cham_teaches_python_06_astronomical_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://www.python.org/static/img/python-logo.png\">\n",
    "\n",
    "# Welcome to my lessons\n",
    "\n",
    "---\n",
    "\n",
    "**Bo Zhang** (NAOC, <mailto:bozhang@nao.cas.cn>) will have a few lessons on python.\n",
    "\n",
    "- These are **very useful knowledge, skills and code styles** when you use `python` to process astronomical data.\n",
    "- All materials can be found on [**my github page**](https://github.com/hypergravity/cham_teaches_python).\n",
    "- **jupyter notebook** (formerly named **ipython notebook**) is recommeded to use\n",
    "\n",
    "---\n",
    "These lectures are organized as below:\n",
    "1. install python\n",
    "2. basic syntax\n",
    "3. numerical computing\n",
    "4. scientific computing\n",
    "5. plotting\n",
    "6. astronomical data processing\n",
    "7. high performance computing\n",
    "8. version control\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Astronomical data format standard - .fits files\n",
    "- HDUs\n",
    " * Primary HDU\n",
    " * Image HDU\n",
    " * BinTable HDU (Binary Table)\n",
    "- HDU header\n",
    " - keywords\n",
    " - column information for tables\n",
    "- HDU data\n",
    "\n",
    "*reference*: \n",
    "- [http://fits.gsfc.nasa.gov/](http://fits.gsfc.nasa.gov/)\n",
    "- [http://fits.gsfc.nasa.gov/fits_wcs.html](http://fits.gsfc.nasa.gov/fits_wcs.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Usually, you will meet these:\n",
    "- 1d image stored using WCS (world coordinate system)\n",
    " - SDSS DR7 spectra\n",
    " - IRAF spectra fits\n",
    "- 2d image stored using WCS (world coordinate system)\n",
    " - all photometry images (2MASS, WISE, ...)\n",
    "- table / catalog\n",
    " - LAMOST catalog\n",
    " - SDSS DR10/12 spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from astropy.io import fits\n",
    "\n",
    "# read hdu list\n",
    "hl = fits.open('./data/lamost_dr2_spectra/spec-55892-F9205_sp09-174.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: ./data/lamost_dr2_spectra/spec-55892-F9205_sp09-174.fits\n",
      "No.    Name         Type      Cards   Dimensions   Format\n",
      "0    Flux        PrimaryHDU     132   (3903, 5)    float32   \n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# show fits info\n",
    "print hl.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T /Primary Header created by MWRFITS v1.11b        \n",
       "BITPIX  =                  -32 /                                                \n",
       "NAXIS   =                    2 / Number of array dimensions                     \n",
       "NAXIS1  =                 3903 /                                                \n",
       "NAXIS2  =                    5 /                                                \n",
       "EXTEND  =                    T /                                                \n",
       "                                                                                \n",
       "COMMENT --------FILE INFORMATION                                                \n",
       "FILENAME= 'spec-55892-F9205_sp09-174.fits' /                                    \n",
       "OBSID   =                20566 / Unique number ID of this spectrum              \n",
       "AUTHOR  = 'LAMOST Pipeline'    / Who compiled the information                   \n",
       "DATA_V  = 'LAMOST DR3'         / Data release version                           \n",
       "EXTEN0  = 'Flux, Inverse, Wavelength, Andmask, Ormask' /                        \n",
       "N_EXTEN =                    1 / The extension number                           \n",
       "EXTNAME = 'Flux    '           / The extension name                             \n",
       "ORIGIN  = 'NAOC-LAMOST'        / Organization responsible for creating this file\n",
       "DATE    = '2015-12-16T13:29:15' / Time when this HDU is created (UTC)           \n",
       "COMMENT --------TELESCOPE PARAMETERS                                            \n",
       "TELESCOP= 'LAMOST  '           / GuoShouJing Telescope                          \n",
       "LONGITUD=               117.58 / [deg] Longitude of site                        \n",
       "LATITUDE=                40.39 / [deg] Latitude of site                         \n",
       "FOCUS   =                19964 / [mm] Telescope focus                           \n",
       "CAMPRO  = 'NEWCAM  '           / Camera program name                            \n",
       "CAMVER  = 'v2.0    '           / Camera program version                         \n",
       "COMMENT --------OBSERVATION PARAMETERS                                          \n",
       "DATE-OBS= '2011-11-26T19:40:26.84' / The observation median UTC                 \n",
       "DATE-BEG= '2011-11-27T03:07:13.0' / The observation start local time            \n",
       "DATE-END= '2011-11-27T04:55:45.0' / The observation end local time              \n",
       "LMJD    =                55892 / Local Modified Julian Day                      \n",
