param = CoreParameter() # Location of the ref data. param.reference_data_path = '/global/cfs/cdirs/e3sm/acme_diags/obs_for_e3sm_diags/climatology/' # Name of the ref obs data, used to find the climo files. param.ref_name = 'ceres_ebaf_toa_v2.8' # Location of the test data. param.test_data_path = '/global/cfs/cdirs/e3sm/acme_diags/obs_for_e3sm_diags/climatology/' # Name of the test obs data, used to find the climo files. param.test_name = 'ceres_ebaf_toa_v4.0' # Name of the folder where the results are stored. # Change `prefix` to use your directory. #prefix = '/global/cfs/cdirs/e3sm/www/<your directory>/examples' param.results_dir = os.path.join(prefix, 'ex7_obs_vs_obs') # What plotsets to run the diags on. param.sets = ['lat_lon'] # Below are more optional arguments. # What seasons to run the diags on. # If not defined, diags is ran on ['ANN', 'DJF', 'MAM', 'JJA', 'SON']. param.seasons = ['ANN'] # 'mpl' is to create matplotlib plots, 'vcs' is for vcs plots. param.backend = 'mpl' runner.sets_to_run = ['lat_lon'] runner.run_diags([param])
# Location of the ref data. param.reference_data_path = ( "/global/cfs/cdirs/e3sm/acme_diags/obs_for_e3sm_diags/climatology/") # Name of the ref obs data, used to find the climo files. param.ref_name = "ceres_ebaf_toa_v2.8" # Location of the test data. param.test_data_path = ( "/global/cfs/cdirs/e3sm/acme_diags/obs_for_e3sm_diags/climatology/") # Name of the test obs data, used to find the climo files. param.test_name = "ceres_ebaf_toa_v4.0" # Name of the folder where the results are stored. # Change `prefix` to use your directory. prefix = "/global/cfs/cdirs/e3sm/www/<your directory>/examples" param.results_dir = os.path.join(prefix, "ex7_obs_vs_obs") # What plotsets to run the diags on. param.sets = ["lat_lon"] # Below are more optional arguments. # What seasons to run the diags on. # If not defined, diags is ran on ['ANN', 'DJF', 'MAM', 'JJA', 'SON']. param.seasons = ["ANN"] # 'mpl' is to create matplotlib plots, 'vcs' is for vcs plots. param.backend = "mpl" runner.sets_to_run = ["lat_lon"] runner.run_diags([param])
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, [])
# Compy #data_prefix = '/compyfs/e3sm_diags_data' #html_prefix = '/compyfs/www/<username>' # Change <username> # Cori data_prefix = '/global/cfs/cdirs/e3sm/acme_diags' html_prefix = '/global/cfs/cdirs/e3sm/www/<username>' # Change <username> param = CoreParameter() param.reference_data_path = os.path.join(data_prefix, 'obs_for_e3sm_diags/climatology') param.test_data_path = os.path.join( data_prefix, 'test_model_data_for_acme_diags/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
import os from acme_diags.parameter.core_parameter import CoreParameter from acme_diags.parameter.area_mean_time_series_parameter import AreaMeanTimeSeriesParameter from acme_diags.run import runner param = CoreParameter() param.reference_data_path = '/compyfs/e3sm_diags_data/obs_for_e3sm_diags/time-series/' param.test_data_path = '/compyfs/e3sm_diags_data/test_model_data_for_acme_diags/time-series/E3SM_v1/' param.test_name = 'e3sm_v1' param.seasons = [ "ANN" ] #seasons shouldn't be a parameter for area_mean_time_series, need to investigate while uncomment this line won't work #param.seasons = [] #seasons shouldn't be a parameter for area_mean_time_series, need to investigate while uncomment this line won't work param.test_timeseries_input = True param.test_start_yr = '1998' param.test_end_yr = '2004' param.ref_timeseries_input = True param.ref_start_yr = '1998' param.ref_end_yr = '2004' param.multiprocessing = True param.num_workers = 40 prefix = '/compyfs/www/zhan429/tests' param.results_dir = os.path.join(prefix, 'multiple_sets_2ts') # Set specific parameters for new sets ts_param = AreaMeanTimeSeriesParameter() ts_param.reference_data_path = '/compyfs/e3sm_diags_data/obs_for_e3sm_diags/time-series/' ts_param.test_data_path = '/compyfs/e3sm_diags_data/test_model_data_for_acme_diags/time-series/E3SM_v1/'
dc_climo_path = os.path.join(test_prefix, "diurnal_climo/" + case + "/1980-2014/rgr") # Define parameters for core sets: 'lat_lon','zonal_mean_xy', 'zonal_mean_2d', 'polar', 'cosp_histogram', 'meridional_mean_2d' param = CoreParameter() param.reference_data_path = obs_climo param.test_data_path = climo_path param.test_name = case param.test_short_name = casename # Define results path. prefix = "/global/cfs/cdirs/e3sm/www/zhang40/tutorials/" param.results_dir = os.path.join(prefix, "run_v230_allsets") param.multiprocessing = True param.num_workers = 30 param.seasons = ["ANN", "JJA"] # Additional parameters: # Short version of test model name being printed as plot titles # param.short_test_name = 'beta0.FC5COSP.ne30' # Specify run_type. Defualt is 'model_vs_obs' # param.run_type = 'model_vs_model' # Specify title of the 3rd panel plot. Defualt is 'Model - Observation' # param.diff_title = 'Difference' # Save subplots as pdf files. # param.output_format_subplot = ['pdf'] # Save netcdf files being plotted. # param.save_netcdf = True # Set specific parameters for new sets
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