Remove unused imports in IPython.extensions

IPython/extensions/cythonmagic.py

@@ -1,313 +1,312 b''
 # -*- coding: utf-8 -*-
 """
 =====================
 Cython related magics
 =====================
 
 Usage
 =====
 
 ``%%cython``
 
 {CYTHON_DOC}
 
 ``%%cython_inline``
 
 {CYTHON_INLINE_DOC}
 
 ``%%cython_pyximport``
 
 {CYTHON_PYXIMPORT_DOC}
 
 Author:
 * Brian Granger
 
 Parts of this code were taken from Cython.inline.
 """
 #-----------------------------------------------------------------------------
 # Copyright (C) 2010-2011, IPython Development Team.
 #
 # Distributed under the terms of the Modified BSD License.
 #
 # The full license is in the file COPYING.txt, distributed with this software.
 #-----------------------------------------------------------------------------
 
 from __future__ import print_function
 
 import imp
 import io
 import os
 import re
 import sys
 import time
 
 try:
     reload
 except NameError:   # Python 3
     from imp import reload
 
 try:
     import hashlib
 except ImportError:
     import md5 as hashlib
 
 from distutils.core import Distribution, Extension
 from distutils.command.build_ext import build_ext
 
 from IPython.core import display
 from IPython.core import magic_arguments
 from IPython.core.magic import Magics, magics_class, cell_magic
-from IPython.testing.skipdoctest import skip_doctest
 from IPython.utils import py3compat
 from IPython.utils.path import get_ipython_cache_dir
 
 import Cython
 from Cython.Compiler.Errors import CompileError
 from Cython.Build.Dependencies import cythonize
 
 
 @magics_class
 class CythonMagics(Magics):
 
     def __init__(self, shell):
         super(CythonMagics,self).__init__(shell)
         self._reloads = {}
         self._code_cache = {}
 
     def _import_all(self, module):
         for k,v in module.__dict__.items():
             if not k.startswith('__'):
                 self.shell.push({k:v})
 
     @cell_magic
     def cython_inline(self, line, cell):
         """Compile and run a Cython code cell using Cython.inline.
 
         This magic simply passes the body of the cell to Cython.inline
         and returns the result. If the variables `a` and `b` are defined
         in the user's namespace, here is a simple example that returns
         their sum::
 
             %%cython_inline
             return a+b
 
         For most purposes, we recommend the usage of the `%%cython` magic.
         """
         locs = self.shell.user_global_ns
         globs = self.shell.user_ns
         return Cython.inline(cell, locals=locs, globals=globs)
 
     @cell_magic
     def cython_pyximport(self, line, cell):
         """Compile and import a Cython code cell using pyximport.
 
         The contents of the cell are written to a `.pyx` file in the current
         working directory, which is then imported using `pyximport`. This
         magic requires a module name to be passed::
 
             %%cython_pyximport modulename
             def f(x):
                 return 2.0*x
 
         The compiled module is then imported and all of its symbols are
         injected into the user's namespace. For most purposes, we recommend
         the usage of the `%%cython` magic.
         """
         module_name = line.strip()
         if not module_name:
             raise ValueError('module name must be given')
         fname = module_name + '.pyx'
         with io.open(fname, 'w', encoding='utf-8') as f:
             f.write(cell)
         if 'pyximport' not in sys.modules:
             import pyximport
             pyximport.install(reload_support=True)
         if module_name in self._reloads:
             module = self._reloads[module_name]
             reload(module)
         else:
             __import__(module_name)
             module = sys.modules[module_name]
             self._reloads[module_name] = module
         self._import_all(module)
 
     @magic_arguments.magic_arguments()
     @magic_arguments.argument(
         '-c', '--compile-args', action='append', default=[],
         help="Extra flags to pass to compiler via the `extra_compile_args` "
              "Extension flag (can be specified  multiple times)."
     )
     @magic_arguments.argument(
         '--link-args', action='append', default=[],
         help="Extra flags to pass to linker via the `extra_link_args` "
              "Extension flag (can be specified  multiple times)."
     )
     @magic_arguments.argument(
         '-l', '--lib', action='append', default=[],
         help="Add a library to link the extension against (can be specified "
              "multiple times)."
     )
     @magic_arguments.argument(
         '-L', dest='library_dirs', metavar='dir', action='append', default=[],
         help="Add a path to the list of libary directories (can be specified "
              "multiple times)."
     )
     @magic_arguments.argument(
         '-I', '--include', action='append', default=[],
         help="Add a path to the list of include directories (can be specified "
              "multiple times)."
     )
     @magic_arguments.argument(
         '-+', '--cplus', action='store_true', default=False,
         help="Output a C++ rather than C file."
     )
     @magic_arguments.argument(
         '-f', '--force', action='store_true', default=False,
         help="Force the compilation of a new module, even if the source has been "
              "previously compiled."
     )
     @magic_arguments.argument(
         '-a', '--annotate', action='store_true', default=False,
         help="Produce a colorized HTML version of the source."
     )
     @cell_magic
     def cython(self, line, cell):
         """Compile and import everything from a Cython code cell.
 
