2-AstroPython.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 1) AstroPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1-ScientificPython.ipynb              chivo_search.ipynb\r\n",
      "2-AstroPython.ipynb                   emhbeta_flux.fits\r\n",
      "3-ipyaladin.ipynb                     emnii6584_flux.fits\r\n",
      "ALMA-OrionKL-band6-cont.fits          emsii6731_flux.fits\r\n",
      "ALMA-OrionKL-band6-methanol.fits      \u001b[34mprep\u001b[m\u001b[m\r\n",
      "ALMA-OrionKL-band6-methanon-mom0.fits view.fits\r\n",
      "Test.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<astropy.io.fits.hdu.image.PrimaryHDU object at 0x107f68cf8>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdulist = fits.open(\"ALMA-OrionKL-band6-cont.fits\")\n",
    "hdulist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on PrimaryHDU in module astropy.io.fits.hdu.image object:\n",
      "\n",
      "class PrimaryHDU(_ImageBaseHDU)\n",
      " |  FITS primary HDU class.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      PrimaryHDU\n",
      " |      _ImageBaseHDU\n",
      " |      astropy.io.fits.hdu.base._ValidHDU\n",
      " |      astropy.io.fits.hdu.base._BaseHDU\n",
      " |      astropy.io.fits.verify._Verify\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, data=None, header=None, do_not_scale_image_data=False, ignore_blank=False, uint=True, scale_back=None)\n",
      " |      Construct a primary HDU.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      data : array or DELAYED, optional\n",
      " |          The data in the HDU.\n",
      " |      \n",
      " |      header : Header instance, optional\n",
      " |          The header to be used (as a template).  If ``header`` is `None`, a\n",
      " |          minimal header will be provided.\n",
      " |      \n",
      " |      do_not_scale_image_data : bool, optional\n",
      " |          If `True`, image data is not scaled using BSCALE/BZERO values\n",
      " |          when read. (default: False)\n",
      " |      \n",
      " |      ignore_blank : bool, optional\n",
      " |          If `True`, the BLANK header keyword will be ignored if present.\n",
      " |          Otherwise, pixels equal to this value will be replaced with\n",
      " |          NaNs. (default: False)\n",
      " |      \n",
      " |      uint : bool, optional\n",
      " |          Interpret signed integer data where ``BZERO`` is the\n",
      " |          central value and ``BSCALE == 1`` as unsigned integer\n",
      " |          data.  For example, ``int16`` data with ``BZERO = 32768``\n",
      " |          and ``BSCALE = 1`` would be treated as ``uint16`` data.\n",
      " |          (default: True)\n",
      " |      \n",
      " |      scale_back : bool, optional\n",
      " |          If `True`, when saving changes to a file that contained scaled\n",
      " |          image data, restore the data to the original type and reapply the\n",
      " |          original BSCALE/BZERO values.  This could lead to loss of accuracy\n",
      " |          if scaling back to integer values after performing floating point\n",
      " |          operations on the data.  Pseudo-unsigned integers are automatically\n",
      " |          rescaled unless scale_back is explicitly set to `False`.\n",
      " |          (default: None)\n",
      " |  \n",
      " |  update_header(self)\n",
      " |      Update the header keywords to agree with the data.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods defined here:\n",
      " |  \n",
      " |  match_header(header) from astropy.io.fits.hdu.base._BaseHDUMeta\n",
      " |      _ImageBaseHDU is sort of an abstract class for HDUs containing image\n",
      " |      data (as opposed to table data) and should never be used directly.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from _ImageBaseHDU:\n",
      " |  \n",
      " |  scale(self, type=None, option='old', bscale=None, bzero=None)\n",
      " |      Scale image data by using ``BSCALE``/``BZERO``.\n",
      " |      \n",
      " |      Call to this method will scale `data` and update the keywords of\n",
      " |      ``BSCALE`` and ``BZERO`` in the HDU's header.  This method should only\n",
      " |      be used right before writing to the output file, as the data will be\n",
      " |      scaled and is therefore not very usable after the call.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      type : str, optional\n",
      " |          destination data type, use a string representing a numpy\n",
      " |          dtype name, (e.g. ``'uint8'``, ``'int16'``, ``'float32'``\n",
      " |          etc.).  If is `None`, use the current data type.\n",
      " |      \n",
      " |      option : str, optional\n",
      " |          How to scale the data: ``\"old\"`` uses the original ``BSCALE`` and\n",
      " |          ``BZERO`` values from when the data was read/created (defaulting to\n",
      " |          1 and 0 if they don't exist). For integer data only, ``\"minmax\"``\n",
      " |          uses the minimum and maximum of the data to scale. User-specified\n",
      " |          ``bscale``/``bzero`` values always take precedence.\n",
      " |      \n",
      " |      bscale, bzero : int, optional\n",
      " |          User-specified ``BSCALE`` and ``BZERO`` values\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from _ImageBaseHDU:\n",
      " |  \n",
      " |  data\n",
      " |      Image/array data as a `~numpy.ndarray`.\n",
      " |      \n",
      " |      Please remember that the order of axes on an Numpy array are opposite\n",
      " |      of the order specified in the FITS file.  For example for a 2D image\n",
      " |      the \"rows\" or y-axis are the first dimension, and the \"columns\" or\n",
      " |      x-axis are the second dimension.\n",
      " |      \n",
      " |      If the data is scaled using the BZERO and BSCALE parameters, this\n",
      " |      attribute returns the data scaled to its physical values unless the\n",
      " |      file was opened with ``do_not_scale_image_data=True``.\n",
      " |  \n",
      " |  header\n",
      " |  \n",
      " |  is_image\n",
      " |  \n",
      " |  section\n",
      " |      Access a section of the image array without loading the entire array\n",
      " |      into memory.  The :class:`Section` object returned by this attribute is\n",
      " |      not meant to be used directly by itself.  Rather, slices of the section\n",
      " |      return the appropriate slice of the data, and loads *only* that section\n",
      " |      into memory.\n",
      " |      \n",
      " |      Sections are mostly obsoleted by memmap support, but should still be\n",
      " |      used to deal with very large scaled images.  See the\n",
      " |      :ref:`data-sections` section of the PyFITS documentation for more\n",
      " |      details.\n",
      " |  \n",
      " |  shape\n",
      " |      Shape of the image array--should be equivalent to ``self.data.shape``.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from _ImageBaseHDU:\n",
      " |  \n",
      " |  standard_keyword_comments = {'BITPIX': 'array data type', 'GCOUNT': 'n...\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from astropy.io.fits.hdu.base._ValidHDU:\n",
      " |  \n",
      " |  add_checksum(self, when=None, override_datasum=False, blocking='standard', checksum_keyword='CHECKSUM', datasum_keyword='DATASUM')\n",
      " |      Add the ``CHECKSUM`` and ``DATASUM`` cards to this HDU with\n",
      " |      the values set to the checksum calculated for the HDU and the\n",
      " |      data respectively.  The addition of the ``DATASUM`` card may\n",
      " |      be overridden.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      when : str, optional\n",
      " |         comment string for the cards; by default the comments\n",
      " |         will represent the time when the checksum was calculated\n",
      " |      \n",
      " |      override_datasum : bool, optional\n",
      " |         add the ``CHECKSUM`` card only\n",
      " |      \n",
      " |      blocking : str, optional\n",
      " |          \"standard\" or \"nonstandard\", compute sum 2880 bytes at a time, or\n",
      " |          not\n",
      " |      \n",
      " |      checksum_keyword : str, optional\n",
      " |          The name of the header keyword to store the checksum value in; this\n",
      " |          is typically 'CHECKSUM' per convention, but there exist use cases\n",
      " |          in which a different keyword should be used\n",
      " |      \n",
      " |      datasum_keyword : str, optional\n",
      " |          See ``checksum_keyword``\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      For testing purposes, first call `add_datasum` with a ``when``\n",
      " |      argument, then call `add_checksum` with a ``when`` argument and\n",
      " |      ``override_datasum`` set to `True`.  This will provide consistent\n",
      " |      comments for both cards and enable the generation of a ``CHECKSUM``\n",
      " |      card with a consistent value.\n",
      " |  \n",
      " |  add_datasum(self, when=None, blocking='standard', datasum_keyword='DATASUM')\n",
      " |      Add the ``DATASUM`` card to this HDU with the value set to the\n",
      " |      checksum calculated for the data.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      when : str, optional\n",
      " |          Comment string for the card that by default represents the\n",
      " |          time when the checksum was calculated\n",
      " |      \n",
      " |      blocking : str, optional\n",
      " |          \"standard\" or \"nonstandard\", compute sum 2880 bytes at a time, or\n",
      " |          not\n",
      " |      \n",
      " |      datasum_keyword : str, optional\n",
      " |          The name of the header keyword to store the datasum value in;\n",
      " |          this is typically 'DATASUM' per convention, but there exist\n",
      " |          use cases in which a different keyword should be used\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      checksum : int\n",
      " |          The calculated datasum\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      For testing purposes, provide a ``when`` argument to enable the comment\n",
      " |      value in the card to remain consistent.  This will enable the\n",
      " |      generation of a ``CHECKSUM`` card with a consistent value.\n",
      " |  \n",
      " |  copy(self)\n",
      " |      Make a copy of the HDU, both header and data are copied.\n",
      " |  \n",
      " |  filebytes(self)\n",
      " |      Calculates and returns the number of bytes that this HDU will write to\n",
      " |      a file.\n",
      " |  \n",
      " |  fileinfo(self)\n",
      " |      Returns a dictionary detailing information about the locations\n",
      " |      of this HDU within any associated file.  The values are only\n",
      " |      valid after a read or write of the associated file with no\n",
      " |      intervening changes to the `HDUList`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      dict or None\n",
      " |      \n",
      " |         The dictionary details information about the locations of\n",
      " |         this HDU within an associated file.  Returns `None` when\n",
      " |         the HDU is not associated with a file.\n",
      " |      \n",
      " |         Dictionary contents:\n",
      " |      \n",
      " |         ========== ================================================\n",
      " |         Key        Value\n",
      " |         ========== ================================================\n",
      " |         file       File object associated with the HDU\n",
      " |         filemode   Mode in which the file was opened (readonly, copyonwrite,\n",
      " |                    update, append, ostream)\n",
      " |         hdrLoc     Starting byte location of header in file\n",
      " |         datLoc     Starting byte location of data block in file\n",
      " |         datSpan    Data size including padding\n",
      " |         ========== ================================================\n",
      " |  \n",
      " |  req_cards(self, keyword, pos, test, fix_value, option, errlist)\n",
      " |      Check the existence, location, and value of a required `Card`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      keyword : str\n",
      " |          The keyword to validate\n",
      " |      \n",
      " |      pos : int, callable\n",
      " |          If an ``int``, this specifies the exact location this card should\n",
      " |          have in the header.  Remember that Python is zero-indexed, so this\n",
      " |          means ``pos=0`` requires the card to be the first card in the\n",
      " |          header.  If given a callable, it should take one argument--the\n",
      " |          actual position of the keyword--and return `True` or `False`.  This\n",
      " |          can be used for custom evaluation.  For example if\n",
      " |          ``pos=lambda idx: idx > 10`` this will check that the keyword's\n",
      " |          index is greater than 10.\n",
      " |      \n",
      " |      test : callable\n",
      " |          This should be a callable (generally a function) that is passed the\n",
      " |          value of the given keyword and returns `True` or `False`.  This can\n",
      " |          be used to validate the value associated with the given keyword.\n",
      " |      \n",
      " |      fix_value : str, int, float, complex, bool, None\n",
      " |          A valid value for a FITS keyword to to use if the given ``test``\n",
      " |          fails to replace an invalid value.  In other words, this provides\n",
      " |          a default value to use as a replacement if the keyword's current\n",
      " |          value is invalid.  If `None`, there is no replacement value and the\n",
      " |          keyword is unfixable.\n",
      " |      \n",
      " |      option : str\n",
      " |          Output verification option.  Must be one of ``\"fix\"``,\n",
      " |          ``\"silentfix\"``, ``\"ignore\"``, ``\"warn\"``, or\n",
      " |          ``\"exception\"``.  May also be any combination of ``\"fix\"`` or\n",
      " |          ``\"silentfix\"`` with ``\"+ignore\"``, ``+warn``, or ``+exception\"\n",
      " |          (e.g. ``\"fix+warn\"``).  See :ref:`verify` for more info.\n",
      " |      \n",
      " |      errlist : list\n",
      " |          A list of validation errors already found in the FITS file; this is\n",
      " |          used primarily for the validation system to collect errors across\n",
      " |          multiple HDUs and multiple calls to `req_cards`.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      If ``pos=None``, the card can be anywhere in the header.  If the card\n",
      " |      does not exist, the new card will have the ``fix_value`` as its value\n",
      " |      when created.  Also check the card's value by using the ``test``\n",
      " |      argument.\n",
      " |  \n",
      " |  verify_checksum(self, blocking='standard')\n",
      " |      Verify that the value in the ``CHECKSUM`` keyword matches the\n",
      " |      value calculated for the current HDU CHECKSUM.\n",
      " |      \n",
      " |      blocking : str, optional\n",
      " |          \"standard\" or \"nonstandard\", compute sum 2880 bytes at a time, or\n",
      " |          not\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      valid : int\n",
      " |         - 0 - failure\n",
      " |         - 1 - success\n",
      " |         - 2 - no ``CHECKSUM`` keyword present\n",
      " |  \n",
      " |  verify_datasum(self, blocking='standard')\n",
      " |      Verify that the value in the ``DATASUM`` keyword matches the value\n",
      " |      calculated for the ``DATASUM`` of the current HDU data.\n",
      " |      \n",
      " |      blocking : str, optional\n",
      " |          \"standard\" or \"nonstandard\", compute sum 2880 bytes at a time, or\n",
      " |          not\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      valid : int\n",
      " |         - 0 - failure\n",
      " |         - 1 - success\n",
      " |         - 2 - no ``DATASUM`` keyword present\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from astropy.io.fits.hdu.base._ValidHDU:\n",
      " |  \n",
      " |  size\n",
      " |      Size (in bytes) of the data portion of the HDU.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from astropy.io.fits.hdu.base._BaseHDU:\n",
      " |  \n",
      " |  writeto(self, name, output_verify='exception', overwrite=False, checksum=False)\n",
      " |      Write the HDU to a new file. This is a convenience method to\n",
      " |      provide a user easier output interface if only one HDU needs\n",
      " |      to be written to a file.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      name : file path, file object or file-like object\n",
      " |          Output FITS file.  If the file object is already opened, it must\n",
      " |          be opened in a writeable mode.\n",
      " |      \n",
      " |      output_verify : str\n",
      " |          Output verification option.  Must be one of ``\"fix\"``,\n",
      " |          ``\"silentfix\"``, ``\"ignore\"``, ``\"warn\"``, or\n",
      " |          ``\"exception\"``.  May also be any combination of ``\"fix\"`` or\n",
      " |          ``\"silentfix\"`` with ``\"+ignore\"``, ``+warn``, or ``+exception\"\n",
      " |          (e.g. ``\"fix+warn\"``).  See :ref:`verify` for more info.\n",
      " |      \n",
      " |      overwrite : bool, optional\n",
      " |          If ``True``, overwrite the output file if it exists. Raises an\n",
      " |          ``OSError`` (``IOError`` for Python 2) if ``False`` and the\n",
      " |          output file exists. Default is ``False``.\n",
      " |      \n",
      " |          .. versionchanged:: 1.3\n",
      " |             ``overwrite`` replaces the deprecated ``clobber`` argument.\n",
      " |      \n",
      " |      checksum : bool\n",
      " |          When `True` adds both ``DATASUM`` and ``CHECKSUM`` cards\n",
      " |          to the header of the HDU when written to the file.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods inherited from astropy.io.fits.hdu.base._BaseHDU:\n",
      " |  \n",
      " |  fromstring(data, checksum=False, ignore_missing_end=False, **kwargs) from astropy.io.fits.hdu.base._BaseHDUMeta\n",
      " |      Creates a new HDU object of the appropriate type from a string\n",
      " |      containing the HDU's entire header and, optionally, its data.\n",
      " |      \n",
      " |      Note: When creating a new HDU from a string without a backing file\n",
      " |      object, the data of that HDU may be read-only.  It depends on whether\n",
      " |      the underlying string was an immutable Python str/bytes object, or some\n",
      " |      kind of read-write memory buffer such as a `memoryview`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      data : str, bytearray, memoryview, ndarray\n",
      " |         A byte string containing the HDU's header and data.\n",
      " |      \n",
      " |      checksum : bool, optional\n",
      " |         Check the HDU's checksum and/or datasum.\n",
      " |      \n",
      " |      ignore_missing_end : bool, optional\n",
      " |         Ignore a missing end card in the header data.  Note that without the\n",
      " |         end card the end of the header may be ambiguous and resulted in a\n",
      " |         corrupt HDU.  In this case the assumption is that the first 2880\n",
      " |         block that does not begin with valid FITS header data is the\n",
      " |         beginning of the data.\n",
      " |      \n",
      " |      kwargs : optional\n",
      " |         May consist of additional keyword arguments specific to an HDU\n",
      " |         type--these correspond to keywords recognized by the constructors of\n",
      " |         different HDU classes such as `PrimaryHDU`, `ImageHDU`, or\n",
      " |         `BinTableHDU`.  Any unrecognized keyword arguments are simply\n",
      " |         ignored.\n",
      " |  \n",
      " |  readfrom(fileobj, checksum=False, ignore_missing_end=False, **kwargs) from astropy.io.fits.hdu.base._BaseHDUMeta\n",
      " |      Read the HDU from a file.  Normally an HDU should be opened with\n",
      " |      :func:`open` which reads the entire HDU list in a FITS file.  But this\n",
      " |      method is still provided for symmetry with :func:`writeto`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      fileobj : file object or file-like object\n",
      " |          Input FITS file.  The file's seek pointer is assumed to be at the\n",
      " |          beginning of the HDU.\n",
      " |      \n",
      " |      checksum : bool\n",
      " |          If `True`, verifies that both ``DATASUM`` and ``CHECKSUM`` card\n",
      " |          values (when present in the HDU header) match the header and data\n",
      " |          of all HDU's in the file.\n",
      " |      \n",
      " |      ignore_missing_end : bool\n",
      " |          Do not issue an exception when opening a file that is missing an\n",
      " |          ``END`` card in the last header.\n",
      " |  \n",
      " |  register_hdu(hducls) from astropy.io.fits.hdu.base._BaseHDUMeta\n",
      " |  \n",
      " |  unregister_hdu(hducls) from astropy.io.fits.hdu.base._BaseHDUMeta\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods inherited from astropy.io.fits.hdu.base._BaseHDU:\n",
      " |  \n",
      " |  __new__(cls, data=None, header=None, *args, **kwargs)\n",
      " |      Iterates through the subclasses of _BaseHDU and uses that class's\n",
      " |      match_header() method to determine which subclass to instantiate.\n",
      " |      \n",
      " |      It's important to be aware that the class hierarchy is traversed in a\n",
      " |      depth-last order.  Each match_header() should identify an HDU type as\n",
      " |      uniquely as possible.  Abstract types may choose to simply return False\n",
      " |      or raise NotImplementedError to be skipped.\n",
      " |      \n",
      " |      If any unexpected exceptions are raised while evaluating\n",
      " |      match_header(), the type is taken to be _CorruptedHDU.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from astropy.io.fits.hdu.base._BaseHDU:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  level\n",
      " |  \n",
      " |  name\n",
      " |  \n",
      " |  ver\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from astropy.io.fits.verify._Verify:\n",
      " |  \n",
      " |  run_option(self, option='warn', err_text='', fix_text='Fixed.', fix=None, fixable=True)\n",
      " |      Execute the verification with selected option.\n",
      " |  \n",
      " |  verify(self, option='warn')\n",
      " |      Verify all values in the instance.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      option : str\n",
