jason-neal/spectrum_overload

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial for spectrum_overload\n",
    "- Jason Neal \n",
    "- September 2016\n",
    "\n",
    "Spectrum is a module based around a class called Spectrum. It allows you easily perform operations between spectra by overloading the operators +-*/.\n",
    "\n",
    "In my reseach involving NIR spectroscopy of exoplanets I need to perform different operations on or between spectra. For example to correct for the telluric absoption lines of earth it is nessary to divide the observations by either a telluric observation or model. I was tired of carrying around multiple objects the dispersion axis and the flux values for each observation and each model. \n",
    "When performing operations between spectra it is nessesary to interpolate either one or both spectra to the same dispersion result to perform the operation at the same wavelength values. \n",
    "\n",
    "This led me to create the Spectrum class so that I could easily subtract or divide two spectra from each other.\n",
    "\n",
    "Below is some basic instuction on how to use it and a telluric correction example\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "from spectrum_overload import Spectrum\n",
    "from astropy.io import fits\n",
    "import copy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You create a spectrum object by passing in the flux and the dispersion values. As positional arguments flux is first. If your dispersion axis has been wavelenght calibrated (its a wavelength rather than pixel positions) then you need to also pass in calibrated=True keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(2000, 2050)\n",
    "y = np.random.rand(len(x))\n",
    "spec_uncalibrated = Spectrum(y, x)\n",
    "spec_calibrated = Spectrum(flux=y, xaxis=x, calibrated=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you need access to the dispersion axis or flux you can do it by the flux and xaxis attributes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "wavelength = spec_uncalibrated.xaxis \n",
    "I = spec_uncalibrated.flux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spectrum has a wave_select metod which allows you to select a section of you spectra between an lower and upper dispersion coordinate(pixel/wavelenght)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#spec_calibrated.wav_select(lower, upper)\n",
    "spec_calibrated.wav_select(2030, 2070)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overloaded Operators:  \n",
    "Warning this section is still to implement in the class!\n",
    "\n",
    "You can preform basic operations with the spectrum objects.\n",
    "\n",
    "The two specta need to be of the same calibration status.\n",
    "If the spectra are calibtrated and their dispersion values are not the same then the second spectrum will be interpolated to the wavelenght values of the first. \n",
    "\n",
    "(At this stage I discoverd another spectum project which talked about overloading operators like this but never actually done it.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "add [1.70203291 1.20704382 1.16797748 0.62837019 1.40323123 0.84373963\n",
      " 1.3903804  0.06070615 1.77941362 1.69980907 1.54330885 1.83766468\n",
      " 1.48231499 1.80771465 1.40542936 0.35509179 0.48959352 0.38532826\n",
      " 1.88849798 0.37656281 1.8370013  0.40189669 1.96548297 0.54524153\n",
      " 1.16443362 1.24792465 0.56880421 1.42732791 1.68579551 0.34775402\n",
      " 1.12921057 1.28120493 0.10417879 1.86802101 0.83211694 1.98394316\n",
      " 0.5018036  0.47683853 0.68797368 1.70136996 1.80879392 0.94254361\n",
      " 0.98815783 1.02281889 1.95058898 1.91379936 0.99529278 1.81349203\n",
      " 0.0552135  1.4813729 ]\n",
      "subtract [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0.]\n",
      "multiply [7.24229010e-01 3.64238695e-01 3.41042847e-01 9.87122733e-02\n",
      " 4.92264473e-01 1.77974141e-01 4.83289411e-01 9.21309106e-04\n",
      " 7.91578208e-01 7.22337717e-01 5.95450552e-01 8.44252868e-01\n",
      " 5.49314429e-01 8.16958064e-01 4.93807924e-01 3.15225441e-02\n",
      " 5.99254539e-02 3.71194672e-02 8.91606153e-01 3.54498881e-02\n",
      " 8.43643440e-01 4.03802368e-02 9.65780822e-01 7.43220827e-02\n",
      " 3.38976413e-01 3.89328985e-01 8.08845569e-02 5.09316240e-01\n",
      " 7.10476625e-01 3.02332145e-02 3.18779130e-01 4.10371517e-01\n",
      " 2.71330522e-03 8.72375626e-01 1.73104650e-01 9.84007615e-01\n",
      " 6.29517126e-02 5.68437463e-02 1.18326947e-01 7.23664934e-01\n",
      " 8.17933865e-01 2.22097114e-01 2.44113976e-01 2.61539618e-01\n",
      " 9.51199344e-01 9.15657001e-01 2.47651928e-01 8.22188337e-01\n",
      " 7.62132742e-04 5.48616420e-01]\n",
      "divide [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "spec1 = Spectrum(y, x, calibrated=True)\n",
    "spec2 = copy.copy(spec1)   # Duplicate to easily see result.  mainly spec1-spec2=0, spec1/spec2=1\n",
    "\n",
    "# this is still to operate.\n",
    "add = spec1 + spec2\n",
    "subtract = spec1 - spec2\n",
    "multiply = spec1 * spec2\n",
    "divide = spec1 / spec2\n",
    "\n",
    "print(\"add\", add.flux)\n",
    "print(\"subtract\", subtract.flux)\n",
    "print(\"multiply\", multiply.flux)\n",
    "print(\"divide\", divide.flux)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Practical Example - Telluric Correction\n",
    "We want to divide an observation by a model of the telluric absorbtion to correct for Earths absoption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function to read header and data from tapas ipac file.\n",
    "def load_telluric(filename):\n",
    "    \"\"\" Returns telluric data and header\n",
    "    if just want the data then call as load_telluric()[0]\n",
    "    or data, __ = load_telluric() \n",