         The contents of the cell are written to a `.pyx` file in the
         directory `IPYTHONDIR/cython` using a filename with the hash of the
         code. This file is then cythonized and compiled. The resulting module
         is imported and all of its symbols are injected into the user's
         namespace. The usage is similar to that of `%%cython_pyximport` but
         you don't have to pass a module name::
 
             %%cython
             def f(x):
                 return 2.0*x
 
         To compile OpenMP codes, pass the required  `--compile-args`
         and `--link-args`.  For example with gcc::
 
             %%cython --compile-args=-fopenmp --link-args=-fopenmp
             ...
         """
         args = magic_arguments.parse_argstring(self.cython, line)
         code = cell if cell.endswith('\n') else cell+'\n'
         lib_dir = os.path.join(get_ipython_cache_dir(), 'cython')
         quiet = True
         key = code, sys.version_info, sys.executable, Cython.__version__
 
         if not os.path.exists(lib_dir):
             os.makedirs(lib_dir)
 
         if args.force:
             # Force a new module name by adding the current time to the
             # key which is hashed to determine the module name.
             key += time.time(),
 
         module_name = "_cython_magic_" + hashlib.md5(str(key).encode('utf-8')).hexdigest()
         module_path = os.path.join(lib_dir, module_name + self.so_ext)
 
         have_module = os.path.isfile(module_path)
         need_cythonize = not have_module
 
         if args.annotate:
             html_file = os.path.join(lib_dir, module_name + '.html')
             if not os.path.isfile(html_file):
                 need_cythonize = True
 
         if need_cythonize:
             c_include_dirs = args.include
             if 'numpy' in code:
                 import numpy
                 c_include_dirs.append(numpy.get_include())
             pyx_file = os.path.join(lib_dir, module_name + '.pyx')
             pyx_file = py3compat.cast_bytes_py2(pyx_file, encoding=sys.getfilesystemencoding())
             with io.open(pyx_file, 'w', encoding='utf-8') as f:
                 f.write(code)
             extension = Extension(
                 name = module_name,
                 sources = [pyx_file],
                 include_dirs = c_include_dirs,
                 library_dirs = args.library_dirs,
                 extra_compile_args = args.compile_args,
                 extra_link_args = args.link_args,
                 libraries = args.lib,
                 language = 'c++' if args.cplus else 'c',
             )
             build_extension = self._get_build_extension()
             try:
                 opts = dict(
                     quiet=quiet,
                     annotate = args.annotate,
                     force = True,
                     )
                 build_extension.extensions = cythonize([extension], **opts)
             except CompileError:
                 return
 
         if not have_module:
             build_extension.build_temp = os.path.dirname(pyx_file)
             build_extension.build_lib  = lib_dir
             build_extension.run()
             self._code_cache[key] = module_name
 
         module = imp.load_dynamic(module_name, module_path)
         self._import_all(module)
 
         if args.annotate:
             try:
                 with io.open(html_file, encoding='utf-8') as f:
                     annotated_html = f.read()
             except IOError as e:
                 # File could not be opened. Most likely the user has a version
                 # of Cython before 0.15.1 (when `cythonize` learned the
                 # `force` keyword argument) and has already compiled this
                 # exact source without annotation.
                 print('Cython completed successfully but the annotated '
                       'source could not be read.', file=sys.stderr)
                 print(e, file=sys.stderr)
             else:
                 return display.HTML(self.clean_annotated_html(annotated_html))
 
     @property
     def so_ext(self):
         """The extension suffix for compiled modules."""
         try:
             return self._so_ext
         except AttributeError:
             self._so_ext = self._get_build_extension().get_ext_filename('')
             return self._so_ext
 
     def _get_build_extension(self):
         dist = Distribution()
         config_files = dist.find_config_files()
         try:
             config_files.remove('setup.cfg')
         except ValueError:
             pass
         dist.parse_config_files(config_files)
         build_extension = build_ext(dist)
         build_extension.finalize_options()
         return build_extension
 
     @staticmethod
     def clean_annotated_html(html):
         """Clean up the annotated HTML source.
 