       "MJD     =                55891 / Modified Julian Day                            \n",
       "LMJMLIST= '80484661-80484699-80484739' / Local Modified Julian Minute list      \n",
       "PLANID  = 'F9205   '           / Plan ID in use                                 \n",
       "RA      =           123.396690 / [deg] Right ascension of object                \n",
       "DEC     =             6.963130 / [deg] Declination of object                    \n",
       "RA_OBS  =           123.396690 / [deg] Right ascension during observing         \n",
       "DEC_OBS =             6.963130 / [deg] Declination during observing             \n",
       "OFFSET  =                    F / Whether there's a offset during observing      \n",
       "OFFSET_V=                 0.00 / Offset value in arcsecond                      \n",
       "DESIG   = 'LAMOST J081335.20+065747.2' / Designation of LAMOST target           \n",
       "FIBERID =                  174 / Fiber ID of Object                             \n",
       "CELL_ID = 'G2826   '           / Fiber Unit ID on the focal plane               \n",
       "X_VALUE =             -171.982 / [mm] X coordinate of object on the focal plane \n",
       "Y_VALUE =             -273.869 / [mm] Y coordinate of object on the focal plane \n",
       "OBJNAME = '257233284699743'    / Name of object                                 \n",
       "OBJTYPE = 'gal     '           / Object type from input catalog                 \n",
       "TSOURCE = 'PILOT   '           / Name of input catalog                          \n",
       "TCOMMENT= 'No      '           / Target information                             \n",
       "TFROM   = '-       '           / Target catalog                                 \n",
       "FIBERTYP= 'Obj     '           / Fiber type of object                           \n",
       "MAGTYPE = 'ugriz   '           / Magnitude type of object                       \n",
       "MAG1    =                24.62 / [mag] Mag1 of object                           \n",
       "MAG2    =                16.40 / [mag] Mag2 of object                           \n",
       "MAG3    =                17.78 / [mag] Mag3 of object                           \n",
       "MAG4    =                15.26 / [mag] Mag4 of object                           \n",
       "MAG5    =                22.84 / [mag] Mag5 of object                           \n",
       "MAG6    =                10.00 / [mag] Mag6 of object                           \n",
       "MAG7    =                10.00 / [mag] Mag7 of object                           \n",
       "OBS_TYPE= 'OBJ     '           / The type of target (OBJ, FLAT, ARC or BIAS)    \n",
       "OBSCOMM = 'Science '           / Science or Test                                \n",
       "RADECSYS= 'FK5     '           / Equatorial coordinate system                   \n",
       "EQUINOX =              2000.00 / Equinox in years                               \n",
       "LAMPLIST= 'lamphgcdne.dat'     / Arc lamp emission line list                    \n",
       "SKYLIST = 'skylines.dat'       / Sky emission line list                         \n",
       "EXPID01 = '09b-20111127030708-8-80484661' / ID string for blue exposure 1       \n",
       "EXPID02 = '09b-20111127034533-9-80484699' / ID string for blue exposure 2       \n",
       "EXPID03 = '09b-20111127042507-10-80484739' / ID string for blue exposure 3      \n",
       "EXPID04 = '09r-20111127030742-8-80484661' / ID string for red exposure 1        \n",
       "EXPID05 = '09r-20111127034606-9-80484699' / ID string for red exposure 2        \n",
       "EXPID06 = '09r-20111127042540-10-80484739' / ID string for red exposure 3       \n",
       "NEXP    =                    3 / Number of valid exposures                      \n",
       "NEXP_B  =                    3 / Number of valid blue exposures                 \n",
       "NEXP_R  =                    3 / Number of valid red exposures                  \n",
       "EXPT_B  =              5400.00 / [s] Blue exposure duration time                \n",
       "EXPT_R  =              5400.00 / [s] Red exposure duration time                 \n",
       "EXPTIME =              5400.00 / [s] Minimum of exposure time for all cameras   \n",
       "BESTEXP =             80484661 / MJM of the best exposure                       \n",
       "SCAMEAN =                 3.10 / [ADU] Mean level of scatter light              \n",
       "COMMENT --------SPECTROGRAPH PARAMETERS                                         \n",
       "SPID    =                    9 / Spectrograph ID                                \n",
       "SPRA    =          123.7406162 / [deg] Average RA of this spectrograph          \n",
       "SPDEC   =            6.8539852 / [deg] Average DEC of this spectrograph         \n",
       "SLIT_MOD= 'x2/3    '           / Slit mode, x1, x2/3 or x1/2                    \n",
       "COMMENT --------WEATHER CONDITION                                               \n",
