def get_ensemble_params(result):
# make a deep copy of the params to not accedientially modify the
# persisted dict
params = deepcopy(result.job_params)
# params['datasets'] is a list of dataset uuids
# and sholud become a list of dicts with datasetinfos
dslist = []
for dsparam in params['datasets']:
dslist.append(getdatasetparams(dsparam))
params['datasets'] = dslist
sdm_projections = []
for uuid in params['sdm_projections']:
sdm_projections.append(getdatasetparams(uuid))
params['sdm_projections'] = sdm_projections
# TODO: quick fix Decimal json encoding through celery
params['thresholds'] = [float(val) for val in params['thresholds']]
workerhints = {
'files': ('datasets', 'sdm_projections',)
}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_traits_params(result):
params = deepcopy(result.job_params)
# get metadata for species_distribution_models
for paramname in ('traits_dataset',):
if not params.get(paramname, None):
continue
uuid = params[paramname]
dsinfo = getdatasetparams(uuid)
if dsinfo['filename'].endswith('.zip'):
# FIXME: too many static assumptions about how an occurrence zip file looks like
# layers:key does not match anything (should it?)
# assumes exactly one file here
# TODO: should I remove 'layers' section here?
dsinfo['zippath'] = dsinfo['layers'].values()[0]['filename']
params[paramname] = dsinfo
# TODO: This assumes we only zip file based layers
envlist = []
envds = params.get('environmental_datasets') or {}
for uuid, layers in envds.items():
dsinfo = getdatasetparams(uuid)
for layer in layers:
dsdata = {
'uuid': dsinfo['uuid'],
'filename': dsinfo['filename'],
'downloadurl': dsinfo['downloadurl'],
# TODO: should we use layer title or URI?
'layer': layer,
'type': dsinfo['layers'][layer]['datatype']
}
# if this is a zip file we'll have to set zippath as well
# FIXME: poor check whether this is a zip file
if dsinfo['filename'].endswith('.zip'):
dsdata['zippath'] = dsinfo['layers'][layer]['filename']
envlist.append(dsdata)
# replace original dict
params['environmental_datasets'] = envlist
# Get the content of the modelling_region BlobFile.
# Note: deepcopy does not copy the content of BlobFile.
if result.job_params['modelling_region']:
params['modelling_region'] = {
'uuid': IUUID(result),
'filename': 'modelling_region.json',
'downloadurl': '{0}/API/em/v1/constraintregion?uuid={1}'.format(getSite().absolute_url(), IUUID(result)),
}
# add hints for worker
workerhints = {
'files': [x for x in ('traits_dataset', 'environmental_datasets', 'modelling_region',) if x in params]
}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_sdm_params(result):
# make a deep copy of the params to not accedientially modify the
# persisted dict
params = deepcopy(result.job_params)
# TODO: names to fix up
# occurrence-> species_occurrence_dataset
# background-> species_absence_dataset
# pseudoabsence['enabled']-> species_pseudo_absence_points,
# pseudoabsence['points']-> species_number_pseudo_absence_points
# layers+ environment{}-> environmental_datasets TODO: turn into list of files
# get all necessary metadata for files, and add worker hints to download files
for paramname in ('species_occurrence_dataset', 'species_absence_dataset'):
# TODO: absence might be none
uuid = params[paramname]
params[paramname] = getdatasetparams(uuid)
# replace all spaces and underscores to '.' (biomod does the same)
# TODO: really necessary?
if params[paramname]:
params[paramname]['species'] = re.sub(
u"[ _]", u".", params[paramname].get('species', u'Unknown'))
# TODO: This assumes we only zip file based layers
envlist = []
for uuid, layers in params['environmental_datasets'].items():
dsinfo = getdatasetparams(uuid)
for layer in layers:
dsdata = {
'uuid': dsinfo['uuid'],
'filename': dsinfo['filename'],
'downloadurl': dsinfo['downloadurl'],
'internalurl': dsinfo['internalurl'],
# TODO: should we use layer title or URI?
'layer': layer,
'type': dsinfo['layers'][layer]['datatype']
}
# if this is a zip file we'll have to set zippath as well
# FIXME: poor check whether this is a zip file
if dsinfo['filename'].endswith('.zip'):
dsdata['zippath'] = dsinfo['layers'][layer]['filename']
envlist.append(dsdata)
# replace original dict
params['environmental_datasets'] = envlist
# add hints for worker to download files
workerhints = {
'files': ('species_occurrence_dataset', 'species_absence_dataset',
'environmental_datasets')
}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_project_params(result):
params = deepcopy(result.job_params)
# get metadata for species_distribution_models
uuid = params['species_distribution_models']
params['species_distribution_models'] = getdatasetparams(uuid)
# do biomod name mangling of species name
params['species_distribution_models']['species'] = re.sub(u"[ _'\"/\(\)\{\}\[\]]", u".", params['species_distribution_models'].get('species', u"Unknown"))
# we need the layers from sdm to fetch correct files for climate_models
# TODO: getdatasetparams should fetch 'layers'
sdmobj = uuidToObject(uuid)
sdmmd = IBCCVLMetadata(sdmobj)
params['species_distribution_models']['layers'] = sdmmd.get('layers_used', None)
# do future climate layers
climatelist = []
for uuid, layers in params['future_climate_datasets'].items():
dsinfo = getdatasetparams(uuid)
for layer in layers:
dsdata = {
'uuid': dsinfo['uuid'],
'filename': dsinfo['filename'],
'downloadurl': dsinfo['downloadurl'],
'layer': layer,
'zippath': dsinfo['layers'][layer]['filename'],
# TODO: add year, gcm, emsc here?
'type': dsinfo['layers'][layer]['datatype'],
}
# if this is a zip file we'll have to set zippath as well
# FIXME: poor check whether this is a zip file
if dsinfo['filename'].endswith('.zip'):
dsdata['zippath'] = dsinfo['layers'][layer]['filename']
climatelist.append(dsdata)
# replace climate_models parameter
params['future_climate_datasets'] = climatelist
params['selected_models'] = 'all'
# projection.name from dsinfo
# FIXME: workaround to get future projection name back, but this works only for file naming scheme with current available data
params['projection_name'], _ = os.path.splitext(dsinfo['filename'])
# TODO: quick fix Decimal json encoding through celery (where is my custom json encoder gone?)
for key, item in params.items():
if isinstance(item, Decimal):
params[key] = float(item)
# add hints for worker
workerhints = {
'files': ('species_distribution_models', 'future_climate_datasets')
}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_sdm_params(result):
# make a deep copy of the params to not accedientially modify the
# persisted dict
params = deepcopy(result.job_params)
# TODO: names to fix up
# occurrence-> species_occurrence_dataset
# background-> species_absence_dataset
# pseudoabsence['enabled']-> species_pseudo_absence_points,
# pseudoabsence['points']-> species_number_pseudo_absence_points
# layers+ environment{}-> environmental_datasets TODO: turn into list of files
# get all necessary metadata for files, and add worker hints to download files
for paramname in ('species_occurrence_dataset', 'species_absence_dataset'):
# TODO: absence might be none
uuid = params[paramname]
params[paramname] = getdatasetparams(uuid)
# replace all spaces and underscores to '.' (biomod does the same)
# TODO: really necessary?
if params[paramname]:
params[paramname]['species'] = re.sub(u"[ _'\"/\(\)\{\}\[\]]", u".", params[paramname].get('species', u'Unknown'))
# TODO: This assumes we only zip file based layers
envlist = []
for uuid, layers in params['environmental_datasets'].items():
dsinfo = getdatasetparams(uuid)
for layer in layers:
