import pandas as pd
import biogeme.database as db
import biogeme.biogeme as bio

pandas = pd.read_table("swissmetro.dat")
database = db.Database("swissmetro",pandas)

# The Pandas data structure is available as database.data. Use all the
# Pandas functions to invesigate the database
#print(database.data.describe())

from headers import *

# Removing some observations can be done directly using pandas.
#remove = (((database.data.PURPOSE != 1) & (database.data.PURPOSE != 3)) | (database.data.CHOICE == 0))
#database.data.drop(database.data[remove].index,inplace=True)

# Here we use the "biogeme" way for backward compatibility
exclude = (( PURPOSE != 1 ) * (  PURPOSE   !=  3  ) +  ( CHOICE == 0 )) > 0
database.remove(exclude)



ASC_CAR = Beta('ASC_CAR',0,None,None,0)
ASC_TRAIN = Beta('ASC_TRAIN',0,None,None,0)
ASC_SM = Beta('ASC_SM',0,None,None,1)
B_TIME = Beta('B_TIME',0,None,None,0)
B_COST = Beta('B_COST',0,None,None,0)

SM_COST =  SM_CO   * (  GA   ==  0  ) 
TRAIN_COST =  TRAIN_CO   * (  GA   ==  0  )

TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED',\
                                 TRAIN_TT / 100.0,database)
TRAIN_COST_SCALED = DefineVariable('TRAIN_COST_SCALED',\
                                   TRAIN_COST / 100,database)
SM_TT_SCALED = DefineVariable('SM_TT_SCALED', SM_TT / 100.0,database)
SM_COST_SCALED = DefineVariable('SM_COST_SCALED', SM_COST / 100,database)
CAR_TT_SCALED = DefineVariable('CAR_TT_SCALED', CAR_TT / 100,database)
CAR_CO_SCALED = DefineVariable('CAR_CO_SCALED', CAR_CO / 100,database)

# Biogeme cannot compute the log of 0. Therefore, whenever the cost
# is 0, the log of 1 computed instead.
LOG_CAR_COST = DefineVariable('LOG_CAR_COST',(CAR_CO_SCALED != 0) * log( CAR_CO_SCALED + 1 * (CAR_CO_SCALED == 0)),database)
LOG_TRAIN_COST = DefineVariable('LOG_TRAIN_COST',(TRAIN_COST_SCALED != 0) * log( TRAIN_COST_SCALED + 1 * (TRAIN_COST_SCALED == 0) ),database)
LOG_SM_COST = DefineVariable('LOG_SM_COST', (SM_COST_SCALED != 0) * log( SM_COST_SCALED + 1 * (SM_COST_SCALED == 0)),database)

V1 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * LOG_TRAIN_COST
V2 = ASC_SM + B_TIME * SM_TT_SCALED + B_COST * LOG_SM_COST
V3 = ASC_CAR + B_TIME * CAR_TT_SCALED + B_COST * LOG_CAR_COST


# Associate utility functions with the numbering of alternatives
V = {1: V1,
     2: V2,
     3: V3}


# Associate the availability conditions with the alternatives
CAR_AV_SP =  DefineVariable('CAR_AV_SP',CAR_AV  * (  SP   !=  0  ),database)
TRAIN_AV_SP =  DefineVariable('TRAIN_AV_SP',TRAIN_AV  * (  SP   !=  0  ),database)

av = {1: TRAIN_AV_SP,
      2: SM_AV,
      3: CAR_AV_SP}

logprob = bioLogLogit(V,av,CHOICE)

biogeme  = bio.BIOGEME(database,logprob)
biogeme.modelName = "04modifVariables"
results = biogeme.estimate()
print("Results=",results)