def compositionValues(aa1, aa2):
    compositionValues = {'S': 1.42, 'R': 0.65, 'L': 0, 'P': 0.39, 'T': 0.71, 'A': 0, 'V': 0, 'G': 0.74,
                         'I': 0, 'F': 0, 'Y': 0.20, 'C': 2.75, 'H': 0.58, 'Q': 0.89, 'N': 1.33, 'K': 0.33,
                         'D': 1.38, 'E': 0.92, 'M': 0, 'W': 0.13}
    dif = round((compositionValues[aa1] - compositionValues[aa2]), 2)
    return (dif)


def polarityValues(aa1, aa2):
    polarityValues = {'S': 9.2, 'R': 10.5, 'L': 4.9, 'P': 8.0, 'T': 8.6, 'A': 8.1, 'V': 5.9, 'G': 9.0,
                      'I': 5.2, 'F': 5.2, 'Y': 6.2, 'C': 5.5, 'H': 10.4, 'Q': 10.5, 'N': 11.6, 'K': 11.3,
                      'D': 13.0, 'E': 12.3, 'M': 5.7, 'W': 5.4}
    dif = round((polarityValues[aa1] - polarityValues[aa2]), 2)
    return (dif)


def volumeValues(aa1, aa2):
    volumeValues = {'S': 32, 'R': 124, 'L': 111, 'P': 32.5, 'T': 61, 'A': 31, 'V': 84, 'G': 3,
                    'I': 111, 'F': 132, 'Y': 136, 'C': 55, 'H': 96, 'Q': 85, 'N': 56, 'K': 119,
                    'D': 54, 'E': 83, 'M': 105, 'W': 170}
    dif = round((volumeValues[aa1] - volumeValues[aa2]), 2)
    return (dif)


def add_physicochemical(df):
    grantham_dict = {
        ('A', 'A'): '0',
        ('A', 'C'): '195',
        ('A', 'D'): '126',
        ('A', 'E'): '107',
        ('A', 'F'): '113',
        ('A', 'G'): '60',
        ('A', 'H'): '86',
        ('A', 'I'): '94',
        ('A', 'K'): '106',
        ('A', 'L'): '96',
        ('A', 'M'): '84',
        ('A', 'N'): '111',
        ('A', 'P'): '27',
        ('A', 'Q'): '91',
        ('A', 'R'): '112',
        ('A', 'S'): '99',
        ('A', 'T'): '58',
        ('A', 'V'): '64',
        ('A', 'W'): '148',
        ('A', 'Y'): '112',
        ('C', 'A'): '195',
        ('C', 'C'): '0',
        ('C', 'D'): '154',
        ('C', 'E'): '170',
        ('C', 'F'): '205',
        ('C', 'G'): '159',
        ('C', 'H'): '174',
        ('C', 'I'): '198',
        ('C', 'K'): '202',
        ('C', 'L'): '198',
        ('C', 'M'): '196',
        ('C', 'N'): '139',
        ('C', 'P'): '169',
        ('C', 'Q'): '154',
        ('C', 'R'): '180',
        ('C', 'S'): '112',
        ('C', 'T'): '149',
        ('C', 'V'): '192',
        ('C', 'W'): '215',
        ('C', 'Y'): '194',
        ('D', 'A'): '126',
        ('D', 'C'): '154',
        ('D', 'D'): '0',
        ('D', 'E'): '45',
        ('D', 'F'): '177',
        ('D', 'G'): '94',
        ('D', 'H'): '81',
        ('D', 'I'): '168',
        ('D', 'K'): '101',
        ('D', 'L'): '172',
        ('D', 'M'): '160',
        ('D', 'N'): '23',
        ('D', 'P'): '108',
        ('D', 'Q'): '61',
        ('D', 'R'): '96',
        ('D', 'S'): '65',
        ('D', 'T'): '85',
        ('D', 'V'): '152',
        ('D', 'W'): '181',
        ('D', 'Y'): '160',
        ('E', 'A'): '107',
        ('E', 'C'): '170',
        ('E', 'D'): '45',
        ('E', 'E'): '0',
        ('E', 'F'): '140',
        ('E', 'G'): '98',
        ('E', 'H'): '40',
        ('E', 'I'): '134',
        ('E', 'K'): '56',
        ('E', 'L'): '138',
        ('E', 'M'): '126',
