import sys
sys.path.insert(0, './')
import MySQLdb
import urllib.parse

import numpy as np
import pandas as pd
from elasticsearch import Elasticsearch, TransportError
from elasticsearch.helpers import bulk

from flask import Flask, g, request

from wqflask import app
from utility.elasticsearch_tools import get_elasticsearch_connection
from utility.tools import ELASTICSEARCH_HOST, ELASTICSEARCH_PORT, SQL_URI


def parse_db_uri():
    """Converts a database URI to the db name, host name, user name, and password"""

    parsed_uri = urllib.parse.urlparse(SQL_URI)

    db_conn_info = dict(
                        db=parsed_uri.path[1:],
                        host=parsed_uri.hostname,
                        user=parsed_uri.username,
                        passwd=parsed_uri.password)

    print(db_conn_info)
    return db_conn_info


def create_dataframe(input_file):
    with open(input_file) as f:
        ncols = len(f.readline().split("\t"))

    input_array = np.loadtxt(open(input_file, "rb"), delimiter="\t", skiprows=1, usecols=list(range(1, ncols)))
    return pd.DataFrame(input_array)

# This function taken from https://github.com/ShawnLYU/Quantile_Normalize


def quantileNormalize(df_input):
    df = df_input.copy()
    # compute rank
    dic = {}
    for col in df:
        dic.update({col: sorted(df[col])})
    sorted_df = pd.DataFrame(dic)
    rank = sorted_df.mean(axis=1).tolist()
    # sort
    for col in df:
        t = np.searchsorted(np.sort(df[col]), df[col])
        df[col] = [rank[i] for i in t]
    return df


def set_data(dataset_name):
    orig_file = "/home/zas1024/cfw_data/" + dataset_name + ".txt"

    sample_list = []
    with open(orig_file, 'r') as orig_fh, open('/home/zas1024/cfw_data/quant_norm.csv', 'r') as quant_fh:
        for i, (line1, line2) in enumerate(zip(orig_fh, quant_fh)):
            trait_dict = {}
            sample_list = []
            if i == 0:
                sample_names = line1.split('\t')[1:]
            else:
                trait_name = line1.split('\t')[0]
                for i, sample in enumerate(sample_names):
                    this_sample = {
                                    "name": sample,
                                    "value": line1.split('\t')[i + 1],
                                    "qnorm": line2.split('\t')[i + 1]
                                  }
                    sample_list.append(this_sample)
                query = """SELECT Species.SpeciesName, InbredSet.InbredSetName, ProbeSetFreeze.FullName
                           FROM Species, InbredSet, ProbeSetFreeze, ProbeFreeze, ProbeSetXRef, ProbeSet
                           WHERE Species.Id = InbredSet.SpeciesId and
                                 InbredSet.Id = ProbeFreeze.InbredSetId and
                                 ProbeFreeze.Id = ProbeSetFreeze.ProbeFreezeId and
                                 ProbeSetFreeze.Name = '%s' and
                                 ProbeSetFreeze.Id = ProbeSetXRef.ProbeSetFreezeId and
                                 ProbeSetXRef.ProbeSetId = ProbeSet.Id and
                                 ProbeSet.Name = '%s'""" % (dataset_name, line1.split('\t')[0])
                Cursor.execute(query)
                result_info = Cursor.fetchone()

                yield {
                    "_index": "traits",
                    "_type": "trait",
                    "_source": {
                        "name": trait_name,
                        "species": result_info[0],
                        "group": result_info[1],
                        "dataset": dataset_name,
                        "dataset_fullname": result_info[2],
                        "samples": sample_list,
                        "transform_types": "qnorm"
                    }
                }


if __name__ == '__main__':
    Conn = MySQLdb.Connect(**parse_db_uri())
    Cursor = Conn.cursor()

    # es = Elasticsearch([{
    #    "host": ELASTICSEARCH_HOST, "port": ELASTICSEARCH_PORT
    # }], timeout=60) if (ELASTICSEARCH_HOST and ELASTICSEARCH_PORT) else None

    es = get_elasticsearch_connection(for_user=False)

    #input_filename = "/home/zas1024/cfw_data/" + sys.argv[1] + ".txt"
    #input_df = create_dataframe(input_filename)
    #output_df = quantileNormalize(input_df)

    #output_df.to_csv('quant_norm.csv', sep='\t')

    #out_filename = sys.argv[1][:-4] + '_quantnorm.txt'

    success, _ = bulk(es, set_data(sys.argv[1]))

    response = es.search(
        index="traits", doc_type="trait", body = {
            "query": {"match": {"name": "ENSMUSG00000028982"}}
        }
    )

    print(response)