def numpy2ri(o):
""" Augmented conversion function, converting numpy arrays into
rpy2.rinterface-level R structures. """
if isinstance(o, numpy.ndarray):
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)
res = ro.r.array(vec, dim=dim)
# R does not support unsigned types:
elif o.dtype.kind == "u":
raise(ValueError("Cannot convert numpy array of unsigned values -- R does not have unsigned integers."))
# Array-of-PyObject is treated like a Python list:
elif o.dtype.kind == "O":
res = conversion.py2ri(list(o))
# Record arrays map onto R data frames:
elif o.dtype.kind == "V":
if o.dtype.names is None:
raise(ValueError("Nothing can be done for this numpy array type %s at the moment." % (o.dtype,)))
df_args = []
for field_name in o.dtype.names:
df_args.append((field_name,
conversion.py2ri(o[field_name])))
res = ro.baseenv["data.frame"].rcall(tuple(df_args), ro.globalenv)
# It should be impossible to get here:
else:
raise(ValueError("Unknown numpy array type."))
else:
res = ro.default_py2ri(o)
return res
def as_ape_object(o):
"""
Returns ``o`` as an ape object.
"""
kwargs = {}
if isinstance(o, dendropy.TreeList):
kwargs["keep.multi"] = True
text = o.as_string("newick")
return _R["read.tree"](text=text, **kwargs)
elif isinstance(o, dendropy.Tree):
kwargs["keep.multi"] = False
text = o.as_string("newick")
return _R["read.tree"](text=text, **kwargs)
# if isinstance(o, dendropy.Tree) or isinstance(o, dendropy.TreeList):
# f = tempfile.NamedTemporaryFile()
# o.write_to_stream(f, "nexus", simple=True, block_titles=False)
# f.flush()
# return _R['read.nexus'](f.name)
elif isinstance(o, dendropy.CharacterMatrix):
f = tempfile.NamedTemporaryFile()
o.write_to_stream(f, "nexus", simple=True, block_titles=False)
f.flush()
return _R["read.nexus.data"](f.name)
else:
return robjects.default_py2ri(o)
def as_ape_object(o):
"""
Returns ``o`` as an ape object.
"""
kwargs = {}
if isinstance(o, dendropy.TreeList):
kwargs['keep.multi'] = True
text = o.as_string("newick")
return _R['read.tree'](text=text, **kwargs)
elif isinstance(o, dendropy.Tree):
kwargs['keep.multi'] = False
text = o.as_string("newick")
return _R['read.tree'](text=text, **kwargs)
# if isinstance(o, dendropy.Tree) or isinstance(o, dendropy.TreeList):
# f = tempfile.NamedTemporaryFile()
# o.write_to_stream(f, "nexus", simple=True, block_titles=False)
# f.flush()
# return _R['read.nexus'](f.name)
elif isinstance(o, dendropy.CharacterMatrix):
f = tempfile.NamedTemporaryFile()
o.write_to_stream(f, "nexus", simple=True, block_titles=False)
f.flush()
return _R['read.nexus.data'](f.name)
else:
return robjects.default_py2ri(o)
def pd_py2ri(o):
"""
"""
res = None
if isinstance(o, pd.DataFrame) and isinstance(o.index, pd.DatetimeIndex):
res = convert_df_to_xts(o)
if res is None:
res = robjects.default_py2ri(o)
return res
def numpy2ri(o):
if isinstance(o, numpy.ndarray):
if not o.dtype.isnative:
raise(ValueError("Cannot pass numpy arrays with non-native byte orders at the moment."))
# The possible kind codes are listed at
# http://numpy.scipy.org/array_interface.shtml
kinds = {
# "t" -> not really supported by numpy
"b": rinterface.LGLSXP,
"i": rinterface.INTSXP,
# "u" -> special-cased below
"f": rinterface.REALSXP,
"c": rinterface.CPLXSXP,
# "O" -> special-cased below
"S": rinterface.STRSXP,
"U": rinterface.STRSXP,
# "V" -> special-cased below
}
# Most types map onto R arrays:
if o.dtype.kind in kinds:
# "F" means "use column-major order"
vec = rinterface.SexpVector(o.ravel("F"), kinds[o.dtype.kind])
dim = rinterface.SexpVector(o.shape, rinterface.INTSXP)
res = ro.r.array(vec, dim=dim)
# R does not support unsigned types:
elif o.dtype.kind == "u":
raise(ValueError("Cannot convert numpy array of unsigned values -- R does not have unsigned integers."))
# Array-of-PyObject is treated like a Python list:
elif o.dtype.kind == "O":
res = ro.conversion.py2ri(list(o))
# Record arrays map onto R data frames:
elif o.dtype.kind == "V":
if o.dtype.names is None:
raise(ValueError("Nothing can be done for this numpy array type %s at the moment." % (o.dtype,)))
df_args = []
for field_name in o.dtype.names:
df_args.append((field_name,
ro.conversion.py2ri(o[field_name])))
res = ro.baseNameSpaceEnv["data.frame"].rcall(tuple(df_args))
# It should be impossible to get here:
else:
raise(ValueError("Unknown numpy array type."))
else:
res = ro.default_py2ri(o)
return res
def pd_py2ri(o):
"""
"""
res = None
if isinstance(o, pd.DataFrame):
if isinstance(o.index, pd.DatetimeIndex):
res = convert_df_to_xts(o)
else:
res = rcom.convert_to_r_dataframe(o)
if isinstance(o, pd.DatetimeIndex):
res = convert_datetime_index(o)
if res is None:
res = robjects.default_py2ri(o)
return res
def pd_py2ri(o):
"""
"""
res = None
if isinstance(o, pd.DataFrame):
if isinstance(o.index, pd.DatetimeIndex):
res = convert_df_to_xts(o)
else:
res = rcom.convert_to_r_dataframe(o)
if isinstance(o, pd.DatetimeIndex):
res = convert_datetime_index(o)
if res is None:
res = robjects.default_py2ri(o)
return res
def numpy2ri(o):
""" Augmented conversion function, converting numpy arrays into
rpy2.rinterface-level R structures. """
# allow array-likes to also function with this module.
if not isinstance(o, numpy.ndarray) and hasattr(o, '__array__'):
o = o.__array__()
if isinstance(o, numpy.ndarray):
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 ?
#FIXME: optimize what is below needed/possible ? (other ways to create R arrays ?)
res = rinterface.baseenv['array'](vec, dim=dim)
# R does not support unsigned types:
elif o.dtype.kind == "u":
raise (ValueError(
"Cannot convert numpy array of unsigned values -- R does not have unsigned integers."
))
# Array-of-PyObject is treated like a Python list:
elif o.dtype.kind == "O":
res = conversion.py2ri(list(o))
# Record arrays map onto R data frames:
elif o.dtype.kind == "V":
if o.dtype.names is None:
raise (ValueError(
"Nothing can be done for this numpy array type %s at the moment."
% (o.dtype, )))
df_args = []
for field_name in o.dtype.names:
df_args.append((field_name, conversion.py2ri(o[field_name])))
res = ro.baseenv["data.frame"].rcall(tuple(df_args), ro.globalenv)
# It should be impossible to get here:
else:
raise (ValueError("Unknown numpy array type."))
else:
res = ro.default_py2ri(o)
return res