def __call__(self):
# respect field level security as defined in plone.autoform
# check if attribute access would be allowed!
# url = guarded_getattr(self.context, 'remoteUrl', None)
exp = self.context
if IProjectionExperiment.providedBy(exp):
if exp.projection_region:
return exp.projection_region.data
if not exp.species_distribution_models:
raise NotFound(self, 'species_distribution_models', self.request)
# Return the SDM's modelling region
sdmuuid = exp.species_distribution_models.keys()[0]
sdmobj = uuidToObject(sdmuuid)
if sdmobj and sdmobj.modelling_region:
return sdmobj.modelling_region.data
elif IExperiment.providedBy(exp):
if exp.modelling_region:
return exp.modelling_region.data
else:
# Move one level up if this is an exp result file
if not IExperiment.providedBy(exp.__parent__):
exp = exp.__parent__
if IExperiment.providedBy(exp.__parent__):
# this is the result folder
if IProjectionExperiment.providedBy(exp.__parent__):
if exp.job_params['projection_region']:
return exp.job_params['projection_region'].data
# Get constraint from SDM experiment result file.
# Use the modelling_region.json file in the sdm result if available.
if not 'species_distribution_models' in exp.job_params:
raise NotFound(self, 'species_distribution_models', self.request)
sdmuuid = exp.job_params['species_distribution_models']
sdmobj = uuidToObject(sdmuuid).__parent__
else:
sdmobj = exp
# Return the modelling_region attribute only if no modelling_region.json file
if not 'modelling_region.json' in sdmobj.keys():
return sdmobj.modelling_region.data
# Redirect to download the modelling_region.json
constraint_region = sdmobj.get('modelling_region.json')
remoteUrl = getattr(constraint_region, 'remoteUrl', None)
if remoteUrl is None:
raise NotFound(self, 'remoteUrl', self.request)
# Generate temp url
tool = getUtility(ISwiftUtility)
try:
url = tool.generate_temp_url(url=remoteUrl)
except:
url = remoteUrl
return self.request.RESPONSE.redirect(url.encode('utf-8'))
else:
raise NotFound(self, 'constraint_region', self.request)
def constraintregion(self):
uuid = self.request.form.get('uuid')
exp = uuidToObject(uuid)
if not exp:
self.record_error('Not Found', 404, 'Experiment not found',
{'parameter': 'uuid'})
raise NotFound(self, 'constraintregion', self.request)
downloadurl = None
if IExperiment.providedBy(exp):
downloadurl = '{}/@@download/modelling_region.json'.format(
exp.absolute_url())
elif not IExperiment.providedBy(exp.__parent__):
# this is an exp result file, so get exp folder
exp = exp.__parent__
if IExperiment.providedBy(exp.__parent__):
# This is an exp result folder
if IProjectionExperiment.providedBy(exp.__parent__):
if exp.job_params['projection_region']:
downloadurl = '{}/@@download/modelling_region.json'.format(
exp.absolute_url())
else:
