Changed image_value functions to return dict of values instead of values

These utils functions are now in line with the functions in openet.core.utils.

Updated some of the test collection IDs to use the new cloud project assets.

openet/ptjpl/image.py

@@ -909,12 +909,12 @@ def from_image_id(cls, image_id, **kwargs):
 
     @classmethod
     def from_landsat_c2_sr(cls, sr_image, cloudmask_args={}, **kwargs):
-        """Returns a PT-JPL Image instance from a Landsat Collection 2 SR image
+        """Returns a PT-JPL Image instance from a Landsat C02 level 2 (SR) image
 
         Parameters
         ----------
-        sr_image : ee.Image
-            A raw Landsat Collection 2 SR image.
+        sr_image : ee.Image, str
+            A raw Landsat Collection 2 level 2 (SR) image or image ID.
         cloudmask_args : dict
             keyword arguments to pass through to cloud mask function
         kwargs : dict
@@ -964,7 +964,9 @@ def from_landsat_c2_sr(cls, sr_image, cloudmask_args={}, **kwargs):
         if 'cloud_score_flag' not in cloudmask_args.keys():
             cloudmask_args['cloud_score_flag'] = True
         if 'cloud_score_pct' not in cloudmask_args.keys():
-            cloudmask_args['cloud_score_pct'] = True
+            cloudmask_args['cloud_score_pct'] = 100
+        if 'filter_flag' not in cloudmask_args.keys():
+            cloudmask_args['filter_flag'] = False
         # QA_RADSAT band will need to be added above if applying saturated masking
         # if 'saturated_flag' not in cloudmask_args.keys():
         #     cloudmask_args['saturated_flag'] = False
@@ -1011,7 +1013,6 @@ def from_landsat_c2_sr(cls, sr_image, cloudmask_args={}, **kwargs):
         # Instantiate the class
         return cls(input_image, **kwargs)
 
-    # CGM - Copied from _crop_type() in the SIMS model.py
     @lazy_property
     def crop_type(self):
         """Crop type
@@ -1038,6 +1039,10 @@ def crop_type(self):
         ------
         ValueError for unsupported crop_type_sources
 
+        Notes
+        -----
+        Copied from _crop_type() in the OpenET SIMS model.py
+
         """
         properties = ee.Dictionary()
 
@@ -1072,7 +1077,8 @@ def crop_type(self):
             crop_type_img = ee.Image(self.crop_type_source).select(['cropland'])
             properties = properties.set('id', crop_type_img.get('system:id'))
         elif (type(self.crop_type_source) is str and
-                'projects/openet/crop_type' in self.crop_type_source.lower()):
+              (('projects/openet/crop_type' in self.crop_type_source.lower()) or
+               ('projects/openet/assets/crop_type' in self.crop_type_source.lower()))):
             # Use the crop_type image closest to the image date
             crop_coll = ee.ImageCollection(self.crop_type_source)
             cdl_year = (

openet/ptjpl/interpolate.py

@@ -143,14 +143,18 @@ def from_scene_et_fraction(
     if type(et_reference_source) is str:
         # Assume a string source is an single image collection ID
         #   not an list of collection IDs or ee.ImageCollection
-        daily_et_ref_coll = ee.ImageCollection(et_reference_source) \
-            .filterDate(start_date, end_date) \
+        daily_et_ref_coll = (
+            ee.ImageCollection(et_reference_source)
+            .filterDate(start_date, end_date)
             .select([et_reference_band], ['et_reference'])
+        )
     # elif isinstance(et_reference_source, computedobject.ComputedObject):
     #     # Interpret computed objects as image collections
-    #     daily_et_reference_coll = et_reference_source \
-    #         .filterDate(self.start_date, self.end_date) \
+    #     daily_et_reference_coll = (
+    #         et_reference_source
+    #         .filterDate(self.start_date, self.end_date)
     #         .select([et_reference_band])
+    #     )
     else:
         raise ValueError(f'unsupported et_reference_source: {et_reference_source}')
 
@@ -212,7 +216,8 @@ def interpolate_prep(img):
     if 'count' in variables:
         aggregate_coll = openet.core.interpolate.aggregate_to_daily(
             image_coll=scene_coll.select(['mask']),
-            start_date=start_date, end_date=end_date)
+            start_date=start_date, end_date=end_date,
+        )
         # The following is needed because the aggregate collection can be
         #   empty if there are no scenes in the target date range but there
         #   are scenes in the interpolation date range.
@@ -232,7 +237,7 @@ def interpolate_prep(img):
         interp_method=interp_method,
         interp_days=interp_days,
         use_joins=use_joins,
-        compute_product=False
+        compute_product=False,
     )
 
