add unit tests to crop production (regression)
This commit is contained in:
Claire Simpson
parent
fa954b9275
commit
570ecbf2a5
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@ -5,9 +5,10 @@ import shutil
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import os
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import numpy
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from osgeo import gdal
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from osgeo import gdal, ogr, osr
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import pandas
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import pygeoprocessing
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from shapely.geometry import Polygon
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gdal.UseExceptions()
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MODEL_DATA_PATH = os.path.join(
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@ -21,6 +22,108 @@ TEST_DATA_PATH = os.path.join(
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'crop_production_model')
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def make_aggregate_vector(path_to_shp):
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"""
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Generate shapefile with two overlapping polygons
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Args:
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path_to_shp (str): path to store watershed results vector
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Outputs:
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None
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"""
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# (xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax)
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shapely_geometry_list = [
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Polygon([(461151, 4923265-50), (461261+50, 4923265-50),
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(461261+50, 4923265), (461151, 4923265)]),
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Polygon([(461261, 4923265-35), (461261+60, 4923265-35),
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(461261+60, 4923265+50), (461261, 4923265+50)])
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]
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srs = osr.SpatialReference()
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srs.ImportFromEPSG(26910)
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projection_wkt = srs.ExportToWkt()
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vector_format = "ESRI Shapefile"
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fields = {"id": ogr.OFTReal}
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attribute_list = [
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{"id": 0},
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{"id": 1},
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]
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pygeoprocessing.shapely_geometry_to_vector(shapely_geometry_list,
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path_to_shp, projection_wkt,
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vector_format, fields,
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attribute_list)
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def make_simple_raster(base_raster_path, array):
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"""Create a raster on designated path with arbitrary values.
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Args:
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base_raster_path (str): the raster path for making the new raster.
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Returns:
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Non
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"""
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# UTM Zone 10N
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srs = osr.SpatialReference()
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srs.ImportFromEPSG(26910)
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projection_wkt = srs.ExportToWkt()
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origin = (461251, 4923245)
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pixel_size = (30, 30)
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no_data = -1
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pygeoprocessing.numpy_array_to_raster(
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array, no_data, pixel_size, origin, projection_wkt,
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base_raster_path)
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def create_nutrient_df():
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"""Creates a nutrient DataFrame for testing."""
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return pandas.DataFrame([
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{'crop': 'corn', 'area (ha)': 21.0, 'production_observed': 0.2,
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'percentrefuse': 7, 'protein': 42., 'lipid': 8, 'energy': 476.,
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'ca': 27.0, 'fe': 15.7, 'mg': 280.0, 'ph': 704.0, 'k': 1727.0,
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'na': 2.0, 'zn': 4.9, 'cu': 1.9, 'fl': 8, 'mn': 2.9, 'se': 0.1,
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'vita': 3.0, 'betac': 16.0, 'alphac': 2.30, 'vite': 0.8,
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'crypto': 1.6, 'lycopene': 0.36, 'lutein': 63.0, 'betat': 0.5,
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'gammat': 2.1, 'deltat': 1.9, 'vitc': 6.8, 'thiamin': 0.4,
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'riboflavin': 1.8, 'niacin': 8.2, 'pantothenic': 0.9,
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'vitb6': 1.4, 'folate': 385.0, 'vitb12': 2.0, 'vitk': 41.0},
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{'crop': 'soybean', 'area (ha)': 5., 'production_observed': 4.,
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'percentrefuse': 9, 'protein': 33., 'lipid': 2., 'energy': 99.,
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'ca': 257., 'fe': 15.7, 'mg': 280., 'ph': 704.0, 'k': 197.0,
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'na': 2., 'zn': 4.9, 'cu': 1.6, 'fl': 3., 'mn': 5.2, 'se': 0.3,
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'vita': 3.0, 'betac': 16.0, 'alphac': 1.0, 'vite': 0.8,
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'crypto': 0.6, 'lycopene': 0.3, 'lutein': 61.0, 'betat': 0.5,
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'gammat': 2.3, 'deltat': 1.2, 'vitc': 3.0, 'thiamin': 0.42,
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'riboflavin': 0.82, 'niacin': 12.2, 'pantothenic': 0.92,
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'vitb6': 5.4, 'folate': 305., 'vitb12': 3., 'vitk': 42.},
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]).set_index('crop')
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def _create_crop_rasters(output_dir, crop_names, file_suffix):
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"""Creates raster files for test setup."""
