def activate():
global original_converter
# If module is already activated, there is nothing to do
if original_converter is not None:
return
original_converter = conversion.make_converter(
'snapshot before pandas conversion', template=conversion.converter)
numpy2ri.activate()
new_converter = conversion.make_converter(
'snapshot before pandas conversion', template=conversion.converter)
numpy2ri.deactivate()
for k, v in py2ri.registry.items():
if k is object:
continue
new_converter.py2ri.register(k, v)
for k, v in ri2ro.registry.items():
if k is object:
continue
new_converter.ri2ro.register(k, v)
for k, v in py2ro.registry.items():
if k is object:
continue
new_converter.py2ro.register(k, v)
for k, v in ri2py.registry.items():
if k is object:
continue
new_converter.ri2py.register(k, v)
conversion.converter = new_converter
name, conversion.ri2ro, conversion.py2ri, conversion.py2ro, conversion.ri2py, lineage = new_converter
def activate():
global original_converter
# If module is already activated, there is nothing to do
if original_converter is not None:
return
original_converter = conversion.make_converter('snapshot before pandas conversion',
template=conversion.converter)
numpy2ri.activate()
new_converter = conversion.make_converter('snapshot before pandas conversion',
template=conversion.converter)
numpy2ri.deactivate()
for k,v in py2ri.registry.items():
if k is object:
continue
new_converter.py2ri.register(k, v)
for k,v in ri2ro.registry.items():
if k is object:
continue
new_converter.ri2ro.register(k, v)
for k,v in py2ro.registry.items():
if k is object:
continue
new_converter.py2ro.register(k, v)
for k,v in ri2py.registry.items():
if k is object:
continue
new_converter.ri2py.register(k, v)
conversion.converter = new_converter
name, conversion.ri2ro, conversion.py2ri, conversion.py2ro, conversion.ri2py, lineage = new_converter
def activate():
global original_converter
# If module is already activated, there is nothing to do
if original_converter is not None:
return
original_converter = conversion.converter
new_converter = conversion.make_converter(template=original_converter)
for k,v in py2ri.registry.items():
if k is object:
continue
new_converter.py2ri.register(k, v)
for k,v in ri2ro.registry.items():
if k is object:
continue
new_converter.ri2ro.register(k, v)
for k,v in py2ro.registry.items():
if k is object:
continue
new_converter.py2ro.register(k, v)
for k,v in ri2py.registry.items():
if k is object:
continue
new_converter.ri2py.register(k, v)
conversion.converter = new_converter
conversion.ri2ro, conversion.py2ri, conversion.py2ro, conversion.ri2py = new_converter
ListVector,
StrVector,
IntVector,
POSIXct)
from rpy2.rinterface import (IntSexpVector,
ListSexpVector)
original_converter = None
# pandas is requiring numpy. We add the numpy conversion will be
# activate in the function activate() below
import rpy2.robjects.numpy2ri as numpy2ri
ISOdatetime = rinterface.baseenv['ISOdatetime']
converter = conversion.make_converter('original pandas conversion')
converter_name, py2ri, ri2ro, py2ro, ri2py, converter_lineage = converter
@py2ri.register(PandasDataFrame)
def py2ri_pandasdataframe(obj):
od = OrderedDict()
for name, values in obj.iteritems():
if values.dtype.kind == 'O':
od[name] = StrVector(values)
else:
od[name] = conversion.py2ri(values)
return DataFrame(od)
@py2ri.register(PandasIndex)
def py2ri_pandasindex(obj):
from pandas.core.index import Index as PandasIndex
from numpy import recarray
from collections import OrderedDict
from rpy2.robjects.vectors import DataFrame, Vector, ListVector, StrVector, IntVector, POSIXct
original_converter = None
# pandas is requiring numpy. We add the numpy conversion will be
# activate in the function activate() below
import rpy2.robjects.numpy2ri as numpy2ri
ISOdatetime = rinterface.baseenv['ISOdatetime']
converter = conversion.make_converter()
py2ri, ri2ro, py2ro, ri2py = converter
@py2ri.register(PandasDataFrame)
def py2ri_pandasdataframe(obj):
od = OrderedDict()
for name, values in obj.iteritems():
if values.dtype.kind == 'O':
od[name] = StrVector(values)
else:
od[name] = conversion.py2ri(values)
return DataFrame(od)
@py2ri.register(PandasIndex)
def py2ri_pandasindex(obj):
else:
def __str__(self):
return unicode_to_str(unicode(self), 'utf-8')
@generic
def pyconverter(pyobj):
"""Convert Python objects to R objects. Add types using the decorator:
@pyconverter.when_type
"""
return pyobj
converter = make_converter('ipython conversion', template=template_converter)
