def test_all_sets_and_all_seasons(self):
ts_param = AreaMeanTimeSeriesParameter()
ts_param.start_yr = "2000"
ts_param.end_yr = "2004"
enso_param = EnsoDiagsParameter()
enso_param.start_yr = "2000"
enso_param.end_yr = "2004"
streamflow_param = StreamflowParameter()
streamflow_param.test_data_path = "unit_test_data/3yr_nc"
streamflow_param.reference_data_path = "unit_test_data"
self.runner.sets_to_run = [
"lat_lon",
"area_mean_time_series",
"enso_diags",
"streamflow",
]
parameters = self.runner.get_final_parameters(
[self.core_param, ts_param, enso_param, streamflow_param])
# Counts the number of each set and each seasons to run the diags on.
set_counter, season_counter = (
collections.Counter(), # type: ignore
collections.Counter(), # type: ignore
)
for param in parameters:
for set_name in param.sets:
set_counter[set_name] += 1
for season in param.seasons:
season_counter[season] += 1
for set_name in set_counter:
count = set_counter[set_name]
if count <= 0:
msg = "Count for {} is invalid: {}"
self.fail(msg.format(set_name, count))
all_season_counts = list(season_counter.values())
# enso_diags and streamflow only run ANN, no seasons
# So, reduce the ANN count by the number of times these appear
all_season_counts[0] -= set_counter["enso_diags"]
all_season_counts[0] -= set_counter["streamflow"]
if not all(all_season_counts[0] == count
for count in all_season_counts):
self.fail("Counts for the seasons don't match: {}".format(
all_season_counts))
def test_all_sets_and_all_seasons(self):
ts_param = AreaMeanTimeSeriesParameter()
ts_param.start_yr = '2000'
ts_param.end_yr = '2004'
enso_param = EnsoDiagsParameter()
enso_param.start_yr = '2000'
enso_param.end_yr = '2004'
streamflow_param = StreamflowParameter()
streamflow_param.test_data_path = 'unit_test_data/3yr_nc'
streamflow_param.reference_data_path = 'unit_test_data'
parameters = self.runner.get_final_parameters(
[self.core_param, ts_param, enso_param, streamflow_param])
# Counts the number of each set and each seasons to run the diags on.
set_counter, season_counter = collections.Counter(
), collections.Counter()
for param in parameters:
for set_name in param.sets:
set_counter[set_name] += 1
for season in param.seasons:
season_counter[season] += 1
for set_name in set_counter:
count = set_counter[set_name]
if count <= 0:
msg = 'Count for {} is invalid: {}'
self.fail(msg.format(set_name, count))
all_season_counts = list(season_counter.values())
# enso_diags and streamflow only run ANN, no seasons
# So, reduce the ANN count by the number of times these appear
all_season_counts[0] -= set_counter['enso_diags']
all_season_counts[0] -= set_counter['streamflow']
if not all(all_season_counts[0] == count
for count in all_season_counts):
self.fail('Counts for the seasons don\'t match: {}'.format(
all_season_counts))
# Running the software with the API:
# python all_sets.py -d all_sets.py
import numpy
from acme_diags.parameter.area_mean_time_series_parameter import AreaMeanTimeSeriesParameter
from acme_diags.parameter.core_parameter import CoreParameter
from acme_diags.parameter.enso_diags_parameter import EnsoDiagsParameter
from acme_diags.parameter.meridional_mean_2d_parameter import MeridionalMean2dParameter
from acme_diags.parameter.zonal_mean_2d_parameter import ZonalMean2dParameter
from acme_diags.run import Run
run_object = Run()
param = CoreParameter()
ts_param = AreaMeanTimeSeriesParameter()
m2d_param = MeridionalMean2dParameter()
m2d_param.plevs = [
200.,
500.,
]
z2d_param = ZonalMean2dParameter()
z2d_param.plevs = [
200.,
300.,
]
enso_param = EnsoDiagsParameter()
enso_param.test_name = 'e3sm_v1'
run_object.run_diags([param, ts_param, m2d_param, z2d_param, enso_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, [])
] # 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(
data_prefix, 'obs_for_e3sm_diags/time-series/')
enso_param.test_data_path = os.path.join(
data_prefix, 'test_model_data_for_acme_diags/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(
data_prefix, 'obs_for_e3sm_diags/time-series/')
qbo_param.test_data_path = os.path.join(
data_prefix, 'test_model_data_for_acme_diags/time-series/E3SM_v1/')
qbo_param.test_name = 'e3sm_v1'
qbo_param.start_yr = '1990'
] # 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(
data_prefix, "obs_for_e3sm_diags/time-series/")
enso_param.test_data_path = os.path.join(
data_prefix, "test_model_data_for_acme_diags/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(
data_prefix, "obs_for_e3sm_diags/time-series/")
qbo_param.test_data_path = os.path.join(
data_prefix, "test_model_data_for_acme_diags/time-series/E3SM_v1/")
qbo_param.test_name = "e3sm_v1"
qbo_param.start_yr = "1990"
qbo_param.reference_data_path = obs_ts qbo_param.test_data_path = ts_path qbo_param.test_name = casename qbo_param.start_yr = "1980" qbo_param.end_yr = "2014" dc_param = DiurnalCycleParameter() dc_param.reference_data_path = obs_climo dc_param.test_data_path = dc_climo_path dc_param.test_name = case dc_param.short_test_name = casename # Plotting diurnal cycle amplitude on different scales. Default is True dc_param.normalize_test_amp = False enso_param = EnsoDiagsParameter() enso_param.reference_data_path = obs_ts enso_param.test_data_path = ts_path enso_param.test_name = casename enso_param.start_yr = "1980" enso_param.end_yr = "2014" ts_param = AreaMeanTimeSeriesParameter() ts_param.reference_data_path = obs_ts ts_param.test_data_path = ts_path ts_param.test_name = casename ts_param.start_yr = "1980" ts_param.end_yr = "2014" streamflow_param = StreamflowParameter() streamflow_param.reference_data_path = obs_ts
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