      " |          Output verification option.  Must be one of ``\"fix\"``,\n",
      " |          ``\"silentfix\"``, ``\"ignore\"``, ``\"warn\"``, or\n",
      " |          ``\"exception\"``.  May also be any combination of ``\"fix\"`` or\n",
      " |          ``\"silentfix\"`` with ``\"+ignore\"``, ``+warn``, or ``+exception\"\n",
      " |          (e.g. ``\"fix+warn\"``).  See :ref:`verify` for more info.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "hdu = hdulist[0]\n",
    "help(hdu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.03462455,  0.03394877,  0.0348247 , ...,  0.00512782,\n",
       "         0.01290684,  0.02093568],\n",
       "       [ 0.04125445,  0.03483354,  0.03083117, ...,  0.00361592,\n",
       "         0.01317338,  0.02610629],\n",
       "       [ 0.04762579,  0.0362675 ,  0.02696897, ..., -0.00290151,\n",
       "         0.01028361,  0.02754407],\n",
       "       ..., \n",
       "       [-0.00706197, -0.00413066, -0.00275087, ..., -0.02907074,\n",
       "        -0.00520752,  0.01757605],\n",
       "       [-0.00493182, -0.00635676, -0.00597036, ..., -0.02548184,\n",
       "        -0.00696347,  0.01069312],\n",
       "       [ 0.00124471, -0.00786869, -0.01027288, ..., -0.01701476,\n",
       "        -0.00597558,  0.00344427]], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdu.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / conforms to FITS standard                      \n",
       "BITPIX  =                  -32 / array data type                                \n",
       "NAXIS   =                    2 / number of array dimensions                     \n",
       "NAXIS1  =                  100                                                  \n",
       "NAXIS2  =                  100                                                  \n",
       "WCSAXES =                    2 / Number of coordinate axes                      \n",
       "CRPIX1  =                 51.0 / Pixel coordinate of reference point            \n",
       "CRPIX2  =                 51.0 / Pixel coordinate of reference point            \n",
       "CDELT1  =  -0.0001111111111111 / [deg] Coordinate increment at reference point  \n",
       "CDELT2  =   0.0001111111111111 / [deg] Coordinate increment at reference point  \n",
       "CUNIT1  = 'deg'                / Units of coordinate increment and value        \n",
       "CUNIT2  = 'deg'                / Units of coordinate increment and value        \n",
       "CTYPE1  = 'RA---SIN'           / Right ascension, orthographic/synthesis project\n",
       "CTYPE2  = 'DEC--SIN'           / Declination, orthographic/synthesis projection \n",
       "CRVAL1  =       83.80979166667 / [deg] Coordinate value at reference point      \n",
       "CRVAL2  =      -5.376388888889 / [deg] Coordinate value at reference point      \n",
       "PV2_1   =                  0.0 / SIN projection parameter                       \n",
       "PV2_2   =                  0.0 / SIN projection parameter                       \n",
       "LONPOLE =                180.0 / [deg] Native longitude of celestial pole       \n",
       "LATPOLE =      -5.376388888889 / [deg] Native latitude of celestial pole        \n",
       "RESTFRQ =       230949718581.1 / [Hz] Line rest frequency                       \n",
       "RADESYS = 'FK5'                / Equatorial coordinate system                   \n",
       "EQUINOX =               2000.0 / [yr] Equinox of equatorial coordinates         \n",
       "SPECSYS = 'TOPOCENT'           / Reference frame of spectral coordinates        \n",
       "OBSGEO-X=       2225142.180269 / [m] ITRF observatory X-coordinate              \n",
       "OBSGEO-Y=      -5440307.370349 / [m] ITRF observatory Y-coordinate              \n",
       "OBSGEO-Z=      -2481029.851874 / [m] ITRF observatory Z-coordinate              \n",
       "MJD-OBS =      55946.063192222 / [d] MJD of observation matching DATE-OBS       \n",
       "DATE-OBS= '2012-01-20T01:30:59.808001' / ISO-8601 observation date matching MJD-"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdu.header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WCS Keywords\n",
       "\n",
       "Number of WCS axes: 2\n",
       "CTYPE : 'RA---SIN'  'DEC--SIN'  \n",
       "CRVAL : 83.809791666669994  -5.3763888888890001  \n",
       "CRPIX : 51.0  51.0  \n",
       "PC1_1 PC1_2  : 1.0  0.0  \n",
       "PC2_1 PC2_2  : 0.0  1.0  \n",
       "CDELT : -0.0001111111111111  0.0001111111111111  \n",
       "NAXIS : 100  100"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astropy.wcs import WCS\n",
    "wcs = WCS(hdu.header)\n",
    "wcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1081260b8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j2F4GJGjVZtJ5ng+ieigwbnZPZ8paW8rZDadTSY3xtEyuyHUaBetLoVJqDJUh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\nQMz6iYrvLP3H8QqPk+HoGZXyskEyGBp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x108dd3c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.subplot(projection=wcs)\n",
    "plt.imshow(hdu.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on WCS in module astropy.wcs.wcs object:\n",
      "\n",
      "class WCS(astropy.wcs.WCSBase)\n",
      " |  WCS objects perform standard WCS transformations, and correct for\n",
      " |  `SIP`_ and `distortion paper`_ table-lookup transformations, based\n",
      " |  on the WCS keywords and supplementary data read from a FITS file.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  header : astropy.io.fits header object, Primary HDU, Image HDU, string, dict-like, or None, optional\n",
      " |      If *header* is not provided or None, the object will be\n",
      " |      initialized to default values.\n",
      " |  \n",
      " |  fobj : An astropy.io.fits file (hdulist) object, optional\n",
      " |      It is needed when header keywords point to a `distortion\n",
      " |      paper`_ lookup table stored in a different extension.\n",
      " |  \n",
      " |  key : str, optional\n",
      " |      The name of a particular WCS transform to use.  This may be\n",
      " |      either ``' '`` or ``'A'``-``'Z'`` and corresponds to the\n",
      " |      ``\"a\"`` part of the ``CTYPEia`` cards.  *key* may only be\n",
      " |      provided if *header* is also provided.\n",
      " |  \n",
      " |  minerr : float, optional\n",
      " |      The minimum value a distortion correction must have in order\n",
      " |      to be applied. If the value of ``CQERRja`` is smaller than\n",
      " |      *minerr*, the corresponding distortion is not applied.\n",
      " |  \n",
      " |  relax : bool or int, optional\n",
      " |      Degree of permissiveness:\n",
      " |  \n",
      " |      - `True` (default): Admit all recognized informal extensions\n",
      " |        of the WCS standard.\n",
      " |  \n",
      " |      - `False`: Recognize only FITS keywords defined by the\n",
      " |        published WCS standard.\n",
      " |  \n",
      " |      - `int`: a bit field selecting specific extensions to accept.\n",
      " |        See :ref:`relaxread` for details.\n",
      " |  \n",
      " |  naxis : int or sequence, optional\n",
      " |      Extracts specific coordinate axes using\n",
      " |      :meth:`~astropy.wcs.Wcsprm.sub`.  If a header is provided, and\n",
      " |      *naxis* is not ``None``, *naxis* will be passed to\n",
      " |      :meth:`~astropy.wcs.Wcsprm.sub` in order to select specific\n",
      " |      axes from the header.  See :meth:`~astropy.wcs.Wcsprm.sub` for\n",
      " |      more details about this parameter.\n",
      " |  \n",
      " |  keysel : sequence of flags, optional\n",
      " |      A sequence of flags used to select the keyword types\n",
      " |      considered by wcslib.  When ``None``, only the standard image\n",
      " |      header keywords are considered (and the underlying wcspih() C\n",
      " |      function is called).  To use binary table image array or pixel\n",
      " |      list keywords, *keysel* must be set.\n",
      " |  \n",
      " |      Each element in the list should be one of the following\n",
      " |      strings:\n",
      " |  \n",
      " |      - 'image': Image header keywords\n",
      " |  \n",
      " |      - 'binary': Binary table image array keywords\n",
      " |  \n",
      " |      - 'pixel': Pixel list keywords\n",
      " |  \n",
      " |      Keywords such as ``EQUIna`` or ``RFRQna`` that are common to\n",
      " |      binary table image arrays and pixel lists (including\n",
      " |      ``WCSNna`` and ``TWCSna``) are selected by both 'binary' and\n",
      " |      'pixel'.\n",
      " |  \n",
      " |  colsel : sequence of int, optional\n",
      " |      A sequence of table column numbers used to restrict the WCS\n",
      " |      transformations considered to only those pertaining to the\n",
      " |      specified columns.  If `None`, there is no restriction.\n",
      " |  \n",
      " |  fix : bool, optional\n",
      " |      When `True` (default), call `~astropy.wcs.Wcsprm.fix` on\n",
      " |      the resulting object to fix any non-standard uses in the\n",
      " |      header.  `FITSFixedWarning` Warnings will be emitted if any\n",
      " |      changes were made.\n",
      " |  \n",
      " |  translate_units : str, optional\n",
      " |      Specify which potentially unsafe translations of non-standard\n",
      " |      unit strings to perform.  By default, performs none.  See\n",
      " |      `WCS.fix` for more information about this parameter.  Only\n",
      " |      effective when ``fix`` is `True`.\n",
      " |  \n",
      " |  Raises\n",
      " |  ------\n",
      " |  MemoryError\n",
      " |       Memory allocation failed.\n",
      " |  \n",
      " |  ValueError\n",
      " |       Invalid key.\n",
      " |  \n",
      " |  KeyError\n",
      " |       Key not found in FITS header.\n",
      " |  \n",
      " |  ValueError\n",
      " |       Lookup table distortion present in the header but *fobj* was\n",
      " |       not provided.\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  \n",
      " |  1. astropy.wcs supports arbitrary *n* dimensions for the core WCS\n",
      " |     (the transformations handled by WCSLIB).  However, the\n",
      " |     `distortion paper`_ lookup table and `SIP`_ distortions must be\n",
      " |     two dimensional.  Therefore, if you try to create a WCS object\n",
      " |     where the core WCS has a different number of dimensions than 2\n",
      " |     and that object also contains a `distortion paper`_ lookup\n",
      " |     table or `SIP`_ distortion, a `ValueError`\n",
      " |     exception will be raised.  To avoid this, consider using the\n",
      " |     *naxis* kwarg to select two dimensions from the core WCS.\n",
      " |  \n",
      " |  2. The number of coordinate axes in the transformation is not\n",
      " |     determined directly from the ``NAXIS`` keyword but instead from\n",
      " |     the highest of:\n",
      " |  \n",
      " |         - ``NAXIS`` keyword\n",
      " |  \n",
      " |         - ``WCSAXESa`` keyword\n",
      " |  \n",
      " |         - The highest axis number in any parameterized WCS keyword.\n",
      " |           The keyvalue, as well as the keyword, must be\n",
      " |           syntactically valid otherwise it will not be considered.\n",
      " |  \n",
      " |     If none of these keyword types is present, i.e. if the header\n",
      " |     only contains auxiliary WCS keywords for a particular\n",
      " |     coordinate representation, then no coordinate description is\n",
      " |     constructed for it.\n",
      " |  \n",
      " |     The number of axes, which is set as the ``naxis`` member, may\n",
      " |     differ for different coordinate representations of the same\n",
      " |     image.\n",
      " |  \n",
      " |  3. When the header includes duplicate keywords, in most cases the\n",
      " |     last encountered is used.\n",
      " |  \n",
      " |  4. `~astropy.wcs.Wcsprm.set` is called immediately after\n",
      " |     construction, so any invalid keywords or transformations will\n",
      " |     be raised by the constructor, not when subsequently calling a\n",
      " |     transformation method.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      WCS\n",
      " |      astropy.wcs.WCSBase\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __copy__(self)\n",
      " |  \n",
      " |  __deepcopy__(self, memo)\n",
      " |  \n",
      " |  __getitem__(self, item)\n",
      " |  \n",
      " |  __init__(self, header=None, fobj=None, key=' ', minerr=0.0, relax=True, naxis=None, keysel=None, colsel=None, fix=True, translate_units='', _do_set=True)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __iter__(self)\n",
      " |  \n",
      " |  __reduce__(self)\n",
      " |      Support pickling of WCS objects.  This is done by serializing\n",
      " |      to an in-memory FITS file and dumping that as a string.\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return a short description. Simply porting the behavior from\n",
      " |      the `printwcs()` method.\n",
      " |  \n",
      " |  all_pix2world(self, *args, **kwargs)\n",
      " |      Transforms pixel coordinates to world coordinates.\n",
      " |      \n",
      " |      Performs all of the following in series:\n",
      " |      \n",
      " |          - Detector to image plane correction (if present in the\n",
      " |            FITS file)\n",
      " |      \n",
      " |          - `SIP`_ distortion correction (if present in the FITS\n",
      " |            file)\n",
      " |      \n",
      " |          - `distortion paper`_ table-lookup correction (if present\n",
      " |            in the FITS file)\n",
      " |      \n",
      " |          - `wcslib`_ \"core\" WCS transformation\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      args : flexible\n",
      " |          There are two accepted forms for the positional arguments:\n",
      " |      \n",
      " |              - 2 arguments: An *N* x *naxis* array of coordinates, and an\n",
      " |                *origin*.\n",
      " |      \n",
      " |              - more than 2 arguments: An array for each axis, followed by\n",
      " |                an *origin*.  These arrays must be broadcastable to one\n",
      " |                another.\n",
      " |      \n",
      " |          Here, *origin* is the coordinate in the upper left corner of the\n",
      " |          image.  In FITS and Fortran standards, this is 1.  In Numpy and C\n",
      " |          standards this is 0.\n",
      " |      \n",
      " |      \n",
      " |          For a transformation that is not two-dimensional, the\n",
      " |          two-argument form must be used.\n",
      " |      \n",
      " |      \n",
      " |      ra_dec_order : bool, optional\n",
      " |          When `True` will ensure that world coordinates are always given\n",
      " |          and returned in as (*ra*, *dec*) pairs, regardless of the order of\n",
      " |          the axes specified by the in the ``CTYPE`` keywords.  Default is\n",
      " |          `False`.\n",
      " |      \n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      \n",
      " |      result : array\n",
      " |          Returns the sky coordinates, in degrees.  If the input was a single array and\n",
      " |          origin, a single array is returned, otherwise a tuple of arrays is\n",
      " |          returned.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The order of the axes for the result is determined by the\n",
      " |      ``CTYPEia`` keywords in the FITS header, therefore it may not\n",
      " |      always be of the form (*ra*, *dec*).  The\n",
      " |      `~astropy.wcs.Wcsprm.lat`, `~astropy.wcs.Wcsprm.lng`,\n",
      " |      `~astropy.wcs.Wcsprm.lattyp` and `~astropy.wcs.Wcsprm.lngtyp`\n",
      " |      members can be used to determine the order of the axes.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      MemoryError\n",
      " |          Memory allocation failed.\n",
      " |      \n",
      " |      SingularMatrixError\n",
      " |          Linear transformation matrix is singular.\n",
      " |      \n",
      " |      InconsistentAxisTypesError\n",
      " |          Inconsistent or unrecognized coordinate axis types.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid parameter value.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |      \n",
      " |      ValueError\n",
      " |          x- and y-coordinate arrays are not the same size.\n",
      " |      \n",
      " |      InvalidTransformError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |      \n",
      " |      InvalidTransformError\n",
      " |          Ill-conditioned coordinate transformation parameters.\n",
      " |  \n",
      " |  all_world2pix(self, *args, **kwargs)\n",
      " |      all_world2pix(*arg, accuracy=1.0e-4, maxiter=20,\n",
      " |      adaptive=False, detect_divergence=True, quiet=False)\n",
      " |      \n",
      " |      Transforms world coordinates to pixel coordinates, using\n",
      " |      numerical iteration to invert the full forward transformation\n",
      " |      `~astropy.wcs.WCS.all_pix2world` with complete\n",
      " |      distortion model.\n",
      " |      \n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      args : flexible\n",
      " |          There are two accepted forms for the positional arguments:\n",
      " |      \n",
      " |              - 2 arguments: An *N* x *naxis* array of coordinates, and an\n",
      " |                *origin*.\n",
      " |      \n",
      " |              - more than 2 arguments: An array for each axis, followed by\n",
      " |                an *origin*.  These arrays must be broadcastable to one\n",
      " |                another.\n",
      " |      \n",
      " |          Here, *origin* is the coordinate in the upper left corner of the\n",
      " |          image.  In FITS and Fortran standards, this is 1.  In Numpy and C\n",
      " |          standards this is 0.\n",
      " |      \n",
      " |      \n",
      " |          For a transformation that is not two-dimensional, the\n",
      " |          two-argument form must be used.\n",
      " |      \n",
      " |      \n",
      " |      ra_dec_order : bool, optional\n",
      " |          When `True` will ensure that world coordinates are always given\n",
      " |          and returned in as (*ra*, *dec*) pairs, regardless of the order of\n",
      " |          the axes specified by the in the ``CTYPE`` keywords.  Default is\n",
      " |          `False`.\n",
      " |      \n",
      " |      \n",
      " |      tolerance : float, optional (Default = 1.0e-4)\n",
      " |          Tolerance of solution. Iteration terminates when the\n",
      " |          iterative solver estimates that the \"true solution\" is\n",
      " |          within this many pixels current estimate, more\n",
      " |          specifically, when the correction to the solution found\n",
      " |          during the previous iteration is smaller\n",
      " |          (in the sense of the L2 norm) than ``tolerance``.\n",
      " |      \n",
      " |      maxiter : int, optional (Default = 20)\n",
      " |          Maximum number of iterations allowed to reach a solution.\n",
      " |      \n",
      " |      quiet : bool, optional (Default = False)\n",
      " |          Do not throw :py:class:`NoConvergence` exceptions when\n",
      " |          the method does not converge to a solution with the\n",
      " |          required accuracy within a specified number of maximum\n",
      " |          iterations set by ``maxiter`` parameter. Instead,\n",
      " |          simply return the found solution.\n",
      " |      \n",
      " |      Other Parameters\n",
      " |      ----------------\n",
      " |      adaptive : bool, optional (Default = False)\n",
      " |          Specifies whether to adaptively select only points that\n",
      " |          did not converge to a solution within the required\n",
      " |          accuracy for the next iteration. Default is recommended\n",
      " |          for HST as well as most other instruments.\n",
      " |      \n",
      " |          .. note::\n",
      " |             The :py:meth:`all_world2pix` uses a vectorized\n",
      " |             implementation of the method of consecutive\n",
      " |             approximations (see ``Notes`` section below) in which it\n",
      " |             iterates over *all* input points *regardless* until\n",
      " |             the required accuracy has been reached for *all* input\n",
      " |             points. In some cases it may be possible that\n",
      " |             *almost all* points have reached the required accuracy\n",
      " |             but there are only a few of input data points for\n",
      " |             which additional iterations may be needed (this\n",
      " |             depends mostly on the characteristics of the geometric\n",
      " |             distortions for a given instrument). In this situation\n",
      " |             it may be advantageous to set ``adaptive`` = `True` in\n",
      " |             which case :py:meth:`all_world2pix` will continue\n",
      " |             iterating *only* over the points that have not yet\n",
      " |             converged to the required accuracy. However, for the\n",
      " |             HST's ACS/WFC detector, which has the strongest\n",
      " |             distortions of all HST instruments, testing has\n",
      " |             shown that enabling this option would lead to a about\n",
      " |             50-100% penalty in computational time (depending on\n",
      " |             specifics of the image, geometric distortions, and\n",
      " |             number of input points to be converted). Therefore,\n",
      " |             for HST and possibly instruments, it is recommended\n",
      " |             to set ``adaptive`` = `False`. The only danger in\n",
      " |             getting this setting wrong will be a performance\n",
      " |             penalty.\n",
      " |      \n",
      " |          .. note::\n",
      " |             When ``detect_divergence`` is `True`,\n",
      " |             :py:meth:`all_world2pix` will automatically switch\n",
      " |             to the adaptive algorithm once divergence has been\n",
      " |             detected.\n",
      " |      \n",
      " |      detect_divergence : bool, optional (Default = True)\n",
      " |          Specifies whether to perform a more detailed analysis\n",
      " |          of the convergence to a solution. Normally\n",
      " |          :py:meth:`all_world2pix` may not achieve the required\n",
      " |          accuracy if either the ``tolerance`` or ``maxiter`` arguments\n",
      " |          are too low. However, it may happen that for some\n",
      " |          geometric distortions the conditions of convergence for\n",
