def get_data(gene_intron, cid, **_):
    from ..fetch_data import get_intron_events, get_chromossome, get_kinship
    from ..fetch_data import get_positions

    gene, intron = gene_intron.split('_')
    intron = int(intron)

    pos = get_positions()
    pos = pos.query("(gene == '%s') & (intron == %d)" % (gene, intron))
    start_pos, end_pos = pos['start_pos'].values[0], pos['end_pos'].values[0]

    df = get_intron_events()
    df = df.loc[(gene, intron)]
    df['iid'] = df['assay'].apply(lambda x: x.split('_')[0].split('.')[0])

    K = get_kinship()
    ws = 5000

    (bim, fam, G) = get_chromossome(cid)
    query = "(chrom == '%d') & (pos >= %d) & (pos <= %d)" % (cid, start_pos-ws, end_pos+ws)
    bim = bim.query(query).sort_index(level=[1])

    snp_idx = bim['i'].values
    snp_ids = bim['snp'].values

    G = G[:,snp_idx]

    data = dict(ntri=[], nsuc=[], idx=[], snp_id=snp_ids)

    for r in df.iterrows():
        data['nsuc'].append(r[1][1])
        data['ntri'].append(r[1][2])
        q = "iid == '%s'" % r[1][3]
        data['idx'].append(fam.query(q)['i'].values[0])

    G = G[data['idx'],:]
    K = K[data['idx'],:][:,data['idx']]

    data['K'] = K
    data['G'] = G
    data['gene'] = gene
    data['intron'] = intron

    return data