Merge pull request #75 from wri/CIF-247-Increase-unit-test-coverage-f…

…or-CIF

Cif 247 increase unit test coverage for cif

city_metrix/layers/impervious_surface.py

@@ -7,8 +7,14 @@
 
 
 class ImperviousSurface(Layer):
-    def __init__(self, **kwargs):
+    """
+    Attributes:
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
+    """
+
+    def __init__(self, spatial_resolution=100, **kwargs):
         super().__init__(**kwargs)
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
         # load impervious_surface
@@ -19,5 +25,5 @@ def get_data(self, bbox):
                                          .sum()
                                          )
 
-        data = get_image_collection(imperv_surf, bbox, 100, "imperv surf")
+        data = get_image_collection(imperv_surf, bbox, self.spatial_resolution, "imperv surf")
         return data.change_year_index

city_metrix/layers/ndvi_sentinel2_gee.py

@@ -12,7 +12,7 @@ class NdviSentinel2(Layer):
     Notebook: https://github.com/wri/cities-cities4forests-indicators/blob/dev-eric/scripts/extract-VegetationCover.ipynb
     Reference: https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index
     """
-    def __init__(self, year=None, spatial_resolution=10, **kwargs):
+    def __init__(self, year=2021, spatial_resolution=10, **kwargs):
         super().__init__(**kwargs)
         self.year = year
         self.spatial_resolution = spatial_resolution

tests/resources/layer_dumps_for_br_lauro_de_freitas/conftest.py

@@ -26,15 +26,16 @@
 def pytest_configure(config):
     qgis_project_file = 'layers_for_br_lauro_de_freitas.qgz'
 
-    source_folder = os.path.dirname(__file__)
-    target_folder = get_target_folder_path()
-    create_target_folder(target_folder, True)
+    if RUN_DUMPS is True:
+        source_folder = os.path.dirname(__file__)
+        target_folder = get_target_folder_path()
+        create_target_folder(target_folder, True)
 
-    source_qgis_file = os.path.join(source_folder, qgis_project_file)
-    target_qgis_file = os.path.join(target_folder, qgis_project_file)
-    shutil.copyfile(source_qgis_file, target_qgis_file)
+        source_qgis_file = os.path.join(source_folder, qgis_project_file)
+        target_qgis_file = os.path.join(target_folder, qgis_project_file)
+        shutil.copyfile(source_qgis_file, target_qgis_file)
 
-    print("\n\033[93m QGIS project file and layer files written to folder %s.\033[0m\n" % target_folder)
+        print("\n\033[93m QGIS project file and layer files written to folder %s.\033[0m\n" % target_folder)
 
 @pytest.fixture
 def target_folder():

tests/resources/layer_dumps_for_br_lauro_de_freitas/test_write_layers_to_qgis_files.py

@@ -21,7 +21,7 @@
     TreeCanopyHeight,
     TreeCover,
     UrbanLandUse,
-    WorldPop, Layer
+    WorldPop, Layer, ImperviousSurface
 )
 from .conftest import RUN_DUMPS, prep_output_path, verify_file_is_populated
 from ...tools.general_tools import get_class_default_spatial_resolution
@@ -62,6 +62,13 @@ def test_write_high_land_surface_temperature(target_folder, bbox_info, target_sp
     HighLandSurfaceTemperature(spatial_resolution=target_resolution).write(bbox_info.bounds, file_path, tile_degrees=None)
     assert verify_file_is_populated(file_path)
 
+@pytest.mark.skipif(RUN_DUMPS == False, reason='Skipping since RUN_DUMPS set to False')
+def test_write_impervious_surface(target_folder, bbox_info, target_spatial_resolution_multiplier):
+    file_path = prep_output_path(target_folder, 'impervious_surface.tif')
+    target_resolution = target_spatial_resolution_multiplier * get_class_default_spatial_resolution(ImperviousSurface())
+    LandSurfaceTemperature(spatial_resolution=target_resolution).write(bbox_info.bounds, file_path, tile_degrees=None)
+    assert verify_file_is_populated(file_path)
+
 @pytest.mark.skipif(RUN_DUMPS == False, reason='Skipping since RUN_DUMPS set to False')
 def test_write_land_surface_temperature(target_folder, bbox_info, target_spatial_resolution_multiplier):
     file_path = prep_output_path(target_folder, 'land_surface_temperature.tif')
@@ -100,7 +107,7 @@ def test_write_ndvi_sentinel2_gee(target_folder, bbox_info, target_spatial_resol
 
 @pytest.mark.skipif(RUN_DUMPS == False, reason='Skipping since RUN_DUMPS set to False')
 def test_write_openbuildings(target_folder, bbox_info, target_spatial_resolution_multiplier):
-    file_path = prep_output_path(target_folder, 'open_buildings.tif')
+    file_path = prep_output_path(target_folder, 'open_buildings.geojson')
     OpenBuildings(bbox_info.country).write(bbox_info.bounds, file_path, tile_degrees=None)
     assert verify_file_is_populated(file_path)
 