         Strips the link to the generated C or C++ file, which we do not
         present to the user.
         """
         r = re.compile('<p>Raw output: <a href="(.*)">(.*)</a>')
         html = '\n'.join(l for l in html.splitlines() if not r.match(l))
         return html
 
 __doc__ = __doc__.format(
                 CYTHON_DOC = ' '*8 + CythonMagics.cython.__doc__,
                 CYTHON_INLINE_DOC = ' '*8 + CythonMagics.cython_inline.__doc__,
                 CYTHON_PYXIMPORT_DOC = ' '*8 + CythonMagics.cython_pyximport.__doc__,
 )
 
 def load_ipython_extension(ip):
     """Load the extension in IPython."""
     ip.register_magics(CythonMagics)

IPython/extensions/rmagic.py

@@ -1,668 +1,667 b''
 # -*- coding: utf-8 -*-
 """
 ======
 Rmagic
 ======
 
 Magic command interface for interactive work with R via rpy2
 
 Usage
 =====
 
 ``%R``
 
 {R_DOC}
 
 ``%Rpush``
 
 {RPUSH_DOC}
 
 ``%Rpull``
 
 {RPULL_DOC}
 
 ``%Rget``
 
 {RGET_DOC}
 
 """
 
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2012 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.
 #-----------------------------------------------------------------------------
 
 import sys
 import tempfile
 from glob import glob
 from shutil import rmtree
-from getopt import getopt
 
 # numpy and rpy2 imports
 
 import numpy as np
 
 import rpy2.rinterface as ri
 import rpy2.robjects as ro
 try:
     from rpy2.robjects import pandas2ri
     pandas2ri.activate()
 except ImportError:
     pandas2ri = None
     from rpy2.robjects import numpy2ri
     numpy2ri.activate()
 
 # IPython imports
 
 from IPython.core.displaypub import publish_display_data
-from IPython.core.magic import (Magics, magics_class, cell_magic, line_magic,
+from IPython.core.magic import (Magics, magics_class, line_magic,
                                 line_cell_magic, needs_local_scope)
 from IPython.testing.skipdoctest import skip_doctest
 from IPython.core.magic_arguments import (
     argument, magic_arguments, parse_argstring
 )
 from IPython.external.simplegeneric import generic
 from IPython.utils.py3compat import str_to_unicode, unicode_to_str, PY3
 
 class RInterpreterError(ri.RRuntimeError):
     """An error when running R code in a %%R magic cell."""
     def __init__(self, line, err, stdout):
         self.line = line
         self.err = err.rstrip()
         self.stdout = stdout.rstrip()
     
     def __unicode__(self):
         s = 'Failed to parse and evaluate line %r.\nR error message: %r' % \
                 (self.line, self.err)
         if self.stdout and (self.stdout != self.err):
             s += '\nR stdout:\n' + self.stdout
         return s
     
     if PY3:
         __str__ = __unicode__
     else:
         def __str__(self):
             return unicode_to_str(unicode(self), 'utf-8')
 
 def Rconverter(Robj, dataframe=False):
     """
     Convert an object in R's namespace to one suitable
     for ipython's namespace.
 
     For a data.frame, it tries to return a structured array.
     It first checks for colnames, then names.
     If all are NULL, it returns np.asarray(Robj), else
     it tries to construct a recarray
 
     Parameters
     ----------
 
     Robj: an R object returned from rpy2
     """
     is_data_frame = ro.r('is.data.frame')
     colnames = ro.r('colnames')
     rownames = ro.r('rownames') # with pandas, these could be used for the index
     names = ro.r('names')
 
     if dataframe:
         as_data_frame = ro.r('as.data.frame')
         cols = colnames(Robj)
         _names = names(Robj)
         if cols != ri.NULL:
             Robj = as_data_frame(Robj)
             names = tuple(np.array(cols))
         elif _names != ri.NULL:
             names = tuple(np.array(_names))
         else: # failed to find names
             return np.asarray(Robj)
         Robj = np.rec.fromarrays(Robj, names = names)
     return np.asarray(Robj)
 
 @generic
 def pyconverter(pyobj):
     """Convert Python objects to R objects. Add types using the decorator:
     
     @pyconverter.when_type
     """
     return pyobj
 
 # The default conversion for lists seems to make them a nested list. That has
 # some advantages, but is rarely convenient, so for interactive use, we convert
 # lists to a numpy array, which becomes an R vector.
 @pyconverter.when_type(list)
 def pyconverter_list(pyobj):
     return np.asarray(pyobj)
 
 if pandas2ri is None:
     # pandas2ri was new in rpy2 2.3.3, so for now we'll fallback to pandas'
     # conversion function.
     try:
         from pandas import DataFrame
         from pandas.rpy.common import convert_to_r_dataframe
         @pyconverter.when_type(DataFrame)
         def pyconverter_dataframe(pyobj):
             return convert_to_r_dataframe(pyobj, strings_as_factors=True)
     except ImportError:
         pass
 
 @magics_class
 class RMagics(Magics):
     """A set of magics useful for interactive work with R via rpy2.
     """
 
     def __init__(self, shell, Rconverter=Rconverter,
                  pyconverter=pyconverter,
                  cache_display_data=False):
         """
         Parameters
         ----------
 
         shell : IPython shell
         
         Rconverter : callable
             To be called on values taken from R before putting them in the
             IPython namespace.
 
         pyconverter : callable
             To be called on values in ipython namespace before 
             assigning to variables in rpy2.
 
         cache_display_data : bool
             If True, the published results of the final call to R are 
             cached in the variable 'display_cache'.
 