      " |          the the method of consecutive approximations used by\n",
      " |          :py:meth:`all_world2pix` may not be satisfied, in which\n",
      " |          case consecutive approximations to the solution will\n",
      " |          diverge regardless of the ``tolerance`` or ``maxiter``\n",
      " |          settings.\n",
      " |      \n",
      " |          When ``detect_divergence`` is `False`, these divergent\n",
      " |          points will be detected as not having achieved the\n",
      " |          required accuracy (without further details). In addition,\n",
      " |          if ``adaptive`` is `False` then the algorithm will not\n",
      " |          know that the solution (for specific points) is diverging\n",
      " |          and will continue iterating and trying to \"improve\"\n",
      " |          diverging solutions. This may result in ``NaN`` or\n",
      " |          ``Inf`` values in the return results (in addition to a\n",
      " |          performance penalties). Even when ``detect_divergence``\n",
      " |          is `False`, :py:meth:`all_world2pix`, at the end of the\n",
      " |          iterative process, will identify invalid results\n",
      " |          (``NaN`` or ``Inf``) as \"diverging\" solutions and will\n",
      " |          raise :py:class:`NoConvergence` unless the ``quiet``\n",
      " |          parameter is set to `True`.\n",
      " |      \n",
      " |          When ``detect_divergence`` is `True`,\n",
      " |          :py:meth:`all_world2pix` will detect points for which\n",
      " |          current correction to the coordinates is larger than\n",
      " |          the correction applied during the previous iteration\n",
      " |          **if** the requested accuracy **has not yet been\n",
      " |          achieved**. In this case, if ``adaptive`` is `True`,\n",
      " |          these points will be excluded from further iterations and\n",
      " |          if ``adaptive`` is `False`, :py:meth:`all_world2pix` will\n",
      " |          automatically switch to the adaptive algorithm. Thus, the\n",
      " |          reported divergent solution will be the latest converging\n",
      " |          solution computed immediately *before* divergence\n",
      " |          has been detected.\n",
      " |      \n",
      " |          .. note::\n",
      " |             When accuracy has been achieved, small increases in\n",
      " |             current corrections may be possible due to rounding\n",
      " |             errors (when ``adaptive`` is `False`) and such\n",
      " |             increases will be ignored.\n",
      " |      \n",
      " |          .. note::\n",
      " |             Based on our testing using HST ACS/WFC images, setting\n",
      " |             ``detect_divergence`` to `True` will incur about 5-20%\n",
      " |             performance penalty with the larger penalty\n",
      " |             corresponding to ``adaptive`` set to `True`.\n",
      " |             Because the benefits of enabling this\n",
      " |             feature outweigh the small performance penalty,\n",
      " |             especially when ``adaptive`` = `False`, it is\n",
      " |             recommended to set ``detect_divergence`` to `True`,\n",
      " |             unless extensive testing of the distortion models for\n",
      " |             images from specific instruments show a good stability\n",
      " |             of the numerical method for a wide range of\n",
      " |             coordinates (even outside the image itself).\n",
      " |      \n",
      " |          .. note::\n",
      " |             Indices of the diverging inverse solutions will be\n",
      " |             reported in the ``divergent`` attribute of the\n",
      " |             raised :py:class:`NoConvergence` exception object.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      \n",
      " |      result : array\n",
      " |          Returns the pixel coordinates.  If the input was a single array and\n",
      " |          origin, a single array is returned, otherwise a tuple of arrays is\n",
      " |          returned.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The order of the axes for the input world array is determined by\n",
      " |      the ``CTYPEia`` keywords in the FITS header, therefore it may\n",
      " |      not always be of the form (*ra*, *dec*).  The\n",
      " |      `~astropy.wcs.Wcsprm.lat`, `~astropy.wcs.Wcsprm.lng`,\n",
      " |      `~astropy.wcs.Wcsprm.lattyp`, and\n",
      " |      `~astropy.wcs.Wcsprm.lngtyp`\n",
      " |      members can be used to determine the order of the axes.\n",
      " |      \n",
      " |      Using the method of fixed-point iterations approximations we\n",
      " |      iterate starting with the initial approximation, which is\n",
      " |      computed using the non-distortion-aware\n",
      " |      :py:meth:`wcs_world2pix` (or equivalent).\n",
      " |      \n",
      " |      The :py:meth:`all_world2pix` function uses a vectorized\n",
      " |      implementation of the method of consecutive approximations and\n",
      " |      therefore it is highly efficient (>30x) when *all* data points\n",
      " |      that need to be converted from sky coordinates to image\n",
      " |      coordinates are passed at *once*. Therefore, it is advisable,\n",
      " |      whenever possible, to pass as input a long array of all points\n",
      " |      that need to be converted to :py:meth:`all_world2pix` instead\n",
      " |      of calling :py:meth:`all_world2pix` for each data point. Also\n",
      " |      see the note to the ``adaptive`` parameter.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      NoConvergence\n",
      " |          The method did not converge to a\n",
      " |          solution to the required accuracy within a specified\n",
      " |          number of maximum iterations set by the ``maxiter``\n",
      " |          parameter. To turn off this exception, set ``quiet`` to\n",
      " |          `True`. Indices of the points for which the requested\n",
      " |          accuracy was not achieved (if any) will be listed in the\n",
      " |          ``slow_conv`` attribute of the\n",
      " |          raised :py:class:`NoConvergence` exception object.\n",
      " |      \n",
      " |          See :py:class:`NoConvergence` documentation for\n",
      " |          more details.\n",
      " |      \n",
      " |      MemoryError\n",
      " |          Memory allocation failed.\n",
      " |      \n",
      " |      SingularMatrixError\n",
      " |          Linear transformation matrix is singular.\n",
      " |      \n",
      " |      InconsistentAxisTypesError\n",
      " |          Inconsistent or unrecognized coordinate axis types.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid parameter value.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |      \n",
      " |      ValueError\n",
      " |          x- and y-coordinate arrays are not the same size.\n",
      " |      \n",
      " |      InvalidTransformError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |      \n",
      " |      InvalidTransformError\n",
      " |          Ill-conditioned coordinate transformation parameters.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> import astropy.io.fits as fits\n",
      " |      >>> import astropy.wcs as wcs\n",
      " |      >>> import numpy as np\n",
      " |      >>> import os\n",
      " |      \n",
      " |      >>> filename = os.path.join(wcs.__path__[0], 'tests/data/j94f05bgq_flt.fits')\n",
      " |      >>> hdulist = fits.open(filename)\n",
      " |      >>> w = wcs.WCS(hdulist[('sci',1)].header, hdulist)\n",
      " |      >>> hdulist.close()\n",
      " |      \n",
      " |      >>> ra, dec = w.all_pix2world([1,2,3], [1,1,1], 1)\n",
      " |      >>> print(ra)  # doctest: +FLOAT_CMP\n",
      " |      [ 5.52645627  5.52649663  5.52653698]\n",
      " |      >>> print(dec)  # doctest: +FLOAT_CMP\n",
      " |      [-72.05171757 -72.05171276 -72.05170795]\n",
      " |      >>> radec = w.all_pix2world([[1,1], [2,1], [3,1]], 1)\n",
      " |      >>> print(radec)  # doctest: +FLOAT_CMP\n",
      " |      [[  5.52645627 -72.05171757]\n",
      " |       [  5.52649663 -72.05171276]\n",
      " |       [  5.52653698 -72.05170795]]\n",
      " |      >>> x, y = w.all_world2pix(ra, dec, 1)\n",
      " |      >>> print(x)  # doctest: +FLOAT_CMP\n",
      " |      [ 1.00000238  2.00000237  3.00000236]\n",
      " |      >>> print(y)  # doctest: +FLOAT_CMP\n",
      " |      [ 0.99999996  0.99999997  0.99999997]\n",
      " |      >>> xy = w.all_world2pix(radec, 1)\n",
      " |      >>> print(xy)  # doctest: +FLOAT_CMP\n",
      " |      [[ 1.00000238  0.99999996]\n",
      " |       [ 2.00000237  0.99999997]\n",
      " |       [ 3.00000236  0.99999997]]\n",
      " |      >>> xy = w.all_world2pix(radec, 1, maxiter=3,\n",
      " |      ...                      tolerance=1.0e-10, quiet=False)\n",
      " |      Traceback (most recent call last):\n",
      " |      ...\n",
      " |      NoConvergence: 'WCS.all_world2pix' failed to converge to the\n",
      " |      requested accuracy. After 3 iterations, the solution is\n",
      " |      diverging at least for one input point.\n",
      " |      \n",
      " |      >>> # Now try to use some diverging data:\n",
      " |      >>> divradec = w.all_pix2world([[1.0, 1.0],\n",
      " |      ...                             [10000.0, 50000.0],\n",
      " |      ...                             [3.0, 1.0]], 1)\n",
      " |      >>> print(divradec)  # doctest: +FLOAT_CMP\n",
      " |      [[  5.52645627 -72.05171757]\n",
      " |       [  7.15976932 -70.8140779 ]\n",
      " |       [  5.52653698 -72.05170795]]\n",
      " |      \n",
      " |      >>> # First, turn detect_divergence on:\n",
      " |      >>> try:  # doctest: +FLOAT_CMP\n",
      " |      ...   xy = w.all_world2pix(divradec, 1, maxiter=20,\n",
      " |      ...                        tolerance=1.0e-4, adaptive=False,\n",
      " |      ...                        detect_divergence=True,\n",
      " |      ...                        quiet=False)\n",
      " |      ... except wcs.wcs.NoConvergence as e:\n",
      " |      ...   print(\"Indices of diverging points: {0}\"\n",
      " |      ...         .format(e.divergent))\n",
      " |      ...   print(\"Indices of poorly converging points: {0}\"\n",
      " |      ...         .format(e.slow_conv))\n",
      " |      ...   print(\"Best solution:\\n{0}\".format(e.best_solution))\n",
      " |      ...   print(\"Achieved accuracy:\\n{0}\".format(e.accuracy))\n",
      " |      Indices of diverging points: [1]\n",
      " |      Indices of poorly converging points: None\n",
      " |      Best solution:\n",
      " |      [[  1.00000238e+00   9.99999965e-01]\n",
      " |       [ -1.99441636e+06   1.44309097e+06]\n",
      " |       [  3.00000236e+00   9.99999966e-01]]\n",
      " |      Achieved accuracy:\n",
      " |      [[  6.13968380e-05   8.59638593e-07]\n",
      " |       [  8.59526812e+11   6.61713548e+11]\n",
      " |       [  6.09398446e-05   8.38759724e-07]]\n",
      " |      >>> raise e\n",
      " |      Traceback (most recent call last):\n",
      " |      ...\n",
      " |      NoConvergence: 'WCS.all_world2pix' failed to converge to the\n",
      " |      requested accuracy.  After 5 iterations, the solution is\n",
      " |      diverging at least for one input point.\n",
      " |      \n",
      " |      >>> # This time turn detect_divergence off:\n",
      " |      >>> try:  # doctest: +FLOAT_CMP\n",
      " |      ...   xy = w.all_world2pix(divradec, 1, maxiter=20,\n",
      " |      ...                        tolerance=1.0e-4, adaptive=False,\n",
      " |      ...                        detect_divergence=False,\n",
      " |      ...                        quiet=False)\n",
      " |      ... except wcs.wcs.NoConvergence as e:\n",
      " |      ...   print(\"Indices of diverging points: {0}\"\n",
      " |      ...         .format(e.divergent))\n",
      " |      ...   print(\"Indices of poorly converging points: {0}\"\n",
      " |      ...         .format(e.slow_conv))\n",
      " |      ...   print(\"Best solution:\\n{0}\".format(e.best_solution))\n",
      " |      ...   print(\"Achieved accuracy:\\n{0}\".format(e.accuracy))\n",
      " |      Indices of diverging points: [1]\n",
      " |      Indices of poorly converging points: None\n",
      " |      Best solution:\n",
      " |      [[ 1.00000009  1.        ]\n",
      " |       [        nan         nan]\n",
      " |       [ 3.00000009  1.        ]]\n",
      " |      Achieved accuracy:\n",
      " |      [[  2.29417358e-06   3.21222995e-08]\n",
      " |       [             nan              nan]\n",
      " |       [  2.27407877e-06   3.13005639e-08]]\n",
      " |      >>> raise e\n",
      " |      Traceback (most recent call last):\n",
      " |      ...\n",
      " |      NoConvergence: 'WCS.all_world2pix' failed to converge to the\n",
      " |      requested accuracy.  After 6 iterations, the solution is\n",
      " |      diverging at least for one input point.\n",
      " |  \n",
      " |  calc_footprint(self, header=None, undistort=True, axes=None, center=True)\n",
      " |      Calculates the footprint of the image on the sky.\n",
      " |      \n",
      " |      A footprint is defined as the positions of the corners of the\n",
      " |      image on the sky after all available distortions have been\n",
      " |      applied.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      header : `~astropy.io.fits.Header` object, optional\n",
      " |          Used to get ``NAXIS1`` and ``NAXIS2``\n",
      " |          header and axes are mutually exclusive, alternative ways\n",
      " |          to provide the same information.\n",
      " |      \n",
      " |      undistort : bool, optional\n",
      " |          If `True`, take SIP and distortion lookup table into\n",
      " |          account\n",
      " |      \n",
      " |      axes : length 2 sequence ints, optional\n",
      " |          If provided, use the given sequence as the shape of the\n",
      " |          image.  Otherwise, use the ``NAXIS1`` and ``NAXIS2``\n",
      " |          keywords from the header that was used to create this\n",
      " |          `WCS` object.\n",
      " |      \n",
      " |      center : bool, optional\n",
      " |          If `True` use the center of the pixel, otherwise use the corner.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      coord : (4, 2) array of (*x*, *y*) coordinates.\n",
      " |          The order is clockwise starting with the bottom left corner.\n",
      " |  \n",
      " |  copy(self)\n",
      " |      Return a shallow copy of the object.\n",
      " |      \n",
      " |      Convenience method so user doesn't have to import the\n",
      " |      :mod:`copy` stdlib module.\n",
      " |      \n",
      " |      .. warning::\n",
      " |          Use `deepcopy` instead of `copy` unless you know why you need a\n",
      " |          shallow copy.\n",
      " |  \n",
      " |  deepcopy(self)\n",
      " |      Return a deep copy of the object.\n",
      " |      \n",
      " |      Convenience method so user doesn't have to import the\n",
      " |      :mod:`copy` stdlib module.\n",
      " |  \n",
      " |  det2im(self, *args)\n",
      " |      Convert detector coordinates to image plane coordinates using\n",
      " |      `distortion paper`_ table-lookup correction.\n",
      " |      \n",
      " |      The output is in absolute pixel coordinates, not relative to\n",
      " |      ``CRPIX``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      \n",
      " |      args : flexible\n",
      " |          There are two accepted forms for the positional arguments:\n",
      " |      \n",
      " |              - 2 arguments: An *N* x *2* array of coordinates, and an\n",
      " |                *origin*.\n",
      " |      \n",
      " |              - more than 2 arguments: An array for each axis, followed by\n",
      " |                an *origin*.  These arrays must be broadcastable to one\n",
      " |                another.\n",
      " |      \n",
      " |          Here, *origin* is the coordinate in the upper left corner of the\n",
      " |          image.  In FITS and Fortran standards, this is 1.  In Numpy and C\n",
      " |          standards this is 0.\n",
      " |      \n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      \n",
      " |      result : array\n",
      " |          Returns the pixel coordinates.  If the input was a single array and\n",
      " |          origin, a single array is returned, otherwise a tuple of arrays is\n",
      " |          returned.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      MemoryError\n",
      " |          Memory allocation failed.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |  \n",
      " |  dropaxis(self, dropax)\n",
      " |      Remove an axis from the WCS.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      wcs : `~astropy.wcs.WCS`\n",
      " |          The WCS with naxis to be chopped to naxis-1\n",
      " |      dropax : int\n",
      " |          The index of the WCS to drop, counting from 0 (i.e., python convention,\n",
      " |          not FITS convention)\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      A new `~astropy.wcs.WCS` instance with one axis fewer\n",
      " |  \n",
      " |  fix(self, translate_units='', naxis=None)\n",
      " |      Perform the fix operations from wcslib, and warn about any\n",
      " |      changes it has made.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      translate_units : str, optional\n",
      " |          Specify which potentially unsafe translations of\n",
      " |          non-standard unit strings to perform.  By default,\n",
      " |          performs none.\n",
      " |      \n",
      " |          Although ``\"S\"`` is commonly used to represent seconds,\n",
      " |          its translation to ``\"s\"`` is potentially unsafe since the\n",
      " |          standard recognizes ``\"S\"`` formally as Siemens, however\n",
      " |          rarely that may be used.  The same applies to ``\"H\"`` for\n",
      " |          hours (Henry), and ``\"D\"`` for days (Debye).\n",
      " |      \n",
      " |          This string controls what to do in such cases, and is\n",
      " |          case-insensitive.\n",
      " |      \n",
      " |          - If the string contains ``\"s\"``, translate ``\"S\"`` to\n",
      " |            ``\"s\"``.\n",
      " |      \n",
      " |          - If the string contains ``\"h\"``, translate ``\"H\"`` to\n",
      " |            ``\"h\"``.\n",
      " |      \n",
      " |          - If the string contains ``\"d\"``, translate ``\"D\"`` to\n",
      " |            ``\"d\"``.\n",
      " |      \n",
      " |          Thus ``''`` doesn't do any unsafe translations, whereas\n",
      " |          ``'shd'`` does all of them.\n",
      " |      \n",
      " |      naxis : int array[naxis], optional\n",
      " |          Image axis lengths.  If this array is set to zero or\n",
      " |          ``None``, then `~astropy.wcs.Wcsprm.cylfix` will not be\n",
      " |          invoked.\n",
      " |  \n",
      " |  footprint_to_file(self, filename='footprint.reg', color='green', width=2, coordsys=None)\n",
      " |      Writes out a `ds9`_ style regions file. It can be loaded\n",
      " |      directly by `ds9`_.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      filename : str, optional\n",
      " |          Output file name - default is ``'footprint.reg'``\n",
      " |      \n",
      " |      color : str, optional\n",
      " |          Color to use when plotting the line.\n",
      " |      \n",
      " |      width : int, optional\n",
      " |          Width of the region line.\n",
      " |      \n",
      " |      coordsys : str, optional\n",
      " |          Coordinate system. If not specified (default), the ``radesys``\n",
      " |          value is used. For all possible values, see\n",
      " |          http://ds9.si.edu/doc/ref/region.html#RegionFileFormat\n",
      " |  \n",
      " |  get_axis_types(self)\n",
      " |      Similar to `self.wcsprm.axis_types <astropy.wcs.Wcsprm.axis_types>`\n",
      " |      but provides the information in a more Python-friendly format.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      result : list of dicts\n",
      " |      \n",
      " |          Returns a list of dictionaries, one for each axis, each\n",
      " |          containing attributes about the type of that axis.\n",
      " |      \n",
      " |          Each dictionary has the following keys:\n",
      " |      \n",
      " |          - 'coordinate_type':\n",
      " |      \n",
      " |            - None: Non-specific coordinate type.\n",
      " |      \n",
      " |            - 'stokes': Stokes coordinate.\n",
      " |      \n",
      " |            - 'celestial': Celestial coordinate (including ``CUBEFACE``).\n",
      " |      \n",
      " |            - 'spectral': Spectral coordinate.\n",
      " |      \n",
      " |          - 'scale':\n",
      " |      \n",
      " |            - 'linear': Linear axis.\n",
      " |      \n",
      " |            - 'quantized': Quantized axis (``STOKES``, ``CUBEFACE``).\n",
      " |      \n",
      " |            - 'non-linear celestial': Non-linear celestial axis.\n",
      " |      \n",
      " |            - 'non-linear spectral': Non-linear spectral axis.\n",
      " |      \n",
      " |            - 'logarithmic': Logarithmic axis.\n",
      " |      \n",
      " |            - 'tabular': Tabular axis.\n",
      " |      \n",
      " |          - 'group'\n",
      " |      \n",
      " |            - Group number, e.g. lookup table number\n",
      " |      \n",
      " |          - 'number'\n",
      " |      \n",
      " |            - For celestial axes:\n",
      " |      \n",
      " |              - 0: Longitude coordinate.\n",
      " |      \n",
      " |              - 1: Latitude coordinate.\n",
      " |      \n",
      " |              - 2: ``CUBEFACE`` number.\n",
      " |      \n",
      " |            - For lookup tables:\n",
      " |      \n",
      " |              - the axis number in a multidimensional table.\n",
      " |      \n",
      " |          ``CTYPEia`` in ``\"4-3\"`` form with unrecognized algorithm code will\n",
      " |          generate an error.\n",
      " |  \n",
      " |  p4_pix2foc(self, *args)\n",
      " |      Convert pixel coordinates to focal plane coordinates using\n",
      " |      `distortion paper`_ table-lookup correction.\n",
      " |      \n",
      " |      The output is in absolute pixel coordinates, not relative to\n",
      " |      ``CRPIX``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      \n",
      " |      args : flexible\n",
      " |          There are two accepted forms for the positional arguments:\n",
      " |      \n",
      " |              - 2 arguments: An *N* x *2* array of coordinates, and an\n",
