def _create_parameter(self):
pdict = ParameterDictionary()
pdict = self._add_location_time_ctxt(pdict)
pres_ctxt = ParameterContext('pressure', param_type=QuantityType(value_encoding=numpy.float32))
pres_ctxt.uom = 'Pascal'
pres_ctxt.fill_value = 0x0
pdict.add_context(pres_ctxt)
temp_ctxt = ParameterContext('temp', param_type=QuantityType(value_encoding=numpy.float32))
temp_ctxt.uom = 'degree_Celsius'
temp_ctxt.fill_value = 0e0
pdict.add_context(temp_ctxt)
cond_ctxt = ParameterContext('conductivity', param_type=QuantityType(value_encoding=numpy.float32))
cond_ctxt.uom = 'unknown'
cond_ctxt.fill_value = 0e0
pdict.add_context(cond_ctxt)
raw_fixed_ctxt = ParameterContext('raw_fixed', param_type=QuantityType(value_encoding=numpy.float32))
raw_fixed_ctxt.uom = 'unknown'
raw_fixed_ctxt.fill_value = 0e0
pdict.add_context(raw_fixed_ctxt)
raw_blob_ctxt = ParameterContext('raw_blob', param_type=QuantityType(value_encoding=numpy.float32))
raw_blob_ctxt.uom = 'unknown'
raw_blob_ctxt.fill_value = 0e0
pdict.add_context(raw_blob_ctxt)
return pdict
def _merge_contexts(cls, contexts, temporal):
pdict = ParameterDictionary()
for context in contexts:
if context.name == temporal:
context.axis = AxisTypeEnum.TIME
pdict.add_context(context, is_temporal=True)
else:
pdict.add_context(context)
return pdict
def _merge_contexts(cls, contexts, temporal):
pdict = ParameterDictionary()
for context in contexts:
if context.name == temporal:
context.axis = AxisTypeEnum.TIME
pdict.add_context(context, is_temporal=True)
else:
pdict.add_context(context)
return pdict