    "\n",
    "    likewise just the header as hdr = load_telluric()[1]\"\"\"\n",
    "    ext = filename.split(\".\")[-1]\n",
    "    if ext == \"ipac\":\n",
    "        tell_hdr = fits.Header()\n",
    "        with open(filename) as f:\n",
    "            col1 = []\n",
    "            col2 = []\n",
    "            for line in f:\n",
    "                #firstchar = line[0]\n",
    "                #print(\"first char =\", firstchar)\n",
    "                if line.startswith(\"\\\\\"):\n",
    "                    # Get the Tapas Header\n",
    "                    line = line[1:] # remove the leading \\\n",
    "                    line = line.strip()\n",
    "                    items = line.split(\"=\")\n",
    "\n",
    "                    tell_hdr[items[0]] = items[1] # Add to header\n",
    "\n",
    "                elif line.startswith(\"|\"):\n",
    "                    # Obtian wavelength scale from piped lines\n",
    "                    if \"in air\" in line:\n",
    "                        tell_hdr[\"WAVSCALE\"] = \"air\"\n",
    "                    elif \"nm|\" in line:\n",
    "                        tell_hdr[\"WAVSCALE\"] = \"vacuum\"\n",
    "                    # Need extra condition to deal with wavenumber\n",
    "                else:\n",
    "                    line = line.strip()\n",
    "                    val1, val2 = line.split()\n",
    "                    col1.append(float(val1))\n",
    "                    col2.append(float(val2))\n",
    "\n",
    "    elif ext == \"fits\":\n",
    "        i_tell = fits.getdata(filename, 1)\n",
    "        tell_hdr = fits.getheader(filename, 1)\n",
    "        # TODO ... Need to get wavelenght scale (air/wavelenght) from fits file somehow... \n",
    "        col1 = i_tell[\"wavelength\"]\n",
    "        col2 = i_tell[\"transmittance\"]\n",
    "\n",
    "    else:\n",
    "        print(\" Could not load file\", filename,\" with extention\", ext)\n",
    "        return None\n",
    "        \n",
    "        # put in ascending order\n",
    "    if col1[-1]-col1[0] < 0:  # wl is backwards\n",
    "            col1 = col1[::-1]\n",
    "            col2 = col2[::-1]                \n",
    "    tell_data = np.array([col1, col2], dtype=\"float64\")\n",
    "\n",
    "    return tell_data, tell_hdr "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load in the spectra \n",
    "# Using already wavelength calibrated slectrum\n",
    "obsname = \"../spectrum_overload/data/spec_wavecal.fits\"\n",
    "tellname = \"../spectrum_overload/data/telluric_data.ipac\"\n",
    "\n",
    "# Load in the data\n",
    "obs_data = fits.getdata(obsname)\n",
    "obs_hdr = fits.getheader(obsname)\n",
    "telluric_data, telluric_header = load_telluric(tellname)\n",
    "\n",
    "## Put data into Spectrum object\n",
    "# Can check for column names using print(obs.columns)\n",
    "# \"Wavelength\" and \"Extracted_DRACS\" are the columns of the fits table\n",
    "observation = Spectrum(obs_data[\"Extracted_DRACS\"], obs_data[\"Wavelength\"], calibrated=True, header=obs_hdr)\n",
    "\n",
    "telluric = Spectrum(telluric_data[1], telluric_data[0], calibrated=True, header=telluric_header)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the telluric model wavelength spans a much longer wavelength than we have observed.\n",
    "It is a good idea to shorten it to the values we have to save memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before  13975\n",
      "After   3218\n"
     ]
    }
   ],
   "source": [
    "# Shorten telluric spectra to just around our observation (1 nm either side)\n",
    "print(\"Before \", len(telluric))\n",
    "telluric.wav_select(min(observation.xaxis)-1, max(observation.xaxis)+1)  \n",
    "print(\"After  \", len(telluric))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the two Spectrum to see what we have\n",
    "plt.figure()\n",
    "plt.plot(telluric.xaxis, telluric.flux, label=\"Telluric\")\n",
    "plt.plot(observation.xaxis, observation.flux, \"r--\", label=\"Observation\")\n",
    "plt.xlabel(\"Wavelength (nm)\")\n",
    "plt.ylabel(\"Flux/Absorption\")\n",
    "#plt.xlim([np.min(observation.xaxis), np.max(observation.xaxis)])   # Veiw this detector only\n",
    "plt.legend(loc=0)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "There can a difference in airmass between the observation and the model which affects the line depth.\n",
    "We can scale the tellruic model by the ratio of airmas to correct the line depths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Airmass Ratio = 0.9588151722519749\n"
     ]
    }
   ],
   "source": [
    "B = observation.header[\"HIERARCH ESO TEL AIRM END\"] / float(telluric.header[\"airmass\"])  # Should probably average airmass of observation (Not just End as used here)\n",
    "print(\"Airmass Ratio = {}\".format(B))\n",
    "scaled_telluric = telluric ** B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now we want to correct for the atmospheric absorption be dividing the observation by the telluric model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Warning! When I attempted a division before applying the telluric.wav_select() above I obtained a MemoryError in the interpolation\n",
    "# This needs to be looked into (it takes a while to run atm)\n",
    "\n",
    "Corrected = observation / scaled_telluric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Result\n",
    "plt.plot(observation.xaxis, observation.flux, label=\"Observation\")\n",
    "plt.plot(Corrected.xaxis, Corrected.flux, \"r--\", label=\"Corrected\")\n",
    "plt.xlabel(\"Wavelength (nm)\")\n",
    "plt.ylabel(\"Flux/Absorption\")\n",
    "plt.xlim([np.min(observation.xaxis), np.max(observation.xaxis)])   # Veiw this detector only\n",
    "plt.legend(loc=0)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}