def test_model_run_2(self):
"""Coastal Blue Carbon: Test CBC without analysis year."""
from natcap.invest.coastal_blue_carbon \
import coastal_blue_carbon as cbc
self.args['analysis_year'] = None
self.args['lulc_baseline_year'] = 2000
self.args['lulc_transition_maps_list'] = [
self.args['lulc_transition_maps_list'][0]
]
self.args['lulc_transition_years_list'] = [2005]
cbc.execute(self.args)
netseq_output_raster = os.path.join(
self.args['workspace_dir'],
'outputs_core/total_net_carbon_sequestration_test.tif')
netseq_array = _read_array(netseq_output_raster)
# (Explanation for why netseq is 10.5.)
# LULC Code: Baseline: 1 --> Year 2000: 1, Year 2005: 2
# Initial Stock from Baseline: 5+5=10
# Sequest:
# 2000-->2005: (1+1.1)*5=10.5
netseq_test = numpy.array([[cbc.NODATA_FLOAT, 10.5], [10.5, 10.5]])
# just a simple regression test. this demonstrates that a NaN value
# will properly propagate across the model. the npv raster was chosen
# because the values are determined by multiple inputs, and any changes
# in those inputs would propagate to this raster.
numpy.testing.assert_allclose(netseq_array,
netseq_test,
rtol=0,
atol=1e-4)
def test_model_no_valuation(self):
"""Coastal Blue Carbon: Test main model without valuation."""
from natcap.invest.coastal_blue_carbon \
import coastal_blue_carbon as cbc
self.args = _get_args(valuation=False, workspace=self.workspace_dir)
self.args['lulc_baseline_year'] = 2000
self.args['lulc_transition_years_list'] = [2005, 2010]
self.args['analysis_year'] = None
cbc.execute(self.args)
netseq_output_raster = os.path.join(
self.args['workspace_dir'],
'outputs_core/total_net_carbon_sequestration_test.tif')
netseq_array = _read_array(netseq_output_raster)
# (Explanation for why netseq is 31.)
# LULC Code: Baseline: 1 --> Year 2000: 1, Year 2005: 2, Year 2010: 2
# Initial Stock from Baseline: 5+5=10
# Sequest:
# 2000-->2005: (1+1.1)*5=10.5, 2005-->2010: (2+2.1)*5=20.5
# Total: 10.5 + 20.5 = 31.
netseq_test = numpy.array([[cbc.NODATA_FLOAT, 31.], [31., 31.]])
# just a simple regression test. this demonstrates that a NaN value
# will properly propagate across the model. the npv raster was chosen
# because the values are determined by multiple inputs, and any changes
# in those inputs would propagate to this raster.
numpy.testing.assert_array_almost_equal(
netseq_array, netseq_test, decimal=4)
def test_no_transitions_with_analysis_year(self):
"""Coastal Blue Carbon: Model can run w/o trans., w/analysis yr."""
from natcap.invest.coastal_blue_carbon \
import coastal_blue_carbon as cbc
args = CBCRefactorTest.create_args(
workspace=self.workspace_dir, transition_tuples=None,
analysis_year=2010)
cbc.execute(args)
def test_no_transitions(self):
"""Coastal Blue Carbon: Verify model can run without transitions."""
from natcap.invest.coastal_blue_carbon \
import coastal_blue_carbon as cbc
args = CBCRefactorTest.create_args(
workspace=self.workspace_dir, transition_tuples=None,
analysis_year=None)
cbc.execute(args)
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_one_transition(self):
"""Coastal Blue Carbon: Verify model can run with 1 transition."""
from natcap.invest.coastal_blue_carbon \
import coastal_blue_carbon as cbc
transition_tuples = [
(numpy.ones((10, 10)), 2010),
]
args = CBCRefactorTest.create_args(workspace=self.workspace_dir,
transition_tuples=transition_tuples,
analysis_year=None)
cbc.execute(args)
def test_duplicate_lulc_classes(self):
"""CBC: Raise an execption if duplicate lulc-classes."""
