def _get_field_(self, format_time=True):
"""
:param bool format_time:
:raises ValueError:
"""
# reference the request dataset's source metadata
source_metadata = self.rd.source_metadata
def _get_temporal_adds_(ref_attrs):
# calendar should default to standard if it is not present and the t_calendar overload is not used.
calendar = self.rd.t_calendar or ref_attrs.get("calendar", None) or "standard"
return {
"units": self.rd.t_units or ref_attrs["units"],
"calendar": calendar,
"format_time": format_time,
"conform_units_to": self.rd.t_conform_units_to,
}
# parameters for the loading loop
to_load = {
"temporal": {
"cls": NcTemporalDimension,
"adds": _get_temporal_adds_,
"axis": "T",
"name_uid": "tid",
"name": "time",
},
"level": {"cls": NcVectorDimension, "adds": None, "axis": "Z", "name_uid": "lid", "name": "level"},
"row": {"cls": NcVectorDimension, "adds": None, "axis": "Y", "name_uid": "yc_id", "name": "yc"},
"col": {"cls": NcVectorDimension, "adds": None, "axis": "X", "name_uid": "xc_id", "name": "xc"},
"realization": {
"cls": NcVectorDimension,
"adds": None,
"axis": "R",
"name_uid": "rlz_id",
"name_value": "rlz",
},
}
loaded = {}
kwds_grid = {}
has_row_column = True
for k, v in to_load.iteritems():
fill = self._get_vector_dimension_(k, v, source_metadata)
if k != "realization" and not isinstance(fill, NcVectorDimension) and fill is not None:
assert k in ("row", "col")
has_row_column = False
kwds_grid[k] = fill
loaded[k] = fill
loaded_keys = set([k for k, v in loaded.iteritems() if v is not None])
if has_row_column:
if not {"temporal", "row", "col"}.issubset(loaded_keys):
raise ValueError("Target variable must at least have temporal, row, and column dimensions.")
kwds_grid = {"row": loaded["row"], "col": loaded["col"]}
else:
shape_src_idx = [source_metadata["dimensions"][xx]["len"] for xx in kwds_grid["row"]["dimensions"]]
src_idx = {
"row": np.arange(0, shape_src_idx[0], dtype=np.int32),
"col": np.arange(0, shape_src_idx[1], dtype=np.int32),
}
name_row = kwds_grid["row"]["name"]
name_col = kwds_grid["col"]["name"]
kwds_grid = {"name_row": name_row, "name_col": name_col, "data": self.rd, "src_idx": src_idx}
grid = NcSpatialGridDimension(**kwds_grid)
spatial = SpatialDimension(name_uid="gid", grid=grid, crs=self.rd.crs, abstraction=self.rd.s_abstraction)
vc = VariableCollection()
for vdict in self.rd:
variable_meta = deepcopy(source_metadata["variables"][vdict["variable"]])
variable_units = vdict["units"] or variable_meta["attrs"].get("units")
dtype = np.dtype(variable_meta["dtype"])
fill_value = variable_meta["fill_value"]
variable = Variable(
vdict["variable"],
vdict["alias"],
units=variable_units,
meta=variable_meta,
data=self.rd,
conform_units_to=vdict["conform_units_to"],
dtype=dtype,
fill_value=fill_value,
attrs=variable_meta["attrs"].copy(),
)
vc.add_variable(variable)
ret = NcField(
variables=vc,
spatial=spatial,
temporal=loaded["temporal"],
level=loaded["level"],
realization=loaded["realization"],
meta=source_metadata.copy(),
uid=self.rd.did,
name=self.rd.name,
attrs=source_metadata["dataset"].copy(),
)
# Apply any subset parameters after the field is loaded.
if self.rd.time_range is not None:
ret = ret.get_between("temporal", min(self.rd.time_range), max(self.rd.time_range))
if self.rd.time_region is not None:
ret = ret.get_time_region(self.rd.time_region)
if self.rd.time_subset_func is not None:
ret = ret.get_time_subset_by_function(self.rd.time_subset_func)
if self.rd.level_range is not None:
try:
ret = ret.get_between("level", min(self.rd.level_range), max(self.rd.level_range))
except AttributeError:
# there may be no level dimension
if ret.level is None:
msg = messages.M4.format(self.rd.alias)
raise ValueError(msg)
else:
raise
return ret
def _get_field_(self, format_time=True):
"""
:param bool format_time:
:raises ValueError:
"""
# reference the request dataset's source metadata
source_metadata = self.rd.source_metadata
def _get_temporal_adds_(ref_attrs):
