def sync_with_r(self):
'''Query the rsession for all objects and try to recreate using the rsession wrapper objects'''
for obj in rsession.r.ls():
#try:
pyobj = rsession.ro.globalEnv.get(str(obj))
typeof = rsession.r['class'](pyobj)[0]
#There are also multivariable time series (is_mts) that we should worry about
#Converting arrays to dataframes flattens the array
if typeof == 'table':
info('FYI, the array object you selected is being imported as a flattened dataframe.')
pyobj = rsession.df(pyobj)
if typeof == 'ts':
#Strip all the data needed to recreate the object
start = rsession.r['start'](pyobj)
end = rsession.r['end'](pyobj)
frequency = rsession.r['frequency'](pyobj)
times = rsession.r['time'](pyobj)
deltat = rsession.r['deltat'](pyobj)
data = numpy.array(pyobj).T
label = str(obj)
self.render_timeseries(data,label=label,start=start,end=end,frequency=frequency,deltat=deltat)
if typeof == 'data.frame':
data = numpy.array(pyobj).T
rownames=pyobj.rownames()
columns = odict()
for col,dat in zip(pyobj.colnames(),pyobj):
ptype = rsession.typeof(dat)
columns[col] = rsession.translate_types(ptype)
self.render_dataframe(data,columns,name=str(obj),rownames=rownames)
if typeof == 'htest':
self.render_description(pyobj,str(obj))
if typeof == 'lm':
self.render_linear_model(pyobj,'Linear Model')
if typeof == 'nls':
self.render_linear_model(pyobj,'Nonlinear Model')
#Vectors
if typeof == 'numeric':
#Cast into dataframe
pyobj = rsession.df(pyobj)
data = numpy.array(pyobj).T
rownames=pyobj.rownames()
columns = odict()
for col,dat in zip(pyobj.colnames(),pyobj):
ptype = rsession.typeof(dat)
columns[col] = rsession.translate_types(ptype)
self.render_dataframe(data,columns,name=str(obj),rownames=rownames)
if config.__devel__:
print('Tring to import to workspace. \n'+str(type(pyobj)) + '\n')
def render_timeseries(self,data,label=None,start=None,end=None,frequency=None,deltat=None,times=None):
'''Time series are handled just like dataframes except we allow for options on frequency/start/end'''
#This only works for ts, not mts at the moment
ptype = rsession.typeof(data[0])
columns = {'V1':rsession.translate_types(ptype)}
if not label:
inc = 0
for unmes in self.robjects.keys():
if 'unnamed' in unmes:
inc += 1
name = 'unnamed' + str(inc+1)
timeseries = rsession.timeseries(data,label=label,start=start,end=end,frequency=frequency,deltat=deltat)
rownames = timeseries.rownames
self.add_r_object(timeseries,label)
self.set_active_robject(timeseries)
self.handle_dataview(data,columns,rownames=rownames,editable=True)