Code cleanup

.github/workflows/tests.yml

@@ -16,6 +16,8 @@ jobs:
       contents: read
       pages: write
       id-token: write
+    env:
+      PROJECT_ID: openet
     steps:
     - uses: actions/checkout@v4
     - name: Set up Python 3.10

CONTRIBUTING.rst

@@ -4,11 +4,6 @@ Contributing to OpenET
 
 Thank you for your interest in supporting the OpenET project.
 
-Versioning
-==========
-
-The OpenET project is currently in Beta and the version numbers will be "0.0.X" until a non-Beta release is made.
-
 Coding Conventions
 ==================
 

openet/core/common.py

@@ -15,8 +15,7 @@ def landsat_c2_sr_cloud_mask(
         filter_flag=False,
         saturated_flag=False,
         sr_cloud_qa_flag=False,
-        # cloud_confidence=3,
-        ):
+):
     """Compute cloud mask for a Landsat Coll. 2 Level 2 (SR) image using multiple approaches
 
     Parameters
@@ -213,10 +212,7 @@ def landsat_c2_sr_lst_correct(sr_image, ndvi):
     image_geom = sr_image.geometry()
     image_extent = image_geom.bounds(1, 'EPSG:4326')
 
-    # # Simple clip extent from image geometry bounds
-    # clip_extent = image_geom.bounds(1, 'EPSG:4326')
-
-    # # Server side approach for getting image extent snapped to the ASTER GED grid
+    # Server side approach for getting image extent snapped to the ASTER GED grid
     buffer_cells = 1
     cellsize = 0.1
     image_xy = ee.Array(image_extent.coordinates().get(0)).transpose().toList()
@@ -230,13 +226,7 @@ def landsat_c2_sr_lst_correct(sr_image, ndvi):
     ymax = ymax.divide(cellsize * buffer_cells).ceil().multiply(cellsize * buffer_cells)
     clip_extent = ee.Geometry.Rectangle([xmin, ymin, xmax, ymax], 'EPSG:4326', False)
 
-    # Landsat image projection for resample/reproject (
-    # image_proj = sr_image.projection()
-    # image_crs = image_proj.crs()
-    # image_geo = ee.List(ee.Dictionary(ee.Algorithms.Describe(image_proj)).get('transform'))
-
     # Aster Global Emissivity Dataset
-
     ged = ee.Image('NASA/ASTER_GED/AG100_003').clip(clip_extent)
 
     veg_emis = 0.99
@@ -276,7 +266,6 @@ def get_matched_c2_t1_image(input_img):
             ee.String(scene_id.get(0)).cat('_').cat(ee.String(scene_id.get(2)))
             .cat('_').cat(ee.String(scene_id.get(3)))
         )
-        # scene_id = ee.String(input_img.get('system:index'))
 
         # Testing if it is any faster to filter each collection separately
         # TODO: Test if adding an extra .filterDate() call helps
@@ -304,7 +293,6 @@ def get_matched_c2_t1_radiance_image(input_img):
             ee.String(scene_id.get(0)).cat('_').cat(ee.String(scene_id.get(2)))
             .cat('_').cat(ee.String(scene_id.get(3)))
         )
-        # scene_id = ee.String(input_img.get('system:index'))
 
         # TODO: Fix error when images that are in the T1_L2 collections but not in the T1,
         #  will fail with a .get() error because matched_img is 'None',
@@ -433,5 +421,4 @@ def get_matched_c2_t1_radiance_image(input_img):
         .divide(Rc).add(1.0).log().pow(-1)
         .multiply(ee.Number(k2.get(spacecraft_id)))
         .rename('lst')
-        # .set({'system:time_start': sr_image.get('system:time_start')})
     )

openet/core/ensemble.py

@@ -83,11 +83,9 @@ def mad(ensemble_img, made_scale=2):
     # Map the band names to the model index
     # The extra combine is to try and account for the ensemble images having
     #   band names that don't map to one of the model names/indexes
-    # band_dict = ee.Dictionary(model_index)
     band_dict = ee.Dictionary(model_index).combine(ee.Dictionary.fromLists(
         output_bands, ee.List.sequence(9, model_count.add(9).subtract(1))))
     band_index = model_names.map(lambda x: band_dict.get(x))
-    # band_index = ee.List.sequence(1, model_count)
 
     # Bit encode the models using the model index values
     # Build the index array from the ensemble images so that the index
@@ -110,22 +108,6 @@ def mad(ensemble_img, made_scale=2):
 
     return output_img
 
-    # DEADBEEF
-    # print(utils.point_image_value(ee.Image(images), [-120, 39], scale=1))
-    # print(utils.point_image_value(ens_median, [-120, 39], scale=1))
-    # print(utils.point_image_value(MADe, [-120, 39], scale=1))
-    # print(utils.point_image_value(upper, [-120, 39], scale=1))
-    # print(utils.point_image_value(lower, [-120, 39], scale=1))
-    # print(utils.point_image_value(count_img, [-120, 39], scale=1))
-    # print(utils.point_image_value(sort_img, [-120, 39], scale=1))
-    # print(utils.point_image_value(model_drop_mean, [-120, 39], scale=1))
-
-    # # Add mean, ens_median, made, upper and lower to ensemble for map display
-    # ens_mean = ensemble_img.reduce(ee.Reducer.mean()).rename(["mean"])
-    # ensemble = ensemble_sims_crop.addBands(ens_mean).addBands(ens_median)
-    #     .addBands(MADe).addBands(upper).addBands(lower)
-    #     .addBands(model_drop_mean)
-
 
 def mean(ensemble_img):
     """Simple arithmetic mean placeholder function