def items(self):
# return dict with keys for experiment
# and subkey 'models' for models within experiment
if self.value:
for experiment_uuid, model_uuids in self.value.items():
item = {}
expbrain = uuidToCatalogBrain(experiment_uuid)
# TODO: we have an experiment_uuid, but can't access the
# experiment (deleted?, access denied?)
# shall we at least try to get some details?
if expbrain is None:
continue
item['title'] = expbrain.Title
item['uuid'] = expbrain.UID
item['brain'] = expbrain
# TODO: what else wolud I need from an experiment?
exp = expbrain.getObject()
expmd = IBCCVLMetadata(exp)
item['resolution'] = expmd.get('resolution')
# now search all datasets within and add infos
pc = getToolByName(self.context, 'portal_catalog')
results = pc.searchResults(path=expbrain.getPath(),
portal_type='Folder',
job_state='COMPLETED')
brains = pc.searchResults(path=[r.getPath() for r in results],
BCCDataGenre=self.genre)
# TODO: maybe as generator?
item['subitems'] = []
for brain in brains:
# FIXME: I need a different list of thresholds for display;
# esp. don't look up threshold, but take vales (threshold
# id and value) from field as is
thresholds = dataset.getThresholds(brain.UID)[brain.UID]
threshold = self.value[experiment_uuid].get(brain.UID)
# is threshold in list?
if threshold and threshold['label'] not in thresholds:
# maybe a custom entered number?
# ... I guess we don't really care as long as we produce the same the user entered. (validate?)
thresholds[threshold['label']] = threshold['label']
dsobj = brain.getObject()
dsmd = IBCCVLMetadata(dsobj)
item['subitems'].append({
'uuid': brain.UID,
'title': brain.Title,
'selected': brain.UID in self.value[experiment_uuid],
'threshold': threshold,
'thresholds': thresholds,
'brain': brain,
'md': dsmd,
'obj': dsobj,
# TODO: this correct? only one layer ever?
'layermd': dsmd['layers'].values()[0]
})
yield item
def items(self):
# return dict with keys for experiment
# and subkey 'models' for models within experiment
if self.value:
for experiment_uuid, model_uuids in self.value.items():
item = {}
expbrain = uuidToCatalogBrain(experiment_uuid)
item['title'] = expbrain.Title
item['uuid'] = expbrain.UID
item['brain'] = expbrain
# TODO: what else wolud I need from an experiment?
exp = expbrain.getObject()
expmd = IBCCVLMetadata(exp)
item['resolution'] = expmd.get('resolution')
# now search all datasets within and add infos
pc = getToolByName(self.context, 'portal_catalog')
brains = pc.searchResults(path=expbrain.getPath(),
BCCDataGenre=self.genre)
# TODO: maybe as generator?
item['datasets'] = []
for brain in brains:
# FIXME: I need a different list of thresholds for display; esp. don't look up threshold, but take vales (threshold id and value) from field as is
thresholds = dataset.getThresholds(brain.UID)[brain.UID]
threshold = self.value[experiment_uuid].get(brain.UID)
# is threshold in list?
if threshold and threshold['label'] not in thresholds:
# maybe a custom entered number?
# ... I guess we don't really care as long as we produce the same the user entered. (validate?)
thresholds[threshold['label']] = threshold['label']
dsobj = brain.getObject()
dsmd = IBCCVLMetadata(dsobj)
item['datasets'].append({
'uuid':
brain.UID,
'title':
brain.Title,
'selected':
brain.UID in self.value[experiment_uuid],
'threshold':
threshold,
'thresholds':
thresholds,
'brain':
brain,
'md':
dsmd,
'obj':
dsobj,
# TODO: this correct? only one layer ever?
'layermd':
dsmd['layers'].values()[0]
})
yield item
def toFieldValue(self, value):
"""Just dispatch it."""
if not len(value):
return self.field.missing_value
# if there is no value, we'll have to fetch it
for exp in value:
for ds, threshold in value[exp].items():
thresholds = dataset.getThresholds(ds)[ds]
if threshold['label'] not in thresholds:
# label not in list: is it a number?
try:
threshold['value'] = Decimal(threshold['label'])
except:
raise ValueError("Invalid '{0}' value for threshold".format(threshold['label']))
else:
threshold['value'] = thresholds[threshold['label']]
return value
def toFieldValue(self, value):
"""Just dispatch it."""
if not len(value):
return self.field.missing_value
# if there is no value, we'll have to fetch it
for exp in value:
for ds, threshold in value[exp].items():
thresholds = dataset.getThresholds(ds)[ds]
if threshold['label'] not in thresholds:
# label not in list: is it a number?
try:
threshold['value'] = Decimal(threshold['label'])
except:
raise ValueError("Invalid '{0}' value for threshold".format(threshold['label']))
else:
threshold['value'] = thresholds[threshold['label']]
return value
def item(self):
# return dict with keys for experiment
# and subkey 'models' for models within experiment
item = {}
if self.value:
experiment_uuid = self.value.keys()[0]
expbrain = uuidToCatalogBrain(experiment_uuid)
if expbrain is None:
return {
'title': u'Not Available',
'uuid': experiment_uuid,
'subitems': [] # models
}
item['title'] = expbrain.Title
item['uuid'] = expbrain.UID
exp = expbrain.getObject()