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_OBSERVED_PRODUCTION_FILE_PATTERN = os.path.join(
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'.', '%s_observed_production%s.tif')
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_CROP_PRODUCTION_FILE_PATTERN = os.path.join(
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'.', '%s_regression_production%s.tif')
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for i, crop in enumerate(crop_names):
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observed_yield_path = os.path.join(
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output_dir,
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_OBSERVED_PRODUCTION_FILE_PATTERN % (crop, file_suffix))
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crop_production_raster_path = os.path.join(
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output_dir,
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_CROP_PRODUCTION_FILE_PATTERN % (crop, file_suffix))
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# Create arbitrary raster arrays
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observed_array = numpy.array([[4, i], [i*3, 4]], dtype=numpy.int16)
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crop_array = numpy.array([[i, 1], [i*2, 3]], dtype=numpy.int16)
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make_simple_raster(observed_yield_path, observed_array)
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make_simple_raster(crop_production_raster_path, crop_array)
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class CropProductionTests(unittest.TestCase):
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"""Tests for the Crop Production model."""
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@ -390,6 +493,304 @@ class CropProductionTests(unittest.TestCase):
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pandas.testing.assert_frame_equal(
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expected_result_table, result_table, check_dtype=False)
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def test_x_yield_op(self):
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"""Test `_x_yield_op"""
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from natcap.invest.crop_production_regression import _x_yield_op
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# make fake data
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y_max = numpy.array([[-1, 3, 2], [4, 5, 3]])
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b_x = numpy.array([[4, 3, 2], [2, 0, 3]])
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c_x = numpy.array([[4, 1, 2], [3, 0, 3]])
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lulc_array = numpy.array([[3, 3, 2], [3, -1, 3]])
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fert_rate = 0.6
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crop_lucode = 3
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pixel_area_ha = 10
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actual_result = _x_yield_op(y_max, b_x, c_x, lulc_array, fert_rate,
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crop_lucode, pixel_area_ha)
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expected_result = numpy.array([[-1, -19.393047, -1],
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[26.776089, -1, 15.1231]])
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numpy.testing.assert_allclose(actual_result, expected_result)
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def test_zero_observed_yield_op(self):
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"""Test `_zero_observed_yield_op`"""
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from natcap.invest.crop_production_regression import \
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_zero_observed_yield_op
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# make fake data
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observed_yield_array = numpy.array([[0, 1, -1], [5, 6, -1]])
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observed_yield_nodata = -1
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actual_result = _zero_observed_yield_op(observed_yield_array,
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observed_yield_nodata)
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expected_result = numpy.array([[0, 1, 0], [5, 6, 0]])
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numpy.testing.assert_allclose(actual_result, expected_result)
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def test_mask_observed_yield_op(self):
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"""Test `_mask_observed_yield_op`"""
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from natcap.invest.crop_production_regression import \
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_mask_observed_yield_op
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# make fake data
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lulc_array = numpy.array([[3, 5, -9999], [3, 3, -1]])
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observed_yield_array = numpy.array([[-1, 5, 4], [8, -9999, 91]])
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observed_yield_nodata = -1
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# note: this observed_yield_nodata value becomes the nodata value in
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# the output array but the values in the observed_yield_array with
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# this value are NOT treated as no data within this function
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landcover_nodata = -9999
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crop_lucode = 3
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pixel_area_ha = 10
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actual_result = _mask_observed_yield_op(
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lulc_array, observed_yield_array, observed_yield_nodata,
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landcover_nodata, crop_lucode, pixel_area_ha)
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expected_result = numpy.array([[-10, 0, -1], [80, -99990, 0]])
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numpy.testing.assert_allclose(actual_result, expected_result)
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def test_tabulate_regression_results(self):
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"""Test `tabulate_regression_results`"""
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from natcap.invest.crop_production_regression import \
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tabulate_regression_results
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def _create_expected_results():
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"""Creates the expected results DataFrame."""