        ('E', 'N'): '42',
        ('E', 'P'): '93',
        ('E', 'Q'): '29',
        ('E', 'R'): '54',
        ('E', 'S'): '80',
        ('E', 'T'): '65',
        ('E', 'V'): '121',
        ('E', 'W'): '152',
        ('E', 'Y'): '122',
        ('F', 'A'): '113',
        ('F', 'C'): '205',
        ('F', 'D'): '177',
        ('F', 'E'): '140',
        ('F', 'F'): '0',
        ('F', 'G'): '153',
        ('F', 'H'): '100',
        ('F', 'I'): '21',
        ('F', 'K'): '102',
        ('F', 'L'): '22',
        ('F', 'M'): '28',
        ('F', 'N'): '158',
        ('F', 'P'): '114',
        ('F', 'Q'): '116',
        ('F', 'R'): '97',
        ('F', 'S'): '155',
        ('F', 'T'): '103',
        ('F', 'V'): '50',
        ('F', 'W'): '40',
        ('F', 'Y'): '22',
        ('G', 'A'): '60',
        ('G', 'C'): '159',
        ('G', 'D'): '94',
        ('G', 'E'): '98',
        ('G', 'F'): '153',
        ('G', 'G'): '0',
        ('G', 'H'): '98',
        ('G', 'I'): '135',
        ('G', 'K'): '127',
        ('G', 'L'): '138',
        ('G', 'M'): '127',
        ('G', 'N'): '80',
        ('G', 'P'): '42',
        ('G', 'Q'): '87',
        ('G', 'R'): '125',
        ('G', 'S'): '56',
        ('G', 'T'): '59',
        ('G', 'V'): '109',
        ('G', 'W'): '184',
        ('G', 'Y'): '147',
        ('H', 'A'): '86',
        ('H', 'C'): '174',
        ('H', 'D'): '81',
        ('H', 'E'): '40',
        ('H', 'F'): '100',
        ('H', 'G'): '98',
        ('H', 'H'): '0',
        ('H', 'I'): '94',
        ('H', 'K'): '32',
        ('H', 'L'): '99',
        ('H', 'M'): '87',
        ('H', 'N'): '68',
        ('H', 'P'): '77',
        ('H', 'Q'): '24',
        ('H', 'R'): '29',
        ('H', 'S'): '89',
        ('H', 'T'): '47',
        ('H', 'V'): '84',
        ('H', 'W'): '115',
        ('H', 'Y'): '83',
        ('I', 'A'): '94',
        ('I', 'C'): '198',
        ('I', 'D'): '168',
        ('I', 'E'): '134',
        ('I', 'F'): '21',
        ('I', 'G'): '135',
        ('I', 'H'): '94',
        ('I', 'I'): '0',
        ('I', 'K'): '102',
        ('I', 'L'): '5',
        ('I', 'M'): '10',
        ('I', 'N'): '149',
        ('I', 'P'): '95',
        ('I', 'Q'): '109',
        ('I', 'R'): '97',
        ('I', 'S'): '142',
        ('I', 'T'): '89',
        ('I', 'V'): '29',
        ('I', 'W'): '61',
        ('I', 'Y'): '33',
        ('K', 'A'): '106',
        ('K', 'C'): '202',
        ('K', 'D'): '101',
        ('K', 'E'): '56',
        ('K', 'F'): '102',
        ('K', 'G'): '127',
        ('K', 'H'): '32',
        ('K', 'I'): '102',
        ('K', 'K'): '0',
        ('K', 'L'): '107',
        ('K', 'M'): '95',
        ('K', 'N'): '94',
        ('K', 'P'): '103',
        ('K', 'Q'): '53',
        ('K', 'R'): '26',
        ('K', 'S'): '121',
        ('K', 'T'): '78',
        ('K', 'V'): '97',
        ('K', 'W'): '110',
        ('K', 'Y'): '85',
        ('L', 'A'): '96',
        ('L', 'C'): '198',
        ('L', 'D'): '172',
        ('L', 'E'): '138',
        ('L', 'F'): '22',
        ('L', 'G'): '138',
        ('L', 'H'): '99',
        ('L', 'I'): '5',
