#!/usr/bin/env python3

import argparse
import json
import os.path

import pandas as pd


def check(outputf, tumorf, normalf=None, genderf=None, cms=None):
    # qc_check = {
    #     'Q30(%)': 85,
    #     'clean_reads_rate(%)': 90,
    #     'mapped_rate(%)': 90,
    #     'capture_rate(reads)': 50,
    #     'mean_depth(dedup)': 500,
    #     'mean_depth(dedup)_umi': 1000,
    #     'coverage(>=80%)': 500,
    #     'coverage(>=80%)_umi': 1000,
    #     'coverage(>=0.2*meanx)': 95,
    #     'coverage(>10x)': 90,
    #     'coverage(>=0.1*meanx)': 90,
    #     'concordence': 99,
    #     'contamination_n': 1,
    #     'contamination_t': 1,
    # }

    qc_t = pd.read_csv(tumorf, sep="\t", index_col=0, header=None).T

    out = open(outputf, 'w')

    ##tumor
    if float(qc_t['Q30(%)'][1]) < 85:
        out.write('\t'.join(("Tumor:", "Q30(%)", str(qc_t['Q30(%)'][1]), "(<85%)\n")))
    if float(qc_t['clean_reads_rate(%)'][1]) < 90:
        out.write('\t'.join(("Tumor:", "clean_reads_rate(%)", str(qc_t['clean_reads_rate(%)'][1]), "(<90%)\n")))
    if float(qc_t['mapped_rate(%)'][1]) < 90:
        out.write('\t'.join(("Tumor:", "mapped_rate(%)", str(qc_t['mapped_rate(%)'][1]), "(<90%)\n")))
    if float(qc_t['capture_rate(reads)'][1]) < 50:
        out.write('\t'.join(("Tumor:", "capture_rate(reads)", str(qc_t['capture_rate(reads)'][1]), "(<50%)\n")))
    if float(qc_t['mean_depth(dedup)'][1]) < 500:
        out.write('\t'.join(("Tumor:", "mean_depth(dedup)", str(qc_t['mean_depth(dedup)'][1]), "(<500X)\n")))
    if float(qc_t['coverage(>=80%)'][1]) < 500:
        out.write('\t'.join(("Tumor:", "coverage(>=80%)", str(qc_t['coverage(>=80%)'][1]), "(<500X)\n")))
    if float(qc_t['coverage(>=0.2*meanx)'][1]) < 95:
        out.write('\t'.join(("Tumor:", "coverage(>=0.2*meanx)", str(qc_t['coverage(>=0.2*meanx)'][1]), "(<95%)\n")))
    if float(qc_t['coverage(>10x)'][1]) < 95:
        out.write('\t'.join(("Tumor:", "coverage(>10x)", str(qc_t['coverage(>10x)'][1]), "(<95%)\n")))

    if os.path.exists(normalf):
        qc_n = pd.read_csv(normalf, sep="\t", index_col=0, header=None).T
        ##normal
        if float(qc_n['Q30(%)'][1]) < 85:
            out.write('\t'.join(("Normal:", "Q30(%)", str(qc_n['Q30(%)'][1]), "(<85%)\n")))
        if float(qc_n['clean_reads_rate(%)'][1]) < 90:
            out.write('\t'.join(("Normal:", "clean_reads_rate(%)", str(qc_n['clean_reads_rate(%)'][1]), "(<90%)\n")))
        if float(qc_n['mapped_rate(%)'][1]) < 90:
            out.write('\t'.join(("Normal:", "mapped_rate(%)", str(qc_n['mapped_rate(%)'][1]), "(<90%)\n")))
        if float(qc_n['capture_rate(reads)'][1]) < 50:
            out.write('\t'.join(("Normal:", "capture_rate(reads)", str(qc_n['capture_rate(reads)'][1]), "(<50%)\n")))
        if float(qc_n['coverage(>=80%)'][1]) < 100:
            out.write('\t'.join(("Normal:", "coverage(>=80%)", str(qc_n['coverage(>=80%)'][1]), "(<100X)\n")))
        if float(qc_n['coverage(>=0.2*meanx)'][1]) < 95:
            out.write(
                '\t'.join(("Normal:", "coverage(>=0.2*meanx)", str(qc_n['coverage(>=0.2*meanx)'][1]), "(<95%)\n")))
        if float(qc_n['coverage(>10x)'][1]) < 95:
            out.write('\t'.join(("Normal:", "coverage(>10x)", str(qc_n['coverage(>10x)'][1]), "(<95%)\n")))
    if os.path.exists(str(genderf)):
        genderf_df = pd.read_csv(genderf, sep='\t')
        genderf_dict = genderf_df.to_dict(orient='records')[0]
        gender_cms = ''
        if os.path.exists(str(cms)):
            cmsf = open(cms, "r")
            cmsj = json.load(cmsf)
            gender_cms_data = cmsj.get('data', [])
            if gender_cms_data:
                gender_cms = gender_cms_data[0].get('gender', '')
        if gender_cms:
            genderf_dict['gender_cms'] = gender_cms
            genderf_r = [k for k, v in genderf_dict.items() if list(genderf_dict.values()).count(v) == 1]
            if genderf_r:
                out.write('\t'.join(("性别鉴定不一致:", str(genderf_dict))))
        else:
            out.write('\t'.join(("性别鉴定:", 'lims系统提供信息有误，请核查')))
    else:
        out.write('\t'.join(("性别鉴定:", '流程未进行性别鉴定评估')))

    out.close()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="Check qcfile")
    parser.add_argument('-n', '--barcode', help="sample's qcfile", required=True)
    parser.add_argument('-s', '--normal', help="normal's file", default='', required=False, nargs='?')
    parser.add_argument('-p', '--gender', help="gender file", default='', required=False, nargs='?')
    parser.add_argument('-c', '--cms', help="cms's file", default='', required=False, nargs='?')
    parser.add_argument('-o', '--output_file', help="Output directory, default ", required=True)

    args = parser.parse_args()
    check(args.output_file, args.barcode, args.normal, args.gender, args.cms)