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)