def test_complete_run(self): # Anvil # Run `source /lcrc/soft/climate/e3sm-unified/load_latest_e3sm_unified.sh` test_data_prefix = '/lcrc/group/e3sm/public_html/e3sm_diags_test_data' ref_data_prefix = '/lcrc/group/e3sm/public_html/diagnostics/observations/Atm' html_prefix = '.' param = CoreParameter() param.reference_data_path = os.path.join(ref_data_prefix, 'climatology') param.test_data_path = os.path.join(test_data_prefix, 'climatology/') param.test_name = '20161118.beta0.FC5COSP.ne30_ne30.edison' param.seasons = [ "ANN", "JJA" ] # Default setting: seasons = ["ANN", "DJF", "MAM", "JJA", "SON"] param.results_dir = os.path.join(html_prefix, 'tutorial_2020_all_sets') param.multiprocessing = True param.num_workers = 30 # Additional parameters: #param.short_test_name = 'beta0.FC5COSP.ne30' #param.run_type = 'model_vs_model' #param.diff_title = 'Difference' #param.output_format = ['png'] #param.output_format_subplot = ['pdf'] #param.save_netcdf = True # Set specific parameters for new sets enso_param = EnsoDiagsParameter() enso_param.reference_data_path = os.path.join(ref_data_prefix, 'time-series/') enso_param.test_data_path = os.path.join(test_data_prefix, 'time-series/E3SM_v1/') enso_param.test_name = 'e3sm_v1' enso_param.start_yr = '1990' enso_param.end_yr = '1999' qbo_param = QboParameter() qbo_param.reference_data_path = os.path.join(ref_data_prefix, 'time-series/') qbo_param.test_data_path = os.path.join(test_data_prefix, 'time-series/E3SM_v1/') qbo_param.test_name = 'e3sm_v1' qbo_param.start_yr = '1990' qbo_param.end_yr = '1999' ts_param = AreaMeanTimeSeriesParameter() ts_param.reference_data_path = os.path.join(ref_data_prefix, 'time-series/') ts_param.test_data_path = os.path.join(test_data_prefix, 'time-series/E3SM_v1/') ts_param.test_name = 'e3sm_v1' ts_param.start_yr = '1990' ts_param.end_yr = '1999' runner.sets_to_run = [ 'lat_lon', 'zonal_mean_xy', 'zonal_mean_2d', 'polar', 'cosp_histogram', 'meridional_mean_2d', 'enso_diags', 'qbo', 'area_mean_time_series' ] runner.run_diags([param, enso_param, qbo_param, ts_param]) actual_images_dir = param.results_dir # The expected_images_file lists all 475 images we expect to compare. # It was generated with the following steps: # cd /lcrc/group/e3sm/public_html/e3sm_diags_test_data/unit_test_complete_run/tutorial_2020_all_sets # find . -type f -name '*.png' > ../expected_images_complete_run.txt expected_images_file = '/lcrc/group/e3sm/public_html/e3sm_diags_test_data/unit_test_complete_run/expected_images_complete_run.txt' expected_images_dir = '/lcrc/group/e3sm/public_html/e3sm_diags_test_data/unit_test_complete_run/tutorial_2020_all_sets' mismatched_images = [] with open(expected_images_file) as f: for line in f: image = line.strip('./').strip('\n') path_to_actual_png = os.path.join(actual_images_dir, image) path_to_expected_png = os.path.join(expected_images_dir, image) actual_png = Image.open(path_to_actual_png).convert('RGB') expected_png = Image.open(path_to_expected_png).convert('RGB') diff = ImageChops.difference(actual_png, expected_png) bbox = diff.getbbox() if not bbox: # If `diff.getbbox()` is None, then the images are in theory equal self.assertIsNone(diff.getbbox()) else: # Sometimes, a few pixels will differ, but the two images appear identical. # https://codereview.stackexchange.com/questions/55902/fastest-way-to-count-non-zero-pixels-using-python-and-pillow nonzero_pixels = diff.crop(bbox).point( lambda x: 255 if x else 0).convert("L").point(bool).getdata() num_nonzero_pixels = sum(nonzero_pixels) print('\npath_to_actual_png={}'.format(path_to_actual_png)) print( 'path_to_expected_png={}'.format(path_to_expected_png)) print('diff has {} nonzero pixels.'.format( num_nonzero_pixels)) width, height = expected_png.size num_pixels = width * height print('total number of pixels={}'.format(num_pixels)) fraction = num_nonzero_pixels / num_pixels print('num_nonzero_pixels/num_pixels fraction={}'.format( fraction)) # Fraction of mismatched pixels should be less than 0.02% if fraction >= 0.0002: mismatched_images.append(image) self.assertEqual(mismatched_images, [])
