Merge branch 'main' of https://github.com/wri/cities-cif into feature…

…/era5

city_metrix/layers/open_street_map.py

@@ -54,10 +54,3 @@ def get_data(self, bbox):
         osm_feature = osm_feature.reset_index()[keep_col]
 
         return osm_feature
-
-    def write(self, output_path):
-        self.data['bbox'] = str(self.data.total_bounds)
-        self.data['osm_class'] = str(self.osm_class.value)
-
-        # Write to a GeoJSON file
-        self.data.to_file(output_path, driver='GeoJSON')

environment.yml

@@ -13,7 +13,7 @@ dependencies:
   - xarray-spatial=0.3.7
   - xee=0.0.3
   - utm=0.7.0
-  - osmnx=1.8.1
+  - osmnx=1.9.0
   - dask[complete]=2023.11.0
   - matplotlib=3.8.2
   - jupyterlab=4.0.10

tests/test_layers.py

@@ -1,132 +1,113 @@
 import ee
-import numpy as np
 import pytest
 
 from city_metrix.layers import (
     Albedo,
     AlosDSM,
     AverageNetBuildingHeight,
+    Era5HottestDay,
     EsaWorldCover,
     EsaWorldCoverClass,
+    HighLandSurfaceTemperature,
+    LandsatCollection2,
     LandSurfaceTemperature,
     NasaDEM,
+    NaturalAreas,
     OpenBuildings,
     OpenStreetMap,
     OpenStreetMapClass,
     OvertureBuildings,
+    Sentinel2Level2,
     SmartSurfaceLULC,
     TreeCanopyHeight,
     TreeCover,
     UrbanLandUse,
-    Era5HottestDay,
+    WorldPop
 )
 from city_metrix.layers.layer import get_image_collection
+from tests.fixtures.bbox_constants import BBOX_BRAZIL_LAURO_DE_FREITAS_1
 
-from .conftest import (
-    LARGE_ZONES,
-    ZONES,
-    MockGroupByLayer,
-    MockLargeGroupByLayer,
-    MockLargeLayer,
-    MockLayer,
-    MockMaskLayer,
-)
-from .fixtures.bbox_constants import *
-
-
-def test_count():
-    counts = MockLayer().groupby(ZONES).count()
-    assert counts.size == 100
-    assert all([count == 100 for count in counts])
-
-
-def test_mean():
-    means = MockLayer().groupby(ZONES).mean()
-    assert means.size == 100
-    assert all([mean == i for i, mean in enumerate(means)])
-
-
-def test_fishnetted_count():
-    counts = MockLargeLayer().groupby(LARGE_ZONES).count()
-    assert counts.size == 100
-    assert all([count == 100 for count in counts])
+EE_IMAGE_DIMENSION_TOLERANCE = 1  # Tolerance compensates for variable results from GEE service
 
+def test_albedo():
+    assert Albedo().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
 
-def test_fishnetted_mean():
-    means = MockLargeLayer().groupby(LARGE_ZONES).mean()
-    assert means.size == 100
-    assert all([mean == i for i, mean in enumerate(means)])
 
+def test_alos_dsm():
+    mean = AlosDSM().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
+    assert mean
 
-def test_masks():
-    counts = MockLayer().mask(MockMaskLayer()).groupby(ZONES).count()
-    assert counts.size == 100
-    for i, count in enumerate(counts):
-        if i % 2 == 0:
-            assert np.isnan(count)
-        else:
-            assert count == 100
 
+def test_average_net_building_height():
+    assert AverageNetBuildingHeight().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
 
-def test_group_by_layer():
-    counts = MockLayer().groupby(ZONES, layer=MockGroupByLayer()).count()
-    assert all([count == {1: 50.0, 2: 50.0} for count in counts])
 
+def test_era_5_hottest_day():
+    mean = Era5HottestDay().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
+    assert mean
+
 
-def test_group_by_large_layer():
-    counts = (
-        MockLargeLayer().groupby(LARGE_ZONES, layer=MockLargeGroupByLayer()).count()
+def test_esa_world_cover():
+    count = (
+        EsaWorldCover(land_cover_class=EsaWorldCoverClass.BUILT_UP)
+        .get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1)
+        .count()
     )
-    assert all([count == {1: 50.0, 2: 50.0} for count in counts])
+    assert count
 
 
 def test_read_image_collection():
     ic = ee.ImageCollection("ESA/WorldCover/v100")
     data = get_image_collection(ic, BBOX_BRAZIL_LAURO_DE_FREITAS_1, 10, "test")
 
-    expected_crs =  32724
+    expected_crs = 32724
     expected_x_dimension = 187
     expected_y_dimension = 199
 
     assert data.rio.crs == expected_crs
-    assert data.dims == {"x": expected_x_dimension, "y": expected_y_dimension}
+    assert (
+        pytest.approx(expected_x_dimension, rel=EE_IMAGE_DIMENSION_TOLERANCE) == "x",
+        pytest.approx(expected_y_dimension, rel=EE_IMAGE_DIMENSION_TOLERANCE) == "y"
+    )
 
 
 def test_read_image_collection_scale():
     ic = ee.ImageCollection("ESA/WorldCover/v100")
     data = get_image_collection(ic, BBOX_BRAZIL_LAURO_DE_FREITAS_1, 100, "test")
-    assert data.dims == {"x": 19, "y": 20}
+    expected_x_dimension = 19
+    expected_y_dimension = 20
+    assert data.dims == {"x": expected_x_dimension, "y": expected_y_dimension}
 
