Merge branch 'main' of github.com:VeinsOfTheEarth/rabpro

.github/workflows/build.yaml

@@ -9,6 +9,9 @@ on:
       - "tests/**"
       - .coveragerc
       - setup.py
+  pull_request:
+    paths:
+      - "rabpro/**"
 
 jobs:
   build:

rabpro/subbasin_stats.py

@@ -151,7 +151,7 @@ def main(
     # Dictionary for determining which rasters and statistics to compute
     control = _get_controls(dataset_list)
     ee.Initialize()
-
+    
     # Create water occurence mask
     occ_mask = ee.Image("JRC/GSW1_3/GlobalSurfaceWater").select("occurrence").lt(90)
 
@@ -168,6 +168,7 @@ def main(
         featureCollection = ee.FeatureCollection(gee_feature_path)
 
     # For each raster
+    datas, tasks = [], []
     for d in control:
         if d.band is None or d.band == "None":
             if d.type == "image":
@@ -254,14 +255,16 @@ def remove_geometry(feat):
         task.start()
 
         if test:
-            return data, task
+            datas.append(data)
+            tasks.append(task)
         else:
-            return (
+            datas.append(
                 table.getDownloadURL(
                     filetype="csv", filename=dataset_to_filename(d.data_id, d.band, tag)
-                ),
-                task,
-            )
+                ))
+            tasks.append(task)
+
+    return datas, tasks
 
 
 def _parse_reducers(stats=None, base=None):
@@ -355,7 +358,7 @@ def _get_controls(datasets):
                 print(
                     f"Warning: requested end date later than expected for {d.data_id}:{d.band}"
                 )
-
+        
         d.stats = set(d.stats + ["count", "mean"])
 
         if "no_data" in gee_dataset["bands"][d.band]:

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="rabpro",
-    version="0.2.2",
+    version="0.3.0",
     author="Example Author",
     author_email="author@example.com",
     description="Package to delineate subbasins and compute statistics.",

tests/test.py

@@ -46,9 +46,9 @@ def stattest(self, stats, datasets, length=1):
         data, task = self.rpo.basin_stats(datasets, folder="rabpro test", test=True)
 
         # Only check one set of stats for time-series data, but check length is equal
-        self.assertEqual(len(data["features"]), length)
+        self.assertEqual(len(data[0]["features"]), length)
 
-        ret_stats = data["features"][0]["properties"]
+        ret_stats = data[0]["features"][0]["properties"]
         print(f"Expected stats: {stats}")
         print(f"Returned stats: {ret_stats}")
 
@@ -62,12 +62,12 @@ def stattest(self, stats, datasets, length=1):
         )
 
         for _ in range(12):
-            status = task.status()["state"]
+            status = task[0].status()["state"]
             if status in ["READY", "RUNNING", "COMPLETED"]:
                 break
             time.sleep(10)
 
-        self.assertTrue(task.status()["state"] in ["READY", "RUNNING", "COMPLETED"])
+        self.assertTrue(task[0].status()["state"] in ["READY", "RUNNING", "COMPLETED"])
 
 
 class MERITTest(DataTestCase):

tests/test_basin_stats.py

@@ -45,7 +45,7 @@ def asdf(feat):
         test=True,
     )
 
-    res = pd.concat([clean_res(feature) for feature in data["features"]])
+    res = pd.concat([clean_res(feature) for feature in data[0]["features"]])
 
     assert all(res["asdf"] == res["max"])
 
@@ -58,7 +58,7 @@ def test_categorical_imgcol():
     )
 
     res = pd.concat(
-        [clean_freqhist(feature, "LC_Type1") for feature in data["features"]]
+        [clean_freqhist(feature, "LC_Type1") for feature in data[0]["features"]]
     )
 
     assert res.shape[1] > 4
@@ -70,7 +70,7 @@ def test_timeindexed_imgcol():
         [Dataset("JRC/GSW1_3/YearlyHistory", "waterClass",)], sb_inc_gdf=gdf, test=True,
     )
 
-    res = pd.concat([clean_res(feature) for feature in data["features"]])
+    res = pd.concat([clean_res(feature) for feature in data[0]["features"]])
 
     assert res["mean"].iloc[0] > 0
     assert res.shape[0] > 0
@@ -91,7 +91,7 @@ def test_timeindexedspecific_imgcol():
         test=True,
     )
 
-    res = pd.concat([clean_res(feature) for feature in data["features"]])
+    res = pd.concat([clean_res(feature) for feature in data[0]["features"]])
 
     assert res.shape[0] == 2
 
@@ -104,7 +104,7 @@ def test_nontimeindexed_imgcol():
         test=True,
     )
 
-    res = pd.concat([clean_res(feature) for feature in data["features"]])
+    res = pd.concat([clean_res(feature) for feature in data[0]["features"]])
 
     assert res.shape[0] > 0
 
@@ -123,7 +123,7 @@ def test_img():
         test=True,
     )
 
-    res = pd.DataFrame(data["features"][0]["properties"], index=[0])
+    res = pd.DataFrame(data[0]["features"][0]["properties"], index=[0])
 
     assert float(res["mean"]) > 0
     assert res.shape[1] == 9