dsdata = {
'uuid': dsinfo['uuid'],
'filename': dsinfo['filename'],
'downloadurl': dsinfo['downloadurl'],
'internalurl': dsinfo['internalurl'],
# TODO: should we use layer title or URI?
'layer': layer,
'type': dsinfo['layers'][layer]['datatype']
}
# if this is a zip file we'll have to set zippath as well
# FIXME: poor check whether this is a zip file
if dsinfo['filename'].endswith('.zip'):
dsdata['zippath'] = dsinfo['layers'][layer]['filename']
envlist.append(dsdata)
# replace original dict
params['environmental_datasets'] = envlist
# add hints for worker to download files
workerhints = {
'files': ('species_occurrence_dataset', 'species_absence_dataset',
'environmental_datasets')
}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_traits_params(result):
params = deepcopy(result.job_params)
# get metadata for species_distribution_models
uuid = params["data_table"]
params["data_table"] = getdatasetparams(uuid)
# add hints for worker
workerhints = {"files": ("data_table",)}
return {"env": {}, "params": params, "worker": workerhints}
def get_traits_params(result):
params = deepcopy(result.job_params)
# get metadata for species_distribution_models
uuid = params['data_table']
params['data_table'] = getdatasetparams(uuid)
# add hints for worker
workerhints = {'files': ('data_table', )}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_project_params(result):
params = deepcopy(result.job_params)
# get metadata for species_distribution_models
uuid = params['species_distribution_models']
params['species_distribution_models'] = getdatasetparams(uuid)
# do biomod name mangling of species name
params['species_distribution_models']['species'] = re.sub(
u"[ _]", u".",
params['species_distribution_models'].get('species', u"Unknown"))
# we need the layers from sdm to fetch correct files for climate_models
# TODO: getdatasetparams should fetch 'layers'
sdmobj = uuidToObject(uuid)
sdmmd = IBCCVLMetadata(sdmobj)
params['species_distribution_models']['layers'] = sdmmd.get(
'layers_used', None)
# do future climate layers
climatelist = []
for uuid, layers in params['future_climate_datasets'].items():
dsinfo = getdatasetparams(uuid)
for layer in layers:
dsdata = {
'uuid': dsinfo['uuid'],
'filename': dsinfo['filename'],
'downloadurl': dsinfo['downloadurl'],
'internalurl': dsinfo['internalurl'],
'layer': layer,
'zippath': dsinfo['layers'][layer]['filename'],
# TODO: add year, gcm, emsc here?
'type': dsinfo['layers'][layer]['datatype'],
}
# if this is a zip file we'll have to set zippath as well
# FIXME: poor check whether this is a zip file
if dsinfo['filename'].endswith('.zip'):
dsdata['zippath'] = dsinfo['layers'][layer]['filename']
climatelist.append(dsdata)
# replace climate_models parameter
params['future_climate_datasets'] = climatelist
params['selected_models'] = 'all'
# projection.name from dsinfo
# FIXME: workaround to get future projection name back, but this works only for file naming scheme with current available data
params['projection_name'], _ = os.path.splitext(dsinfo['filename'])
# add hints for worker
workerhints = {
'files': ('species_distribution_models', 'future_climate_datasets')
}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_traits_params(result):
params = deepcopy(result.job_params)
# get metadata for species_distribution_models
uuid = params['data_table']
params['data_table'] = getdatasetparams(uuid)
# add hints for worker
workerhints = {
'files': ('data_table', )
}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_project_params(result):
params = deepcopy(result.job_params)
# get metadata for species_distribution_models
uuid = params["species_distribution_models"]
params["species_distribution_models"] = getdatasetparams(uuid)
# do biomod name mangling of species name
params["species_distribution_models"]["species"] = re.sub(
u"[ _'\"/\(\)\{\}\[\]]", u".", params["species_distribution_models"].get("species", u"Unknown")
)
# we need the layers from sdm to fetch correct files for climate_models
# TODO: getdatasetparams should fetch 'layers'
sdmobj = uuidToObject(uuid)
sdmmd = IBCCVLMetadata(sdmobj)
params["species_distribution_models"]["layers"] = sdmmd.get("layers_used", None)
# do future climate layers
climatelist = []
for uuid, layers in params["future_climate_datasets"].items():
dsinfo = getdatasetparams(uuid)
for layer in layers:
dsdata = {
"uuid": dsinfo["uuid"],
"filename": dsinfo["filename"],
"downloadurl": dsinfo["downloadurl"],
"internalurl": dsinfo["internalurl"],
"layer": layer,
"zippath": dsinfo["layers"][layer]["filename"],
# TODO: add year, gcm, emsc here?
"type": dsinfo["layers"][layer]["datatype"],
}
# if this is a zip file we'll have to set zippath as well
# FIXME: poor check whether this is a zip file
if dsinfo["filename"].endswith(".zip"):
dsdata["zippath"] = dsinfo["layers"][layer]["filename"]
climatelist.append(dsdata)
# replace climate_models parameter
params["future_climate_datasets"] = climatelist
params["selected_models"] = "all"
# projection.name from dsinfo
# FIXME: workaround to get future projection name back, but this works only for file naming scheme with current available data
params["projection_name"], _ = os.path.splitext(dsinfo["filename"])
# add hints for worker
workerhints = {"files": ("species_distribution_models", "future_climate_datasets")}
return {"env": {}, "params": params, "worker": workerhints}
def get_ensemble_params(result):
# make a deep copy of the params to not accedientially modify the
# persisted dict
params = deepcopy(result.job_params)
# params['datasets'] is a list of dataset uuids
# and sholud become a list of dicts with datasetinfos
dslist = []
for dsparam in params['datasets']:
dslist.append(getdatasetparams(dsparam))
# replace datasets param
params['datasets'] = dslist
workerhints = {'files': ('datasets', )}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_ensemble_params(result):
# make a deep copy of the params to not accedientially modify the
# persisted dict
params = deepcopy(result.job_params)
# params['datasets'] is a list of dataset uuids
# and sholud become a list of dicts with datasetinfos
dslist = []
for dsparam in params["datasets"]:
dslist.append(getdatasetparams(dsparam))
# replace datasets param
params["datasets"] = dslist
workerhints = {"files": ("datasets",)}
return {"env": {}, "params": params, "worker": workerhints}
def get_biodiverse_params(result):
# make a deep copy of the params to not accedientially modify the
# persisted dict
params = deepcopy(result.job_params)
# params['projections'] is a list of dicts with 'threshold' and 'uuid'
# and sholud become a list of dicts with datasetinfos + threshold?
dslist = []
for dsparam in params['projections']:
dsinfo = getdatasetparams(dsparam['dataset'])
dsinfo['threshold'] = dsparam['threshold']
dslist.append(dsinfo)
# replace projections param
params['projections'] = dslist
workerhints = {'files': ('projections', )}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_ensemble_params(result):
# make a deep copy of the params to not accedientially modify the
# persisted dict
params = deepcopy(result.job_params)
# params['datasets'] is a list of dataset uuids
# and sholud become a list of dicts with datasetinfos
dslist = []
for dsparam in params['datasets']:
dslist.append(getdatasetparams(dsparam))
# replace datasets param
params['datasets'] = dslist
workerhints = {
'files': ('datasets', )
}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_biodiverse_params(result):
# make a deep copy of the params to not accedientially modify the
# persisted dict
params = deepcopy(result.job_params)
# params['projections'] is a list of dicts with 'threshold' and 'uuid'
# and sholud become a list of dicts with datasetinfos + threshold?
dslist = []
for dsparam in params['projections']:
dsinfo = getdatasetparams(dsparam['dataset'])
dsinfo['threshold'] = dsparam['threshold']
dslist.append(dsinfo)
# replace projections param
params['projections'] = dslist
workerhints = {
'files': ('projections', )
}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_biodiverse_params(result):
# make a deep copy of the params to not accedientially modify the
# persisted dict
params = deepcopy(result.job_params)
# params['projections'] is a list of dicts with 'threshold' and 'uuid'
# and sholud become a list of dicts with datasetinfos + threshold?
dslist = []
for dsparam in params['projections']:
dsinfo = getdatasetparams(dsparam['dataset'])
dsinfo['threshold'] = dsparam['threshold']
dslist.append(dsinfo)
# replace projections param
params['projections'] = dslist
# TODO: quick fix Decimal json encoding through celery (where is my custom
# json encoder gone?)
# -> problem is oslo jsonutils, whihch patches anyjson with it's own
# loads/dumps methods.
# we would normally use simplejson, which supports decimal, but oslo
# patches it in a way so that decimal no longer works
for key, item in params.items():
if isinstance(item, Decimal):
params[key] = float(item)
# ptach threshold vasue as well
for pds in params['projections']:
thresholds = pds['threshold']
for key, item in thresholds.items():
if isinstance(item, Decimal):
thresholds[key] = float(item)
workerhints = {
'files': ('projections', )
}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_sdm_params(result):
# make a deep copy of the params to not accedientially modify the
# persisted dict
params = deepcopy(result.job_params)
# TODO: names to fix up
# occurrence-> species_occurrence_dataset
# background-> species_absence_dataset
# pseudoabsence['enabled']-> species_pseudo_absence_points,
# pseudoabsence['points']-> species_number_pseudo_absence_points
# layers+ environment{}-> environmental_datasets TODO: turn into list of files
# get all necessary metadata for files, and add worker hints to download files
for paramname in ('species_occurrence_dataset', 'species_absence_dataset'):
# Skip empty or non existing params
if not params.get(paramname, None):
continue
uuid = params[paramname]
dsinfo = getdatasetparams(uuid)
if dsinfo['filename'].endswith('.zip'):
