def test_no_aoi(self):
"""WindEnergy: testing base case w/o AOI, distances, or valuation."""
from natcap.invest import wind_energy
args = WindEnergyRegressionTests.generate_base_args(self.workspace_dir)
# Also test on input bathymetry that has equal x, y pixel sizes
args['bathymetry_path'] = os.path.join(
SAMPLE_DATA, 'resampled_global_dem_equal_pixel.tif')
wind_energy.execute(args)
raster_results = [
'density_W_per_m2.tif', 'harvested_energy_MWhr_per_yr.tif'
]
for raster_path in raster_results:
pygeoprocessing.testing.assert_rasters_equal(
os.path.join(args['workspace_dir'], 'output', raster_path),
os.path.join(REGRESSION_DATA, 'noaoi', raster_path), 1E-6)
vector_path = 'wind_energy_points.shp'
WindEnergyRegressionTests._assert_vectors_equal(
os.path.join(args['workspace_dir'], 'output', vector_path),
os.path.join(REGRESSION_DATA, 'noaoi', vector_path))
def test_no_distances(self):
"""WindEnergy: testing case w/ AOI and land poly, but w/o distances."""
from natcap.invest import wind_energy
args = WindEnergyRegressionTests.generate_base_args(self.workspace_dir)
args['aoi_vector_path'] = os.path.join(SAMPLE_DATA,
'New_England_US_Aoi.shp')
args['land_polygon_vector_path'] = os.path.join(
SAMPLE_DATA, 'simple_north_america_polygon.shp')
wind_energy.execute(args)
raster_results = [
'density_W_per_m2.tif', 'harvested_energy_MWhr_per_yr.tif'
]
for raster_path in raster_results:
pygeoprocessing.testing.assert_rasters_equal(
os.path.join(args['workspace_dir'], 'output', raster_path),
os.path.join(REGRESSION_DATA, 'nodistances', raster_path))
vector_path = 'wind_energy_points.shp'
WindEnergyRegressionTests._assert_vectors_equal(
os.path.join(args['workspace_dir'], 'output', vector_path),
os.path.join(REGRESSION_DATA, 'nodistances', vector_path))
def test_clip_vector_value_error(self):
"""WindEnergy: Test AOI doesn't intersect Wind Data points."""
from natcap.invest import wind_energy
from natcap.invest.utils import _assert_vectors_equal
args = WindEnergyRegressionTests.generate_base_args(self.workspace_dir)
args['aoi_vector_path'] = os.path.join(SAMPLE_DATA,
'New_England_US_Aoi.shp')
# Make up some Wind Data points that live outside AOI
wind_data_csv = os.path.join(args['workspace_dir'],
'temp-wind-data.csv')
with open(wind_data_csv, 'w') as open_table:
open_table.write('LONG,LATI,LAM,K,REF\n')
open_table.write('-60.5,25.0,7.59,2.6,10\n')
open_table.write('-59.5,24.0,7.59,2.6,10\n')
open_table.write('-58.5,24.5,7.59,2.6,10\n')
open_table.write('-58.95,24.95,7.59,2.6,10\n')
open_table.write('-57.95,24.95,7.59,2.6,10\n')
open_table.write('-57.95,25.95,7.59,2.6,10\n')
args['wind_data_path'] = wind_data_csv
# AOI and wind data should not overlap, leading to a ValueError in
# clip_vector_by_vector
with self.assertRaises(ValueError) as cm:
wind_energy.execute(args)
self.assertTrue(
"returned 0 features. If an AOI was" in str(cm.exception))
def test_no_land_polygon(self):
"""WindEnergy: testing case w/ AOI but w/o land poly or distances."""
