Merge pull request #24 from python4astronomers/v2

Update for astropy 1.0, Python 3, and generally refresh

source/astropy-UVES/UVES.rst

@@ -1,4 +1,4 @@
-Analyzing UVES Spectroscopy with Astropy
+Astropy II: Analyzing UVES Spectroscopy
 ========================================
 
 This tutorial follows my real live data analysis of MN Lup and the code developed
@@ -25,15 +25,21 @@ Please download this
 the content, either by clicking on the link or by executing this
 python code::
 
-    import urllib2, tarfile
+    from astropy.extern.six.moves.urllib import request
+    import tarfile
     url = 'http://python4astronomers.github.io/_downloads/astropy_UVES.tar.gz'
-    tarfile.open(fileobj=urllib2.urlopen(url), mode='r|*').extractall()
+    tarfile.open(fileobj=request.urlopen(url), mode='r|*').extractall()
     cd UVES
     ls
 
-Then start up IPython with the ``--pylab`` option to enable easy plotting::
+Then start up IPython with the ``--matplotlib`` option and do the usual imports
+to enable interactive plotting::
+
+    $ ipython --matplotlib
+
+    import numpy as np
+    import matplotlib.pyplot as plt
 
-    ipython --pylab
 
 Acknowledgments
 ---------------
@@ -253,7 +259,7 @@ than two lines::
     # Let's just print the setup on the screen
     # We'll see if it's all the same.
     for f in filelist:
-        print read_setup(f)
+        print(read_setup(f))
 
 .. raw:: html
 
@@ -280,12 +286,12 @@ Units and constants in astropy
 Often, one has to keep track of the units for certain values. Was the wavelength
 given in Angstrom or in nm? In X-ray observations, a common unit of wavelength is
 keV. How many nm is 0.65 keV?
-`astropy.units <http://docs.astropy.org/en/v0.2.1/units/index.html>`_
+`astropy.units <http://docs.astropy.org/en/stable/units/index.html>`_
 offers a framework that can take
 care of this book-keeping and propagate the units through many (but not all)
 mathematical operations (e.g. addition, division, multiplication). 
 Furthermore, 
-`astropy.constants <http://docs.astropy.org/en/v0.2.1/constants/index.html>`_  supplies the values of
+`astropy.constants <http://docs.astropy.org/en/stable/constants/index.html>`_  supplies the values of
 many physical and astronomical constants.
 The easiest way to attach a unit to a number is by multiplication::
 
@@ -326,13 +332,13 @@ energy conservation:
 So, let us calculate the free-fall velocity for MN Lup::
 
     >>> v_accr = (2.* G * M_MN_Lup/R_MN_Lup)**0.5 
-    >>> print v_accr
+    >>> print(v_accr)
     504469.027564 m / (s)
     >>> # Maybe astronomers prefer it in the traditional cgs system?
-    >>> print v_accr.cgs
+    >>> print(v_accr.cgs)
     50446902.7564 cm / (s)
     >>> # Or in some really obscure unit?
-    >>> print v_accr.to(u.mile / u.hour)
+    >>> print(v_accr.to(u.mile / u.hour))
     1128465.07598 mi / (h)
 
 How does the accretion velocity relate to the rotational velocity?
@@ -374,7 +380,7 @@ to tell astropy that this number is dimensionless and does not carry any scaling
     wavelength = wavelength * (1. * u.dimensionless_unscaled+ heliocentric/c)
 
 I want to mention one more feature here (check out 
-`astropy.units <http://docs.astropy.org/en/v0.2.1/units/index.html>`_ for
+`astropy.units <http://docs.astropy.org/en/stable/units/index.html>`_ for
 more): The ability to convert the spectral axis to frequencies or energies.
 Normally, a unit of length is not equivalent to a unit of energy or to a
 frequency, but this conversion makes sense for the wavelength of a spectrum.
@@ -403,7 +409,7 @@ This is how it can be done::
 The values from evolutionary tracks are indeed consistent with the 
 spectroscopically estimated surface gravity::
    
-    >>> print np.log10((G*M_MN_Lup/R_MN_Lup**2).cgs)
+    >>> print(np.log10((G*M_MN_Lup/R_MN_Lup**2).cgs))
     4.3080943799180433
 
 .. raw:: html
@@ -427,7 +433,7 @@ spectroscopically estimated surface gravity::
     of the following wavelengths relative to :math:`H_\alpha`::
 
         waveclosetoHa = np.array([6562.,6563,6565.]) * u.AA
-        print wave2doppler(waveclosetoHa, 656.489 * u.nm)
+        print(wave2doppler(waveclosetoHa, 656.489 * u.nm))
 
     I get -132, -86 and +5 km/s.
 
