def test_extract_snapshots(self):
"""CBC: Extract snapshots from a snapshot CSV."""
csv_path = os.path.join(self.workspace_dir, 'snapshots.csv')
transition_years = (2000, 2010, 2020)
transition_rasters = []
with open(csv_path, 'w') as transitions_csv:
# Check that we can interpret varying case.
transitions_csv.write('snapshot_YEAR,raster_PATH\n')
for transition_year in transition_years:
# Write absolute paths.
transition_file_path = os.path.join(self.workspace_dir,
f'{transition_year}.tif)')
transition_rasters.append(transition_file_path)
transitions_csv.write(
f'{transition_year},{transition_file_path}\n')
# Make one path relative to the workspace, where the transitions
# CSV also lives.
# The expected raster path is absolute.
transitions_csv.write('2030,some_path.tif\n')
transition_years += (2030, )
transition_rasters.append(
os.path.join(self.workspace_dir, 'some_path.tif'))
extracted_transitions = (
coastal_blue_carbon._extract_snapshots_from_table(csv_path))
self.assertEqual(extracted_transitions,
dict(zip(transition_years, transition_rasters)))
def test_model_one_transition_no_analysis_year(self):
"""CBC: Test the model on one transition with no analysis year.
This test came up while looking into an issue reported on the forums:
https://community.naturalcapitalproject.org/t/coastal-blue-carbon-negative-carbon-stocks/780/12
"""
args = TestCBC2._create_model_args(self.workspace_dir)
args['workspace_dir'] = os.path.join(self.workspace_dir, 'workspace')
prior_snapshots = coastal_blue_carbon._extract_snapshots_from_table(
args['landcover_snapshot_csv'])
baseline_year = min(prior_snapshots.keys())
baseline_raster = prior_snapshots[baseline_year]
with open(args['landcover_snapshot_csv'], 'w') as snapshot_csv:
snapshot_csv.write('snapshot_year,raster_path\n')
snapshot_csv.write(f'{baseline_year},{baseline_raster}\n')
snapshot_csv.write(f'2010,{prior_snapshots[2010]}\n')
del args['analysis_year']
# Use valuation parameters rather than price table.
args['use_price_table'] = False
args['inflation_rate'] = 4
args['price'] = 1.0
coastal_blue_carbon.execute(args)
# Check sequestration raster
expected_sequestration_2000_to_2010 = numpy.array([[83.5, 0.]],
dtype=numpy.float32)
raster_path = os.path.join(args['workspace_dir'], 'output',
('total-net-carbon-sequestration-between-'
'2000-and-2010.tif'))
try:
raster = gdal.OpenEx(raster_path)
numpy.testing.assert_allclose(raster.ReadAsArray(),
expected_sequestration_2000_to_2010)
finally:
raster = None
# Check valuation raster
# Discount rate here matches the inflation rate, so the value of the 10
# years' accumulation is just 1*(10 years of accumulation).
expected_net_present_value_at_2010 = numpy.array([[835.0, 0.]],
dtype=numpy.float32)
raster_path = os.path.join(args['workspace_dir'], 'output',
'net-present-value-at-2010.tif')
try:
raster = gdal.OpenEx(raster_path)
numpy.testing.assert_allclose(raster.ReadAsArray(),
expected_net_present_value_at_2010,
rtol=1e-6)
finally:
raster = None
def test_model_no_transitions(self):
"""CBC: Test model without transitions.
When the model executes without transitions, we still evaluate carbon
sequestration (accumulation only) for the whole baseline period.
"""
args = TestCBC2._create_model_args(self.workspace_dir)
args['workspace_dir'] = os.path.join(self.workspace_dir, 'workspace')
prior_snapshots = coastal_blue_carbon._extract_snapshots_from_table(
args['landcover_snapshot_csv'])
baseline_year = min(prior_snapshots.keys())
baseline_raster = prior_snapshots[baseline_year]
with open(args['landcover_snapshot_csv'], 'w') as snapshot_csv:
snapshot_csv.write('snapshot_year,raster_path\n')
snapshot_csv.write(f'{baseline_year},{baseline_raster}\n')
args['analysis_year'] = baseline_year + 10
# Use valuation parameters rather than price table.
args['use_price_table'] = False
args['inflation_rate'] = 4
args['price'] = 1.0
coastal_blue_carbon.execute(args)
# Check sequestration raster
expected_sequestration_2000_to_2010 = numpy.array([[83.5, 0.]],
dtype=numpy.float32)
raster_path = os.path.join(args['workspace_dir'], 'output',
('total-net-carbon-sequestration-between-'
'2000-and-2010.tif'))
try:
raster = gdal.OpenEx(raster_path)
numpy.testing.assert_allclose(raster.ReadAsArray(),
expected_sequestration_2000_to_2010)
finally:
raster = None
# Check valuation raster
# Discount rate here matches the inflation rate, so the value of the 10
# years' accumulation is just 1*(10 years of accumulation).
expected_net_present_value_at_2010 = numpy.array([[835.0, 0.]],
dtype=numpy.float32)
raster_path = os.path.join(args['workspace_dir'], 'output',
'net-present-value-at-2010.tif')
try:
raster = gdal.OpenEx(raster_path)
numpy.testing.assert_allclose(raster.ReadAsArray(),
expected_net_present_value_at_2010,
rtol=1e-6)
finally:
raster = None
def test_validation(self):
"""CBC: Test custom validation."""
args = TestCBC2._create_model_args(self.workspace_dir)
args['workspace_dir'] = self.workspace_dir
# verify validation passes on basic set of arguments.
validation_warnings = coastal_blue_carbon.validate(args)
self.assertEqual([], validation_warnings)
# Now work through the extra validation warnings.
# Create an invalid transitions table.
invalid_raster_path = os.path.join(self.workspace_dir,
'invalid_raster.tif')
with open(invalid_raster_path, 'w') as raster:
raster.write('not a raster')
# Write over the landcover snapshot CSV
prior_snapshots = coastal_blue_carbon._extract_snapshots_from_table(
args['landcover_snapshot_csv'])
baseline_year = min(prior_snapshots)
with open(args['landcover_snapshot_csv'], 'w') as snapshot_table:
snapshot_table.write('snapshot_year,raster_path\n')
snapshot_table.write(
f'{baseline_year},{prior_snapshots[baseline_year]}\n')
snapshot_table.write(f"{baseline_year + 10},{invalid_raster_path}")
# analysis year must be >= the last transition year.
args['analysis_year'] = baseline_year
validation_warnings = coastal_blue_carbon.validate(args)
self.assertEqual(len(validation_warnings), 2)
self.assertIn(
coastal_blue_carbon.INVALID_SNAPSHOT_RASTER_MSG.format(
snapshot_year=baseline_year + 10), validation_warnings[0][1])
self.assertIn(
coastal_blue_carbon.INVALID_ANALYSIS_YEAR_MSG.format(
analysis_year=2000, latest_year=2010),
validation_warnings[1][1])