helpers.py 文件源码

def format2tidy(df, subjname, listname, subjgroup, **attrs):

    melted_df = pd.melt(df.T)
    melted_df[subjname]=""
    for idx,sub in enumerate(melted_df['Subject'].unique()):
        melted_df.loc[melted_df['Subject']==sub,subjname]=subjgroup[idx]
    if attrs['analysis_type'] in ['spc']:
        base = list(df.columns)
        melted_df['Position'] = base * int(melted_df.shape[0] / len(base))
        melted_df.columns = ['Subject', listname, 'Proportion Recalled', subjname, 'Position']
    elif attrs['analysis_type'] in ['pfr', 'pnr']:
        base = list(df.columns)
        melted_df['Position'] = base * int(melted_df.shape[0] / len(base))
        melted_df.columns = ['Subject', listname, 'Probability of Recall: Position ' + str(attrs['n']), subjname, 'Position']
    elif attrs['analysis_type'] is 'lagcrp':
        base = range(int(-len(df.columns.values)/2),int(len(df.columns.values)/2)+1)
        melted_df['Position'] = base * int(melted_df.shape[0] / len(base))
        melted_df.columns = ['Subject', listname, 'Conditional Response Probability', subjname, 'Position']
    elif attrs['analysis_type'] is 'fingerprint' or attrs['analysis_type'] is 'fingerprint_temporal':
        base = list(df.columns.values)
        melted_df['Feature'] = base * int(melted_df.shape[0] / len(base))
        melted_df.columns = ['Subject', listname, 'Clustering Score', subjname, 'Feature']
    elif attrs['analysis_type'] is 'accuracy':
        melted_df.columns = ['Subject', listname, 'Accuracy', subjname]
    elif attrs['analysis_type'] is 'temporal':
        melted_df.columns = ['Subject', listname, 'Temporal Clustering Score', subjname]


    return melted_df