import datetime
import time
from urllib.parse import quote

import requests
import math
import tqdm
import yaml
import pickle
import pandas as pd
import numpy as np
import os
import re
import json
from itertools import compress


def read_config(file_to_read):
    with open(file_to_read, "r") as f:
        config = yaml.load(f, Loader=yaml.SafeLoader)
    return config


def read_from_inspire(formatted_query, silent=False):
    response = requests.get(formatted_query)
    while response.status_code != 200:
        time.sleep(1.0)
        print("retrieving failed, with status code: " +
              str(response.status_code))
        print("query is:")
        print(formatted_query)
        print("trying again...")

        response = requests.get(formatted_query)
        #  response.raise_for_status()  # raises exception when not a 2xx response
    else:
        data = response.json()
        if not silent:
            print("data retrieved.")
    return data


def get_publication_query(publications, clickable):
    ids = []
    for publication in publications:
        ids.append(publication.id)
    result = get_publication_by_id(id_list=ids)
    if not clickable:
        return result
    else:
        return result.replace("api/", "").replace("fields=titles,authors,id",
                                                  "")


def get_publication_by_id(id_list, size=500):
    other_query = 'https://inspirehep.net/api/literature?fields=titles,authors,id&sort=mostrecent&size={size}&page={page}&q='
    id_template = 'recid%3A{id} or '

    id_query = ""
    for id in id_list:
        id_query += id_template.format(id=id)
    else:
        id_query = id_query[:-4]
    id_query = id_query.replace(" ", "%20")
    return other_query.format(page='1', size=size) + id_query


def build_time_query(lower_date=None, upper_date=None):
    time_query_low = 'date>{lower_date}'
    time_query_up = 'date<{upper_date}'
    if upper_date is not None and lower_date is not None:
        return time_query_low.format(
            lower_date=lower_date) + " and " + time_query_up.format(
                upper_date=upper_date)
    elif lower_date is not None:
        return time_query_low.format(lower_date=lower_date)
    else:
        return ""


def build_people_query(file_to_read):
    # This needs to be replaced with the csv from Anja
    with open(file_to_read, "r") as f:
        people_to_match = [line.strip() for line in f]

    author_query = '(author%3A{name})'
    people_query = ""
    for author in people_to_match:
        people_query += author_query.format(name=author) + " or "
    people_query = people_query[:-4]
    people_query = people_query.replace(" ", "%20")
    return people_query, people_to_match


def build_person_query(person, size=25, search_type="authors"):
    if search_type not in ["authors", "literature"]:
        raise ValueError("search_type must be either authors or literature")

    query_template = build_query_template(lower_date=None, upper_date=None, add_institute=False, search_type=search_type) + "&q={person}".format(person=quote(person))
    query_template = query_template.format(size=size, page=1)
    return query_template

def build_query_template(lower_date, upper_date, add_institute=True, search_type="literature"):
    # building query command
    institute_query = 'https://inspirehep.net/api/{search_type}?sort=mostrecent&size={size}&page={page}'
    if add_institute:
        institute_query += "&q=aff:{institute}"
    time_query = build_time_query(lower_date=lower_date, upper_date=upper_date)
    if time_query != "":
        institute_and_time_query = institute_query + " and " + time_query
    else:
        institute_and_time_query = institute_query

    matches = re.findall(r'\{(.*?)\}', institute_and_time_query)
    replace_dict = {}
    for match in matches:
        if match != "search_type":
            replace_dict[match] = "{" + match + "}"
        else:
            replace_dict[match] = search_type
    institute_and_time_query = institute_and_time_query.format(
        **replace_dict)

    institute_and_time_query = institute_and_time_query.replace(" ", "%20")
    institute_and_time_query = institute_and_time_query.replace("\n", "")
    return institute_and_time_query


def get_matched_authors(publications, institute, people_to_exclude):
    all_authors = []
    for pub in publications:
        for auth in pub.author_objects:
            if auth.affiliations is not None and institute in auth.affiliations:
                for name in people_to_exclude:
                    if name in auth.full_name:
                        print("excluding: " + auth.full_name,
                              "with publication", pub.title)
                        break
                else:
                    all_authors.append(auth)

    all_authors_named = list(set([auth.full_name for auth in all_authors]))
    return all_authors_named


def get_data(global_query, retrieve, institute_and_time_query, config):
    if retrieve:
        # retrieving data
        data = read_from_inspire(formatted_query=global_query)
        total_hits = data["hits"]["total"]
        n_pages = int(total_hits / int(config["size"])) + 1
        for i in tqdm.tqdm(range(n_pages)):
            if i > 0:
                time.sleep(1.0)
                this_query = institute_and_time_query.format(
                    page=str(i + 1),
                    size=str(config["size"]),
                    institute=quote(config["institute"]))
                temp_data = read_from_inspire(formatted_query=this_query)
                data["hits"]["hits"] += temp_data["hits"]["hits"]
        else:
            data, df = apply_cleaning_to_data(data=data, config=config)
    else:
        data = read_data(filename=config["cache_file"])
        data, df = apply_cleaning_to_data(data=data, config=config)
        total_hits = data["hits"]["total"]
    return data


def read_data(filename):
    print("Reading data...")
    with open(filename, "rb") as f:
        data = pickle.load(f)
    return data