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return pandas.DataFrame([
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{'crop': 'corn', 'area (ha)': 20.0,
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'production_observed': 8.0, 'production_modeled': 4.0,
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'protein_modeled': 1562400.0, 'protein_observed': 3124800.0,
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'lipid_modeled': 297600.0, 'lipid_observed': 595200.0,
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'energy_modeled': 17707200.0, 'energy_observed': 35414400.0,
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'ca_modeled': 1004400.0, 'ca_observed': 2008800.0,
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'fe_modeled': 584040.0, 'fe_observed': 1168080.0,
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'mg_modeled': 10416000.0, 'mg_observed': 20832000.0,
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'ph_modeled': 26188800.0, 'ph_observed': 52377600.0,
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'k_modeled': 64244400.0, 'k_observed': 128488800.0,
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'na_modeled': 74400.0, 'na_observed': 148800.0,
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'zn_modeled': 182280.0, 'zn_observed': 364560.0,
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'cu_modeled': 70680.0, 'cu_observed': 141360.0,
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'fl_modeled': 297600.0, 'fl_observed': 595200.0,
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'mn_modeled': 107880.0, 'mn_observed': 215760.0,
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'se_modeled': 3720.0, 'se_observed': 7440.0,
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'vita_modeled': 111600.0, 'vita_observed': 223200.0,
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'betac_modeled': 595200.0, 'betac_observed': 1190400.0,
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'alphac_modeled': 85560.0, 'alphac_observed': 171120.0,
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'vite_modeled': 29760.0, 'vite_observed': 59520.0,
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'crypto_modeled': 59520.0, 'crypto_observed': 119040.0,
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'lycopene_modeled': 13392.0, 'lycopene_observed': 26784.0,
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'lutein_modeled': 2343600.0, 'lutein_observed': 4687200.0,
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'betat_modeled': 18600.0, 'betat_observed': 37200.0,
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'gammat_modeled': 78120.0, 'gammat_observed': 156240.0,
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'deltat_modeled': 70680.0, 'deltat_observed': 141360.0,
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'vitc_modeled': 252960.0, 'vitc_observed': 505920.0,
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'thiamin_modeled': 14880.0, 'thiamin_observed': 29760.0,
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'riboflavin_modeled': 66960.0, 'riboflavin_observed': 133920.0,
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'niacin_modeled': 305040.0, 'niacin_observed': 610080.0,
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'pantothenic_modeled': 33480.0, 'pantothenic_observed': 66960.0,
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'vitb6_modeled': 52080.0, 'vitb6_observed': 104160.0,
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'folate_modeled': 14322000.0, 'folate_observed': 28644000.0,
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'vitb12_modeled': 74400.0, 'vitb12_observed': 148800.0,
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'vitk_modeled': 1525200.0, 'vitk_observed': 3050400.0},
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{'crop': 'soybean', 'area (ha)': 40.0,
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'production_observed': 12.0, 'production_modeled': 7.0,
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'protein_modeled': 2102100.0, 'protein_observed': 3603600.0,
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'lipid_modeled': 127400.0, 'lipid_observed': 218400.0,
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'energy_modeled': 6306300.0, 'energy_observed': 10810800.0,
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'ca_modeled': 16370900.0, 'ca_observed': 28064400.0,
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'fe_modeled': 1000090.0, 'fe_observed': 1714440.0,
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'mg_modeled': 17836000.0, 'mg_observed': 30576000.0,
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'ph_modeled': 44844800.0, 'ph_observed': 76876800.0,
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'k_modeled': 12548900.0, 'k_observed': 21512400.0,
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'na_modeled': 127400.0, 'na_observed': 218400.0,
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'zn_modeled': 312130.0, 'zn_observed': 535080.0,
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'cu_modeled': 101920.0, 'cu_observed': 174720.0,
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'fl_modeled': 191100.0, 'fl_observed': 327600.0,
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'mn_modeled': 331240.0, 'mn_observed': 567840.0,
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'se_modeled': 19110.0, 'se_observed': 32760.0,
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'vita_modeled': 191100.0, 'vita_observed': 327600.0,
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'betac_modeled': 1019200.0, 'betac_observed': 1747200.0,
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'alphac_modeled': 63700.0, 'alphac_observed': 109200.0,
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'vite_modeled': 50960.0, 'vite_observed': 87360.0,
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'crypto_modeled': 38220.0, 'crypto_observed': 65520.0,
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'lycopene_modeled': 19110.0, 'lycopene_observed': 32760.0,
