def __init__(self, *args, **kwargs):
od = OrdDict()
for item in args:
od[None] = conversion.py2ro(item)
for k, v in kwargs.iteritems():
od[k] = conversion.py2ro(v)
res = self._constructor.rcall(od.items(), robjects.globalenv)
self.__sexp__ = res.__sexp__
def assign(self, index, value):
""" Assign a given value to a given index position in the vector """
if not (isinstance(index, rlc.TaggedList) | \
isinstance(index, rlc.ArgsDict)):
args = rlc.TaggedList([conversion.py2ro(index), ])
else:
for i in xrange(len(index)):
index[i] = conversion.py2ro(index[i])
args = index
args.append(conversion.py2ro(value))
args.insert(0, self)
res = r["[<-"].rcall(args.items())
res = conversion.ri2py(res)
return res
def subset(self, *args, **kwargs):
""" Subset the "R-way.", using R's "[" function.
In a nutshell, R indexing differs from Python's on:
- indexing can be done with integers or strings (that are 'names')
- an index equal to TRUE will mean everything selected (because of the recycling rule)
- integer indexing starts at one
- negative integer indexing means exclusion of the given integers
- an index is itself a vector of elements to select
"""
args = [conversion.py2ro(x) for x in args]
for k, v in kwargs.itervalues():
args[k] = conversion.py2ro(v)
res = r["["](*([self, ] + [x for x in args]), **kwargs)
return res
def new(cls,
geom, geom_params,
stat, stat_params,
data,
aesthetics,
position,
params):
args = [conversion.py2ro(x) for x in (geom, geom_params,
stat, stat_params,
data,
aesthetics,
position,
params)]
res = cls(cls.contructor)(*args)
return res
def __setitem__(self, item, value):
robj = conversion.py2ro(value)
super(REnvironment, self).__setitem__(item, robj)
def setdim(self, value):
value = conversion.py2ro(value)
res = r["dim<-"](self, value)
#FIXME: not properly done
raise(Exception("Not yet implemented"))