# -*- coding: utf-8 -*-
# Copyright (c) 2017 Tomek Wójcik <tomek@bthlabs.pl>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# THE SOFTWARE.
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"""
q3stats.lib.charts.player
=========================

This module contains functions for genering player charts.
"""

from collections import defaultdict

import six

from q3stats.lib import defs, queries
from q3stats.models import Game, Score


def get_player_wins_chart(session, player, agg_by='date'):
    """Returns wins chart data for *player*.

    Use the *agg_by* kwargs to specify aggregation level. Valid values are
    ``date`` and ``map``."""
    assert agg_by in ('date', 'map')

    categories = []
    wins_serie = []
    losses_serie = []

    player_sessions = queries.get_player_sessions(session, player)
    if player_sessions:
        scores = session.query(Score).\
            join(Score.game).\
            filter(Game.date.in_(player_sessions)).\
            filter(Score.player == player).\
            all()

        if scores:
            intermediary = defaultdict(lambda: [0, 0])
            for score in scores:
                agg = getattr(score.game, agg_by)
                if score.score == score.game.fraglimit:
                    intermediary[agg][0] += 1
                else:
                    intermediary[agg][1] += 1

            categories = sorted(intermediary.keys())
            for key in categories:
                wins_serie.append(intermediary[key][0])
                losses_serie.append(intermediary[key][1])

    return categories, wins_serie, losses_serie


def get_player_avg_accuracy_chart(session, player, agg_by='date'):
    """Returns avg accuracy chart data for *player*.

    Use the *agg_by* kwargs to specify aggregation level. Valid values are
    ``date`` and ``map``."""
    assert agg_by in ('date', 'map')

    categories = []
    series = []

    player_sessions = queries.get_player_sessions(session, player)
    if player_sessions:
        scores = session.query(Score).\
            join(Score.game).\
            filter(Game.date.in_(player_sessions)).\
            filter(Score.player == player).\
            all()

        if scores:
            intermediary = defaultdict(lambda: defaultdict(list))
            for score in scores:
                for weapon, weapon_stats in six.iteritems(score.weapons):
                    if weapon_stats['shots'] > 0:
                        agg = getattr(score.game, agg_by)
                        intermediary[agg][weapon].append(
                            weapon_stats['hits'] / float(weapon_stats['shots'])
                        )

            categories = sorted(intermediary.keys())

            weapons = list(defs.WEAPON_NAMES.keys())
            weapons.remove('G')
            weapons.sort()

            for weapon in weapons:
                serie = {
                    'name': defs.WEAPON_NAMES[weapon],
                    'data': []
                }

                for key in categories:
                    accuracies = intermediary[key][weapon]
                    if not accuracies:
                        serie['data'].append(None)
                    else:
                        serie['data'].append(
                            round(sum(accuracies) / len(accuracies), 4) * 100
                        )

                series.append(serie)

    return categories, series