 
-def test_tree_cover():
-    actual = TreeCover().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
-    expected = 54.0
-    tolerance = 0.1
-    assert (
-        pytest.approx(expected, rel=tolerance) == actual
-    )
+def test_high_land_surface_temperature():
+    data = HighLandSurfaceTemperature().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1)
+    assert data.any()
 
 
-def test_albedo():
-    assert Albedo().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
+def test_land_surface_temperature():
+    mean_lst = LandSurfaceTemperature().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
+    assert mean_lst
+
+
+def test_landsat_collection_2():
+    bands = ['green']
+    data = LandsatCollection2(bands).get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1)
+    assert data.any()
 
 
-def test_lst():
-    mean = LandSurfaceTemperature().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
+def test_nasa_dem():
+    mean = NasaDEM().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
     assert mean
 
 
-def test_esa():
-    count = (
-        EsaWorldCover(land_cover_class=EsaWorldCoverClass.BUILT_UP)
-        .get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1)
-        .count()
-    )
-    assert count
+def test_natural_areas():
+    data = NaturalAreas().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1)
+    assert data.any()
 
 
-def test_average_net_building_height():
-    assert AverageNetBuildingHeight().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
+def test_openbuildings():
+    count = OpenBuildings().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).count().sum()
+    assert count
 
 
 def test_open_street_map():
@@ -139,39 +120,40 @@ def test_open_street_map():
     assert count
 
 
-def test_urban_land_use():
-    assert UrbanLandUse().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).count()
-
-
-def test_openbuildings():
-    count = OpenBuildings().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).count().sum()
+def test_overture_buildings():
+    count = OvertureBuildings().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).count().sum()
     assert count
 
 
-def test_tree_canopy_hight():
-    count = TreeCanopyHeight().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).count()
-    assert count
-
-
-def test_alos_dsm():
-    mean = AlosDSM().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
-    assert mean
+def test_sentinel_2_level2():
+    sentinel_2_bands = ["green"]
+    data = Sentinel2Level2(sentinel_2_bands).get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1)
+    assert data.any()
 
 
 def test_smart_surface_lulc():
     count = SmartSurfaceLULC().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).count()
     assert count
 
 
-def test_overture_buildings():
-    count = OvertureBuildings().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).count().sum()
+def test_tree_canopy_height():
+    count = TreeCanopyHeight().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).count()
     assert count
 
 
-def test_nasa_dem():
-    mean = NasaDEM().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
-    assert mean
+def test_tree_cover():
+    actual = TreeCover().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
+    expected = 54.0
+    tolerance = 0.1
+    assert (
+            pytest.approx(expected, rel=tolerance) == actual
+    )
 
-def test_era_5_hottest_day():
-    mean = Era5HottestDay().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).mean()
-    assert mean
+
+def test_urban_land_use():
+    assert UrbanLandUse().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1).count()
+
+
+def test_world_pop():
+    data = WorldPop().get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1)
+    assert data.any()

tests/test_methods.py

@@ -0,0 +1,58 @@
+import numpy as np
+
+from .conftest import (
+    LARGE_ZONES,
+    ZONES,
+    MockGroupByLayer,
+    MockLargeGroupByLayer,
+    MockLargeLayer,
+    MockLayer,
+    MockMaskLayer,
+)
+from .fixtures.bbox_constants import *
+
+
+def test_count():
+    counts = MockLayer().groupby(ZONES).count()
+    assert counts.size == 100
+    assert all([count == 100 for count in counts])
+
+
+def test_mean():
+    means = MockLayer().groupby(ZONES).mean()
+    assert means.size == 100
+    assert all([mean == i for i, mean in enumerate(means)])
+
+
+def test_fishnetted_count():
+    counts = MockLargeLayer().groupby(LARGE_ZONES).count()
+    assert counts.size == 100
+    assert all([count == 100 for count in counts])
+
+
+def test_fishnetted_mean():
+    means = MockLargeLayer().groupby(LARGE_ZONES).mean()
+    assert means.size == 100
+    assert all([mean == i for i, mean in enumerate(means)])
+
+
+def test_masks():
+    counts = MockLayer().mask(MockMaskLayer()).groupby(ZONES).count()
+    assert counts.size == 100
+    for i, count in enumerate(counts):
+        if i % 2 == 0:
+            assert np.isnan(count)
+        else:
+            assert count == 100
+
+
+def test_group_by_layer():
+    counts = MockLayer().groupby(ZONES, layer=MockGroupByLayer()).count()
+    assert all([count == {1: 50.0, 2: 50.0} for count in counts])
+
+
+def test_group_by_large_layer():
+    counts = (
+        MockLargeLayer().groupby(LARGE_ZONES, layer=MockLargeGroupByLayer()).count()
+    )
+    assert all([count == {1: 50.0, 2: 50.0} for count in counts])