def outdata(self):
mask = ma.mask_or(self.colmask, self.resmask)
clean = ma.masked_array(data=self.data, mask = mask, fill_value = "NA")
ldata = clean.filled(np.nan).tolist()
for i, row in enumerate(ldata):
for j, col in enumerate(row):
if np.isnan(ldata[i][j]): ldata[i][j] = "NA"
fulldata = vstack((hstack((array(self.indname),self.colnames)),
hstack((transpose(np.atleast_2d(self.ind)), ldata))))
return fulldata
def mask(self):
out = ma.make_mask_none(self.data.shape)
for mask in self.masks: #(self.resmask, self.colmask, self.incmask, self.fitmask):
out = ma.mask_or(out, mask)
return out
