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Multi scene objs #488

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Multi scene objs #488

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38f6779
Only re-initialize trackerdict[index][Results] if it's empty
cdeline Nov 9, 2023
4551a8b
add customname to keys of trackerdict['Results']
cdeline Nov 9, 2023
35f6f6b
convert self.scene into self.scenes (list) and add _getradfiles() whi…
cdeline Nov 10, 2023
bd3c73c
add scene.appendtoScene, which is basically a duplicate of radianceOb…
cdeline Nov 10, 2023
2132aff
Initial commit - update trackerdict[scene] to [scenes] (list). Initi…
cdeline Nov 10, 2023
90f8f80
load._exportTrackerDict updated to iterate on sceneNum. _getradfiles…
cdeline Nov 10, 2023
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105 changes: 53 additions & 52 deletions bifacial_radiance/load.py
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Original file line number Diff line number Diff line change
Expand Up @@ -333,7 +333,7 @@ def _exportTrackerDict(trackerdict, savefile, reindex=False, monthlyyearly=False
# add trackerdict Results (not all simulations will have results)
try:
results = pd.concat([df(data=value['Results'],index=[key]*len(value['Results'])) for (key,value) in trackerdict.items()])
results = results[['rowWanted','modWanted','Wm2Front','Wm2Back']]
results = results[['rowWanted','modWanted','sceneNum','Wm2Front','Wm2Back']]
d = results.join(d)
except KeyError:
pass
Expand All @@ -357,57 +357,58 @@ def _exportTrackerDict(trackerdict, savefile, reindex=False, monthlyyearly=False
D4join = pd.DataFrame()
for rownum in d['rowWanted'].unique():
for modnum in d['modWanted'].unique():
mask = (d['rowWanted']==rownum) & (d['modWanted']==modnum)
print(modnum)
# Gfront_mean.append(filledFront[mask].sum(axis=0).mean())
D2 = d[mask].copy()
D2['timestamp'] = pd.to_datetime(D2['measdatetime'], format="%Y-%m-%d_%H%M")
D2 = D2.set_index('timestamp')
# D2 = D2.set_index(D2['timestamp'])

# Determine if data is sub-hourly
if len(D2) > 1:
if (D2.index[1]-D2.index[0]).total_seconds() / 60 < 60.0:
# Subhourly to hourly data averages, doesn't sum
# So we get average hourly irradiance as well as Wh on
# results of power.
D2b = D2.copy()
D2b = D2b.groupby(pd.PeriodIndex(D2b.index, freq="H")).mean().reset_index()
D2b['BGG'] = D2b['Grear_mean']*100/D2b['Gfront_mean']
D2b['BGE'] = (D2b['Pout']-D2b['Pout_Gfront'])*100/D2b['Pout']
D2b['Mismatch'] = (D2b['Pout_raw']-D2b['Pout'])*100/D2b['Pout_raw']
D2b['rowWanted'] = rownum
D2b['modWanted'] = modnum
D2b.drop(columns=['theta', 'surf_tilt', 'surf_azm'], inplace=True)
D2b=D2b.reset_index()
D2join = pd.concat([D2join, D2b], ignore_index=True, sort=False)

D3 = D2.groupby(pd.PeriodIndex(D2.index, freq="M")).sum().reset_index()
D3['BGG'] = D3['Grear_mean']*100/D3['Gfront_mean']
D3['BGE'] = (D3['Pout']-D3['Pout_Gfront'])*100/D3['Pout']
D3['Mismatch'] = (D3['Pout_raw']-D3['Pout'])*100/D3['Pout_raw']
D3['rowWanted'] = rownum
D3['modWanted'] = modnum
D3m = D2.groupby(pd.PeriodIndex(D2.index, freq="M")).mean().reset_index()
D3['temp_air'] = D3m['temp_air']
D3['wind_speed'] = D3m['wind_speed']
D3.drop(columns=['theta', 'surf_tilt', 'surf_azm'], inplace=True)

