Notebooks/Using_astropy.ipynb

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     "text": [
      "Sun Sep 24 07:17:22 2023\n",
      "3.10.11 (main, Apr 20 2023, 19:02:41) [GCC 11.2.0]\n"
     ]
    }
   ],
   "source": [
    "# The following is to know when this notebook has been run and with which python version.\n",
    "import time, sys\n",
    "print(time.ctime())\n",
    "print(sys.version.split('|')[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# G The astropy package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Astropy Project is a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages. More informations here: http://www.astropy.org/\n",
    "\n",
    "The astropy group received US$ 1 million last year to found astropy for 3 years.\n",
    "\n",
    "https://www.slideshare.net/KelleCruz/astropy-project-update-for-adass\n",
    "\n",
    "https://learn.astropy.org/"
   ]
  },
  {
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   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constants and Units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://docs.astropy.org/en/stable/constants/index.html\n",
    "\n",
    "http://docs.astropy.org/en/stable/units/index.html"
   ]
  },
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     "text": [
      "5.1\n",
      "Help on package astropy.constants in astropy:\n",
      "\n",
      "NAME\n",
      "    astropy.constants\n",
      "\n",
      "DESCRIPTION\n",
      "    Contains astronomical and physical constants for use in Astropy or other\n",
      "    places.\n",
      "    \n",
      "    A typical use case might be::\n",
      "    \n",
      "        >>> from astropy.constants import c, m_e\n",
      "        >>> # ... define the mass of something you want the rest energy of as m ...\n",
      "        >>> m = m_e\n",
      "        >>> E = m * c**2\n",
      "        >>> E.to('MeV')  # doctest: +FLOAT_CMP\n",
      "        <Quantity 0.510998927603161 MeV>\n",
      "    \n",
      "    The following constants are available:\n",
      "    \n",
      "    ========== ============== ================ =========================\n",
      "       Name        Value            Unit       Description\n",
      "    ========== ============== ================ =========================\n",
      "        G        6.6743e-11     m3 / (kg s2)   Gravitational constant\n",
      "       N_A     6.02214076e+23    1 / (mol)     Avogadro's number\n",
      "        R        8.31446262     J / (K mol)    Gas constant\n",
      "       Ryd       10973731.6       1 / (m)      Rydberg constant\n",
      "        a0     5.29177211e-11        m         Bohr radius\n",
      "      alpha    0.00729735257                   Fine-structure constant\n",
      "       atm         101325            Pa        Standard atmosphere\n",
      "      b_wien   0.00289777196        m K        Wien wavelength displacement law constant\n",
      "        c        299792458        m / (s)      Speed of light in vacuum\n",
      "        e      1.60217663e-19        C         Electron charge\n",
      "       eps0    8.85418781e-12       F/m        Vacuum electric permittivity\n",
      "        g0        9.80665          m / s2      Standard acceleration of gravity\n",
      "        h      6.62607015e-34       J s        Planck constant\n",
      "       hbar    1.05457182e-34       J s        Reduced Planck constant\n",
      "       k_B      1.380649e-23      J / (K)      Boltzmann constant\n",
      "       m_e     9.1093837e-31         kg        Electron mass\n",
      "       m_n     1.6749275e-27         kg        Neutron mass\n",
      "       m_p     1.67262192e-27        kg        Proton mass\n",
      "       mu0     1.25663706e-06       N/A2       Vacuum magnetic permeability\n",
      "       muB     9.27401008e-24       J/T        Bohr magneton\n",
      "     sigma_T   6.65245873e-29        m2        Thomson scattering cross-section\n",
      "     sigma_sb  5.67037442e-08   W / (K4 m2)    Stefan-Boltzmann constant\n",
      "        u      1.66053907e-27        kg        Atomic mass\n",
      "     GM_earth   3.986004e+14     m3 / (s2)     Nominal Earth mass parameter\n",
      "      GM_jup   1.2668653e+17     m3 / (s2)     Nominal Jupiter mass parameter\n",
      "      GM_sun   1.3271244e+20     m3 / (s2)     Nominal solar mass parameter\n",
      "      L_bol0     3.0128e+28          W         Luminosity for absolute bolometric magnitude 0\n",
      "      L_sun      3.828e+26           W         Nominal solar luminosity\n",
      "     M_earth   5.97216787e+24        kg        Earth mass\n",
      "      M_jup    1.8981246e+27         kg        Jupiter mass\n",
      "      M_sun    1.98840987e+30        kg        Solar mass\n",
      "     R_earth      6378100            m         Nominal Earth equatorial radius\n",
      "      R_jup       71492000           m         Nominal Jupiter equatorial radius\n",
      "      R_sun      695700000           m         Nominal solar radius\n",
      "        au     1.49597871e+11        m         Astronomical Unit\n",
      "       kpc     3.08567758e+19        m         Kiloparsec\n",
      "        pc     3.08567758e+16        m         Parsec\n",
      "    ========== ============== ================ =========================\n",
      "\n",
      "PACKAGE CONTENTS\n",
      "    astropyconst13\n",
      "    astropyconst20\n",
      "    astropyconst40\n",
      "    cgs\n",
      "    codata2010\n",
      "    codata2014\n",
      "    codata2018\n",
      "    config\n",
      "    constant\n",
      "    iau2012\n",
      "    iau2015\n",
      "    si\n",
      "    tests (package)\n",
      "    utils\n",
      "\n",
      "SUBMODULES\n",
      "    codata\n",
      "    iaudata\n",
      "\n",
      "DATA\n",
      "    G = <<class 'astropy.constants.codata2018.CODATA2018...e-15 unit='m3 /...\n",
      "    GM_earth = <<class 'astropy.constants.iau2015.IAU2015'> nam...it='m3 /...\n",
      "    GM_jup = <<class 'astropy.constants.iau2015.IAU2015'> nam...it='m3 / s...\n",
      "    GM_sun = <<class 'astropy.constants.iau2015.IAU2015'> nam...it='m3 / s...\n",
      "    L_bol0 = <<class 'astropy.constants.iau2015.IAU2015'> nam...0.0 unit='...\n",
      "    L_sun = <<class 'astropy.constants.iau2015.IAU2015'> nam...0.0 unit='W...\n",
      "    M_earth = <<class 'astropy.constants.iau2015.IAU2015'> nam...eference=...\n",
      "    M_jup = <<class 'astropy.constants.iau2015.IAU2015'> nam...eference='I...\n",
      "    M_sun = <<class 'astropy.constants.iau2015.IAU2015'> nam...eference='I...\n",
      "    N_A = <<class 'astropy.constants.codata2018.CODATA2018...ainty=0.0 uni...\n",
      "    R = <<class 'astropy.constants.codata2018.CODATA2018...y=0.0 unit='J /...\n",
      "    R_earth = <<class 'astropy.constants.iau2015.IAU2015'> nam...0.0 unit=...\n",
      "    R_jup = <<class 'astropy.constants.iau2015.IAU2015'> nam...0.0 unit='m...\n",
      "    R_sun = <<class 'astropy.constants.iau2015.IAU2015'> nam...0.0 unit='m...\n",
      "    Ryd = <<class 'astropy.constants.codata2018.CODATA2018...nty=2.1e-05 u...\n",
      "    a0 = <<class 'astropy.constants.codata2018.CODATA2018...certainty=8e-2...\n",
      "    alpha = <<class 'astropy.constants.codata2018.CODATA2018...ertainty=1....\n",
      "    atm = <<class 'astropy.constants.codata2018.CODATA2018...ncertainty=0....\n",
      "    au = <<class 'astropy.constants.iau2015.IAU2015'> nam...=0.0 unit='m' ...\n",
      "    b_wien = <<class 'astropy.constants.codata2018.CODATA2018...certainty=...\n",
      "    c = <<class 'astropy.constants.codata2018.CODATA2018...rtainty=0.0 uni...\n",
      "    e = <<class 'astropy.constants.codata2018.EMCODATA20...uncertainty=0.0...\n",
      "    eps0 = <<class 'astropy.constants.codata2018.EMCODATA20...nty=1.3e-21 ...\n",
      "    g0 = <<class 'astropy.constants.codata2018.CODATA2018...tainty=0.0 uni...\n",
      "    h = <<class 'astropy.constants.codata2018.CODATA2018...certainty=0.0 u...\n",
      "    hbar = <<class 'astropy.constants.codata2018.CODATA2018...certainty=0....\n",
      "    k_B = <<class 'astropy.constants.codata2018.CODATA2018...rtainty=0.0 u...\n",
      "    kpc = <<class 'astropy.constants.iau2015.IAU2015'> nam...ived from au ...\n",
      "    m_e = <<class 'astropy.constants.codata2018.CODATA2018...tainty=2.8e-4...\n",
      "    m_n = <<class 'astropy.constants.codata2018.CODATA2018...tainty=9.5e-3...\n",
      "    m_p = <<class 'astropy.constants.codata2018.CODATA2018...tainty=5.1e-3...\n",
      "    mu0 = <<class 'astropy.constants.codata2018.CODATA2018...ty=1.9e-16 un...\n",
      "    muB = <<class 'astropy.constants.codata2018.CODATA2018...nty=2.8e-33 u...\n",
      "    pc = <<class 'astropy.constants.iau2015.IAU2015'> nam...ived from au +...\n",
      "    sigma_T = <<class 'astropy.constants.codata2018.CODATA2018...ertainty=...\n",
      "    sigma_sb = <<class 'astropy.constants.codata2018.CODATA2018...y=0.0 un...\n",
      "    u = <<class 'astropy.constants.codata2018.CODATA2018...ertainty=5e-37 ...\n",
      "\n",
      "FILE\n",
      "    /home/morisset/anaconda3/envs/ML/lib/python3.10/site-packages/astropy/constants/__init__.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import astropy\n",
    "print(astropy.__version__)\n",
    "from astropy import constants as const\n",
    "from astropy import units as u\n",
    "help(const)"
   ]
  },
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     "text": [
      "  Name   = Speed of light in vacuum\n",
      "  Value  = 299792458.0\n",
      "  Uncertainty  = 0.0\n",
      "  Unit  = m / s\n",
      "  Reference = CODATA 2018\n"
     ]
    }
   ],
   "source": [
    "# Pretty printing\n",
    "print(const.c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0660139378555056e-07 Mpc / yr\n"
     ]
    }
   ],
   "source": [
    "# .to change the unit\n",
    "print(const.c.to('Mpc/yr'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/latex": [
       "$8.9875518 \\times 10^{16} \\; \\mathrm{\\frac{m^{2}}{s^{2}}}$"
      ],
      "text/plain": [
       "<Quantity 8.98755179e+16 m2 / s2>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# basic operations are managed\n",
    "const.c ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
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   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$17314.516 \\; \\mathrm{\\frac{m^{1/2}}{s^{1/2}}}$"
      ],
      "text/plain": [
       "<Quantity 17314.51581766 m(1/2) / s(1/2)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(const.c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17314.51581766005 m(1/2) / s(1/2)\n"
     ]
    }
   ],
   "source": [
    "print(np.sqrt(const.c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.965229612094153e+30 kg\n",
      "8.225977685950412e+21 g m3 / (AU2 s2)\n"
     ]
    }
   ],
   "source": [
    "# Following the units\n",
    "M1 = 3 * const.M_sun\n",
    "M2 = 100 * u.g\n",
    "Dist = 2.2 * u.au\n",
    "F = const.G * M1 * M2 / Dist ** 2\n",
    "print(M1)\n",
    "print(F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/latex": [
       "$8.2259777 \\times 10^{21} \\; \\mathrm{\\frac{m^{3}\\,g}{AU^{2}\\,s^{2}}}$"
      ],
      "text/plain": [
