS_And_Equations.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Spectral Analysis\n",
        "\n",
        "* Spectral analysis in astronomy involves examining the spectrum of light emitted, absorbed, or scattered by celestial objects. This analysis is fundamental because it provides a wealth of information about the object, such as its chemical composition, temperature, density, mass, distance, luminosity, and relative motion.\n",
        "* The light from an astronomical object, when passed through a prism or a diffraction grating, spreads out to form a spectrum. Analyzing these patterns helps astronomers determine a lot about the object in question. For example, the specific wavelengths of light that are absorbed or emitted can identify the elements present in a star:\n",
        "  - **Continuous Spectrum**: A seamless gradation of colors or wavelengths, typically seen in solid, liquid, or densely packed materials.\n",
        "  - **Emission Spectrum**: Consists of bright lines or bands on a dark background. It's characteristic of thin gases in which atoms emit light at specific wavelengths.\n",
        "  - **Absorption Spectrum**: Shows dark lines or bands in the spectrum, formed when light passes through a cooler, diffuse gas.\n",
        "* Modern spectrographs can produce spectra with high wavelength resolution which allows for the detailed analysis of absorption lines.\n",
        "* Spectral analysis in astronomy involves several key formulas, each revealing different aspects of the celestial objects being studied:\n",
        "  - **Wien's Displacement Law**: This law helps in determining the temperature of a star. It states that the wavelength at which a blackbody radiates most intensely is inversely proportional to its temperature.\n",
        "  > $\\lambda_{\\text{max}} = \\frac{b}{T}$\n",
        "\n",
        "  where:\n",
        "\n",
        "  $\\lambda_{\\text{max}}$: the wavelength at which the emission is strongest\n",
        "\n",
        "  $T$: the temperature of the blackbody in Kelvin\n",
        "\n",
        "  $b$: Wien's displacement constant, approximately ($2.897 \\times 10^{-3}mK$)\n",
        "\n",
        "  - **Doppler Shift**: Used to determine the radial velocity of a star or galaxy. If an object is moving towards us, its spectral lines are shifted towards shorter wavelengths (blue shift). If it's moving away, the shift is towards longer wavelengths (red shift).\n",
        "  > $\\frac{\\Delta \\lambda}{\\lambda} = \\frac{v}{c}$\n",
        "\n",
        "  where:\n",
        "\n",
        "  $\\frac{\\Delta \\lambda}{\\lambda}$: the change in wavelength ($\\Delta \\lambda = \\lambda_{\\text{observed wavelength}} - \\lambda_{\\text{emitted wavelength}}$)\n",
        "\n",
        "  $\\lambda$: the original wavelength\n",
        "\n",
        "  $v$: the velocity of the object relative to the observer\n",
        "\n",
        "  $c$: the speed of light\n",
        "\n",
        "  - **Stefan-Boltzmann Law**: This law relates the total energy radiated per unit surface area of a blackbody to its temperature.\n",
        "  > $F = \\sigma T^4$\n",
        "\n",
        "  where:\n",
        "\n",
        "  $F$: the total energy radiated per unit surface area\n",
        "\n",
        "  $\\sigma$: the Stefan-Boltzmann constant, approximately ($5.67 \\times 10^{-8} W m^{-2}K^{-4}$)\n",
        "\n",
        "  $T$: the temperature in Kelvin\n",
        "\n",
        "  - **Rydberg Formula for Hydrogen**: It's used to predict the wavelengths of the spectral lines of hydrogen.\n",
        "  > $\\frac{1}{\\lambda} = R \\left( \\frac{1}{n_1^2} - \\frac{1}{n_2^2} \\right)$\n",
        "\n",
        "  where:\n",
        "\n",
        "  $\\lambda$: the wavelength of the light\n",
        "\n",
        "  $R$: the Rydberg constant, approximately ($1.097 \\times 10^7 m^{-1}$)\n",
        "\n",
        "  $n_1$ and $n_2$: integers (energy levels), with $n_2 > n_1$\n"
      ],
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "elGedDxuFPK2"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from astropy.constants import c,sigma_sb\n",
        "plt.style.use(\"dark_background\")\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def WienLaw(temperature:int or float)->int or float:\n",
        "  \"\"\"\n",
        "    Calculate the peak wavelength (lambda_max) for a given temperature using Wien's Displacement Law.\n",
        "    :param temperature: Temperature in Kelvin\n",
