def _toDataColumn_object(self, data, mask):
data, mask = self._checkNumpy(data, mask)
if isinstance(data, NP.ndarray) and (mask is None or isinstance(mask, NP.ndarray)) and data.dtype == self.dtype:
pass # proceed to return statement (after checking values and intervals)
else:
data, mask = self._checkNonNumpy(data, mask)
data = NP.array(data, dtype=self.dtype)
if mask is None:
mask = NP("fromiter", (defs.MISSING if (isinstance(d, float) and math.isnan(d)) else defs.VALID for d in data), dtype=defs.maskType, count=len(data))
else:
mask = NP("fromiter", (defs.MISSING if (m != 0 or (isinstance(data[i], float) and math.isnan(data[i]))) else defs.VALID for i, m in enumerate(mask)), dtype=defs.maskType, count=len(mask))
if not mask.any():
mask = None
data, mask = self._checkValues(data, mask)
data, mask = self._checkIntervals(data, mask)
return DataColumn(self, data, mask)
