def addSpeciesInfo(bccvlmd, result):
if ISDMExperiment.providedBy(result.__parent__) or IMSDMExperiment.providedBy(result.__parent__):
spds = uuidToObject(result.job_params['species_occurrence_dataset'])
if IProjectionExperiment.providedBy(result.__parent__):
spds = uuidToObject(result.job_params['species_distribution_models'])
speciesmd = IBCCVLMetadata(spds).get('species', None)
if speciesmd:
bccvlmd['species'] = speciesmd.copy()
def addSpeciesInfo(bccvlmd, result):
if ISDMExperiment.providedBy(result.__parent__) or \
IMSDMExperiment.providedBy(result.__parent__) or \
IMMExperiment.providedBy(result.__parent__):
spds = uuidToObject(result.job_params['species_occurrence_dataset'])
if IProjectionExperiment.providedBy(result.__parent__):
spds = uuidToObject(result.job_params['species_distribution_models'])
speciesmd = IBCCVLMetadata(spds).get('species', None)
if speciesmd:
bccvlmd['species'] = speciesmd.copy()
def experiment_inputs(self, context=None):
# return visualisable input datasets for experiment
# - used in overlay and compare pages
if context is None:
context = self.context
pc = getToolByName(self.context, 'portal_catalog')
if ISDMExperiment.providedBy(context):
# for sdm we return selected occurrence and absence dataset
# TODO: once available include pesudo absences from result
for dsuuid in (context.species_occurrence_dataset,
context.species_absence_dataset):
brain = uuidToCatalogBrain(dsuuid)
if brain:
yield brain
elif IMMExperiment.providedBy(context):
# for mme we return selected occurrence dataset only
# TODO: once available include pesudo absences from result
for dsuuid in (context.species_occurrence_dataset,):
brain = uuidToCatalogBrain(dsuuid)
if brain:
yield brain
elif IMSDMExperiment.providedBy(context):
# muilt species sdm inputs
for dsuuid in (context.species_occurrence_collections):
brain = uuidToCatalogBrain(dsuuid)
if brain:
yield brain
elif IProjectionExperiment.providedBy(context):
# one experiment - multiple models
for sdmuuid, models in context.species_distribution_models.items():
sdm = uuidToObject(sdmuuid)
if not sdm:
continue
for model in models:
# yield current projections for each model
model_brain = uuidToCatalogBrain(model)
if not model_brain:
continue
# Return only constraint SDM projection
res_path = model_brain.getPath().rsplit('/', 1)
for projection in pc.searchResults(path=res_path,
BCCDataGenre='DataGenreCP'):
yield projection
def experiment_inputs(self, context=None):
# return visualisable input datasets for experiment
# - used in overlay and compare pages
if context is None:
context = self.context
pc = getToolByName(self.context, 'portal_catalog')
if ISDMExperiment.providedBy(context):
# for sdm we return selected occurrence and absence dataset
# TODO: once available include pesudo absences from result
for dsuuid in (context.species_occurrence_dataset,
context.species_absence_dataset):
brain = uuidToCatalogBrain(dsuuid)
if brain:
yield brain
elif IMMExperiment.providedBy(context):
# for mme we return selected occurrence dataset only
# TODO: once available include pesudo absences from result
for dsuuid in (context.species_occurrence_dataset, ):
brain = uuidToCatalogBrain(dsuuid)
if brain:
yield brain
elif IMSDMExperiment.providedBy(context):
# muilt species sdm inputs
for dsuuid in (context.species_occurrence_collections):
brain = uuidToCatalogBrain(dsuuid)
if brain:
yield brain
elif IProjectionExperiment.providedBy(context):
# one experiment - multiple models
for sdmuuid, models in context.species_distribution_models.items():
sdm = uuidToObject(sdmuuid)
if not sdm:
continue
for model in models:
# yield current projections for each model
model_brain = uuidToCatalogBrain(model)
if not model_brain:
continue
# Return only constraint SDM projection
res_path = model_brain.getPath().rsplit('/', 1)
for projection in pc.searchResults(
path=res_path, BCCDataGenre='DataGenreCP'):
yield projection