machine_path_prefix = '/p/user_pub/e3sm/e3sm_diags_data/' #param.reference_data_path = os.path.join(machine_path_prefix, 'obs_for_e3sm_diags/time-series/') #param.test_data_path = os.path.join(machine_path_prefix, 'test_model_data_for_acme_diags/time-series/CESM1-CAM5_cmip/') param.reference_data_path = '/p/user_pub/e3sm/zhang40/e3sm_cmip6_xmls/v1_water_cycle/amip/' param.test_data_path = '/p/user_pub/e3sm/zhang40/e3sm_cmip6_xmls/v1_water_cycle/amip/r1i1p1f1' param.test_name = 'e3sm_v1_amip_r1' #For compy #prefix = '/compyfs/www/zhan429/doc_examples/' #For cori #prefix = '/global/project/projectdirs/acme/www/zhang40/' #For acme1 #prefix = '/var/www/acme/acme-diags/zhang40/tests/' #param.results_dir = os.path.join(prefix, 'area_mean_amip_r1-3') param.multiprocessing = True param.num_workers = 25 # We're passing in this new object as well, in # addition to the CoreParameter object. ts_param = AreaMeanTimeSeriesParameter() ts_param.ref_names = ['r2i1p1f1', 'r3i1p1f1'] #This setting plot model data only ts_param.start_yr = '1870' ts_param.end_yr = '2014' runner.sets_to_run = ['area_mean_time_series'] runner.run_diags([param, ts_param])
def run_all_sets(html_prefix, d): param = CoreParameter() param.reference_data_path = d["obs_climo"] param.test_data_path = d["test_climo"] param.test_name = "20161118.beta0.FC5COSP.ne30_ne30.edison" param.seasons = [ "ANN", "JJA", ] # Default setting: seasons = ["ANN", "DJF", "MAM", "JJA", "SON"] param.results_dir = os.path.join(html_prefix, "v2_4_0_all_sets") param.multiprocessing = True param.num_workers = 30 # Set specific parameters for new sets enso_param = EnsoDiagsParameter() enso_param.reference_data_path = d["obs_ts"] enso_param.test_data_path = d["test_ts"] enso_param.test_name = "e3sm_v1" enso_param.start_yr = "1990" enso_param.end_yr = "1999" qbo_param = QboParameter() qbo_param.reference_data_path = d["obs_ts"] qbo_param.test_data_path = d["test_ts"] qbo_param.test_name = "e3sm_v1" qbo_param.start_yr = "1990" qbo_param.end_yr = "1999" ts_param = AreaMeanTimeSeriesParameter() ts_param.reference_data_path = d["obs_ts"] ts_param.test_data_path = d["test_ts"] ts_param.test_name = "e3sm_v1" ts_param.start_yr = "1990" ts_param.end_yr = "1999" dc_param = DiurnalCycleParameter() dc_param.reference_data_path = d["dc_obs_climo"] dc_param.test_data_path = d["dc_test_climo"] dc_param.test_name = "20180215.DECKv1b_H1.ne30_oEC.edison" dc_param.short_test_name = "DECKv1b_H1.ne30_oEC" # Plotting diurnal cycle amplitude on different scales. Default is True dc_param.normalize_test_amp = False streamflow_param = StreamflowParameter() streamflow_param.reference_data_path = d["streamflow_obs_ts"] streamflow_param.test_data_path = d["streamflow_test_ts"] streamflow_param.test_name = "20180215.DECKv1b_H1.ne30_oEC.edison" streamflow_param.test_start_yr = "1980" streamflow_param.test_end_yr = "2014" # Streamflow gauge station data range from year 1986 to 1995 streamflow_param.ref_start_yr = "1986" streamflow_param.ref_end_yr = "1995" arm_param = ARMDiagsParameter() arm_param.reference_data_path = d["arm_obs"] arm_param.ref_name = "armdiags" arm_param.test_data_path = d["arm_test"] arm_param.test_name = "20210122.F2010.armsites" arm_param.run_type = "model_vs_obs" arm_param.test_start_yr = "0001" arm_param.test_end_yr = "0001" arm_param.ref_start_yr = "0001" arm_param.ref_end_yr = "0001" runner.sets_to_run = [ "lat_lon", "zonal_mean_xy", "zonal_mean_2d", "polar", "cosp_histogram", "meridional_mean_2d", "enso_diags", "qbo", "area_mean_time_series", "diurnal_cycle", "streamflow", "arm_diags", ] runner.run_diags([ param, enso_param, qbo_param, ts_param, dc_param, streamflow_param, arm_param ]) return param.results_dir