        ('L', 'K'): '107',
        ('L', 'L'): '0',
        ('L', 'M'): '15',
        ('L', 'N'): '153',
        ('L', 'P'): '98',
        ('L', 'Q'): '113',
        ('L', 'R'): '102',
        ('L', 'S'): '145',
        ('L', 'T'): '92',
        ('L', 'V'): '32',
        ('L', 'W'): '61',
        ('L', 'Y'): '36',
        ('M', 'A'): '84',
        ('M', 'C'): '196',
        ('M', 'D'): '160',
        ('M', 'E'): '126',
        ('M', 'F'): '28',
        ('M', 'G'): '127',
        ('M', 'H'): '87',
        ('M', 'I'): '10',
        ('M', 'K'): '95',
        ('M', 'L'): '15',
        ('M', 'M'): '0',
        ('M', 'N'): '142',
        ('M', 'P'): '87',
        ('M', 'Q'): '101',
        ('M', 'R'): '91',
        ('M', 'S'): '135',
        ('M', 'T'): '81',
        ('M', 'V'): '21',
        ('M', 'W'): '67',
        ('M', 'Y'): '36',
        ('N', 'A'): '111',
        ('N', 'C'): '139',
        ('N', 'D'): '23',
        ('N', 'E'): '42',
        ('N', 'F'): '158',
        ('N', 'G'): '80',
        ('N', 'H'): '68',
        ('N', 'I'): '149',
        ('N', 'K'): '94',
        ('N', 'L'): '153',
        ('N', 'M'): '142',
        ('N', 'N'): '0',
        ('N', 'P'): '91',
        ('N', 'Q'): '46',
        ('N', 'R'): '86',
        ('N', 'S'): '46',
        ('N', 'T'): '65',
        ('N', 'V'): '133',
        ('N', 'W'): '174',
        ('N', 'Y'): '143',
        ('P', 'A'): '27',
        ('P', 'C'): '169',
        ('P', 'D'): '108',
        ('P', 'E'): '93',
        ('P', 'F'): '114',
        ('P', 'G'): '42',
        ('P', 'H'): '77',
        ('P', 'I'): '95',
        ('P', 'K'): '103',
        ('P', 'L'): '98',
        ('P', 'M'): '87',
        ('P', 'N'): '91',
        ('P', 'P'): '0',
        ('P', 'Q'): '76',
        ('P', 'R'): '103',
        ('P', 'S'): '74',
        ('P', 'T'): '38',
        ('P', 'V'): '68',
        ('P', 'W'): '147',
        ('P', 'Y'): '110',
        ('Q', 'A'): '91',
        ('Q', 'C'): '154',
        ('Q', 'D'): '61',
        ('Q', 'E'): '29',
        ('Q', 'F'): '116',
        ('Q', 'G'): '87',
        ('Q', 'H'): '24',
        ('Q', 'I'): '109',
        ('Q', 'K'): '53',
        ('Q', 'L'): '113',
        ('Q', 'M'): '101',
        ('Q', 'N'): '46',
        ('Q', 'P'): '76',
        ('Q', 'Q'): '0',
        ('Q', 'R'): '43',
        ('Q', 'S'): '68',
        ('Q', 'T'): '42',
        ('Q', 'V'): '96',
        ('Q', 'W'): '130',
        ('Q', 'Y'): '99',
        ('R', 'A'): '112',
        ('R', 'C'): '180',
        ('R', 'D'): '96',
        ('R', 'E'): '54',
        ('R', 'F'): '97',
        ('R', 'G'): '125',
        ('R', 'H'): '29',
        ('R', 'I'): '97',
        ('R', 'K'): '26',
        ('R', 'L'): '102',
        ('R', 'M'): '91',
        ('R', 'N'): '86',
        ('R', 'P'): '103',
        ('R', 'Q'): '43',
        ('R', 'R'): '0',
        ('R', 'S'): '110',
        ('R', 'T'): '71',
        ('R', 'V'): '96',
        ('R', 'W'): '101',
        ('R', 'Y'): '77',
        ('S', 'A'): '99',
        ('S', 'C'): '112',
        ('S', 'D'): '65',
        ('S', 'E'): '80',
        ('S', 'F'): '155',
        ('S', 'G'): '56',