# FIXME: too many static assumptions about how an occurrence zip file looks like
# layers:key does not match anything (should it?)
# assumes exactly one file here
# TODO: should I remove 'layers' section here?
dsinfo['zippath'] = dsinfo['layers'].values()[0]['filename']
params[paramname] = dsinfo
# replace all spaces and underscores to '.' (biomod does the same)
# TODO: really necessary?
if params[paramname]:
params[paramname]['species'] = re.sub(u"[ _,'\"/\(\)\{\}\[\]]", u".", params[paramname].get('species', u'Unknown'))
# TODO: This assumes we only zip file based layers
envlist = []
for uuid, layers in params['environmental_datasets'].items():
dsinfo = getdatasetparams(uuid)
for layer in layers:
dsdata = {
'uuid': dsinfo['uuid'],
'filename': dsinfo['filename'],
'downloadurl': dsinfo['downloadurl'],
# TODO: should we use layer title or URI?
'layer': layer,
'type': dsinfo['layers'][layer]['datatype']
}
# if this is a zip file we'll have to set zippath as well
# FIXME: poor check whether this is a zip file
if dsinfo['filename'].endswith('.zip'):
dsdata['zippath'] = dsinfo['layers'][layer]['filename']
envlist.append(dsdata)
# replace original dict
params['environmental_datasets'] = envlist
# TODO: quick fix Decimal json encoding through celery (where is my custom json encoder gone?)
for key, item in params.items():
if isinstance(item, Decimal):
params[key] = float(item)
# add hints for worker to download files
workerhints = {
# only those parameters that are actually in params dict
'files': [x for x in ('species_occurrence_dataset', 'species_absence_dataset', 'environmental_datasets') if x in params]
}
return {'env': {}, 'params': params, 'worker': workerhints}
def get_traits_params(result):
params = deepcopy(result.job_params)
# get metadata for species_distribution_models
for paramname in ('traits_dataset', ):
if not params.get(paramname, None):
continue
uuid = params[paramname]
dsinfo = getdatasetparams(uuid)
if dsinfo['filename'].endswith('.zip'):
# FIXME: too many static assumptions about how an occurrence zip file looks like
# layers:key does not match anything (should it?)
# assumes exactly one file here
# TODO: should I remove 'layers' section here?
dsinfo['zippath'] = dsinfo['layers'].values()[0]['filename']
params[paramname] = dsinfo
# TODO: This assumes we only zip file based layers
envlist = []
envds = params.get('environmental_datasets') or {}
for uuid, layers in envds.items():
dsinfo = getdatasetparams(uuid)
for layer in layers:
dsdata = {
'uuid': dsinfo['uuid'],
'filename': dsinfo['filename'],
'downloadurl': dsinfo['downloadurl'],
# TODO: should we use layer title or URI?
'layer': layer,
'type': dsinfo['layers'][layer]['datatype']
}
# if this is a zip file we'll have to set zippath as well
# FIXME: poor check whether this is a zip file
if dsinfo['filename'].endswith('.zip'):
dsdata['zippath'] = dsinfo['layers'][layer]['filename']
envlist.append(dsdata)
# replace original dict
params['environmental_datasets'] = envlist
# Get the content of the modelling_region BlobFile.
# Note: deepcopy does not copy the content of BlobFile.
if result.job_params['modelling_region']:
params['modelling_region'] = {
'uuid':
IUUID(result),
'filename':
'modelling_region.json',
'downloadurl':
'{0}/API/em/v1/constraintregion?uuid={1}'.format(
getSite().absolute_url(), IUUID(result)),
}
# add hints for worker
workerhints = {
'files': [
x for x in (
'traits_dataset',
'environmental_datasets',
'modelling_region',
) if x in params
]
}
return {'env': {}, 'params': params, 'worker': workerhints}
def __generateParameters(self, params, portal_type):
# This code formats the input parameters to experiments, and is a mirror "copy" of get_sdm_params,
# get_project_params, get_biodiverse_params, get_traits_params, get_ensemble_params in org.bccvl.compute.
inp = deepcopy(params)
for key, val in inp.items():
if key in ('modelling_region', 'projection_region'):
if val:
val = params[key].data
else:
val = '{0}/API/em/v1/constraintregion?uuid={1}'.format(getSite().absolute_url(), IUUID(self.context))
if key in ('species_occurrence_dataset', 'species_absence_dataset'):
if val:
val = getdatasetparams(val)
val['species'] = re.sub(u"[ _,\-'\"/\(\)\{\}\[\]]", u".", val.get('species', u'Unknown'))
if key in ('environmental_datasets', 'future_climate_datasets'):
envlist = []
for uuid, layers in val.items():
dsinfo = getdatasetparams(uuid)
for layer in layers:
