def test_crop_production_percentile_bad_crop(self):
"""Crop Production: test crop production with a bad crop name."""
from natcap.invest import crop_production_percentile
args = {
'workspace_dir':
self.workspace_dir,
'results_suffix':
'',
'landcover_raster_path':
os.path.join(SAMPLE_DATA_PATH, 'landcover.tif'),
'landcover_to_crop_table_path':
os.path.join(self.workspace_dir, 'landcover_to_badcrop_table.csv'),
'aggregate_polygon_path':
os.path.join(SAMPLE_DATA_PATH, 'aggregate_shape.shp'),
'model_data_path':
MODEL_DATA_PATH,
'n_workers':
'-1'
}
with open(args['landcover_to_crop_table_path'],
'w') as landcover_crop_table:
landcover_crop_table.write('crop_name,lucode\nfakecrop,20\n')
with self.assertRaises(ValueError):
crop_production_percentile.execute(args)
def test_crop_production_percentile_no_nodata(self):
"""Crop Production: test percentile model with undefined nodata raster.
Test with a landcover raster input that has no nodata value
defined.
"""
from natcap.invest import crop_production_percentile
args = {
'workspace_dir': self.workspace_dir,
'results_suffix': '',
'landcover_raster_path': os.path.join(
SAMPLE_DATA_PATH, 'landcover.tif'),
'landcover_to_crop_table_path': os.path.join(
SAMPLE_DATA_PATH, 'landcover_to_crop_table.csv'),
'model_data_path': MODEL_DATA_PATH,
'n_workers': '-1'
}
# Create a raster based on the test data geotransform, but smaller and
# with no nodata value defined.
base_lulc_info = pygeoprocessing.get_raster_info(
args['landcover_raster_path'])
base_geotransform = base_lulc_info['geotransform']
origin_x = base_geotransform[0]
origin_y = base_geotransform[3]
n = 9
gtiff_driver = gdal.GetDriverByName('GTiff')
raster_path = os.path.join(self.workspace_dir, 'small_raster.tif')
new_raster = gtiff_driver.Create(
raster_path, n, n, 1, gdal.GDT_Int32, options=[
'TILED=YES', 'BIGTIFF=YES', 'COMPRESS=LZW',
'BLOCKXSIZE=16', 'BLOCKYSIZE=16'])
new_raster.SetProjection(base_lulc_info['projection'])
new_raster.SetGeoTransform([origin_x, 1.0, 0.0, origin_y, 0.0, -1.0])
new_band = new_raster.GetRasterBand(1)
array = numpy.array(range(n*n), dtype=numpy.int32).reshape((n, n))
array.fill(20) # 20 is present in the landcover_to_crop_table
new_band.WriteArray(array)
new_raster.FlushCache()
new_band = None
new_raster = None
args['landcover_raster_path'] = raster_path
crop_production_percentile.execute(args)
result_table_path = os.path.join(
args['workspace_dir'], 'result_table.csv')
expected_result_table_path = os.path.join(
TEST_DATA_PATH, 'expected_result_table_no_nodata.csv')
expected_result_table = pandas.read_csv(
expected_result_table_path)
result_table = pandas.read_csv(
result_table_path)
pandas.testing.assert_frame_equal(
expected_result_table, result_table, check_dtype=False)
def test_crop_production_percentile_missing_climate_bin(self):
"""Crop Production: test crop percentile with a missing climate bin."""
