q3stats/q3stats/lib/charts/player.py

126 lines
4.3 KiB
Python

# -*- 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:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# 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
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
"""
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