# Get constraint from SDM experiment result file.
# Use the modelling_region.json file in the sdm result if available.
if not 'species_distribution_models' in exp.job_params:
self.record_error(
'NotFound', 404, 'SDM model not found',
{'parameter': 'species_distribution_models'})
raise NotFound(self, 'species_distribution_models',
self.request)
sdmuuid = exp.job_params['species_distribution_models']
sdmobj = uuidToObject(sdmuuid).__parent__
# Return the modelling_region attribute only if no modelling_region.json file
if not 'modelling_region.json' in sdmobj.keys():
downloadurl = '{}/@@download/modelling_region.json'.format(
sdmobj.absolute_url())
else:
# Redirect to download the modelling_region.json
constraint_region = sdmobj.get('modelling_region.json')
remoteUrl = getattr(constraint_region, 'remoteUrl',
None)
if remoteUrl is None:
raise NotFound(self, 'remoteUrl', self.request)
# Generate temp url
tool = getUtility(ISwiftUtility)
try:
downloadurl = tool.generate_temp_url(url=remoteUrl)
except:
downloadurl = remoteUrl
else:
downloadurl = '{}/@@download/modelling_region.json'.format(
exp.absolute_url())
if downloadurl is None:
self.record_error('Not Found', 404, 'Constraint region not found',
{'parameter': 'uuid'})
raise NotFound(self, 'constraintregion', self.request)
return self.request.RESPONSE.redirect(downloadurl.encode('utf-8'))
def addSpeciesInfo(bccvlmd, result):
if ISDMExperiment.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__):
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 constraintregion(self):
uuid = self.request.form.get('uuid')
exp = uuidToObject(uuid)
if not exp:
self.record_error('Not Found', 404,
'Experiment not found',
{'parameter': 'uuid'})
raise NotFound(self, 'constraintregion', self.request)
downloadurl = None
if IExperiment.providedBy(exp):
downloadurl = '{}/@@download/modelling_region.json'.format(exp.absolute_url())
elif not IExperiment.providedBy(exp.__parent__):
# this is an exp result file, so get exp folder
exp = exp.__parent__
if IExperiment.providedBy(exp.__parent__):
# This is an exp result folder
if IProjectionExperiment.providedBy(exp.__parent__):
if exp.job_params['projection_region']:
downloadurl = '{}/@@download/modelling_region.json'.format(exp.absolute_url())
else:
# Get constraint from SDM experiment result file.
# Use the modelling_region.json file in the sdm result if available.
if not 'species_distribution_models' in exp.job_params:
self.record_error('NotFound', 404,
'SDM model not found',
{'parameter': 'species_distribution_models'})
raise NotFound(self, 'species_distribution_models', self.request)
sdmuuid = exp.job_params['species_distribution_models']
sdmobj = uuidToObject(sdmuuid).__parent__
# Return the modelling_region attribute only if no modelling_region.json file
if not 'modelling_region.json' in sdmobj.keys():
downloadurl = '{}/@@download/modelling_region.json'.format(sdmobj.absolute_url())
else:
# Redirect to download the modelling_region.json
constraint_region = sdmobj.get('modelling_region.json')
remoteUrl = getattr(constraint_region, 'remoteUrl', None)
if remoteUrl is None:
raise NotFound(self, 'remoteUrl', self.request)
# Generate temp url
tool = getUtility(ISwiftUtility)
try:
downloadurl = tool.generate_temp_url(url=remoteUrl)
except:
downloadurl = remoteUrl
else:
downloadurl = '{}/@@download/modelling_region.json'.format(exp.absolute_url())
if downloadurl is None:
self.record_error('Not Found', 404,
'Constraint region not found',
{'parameter': 'uuid'})
raise NotFound(self, 'constraintregion', self.request)
return self.request.RESPONSE.redirect(downloadurl.encode('utf-8'))
def experiment_reference_indexer(object, **kw):
# TODO: Add Ensemble -> SDM, Proj, Biodiv, Biodiverse -> SDM, Proj
if IProjectionExperiment.providedBy(object):
return object.species_distribution_models.keys()
elif IEnsembleExperiment.providedBy(object):
return object.datasets.keys()
elif IBiodiverseExperiment.providedBy(object):
return object.projection.keys()
else:
pass
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
def __createExpmetadata(self, job_params):
# To do: add other R package versions dynamically
# Get experiment title
self.md['Model specifications'] = {
'Title': self.context.title,
'Date/time run': self.context.creation_date.__str__(),
'Description': self.context.description or ''
}
# iterate over all input datasets and add them as entities
self.md['Input datasets:'] = {}
for key in ('species_occurrence_dataset', 'species_absence_dataset',
'traits_dataset'):
spmd = {}
if not job_params.has_key(key):
continue
dsbrain = uuidToCatalogBrain(job_params[key])
if not dsbrain:
continue
ds = dsbrain.getObject()
mdata = IBCCVLMetadata(ds)
if mdata and mdata.get('rows', None):
spmd = {'Title': "{} ({})".format(ds.title, mdata.get('rows'))}
else:
spmd = {'Title': ds.title}
info = IDownloadInfo(ds)
spmd['Download URL'] = info['url']
coll = ds
while not (ISiteRoot.providedBy(coll)
or ICollection.providedBy(coll)):
coll = coll.__parent__
spmd['Description'] = ds.description or coll.description or ''
attribution = ds.attribution or getattr(coll, 'attribution') or ''
if isinstance(attribution, list):
attribution = '\n'.join([att.raw for att in attribution])
spmd['Attribution'] = attribution
self.md['Input datasets:'][key] = spmd
key = 'traits_dataset_params'
if key in job_params:
self.md['Input datasets:'][key] = job_params.get(key, {})