         """
         super(RMagics, self).__init__(shell)
         self.cache_display_data = cache_display_data
 
         self.r = ro.R()
 
         self.Rstdout_cache = []
         self.pyconverter = pyconverter
         self.Rconverter = Rconverter
 
     def eval(self, line):
         '''
         Parse and evaluate a line with rpy2.
         Returns the output to R's stdout() connection
         and the value of eval(parse(line)).
         '''
         old_writeconsole = ri.get_writeconsole()
         ri.set_writeconsole(self.write_console)
         try:
             value = ri.baseenv['eval'](ri.parse(line))
         except (ri.RRuntimeError, ValueError) as exception:
             warning_or_other_msg = self.flush() # otherwise next return seems to have copy of error
             raise RInterpreterError(line, str_to_unicode(str(exception)), warning_or_other_msg)
         text_output = self.flush()
         ri.set_writeconsole(old_writeconsole)
         return text_output, value
 
     def write_console(self, output):
         '''
         A hook to capture R's stdout in a cache.
         '''
         self.Rstdout_cache.append(output)
 
     def flush(self):
         '''
         Flush R's stdout cache to a string, returning the string.
         '''
         value = ''.join([str_to_unicode(s, 'utf-8') for s in self.Rstdout_cache])
         self.Rstdout_cache = []
         return value
 
     @skip_doctest
     @needs_local_scope
     @line_magic
     def Rpush(self, line, local_ns=None):
         '''
         A line-level magic for R that pushes
         variables from python to rpy2. The line should be made up
         of whitespace separated variable names in the IPython
         namespace::
 
             In [7]: import numpy as np
 
             In [8]: X = np.array([4.5,6.3,7.9])
 
             In [9]: X.mean()
             Out[9]: 6.2333333333333343
 
             In [10]: %Rpush X
 
             In [11]: %R mean(X)
             Out[11]: array([ 6.23333333])
 
         '''
         if local_ns is None:
             local_ns = {}
 
         inputs = line.split(' ')
         for input in inputs:
             try:
                 val = local_ns[input]
             except KeyError:
                 try:
                     val = self.shell.user_ns[input]
                 except KeyError:
                     # reraise the KeyError as a NameError so that it looks like
                     # the standard python behavior when you use an unnamed
                     # variable
                     raise NameError("name '%s' is not defined" % input)
 
             self.r.assign(input, self.pyconverter(val))
 
     @skip_doctest
     @magic_arguments()
     @argument(
         '-d', '--as_dataframe', action='store_true',
         default=False,
         help='Convert objects to data.frames before returning to ipython.'
         )
     @argument(
         'outputs',
         nargs='*',
         )
     @line_magic
     def Rpull(self, line):
         '''
         A line-level magic for R that pulls
         variables from python to rpy2::
 
             In [18]: _ = %R x = c(3,4,6.7); y = c(4,6,7); z = c('a',3,4)
 
             In [19]: %Rpull x  y z
 
             In [20]: x
             Out[20]: array([ 3. ,  4. ,  6.7])
 
             In [21]: y
             Out[21]: array([ 4.,  6.,  7.])
 
             In [22]: z
             Out[22]:
             array(['a', '3', '4'],
                   dtype='|S1')
 
 
         If --as_dataframe, then each object is returned as a structured array
         after first passed through "as.data.frame" in R before
         being calling self.Rconverter. 
         This is useful when a structured array is desired as output, or
         when the object in R has mixed data types. 
         See the %%R docstring for more examples.
 
         Notes
         -----
 
         Beware that R names can have '.' so this is not fool proof.
         To avoid this, don't name your R objects with '.'s...
 
         '''
         args = parse_argstring(self.Rpull, line)
         outputs = args.outputs
         for output in outputs:
             self.shell.push({output:self.Rconverter(self.r(output),dataframe=args.as_dataframe)})
 
     @skip_doctest
     @magic_arguments()
     @argument(
         '-d', '--as_dataframe', action='store_true',
         default=False,
         help='Convert objects to data.frames before returning to ipython.'
         )
     @argument(
         'output',
         nargs=1,
         type=str,
         )
     @line_magic
     def Rget(self, line):
         '''
         Return an object from rpy2, possibly as a structured array (if possible).
         Similar to Rpull except only one argument is accepted and the value is 
         returned rather than pushed to self.shell.user_ns::
 
             In [3]: dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')]
 
             In [4]: datapy = np.array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c'), (4, 5, 'e')], dtype=dtype)
 
             In [5]: %R -i datapy
 
             In [6]: %Rget datapy
             Out[6]: 
             array([['1', '2', '3', '4'],
                    ['2', '3', '2', '5'],
                    ['a', 'b', 'c', 'e']], 
                   dtype='|S1')
 
             In [7]: %Rget -d datapy
             Out[7]: 
             array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c'), (4, 5.0, 'e')], 
                   dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])
 
