def test_run_diagnostic_log_level(tmp_path, monkeypatch, flag):
"""Test if setting the log level from the command line works."""
settings = create_settings(tmp_path)
settings_file = write_settings(settings)
monkeypatch.setattr(sys, 'argv', ['', flag, 'error', settings_file])
with shared.run_diagnostic():
assert shared._base.logger.getEffectiveLevel() == logging.ERROR
def test_rerun_diagnostic_raises(tmp_path, monkeypatch):
"""Test if re-running the diagnostic script fails when output exists."""
settings = create_settings(tmp_path)
settings_file = write_settings(settings)
create_run_content(settings)
monkeypatch.setattr(sys, 'argv', ['', settings_file])
with pytest.raises(FileExistsError):
with shared.run_diagnostic():
pass
def test_run_diagnostic(tmp_path, monkeypatch):
settings = create_settings(tmp_path)
settings_file = write_settings(settings)
monkeypatch.setattr(sys, 'argv', ['', settings_file])
# Create files created by ESMValCore
for filename in ('log.txt', 'profile.bin', 'resource_usage.txt'):
file = Path(settings['run_dir']) / filename
file.touch()
with shared.run_diagnostic() as cfg:
assert 'example_setting' in cfg
def test_rerun_diagnostic_flag(tmp_path, monkeypatch, flag):
"""Test if re-running the diagnostic script works."""
exist = flag in {'-i', '--ignore'}
settings = create_settings(tmp_path)
settings_file = write_settings(settings)
create_run_content(settings)
monkeypatch.setattr(sys, 'argv', ['', flag, settings_file])
with shared.run_diagnostic():
assert not (Path(settings['run_dir']) /
'diagnostic_provenance.yml').exists()
for file in (
Path(settings['run_dir']) / 'tmp.nc',
Path(settings['work_dir']) / 'example_output.txt',
Path(settings['plot_dir']) / 'example_output.txt',
):
assert file.exists() == exist
# run the metric
metrics = metric_function(run_obj)
# check duplication
duplicate_metrics = list(
set(all_metrics.keys()) & set(metrics.keys()))
if duplicate_metrics:
raise AssertionError('Duplicate Metrics ' +
str(duplicate_metrics))
all_metrics.update(metrics)
# write metrics to file
with open(os.path.join(run_obj['dump_output'], 'metrics.csv'),
'w') as file_handle:
writer = csv.writer(file_handle)
for metric in all_metrics.items():
writer.writerow(metric)
# multimodel functions
if hasattr(area_package, 'multi_functions'):
for multi_function in area_package.multi_functions:
multi_function(run_obj)
else:
logger.info('# Area has no multi functions.')
if __name__ == '__main__':
with run_diagnostic() as config:
run_area(config)
group_metadata,
)
import os
import logging
logger = logging.getLogger(os.path.basename(__file__))
def main(cfg):
# The config object is a dict of all the metadata from the pre-processor
logger.debug(cfg)
projects = group_metadata(cfg["input_data"].values(), "project")
for k, p in projects.items():
m_list = set()
for ds in p:
if k == "CORDEX":
ds_str = f"{ds['driver']} - {ds['dataset']}"
else:
ds_str = ds["dataset"]
m_list.add(ds_str)
print(f"{k} - {len(m_list)} models:")
print(m_list)
if __name__ == "__main__":
with run_diagnostic() as cfg:
main(cfg)
def main():
"""Run Eady Growth Rate diagnostic."""
with run_diagnostic() as config:
EadyGrowthRate(config).compute()
if model == datainfo.get('reference_dataset', None):
update_reference(my_catch, model, rivervalues, var)
else:
update_plotdata(identifier, plotdata, rivervalues, var)
# Append to cubelist for temporary output
if model not in allcubes.keys():
allcubes[model] = []
allcubes[model].append(cube)
# Write regridded and temporal aggregated netCDF data files (one per model)
# to do: update attributes, something fishy with unlimited dimension
for model, mcube in allcubes.items():
filepath = os.path.join(cfg[diag.names.WORK_DIR],
'_'.join(['postproc', model]) + '.nc')
if cfg[diag.names.WRITE_NETCDF]:
iris.save(mcube, filepath)
logger.info("Writing %s", filepath)
# Write plotdata as ascii files for user information
write_plotdata(cfg, plotdata, my_catch)
# Plot catchment data
make_catchment_plots(cfg, plotdata, my_catch)
if __name__ == '__main__':
with diag.run_diagnostic() as config:
main(config)