skip sympy tests if sympy not installed

IPython/extensions/sympy_printing.py

@@ -1,65 +1,72 b''
 """A print function that pretty prints sympy Basic objects.
 
 Authors:
 * Brian Granger
 """
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2008-2011  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-----------------------------------------------------------------------------
 
 #-----------------------------------------------------------------------------
 # Imports
 #-----------------------------------------------------------------------------
 
 from IPython.lib.latextools import latex_to_png
+from IPython.testing import decorators as dec
+
+try:
+    from sympy import pretty, latex
+except ImportError:
+    pass
 
-from sympy import pretty, latex
 
 #-----------------------------------------------------------------------------
 # Definitions of magic functions for use with IPython
 #-----------------------------------------------------------------------------
 
[e-mail]_not_sympy
 def print_basic_unicode(o, p, cycle):
     """A function to pretty print sympy Basic objects."""
     if cycle:
         return p.text('Basic(...)')
     out = pretty(o, use_unicode=True)
     if '\n' in out:
         p.text(u'\n')
     p.text(out)
 
 
[e-mail]_not_sympy
 def print_png(o):
     """A funciton to display sympy expression using LaTex -> PNG."""
     s = latex(o, mode='inline')
     # mathtext does not understand certain latex flags, so we try to replace
     # them with suitable subs.
     s = s.replace('\\operatorname','')
     s = s.replace('\\overline', '\\bar')
     png = latex_to_png(s, encode=True)
     return png
 
 _loaded = False
 
-
[e-mail]_not_sympy
 def load_ipython_extension(ip):
     """Load the extension in IPython."""
     global _loaded
     if not _loaded:
         plaintext_formatter = ip.display_formatter.formatters['text/plain']
         plaintext_formatter.for_type_by_name(
             'sympy.core.basic', 'Basic', print_basic_unicode
         )
         plaintext_formatter.for_type_by_name(
             'sympy.matrices.matrices', 'Matrix', print_basic_unicode
         )
 
         png_formatter = ip.display_formatter.formatters['image/png']
         png_formatter.for_type_by_name(
             'sympy.core.basic', 'Basic', print_png
         )
         _loaded = True
 

IPython/testing/decorators.py

@@ -1,324 +1,326 b''
 """Decorators for labeling test objects.
 
 Decorators that merely return a modified version of the original function
 object are straightforward.  Decorators that return a new function object need
 to use nose.tools.make_decorator(original_function)(decorator) in returning the
 decorator, in order to preserve metadata such as function name, setup and
 teardown functions and so on - see nose.tools for more information.
 
 This module provides a set of useful decorators meant to be ready to use in
 your own tests.  See the bottom of the file for the ready-made ones, and if you
 find yourself writing a new one that may be of generic use, add it here.
 
 Included decorators:
 
 
 Lightweight testing that remains unittest-compatible.
 
 - @parametric, for parametric test support that is vastly easier to use than
   nose's for debugging. With ours, if a test fails, the stack under inspection
   is that of the test and not that of the test framework.
 
 - An @as_unittest decorator can be used to tag any normal parameter-less
   function as a unittest TestCase.  Then, both nose and normal unittest will
   recognize it as such.  This will make it easier to migrate away from Nose if
   we ever need/want to while maintaining very lightweight tests.
 
 NOTE: This file contains IPython-specific decorators and imports the
 numpy.testing.decorators file, which we've copied verbatim.  Any of our own
 code will be added at the bottom if we end up extending this.
 
 Authors
 -------
 
 - Fernando Perez <[e-mail]>
 """
 
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2009-2010  The IPython Development Team
 #
 #  Distributed under the terms of the BSD License.  The full license is in
 #  the file COPYING, distributed as part of this software.
 #-----------------------------------------------------------------------------
 
 #-----------------------------------------------------------------------------
 # Imports
 #-----------------------------------------------------------------------------
 
 # Stdlib imports
 import inspect
 import sys
 import unittest
 
 # Third-party imports
 
 # This is Michele Simionato's decorator module, kept verbatim.
 from IPython.external.decorator import decorator, update_wrapper
 
