def create_presamples(self, name=None, id_=None, overwrite=False, dirpath=None,
seed='sequential'):
"""Create a presamples package from generated samples
Parameters
-----------
name: str, optional
A human-readable name for these samples.
\id_: str, optional
Unique id for this collection of presamples. Optional, generated automatically if not set.
overwrite: bool, default=False
If True, replace an existing presamples package with the same ``\id_`` if it exists.
dirpath: str, optional
An optional directory path where presamples can be created. If None, a subdirectory in the ``project`` folder.
seed: {None, int, "sequential"}, optional, default="sequential"
Seed used by indexer to return array columns in random order. Can be an integer, "sequential" or None.
"""
if not all([self.matrix_samples is not None, self.matrix_indices]):
warnings.warn("No presamples created because there were no matrix data. "
"Make sure to run `add_samples_for_all_acts` or "
"`add_samples_for_act` for a set of acts first.")
return
id_, dirpath = create_presamples_package(
matrix_data=split_inventory_presamples(self.matrix_samples, self.matrix_indices),
name=name, id_=id_, overwrite=overwrite, dirpath=dirpath, seed=seed)
print("Presamples with id_ {} written at {}".format(id_, dirpath))
return id_, dirpath
def presamples_from_scenarios(self, name: str, scenarios: Iterable[Tuple[str, Iterable]]) -> (str, str):
""" When given a iterable of multiple parameter scenarios, construct
a presamples package with all of the recalculated exchange amounts.
"""
samples, indices = self.arrays_from_scenarios(scenarios)
arrays = ps.split_inventory_presamples(samples, indices)
ps_id, ps_path = ps.create_presamples_package(
matrix_data=arrays, name=name, seed="sequential"
)
return ps_id, ps_path
def presamples_from_scenarios(
self, name: str,
scenarios: Iterable[Tuple[str, Iterable]]) -> (str, str):
""" When given a iterable of multiple parameter scenarios, construct
a presamples package with all of the recalculated exchange amounts.
"""
sample_data, indice_data = zip(*(self.recalculate_scenario(values)
for _, values in scenarios))
samples = np.concatenate(sample_data, axis=1)
indices = next(iter(indice_data))
arrays = ps.split_inventory_presamples(samples, indices)
ps_id, ps_path = ps.create_presamples_package(matrix_data=arrays,
name=name,
seed="sequential")
return ps_id, ps_path
def process_arrays_to_package(indices: np.ndarray, values: np.ndarray) -> list:
""" Follow along `presamples` path of splitting the inventories and
formatting the exchange indexes into numpy arrays.
Instead of writing the entire thing to a file, return the list of formatted
data.
"""
package = []
arrays = ps.split_inventory_presamples(values, indices)
for obj in arrays:
samples, indices, kind, *other = obj
samples = to_2d(to_array(samples))
# Convert the key indexes to the actual db ids
indices, metadata = format_matrix_data(indices, kind, *other)
metadata["type"] = kind
package.append((samples, indices, metadata))
return package