def __init__(self, *args, **kwargs): od = OrdDict() for item in args: od[None] = conversion.py2ro(item) for k, v in kwargs.items(): od[k] = conversion.py2ro(v) res = self._constructor.rcall(tuple(od.items()), robjects.globalenv) self.__sexp__ = res.__sexp__
def new(cls, *args, **kwargs): """ Constructor for the class Layer. """ for i, elt in enumerate(args): args[i] = conversion.py2ro(elt) for k in kwargs: kwargs[k] = conversion.py2ro(kwargs[k]) res = cls(cls.contructor)(*args, **kwargs) return res
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 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 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"))
for cell in all_cells_clusters23: total_transcript_counts = cell.total_estimated_counts - cell.spikeins.est_counts.sum( ) est_counts = cell.est_counts.transpose() #*total_transcript_counts/1e3 counts_data.loc[:, cell.id] = np.floor(est_counts) factors = pd.DataFrame(np.zeros((1, number_of_cells), dtype='int16'), columns=list_of_cell_names) fact = '' for cell in all_cells_clusters23: factors.loc[:, cell.id] = str(cell.clusterID) fact += str(cell.clusterID) scde = importr("scde") counts_data_r = conversion.py2ro(counts_data) factors_r = conversion.py2ro(factors) fact_r = ro.FactorVector(fact) factors_r.colnames = list_of_cell_names counts_data_r.colnames = list_of_cell_names r("""counts_data_int = apply(""" + counts_data_r.r_repr() + """,2, function(x) {storage.mode(x) = 'integer';x}) """) r("""facts = """ + fact_r.r_repr()) r("""names(facts) = colnames(counts_data_int)""") # r("o.ifm = scde.error.models(counts = counts_data_int, groups = facts, n.cores = 1, linear.fit=F, local.theta.fit=F, threshold.segmentation = TRUE, save.crossfit.plots = FALSE, save.model.plots = FALSE, verbose = 1)") # r("""save(o.ifm, file = "/scratch/PI/mcovert/dvanva/sequencing/75min_scde_fit_linear.RData")""")
output = open("DMBC_Prediction.csv", "w") training = pd.read_csv("training.csv") test = pd.read_csv("test.csv") training2 = pd.DataFrame.to_csv(training, sep=",", index=False, line_terminator='\n') test2 = pd.DataFrame.to_csv(test, sep=",", index=False, line_terminator='\n') print(training2) print(test2) r_training = conversion.py2ro(training2) robjects.r.assign("my_training", r_training) robjects.r("save(my_training, file='training.RData')") r_test = conversion.py2ro(test2) robjects.r.assign("my_test", r_test) robjects.r("save(my_test, file='test.RData')") x = robjects.r(''' library(DMBC) load("training.RData") training_time <- get("training") load("test.RData") test_time <- get("test") auc_out <- Cal_AUC(loocv(training_time)) dmbc_predict(data=training_time,testSet=test_time,auc_out=auc_out)