       "<Quantity 8.22597769e+21 g m3 / (AU2 s2)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0003675671602160826 N\n"
     ]
    }
   ],
   "source": [
    "# Convert in more classical unit\n",
    "print(F.to(u.N))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "q = 42.0 * u.meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$1764 \\; \\mathrm{m^{2}}$"
      ],
      "text/plain": [
       "<Quantity 1764. m2>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1764.0\n",
      "1764.0\n"
     ]
    }
   ],
   "source": [
    "# Extract only the value\n",
    "print((q**2).value)\n",
    "print(q.value**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'astropy.units.quantity.Quantity'>\n",
      "[2.803206e-09 2.803206e-09] m3 / (kg s2)\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([q.value, q.value]) * const.G\n",
    "print(type(arr))\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[42,~126] \\; \\mathrm{m}$"
      ],
      "text/plain": [
       "<Quantity [ 42., 126.] m>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.ones(2) * q\n",
    "arr2[1] = q*3\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'astropy.units.quantity.Quantity'>\n",
      "[2.803206e-09 2.803206e-09] m4 / (kg s2)\n"
     ]
    }
   ],
   "source": [
    "arr = np.ones(2) * q * const.G\n",
    "print(type(arr))\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.008746355685131196 km s / m\n",
      "8.746355685131196 s\n"
     ]
    }
   ],
   "source": [
    "# Resolving redondant units\n",
    "d = 3 * u.km\n",
    "v = 343 * u.meter / u.second # sound velocity\n",
    "t = d / v\n",
    "print(t)\n",
    "print(t.decompose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30856775814913.67 km\n"
     ]
    }
   ],
   "source": [
    "x = 1.0 * u.parsec\n",
    "print(x.to(u.km))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "lam = 5007 * u.angstrom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500.70000000000005 nm\n",
      "0.5007000000000001 micron\n",
      "0.5007000000000001 um\n"
     ]
    }
   ],
   "source": [
    "print(lam.to(u.nm))\n",
    "print(lam.to(u.micron))\n",
    "print(lam.to(u.um))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "598746.6706610745 GHz\n"
     ]
    }
   ],
   "source": [
    "# Some transformations needs extra information, available from u.special\n",
    "print(lam.to(u.GHz, equivalencies=u.spectral()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.modeling.models import BlackBody\n",
    "\n",
    "# wavelengths and spectrum are 1D arrays\n",
    "# wavelengths between 1000 and ~ 30000 A\n",
    "temp = 5500 * u.K\n",
    "wavelengths = np.logspace(3, 4.5, num=1000) * u.AA\n",
    "wavelengths = wavelengths.to(u.um)\n",
    "bb_lam = BlackBody(temp, scale=1.0 * u.erg / (u.cm ** 2 * u.AA * u.s * u.sr))\n",
    "spectrum = bb_lam(wavelengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy import visualization\n",
    "with visualization.quantity_support():\n",
    "    f, ax = plt.subplots()\n",
    "    ax.plot(wavelengths, spectrum)\n",
    "    ax.set_xscale('log')\n",
    "    ax.set_yscale('log')\n",
    "    #ax.set_xlabel(f'Wavelengths [{wavelengths.unit}]')\n",
    "    #ax.set_ylabel(f'Flux [{spectrum.unit}]');\n",
    "    ax.set_title(fr'BB @ {temp:.0f} with Bol. Flux={bb_lam.bolometric_flux:.2e}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More in http://docs.astropy.org/en/stable/units/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://docs.astropy.org/en/stable/table/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from astropy.table import Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " a   b   c \n",
      "--- --- ---\n",
      "  1 2.0   x\n",
      "  4 5.0   y\n",
      "  5 8.2   z\n"
     ]
    }
   ],
   "source": [
    "# create a table with non homogeneous types\n",
    "a = [1, 4, 5]\n",
    "b = [2.0, 5.0, 8.2]\n",
    "c = ['x', 'y', 'z']\n",
    "t = Table((a, b, c), names=('a', 'b', 'c'), meta={'name': 'first table'})\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=3</i>\n",
       "<table id=\"table139749111669056\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>b</th><th>c</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>str1</th></tr></thead>\n",
       "<tr><td>1</td><td>2.0</td><td>x</td></tr>\n",
       "<tr><td>4</td><td>5.0</td><td>y</td></tr>\n",
       "<tr><td>5</td><td>8.2</td><td>z</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "  a      b     c  \n",
       "int64 float64 str1\n",
       "----- ------- ----\n",
       "    1     2.0    x\n",
       "    4     5.0    y\n",
       "    5     8.2    z"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pretty output\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=3</i>\n",
       "<table id=\"table139749111669056\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>b</th><th>c</th></tr></thead>\n",
       "<thead><tr><th></th><th>s</th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>str1</th></tr></thead>\n",
       "<tr><td>1</td><td>2.000</td><td>x</td></tr>\n",
       "<tr><td>4</td><td>5.000</td><td>y</td></tr>\n",
       "<tr><td>5</td><td>8.200</td><td>z</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "  a      b     c  \n",
       "         s        \n",
       "int64 float64 str1\n",
       "----- ------- ----\n",
       "    1   2.000    x\n",
       "    4   5.000    y\n",
       "    5   8.200    z"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One can change the output format\n",
    "t['b'].format = '7.3f'\n",
    "t['b'].format = '{:.3f}'\n",
    "# and add units\n",
    "t['b'].unit = 's'\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t.show_in_browser(jsviewer=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access the column names\n",
    "t.colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# length of the table (number of rows)\n",
    "len(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Acces one element\n",
    "t['a'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=3</i>\n",
       "<table id=\"table139749111669056\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>b</th><th>c</th></tr></thead>\n",
       "<thead><tr><th></th><th>s</th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>str1</th></tr></thead>\n",
       "<tr><td>1</td><td>2.000</td><td>x</td></tr>\n",
       "<tr><td>10</td><td>5.000</td><td>y</td></tr>\n",
       "<tr><td>5</td><td>8.200</td><td>z</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "  a      b     c  \n",
       "         s        \n",
       "int64 float64 str1\n",
       "----- ------- ----\n",
       "    1   2.000    x\n",
       "   10   5.000    y\n",
       "    5   8.200    z"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modify one element\n",
    "t['a'][1] = 10\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# easy add column:\n",
    "t['d'] = [1, 2, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=3</i>\n",
       "<table id=\"table139749111669056\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>b</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th></th><th>s</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>str1</th><th>int64</th></tr></thead>\n",
       "<tr><td>1</td><td>2.000</td><td>x</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>5.000</td><td>y</td><td>2</td></tr>\n",
       "<tr><td>5</td><td>8.200</td><td>z</td><td>3</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "  a      b     c     d  \n",
       "         s              \n",
       "int64 float64 str1 int64\n",
       "----- ------- ---- -----\n",
       "    1   2.000    x     1\n",
       "   10   5.000    y     2\n",
       "    5   8.200    z     3"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=3</i>\n",
       "<table id=\"table139749111669056\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>A</th><th>b</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th></th><th>s</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>str1</th><th>int64</th></tr></thead>\n",
       "<tr><td>1</td><td>2.000</td><td>x</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>5.000</td><td>y</td><td>2</td></tr>\n",
       "<tr><td>5</td><td>8.200</td><td>z</td><td>3</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       "  A      b     c     d  \n",
       "         s              \n",
       "int64 float64 str1 int64\n",
       "----- ------- ---- -----\n",
       "    1   2.000    x     1\n",
       "   10   5.000    y     2\n",
       "    5   8.200    z     3"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.rename_column('a', 'A')\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=4</i>\n",
       "<table id=\"table139749111669056\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>A</th><th>b</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th></th><th>s</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>str1</th><th>int64</th></tr></thead>\n",
       "<tr><td>1</td><td>2.000</td><td>x</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>5.000</td><td>y</td><td>2</td></tr>\n",
       "<tr><td>5</td><td>8.200</td><td>z</td><td>3</td></tr>\n",
       "<tr><td>-6</td><td>-9.300</td><td>r</td><td>10</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=4>\n",
       "  A      b     c     d  \n",
       "         s              \n",
       "int64 float64 str1 int64\n",
       "----- ------- ---- -----\n",
       "    1   2.000    x     1\n",
       "   10   5.000    y     2\n",
       "    5   8.200    z     3\n",
       "   -6  -9.300    r    10"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.add_row([-6.6, -9.3, 'r', 10])\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=5</i>\n",
       "<table id=\"table139749111669056\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>A</th><th>b</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th></th><th>s</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>str1</th><th>int64</th></tr></thead>\n",
       "<tr><td>1</td><td>2.000</td><td>x</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>5.000</td><td>y</td><td>2</td></tr>\n",
       "<tr><td>5</td><td>8.200</td><td>z</td><td>3</td></tr>\n",
       "<tr><td>-6</td><td>-9.300</td><td>r</td><td>10</td></tr>\n",
       "<tr><td>-9</td><td>40.000</td><td>q</td><td>10</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=5>\n",
       "  A      b     c     d  \n",
       "         s              \n",
       "int64 float64 str1 int64\n",
       "----- ------- ---- -----\n",
       "    1   2.000    x     1\n",
       "   10   5.000    y     2\n",
       "    5   8.200    z     3\n",
       "   -6  -9.300    r    10\n",
       "   -9  40.000    q    10"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.add_row([-9, 40, 'q', 10.1])\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=5</i>\n",
       "<table id=\"table139749111666608\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>A</th><th>b</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th></th><th>s</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>str1</th><th>int64</th></tr></thead>\n",
       "<tr><td>--</td><td>2.000</td><td>x</td><td>1</td></tr>\n",
       "<tr><td>--</td><td>5.000</td><td>y</td><td>2</td></tr>\n",