      " |                *origin*.\n",
      " |      \n",
      " |              - more than 2 arguments: An array for each axis, followed by\n",
      " |                an *origin*.  These arrays must be broadcastable to one\n",
      " |                another.\n",
      " |      \n",
      " |          Here, *origin* is the coordinate in the upper left corner of the\n",
      " |          image.  In FITS and Fortran standards, this is 1.  In Numpy and C\n",
      " |          standards this is 0.\n",
      " |      \n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      \n",
      " |      result : array\n",
      " |          Returns the focal coordinates.  If the input was a single array and\n",
      " |          origin, a single array is returned, otherwise a tuple of arrays is\n",
      " |          returned.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      MemoryError\n",
      " |          Memory allocation failed.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |  \n",
      " |  pix2foc(self, *args)\n",
      " |      Convert pixel coordinates to focal plane coordinates using the\n",
      " |      `SIP`_ polynomial distortion convention and `distortion\n",
      " |      paper`_ table-lookup correction.\n",
      " |      \n",
      " |      The output is in absolute pixel coordinates, not relative to\n",
      " |      ``CRPIX``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      \n",
      " |      args : flexible\n",
      " |          There are two accepted forms for the positional arguments:\n",
      " |      \n",
      " |              - 2 arguments: An *N* x *2* array of coordinates, and an\n",
      " |                *origin*.\n",
      " |      \n",
      " |              - more than 2 arguments: An array for each axis, followed by\n",
      " |                an *origin*.  These arrays must be broadcastable to one\n",
      " |                another.\n",
      " |      \n",
      " |          Here, *origin* is the coordinate in the upper left corner of the\n",
      " |          image.  In FITS and Fortran standards, this is 1.  In Numpy and C\n",
      " |          standards this is 0.\n",
      " |      \n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      \n",
      " |      result : array\n",
      " |          Returns the focal coordinates.  If the input was a single array and\n",
      " |          origin, a single array is returned, otherwise a tuple of arrays is\n",
      " |          returned.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      MemoryError\n",
      " |          Memory allocation failed.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |  \n",
      " |  printwcs(self)\n",
      " |  \n",
      " |  reorient_celestial_first(self)\n",
      " |      Reorient the WCS such that the celestial axes are first, followed by\n",
      " |      the spectral axis, followed by any others.\n",
      " |      Assumes at least celestial axes are present.\n",
      " |  \n",
      " |  sip_foc2pix(self, *args)\n",
      " |      Convert focal plane coordinates to pixel coordinates using the\n",
      " |      `SIP`_ polynomial distortion convention.\n",
      " |      \n",
      " |      FITS WCS `distortion paper`_ table lookup distortion\n",
      " |      correction is not applied, even if that information existed in\n",
      " |      the FITS file that initialized this `~astropy.wcs.WCS` object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      \n",
      " |      args : flexible\n",
      " |          There are two accepted forms for the positional arguments:\n",
      " |      \n",
      " |              - 2 arguments: An *N* x *2* array of coordinates, and an\n",
      " |                *origin*.\n",
      " |      \n",
      " |              - more than 2 arguments: An array for each axis, followed by\n",
      " |                an *origin*.  These arrays must be broadcastable to one\n",
      " |                another.\n",
      " |      \n",
      " |          Here, *origin* is the coordinate in the upper left corner of the\n",
      " |          image.  In FITS and Fortran standards, this is 1.  In Numpy and C\n",
      " |          standards this is 0.\n",
      " |      \n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      \n",
      " |      result : array\n",
      " |          Returns the pixel coordinates.  If the input was a single array and\n",
      " |          origin, a single array is returned, otherwise a tuple of arrays is\n",
      " |          returned.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      MemoryError\n",
      " |          Memory allocation failed.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |  \n",
      " |  sip_pix2foc(self, *args)\n",
      " |      Convert pixel coordinates to focal plane coordinates using the\n",
      " |      `SIP`_ polynomial distortion convention.\n",
      " |      \n",
      " |      The output is in pixel coordinates, relative to ``CRPIX``.\n",
      " |      \n",
      " |      FITS WCS `distortion paper`_ table lookup correction is not\n",
      " |      applied, even if that information existed in the FITS file\n",
      " |      that initialized this :class:`~astropy.wcs.WCS` object.  To\n",
      " |      correct for that, use `~astropy.wcs.WCS.pix2foc` or\n",
      " |      `~astropy.wcs.WCS.p4_pix2foc`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      \n",
      " |      args : flexible\n",
      " |          There are two accepted forms for the positional arguments:\n",
      " |      \n",
      " |              - 2 arguments: An *N* x *2* array of coordinates, and an\n",
      " |                *origin*.\n",
      " |      \n",
      " |              - more than 2 arguments: An array for each axis, followed by\n",
      " |                an *origin*.  These arrays must be broadcastable to one\n",
      " |                another.\n",
      " |      \n",
      " |          Here, *origin* is the coordinate in the upper left corner of the\n",
      " |          image.  In FITS and Fortran standards, this is 1.  In Numpy and C\n",
      " |          standards this is 0.\n",
      " |      \n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      \n",
      " |      result : array\n",
      " |          Returns the focal coordinates.  If the input was a single array and\n",
      " |          origin, a single array is returned, otherwise a tuple of arrays is\n",
      " |          returned.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      MemoryError\n",
      " |          Memory allocation failed.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |  \n",
      " |  slice(self, view, numpy_order=True)\n",
      " |      Slice a WCS instance using a Numpy slice. The order of the slice should\n",
      " |      be reversed (as for the data) compared to the natural WCS order.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      view : tuple\n",
      " |          A tuple containing the same number of slices as the WCS system.\n",
      " |          The ``step`` method, the third argument to a slice, is not\n",
      " |          presently supported.\n",
      " |      numpy_order : bool\n",
      " |          Use numpy order, i.e. slice the WCS so that an identical slice\n",
      " |          applied to a numpy array will slice the array and WCS in the same\n",
      " |          way. If set to `False`, the WCS will be sliced in FITS order,\n",
      " |          meaning the first slice will be applied to the *last* numpy index\n",
      " |          but the *first* WCS axis.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      wcs_new : `~astropy.wcs.WCS`\n",
      " |          A new resampled WCS axis\n",
      " |  \n",
      " |  sub(self, axes=None)\n",
      " |      sub(axes)\n",
      " |      \n",
      " |      Extracts the coordinate description for a subimage from a\n",
      " |      `~astropy.wcs.WCS` object.\n",
      " |      \n",
      " |      The world coordinate system of the subimage must be separable in the\n",
      " |      sense that the world coordinates at any point in the subimage must\n",
      " |      depend only on the pixel coordinates of the axes extracted.  In\n",
      " |      practice, this means that the ``PCi_ja`` matrix of the original image\n",
      " |      must not contain non-zero off-diagonal terms that associate any of the\n",
      " |      subimage axes with any of the non-subimage axes.\n",
      " |      \n",
      " |      `sub` can also add axes to a wcsprm object.  The new axes will be\n",
      " |      created using the defaults set by the Wcsprm constructor which produce\n",
      " |      a simple, unnamed, linear axis with world coordinates equal to the\n",
      " |      pixel coordinate.  These default values can be changed before\n",
      " |      invoking `set`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      axes : int or a sequence.\n",
      " |      \n",
      " |          - If an int, include the first *N* axes in their original order.\n",
      " |      \n",
      " |          - If a sequence, may contain a combination of image axis numbers\n",
      " |            (1-relative) or special axis identifiers (see below).  Order is\n",
      " |            significant; ``axes[0]`` is the axis number of the input image\n",
      " |            that corresponds to the first axis in the subimage, etc.  Use an\n",
      " |            axis number of 0 to create a new axis using the defaults.\n",
      " |      \n",
      " |          - If ``0``, ``[]`` or ``None``, do a deep copy.\n",
      " |      \n",
      " |          Coordinate axes types may be specified using either strings or\n",
      " |          special integer constants.  The available types are:\n",
      " |      \n",
      " |          - ``'longitude'`` / ``WCSSUB_LONGITUDE``: Celestial longitude\n",
      " |      \n",
      " |          - ``'latitude'`` / ``WCSSUB_LATITUDE``: Celestial latitude\n",
      " |      \n",
      " |          - ``'cubeface'`` / ``WCSSUB_CUBEFACE``: Quadcube ``CUBEFACE`` axis\n",
      " |      \n",
      " |          - ``'spectral'`` / ``WCSSUB_SPECTRAL``: Spectral axis\n",
      " |      \n",
      " |          - ``'stokes'`` / ``WCSSUB_STOKES``: Stokes axis\n",
      " |      \n",
      " |          - ``'celestial'`` / ``WCSSUB_CELESTIAL``: An alias for the\n",
      " |            combination of ``'longitude'``, ``'latitude'`` and ``'cubeface'``.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      new_wcs : `~astropy.wcs.WCS` object\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      MemoryError\n",
      " |          Memory allocation failed.\n",
      " |      \n",
      " |      InvalidSubimageSpecificationError\n",
      " |          Invalid subimage specification (no spectral axis).\n",
      " |      \n",
      " |      NonseparableSubimageCoordinateSystem\n",
      " |          Non-separable subimage coordinate system.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Combinations of subimage axes of particular types may be extracted in\n",
      " |      the same order as they occur in the input image by combining the\n",
      " |      integer constants with the 'binary or' (``|``) operator.  For\n",
      " |      example::\n",
      " |      \n",
      " |          wcs.sub([WCSSUB_LONGITUDE | WCSSUB_LATITUDE | WCSSUB_SPECTRAL])\n",
      " |      \n",
      " |      would extract the longitude, latitude, and spectral axes in the same\n",
      " |      order as the input image.  If one of each were present, the resulting\n",
      " |      object would have three dimensions.\n",
      " |      \n",
      " |      For convenience, ``WCSSUB_CELESTIAL`` is defined as the combination\n",
      " |      ``WCSSUB_LONGITUDE | WCSSUB_LATITUDE | WCSSUB_CUBEFACE``.\n",
      " |      \n",
      " |      The codes may also be negated to extract all but the types specified,\n",
      " |      for example::\n",
      " |      \n",
      " |          wcs.sub([\n",
      " |            WCSSUB_LONGITUDE,\n",
      " |            WCSSUB_LATITUDE,\n",
      " |            WCSSUB_CUBEFACE,\n",
      " |            -(WCSSUB_SPECTRAL | WCSSUB_STOKES)])\n",
      " |      \n",
      " |      The last of these specifies all axis types other than spectral or\n",
      " |      Stokes.  Extraction is done in the order specified by ``axes``, i.e. a\n",
      " |      longitude axis (if present) would be extracted first (via ``axes[0]``)\n",
      " |      and not subsequently (via ``axes[3]``).  Likewise for the latitude and\n",
      " |      cubeface axes in this example.\n",
      " |      \n",
      " |      The number of dimensions in the returned object may be less than or\n",
      " |      greater than the length of ``axes``.  However, it will never exceed the\n",
      " |      number of axes in the input image.\n",
      " |  \n",
      " |  swapaxes(self, ax0, ax1)\n",
      " |      Swap axes in a WCS.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      wcs : `~astropy.wcs.WCS`\n",
      " |          The WCS to have its axes swapped\n",
      " |      ax0 : int\n",
      " |      ax1 : int\n",
      " |          The indices of the WCS to be swapped, counting from 0 (i.e., python\n",
      " |          convention, not FITS convention)\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      A new `~astropy.wcs.WCS` instance with the same number of axes, but two\n",
      " |      swapped\n",
      " |  \n",
      " |  to_fits(self, relax=False, key=None)\n",
      " |      Generate an `astropy.io.fits.HDUList` object with all of the\n",
      " |      information stored in this object.  This should be logically identical\n",
      " |      to the input FITS file, but it will be normalized in a number of ways.\n",
      " |      \n",
      " |      See `to_header` for some warnings about the output produced.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      \n",
      " |      relax : bool or int, optional\n",
      " |          Degree of permissiveness:\n",
      " |      \n",
      " |          - `False` (default): Write all extensions that are\n",
      " |            considered to be safe and recommended.\n",
      " |      \n",
      " |          - `True`: Write all recognized informal extensions of the\n",
      " |            WCS standard.\n",
      " |      \n",
      " |          - `int`: a bit field selecting specific extensions to\n",
      " |            write.  See :ref:`relaxwrite` for details.\n",
      " |      \n",
      " |      key : str\n",
      " |          The name of a particular WCS transform to use.  This may be\n",
      " |          either ``' '`` or ``'A'``-``'Z'`` and corresponds to the ``\"a\"``\n",
      " |          part of the ``CTYPEia`` cards.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      hdulist : `astropy.io.fits.HDUList`\n",
      " |  \n",
      " |  to_header(self, relax=None, key=None)\n",
      " |      Generate an `astropy.io.fits.Header` object with the basic WCS\n",
      " |      and SIP information stored in this object.  This should be\n",
      " |      logically identical to the input FITS file, but it will be\n",
      " |      normalized in a number of ways.\n",
      " |      \n",
      " |      .. warning::\n",
      " |      \n",
      " |        This function does not write out FITS WCS `distortion\n",
      " |        paper`_ information, since that requires multiple FITS\n",
      " |        header data units.  To get a full representation of\n",
      " |        everything in this object, use `to_fits`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      relax : bool or int, optional\n",
      " |          Degree of permissiveness:\n",
      " |      \n",
      " |          - `False` (default): Write all extensions that are\n",
      " |            considered to be safe and recommended.\n",
      " |      \n",
      " |          - `True`: Write all recognized informal extensions of the\n",
      " |            WCS standard.\n",
      " |      \n",
      " |          - `int`: a bit field selecting specific extensions to\n",
      " |            write.  See :ref:`relaxwrite` for details.\n",
      " |      \n",
      " |          If the ``relax`` keyword argument is not given and any\n",
      " |          keywords were omitted from the output, an\n",
      " |          `~astropy.utils.exceptions.AstropyWarning` is displayed.\n",
      " |          To override this, explicitly pass a value to ``relax``.\n",
      " |      \n",
      " |      key : str\n",
      " |          The name of a particular WCS transform to use.  This may be\n",
      " |          either ``' '`` or ``'A'``-``'Z'`` and corresponds to the ``\"a\"``\n",
      " |          part of the ``CTYPEia`` cards.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      header : `astropy.io.fits.Header`\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The output header will almost certainly differ from the input in a\n",
      " |      number of respects:\n",
      " |      \n",
      " |        1. The output header only contains WCS-related keywords.  In\n",
      " |           particular, it does not contain syntactically-required\n",
      " |           keywords such as ``SIMPLE``, ``NAXIS``, ``BITPIX``, or\n",
      " |           ``END``.\n",
      " |      \n",
      " |        2. Deprecated (e.g. ``CROTAn``) or non-standard usage will\n",
      " |           be translated to standard (this is partially dependent on\n",
      " |           whether ``fix`` was applied).\n",
      " |      \n",
      " |        3. Quantities will be converted to the units used internally,\n",
      " |           basically SI with the addition of degrees.\n",
      " |      \n",
      " |        4. Floating-point quantities may be given to a different decimal\n",
      " |           precision.\n",
      " |      \n",
      " |        5. Elements of the ``PCi_j`` matrix will be written if and\n",
      " |           only if they differ from the unit matrix.  Thus, if the\n",
      " |           matrix is unity then no elements will be written.\n",
      " |      \n",
      " |        6. Additional keywords such as ``WCSAXES``, ``CUNITia``,\n",
      " |           ``LONPOLEa`` and ``LATPOLEa`` may appear.\n",
      " |      \n",
      " |        7. The original keycomments will be lost, although\n",
      " |           `to_header` tries hard to write meaningful comments.\n",
      " |      \n",
      " |        8. Keyword order may be changed.\n",
      " |  \n",
      " |  to_header_string(self, relax=None)\n",
      " |      Identical to `to_header`, but returns a string containing the\n",
      " |      header cards.\n",
      " |  \n",
      " |  wcs_pix2world(self, *args, **kwargs)\n",
      " |      Transforms pixel coordinates to world coordinates by doing\n",
      " |      only the basic `wcslib`_ transformation.\n",
      " |      \n",
      " |      No `SIP`_ or `distortion paper`_ table lookup correction is\n",
      " |      applied.  To perform distortion correction, see\n",
      " |      `~astropy.wcs.WCS.all_pix2world`,\n",
      " |      `~astropy.wcs.WCS.sip_pix2foc`, `~astropy.wcs.WCS.p4_pix2foc`,\n",
      " |      or `~astropy.wcs.WCS.pix2foc`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      args : flexible\n",
      " |          There are two accepted forms for the positional arguments:\n",
      " |      \n",
      " |              - 2 arguments: An *N* x *naxis* array of coordinates, and an\n",
      " |                *origin*.\n",
      " |      \n",
      " |              - more than 2 arguments: An array for each axis, followed by\n",
      " |                an *origin*.  These arrays must be broadcastable to one\n",
      " |                another.\n",
      " |      \n",
      " |          Here, *origin* is the coordinate in the upper left corner of the\n",
      " |          image.  In FITS and Fortran standards, this is 1.  In Numpy and C\n",
      " |          standards this is 0.\n",
      " |      \n",
      " |      \n",
      " |          For a transformation that is not two-dimensional, the\n",
      " |          two-argument form must be used.\n",
      " |      \n",
      " |      \n",
      " |      ra_dec_order : bool, optional\n",
      " |          When `True` will ensure that world coordinates are always given\n",
      " |          and returned in as (*ra*, *dec*) pairs, regardless of the order of\n",
      " |          the axes specified by the in the ``CTYPE`` keywords.  Default is\n",
      " |          `False`.\n",
      " |      \n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      \n",
      " |      result : array\n",
      " |          Returns the world coordinates, in degrees.  If the input was a single array and\n",
      " |          origin, a single array is returned, otherwise a tuple of arrays is\n",
      " |          returned.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      MemoryError\n",
      " |          Memory allocation failed.\n",
      " |      \n",
      " |      SingularMatrixError\n",
      " |          Linear transformation matrix is singular.\n",
      " |      \n",
      " |      InconsistentAxisTypesError\n",
      " |          Inconsistent or unrecognized coordinate axis types.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid parameter value.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |      \n",
      " |      ValueError\n",
      " |          x- and y-coordinate arrays are not the same size.\n",
      " |      \n",
      " |      InvalidTransformError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |      \n",
      " |      InvalidTransformError\n",
      " |          Ill-conditioned coordinate transformation parameters.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The order of the axes for the result is determined by the\n",
      " |      ``CTYPEia`` keywords in the FITS header, therefore it may not\n",
      " |      always be of the form (*ra*, *dec*).  The\n",
      " |      `~astropy.wcs.Wcsprm.lat`, `~astropy.wcs.Wcsprm.lng`,\n",
      " |      `~astropy.wcs.Wcsprm.lattyp` and `~astropy.wcs.Wcsprm.lngtyp`\n",
      " |      members can be used to determine the order of the axes.\n",
      " |  \n",
      " |  wcs_world2pix(self, *args, **kwargs)\n",
      " |      Transforms world coordinates to pixel coordinates, using only\n",
      " |      the basic `wcslib`_ WCS transformation.  No `SIP`_ or\n",
      " |      `distortion paper`_ table lookup transformation is applied.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      args : flexible\n",
      " |          There are two accepted forms for the positional arguments:\n",
      " |      \n",
      " |              - 2 arguments: An *N* x *naxis* array of coordinates, and an\n",
      " |                *origin*.\n",
      " |      \n",
      " |              - more than 2 arguments: An array for each axis, followed by\n",
      " |                an *origin*.  These arrays must be broadcastable to one\n",
      " |                another.\n",
      " |      \n",
