def get(self,format_time=True):
def _get_temporal_adds_(ref_attrs):
## calendar should default to standard if it is not present.
calendar = ref_attrs.get('calendar',None) or self.t_calendar
return({'units':self.t_units or ref_attrs['units'],
'calendar':calendar,
'format_time':format_time})
## parameters for the loading loop
to_load = {'temporal':{'cls':NcTemporalDimension,'adds':_get_temporal_adds_,'axis':'T','name_uid':'tid','name_value':'time'},
'level':{'cls':NcVectorDimension,'adds':None,'axis':'Z','name_uid':'lid','name_value':'level'},
'row':{'cls':NcVectorDimension,'adds':None,'axis':'Y','name_uid':'row_id','name_value':'row'},
'col':{'cls':NcVectorDimension,'adds':None,'axis':'X','name_uid':'col_id','name_value':'col'},
'realization':{'cls':NcVectorDimension,'adds':None,'axis':'R','name_uid':'rlz_id','name_value':'rlz'}}
loaded = {}
for k,v in to_load.iteritems():
## this is the string axis representation
axis_value = v['axis'] or v['cls']._axis
## pull the axis information out of the dimension map
ref_axis = self._source_metadata['dim_map'].get(axis_value)
## if the axis is not represented, fill it with none. this happens
## when a dataset does not have a vertical level or projection axis
## for example.
if ref_axis is None:
fill = None
else:
ref_variable = self._source_metadata['variables'].get(ref_axis['variable'])
## for data with a projection/realization axis there may be no
## associated variable.
try:
ref_variable['axis'] = ref_axis
except TypeError:
if axis_value == 'R' and ref_variable is None:
ref_variable = {'axis':ref_axis,'name':ref_axis['dimension'],'attrs':{}}
## extract the data length to use when creating the source index
## arrays.
length = self._source_metadata['dimensions'][ref_axis['dimension']]['len']
src_idx = np.arange(0,length)
## assemble parameters for creating the dimension class then initialize
## the class.
kwds = dict(name_uid=v['name_uid'],name_value=v['name_value'],src_idx=src_idx,
data=self,meta=ref_variable,axis=axis_value,name=ref_variable.get('name'))
if v['adds'] is not None:
kwds.update(v['adds'](ref_variable['attrs']))
kwds.update({'name':ref_variable.get('name')})
fill = v['cls'](**kwds)
loaded[k] = fill
assert_raise(set(('temporal','row','col')).issubset(set([k for k,v in loaded.iteritems() if v != None])),
logger='request',exc=ValueError('Target variable must at least have temporal, row, and column dimensions.'))
grid = SpatialGridDimension(row=loaded['row'],col=loaded['col'])
crs = None
if self.s_crs is not None:
crs = self.s_crs
else:
crs = self._get_crs_()
if crs is None:
ocgis_lh('No "grid_mapping" attribute available assuming WGS84: {0}'.format(self.uri),
'request',logging.WARN)
crs = CFWGS84()
## rotated pole coordinate systems require transforming the coordinates to
## WGS84 before they may be loaded.
if isinstance(crs,CFRotatedPole):
msg = 'CFRotatedPole projection found. Transforming coordinates to WGS84 and replacing the CRS with CFWGS84'
ocgis_lh(msg=msg,logger='request.nc',level=logging.WARN)
grid = get_rotated_pole_spatial_grid_dimension(crs,grid)
crs = CFWGS84()
spatial = SpatialDimension(name_uid='gid',grid=grid,crs=crs,abstraction=self.s_abstraction)
variable_meta = self._source_metadata['variables'][self.variable]
variable_units = variable_meta['attrs'].get('units')
variable = Variable(self.variable,self.alias,variable_units,meta=variable_meta,
data=self)
vc = VariableCollection(variables=[variable])
ret = NcField(variables=vc,spatial=spatial,temporal=loaded['temporal'],level=loaded['level'],
realization=loaded['realization'],meta=self._source_metadata,uid=self.did)
## apply any subset parameters after the field is loaded
if self.time_range is not None:
ret = ret.get_between('temporal',min(self.time_range),max(self.time_range))
if self.time_region is not None:
ret = ret.get_time_region(self.time_region)
if self.level_range is not None:
ret = ret.get_between('level',min(self.level_range),max(self.level_range))
return(ret)
def get_field(self, format_time=True, interpolate_spatial_bounds=False):
"""
:param bool format_time:
:param bool interpolate_spatial_bounds:
:raises ValueError:
"""
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})