args = TestCBC2._create_model_args(self.workspace_dir)
args['workspace_dir'] = os.path.join(self.workspace_dir, 'workspace')
with open(args['biophysical_table_path'], 'r') as table:
lines = table.readlines()
with open(args['biophysical_table_path'], 'a') as table:
last_line_contents = lines[-1].strip().split(',')
last_line_contents[0] = '3' # assign a new code
table.write(','.join(last_line_contents))
with self.assertRaises(ValueError) as context:
coastal_blue_carbon.execute(args)
self.assertIn("`lulc-class` column must be unique",
str(context.exception))
def test_1_transition_passes(self):
"""Coastal Blue Carbon: Test model runs with only 1 transition.
This is a regression test addressing issue #3572
(see: https://bitbucket.org/natcap/invest/issues/3572)
"""
from natcap.invest.coastal_blue_carbon \
import coastal_blue_carbon as cbc
self.args['lulc_transition_maps_list'] = \
[self.args['lulc_transition_maps_list'][0]]
self.args['lulc_transition_years_list'] = \
[self.args['lulc_transition_years_list'][0]]
self.args['analysis_year'] = None
try:
cbc.execute(self.args)
except AttributeError as error:
LOGGER.exception("Here's the traceback encountered: %s" % error)
self.fail('CBC should not crash when only 1 transition provided')
def test_model_run(self):
"""Coastal Blue Carbon: Test run function in main model."""
from natcap.invest.coastal_blue_carbon \
import coastal_blue_carbon as cbc
self.args['suffix'] = 'xyz'
self.args['lulc_baseline_year'] = 2000
self.args['lulc_transition_years_list'] = [2005, 2010]
self.args['analysis_year'] = None
cbc.execute(self.args)
netseq_output_raster = os.path.join(
self.args['workspace_dir'],
'outputs_core/total_net_carbon_sequestration_test.tif')
npv_output_raster = os.path.join(
self.args['workspace_dir'],
'outputs_core/net_present_value_at_2010_test.tif')
netseq_array = _read_array(netseq_output_raster)
npv_array = _read_array(npv_output_raster)
# (Explanation for why netseq is 31.)
# LULC Code: Baseline: 1 --> Year 2000: 1, Year 2005: 2, Year 2010: 2
# Initial Stock from Baseline: 5+5=10
# Sequest:
# 2000-->2005: (1+1.1)*5=10.5, 2005-->2010: (2+2.1)*5=20.5
# Total: 10.5 + 20.5 = 31.
netseq_test = numpy.array([[cbc.NODATA_FLOAT, 31.], [31., 31.]])
npv_test = numpy.array([[cbc.NODATA_FLOAT, 60.27801514],
[60.27801514, 60.27801514]])
# just a simple regression test. this demonstrates that a NaN value
# will properly propagate across the model. the npv raster was chosen
# because the values are determined by multiple inputs, and any changes
# in those inputs would propagate to this raster.
numpy.testing.assert_allclose(netseq_array,
netseq_test,
rtol=0,
atol=1e-4)
numpy.testing.assert_allclose(npv_array, npv_test, rtol=0, atol=1e-4)
def test_binary(self):
"""Coastal Blue Carbon: Test CBC model against InVEST-Data."""
