diff --git a/bifacial_radiance/main.py b/bifacial_radiance/main.py index 9fcc0344..6efd2e90 100644 --- a/bifacial_radiance/main.py +++ b/bifacial_radiance/main.py @@ -3126,7 +3126,7 @@ def _printRow(analysisobj, key): - else: + else: #cumulative analysis if module is None: for key in keys: # loop over trackerdict to find first available module try: @@ -3139,7 +3139,8 @@ def _printRow(analysisobj, key): module_local = module self.compiledResults = performance.calculatePerformanceGencumsky(results=self.results, bifacialityfactor=module_local.bifi, - fillcleanedSensors=False) + fillcleanedSensors=False).rename( + columns={'Wm2Front':'Whm2Front', 'Wm2Back':'Whm2Back'}) self.compiledResults.to_csv(os.path.join('results', 'Cumulative_Results.csv'), float_format='%0.3f', index=False) diff --git a/bifacial_radiance/performance.py b/bifacial_radiance/performance.py index e0a378e9..417c8ff3 100644 --- a/bifacial_radiance/performance.py +++ b/bifacial_radiance/performance.py @@ -453,14 +453,14 @@ def _dict2DF(df, key): dfst = pd.DataFrame(zip(cumRow, cumMod, cumScene, cumFront, cumWM2, cumBack, Grear_mean, POA_eff), columns=('rowNum', 'modNum','sceneNum', 'Gfront_mean', - 'Wm2Front', 'Wm2Back', 'Grear_mean', + 'Whm2Front', 'Whm2Back', 'Grear_mean', 'POA_eff')) dfst['BGG'] = dfst['Grear_mean']*100*bifacialityfactor/dfst['Gfront_mean'] # Reordering columns cols = ['rowNum', 'modNum','sceneNum', 'BGG', 'Gfront_mean', 'Grear_mean', 'POA_eff', - 'Wm2Front','Wm2Back'] + 'Whm2Front','Whm2Back'] dfst = dfst[cols] return dfst