@@ -441,7 +447,7 @@ spectroscopically estimated surface gravity::
         doppler = ((w-w0)/w0 * c)
         return doppler
 
-    print wave2doppler(waveclosetoHa, 656.489 * u.nm).decompose().to(u.km/u.s)
+    print(wave2doppler(waveclosetoHa, 656.489 * u.nm).decompose().to(u.km/u.s))
 
 .. raw:: html
 
@@ -478,7 +484,7 @@ spectroscopically estimated surface gravity::
 
 Converting times
 ----------------
-`astropy.time <http://docs.astropy.org/en/v0.2.1/time/index.html>`_ 
+`astropy.time <http://docs.astropy.org/en/stable/time/index.html>`_ 
 provides methods to convert times and dates between different
 systems and formats. Since the ESO fits headers already contain the time of the
 observation in different systems, we could just read the keyword in the time
@@ -579,7 +585,7 @@ for the first spectrum::
 
     ew = fcaII[0,:] - 1.
     ew = ew[:-1] * np.diff(wcaII.to(u.AA).value)
-    print ew.sum()
+    print(ew.sum())
 
 Using ``numpy`` array notation we can actually process all spectra at once::
 
@@ -589,7 +595,7 @@ Using ``numpy`` array notation we can actually process all spectra at once::
 Now, we want to generate a LaTeX table of the observation times, period
 and equivalent width that we can directly paste into our manuscript. To do so,
 we first collect all the columns and make an ``astropy.table.Table`` object. (Please
-check `astropy.table <http://docs.astropy.org/en/v0.2.1/table/index.html>`_
+check `astropy.table <http://docs.astropy.org/en/stable/table/index.html>`_
 or :ref:`tabular-data` for more
 details on ``Table``). So, here is the code::
 

source/astropy/astropy.rst

@@ -1,5 +1,5 @@
-Astropy
-=======
+Astropy I: core functions
+=========================
 
 `Astropy <http://www.astropy.org>`_ is a community-developed core Python
 package for Astronomy (with the term used in the broad sense, from Solar
@@ -32,15 +32,20 @@ Please download this
 the content, either by clicking on the link or by executing this
 python code::
 
-    import urllib2, tarfile
+    from astropy.extern.six.moves.urllib import request
+    import tarfile
     url = 'http://python4astronomers.github.io/_downloads/astropy_examples.tar'
-    tarfile.open(fileobj=urllib2.urlopen(url), mode='r|').extractall()
+    tarfile.open(fileobj=request.urlopen(url), mode='r|').extractall()
     cd data
     ls
 
-Then start up IPython with the ``--pylab`` option to enable easy plotting::
+Then start up IPython with the ``--matplotlib`` option to enable interactive
+plotting and then import numpy and matplotlib::
 
-    ipython --pylab
+    $ ipython --matplotlib
+
+    import numpy as np
+    import matplotlib.pyplot as plt
 
 Acknowledgments
 ---------------

source/astropy/coordinates.rst

@@ -92,10 +92,10 @@ Practical Exercises
 
     >>> from astropy import coordinates as coord
     >>> crab = coord.ICRSCoordinates.from_name('M1')
-    >>> print crab
+    >>> print(crab)
     <ICRSCoordinates RA=83.63308 deg, Dec=22.01450 deg>
     >>> crab_gal = crab.transform_to(coord.GalacticCoordinates)
-    >>> print crab_gal
+    >>> print(crab_gal)
     <GalacticCoordinates l=-175.44248 deg, b=-5.78434 deg>
 
 .. raw:: html

source/astropy/fits.rst

@@ -1,9 +1,11 @@
+.. include:: ../references.rst
+
 .. _handling-fits-files:
 
 Handling FITS files
 ===================
 
-.. note:: If you are already familiar with PyFITS, `astropy.io.fits` is in
+.. note:: If you are already familiar with PyFITS, `astropy.io.fits`_ is in
           fact the same code as the latest version of PyFITS, and you can
           adapt old scripts that use PyFITS to use Astropy by simply doing::
 
@@ -17,7 +19,7 @@ Documentation
 -------------
 
 For more information about the features presented below, you can read the
-`astropy.io.fits <http://docs.astropy.org/en/v0.2/io/fits/index.html>`_ docs.
+`astropy.io.fits`_ docs.
 
