Misc. formatting cleanup and flake8 changes

Letting line lengths go to ~100 where it seems to improves readability.  Also, trying to move away from using trailing slashes.

Author: cgmorton

openet/ssebop/__init__.py

@@ -9,4 +9,6 @@
 
 MODEL_NAME = 'SSEBOP'
 
-# __version__ = metadata.version(__package__ or __name__)
+# # __version__ = metadata.version(__package__ or __name__)
+# __version__ = metadata.version(__package__.replace('.', '-') or __name__.replace('.', '-'))
+# # __version__ = metadata.version('openet-ssebop')

openet/ssebop/collection.py

@@ -1,8 +1,8 @@
 import datetime
 import logging
-import pprint
+# import pprint
 
-from dateutil.relativedelta import *
+from dateutil.relativedelta import relativedelta
 import ee
 import openet.core.interpolate
 # TODO: import utils from openet.core
@@ -172,30 +172,36 @@ def from_scene_et_fraction(
     if type(et_reference_source) is str:
         # Assume a string source is a single image collection ID
         #   not a list of collection IDs or ee.ImageCollection
-        if (et_reference_date_type is None or
-                et_reference_date_type.lower() == 'daily'):
-            daily_et_ref_coll = ee.ImageCollection(et_reference_source) \
-                .filterDate(start_date, end_date) \
+        if (et_reference_date_type is None) or (et_reference_date_type.lower() == 'daily'):
+            daily_et_ref_coll = (
+                ee.ImageCollection(et_reference_source)
+                .filterDate(start_date, end_date)
                 .select([et_reference_band], ['et_reference'])
+            )
         elif et_reference_date_type.lower() == 'doy':
             # Assume the image collection is a climatology with a "DOY" property
             def doy_image(input_img):
                 """Return the doy-based reference et with daily time properties from GRIDMET"""
                 image_date = ee.Algorithms.Date(input_img.get('system:time_start'))
                 image_doy = ee.Number(image_date.getRelative('day', 'year')).add(1).int()
-                doy_coll = ee.ImageCollection(et_reference_source)\
-                    .filterMetadata('DOY', 'equals', image_doy)\
+                doy_coll = (
+                    ee.ImageCollection(et_reference_source)
+                    .filterMetadata('DOY', 'equals', image_doy)
                     .select([et_reference_band], ['et_reference'])
+                )
                 # CGM - Was there a reason to use rangeContains if limiting to one DOY?
                 #     .filter(ee.Filter.rangeContains('DOY', doy, doy))\
                 return ee.Image(doy_coll.first())\
                     .set({'system:index': input_img.get('system:index'),
                           'system:time_start': input_img.get('system:time_start')})
             # Note, the collection and band that are used are important as
             #   long as they are daily and available for the time period
-            daily_et_ref_coll = ee.ImageCollection('IDAHO_EPSCOR/GRIDMET')\
-                .filterDate(start_date, end_date).select(['eto'])\
+            daily_et_ref_coll = (
+                ee.ImageCollection('IDAHO_EPSCOR/GRIDMET')
+                .filterDate(start_date, end_date)
+                .select(['eto'])
                 .map(doy_image)
+            )
     # elif isinstance(et_reference_source, computedobject.ComputedObject):
     #     # Interpret computed objects as image collections
     #     daily_et_reference_coll = ee.ImageCollection(et_reference_source)\
@@ -208,9 +214,12 @@ def doy_image(input_img):
     # 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 input_img.multiply(et_reference_factor) \
-                .copyProperties(input_img) \
+            return (
+                input_img.multiply(et_reference_factor)
+                .copyProperties(input_img)
                 .set({'system:time_start': input_img.get('system:time_start')})
+            )
+
         daily_et_ref_coll = daily_et_ref_coll.map(et_reference_adjust)
 