     # Compute ET from ETf and ETr (if necessary)
@@ -506,8 +511,8 @@ def from_scene_et_actual(
         #     logging.debug('et_reference_resample was not set, default to nearest')
         #     # raise ValueError('et_reference_resample was not set')
 
-        # Assume a string source is an single image collection ID
-        #   not an list of collection IDs or ee.ImageCollection
+        # 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)

openet/ptjpl/tests/test_a_utils.py

@@ -47,7 +47,7 @@ def test_point_coll_value(image_id, image_date, xy, scale, expected, tol):
 
 def test_c_to_k(c=20, k=293.15, tol=0.000001):
     output = utils.constant_image_value(utils.c_to_k(ee.Image.constant(c)))
-    assert abs(output - k) <= tol
+    assert abs(output['constant'] - k) <= tol
 
 
 @pytest.mark.parametrize(

openet/ptjpl/tests/test_b_daily_integration.py

@@ -40,7 +40,7 @@ def test_day_angle_rad_from_doy_image(rn, hour_of_day, sunrise_hour,
         sunrise_hour=ee.Image.constant(sunrise_hour),
         daylight_hours=ee.Image.constant(daylight_hours),
     ))
-    assert abs(output - expected) <= tol
+    assert abs(output['constant'] - expected) <= tol
 
 
 @pytest.mark.parametrize(

openet/ptjpl/tests/test_b_daylight_hours.py

@@ -38,7 +38,7 @@ def test_sha_deg_from_doy_lat(doy, latitude, expected, tol=0.000001):
         ee.Number(doy), ee.Image.constant(latitude)
     ))
     # output = utils.getinfo(dh.sha_deg_from_doy_lat(ee.Number(doy), ee.Number(latitude)))
-    assert abs(output - expected) <= tol
+    assert abs(output['constant'] - expected) <= tol
 
 
 @pytest.mark.parametrize(

openet/ptjpl/tests/test_b_landsat.py

@@ -32,6 +32,11 @@ def sr_image(blue=0.2, green=0.2, red=0.2, nir=0.7, swir1=0.2, swir2=0.2, bt=300
 
 # Test the static methods of the class first
 # Do these need to be inside the TestClass?
+def test_Image_ndvi_band_name():
+    output = landsat.ndvi(sr_image()).getInfo()['bands'][0]['id']
+    assert output == 'ndvi'
+
+
 @pytest.mark.parametrize(
     'red, nir, expected',
     [
@@ -49,22 +54,22 @@ def sr_image(blue=0.2, green=0.2, red=0.2, nir=0.7, swir1=0.2, swir2=0.2, bt=300
 )
 def test_Image_ndvi_calculation(red, nir, expected, tol=0.000001):
     output = utils.constant_image_value(landsat.ndvi(sr_image(red=red, nir=nir)))
-    assert abs(output - expected) <= tol
+    assert abs(output['ndvi'] - expected) <= tol
 
 
-def test_Image_ndvi_band_name():
-    output = landsat.ndvi(sr_image()).getInfo()['bands'][0]['id']
-    assert output == 'ndvi'
+def test_Image_ndwi_band_name():
+    output = landsat.ndwi(sr_image()).getInfo()['bands'][0]['id']
+    assert output == 'ndwi'
 
 
 def test_Image_ndwi_calculation(green=0.2, nir=0.2, expected=0, tol=0.000001):
     output = utils.constant_image_value(landsat.ndwi(sr_image(green=green, nir=nir)))
-    assert abs(output - expected) <= tol
+    assert abs(output['ndwi'] - expected) <= tol
 
 
-def test_Image_ndwi_band_name():
-    output = landsat.ndwi(sr_image()).getInfo()['bands'][0]['id']
-    assert output == 'ndwi'
+def test_Image_mndwi_band_name():
+    output = landsat.mndwi(sr_image()).getInfo()['bands'][0]['id']
+    assert output == 'mndwi'
 
 
 @pytest.mark.parametrize(
@@ -76,12 +81,12 @@ def test_Image_ndwi_band_name():
 )
 def test_Image_mndwi_calculation(green, swir2, expected, tol=0.000001):
     output = utils.constant_image_value(landsat.mndwi(sr_image(green=green, swir2=swir2)))
-    assert abs(output - expected) <= tol
+    assert abs(output['mndwi'] - expected) <= tol
 
 
-def test_Image_mndwi_band_name():
-    output = landsat.mndwi(sr_image()).getInfo()['bands'][0]['id']
-    assert output == 'mndwi'
+def test_Image_wri_band_name():
+    output = landsat.wri(sr_image()).getInfo()['bands'][0]['id']
+    assert output == 'wri'
 