       "<tr><td>5</td><td>8.200</td><td>z</td><td>3</td></tr>\n",
       "<tr><td>-6</td><td>-9.300</td><td>r</td><td>10</td></tr>\n",
       "<tr><td>-9</td><td>40.000</td><td>q</td><td>10</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=5>\n",
       "  A      b     c     d  \n",
       "         s              \n",
       "int64 float64 str1 int64\n",
       "----- ------- ---- -----\n",
       "   --   2.000    x     1\n",
       "   --   5.000    y     2\n",
       "    5   8.200    z     3\n",
       "   -6  -9.300    r    10\n",
       "   -9  40.000    q    10"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Masked values\n",
    "t2 = Table(t, masked=True)\n",
    "t2['A'].mask = [True, True, False, False, False, False] # True is for the masked values!!\n",
    "t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=5</i>\n",
       "<table id=\"table139749111666608\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>A</th><th>b</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th></th><th>s</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>str1</th><th>int64</th></tr></thead>\n",
       "<tr><td>--</td><td>2.000</td><td>x</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>5.000</td><td>y</td><td>2</td></tr>\n",
       "<tr><td>5</td><td>8.200</td><td>z</td><td>3</td></tr>\n",
       "<tr><td>-6</td><td>-9.300</td><td>r</td><td>10</td></tr>\n",
       "<tr><td>-9</td><td>40.000</td><td>q</td><td>10</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=5>\n",
       "  A      b     c     d  \n",
       "         s              \n",
       "int64 float64 str1 int64\n",
       "----- ------- ---- -----\n",
       "   --   2.000    x     1\n",
       "   10   5.000    y     2\n",
       "    5   8.200    z     3\n",
       "   -6  -9.300    r    10\n",
       "   -9  40.000    q    10"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2['A'].mask = [True, False, False, False, False, False] # True is for the masked values!!\n",
    "t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=5</i>\n",
       "<table id=\"table139749111669056\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>A</th><th>b</th><th>c</th><th>d</th><th>A2</th></tr></thead>\n",
       "<thead><tr><th>m</th><th>s</th><th></th><th></th><th>m</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>str1</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>1</td><td>2.000</td><td>x</td><td>1</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>5.000</td><td>y</td><td>2</td><td>100</td></tr>\n",
       "<tr><td>5</td><td>8.200</td><td>z</td><td>3</td><td>25</td></tr>\n",
       "<tr><td>-6</td><td>-9.300</td><td>r</td><td>10</td><td>36</td></tr>\n",
       "<tr><td>-9</td><td>40.000</td><td>q</td><td>10</td><td>81</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=5>\n",
       "  A      b     c     d     A2 \n",
       "  m      s                 m  \n",
       "int64 float64 str1 int64 int64\n",
       "----- ------- ---- ----- -----\n",
       "    1   2.000    x     1     1\n",
       "   10   5.000    y     2   100\n",
       "    5   8.200    z     3    25\n",
       "   -6  -9.300    r    10    36\n",
       "   -9  40.000    q    10    81"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creat a table from a table. Use QTable to manage units.\n",
    "from astropy.table import QTable\n",
    "t['A'].unit = u.m\n",
    "t['A2'] = t['A']**2\n",
    "t\n",
    "# !!!! WRONG UNIT FOR A2!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>QTable length=5</i>\n",
       "<table id=\"table139749265662896\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a2</th><th>b2</th><th>a2/b2</th></tr></thead>\n",
       "<thead><tr><th>m2</th><th>s2</th><th>m2 / s2</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>1.0</td><td>4.000</td><td>0.25</td></tr>\n",
       "<tr><td>100.0</td><td>25.000</td><td>4.0</td></tr>\n",
       "<tr><td>25.0</td><td>67.240</td><td>0.37180249851279</td></tr>\n",
       "<tr><td>36.0</td><td>86.490</td><td>0.4162330905306971</td></tr>\n",
       "<tr><td>81.0</td><td>1600.000</td><td>0.050625</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<QTable length=5>\n",
       "   a2      b2          a2/b2       \n",
       "   m2      s2         m2 / s2      \n",
       "float64 float64       float64      \n",
       "------- -------- ------------------\n",
       "    1.0    4.000               0.25\n",
       "  100.0   25.000                4.0\n",
       "   25.0   67.240   0.37180249851279\n",
       "   36.0   86.490 0.4162330905306971\n",
       "   81.0 1600.000           0.050625"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tq = QTable(t)\n",
    "t2 = QTable([tq['A']**2, tq['b']**2, tq['A']**2 / tq['b']**2], names=('a2', 'b2', 'a2/b2'))\n",
    "t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Managing columns\n",
    "from astropy.table import Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=2</i>\n",
       "<table id=\"table139750199735152\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>col0</th><th>col1</th><th>axis</th><th>axis obj</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>object</th><th>str1</th><th>object</th></tr></thead>\n",
       "<tr><td>1</td><td>[2, 5, 4, 5, 6, 3]</td><td>x</td><td>Axes(0.125,0.11;0.352273x0.77)</td></tr>\n",
       "<tr><td>4</td><td>[5, 7, 6]</td><td>y</td><td>Axes(0.547727,0.11;0.352273x0.77)</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       " col0        col1        axis              axis obj            \n",
       "int64       object       str1               object             \n",
       "----- ------------------ ---- ---------------------------------\n",
       "    1 [2, 5, 4, 5, 6, 3]    x    Axes(0.125,0.11;0.352273x0.77)\n",
       "    4          [5, 7, 6]    y Axes(0.547727,0.11;0.352273x0.77)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a table combining different formats\n",
    "a = (1, 4)\n",
    "b = np.array([[2, 5, 4, 5,6, 3], [5,  7, 6]], dtype=object)  # vector column\n",
    "c = Column(['x', 'y'], name='axis')\n",
    "f, (ax1, ax2) = plt.subplots(1,2)\n",
    "d = Column([ax1, ax2], name='axis obj')\n",
    "\n",
    "tup = (a, b, c, d)\n",
    "t3 = Table(tup)  # Data column named \"c\" has a name \"axis\" in that table\n",
    "t3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=2</i>\n",
       "<table id=\"table139749261939600\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>b</th><th>c</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>str1</th></tr></thead>\n",
       "<tr><td>1</td><td>2.0</td><td>x</td></tr>\n",
       "<tr><td>4</td><td>5.0</td><td>y</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "  a      b     c  \n",
       "int64 float64 str1\n",
       "----- ------- ----\n",
       "    1     2.0    x\n",
       "    4     5.0    y"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table from a dictionnary\n",
    "rr = {'a': [1, 4],\n",
    "      'b': [2.0, 5.0],\n",
    "      'c': ('x', 'y')}\n",
    "t4 = Table(rr)\n",
    "t4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=2</i>\n",
       "<table id=\"table139749261579392\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>b</th><th>c</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>5</td><td>10</td><td>--</td></tr>\n",
       "<tr><td>--</td><td>30</td><td>15</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "  a     b     c  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "    5    10    --\n",
       "   --    30    15"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create table row by row\n",
    "t5 = Table(rows=[{'a': 5, 'b': 10}, {'c': 15, 'b': 30}])\n",
    "t5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 2., b'x') (4, 5., b'y')]\n",
      "........................\n",
      " a   b   c \n",
      "--- --- ---\n",
      "  1 2.0   x\n",
      "  4 5.0   y\n"
     ]
    }
   ],
   "source": [
    "# Numpy structured array\n",
    "arr = np.array([(1, 2.0, 'x'),\n",
    "                (4, 5.0, 'y')],\n",
    "               dtype=[('a', 'i8'), ('b', 'f8'), ('c', 'S2')])\n",
    "print(arr)\n",
    "print('........................')\n",
    "t6 = Table(arr)\n",
    "print(t6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python arrays versus `numpy` arrays as input\n",
    "\n",
    "There is a slightly subtle issue that is important to understand in the way that Table objects are created. Any data input that looks like a Python list (including a tuple) is considered to be a list of columns. In contrast an homogeneous numpy array input is interpreted as a list of rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=3</i>\n",
       "<table id=\"table139749262513424\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>col0</th><th>col1</th><th>col2</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>1</td><td>4</td><td>7</td></tr>\n",
       "<tr><td>2</td><td>5</td><td>8</td></tr>\n",
       "<tr><td>3</td><td>6</td><td>9</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=3>\n",
       " col0  col1  col2\n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "    1     4     7\n",
       "    2     5     8\n",
       "    3     6     9"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t7 = Table(((1,2,3), (4,5,6), (7,8,9)))\n",
    "t7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "col0 col1 col2\n",
      "---- ---- ----\n",
      "   1    2    3\n",
      "   4    5    6\n",
      "   7    8    9\n"
     ]
    }
   ],
   "source": [
    "arr7 = np.array(((1,2,3), (4,5,6), (7,8,9)))\n",
    "t7 = Table(arr7)\n",
    "print(arr7)\n",
    "print(t7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(99, 2., b'x') ( 4, 5., b'y')]\n",
      " a   b   c \n",
      "--- --- ---\n",
      " 99 2.0   x\n",
      "  4 5.0   y\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([(1, 2.0, 'x'),\n",
    "                (4, 5.0, 'y')],\n",
    "               dtype=[('a', 'i8'), ('b', 'f8'), ('c', 'S2')])\n",
    "t6 = Table(arr, copy=False) # pointing to the original data.\n",
    "arr['a'][0] = 99\n",
    "print(arr)\n",
    "print(t6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TableColumns names=('a','b','c')>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t6.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t6.colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(99, 2., b'x'), ( 4, 5., b'y')],\n",
       "      dtype=[('a', '<i8'), ('b', '<f8'), ('c', 'S2')])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One can obtain a numpy structured array from a Table\n",
    "np.array(t6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "col0 col1 col2 col3 col4 col5 col6 ... col83 col84 col85 col86 col87 col88 col89\n",
      "---- ---- ---- ---- ---- ---- ---- ... ----- ----- ----- ----- ----- ----- -----\n",
      "   0    1    2    3    4    5    6 ...    83    84    85    86    87    88    89\n",
      "  90   91   92   93   94   95   96 ...   173   174   175   176   177   178   179\n",
      " 180  181  182  183  184  185  186 ...   263   264   265   266   267   268   269\n",
      " 270  271  272  273  274  275  276 ...   353   354   355   356   357   358   359\n",
      " 360  361  362  363  364  365  366 ...   443   444   445   446   447   448   449\n",
      " 450  451  452  453  454  455  456 ...   533   534   535   536   537   538   539\n",