     # Initialize variable list to only variables that can be interpolated
@@ -235,9 +244,7 @@ def et_reference_adjust(input_img):
     # For count, compute the composite/mosaic image for the mask band only
     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,
+            image_coll=scene_coll.select(['mask']), start_date=start_date, end_date=end_date,
         )
 
         # The following is needed because the aggregate collection can be
@@ -247,7 +254,7 @@ def et_reference_adjust(input_img):
         #   bands will be which causes a non-homogeneous image collection.
         aggregate_coll = aggregate_coll.merge(
             ee.Image.constant(0).rename(['mask'])
-                .set({'system:time_start': ee.Date(start_date).millis()})
+            .set({'system:time_start': ee.Date(start_date).millis()})
         )
 
     # Interpolate to a daily time step
@@ -294,13 +301,13 @@ 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_coll \
-            et_reference_img = daily_et_ref_coll \
-                .filterDate(agg_start_date, agg_end_date) \
+            et_reference_img = (
+                daily_et_ref_coll
+                .filterDate(agg_start_date, agg_end_date)
                 .select(['et_reference']).sum()
+            )
 
         image_list = []
         if 'et' in variables:
@@ -309,25 +316,26 @@ 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 = daily_coll \
-                .filterDate(agg_start_date, agg_end_date) \
+            ndvi_img = (
+                daily_coll.filterDate(agg_start_date, agg_end_date)
                 .mean().select(['ndvi']).float()
+            )
             image_list.append(ndvi_img)
         if 'count' in variables:
-            count_img = aggregate_coll \
-                .filterDate(agg_start_date, agg_end_date) \
+            count_img = (
+                aggregate_coll.filterDate(agg_start_date, agg_end_date)
                 .select(['mask']).sum().rename('count').uint8()
+            )
             image_list.append(count_img)
 
         return ee.Image(image_list) \
             .set({
                 'system:index': ee.Date(agg_start_date).format(date_format),
-                'system:time_start': ee.Date(agg_start_date).millis()})
+                'system:time_start': ee.Date(agg_start_date).millis(),
+            })
         #     .set(interp_properties) \
 
     # Combine input, interpolated, and derived values
@@ -386,6 +394,5 @@ def agg_annual(agg_start_date):
     elif t_interval.lower() == 'custom':
         # Returning an ImageCollection to be consistent
         return ee.ImageCollection(aggregate_image(
-            agg_start_date=start_date, agg_end_date=end_date,
-            date_format='YYYYMMdd',
+            agg_start_date=start_date, agg_end_date=end_date, date_format='YYYYMMdd',
         ))

openet/ssebop/image.py

@@ -22,23 +22,19 @@ def emissivity(landsat_image):
 
     """
     ndvi_img = ndvi(landsat_image)
-    Pv = ndvi_img.expression('((ndvi - 0.2) / 0.3) ** 2', {'ndvi': ndvi_img})
+    pv = ndvi_img.expression('((ndvi - 0.2) / 0.3) ** 2', {'ndvi': ndvi_img})
     # ndviRangevalue = ndvi_image.where(
     #     ndvi_image.gte(0.2).And(ndvi_image.lte(0.5)), ndvi_image
     # )
-    # Pv = ndviRangevalue.expression(
+    # pv = ndviRangevalue.expression(
     #     '(((ndviRangevalue - 0.2) / 0.3) ** 2',
     #     {'ndviRangevalue':ndviRangevalue}
     # )
 
     # Assuming typical Soil Emissivity of 0.97 and Veg Emissivity of 0.99
     #   and shape Factor mean value of 0.553
-    dE = Pv.expression(
-        '(1 - 0.97) * (1 - Pv) * (0.55 * 0.99)', {'Pv': Pv}
-    )
-    RangeEmiss = dE.expression(
-        '(0.99 * Pv) + (0.97 * (1 - Pv)) + dE', {'Pv': Pv, 'dE': dE}
-    )
+    de = pv.expression('(1 - 0.97) * (1 - Pv) * (0.55 * 0.99)', {'Pv': pv})
+    RangeEmiss = de.expression('(0.99 * Pv) + (0.97 * (1 - Pv)) + dE', {'Pv': pv, 'dE': de})
 
     return (
         ndvi_img
@@ -128,8 +124,7 @@ def ndvi(landsat_image):
     ee.Image
 