# pseudo-absence metadata.
key = u"pseudo_absence_dataset"
pa_file = self.context.get('pseudo_absences.csv')
pa_url = ""
pa_title = ""
if pa_file:
pa_title = pa_file.title
pa_url = pa_file.absolute_url()
pa_url = '{}/@@download/{}'.format(pa_url,
os.path.basename(pa_url))
pamd = {
'Title': pa_title,
'Download URL': pa_url,
'Pseudo-absence Strategy': job_params.get('pa_strategy', ''),
'Pseudo-absence Ratio': str(job_params.get('pa_ratio', ''))
}
if job_params.get('pa_strategy', '') == 'disc':
pamd['Minimum distance'] = str(
job_params.get('pa_disk_min', ''))
pamd['Maximum distance'] = str(
job_params.get('pa_disk_max', ''))
if job_params.get('pa_strategy', '') == 'sre':
pamd['Quantile'] = str(job_params.get('pa_sre_quant', ''))
self.md['Input datasets:'][key] = pamd
for key in ['environmental_datasets', 'future_climate_datasets']:
if key not in job_params:
continue
env_list = []
layer_vocab = getUtility(IVocabularyFactory,
'layer_source')(self.context)
for uuid, layers in job_params[key].items():
ds = uuidToObject(uuid)
coll = ds
while not (ISiteRoot.providedBy(coll)
or ICollection.providedBy(coll)):
coll = coll.__parent__
description = ds.description or coll.description
attribution = ds.attribution or getattr(coll,
'attribution') or ''
if isinstance(attribution, list):
attribution = '\n'.join([att.raw for att in attribution])
layer_titles = [
layer_vocab.getLayerTitle(layer) for layer in layers
]
env_list.append({
'Title': ds.title,
'Layers': u'\n'.join(layer_titles),
'Description': description,
'Attribution': attribution
})
self.md['Input datasets:'][key] = env_list
key = "datasets"
if key in job_params:
dataset_list = []
for uid in job_params[key]:
dsbrain = uuidToCatalogBrain(uid)
if dsbrain:
ds = dsbrain.getObject()
# get the source experiment
source_exp = ds.__parent__
while not IExperiment.providedBy(source_exp):
source_exp = source_exp.__parent__
dataset_list.append({
'Source experiment':
source_exp.title,
'Title':
ds.title,
'Description':
ds.description,
'Download URL':
'{}/@@download/file/{}'.format(
ds.absolute_url(),
os.path.basename(ds.absolute_url())),
'Algorithm':
ds.__parent__.job_params.get('function', ''),
'Species':
IBCCVLMetadata(ds).get('species',
{}).get('scientificName', ''),
'Resolution':
IBCCVLMetadata(ds).get('resolution', '')
})
self.md['Input datasets:'][key] = dataset_list
key = 'species_distribution_models'
if key in job_params:
dsbrain = uuidToCatalogBrain(job_params[key])
if dsbrain:
ds = dsbrain.getObject()
# get the source experiment
source_exp = ds.__parent__
while not IExperiment.providedBy(source_exp):
source_exp = source_exp.__parent__
# get the threshold
threshold = self.context.species_distribution_models.get(
source_exp.UID(), {}).get(ds.UID())
self.md['Input datasets:'][key] = {
'Source experiment':
source_exp.title,
'Title':
ds.title,
'Description':
ds.description,
'Download URL':
'{}/@@download/file/{}'.format(
ds.absolute_url(),
os.path.basename(ds.absolute_url())),
'Algorithm':
ds.__parent__.job_params.get('function', ''),
