[CIF-183] Fixed the pytest setup

.gitignore

@@ -163,3 +163,4 @@ cython_debug/
 /keys
 keys/
 wri-gee-358d958ce7c6.json
+citiesindicators-fe8fd6514c70.json

environment.yml

@@ -13,13 +13,12 @@ dependencies:
   - xarray-spatial=0.3.7
   - xee=0.0.3
   - utm=0.7.0
-  - osmnx=1.8.1
+  - osmnx=1.9.3
   - dask[complete]=2023.11.0
   - matplotlib=3.8.2
-  - jupyterlab=4.0.10
   - s3fs=2024.5.0
   - geemap=0.32.0
   - pip=23.3.1
   - boto3=1.34.124
   - pip:
-    - cartoframes==1.2.5
+      - cartoframes==1.2.5

pytest.ini

@@ -0,0 +1,2 @@
+[pytest]
+testpaths = tests

tests/layers.py → tests/test_layers.py

@@ -1,12 +1,32 @@
 import ee
+import numpy as np
+import pytest
 
-from city_metrix.layers import LandsatCollection2, Albedo, LandSurfaceTemperature, EsaWorldCover, EsaWorldCoverClass, TreeCover, AverageNetBuildingHeight, OpenStreetMap, OpenStreetMapClass, UrbanLandUse, OpenBuildings, TreeCanopyHeight, AlosDSM
+from city_metrix.layers import (
+    Albedo,
+    AlosDSM,
+    AverageNetBuildingHeight,
+    EsaWorldCover,
+    EsaWorldCoverClass,
+    LandSurfaceTemperature,
+    OpenBuildings,
+    OpenStreetMap,
+    OpenStreetMapClass,
+    TreeCanopyHeight,
+    TreeCover,
+    UrbanLandUse,
+)
 from city_metrix.layers.layer import get_image_collection
-from .conftest import MockLayer, MockMaskLayer, ZONES, LARGE_ZONES, MockLargeLayer, MockGroupByLayer, \
-    MockLargeGroupByLayer
 
-import pytest
-import numpy as np
+from .conftest import (
+    LARGE_ZONES,
+    ZONES,
+    MockGroupByLayer,
+    MockLargeGroupByLayer,
+    MockLargeLayer,
+    MockLayer,
+    MockMaskLayer,
+)
 
 
 def test_count():
@@ -49,29 +69,39 @@ def test_group_by_layer():
 
 
 def test_group_by_large_layer():
-    counts = MockLargeLayer().groupby(LARGE_ZONES, layer=MockLargeGroupByLayer()).count()
+    counts = (
+        MockLargeLayer().groupby(LARGE_ZONES, layer=MockLargeGroupByLayer()).count()
+    )
     assert all([count == {1: 50.0, 2: 50.0} for count in counts])
 
 
-SAMPLE_BBOX = (-38.35530428121955, -12.821710300686393, -38.33813814352424, -12.80363249765361)
+SAMPLE_BBOX = (
+    -38.35530428121955,
+    -12.821710300686393,
+    -38.33813814352424,
+    -12.80363249765361,
+)
 
 
 def test_read_image_collection():
     ic = ee.ImageCollection("ESA/WorldCover/v100")
     data = get_image_collection(ic, SAMPLE_BBOX, 10, "test")
 
     assert data.rio.crs == 32724
-    assert data.dims == {'x': 187, 'y': 200}
+    assert data.dims == {"x": 187, "y": 200}
 
 
 def test_read_image_collection_scale():
     ic = ee.ImageCollection("ESA/WorldCover/v100")
     data = get_image_collection(ic, SAMPLE_BBOX, 100, "test")
-    assert data.dims == {'x': 19, 'y': 20}
+    assert data.dims == {"x": 19, "y": 20}
 
 
 def test_tree_cover():
-    assert pytest.approx(53.84184165912419, rel=0.001) == TreeCover().get_data(SAMPLE_BBOX).mean()
+    assert (
+        pytest.approx(53.84184165912419, rel=0.001)
+        == TreeCover().get_data(SAMPLE_BBOX).mean()
+    )
 
 
 def test_albedo():
@@ -84,27 +114,42 @@ def test_lst():
 
 
 def test_esa():
-    count = EsaWorldCover(land_cover_class=EsaWorldCoverClass.BUILT_UP).get_data(SAMPLE_BBOX).count()
+    count = (
+        EsaWorldCover(land_cover_class=EsaWorldCoverClass.BUILT_UP)
+        .get_data(SAMPLE_BBOX)
+        .count()
+    )
     assert count
 
+
 def test_average_net_building_height():
     assert AverageNetBuildingHeight().get_data(SAMPLE_BBOX).mean()
 
+
 def test_open_street_map():
-    count = OpenStreetMap(osm_class=OpenStreetMapClass.ROAD).get_data(SAMPLE_BBOX).count().sum()
+    count = (
+        OpenStreetMap(osm_class=OpenStreetMapClass.ROAD)
+        .get_data(SAMPLE_BBOX)
+        .count()
+        .sum()
+    )
     assert count
 
+
 def test_urban_land_use():
     assert UrbanLandUse().get_data(SAMPLE_BBOX).count()
 
+
 def test_openbuildings():
     count = OpenBuildings().get_data(SAMPLE_BBOX).count().sum()
     assert count
 
+
 def test_tree_canopy_hight():
     count = TreeCanopyHeight().get_data(SAMPLE_BBOX).count()
     assert count
-
+
+
 def test_AlosDSM():
     mean = AlosDSM().get_data(SAMPLE_BBOX).mean()
     assert mean