from natcap.invest import wind_energy
from natcap.invest.utils import _assert_vectors_equal
args = WindEnergyRegressionTests.generate_base_args(self.workspace_dir)
args['aoi_vector_path'] = os.path.join(SAMPLE_DATA,
'New_England_US_Aoi.shp')
wind_energy.execute(args)
raster_results = [
'density_W_per_m2.tif', 'harvested_energy_MWhr_per_yr.tif'
]
for raster_path in raster_results:
model_array = pygeoprocessing.raster_to_numpy_array(
os.path.join(args['workspace_dir'], 'output', raster_path))
reg_array = pygeoprocessing.raster_to_numpy_array(
os.path.join(REGRESSION_DATA, 'nolandpoly', raster_path))
numpy.testing.assert_allclose(model_array, reg_array)
vector_path = 'wind_energy_points.shp'
_assert_vectors_equal(
os.path.join(args['workspace_dir'], 'output', vector_path),
os.path.join(REGRESSION_DATA, 'nolandpoly', vector_path))
def test_val_gridpts_windprice(self):
"""WindEnergy: testing Valuation w/ grid pts and wind price."""
from natcap.invest import wind_energy
from natcap.invest.utils import _assert_vectors_equal
args = WindEnergyRegressionTests.generate_base_args(self.workspace_dir)
args['aoi_vector_path'] = os.path.join(SAMPLE_DATA,
'New_England_US_Aoi.shp')
args['land_polygon_vector_path'] = os.path.join(
SAMPLE_DATA, 'simple_north_america_polygon.shp')
args['min_distance'] = 0
args['max_distance'] = 200000
args['valuation_container'] = True
args['foundation_cost'] = 2
args['discount_rate'] = 0.07
# Test that only grid points are provided in grid_points_path
args['grid_points_path'] = os.path.join(SAMPLE_DATA,
'resampled_grid_pts.csv')
args['price_table'] = False
args['wind_price'] = 0.187
args['rate_change'] = 0.2
wind_energy.execute(args)
# Make sure the output files were created.
vector_path = 'wind_energy_points.shp'
self.assertTrue(
os.path.exists(
os.path.join(args['workspace_dir'], 'output', vector_path)))
# Run through the model again, which should mean deleting shapefiles
# that have already been made, but which need to be created again.
wind_energy.execute(args)
raster_results = [
'carbon_emissions_tons.tif', 'levelized_cost_price_per_kWh.tif',
'npv_US_millions.tif'
]
for raster_path in raster_results:
model_array = pygeoprocessing.raster_to_numpy_array(
os.path.join(args['workspace_dir'], 'output', raster_path))
reg_array = pygeoprocessing.raster_to_numpy_array(
os.path.join(REGRESSION_DATA, 'pricevalgrid', raster_path))
numpy.testing.assert_allclose(model_array, reg_array)
vector_path = 'wind_energy_points.shp'
_assert_vectors_equal(
os.path.join(args['workspace_dir'], 'output', vector_path),
os.path.join(REGRESSION_DATA, 'pricevalgrid', vector_path))
def test_val_land_grid_points(self):
"""WindEnergy: testing Valuation w/ grid/land pts and wind price."""
from natcap.invest import wind_energy
from natcap.invest.utils import _assert_vectors_equal
args = WindEnergyRegressionTests.generate_base_args(self.workspace_dir)
args['aoi_vector_path'] = os.path.join(SAMPLE_DATA,
'New_England_US_Aoi.shp')
args['land_polygon_vector_path'] = os.path.join(
SAMPLE_DATA, 'simple_north_america_polygon.shp')
args['min_distance'] = 0
args['max_distance'] = 200000
args['valuation_container'] = True
args['foundation_cost'] = 2
args['discount_rate'] = 0.07
# there was no sample data that provided landing points, thus for
# testing, grid points in 'resampled_grid_pts.csv' were duplicated and
# marked as land points. So the distances will be zero, keeping the
# result the same but testing that section of code
args['grid_points_path'] = os.path.join(SAMPLE_DATA,
'resampled_grid_land_pts.csv')
args['price_table'] = False
args['wind_price'] = 0.187
args['rate_change'] = 0.2
wind_energy.execute(args)
raster_results = [
'carbon_emissions_tons.tif', 'levelized_cost_price_per_kWh.tif',
'npv_US_millions.tif'
]
for raster_path in raster_results:
model_array = pygeoprocessing.raster_to_numpy_array(
os.path.join(args['workspace_dir'], 'output', raster_path))
reg_array = pygeoprocessing.raster_to_numpy_array(
os.path.join(REGRESSION_DATA, 'pricevalgridland', raster_path))
# loosened tolerance to pass against GDAL 2.2.4 and 2.4.1
numpy.testing.assert_allclose(model_array, reg_array, rtol=1e-04)
vector_path = 'wind_energy_points.shp'
_assert_vectors_equal(
os.path.join(args['workspace_dir'], 'output', vector_path),
os.path.join(REGRESSION_DATA, 'pricevalgridland', vector_path))
def test_valuation_taskgraph(self):
"""WindEnergy: testing Valuation with async TaskGraph."""
