def test_system_units_validation_equal_units(self):
# Heat index coefficients require the data be in specific units.
field = self.get_field(name='tasmax', units='fahrenheit', with_value=True)
field_rhs = self.get_field(name='rhsmax', units='percent', with_value=True)
with orphaned(field_rhs['rhsmax']):
field.add_variable(field_rhs['rhsmax'], is_data=True)
self.assertEqual(set(get_variable_names(field.get_by_tag(TagName.DATA_VARIABLES))),
set(['tasmax', 'rhsmax']))
hi = HeatIndex(field=field, parms={'tas': 'tasmax', 'rhs': 'rhsmax'})
vc = hi.execute()
self.assertIsInstance(vc, VariableCollection)
def _write_variable_collection_main_(cls, vc, opened_or_path, write_mode,
**kwargs):
assert write_mode is not None
dataset_kwargs = kwargs.get('dataset_kwargs', {})
variable_kwargs = kwargs.get('variable_kwargs', {})
# When filling a dataset, we use append mode.
if write_mode == MPIWriteMode.FILL:
mode = 'a'
else:
mode = 'w'
# For an asynchronous write, treat everything like a single rank.
if write_mode == MPIWriteMode.ASYNCHRONOUS:
possible_ranks = [0]
else:
possible_ranks = vm.ranks
# Write the data on each rank.
for idx, rank_to_write in enumerate(possible_ranks):
# The template write only occurs on the first rank.
if write_mode == MPIWriteMode.TEMPLATE and rank_to_write != 0:
pass
# If this is not a template write, fill the data.
elif write_mode == MPIWriteMode.ASYNCHRONOUS or vm.rank == rank_to_write:
with driver_scope(cls,
opened_or_path=opened_or_path,
mode=mode,
**dataset_kwargs) as dataset:
# Write global attributes if we are not filling data.
if write_mode != MPIWriteMode.FILL:
vc.write_attributes_to_netcdf_object(dataset)
# This is the main variable write loop.
variables_to_write = get_variables_to_write(vc)
for variable in variables_to_write:
# Load the variable's data before orphaning. The variable needs its parent to know which
# group it is in.
variable.load()
# Call the individual variable write method in fill mode. Orphaning is required as a
# variable will attempt to write its parent first.
with orphaned(variable, keep_dimensions=True):
variable.write(dataset,
write_mode=write_mode,
**variable_kwargs)
# Recurse the children.
for child in list(vc.children.values()):
if write_mode != MPIWriteMode.FILL:
group = nc.Group(dataset, child.name)
else:
group = dataset.groups[child.name]
child.write(group, write_mode=write_mode, **kwargs)
dataset.sync()
vm.barrier()
def test_calculate(self):
tasmin = self.test_data.get_rd('cancm4_tasmin_2001')
tasmax = self.test_data.get_rd('cancm4_tasmax_2001')
field = tasmin.get()
field_tasmax = tasmax.get()
with orphaned(field_tasmax['tasmax']):
field.add_variable(field_tasmax['tasmax'], is_data=True)
field = field.get_field_slice({'time': slice(0, 600), 'y': slice(25, 50), 'x': slice(25, 50)})
tgd = field.temporal.get_grouping(['month'])
dtr = IcclimETR(field=field, tgd=tgd)
ret = dtr.execute()
self.assertEqual(ret['icclim_ETR'].get_value().shape, (12, 25, 25))
def test_calculation_file_only_two_variables(self):
rd = self.test_data.get_rd('cancm4_tas')
rd2 = self.test_data.get_rd('cancm4_tasmax_2001')
field = rd.get()
field2 = rd2.get()
with orphaned(field2['tasmax']):
field.add_variable(field2['tasmax'], is_data=True)
field = field.get_field_slice({'time': slice(0, 10)})
expr = 'foo=log(1000*(tasmax-tas))/3'
ef = EvalFunction(expr=expr, field=field, file_only=True)
ret = ef.execute()
self.assertEqual(ret['foo']._value, None)
def test_system_units_validation_wrong_units(self):
