"""

fetches biology for simbad

"""

import pandas as pd

from clintk.utils.connection import get_engine, sql2df

from datetime import timedelta

from bs4 import BeautifulSoup

from io import StringIO

import requests

import argparse

def fetch(url, header_path, id, ip, dbase, targets_table):

    """

    il suffit de concatener toutes les tables extraites pour ensuite les fold

    url : str

        url to the location of biology files

    header_path : str

        path to csv file containing header

    id : str

        login to the sql database

    ip : str

        ip adress to the sql server

    dbase : str

        name of the database on the given server

    targets_table : str

        name of the table containing targets information

    @TODO ne need to fetch targets_table from sql since already loaded by

    @TODO main function

    Returns

    -------

    """

    # url = 'http://esimbad/testGSAV7/reslabo?FENID=resLaboPatDitep&NIP={}' \

    #       '&STARTDATE={}&ENDDATE={}'

    # header_path = '~/workspace/data/biology/header.csv'

    # constant names specific to our database

    KEY1 = 'id'

    KEY2 = 'NIP'

    header = pd.read_csv(header_path, sep=';', encoding='latin1').columns

    engine = get_engine(id, ip, dbase)

    df_ids = sql2df(engine, targets_table)

    df_ids.rename({'nip': KEY2}, inplace=True, axis=1)

    df_ids['patient_id'] = df_ids[KEY1]

    cols = [KEY2, 'Analyse', 'Resultat', 'Date prelvt']

    df_res = pd.DataFrame(data=None, columns=cols)

    for index, row in df_ids.iterrows():

        nip = row[KEY2].replace(' ', '')

        # patient_id = row['patient_id']

        # c1j1_date = row[C1J1].date()

        # start_date = c1j1_date - timedelta(weeks=8)

        start_date = row['prescreen']

        end_date = start_date + timedelta(weeks=4)

        start = str(start_date).replace('-', '')

        stop = str(end_date).replace('-', '')

        req = requests.get(url.format(nip, start, stop))

        values = BeautifulSoup(req.content, 'html.parser').body.text

        new_df = pd.read_csv(StringIO(values), sep=';', header=None,

                             index_col=False, names=header)

        new_df = new_df.loc[:, cols + ['LC']]

        # normalize nip

        new_df[KEY2] = row[KEY2]

        new_df.drop('LC', axis=1, inplace=True)

        df_res = pd.concat([df_res, new_df], axis=0,

                           sort=False, ignore_index=True)

    return df_res

def fetch_and_fold(url, header, id, ip, db, targets):

    key1, key2, date = 'patient_id', 'nip', 'date'

    # engine for sql connection

    engine = get_engine(id, ip, db)

    # fetching targets table

    df_targets = sql2df(engine, 'patient_target_simbad')

    df_targets['prescreen'] = df_targets.loc[:, 'prescreen'].dt.date

    # fetching features

    # url = 'http://esimbad/testGSAV7/reslabo?FENID=resLaboPatDitep&NIP={}' \

    #       '&STARTDATE={}&ENDDATE={}'

    #

    # header_path = '~/workspace/data/biology/header.csv'

    url =url

    header_path = header

    # fetching features

    df_bio = fetch(url, header_path, id, ip, db, targets)

    # parse_dates

    df_bio['Date prelvt'] = pd.to_datetime(df_bio['Date prelvt'],

                                           errors='coerce',

                                           format='%Y%m%d').dt.date

    df_bio.dropna(inplace=True)

    df_bio.rename({'Date prelvt': date, 'Analyse': 'feature',

                   'Resultat': 'value'}, inplace=True, axis=1)

    # joining with targets

    df_bio = df_bio.merge(df_targets, on=None, left_on='NIP',

                          right_on='nip').drop('NIP', axis=1)

    df_bio.rename({'id': 'patient_id'}, axis=1, inplace=True)

    df_bio['value'] = pd.to_numeric(df_bio.loc[:, 'value'], errors='coerce',

                                    downcast='float')

    df_bio = df_bio.loc[:, [key1, key2, 'feature', 'value', date]]

    # df_bio already folded

    print('done')

    return df_bio

def main_fetch_and_fold():

    description = 'Folding biology measures from Ventura Care'

    parser = argparse.ArgumentParser(description=description)

    parser.add_argument('--url', '-u',

                        help='url to where measures are stored')

    parser.add_argument('--header', '-H',

                        help='path to the header file to read csv')

    parser.add_argument('--id', '-I',

                        help='id to connect to sql server')

    parser.add_argument('--ip', '-a',

                        help='ip adress of the sql server')

    parser.add_argument('--db', '-d',

                        help='name of the database on the sql server')

    parser.add_argument('--targets', '-t',

                        help='name of the table containing targets on the db')

    parser.add_argument('--output', '-o',

                        help='output path to write the folded result')

    args = parser.parse_args()

    df_bio = fetch_and_fold(args.url, args.header,args.id, args.ip,

                            args.db, args.targets)

    # df_bio already folded

    output = args.output

    df_bio.to_csv(output, encoding='utf-8', sep=';')

    print('done')

    return df_bio

if __name__ == "__main__":

    main_fetch_and_fold()