 
 @pytest.mark.parametrize(
@@ -95,12 +100,12 @@ def test_Image_wri_calculation(green, red, nir, swir2, expected, tol=0.000001):
     output = utils.constant_image_value(landsat.wri(
         sr_image(green=green, red=red, nir=nir, swir2=swir2)
     ))
-    assert abs(output - expected) <= tol
+    assert abs(output['wri'] - expected) <= tol
 
 
-def test_Image_wri_band_name():
-    output = landsat.wri(sr_image()).getInfo()['bands'][0]['id']
-    assert output == 'wri'
+def test_Image_emissivity_ptjpl_band_name():
+    output = landsat.emissivity_ptjpl(sr_image()).getInfo()['bands'][0]['id']
+    assert output == 'emissivity'
 
 
 @pytest.mark.parametrize(
@@ -121,11 +126,11 @@ def test_Image_wri_band_name():
 )
 def test_Image_emissivity_ptjpl_calculation(red, nir, expected, tol=0.000001):
     output = utils.constant_image_value(landsat.emissivity_ptjpl(sr_image(red=red, nir=nir)))
-    assert abs(output - expected) <= tol
+    assert abs(output['emissivity'] - expected) <= tol
 
 
-def test_Image_emissivity_disalexi_band_name():
-    output = landsat.emissivity_ptjpl(sr_image()).getInfo()['bands'][0]['id']
+def test_Image_emissivity_metric_band_name():
+    output = landsat.emissivity_metric(sr_image()).getInfo()['bands'][0]['id']
     assert output == 'emissivity'
 
 
@@ -146,12 +151,12 @@ def test_Image_emissivity_disalexi_band_name():
 )
 def test_Image_emissivity_metric_calculation(red, nir, expected, tol=0.000001):
     output = utils.constant_image_value(landsat.emissivity_metric(sr_image(red=red, nir=nir)))
-    assert abs(output - expected) <= tol
+    assert abs(output['emissivity'] - expected) <= tol
 
 
-def test_Image_emissivity_metric_band_name():
-    output = landsat.emissivity_metric(sr_image()).getInfo()['bands'][0]['id']
-    assert output == 'emissivity'
+def test_Image_lst_band_name():
+    output = landsat.lst(sr_image()).getInfo()['bands'][0]['id']
+    assert output == 'lst'
 
 
 @pytest.mark.parametrize(
@@ -163,12 +168,12 @@ def test_Image_emissivity_metric_band_name():
 )
 def test_Image_lst_calculation(red, nir, bt, expected, tol=0.000001):
     output = utils.constant_image_value(landsat.lst(sr_image(red=red, nir=nir, bt=bt)))
-    assert abs(output - expected) <= tol
+    assert abs(output['lst'] - expected) <= tol
 
 
-def test_Image_lst_band_name():
-    output = landsat.lst(sr_image()).getInfo()['bands'][0]['id']
-    assert output == 'lst'
+def test_Image_albedo_band_name():
+    output = landsat.albedo_metric(sr_image()).getInfo()['bands'][0]['id']
+    assert output == 'albedo'
 
 
 @pytest.mark.parametrize(
@@ -181,9 +186,4 @@ def test_Image_albedo_calculation(blue, green, red, nir, swir1, swir2, expected,
     output = utils.constant_image_value(landsat.albedo_metric(
         sr_image(blue=blue, green=green, red=red, nir=nir, swir1=swir1, swir2=swir2)
     ))
-    assert abs(output - expected) <= tol
-
-
-def test_Image_albedo_band_name():
-    output = landsat.albedo_metric(sr_image()).getInfo()['bands'][0]['id']
-    assert output == 'albedo'
+    assert abs(output['albedo'] - expected) <= tol

openet/ptjpl/tests/test_b_meteorology.py

@@ -68,8 +68,8 @@ def test_pascal_to_kilopascal(P_Pa, expected, tol=0.000001):
 def test_meteorology_svp(Ta_C, Ea_kPa, expected, tol=0.000001):
     # CGM - This test uses images due to .updateMask() call in meteorology
     SVP, VPD, RH = meteorology.meteorology(ee.Image.constant(Ta_C), ee.Image.constant(Ea_kPa))
-    output = utils.constant_image_value(SVP)
-    assert abs(output - expected) <= tol
+    output = utils.constant_image_value(SVP.rename(['svp']))
+    assert abs(output['svp'] - expected) <= tol
 