         '''
         args = parse_argstring(self.Rget, line)
         output = args.output
         return self.Rconverter(self.r(output[0]),dataframe=args.as_dataframe)
 
 
     @skip_doctest
     @magic_arguments()
     @argument(
         '-i', '--input', action='append',
         help='Names of input variable from shell.user_ns to be assigned to R variables of the same names after calling self.pyconverter. Multiple names can be passed separated only by commas with no whitespace.'
         )
     @argument(
         '-o', '--output', action='append',
         help='Names of variables to be pushed from rpy2 to shell.user_ns after executing cell body and applying self.Rconverter. Multiple names can be passed separated only by commas with no whitespace.'
         )
     @argument(
         '-w', '--width', type=int,
         help='Width of png plotting device sent as an argument to *png* in R.'
         )
     @argument(
         '-h', '--height', type=int,
         help='Height of png plotting device sent as an argument to *png* in R.'
         )
 
     @argument(
         '-d', '--dataframe', action='append',
         help='Convert these objects to data.frames and return as structured arrays.'
         )
     @argument(
         '-u', '--units', type=unicode, choices=["px", "in", "cm", "mm"],
         help='Units of png plotting device sent as an argument to *png* in R. One of ["px", "in", "cm", "mm"].'
         )
     @argument(
         '-r', '--res', type=int,
         help='Resolution of png plotting device sent as an argument to *png* in R. Defaults to 72 if *units* is one of ["in", "cm", "mm"].'
         )
     @argument(
         '-p', '--pointsize', type=int,
         help='Pointsize of png plotting device sent as an argument to *png* in R.'
         )
     @argument(
         '-b', '--bg',
         help='Background of png plotting device sent as an argument to *png* in R.'
         )
     @argument(
         '-n', '--noreturn',
         help='Force the magic to not return anything.',
         action='store_true',
         default=False
         )
     @argument(
         'code',
         nargs='*',
         )
     @needs_local_scope
     @line_cell_magic
     def R(self, line, cell=None, local_ns=None):
         '''
         Execute code in R, and pull some of the results back into the Python namespace.
 
         In line mode, this will evaluate an expression and convert the returned value to a Python object.
         The return value is determined by rpy2's behaviour of returning the result of evaluating the
         final line. 
 
         Multiple R lines can be executed by joining them with semicolons::
 
             In [9]: %R X=c(1,4,5,7); sd(X); mean(X)
             Out[9]: array([ 4.25])
 
         As a cell, this will run a block of R code, without bringing anything back by default::
 
             In [10]: %%R
                ....: Y = c(2,4,3,9)
                ....: print(summary(lm(Y~X)))
                ....:
 
             Call:
             lm(formula = Y ~ X)
 
             Residuals:
                 1     2     3     4
              0.88 -0.24 -2.28  1.64
 
             Coefficients:
                         Estimate Std. Error t value Pr(>|t|)
             (Intercept)   0.0800     2.3000   0.035    0.975
             X             1.0400     0.4822   2.157    0.164
 
             Residual standard error: 2.088 on 2 degrees of freedom
             Multiple R-squared: 0.6993,Adjusted R-squared: 0.549
             F-statistic: 4.651 on 1 and 2 DF,  p-value: 0.1638
 
         In the notebook, plots are published as the output of the cell.
 
         %R plot(X, Y)
 
         will create a scatter plot of X bs Y.
 
         If cell is not None and line has some R code, it is prepended to
         the R code in cell.
 
         Objects can be passed back and forth between rpy2 and python via the -i -o flags in line::
 
             In [14]: Z = np.array([1,4,5,10])
 
             In [15]: %R -i Z mean(Z)
             Out[15]: array([ 5.])
 
 
             In [16]: %R -o W W=Z*mean(Z)
             Out[16]: array([  5.,  20.,  25.,  50.])
 
             In [17]: W
             Out[17]: array([  5.,  20.,  25.,  50.])
 
         The return value is determined by these rules:
 
         * If the cell is not None, the magic returns None.
 
         * If the cell evaluates as False, the resulting value is returned
         unless the final line prints something to the console, in
         which case None is returned.
 
         * If the final line results in a NULL value when evaluated
         by rpy2, then None is returned.
 
         * No attempt is made to convert the final value to a structured array.
         Use the --dataframe flag or %Rget to push / return a structured array.
 
         * If the -n flag is present, there is no return value.
 
         * A trailing ';' will also result in no return value as the last
         value in the line is an empty string.
 