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'lutein_modeled': 3885700.0, 'lutein_observed': 6661200.0,
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'betat_modeled': 31850.0, 'betat_observed': 54600.0,
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'gammat_modeled': 146510.0, 'gammat_observed': 251160.0,
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'deltat_modeled': 76440.0, 'deltat_observed': 131040.0,
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'vitc_modeled': 191100.0, 'vitc_observed': 327600.0,
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'thiamin_modeled': 26754.0, 'thiamin_observed': 45864.0,
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'riboflavin_modeled': 52234.0, 'riboflavin_observed': 89544.0,
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'niacin_modeled': 777140.0, 'niacin_observed': 1332240.0,
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'pantothenic_modeled': 58604.0, 'pantothenic_observed': 100464.0,
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'vitb6_modeled': 343980.0, 'vitb6_observed': 589680.0,
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'folate_modeled': 19428500.0, 'folate_observed': 33306000.0,
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'vitb12_modeled': 191100.0, 'vitb12_observed': 327600.0,
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'vitk_modeled': 2675400.0, 'vitk_observed': 4586400.0}])
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nutrient_df = create_nutrient_df()
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pixel_area_ha = 10
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workspace_dir = self.workspace_dir
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output_dir = os.path.join(workspace_dir, "OUTPUT")
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os.makedirs(output_dir, exist_ok=True)
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landcover_raster_path = os.path.join(workspace_dir, "landcover.tif")
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landcover_nodata = -1
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make_simple_raster(landcover_raster_path,
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numpy.array([[2, 1], [2, 3]], dtype=numpy.int16))
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file_suffix = "v1"
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target_table_path = os.path.join(workspace_dir, "output_table.csv")
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crop_names = ["corn", "soybean"]
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_create_crop_rasters(output_dir, crop_names, file_suffix)
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tabulate_regression_results(
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nutrient_df, crop_names, pixel_area_ha,
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landcover_raster_path, landcover_nodata,
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output_dir, file_suffix, target_table_path
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)
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# Read only the first 2 crop's data (skipping total area)
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actual_result_table = pandas.read_csv(target_table_path, nrows=2,
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header=0)
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expected_result_table = _create_expected_results()
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# Compare expected vs actual
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pandas.testing.assert_frame_equal(actual_result_table,
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expected_result_table)
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def test_aggregate_regression_results_to_polygons(self):
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"""Test `aggregate_regression_results_to_polygons`"""
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from natcap.invest.crop_production_regression import \
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aggregate_regression_results_to_polygons
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def _create_expected_agg_table():
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"""Create expected output results"""
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# Define the new values manually
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return pandas.DataFrame([
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{"FID": 0, "corn_modeled": 1, "corn_observed": 4,
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"soybean_modeled": 2, "soybean_observed": 5,
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"protein_modeled": 991200, "protein_observed": 3063900,
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"lipid_modeled": 110800, "lipid_observed": 388600,
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"energy_modeled": 6228600, "energy_observed": 22211700,
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"ca_modeled": 4928500, "ca_observed": 12697900,
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"fe_modeled": 431750, "fe_observed": 1298390,
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"mg_modeled": 7700000, "mg_observed": 23156000,
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"ph_modeled": 19360000, "ph_observed": 58220800,
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"k_modeled": 19646500, "k_observed": 73207900,
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"na_modeled": 55000, "na_observed": 165400,
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"zn_modeled": 134750, "zn_observed": 405230,
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"cu_modeled": 46790, "cu_observed": 143480,
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"fl_modeled": 129000, "fl_observed": 434100,
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"mn_modeled": 121610, "mn_observed": 344480,