       "TEMPCCDB=              -113.10 / [deg] The temperature of blue CCD              \n",
       "TEMPCCDR=              -122.20 / [deg] The temperature of red CCD               \n",
       "SEEING  =                 3.00 / [arcsec] Seeing during exposure                \n",
       "MOONPHA =                 0.01 / [day] Moon phase for a 29.53 days period       \n",
       "TEMP_AIR=                 1.60 / [deg] Temperature outside dome                 \n",
       "TEMP_FP =                 6.30 / [degree celsius] Temprature of the focalplane  \n",
       "DEWPOINT=                -8.60 / [deg]                                          \n",
       "DUST    = '        '           / Reservation                                    \n",
       "HUMIDITY=                 0.50 /                                                \n",
       "WINDD   =                18.70 / [deg] Wind direction                           \n",
       "WINDS   =                 1.40 / [m/s] Wind speed                               \n",
       "SKYLEVEL= '        '           / Reservation                                    \n",
       "COMMENT --------DATA REDUCTION PARAMETERS                                       \n",
       "EXTRACT = 'aperture'           / Extraction method                              \n",
       "SFLATTEN=                    T / Super flat has been applied                    \n",
       "PCASKYSB=                    T / PCA sky-subtraction has been applied           \n",
       "NSKIES  =                   20 / Sky fiber number                               \n",
       "SKYCHI2 =                  2.4 / Mean chi^2 of sky-subtraction                  \n",
       "SCHI2MIN=                  1.6 / Minimum chi^2 of sky-subtraction               \n",
       "SCHI2MAX=                  3.2 / Maximum chi^2 of sky-subtraction               \n",
       "NSTD    =                    5 / Number of (good) standard stars                \n",
       "FSTAR   = '60-76-89-135-152'   / FiberID of flux standard stars                 \n",
       "FCBY    = 'catalog '           / Standard stars origin (auto, manual or catalog)\n",
       "HELIO   =                    T / Heliocentric correction                        \n",
       "HELIO_RV=            -25.38969 / [km/s] Heliocentric correction                 \n",
       "VACUUM  =                    T / Wavelengths are in vacuum                      \n",
       "NWORDER =                    2 / Number of linear-log10 coefficients            \n",
       "WFITTYPE= 'LOG-LINEAR'         / Linear-log10 dispersion                        \n",
       "COEFF0  =               3.5682 / Central wavelength (log10) of first pixel      \n",
       "COEFF1  =               0.0001 / Log10 dispersion per pixel                     \n",
       "WAT0_001= 'system=linear'      /                                                \n",
       "WAT1_001= 'wtype=linear label=Wavelength units=Angstroms' /                     \n",
       "CRVAL1  =               3.5682 / Central wavelength (log10) of first pixel      \n",
       "CD1_1   =               0.0001 / Log10 dispersion per pixel                     \n",
       "CRPIX1  =                    1 / Starting pixel (1-indexed)                     \n",
       "CTYPE1  = 'LINEAR  '           /                                                \n",
       "DC-FLAG =                    1 / Log-linear flag                                \n",
       "COMMENT --------SPECTRA ANALYSIS RESULTS                                        \n",
       "VERSPIPE= 'v2.7.5  '           / Version of Pipeline                            \n",
       "CLASS   = 'STAR    '           / Class of object                                \n",
       "SUBCLASS= 'F0      '           / Subclass of object                             \n",
       "Z       =          -0.00001500 / Redshift of object                             \n",
       "Z_ERR   =           0.00007800 / Redshift error of object                       \n",
       "ZFLAG   = 'LASP    '           / Which method computes the redshift             \n",
       "SNRU    =               164.85 / SNR of u filter                                \n",
       "SNRG    =               446.69 / SNR of g filter                                \n",
       "SNRR    =               590.62 / SNR of r filter                                \n",
       "SNRI    =               570.01 / SNR of i filter                                \n",
       "SNRZ    =               376.18 / SNR of z filter                                "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read HDU header\n",
    "hl[0].header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.73385332e+04,   1.78001680e+04,   1.74559727e+04, ...,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00],\n",
       "       [  5.20499998e-05,   5.11758699e-05,   5.23977396e-05, ...,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00],\n",
       "       [  3.69998633e+03,   3.70083838e+03,   3.70169067e+03, ...,\n",