      " 540  541  542  543  544  545  546 ...   623   624   625   626   627   628   629\n",
      " 630  631  632  633  634  635  636 ...   713   714   715   716   717   718   719\n",
      " 720  721  722  723  724  725  726 ...   803   804   805   806   807   808   809\n",
      " 810  811  812  813  814  815  816 ...   893   894   895   896   897   898   899\n",
      " ...  ...  ...  ...  ...  ...  ... ...   ...   ...   ...   ...   ...   ...   ...\n",
      "8010 8011 8012 8013 8014 8015 8016 ...  8093  8094  8095  8096  8097  8098  8099\n",
      "8100 8101 8102 8103 8104 8105 8106 ...  8183  8184  8185  8186  8187  8188  8189\n",
      "8190 8191 8192 8193 8194 8195 8196 ...  8273  8274  8275  8276  8277  8278  8279\n",
      "8280 8281 8282 8283 8284 8285 8286 ...  8363  8364  8365  8366  8367  8368  8369\n",
      "8370 8371 8372 8373 8374 8375 8376 ...  8453  8454  8455  8456  8457  8458  8459\n",
      "8460 8461 8462 8463 8464 8465 8466 ...  8543  8544  8545  8546  8547  8548  8549\n",
      "8550 8551 8552 8553 8554 8555 8556 ...  8633  8634  8635  8636  8637  8638  8639\n",
      "8640 8641 8642 8643 8644 8645 8646 ...  8723  8724  8725  8726  8727  8728  8729\n",
      "8730 8731 8732 8733 8734 8735 8736 ...  8813  8814  8815  8816  8817  8818  8819\n",
      "8820 8821 8822 8823 8824 8825 8826 ...  8903  8904  8905  8906  8907  8908  8909\n",
      "8910 8911 8912 8913 8914 8915 8916 ...  8993  8994  8995  8996  8997  8998  8999\n",
      "Length = 100 rows\n"
     ]
    }
   ],
   "source": [
    "arr = np.arange(9000).reshape(100, 90)  # 100 rows x 90 columns array\n",
    "t = Table(arr)\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t.show_in_browser(jsviewer=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=5</i>\n",
       "<table id=\"table139749261936480\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>b</th><th>c</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>0</td><td>1</td><td>2</td></tr>\n",
       "<tr><td>3</td><td>4</td><td>5</td></tr>\n",
       "<tr><td>6</td><td>7</td><td>8</td></tr>\n",
       "<tr><td>9</td><td>10</td><td>11</td></tr>\n",
       "<tr><td>12</td><td>13</td><td>14</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=5>\n",
       "  a     b     c  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "    0     1     2\n",
       "    3     4     5\n",
       "    6     7     8\n",
       "    9    10    11\n",
       "   12    13    14"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a simple table to play with\n",
    "arr = np.arange(15).reshape(5, 3)\n",
    "t = Table(arr, names=('a', 'b', 'c'), meta={'keywords': {'key1': 'val1'}}, masked=True)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=5</i>\n",
       "<table id=\"table139749261936480\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>b</th><th>c</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>1</td><td>1</td><td>2</td></tr>\n",
       "<tr><td>-2</td><td>4</td><td>5</td></tr>\n",
       "<tr><td>3</td><td>7</td><td>8</td></tr>\n",
       "<tr><td>-4</td><td>10</td><td>11</td></tr>\n",
       "<tr><td>5</td><td>13</td><td>14</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=5>\n",
       "  a     b     c  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "    1     1     2\n",
       "   -2     4     5\n",
       "    3     7     8\n",
       "   -4    10    11\n",
       "    5    13    14"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t['a'] = [1, -2, 3, -4, 5]  # Set all\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=5</i>\n",
       "<table id=\"table139749261936480\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>b</th><th>c</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>1</td><td>1</td><td>2</td></tr>\n",
       "<tr><td>-2</td><td>4</td><td>5</td></tr>\n",
       "<tr><td>30</td><td>7</td><td>8</td></tr>\n",
       "<tr><td>-4</td><td>10</td><td>11</td></tr>\n",
       "<tr><td>5</td><td>13</td><td>14</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=5>\n",
       "  a     b     c  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "    1     1     2\n",
       "   -2     4     5\n",
       "   30     7     8\n",
       "   -4    10    11\n",
       "    5    13    14"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t['a'][2] = 30 # set one\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=5</i>\n",
       "<table id=\"table139749261936480\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>b</th><th>c</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>1</td><td>1</td><td>2</td></tr>\n",
       "<tr><td>8</td><td>9</td><td>10</td></tr>\n",
       "<tr><td>30</td><td>7</td><td>8</td></tr>\n",
       "<tr><td>-4</td><td>10</td><td>11</td></tr>\n",
       "<tr><td>5</td><td>13</td><td>14</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=5>\n",
       "  a     b     c  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "    1     1     2\n",
       "    8     9    10\n",
       "   30     7     8\n",
       "   -4    10    11\n",
       "    5    13    14"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set one row\n",
    "t[1] = (8, 9, 10)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=5</i>\n",
       "<table id=\"table139749261936480\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>b</th><th>c</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>99</td><td>1</td><td>2</td></tr>\n",
       "<tr><td>99</td><td>9</td><td>10</td></tr>\n",
       "<tr><td>99</td><td>7</td><td>8</td></tr>\n",
       "<tr><td>99</td><td>10</td><td>11</td></tr>\n",
       "<tr><td>99</td><td>13</td><td>14</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=5>\n",
       "  a     b     c  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "   99     1     2\n",
       "   99     9    10\n",
       "   99     7     8\n",
       "   99    10    11\n",
       "   99    13    14"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set a whole column\n",
    "t['a'] = 99\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=5</i>\n",
       "<table id=\"table139749261936480\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>b</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>99</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>99</td><td>9</td><td>10</td><td>2</td></tr>\n",
       "<tr><td>99</td><td>7</td><td>8</td><td>3</td></tr>\n",
       "<tr><td>99</td><td>10</td><td>11</td><td>4</td></tr>\n",
       "<tr><td>99</td><td>13</td><td>14</td><td>5</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=5>\n",
       "  a     b     c     d  \n",
       "int64 int64 int64 int64\n",
       "----- ----- ----- -----\n",
       "   99     1     2     1\n",
       "   99     9    10     2\n",
       "   99     7     8     3\n",
       "   99    10    11     4\n",
       "   99    13    14     5"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a column\n",
    "t.add_column(Column(np.array([1,2,3,4,5]), name='d'))\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=5</i>\n",
       "<table id=\"table139749261936480\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>99</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>99</td><td>10</td><td>2</td></tr>\n",
       "<tr><td>99</td><td>8</td><td>3</td></tr>\n",
       "<tr><td>99</td><td>11</td><td>4</td></tr>\n",
       "<tr><td>99</td><td>14</td><td>5</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=5>\n",
       "  a     c     d  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "   99     2     1\n",
       "   99    10     2\n",
       "   99     8     3\n",
       "   99    11     4\n",
       "   99    14     5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove a column\n",
    "t.remove_column('b')\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=6</i>\n",
       "<table id=\"table139749261936480\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>99</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>99</td><td>10</td><td>2</td></tr>\n",
       "<tr><td>99</td><td>8</td><td>3</td></tr>\n",
       "<tr><td>99</td><td>11</td><td>4</td></tr>\n",
       "<tr><td>99</td><td>14</td><td>5</td></tr>\n",
       "<tr><td>-8</td><td>-9</td><td>10</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=6>\n",
       "  a     c     d  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "   99     2     1\n",
       "   99    10     2\n",
       "   99     8     3\n",
       "   99    11     4\n",
       "   99    14     5\n",
       "   -8    -9    10"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a row\n",
    "t.add_row([-8, -9, 10])\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=4</i>\n",
       "<table id=\"table139749261936480\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>99</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>99</td><td>11</td><td>4</td></tr>\n",
       "<tr><td>99</td><td>14</td><td>5</td></tr>\n",
       "<tr><td>-8</td><td>-9</td><td>10</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=4>\n",
       "  a     c     d  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "   99     2     1\n",
       "   99    11     4\n",
       "   99    14     5\n",
       "   -8    -9    10"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove some rows\n",
    "t.remove_rows([1, 2])\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=4</i>\n",
       "<table id=\"table139749261936480\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>-8</td><td>-9</td><td>10</td></tr>\n",
       "<tr><td>99</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>99</td><td>11</td><td>4</td></tr>\n",
       "<tr><td>99</td><td>14</td><td>5</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=4>\n",
       "  a     c     d  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "   -8    -9    10\n",
       "   99     2     1\n",
       "   99    11     4\n",
       "   99    14     5"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the Table using one column\n",
    "t.sort('c')\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False  True  True]\n"
     ]
    }
   ],
   "source": [
    "filter = (t['a'] > 50) & (t['d'] > 3)\n",
    "print(filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=2</i>\n",
       "<table id=\"table139749261939456\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>99</td><td>11</td><td>4</td></tr>\n",
       "<tr><td>99</td><td>14</td><td>5</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=2>\n",
       "  a     c     d  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "   99    11     4\n",
       "   99    14     5"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table masked=True length=4</i>\n",
       "<table id=\"table140282357763904\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>--</td><td>-9</td><td>10</td></tr>\n",
       "<tr><td>99</td><td>2</td><td>--</td></tr>\n",
       "<tr><td>99</td><td>11</td><td>4</td></tr>\n",
       "<tr><td>99</td><td>14</td><td>5</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table masked=True length=4>\n",
       "  a     c     d  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "   --    -9    10\n",
       "   99     2    --\n",
       "   99    11     4\n",
       "   99    14     5"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t['a'].mask = ~(t['a'] > 50)\n",