        "    :return: Peak wavelength in meters\n",
        "  \"\"\"\n",
        "  wienConstant = 2.897e-3  # Wien's displacement constant in meters Kelvin\n",
        "  return wienConstant/temperature"
      ],
      "metadata": {
        "id": "-9wYwa-8Ib1a"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def DopplerShift(wavelength:int or float,velocity:int or float)->int or float:\n",
        "  \"\"\"\n",
        "    Calculate the Doppler Shift of a wavelength for a given velocity.\n",
        "    :param wavelength: Original wavelength in meters\n",
        "    :param velocity: Velocity in meters/second\n",
        "    :return: Shifted wavelength in meters\n",
        "  \"\"\"\n",
        "  return wavelength*(1+velocity/c.value)"
      ],
      "metadata": {
        "id": "ggLGNNPIIoad"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def StefanBoltzmannLaw(temperature:int or float)->int or float:\n",
        "  \"\"\"\n",
        "    Calculate the total energy radiated per unit surface area using Stefan-Boltzmann Law.\n",
        "    :param temperature: Temperature in Kelvin\n",
        "    :return: Total energy radiated in W/m^2\n",
        "  \"\"\"\n",
        "  return sigma_sb.value*temperature**4"
      ],
      "metadata": {
        "id": "bUdVokc3I8ld"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def RydbergFormula(n1:int or float,n2:int or float)->int or float:\n",
        "  \"\"\"\n",
        "    Calculate the wavelength of light emitted during a transition of an electron\n",
        "    between two levels in a hydrogen atom using the Rydberg Formula.\n",
        "    :param n1: Lower energy level\n",
        "    :param n2: Higher energy level\n",
        "    :return: Wavelength in meters\n",
        "  \"\"\"\n",
        "  rydbergConstant = 1.097e7 # Rydberg constant in m^-1\n",
        "  return 1/(rydbergConstant*(1/n1**2-1/n2**2))"
      ],
      "metadata": {
        "id": "V7y7-31qJ5Z2"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temperature = 5778  # Temperature of the Sun in Kelvin\n",
        "peakWavelength = WienLaw(temperature)\n",
        "print(f\"Wien's Law - Peak Wavelength for Sun: {peakWavelength} m\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8cfDV7dKITe",
        "outputId": "e16ea75b-8e52-4e7e-feb9-4253ceebbaf4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wien's Law - Peak Wavelength for Sun: 5.013845621322256e-07 m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wavelength = 656.3e-9  # H-alpha line in meters\n",
        "velocity = 300000  # Velocity in m/s\n",
        "dopplerShift = DopplerShift(wavelength,velocity)\n",
        "print(f\"Doppler Shift: {dopplerShift} m\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cf6PXybqLMU0",
        "outputId": "c9195bcc-9e71-453f-c2e8-1ee780662752"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Doppler Shift: 6.569567543470356e-07 m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "radiationEnergy = StefanBoltzmannLaw(temperature)\n",
        "print(f\"Stefan-Boltzmann Law - Radiated Energy: {radiationEnergy} W/m^2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6oH7rfoMLZkF",
        "outputId": "0fbd94d1-096a-44e6-8937-acc766e32480"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stefan-Boltzmann Law - Radiated Energy: 63200699.73684546 W/m^2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n1,n2 = 2,3  # Transition from level 2 to 3 in hydrogen\n",
        "wavelengthH = RydbergFormula(n1,n2)\n",
        "print(f\"Rydberg Formula - Wavelength for H transition: {wavelengthH} m\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UbYuHaR4Lk8n",
        "outputId": "0fcc7b8f-8d10-4f63-b0d3-b7db99cf98c1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rydberg Formula - Wavelength for H transition: 6.563354603463993e-07 m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example"
      ],
      "metadata": {
        "id": "Fs4I0qZHY57S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temperatureBetelgeuse = 3600 # Kelvin\n",
        "velocityBetelgeuse = 20e3  # m/s (20 km/s towards Earth)"
      ],
      "metadata": {
        "id": "5VTsjiLuYwpF"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "peakWavelength = WienLaw(temperature)\n",
        "print(f\"Peak Wavelength for Betelgeuse: {peakWavelength} m\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13tParkmY45P",