# The default conversion for lists is currently to make them an R list. That has
# some advantages, but can be inconvenient (and, it's inconsistent with the way
# python lists are automatically converted by numpy functions), so for
# interactive use in the rmagic, we call unlist, which converts lists to vectors
# **if the list was of uniform (atomic) type**.
@pyconverter.when_type(list)
def pyconverter_list(pyobj):
# simplify2array is a utility function, but nice for us
return ro.r.simplify2array(pyobj)
@magics_class
class RMagics(Magics):
# "V" -> special-cased below
#FIXME: datetime64 ?
#"datetime64":
}
#FIXME: the following would need further thinking & testing on
# 32bits architectures
_kinds['float64'] = rinterface.REALSXP
_vectortypes = (rinterface.LGLSXP,
rinterface.INTSXP,
rinterface.REALSXP,
rinterface.CPLXSXP,
rinterface.STRSXP)
converter = conversion.make_converter('original numpy conversion')
converter_name, py2ri, ri2ro, py2ro, ri2py, converter_lineage = converter
@py2ri.register(numpy.ndarray)
def numpy2ri(o):
""" Augmented conversion function, converting numpy arrays into
rpy2.rinterface-level R structures. """
if not o.dtype.isnative:
raise(ValueError("Cannot pass numpy arrays with non-native byte orders at the moment."))
# Most types map onto R arrays:
if o.dtype.kind in _kinds:
# "F" means "use column-major order"
vec = SexpVector(o.ravel("F"), _kinds[o.dtype.kind])
dim = SexpVector(o.shape, INTSXP)
#FIXME: no dimnames ?
import numpy
from collections import OrderedDict
from rpy2.robjects.vectors import (DataFrame, Vector, ListVector, StrVector,
IntVector, POSIXct)
from rpy2.rinterface import (IntSexpVector, ListSexpVector)
original_converter = None
# pandas is requiring numpy. We add the numpy conversion will be
# activate in the function activate() below
import rpy2.robjects.numpy2ri as numpy2ri
ISOdatetime = rinterface.baseenv['ISOdatetime']
converter = conversion.make_converter('original pandas conversion')
converter_name, py2ri, ri2ro, py2ro, ri2py, converter_lineage = converter
@py2ri.register(PandasDataFrame)
def py2ri_pandasdataframe(obj):
od = OrderedDict()
for name, values in obj.iteritems():
if values.dtype.kind == 'O':
od[name] = StrVector(values)
else:
od[name] = conversion.py2ri(values)
return DataFrame(od)
@py2ri.register(PandasIndex)
if PY3:
__str__ = __unicode__
else:
def __str__(self):
return unicode_to_str(unicode(self), 'utf-8')
@generic
def pyconverter(pyobj):
"""Convert Python objects to R objects. Add types using the decorator:
@pyconverter.when_type
"""
return pyobj
converter = make_converter('ipython conversion',
template = template_converter)
# The default conversion for lists is currently to make them an R list. That has
# some advantages, but can be inconvenient (and, it's inconsistent with the way
# python lists are automatically converted by numpy functions), so for
# interactive use in the rmagic, we call unlist, which converts lists to vectors
# **if the list was of uniform (atomic) type**.
@pyconverter.when_type(list)
def pyconverter_list(pyobj):
# simplify2array is a utility function, but nice for us
return ro.r.simplify2array(pyobj)
@magics_class
class RMagics(Magics):