    "t['d'].mask = ~(t['d'] > 3)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting tab1.dat\n"
     ]
    }
   ],
   "source": [
    "%%writefile tab1.dat\n",
    "#name    obs_date    mag_b  mag_v\n",
    "M31     2012-01-02  17.0   17.5\n",
    "M31     2012-01-02  17.1   17.4\n",
    "M101    2012-01-02  15.1   13.5\n",
    "M82     2012-02-14  16.2   14.5\n",
    "M31     2012-02-14  16.9   17.3\n",
    "M82     2012-02-14  15.2   15.5\n",
    "M101    2012-02-14  15.0   13.6\n",
    "M82     2012-03-26  15.7   16.5\n",
    "M101    2012-03-26  15.1   13.5\n",
    "M101    2012-03-26  14.8   14.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# directly read a Table from an ascii file\n",
    "obs = Table.read('tab1.dat', format='ascii')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=10</i>\n",
       "<table id=\"table139749259901968\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>name</th><th>obs_date</th><th>mag_b</th><th>mag_v</th></tr></thead>\n",
       "<thead><tr><th>str4</th><th>str10</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>M31</td><td>2012-01-02</td><td>17.0</td><td>17.5</td></tr>\n",
       "<tr><td>M31</td><td>2012-01-02</td><td>17.1</td><td>17.4</td></tr>\n",
       "<tr><td>M101</td><td>2012-01-02</td><td>15.1</td><td>13.5</td></tr>\n",
       "<tr><td>M82</td><td>2012-02-14</td><td>16.2</td><td>14.5</td></tr>\n",
       "<tr><td>M31</td><td>2012-02-14</td><td>16.9</td><td>17.3</td></tr>\n",
       "<tr><td>M82</td><td>2012-02-14</td><td>15.2</td><td>15.5</td></tr>\n",
       "<tr><td>M101</td><td>2012-02-14</td><td>15.0</td><td>13.6</td></tr>\n",
       "<tr><td>M82</td><td>2012-03-26</td><td>15.7</td><td>16.5</td></tr>\n",
       "<tr><td>M101</td><td>2012-03-26</td><td>15.1</td><td>13.5</td></tr>\n",
       "<tr><td>M101</td><td>2012-03-26</td><td>14.8</td><td>14.3</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=10>\n",
       "name  obs_date   mag_b   mag_v \n",
       "str4   str10    float64 float64\n",
       "---- ---------- ------- -------\n",
       " M31 2012-01-02    17.0    17.5\n",
       " M31 2012-01-02    17.1    17.4\n",
       "M101 2012-01-02    15.1    13.5\n",
       " M82 2012-02-14    16.2    14.5\n",
       " M31 2012-02-14    16.9    17.3\n",
       " M82 2012-02-14    15.2    15.5\n",
       "M101 2012-02-14    15.0    13.6\n",
       " M82 2012-03-26    15.7    16.5\n",
       "M101 2012-03-26    15.1    13.5\n",
       "M101 2012-03-26    14.8    14.3"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=10</i>\n",
       "<table id=\"table139749261938064\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>name</th><th>obs_date</th><th>mag_b</th><th>mag_v</th></tr></thead>\n",
       "<thead><tr><th>str4</th><th>str10</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>M101</td><td>2012-01-02</td><td>15.1</td><td>13.5</td></tr>\n",
       "<tr><td>M101</td><td>2012-02-14</td><td>15.0</td><td>13.6</td></tr>\n",
       "<tr><td>M101</td><td>2012-03-26</td><td>15.1</td><td>13.5</td></tr>\n",
       "<tr><td>M101</td><td>2012-03-26</td><td>14.8</td><td>14.3</td></tr>\n",
       "<tr><td>M31</td><td>2012-01-02</td><td>17.0</td><td>17.5</td></tr>\n",
       "<tr><td>M31</td><td>2012-01-02</td><td>17.1</td><td>17.4</td></tr>\n",
       "<tr><td>M31</td><td>2012-02-14</td><td>16.9</td><td>17.3</td></tr>\n",
       "<tr><td>M82</td><td>2012-02-14</td><td>16.2</td><td>14.5</td></tr>\n",
       "<tr><td>M82</td><td>2012-02-14</td><td>15.2</td><td>15.5</td></tr>\n",
       "<tr><td>M82</td><td>2012-03-26</td><td>15.7</td><td>16.5</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=10>\n",
       "name  obs_date   mag_b   mag_v \n",
       "str4   str10    float64 float64\n",
       "---- ---------- ------- -------\n",
       "M101 2012-01-02    15.1    13.5\n",
       "M101 2012-02-14    15.0    13.6\n",
       "M101 2012-03-26    15.1    13.5\n",
       "M101 2012-03-26    14.8    14.3\n",
       " M31 2012-01-02    17.0    17.5\n",
       " M31 2012-01-02    17.1    17.4\n",
       " M31 2012-02-14    16.9    17.3\n",
       " M82 2012-02-14    16.2    14.5\n",
       " M82 2012-02-14    15.2    15.5\n",
       " M82 2012-03-26    15.7    16.5"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group data\n",
    "obs_by_name = obs.group_by('name')\n",
    "obs_by_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name\n",
      "----\n",
      "M101\n",
      " M31\n",
      " M82\n"
     ]
    }
   ],
   "source": [
    "print(obs_by_name.groups.keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name  obs_date \n",
      "---- ----------\n",
      "M101 2012-01-02\n",
      "M101 2012-02-14\n",
      "M101 2012-03-26\n",
      " M31 2012-01-02\n",
      " M31 2012-02-14\n",
      " M82 2012-02-14\n",
      " M82 2012-03-26\n"
     ]
    }
   ],
   "source": [
    "# Using 2 keys to group\n",
    "print(obs.group_by(['name', 'obs_date']).groups.keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name  obs_date  mag_b mag_v\n",
      "---- ---------- ----- -----\n",
      " M31 2012-01-02  17.0  17.5\n",
      " M31 2012-01-02  17.1  17.4\n",
      " M31 2012-02-14  16.9  17.3\n"
     ]
    }
   ],
   "source": [
    "# Extracting a group\n",
    "print(obs_by_name.groups[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False False]\n",
      "name  obs_date  mag_b mag_v\n",
      "---- ---------- ----- -----\n",
      "M101 2012-01-02  15.1  13.5\n",
      "M101 2012-02-14  15.0  13.6\n",
      "M101 2012-03-26  15.1  13.5\n",
      "M101 2012-03-26  14.8  14.3\n"
     ]
    }
   ],
   "source": [
    "# Using a mask to select entries\n",
    "mask = obs_by_name.groups.keys['name'] == 'M101'\n",
    "print(mask)\n",
    "print(obs_by_name.groups[mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name       mag_b              mag_v       \n",
      "---- ------------------ ------------------\n",
      "M101 15.000000000000002 13.725000000000001\n",
      " M31               17.0 17.400000000000002\n",
      " M82 15.699999999999998               15.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Cannot aggregate column 'obs_date' with type '<U10': ufunc 'add' did not contain a loop with signature matching types (dtype('<U10'), dtype('<U10')) -> None [astropy.table.groups]\n"
     ]
    }
   ],
   "source": [
    "# Some functions can be applied to the elements of a group\n",
    "obs_mean = obs_by_name.groups.aggregate(np.mean) \n",
    "print(obs_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name       mag_v              mag_b       \n",
      "---- ------------------ ------------------\n",
      "M101 13.725000000000001 15.000000000000002\n",
      " M31 17.400000000000002               17.0\n",
      " M82               15.5 15.699999999999998\n"
     ]
    }
   ],
   "source": [
    "print(obs_by_name['name', 'mag_v', 'mag_b'].groups.aggregate(np.mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# creat a new Table on the fly\n",
    "obs1 = Table.read(\"\"\"name    obs_date    mag_b  logLx\n",
    "M31     2012-01-02  17.0   42.5\n",
    "M82     2012-10-29  16.2   43.5\n",
    "M101    2012-10-31  15.1   44.5\"\"\", format='ascii')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# this is used to stack Tables\n",
    "from astropy.table import vstack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=13</i>\n",
       "<table id=\"table139749259901152\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>name</th><th>obs_date</th><th>mag_b</th><th>mag_v</th><th>logLx</th></tr></thead>\n",
       "<thead><tr><th>str4</th><th>str10</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>M31</td><td>2012-01-02</td><td>17.0</td><td>17.5</td><td>--</td></tr>\n",
       "<tr><td>M31</td><td>2012-01-02</td><td>17.1</td><td>17.4</td><td>--</td></tr>\n",
       "<tr><td>M101</td><td>2012-01-02</td><td>15.1</td><td>13.5</td><td>--</td></tr>\n",
       "<tr><td>M82</td><td>2012-02-14</td><td>16.2</td><td>14.5</td><td>--</td></tr>\n",
       "<tr><td>M31</td><td>2012-02-14</td><td>16.9</td><td>17.3</td><td>--</td></tr>\n",
       "<tr><td>M82</td><td>2012-02-14</td><td>15.2</td><td>15.5</td><td>--</td></tr>\n",
       "<tr><td>M101</td><td>2012-02-14</td><td>15.0</td><td>13.6</td><td>--</td></tr>\n",
       "<tr><td>M82</td><td>2012-03-26</td><td>15.7</td><td>16.5</td><td>--</td></tr>\n",
       "<tr><td>M101</td><td>2012-03-26</td><td>15.1</td><td>13.5</td><td>--</td></tr>\n",
       "<tr><td>M101</td><td>2012-03-26</td><td>14.8</td><td>14.3</td><td>--</td></tr>\n",
       "<tr><td>M31</td><td>2012-01-02</td><td>17.0</td><td>--</td><td>42.5</td></tr>\n",
       "<tr><td>M82</td><td>2012-10-29</td><td>16.2</td><td>--</td><td>43.5</td></tr>\n",
       "<tr><td>M101</td><td>2012-10-31</td><td>15.1</td><td>--</td><td>44.5</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=13>\n",
       "name  obs_date   mag_b   mag_v   logLx \n",
       "str4   str10    float64 float64 float64\n",
       "---- ---------- ------- ------- -------\n",
       " M31 2012-01-02    17.0    17.5      --\n",
       " M31 2012-01-02    17.1    17.4      --\n",
       "M101 2012-01-02    15.1    13.5      --\n",
       " M82 2012-02-14    16.2    14.5      --\n",
       " M31 2012-02-14    16.9    17.3      --\n",
       " M82 2012-02-14    15.2    15.5      --\n",
       "M101 2012-02-14    15.0    13.6      --\n",
       " M82 2012-03-26    15.7    16.5      --\n",
       "M101 2012-03-26    15.1    13.5      --\n",
       "M101 2012-03-26    14.8    14.3      --\n",
       " M31 2012-01-02    17.0      --    42.5\n",
       " M82 2012-10-29    16.2      --    43.5\n",
       "M101 2012-10-31    15.1      --    44.5"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs = vstack([obs, obs1])\n",
    "tvs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting data6.dat\n"
     ]
    }
   ],
   "source": [
    "%%writefile data6.dat\n",
    "Line        Iobs     lambda  rel_er   Obs_code\n",
    "H  1  4861A 1.00000    4861. 0.08000  Anabel                               \n",
    "H  1  6563A 2.8667     6563. 0.19467  Anabel                               \n",
    "H  1  4340A 0.4933     4340. 0.03307  Anabel                               \n",
    "H  1  4102A 0.2907     4102. 0.02229  Anabel                               \n",
    "H  1  3970A 0.1800     3970. 0.01253  Anabel                               \n",
    "N  2  6584A 2.1681     6584. 0.08686  Anabel                               \n",
    "N  2 121.7m 0.0044621217000. 0.20000  Liu                                  \n",
    "O  1  6300A 0.0147     6300. 0.00325  Anabel                               \n",
    "TOTL  2326A 0.07900    2326. 0.20000  Adams                                \n",
    "C  2 157.6m 0.00856 1576000. 0.20000  Liu                                  \n",
    "O  1 63.17m 0.13647  631700. 0.10000  Liu                                  \n",
    "O  1 145.5m 0.00446 1455000. 0.200    Liu                                  \n",
    "TOTL  3727A 0.77609    3727. 0.200    Torres-Peimbert                      \n",
    "S II  4070A 0.06174    4070. 0.200    Torres-Peimbert                      \n",
    "S II  4078A 0.06174    4078. 0.200    Torres-Peimbert                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=15</i>\n",