 
 @pytest.mark.parametrize(
@@ -82,11 +82,11 @@ def test_meteorology_svp(Ta_C, Ea_kPa, expected, tol=0.000001):
 def test_meteorology_vpd(Ta_C, Ea_kPa, expected, tol=0.000001):
     # CGM - This test uses images due to .updateMask() call in meteorology
     SVP, VPD, RH = meteorology.meteorology(ee.Image.constant(Ta_C), ee.Image.constant(Ea_kPa))
-    output = utils.constant_image_value(VPD)
-    if expected is None and output is None:
+    output = utils.constant_image_value(VPD.rename(['vpd']))
+    if expected is None and output['vpd'] is None:
         assert True
     else:
-        assert abs(output - expected) <= tol
+        assert abs(output['vpd'] - expected) <= tol
 
 
 @pytest.mark.parametrize(
@@ -99,8 +99,8 @@ def test_meteorology_vpd(Ta_C, Ea_kPa, expected, tol=0.000001):
 def test_meteorology_rh(Ta_C, Ea_kPa, expected, tol=0.000001):
     # CGM - This test uses images due to .updateMask() call in meteorology
     SVP, VPD, RH = meteorology.meteorology(ee.Image.constant(Ta_C), ee.Image.constant(Ea_kPa))
-    output = utils.constant_image_value(RH)
-    assert abs(output - expected) <= tol
+    output = utils.constant_image_value(RH.rename(['rh']))
+    assert abs(output['rh'] - expected) <= tol
 
 
 @pytest.mark.parametrize(

openet/ptjpl/tests/test_b_vegetation.py

@@ -7,7 +7,7 @@
 
 def test_savi_from_ndvi(ndvi=0.5, expected=0.5 * 0.45 + 0.132, tol=0.000001):
     output = utils.constant_image_value(vegetation.savi_from_ndvi(ee.Image.constant(ndvi)))
-    assert abs(output - expected) <= tol
+    assert abs(output['constant'] - expected) <= tol
 
 
 @pytest.mark.parametrize(
@@ -20,14 +20,14 @@ def test_savi_from_ndvi(ndvi=0.5, expected=0.5 * 0.45 + 0.132, tol=0.000001):
 )
 def test_fAPAR_from_savi(savi, expected, tol=0.000001):
     output = utils.constant_image_value(vegetation.fAPAR_from_savi(ee.Image.constant(savi)))
-    assert abs(output - expected) <= tol
+    assert abs(output['constant'] - expected) <= tol
 
 
 @pytest.mark.parametrize(
     'ndvi, expected', [[0.5, (0.45 * 0.5 + 0.132) * 1.3632 - 0.048]])
 def test_fAPAR_from_ndvi(ndvi, expected, tol=0.000001):
     output = utils.constant_image_value(vegetation.fAPAR_from_ndvi(ee.Image.constant(ndvi)))
-    assert abs(output - expected) <= tol
+    assert abs(output['constant'] - expected) <= tol
 
 
 @pytest.mark.parametrize(
@@ -40,4 +40,4 @@ def test_fAPAR_from_ndvi(ndvi, expected, tol=0.000001):
 )
 def test_fIPAR_from_ndvi(ndvi, expected, tol=0.000001):
     output = utils.constant_image_value(vegetation.fIPAR_from_ndvi(ee.Image.constant(ndvi)))
-    assert abs(output - expected) <= tol
+    assert abs(output['constant'] - expected) <= tol

openet/ptjpl/tests/test_c_ptjpl.py

@@ -34,9 +34,9 @@ def test_Model_Rns_calculation(Rn, LAI, water_mask, expected, tol=0.001):
         water_mask=ee.Image.constant(water_mask)
     ))
     if expected is None:
-        assert output is None
+        assert output['constant'] is None
     else:
-        assert abs(output - expected) <= tol
+        assert abs(output['constant'] - expected) <= tol
 
 
 # @pytest.mark.parametrize(
@@ -53,7 +53,7 @@ def test_Model_Rns_calculation(Rn, LAI, water_mask, expected, tol=0.001):
 #         Rn=ee.Image.constant(Rn), fIPAR=ee.Image.constant(fIPAR),
 #         Rns=ee.Image.constant(Rns), W=ee.Image.constant(W),
 #         water_mask=ee.Image.constant(water_mask)))
-#     assert abs(output - expected) <= tol
+#     assert abs(output['constant'] - expected) <= tol
 
 
 # @pytest.mark.parametrize(
@@ -83,5 +83,5 @@ def test_Model_Rns_calculation(Rn, LAI, water_mask, expected, tol=0.001):
 #     )
 #     output = utils.constant_image_value(output_image)
 #     logging.debug(f'\n  Target values: {expected}')
-#     logging.debug(f'  Output values: {output}')
-#     assert abs(output - expected) <= tol
+#     logging.debug(f'  Output values: {output['constant']}')
+#     assert abs(output['constant'] - expected) <= tol