        ('S', 'H'): '89',
        ('S', 'I'): '142',
        ('S', 'K'): '121',
        ('S', 'L'): '145',
        ('S', 'M'): '135',
        ('S', 'N'): '46',
        ('S', 'P'): '74',
        ('S', 'Q'): '68',
        ('S', 'R'): '110',
        ('S', 'S'): '0',
        ('S', 'T'): '58',
        ('S', 'V'): '124',
        ('S', 'W'): '177',
        ('S', 'Y'): '144',
        ('T', 'A'): '58',
        ('T', 'C'): '149',
        ('T', 'D'): '85',
        ('T', 'E'): '65',
        ('T', 'F'): '103',
        ('T', 'G'): '59',
        ('T', 'H'): '47',
        ('T', 'I'): '89',
        ('T', 'K'): '78',
        ('T', 'L'): '92',
        ('T', 'M'): '81',
        ('T', 'N'): '65',
        ('T', 'P'): '38',
        ('T', 'Q'): '42',
        ('T', 'R'): '71',
        ('T', 'S'): '58',
        ('T', 'T'): '0',
        ('T', 'V'): '69',
        ('T', 'W'): '128',
        ('T', 'Y'): '92',
        ('V', 'A'): '64',
        ('V', 'C'): '192',
        ('V', 'D'): '152',
        ('V', 'E'): '121',
        ('V', 'F'): '50',
        ('V', 'G'): '109',
        ('V', 'H'): '84',
        ('V', 'I'): '29',
        ('V', 'K'): '97',
        ('V', 'L'): '32',
        ('V', 'M'): '21',
        ('V', 'N'): '133',
        ('V', 'P'): '68',
        ('V', 'Q'): '96',
        ('V', 'R'): '96',
        ('V', 'S'): '124',
        ('V', 'T'): '69',
        ('V', 'V'): '0',
        ('V', 'W'): '88',
        ('V', 'Y'): '55',
        ('W', 'A'): '148',
        ('W', 'C'): '215',
        ('W', 'D'): '181',
        ('W', 'E'): '152',
        ('W', 'F'): '40',
        ('W', 'G'): '184',
        ('W', 'H'): '115',
        ('W', 'I'): '61',
        ('W', 'K'): '110',
        ('W', 'L'): '61',
        ('W', 'M'): '67',
        ('W', 'N'): '174',
        ('W', 'P'): '147',
        ('W', 'Q'): '130',
        ('W', 'R'): '101',
        ('W', 'S'): '177',
        ('W', 'T'): '128',
        ('W', 'V'): '88',
        ('W', 'W'): '0',
        ('W', 'Y'): '37',
        ('Y', 'A'): '112',
        ('Y', 'C'): '194',
        ('Y', 'D'): '160',
        ('Y', 'E'): '122',
        ('Y', 'F'): '22',
        ('Y', 'G'): '147',
        ('Y', 'H'): '83',
        ('Y', 'I'): '33',
        ('Y', 'K'): '85',
        ('Y', 'L'): '36',
        ('Y', 'M'): '36',
        ('Y', 'N'): '143',
        ('Y', 'P'): '110',
        ('Y', 'Q'): '99',
        ('Y', 'R'): '77',
        ('Y', 'S'): '144',
        ('Y', 'T'): '92',
        ('Y', 'V'): '55',
        ('Y', 'W'): '37',
        ('Y', 'Y'): '0'
    }
    for i in df.index:
        try:
            df.at[i, 'composition'] = compositionValues(df.at[i, 'wt'], df.at[i, 'mut'])
            df.at[i, 'polarity'] = polarityValues(df.at[i, 'wt'], df.at[i, 'mut'])
            df.at[i, 'volume'] = volumeValues(df.at[i, 'wt'], df.at[i, 'mut'])
            df.at[i, 'granthamScore'] = grantham_dict[df.at[i, 'wt'], df.at[i, 'mut']]
        except:
            KeyError
            df.at[i, 'composition'] = 'nan'
            df.at[i, 'polarity'] = 'nan'
            df.at[i, 'volume'] = 'nan'
            df.at[i, 'granthamScore'] = 'nan'
    return df