from natcap.invest.coastal_blue_carbon \
import coastal_blue_carbon as cbc
args = {
'workspace_dir': self.args['workspace_dir'],
'carbon_pool_initial_uri': os.path.join(
REGRESSION_DATA,
'outputs_preprocessor/carbon_pool_initial_sample.csv'),
'carbon_pool_transient_uri': os.path.join(
REGRESSION_DATA,
'outputs_preprocessor/carbon_pool_transient_sample.csv'),
'discount_rate': 6.0,
'do_economic_analysis': True,
'do_price_table': True,
'inflation_rate': 3.0,
'lulc_lookup_uri': os.path.join(
REGRESSION_DATA, 'inputs', 'lulc_lookup.csv'),
'lulc_baseline_map_uri': os.path.join(
REGRESSION_DATA, 'inputs/GBJC_2010_mean_Resample.tif'),
'lulc_baseline_year': 2010,
'lulc_transition_maps_list': [
os.path.join(
REGRESSION_DATA, 'inputs/GBJC_2030_mean_Resample.tif'),
os.path.join(
REGRESSION_DATA, 'inputs/GBJC_2050_mean_Resample.tif')],
'lulc_transition_years_list': [2030, 2050],
'price_table_uri': os.path.join(
REGRESSION_DATA, 'inputs/Price_table_SCC3.csv'),
'lulc_transition_matrix_uri': os.path.join(
REGRESSION_DATA, 'outputs_preprocessor/transitions_sample.csv'),
'price': 10.0,
'results_suffix': '150225'
}
cbc.execute(args)
npv_raster = os.path.join(
os.path.join(
args['workspace_dir'],
'outputs_core/net_present_value_at_2050_150225.tif'))
npv_array = _read_array(npv_raster)
# this is just a regression test, but it will capture all values
# in the net present value raster. the npv raster was chosen because
# the values are determined by multiple inputs, and any changes in
# those inputs would propagate to this raster.
u = numpy.unique(npv_array)
u.sort()
a = numpy.array([-76992.05, -40101.57, -34930., -34821.32,
0., 108.68, 6975.94, 7201.22, 7384.99],
dtype=numpy.float32)
a.sort()
numpy.testing.assert_array_almost_equal(u, a, decimal=2)
# walk through all files in the workspace and assert that outputs have
# the file suffix.
non_suffixed_files = []
for root_dir, dirnames, filenames in os.walk(self.args['workspace_dir']):
for filename in filenames:
if not filename.lower().endswith('.txt'): # ignore logfile
basename, extension = os.path.splitext(filename)
if not basename.endswith('_150225'):
path_rel_to_workspace = os.path.relpath(
os.path.join(root_dir, filename),
self.args['workspace_dir'])
non_suffixed_files.append(path_rel_to_workspace)
if non_suffixed_files:
self.fail('%s files are missing suffixes: %s' %
(len(non_suffixed_files),
pprint.pformat(non_suffixed_files)))
def test_model(self):
"""CBC: Test the model's execution."""
args = TestCBC2._create_model_args(self.workspace_dir)
args['workspace_dir'] = os.path.join(self.workspace_dir, 'workspace')
coastal_blue_carbon.execute(args)
# Sample values calculated by hand. Pixel 0 only accumulates. Pixel 1
# has no accumulation (per the biophysical table) and also has no
# emissions.
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
expected_sequestration_2010_to_2020 = numpy.array([[-176.9792, 73.5]],
dtype=numpy.float32)
raster_path = os.path.join(args['workspace_dir'], 'output',
('total-net-carbon-sequestration-between-'
'2010-and-2020.tif'))
try:
raster = gdal.OpenEx(raster_path)
numpy.testing.assert_allclose(raster.ReadAsArray(),
expected_sequestration_2010_to_2020,
rtol=1e-6)
finally:
raster = None
expected_sequestration_2020_to_2030 = numpy.array([[73.5, -28.698004]],
dtype=numpy.float32)
raster_path = os.path.join(args['workspace_dir'], 'output',
('total-net-carbon-sequestration-between-'
'2020-and-2030.tif'))
try:
raster = gdal.OpenEx(raster_path)
numpy.testing.assert_allclose(raster.ReadAsArray(),
expected_sequestration_2020_to_2030,
rtol=1e-6)
finally:
raster = None
# Total sequestration is the sum of all the previous sequestration.
expected_total_sequestration = (expected_sequestration_2000_to_2010 +
expected_sequestration_2010_to_2020 +
expected_sequestration_2020_to_2030)
raster_path = os.path.join(args['workspace_dir'], 'output',
'total-net-carbon-sequestration.tif')
try:
raster = gdal.OpenEx(raster_path)
numpy.testing.assert_allclose(raster.ReadAsArray(),
expected_total_sequestration,
rtol=1e-6)
finally:
raster = None
expected_net_present_value_at_2030 = numpy.array(
[[-373.67245, 837.93445]], dtype=numpy.float32)
raster_path = os.path.join(args['workspace_dir'], 'output',
'net-present-value-at-2030.tif')
try:
raster = gdal.OpenEx(raster_path)
numpy.testing.assert_allclose(raster.ReadAsArray(),
expected_net_present_value_at_2030,
rtol=1e-6)
finally:
raster = None
# For emissions, make sure that each of the emissions sum rasters in
# the output folder match the sum of all annual emissions from the time
# period.
for emissions_raster_path in glob.glob(
os.path.join(args['workspace_dir'], 'output',
'carbon-emissions-between-*.tif')):
try:
raster = gdal.OpenEx(emissions_raster_path)
band = raster.GetRasterBand(1)
emissions_in_period = band.ReadAsArray()
finally:
band = None
raster = None
basename = os.path.splitext(
os.path.basename(emissions_raster_path))[0]
parts = basename.split('-')
start_year = int(parts[3])
end_year = int(parts[5])
summed_emissions_over_time_period = numpy.array(
[[0.0, 0.0]], dtype=numpy.float32)
for year in range(start_year, end_year):
for pool in ('soil', 'biomass'):
yearly_emissions_raster = os.path.join(
args['workspace_dir'], 'intermediate',
f'emissions-{pool}-{year}.tif')
try:
raster = gdal.OpenEx(yearly_emissions_raster)
band = raster.GetRasterBand(1)
summed_emissions_over_time_period += band.ReadAsArray()
finally:
band = None
raster = None
numpy.testing.assert_allclose(emissions_in_period,
summed_emissions_over_time_period)
def test_model_no_analysis_year_no_price_table(self):
"""CBC: Test the model's execution."""
args = TestCBC2._create_model_args(self.workspace_dir)
args['workspace_dir'] = os.path.join(self.workspace_dir, 'workspace')
del args['analysis_year'] # final year is 2020.
args['use_price_table'] = False
args['inflation_rate'] = 5
args['price'] = 10.0
coastal_blue_carbon.execute(args)
# Sample values calculated by hand. Pixel 0 only accumulates. Pixel 1
# has no accumulation (per the biophysical table) and also has no
# emissions.
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
expected_sequestration_2010_to_2020 = numpy.array([[-176.9792, 73.5]],
dtype=numpy.float32)
raster_path = os.path.join(args['workspace_dir'], 'output',
('total-net-carbon-sequestration-between-'
'2010-and-2020.tif'))
try:
raster = gdal.OpenEx(raster_path)
numpy.testing.assert_allclose(raster.ReadAsArray(),
expected_sequestration_2010_to_2020,
rtol=1e-6)
finally:
raster = None
# Total sequestration is the sum of all the previous sequestration.
expected_total_sequestration = (expected_sequestration_2000_to_2010 +
expected_sequestration_2010_to_2020)
raster_path = os.path.join(args['workspace_dir'], 'output',
'total-net-carbon-sequestration.tif')
try:
raster = gdal.OpenEx(raster_path)
numpy.testing.assert_allclose(raster.ReadAsArray(),
expected_total_sequestration,
rtol=1e-6)
finally:
raster = None
expected_net_present_value_at_2020 = numpy.array(
[[-20506.314, 16123.521]], dtype=numpy.float32)
raster_path = os.path.join(args['workspace_dir'], 'output',
'net-present-value-at-2020.tif')
try:
raster = gdal.OpenEx(raster_path)
numpy.testing.assert_allclose(raster.ReadAsArray(),
expected_net_present_value_at_2020,
rtol=1e-6)
finally:
raster = None
