def create_coverage_dataset(self,name,wms_url,netcdf_url,low_res_url,
data_range_from, data_range_to, is_categorical):
"""
Creates a coverage dataset in the EcoMaps DB
@param name: Display name of the dataset
@param wms_url: Endpoint for the mapping server
@param netcdf_url: URL of the OpenDAP endpoint for this dataset
@param low_res_url: URL for accessing the NetCDF file over the HTTP protocol
@param data_range_from: Low range for the data
@param data_range_to: High range for the data
@param is_categorical: Set to true if the data is categorical (not continuous)
"""
with self.transaction_scope() as session:
dataset_type = session.query(DatasetType).filter(DatasetType.type=='Coverage').one()
dataset = Dataset()
dataset.name = name
dataset.dataset_type = dataset_type
dataset.netcdf_url = netcdf_url
dataset.wms_url = wms_url
dataset.low_res_url = low_res_url
dataset.data_range_from = data_range_from
dataset.data_range_to = data_range_to
dataset.is_categorical = is_categorical
session.add(dataset)
def test_analysis_run(self):
handler = logging.StreamHandler(sys.stderr)
handler.setLevel(logging.DEBUG)
log.setLevel(logging.DEBUG)
try:
#log.basicConfig(stream=sys.stderr)
log.addHandler(handler)
with working_directory(
EcomapsAnalysisWorkingDirectory(),
os.path.join(os.path.dirname(__file__), '../code_root')) as dir:
# CEH Chess Data
coverage_ds = Dataset()
#coverage_ds.name = 'CHESS 1971 01'
#coverage_ds.netcdf_url = 'http://*****:*****@test.com')
analysis.run(point_url='http://thredds-prod.nerc-lancaster.ac.uk/thredds/dodsC/ECOMAPSDetail/ECOMAPSInputLOI01.nc',
coverage_dict=coverage_dict,
progress_fn=progress)
finally:
log.removeHandler(handler)
# Remove me to test analysis code
return
def create_point_dataset(self,name,wms_url,netcdf_url):
"""
Creates a point dataset in the EcoMaps DB
@param name: Display name of the dataset
@param wms_url: Endpoint for the mapping server
@param netcdf_url: URL of the OpenDAP endpoint for this dataset
"""
with self.transaction_scope() as session:
dataset_type = session.query(DatasetType).filter(DatasetType.type=='Point').one()
dataset = Dataset()
dataset.name = name
dataset.dataset_type = dataset_type
dataset.netcdf_url = netcdf_url
dataset.wms_url = wms_url
dataset.low_res_url = None
session.add(dataset)
def registerThreddsDatasets(url, types, session):
"""Scan over the given url for thredds datasets. Add to the session"""
xml = parse(urllib2.urlopen(url))
for dataset in xml.getElementsByTagName("dataset"):
if dataset.hasAttribute("urlPath"):
# Here we should lookup the sevicename which (contained in this element)
# and find out the services base.
ds = Dataset()
ds.dataset_type = types["GRID"] # Set to GRID type by default
# See if a dataType has been defined for this dataset. If so, look
# it up
dataTypes = dataset.getElementsByTagName("dataType")
if dataTypes.length == 1:
ds.dataset_type = types[dataTypes[0].firstChild.nodeValue]
path = dataset.attributes["urlPath"].value
ds.name = dataset.attributes["name"].value
ds.wms_url = urljoin(url, "/thredds/wms/" + path + "?service=WMS&version=1.3.0&request=GetCapabilities")
ds.netcdf_url = urljoin(url, "/thredds/dodsC/" + path)
session.add(ds) # Register the dataset to the session
# Group sibling catalogRefs together. If any of these have an aggregation we will scan them
# otherwise just scan all of the catalogueRegs
catalogRefs = xml.getElementsByTagName("catalogRef")
for key, group in groupby(catalogRefs, lambda e: e.parentNode):
groupList = list(group)
aggregations = filter(lambda x: x.attributes["xlink:title"].value.lower().endswith("aggregation"), groupList)
scan = aggregations if len(aggregations) > 0 else groupList # Were there any aggregations?
for catRef in scan:
# Check if the current catRef node has any sibling datasets which are aggregations.
# If it does, ignore this catRef
if not hasSiblingAggregationDatasets(catRef):
path = urljoin(url, catRef.attributes["xlink:href"].value)
registerThreddsDatasets(path, types, session)
def run(self, analysis_obj):
"""Runs the analysis, updating the model object passed in with a result file URL and a
PNG image (base64 encoded)
Params:
analysis: Ecomaps analysis model to update
"""
self._analysis_obj = analysis_obj
with working_directory(EcomapsAnalysisWorkingDirectory(),
os.path.join(os.path.dirname(__file__), self._source_dir)) as dir:
log.debug("Analysis for %s has started" % self._analysis_obj.name)
#RUN
analysis = EcomapsAnalysis(dir,
analysis_obj.run_by_user.name,
analysis_obj.run_by_user.email)
file_name = "%s_%s.nc" % (self._analysis_obj.name.replace(' ', '-'), str(datetime.datetime.now().isoformat()).replace(':', '-'))
coverage_dict = {}
for ds in analysis_obj.coverage_datasets:
# Make a sensible data structure to tie the columns chosen
# for each dataset with any time slice information
coverage_dict[ds.dataset] = [(c.column, c.time_index) for c in ds.columns]
# Now we have enough information to kick the analysis off
output_file_loc, map_image_file_loc, \
fit_image_file_loc = analysis.run(analysis_obj.point_dataset.netcdf_url,
coverage_dict, self._update_progress)
# Write the result image to
with open(map_image_file_loc, "rb") as img:
encoded_image = base64.b64encode(img.read())
self._analysis_obj.result_image = encoded_image
with open(fit_image_file_loc, "rb") as img:
encoded_image = base64.b64encode(img.read())
self._analysis_obj.fit_image = encoded_image
# Grab the "convenience" values from the dataset, which
# we'll store against the analysis, saves looking in the netCDF each time
try:
netCdf = NetCDFDataset(output_file_loc, 'r', format='NETCDF4')
self._analysis_obj.aic = str(netCdf.AIC)
self._analysis_obj.model_formula = netCdf.model_formula
except:
log.warning('Failed to get netCDF attributes at the end of %s' % self._analysis_obj.name)
# Copy the result file to the ecomaps THREDDS server
# Set the file name to the name of the analysis + a bit of uniqueness
shutil.copyfile(output_file_loc, os.path.join(self._netcdf_file_store, file_name))
# Generate a WMS URL for the output file...
wms_url = self._thredds_wms_format % file_name
# Create a result dataset
result_ds = Dataset()
result_ds.name = self._analysis_obj.name
result_ds.wms_url = wms_url
# 3 = result dataset
result_ds.dataset_type_id = 3
result_ds.netcdf_url = self._open_ndap_format % file_name
result_ds.viewable_by_user_id = analysis_obj.run_by_user.id
# Tidy up the analysis object
self._save_analysis(result_ds)
self._update_progress('Complete', True)