'Species':
IBCCVLMetadata(ds).get('species',
{}).get('scientificName', ''),
'Threshold':
"{}({})".format(threshold.get('label', ''),
str(threshold.get('value', '')))
}
key = 'projections'
if key in job_params:
for pds in job_params[key]:
threshold = pds.get('threshold', {})
dsbrain = uuidToCatalogBrain(pds.get('dataset'))
if dsbrain:
ds = dsbrain.getObject()
# get the source experiment
source_exp = ds.__parent__
while not IExperiment.providedBy(source_exp):
source_exp = source_exp.__parent__
self.md['Input datasets:'][key] = {
'Source experiment':
source_exp.title,
'Title':
ds.title,
'Description':
ds.description,
'Download URL':
'{}/@@download/file/{}'.format(
ds.absolute_url(),
os.path.basename(ds.absolute_url())),
'Algorithm':
ds.__parent__.job_params.get('function', ''),
'Species':
IBCCVLMetadata(ds).get('species',
{}).get('scientificName', ''),
'Threshold':
"{}({})".format(threshold.get('label', ''),
str(threshold.get('value', ''))),
'Biodiverse Cell size (m)':
str(job_params.get('cluster_size', ''))
}
# Projection experiment does not have algorithm as input
if not IProjectionExperiment.providedBy(self.context.__parent__):
for key in ['function', 'algorithm']:
if key in job_params:
self.md['Algorithm settings:'] = {
'Algorithm Name':
job_params[key],
'Configuration options':
self.__algoConfigOption(job_params[key], job_params)
}
# Construct the text
mdtext = StringIO.StringIO()
for heading in [
'BCCVL model outputs guide', 'System specifications',
'Model specifications', 'Input datasets:',
'Algorithm settings:', 'Model outputs:'
]:
mdtext.write(self.__getMetadataText(heading, self.md))
return mdtext.getvalue()
def __createExpmetadata(self, job_params):
# To do: add other R package versions dynamically
# Get experiment title
self.md['Model specifications'] = {
'Title': self.context.title,
'Date/time run': self.context.creation_date.__str__(),
'Description': self.context.description or ''
}
# iterate over all input datasets and add them as entities
self.md['Input datasets:'] = {}
for key in ('species_occurrence_dataset', 'species_absence_dataset', 'traits_dataset'):
spmd = {}
if not job_params.has_key(key):
continue
dsbrain = uuidToCatalogBrain(job_params[key])
if not dsbrain:
continue
ds = dsbrain.getObject()
mdata = IBCCVLMetadata(ds)
if mdata and mdata.get('rows', None):
spmd = {'Title': "{} ({})".format(ds.title, mdata.get('rows'))}
else:
spmd = {'Title': ds.title}
info = IDownloadInfo(ds)
spmd['Download URL'] = info['url']
coll = ds
while not (ISiteRoot.providedBy(coll) or ICollection.providedBy(coll)):
coll = coll.__parent__
spmd['Description'] = ds.description or coll.description or ''
attribution = ds.attribution or getattr(coll, 'attribution') or ''
if isinstance(attribution, list):
attribution = '\n'.join([att.raw for att in attribution])
spmd['Attribution'] = attribution
self.md['Input datasets:'][key] = spmd
key = 'traits_dataset_params'
if key in job_params:
self.md['Input datasets:'][key] = job_params.get(key, {})