@@ -113,7 +120,7 @@ def test_write_openbuildings(target_folder, bbox_info, target_spatial_resolution
 
 @pytest.mark.skipif(RUN_DUMPS == False, reason='Skipping since RUN_DUMPS set to False')
 def test_write_overture_buildings(target_folder, bbox_info, target_spatial_resolution_multiplier):
-    file_path = prep_output_path(target_folder, 'overture_buildings.tif')
+    file_path = prep_output_path(target_folder, 'overture_buildings.geojson')
     OvertureBuildings().write(bbox_info.bounds, file_path, tile_degrees=None)
     assert verify_file_is_populated(file_path)
 

tests/test_layer_metrics.py

@@ -0,0 +1,106 @@
+import ee
+import pytest
+
+from city_metrix.layers import NdviSentinel2, TreeCover, Albedo, AlosDSM
+from tests.resources.bbox_constants import BBOX_BRA_LAURO_DE_FREITAS_1
+from city_metrix.layers.layer import get_image_collection
+
+EE_IMAGE_DIMENSION_TOLERANCE = 1  # Tolerance compensates for variable results from GEE service
+COUNTRY_CODE_FOR_BBOX = 'BRA'
+BBOX = BBOX_BRA_LAURO_DE_FREITAS_1
+
+'''
+Evalues various metrics of returned datasets, such as max value, min value, mean value, and 
+array-dimension size.
+'''
+
+def test_read_image_collection():
+    ic = ee.ImageCollection("ESA/WorldCover/v100")
+    data = get_image_collection(ic, BBOX, 10, "test")
+
+    expected_crs = 32724
+    expected_x_size = 186
+    expected_y_size = 199
+
+    actual_crs = data.rio.crs
+    actual_x_size = data['x'].size
+    actual_y_size = data['y'].size
+
+    assert expected_crs == actual_crs
+    assert (
+        pytest.approx(expected_x_size, rel=EE_IMAGE_DIMENSION_TOLERANCE) == actual_x_size,
+        pytest.approx(expected_y_size, rel=EE_IMAGE_DIMENSION_TOLERANCE) == actual_y_size
+    )
+
+def test_read_image_collection_scale():
+    ic = ee.ImageCollection("ESA/WorldCover/v100")
+    data = get_image_collection(ic, BBOX, 100, "test")
+
+    expected_x_size = 19
+    expected_y_size = 20
+
+    actual_x_size = data['x'].size
+    actual_y_size = data['y'].size
+
+    assert (
+        pytest.approx(expected_x_size, rel=EE_IMAGE_DIMENSION_TOLERANCE) == actual_x_size,
+        pytest.approx(expected_y_size, rel=EE_IMAGE_DIMENSION_TOLERANCE) == actual_y_size
+    )
+
+def test_albedo_metrics():
+    data = Albedo().get_data(BBOX)
+
+    # Bounding values
+    expected_min_value = _convert_fraction_to_rounded_percent(0.03)
+    expected_max_value = _convert_fraction_to_rounded_percent(0.34)
+    actual_min_value = _convert_fraction_to_rounded_percent(data.values.min())
+    actual_max_value = _convert_fraction_to_rounded_percent(data.values.max())
+
+    # Value range
+    assert expected_min_value == actual_min_value
+    assert expected_max_value == actual_max_value
+
+
+def test_alos_dsm_metrics():
+    data = AlosDSM().get_data(BBOX)
+
+    # Bounding values
+    expected_min_value = 16
+    expected_max_value = 86
+    actual_min_value = _convert_to_rounded_integer(data.values.min())
+    actual_max_value = _convert_to_rounded_integer(data.values.max())
+
+    # Value range
+    assert expected_min_value == actual_min_value
+    assert expected_max_value == actual_max_value
+
+
+def test_ndvi_metrics():
+    data = NdviSentinel2(year=2023).get_data(BBOX)
+
+    # Bounding values
+    expected_min_value = _convert_fraction_to_rounded_percent(0.21)
+    expected_max_value = _convert_fraction_to_rounded_percent(0.85)
+    actual_min_value = _convert_fraction_to_rounded_percent(data.values.min())
+    actual_max_value = _convert_fraction_to_rounded_percent(data.values.max())
+
+    # Value range
+    assert actual_min_value == expected_min_value
+    assert actual_max_value == expected_max_value
+
+
+def test_tree_cover_metrics():
+    expected_mean_value = 54.0
+    actual_mean_value = TreeCover().get_data(BBOX).mean()
+    tolerance = 0.1
+    assert (
+            pytest.approx(expected_mean_value, rel=tolerance) == actual_mean_value
+    )
+
+def _convert_fraction_to_rounded_percent(fraction):
+        return _convert_to_rounded_integer(fraction * 100)
+
+
+def _convert_to_rounded_integer(value):
+    return int(round((value)))
+