dsdata = {
'uuid': dsinfo['uuid'],
'filename': dsinfo['filename'],
'downloadurl': dsinfo['downloadurl'],
# TODO: should we use layer title or URI?
'layer': layer,
'type': dsinfo['layers'][layer]['datatype']
}
# if this is a zip file we'll have to set zippath as well
# FIXME: poor check whether this is a zip file
if dsinfo['filename'].endswith('.zip'):
dsdata['zippath'] = dsinfo['layers'][layer]['filename']
envlist.append(dsdata)
val = envlist
# for SDM model as input to Climate Change experiement
if key == 'species_distribution_models':
if val:
uuid = val
val = getdatasetparams(uuid)
val['species'] = re.sub(u"[ _\-'\"/\(\)\{\}\[\]]", u".", val.get('species', u"Unknown"))
sdmobj = uuidToObject(uuid)
sdmmd = IBCCVLMetadata(sdmobj)
val['layers'] = sdmmd.get('layers_used', None)
# do SDM projection results
sdm_projections = []
for resuuid in inp['sdm_projections']:
sdm_projections.append(getdatasetparams(resuuid))
inp['sdm_projections'] = sdm_projections
# for projection as input to Biodiverse experiment
if key == 'projections':
dslist = []
for dsparam in val:
dsinfo = getdatasetparams(dsparam['dataset'])
dsinfo['threshold'] = dsparam['threshold']
# Convert threshold value from Decimal to float
for thkey, thvalue in dsinfo['threshold'].items():
if isinstance(thvalue, Decimal):
dsinfo['threshold'][thkey] = float(thvalue)
dslist.append(dsinfo)
# replace projections param
val = dslist
# projection models as input to Ensemble experiment
if key == 'datasets':
dslist = []
for uuid in val:
dslist.append(getdatasetparams(uuid))
# replace datasets param
val = dslist
# for trait dataset as input to Species Trait Modelling experiment
if key == 'traits_dataset':
dsinfo = getdatasetparams(val)
if dsinfo['filename'].endswith('.zip'):
dsinfo['zippath'] = dsinfo['layers'].values()[0]['filename']
val = dsinfo
if isinstance(val, Decimal):
val = float(val)
inp[key] = val
if portal_type == ('org.bccvl.content.sdmexperiment',
'org.bccvl.content.msdmexperiment',
'org.bccvl.content.mmexperiment'):
inp.update({
'rescale_all_models': False,
'selected_models': 'all',
'modeling_id': 'bccvl',
# generic dismo params
'tails': 'both',
})
elif portal_type == 'org.bccvl.content.projectionexperiment':
inp.update({
'selected_models': 'all',
'projection_name': os.path.splitext(dsinfo['filename'])[0]
})
inputParams = {
# example of input/ouput directories
'env': {
'inputdir': './input',
'outputdir': './output',
'scriptdir': './script',
'workdir': './workdir'
},
'params': inp
}
return json.dumps(inputParams, default=str, indent=4)
def get_project_params(result):
params = deepcopy(result.job_params)
# get metadata for species_distribution_models
uuid = params['species_distribution_models']
params['species_distribution_models'] = getdatasetparams(uuid)
# do biomod name mangling of species name
params['species_distribution_models']['species'] = re.sub(u"[ _\-'\"/\(\)\{\}\[\]]", u".", params['species_distribution_models'].get('species', u"Unknown"))
# we need the layers from sdm to fetch correct files for climate_models
# TODO: getdatasetparams should fetch 'layers'
sdmobj = uuidToObject(uuid)
sdmmd = IBCCVLMetadata(sdmobj)
params['species_distribution_models']['layers'] = sdmmd.get('layers_used', None)
# do SDM projection results
sdm_projections = []
for resuuid in params['sdm_projections']:
sdm_projections.append(getdatasetparams(resuuid))
params['sdm_projections'] = sdm_projections
# do future climate layers
climatelist = []
for uuid, layers in params['future_climate_datasets'].items():
dsinfo = getdatasetparams(uuid)
for layer in layers:
dsdata = {
'uuid': dsinfo['uuid'],
'filename': dsinfo['filename'],
'downloadurl': dsinfo['downloadurl'],
'layer': layer,
# TODO: add year, gcm, emsc here?
'type': dsinfo['layers'][layer]['datatype'],
}
# if this is a zip file we'll have to set zippath as well
# FIXME: poor check whether this is a zip file
if dsinfo['filename'].endswith('.zip'):
dsdata['zippath'] = dsinfo['layers'][layer]['filename']
# FIXME: workaround to get future projection name back, but this works only for file naming scheme with current available data
if params['selected_future_layers'] and layer in params['selected_future_layers']:
params['projection_name'], _ = os.path.splitext(dsinfo['filename'])
climatelist.append(dsdata)
# replace climate_models parameter
params['future_climate_datasets'] = climatelist
params['selected_models'] = 'all'
# In case no future climate layer is selected
if not params.get('projection_name'):
params['projection_name'], _ = os.path.splitext(dsinfo['filename'])
# TODO: quick fix Decimal json encoding through celery (where is my custom json encoder gone?)
for key, item in params.items():
if isinstance(item, Decimal):
params[key] = float(item)
# Get the content of the projection_region BlobFile.
# Note: deepcopy does not copy the content of BlobFile.
params['projection_region'] = {
'uuid': IUUID(result),
'filename': 'projection_region.json',
'downloadurl': '{0}/API/em/v1/constraintregion?uuid={1}'.format(getSite().absolute_url(), IUUID(result)),
}
# add hints for worker
workerhints = {
'files': ('species_distribution_models', 'future_climate_datasets', 'sdm_projections', 'projection_region',)
}
return {'env': {}, 'params': params, 'worker': workerhints}
def __generateParameters(self, params, portal_type):
# This code formats the input parameters to experiments, and is a mirror "copy" of get_sdm_params,
# get_project_params, get_biodiverse_params, get_traits_params, get_ensemble_params in org.bccvl.compute.
inp = deepcopy(params)
for key, val in inp.items():
if key in ('modelling_region', 'projection_region'):
if val:
val = params[key].data
else:
val = '{0}/API/em/v1/constraintregion?uuid={1}'.format(
getSite().absolute_url(), IUUID(self.context))
if key in ('species_occurrence_dataset',
'species_absence_dataset'):
if val:
val = getdatasetparams(val)
val['species'] = re.sub(u"[ _,\-'\"/\(\)\{\}\[\]]", u".",
val.get('species', u'Unknown'))
if key in ('environmental_datasets', 'future_climate_datasets'):
envlist = []
for uuid, layers in val.items():
dsinfo = getdatasetparams(uuid)
for layer in layers:
dsdata = {
'uuid': dsinfo['uuid'],
'filename': dsinfo['filename'],
'downloadurl': dsinfo['downloadurl'],
# TODO: should we use layer title or URI?
'layer': layer,
'type': dsinfo['layers'][layer]['datatype']
}
# if this is a zip file we'll have to set zippath as well
# FIXME: poor check whether this is a zip file
if dsinfo['filename'].endswith('.zip'):
dsdata['zippath'] = dsinfo['layers'][layer][
'filename']
envlist.append(dsdata)
val = envlist
# for SDM model as input to Climate Change experiement
if key == 'species_distribution_models':
if val:
uuid = val
val = getdatasetparams(uuid)
val['species'] = re.sub(u"[ _\-'\"/\(\)\{\}\[\]]", u".",
val.get('species', u"Unknown"))
sdmobj = uuidToObject(uuid)
sdmmd = IBCCVLMetadata(sdmobj)
val['layers'] = sdmmd.get('layers_used', None)
# do SDM projection results
sdm_projections = []
for resuuid in inp['sdm_projections']:
sdm_projections.append(getdatasetparams(resuuid))
inp['sdm_projections'] = sdm_projections
# for projection as input to Biodiverse experiment
if key == 'projections':
dslist = []
for dsparam in val:
dsinfo = getdatasetparams(dsparam['dataset'])
dsinfo['threshold'] = dsparam['threshold']
# Convert threshold value from Decimal to float
for thkey, thvalue in dsinfo['threshold'].items():
if isinstance(thvalue, Decimal):
dsinfo['threshold'][thkey] = float(thvalue)
dslist.append(dsinfo)
# replace projections param
val = dslist
