def grid_search_subhalo_centres_as_array_from_grid_search_result(
grid_search_result, ) -> [(float, float)]:
if grid_search_result.no_dimensions != 2:
raise exc.AggregatorException(
"The GridSearchResult is not dimensions 2, meaning a 2D array cannot be made."
)
return [
res.samples.median_pdf_instance.galaxies.subhalo.mass.centre
for results in grid_search_result.results_reshaped for res in results
]
def grid_search_subhalo_masses_as_array(aggregator: af.Aggregator) -> al.Array:
grid_search_result_gen = aggregator.values("grid_search_result")
grid_search_results = list(filter(None, list(grid_search_result_gen)))
if len(grid_search_results) != 1:
raise exc.AggregatorException(
"There is more than one grid search result in the aggregator - please filter the"
"aggregator.")
return grid_search_subhalo_masses_as_array_from_grid_search_result(
grid_search_result=grid_search_results[0])
def grid_search_log_evidences_as_array_from_grid_search_result(
grid_search_result,
use_log_evidences=True,
use_stochastic_log_evidences: bool = False,
) -> al.Array2D:
if grid_search_result.no_dimensions != 2:
raise exc.AggregatorException(
"The GridSearchResult is not dimensions 2, meaning a 2D array cannot be made."
)
if use_log_evidences and not use_stochastic_log_evidences:
values = [
value for values in grid_search_result.log_evidence_values
for value in values
]
elif use_stochastic_log_evidences:
stochastic_log_evidences = []
for result in grid_search_result.results:
stochastic_log_evidences_json_file = path.join(
result.search.paths.output_path,
"stochastic_log_evidences.json")
try:
with open(stochastic_log_evidences_json_file, "r") as f:
stochastic_log_evidences_array = np.asarray(json.load(f))
except FileNotFoundError:
raise FileNotFoundError(
f"File not found at {result.search.paths.output_path}")
stochastic_log_evidences.append(
np.median(stochastic_log_evidences_array))
values = stochastic_log_evidences
else:
values = [
value for values in grid_search_result.max_log_likelihood_values
for value in values
]
return al.Array2D.manual_yx_and_values(
y=[centre[0] for centre in grid_search_result.physical_centres_lists],
x=[centre[1] for centre in grid_search_result.physical_centres_lists],
values=values,
pixel_scales=grid_search_result.physical_step_sizes,
shape_native=grid_search_result.shape,
)
def grid_search_result_as_array(
aggregator: af.Aggregator,
use_log_evidences: bool = True,
use_stochastic_log_evidences: bool = False,
) -> np.ndarray:
grid_search_result_gen = aggregator.values("grid_search_result")
grid_search_results = list(filter(None, list(grid_search_result_gen)))
if len(grid_search_results) == 0:
raise exc.AggregatorException(
"There is no grid search resultin the aggregator.")
elif len(grid_search_results) > 1:
raise exc.AggregatorException(
"There is more than one grid search result in the aggregator - please filter the"
"aggregator.")
return grid_search_log_evidences_as_array_from_grid_search_result(
grid_search_result=grid_search_results[0],
use_log_evidences=use_log_evidences,
use_stochastic_log_evidences=use_stochastic_log_evidences,
)
def grid_search_subhalo_masses_as_array_from_grid_search_result(
grid_search_result, ) -> [float]:
if grid_search_result.no_dimensions != 2:
raise exc.AggregatorException(
"The GridSearchResult is not dimensions 2, meaning a 2D array cannot be made."
)
masses = [
res.samples.median_pdf_instance.galaxies.subhalo.mass.mass_at_200
for results in grid_search_result.results_reshaped for res in results
]
return al.Array2D.manual_yx_and_values(
y=[centre[0] for centre in grid_search_result.physical_centres_lists],
x=[centre[1] for centre in grid_search_result.physical_centres_lists],
values=masses,
pixel_scales=grid_search_result.physical_step_sizes,
shape_native=grid_search_result.shape,
)