Fix print statements for Py3

source/astropy-UVES/UVES.rst

@@ -253,7 +253,7 @@ 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
 
@@ -427,7 +427,7 @@ 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 +441,7 @@ 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
 
@@ -579,7 +579,7 @@ 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::
 

source/astropy/wcs.rst

@@ -160,7 +160,7 @@ image have a valid position on the sky.
     values = image[py, px]
 
     # Print out the values
-    print values
+    print(values)
 
 which gives::
 

source/core/ipython.rst

@@ -35,7 +35,7 @@ matplotlib routines and are widely used in scientific code.
   `import <http://docs.python.org/reference/simple_stmts.html#import>`_ statement 
   makes the functions in a module available::
 
-    print time.ctime()    # will fail
+    print(time.ctime())   # will fail
     # need to import the time module first
     import time
     time.ctime()          # prints the system time
@@ -122,7 +122,7 @@ Try::
 
   y = dict((x, 'value is %d' % x**2) for x in range(10))
   y
-  print y
+  print(y)
 
 Further resources
 ^^^^^^^^^^^^^^^^^^

source/files/asciifiles.rst

@@ -90,12 +90,12 @@ But what we'd really like to do is read the file line by line. There are several
 
     f = open('data.txt', 'r')
     for line in f:
-        print repr(line)
+        print(repr(line))
 
 Notice the indent before ``print``, which is necessary to indicate that we are inside the loop (there is no ``end for`` in Python). Note that we are using ``repr()`` to show any invisible characters (this will be useful in a minute). The output should now look something like this::
 
     >>> for line in f:
-            print repr(line)
+            print(repr(line))
 
     'RAJ        DEJ                          Jmag   e_Jmag\n'
     '2000 (deg) 2000 (deg) 2MASS             (mag)  (mag) \n'
@@ -178,7 +178,7 @@ We can put all this together to write a little script to read the data from the
         columns = line.split()
         name = columns[2]
         j = float(columns[3])
-        print name, j
+        print(name, j)
         
     f.close()
 

source/fitting/classes.rst

@@ -25,12 +25,12 @@ declaration indicating a no-op.
 Class instances are mutable, meaning that attributes and functions can be added
 after instantiation::
 
-  print h.msg
+  print(h.msg)
 
 There is no attribute ``msg``, so add one::
 
   h.msg = "Hello World!"
-  print h.msg
+  print(h.msg)
 
 Create a class with a static string attribute ``msg1`` and a class method
 ``echo`` to print the attribute.  Comments begin with a ``#`` and extend to the
@@ -40,12 +40,12 @@ end of the line::
       # static attribute string "msg1"
       msg1 = "Hello"
       def echo(self):
-          print self.msg1
+          print(self.msg1)
 
-  print "Hello's msg1:", Hello.msg1
+  print("Hello's msg1: {0}".format(Hello.msg1))
   h = Hello()
   h.echo()
-  print h
+  print(h)
 
 Create a class with a constructor definition.  Initialize an attribute ``msg2``
 at class instance creation time::
@@ -56,11 +56,11 @@ at class instance creation time::
           # attribute "msg2" initialized in constructor
           self.msg2 = msg2
       def echo(self):
-          print self.msg2
+          print(self.msg2)
 
   w = World()
   w.echo()
-  print w
+  print(w)
 
 
 Multiple Inheritance
@@ -87,7 +87,7 @@ return the attribute ``msg3`` when the class instance is printed with
 
   hw = HelloWorld()
   hw.echo()
-  print hw
+  print(hw)
 
 
 Class ``HelloWorld`` is of type ``Hello``, ``World``, and ``HelloWorld``::
@@ -126,12 +126,12 @@ language operators.  Define how a class behaves using the '+' operator::
   class Hello:
       msg = "Hello"
       def __add__(self, lhs):
-          print self.msg + lhs.msg
+          print(self.msg + lhs.msg)
 
   class World:
       msg = "World"
       def __add__(self, rhs):
-          print self.msg + rhs.msg
+          print(self.msg + rhs.msg)
 
   Hello() + World()
   World() + Hello()

source/fitting/image.rst

@@ -340,7 +340,7 @@ ERF(sigma/SQRT(2)).  We can invert this operation using the inverse
 error-function found in SciPy in the special functions module::
 
   import scipy.special
-  print scipy.special.erfinv(0.90)*numpy.sqrt(2)
+  print(scipy.special.erfinv(0.90) * numpy.sqrt(2))
 