        "outputId": "9a040d72-c66a-4338-fb2b-851256748bff"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Peak Wavelength for Betelgeuse: 5.013845621322256e-07 m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wavelengthInterval = np.linspace(0.1e6,3e6,1000) # Wavelengths range from 0.1 to 3 micrometers"
      ],
      "metadata": {
        "id": "pjs4NvH3ZHow"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "intensity = 1 / (wavelengthInterval**5*(np.exp(1/(wavelengthInterval*temperatureBetelgeuse))-1))"
      ],
      "metadata": {
        "id": "mRjf3wIsZNmy"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shiftedPeakWavelength = DopplerShift(peakWavelength,-velocityBetelgeuse) # Negative velocity for approaching"
      ],
      "metadata": {
        "id": "UMpgWGHOZYoj"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "plt.plot(wavelengthInterval*1e9,intensity,label=\"Simulated Spectrum\")\n",
        "plt.axvline(peakWavelength*1e9,color=\"r\",linestyle=\"--\",label=\"Peak Wavelength\")\n",
        "plt.axvline(shiftedPeakWavelength*1e9,color=\"g\",linestyle=\"--\",label=\"Doppler Shifted Peak\")\n",
        "plt.xlabel(\"Wavelength (nm)\")\n",
        "plt.ylabel(\"Intensity (Arbitrary Units)\")\n",
        "plt.title(\"Simulated Spectrum of Betelgeuse\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "KXijRCftZjzX",
        "outputId": "c656e4ee-3a8e-4f2d-9484-1d2637998a0c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# FITS File\n",
        "\n",
        "* FITS (Flexible Image Transport System) is a file format widely used in astronomy. It's designed to store, transmit, and manipulate scientific images and data, particularly in astronomy. FITS files are advantageous because:\n",
        "  - They can handle a wide range of data types, including images, multi-dimensional arrays (like spectra), and tables.\n",
        "  - They include both data and metadata (information about the data) in a single file.\n",
        "  - They are standardized, ensuring compatibility across different software and observatories.\n",
        "* In practical terms, astronomers often collect data from telescopes in FITS format. This data might include images of celestial bodies, light spectra, or other observational data."
      ],
      "metadata": {
        "id": "DQTbTH7GLyUS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/driveFiles\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1UfSpzyLurF",
        "outputId": "18604dd5-7c15-4f88-9ab4-1e3a5bdd7be4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/driveFiles\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from astropy.io import fits"
      ],
      "metadata": {
        "id": "9Hh-Zcttaqmf"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**For real data**: https://drive.google.com/file/d/1HL8WxFv1vD-BrAKIDh6ynZQXbhG7r83J/view?usp=sharing"
      ],
      "metadata": {
        "id": "G8fgaKInamM3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataSpectrumPath = \"/content/driveFiles/MyDrive/Astronomy/Project Data/fits_file/spectral_data/ADP.2014-10-29T09_42_08.747.fits\""
      ],
      "metadata": {
        "id": "Hlkw1HLuaV5o"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fitsFileRead = fits.open(dataSpectrumPath)"
      ],
      "metadata": {
        "id": "xiRhEF-FahfX"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"INFORMATION:\\n\\n\")\n",
        "fitsFileRead.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLhcpO0May1m",
        "outputId": "13c2f833-f4d3-4856-bbcf-f22889bc6eda"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFORMATION:\n",
            "\n",
            "\n",
            "Filename: /content/driveFiles/MyDrive/Astronomy/Project Data/fits_file/spectral_data/ADP.2014-10-29T09_42_08.747.fits\n",
            "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
            "  0  PRIMARY       1 PrimaryHDU     788   ()      \n",
            "  1  SPECTRUM      1 BinTableHDU     71   1R x 6C   [134944D, 134944E, 134944E, 134944E, 134944E, 134944E]   \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For a more detaileddescription of the columns:"
      ],
      "metadata": {
        "id": "TUBWf3Wea7Ar"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fitsFileRead[1].columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQS3Askqa2_4",
        "outputId": "45da293f-d926-475d-d3b1-d345961f4e9d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColDefs(\n",
              "    name = 'WAVE'; format = '134944D'; unit = 'Angstrom'\n",
              "    name = 'FLUX_REDUCED'; format = '134944E'; unit = 'adu'\n",
