def test_registry(self):
"""Test component registry functionality."""
reg = cmp.registry()
# get a list of all components
registry_list = reg.list_registry()
# get a list of population components
registry_asset = reg.list_related(asset='population')
# ensure this component has been registered
self.assertTrue(TestPopTellCounties.COMPONENT_NAME in registry_list)
# ensure component is findable by asset name
self.assertTrue(TestPopTellCounties.COMPONENT_NAME in registry_asset)
# check asset function
my_asset_function = reg.get_component(TestPopTellCounties.COMPONENT_NAME)
df = my_asset_function(raster_list=[TestPopTellCounties.RASTER_FILE],
county_shapefile=TestPopTellCounties.COUNTY_SHAPEFILE,
year_list=[2020],
state_name='alabama')
pd.testing.assert_frame_equal(TestPopTellCounties.EXPECTED_OUTPUT, df)
def test_r_demo(self):
"""Test the R demo code."""
reg = cmp.registry()
# get R demo function that returns the sum of two squares
fx = reg.get_component('demo_r')
result = fx(2, 4)
self.assertEqual(20, result)
def test_future_balancing_authority_aggregation(self):
"""Ensure that historical counties to BAs aggregation produces the same result."""
registry = cmp.registry()
wrf_to_tell_balancing_authorities = registry.get_component(
self.BA_COMPONENT_NAME)
# remove output file if it exists
try:
os.remove(
f'{self.data_path}/PSEI_WRF_Hourly_Mean_Meteorology_2059.csv')
except OSError:
pass
# generate results
wrf_to_tell_balancing_authorities(
year=2059,
is_historical=False,
balancing_authority_to_fips_file=
f'{self.data_path}/fips_service_match_2019.csv',
county_population_by_year_file=
f'{self.data_path}/ssp3_county_population.csv',
county_data_directory=f'{self.data_path}',
output_directory=f'{self.data_path}',
output_file_infix='WRF_Hourly_Mean_Meteorology',
county_data_prefix='',
county_data_suffix='county_test_data',
county_data_time_format='%Y_%m_%d_%H',
variables=['T2', 'Q2', 'U10', 'V10', 'SWDOWN', 'GLW'],
precisions=[2, 5, 2, 2, 2, 2],
)
# load the output and check results
data = pd.read_csv(
f'{self.data_path}/PSEI_WRF_Hourly_Mean_Meteorology_2059.csv')
validation_data = pd.DataFrame({
'Time_UTC':
np.array(['2059-01-01 01:00:00', '2059-01-01 02:00:00']),
'T2':
np.array([270.14, 269.46], dtype=float),
'Q2':
np.array([0.00242, 0.0023], dtype=float),
'SWDOWN':
np.array([0.0, 0.0], dtype=float),
'GLW':
np.array([218.26, 217.44], dtype=float),
'WSPD':
np.array([1.92, 2.33], dtype=float),
})
pd.testing.assert_frame_equal(validation_data, data)
def test_dummy_value(self):
"""Test for initial dummy value return."""
# get result
registry = im3c.registry()
method = registry.get_component(registry.list_related('gcam')[0])
result = method(0, 0)
# function should return 0
self.assertEqual(
0,
result,
msg=
f"Result for `gcam_cerf_expansion_plan` returned {result} instead of 0"
)
def test_county_aggregation(self):
"""Ensure that a single time slice and county produces the same result."""
registry = cmp.registry()
wrf_to_tell_counties = registry.get_component(
self.COUNTY_COMPONENT_NAME)
# remove weights file if it exists
try:
os.remove(f'{self.data_path}/test_weights.parquet')
except OSError:
pass
# remove output files if they exist
try:
os.remove(
f'{self.data_path}/2019_01_01_01_UTC_County_Mean_Meteorology.csv'
)
except OSError:
pass
try:
os.remove(
f'{self.data_path}/2019_01_01_02_UTC_County_Mean_Meteorology.csv'
)
except OSError:
pass
# generate the weights and output files
wrf_to_tell_counties(
wrf_file=f'{self.data_path}/test_data.nc',
wrf_variables=['T2', 'Q2', 'U10', 'V10', 'SWDOWN', 'GLW'],
precisions=[2, 5, 2, 2, 2, 2],
county_shapefile=f'{self.data_path}/test_counties.shp',
weight_and_mapping_file=f'{self.data_path}/test_weights.parquet',
output_directory=self.data_path,
output_filename_suffix='_County_Mean_Meteorology',
n_jobs=-1,
)
# load the output files and check results
slice_one = pd.read_csv(
f'{self.data_path}/2019_01_01_01_UTC_County_Mean_Meteorology.csv')
slice_two = pd.read_csv(
f'{self.data_path}/2019_01_01_02_UTC_County_Mean_Meteorology.csv')
validation_data = pd.DataFrame({
'FIPS':
np.array([1001, 1001], dtype=int),
'T2':
np.array([293.03, 293.12], dtype=float),
'Q2':
np.array([0.01315, 0.01344], dtype=float),
'U10':
np.array([-0.46, 0.81], dtype=float),
'V10':
np.array([7.81, 7.06], dtype=float),
'SWDOWN':
np.array([0.0, 0.0], dtype=float),
'GLW':
np.array([411.66, 411.7], dtype=float),
})
pd.testing.assert_frame_equal(
validation_data,
pd.concat([slice_one, slice_two], ignore_index=True))