Refactor to prefer rpy2's pandas2ri conversion system.

IPython/extensions/rmagic.py

@@ -1,638 +1,660 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
-from rpy2.robjects.numpy2ri import numpy2ri
-ro.conversion.py2ri = numpy2ri
+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,
                                 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."""
-    if 'pandas' in sys.modules:
-        # We only do this if pandas is already loaded
-        from pandas import DataFrame
-        if isinstance(pyobj, DataFrame):
-            from pandas.rpy.common import convert_to_r_dataframe
-            return convert_to_r_dataframe(pyobj, strings_as_factors=True)
+    """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.
[e-mail]_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 = ''
         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
 
         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)