def set_up_lci_calculations(activity_list, result_dir, worker_id,
database_name, samples_batch, project_name):
"""Dispatch LCI calculation for a list of activities
Parameters
--------------
activity_list : list
List of codes to activities for which LCI arrays should be calculated
result_dir : str
Path to directory where results are stored
worker_id : int
Identification of the worker if using MultiProcessing, used only for
error messages.
database_name : str
Name of the LCI database
samples_batch : int
Integer id for sample batch. Used for campaigns names and for
generating a seed for the RNG. The maximum value is 14.
project_name : str
Name of the brightway2 project where the database is imported
Returns
---------
None
"""
projects.set_current(_check_project(project_name))
g_samples_dir = _get_lci_dir(result_dir,
'probabilistic',
samples_batch,
must_exist=False)
g_samples_dir_temp = g_samples_dir / "temp"
g_samples_dir_temp.mkdir(exist_ok=True, parents=True)
ref_bio_dict = get_ref_bio_dict_from_common_files(
Path(result_dir) / "common_files")
campaign = _get_campaign(str(samples_batch), expect_base_presamples=True)
presample_paths = [p for p in campaign]
assert presample_paths
pps = [presamples.PresamplesPackage(p) for p in presample_paths]
iterations = list(set([pp.ncols for pp in pps]))
assert len(iterations) == 1
total_iterations = iterations[0]
print("Worker ID {}, requested iterations: {}".format(
worker_id, total_iterations))
g_dimensions = len(ref_bio_dict)
times = []
for act_i, act_code in enumerate(activity_list):
t0 = time.time()
try:
calculate_lci_array(database_name, act_code, presample_paths,
g_dimensions, total_iterations, g_samples_dir)
times.append(time.time() - t0)
except Exception as err:
print("************Failure for {} by {}: {}************".format(
act_code, worker_id, err))
print("Worker ID: {}\n\tTotal of {} LCIs of {} iterations"
"\n\tTotal time: {} minutes\n\tAverage time: {} minutes per LCI".
format(worker_id, len(activity_list), total_iterations,
sum(times) / 60, (sum(times) / len(times)) / 60))
def __init__(self, cs_name: str, ps_name: str):
self.package = ps.PresamplesPackage(get_package_path(ps_name))
self.resource = ps.PresampleResource.get_or_none(
name=self.package.name)
if not self.package:
raise ValueError(
"Presamples package with name or id '{}' not found.".format(
ps_name))
super().__init__(cs_name)
self.total = self.package.ncols
self._current_index = 0
# Construct an index dictionary similar to fu_index and method_index
self.presamples_index = {
k: i
for i, k in enumerate(self.scenario_names)
}
# Rebuild numpy arrays with presample dimension included.
self.lca_scores = np.zeros(
(len(self.func_units), len(self.methods), self.total))
self.elementary_flow_contributions = np.zeros(
(len(self.func_units), len(self.methods), self.total,
self.lca.biosphere_matrix.shape[0]))
self.process_contributions = np.zeros(
(len(self.func_units), len(self.methods), self.total,
self.lca.technosphere_matrix.shape[0]))