      " |          Here, *origin* is the coordinate in the upper left corner of the\n",
      " |          image.  In FITS and Fortran standards, this is 1.  In Numpy and C\n",
      " |          standards this is 0.\n",
      " |      \n",
      " |      \n",
      " |          For a transformation that is not two-dimensional, the\n",
      " |          two-argument form must be used.\n",
      " |      \n",
      " |      \n",
      " |      ra_dec_order : bool, optional\n",
      " |          When `True` will ensure that world coordinates are always given\n",
      " |          and returned in as (*ra*, *dec*) pairs, regardless of the order of\n",
      " |          the axes specified by the in the ``CTYPE`` keywords.  Default is\n",
      " |          `False`.\n",
      " |      \n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      \n",
      " |      result : array\n",
      " |          Returns the pixel coordinates.  If the input was a single array and\n",
      " |          origin, a single array is returned, otherwise a tuple of arrays is\n",
      " |          returned.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The order of the axes for the input world array is determined by\n",
      " |      the ``CTYPEia`` keywords in the FITS header, therefore it may\n",
      " |      not always be of the form (*ra*, *dec*).  The\n",
      " |      `~astropy.wcs.Wcsprm.lat`, `~astropy.wcs.Wcsprm.lng`,\n",
      " |      `~astropy.wcs.Wcsprm.lattyp` and `~astropy.wcs.Wcsprm.lngtyp`\n",
      " |      members can be used to determine the order of the axes.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      MemoryError\n",
      " |          Memory allocation failed.\n",
      " |      \n",
      " |      SingularMatrixError\n",
      " |          Linear transformation matrix is singular.\n",
      " |      \n",
      " |      InconsistentAxisTypesError\n",
      " |          Inconsistent or unrecognized coordinate axis types.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid parameter value.\n",
      " |      \n",
      " |      ValueError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |      \n",
      " |      ValueError\n",
      " |          x- and y-coordinate arrays are not the same size.\n",
      " |      \n",
      " |      InvalidTransformError\n",
      " |          Invalid coordinate transformation parameters.\n",
      " |      \n",
      " |      InvalidTransformError\n",
      " |          Ill-conditioned coordinate transformation parameters.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  axis_type_names\n",
      " |      World names for each coordinate axis\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      A list of names along each axis\n",
      " |  \n",
      " |  celestial\n",
      " |      A copy of the current WCS with only the celestial axes included\n",
      " |  \n",
      " |  has_celestial\n",
      " |  \n",
      " |  is_celestial\n",
      " |  \n",
      " |  pixel_scale_matrix\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from astropy.wcs.WCSBase:\n",
      " |  \n",
      " |  __new__(*args, **kwargs) from builtins.type\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from astropy.wcs.WCSBase:\n",
      " |  \n",
      " |  cpdis1\n",
      " |      `~astropy.wcs.DistortionLookupTable`\n",
      " |      \n",
      " |      The pre-linear transformation distortion lookup table, ``CPDIS1``.\n",
      " |  \n",
      " |  cpdis2\n",
      " |      `~astropy.wcs.DistortionLookupTable`\n",
      " |      \n",
      " |      The pre-linear transformation distortion lookup table, ``CPDIS2``.\n",
      " |  \n",
      " |  det2im1\n",
      " |      A `~astropy.wcs.DistortionLookupTable` object for detector to image plane\n",
      " |      correction in the *x*-axis.\n",
      " |  \n",
      " |  det2im2\n",
      " |      A `~astropy.wcs.DistortionLookupTable` object for detector to image plane\n",
      " |      correction in the *y*-axis.\n",
      " |  \n",
      " |  sip\n",
      " |      Get/set the `~astropy.wcs.Sip` object for performing `SIP`_ distortion\n",
      " |      correction.\n",
      " |  \n",
      " |  wcs\n",
      " |      A `~astropy.wcs.Wcsprm` object to perform the basic `wcslib`_ WCS\n",
      " |      transformation.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(wcs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 83.80979167,  -5.37638889]),\n",
       " array([ 83.80956944,  -5.37616667]),\n",
       " array([ 83.81034722,  -5.37694444])]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resol  = wcs.wcs.cdelt\n",
    "center = wcs.wcs.crval\n",
    "lpoints = [center, center + 2*resol, center -5*resol]\n",
    "lpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$2.8284271 \\; \\mathrm{{}^{\\prime\\prime}}$"
      ],
      "text/plain": [
       "<Quantity 2.828427124733067 arcsec>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import astropy.units as u\n",
    "import numpy as np\n",
    "diff = lpoints[0] - lpoints[2]\n",
    "dist = np.sqrt(diff[0]**2 + diff[1]**2)* u.deg\n",
    "dist.to(\"arcsec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 50.        ,  50.        ],\n",
       "       [ 51.99120205,  51.99999964],\n",
       "       [ 45.02200124,  44.99999774]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lpix = wcs.all_world2pix(lpoints,0)\n",
    "lpix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[50 50]\n",
      " [51 51]\n",
      " [45 44]]\n",
      "0.110144\n",
      "0.00510559\n",
      "0.000227621\n"
     ]
    }
   ],
   "source": [
    "lpix_int=lpix.astype(\"int\")\n",
    "print(lpix_int)\n",
    "for pix in lpix_int:\n",
    "    print(hdu.data[pix[0],pix[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SpTDkZ63nCtVRlh+rk7O3+GmlAnFp35o7F5g7F5ds2Ig3p6zG4sV7Nj1WREBv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\nMxVrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eab86a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(projection=wcs)\n",
    "plt.imshow(hdu.data,cmap=\"gray_r\")\n",
    "plt.scatter(lpix[:,0],lpix[:,1],c='red',marker='.')\n",
    "ini = lpix[0]\n",
    "end = lpix[2]\n",
    "plt.plot([ini[0],end[0]],[ini[1],end[1]],c=\"blue\")\n",
    "dist = np.around(dist.to(\"arcsec\"),decimals=2)\n",
    "plt.annotate(dist, xy=(ini+end)/2, xytext=(50, 30),\n",
    "           arrowprops=dict(headwidth=4,width=1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1-ScientificPython.ipynb              chivo_search.ipynb\r\n",
      "2-AstroPython.ipynb                   emhbeta_flux.fits\r\n",
      "3-ipyaladin.ipynb                     emnii6584_flux.fits\r\n",
      "ALMA-OrionKL-band6-cont.fits          emsii6731_flux.fits\r\n",
      "ALMA-OrionKL-band6-methanol.fits      \u001b[34mprep\u001b[m\u001b[m\r\n",
      "ALMA-OrionKL-band6-methanon-mom0.fits view.fits\r\n",
      "Test.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# http://muse-vlt.eu/science/m42/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WCS Keywords\n",
       "\n",
       "Number of WCS axes: 2\n",
       "CTYPE : ''  ''  \n",
       "CRVAL : 0.0  0.0  \n",
       "CRPIX : 0.0  0.0  \n",
       "PC1_1 PC1_2  : 1.0  0.0  \n",
       "PC2_1 PC2_2  : 0.0  1.0  \n",
       "CDELT : 1.0  1.0  \n",
       "NAXIS : 1766  1476"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H = fits.open(\"emhbeta_flux.fits\")[0]\n",
    "N = fits.open(\"emnii6584_flux.fits\")[0]\n",
    "S = fits.open(\"emsii6731_flux.fits\")[0]\n",
    "wcs = WCS(H.header)\n",
    "wcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def stand(A):\n",
    "    A=np.power(A,1/3)\n",
    "    A=A-A.min()\n",
    "    A=A/A.max()\n",
    "    return(A)\n",
    "\n",
    "nH =stand(H.data)\n",
    "nN =stand(N.data)\n",
    "nS =stand(S.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 1765.5, 1475.5, -0.5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q9dnD765KFDHXMFTyWX3ntL+e0n5u/B+nA0fm9FMbTVsmN7GxHtnlsYcpLe4s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7AAAg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q0RYk61YI+tAnMcWTVIIFjQ0FFUWNbMh2jqOEY4es7I0dk+5+0ALTrdsIs/RschW/90mCXa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\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F7Ma6Gv/w+8LHP3zgn95f+N43A5idTO91AysZAKxE3GRs8g4gL6bePgJQo1L28SL4r5vO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\nLTba5BlOaWiqmpq1lVgfDAS62SW+v6j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UIPG9EfbsqbGHAp2MkuBZjXMtukqy2iXSIc9a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Za+LoHPFggbw0SzIpXviD4XkcB95t8mwvtD6QfVaK3uzyfboDCc9QAMWrXW4Ds5JpZ99707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\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rFJHm3za5kTnhFMqwL4sf3FaHAZCNNMgo0E9LJ4+3y1RuMEFvSf+BGQAh9yObED2L23Cb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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eb38748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "rgbimg = np.array([nH.T,nN.T,nS.T]).T\n",
    "plt.imshow(rgbimg)\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Orion3D = fits.open(\"ALMA-OrionKL-band6-methanol.fits\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore', Warning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WCS Keywords\n",
       "\n",
       "Number of WCS axes: 4\n",
       "CTYPE : 'RA---SIN'  'DEC--SIN'  'FREQ'  'STOKES'  \n",
       "CRVAL : 83.809791666669994  -5.3763888888890001  229763161321.39999  1.0  \n",
       "CRPIX : 51.0  51.0  1.0  1.0  \n",
       "PC1_1 PC1_2 PC1_3 PC1_4  : 1.0  0.0  0.0  0.0  \n",
       "PC2_1 PC2_2 PC2_3 PC2_4  : 0.0  1.0  0.0  0.0  \n",
       "PC3_1 PC3_2 PC3_3 PC3_4  : 0.0  0.0  1.0  0.0  \n",
       "PC4_1 PC4_2 PC4_3 PC4_4  : 0.0  0.0  0.0  1.0  \n",
       "CDELT : -0.0001111111111111  0.0001111111111111  -488336.1207886  1.0  \n",
       "NAXIS : 100  100  41  1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcs = WCS(Orion3D.header)\n",
    "wcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 100, 100)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data = Orion3D.data[0]\n",
    "my_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WCS Keywords\n",
       "\n",
       "Number of WCS axes: 3\n",
       "CTYPE : 'RA---SIN'  'DEC--SIN'  'FREQ'  \n",
       "CRVAL : 83.809791666669994  -5.3763888888890001  229763161321.39999  \n",
       "CRPIX : 51.0  51.0  1.0  \n",
       "PC1_1 PC1_2 PC1_3  : 1.0  0.0  0.0  \n",
       "PC2_1 PC2_2 PC2_3  : 0.0  1.0  0.0  \n",
       "PC3_1 PC3_2 PC3_3  : 0.0  0.0  1.0  \n",
       "CDELT : -0.0001111111111111  0.0001111111111111  -488336.1207886  \n",
       "NAXIS : 100  100  41  1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_wcs = wcs.dropaxis(3)\n",
    "my_wcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vmax = my_data.max()\n",
    "vmin = my_data.min()\n",
    "\n",
    "def func(i):\n",
    "    plt.subplot(projection=my_wcs.dropaxis(2))\n",
    "    plt.imshow(my_data[i],cmap=\"seismic\",vmax=vmax,vmin=vmin)\n",
    "    plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7529dcb238c7428fa0e9d30f75b79471",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.func>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipywidgets import interact\n",
    "interact(func,i=(0,40,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) AplPy\n",
    "``conda install -c astropy aplpy``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Auto-setting vmin to -1.115e+01 [aplpy.core]\n",
      "INFO: Auto-setting vmax to  7.030e+01 [aplpy.core]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Uo55C+byUze2Ll8gWJkxt2rZR6v23fQZNa4vJKMLWYMXWAMaEjQFXJQwlTJiM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27SxwtuxxFLvzakL/cLzZPqYl7zH9nYcA01FUuZ1LWi4PYUCoV9lKVOoVAcFVS2v0jEXUeXb07e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\nwmLDDw8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1284f2ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import aplpy\n",
    "imgi = fits.open(\"ALMA-OrionKL-band6-methanon-mom0.fits\")[0]\n",
    "fig = aplpy.FITSFigure(imgi)\n",
    "fig.show_grayscale()\n",
    "fig.show_grid()\n",
    "fig.show_contour()\n",
    "fig.add_colorbar()\n",
    "fig.add_label(imgi.header['CRVAL1'],imgi.header['CRVAL2'],\"Center\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Astroquery\n",
    "\n",
    "``conda install -c astropy astroquery``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import astroquery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'All:GOODS/HDF/CDF': ['GOODS: Chandra ACIS HB',\n",
      "                       'GOODS: Chandra ACIS FB',\n",
      "                       'GOODS: Chandra ACIS SB',\n",
      "                       'GOODS: VLT VIMOS U',\n",
      "                       'GOODS: VLT VIMOS R',\n",
      "                       'GOODS: HST ACS B',\n",
      "                       'GOODS: HST ACS V',\n",
      "                       'GOODS: HST ACS I',\n",
      "                       'GOODS: HST ACS Z',\n",
      "                       'Hawaii HDF U',\n",
      "                       'Hawaii HDF B',\n",
      "                       'Hawaii HDF V0201',\n",
      "                       'Hawaii HDF V0401',\n",
      "                       'Hawaii HDF R',\n",
      "                       'Hawaii HDF I',\n",
      "                       'Hawaii HDF z',\n",
      "                       'Hawaii HDF HK',\n",
      "                       'GOODS: HST NICMOS',\n",
      "                       'GOODS: VLT ISAAC J',\n",
      "                       'GOODS: VLT ISAAC H',\n",
      "                       'GOODS: VLT ISAAC Ks',\n",
      "                       'HUDF: VLT ISAAC Ks',\n",
      "                       'GOODS: Spitzer IRAC 3.6',\n",
      "                       'GOODS: Spitzer IRAC 4.5',\n",
      "                       'GOODS: Spitzer IRAC 5.8',\n",
      "                       'GOODS: Spitzer IRAC 8.0',\n",
      "                       'GOODS: Spitzer MIPS 24',\n",
      "                       'GOODS: Herschel 100',\n",
      "                       'GOODS: Herschel 160',\n",
      "                       'GOODS: Herschel 250',\n",
      "                       'GOODS: Herschel 350',\n",
      "                       'GOODS: Herschel 500',\n",
      "                       'CDFS: LESS',\n",
      "                       'GOODS: VLA North'],\n",
      " 'All:HiPS': ['UltraVista-H',\n",
      "              'UltraVista-J',\n",
      "              'UltraVista-Ks',\n",
      "              'UltraVista-NB118',\n",
      "              'UltraVista-Y',\n",
      "              'CFHTLS-W-u',\n",
      "              'CFHTLS-W-g',\n",
      "              'CFHTLS-W-r',\n",
      "              'CFHTLS-W-i',\n",
      "              'CFHTLS-W-z',\n",
      "              'CFHTLS-D-u',\n",
      "              'CFHTLS-D-g',\n",
      "              'CFHTLS-D-r',\n",
      "              'CFHTLS-D-i',\n",
      "              'CFHTLS-D-z'],\n",
      " 'GammaRay': ['Fermi 5',\n",
      "              'Fermi 4',\n",
      "              'Fermi 3',\n",
      "              'Fermi 2',\n",
      "              'Fermi 1',\n",
      "              'EGRET (3D)',\n",
      "              'EGRET <100 MeV',\n",
      "              'EGRET >100 MeV',\n",
      "              'COMPTEL'],\n",
      " 'HardX-ray': ['INT GAL 17-35 Flux',\n",
      "               'INT GAL 17-60 Flux',\n",
      "               'INT GAL 35-80 Flux',\n",
      "               'INTEGRAL/SPI GC',\n",
      "               'GRANAT/SIGMA',\n",
      "               'RXTE Allsky 3-8keV Flux',\n",
      "               'RXTE Allsky 3-20keV Flux',\n",
      "               'RXTE Allsky 8-20keV Flux'],\n",
      " 'IR:2MASS class=': ['2MASS-J', '2MASS-H', '2MASS-K'],\n",
      " 'IR:AKARI class=': ['AKARI N60', 'AKARI WIDE-S', 'AKARI WIDE-L', 'AKARI N160'],\n",
      " 'IR:IRAS': ['IRIS  12',\n",
      "             'IRIS  25',\n",
      "             'IRIS  60',\n",
      "             'IRIS 100',\n",
      "             'SFD100m',\n",
      "             'SFD Dust Map',\n",
      "             'IRAS  12 micron',\n",
      "             'IRAS  25 micron',\n",
      "             'IRAS  60 micron',\n",
      "             'IRAS 100 micron'],\n",
      " 'IR:Planck': ['Planck 857',\n",
      "               'Planck 545',\n",
      "               'Planck 353',\n",
      "               'Planck 217',\n",
      "               'Planck 143',\n",
      "               'Planck 100',\n",
      "               'Planck 070',\n",
      "               'Planck 044',\n",
      "               'Planck 030'],\n",
      " 'IR:UKIDSS class=': ['UKIDSS-Y', 'UKIDSS-J', 'UKIDSS-H', 'UKIDSS-K'],\n",
      " 'IR:WISE class=': ['WISE 3.4', 'WISE 4.6', 'WISE 12', 'WISE 22'],\n",
      " 'IR:WMAP&COBE': ['WMAP ILC',\n",
      "                  'WMAP Ka',\n",
      "                  'WMAP K',\n",
      "                  'WMAP Q',\n",
      "                  'WMAP V',\n",
      "                  'WMAP W',\n",
      "                  'COBE DIRBE/AAM',\n",
      "                  'COBE DIRBE/ZSMA'],\n",
      " 'Optical:DSS class=': ['DSS',\n",
      "                        'DSS1 Blue',\n",
      "                        'DSS1 Red',\n",
      "                        'DSS2 Red',\n",
      "                        'DSS2 Blue',\n",
      "                        'DSS2 IR'],\n",
      " 'Optical:SDSS': ['SDSSg',\n",
      "                  'SDSSi',\n",
      "                  'SDSSr',\n",
      "                  'SDSSu',\n",
      "                  'SDSSz',\n",
      "                  'SDSSdr7g',\n",
      "                  'SDSSdr7i',\n",
      "                  'SDSSdr7r',\n",
      "                  'SDSSdr7u',\n",
      "                  'SDSSdr7z'],\n",
      " 'OtherOptical': ['Mellinger Red',\n",
      "                  'Mellinger Green',\n",
      "                  'Mellinger Blue',\n",
      "                  'NEAT',\n",
      "                  'H-Alpha Comp',\n",
      "                  'SHASSA H',\n",
      "                  'SHASSA CC',\n",
      "                  'SHASSA C',\n",
      "                  'SHASSA Sm'],\n",
      " 'ROSATDiffuse class=': ['RASS Background 1',\n",
      "                         'RASS Background 2',\n",
      "                         'RASS Background 3',\n",
      "                         'RASS Background 4',\n",
      "                         'RASS Background 5',\n",
      "                         'RASS Background 6',\n",
      "                         'RASS Background 7'],\n",
      " 'ROSATw/sources class=': ['RASS-Cnt Soft',\n",
      "                           'RASS-Cnt Hard',\n",
      "                           'RASS-Cnt Broad',\n",
      "                           'PSPC 2.0 Deg-Int',\n",
      "                           'PSPC 1.0 Deg-Int',\n",
      "                           'PSPC 0.6 Deg-Int',\n",
      "                           'HRI'],\n",
      " 'Radio:GHz': ['CO',\n",
      "               'GB6 (4850MHz)',\n",
      "               'VLA FIRST (1.4 GHz)',\n",
      "               'NVSS',\n",
      "               'Stripe82VLA',\n",
      "               '1420MHz (Bonn)',\n",
      "               'HI4PI',\n",
      "               'EBHIS',\n",
      "               'nH'],\n",
      " 'Radio:MHz class=': ['SUMSS 843 MHz',\n",
      "                      '0408MHz',\n",
      "                      'WENSS',\n",
      "                      'TGSS ADR1',\n",
      "                      'VLSSr',\n",
      "                      '0035MHz'],\n",
      " 'SoftX-ray class=': ['SwiftXRTCnt',\n",
      "                      'SwiftXRTExp',\n",
      "                      'SwiftXRTInt',\n",
      "                      'HEAO 1 A-2'],\n",
      " 'SwiftUVOT class=': ['UVOT WHITE Intensity',\n",
      "                      'UVOT V Intensity',\n",
      "                      'UVOT B Intensity',\n",
      "                      'UVOT U Intensity',\n",
      "                      'UVOT UVW1 Intensity',\n",
      "                      'UVOT UVM2 Intensity',\n",
      "                      'UVOT UVW2 Intensity'],\n",
      " 'UV': ['GALEX Near UV',\n",
      "        'GALEX Far UV',\n",
      "        'ROSAT WFC F1',\n",
      "        'ROSAT WFC F2',\n",
      "        'EUVE 83 A',\n",
      "        'EUVE 171 A',\n",
      "        'EUVE 405 A',\n",
      "        'EUVE 555 A'],\n",
      " 'X-ray:SwiftBAT': ['BAT SNR 14-195',\n",
      "                    'BAT SNR 14-20',\n",
      "                    'BAT SNR 20-24',\n",
      "                    'BAT SNR 24-35',\n",
      "                    'BAT SNR 35-50',\n",
      "                    'BAT SNR 50-75',\n",
      "                    'BAT SNR 75-100',\n",
      "                    'BAT SNR 100-150',\n",
      "                    'BAT SNR 150-195'],\n",
      " 'overlay_blue': ['Fermi 5',\n",
      "                  'Fermi 4',\n",
      "                  'Fermi 3',\n",
      "                  'Fermi 2',\n",
      "                  'Fermi 1',\n",
      "                  'EGRET (3D)',\n",
      "                  'EGRET <100 MeV',\n",
      "                  'EGRET >100 MeV',\n",
      "                  'COMPTEL',\n",
      "                  'INT GAL 17-35 Flux',\n",
      "                  'INT GAL 17-60 Flux',\n",
      "                  'INT GAL 35-80 Flux',\n",
      "                  'INTEGRAL/SPI GC',\n",
      "                  'GRANAT/SIGMA',\n",
      "                  'RXTE Allsky 3-8keV Flux',\n",
      "                  'RXTE Allsky 3-20keV Flux',\n",
      "                  'RXTE Allsky 8-20keV Flux',\n",
      "                  'BAT SNR 14-195',\n",
      "                  'BAT SNR 14-20',\n",
      "                  'BAT SNR 20-24',\n",
      "                  'BAT SNR 24-35',\n",
      "                  'BAT SNR 35-50',\n",
      "                  'BAT SNR 50-75',\n",
      "                  'BAT SNR 75-100',\n",
      "                  'BAT SNR 100-150',\n",
      "                  'BAT SNR 150-195',\n",
      "                  'SwiftXRTCnt',\n",
      "                  'SwiftXRTExp',\n",
      "                  'SwiftXRTInt',\n",
      "                  'HEAO 1 A-2',\n",
      "                  'RASS-Cnt Soft',\n",
      "                  'RASS-Cnt Hard',\n",
      "                  'RASS-Cnt Broad',\n",
      "                  'PSPC 2.0 Deg-Int',\n",
      "                  'PSPC 1.0 Deg-Int',\n",
      "                  'PSPC 0.6 Deg-Int',\n",
      "                  'HRI',\n",
      "                  'RASS Background 1',\n",
      "                  'RASS Background 2',\n",
      "                  'RASS Background 3',\n",
      "                  'RASS Background 4',\n",
      "                  'RASS Background 5',\n",
      "                  'RASS Background 6',\n",
      "                  'RASS Background 7',\n",
      "                  'GALEX Near UV',\n",
      "                  'GALEX Far UV',\n",
      "                  'ROSAT WFC F1',\n",
      "                  'ROSAT WFC F2',\n",
      "                  'EUVE 83 A',\n",
      "                  'EUVE 171 A',\n",
