split long comments into multiple lines

hopp/simulation/technologies/resource/nsrdb_data.py

@@ -98,10 +98,15 @@ def download_resource(self):
             self.nsrdb_latitude = f.meta['latitude'].iloc[site_gid]
             self.nsrdb_longitude = f.meta['longitude'].iloc[site_gid]
 
-            # extract remaining datapoints: year, month, day, hour, minute, dn, df, gh, wspd,tdry, pres, tdew
-            # NOTE: datasets have readings at 0 and 30 minutes each hour, HOPP/SAM workflow requires only 30 minute reading values -> filter 0 minute readings with [1::2]
-            # NOTE: datasets are not auto shifted by timezone offset -> wrap extraction in SAMResource.roll_timeseries(input_array, timezone, #steps in an hour=1) to roll timezones
-            # NOTE: solar_resource.py code references solar_zenith_angle and RH = relative_humidity but I couldn't find them actually being utilized. Captured them below just in case.
+            # extract remaining datapoints: 
+            # year, month, day, hour, minute, dn, df, gh, wspd,tdry, pres, tdew
+
+            # 1) NOTE: datasets have readings at 0 and 30 minutes each hour, 
+            # HOPP/SAM workflow requires only 30 minute reading values -> filter 0 minute readings with [1::2]
+            # 2) NOTE: datasets are not auto shifted by timezone offset 
+            # -> wrap extraction in SAMResource.roll_timeseries(input_array, timezone, #steps in an hour=1) to roll timezones
+            # 3) NOTE: solar_resource.py code references solar_zenith_angle and RH = relative_humidity but I couldn't find them 
+            # actually being utilized. Captured them below just in case.
             self.year_arr = f.time_index.year.values[1::2]
             self.month_arr = f.time_index.month.values[1::2]
             self.day_arr = f.time_index.day.values[1::2]

hopp/simulation/technologies/resource/wind_toolkit_data.py

@@ -125,7 +125,8 @@ def download_resource(self):
             # instantiate temp dictionary to hold each attributes dataset
             self.wind_dict = {}
             # loop through hub heights to download, capture datasets
-            # NOTE: datasets are not auto shifted by timezone offset -> wrap extraction in SAMResource.roll_timeseries(input_array, timezone, #steps in an hour=1) to roll timezones
+            # NOTE: datasets are not auto shifted by timezone offset 
+            # -> wrap extraction in SAMResource.roll_timeseries(input_array, timezone, #steps in an hour=1) to roll timezones
             # NOTE: pressure datasets unit = Pa, convert to atm via division by 101325
             for h in self.data_hub_heights:
                 self.wind_dict['temperature_{height}m_arr'.format(height=h)] = SAMResource.roll_timeseries((f['temperature_{height}m'.format(height=h), :, site_gid]), time_zone, 1)
@@ -164,7 +165,8 @@ def format_data(self):
                                                        self.wind_dict['winddirection_{h}m_arr'.format(h=self.data_hub_heights[1])])]
 
         elif len(self.data_hub_heights) == 1:
-            # NOTE: Unsure if SAM/PySAM is sensitive to data types ie: floats with long precision vs to 2 or 3 decimals. If not sensitive, can remove following 4 lines of code to increase computational efficiency
+            # NOTE: Unsure if SAM/PySAM is sensitive to data types ie: floats with long precision vs to 2 or 3 decimals. 
+            # If not sensitive, can remove following 4 lines of code to increase computational efficiency
             self.wind_dict['temperature_{h}m_arr'.format(h=self.data_hub_heights[0])] = np.round((self.wind_dict['temperature_{h}m_arr'.format(h=self.data_hub_heights[0])]), decimals=1)
             self.wind_dict['pressure_{h}m_arr'.format(h=self.data_hub_heights[0])] = np.round((self.wind_dict['pressure_{h}m_arr'.format(h=self.data_hub_heights[0])]), decimals=2)
             self.wind_dict['windspeed_{h}m_arr'.format(h=self.data_hub_heights[0])] = np.round((self.wind_dict['windspeed_{h}m_arr'.format(h=self.data_hub_heights[0])]), decimals=3)