from natcap.invest import wind_energy
from natcap.invest.utils import _assert_vectors_equal
args = WindEnergyRegressionTests.generate_base_args(self.workspace_dir)
# Also use an already projected bathymetry
args['bathymetry_path'] = os.path.join(
SAMPLE_DATA, 'resampled_global_dem_projected.tif')
args['aoi_vector_path'] = os.path.join(SAMPLE_DATA,
'New_England_US_Aoi.shp')
args['land_polygon_vector_path'] = os.path.join(
SAMPLE_DATA, 'simple_north_america_polygon.shp')
args['min_distance'] = 0
args['max_distance'] = 200000
args['valuation_container'] = True
args['foundation_cost'] = 2000000
args['discount_rate'] = 0.07
args['price_table'] = True
args['wind_schedule'] = os.path.join(SAMPLE_DATA,
'price_table_example.csv')
args['wind_price'] = 0.187
args['rate_change'] = 0.2
args['avg_grid_distance'] = 4
args['n_workers'] = 1
wind_energy.execute(args)
raster_results = [
'carbon_emissions_tons.tif', 'levelized_cost_price_per_kWh.tif',
'npv.tif'
]
for raster_path in raster_results:
model_array = pygeoprocessing.raster_to_numpy_array(
os.path.join(args['workspace_dir'], 'output', raster_path))
reg_array = pygeoprocessing.raster_to_numpy_array(
os.path.join(REGRESSION_DATA, 'priceval', raster_path))
numpy.testing.assert_allclose(model_array, reg_array, rtol=1e-6)
vector_path = 'wind_energy_points.shp'
_assert_vectors_equal(
os.path.join(args['workspace_dir'], 'output', vector_path),
os.path.join(REGRESSION_DATA, 'priceval', vector_path))
def test_val_no_grid_land_pts(self):
"""WindEnergy: testing Valuation without grid or land points."""
from natcap.invest import wind_energy
args = WindEnergyRegressionTests.generate_base_args(self.workspace_dir)
# Also use an already projected bathymetry
args['bathymetry_path'] = os.path.join(
SAMPLE_DATA, 'resampled_global_dem_projected.tif')
args['aoi_vector_path'] = os.path.join(SAMPLE_DATA,
'New_England_US_Aoi.shp')
args['land_polygon_vector_path'] = os.path.join(
SAMPLE_DATA, 'simple_north_america_polygon.shp')
args['min_distance'] = 0
args['max_distance'] = 200000
args['valuation_container'] = True
args['foundation_cost'] = 2
args['discount_rate'] = 0.07
args['price_table'] = True
args['wind_schedule'] = os.path.join(SAMPLE_DATA,
'price_table_example.csv')
args['wind_price'] = 0.187
args['rate_change'] = 0.2
args['avg_grid_distance'] = 4
wind_energy.execute(args)
raster_results = [
'carbon_emissions_tons.tif', 'levelized_cost_price_per_kWh.tif',
'npv_US_millions.tif'
]
for raster_path in raster_results:
pygeoprocessing.testing.assert_rasters_equal(
os.path.join(args['workspace_dir'], 'output', raster_path),
os.path.join(REGRESSION_DATA, 'priceval', raster_path), 1E-6)
vector_path = 'wind_energy_points.shp'
WindEnergyRegressionTests._assert_vectors_equal(
os.path.join(args['workspace_dir'], 'output', vector_path),
os.path.join(REGRESSION_DATA, 'priceval', vector_path))