# Heat index coefficients require the data be in specific units.
field = self.get_field(name='tasmax', units='kelvin', with_value=True)
field_rhs = self.get_field(name='rhsmax', units='percent', with_value=True)
with orphaned(field_rhs['rhsmax']):
field.add_variable(field_rhs['rhsmax'], is_data=True)
self.assertEqual(set(get_variable_names(field.get_by_tag(TagName.DATA_VARIABLES))),
{'tasmax', 'rhsmax'})
hi = HeatIndex(field=field, parms={'tas': 'tasmax', 'rhs': 'rhsmax'})
with self.assertRaises(UnitsValidationError):
hi.execute()
def test_calculation_file_only_two_variables(self):
rd = self.test_data.get_rd('cancm4_tas')
rd2 = self.test_data.get_rd('cancm4_tasmax_2001')
field = rd.get()
field2 = rd2.get()
with orphaned(field2['tasmax']):
field.add_variable(field2['tasmax'], is_data=True)
field = field.get_field_slice({'time': slice(0, 10)})
expr = 'foo=log(1000*(tasmax-tas))/3'
ef = EvalFunction(expr=expr, field=field, file_only=True)
ret = ef.execute()
self.assertEqual(ret['foo']._value, None)
def _write_variable_collection_main_(cls, vc, opened_or_path, write_mode, **kwargs):
assert write_mode is not None
dataset_kwargs = kwargs.get('dataset_kwargs', {})
variable_kwargs = kwargs.get('variable_kwargs', {})
# When filling a dataset, we use append mode.
if write_mode == MPIWriteMode.FILL:
mode = 'a'
else:
mode = 'w'
# For an asynchronous write, treat everything like a single rank.
if write_mode == MPIWriteMode.ASYNCHRONOUS:
possible_ranks = [0]
else:
possible_ranks = vm.ranks
# Write the data on each rank.
for idx, rank_to_write in enumerate(possible_ranks):
# The template write only occurs on the first rank.
if write_mode == MPIWriteMode.TEMPLATE and rank_to_write != 0:
pass
# If this is not a template write, fill the data.
elif write_mode == MPIWriteMode.ASYNCHRONOUS or vm.rank == rank_to_write:
with driver_scope(cls, opened_or_path=opened_or_path, mode=mode, **dataset_kwargs) as dataset:
# Write global attributes if we are not filling data.
if write_mode != MPIWriteMode.FILL:
vc.write_attributes_to_netcdf_object(dataset)
# This is the main variable write loop.
variables_to_write = get_variables_to_write(vc)
for variable in variables_to_write:
# Load the variable's data before orphaning. The variable needs its parent to know which
# group it is in.
variable.load()
# Call the individual variable write method in fill mode. Orphaning is required as a
# variable will attempt to write its parent first.
with orphaned(variable, keep_dimensions=True):
variable.write(dataset, write_mode=write_mode, **variable_kwargs)
# Recurse the children.
for child in list(vc.children.values()):
if write_mode != MPIWriteMode.FILL:
group = nc.Group(dataset, child.name)
else:
group = dataset.groups[child.name]
child.write(group, write_mode=write_mode, **kwargs)
dataset.sync()
vm.barrier()
def test_system_units_validation_equal_units(self):
# Heat index coefficients require the data be in specific units.
field = self.get_field(name='tasmax',
units='fahrenheit',
with_value=True)
field_rhs = self.get_field(name='rhsmax',
units='percent',
with_value=True)
with orphaned(field_rhs['rhsmax']):
field.add_variable(field_rhs['rhsmax'], is_data=True)
self.assertEqual(
set(get_variable_names(field.get_by_tag(TagName.DATA_VARIABLES))),
set(['tasmax', 'rhsmax']))
hi = HeatIndex(field=field, parms={'tas': 'tasmax', 'rhs': 'rhsmax'})
vc = hi.execute()
self.assertIsInstance(vc, VariableCollection)
def test_system_units_validation_wrong_units(self):
