from multiprocessing import Pool
import requests
import re
from bs4 import BeautifulSoup
from itertools import chain
from collections import Counter
from timeit import default_timer as timer
import pandas as pd
from datetime import datetime


def get_table_rows(fname="stats.html"):
    """
    Extract the table rows from the statistics

    :param fname: string name of the file stored in `data` directory

    :return table_rows: list of BeautifulSoup objects to be passed to `process_in_parallel`
    """

    soup = BeautifulSoup(open(f"data/{fname}", "r", encoding="utf8"), features="lxml")
    table_rows = soup.find_all(attrs={"class": "sortableTable-row js-statsTableRow"})
    print(f"Found {len(table_rows)} entries in table.")
    return table_rows


def convert_timestamp(ts: int, tz: str):
    """Convert a unix timestamp to a date timestamp"""
    return (
        pd.to_datetime(ts, origin="unix", unit="ms")
        .tz_localize("UTC")
        .tz_convert(tz)
        .tz_localize(None)
    )


def process_entry(entry, parallel=True, tz="America/Chicago"):
    """
    Extract data from one entry in table

    :param entry: BeautifulSoup tag
    :param parallel: Boolean for whether function is being run in parallel
    :param tz: string representing timezone for started and published time

    :return entry_dict: dictionary with data about entry

    """
    # Convert to soup when running in parallel
    if parallel:
        entry = BeautifulSoup(entry, features="lxml").body.tr

    entry_dict = {}
    # Extract information
    for value, key in zip(
        entry.find_all(attrs={"class": "sortableTable-value"}),
        ["published_date", "views", "reads", "ratio", "fans"],
    ):
        entry_dict[key] = float(value.text) if key == "ratio" else int(value.text)

    entry_dict["read_time"] = int(
        entry.find_all(attrs={"class": "readingTime"})[0].get("title").split(" ")[0]
    )

    # Unlisted vs published
    entry_dict["type"] = (
        "unlisted" if len(entry.find_all(text=" Unlisted")) > 0 else "published"
    )

    # Publication
    publication = entry.find_all(attrs={"class": "sortableTable-text"})
    if "In" in publication[0].text:
        entry_dict["publication"] = publication[0].text.split("In ")[1].split("View")[0]
    else:
        entry_dict["publication"] = "None"

    # Convert datetimes
    entry_dict["published_date"] = convert_timestamp(
        entry_dict["published_date"], tz=tz
    )
    entry_dict["started_date"] = convert_timestamp(entry.get("data-timestamp"), tz=tz)

    # Get the link
    link = entry.find_all(text="View story", attrs={"class": "sortableTable-link"})[
        0
    ].get("href")
    entry_dict["link"] = link
    # Retrieve the article and create a soup
    entry = requests.get(link).content
    entry_soup = BeautifulSoup(entry, features="lxml")

    # Get the title
    try:
        title = entry_soup.h1.text
    except:
        title = "response"

    title_word_count = len(re.findall(r"[\w']+|[.,!?;]", title))

    # Main text entries
    entry_text = [
        p.text for p in entry_soup.find_all(["h1", "h2", "h3", "p", "blockquote"])
    ]

    # Make sure to catch everything
    entry_text.extend(
        s.text
        for s in entry_soup.find_all(attrs={"class": "graf graf--li graf-after--li"})
    )
    entry_text.extend(
        s.text
        for s in entry_soup.find_all(attrs={"class": "graf graf--li graf-after--p"})
    )
    entry_text.extend(
        s.text
        for s in entry_soup.find_all(
            attrs={"class": "graf graf--li graf-after--blockquote"}
        )
    )
    entry_text.extend(
        s.text
        for s in entry_soup.find_all(
            attrs={"class": "graf graf--li graf-after--pullquote"}
        )
    )

    entry_text = " ".join(entry_text)