       "<table id=\"table139749262512128\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Line</th><th>Iobs</th><th>lambda</th><th>rel_er</th><th>Obs_code</th></tr></thead>\n",
       "<thead><tr><th>str11</th><th>float64</th><th>float64</th><th>float64</th><th>str15</th></tr></thead>\n",
       "<tr><td>H  1  4861A</td><td>1.0</td><td>4861.0</td><td>0.08</td><td>Anabel</td></tr>\n",
       "<tr><td>H  1  6563A</td><td>2.8667</td><td>6563.0</td><td>0.19467</td><td>Anabel</td></tr>\n",
       "<tr><td>H  1  4340A</td><td>0.4933</td><td>4340.0</td><td>0.03307</td><td>Anabel</td></tr>\n",
       "<tr><td>H  1  4102A</td><td>0.2907</td><td>4102.0</td><td>0.02229</td><td>Anabel</td></tr>\n",
       "<tr><td>H  1  3970A</td><td>0.18</td><td>3970.0</td><td>0.01253</td><td>Anabel</td></tr>\n",
       "<tr><td>N  2  6584A</td><td>2.1681</td><td>6584.0</td><td>0.08686</td><td>Anabel</td></tr>\n",
       "<tr><td>N  2 121.7m</td><td>0.004462</td><td>1217000.0</td><td>0.2</td><td>Liu</td></tr>\n",
       "<tr><td>O  1  6300A</td><td>0.0147</td><td>6300.0</td><td>0.00325</td><td>Anabel</td></tr>\n",
       "<tr><td>TOTL  2326A</td><td>0.079</td><td>2326.0</td><td>0.2</td><td>Adams</td></tr>\n",
       "<tr><td>C  2 157.6m</td><td>0.00856</td><td>1576000.0</td><td>0.2</td><td>Liu</td></tr>\n",
       "<tr><td>O  1 63.17m</td><td>0.13647</td><td>631700.0</td><td>0.1</td><td>Liu</td></tr>\n",
       "<tr><td>O  1 145.5m</td><td>0.00446</td><td>1455000.0</td><td>0.2</td><td>Liu</td></tr>\n",
       "<tr><td>TOTL  3727A</td><td>0.77609</td><td>3727.0</td><td>0.2</td><td>Torres-Peimbert</td></tr>\n",
       "<tr><td>S II  4070A</td><td>0.06174</td><td>4070.0</td><td>0.2</td><td>Torres-Peimbert</td></tr>\n",
       "<tr><td>S II  4078A</td><td>0.06174</td><td>4078.0</td><td>0.2</td><td>Torres-Peimbert</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=15>\n",
       "    Line      Iobs     lambda   rel_er     Obs_code   \n",
       "   str11    float64   float64  float64      str15     \n",
       "----------- -------- --------- ------- ---------------\n",
       "H  1  4861A      1.0    4861.0    0.08          Anabel\n",
       "H  1  6563A   2.8667    6563.0 0.19467          Anabel\n",
       "H  1  4340A   0.4933    4340.0 0.03307          Anabel\n",
       "H  1  4102A   0.2907    4102.0 0.02229          Anabel\n",
       "H  1  3970A     0.18    3970.0 0.01253          Anabel\n",
       "N  2  6584A   2.1681    6584.0 0.08686          Anabel\n",
       "N  2 121.7m 0.004462 1217000.0     0.2             Liu\n",
       "O  1  6300A   0.0147    6300.0 0.00325          Anabel\n",
       "TOTL  2326A    0.079    2326.0     0.2           Adams\n",
       "C  2 157.6m  0.00856 1576000.0     0.2             Liu\n",
       "O  1 63.17m  0.13647  631700.0     0.1             Liu\n",
       "O  1 145.5m  0.00446 1455000.0     0.2             Liu\n",
       "TOTL  3727A  0.77609    3727.0     0.2 Torres-Peimbert\n",
       "S II  4070A  0.06174    4070.0     0.2 Torres-Peimbert\n",
       "S II  4078A  0.06174    4078.0     0.2 Torres-Peimbert"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = Table.read('data6.dat', format='ascii.fixed_width', \n",
    "               col_starts=(0, 12, 20, 29, 38))\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=15</i>\n",
       "<table id=\"table140282359161472\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Line</th><th>Iobs</th><th>lambda</th><th>rel_er</th><th>Obs_code</th></tr></thead>\n",
       "<thead><tr><th>str11</th><th>float64</th><th>float64</th><th>float64</th><th>str15</th></tr></thead>\n",
       "<tr><td>TOTL  2326A</td><td>0.079</td><td>2326.0</td><td>0.2</td><td>Adams</td></tr>\n",
       "<tr><td>H  1  4861A</td><td>1.0</td><td>4861.0</td><td>0.08</td><td>Anabel</td></tr>\n",
       "<tr><td>H  1  6563A</td><td>2.8667</td><td>6563.0</td><td>0.19467</td><td>Anabel</td></tr>\n",
       "<tr><td>H  1  4340A</td><td>0.4933</td><td>4340.0</td><td>0.03307</td><td>Anabel</td></tr>\n",
       "<tr><td>H  1  4102A</td><td>0.2907</td><td>4102.0</td><td>0.02229</td><td>Anabel</td></tr>\n",
       "<tr><td>H  1  3970A</td><td>0.18</td><td>3970.0</td><td>0.01253</td><td>Anabel</td></tr>\n",
       "<tr><td>N  2  6584A</td><td>2.1681</td><td>6584.0</td><td>0.08686</td><td>Anabel</td></tr>\n",
       "<tr><td>O  1  6300A</td><td>0.0147</td><td>6300.0</td><td>0.00325</td><td>Anabel</td></tr>\n",
       "<tr><td>N  2 121.7m</td><td>0.004462</td><td>1217000.0</td><td>0.2</td><td>Liu</td></tr>\n",
       "<tr><td>C  2 157.6m</td><td>0.00856</td><td>1576000.0</td><td>0.2</td><td>Liu</td></tr>\n",
       "<tr><td>O  1 63.17m</td><td>0.13647</td><td>631700.0</td><td>0.1</td><td>Liu</td></tr>\n",
       "<tr><td>O  1 145.5m</td><td>0.00446</td><td>1455000.0</td><td>0.2</td><td>Liu</td></tr>\n",
       "<tr><td>TOTL  3727A</td><td>0.77609</td><td>3727.0</td><td>0.2</td><td>Torres-Peimbert</td></tr>\n",
       "<tr><td>S II  4070A</td><td>0.06174</td><td>4070.0</td><td>0.2</td><td>Torres-Peimbert</td></tr>\n",
       "<tr><td>S II  4078A</td><td>0.06174</td><td>4078.0</td><td>0.2</td><td>Torres-Peimbert</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=15>\n",
       "    Line      Iobs     lambda   rel_er     Obs_code   \n",
       "   str11    float64   float64  float64      str15     \n",
       "----------- -------- --------- ------- ---------------\n",
       "TOTL  2326A    0.079    2326.0     0.2           Adams\n",
       "H  1  4861A      1.0    4861.0    0.08          Anabel\n",
       "H  1  6563A   2.8667    6563.0 0.19467          Anabel\n",
       "H  1  4340A   0.4933    4340.0 0.03307          Anabel\n",
       "H  1  4102A   0.2907    4102.0 0.02229          Anabel\n",
       "H  1  3970A     0.18    3970.0 0.01253          Anabel\n",
       "N  2  6584A   2.1681    6584.0 0.08686          Anabel\n",
       "O  1  6300A   0.0147    6300.0 0.00325          Anabel\n",
       "N  2 121.7m 0.004462 1217000.0     0.2             Liu\n",
       "C  2 157.6m  0.00856 1576000.0     0.2             Liu\n",
       "O  1 63.17m  0.13647  631700.0     0.1             Liu\n",
       "O  1 145.5m  0.00446 1455000.0     0.2             Liu\n",
       "TOTL  3727A  0.77609    3727.0     0.2 Torres-Peimbert\n",
       "S II  4070A  0.06174    4070.0     0.2 Torres-Peimbert\n",
       "S II  4078A  0.06174    4078.0     0.2 Torres-Peimbert"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.group_by('Obs_code')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a lot of possibilities of joining Tables, see http://docs.astropy.org/en/stable/table/operations.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas and Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = t.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>a</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-8</td>\n",
       "      <td>-9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   c   d\n",
       "0  -8  -9  10\n",
       "1  99   2   1\n",
       "2  99  11   4\n",
       "3  99  14   5"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=4</i>\n",
       "<table id=\"table139749259901872\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>a</th><th>c</th><th>d</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>-8</td><td>-9</td><td>10</td></tr>\n",
       "<tr><td>99</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>99</td><td>11</td><td>4</td></tr>\n",
       "<tr><td>99</td><td>14</td><td>5</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=4>\n",
       "  a     c     d  \n",
       "int64 int64 int64\n",
       "----- ----- -----\n",
       "   -8    -9    10\n",
       "   99     2     1\n",
       "   99    11     4\n",
       "   99    14     5"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = Table.from_pandas(df)\n",
    "t2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading from CDS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look for data on \"Diffuse gas\" at Vizier: https://vizier.u-strasbg.fr/viz-bin/VizieR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = Table.read(\"http://cdsarc.unistra.fr/ftp/J/other/RMxAA/45.261/digeda.dat\", \n",
    "               format='ascii.cds',\n",
    "               readme='http://cdsarc.unistra.fr/ftp/J/other/RMxAA/45.261/ReadMe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t = Table.read(\"ftp://cdsarc.u-strasbg.fr/pub/cats/J/other/RMxAA/45.261/digeda.dat\", \n",
    "               format='ascii.cds', \n",
    "               readme='ftp://cdsarc.u-strasbg.fr/pub/cats/J/other/RMxAA/45.261/ReadMe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=1061</i>\n",
       "<table id=\"table139749149464256\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>ObsID</th><th>Pos</th><th>I3727</th><th>I4363</th><th>IHb</th><th>I4959</th><th>I5007</th><th>I5876</th><th>I6300</th><th>I6548</th><th>IHa</th><th>I6583</th><th>I6716</th><th>I6731</th><th>I16+31</th><th>Te</th><th>R16/31</th><th>R07/59</th><th>RHa/Hb</th><th>EM(Ha)</th><th>RefID</th><th>MType</th><th>Slit</th><th>Region</th><th>GalID</th><th>RefN</th></tr></thead>\n",
       "<thead><tr><th></th><th>pc</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>10000 K</th><th></th><th></th><th></th><th>pc / cm6</th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>1</td><td>0.03</td><td>--</td><td>--</td><td>1.0</td><td>0.2</td><td>0.6</td><td>--</td><td>--</td><td>--</td><td>2.86</td><td>1.0</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>1</td><td>12</td><td>3</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>2</td><td>0.03</td><td>--</td><td>--</td><td>1.0</td><td>0.33</td><td>0.96</td><td>--</td><td>--</td><td>--</td><td>2.86</td><td>1.32</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>1</td><td>12</td><td>3</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>3</td><td>0.05</td><td>--</td><td>--</td><td>1.0</td><td>0.32</td><td>0.95</td><td>--</td><td>--</td><td>--</td><td>2.86</td><td>1.11</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>1</td><td>12</td><td>3</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>4</td><td>0.06</td><td>--</td><td>--</td><td>1.0</td><td>0.12</td><td>0.36</td><td>--</td><td>--</td><td>--</td><td>2.86</td><td>1.06</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>1</td><td>12</td><td>3</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>5</td><td>0.07</td><td>--</td><td>--</td><td>1.0</td><td>0.27</td><td>0.8</td><td>--</td><td>--</td><td>--</td><td>2.86</td><td>1.08</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>1</td><td>12</td><td>3</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>6</td><td>0.12</td><td>--</td><td>--</td><td>1.0</td><td>0.31</td><td>0.9</td><td>--</td><td>--</td><td>--</td><td>2.86</td><td>1.06</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>1</td><td>12</td><td>3</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>7</td><td>0.13</td><td>--</td><td>--</td><td>1.0</td><td>0.29</td><td>0.85</td><td>--</td><td>--</td><td>--</td><td>2.86</td><td>1.42</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>1</td><td>12</td><td>3</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>8</td><td>0.15</td><td>--</td><td>--</td><td>1.0</td><td>0.3</td><td>0.89</td><td>--</td><td>--</td><td>--</td><td>2.86</td><td>0.94</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>1</td><td>12</td><td>3</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>9</td><td>0.15</td><td>--</td><td>--</td><td>1.0</td><td>0.57</td><td>1.67</td><td>--</td><td>--</td><td>--</td><td>2.86</td><td>0.86</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>1</td><td>12</td><td>3</td><td>1</td><td>2</td><td>1</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>1052</td><td>-1.0</td><td>--</td><td>--</td><td>0.35</td><td>--</td><td>0.2</td><td>--</td><td>--</td><td>--</td><td>1.0</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>18</td><td>2</td><td>3</td><td>3</td><td>92</td><td>44</td></tr>\n",