 .. raw:: html
 
@@ -355,7 +355,7 @@ Save the 90% calculated parameter limits::
   f.write("NAME VALUE MIN MAX\n")
   for name, val, minval, maxval in zip(results.parnames,results.parvals,results.parmins,results.parmaxes):
       line = [name, str(val), str(val+minval), str(val+maxval)]
-      print line
+      print(line)
       f.write(" ".join(line)+"\n")
 
   f.close()

source/fitting/low-level.rst

@@ -9,17 +9,20 @@ Explore the Sherpa Object Model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 In a new working directory, download a MAST spectrum of :download:`3C 273 <./3c273.fits>`
-and start IPython::
+and start IPython along with the standard imports::
 
-  $ ipython --pylab
+  $ ipython --matplotlib
+
+  import numpy as np
+  import matplotlib.pyplot as plt
 
 If you have trouble accessing the spectrum you can download it straight away
 using Python::
 
   import urllib2
   url = 'http://python4astronomers.github.com/_downloads/3c273.fits'
   open('3c273.fits', 'wb').write(urllib2.urlopen(url).read())
-  ls
+  %ls
 
 Import a few Sherpa classes needed to characterize a fit::
 
@@ -29,10 +32,10 @@ Import a few Sherpa classes needed to characterize a fit::
   from sherpa.optmethods import LevMar
   from sherpa.fit import Fit
 
-Import the Python FITS reader ``pyfits`` and open the spectrum as a table::
+Import the Python FITS reader ``astropy.io.fits`` and open the spectrum as a table::
 
-  import pyfits
-  dat = pyfits.open('3c273.fits')[1].data
+  from astropy.io import fits
+  dat = fits.open('3c273.fits')[1].data
 
 Access the `WAVELENGTH` and `FLUX` columns from the pyFITS ``RecArray``.  Populate
 variables represented as ``wave``, ``flux``, and ``err``.  Normalize the flux and assume
@@ -47,13 +50,13 @@ Create a Sherpa ``Data1D`` data set from the NumPy arrays ``wave``, ``flux``, an
 ``x``, ``y``, and ``staterror``::
 
   data = Data1D('3C 273', wave, flux, err)
-  print data
+  print(data)
 
 Array access::
 
-  print 'x', data.x
-  print 'y', data.y
-  print 'err',  data.staterror
+  print('x {0}'.format(data.x))
+  print('y {0}'.format(data.y))
+  print('err {0}'.format(data.staterror))
 
 
 Define a convenience function ``plot_data`` that calls the matplotlib functions
@@ -65,10 +68,10 @@ in the Sherpa data set using our new function and its default arguments::
 
   def plot_data(x, y, err=None, fmt='.', clear=True):
       if clear:
-          clf()
-      plot(x, y, fmt)
+          plt.clf()
+      plt.plot(x, y, fmt)
       if err is not None:
-          errorbar(x, y, err, fmt=None, ecolor='b')
+          plt.errorbar(x, y, err, fmt=None, ecolor='b')
 
   plot_data(data.x, data.y, data.staterror)
 
@@ -83,21 +86,21 @@ the ``name`` and ``val`` attributes::
   pl = PowLaw1D('pl')
   pl.pars
   for par in pl.pars:
-      print par.name, par.val
+      print('{0} {1}'.format(par.name, par.val))
 
-  print pl
+  print(pl)
 
 Set the power-law reference to be 4000 Angstroms and print out the ``PowLaw1D``
 object and its parameter information.  Each model parameter is accessible as an
 attribute its model.  For example, the power-law amplitude is referenced with
 ``pl.ampl``::
 
   pl.ref = 4000.
-  print pl
+  print(pl)
 
 Model parameters are themselves class objects::
 
-  print pl.ampl
+  print(pl.ampl)
 
 
 .. admonition:: Exercise (for the interested reader): Special methods and properties
@@ -151,9 +154,9 @@ optimization method.  Fit the spectrum to a power-law with least squares
 
   f = Fit(data, pl, Chi2DataVar(), LevMar())
   result = f.fit()
-  print result
+  print(result)
   # or alternatively
-  print result.format()
+  print(result.format())
 
 Over-plot the fitted model atop the data points using our convenience function
 ``plot_data``.  This time calculate the model using the best-fit parameter

source/intro/comparison.rst

@@ -59,7 +59,7 @@ Python::
   y = v + x * 12         # OK
   y = str(v) + "+x*12"   # Two strings can be added (concatenated)
   y = str(v+x*12)        # Make a string from the number
-  print x, y, v          # print x, y and v. 
+  print(x, y, v)         # print x, y and v. 
 