from natcap.invest import crop_production_percentile
args = {
'workspace_dir':
self.workspace_dir,
'results_suffix':
'',
'landcover_raster_path':
os.path.join(SAMPLE_DATA_PATH, 'landcover.tif'),
'landcover_to_crop_table_path':
os.path.join(SAMPLE_DATA_PATH, 'landcover_to_crop_table.csv'),
'aggregate_polygon_path':
os.path.join(SAMPLE_DATA_PATH, 'aggregate_shape.shp'),
'model_data_path':
MODEL_DATA_PATH,
'n_workers':
'-1'
}
# copy model data directory to a temp location so that hard coded
# data paths can be altered for this test.
tmp_copy_model_data_path = os.path.join(self.workspace_dir,
'tmp_model_data')
shutil.copytree(MODEL_DATA_PATH, tmp_copy_model_data_path)
# remove a row from the wheat percentile yield table so that a wheat
# climate bin value is missing
climate_bin_wheat_table_path = os.path.join(
MODEL_DATA_PATH, 'climate_percentile_yield_tables',
'wheat_percentile_yield_table.csv')
bad_climate_bin_wheat_table_path = os.path.join(
tmp_copy_model_data_path, 'climate_percentile_yield_tables',
'wheat_percentile_yield_table.csv')
os.remove(bad_climate_bin_wheat_table_path)
table_df = pandas.read_csv(climate_bin_wheat_table_path)
table_df = table_df[table_df['climate_bin'] != 40]
table_df.to_csv(bad_climate_bin_wheat_table_path)
table_df = None
args['model_data_path'] = tmp_copy_model_data_path
with self.assertRaises(ValueError) as context:
crop_production_percentile.execute(args)
self.assertTrue(
"The missing values found in the wheat Climate Bin raster but not"
" the table are: [40]" in str(context.exception))
def test_crop_production_percentile(self):
"""Crop Production: test crop production percentile regression."""
from natcap.invest import crop_production_percentile
args = {
'workspace_dir':
self.workspace_dir,
'results_suffix':
'',
'landcover_raster_path':
os.path.join(SAMPLE_DATA_PATH, 'landcover.tif'),
'landcover_to_crop_table_path':
os.path.join(SAMPLE_DATA_PATH, 'landcover_to_crop_table.csv'),
'aggregate_polygon_path':
os.path.join(SAMPLE_DATA_PATH, 'aggregate_shape.shp'),
'model_data_path':
MODEL_DATA_PATH,
'n_workers':
'-1'
}
crop_production_percentile.execute(args)
agg_result_table_path = os.path.join(args['workspace_dir'],
'aggregate_results.csv')
expected_agg_result_table_path = os.path.join(
TEST_DATA_PATH, 'expected_aggregate_results.csv')
expected_agg_result_table = pandas.read_csv(
expected_agg_result_table_path)
agg_result_table = pandas.read_csv(agg_result_table_path)
pandas.testing.assert_frame_equal(expected_agg_result_table,
agg_result_table,
check_dtype=False)
result_table_path = os.path.join(args['workspace_dir'],
'result_table.csv')
expected_result_table_path = os.path.join(TEST_DATA_PATH,
'expected_result_table.csv')
expected_result_table = pandas.read_csv(expected_result_table_path)
result_table = pandas.read_csv(result_table_path)
pandas.testing.assert_frame_equal(expected_result_table,
result_table,
check_dtype=False)
def test_crop_production_percentile(self):
"""Crop Production: test crop production percentile regression."""
from natcap.invest import crop_production_percentile
args = {
'workspace_dir': self.workspace_dir,
'results_suffix': '',
'landcover_raster_path': os.path.join(
SAMPLE_DATA_PATH, 'landcover.tif'),
'landcover_to_crop_table_path': os.path.join(
SAMPLE_DATA_PATH, 'landcover_to_crop_table.csv'),
'aggregate_polygon_path': os.path.join(
SAMPLE_DATA_PATH, 'aggregate_shape.shp'),
'aggregate_polygon_id': 'id',
'model_data_path': MODEL_DATA_PATH
}
crop_production_percentile.execute(args)
result_table_path = os.path.join(
args['workspace_dir'], 'aggregate_results.csv')
expected_result_table_path = os.path.join(
TEST_DATA_PATH, 'expected_aggregate_results.csv')
pygeoprocessing.testing.assert_csv_equal(
expected_result_table_path, result_table_path)