# Heat index coefficients require the data be in specific units.
field = self.get_field(name='tasmax', units='kelvin', with_value=True)
field_rhs = self.get_field(name='rhsmax',
units='percent',
with_value=True)
with orphaned(field_rhs['rhsmax']):
field.add_variable(field_rhs['rhsmax'], is_data=True)
self.assertEqual(
set(get_variable_names(field.get_by_tag(TagName.DATA_VARIABLES))),
{'tasmax', 'rhsmax'})
hi = HeatIndex(field=field, parms={'tas': 'tasmax', 'rhs': 'rhsmax'})
with self.assertRaises(UnitsValidationError):
hi.execute()
def test_calculation_two_variables_exp_only(self):
rd = self.test_data.get_rd('cancm4_tas')
rd2 = self.test_data.get_rd('cancm4_tasmax_2001')
field = rd.get()
field2 = rd2.get()
with orphaned(field2['tasmax']):
field.add_variable(field2['tasmax'], is_data=True)
field = field.get_field_slice({'time': slice(0, 10)})
expr = 'foo=log(1000*(tasmax-tas))/3'
ef = EvalFunction(expr=expr, field=field)
ret = ef.execute()
self.assertEqual(list(ret.keys()), ['foo'])
tas = field['tas']
tasmax = field['tasmax']
actual_value = np.log(1000 * (tasmax.get_value() - tas.get_value())) / 3
self.assertNumpyAll(ret['foo'].get_value(), actual_value)
def test_calculation_two_variables_exp_only(self):
rd = self.test_data.get_rd('cancm4_tas')
rd2 = self.test_data.get_rd('cancm4_tasmax_2001')
field = rd.get()
field2 = rd2.get()
with orphaned(field2['tasmax']):
field.add_variable(field2['tasmax'], is_data=True)
field = field.get_field_slice({'time': slice(0, 10)})
expr = 'foo=log(1000*(tasmax-tas))/3'
ef = EvalFunction(expr=expr, field=field)
ret = ef.execute()
self.assertEqual(list(ret.keys()), ['foo'])
tas = field['tas']
tasmax = field['tasmax']
actual_value = np.log(1000 *
(tasmax.get_value() - tas.get_value())) / 3
self.assertNumpyAll(ret['foo'].get_value(), actual_value)
def test_with_eval_function_two_variables(self):
funcs = [{'func': 'tas_out=tas+tas2', 'ref': EvalFunction}]
engine = self.get_engine(funcs=funcs, grouping=None)
rd = self.test_data.get_rd('cancm4_tas')
rd2 = self.test_data.get_rd('cancm4_tas', kwds={'rename_variable': 'tas2'})
field = rd.get()
field2 = rd2.get()
with orphaned(field2['tas2']):
field.add_variable(field2['tas2'], is_data=True)
field = field.get_field_slice({'time': slice(0, 100), 'y': slice(0, 10), 'x': slice(0, 10)})
desired = SpatialCollection()
desired.add_field(field, None)
actual = deepcopy(desired)
engine.execute(desired)
tas_out = desired.get_element(variable_name='tas_out').get_value()
tas = actual.get_element(variable_name='tas').get_value()
tas2 = actual.get_element(variable_name='tas2').get_value()
self.assertNumpyAll(tas_out, tas + tas2)
def test_with_eval_function_two_variables(self):
funcs = [{'func': 'tas_out=tas+tas2', 'ref': EvalFunction}]
engine = self.get_engine(funcs=funcs, grouping=None)
rd = self.test_data.get_rd('cancm4_tas')
rd2 = self.test_data.get_rd('cancm4_tas', kwds={'rename_variable': 'tas2'})
field = rd.get()
field2 = rd2.get()
with orphaned(field2['tas2']):
field.add_variable(field2['tas2'], is_data=True)
field = field.get_field_slice({'time': slice(0, 100), 'y': slice(0, 10), 'x': slice(0, 10)})
desired = SpatialCollection()
desired.add_field(field, None)
actual = deepcopy(desired)
engine.execute(desired)
tas_out = desired.get_element(variable_name='tas_out').get_value()
tas = actual.get_element(variable_name='tas').get_value()
tas2 = actual.get_element(variable_name='tas2').get_value()
self.assertNumpyAll(tas_out, tas + tas2)
def execute(self, coll, file_only=False, tgds=None):
"""
:param :class:~`ocgis.SpatialCollection` coll:
:param bool file_only:
:param dict tgds: {'field_alias': :class:`ocgis.interface.base.dimension.temporal.TemporalGroupDimension`,...}
"""
