def append_values(self, timestamp):
# =======
# for prop in self._data:
# self._data[prop] = None
# def get_current_data(self):
# curr_data = {}
# for prop in self.properties:
# value = self._obj.GetValue(prop, convert=True)
# if len(value.make_columns()) > 1:
# for column, val in zip(value.make_columns(), value.value):
# curr_data[column] = val
# else:
# curr_data[value.make_columns()[0]] = value.value
# return curr_data
# def append_values(self):
# >>>>>>> bug_fixes_merge
curr_data = {}
cached_values = {}
for prop in self._properties:
if not prop.should_sample_value(self._step_number):
continue
prop_key = self._prop_key(prop)
value_key = self._value_key(prop)
# Don't re-read the same value multiple times.
if value_key in cached_values:
value = cached_values[value_key]
else:
value = self._get_value_func_by_type[prop.store_values_type](
prop, prop_key, timestamp)
if value is not None:
cached_values[value_key] = value
if not self._should_store_by_type[prop.store_values_type](
prop, prop_key, value):
continue
if len(value.make_columns()) > 1:
for column, val in zip(value.make_columns(), value.value):
curr_data[column] = val
else:
curr_data[value.make_columns()[0]] = value.value
if self._data[prop_key] is None:
path = f"Exports/{self._scenario}/{prop.elem_class}/{self._name}/{prop.storage_name}"
self._data[prop_key] = ValueContainer(
value,
self._hdf_store,
path,
prop.get_max_size(self._num_steps),
dataset_property_type=prop.get_dataset_property_type(),
max_chunk_bytes=self._max_chunk_bytes,
store_timestamp=prop.should_store_timestamp(),
)
self._data[prop_key].append(value, timestamp=timestamp)
self._step_number += 1
return curr_data
def make_container(hdf_store, path, prop, num_steps, max_chunk_bytes,
values, elem_names):
"""Return an instance of ValueContainer for storing values."""
container = ValueContainer(
values,
hdf_store,
path,
prop.get_max_size(num_steps),
elem_names,
prop.get_dataset_property_type(),
max_chunk_bytes=max_chunk_bytes,
store_time_step=prop.should_store_time_step(),
)
logger.debug("Created storage container path=%s", path)
return container
def export_sums(self):
"""Export properties stored as sums to disk."""
for key, value in self._sums.items():
path = f"Exports/{self._scenario}/{key[0]}/{self._name}/{key[1]}"
container = ValueContainer(
value,
self._hdf_store,
path,
1,
max_chunk_bytes=self._max_chunk_bytes,
dataset_property_type=DatasetPropertyType.NUMBER,
)
container.append(value)
container.flush_data()
def export_change_counts(self):
"""Export properties stored as change counts to disk."""
for key, val in self._change_counts.items():
elem_class = key[0]
prop = key[1]
value = ValueByNumber(self._name, prop, val[1])
path = f"Exports/{self._scenario}/{elem_class}/{self._name}/{prop}"
container = ValueContainer(
value,
self._hdf_store,
path,
1,
max_chunk_bytes=self._max_chunk_bytes,
dataset_property_type=DatasetPropertyType.NUMBER,
)
container.append(value)
container.flush_data()