import typing

import pandas
from dataclasses import dataclass
from math import floor
from datetime import datetime


def _add_accumulated_score(df: pandas.DataFrame):
    acc_col = pandas.Series([0.0]).repeat(len(df)).reset_index(drop=True)

    acc = 0.0
    for i, row in enumerate(df.itertuples()):
        acc += row.score
        acc_col[i] = acc

    df['accumulated_score'] = acc_col


def load_score_log(path: str) -> pandas.DataFrame:
    return pandas.read_csv(path, sep=',',
                           dtype={'score': int, 'sourcename': str, 'name': str, 'mapx': int, 'mapy': int},
                           parse_dates=['when'], date_format='%d/%m/%Y %H:%M')


def _calc_duration(row) -> int:
    score_per = None
    if row['sourcename'] == 'Capture':
        score_per = 1.0
    elif row['sourcename'] == 'Output Boost':
        score_per = 0.1
    else:
        return 0

    return int(floor(row['score'] / score_per))


def _calc_event_start(row) -> pandas.Timestamp:
    return pandas.Timestamp(row['when'].timestamp() - row['seconds'], unit='s')


def extend_score_log(scores: pandas.DataFrame):
    scores.sort_values('when', inplace=True)
    _add_accumulated_score(scores)

    scores['seconds'] = scores.apply(_calc_duration, axis=1)
    scores['when_start'] = scores.apply(_calc_event_start, axis=1)


def generate_station_stats(score_log: pandas.DataFrame) -> pandas.DataFrame:
    station_count = len(score_log['name'].unique())

    # every station in the score log should have a first visit, so create summary based on that
    summary = score_log[score_log['sourcename'] == 'First Visit'][['name', 'mapx', 'mapy', 'when']]
    summary.rename(columns={'when': 'first_visit'}, inplace=True)

    assert len(summary) == station_count

    common_join_args = {'on': 'name', 'how': 'left', 'validate': '1:1'}

    # add total score
    summary = pandas.merge(summary, score_log[['name', 'score']].groupby('name').sum(), **common_join_args)
    summary.rename(columns={'score': 'total_score'}, inplace=True)
    assert len(summary) == station_count

    boosts = score_log[score_log['sourcename'] == 'Output Boost'][['name', 'score']].groupby('name')

    # add total boosts
    total_boosts = boosts.sum()
    total_boosts['totalboostduration'] = total_boosts['score'].apply(lambda x: 10 * x)
    total_boosts.rename(columns={'score': 'totalboostscore'}, inplace=True)
    summary = pandas.merge(summary, total_boosts, **common_join_args)
    assert len(summary) == station_count

    # add max boosts
    max_boosts = boosts.max()
    max_boosts['maxboostduration'] = max_boosts['score'].apply(lambda x: 10 * x)
    max_boosts.rename(columns={'score': 'maxboostscore'}, inplace=True)
    summary = pandas.merge(summary, max_boosts, **common_join_args)
    assert len(summary) == station_count

    visits = score_log[(score_log['sourcename'] == 'Visit') | (score_log['sourcename'] == 'First Visit')][
        ['name', 'score']].groupby('name')

    # add total visits (count)
    summary = pandas.merge(summary, visits.count(), **common_join_args)
    summary.rename(columns={'score': 'totalvisits'}, inplace=True)
    assert len(summary) == station_count

    captures = score_log[score_log['sourcename'] == 'Capture'][['name', 'score']].groupby('name')

    # add captures (count)
    summary = pandas.merge(summary, captures.count(), **common_join_args)
    summary.rename(columns={'score': 'captures'}, inplace=True)
    assert len(summary) == station_count

    # add max held duration (max capture score)
    summary = pandas.merge(summary, captures.max(), **common_join_args)
    summary.rename(columns={'score': 'maxheldduration'}, inplace=True)
    assert len(summary) == station_count

    # add total held duration (sum capture score)
    summary = pandas.merge(summary, captures.sum(), **common_join_args)
    summary.rename(columns={'score': 'totalheldduration'}, inplace=True)

    assert len(summary) == station_count
    return summary


def generate_score_per_second(score_log: pandas.DataFrame) -> pandas.DataFrame:
    @dataclass
    class ScoreSecond:
        name: str
        sourcename: str
        when: datetime
        score: float
        once: bool
        event_start: bool
        mapx: int
        mapy: int

    def row_to_scoreseconds(row) -> typing.Iterator[ScoreSecond]:
        score_per = row.score / row.seconds
        for elapsed in range(0, row.seconds):
            timestamp = pandas.Timestamp(row.when_start.timestamp() + elapsed, unit='s')
            yield ScoreSecond(name=row.name, sourcename=row.sourcename, mapx=row.mapx, mapy=row.mapy, when=timestamp,
                              score=score_per, once=False, event_start=(elapsed == 0))

    def gen_scoreseconds() -> typing.Iterator[ScoreSecond]:
        for row in score_log.itertuples():
            if row.seconds == 0:  # one-off
                yield ScoreSecond(name=row.name, sourcename=row.sourcename, mapx=row.mapx, mapy=row.mapy, when=row.when,
                                  score=row.score, once=True, event_start=True)
            else:
                yield from row_to_scoreseconds(row)

    scoreseconds = pandas.DataFrame(gen_scoreseconds())
    scoreseconds.sort_values(by=['when'], inplace=True)
    scoreseconds.reset_index(drop=True, inplace=True)

    _add_accumulated_score(scoreseconds)
    return scoreseconds