      "                  'EUVE 405 A',\n",
      "                  'EUVE 555 A',\n",
      "                  'UVOT WHITE Intensity',\n",
      "                  'UVOT V Intensity',\n",
      "                  'UVOT B Intensity',\n",
      "                  'UVOT U Intensity',\n",
      "                  'UVOT UVW1 Intensity',\n",
      "                  'UVOT UVM2 Intensity',\n",
      "                  'UVOT UVW2 Intensity',\n",
      "                  'DSS',\n",
      "                  'DSS1 Blue',\n",
      "                  'DSS1 Red',\n",
      "                  'DSS2 Red',\n",
      "                  'DSS2 Blue',\n",
      "                  'DSS2 IR',\n",
      "                  'SDSSg',\n",
      "                  'SDSSi',\n",
      "                  'SDSSr',\n",
      "                  'SDSSu',\n",
      "                  'SDSSz',\n",
      "                  'SDSSdr7g',\n",
      "                  'SDSSdr7i',\n",
      "                  'SDSSdr7r',\n",
      "                  'SDSSdr7u',\n",
      "                  'SDSSdr7z',\n",
      "                  'Mellinger Red',\n",
      "                  'Mellinger Green',\n",
      "                  'Mellinger Blue',\n",
      "                  'NEAT',\n",
      "                  'H-Alpha Comp',\n",
      "                  'SHASSA H',\n",
      "                  'SHASSA CC',\n",
      "                  'SHASSA C',\n",
      "                  'SHASSA Sm',\n",
      "                  'IRIS  12',\n",
      "                  'IRIS  25',\n",
      "                  'IRIS  60',\n",
      "                  'IRIS 100',\n",
      "                  'SFD100m',\n",
      "                  'SFD Dust Map',\n",
      "                  'IRAS  12 micron',\n",
      "                  'IRAS  25 micron',\n",
      "                  'IRAS  60 micron',\n",
      "                  'IRAS 100 micron',\n",
      "                  '2MASS-J',\n",
      "                  '2MASS-H',\n",
      "                  '2MASS-K',\n",
      "                  'UKIDSS-Y',\n",
      "                  'UKIDSS-J',\n",
      "                  'UKIDSS-H',\n",
      "                  'UKIDSS-K',\n",
      "                  'WISE 3.4',\n",
      "                  'WISE 4.6',\n",
      "                  'WISE 12',\n",
      "                  'WISE 22',\n",
      "                  'AKARI N60',\n",
      "                  'AKARI WIDE-S',\n",
      "                  'AKARI WIDE-L',\n",
      "                  'AKARI N160',\n",
      "                  'Planck 857',\n",
      "                  'Planck 545',\n",
      "                  'Planck 353',\n",
      "                  'Planck 217',\n",
      "                  'Planck 143',\n",
      "                  'Planck 100',\n",
      "                  'Planck 070',\n",
      "                  'Planck 044',\n",
      "                  'Planck 030',\n",
      "                  'WMAP ILC',\n",
      "                  'WMAP Ka',\n",
      "                  'WMAP K',\n",
      "                  'WMAP Q',\n",
      "                  'WMAP V',\n",
      "                  'WMAP W',\n",
      "                  'COBE DIRBE/AAM',\n",
      "                  'COBE DIRBE/ZSMA',\n",
      "                  'CO',\n",
      "                  'GB6 (4850MHz)',\n",
      "                  'VLA FIRST (1.4 GHz)',\n",
      "                  'NVSS',\n",
      "                  'Stripe82VLA',\n",
      "                  '1420MHz (Bonn)',\n",
      "                  'HI4PI',\n",
      "                  'EBHIS',\n",
      "                  'nH',\n",
      "                  'SUMSS 843 MHz',\n",
      "                  '0408MHz',\n",
      "                  'WENSS',\n",
      "                  'TGSS ADR1',\n",
      "                  'VLSSr',\n",
      "                  '0035MHz',\n",
      "                  'GOODS: Chandra ACIS HB',\n",
      "                  'GOODS: Chandra ACIS FB',\n",
      "                  'GOODS: Chandra ACIS SB',\n",
      "                  'GOODS: VLT VIMOS U',\n",
      "                  'GOODS: VLT VIMOS R',\n",
      "                  'GOODS: HST ACS B',\n",
      "                  'GOODS: HST ACS V',\n",
      "                  'GOODS: HST ACS I',\n",
      "                  'GOODS: HST ACS Z',\n",
      "                  'Hawaii HDF U',\n",
      "                  'Hawaii HDF B',\n",
      "                  'Hawaii HDF V0201',\n",
      "                  'Hawaii HDF V0401',\n",
      "                  'Hawaii HDF R',\n",
      "                  'Hawaii HDF I',\n",
      "                  'Hawaii HDF z',\n",
      "                  'Hawaii HDF HK',\n",
      "                  'GOODS: HST NICMOS',\n",
      "                  'GOODS: VLT ISAAC J',\n",
      "                  'GOODS: VLT ISAAC H',\n",
      "                  'GOODS: VLT ISAAC Ks',\n",
      "                  'HUDF: VLT ISAAC Ks',\n",
      "                  'GOODS: Spitzer IRAC 3.6',\n",
      "                  'GOODS: Spitzer IRAC 4.5',\n",
      "                  'GOODS: Spitzer IRAC 5.8',\n",
      "                  'GOODS: Spitzer IRAC 8.0',\n",
      "                  'GOODS: Spitzer MIPS 24',\n",
      "                  'GOODS: Herschel 100',\n",
      "                  'GOODS: Herschel 160',\n",
      "                  'GOODS: Herschel 250',\n",
      "                  'GOODS: Herschel 350',\n",
      "                  'GOODS: Herschel 500',\n",
      "                  'CDFS: LESS',\n",
      "                  'GOODS: VLA North',\n",
      "                  'UltraVista-H',\n",
      "                  'UltraVista-J',\n",
      "                  'UltraVista-Ks',\n",
      "                  'UltraVista-NB118',\n",
      "                  'UltraVista-Y',\n",
      "                  'CFHTLS-W-u',\n",
      "                  'CFHTLS-W-g',\n",
      "                  'CFHTLS-W-r',\n",
      "                  'CFHTLS-W-i',\n",
      "                  'CFHTLS-W-z',\n",
      "                  'CFHTLS-D-u',\n",
      "                  'CFHTLS-D-g',\n",
      "                  'CFHTLS-D-r',\n",
      "                  'CFHTLS-D-i',\n",
      "                  'CFHTLS-D-z',\n",
      "                  'None '],\n",
      " 'overlay_green': ['None \\n'\n",
      "                   ' Fermi 5Fermi 4Fermi 3Fermi 2Fermi 1EGRET (3D)EGRET <100 '\n",
      "                   'MeVEGRET >100 MeVCOMPTELINT GAL 17-35 FluxINT GAL 17-60 '\n",
      "                   'FluxINT GAL 35-80 FluxINTEGRAL/SPI GCGRANAT/SIGMARXTE '\n",
      "                   'Allsky 3-8keV FluxRXTE Allsky 3-20keV FluxRXTE Allsky '\n",
      "                   '8-20keV FluxBAT SNR 14-195BAT SNR 14-20BAT SNR 20-24BAT '\n",
      "                   'SNR 24-35BAT SNR 35-50BAT SNR 50-75BAT SNR 75-100BAT SNR '\n",
      "                   '100-150BAT SNR '\n",
      "                   '150-195SwiftXRTCntSwiftXRTExpSwiftXRTIntHEAO 1 A-2RASS-Cnt '\n",
      "                   'SoftRASS-Cnt HardRASS-Cnt BroadPSPC 2.0 Deg-IntPSPC 1.0 '\n",
      "                   'Deg-IntPSPC 0.6 Deg-IntHRIRASS Background 1RASS Background '\n",
      "                   '2RASS Background 3RASS Background 4RASS Background 5RASS '\n",
      "                   'Background 6RASS Background 7GALEX Near UVGALEX Far '\n",
      "                   'UVROSAT WFC F1ROSAT WFC F2EUVE 83 AEUVE 171 AEUVE 405 '\n",
      "                   'AEUVE 555 AUVOT WHITE IntensityUVOT V IntensityUVOT B '\n",
      "                   'IntensityUVOT U IntensityUVOT UVW1 IntensityUVOT UVM2 '\n",
      "                   'IntensityUVOT UVW2 IntensityDSSDSS1 BlueDSS1 RedDSS2 '\n",
      "                   'RedDSS2 BlueDSS2 '\n",
      "                   'IRSDSSgSDSSiSDSSrSDSSuSDSSzSDSSdr7gSDSSdr7iSDSSdr7rSDSSdr7uSDSSdr7zMellinger '\n",
      "                   'RedMellinger GreenMellinger BlueNEATH-Alpha CompSHASSA '\n",
      "                   'HSHASSA CCSHASSA CSHASSA SmIRIS  12IRIS  25IRIS  60IRIS '\n",
      "                   '100SFD100mSFD Dust MapIRAS  12 micronIRAS  25 micronIRAS  '\n",
      "                   '60 micronIRAS 100 '\n",
      "                   'micron2MASS-J2MASS-H2MASS-KUKIDSS-YUKIDSS-JUKIDSS-HUKIDSS-KWISE '\n",
      "                   '3.4WISE 4.6WISE 12WISE 22AKARI N60AKARI WIDE-SAKARI '\n",
      "                   'WIDE-LAKARI N160Planck 857Planck 545Planck 353Planck '\n",
      "                   '217Planck 143Planck 100Planck 070Planck 044Planck 030WMAP '\n",
      "                   'ILCWMAP KaWMAP KWMAP QWMAP VWMAP WCOBE DIRBE/AAMCOBE '\n",
      "                   'DIRBE/ZSMACOGB6 (4850MHz)VLA FIRST (1.4 '\n",
      "                   'GHz)NVSSStripe82VLA1420MHz (Bonn)HI4PIEBHISnHSUMSS 843 '\n",
      "                   'MHz0408MHzWENSSTGSS ADR1VLSSr0035MHzGOODS: Chandra ACIS '\n",
      "                   'HBGOODS: Chandra ACIS FBGOODS: Chandra ACIS SBGOODS: VLT '\n",
      "                   'VIMOS UGOODS: VLT VIMOS RGOODS: HST ACS BGOODS: HST ACS '\n",
      "                   'VGOODS: HST ACS IGOODS: HST ACS ZHawaii HDF UHawaii HDF '\n",
      "                   'BHawaii HDF V0201Hawaii HDF V0401Hawaii HDF RHawaii HDF '\n",
      "                   'IHawaii HDF zHawaii HDF HKGOODS: HST NICMOSGOODS: VLT '\n",
      "                   'ISAAC JGOODS: VLT ISAAC HGOODS: VLT ISAAC KsHUDF: VLT '\n",
      "                   'ISAAC KsGOODS: Spitzer IRAC 3.6GOODS: Spitzer IRAC '\n",
      "                   '4.5GOODS: Spitzer IRAC 5.8GOODS: Spitzer IRAC 8.0GOODS: '\n",
      "                   'Spitzer MIPS 24GOODS: Herschel 100GOODS: Herschel '\n",
      "                   '160GOODS: Herschel 250GOODS: Herschel 350GOODS: Herschel '\n",
      "                   '500CDFS: LESSGOODS: VLA '\n",
      "                   'NorthUltraVista-HUltraVista-JUltraVista-KsUltraVista-NB118UltraVista-YCFHTLS-W-uCFHTLS-W-gCFHTLS-W-rCFHTLS-W-iCFHTLS-W-zCFHTLS-D-uCFHTLS-D-gCFHTLS-D-rCFHTLS-D-iCFHTLS-D-z\\n',\n",
      "                   'Fermi 5',\n",
      "                   'Fermi 4',\n",
      "                   'Fermi 3',\n",
      "                   'Fermi 2',\n",
      "                   'Fermi 1',\n",
      "                   'EGRET (3D)',\n",
      "                   'EGRET <100 MeV',\n",
      "                   'EGRET >100 MeV',\n",
      "                   'COMPTEL',\n",
      "                   'INT GAL 17-35 Flux',\n",
      "                   'INT GAL 17-60 Flux',\n",
      "                   'INT GAL 35-80 Flux',\n",
      "                   'INTEGRAL/SPI GC',\n",
      "                   'GRANAT/SIGMA',\n",
      "                   'RXTE Allsky 3-8keV Flux',\n",
      "                   'RXTE Allsky 3-20keV Flux',\n",
      "                   'RXTE Allsky 8-20keV Flux',\n",
      "                   'BAT SNR 14-195',\n",
      "                   'BAT SNR 14-20',\n",
      "                   'BAT SNR 20-24',\n",
      "                   'BAT SNR 24-35',\n",
      "                   'BAT SNR 35-50',\n",
      "                   'BAT SNR 50-75',\n",
      "                   'BAT SNR 75-100',\n",
      "                   'BAT SNR 100-150',\n",
      "                   'BAT SNR 150-195',\n",
      "                   'SwiftXRTCnt',\n",
      "                   'SwiftXRTExp',\n",
      "                   'SwiftXRTInt',\n",
      "                   'HEAO 1 A-2',\n",
      "                   'RASS-Cnt Soft',\n",
      "                   'RASS-Cnt Hard',\n",
      "                   'RASS-Cnt Broad',\n",
      "                   'PSPC 2.0 Deg-Int',\n",
      "                   'PSPC 1.0 Deg-Int',\n",
      "                   'PSPC 0.6 Deg-Int',\n",
      "                   'HRI',\n",
      "                   'RASS Background 1',\n",
      "                   'RASS Background 2',\n",
      "                   'RASS Background 3',\n",
      "                   'RASS Background 4',\n",
      "                   'RASS Background 5',\n",
      "                   'RASS Background 6',\n",
      "                   'RASS Background 7',\n",
      "                   'GALEX Near UV',\n",
      "                   'GALEX Far UV',\n",
      "                   'ROSAT WFC F1',\n",
      "                   'ROSAT WFC F2',\n",
      "                   'EUVE 83 A',\n",
      "                   'EUVE 171 A',\n",
      "                   'EUVE 405 A',\n",
      "                   'EUVE 555 A',\n",
      "                   'UVOT WHITE Intensity',\n",
      "                   'UVOT V Intensity',\n",
      "                   'UVOT B Intensity',\n",
      "                   'UVOT U Intensity',\n",
      "                   'UVOT UVW1 Intensity',\n",
      "                   'UVOT UVM2 Intensity',\n",
      "                   'UVOT UVW2 Intensity',\n",
      "                   'DSS',\n",
      "                   'DSS1 Blue',\n",
      "                   'DSS1 Red',\n",
      "                   'DSS2 Red',\n",
      "                   'DSS2 Blue',\n",
      "                   'DSS2 IR',\n",
      "                   'SDSSg',\n",
      "                   'SDSSi',\n",
      "                   'SDSSr',\n",
      "                   'SDSSu',\n",
      "                   'SDSSz',\n",
      "                   'SDSSdr7g',\n",
      "                   'SDSSdr7i',\n",
      "                   'SDSSdr7r',\n",
      "                   'SDSSdr7u',\n",
      "                   'SDSSdr7z',\n",
      "                   'Mellinger Red',\n",
      "                   'Mellinger Green',\n",
      "                   'Mellinger Blue',\n",
      "                   'NEAT',\n",
      "                   'H-Alpha Comp',\n",
      "                   'SHASSA H',\n",
      "                   'SHASSA CC',\n",
      "                   'SHASSA C',\n",
      "                   'SHASSA Sm',\n",
      "                   'IRIS  12',\n",
      "                   'IRIS  25',\n",
      "                   'IRIS  60',\n",
      "                   'IRIS 100',\n",
      "                   'SFD100m',\n",
      "                   'SFD Dust Map',\n",
      "                   'IRAS  12 micron',\n",
      "                   'IRAS  25 micron',\n",
      "                   'IRAS  60 micron',\n",
      "                   'IRAS 100 micron',\n",
      "                   '2MASS-J',\n",
      "                   '2MASS-H',\n",
      "                   '2MASS-K',\n",
      "                   'UKIDSS-Y',\n",
      "                   'UKIDSS-J',\n",
      "                   'UKIDSS-H',\n",
      "                   'UKIDSS-K',\n",
      "                   'WISE 3.4',\n",
      "                   'WISE 4.6',\n",
      "                   'WISE 12',\n",
      "                   'WISE 22',\n",
      "                   'AKARI N60',\n",
      "                   'AKARI WIDE-S',\n",
      "                   'AKARI WIDE-L',\n",
      "                   'AKARI N160',\n",
      "                   'Planck 857',\n",
      "                   'Planck 545',\n",
      "                   'Planck 353',\n",
      "                   'Planck 217',\n",
      "                   'Planck 143',\n",
      "                   'Planck 100',\n",
      "                   'Planck 070',\n",
      "                   'Planck 044',\n",
      "                   'Planck 030',\n",
      "                   'WMAP ILC',\n",
      "                   'WMAP Ka',\n",
      "                   'WMAP K',\n",
      "                   'WMAP Q',\n",
      "                   'WMAP V',\n",
      "                   'WMAP W',\n",
      "                   'COBE DIRBE/AAM',\n",
      "                   'COBE DIRBE/ZSMA',\n",
      "                   'CO',\n",
      "                   'GB6 (4850MHz)',\n",
      "                   'VLA FIRST (1.4 GHz)',\n",
      "                   'NVSS',\n",
      "                   'Stripe82VLA',\n",
      "                   '1420MHz (Bonn)',\n",
      "                   'HI4PI',\n",
      "                   'EBHIS',\n",
      "                   'nH',\n",
      "                   'SUMSS 843 MHz',\n",
      "                   '0408MHz',\n",
      "                   'WENSS',\n",
      "                   'TGSS ADR1',\n",
      "                   'VLSSr',\n",
      "                   '0035MHz',\n",
      "                   'GOODS: Chandra ACIS HB',\n",
      "                   'GOODS: Chandra ACIS FB',\n",
      "                   'GOODS: Chandra ACIS SB',\n",
      "                   'GOODS: VLT VIMOS U',\n",
      "                   'GOODS: VLT VIMOS R',\n",
      "                   'GOODS: HST ACS B',\n",
      "                   'GOODS: HST ACS V',\n",
      "                   'GOODS: HST ACS I',\n",
      "                   'GOODS: HST ACS Z',\n",
      "                   'Hawaii HDF U',\n",
      "                   'Hawaii HDF B',\n",
      "                   'Hawaii HDF V0201',\n",
      "                   'Hawaii HDF V0401',\n",
      "                   'Hawaii HDF R',\n",
      "                   'Hawaii HDF I',\n",
      "                   'Hawaii HDF z',\n",
      "                   'Hawaii HDF HK',\n",
      "                   'GOODS: HST NICMOS',\n",
      "                   'GOODS: VLT ISAAC J',\n",
      "                   'GOODS: VLT ISAAC H',\n",
      "                   'GOODS: VLT ISAAC Ks',\n",
      "                   'HUDF: VLT ISAAC Ks',\n",
      "                   'GOODS: Spitzer IRAC 3.6',\n",
      "                   'GOODS: Spitzer IRAC 4.5',\n",
      "                   'GOODS: Spitzer IRAC 5.8',\n",
      "                   'GOODS: Spitzer IRAC 8.0',\n",
      "                   'GOODS: Spitzer MIPS 24',\n",
      "                   'GOODS: Herschel 100',\n",
      "                   'GOODS: Herschel 160',\n",
      "                   'GOODS: Herschel 250',\n",
      "                   'GOODS: Herschel 350',\n",
      "                   'GOODS: Herschel 500',\n",
      "                   'CDFS: LESS',\n",
      "                   'GOODS: VLA North',\n",
      "                   'UltraVista-H',\n",
      "                   'UltraVista-J',\n",
      "                   'UltraVista-Ks',\n",
      "                   'UltraVista-NB118',\n",
      "                   'UltraVista-Y',\n",
      "                   'CFHTLS-W-u',\n",
      "                   'CFHTLS-W-g',\n",
      "                   'CFHTLS-W-r',\n",
      "                   'CFHTLS-W-i',\n",
      "                   'CFHTLS-W-z',\n",
      "                   'CFHTLS-D-u',\n",
      "                   'CFHTLS-D-g',\n",
      "                   'CFHTLS-D-r',\n",
      "                   'CFHTLS-D-i',\n",
      "                   'CFHTLS-D-z'],\n",
      " 'overlay_red': ['None \\n'\n",
      "                 ' Fermi 5Fermi 4Fermi 3Fermi 2Fermi 1EGRET (3D)EGRET <100 '\n",
      "                 'MeVEGRET >100 MeVCOMPTELINT GAL 17-35 FluxINT GAL 17-60 '\n",
      "                 'FluxINT GAL 35-80 FluxINTEGRAL/SPI GCGRANAT/SIGMARXTE Allsky '\n",
      "                 '3-8keV FluxRXTE Allsky 3-20keV FluxRXTE Allsky 8-20keV '\n",
      "                 'FluxBAT SNR 14-195BAT SNR 14-20BAT SNR 20-24BAT SNR 24-35BAT '\n",
      "                 'SNR 35-50BAT SNR 50-75BAT SNR 75-100BAT SNR 100-150BAT SNR '\n",
      "                 '150-195SwiftXRTCntSwiftXRTExpSwiftXRTIntHEAO 1 A-2RASS-Cnt '\n",
      "                 'SoftRASS-Cnt HardRASS-Cnt BroadPSPC 2.0 Deg-IntPSPC 1.0 '\n",
      "                 'Deg-IntPSPC 0.6 Deg-IntHRIRASS Background 1RASS Background '\n",
      "                 '2RASS Background 3RASS Background 4RASS Background 5RASS '\n",
      "                 'Background 6RASS Background 7GALEX Near UVGALEX Far UVROSAT '\n",
      "                 'WFC F1ROSAT WFC F2EUVE 83 AEUVE 171 AEUVE 405 AEUVE 555 '\n",
      "                 'AUVOT WHITE IntensityUVOT V IntensityUVOT B IntensityUVOT U '\n",
      "                 'IntensityUVOT UVW1 IntensityUVOT UVM2 IntensityUVOT UVW2 '\n",
      "                 'IntensityDSSDSS1 BlueDSS1 RedDSS2 RedDSS2 BlueDSS2 '\n",
      "                 'IRSDSSgSDSSiSDSSrSDSSuSDSSzSDSSdr7gSDSSdr7iSDSSdr7rSDSSdr7uSDSSdr7zMellinger '\n",
      "                 'RedMellinger GreenMellinger BlueNEATH-Alpha CompSHASSA '\n",
      "                 'HSHASSA CCSHASSA CSHASSA SmIRIS  12IRIS  25IRIS  60IRIS '\n",
      "                 '100SFD100mSFD Dust MapIRAS  12 micronIRAS  25 micronIRAS  60 '\n",
      "                 'micronIRAS 100 '\n",
      "                 'micron2MASS-J2MASS-H2MASS-KUKIDSS-YUKIDSS-JUKIDSS-HUKIDSS-KWISE '\n",
      "                 '3.4WISE 4.6WISE 12WISE 22AKARI N60AKARI WIDE-SAKARI '\n",
      "                 'WIDE-LAKARI N160Planck 857Planck 545Planck 353Planck '\n",
      "                 '217Planck 143Planck 100Planck 070Planck 044Planck 030WMAP '\n",
      "                 'ILCWMAP KaWMAP KWMAP QWMAP VWMAP WCOBE DIRBE/AAMCOBE '\n",
      "                 'DIRBE/ZSMACOGB6 (4850MHz)VLA FIRST (1.4 '\n",
      "                 'GHz)NVSSStripe82VLA1420MHz (Bonn)HI4PIEBHISnHSUMSS 843 '\n",
      "                 'MHz0408MHzWENSSTGSS ADR1VLSSr0035MHzGOODS: Chandra ACIS '\n",
      "                 'HBGOODS: Chandra ACIS FBGOODS: Chandra ACIS SBGOODS: VLT '\n",
      "                 'VIMOS UGOODS: VLT VIMOS RGOODS: HST ACS BGOODS: HST ACS '\n",
      "                 'VGOODS: HST ACS IGOODS: HST ACS ZHawaii HDF UHawaii HDF '\n",
      "                 'BHawaii HDF V0201Hawaii HDF V0401Hawaii HDF RHawaii HDF '\n",
      "                 'IHawaii HDF zHawaii HDF HKGOODS: HST NICMOSGOODS: VLT ISAAC '\n",
      "                 'JGOODS: VLT ISAAC HGOODS: VLT ISAAC KsHUDF: VLT ISAAC '\n",
      "                 'KsGOODS: Spitzer IRAC 3.6GOODS: Spitzer IRAC 4.5GOODS: '\n",
      "                 'Spitzer IRAC 5.8GOODS: Spitzer IRAC 8.0GOODS: Spitzer MIPS '\n",
      "                 '24GOODS: Herschel 100GOODS: Herschel 160GOODS: Herschel '\n",
      "                 '250GOODS: Herschel 350GOODS: Herschel 500CDFS: LESSGOODS: '\n",
      "                 'VLA '\n",
      "                 'NorthUltraVista-HUltraVista-JUltraVista-KsUltraVista-NB118UltraVista-YCFHTLS-W-uCFHTLS-W-gCFHTLS-W-rCFHTLS-W-iCFHTLS-W-zCFHTLS-D-uCFHTLS-D-gCFHTLS-D-rCFHTLS-D-iCFHTLS-D-z\\n',\n",
      "                 'Fermi 5',\n",
      "                 'Fermi 4',\n",
      "                 'Fermi 3',\n",
      "                 'Fermi 2',\n",
      "                 'Fermi 1',\n",
      "                 'EGRET (3D)',\n",
      "                 'EGRET <100 MeV',\n",
      "                 'EGRET >100 MeV',\n",
      "                 'COMPTEL',\n",
      "                 'INT GAL 17-35 Flux',\n",
      "                 'INT GAL 17-60 Flux',\n",
      "                 'INT GAL 35-80 Flux',\n",
      "                 'INTEGRAL/SPI GC',\n",
      "                 'GRANAT/SIGMA',\n",
      "                 'RXTE Allsky 3-8keV Flux',\n",
      "                 'RXTE Allsky 3-20keV Flux',\n",
      "                 'RXTE Allsky 8-20keV Flux',\n",
      "                 'BAT SNR 14-195',\n",
      "                 'BAT SNR 14-20',\n",
      "                 'BAT SNR 20-24',\n",