 IDL
 -----

source/intro/quick-tour.rst

@@ -109,9 +109,9 @@ uncertainties.
   popt, pcov = curve_fit(gaussian, x, y, sigma=e)
 
   # Print results
-  print "Scale =  %.3f +/- %.3f" % (popt[0], sqrt(pcov[0, 0]))
-  print "Offset = %.3f +/- %.3f" % (popt[1], sqrt(pcov[1, 1]))
-  print "Sigma =  %.3f +/- %.3f" % (popt[2], sqrt(pcov[2, 2]))
+  print("Scale =  %.3f +/- %.3f" % (popt[0], sqrt(pcov[0, 0])))
+  print("Offset = %.3f +/- %.3f" % (popt[1], sqrt(pcov[1, 1])))
+  print("Sigma =  %.3f +/- %.3f" % (popt[2], sqrt(pcov[2, 2])))
 
   # Plot data
   errorbar(x, y, yerr=e, linewidth=1, color='black', fmt=None)
@@ -213,7 +213,7 @@ together other codes and doing system type tasks.
       for noise in noises:
           # Run the compiled code "make_data" to make data as a list of x, y, y_smooth
           cmd = 'make_data %s %s %s' % (freq, noise, smoothing)
-          print 'Running', cmd
+          print('Running {0}'.format(cmd))
           out = os.popen(cmd).read()
           # out now contains the output from <cmd> as a single string
 

source/local/cfa.rst

@@ -92,8 +92,8 @@ Now within ``sherpa`` do::
 
   import scipy
   import scipy.linalg
-  print scipy.__version__
-  print scipy.linalg.eig([[1,2],[3,4]])
+  print(scipy.__version__)
+  print(scipy.linalg.eig([[1,2],[3,4]]))
 
 Now quit and install the rest::
 
@@ -117,7 +117,7 @@ Once again fire up ``sherpa`` or ``chips`` and do::
   import atpy
   import aplpy
 
-  print np.__version__
+  print(np.__version__)
 
   x = np.linspace(0, 20, 100)
   plt.plot(x, np.sin(x))

source/plotting/matplotlib.rst

@@ -781,7 +781,7 @@ clarify the relationship between **pylab** and **pyplot**.
 matplotlib work like MATLAB.  This is just a package module that you can import::
 
   import matplotlib.pyplot
-  print sorted(dir(matplotlib.pyplot))
+  print(sorted(dir(matplotlib.pyplot)))
 
 Likewise pylab is also a module provided by matplotlib that you can import::
 

source/vo/votools.rst

@@ -156,7 +156,7 @@ The ``Siap`` acronym stands for *Simple Image Access Protocol* and as with
     The returned array contains **66** fields! 
     ::
         
-        print image_table.dtype.names     
+        print(image_table.dtype.names)
         
     If all we want is the actual data then the important column is **URL**
     while the **filename** column is also useful.
@@ -183,16 +183,16 @@ We use `pyfits`_ to open the image file and examine its contents.
 
     import pyfits
     import aplpy
-    import matplotlib.pyplot as mpl
+    import matplotlib.pyplot as plt
     
     hdulist = pyfits.open(filename)
 
     # check for multiple FITS extensions and their contents
     # in this case the "PRIMARY" header is empty
     for hdu in hdulist:
-        print hdu.name, type(hdu.data)
+        print('name: {0}  type: {1}'.format(hdu.name, type(hdu.data)))
     
-    fig = mpl.figure(figsize=(15, 7))
+    fig = plt.figure(figsize=(15, 7))
     f1 = aplpy.FITSFigure(filename, hdu=1, subplot=[0.1,0.1,0.3,0.65], figure=fig)    
     f1.set_tick_labels_font(size='x-small')
     f1.set_axis_labels_font(size='small')
@@ -211,7 +211,3 @@ We use `pyfits`_ to open the image file and examine its contents.
     f3.hide_yaxis_label()
     f3.hide_ytick_labels()
     f3.show_colorscale(cmap='spring')
-
-        
-        
-