              "    name = 'ERR_REDUCED'; format = '134944E'; unit = 'adu'\n",
              "    name = 'BGFLUX_REDUCED'; format = '134944E'; unit = 'adu'\n",
              "    name = 'FLUX'; format = '134944E'; unit = '10^-16 erg/cm^2/s/Angstrom'\n",
              "    name = 'ERR'; format = '134944E'; unit = '10^-16 erg/cm^2/s/Angstrom'\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analog-to-Digital Units (ADU), are a unit of measurement used in astronomy to quantify the flux (the amount of light) captured by a CCD (Charge-Coupled Device) camera in a telescope. The ADU is specific to digital imaging and is a result of the analog-to-digital conversion process.**"
      ],
      "metadata": {
        "id": "aVw-2IFfb-CV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For raw data:"
      ],
      "metadata": {
        "id": "PX5aaIivbCMD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "specData = fitsFileRead[1].data\n",
        "fitsFileRead.close()"
      ],
      "metadata": {
        "id": "fJQY4sZxa-ii"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wavelength = specData[0][0]\n",
        "flux = specData[0][4]"
      ],
      "metadata": {
        "id": "s9_dphArbMSd"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(wavelength)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lUF1VzlMbG9b",
        "outputId": "1ad72d66-5044-43c5-a929-68a54b886fe6"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wavelength.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UoTcoP5Pc8Q6",
        "outputId": "a22aabe9-2887-4f1f-d299-438df28f07d5"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(134944,)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(flux)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QIL6z5HgbJ53",
        "outputId": "eab80631-eca9-4c5b-9c53-25bfb552c886"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "flux.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8RVyCtRGc9dY",
        "outputId": "57c2fc94-c8a5-439f-fd6b-0a1c7cc33fdb"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(134944,)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Normalize the spectrum by dividing through the peak value:"
      ],
      "metadata": {
        "id": "ONkmWwwmbhya"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "normalizeSpec = np.max(flux)\n",
        "flux = flux/normalizeSpec\n",
        "wavelength = wavelength*0.1"
      ],
      "metadata": {
        "id": "1cdztF5tbe9l"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "plt.plot(wavelength,flux,linestyle=\"-\",color=\"red\",label=\"Wavelength-Flux\")\n",
        "plt.xlabel(\"$\\lambda$ / nm\")\n",
        "plt.ylabel(\"Flux / ADU\")\n",
        "plt.xlim(587,590)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 698
        },
        "id": "-mWI0dqTbr93",
        "outputId": "677a123e-328d-4a4e-c5b9-d3111293c584"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wienConstant = 2.8977729e-3  # Wien's displacement constant in meters K"
      ],
      "metadata": {
        "id": "jMveMd0xcK9C"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wavelengthM = specData[0][0]*1e-10 # Convert Angstrom to meters\n",
        "flux = specData[0][4]"
      ],
      "metadata": {
        "id": "oZZMC5tndOFN"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "peakWavelength = wavelengthM[np.argmax(flux)] # Estimate the peak wavelength (in meters)"
      ],
      "metadata": {
        "id": "mx5YcHSudeOK"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "estimatedTemperature = wienConstant/peakWavelength"
      ],
      "metadata": {
        "id": "G6nykArjdjt2"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=((10,8)))\n",
        "plt.plot(wavelengthM*1e10,flux,color=\"white\",label=\"Spectrum\") # Wavelength converted back to Angstroms for plotting\n",
        "plt.axvline(peakWavelength*1e10,color=\"r\",linestyle=\"--\",label=f\"Peak Wavelength: {peakWavelength*1e10:.2f} A\")\n",
        "plt.xlabel(\"Wavelength (Angstrom)\")\n",
        "plt.ylabel(\"Flux (ADU)\")\n",
        "plt.title(f\"Spectral Analysis - Estimated Temperature: {estimatedTemperature:.2f} K\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "ulPStRdNdquA",
        "outputId": "2ff3763d-c82a-4594-f22e-adf831c75c0f"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example\n",
        "- A star similar to our Sun with a temperature of about 5778 K.\n",
        "- A radial velocity of 30 km/s away from us, resulting in a redshift."