      "                 'BAT SNR 24-35',\n",
      "                 'BAT SNR 35-50',\n",
      "                 'BAT SNR 50-75',\n",
      "                 'BAT SNR 75-100',\n",
      "                 'BAT SNR 100-150',\n",
      "                 'BAT SNR 150-195',\n",
      "                 'SwiftXRTCnt',\n",
      "                 'SwiftXRTExp',\n",
      "                 'SwiftXRTInt',\n",
      "                 'HEAO 1 A-2',\n",
      "                 'RASS-Cnt Soft',\n",
      "                 'RASS-Cnt Hard',\n",
      "                 'RASS-Cnt Broad',\n",
      "                 'PSPC 2.0 Deg-Int',\n",
      "                 'PSPC 1.0 Deg-Int',\n",
      "                 'PSPC 0.6 Deg-Int',\n",
      "                 'HRI',\n",
      "                 'RASS Background 1',\n",
      "                 'RASS Background 2',\n",
      "                 'RASS Background 3',\n",
      "                 'RASS Background 4',\n",
      "                 'RASS Background 5',\n",
      "                 'RASS Background 6',\n",
      "                 'RASS Background 7',\n",
      "                 'GALEX Near UV',\n",
      "                 'GALEX Far UV',\n",
      "                 'ROSAT WFC F1',\n",
      "                 'ROSAT WFC F2',\n",
      "                 'EUVE 83 A',\n",
      "                 'EUVE 171 A',\n",
      "                 'EUVE 405 A',\n",
      "                 'EUVE 555 A',\n",
      "                 'UVOT WHITE Intensity',\n",
      "                 'UVOT V Intensity',\n",
      "                 'UVOT B Intensity',\n",
      "                 'UVOT U Intensity',\n",
      "                 'UVOT UVW1 Intensity',\n",
      "                 'UVOT UVM2 Intensity',\n",
      "                 'UVOT UVW2 Intensity',\n",
      "                 'DSS',\n",
      "                 'DSS1 Blue',\n",
      "                 'DSS1 Red',\n",
      "                 'DSS2 Red',\n",
      "                 'DSS2 Blue',\n",
      "                 'DSS2 IR',\n",
      "                 'SDSSg',\n",
      "                 'SDSSi',\n",
      "                 'SDSSr',\n",
      "                 'SDSSu',\n",
      "                 'SDSSz',\n",
      "                 'SDSSdr7g',\n",
      "                 'SDSSdr7i',\n",
      "                 'SDSSdr7r',\n",
      "                 'SDSSdr7u',\n",
      "                 'SDSSdr7z',\n",
      "                 'Mellinger Red',\n",
      "                 'Mellinger Green',\n",
      "                 'Mellinger Blue',\n",
      "                 'NEAT',\n",
      "                 'H-Alpha Comp',\n",
      "                 'SHASSA H',\n",
      "                 'SHASSA CC',\n",
      "                 'SHASSA C',\n",
      "                 'SHASSA Sm',\n",
      "                 'IRIS  12',\n",
      "                 'IRIS  25',\n",
      "                 'IRIS  60',\n",
      "                 'IRIS 100',\n",
      "                 'SFD100m',\n",
      "                 'SFD Dust Map',\n",
      "                 'IRAS  12 micron',\n",
      "                 'IRAS  25 micron',\n",
      "                 'IRAS  60 micron',\n",
      "                 'IRAS 100 micron',\n",
      "                 '2MASS-J',\n",
      "                 '2MASS-H',\n",
      "                 '2MASS-K',\n",
      "                 'UKIDSS-Y',\n",
      "                 'UKIDSS-J',\n",
      "                 'UKIDSS-H',\n",
      "                 'UKIDSS-K',\n",
      "                 'WISE 3.4',\n",
      "                 'WISE 4.6',\n",
      "                 'WISE 12',\n",
      "                 'WISE 22',\n",
      "                 'AKARI N60',\n",
      "                 'AKARI WIDE-S',\n",
      "                 'AKARI WIDE-L',\n",
      "                 'AKARI N160',\n",
      "                 'Planck 857',\n",
      "                 'Planck 545',\n",
      "                 'Planck 353',\n",
      "                 'Planck 217',\n",
      "                 'Planck 143',\n",
      "                 'Planck 100',\n",
      "                 'Planck 070',\n",
      "                 'Planck 044',\n",
      "                 'Planck 030',\n",
      "                 'WMAP ILC',\n",
      "                 'WMAP Ka',\n",
      "                 'WMAP K',\n",
      "                 'WMAP Q',\n",
      "                 'WMAP V',\n",
      "                 'WMAP W',\n",
      "                 'COBE DIRBE/AAM',\n",
      "                 'COBE DIRBE/ZSMA',\n",
      "                 'CO',\n",
      "                 'GB6 (4850MHz)',\n",
      "                 'VLA FIRST (1.4 GHz)',\n",
      "                 'NVSS',\n",
      "                 'Stripe82VLA',\n",
      "                 '1420MHz (Bonn)',\n",
      "                 'HI4PI',\n",
      "                 'EBHIS',\n",
      "                 'nH',\n",
      "                 'SUMSS 843 MHz',\n",
      "                 '0408MHz',\n",
      "                 'WENSS',\n",
      "                 'TGSS ADR1',\n",
      "                 'VLSSr',\n",
      "                 '0035MHz',\n",
      "                 'GOODS: Chandra ACIS HB',\n",
      "                 'GOODS: Chandra ACIS FB',\n",
      "                 'GOODS: Chandra ACIS SB',\n",
      "                 'GOODS: VLT VIMOS U',\n",
      "                 'GOODS: VLT VIMOS R',\n",
      "                 'GOODS: HST ACS B',\n",
      "                 'GOODS: HST ACS V',\n",
      "                 'GOODS: HST ACS I',\n",
      "                 'GOODS: HST ACS Z',\n",
      "                 'Hawaii HDF U',\n",
      "                 'Hawaii HDF B',\n",
      "                 'Hawaii HDF V0201',\n",
      "                 'Hawaii HDF V0401',\n",
      "                 'Hawaii HDF R',\n",
      "                 'Hawaii HDF I',\n",
      "                 'Hawaii HDF z',\n",
      "                 'Hawaii HDF HK',\n",
      "                 'GOODS: HST NICMOS',\n",
      "                 'GOODS: VLT ISAAC J',\n",
      "                 'GOODS: VLT ISAAC H',\n",
      "                 'GOODS: VLT ISAAC Ks',\n",
      "                 'HUDF: VLT ISAAC Ks',\n",
      "                 'GOODS: Spitzer IRAC 3.6',\n",
      "                 'GOODS: Spitzer IRAC 4.5',\n",
      "                 'GOODS: Spitzer IRAC 5.8',\n",
      "                 'GOODS: Spitzer IRAC 8.0',\n",
      "                 'GOODS: Spitzer MIPS 24',\n",
      "                 'GOODS: Herschel 100',\n",
      "                 'GOODS: Herschel 160',\n",
      "                 'GOODS: Herschel 250',\n",
      "                 'GOODS: Herschel 350',\n",
      "                 'GOODS: Herschel 500',\n",
      "                 'CDFS: LESS',\n",
      "                 'GOODS: VLA North',\n",
      "                 'UltraVista-H',\n",
      "                 'UltraVista-J',\n",
      "                 'UltraVista-Ks',\n",
      "                 'UltraVista-NB118',\n",
      "                 'UltraVista-Y',\n",
      "                 'CFHTLS-W-u',\n",
      "                 'CFHTLS-W-g',\n",
      "                 'CFHTLS-W-r',\n",
      "                 'CFHTLS-W-i',\n",
      "                 'CFHTLS-W-z',\n",
      "                 'CFHTLS-D-u',\n",
      "                 'CFHTLS-D-g',\n",
      "                 'CFHTLS-D-r',\n",
      "                 'CFHTLS-D-i',\n",
      "                 'CFHTLS-D-z']}\n"
     ]
    }
   ],
   "source": [
    "from astroquery.skyview import SkyView\n",
    "dd=SkyView.list_surveys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['https://skyview.gsfc.nasa.gov/tempspace/fits/skv20488732635370.fits']\n"
     ]
    }
   ],
   "source": [
    "res=SkyView.get_image_list('Orion',['SDSSg'])\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('view.fits', <http.client.HTTPMessage at 0x1260d9470>)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import urllib.request\n",
    "urllib.request.urlretrieve(res[0],'view.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / conforms to FITS standard                      \n",
       "BITPIX  =                  -32 / array data type                                \n",
       "NAXIS   =                    2 / number of array dimensions                     \n",
       "NAXIS1  =                  100                                                  \n",
       "NAXIS2  =                  100                                                  \n",
       "WCSAXES =                    2 / Number of coordinate axes                      \n",
       "CRPIX1  =                 51.0 / Pixel coordinate of reference point            \n",
       "CRPIX2  =                 51.0 / Pixel coordinate of reference point            \n",
       "CDELT1  =  -0.0001111111111111 / [deg] Coordinate increment at reference point  \n",
       "CDELT2  =   0.0001111111111111 / [deg] Coordinate increment at reference point  \n",
       "CUNIT1  = 'deg'                / Units of coordinate increment and value        \n",
       "CUNIT2  = 'deg'                / Units of coordinate increment and value        \n",
       "CTYPE1  = 'RA---SIN'           / Right ascension, orthographic/synthesis project\n",
       "CTYPE2  = 'DEC--SIN'           / Declination, orthographic/synthesis projection \n",
       "CRVAL1  =       83.80979166667 / [deg] Coordinate value at reference point      \n",
       "CRVAL2  =      -5.376388888889 / [deg] Coordinate value at reference point      \n",
       "PV2_1   =                  0.0 / SIN projection parameter                       \n",
       "PV2_2   =                  0.0 / SIN projection parameter                       \n",
       "LONPOLE =                180.0 / [deg] Native longitude of celestial pole       \n",
       "LATPOLE =      -5.376388888889 / [deg] Native latitude of celestial pole        \n",
       "RESTFRQ =       230949718581.1 / [Hz] Line rest frequency                       \n",
       "RADESYS = 'FK5'                / Equatorial coordinate system                   \n",
       "EQUINOX =               2000.0 / [yr] Equinox of equatorial coordinates         \n",
       "SPECSYS = 'TOPOCENT'           / Reference frame of spectral coordinates        \n",
       "OBSGEO-X=       2225142.180269 / [m] ITRF observatory X-coordinate              \n",
       "OBSGEO-Y=      -5440307.370349 / [m] ITRF observatory Y-coordinate              \n",
       "OBSGEO-Z=      -2481029.851874 / [m] ITRF observatory Z-coordinate              \n",
       "MJD-OBS =      55946.063192222 / [d] MJD of observation matching DATE-OBS       \n",
       "DATE-OBS= '2012-01-20T01:30:59.808001' / ISO-8601 observation date matching MJD-"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdu.header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "side = 0.0001111111111111*51"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Auto-setting vmin to  2.244e+00 [aplpy.core]\n",
      "INFO: Auto-setting vmax to  2.239e+02 [aplpy.core]\n"
     ]
    },
    {
     "data": {
      "image/png": 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q4KYybAXWLlCFsijg3xw6w565adMC82EnvLZYO7KTkxO8+93vBgC8/e1vB3AR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Oj5Zg/reH5+vljdqa2nIrVr5s2KDJnaJQBZN8WmKbaRY8UhMB1WTyLw3eDODRkihxfFRuIqmPtk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1263ca048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hdulist = fits.open(\"view.fits\")\n",
    "view = hdulist[0] \n",
    "fig = aplpy.FITSFigure(view)\n",
    "fig.show_grayscale()\n",
    "fig.show_markers(center[0],center[1])\n",
    "fig.show_rectangles(center[0],center[1],width=side,height=side,edgecolor=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#from astroquery.vizier import Vizier\n",
    "from astroquery.simbad import Simbad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=128&gt;\n",
       "<table id=\"table4936449944-591364\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>idx</th><th>MAIN_ID</th><th>RA</th><th>DEC</th><th>RA_PREC</th><th>DEC_PREC</th><th>COO_ERR_MAJA</th><th>COO_ERR_MINA</th><th>RA_2</th><th>COO_ERR_ANGLE</th><th>DEC_2</th><th>RA_PREC_2</th><th>DEC_PREC_2</th><th>COO_ERR_MAJA_2</th><th>COO_ERR_MINA_2</th><th>COO_ERR_ANGLE_2</th><th>COO_QUAL</th><th>COO_WAVELENGTH</th><th>COO_BIBCODE</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th>&quot;h:m:s&quot;</th><th>&quot;d:m:s&quot;</th><th></th><th></th><th>mas</th><th>mas</th><th>&quot;h:m:s&quot;</th><th>deg</th><th>&quot;d:m:s&quot;</th><th></th><th></th><th>mas</th><th>mas</th><th>deg</th><th></th><th></th><th></th></tr></thead>\n",
       "<tr><td>0</td><td>[BE83] IR 208.99-19.39B</td><td>05 35 14.3</td><td>-05 22 35</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.3</td><td>0</td><td>-05 22 35</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>1</td><td>CXOONC J053514.3-052235</td><td>05 35 14.368</td><td>-05 22 35.97</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.368</td><td>0</td><td>-05 22 35.97</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2005ApJS..160..319G</td></tr>\n",
       "<tr><td>2</td><td>[FDB2011] MF2</td><td>05 35 14.44</td><td>-05 22 34.4</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.44</td><td>0</td><td>-05 22 34.4</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>3</td><td>MLLA  585</td><td>05 35 14.250</td><td>-05 22 35.21</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.250</td><td>0</td><td>-05 22 35.21</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>1999AJ....117.1375S</td></tr>\n",
       "<tr><td>4</td><td>[CW97] 3</td><td>05 35 14.46</td><td>-05 22 35.0</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.46</td><td>0</td><td>-05 22 35.0</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>5</td><td>[JGS92]  2</td><td>05 35 14.416</td><td>-05 22 36.39</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.416</td><td>0</td><td>-05 22 36.39</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>6</td><td>[FW2011] C34</td><td>05 35 14.4640</td><td>-05 22 35.350</td><td>8</td><td>8</td><td>--</td><td>--</td><td>05 35 14.4640</td><td>0</td><td>-05 22 35.350</td><td>8</td><td>8</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>7</td><td>[FW2011] J053514.431-052233.457</td><td>05 35 14.431</td><td>-05 22 33.46</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.431</td><td>0</td><td>-05 22 33.46</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>8</td><td>[VB99] OMC 1 N0</td><td>05 35 14.4</td><td>-05 22 33</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.4</td><td>0</td><td>-05 22 33</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>1999ApJ...526..819V</td></tr>\n",
       "<tr><td>9</td><td>[FDB2011] MF3</td><td>05 35 14.31</td><td>-05 22 37.2</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.31</td><td>0</td><td>-05 22 37.2</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>10</td><td>MLLA  591</td><td>05 35 14.218</td><td>-05 22 33.79</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.218</td><td>0</td><td>-05 22 33.79</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>1999AJ....117.1375S</td></tr>\n",
       "<tr><td>11</td><td>NAME Ori N</td><td>05 35 14.359</td><td>-05 22 32.69</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.359</td><td>0</td><td>-05 22 32.69</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2005ApJS..160..319G</td></tr>\n",
       "<tr><td>12</td><td>[FDB2011] MF12</td><td>05 35 14.18</td><td>-05 22 34.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.18</td><td>0</td><td>-05 22 34.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>13</td><td>[GD77] A</td><td>05 35 14.19</td><td>-05 22 34.0</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>05 35 14.19</td><td>90</td><td>-05 22 34.0</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>90</td><td>D</td><td>I</td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>14</td><td>[FW2011] J053514.20-052237.15</td><td>05 35 14.20</td><td>-05 22 37.1</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.20</td><td>0</td><td>-05 22 37.1</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>15</td><td>[RLK73] IRc 22</td><td>05 35 14.55</td><td>-05 22 34.2</td><td>6</td><td>6</td><td>200.000</td><td>200.000</td><td>05 35 14.55</td><td>90</td><td>-05 22 34.2</td><td>6</td><td>6</td><td>200.000</td><td>200.000</td><td>90</td><td>D</td><td></td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>16</td><td>[CW97] 4</td><td>05 35 14.24</td><td>-05 22 37.8</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.24</td><td>0</td><td>-05 22 37.8</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>17</td><td>[FDB2011] MF6</td><td>05 35 14.50</td><td>-05 22 32.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.50</td><td>0</td><td>-05 22 32.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>18</td><td>[CW97] 2</td><td>05 35 14.52</td><td>-05 22 32.8</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.52</td><td>0</td><td>-05 22 32.8</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>19</td><td>[FDB2011] MF14</td><td>05 35 14.16</td><td>-05 22 37.0</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.16</td><td>0</td><td>-05 22 37.0</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>20</td><td>NAME SMA 1</td><td>05 35 14.488</td><td>-05 22 32.10</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.488</td><td>0</td><td>-05 22 32.10</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>21</td><td>[SUK94] Peak 8</td><td>05 35 14.16</td><td>-05 22 37.3</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.16</td><td>0</td><td>-05 22 37.3</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>22</td><td>[GD77] B</td><td>05 35 14.15</td><td>-05 22 37.4</td><td>6</td><td>6</td><td>200.000</td><td>200.000</td><td>05 35 14.15</td><td>90</td><td>-05 22 37.4</td><td>6</td><td>6</td><td>200.000</td><td>200.000</td><td>90</td><td>D</td><td>I</td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>23</td><td>[GC94b] 101</td><td>05 35 14.6</td><td>-05 22 34</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.6</td><td>0</td><td>-05 22 34</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>24</td><td>[SCH2001]  21</td><td>05 35 14.099</td><td>-05 22 36.35</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.099</td><td>0</td><td>-05 22 36.35</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>25</td><td>[FDB2011] MF1</td><td>05 35 14.09</td><td>-05 22 36.7</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.09</td><td>0</td><td>-05 22 36.7</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>26</td><td>[SCE2006] 25</td><td>05 35 14.50</td><td>-05 22 38.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.50</td><td>0</td><td>-05 22 38.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>C</td><td>R</td><td>2014ApJ...790...49K</td></tr>\n",
       "<tr><td>27</td><td>[CW97] 5</td><td>05 35 14.27</td><td>-05 22 30.9</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.27</td><td>0</td><td>-05 22 30.9</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>28</td><td>GMR R</td><td>05 35 14.08</td><td>-05 22 36.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.08</td><td>0</td><td>-05 22 36.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>29</td><td>[FDB2011] Cb1</td><td>05 35 14.080</td><td>-05 22 36.73</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.080</td><td>0</td><td>-05 22 36.73</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>30</td><td>LBLS r</td><td>05 35 14.50</td><td>-05 22 31.2</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.50</td><td>0</td><td>-05 22 31.2</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>31</td><td>[FW2011] C32</td><td>05 35 14.0700</td><td>-05 22 36.581</td><td>8</td><td>8</td><td>--</td><td>--</td><td>05 35 14.0700</td><td>0</td><td>-05 22 36.581</td><td>8</td><td>8</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>32</td><td>Parenago  1822</td><td>05 35 14.07</td><td>-05 22 36.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.07</td><td>0</td><td>-05 22 36.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2009ApJS..183..261D</td></tr>\n",
       "<tr><td>33</td><td>[FW2011] J053514.278-052230.647</td><td>05 35 14.278</td><td>-05 22 30.65</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.278</td><td>0</td><td>-05 22 30.65</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>34</td><td>Parenago  1839</td><td>05 35 14.64</td><td>-05 22 33.7</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.64</td><td>0</td><td>-05 22 33.7</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2009ApJS..183..261D</td></tr>\n",
       "<tr><td>35</td><td>[HLB98] Onsala  40</td><td>05 35 14.5</td><td>-05 22 31</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.5</td><td>0</td><td>-05 22 31</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>1998A&amp;AS..132..211H</td></tr>\n",
       "<tr><td>36</td><td>[FDB2011] MF13</td><td>05 35 14.09</td><td>-05 22 32.5</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.09</td><td>0</td><td>-05 22 32.5</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>37</td><td>[JGS92] 17</td><td>05 35 14.096</td><td>-05 22 37.67</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.096</td><td>0</td><td>-05 22 37.67</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>38</td><td>[FDB2011] MF11</td><td>05 35 14.45</td><td>-05 22 30.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.45</td><td>0</td><td>-05 22 30.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>39</td><td>[FDB2011] Ca</td><td>05 35 14.560</td><td>-05 22 31.50</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.560</td><td>0</td><td>-05 22 31.50</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>40</td><td>[RLK73] IRc  2c</td><td>05 35 14.412</td><td>-05 22 30.36</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.412</td><td>0</td><td>-05 22 30.36</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2005ApJS..160..319G</td></tr>\n",
       "<tr><td>41</td><td>[RLK73] IRc  8</td><td>05 35 14.68</td><td>-05 22 35.1</td><td>6</td><td>6</td><td>200.000</td><td>200.000</td><td>05 35 14.68</td><td>90</td><td>-05 22 35.1</td><td>6</td><td>6</td><td>200.000</td><td>200.000</td><td>90</td><td>D</td><td>I</td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>42</td><td>[RLK73] IRc  2a</td><td>05 35 14.511</td><td>-05 22 30.40</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.511</td><td>0</td><td>-05 22 30.40</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>43</td><td>[FDB2011] MF24</td><td>05 35 14.68</td><td>-05 22 36.7</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.68</td><td>0</td><td>-05 22 36.7</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>44</td><td>[UMM2015] G208.993-09.387</td><td>05 35 14</td><td>-05 22.6</td><td>4</td><td>4</td><td>--</td><td>--</td><td>05 35 14</td><td>0</td><td>-05 22.6</td><td>4</td><td>4</td><td>--</td><td>--</td><td>0</td><td>D</td><td>s</td><td>2015MNRAS.446.3461U</td></tr>\n",