def write_data(filename, data):
    print("Writing data...")
    with open(filename, "wb") as f:
        pickle.dump(data, f)


def get_earliest_date(row):
    if "earliest_date" in row["metadata"]:
        return row["metadata"]["earliest_date"]
    else:
        return None


def apply_cleaning_to_data(data, config):
    df = convert_to_pandas(data)
    if config["lower_date"] is not None:
        masker_low = df["earliest_date"] > config["lower_date"]
    else:
        masker_low = np.array([True] * len(df))

    if config["upper_date"] is not None:
        masker_up = df["earliest_date"] < config["upper_date"]
    else:
        masker_up = np.array([True] * len(df))

    masker = masker_low & masker_up

    filtered_list = list(compress(data["hits"]["hits"], masker))
    data["hits"]["total"] = len(filtered_list)
    data["hits"]["hits"] = filtered_list
    return data, df[masker]


def get_tarball_of_publications(publications, link_type, target_dir, tarball_name):
    if not os.path.exists(target_dir):
        os.makedirs(target_dir)

    ids = []
    for pub in publications:
        link = pub.links[link_type]
        ids.append(pub.id)
        cmd = "wget -P {target_dir} {link}".format(target_dir=target_dir,
                                                   link=link)
        os.system(cmd)
    else:
        print("Downloaded {} publication files".format(len(ids)))

    # Create tarball of all files
    cmd = "tar -czvf {tarball_name} -C {target_dir} .".format(
        tarball_name=tarball_name, target_dir=target_dir)
    os.system(cmd)


def convert_to_pandas(data):
    df = pd.DataFrame(data["hits"]["hits"])
    updated_date = df.apply(lambda row: datetime.datetime.strptime(
        row["updated"], "%Y-%m-%dT%H:%M:%S.%f+00:00"),
                            axis=1)
    created_date = df.apply(
        lambda row: datetime.datetime.strptime(row["created"][:4], "%Y"),
        axis=1)
    df["updated_date"] = pd.to_datetime(updated_date)
    df["created_date"] = pd.to_datetime(created_date)
    earliest_date = df.apply(lambda row: get_earliest_date(row=row), axis=1)
    df["earliest_date"] = pd.to_datetime(earliest_date)
    return df

def match_publications_by_keywords(publications, keywords):
    publications_without_keywords = []
    matched_publications = []
    unmatched_publications = []

    for pub in tqdm.tqdm(publications):
        if pub.keywords is not None:
            for keyword in keywords:
                if keyword in pub.keywords:
                    matched_publications.append(pub)
                    break
            else:
                unmatched_publications.append(pub)
        else:
            publications_without_keywords.append(pub)
            continue
    return publications_without_keywords, matched_publications, unmatched_publications

def match_publications_by_authors(publications, name_proposal_data, institute):
    matched_publications = []
    unmatched_publications = []

    for pub in publications:
        matched = False
        for author_to_check in name_proposal_data:
            if author_to_check in pub.author_bais:
                idx = pub.author_bais.index(author_to_check)
                candidate_author = pub.author_objects[idx]
                if candidate_author.affiliations is not None:
                    for affiliation in candidate_author.affiliations:
                        if affiliation == institute:
                            matched_publications.append(pub)
                            matched = True
                            break
            if matched:
                break
        if not matched:
            unmatched_publications.append(pub)
    return matched_publications, unmatched_publications

def get_clickable_links(publications):
    clickable_links = []
    for i in range(math.ceil(len(publications) / 100)):
        print(100 * i, 100 * (i + 1))
        print(len(publications[100 * i:100 * (i + 1)]))

        clickalbe_link = get_publication_query(
            publications[100 * i:100 * (i + 1)], clickable=True)
        clickable_links.append(clickalbe_link)
    return clickable_links

def check_missing_publications_on_disk(publications, target_dir, link_type):
    missing_publications = []
    for pub in publications:
        path_to_check = os.path.join(target_dir, pub.id + "?format={}".format(link_type))
        if not os.path.exists(path_to_check):
            missing_publications.append(pub)
    return missing_publications

def build_name_proposal_from_excel(path_to_excel):
    df = pd.read_excel(path_to_excel)
    name_proposal_data = []
    for i in range(len(df)):
        data = df.iloc[i]["inspire_names"]
        splitted_data = data.split(";")
        for split in splitted_data:
            name_proposal_data.append(split)
    return name_proposal_data

def get_inspire_bai(file):
    with open(file, "r") as f:
        data = json.load(f)
    l_bais = []
    for idx, item in enumerate(data["hits"]["hits"]):
        if "ids" in item["metadata"]:
            schemas = [id["schema"] for id in item["metadata"]["ids"]]
            if "INSPIRE BAI" not in schemas:
                print("No INSPIRE BAI")
            else:
                idx_scheme = schemas.index("INSPIRE BAI")
                bai = item["metadata"]["ids"][idx_scheme]["value"]
                l_bais.append(bai)
        else:
            print("No ids found")
        if idx > 4:
            break
    return l_bais

def get_inspire_bais_from_filelist(filelist):
    l_bais = []
    for file in filelist:
        l_bais += get_inspire_bai(file)
    return l_bais