       "          9.08238965e+03,   9.08448047e+03,   9.08657324e+03],\n",
       "       [  0.00000000e+00,   0.00000000e+00,   0.00000000e+00, ...,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00],\n",
       "       [  0.00000000e+00,   0.00000000e+00,   0.00000000e+00, ...,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00]], dtype=float32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read HDU data\n",
    "hl[0].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3699.98531173  3700.83736292  3701.68961033 ...,  9082.3869326\n",
      "  9084.47847027  9086.57048958]\n",
      "[ 17338.53320312  17800.16796875  17455.97265625 ...,      0.              0.\n",
      "      0.        ]\n"
     ]
    }
   ],
   "source": [
    "# transform data to WVS coordinates\n",
    "naxis1 = hl[0].header['NAXIS1']\n",
    "crval1 = hl[0].header['CRVAL1']\n",
    "crpix1 = hl[0].header['CRPIX1']\n",
    "cdelt1 = hl[0].header['CD1_1']\n",
    "\n",
    "wave = 10**( crval1 + (np.arange(naxis1)+1-crpix1)*cdelt1)\n",
    "print wave\n",
    "\n",
    "flux = hl[0].data[0, :]\n",
    "print flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7eff25e74bd0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LX7etLQyF7n3ccNdi4lZJBQAAAFodwRCJ/vlPe4wGQxfSenqkzs6wQijlH9vapFNPtUHS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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff27f08750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,8 ))\n",
    "plt.plot(wave, flux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Table.pprint of <Table length=3903>\n",
       "     wave       flux \n",
       "   float64    float32\n",
       "------------- -------\n",
       "3699.98531173 17338.5\n",
       "3700.83736292 17800.2\n",
       "3701.68961033 17456.0\n",
       "  3702.542054 17002.9\n",
       "3703.39469398 15677.3\n",
       " 3704.2475303 14652.4\n",
       "3705.10056302 14114.7\n",
       "3705.95379218 13921.9\n",
       "3706.80721783 14344.3\n",
       "   3707.66084 14902.9\n",
       "          ...     ...\n",
       "9067.75964584     0.0\n",
       "9069.84781506     0.0\n",
       "9071.93646515     0.0\n",
       "9074.02559623     0.0\n",
       " 9076.1152084     0.0\n",
       "9078.20530178     0.0\n",
       "9080.29587648     0.0\n",
       " 9082.3869326     0.0\n",
       "9084.47847027     0.0\n",
       "9086.57048958     0.0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astropy.table import Table, Column\n",
    "spec_table = Table([Column(wave, 'wave'),\n",
    "                    Column(flux, 'flux')])\n",
    "# Table.write('./data/lamost_dr2_spectra/test_spec_table.fits')\n",
    "spec_table.pprint\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# how to convert (ra, dec) to (l, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: TkAgg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab\n",
    "from astropy.coordinates import SkyCoord, Longitude, Latitude\n",
    "import astropy.units as u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (ICRS): (ra, dec) in deg\n",
       "    [(1.0, 4.5), (2.0, 5.2), (3.0, 6.3)]>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ra = Longitude([1, 2, 3], unit=u.deg)  # Could also use Angle\n",
    "dec = np.array([4.5, 5.2, 6.3]) * u.deg  # Astropy Quantity\n",
    "c = SkyCoord(ra, dec, frame='icrs')\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dtype = object\n",
      "unit = deg,deg\n",
      "class = SkyCoord\n",
      "n_bad = 0\n",
      "length = 3\n"
     ]
    }
   ],
   "source": [
    "c.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (Galactic): (l, b) in deg\n",
       "    [(101.22678436, -56.35949807), (103.32241344, -56.00907284),\n",
       "     (105.56483231, -55.23635591)]>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.galactic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Longitude [ 101.22678436, 103.32241344, 105.56483231] deg>,\n",
       " <Latitude [-56.35949807,-56.00907284,-55.23635591] deg>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.galactic.l, c.galactic.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2-D data (images)\n",
    "recommended packages: **aplpy**, **healpy**\n",
    "\n",
    "temporally these will not be introduced in our course"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# HOMEWORK\n",
    "1. download a LAMOST spectra, and write a function to read data in the fits file.\n",
    "\n",
    "    requirements:\n",
    "    1. usage\n",
    "        ```python\n",
    "        spec = read_spectrum_lamost('filepath')\n",
    "        ```\n",
    "    2. `spec` should be a 5-column table, including these data:\n",
    "    \n",
    "        `wavelength, flux, inverse variance, and-mask, or-mask`\n",
    "\n",
    "hint: the data structure of LAMOST DR2 spectra fits files can be found [here](http://dr2.lamost.org/doc/data-production-description#toc_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11+"
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 },
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