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
