"""

fetches biology for validation cohort

"""

import pandas as pd

from clintk.utils import Unfolder

from datetime import timedelta

from bs4 import BeautifulSoup

from io import StringIO

import requests

def fetch(url, header_path, df_ids):

    """

    Parameters

    ----------

    url : str

        url to the location of biology files

    header_path : str

        path to csv file containing header

    df_ids : pd.DataFrame

        df containing target info

        columns should be [nip, date_sign_ok]

    Returns

    -------

    """

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

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

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

    for index, row in df_ids.iterrows():

        nip = row['nip']

        start_date = row['DATE SIGN_OK']

        end_date = start_date + timedelta(weeks=4)

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

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

        req = requests.get(url.format(nip.replace(' ', ''), 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]

        new_df['nip'] = nip

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

                           sort=False, ignore_index=True)

    return df_res

def fetch_and_fold(url, header_path, targets):

    df_bio = fetch(url, header_path, targets)

    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)

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

                                    downcast='float')

    return df_bio

def main_fetch():

    base_path = 'data/cohorte_validation'

    ## ditep

    path = base_path + '/ditep_inclus.csv'

    ditep_ok = pd.read_csv(path, sep=';',

                           parse_dates=[-2]).loc[:, ['nip','DATE SIGN_OK']]

    path = base_path + '/ditep_sf.csv'

    ditep_sf = pd.read_csv(path, sep=';',

                           parse_dates=[-2]).loc[:, ['nip', 'DATE SIGN_OK']]

    ditep = pd.concat([ditep_ok, ditep_sf], ignore_index=True)

    ditep['DATE SIGN_OK'] = ditep['DATE SIGN_OK'].dt.date

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

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

    header_path = '/home/v_charvet/workspace/data/biology/header.csv'

    bio_ditep = fetch_and_fold(url, header_path, ditep)

    # poumon

    path = base_path + '/poumons_inclusion.csv'

    poumon_ok = pd.read_csv(path, sep=';',

                            parse_dates=[-2]).loc[:, ['nip', 'DATE_SIGN_OK']]

    path = base_path + '/poumons_sf.csv'

    poumon_sf = pd.read_csv(path, sep=';',

                            parse_dates=[-2]).loc[:, ['nip', 'DATE_SIGN_OK']]

    poumon = pd.concat([poumon_ok, poumon_sf], ignore_index=True)

    poumon.rename({'DATE_SIGN_OK': 'DATE SIGN_OK'}, axis=1, inplace=True)

    poumon['DATE SIGN_OK'] = poumon['DATE SIGN_OK'].dt.date

    bio_poumon = fetch_and_fold(url, header_path, poumon)

    #unfolding features

    bio_ditep['null_id'] = [1] * bio_ditep.shape[0]

    bio_poumon['null_id'] = [1] * bio_poumon.shape[0]

    unfolder = Unfolder('nip', 'null_id', 'feature', 'value', 'date', False, -1)

    ditep_unfold = unfolder.fit(bio_ditep).unfold()

    poumon_unfold = unfolder.fit(bio_poumon).unfold()

    ditep_unfold.to_csv('data/ditep_bio_unfold.csv', sep=';')

    poumon_unfold.to_csv('data/poumons_bio_unfold.csv', sep=';')

    return bio_ditep, bio_poumon

if __name__ == "__main__":

    main_fetch()