Formatting and misc. cleanup to match openet-core

Open-ET · Mar 12, 2024 · e5d364d · e5d364d
1 parent e2498d2
commit e5d364d
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Showing 3 changed files with 14 additions and 52 deletions.
diff --git a/openet/ptjpl/__init__.py b/openet/ptjpl/__init__.py
@@ -1,11 +1,10 @@
-# from importlib import metadata
-
 from .collection import Collection
 from .image import Image
 from . import interpolate
 
 MODEL_NAME = 'PTJPL'
 
+# from importlib import metadata
 # # __version__ = metadata.version(__package__ or __name__)
 # __version__ = metadata.version(__package__.replace('.', '-') or __name__.replace('.', '-'))
 # # __version__ = metadata.version('openet-ptjpl')
diff --git a/openet/ptjpl/collection.py b/openet/ptjpl/collection.py
@@ -489,9 +489,8 @@ def interpolate(
             if type(self.model_args['et_reference_source']) is str:
                 # Assume a string source is a single image collection ID
                 #   not a list of collection IDs or ee.ImageCollection
-                daily_et_ref_coll_id = self.model_args['et_reference_source']
                 daily_et_ref_coll = (
-                    ee.ImageCollection(daily_et_ref_coll_id)
+                    ee.ImageCollection(self.model_args['et_reference_source'])
                     .filterDate(start_date, end_date)
                     .select([self.model_args['et_reference_band']], ['et_reference'])
                 )
@@ -682,12 +681,7 @@ def aggregate_image(agg_start_date, agg_end_date, date_format):
 
             """
             if ('et' in variables) or ('et_fraction' in variables):
-                et_img = (
-                    daily_coll
-                    .filterDate(agg_start_date, agg_end_date)
-                    .select(['et'])
-                    .sum()
-                )
+                et_img = daily_coll.filterDate(agg_start_date, agg_end_date).select(['et']).sum()
 
             if ('et_reference' in variables) or ('et_fraction' in variables):
                 # Get the reference ET image from the reference ET collection,
@@ -711,9 +705,7 @@ def aggregate_image(agg_start_date, agg_end_date, date_format):
             if 'et_reference' in variables:
                 image_list.append(et_reference_img.float())
             if 'et_fraction' in variables:
-                image_list.append(
-                    et_img.divide(et_reference_img).rename(['et_fraction']).float()
-                )
+                image_list.append(et_img.divide(et_reference_img).rename(['et_fraction']).float())
             if 'ndvi' in variables:
                 ndvi_img = (
                     daily_coll.filterDate(agg_start_date, agg_end_date)

diff --git a/openet/ptjpl/interpolate.py b/openet/ptjpl/interpolate.py
@@ -256,16 +256,12 @@ def interpolate_prep(img):
             img.select(interp_vars).double().multiply(ee.Number(img.get('scale_factor')))
             .addBands([mask_img, time_img])
             .set({'system:time_start': ee.Number(img.get('system:time_start'))})
+            # .set({'image_id': ee.String(img.get('system:index'))})
         )
-        #     .set({'image_id': ee.String(img.get('system:index'))})
 
     # Filter scene collection to the interpolation range
     # This may not be needed since scene_coll was built to this range
-    scene_coll = (
-        scene_coll
-        .filterDate(interp_start_date, interp_end_date)
-        .map(interpolate_prep)
-    )
+    scene_coll = scene_coll.filterDate(interp_start_date, interp_end_date).map(interpolate_prep)
 
     # For count, compute the composite/mosaic image for the mask band only
     if 'count' in variables:
@@ -343,20 +339,11 @@ def aggregate_image(agg_start_date, agg_end_date, date_format):
 
         """
         if ('et' in variables) or ('et_fraction' in variables):
-            et_img = (
-                daily_coll
-                .filterDate(agg_start_date, agg_end_date)
-                .select(['et'])
-                .sum()
-            )
+            et_img = daily_coll.filterDate(agg_start_date, agg_end_date).select(['et']).sum()
 
         if ('et_reference' in variables) or ('et_fraction' in variables):
-            et_reference_img = (
-                daily_et_ref_coll
-                .filterDate(agg_start_date, agg_end_date)
-                .select(['et_reference'])
-                .sum()
-            )
+            et_reference_img = daily_et_ref_coll.filterDate(agg_start_date, agg_end_date).sum()
+
             if et_reference_resample and (et_reference_resample in ['bilinear', 'bicubic']):
                 et_reference_img = (
                     et_reference_img
@@ -371,9 +358,7 @@ def aggregate_image(agg_start_date, agg_end_date, date_format):
             image_list.append(et_reference_img.float())
         if 'et_fraction' in variables:
             # Compute average et fraction over the aggregation period
-            image_list.append(
-                et_img.divide(et_reference_img).rename(['et_fraction']).float()
-            )
+            image_list.append(et_img.divide(et_reference_img).rename(['et_fraction']).float())
         if 'ndvi' in variables:
             # Compute average ndvi over the aggregation period
             ndvi_img = (
@@ -624,15 +609,13 @@ def from_scene_et_actual(
 
         # Assume a string source is a single image collection ID
         #   not a list of collection IDs or ee.ImageCollection
-        daily_et_ref_coll_id = model_args['et_reference_source']
         daily_et_ref_coll = (
-            ee.ImageCollection(daily_et_ref_coll_id)
+            ee.ImageCollection(model_args['et_reference_source'])
             .filterDate(start_date, end_date)
             .select([model_args['et_reference_band']], ['et_reference'])
         )
 
         # Scale reference ET images (if necessary)
-        # CGM - Resampling is not working correctly so not including for now
         if et_reference_factor and (et_reference_factor != 1):
             def et_reference_adjust(input_img):
                 return (
@@ -642,7 +625,6 @@ def et_reference_adjust(input_img):
                 )
             daily_et_ref_coll = daily_et_ref_coll.map(et_reference_adjust)
 
-
     # Target collection needs to be filtered to the same date range as the
     #   scene collection in order to normalize the scenes.
     # It will be filtered again to the start/end when it is sent into
@@ -766,21 +748,12 @@ def aggregate_image(agg_start_date, agg_end_date, date_format):
 
         """
         if ('et' in variables) or ('et_fraction' in variables):
-            et_img = (
-                daily_coll
-                .filterDate(agg_start_date, agg_end_date)
-                .select(['et'])
-                .sum()
-            )
+            et_img = daily_coll.filterDate(agg_start_date, agg_end_date).select(['et']).sum()
 
         if ('et_reference' in variables) or ('et_fraction' in variables):
             # Get the reference ET image from the reference ET collection,
             #   not the interpolated collection
-            et_reference_img = (
-                daily_et_ref_coll
-                .filterDate(agg_start_date, agg_end_date)
-                .sum()
-            )
+            et_reference_img = daily_et_ref_coll.filterDate(agg_start_date, agg_end_date).sum()
             if et_reference_resample and (et_reference_resample in ['bilinear', 'bicubic']):
                 et_reference_img = (
                     et_reference_img
@@ -795,9 +768,7 @@ def aggregate_image(agg_start_date, agg_end_date, date_format):
             image_list.append(et_reference_img.float())
         if 'et_fraction' in variables:
             # Compute average et fraction over the aggregation period
-            image_list.append(
-                et_img.divide(et_reference_img).rename(['et_fraction']).float()
-            )
+            image_list.append(et_img.divide(et_reference_img).rename(['et_fraction']).float())
         # if 'ndvi' in variables:
         #     # Compute average ndvi over the aggregation period
         #     ndvi_img = (