def set_weapon_accuracy(self):
def parse_pct(pct):
if isinstance(pct, int):
return pct
return eval(pct.split("%")[0]) / 100.
heroes = self.hero_crawler("Combat", "Weapon Accuracy", evl=False)
wpn_acc = HerosStats(
"Combat: Weapon Accuracy",
map(lambda hero: (hero.name, parse_pct(str(hero.value))), heroes))
logging.debug("%s: %s" % (wpn_acc.stat_name, wpn_acc))
self.wpn_acc = wpn_acc
def set_total_dmg(self):
def remove_commas(seq):
if isinstance(seq, int):
return seq
return eval("".join(seq.split(",")))
heroes = self.hero_crawler("Combat", "All Damage Done", evl=False)
total_dmg = HerosStats(
"Combat: Damage Done",
map(lambda hero: (hero.name, remove_commas(str(hero.value))),
heroes))
logging.debug("%s: %s" % (total_dmg.stat_name, total_dmg))
self.total_dmg = total_dmg
def get_top_three(col):
"""
Finds the top 3 heros in the stat specified by `col`.
Only looks for heros that have more than 5 games played.
:param col: The stat to reference
:type col: str
"""
top_stat = df[df['Games Played'] > 5][col].nlargest(3)
logging.info("\n%s" % top_stat)
top_stat_hs = HerosStats(col, zip(top_stat.index, top_stat))
logging.debug("%s (Top 3): %s" %
(top_stat_hs.stat_name, top_stat_hs))
return top_stat_hs
def data_packer(total_stat, mins_played):
"""
This helper function serves to calculate a stat per minute, given by `total_stat`.
:param total_stat: The HerosStats instance to calculate per minute on
:param mis_played: The HeroStats instance of minutes played
:type total_stat: HerosStats
:type total_stat: HerosStats
:returns: HerosStats
"""
logging.debug("...Data Packing stat: %s" % (total_stat.stat_name))
stat_min = np.array(total_stat.values) / np.array(
mins_played.values)
stat_min_hs = HerosStats(total_stat.stat_name + "/Minute",
zip(total_stat.values, stat_min))
return stat_min_hs
def hero_crawler(self, cat, subcat, evl=True):
"""
This method is intended to be used whenever data is requested in such
a way where a mapping is directly accomplished by response indexing.
Populates a list of heros mapping heros to data, in this specified
by the parameters.
Example return:
>>> self.set_avg_elims()
>>> [(u'Pharah', 23.13), (u'McCree', 22.38), (u'Widowmaker', 22.31)]
.. note:: Need to subclass a sort of hero data class to better repr this data type.
Something like... when printed of course.
HeroDataBin(stat=Average Eliminations Per Game, data=[(u'Pharah', 23.13), (u'McCree', 22.38), (u'Widowmaker', 22.31)])
:param cat: The 'parent' category of the data, e.g. "Average" or "Game"
:param subcat: The subcategory corresponging to param cat.
e.g. if cat is "Best", subcat can be "All Damage Done - Most in Game".
:returns: HerosStats object
"""
values = []
for hero in self.heros:
try:
val = self.data[hero][cat][subcat]
if evl:
values.append(eval(val)) # need to use eval() for strings
else:
values.append(val)
except:
logging.warning("NO DATA FOR %s ON KEYS %s AND/OR %s" %
(hero, cat, subcat))
values.append(1)
stat_name = cat + ": " + subcat
hs = HerosStats(stat_name, zip(self.heros, values))
return hs
def set_minutes_played(self):
def strtime_eval(time_str):
if "second" in time_str:
return 1
if time_str == "--":
return 1
assert "hour" in time_str or "minute" in time_str, "malformed input: '" + time_str + "'"
split = time_str.split(" ")
rawnum = eval(split[0])
if "hour" in split[1]:
time = rawnum * 60
elif "minute" in split[1]:
time = rawnum
return float(time)
mins_played = HerosStats(
"Game: Time Played",
map(lambda hero: (hero.name, strtime_eval(hero.value)),
self.hero_crawler("Game", "Time Played", evl=False)))
logging.debug("%s: %s" % (mins_played.stat_name, mins_played))
self.mins_played = mins_played
def make_summary_table(self):
"""
Summary will correspond to top 3 played heros in variety of stats.
elims/min?
deaths/min?
dmg/min?
solokills/min?
weapon acc?
winrate?
most dmg ingame?
avg time on fire?
"""
def data_packer(total_stat, mins_played):
"""
This helper function serves to calculate a stat per minute, given by `total_stat`.
:param total_stat: The HerosStats instance to calculate per minute on
:param mis_played: The HeroStats instance of minutes played
:type total_stat: HerosStats
:type total_stat: HerosStats
:returns: HerosStats
"""
logging.debug("...Data Packing stat: %s" % (total_stat.stat_name))
stat_min = np.array(total_stat.values) / np.array(
mins_played.values)
stat_min_hs = HerosStats(total_stat.stat_name + "/Minute",
zip(total_stat.values, stat_min))
return stat_min_hs
def get_top_three(col):
"""
Finds the top 3 heros in the stat specified by `col`.
Only looks for heros that have more than 5 games played.
:param col: The stat to reference
:type col: str
"""
top_stat = df[df['Games Played'] > 5][col].nlargest(3)
logging.info("\n%s" % top_stat)
top_stat_hs = HerosStats(col, zip(top_stat.index, top_stat))
logging.debug("%s (Top 3): %s" %
(top_stat_hs.stat_name, top_stat_hs))
return top_stat_hs
elims_min_hs = data_packer(self.total_elims, self.mins_played)
deaths_min_hs = data_packer(self.total_deaths, self.mins_played)
dmg_min_hs = data_packer(self.total_dmg, self.mins_played)
solo_kills_hs = data_packer(self.total_solo_kills, self.mins_played)
wpn_acc = self.wpn_acc
games_played = self.games_played
data = {
"Elims/Min": elims_min_hs.values,
"Deaths/Min": deaths_min_hs.values,
"Dmg/Min": dmg_min_hs.values,
"SoloKills/Min": solo_kills_hs.values,
"Weapon Accuracy": wpn_acc.values,
"Games Played": games_played.values
}
df = pd.DataFrame(data=data, index=self.heros)
logging.info("\n%s" % df)
self.elimspm = HerosStats("Elims/Min", zip(df.index, df['Elims/Min']))
self.deathspm = HerosStats("Deaths/Min", zip(df.index,
df['Deaths/Min']))
self.dmgpm = HerosStats("Dmg/Min", zip(df.index, df['Dmg/Min']))
self.solokills = HerosStats("SoloKills/Min",
zip(df.index, df['SoloKills/Min']))
logging.debug("\n%s" % self.elimspm)
logging.debug("\n%s" % self.deathspm)
logging.debug("\n%s" % self.dmgpm)
logging.debug("\n%s" % self.solokills)
self.top_elimspm = get_top_three('Elims/Min')
self.top_deathspm = get_top_three('Deaths/Min')
self.top_dmgs = get_top_three('Dmg/Min')
self.top_solokills = get_top_three('SoloKills/Min')
self.top_wpnacc = get_top_three('Weapon Accuracy')
self.top_played = get_top_three('Games Played')