       "<tr><td>1053</td><td>-1.0</td><td>--</td><td>--</td><td>0.35</td><td>--</td><td>0.5</td><td>--</td><td>--</td><td>--</td><td>1.0</td><td>--</td><td>0.25</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>18</td><td>2</td><td>3</td><td>3</td><td>92</td><td>44</td></tr>\n",
       "<tr><td>1054</td><td>-1.0</td><td>--</td><td>--</td><td>0.35</td><td>--</td><td>0.23</td><td>--</td><td>--</td><td>--</td><td>1.0</td><td>--</td><td>0.15</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>18</td><td>2</td><td>3</td><td>3</td><td>92</td><td>44</td></tr>\n",
       "<tr><td>1055</td><td>-1.0</td><td>--</td><td>--</td><td>0.35</td><td>--</td><td>0.6</td><td>--</td><td>--</td><td>--</td><td>1.0</td><td>--</td><td>0.14</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>18</td><td>2</td><td>3</td><td>1</td><td>92</td><td>44</td></tr>\n",
       "<tr><td>1056</td><td>-1.0</td><td>--</td><td>--</td><td>0.35</td><td>--</td><td>0.33</td><td>--</td><td>--</td><td>--</td><td>1.0</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>18</td><td>2</td><td>3</td><td>3</td><td>92</td><td>44</td></tr>\n",
       "<tr><td>1057</td><td>-1.0</td><td>--</td><td>--</td><td>0.35</td><td>--</td><td>0.56</td><td>--</td><td>--</td><td>--</td><td>1.0</td><td>--</td><td>0.35</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>18</td><td>2</td><td>3</td><td>1</td><td>92</td><td>44</td></tr>\n",
       "<tr><td>1058</td><td>-1.0</td><td>--</td><td>--</td><td>0.35</td><td>--</td><td>0.3</td><td>--</td><td>--</td><td>--</td><td>1.0</td><td>--</td><td>0.24</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>18</td><td>2</td><td>3</td><td>3</td><td>92</td><td>44</td></tr>\n",
       "<tr><td>1059</td><td>-1.0</td><td>--</td><td>--</td><td>0.35</td><td>--</td><td>0.54</td><td>--</td><td>--</td><td>--</td><td>1.0</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>18</td><td>2</td><td>3</td><td>3</td><td>92</td><td>44</td></tr>\n",
       "<tr><td>1060</td><td>-1.0</td><td>--</td><td>--</td><td>0.35</td><td>--</td><td>1.33</td><td>--</td><td>--</td><td>--</td><td>1.0</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>18</td><td>2</td><td>3</td><td>1</td><td>92</td><td>44</td></tr>\n",
       "<tr><td>1061</td><td>-1.0</td><td>--</td><td>--</td><td>0.35</td><td>--</td><td>0.44</td><td>--</td><td>--</td><td>--</td><td>1.0</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td><td>18</td><td>2</td><td>3</td><td>3</td><td>92</td><td>44</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=1061>\n",
       "ObsID   Pos    I3727   I4363    IHb    I4959  ... MType  Slit Region GalID  RefN\n",
       "         pc                                   ...                               \n",
       "int64 float64 float64 float64 float64 float64 ... int64 int64 int64  int64 int64\n",
       "----- ------- ------- ------- ------- ------- ... ----- ----- ------ ----- -----\n",
       "    1    0.03      --      --     1.0     0.2 ...    12     3      1     2     1\n",
       "    2    0.03      --      --     1.0    0.33 ...    12     3      1     2     1\n",
       "    3    0.05      --      --     1.0    0.32 ...    12     3      1     2     1\n",
       "    4    0.06      --      --     1.0    0.12 ...    12     3      1     2     1\n",
       "    5    0.07      --      --     1.0    0.27 ...    12     3      1     2     1\n",
       "    6    0.12      --      --     1.0    0.31 ...    12     3      1     2     1\n",
       "    7    0.13      --      --     1.0    0.29 ...    12     3      1     2     1\n",
       "    8    0.15      --      --     1.0     0.3 ...    12     3      1     2     1\n",
       "    9    0.15      --      --     1.0    0.57 ...    12     3      1     2     1\n",
       "  ...     ...     ...     ...     ...     ... ...   ...   ...    ...   ...   ...\n",
       " 1052    -1.0      --      --    0.35      -- ...     2     3      3    92    44\n",
       " 1053    -1.0      --      --    0.35      -- ...     2     3      3    92    44\n",
       " 1054    -1.0      --      --    0.35      -- ...     2     3      3    92    44\n",
       " 1055    -1.0      --      --    0.35      -- ...     2     3      1    92    44\n",
       " 1056    -1.0      --      --    0.35      -- ...     2     3      3    92    44\n",
       " 1057    -1.0      --      --    0.35      -- ...     2     3      1    92    44\n",
       " 1058    -1.0      --      --    0.35      -- ...     2     3      3    92    44\n",
       " 1059    -1.0      --      --    0.35      -- ...     2     3      3    92    44\n",
       " 1060    -1.0      --      --    0.35      -- ...     2     3      1    92    44\n",
       " 1061    -1.0      --      --    0.35      -- ...     2     3      3    92    44"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t.show_in_browser(jsviewer=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "ax.scatter(np.log10(t['I6583']), np.log10(t['I5007']), c=t['RefN'], edgecolor='None')\n",
    "ax.set_xlabel(r'log [NII]/H$\\alpha$')\n",
    "ax.set_ylabel(r'log [OIII]/H$\\beta$');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t = Table.read(\"ftp://cdsarc.u-strasbg.fr/pub/cats/VII/253/snrs.dat\",\n",
    "readme=\"ftp://cdsarc.u-strasbg.fr/pub/cats/VII/253/ReadMe\",\n",
    "format=\"ascii.cds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=274</i>\n",
       "<table id=\"table139749141307664\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>SNR</th><th>RAh</th><th>RAm</th><th>RAs</th><th>DE-</th><th>DEd</th><th>DEm</th><th>MajDiam</th><th>---</th><th>MinDiam</th><th>u_MinDiam</th><th>type</th><th>l_S(1GHz)</th><th>S(1GHz)</th><th>u_S(1GHz)</th><th>Sp-Index</th><th>u_Sp-Index</th><th>Names</th></tr></thead>\n",
       "<thead><tr><th></th><th>h</th><th>min</th><th>s</th><th></th><th>deg</th><th>arcmin</th><th>arcmin</th><th></th><th>arcmin</th><th></th><th></th><th></th><th>Jy</th><th></th><th></th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>str11</th><th>int64</th><th>int64</th><th>int64</th><th>str1</th><th>int64</th><th>int64</th><th>float64</th><th>str1</th><th>float64</th><th>str1</th><th>str2</th><th>str1</th><th>float64</th><th>str1</th><th>float64</th><th>str1</th><th>str26</th></tr></thead>\n",
       "<tr><td>G000.0+00.0</td><td>17</td><td>45</td><td>44</td><td>-</td><td>29</td><td>0</td><td>3.5</td><td>x</td><td>2.5</td><td>--</td><td>S</td><td>--</td><td>100.0</td><td>?</td><td>0.8</td><td>?</td><td>Sgr A East</td></tr>\n",
       "<tr><td>G000.3+00.0</td><td>17</td><td>46</td><td>15</td><td>-</td><td>28</td><td>38</td><td>15.0</td><td>x</td><td>8.0</td><td>--</td><td>S</td><td>--</td><td>22.0</td><td>--</td><td>0.6</td><td>--</td><td>--</td></tr>\n",
       "<tr><td>G000.9+00.1</td><td>17</td><td>47</td><td>21</td><td>-</td><td>28</td><td>9</td><td>8.0</td><td>--</td><td>--</td><td>--</td><td>C</td><td>--</td><td>18.0</td><td>?</td><td>--</td><td>v</td><td>--</td></tr>\n",
       "<tr><td>G001.0-00.1</td><td>17</td><td>48</td><td>30</td><td>-</td><td>28</td><td>9</td><td>8.0</td><td>--</td><td>--</td><td>--</td><td>S</td><td>--</td><td>15.0</td><td>--</td><td>0.6</td><td>?</td><td>--</td></tr>\n",
       "<tr><td>G001.4-00.1</td><td>17</td><td>49</td><td>39</td><td>-</td><td>27</td><td>46</td><td>10.0</td><td>--</td><td>--</td><td>--</td><td>S</td><td>--</td><td>2.0</td><td>?</td><td>--</td><td>?</td><td>--</td></tr>\n",
       "<tr><td>G001.9+00.3</td><td>17</td><td>48</td><td>45</td><td>-</td><td>27</td><td>10</td><td>1.5</td><td>--</td><td>--</td><td>--</td><td>S</td><td>--</td><td>0.6</td><td>--</td><td>0.6</td><td>--</td><td>--</td></tr>\n",
       "<tr><td>G003.7-00.2</td><td>17</td><td>55</td><td>26</td><td>-</td><td>25</td><td>50</td><td>14.0</td><td>x</td><td>11.0</td><td>--</td><td>S</td><td>--</td><td>2.3</td><td>--</td><td>0.65</td><td>--</td><td>--</td></tr>\n",
       "<tr><td>G003.8+00.3</td><td>17</td><td>52</td><td>55</td><td>-</td><td>25</td><td>28</td><td>18.0</td><td>--</td><td>--</td><td>--</td><td>S?</td><td>--</td><td>3.0</td><td>?</td><td>0.6</td><td>--</td><td>--</td></tr>\n",
       "<tr><td>G004.2-03.5</td><td>18</td><td>8</td><td>55</td><td>-</td><td>27</td><td>3</td><td>28.0</td><td>--</td><td>--</td><td>--</td><td>S</td><td>--</td><td>3.2</td><td>?</td><td>0.6</td><td>?</td><td>--</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>G356.3-00.3</td><td>17</td><td>37</td><td>56</td><td>-</td><td>32</td><td>16</td><td>11.0</td><td>x</td><td>7.0</td><td>--</td><td>S</td><td>--</td><td>3.0</td><td>?</td><td>--</td><td>?</td><td>--</td></tr>\n",
       "<tr><td>G356.3-01.5</td><td>17</td><td>42</td><td>35</td><td>-</td><td>32</td><td>52</td><td>20.0</td><td>x</td><td>15.0</td><td>--</td><td>S</td><td>--</td><td>3.0</td><td>?</td><td>--</td><td>?</td><td>--</td></tr>\n",
       "<tr><td>G357.7-00.1</td><td>17</td><td>40</td><td>29</td><td>-</td><td>30</td><td>58</td><td>8.0</td><td>x</td><td>3.0</td><td>?</td><td>?</td><td>--</td><td>37.0</td><td>--</td><td>0.4</td><td>--</td><td>MSH 17-39</td></tr>\n",
       "<tr><td>G357.7+00.3</td><td>17</td><td>38</td><td>35</td><td>-</td><td>30</td><td>44</td><td>24.0</td><td>--</td><td>--</td><td>--</td><td>S</td><td>--</td><td>10.0</td><td>--</td><td>0.4</td><td>?</td><td>--</td></tr>\n",
       "<tr><td>G358.0+03.8</td><td>17</td><td>26</td><td>0</td><td>-</td><td>28</td><td>36</td><td>38.0</td><td>--</td><td>--</td><td>--</td><td>S</td><td>--</td><td>1.5</td><td>?</td><td>--</td><td>?</td><td>--</td></tr>\n",
       "<tr><td>G358.1+00.1</td><td>17</td><td>37</td><td>0</td><td>-</td><td>29</td><td>59</td><td>20.0</td><td>--</td><td>--</td><td>--</td><td>S</td><td>--</td><td>2.0</td><td>?</td><td>--</td><td>?</td><td>--</td></tr>\n",
       "<tr><td>G358.5-00.9</td><td>17</td><td>46</td><td>10</td><td>-</td><td>30</td><td>40</td><td>17.0</td><td>--</td><td>--</td><td>--</td><td>S</td><td>--</td><td>4.0</td><td>?</td><td>--</td><td>?</td><td>--</td></tr>\n",
       "<tr><td>G359.0-00.9</td><td>17</td><td>46</td><td>50</td><td>-</td><td>30</td><td>16</td><td>23.0</td><td>--</td><td>--</td><td>--</td><td>S</td><td>--</td><td>23.0</td><td>--</td><td>0.5</td><td>--</td><td>--</td></tr>\n",
       "<tr><td>G359.1-00.5</td><td>17</td><td>45</td><td>30</td><td>-</td><td>29</td><td>57</td><td>24.0</td><td>--</td><td>--</td><td>--</td><td>S</td><td>--</td><td>14.0</td><td>--</td><td>0.4</td><td>?</td><td>--</td></tr>\n",
       "<tr><td>G359.1+00.9</td><td>17</td><td>39</td><td>36</td><td>-</td><td>29</td><td>11</td><td>12.0</td><td>x</td><td>11.0</td><td>--</td><td>S</td><td>--</td><td>2.0</td><td>?</td><td>--</td><td>?</td><td>--</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=274>\n",