## this dictionary contains additional keyword arguments for the row
## and column dimensions.
adds_row_col = {'interpolate_bounds': interpolate_spatial_bounds}
## parameters for the loading loop
to_load = {'temporal': {'cls': NcTemporalDimension, 'adds': _get_temporal_adds_, 'axis': 'T', 'name_uid': 'tid',
'name_value': 'time'},
'level': {'cls': NcVectorDimension, 'adds': None, 'axis': 'Z', 'name_uid': 'lid',
'name_value': 'level'},
'row': {'cls': NcVectorDimension, 'adds': adds_row_col, 'axis': 'Y', 'name_uid': 'row_id',
'name_value': 'row'},
'col': {'cls': NcVectorDimension, 'adds': adds_row_col, 'axis': 'X', 'name_uid': 'col_id',
'name_value': 'col'},
'realization': {'cls': NcVectorDimension, 'adds': None, 'axis': 'R', 'name_uid': 'rlz_id',
'name_value': 'rlz'}}
loaded = {}
for k, v in to_load.iteritems():
## this is the string axis representation
axis_value = v['axis'] or v['cls']._axis
## pull the axis information out of the dimension map
ref_axis = self.rd.source_metadata['dim_map'].get(axis_value)
ref_axis = self.rd.source_metadata['dim_map'].get(axis_value)
## if the axis is not represented, fill it with none. this happens
## when a dataset does not have a vertical level or projection axis
## for example.
if ref_axis is None:
fill = None
else:
ref_variable = self.rd.source_metadata['variables'].get(ref_axis['variable'])
## for data with a projection/realization axis there may be no
## associated variable.
try:
ref_variable['axis'] = ref_axis
except TypeError:
if axis_value == 'R' and ref_variable is None:
ref_variable = {'axis': ref_axis, 'name': ref_axis['dimension'], 'attrs': {}}
## extract the data length to use when creating the source index
## arrays.
length = self.rd.source_metadata['dimensions'][ref_axis['dimension']]['len']
src_idx = np.arange(0, length, dtype=constants.np_int)
## get the target data type for the dimension
try:
dtype = np.dtype(ref_variable['dtype'])
## the realization dimension may not be a associated with a variable
except KeyError:
if k == 'realization' and ref_variable['axis']['variable'] is None:
dtype = None
else:
raise
## assemble parameters for creating the dimension class then initialize
## the class.
kwds = dict(name_uid=v['name_uid'], name_value=v['name_value'], src_idx=src_idx,
data=self.rd, meta=ref_variable, axis=axis_value, name=ref_variable.get('name'),
dtype=dtype)
## there may be additional parameters for each dimension.
if v['adds'] is not None:
try:
kwds.update(v['adds'](ref_variable['attrs']))
## adds may not be a callable object. assume they are a
## dictionary.
except TypeError:
kwds.update(v['adds'])
kwds.update({'name': ref_variable.get('name')})
fill = v['cls'](**kwds)
loaded[k] = fill
assert_raise(set(('temporal', 'row', 'col')).issubset(set([k for k, v in loaded.iteritems() if v != None])),
logger='request',
exc=ValueError('Target variable must at least have temporal, row, and column dimensions.'))
grid = SpatialGridDimension(row=loaded['row'], col=loaded['col'])
# crs = None
# if rd.crs is not None:
# crs = rd.crs
# else:
# crs = rd._get_crs_(rd._variable[0])
# if crs is None:
# ocgis_lh('No "grid_mapping" attribute available assuming WGS84: {0}'.format(rd.uri),
# 'request', logging.WARN)
# crs = CFWGS84()
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(self.rd._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)
vc.add_variable(variable)
ret = NcField(variables=vc, spatial=spatial, temporal=loaded['temporal'], level=loaded['level'],
realization=loaded['realization'], meta=deepcopy(self.rd._source_metadata), uid=self.rd.did,
name=self.rd.name)
## 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.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 == None:
msg = ("A level subset was requested but the target dataset does not have a level dimension. The "
"dataset's alias is: {0}".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, "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