       "<tr><td>45</td><td>[FDB2011] MF28</td><td>05 35 14.00</td><td>-05 22 35.3</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.00</td><td>0</td><td>-05 22 35.3</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>46</td><td>[RLK73] IRc  2d</td><td>05 35 14.45</td><td>-05 22 29.9</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>05 35 14.45</td><td>90</td><td>-05 22 29.9</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>90</td><td>D</td><td>I</td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>47</td><td>[LRY2000]  67</td><td>05 35 14.708</td><td>-05 22 35.62</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.708</td><td>0</td><td>-05 22 35.62</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2000ApJ...540.1016L</td></tr>\n",
       "<tr><td>48</td><td>MLLA  587</td><td>05 35 14.71</td><td>-05 22 35.5</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.71</td><td>0</td><td>-05 22 35.5</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2000ApJ...540..236H</td></tr>\n",
       "<tr><td>49</td><td>SH  2-281</td><td>05 35 14.5</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.5</td><td>0</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>50</td><td>[BE83] IR 208.99-13.39C</td><td>05 35 14.5</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.5</td><td>0</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>51</td><td>[BE83] Maser 208.99-19.39C</td><td>05 35 14.5</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.5</td><td>0</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>52</td><td>[PCC93]  69</td><td>05 35 14.5</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.5</td><td>0</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>53</td><td>NAME Orion Core</td><td>05 35 14.5</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.5</td><td>0</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2001ApJ...560..792I</td></tr>\n",
       "<tr><td>54</td><td>[HLB98] SEST   3</td><td>05 35 14.5</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.5</td><td>0</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>1998A&amp;AS..132..211H</td></tr>\n",
       "<tr><td>55</td><td>[FW2011] J053514.579-052230.679</td><td>05 35 14.579</td><td>-05 22 30.68</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.579</td><td>0</td><td>-05 22 30.68</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>56</td><td>[CW97] 1</td><td>05 35 14.59</td><td>-05 22 30.8</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.59</td><td>0</td><td>-05 22 30.8</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>57</td><td>[FDB2011] Cb2</td><td>05 35 14.010</td><td>-05 22 37.24</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.010</td><td>0</td><td>-05 22 37.24</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>58</td><td>[FW2011] J053514.656-052238.363</td><td>05 35 14.656</td><td>-05 22 38.36</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.656</td><td>0</td><td>-05 22 38.36</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>59</td><td>[RLK73] IRc  2b</td><td>05 35 14.473</td><td>-05 22 29.61</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.473</td><td>0</td><td>-05 22 29.61</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>B</td><td></td><td>1993ApJ...406..112D</td></tr>\n",
       "<tr><td>60</td><td>[JGS92]  9A</td><td>05 35 13.982</td><td>-05 22 36.63</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 13.982</td><td>0</td><td>-05 22 36.63</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>61</td><td>[JGS92]  9B</td><td>05 35 13.982</td><td>-05 22 36.63</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 13.982</td><td>0</td><td>-05 22 36.63</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>62</td><td>[JGS92]  9</td><td>05 35 13.982</td><td>-05 22 36.63</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 13.982</td><td>0</td><td>-05 22 36.63</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>63</td><td>[RLK73] IRc 20</td><td>05 35 14.16</td><td>-05 22 29.8</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>05 35 14.16</td><td>90</td><td>-05 22 29.8</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>90</td><td>D</td><td></td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>64</td><td>[RLK73] IRc  2e</td><td>05 35 14.44</td><td>-05 22 29.2</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>05 35 14.44</td><td>90</td><td>-05 22 29.2</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>90</td><td>D</td><td></td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>65</td><td>[SCB99]  43</td><td>05 35 14.752</td><td>-05 22 35.16</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.752</td><td>0</td><td>-05 22 35.16</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>1999AJ....117.1375S</td></tr>\n",
       "<tr><td>66</td><td>EQ 053246-052422</td><td>05 35 14.3</td><td>-05 22 29</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.3</td><td>0</td><td>-05 22 29</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>67</td><td>[RLK73] IRc  2</td><td>05 35 14.49</td><td>-05 22 29.3</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.49</td><td>0</td><td>-05 22 29.3</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2002ApJ...573..366M</td></tr>\n",
       "<tr><td>68</td><td>COUP J053514.7-052238</td><td>05 35 14.717</td><td>-05 22 38.28</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.717</td><td>0</td><td>-05 22 38.28</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>1999AJ....117.1375S</td></tr>\n",
       "<tr><td>69</td><td>[FDB2011] MF4</td><td>05 35 14.15</td><td>-05 22 29.3</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.15</td><td>0</td><td>-05 22 29.3</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>70</td><td>[RLK73] IRc  3S</td><td>05 35 14.02</td><td>-05 22 30.8</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>05 35 14.02</td><td>90</td><td>-05 22 30.8</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>90</td><td>D</td><td></td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>71</td><td>MLLA  602</td><td>05 35 13.960</td><td>-05 22 32.13</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 13.960</td><td>0</td><td>-05 22 32.13</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2000ApJ...540.1016L</td></tr>\n",
       "<tr><td>72</td><td>[JGS92]  8</td><td>05 35 14.210</td><td>-05 22 41.20</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.210</td><td>0</td><td>-05 22 41.20</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>73</td><td>[FW2011] C33</td><td>05 35 14.6130</td><td>-05 22 29.688</td><td>8</td><td>8</td><td>--</td><td>--</td><td>05 35 14.6130</td><td>0</td><td>-05 22 29.688</td><td>8</td><td>8</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>74</td><td>LBLS j</td><td>05 35 14.02</td><td>-05 22 39.4</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.02</td><td>0</td><td>-05 22 39.4</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>75</td><td>COUP J053513.9-052235</td><td>05 35 13.910</td><td>-05 22 35.72</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 13.910</td><td>0</td><td>-05 22 35.72</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2005ApJS..160..319G</td></tr>\n",
       "<tr><td>76</td><td>COUP J053513.9-052237</td><td>05 35 13.939</td><td>-05 22 37.49</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 13.939</td><td>0</td><td>-05 22 37.49</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2005ApJS..160..319G</td></tr>\n",
       "<tr><td>77</td><td>[OW94] 147-234</td><td>05 35 14.8</td><td>-05 22 34</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.8</td><td>0</td><td>-05 22 34</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>78</td><td>[MGR2000b] 1</td><td>05 35 14.8</td><td>-05 22 34</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.8</td><td>0</td><td>-05 22 34</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2000AJ....120.1954M</td></tr>\n",
       "<tr><td>79</td><td>[JGS92]  4</td><td>05 35 13.901</td><td>-05 22 33.90</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 13.901</td><td>0</td><td>-05 22 33.90</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>80</td><td>[RLK73] IRc 11</td><td>05 35 14.71</td><td>-05 22 30.8</td><td>6</td><td>6</td><td>200.000</td><td>200.000</td><td>05 35 14.71</td><td>90</td><td>-05 22 30.8</td><td>6</td><td>6</td><td>200.000</td><td>200.000</td><td>90</td><td>D</td><td></td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>81</td><td>[FW2011] J053514.309-052241.839</td><td>05 35 14.309</td><td>-05 22 41.84</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.309</td><td>0</td><td>-05 22 41.84</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>82</td><td>OH 209.0   -19.4</td><td>05 35 14.4</td><td>-05 22 28</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.4</td><td>0</td><td>-05 22 28</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>83</td><td>[RLK73] IRc  3N</td><td>05 35 14.01</td><td>-05 22 30.1</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>05 35 14.01</td><td>90</td><td>-05 22 30.1</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>90</td><td>D</td><td></td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>84</td><td>[RLK73] IRc 21</td><td>05 35 14.41</td><td>-05 22 27.9</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>05 35 14.41</td><td>90</td><td>-05 22 27.9</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>90</td><td>D</td><td></td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>85</td><td>NAME OMC 1 Cluster</td><td>05 35 14</td><td>-05 22.5</td><td>4</td><td>4</td><td>--</td><td>--</td><td>05 35 14</td><td>0</td><td>-05 22.5</td><td>4</td><td>4</td><td>--</td><td>--</td><td>0</td><td>E</td><td></td><td>2003A&amp;A...397..177B</td></tr>\n",
       "<tr><td>86</td><td>[FW2011] J053514.253-052227.820</td><td>05 35 14.253</td><td>-05 22 27.82</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.253</td><td>0</td><td>-05 22 27.82</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>87</td><td>[FW2011] J053514.614-052228.696</td><td>05 35 14.614</td><td>-05 22 28.70</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.614</td><td>0</td><td>-05 22 28.70</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>88</td><td>[SBE98] CB4</td><td>05 35 14.70</td><td>-05 22 29.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.70</td><td>0</td><td>-05 22 29.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2006ApJ...642..339S</td></tr>\n",
       "<tr><td>89</td><td>[DRS2009] 1494</td><td>05 35 14.51</td><td>-05 22 42.2</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.51</td><td>0</td><td>-05 22 42.2</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2009ApJS..183..261D</td></tr>\n",
       "<tr><td>90</td><td>[LRY2000]  69</td><td>05 35 14.718</td><td>-05 22 29.77</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.718</td><td>0</td><td>-05 22 29.77</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2000ApJ...540.1016L</td></tr>\n",
       "<tr><td>91</td><td>[EC2006] MM4</td><td>05 35 14.86</td><td>-05 22 35.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.86</td><td>0</td><td>-05 22 35.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2006ApJ...641.1162E</td></tr>\n",
       "<tr><td>92</td><td>MLLA  603</td><td>05 35 14.82</td><td>-05 22 31.9</td><td>6</td><td>6</td><td>150.000</td><td>150.000</td><td>05 35 14.82</td><td>90</td><td>-05 22 31.9</td><td>6</td><td>6</td><td>150.000</td><td>150.000</td><td>90</td><td>C</td><td>O</td><td>2009A&amp;A...502..883R</td></tr>\n",
       "<tr><td>93</td><td>NAME OHC-E</td><td>05 35 14.730</td><td>-05 22 29.83</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.730</td><td>0</td><td>-05 22 29.83</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2015ApJ...808..146R</td></tr>\n",
       "<tr><td>94</td><td>[SCE2006] 14</td><td>05 35 14.73</td><td>-05 22 29.8</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.73</td><td>0</td><td>-05 22 29.8</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2000ApJ...540..236H</td></tr>\n",
       "<tr><td>95</td><td>[SCE2006] 15</td><td>05 35 14.732</td><td>-05 22 29.72</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.732</td><td>0</td><td>-05 22 29.72</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2005ApJS..160..319G</td></tr>\n",
       "<tr><td>96</td><td>[RLK73] IRc 12</td><td>05 35 14.81</td><td>-05 22 31.3</td><td>6</td><td>6</td><td>200.000</td><td>200.000</td><td>05 35 14.81</td><td>90</td><td>-05 22 31.3</td><td>6</td><td>6</td><td>200.000</td><td>200.000</td><td>90</td><td>D</td><td></td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>97</td><td>[FDB2011] MF10</td><td>05 35 14.68</td><td>-05 22 28.9</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.68</td><td>0</td><td>-05 22 28.9</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>98</td><td>TPSC 77</td><td>05 35 14.74</td><td>-05 22 29.7</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.74</td><td>0</td><td>-05 22 29.7</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2000AJ....120.3162L</td></tr>\n",
       "<tr><td>99</td><td>[FW2011] J053514.797-052230.557</td><td>05 35 14.797</td><td>-05 22 30.56</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.797</td><td>0</td><td>-05 22 30.56</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>100</td><td>[HAB84] 44</td><td>05 35 14.66</td><td>-05 22 41.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.66</td><td>0</td><td>-05 22 41.6</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>101</td><td>[JGS92] 12</td><td>05 35 13.931</td><td>-05 22 29.91</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 13.931</td><td>0</td><td>-05 22 29.91</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>102</td><td>[FDB2011] MF5</td><td>05 35 14.12</td><td>-05 22 27.7</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.12</td><td>0</td><td>-05 22 27.7</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>103</td><td>[FW2011] J053514.155-052227.193</td><td>05 35 14.155</td><td>-05 22 27.19</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.155</td><td>0</td><td>-05 22 27.19</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>104</td><td>CXOONC J053513.9-052229</td><td>05 35 13.911</td><td>-05 22 29.85</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 13.911</td><td>0</td><td>-05 22 29.85</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2005ApJS..160..319G</td></tr>\n",
       "<tr><td>105</td><td>[OW94] 139-230</td><td>05 35 13.9</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 13.9</td><td>0</td><td>-05 22 30</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2006ApJ...642..339S</td></tr>\n",
       "<tr><td>106</td><td>[SCB99]  46</td><td>05 35 14.782</td><td>-05 22 29.60</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.782</td><td>0</td><td>-05 22 29.60</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>1999AJ....117.1375S</td></tr>\n",
       "<tr><td>107</td><td>[SCB99]  45</td><td>05 35 14.770</td><td>-05 22 29.34</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.770</td><td>0</td><td>-05 22 29.34</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>1999AJ....117.1375S</td></tr>\n",
       "<tr><td>108</td><td>[FW2011] J053514.556-052227.128</td><td>05 35 14.556</td><td>-05 22 27.13</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.556</td><td>0</td><td>-05 22 27.13</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>109</td><td>2MASS J05351486-0522315</td><td>05 35 14.870</td><td>-05 22 31.57</td><td>7</td><td>7</td><td>120.000</td><td>110.000</td><td>05 35 14.870</td><td>39</td><td>-05 22 31.57</td><td>7</td><td>7</td><td>120.000</td><td>110.000</td><td>39</td><td>B</td><td>I</td><td>2003yCat.2246....0C</td></tr>\n",
       "<tr><td>110</td><td>[FDB2011] Cc</td><td>05 35 14.080</td><td>-05 22 27.50</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.080</td><td>0</td><td>-05 22 27.50</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>111</td><td>[FDB2011] MF7</td><td>05 35 14.05</td><td>-05 22 42.3</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.05</td><td>0</td><td>-05 22 42.3</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>112</td><td>LBLS f</td><td>05 35 13.78</td><td>-05 22 33.4</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 13.78</td><td>0</td><td>-05 22 33.4</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>113</td><td>[RLK73] IRc  6</td><td>05 35 14.18</td><td>-05 22 26.7</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.18</td><td>0</td><td>-05 22 26.7</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>I</td><td>1981ApJ...244..869D</td></tr>\n",
       "<tr><td>114</td><td>[BPN78] Pk 4</td><td>05 35 13.79</td><td>-05 22 31.8</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 13.79</td><td>0</td><td>-05 22 31.8</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>115</td><td>[FDB2011] MF9</td><td>05 35 14.11</td><td>-05 22 43.3</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.11</td><td>0</td><td>-05 22 43.3</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>116</td><td>[FW2011] J053514.096-052226.734</td><td>05 35 14.096</td><td>-05 22 26.73</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.096</td><td>0</td><td>-05 22 26.73</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>117</td><td>V* V1330 Ori</td><td>05 35 14.90</td><td>-05 22 39.2</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 14.90</td><td>0</td><td>-05 22 39.2</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>C</td><td>O</td><td>2009ApJS..183..261D</td></tr>\n",
       "<tr><td>118</td><td>[JGS92]  5</td><td>05 35 14.219</td><td>-05 22 25.92</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.219</td><td>0</td><td>-05 22 25.92</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>119</td><td>[FW2011] J053513.981-052242.611</td><td>05 35 13.981</td><td>-05 22 42.61</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 13.981</td><td>0</td><td>-05 22 42.61</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>120</td><td>[CBA97] 8</td><td>05 35 14.33</td><td>-05 22 25.2</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>05 35 14.33</td><td>90</td><td>-05 22 25.2</td><td>6</td><td>6</td><td>100.000</td><td>100.000</td><td>90</td><td>D</td><td></td><td>2004AJ....128..363S</td></tr>\n",
       "<tr><td>121</td><td>[FDB2011] MF23</td><td>05 35 15.01</td><td>-05 22 34.8</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 15.01</td><td>0</td><td>-05 22 34.8</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td>m</td><td>2011A&amp;A...532A..32F</td></tr>\n",
       "<tr><td>122</td><td>MLLA  594</td><td>05 35 15.01</td><td>-05 22 33.9</td><td>6</td><td>6</td><td>--</td><td>--</td><td>05 35 15.01</td><td>0</td><td>-05 22 33.9</td><td>6</td><td>6</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td>2002ApJ...573..366M</td></tr>\n",
       "<tr><td>123</td><td>[FW2011] J053514.519-052244.609</td><td>05 35 14.519</td><td>-05 22 44.61</td><td>7</td><td>7</td><td>--</td><td>--</td><td>05 35 14.519</td><td>0</td><td>-05 22 44.61</td><td>7</td><td>7</td><td>--</td><td>--</td><td>0</td><td>C</td><td>m</td><td>2011ApJ...742...64F</td></tr>\n",
       "<tr><td>124</td><td>[BE83] Maser 208.99-19.39B</td><td>05 35 14.4</td><td>-05 22 25</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.4</td><td>0</td><td>-05 22 25</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>125</td><td>Caswell OH 208.99-19.38</td><td>05 35 14.4</td><td>-05 22 25</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 14.4</td><td>0</td><td>-05 22 25</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "<tr><td>126</td><td>[OW94] 150-231</td><td>05 35 14.98</td><td>-05 22 31.4</td><td>6</td><td>6</td><td>150.000</td><td>150.000</td><td>05 35 14.98</td><td>90</td><td>-05 22 31.4</td><td>6</td><td>6</td><td>150.000</td><td>150.000</td><td>90</td><td>C</td><td>O</td><td>2009A&amp;A...502..883R</td></tr>\n",
       "<tr><td>127</td><td>[OW94] 136-233</td><td>05 35 13.7</td><td>-05 22 32</td><td>5</td><td>5</td><td>--</td><td>--</td><td>05 35 13.7</td><td>0</td><td>-05 22 32</td><td>5</td><td>5</td><td>--</td><td>--</td><td>0</td><td>D</td><td></td><td></td></tr>\n",
       "</table><style>table.dataTable {clear: both; width: auto !important; margin: 0 !important;}\n",
       ".dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{\n",
       "display: inline-block; margin-right: 1em; }\n",
       ".paginate_button { margin-right: 5px; }\n",
       "</style>\n",
       "<script>\n",
       "\n",
       "var astropy_sort_num = function(a, b) {\n",
       "    var a_num = parseFloat(a);\n",
       "    var b_num = parseFloat(b);\n",
       "\n",
       "    if (isNaN(a_num) && isNaN(b_num))\n",
       "        return ((a < b) ? -1 : ((a > b) ? 1 : 0));\n",
       "    else if (!isNaN(a_num) && !isNaN(b_num))\n",
       "        return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));\n",
       "    else\n",
       "        return isNaN(a_num) ? -1 : 1;\n",
       "}\n",
       "\n",
       "require.config({paths: {\n",
       "    datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n",
       "}});\n",
       "require([\"datatables\"], function(){\n",
       "    console.log(\"$('#table4936449944-591364').dataTable()\");\n",
       "    \n",
       "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n",
       "    \"optionalnum-asc\": astropy_sort_num,\n",
       "    \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n",
       "});\n",
       "\n",
       "    $('#table4936449944-591364').dataTable({\n",
       "        order: [],\n",
       "        pageLength: 50,\n",
       "        lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n",
       "        pagingType: \"full_numbers\",\n",
       "        columnDefs: [{targets: [0, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15], type: \"optionalnum\"}]\n",
       "    });\n",
       "});\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astropy.coordinates import SkyCoord\n",
    "c = SkyCoord(ra=center[0]*u.deg, dec=center[1]*u.deg)\n",
    "tab = Simbad.query_region(c, radius=side/2.0*u.deg)\n",
    "tab.show_in_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Auto-setting vmin to  2.244e+00 [aplpy.core]\n",
      "INFO: Auto-setting vmax to  2.239e+02 [aplpy.core]\n"
     ]
    },
    {
     "data": {
      "image/png": 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SqE9JYe+kSQxctIit119Pdb9+9HnlFUZedx0xhYX4kpLIfuIJog8cYNdvf0vZ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dt9ZDenpxJyiRPvmHLb4/F44ZoP9ajNPtSTC4LbRBhsG8SLBfOMoiEPUvVDNsSI683KtG2r7Fe7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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e78d518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from astropy.coordinates import Angle\n",
    "fig = aplpy.FITSFigure(view)\n",
    "fig.show_grayscale()\n",
    "fig.show_markers(center[0],center[1],c=\"cyan\")\n",
    "fig.show_rectangles(center[0],center[1],width=side,height=side,edgecolor=\"blue\")\n",
    "ra_list=[]\n",
    "dec_list=[]\n",
    "for row in tab:\n",
    "    p = SkyCoord(row['RA'],row['DEC'], unit=(u.hourangle, u.deg))\n",
    "    ra_list.append(p.ra.value)\n",
    "    dec_list.append(p.dec.value)\n",
    "fig.show_markers(ra_list,dec_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Visit affliation page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}