import Data_Analysis as DA
import pandas as pd

accuracy = pd.read_csv(f'results/accMeanDf.csv')

propData = DA.getPropertyDataFromDB()
propData = pd.DataFrame(propData, columns =['property_id', 'region', 'geoLocation'])
propData = propData.drop(columns=['geoLocation'])
#print(propData)

merge = pd.merge(propData, accuracy, on="property_id")
#print(merge)

#1 = Heidiland, 2 = Davos, 3 = Engadin 4 = St.Moritz

heidiAcc = merge[merge['region'] == 1]
davosAcc = merge[merge['region'] == 2]
EngadAcc = merge[merge['region'] == 3]
StMorAcc = merge[merge['region'] == 4]


heidiMean = heidiAcc.mean(axis=0)
davosMean = davosAcc.mean(axis=0)
EngadMean = EngadAcc.mean(axis=0)
StMorMean = StMorAcc.mean(axis=0)

heidiSDev = heidiAcc.std(axis=0)
davosSDev = davosAcc.std(axis=0)
EngadSDev = EngadAcc.std(axis=0)
StMorSDev = StMorAcc.std(axis=0)


accuracyOverview = pd.DataFrame()

accuracyOverview.insert(0, "St. Moritz StdDev", StMorSDev, True)
accuracyOverview.insert(0, "St. Moritz Mean", StMorMean, True)
accuracyOverview.insert(0, "Engadin StdDev", EngadSDev, True)
accuracyOverview.insert(0, "Engadin Mean", EngadMean, True)
accuracyOverview.insert(0, "Davos StdDev", davosSDev, True)
accuracyOverview.insert(0, "Davos Mean", davosMean, True)
accuracyOverview.insert(0, "Heidi StdDev", heidiSDev, True)
accuracyOverview.insert(0, "Heidi Mean", heidiMean, True)


accuracyOverview.drop(index=accuracyOverview.index[0], axis=0, inplace=True)
accuracyOverview.drop(index=accuracyOverview.index[0], axis=0, inplace=True)
accuracyOverview.to_csv('results/accuracyOverview.csv', index=False)

#delete unused DF's
del merge, accuracy, propData
del heidiAcc, davosAcc, EngadAcc, StMorAcc
del heidiMean, davosMean, EngadMean, StMorMean
del heidiSDev, davosSDev, EngadSDev, StMorSDev


print(accuracyOverview)