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"se_modeled": 6390, "se_observed": 17370,
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"vita_modeled": 82500, "vita_observed": 248100,
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"betac_modeled": 440000, "betac_observed": 1323200,
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"alphac_modeled": 39590, "alphac_observed": 131060,
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"vite_modeled": 22000, "vite_observed": 66160,
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"crypto_modeled": 25800, "crypto_observed": 86820,
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"lycopene_modeled": 8808, "lycopene_observed": 27042,
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"lutein_modeled": 1696100, "lutein_observed": 5119100,
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"betat_modeled": 13750, "betat_observed": 41350,
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"gammat_modeled": 61390, "gammat_observed": 182770,
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"deltat_modeled": 39510, "deltat_observed": 125280,
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"vitc_modeled": 117840, "vitc_observed": 389460,
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"thiamin_modeled": 11364, "thiamin_observed": 33990,
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"riboflavin_modeled": 31664, "riboflavin_observed": 104270,
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"niacin_modeled": 298300, "niacin_observed": 860140,
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"pantothenic_modeled": 25114, "pantothenic_observed": 75340,
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"vitb6_modeled": 111300, "vitb6_observed": 297780,
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"folate_modeled": 9131500, "folate_observed": 28199500,
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"vitb12_modeled": 73200, "vitb12_observed": 210900,
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"vitk_modeled": 1145700, "vitk_observed": 3436200},
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{"FID": 1, "corn_modeled": 4, "corn_observed": 8,
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"soybean_modeled": 7, "soybean_observed": 12,
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"protein_modeled": 3664500, "protein_observed": 6728400,
|
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"lipid_modeled": 425000, "lipid_observed": 813600,
|
||||
"energy_modeled": 24013500, "energy_observed": 46225200,
|
||||
"ca_modeled": 17375300, "ca_observed": 30073200,
|
||||
"fe_modeled": 1584130, "fe_observed": 2882520,
|
||||
"mg_modeled": 28252000, "mg_observed": 51408000,
|
||||
"ph_modeled": 71033600, "ph_observed": 129254400,
|
||||
"k_modeled": 76793300, "k_observed": 150001200,
|
||||
"na_modeled": 201800, "na_observed": 367200,
|
||||
"zn_modeled": 494410, "zn_observed": 899640,
|
||||
"cu_modeled": 172600, "cu_observed": 316080,
|
||||
"fl_modeled": 488700, "fl_observed": 922800,
|
||||
"mn_modeled": 439120, "mn_observed": 783600,
|
||||
"se_modeled": 22830, "se_observed": 40200,
|
||||
"vita_modeled": 302700, "vita_observed": 550800,
|
||||
"betac_modeled": 1614400, "betac_observed": 2937600,
|
||||
"alphac_modeled": 149260, "alphac_observed": 280320,
|
||||
"vite_modeled": 80720, "vite_observed": 146880,
|
||||
"crypto_modeled": 97740, "crypto_observed": 184560,
|
||||
"lycopene_modeled": 32502, "lycopene_observed": 59544,
|
||||
"lutein_modeled": 6229300, "lutein_observed": 11348400,
|
||||
"betat_modeled": 50450, "betat_observed": 91800,
|
||||
"gammat_modeled": 224630, "gammat_observed": 407400,
|
||||
"deltat_modeled": 147120, "deltat_observed": 272400,
|
||||
"vitc_modeled": 444060, "vitc_observed": 833520,
|
||||
"thiamin_modeled": 41634, "thiamin_observed": 75624,
|
||||
"riboflavin_modeled": 119194, "riboflavin_observed": 223464,
|
||||
"niacin_modeled": 1082180, "niacin_observed": 1942320,
|
||||
"pantothenic_modeled": 92084, "pantothenic_observed": 167424,
|
||||
"vitb6_modeled": 396060, "vitb6_observed": 693840,
|
||||
"folate_modeled": 33750500, "folate_observed": 61950000,
|
||||
"vitb12_modeled": 265500, "vitb12_observed": 476400,
|
||||
"vitk_modeled": 4200600, "vitk_observed": 7636800}
|
||||
], dtype=float)
|
||||
|
||||
workspace = "/Users/simpson2/Desktop/output_test_cp"
|
||||
|
||||
base_aggregate_vector_path = os.path.join(workspace,
|
||||
"agg_vector.shp")
|
||||
make_aggregate_vector(base_aggregate_vector_path)
|
||||
|
||||
target_aggregate_vector_path = os.path.join(workspace,
|
||||
"agg_vector_prj.shp")
|
||||
|
||||
spatial_ref = osr.SpatialReference()
|
||||
spatial_ref.ImportFromEPSG(26910) # EPSG:4326 for WGS84
|
||||
landcover_raster_projection = spatial_ref.ExportToWkt()
|
||||
|
||||
crop_names = ['corn', 'soybean']
|
||||
nutrient_df = create_nutrient_df()
|
||||
output_dir = os.path.join(workspace, "OUTPUT")
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
file_suffix = 'test'
|
||||
target_aggregate_table_path = '' # unused
|
||||
|
||||
_create_crop_rasters(output_dir, crop_names, file_suffix)
|
||||
|
||||
aggregate_regression_results_to_polygons(
|
||||
base_aggregate_vector_path, target_aggregate_vector_path,
|
||||
landcover_raster_projection, crop_names,
|
||||
nutrient_df, output_dir, file_suffix,
|
||||
target_aggregate_table_path)
|
||||
|
||||
_AGGREGATE_TABLE_FILE_PATTERN = os.path.join(
|
||||
'.','aggregate_results%s.csv')
|
||||
|
||||
aggregate_table_path = os.path.join(
|
||||
output_dir, _AGGREGATE_TABLE_FILE_PATTERN % file_suffix)
|
||||
|
||||
actual_aggregate_table = pandas.read_csv(aggregate_table_path,
|
||||
dtype=float)
|
||||
print(actual_aggregate_table)
|
||||
|
||||
expected_aggregate_table = _create_expected_agg_table()
|
||||
|
||||
pandas.testing.assert_frame_equal(
|
||||
actual_aggregate_table, expected_aggregate_table)
|
||||
|
||||
|
||||
|
||||
class CropValidationTests(unittest.TestCase):
|
||||
"""Tests for the Crop Productions' MODEL_SPEC and validation."""
|
||||
|
|
|
|||
Loading…
Reference in New Issue