bug fixes, clipping in GEE

daviddkovacs · Dec 22, 2024 · 62c5a2e · 62c5a2e
1 parent 8b69111
commit 62c5a2e
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Showing 2 changed files with 10 additions and 19 deletions.
diff --git a/pyeogpr/gee_processing.py b/pyeogpr/gee_processing.py
@@ -95,7 +95,7 @@ def __init__(
                 bounding_box[0], bounding_box[1], bounding_box[2], bounding_box[3]
             )
         if "projects/ee-" in bounding_box:  # So it accepts shps too!
-            self.bbox = ee.FeatureCollection(bounding_box)
+            self.bbox = ee.FeatureCollection(bounding_box).geometry()
             # self.bbox_fc2geo = ee.FeatureCollection(bounding_box).geometry()
 
         self.temporal_extent = temporal_extent
@@ -115,11 +115,9 @@ def __init__(
             spec.loader.exec_module(user_module)
 
             self.model_imported = user_module
-            print(self.model_imported.hyp_sig0_GREEN)
 
         else:
             url = f"https://raw.githubusercontent.com/daviddkovacs/pyeogpr/refs/heads/main/models/{search_sensor}_{self.biovar}_GEE.py"
-            print(url)
             response = requests.get(url)
 
             with open("model_imported.py", "w") as f:
@@ -130,7 +128,7 @@ def __init__(
             import model_imported
 
             self.model_imported = model_imported
-            print(model_imported.hyp_sig0_GREEN)
+
 
         self.imcol = ee.ImageCollection(self.sensor).filterDate(
             ee.Date(self.temporal_extent[0]), ee.Date(self.temporal_extent[1])
@@ -224,11 +222,11 @@ def calculate_GREEN(self, fecha_inicio, fecha_fin, variable):
         model = self.model_imported
         image = ee.Image(
             self.imcol.filterDate(fecha_inicio, fecha_fin)
-            .filterBounds(ee.FeatureCollection(self.bbox))
+            .filterBounds(self.bbox)
             .map(self.maskS3badPixels)
             .select(model.bands)  # .cast(model.bands_dict, model.bands)
             .max()
-            .clipToCollection(ee.FeatureCollection(self.bbox))
+            .clip(self.bbox)
         )
         # .clip(fc));
         # TODO: I should play around with this .clip() here..
@@ -338,24 +336,17 @@ def maploop(self):
                 .eq(80)
             )
             lakemask = lakes.eq(0)
-            # image_export = image_export.mask(lakemask)
-            # image_export = image_export.mask(bare_cover)
+            image_export = image_export.mask(lakemask)
+            image_export = image_export.mask(bare_cover)
 
-            # SHP-bbox misery
-            region = None
-            if type(self.bbox) is list:
-                region=self.bbox
-                image_export = image_export.clip(region)
-            elif isinstance(self.bbox, ee.FeatureCollection):
-                region = self.bbox.geometry()
-                image_export = image_export.clip(region)
 
             image_export = image_export.set(
                 "system:time_start",
                 ee.Date(self.temporal_extent[0])
                 .advance(ee.Number(i).multiply(self.timeWindows), "days")
                 .millis(),
             )
+            image_export = image_export.clip(self.bbox)
 
             # Export the image to an asset
             exportar = ee.batch.Export.image.toAsset(
@@ -368,7 +359,7 @@ def maploop(self):
                 maxPixels=17210617060,
                 description=self.getInputDates(i)["fecha_str"] + "_" + self.biovar,
                 scale=self.spatial_resolution,
-                region = region,  # .bounds().getInfo()['coordinates']
+                region = self.bbox,  # .bounds().getInfo()['coordinates']
             )
             exportar.start()
             exportar.status()
diff --git a/pyeogpr/openeo_processing.py b/pyeogpr/openeo_processing.py
@@ -27,7 +27,8 @@ class Datacube:
             - SENTINEL2_L1C: Cab, Cm, Cw, FVC, LAI, laiCab, laiCm, laiCw
             - SENTINEL2_L2A: Cab, Cm, Cw, FVC, LAI, laiCab, laiCm, laiCw, CNC_Cab, CNC_Cprot
             - SENTINEL3_OLCI_L1B: FAPAR, FVC, LAI, LCC
-
+            - for own model, simply put the directory of your model
+            
         bounding_box : list
             Your region of interest. Insert bbox as list. Can be selected from https://geojson.io/
             (e.g.: [-4.55, 42.73,-4.48, 42.77])
@@ -219,7 +220,6 @@ def process_map(self, gapfill=False, fileformat="nc"):
         fileformat : string
                 For netCDF4: "nc", for tiff: "tiff".
         
-                Insert the dir of the model you developed with ARTMO. Just simply put the directory.
             
 
         """