 # We already have python3-compliant code for parametric tests
 if sys.version[0]=='2':
     from _paramtestpy2 import parametric, ParametricTestCase
 else:
     from _paramtestpy3 import parametric, ParametricTestCase
 
 # Expose the unittest-driven decorators
 from ipunittest import ipdoctest, ipdocstring
 
 # Grab the numpy-specific decorators which we keep in a file that we
 # occasionally update from upstream: decorators.py is a copy of
 # numpy.testing.decorators, we expose all of it here.
 from IPython.external.decorators import *
 
 #-----------------------------------------------------------------------------
 # Classes and functions
 #-----------------------------------------------------------------------------
 
 # Simple example of the basic idea
 def as_unittest(func):
     """Decorator to make a simple function into a normal test via unittest."""
     class Tester(unittest.TestCase):
         def test(self):
             func()
 
     Tester.__name__ = func.__name__
 
     return Tester
 
 # Utility functions
 
 def apply_wrapper(wrapper,func):
     """Apply a wrapper to a function for decoration.
 
     This mixes Michele Simionato's decorator tool with nose's make_decorator,
     to apply a wrapper in a decorator so that all nose attributes, as well as
     function signature and other properties, survive the decoration cleanly.
     This will ensure that wrapped functions can still be well introspected via
     IPython, for example.
     """
     import nose.tools
 
     return decorator(wrapper,nose.tools.make_decorator(func)(wrapper))
 
 
 def make_label_dec(label,ds=None):
     """Factory function to create a decorator that applies one or more labels.
 
     Parameters
     ----------
       label : string or sequence
       One or more labels that will be applied by the decorator to the functions
     it decorates.  Labels are attributes of the decorated function with their
     value set to True.
 
       ds : string
       An optional docstring for the resulting decorator.  If not given, a
       default docstring is auto-generated.
 
     Returns
     -------
       A decorator.
 
     Examples
     --------
 
     A simple labeling decorator:
     >>> slow = make_label_dec('slow')
     >>> print slow.__doc__
     Labels a test as 'slow'.
 
     And one that uses multiple labels and a custom docstring:
     >>> rare = make_label_dec(['slow','hard'],
     ... "Mix labels 'slow' and 'hard' for rare tests.")
     >>> print rare.__doc__
     Mix labels 'slow' and 'hard' for rare tests.
 
     Now, let's test using this one:
     >>> @rare
     ... def f(): pass
     ...
     >>>
     >>> f.slow
     True
     >>> f.hard
     True
     """
 
     if isinstance(label,basestring):
         labels = [label]
     else:
         labels = label
         
     # Validate that the given label(s) are OK for use in setattr() by doing a
     # dry run on a dummy function.
     tmp = lambda : None
     for label in labels:
         setattr(tmp,label,True)
 
     # This is the actual decorator we'll return
     def decor(f):
         for label in labels:
             setattr(f,label,True)
         return f
     
     # Apply the user's docstring, or autogenerate a basic one
     if ds is None:
         ds = "Labels a test as %r." % label
     decor.__doc__ = ds
     
     return decor
 
 
 # Inspired by numpy's skipif, but uses the full apply_wrapper utility to
 # preserve function metadata better and allows the skip condition to be a
 # callable.
 def skipif(skip_condition, msg=None):
     ''' Make function raise SkipTest exception if skip_condition is true
 
     Parameters
     ----------
     skip_condition : bool or callable. 
     Flag to determine whether to skip test.  If the condition is a 
     callable, it is used at runtime to dynamically make the decision.  This 
     is useful for tests that may require costly imports, to delay the cost 
     until the test suite is actually executed.        
     msg : string
         Message to give on raising a SkipTest exception
 
    Returns
    -------
    decorator : function
        Decorator, which, when applied to a function, causes SkipTest
        to be raised when the skip_condition was True, and the function
        to be called normally otherwise.
 
