def setUp(self):
super(AnalysisServiceTest, self).setUp()
self.sample_analysis = Analysis()
self.sample_analysis.name = 'Test User'
self.sample_analysis.user_id = 1
self.sample_analysis.point_data_dataset_id = 2
self.sample_analysis.coverage_dataset_ids = ['1_LandCover','3_LandCover']
self.sample_analysis.parameters = []
self.sample_analysis.id = 12
self.sample_analysis.input_hash = 12345
self.sample_analysis.model_id = 1
self.sample_analysis.description = 'Analysis for testing purposes'
results_dataset = Dataset()
results_dataset.type = 'Result'
results_dataset.viewable_by_user_id = 1
results_dataset.name = 'Example Results Dataset 1'
self.sample_analysis.result_dataset = results_dataset
def _populate_session(self):
with self._service.transaction_scope() as session:
user = User()
user.username = "******"
user.name = "Test User"
user.email = "*****@*****.**"
user.access_level = "CEH"
session.add(user)
pointDst = DatasetType()
pointDst.type = "Point"
coverDst = DatasetType()
coverDst.type = "Coverage"
resultDst = DatasetType()
resultDst.type = "Result"
session.add(pointDst)
session.add(coverDst)
session.add(resultDst)
dataset_a = Dataset()
dataset_a.dataset_type = pointDst
dataset_a.viewable_by_user_id = self._user_id
dataset_a.name = "Dataset1"
session.add(dataset_a)
dataset_b = Dataset()
dataset_b.dataset_type = pointDst
dataset_b.name = "Dataset2"
session.add(dataset_b)
dataset_c = Dataset()
dataset_c.dataset_type = pointDst
dataset_c.viewable_by_user_id = self._another_user_id
dataset_c.name = "Dataset3"
session.add(dataset_c)
dataset_d = Dataset()
dataset_d.dataset_type = resultDst
dataset_d.name = "Results Dataset 1"
dataset_d.viewable_by_user_id = 1
session.add(dataset_d)
analysis_a = Analysis()
analysis_a.point_dataset = dataset_a
analysis_a.coverage_datasets.append(AnalysisCoverageDataset(dataset_b))
analysis_a.viewable_by = self._user_id
analysis_a.result_dataset = dataset_d
analysis_a.deleted = False
analysis_b = Analysis()
analysis_b.point_dataset = dataset_a
analysis_b.coverage_datasets.append(AnalysisCoverageDataset(dataset_b))
analysis_b.run_by = self._user_id
analysis_b.result_dataset = dataset_d
analysis_b.deleted = False
analysis_c = Analysis()
analysis_c.point_dataset = dataset_a
analysis_c.coverage_datasets.append(AnalysisCoverageDataset(dataset_b))
analysis_c.viewable_by = self._another_user_id
analysis_c.result_dataset = dataset_d
analysis_c.deleted = False
session.add(analysis_a)
session.add(analysis_b)
session.add(analysis_c)
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)