# projection models as input to Ensemble experiment
if key == 'datasets':
dslist = []
for uuid in val:
dslist.append(getdatasetparams(uuid))
# replace datasets param
val = dslist
# for trait dataset as input to Species Trait Modelling experiment
if key == 'traits_dataset':
dsinfo = getdatasetparams(val)
if dsinfo['filename'].endswith('.zip'):
dsinfo['zippath'] = dsinfo['layers'].values(
)[0]['filename']
val = dsinfo
if isinstance(val, Decimal):
val = float(val)
inp[key] = val
if portal_type == ('org.bccvl.content.sdmexperiment',
'org.bccvl.content.msdmexperiment',
'org.bccvl.content.mmexperiment'):
inp.update({
'rescale_all_models': False,
'selected_models': 'all',
'modeling_id': 'bccvl',
# generic dismo params
'tails': 'both',
})
elif portal_type == 'org.bccvl.content.projectionexperiment':
inp.update({
'selected_models':
'all',
'projection_name':
os.path.splitext(dsinfo['filename'])[0]
})
inputParams = {
# example of input/ouput directories
'env': {
'inputdir': './input',
'outputdir': './output',
'scriptdir': './script',
'workdir': './workdir'
},
'params': inp
}
return json.dumps(inputParams, default=str, indent=4)
def get_sdm_params(result):
# make a deep copy of the params to not accedientially modify the
# persisted dict
params = deepcopy(result.job_params)
# TODO: names to fix up
# occurrence-> species_occurrence_dataset
# background-> species_absence_dataset
# pseudoabsence['enabled']-> species_pseudo_absence_points,
# pseudoabsence['points']-> species_number_pseudo_absence_points
# layers+ environment{}-> environmental_datasets TODO: turn into list of files
# get all necessary metadata for files, and add worker hints to download files
for paramname in ('species_occurrence_dataset', 'species_absence_dataset'):
# Skip empty or non existing params
if not params.get(paramname, None):
continue
uuid = params[paramname]
dsinfo = getdatasetparams(uuid)
if dsinfo['filename'].endswith('.zip'):
# FIXME: too many static assumptions about how an occurrence zip file looks like
# layers:key does not match anything (should it?)
# assumes exactly one file here
# TODO: should I remove 'layers' section here?
dsinfo['zippath'] = dsinfo['layers'].values()[0]['filename']
params[paramname] = dsinfo
# replace all spaces and underscores to '.' (biomod does the same)
# TODO: really necessary?
if params[paramname]:
params[paramname]['species'] = re.sub(
u"[ _\-,'\"/\(\)\{\}\[\]]", u".",
params[paramname].get('species', u'Unknown'))
# TODO: This assumes we only zip file based layers
envlist = []
for uuid, layers in params['environmental_datasets'].items():
dsinfo = getdatasetparams(uuid)
for layer in layers:
dsdata = {
'uuid': dsinfo['uuid'],
'filename': dsinfo['filename'],
'downloadurl': dsinfo['downloadurl'],
# TODO: should we use layer title or URI?
'layer': layer,
'type': dsinfo['layers'][layer]['datatype']
}
# if this is a zip file we'll have to set zippath as well
# FIXME: poor check whether this is a zip file
if dsinfo['filename'].endswith('.zip'):
dsdata['zippath'] = dsinfo['layers'][layer]['filename']
envlist.append(dsdata)
# replace original dict
params['environmental_datasets'] = envlist
# TODO: quick fix Decimal json encoding through celery (where is my custom json encoder gone?)
for key, item in params.items():
if isinstance(item, Decimal):
params[key] = float(item)
# Pass the url to get modelling region as input file
if result.job_params['modelling_region']:
params['modelling_region'] = {
'uuid':
IUUID(result),
'filename':
'modelling_region.json',
'downloadurl':
'{0}/API/em/v1/constraintregion?uuid={1}'.format(
getSite().absolute_url(), IUUID(result)),
}
# add hints for worker to download files
workerhints = {
# only those parameters that are actually in params dict
'files': [
x for x in (
'species_occurrence_dataset',
'species_absence_dataset',
'environmental_datasets',
'modelling_region',
) if x in params
]
}
return {'env': {}, 'params': params, 'worker': workerhints}