     Notes
     -----
     You will see from the code that we had to further decorate the
     decorator with the nose.tools.make_decorator function in order to
     transmit function name, and various other metadata.
     '''
 
     def skip_decorator(f):
         # Local import to avoid a hard nose dependency and only incur the 
         # import time overhead at actual test-time. 
         import nose
 
         # Allow for both boolean or callable skip conditions.
         if callable(skip_condition):
             skip_val = skip_condition
         else:
             skip_val = lambda : skip_condition
 
         def get_msg(func,msg=None):
             """Skip message with information about function being skipped."""
             if msg is None: out = 'Test skipped due to test condition.'
             else: out = msg
             return "Skipping test: %s. %s" % (func.__name__,out)
 
         # We need to define *two* skippers because Python doesn't allow both
         # return with value and yield inside the same function.
         def skipper_func(*args, **kwargs):
             """Skipper for normal test functions."""
             if skip_val():
                 raise nose.SkipTest(get_msg(f,msg))
             else:
                 return f(*args, **kwargs) 
 
         def skipper_gen(*args, **kwargs):
             """Skipper for test generators."""
             if skip_val():
                 raise nose.SkipTest(get_msg(f,msg))
             else:
                 for x in f(*args, **kwargs):
                     yield x
 
         # Choose the right skipper to use when building the actual generator.
         if nose.util.isgenerator(f):
             skipper = skipper_gen
         else:
             skipper = skipper_func
             
         return nose.tools.make_decorator(f)(skipper)
 
     return skip_decorator
 
 # A version with the condition set to true, common case just to attacha message
 # to a skip decorator
 def skip(msg=None):
     """Decorator factory - mark a test function for skipping from test suite.
 
     Parameters
     ----------
       msg : string
         Optional message to be added.
 
     Returns
     -------
        decorator : function
          Decorator, which, when applied to a function, causes SkipTest
          to be raised, with the optional message added.
       """
 
     return skipif(True,msg)
 
 
 def onlyif(condition, msg):
     """The reverse from skipif, see skipif for details."""
 
     if callable(condition):
         skip_condition = lambda : not condition()
     else:
         skip_condition = lambda : not condition
 
     return skipif(skip_condition, msg)
 
 #-----------------------------------------------------------------------------
 # Utility functions for decorators
-def numpy_not_available():
-    """Can numpy be imported?  Returns true if numpy does NOT import.
+def module_not_available(module):
+    """Can module be imported?  Returns true if module does NOT import.
 
-    This is used to make a decorator to skip tests that require numpy to be
+    This is used to make a decorator to skip tests that require module to be
     available, but delay the 'import numpy' to test execution time.
     """
     try:
-        import numpy
-        np_not_avail = False
+        mod = __import__(module)
+        mod_not_avail = False
     except ImportError:
-        np_not_avail = True
+        mod_not_avail = True
 
-    return np_not_avail
+    yield mod_not_avail
 
 #-----------------------------------------------------------------------------
 # Decorators for public use
 
 skip_doctest = make_label_dec('skip_doctest',
     """Decorator - mark a function or method for skipping its doctest.
 
     This decorator allows you to mark a function whose docstring you wish to
     omit from testing, while preserving the docstring for introspection, help,
     etc.""")                              
 
 # Decorators to skip certain tests on specific platforms.
 skip_win32 = skipif(sys.platform == 'win32',
                     "This test does not run under Windows")
 skip_linux = skipif(sys.platform == 'linux2',
                     "This test does not run under Linux")
 skip_osx = skipif(sys.platform == 'darwin',"This test does not run under OS X")
 
 
 # Decorators to skip tests if not on specific platforms.
 skip_if_not_win32 = skipif(sys.platform != 'win32',
                            "This test only runs under Windows")
 skip_if_not_linux = skipif(sys.platform != 'linux2',
                            "This test only runs under Linux")
 skip_if_not_osx = skipif(sys.platform != 'darwin',
                          "This test only runs under OSX")
 
 # Other skip decorators
-skipif_not_numpy = skipif(numpy_not_available,"This test requires numpy")
+skipif_not_numpy = skipif(module_not_available('numpy'),"This test requires numpy")
 
-skip_known_failure = skip('This test is known to fail')
+skipif_not_sympy = skipif(module_not_available('sympy'),"This test requires sympy")
+
+skip_known_failure = knownfailureif(True,'This test is known to fail')
 
 # A null 'decorator', useful to make more readable code that needs to pick
 # between different decorators based on OS or other conditions
 null_deco = lambda f: f