      ],
      "metadata": {
        "id": "KDmwG4IrfiG1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.constants import c,h,k"
      ],
      "metadata": {
        "id": "BH2b8PiSek_0"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temperature = 5778  # Temperature in Kelvin (approximate temperature of the Sun)\n",
        "velocity = 30e3  # Radial velocity in m/s (away from the observer)"
      ],
      "metadata": {
        "id": "5gvJZ6fifog0"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def PeakWavelength(temperature):\n",
        "    wienConstant = 2.8977729e-3  # m.K\n",
        "    return wienConstant / temperature"
      ],
      "metadata": {
        "id": "m_MnCEVBgaqZ"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def PlanckLaw(wavelength:int or float,temperature:int or float)->int or float:\n",
        "  result = (2.0*h*c**2)/(wavelength**5*(np.exp(h*c/(wavelength*k*temperature))-1))\n",
        "  return result"
      ],
      "metadata": {
        "id": "e5TO9cc8fsPS"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wavelengths = np.linspace(100e-9,2000e-9,500) # Wavelengths from 100 nm to 2000 nm"
      ],
      "metadata": {
        "id": "sAv0iqTBf8ht"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spectrum = PlanckLaw(wavelengths,temperature)"
      ],
      "metadata": {
        "id": "Mzph-tgcgC3Z"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shiftedWavelength = wavelengths * (1+velocity/c)\n",
        "shiftedSpectrum = PlanckLaw(shiftedWavelength,temperature)"
      ],
      "metadata": {
        "id": "OAsw0FUvgGxq"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "plt.plot(wavelengths*1e9,spectrum,label='Original Spectrum')\n",
        "plt.plot(shiftedWavelength*1e9,shiftedSpectrum,linestyle='--',label='Doppler Shifted Spectrum')\n",
        "plt.axvline(PeakWavelength(temperature)*1e9,color='r',linestyle=':',label='Peak Wavelength')\n",
        "plt.xlabel('Wavelength (nm)')\n",
        "plt.ylabel('Spectral Radiance')\n",
        "plt.title('Simulated Stellar Spectrum with Doppler Shift')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "BuMmZLzIgQOk",
        "outputId": "54456359-89ea-4378-fb5b-df8b8f5cf7b5"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- This script uses Planck's Law to simulate a blackbody spectrum for a star with a given temperature.\n",
        "- The peak wavelength is estimated using Wien's Displacement Law.\n",
        "- The Doppler Shift is simulated by adjusting the wavelengths to simulate a redshift due to the star moving away.\n",
        "- The original and Doppler-shifted spectra are plotted for comparison."
      ],
      "metadata": {
        "id": "n9iS9t1TgmHE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **Blackbody Radiation and Stellar Temperature**:\n",
        "\n",
        "Stars, including our Sun, can often be approximated as blackbodies, objects that absorb all incident electromagnetic radiation and re-emit it.\n",
        "The simulation uses Planck's Law to model the blackbody radiation, which gives a good approximation of the energy output across different wavelengths for stars.\n",
        "\n",
        "Wien's Displacement Law helps determine the peak wavelength of this radiation, which is inversely proportional to the temperature of the star. This allows astronomers to estimate the temperature of stars based on their spectrum.\n",
        "\n",
        "- **Doppler Shift and Stellar Motion**:\n",
        "\n",
        "The Doppler Shift is a phenomenon observed when there is relative motion between a light source (like a star) and an observer.\n",
        "In the simulation, applying a Doppler Shift to the spectrum models how the light from a star is redshifted (wavelengths stretched) or blueshifted (wavelengths compressed) due to its motion towards or away from us.\n",
        "This is crucial in astronomy for measuring the radial velocities of stars and galaxies, which can reveal information about their motion in the universe, such as orbital dynamics, expansion of the universe, and more.\n",
        "\n",
        "- **Spectral Analysis**:\n",
        "\n",
        "While the simulation primarily focuses on the continuum spectrum of a star, real spectral analysis also involves studying absorption and emission lines.\n",
        "These lines are fingerprints of different elements in the star's atmosphere, allowing astronomers to determine its composition.\n",
        "\n",
        "- **Astrophysical Research and Observations**:\n",
        "\n",
        "Simulations like this provide a foundation for understanding real observations. When astronomers observe a star, they can compare its spectrum to such models to derive physical properties.\n",
        "It also helps in calibrating instruments and understanding the limitations and capabilities of observational data."
      ],
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}