         The --dataframe argument will attempt to return structured arrays. 
         This is useful for dataframes with
         mixed data types. Note also that for a data.frame, 
         if it is returned as an ndarray, it is transposed::
 
             In [18]: dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')]
 
             In [19]: datapy = np.array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c'), (4, 5, 'e')], dtype=dtype)
 
             In [20]: %%R -o datar
             datar = datapy
                ....: 
 
             In [21]: datar
             Out[21]: 
             array([['1', '2', '3', '4'],
                    ['2', '3', '2', '5'],
                    ['a', 'b', 'c', 'e']], 
                   dtype='|S1')
 
             In [22]: %%R -d datar
             datar = datapy
                ....: 
 
             In [23]: datar
             Out[23]: 
             array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c'), (4, 5.0, 'e')], 
                   dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])
 
         The --dataframe argument first tries colnames, then names.
         If both are NULL, it returns an ndarray (i.e. unstructured)::
 
             In [1]: %R mydata=c(4,6,8.3); NULL
 
             In [2]: %R -d mydata
 
             In [3]: mydata
             Out[3]: array([ 4. ,  6. ,  8.3])
 
             In [4]: %R names(mydata) = c('a','b','c'); NULL
 
             In [5]: %R -d mydata
 
             In [6]: mydata
             Out[6]: 
             array((4.0, 6.0, 8.3), 
                   dtype=[('a', '<f8'), ('b', '<f8'), ('c', '<f8')])
 
             In [7]: %R -o mydata
 
             In [8]: mydata
             Out[8]: array([ 4. ,  6. ,  8.3])
 
         '''
 
         args = parse_argstring(self.R, line)
 
         # arguments 'code' in line are prepended to
         # the cell lines
 
         if cell is None:
             code = ''
             return_output = True
             line_mode = True
         else:
             code = cell
             return_output = False
             line_mode = False
 
         code = ' '.join(args.code) + code
 
         # if there is no local namespace then default to an empty dict
         if local_ns is None:
             local_ns = {}
 
         if args.input:
             for input in ','.join(args.input).split(','):
                 try:
                     val = local_ns[input]
                 except KeyError:
                     try:
                         val = self.shell.user_ns[input]
                     except KeyError:
                         raise NameError("name '%s' is not defined" % input)
                 self.r.assign(input, self.pyconverter(val))
 
         if getattr(args, 'units') is not None:
             if args.units != "px" and getattr(args, 'res') is None:
                 args.res = 72
             args.units = '"%s"' % args.units
 
         png_argdict = dict([(n, getattr(args, n)) for n in ['units', 'res', 'height', 'width', 'bg', 'pointsize']])
         png_args = ','.join(['%s=%s' % (o,v) for o, v in png_argdict.items() if v is not None])
         # execute the R code in a temporary directory
 
         tmpd = tempfile.mkdtemp()
         self.r('png("%s/Rplots%%03d.png",%s)' % (tmpd.replace('\\', '/'), png_args))
 
         text_output = ''
         try:
             if line_mode:
                 for line in code.split(';'):
                     text_result, result = self.eval(line)
                     text_output += text_result
                 if text_result:
                     # the last line printed something to the console so we won't return it
                     return_output = False
             else:
                 text_result, result = self.eval(code)
                 text_output += text_result
         
         except RInterpreterError as e:
             print(e.stdout)
             if not e.stdout.endswith(e.err):
                 print(e.err)
             rmtree(tmpd)
             return
 
         self.r('dev.off()')
 
         # read out all the saved .png files
 
         images = [open(imgfile, 'rb').read() for imgfile in glob("%s/Rplots*png" % tmpd)]
 
         # now publish the images
         # mimicking IPython/zmq/pylab/backend_inline.py
         fmt = 'png'
         mimetypes = { 'png' : 'image/png', 'svg' : 'image/svg+xml' }
         mime = mimetypes[fmt]
 
         # publish the printed R objects, if any
 
         display_data = []
         if text_output:
             display_data.append(('RMagic.R', {'text/plain':text_output}))
 
         # flush text streams before sending figures, helps a little with output
         for image in images:
             # synchronization in the console (though it's a bandaid, not a real sln)
             sys.stdout.flush(); sys.stderr.flush()
             display_data.append(('RMagic.R', {mime: image}))
 
         # kill the temporary directory
         rmtree(tmpd)
 
         # try to turn every output into a numpy array
         # this means that output are assumed to be castable
         # as numpy arrays
 
         if args.output:
             for output in ','.join(args.output).split(','):
                 self.shell.push({output:self.Rconverter(self.r(output), dataframe=False)})
 
         if args.dataframe:
             for output in ','.join(args.dataframe).split(','):
                 self.shell.push({output:self.Rconverter(self.r(output), dataframe=True)})
 
         for tag, disp_d in display_data:
             publish_display_data(tag, disp_d)
 
         # this will keep a reference to the display_data
         # which might be useful to other objects who happen to use
         # this method
 
         if self.cache_display_data:
             self.display_cache = display_data
 
         # if in line mode and return_output, return the result as an ndarray
         if return_output and not args.noreturn:
             if result != ri.NULL:
                 return self.Rconverter(result, dataframe=False)
 