D4 = D2.groupby(pd.PeriodIndex(D2.index, freq="Y")).sum().reset_index()
D4['BGG'] = D4['Grear_mean']*100/D4['Gfront_mean']
D4['BGE'] = (D4['Pout']-D4['Pout_Gfront'])*100/D4['Pout']
D4['Mismatch'] = (D4['Pout_raw']-D4['Pout'])*100/D4['Pout_raw']
D4['rowWanted'] = rownum
D4['modWanted'] = modnum
D4m = D2.groupby(pd.PeriodIndex(D2.index, freq="Y")).mean().reset_index()
D4['temp_air'] = D4m['temp_air']
D4['wind_speed'] = D4m['wind_speed']
D4.drop(columns=['theta', 'surf_tilt', 'surf_azm'], inplace=True)

D3=D3.reset_index()
D4=D4.reset_index()
D3join = pd.concat([D3join, D3], ignore_index=True, sort=False)
D4join = pd.concat([D4join, D4], ignore_index=True, sort=False)
for sceneNum in d['sceneNum'].unique():#TODO: is sceneNum iteration required here?
mask = (d['rowWanted']==rownum) & (d['modWanted']==modnum) & (d['sceneNum']==sceneNum)
print(modnum)
# Gfront_mean.append(filledFront[mask].sum(axis=0).mean())
D2 = d[mask].copy()
D2['timestamp'] = pd.to_datetime(D2['measdatetime'], format="%Y-%m-%d_%H%M")
D2 = D2.set_index('timestamp')
# D2 = D2.set_index(D2['timestamp'])

# Determine if data is sub-hourly
if len(D2) > 1:
if (D2.index[1]-D2.index[0]).total_seconds() / 60 < 60.0:
# Subhourly to hourly data averages, doesn't sum
# So we get average hourly irradiance as well as Wh on
# results of power.
D2b = D2.copy()
D2b = D2b.groupby(pd.PeriodIndex(D2b.index, freq="H")).mean().reset_index()
D2b['BGG'] = D2b['Grear_mean']*100/D2b['Gfront_mean']
D2b['BGE'] = (D2b['Pout']-D2b['Pout_Gfront'])*100/D2b['Pout']
D2b['Mismatch'] = (D2b['Pout_raw']-D2b['Pout'])*100/D2b['Pout_raw']
D2b['rowWanted'] = rownum
D2b['modWanted'] = modnum
D2b.drop(columns=['theta', 'surf_tilt', 'surf_azm'], inplace=True)
D2b=D2b.reset_index()
D2join = pd.concat([D2join, D2b], ignore_index=True, sort=False)

D3 = D2.groupby(pd.PeriodIndex(D2.index, freq="M")).sum().reset_index()
D3['BGG'] = D3['Grear_mean']*100/D3['Gfront_mean']
D3['BGE'] = (D3['Pout']-D3['Pout_Gfront'])*100/D3['Pout']
D3['Mismatch'] = (D3['Pout_raw']-D3['Pout'])*100/D3['Pout_raw']
D3['rowWanted'] = rownum
D3['modWanted'] = modnum
D3m = D2.groupby(pd.PeriodIndex(D2.index, freq="M")).mean().reset_index()
D3['temp_air'] = D3m['temp_air']
D3['wind_speed'] = D3m['wind_speed']
D3.drop(columns=['theta', 'surf_tilt', 'surf_azm'], inplace=True)

D4 = D2.groupby(pd.PeriodIndex(D2.index, freq="Y")).sum().reset_index()
D4['BGG'] = D4['Grear_mean']*100/D4['Gfront_mean']
D4['BGE'] = (D4['Pout']-D4['Pout_Gfront'])*100/D4['Pout']
D4['Mismatch'] = (D4['Pout_raw']-D4['Pout'])*100/D4['Pout_raw']
D4['rowWanted'] = rownum
D4['modWanted'] = modnum
D4m = D2.groupby(pd.PeriodIndex(D2.index, freq="Y")).mean().reset_index()
D4['temp_air'] = D4m['temp_air']
D4['wind_speed'] = D4m['wind_speed']
D4.drop(columns=['theta', 'surf_tilt', 'surf_azm'], inplace=True)

D3=D3.reset_index()
D4=D4.reset_index()
D3join = pd.concat([D3join, D3], ignore_index=True, sort=False)
D4join = pd.concat([D4join, D4], ignore_index=True, sort=False)

savefile2 = savefile[:-4]+'_Hourly.csv'
savefile3 = savefile[:-4]+'_Monthly.csv'
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