 
 
@@ -108,7 +110,8 @@ header keywords using standard item notation::
     >>> hdu.header['INSTRUME']
     'LAT'
 
-Provided that we started up ``ipython`` with the ``--pylab`` flag, we can plot
+Provided that we started up ``ipython`` with the ``--matplotlib`` flag and did
+``import matplotlib.pyplot as plt``, we can plot
 one of the slices in photon energy::
 
     >>> plt.imshow(hdu.data[0,:,:], origin='lower')
@@ -215,14 +218,14 @@ Accessing Tabular Data
 
 In Astropy 0.2, FITS tables cannot be read/written directly from the ``Table``
 class. To create a ``Table`` object from a FITS table, you can use
-``astropy.io.fits``::
+`astropy.io.fits`_::
 
     >>> from astropy.io import fits
     >>> from astropy.table import Table
     >>> data = fits.getdata('catalog.fits', 1)
     >>> t = Table(data)
     
-and to write out, you can use ``astropy.io.fits``, converting the table to a
+and to write out, you can use `astropy.io.fits`_, converting the table to a
 Numpy array::
 
     >>> fits.writeto('new_catalog.fits', np.array(t))
@@ -285,7 +288,7 @@ Practical Exercises
     Using Matplotlib, make an all-sky plot of the LAT Background Model in the
     Plate Carée projection showing the LAT Point Source Catalog overlaid with
     markers, and with the correct coordinates on the axes. You should do this
-    using only ``astropy.io.fits``, Numpy, and Matplotlib (no WCS or
+    using only `astropy.io.fits`_, Numpy, and Matplotlib (no WCS or
     coordinate conversion library). Hint: the -CAR projection is such that the
     x pixel position is proportional to longitude, and the y pixel position to
     latitude. Bonus points for a pretty colormap.

source/astropy/tables.rst

@@ -17,7 +17,7 @@ Documentation
 -------------
 
 For more information about the features presented below, you can read the
-`astropy.table <http://docs.astropy.org/en/v0.2.1/table/index.html>`_ docs.
+`astropy.table <http://docs.astropy.org/en/stable/table/index.html>`_ docs.
 
 
 Constructing and Manipulating tables
@@ -43,7 +43,7 @@ about the table values and column definitions as follows::
 If you print the table (either from the noteboook or in a text console
 session) then a formatted version appears::
 
-  >>> print t
+  >>> print(t)
     a   b   c
   --- --- ---
     1 2.0   x
@@ -79,7 +79,7 @@ arrays::
 One can retrieve a subset of a table by rows (using a slice) or columns (using
 column names), where the subset is returned as a new table::
 
-    >>> print t[0:2]      # Table object with rows 0 and 1
+    >>> print(t[0:2])      # Table object with rows 0 and 1
      a   b   c
     --- --- ---
       1 2.0   x
@@ -99,7 +99,7 @@ Modifying table values in place is flexible and works as one would expect::
     >>> t[1] = (8, 9.0, "W")        # Set all row values
     >>> t[1]['b'] = -9              # Set column 'b' of row 1
     >>> t[0:2]['b'] = 100.0         # Set column 'c' of rows 0 and 1
-    >>> print t
+    >>> print(t)
      a    b    c
     --- ----- ---
      -1 100.0   x
@@ -132,7 +132,7 @@ Lastly, one can create a table with support for missing values, for example by s
            fill_value = (999999, 1e+20, 'N'),
                 dtype = [('a', '<i8'), ('b', '<f8'), ('c', '|S1')])
 
-    >>> print t
+    >>> print(t)
      a   b   c
     --- --- ---
      -- 2.0   x
@@ -159,7 +159,7 @@ You can read this in as a ``Table`` object by simply doing::
 (just ignore the warnings, which are due to Vizier not complying with the VO
 standard). We can see a quick overview of the table with::
 
-    >>> print t
+    >>> print(t)
          _1RXS        RAJ2000   DEJ2000  PosErr NewFlag   Count    e_Count   HR1  e_HR1  HR2  e_HR2 Extent
     ---------------- --------- --------- ------ ------- --------- --------- ----- ----- ----- ----- ------
     J000000.0-392902   0.00000 -39.48403     19    __..      0.13     0.035  0.69  0.25  0.28  0.24      0
@@ -178,7 +178,8 @@ standard). We can see a quick overview of the table with::
     J235944.7+220014 359.93625  22.00389     17    __..     0.052     0.015 -0.01  0.27  0.37  0.35      0
     J235959.1+083355 359.99625   8.56528     10    __..      0.12     0.018  0.54  0.13  0.10  0.17      9
 
-Since we are using IPython with the ``--pylab`` option, we can easily make a
+Since we are using IPython with the ``--matplotlib`` option along with 
+``import matplotlib.pyplot as plt``, we can easily make a
 histogram of the count rates::
 
     >>> plt.hist(t['Count'], range=[0., 2], bins=100)
@@ -218,7 +219,7 @@ Practical Exercises
 ::
 
     >>> t.keep_columns(['RAJ2000', 'DEJ2000', 'Count'])
-    >>> print t
+    >>> print(t)
      RAJ2000   DEJ2000    Count
     --------- --------- ---------
       0.00000 -39.48403      0.13
@@ -239,7 +240,7 @@ Practical Exercises
     Note that you can also do this with::
     
     >>> t_new = t['RAJ2000', 'DEJ2000', 'Count']
-    >>> print t_new
+    >>> print(t_new)
      RAJ2000   DEJ2000    Count
     --------- --------- ---------
       0.00000 -39.48403      0.13