     """
-    return ee.Image(landsat_image).normalizedDifference(['nir', 'red'])\
-        .rename(['ndvi'])
+    return ee.Image(landsat_image).normalizedDifference(['nir', 'red']).rename(['ndvi'])
 
 
 def ndwi(landsat_image):
@@ -145,8 +140,7 @@ def ndwi(landsat_image):
     ee.Image
 
     """
-    return ee.Image(landsat_image).normalizedDifference(['green', 'swir1'])\
-        .rename(['ndwi'])
+    return ee.Image(landsat_image).normalizedDifference(['green', 'swir1']).rename(['ndwi'])
 
 
 def landsat_c2_qa_water_mask(landsat_image):

openet/ssebop/interpolate.py

@@ -216,8 +216,10 @@ def elr_adjust(temperature, elevation, radius=80):
     # Then generate the smoothed elevation image
     elev_tmax_smoothed = (
         elev_tmax_fine
-        .reduceNeighborhood(reducer=ee.Reducer.median(),
-                            kernel=ee.Kernel.square(radius=radius, units='pixels'))
+        .reduceNeighborhood(
+            reducer=ee.Reducer.median(),
+            kernel=ee.Kernel.square(radius=radius, units='pixels')
+        )
         .reproject(crs=tmax_projection)
     )
 

openet/ssebop/landsat.py

@@ -38,16 +38,18 @@ def getinfo(ee_obj, n=4):
 # I'm not sure how to make them fixtures and allow input parameters
 def constant_image_value(image, crs='EPSG:32613', scale=1):
     """Extract the output value from a calculation done with constant images"""
-    return getinfo(ee.Image(image).reduceRegion(
-        reducer=ee.Reducer.first(), scale=scale,
-        geometry=ee.Geometry.Rectangle([0, 0, 10, 10], crs, False)))
+    rr_params = {
+        'reducer': ee.Reducer.first(),
+        'geometry': ee.Geometry.Rectangle([0, 0, 10, 10], crs, False),
+        'scale': scale,
+    }
+    return getinfo(ee.Image(image).reduceRegion(**rr_params))
 
 
 def point_image_value(image, xy, scale=1):
     """Extract the output value from a calculation at a point"""
-    return getinfo(ee.Image(image).reduceRegion(
-        reducer=ee.Reducer.first(), geometry=ee.Geometry.Point(xy),
-        scale=scale))
+    rr_params = {'reducer': ee.Reducer.first(), 'geometry': ee.Geometry.Point(xy), 'scale': scale}
+    return getinfo(ee.Image(image).reduceRegion(**rr_params))
 
 
 def point_coll_value(coll, xy, scale=1):
@@ -62,8 +64,7 @@ def point_coll_value(coll, xy, scale=1):
         col_dict[k] = i + 4
         info_dict[k] = {}
     for row in output[1:]:
-        date = datetime.datetime.utcfromtimestamp(row[3] / 1000.0).strftime(
-            '%Y-%m-%d')
+        date = datetime.datetime.utcfromtimestamp(row[3] / 1000.0).strftime('%Y-%m-%d')
         for k, v in col_dict.items():
             info_dict[k][date] = row[col_dict[k]]
     return info_dict
@@ -97,8 +98,7 @@ def date_to_time_0utc(date):
     ee.Number
 
     """
-    return ee.Date.fromYMD(date.get('year'), date.get('month'),
-                           date.get('day')).millis()
+    return ee.Date.fromYMD(date.get('year'), date.get('month'), date.get('day')).millis()
     # Extra operations are needed since update() does not set milliseconds to 0.
     # return date.update(hour=0, minute=0, second=0).millis()\
     #     .divide(1000).floor().multiply(1000)