# calendar should default to standard if it is not present and the t_calendar overload is not used.
calendar = self.rd.t_calendar or ref_attrs.get('calendar', None) or 'standard'
return {'units': self.rd.t_units or ref_attrs['units'], 'calendar': calendar, 'format_time': format_time,
'conform_units_to': self.rd.t_conform_units_to}
# parameters for the loading loop
to_load = {'temporal': {'cls': NcTemporalDimension, 'adds': _get_temporal_adds_, 'axis': 'T', 'name_uid': 'tid',
'name': 'time'},
'level': {'cls': NcVectorDimension, 'adds': None, 'axis': 'Z', 'name_uid': 'lid', 'name': 'level'},
'row': {'cls': NcVectorDimension, 'adds': None, 'axis': 'Y', 'name_uid': 'yc_id', 'name': 'yc'},
'col': {'cls': NcVectorDimension, 'adds': None, 'axis': 'X', 'name_uid': 'xc_id', 'name': 'xc'},
'realization': {'cls': NcVectorDimension, 'adds': None, 'axis': 'R', 'name_uid': 'rlz_id',
'name_value': 'rlz'}}
loaded = {}
kwds_grid = {}
has_row_column = True
for k, v in to_load.iteritems():
fill = self._get_vector_dimension_(k, v, source_metadata)
if k != 'realization' and not isinstance(fill, NcVectorDimension) and fill is not None:
assert k in ('row', 'col')
has_row_column = False
kwds_grid[k] = fill
loaded[k] = fill
loaded_keys = set([k for k, v in loaded.iteritems() if v is not None])
if has_row_column:
if not {'temporal', 'row', 'col'}.issubset(loaded_keys):
raise ValueError('Target variable must at least have temporal, row, and column dimensions.')
kwds_grid = {'row': loaded['row'], 'col': loaded['col']}
else:
shape_src_idx = [source_metadata['dimensions'][xx]['len'] for xx in kwds_grid['row']['dimensions']]
src_idx = {'row': np.arange(0, shape_src_idx[0], dtype=np.int32),
'col': np.arange(0, shape_src_idx[1], dtype=np.int32)}
name_row = kwds_grid['row']['name']
name_col = kwds_grid['col']['name']
kwds_grid = {'name_row': name_row, 'name_col': name_col, 'request_dataset': self.rd, 'src_idx': src_idx}
grid = NcSpatialGridDimension(**kwds_grid)
spatial = SpatialDimension(name_uid='gid', grid=grid, crs=self.rd.crs, abstraction=self.rd.s_abstraction)
vc = VariableCollection()
for vdict in self.rd:
variable_meta = deepcopy(source_metadata['variables'][vdict['variable']])
variable_units = vdict['units'] or variable_meta['attrs'].get('units')
attrs = variable_meta['attrs'].copy()
if variable_meta['dtype_packed'] is None:
dtype = np.dtype(variable_meta['dtype'])
fill_value = variable_meta['fill_value']
else:
dtype = np.dtype(variable_meta['dtype_packed'])
fill_value = variable_meta['fill_value_packed']
# Remove scale factors and offsets from the metadata.
attrs.pop('scale_factor')
attrs.pop('add_offset', None)
attrs.pop('missing_value', None)
attrs.pop('_Fill_Value', None)
variable = Variable(vdict['variable'], vdict['alias'], units=variable_units, meta=variable_meta,
request_dataset=self.rd, conform_units_to=vdict['conform_units_to'], dtype=dtype,
fill_value=fill_value, attrs=attrs)
vc.add_variable(variable)
ret = NcField(variables=vc, spatial=spatial, temporal=loaded['temporal'], level=loaded['level'],
realization=loaded['realization'], meta=source_metadata.copy(), uid=self.rd.did,
name=self.rd.name, attrs=source_metadata['dataset'].copy())
# Apply any subset parameters after the field is loaded.
if self.rd.time_range is not None:
ret = ret.get_between('temporal', min(self.rd.time_range), max(self.rd.time_range))
if self.rd.time_region is not None:
ret = ret.get_time_region(self.rd.time_region)
if self.rd.time_subset_func is not None:
ret = ret.get_time_subset_by_function(self.rd.time_subset_func)
if self.rd.level_range is not None:
try:
ret = ret.get_between('level', min(self.rd.level_range), max(self.rd.level_range))
except AttributeError:
# there may be no level dimension
if ret.level is None:
msg = messages.M4.format(self.rd.alias)
raise ValueError(msg)
else:
raise
return ret