 __doc__ = __doc__.format(
                 R_DOC = ' '*8 + RMagics.R.__doc__,
                 RPUSH_DOC = ' '*8 + RMagics.Rpush.__doc__,
                 RPULL_DOC = ' '*8 + RMagics.Rpull.__doc__,
                 RGET_DOC = ' '*8 + RMagics.Rget.__doc__
 )
 
 
 def load_ipython_extension(ip):
     """Load the extension in IPython."""
     ip.register_magics(RMagics)
     # Initialising rpy2 interferes with readline. Since, at this point, we've
     # probably just loaded rpy2, we reset the delimiters. See issue gh-2759.
     if ip.has_readline:
         ip.readline.set_completer_delims(ip.readline_delims)

IPython/extensions/sympyprinting.py

@@ -1,171 +1,169 b''
 """
 A print function that pretty prints sympy Basic objects.
 
 :moduleauthor: Brian Granger
 
 Usage
 =====
 
 Once the extension is loaded, Sympy Basic objects are automatically
 pretty-printed.
 
 As of SymPy 0.7.2, maintenance of this extension has moved to SymPy under
 sympy.interactive.ipythonprinting, any modifications to account for changes to
 SymPy should be submitted to SymPy rather than changed here. This module is
 maintained here for backwards compatablitiy with old SymPy versions.
 
 """
 #-----------------------------------------------------------------------------
 #  Copyright (C) 2008  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
-# use @dec.skipif_not_sympy to skip tests requiring sympy
 
 try:
     from sympy import pretty, latex
 except ImportError:
     pass
 
 import warnings
 
 #-----------------------------------------------------------------------------
 # Definitions of special display functions for use with IPython
 #-----------------------------------------------------------------------------
 
 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)
 
 
 def print_png(o):
     """
     A function to display sympy expression using inline style LaTeX in 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)
     return png
 
 
 def print_display_png(o):
     """
     A function to display sympy expression using display style LaTeX in PNG.
     """
     s = latex(o, mode='plain')
     s = s.strip('$')
     # As matplotlib does not support display style, dvipng backend is
     # used here.
     png = latex_to_png(s, backend='dvipng', wrap=True)
     return png
 
 
 def can_print_latex(o):
     """
     Return True if type o can be printed with LaTeX.
 
     If o is a container type, this is True if and only if every element of o
     can be printed with LaTeX.
     """
     import sympy
     if isinstance(o, (list, tuple, set, frozenset)):
         return all(can_print_latex(i) for i in o)
     elif isinstance(o, dict):
         return all((isinstance(i, basestring) or can_print_latex(i)) and can_print_latex(o[i]) for i in o)
     elif isinstance(o,(sympy.Basic, sympy.matrices.Matrix, int, long, float)):
         return True
     return False
 
 def print_latex(o):
     """A function to generate the latex representation of sympy
     expressions."""
     if can_print_latex(o):
         s = latex(o, mode='plain')
         s = s.replace('\\dag','\\dagger')
         s = s.strip('$')
         return '$$%s$$' % s
     # Fallback to the string printer
     return None
 
 _loaded = False
 
 def load_ipython_extension(ip):
     """Load the extension in IPython."""
     import sympy
 
     # sympyprinting extension has been moved to SymPy as of 0.7.2, if it
     # exists there, warn the user and import it
     try:
         import sympy.interactive.ipythonprinting
     except ImportError:
         pass
     else:
         warnings.warn("The sympyprinting extension in IPython is deprecated, "
             "use 'from sympy import init_printing; init_printing()'")
         ip.extension_manager.load_extension('sympy.interactive.ipythonprinting')
         return
 
     global _loaded
     if not _loaded:
         plaintext_formatter = ip.display_formatter.formatters['text/plain']
 
         for cls in (object, str):
             plaintext_formatter.for_type(cls, print_basic_unicode)
 
         printable_containers = [list, tuple]
 
         # set and frozen set were broken with SymPy's latex() function, but
         # was fixed in the 0.7.1-git development version. See
         # http://code.google.com/p/sympy/issues/detail?id=3062.
         if sympy.__version__ > '0.7.1':
             printable_containers += [set, frozenset]
         else:
             plaintext_formatter.for_type(cls, print_basic_unicode)
 
         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
         )
         png_formatter.for_type_by_name(
             'sympy.matrices.matrices', 'Matrix', print_display_png
         )
         for cls in [dict, int, long, float] + printable_containers:
             png_formatter.for_type(cls, print_png)
 
         latex_formatter = ip.display_formatter.formatters['text/latex']
         latex_formatter.for_type_by_name(
             'sympy.core.basic', 'Basic', print_latex
         )
         latex_formatter.for_type_by_name(
             'sympy.matrices.matrices', 'Matrix', print_latex
         )
 
         for cls in printable_containers:
             # Use LaTeX only if every element is printable by latex
             latex_formatter.for_type(cls, print_latex)
 