from ocgis import VariableCollection
# Select which dictionary will hold the temporal group dimensions.
if tgds is None:
tgds_to_use = self._tgds
tgds_overloaded = False
else:
tgds_to_use = tgds
tgds_overloaded = True
# Group the variables. If grouping is None, calculations are performed on each element.
if self.grouping is not None:
ocgis_lh('Setting temporal groups: {0}'.format(self.grouping),
'calc.engine')
for field in coll.iter_fields():
if tgds_overloaded:
assert field.name in tgds_to_use
else:
if field.name not in tgds_to_use:
tgds_to_use[field.name] = field.time.get_grouping(
self.grouping)
# Iterate over functions.
for ugid, container in list(coll.children.items()):
for field_name, field in list(container.children.items()):
new_temporal = tgds_to_use.get(field_name)
if new_temporal is not None:
new_temporal = new_temporal.copy()
# If the engine has a grouping, ensure it is equivalent to the new temporal dimension.
if self.grouping is not None:
try:
compare = set(new_temporal.grouping) == set(
self.grouping)
# Types may be unhashable, compare directly.
except TypeError:
compare = new_temporal.grouping == self.grouping
if not compare:
msg = 'Engine temporal grouping and field temporal grouping are not equivalent. Perhaps ' \
'optimizations are incorrect?'
ocgis_lh(logger='calc.engine', exc=ValueError(msg))
out_vc = VariableCollection()
for f in self.funcs:
try:
ocgis_lh('Calculating: {0}'.format(f['func']),
logger='calc.engine')
# Initialize the function.
function = f['ref'](
alias=f['name'],
dtype=None,
field=field,
file_only=file_only,
vc=out_vc,
parms=f['kwds'],
tgd=new_temporal,
calc_sample_size=self.calc_sample_size,
meta_attrs=f.get('meta_attrs'),
spatial_aggregation=self.spatial_aggregation)
# Allow a calculation to create a temporal aggregation after initialization.
if new_temporal is None and function.tgd is not None:
new_temporal = function.tgd.extract()
except KeyError:
# Likely an eval function which does not have the name key.
function = EvalFunction(
field=field,
file_only=file_only,
vc=out_vc,
expr=self.funcs[0]['func'],
meta_attrs=self.funcs[0].get('meta_attrs'))
ocgis_lh('calculation initialized',
logger='calc.engine',
level=logging.DEBUG)
# Return the variable collection from the calculations.
out_vc = function.execute()
for dv in out_vc.values():
# Any outgoing variables from a calculation must have an associated data type.
try:
assert dv.dtype is not None
except AssertionError:
assert isinstance(dv.dtype, np.dtype)
# If this is a file only operation, there should be no computed values.
if file_only:
assert dv._value is None
ocgis_lh('calculation finished',
logger='calc.engine',
level=logging.DEBUG)
# Try to mark progress. Okay if it is not there.
try:
self._progress.mark()
except AttributeError:
pass
out_field = function.field.copy()
function_tag = function.tag
# Format the returned field. Doing things like removing original data variables and modifying the
# time dimension if necessary. Field functions handle all field modifications on their own, so bypass
# in that case.
if new_temporal is not None:
new_temporal = new_temporal.extract()
format_return_field(function_tag,
out_field,
new_temporal=new_temporal)
# Add the calculation variables.
for variable in list(out_vc.values()):
with orphaned(variable):
out_field.add_variable(variable)
# Tag the calculation data as data variables.
out_field.append_to_tags(function_tag, list(out_vc.keys()))
coll.children[ugid].children[field_name] = out_field
return coll