# TODO: To get layers of all subsets?
if getattr(exp, 'datasubsets', None):
env_datasets = exp.datasubsets[0].get('environmental_datasets')
item['layers'] = set((chain(*env_datasets.values())))
else:
item['layers'] = set(
(chain(*exp.environmental_datasets.values())))
expmd = IBCCVLMetadata(exp)
item['resolution'] = expmd.get('resolution')
# now search all models within and add infos
pc = getToolByName(self.context, 'portal_catalog')
# only get result folders that are completed
brains = pc.searchResults(path={
'query': expbrain.getPath(),
'depth': 1
},
portal_type='Folder',
job_state='COMPLETED')
# TODO: maybe as generator?
item['subitems'] = []
for fldbrain in brains:
# Get the SDM model from result folder
brain = pc.searchResults(path=fldbrain.getPath(),
BCCDataGenre=self.genre)
if not brain:
# ignore this folder, as it does not have a result we want
continue
brain = brain[0]
# get algorithm term
algoid = getattr(brain.getObject(), 'job_params',
{}).get('function')
algobrain = self.algo_dict.get(algoid, None)
# Filter out geographic models
if algobrain.getObject().algorithm_category == 'geographic':
continue
# FIXME: I need a different list of thresholds for display;
# esp. don't look up threshold, but take vales (threshold
# id and value) from field as is
thresholds = dataset.getThresholds(brain.UID)[brain.UID]
threshold = self.value[experiment_uuid].get(brain.UID)
# is threshold in list?
if threshold and threshold['label'] not in thresholds:
# maybe a custom entered number?
# ... I guess we don't really care as long as we produce the same the user entered. (validate?)
thresholds[threshold['label']] = threshold['label']
# current projection tiff file, and its metadata
cpbrains = pc.searchResults(path=expbrain.getPath(),
BCCDataGenre=['DataGenreCP'])
cpmd = IBCCVLMetadata(cpbrains[0].getObject())
item['subitems'].append({
'item':
brain,
'uuid':
brain.UID,
'title':
brain.Title,
'selected':
brain.UID in self.value[experiment_uuid],
'algorithm':
algobrain,
'threshold':
threshold,
'thresholds':
thresholds,
'layermd':
cpmd['layers'].values()[0]
})
return item
def item(self):
# return dict with keys for experiment
# and subkey 'models' for models within experiment
item = {}
if self.value:
experiment_uuid = self.value.keys()[0]
expbrain = uuidToCatalogBrain(experiment_uuid)
if expbrain is None:
return {
'title': u'Not Available',
'uuid': experiment_uuid,
'subitems': [] # models
}
item['title'] = expbrain.Title
item['uuid'] = expbrain.UID
exp = expbrain.getObject()
# TODO: To get layers of all subsets?
if getattr(exp, 'datasubsets', None):
env_datasets = exp.datasubsets[0].get('environmental_datasets')
item['layers'] = set((chain(*env_datasets.values())))
else:
item['layers'] = set((chain(*exp.environmental_datasets.values())))
expmd = IBCCVLMetadata(exp)
item['resolution'] = expmd.get('resolution')
# now search all models within and add infos
pc = getToolByName(self.context, 'portal_catalog')
# only get result folders that are completed
brains = pc.searchResults(path={'query': expbrain.getPath(), 'depth': 1},
portal_type='Folder',
job_state='COMPLETED')
# TODO: maybe as generator?
item['subitems'] = []
for fldbrain in brains:
# Get the SDM model from result folder
brain = pc.searchResults(path=fldbrain.getPath(),
BCCDataGenre=self.genre)
if not brain:
# ignore this folder, as it does not have a result we want
continue
brain = brain[0]
# get algorithm term
algoid = getattr(brain.getObject(), 'job_params',
{}).get('function')
algobrain = self.algo_dict.get(algoid, None)
# Filter out geographic models
if algobrain.getObject().algorithm_category == 'geographic':
continue
# FIXME: I need a different list of thresholds for display;
# esp. don't look up threshold, but take vales (threshold
# id and value) from field as is
thresholds = dataset.getThresholds(brain.UID)[brain.UID]
threshold = self.value[experiment_uuid].get(brain.UID)
# is threshold in list?
if threshold and threshold['label'] not in thresholds:
# maybe a custom entered number?
# ... I guess we don't really care as long as we produce the same the user entered. (validate?)
thresholds[threshold['label']] = threshold['label']
# current projection tiff file, and its metadata
cpbrains = pc.searchResults(path=expbrain.getPath(),
BCCDataGenre=['DataGenreCP'])
cpmd = IBCCVLMetadata(cpbrains[0].getObject())
item['subitems'].append(
{'item': brain,
'uuid': brain.UID,
'title': brain.Title,
'selected': brain.UID in self.value[experiment_uuid],
'algorithm': algobrain,
'threshold': threshold,
'thresholds': thresholds,
'layermd': cpmd['layers'].values()[0]
}
)
return item
def getThresholds(self, datasets, thresholds=None):
# datasets: a future projection dataset as a result of projectien experiment
# thresholds: list of names to retrieve or all
return dataset.getThresholds(datasets, thresholds)
