def mask_array_construct(self):
data = with_mode(0, r.read_table)(self.dataset_source, row_names=1)
'''
!Important!
if the dataset_source has too few data, conversion from R to python will be a problem.
The whole data matrix will be converted to a python string matrix.
R's NA is not converted to nan in python.
The problem has been found.
r.as_matrix converts small dataset to character type.
r.matrix won't rig the class type, but it rigs the structure.
The only to sovle this is add a colClasses vector to r.read_table.
such as: colClasses=c('character',rep('double',11))
But you have to know the no_of_cols in advance.
As our dataset is really big, this problem hasn't appeared.
'''
#print r.as_matrix(data)
array = ma.masked_inside(r.as_matrix(data), -1.0e20, 1.0e20)
#all are set to be masked except nan. weird! So have to do a converse.
self.mask_array = ma.array(array, mask=ma.logical_not(ma.getmask(array)))
self.genelabels = r.rownames(data)
self.no_of_genes = len(self.genelabels)
self.no_of_cols = len(array[0])
self.mask_matrix=ma.identity(self.no_of_cols)
del array ,data
def mask_array_construct(self): data =with_mode(0, r.read_table)(self.dataset_source) ''' !Important! if the dataset_source has too few data, conversion from R to python will be a problem. The whole data matrix will be converted to a python string matrix. R's NA is not converted to nan in python. ''' #print r.as_matrix(data) array = ma.masked_inside(r.as_matrix(data), -1.0e20, 1.0e20) #all are set to be masked except nan. weird! So have to do a converse. self.mask_array = ma.array(array, mask=ma.logical_not(ma.getmask(array))) self.genelabels = r.rownames(data) self.no_of_genes = len(self.genelabels) self.no_of_cols = len(array[0]) self.mask_matrix=ma.identity(self.no_of_cols) del array ,data
