def get_iter(self):
ref_value,ref_bounds = self._get_iter_value_bounds_()
if ref_bounds is None:
has_bounds = False
else:
has_bounds = True
ref_uid = self.uid
ref_name_value = self.name_value
assert_raise(self.name_value != None,logger='interface.dimension.base',
exc=ValueError('The "name_value" attribute is required for iteration.'))
ref_name_uid = self.name_uid
ref_name_bounds_lower = '{0}_lower'.format(self.name_bounds)
ref_name_bounds_upper = '{0}_upper'.format(self.name_bounds)
for ii in range(self.value.shape[0]):
yld = {ref_name_value:ref_value[ii],ref_name_uid:ref_uid[ii]}
if has_bounds:
yld.update({ref_name_bounds_lower:ref_bounds[ii,0],
ref_name_bounds_upper:ref_bounds[ii,1]})
else:
yld.update({ref_name_bounds_lower:None,
ref_name_bounds_upper:None})
yield(ii,yld)
def variables(self,value):
if isinstance(value,Variable):
value = VariableCollection(variables=[value])
assert_raise(isinstance(value,VariableCollection),exc=ValueError('The "variables" keyword must be a Variable object.'))
self._variables = value
for v in value.itervalues():
v._field = self
if v._value is not None:
assert(v._value.shape == self.shape)
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