       "    SNR      RAh   RAm   RAs  DE-  ... u_S(1GHz) Sp-Index u_Sp-Index   Names   \n",
       "              h    min    s        ...                                         \n",
       "   str11    int64 int64 int64 str1 ...    str1   float64     str1      str26   \n",
       "----------- ----- ----- ----- ---- ... --------- -------- ---------- ----------\n",
       "G000.0+00.0    17    45    44    - ...         ?      0.8          ? Sgr A East\n",
       "G000.3+00.0    17    46    15    - ...        --      0.6         --         --\n",
       "G000.9+00.1    17    47    21    - ...         ?       --          v         --\n",
       "G001.0-00.1    17    48    30    - ...        --      0.6          ?         --\n",
       "G001.4-00.1    17    49    39    - ...         ?       --          ?         --\n",
       "G001.9+00.3    17    48    45    - ...        --      0.6         --         --\n",
       "G003.7-00.2    17    55    26    - ...        --     0.65         --         --\n",
       "G003.8+00.3    17    52    55    - ...         ?      0.6         --         --\n",
       "G004.2-03.5    18     8    55    - ...         ?      0.6          ?         --\n",
       "        ...   ...   ...   ...  ... ...       ...      ...        ...        ...\n",
       "G356.3-00.3    17    37    56    - ...         ?       --          ?         --\n",
       "G356.3-01.5    17    42    35    - ...         ?       --          ?         --\n",
       "G357.7-00.1    17    40    29    - ...        --      0.4         --  MSH 17-39\n",
       "G357.7+00.3    17    38    35    - ...        --      0.4          ?         --\n",
       "G358.0+03.8    17    26     0    - ...         ?       --          ?         --\n",
       "G358.1+00.1    17    37     0    - ...         ?       --          ?         --\n",
       "G358.5-00.9    17    46    10    - ...         ?       --          ?         --\n",
       "G359.0-00.9    17    46    50    - ...        --      0.5         --         --\n",
       "G359.1-00.5    17    45    30    - ...        --      0.4          ?         --\n",
       "G359.1+00.9    17    39    36    - ...         ?       --          ?         --"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t.show_in_browser(jsviewer=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t[0:10].write('tab_cds1.tex', format='latex', overwrite=True, formats={'Sp-Index': '%0.2f'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}\n",
      "\\begin{tabular}{cccccccccccccccccc}\n",
      "SNR & RAh & RAm & RAs & DE- & DEd & DEm & MajDiam & --- & MinDiam & u_MinDiam & type & l_S(1GHz) & S(1GHz) & u_S(1GHz) & Sp-Index & u_Sp-Index & Names \\\\\n",
      " & $\\mathrm{h}$ & $\\mathrm{min}$ & $\\mathrm{s}$ &  & $\\mathrm{deg}$ & $\\mathrm{arcmin}$ & $\\mathrm{arcmin}$ &  & $\\mathrm{arcmin}$ &  &  &  & $\\mathrm{Jy}$ &  &  &  &  \\\\\n",
      "G000.0+00.0 & 17 & 45 & 44 & - & 29 & 0 & 3.5 & x & 2.5 &  & S &  & 100.0 & ? & 0.80 & ? & Sgr A East \\\\\n",
      "G000.3+00.0 & 17 & 46 & 15 & - & 28 & 38 & 15.0 & x & 8.0 &  & S &  & 22.0 &  & 0.60 &  &  \\\\\n",
      "G000.9+00.1 & 17 & 47 & 21 & - & 28 & 9 & 8.0 &  &  &  & C &  & 18.0 & ? &  & v &  \\\\\n",
      "G001.0-00.1 & 17 & 48 & 30 & - & 28 & 9 & 8.0 &  &  &  & S &  & 15.0 &  & 0.60 & ? &  \\\\\n",
      "G001.4-00.1 & 17 & 49 & 39 & - & 27 & 46 & 10.0 &  &  &  & S &  & 2.0 & ? &  & ? &  \\\\\n",
      "G001.9+00.3 & 17 & 48 & 45 & - & 27 & 10 & 1.5 &  &  &  & S &  & 0.6 &  & 0.60 &  &  \\\\\n",
      "G003.7-00.2 & 17 & 55 & 26 & - & 25 & 50 & 14.0 & x & 11.0 &  & S &  & 2.3 &  & 0.65 &  &  \\\\\n",
      "G003.8+00.3 & 17 & 52 & 55 & - & 25 & 28 & 18.0 &  &  &  & S? &  & 3.0 & ? & 0.60 &  &  \\\\\n",
      "G004.2-03.5 & 18 & 8 & 55 & - & 27 & 3 & 28.0 &  &  &  & S &  & 3.2 & ? & 0.60 & ? &  \\\\\n",
      "G004.5+06.8 & 17 & 30 & 42 & - & 21 & 29 & 3.0 &  &  &  & S &  & 19.0 &  & 0.64 &  & Kepler, SN1604, 3C358 \\\\\n",
      "\\end{tabular}\n",
      "\\end{table}\n"
     ]
    }
   ],
   "source": [
    "!cat tab_cds1.tex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t[10:20].write('tab_cds1.ascii', format='ascii', delimiter=';', formats={'Sp-Index': '%0.2f'}, overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNR;RAh;RAm;RAs;DE-;DEd;DEm;MajDiam;---;MinDiam;u_MinDiam;type;l_S(1GHz);S(1GHz);u_S(1GHz);Sp-Index;u_Sp-Index;Names\n",
      "G004.8+06.2;17;33;25;-;21;34;18.0;;;;S;;3.0;;0.60;;\n",
      "G005.2-02.6;18;7;30;-;25;45;18.0;;;;S;;2.6;?;0.60;?;\n",
      "G005.4-01.2;18;2;10;-;24;54;35.0;;;;C?;;35.0;?;0.20;?;Milne 56\n",
      "G005.5+00.3;17;57;4;-;24;0;15.0;x;12.0;;S;;5.5;;0.70;;\n",
      "G005.9+03.1;17;47;20;-;22;16;20.0;;;;S;;3.3;?;0.40;?;\n",
      "G006.1+00.5;17;57;29;-;23;25;18.0;x;12.0;;S;;4.5;;0.90;;\n",
      "G006.1+01.2;17;54;55;-;23;5;30.0;x;26.0;;F;;4.0;?;0.30;?;\n",
      "G006.4-00.1;18;0;30;-;23;26;48.0;;;;C;;310.0;;;v;W28\n",
      "G006.4+04.0;17;45;10;-;21;22;31.0;;;;S;;1.3;?;0.40;?;\n",
      "G006.5-00.4;18;2;11;-;23;34;18.0;;;;S;;27.0;;0.60;;\n"
     ]
    }
   ],
   "source": [
    "!cat tab_cds1.ascii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "t[10:20].write('tab_cds2.ascii', format='ascii.fixed_width', delimiter='', formats={'Sp-Index': '%0.2f'}, overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         SNR  RAh  RAm  RAs  DE-  DEd  DEm  MajDiam  ---  MinDiam  u_MinDiam  type  l_S(1GHz)  S(1GHz)  u_S(1GHz)  Sp-Index  u_Sp-Index     Names \n",
      " G004.8+06.2   17   33   25    -   21   34     18.0                              S                 3.0                 0.60                       \n",
      " G005.2-02.6   18    7   30    -   25   45     18.0                              S                 2.6          ?      0.60           ?           \n",
      " G005.4-01.2   18    2   10    -   24   54     35.0                             C?                35.0          ?      0.20           ?  Milne 56 \n",
      " G005.5+00.3   17   57    4    -   24    0     15.0    x     12.0                S                 5.5                 0.70                       \n",
      " G005.9+03.1   17   47   20    -   22   16     20.0                              S                 3.3          ?      0.40           ?           \n",
      " G006.1+00.5   17   57   29    -   23   25     18.0    x     12.0                S                 4.5                 0.90                       \n",
      " G006.1+01.2   17   54   55    -   23    5     30.0    x     26.0                F                 4.0          ?      0.30           ?           \n",
      " G006.4-00.1   18    0   30    -   23   26     48.0                              C               310.0                                v       W28 \n",
      " G006.4+04.0   17   45   10    -   21   22     31.0                              S                 1.3          ?      0.40           ?           \n",
      " G006.5-00.4   18    2   11    -   23   34     18.0                              S                27.0                 0.60                       \n"
     ]
    }
   ],
   "source": [
    "!cat tab_cds2.ascii"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The astropy Table can also read FITS files (if containing tables), VO tables and hdf5 format. See more there: http://docs.astropy.org/en/stable/io/unified.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time and Dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The astropy.time package provides functionality for manipulating times and dates. Specific emphasis is placed on supporting time scales (e.g. UTC, TAI, UT1, TDB) and time representations (e.g. JD, MJD, ISO 8601) that are used in astronomy and required to calculate, e.g., sidereal times and barycentric corrections. It uses Cython to wrap the C language ERFA time and calendar routines, using a fast and memory efficient vectorization scheme. More here: http://docs.astropy.org/en/stable/time/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coordinates package provides classes for representing a variety of celestial/spatial coordinates, as well as tools for converting between common coordinate systems in a uniform way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (ICRS): (ra, dec) in deg\n",
       "    (10.5, 41.2)>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = SkyCoord(ra=10.5*u.degree, dec=41.2*u.degree, frame='icrs')\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SkyCoord (ICRS): (ra, dec) in deg\n",
       "    (10.5, 41.2)>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = SkyCoord('0 42 00 +41 12 00', frame='icrs', unit=(u.hourangle, u.deg))\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10d30m00s 41d12m00s\n"
     ]
    }
   ],
   "source": [
    "print(c.ra, c.dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'10.5 41.2'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.to_string('decimal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$41^\\circ12{}^\\prime00{}^{\\prime\\prime}$\n"
     ]
    }
   ],
   "source": [
    "print(c.dec.to_string(format='latex'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$41^\\circ12{}^\\prime00{}^{\\prime\\prime}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "astropy.modeling provides a framework for representing models and performing model evaluation and fitting. It currently supports 1-D and 2-D models and fitting with parameter constraints.\n",
    "\n",
    "It is designed to be easily extensible and flexible. Models do not reference fitting algorithms explicitly and new fitting algorithms may be added without changing the existing models (though not all models can be used with all fitting algorithms due to constraints such as model linearity).\n",
    "\n",
    "The goal is to eventually provide a rich toolset of models and fitters such that most users will not need to define new model classes, nor special purpose fitting routines (while making it reasonably easy to do when necessary).\n",
    "\n",
    "http://docs.astropy.org/en/stable/modeling/index.html\n",
    "\n",
    "More examples: https://learn.astropy.org/rst-tutorials/Models-Quick-Fit.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolution and filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "astropy.convolution provides convolution functions and kernels that offers improvements compared to the scipy scipy.ndimage convolution routines, including:\n",
    "\n",
    "* Proper treatment of NaN values\n",
    "* A single function for 1-D, 2-D, and 3-D convolution\n",
    "* Improved options for the treatment of edges\n",
    "* Both direct and Fast Fourier Transform (FFT) versions\n",
    "* Built-in kernels that are commonly used in Astronomy\n",
    "\n",
    "More on http://docs.astropy.org/en/stable/convolution/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CCD reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ccdproc is is an Astropy affiliated package for basic data reductions of CCD images. It provides the essential tools for processing of CCD images in a framework that provides error propagation and bad pixel tracking throughout the reduction process.\n",
    "\n",
    "https://ccdproc.readthedocs.io/en/latest/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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