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 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)