    # Word count
    word_count = len(re.findall(r"[\w']+|[.,!?;]", entry_text))

    # Number of claps
    clap_pattern = re.compile(
        "^[0-9]{1,} claps|^[0-9]{1,}.[0-9]{1,}K claps|^[0-9]{1,}K claps"
    )
    claps = entry_soup.find_all(text=clap_pattern)

    if len(claps) > 0:
        if "K" in claps[0]:
            clap_number = int(1e3 * float(claps[0].split("K")[0]))
        else:
            clap_number = int(claps[0].split(" ")[0])
    else:
        clap_number = 0

    # Post tags
    tags = entry_soup.find_all(attrs={"class": "tags tags--postTags tags--borderless"})
    tags = [li.text for li in tags[0].find_all("li")]

    # Responses to entry
    responses = entry_soup.find_all(
        attrs={
            "class": "button button--chromeless u-baseColor--buttonNormal u-marginRight12",
            "data-action": "scroll-to-responses",
        }
    )
    num_responses = int(responses[0].text) if len(responses) > 0 else 0

    # Store in dictionary
    entry_dict["title"] = title
    entry_dict["title_word_count"] = title_word_count
    entry_dict["text"] = entry_text
    entry_dict["word_count"] = word_count
    entry_dict["claps"] = clap_number
    entry_dict["tags"] = tags
    entry_dict["num_responses"] = num_responses

    # Time since publication
    entry_dict["days_since_publication"] = (
        datetime.now() - entry_dict["published_date"]
    ).total_seconds() / (3600 * 24)

    return entry_dict


def process_in_parallel(table_rows, processes=20):
    """
    Process all the stats in a table in parallel

    :note: make sure to set the correct time zone in `process_entry`
    :note: running on Mac may first require setting
    export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES
    from the command line to enable parallel processing

    :param table_rows: BeautifulSoup table rows

    :param processes: integer number of processes (threads) to use in parallel

    :return df: dataframe of information about each post

    """
    # Convert to strings for multiprocessing
    table_rows_str = [str(r) for r in table_rows]

    # Process each article in paralllel
    pool = Pool(processes=processes)
    results = []
    start = timer()
    for i, r in enumerate(pool.imap_unordered(process_entry, table_rows_str)):
        # Report progress
        print(f"{100 * i / len(table_rows_str):.2f}% complete.", end="\r")
        results.append(r)
    pool.close()
    pool.join()
    end = timer()
    print(f"Processed {len(table_rows_str)} articles in {end-start:.2f} seconds.")

    # Convert to dataframe
    df = pd.DataFrame(results)
    # Rename ratio
    df.rename(columns={"ratio": "read_ratio"}, inplace=True)
    # Add extra columns with more data
    df["claps_per_word"] = df["claps"] / df["word_count"]
    df["editing_days"] = (
        (df["published_date"] - df["started_date"]).dt.total_seconds() / (60 * 60 * 24)
    ).astype(int)

    # Rounding
    df["published_date"] = df["published_date"].dt.round("min")
    df["started_date"] = df["started_date"].dt.round("min")
    df["read_ratio"] = df["read_ratio"].round(2)

    # 5 most common tags (might want to include more tags)
    n = 5
    all_tags = list(chain(*df["tags"].tolist()))
    tag_counts = Counter(all_tags)
    tags = tag_counts.most_common(n)

    # Adding columns with indication of tag
    for tag, count in tags:
        flag = [1 if tag in tags else 0 for tags in df["tags"]]
        df.loc[:, f"<tag>{tag}"] = flag

    df.sort_values("published_date", inplace=True)
    return df


def get_data(fname="stats.html", processes=20):
    """
    Retrieve medium article statistics

    :note: running on Mac may first require setting
    export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES
    from the command line to enable parallel processing

    :param fname: file name (should be 'stats.html')
    :param processes: integer number of processes

    :return df: dataframe of article data
    """
    t = get_table_rows(fname=fname)
    return process_in_parallel(t, processes=processes)