         _loaded = True

IPython/extensions/tests/test_rmagic.py

@@ -1,123 +1,122 b''
 from StringIO import StringIO
 
 import numpy as np
-from IPython.core.interactiveshell import InteractiveShell
 from IPython.testing.decorators import skip_without
 from IPython.extensions import rmagic
 from rpy2 import rinterface
 import nose.tools as nt
 
 ip = get_ipython()
 ip.magic('load_ext rmagic')
 
 
 def test_push():
     rm = rmagic.RMagics(ip)
     ip.push({'X':np.arange(5), 'Y':np.array([3,5,4,6,7])})
     ip.run_line_magic('Rpush', 'X Y')
     np.testing.assert_almost_equal(np.asarray(rm.r('X')), ip.user_ns['X'])
     np.testing.assert_almost_equal(np.asarray(rm.r('Y')), ip.user_ns['Y'])
 
 def test_push_localscope():
     """Test that Rpush looks for variables in the local scope first."""
     ip.run_cell('''
 def rmagic_addone(u):
     %Rpush u
     %R result = u+1
     %Rpull result
     return result[0]
 u = 0
 result = rmagic_addone(12344)
     ''')
     result = ip.user_ns['result']
     np.testing.assert_equal(result, 12345)
 
 @skip_without('pandas')
 def test_push_dataframe():
     from pandas import DataFrame
     rm = rmagic.RMagics(ip)
     df = DataFrame([{'a': 1, 'b': 'bar'}, {'a': 5, 'b': 'foo', 'c': 20}])
     ip.push({'df':df})
     ip.run_line_magic('Rpush', 'df')
     
     # This is converted to factors, which are currently converted back to Python
     # as integers, so for now we test its representation in R.
     sio = StringIO()
     rinterface.set_writeconsole(sio.write)
     try:
         rm.r('print(df$b[1])')
         nt.assert_in('[1] bar', sio.getvalue())
     finally:
         rinterface.set_writeconsole(None)
     
     # Values come packaged in arrays, so we unbox them to test.
     nt.assert_equal(rm.r('df$a[2]')[0], 5)
     missing = rm.r('df$c[1]')[0]
     assert np.isnan(missing), missing
 
 def test_pull():
     rm = rmagic.RMagics(ip)
     rm.r('Z=c(11:20)')
     ip.run_line_magic('Rpull', 'Z')
     np.testing.assert_almost_equal(np.asarray(rm.r('Z')), ip.user_ns['Z'])
     np.testing.assert_almost_equal(ip.user_ns['Z'], np.arange(11,21))
 
 def test_Rconverter():
     datapy= np.array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], 
           dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])
     ip.user_ns['datapy'] = datapy
     ip.run_line_magic('Rpush', 'datapy')
 
     # test to see if a copy is being made
     v = ip.run_line_magic('Rget', '-d datapy')
     w = ip.run_line_magic('Rget', '-d datapy')
     np.testing.assert_almost_equal(w['x'], v['x'])
     np.testing.assert_almost_equal(w['y'], v['y'])
     nt.assert_true(np.all(w['z'] == v['z']))
     np.testing.assert_equal(id(w.data), id(v.data))
     nt.assert_equal(w.dtype, v.dtype)
 
     ip.run_cell_magic('R', ' -d datar', 'datar=datapy')
 
     u = ip.run_line_magic('Rget', ' -d datar')
     np.testing.assert_almost_equal(u['x'], v['x'])
     np.testing.assert_almost_equal(u['y'], v['y'])
     nt.assert_true(np.all(u['z'] == v['z']))
     np.testing.assert_equal(id(u.data), id(v.data))
     nt.assert_equal(u.dtype, v.dtype)
 
 
 def test_cell_magic():
 
     ip.push({'x':np.arange(5), 'y':np.array([3,5,4,6,7])})
     snippet = '''
     print(summary(a))
     plot(x, y, pch=23, bg='orange', cex=2)
     plot(x, x)
     print(summary(x))
     r = resid(a)
     xc = coef(a)
     '''
     ip.run_cell_magic('R', '-i x,y -o r,xc -w 150 -u mm a=lm(y~x)', snippet)
     np.testing.assert_almost_equal(ip.user_ns['xc'], [3.2, 0.9])
     np.testing.assert_almost_equal(ip.user_ns['r'], np.array([-0.2,  0.9, -1. ,  0.1,  0.2]))
 
 
 def test_rmagic_localscope():
     ip.push({'x':0})
     ip.run_line_magic('R', '-i x -o result result <-x+1')
     result = ip.user_ns['result']
     nt.assert_equal(result[0], 1)
 
     ip.run_cell('''def rmagic_addone(u):
     %R -i u -o result result <- u+1
     return result[0]''')
     ip.run_cell('result = rmagic_addone(1)')
     result = ip.user_ns['result']
     nt.assert_equal(result, 2)
 
     nt.assert_raises(
         NameError,
         ip.run_line_magic,
         "R",
         "-i var_not_defined 1+1")