import math
import numpy as np

#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
H7XANTHINE= {
    1: ([7.55], [1]),
    2: ([4.47], [3]),
    3: ([7.72], [1]),
    4: ([7.655], [1]),
}
C7XANTHINE= {
    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
HNMR2= {
    1: ([11.10], [1]),
    2: ([3.33], [3]),
    3: ([3.84], [3]),
    4: ([7.97], [1]),
}

CNMR2= {
    1: ([154.9], [1]),
    2: ([149.8], [1]),
    3: ([107.1], [1]),
    4: ([151.0], [1]),
    5: ([142.8], [1]),
    6: ([29.3], [1]),
    7: ([33.9], [1]),
}

def overlap(listref, listnew):
    twoleft = np.sum(np.multiply(np.concatenate((listref, [0, 0])),  np.concatenate(([0, 0], listnew))))
    oneleft = np.sum(np.multiply(np.concatenate((listref, [0])),  np.concatenate(([0], listnew))))
    neutral = np.sum(np.multiply(listref,listnew))
    oneright = np.sum(np.multiply(np.concatenate(([0], listref)),  np.concatenate((listnew, [0]))))
    tworight = np.sum(np.multiply(np.concatenate(([0, 0], listref)),  np.concatenate((listnew, [0, 0]))))
    overlap = (oneleft + oneright)* 0.5 + neutral
    return overlap

def bin_array(spectra, highest_ppm, lowest_ppm, bin_width):
    binnumber = math.ceil((highest_ppm - lowest_ppm)/bin_width)
    bin = [0] * binnumber
    for peak in spectra:
        (shift, height) = spectra[peak]
        binindex = math.floor((shift[0] - lowest_ppm) / bin_width)
        bin[binindex] += height[0]
    normalizedbin = np.divide(bin, np.sum(bin))
    return normalizedbin

def similarity_nmr(spectraref, spectranew, bin_width, highest_ppm, lowest_ppm):

    #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.
    #5.4.2 Eliminating X–H signals from 1H NMR spectra
    binref = bin_array(spectraref, highest_ppm, lowest_ppm, bin_width)
    binnew = bin_array(spectranew, highest_ppm, lowest_ppm, bin_width)
    crosscorr = overlap(binref, binnew)
    refselfcorr = overlap(binref, binref)
    newselfcorr = overlap(binnew, binnew)
    simidx = crosscorr / math.sqrt(refselfcorr * newselfcorr)
    return(simidx)

def main():
    print(similarity_nmr(H1XANTHINE, HNMR1, 0.4, 13, 3))
    print(similarity_nmr(H3XANTHINE, HNMR1, 0.4, 13, 3))
    print(similarity_nmr(H7XANTHINE, HNMR1, 0.4, 13, 3))

if __name__ == "__main__":
    main()