# pseudo-absence metadata.
key = u"pseudo_absence_dataset"
pa_file = self.context.get('pseudo_absences.csv')
pa_url = ""
pa_title = ""
if pa_file:
pa_title = pa_file.title
pa_url = pa_file.absolute_url()
pa_url = '{}/@@download/{}'.format(pa_url, os.path.basename(pa_url))
pamd = {
'Title': pa_title,
'Download URL': pa_url,
'Pseudo-absence Strategy': job_params.get('pa_strategy', ''),
'Pseudo-absence Ratio' : str(job_params.get('pa_ratio', ''))
}
if job_params.get('pa_strategy', '') == 'disc':
pamd['Minimum distance'] = str(job_params.get('pa_disk_min', ''))
pamd['Maximum distance'] = str(job_params.get('pa_disk_max', ''))
if job_params.get('pa_strategy', '') == 'sre':
pamd['Quantile'] = str(job_params.get('pa_sre_quant', ''))
self.md['Input datasets:'][key] = pamd
for key in ['environmental_datasets', 'future_climate_datasets']:
if key not in job_params:
continue
env_list = []
layer_vocab = getUtility(IVocabularyFactory, 'layer_source')(self.context)
for uuid, layers in job_params[key].items():
ds = uuidToObject(uuid)
coll = ds
while not (ISiteRoot.providedBy(coll) or ICollection.providedBy(coll)):
coll = coll.__parent__
description = ds.description or coll.description
attribution = ds.attribution or getattr(coll, 'attribution') or ''
if isinstance(attribution, list):
attribution = '\n'.join([att.raw for att in attribution])
layer_titles = [layer_vocab.getLayerTitle(layer) for layer in layers]
env_list.append({
'Title': ds.title,
'Layers': u'\n'.join(layer_titles),
'Description': description,
'Attribution': attribution
})
self.md['Input datasets:'][key] = env_list
key = "datasets"
if key in job_params:
dataset_list = []
for uid in job_params[key]:
dsbrain = uuidToCatalogBrain(uid)
if dsbrain:
ds = dsbrain.getObject()
# get the source experiment
source_exp = ds.__parent__
while not IExperiment.providedBy(source_exp):
source_exp = source_exp.__parent__
dataset_list.append({
'Source experiment': source_exp.title,
'Title': ds.title,
'Description': ds.description,
'Download URL': '{}/@@download/file/{}'.format(ds.absolute_url(), os.path.basename(ds.absolute_url()))
,
'Algorithm': ds.__parent__.job_params.get('function', ''),
'Species': IBCCVLMetadata(ds).get('species', {}).get('scientificName', ''),
'Resolution': IBCCVLMetadata(ds).get('resolution', '')
})
self.md['Input datasets:'][key] = dataset_list
key = 'species_distribution_models'
if key in job_params:
dsbrain = uuidToCatalogBrain(job_params[key])
if dsbrain:
ds = dsbrain.getObject()
# get the source experiment
source_exp = ds.__parent__
while not IExperiment.providedBy(source_exp):
source_exp = source_exp.__parent__
# get the threshold
threshold = self.context.species_distribution_models.get(source_exp.UID(), {}).get(ds.UID())
self.md['Input datasets:'][key] = {
'Source experiment': source_exp.title,
'Title': ds.title,
'Description': ds.description,
'Download URL': '{}/@@download/file/{}'.format(ds.absolute_url(), os.path.basename(ds.absolute_url()))
,
'Algorithm': ds.__parent__.job_params.get('function', ''),
'Species': IBCCVLMetadata(ds).get('species', {}).get('scientificName', ''),
'Threshold': "{}({})".format(threshold.get('label', ''), str(threshold.get('value', '')))
}
key = 'projections'
if key in job_params:
for pds in job_params[key]:
threshold = pds.get('threshold', {})
dsbrain = uuidToCatalogBrain(pds.get('dataset'))
if dsbrain:
ds = dsbrain.getObject()
# get the source experiment
source_exp = ds.__parent__
while not IExperiment.providedBy(source_exp):
source_exp = source_exp.__parent__
self.md['Input datasets:'][key] = {
'Source experiment': source_exp.title,
'Title': ds.title,
'Description': ds.description,
'Download URL': '{}/@@download/file/{}'.format(ds.absolute_url(), os.path.basename(ds.absolute_url()))
,
'Algorithm': ds.__parent__.job_params.get('function', ''),
'Species': IBCCVLMetadata(ds).get('species', {}).get('scientificName', ''),
'Threshold': "{}({})".format(threshold.get('label', ''), str(threshold.get('value', ''))),
'Biodiverse Cell size (m)': str(job_params.get('cluster_size', ''))
}
# Projection experiment does not have algorithm as input
if not IProjectionExperiment.providedBy(self.context.__parent__):
for key in ['function', 'algorithm']:
if key in job_params:
self.md['Algorithm settings:'] = {
'Algorithm Name': job_params[key],
'Configuration options': self.__algoConfigOption(job_params[key], job_params)
}
# Construct the text
mdtext = StringIO.StringIO()
for heading in [
'BCCVL model outputs guide',
'System specifications',
'Model specifications',
'Input datasets:',
'Algorithm settings:',
'Model outputs:']:
mdtext.write(self.__getMetadataText(heading, self.md))
return mdtext.getvalue()