#Also ILP? Minimize distance becuse two vectors might be shifted to oneanother (high weight on height distance to keep same height)
#Zu hohe Komplexität bei Skalierung
import gurobipy as gp
from gurobipy import GRB, Model, quicksum

#Xanthine
HXANTHINE= {
    1: ([7.96], [1]),
    2: ([9.45], [1]),
    3: ([7.725], [1]),
    4: ([7.625], [1]),
}
CXANTHINE= {
    1: ([159.40], [1]),
    2: ([164.01], [1]),
    3: ([120.94], [1]),
    4: ([161.24], [1]),
    5: ([146.98], [1]),
}

#1-Methylxanthine
H1XANTHINE= {
    1: ([7.93], [1]),
    2: ([9.45], [1]),
    3: ([4.05], [3]),
    4: ([7.91], [1]),
}
C1XANTHINE= {
    1: ([161.50], [1]),
    2: ([166.28], [1]),
    3: ([120.65], [1]),
    4: ([158.74], [1]),
    5: ([146.25], [1]),
    6: ([38.55], [1]),
}

#3-Methylxanthine
H3XANTHINE= {
    1: ([4.15], [3]),
    2: ([7.73], [1]),
    3: ([7.99], [1]),
    4: ([9.49], [1]),
}
C3XANTHINE= {
    1: ([161.83], [1]),
    2: ([163.37], [1]),
    3: ([121.24], [1]),
    4: ([163.15], [1]),
    5: ([146.49], [1]),
    6: ([39.71], [1]),
}

#7-Methylxanthine
H3XANTHINE= {
    1: ([7.55], [1]),
    2: ([4.47], [3]),
    3: ([7.72], [1]),
    4: ([7.655], [1]),
}
C3XANTHINE= {
    1: ([159.50], [1]),
    2: ([165.47], [1]),
    3: ([122.15], [1]),
    4: ([162.31], [1]),
    5: ([151.55], [1]),
    6: ([45.06], [1]),
}

#Theophylline
H13XANTHINE= {
    1: ([4.03], [3]),
    2: ([7.98], [1]),
    3: ([4.19], [3]),
    4: ([9.49], [1]),
}
C13XANTHINE= {
    1: ([163.77], [1]),
    2: ([165.26], [1]),
    3: ([120.73], [1]),
    4: ([160.99], [1]),
    5: ([145.80], [1]),
    6: ([40.42], [1]),
    7: ([37.60], [1]),
}

#Paraxanthine
H17XANTHINE= {
    1: ([4.50], [3]),
    2: ([7.70], [1]),
    3: ([3.98], [3]),
    4: ([7.82], [1]),
}
C17XANTHINE= {
    1: ([161.41], [1]),
    2: ([167.17], [1]),
    3: ([121.81], [1]),
    4: ([160.18], [1]),
    5: ([151.09], [1]),
    6: ([45.17], [1]),
    7: ([36.96], [1]),
}

#Theobromine
H37XANTHINE= {
    1: ([4.49], [3]),
    2: ([7.75], [1]),
    3: ([4.11], [3]),
    4: ([7.65], [1]),
}
C37XANTHINE= {
    1: ([161.76], [1]),
    2: ([164.86], [1]),
    3: ([122.51], [1]),
    4: ([164.29], [1]),
    5: ([151.10], [1]),
    6: ([39.33], [1]),
    7: ([45.07], [1]),
}

#Caffeine
H37XANTHINE= {
    1: ([7.73], [1]),
    2: ([4.15], [3]),
    3: ([4.52], [3]),
    4: ([4.01], [3]),
}
C37XANTHINE= {
    1: ([163.66], [1]),
    2: ([166.66], [1]),
    3: ([122.03], [1]),
    4: ([162.23], [1]),
    5: ([150.50], [1]),
    6: ([40.09], [1]),
    7: ([45.23], [1]),
    8: ([37.17], [1]),
}

#Neue Quelle für 13CNMR für beide
#Experimental 7-Methylxanthine nmr
HNMR1= {
    1: ([10.85], [1]),
    2: ([11.50], [1]),
    3: ([3.82], [3]),
    4: ([7.88], [1]),
}



#Experimental Theobromine nmr
HNMR1= {
    1: ([11.10], [1]),
    2: ([3.33], [3]),
    3: ([3.84], [3]),
    4: ([7.97], [1]),
}

def build_model(name, nmrnode, nmrmeasured, excluded_support=None):
    model = Model(name)
    #Maximize likelihood or minimize Deviation
    #Values for two spectra and optimize largest for both different?
    #Spectra in Nodes to allow maximize overlapp with both spectra or one spectra.
    #Vergleiche PCA, Binning (Größere Schwankungen zwischen den Methoden)