def scouting_freq_and_cat(replay):
'''scouting_freq_and_cat takes in a previously loaded replay
from sc2reader and returns the scouting frequency (instances per second)
for each player, how their scouting behavior is categorized, as well as
the winner of the game.'''
r = replay
try:
frames = r.frames
seconds = r.real_length.total_seconds()
team1_scouting_states, team2_scouting_states = final_scouting_states(r)
team1_num_times, team1_time = scouting_stats(team1_scouting_states)
team2_num_times, team2_time = scouting_stats(team2_scouting_states)
team1_freq = team1_num_times / seconds
team2_freq = team2_num_times / seconds
team1_frames = scouting_timeframe_list1(team1_scouting_states)
team2_frames = scouting_timeframe_list1(team2_scouting_states)
battles, harassing = battle_detector.buildBattleList(r)
team1_cat = categorize_player(team1_frames, battles, harassing, frames)
team2_cat = categorize_player(team2_frames, battles, harassing, frames)
return team1_freq, team1_cat, team2_freq, team2_cat, r.winner.number
except:
print(replay.filename, "contains errors within scouting_detector")
raise
def scouting_response(replay):
'''scouting_response takes in a previously loaded replay and returns the
average interval in between scouting and battles for each player. This is
a start on exploring responses to scouting.'''
r = replay
try:
factors = sc2reader.constants.GAME_SPEED_FACTOR
# scale = 16*factors[r.expansion][r.speed]
# it appears the scale is always 22.4 in our dataset, despite documentation to the contrary
scale = 22.4
team1_scouting_states, team2_scouting_states = final_scouting_states(r)
battles, harassing = battle_detector.buildBattleList(r)
team1_scouting_frames = scouting_timeframe_list1(team1_scouting_states)
team2_scouting_frames = scouting_timeframe_list1(team2_scouting_states)
team1_avg = avg_interval_before_battle(team1_scouting_frames, battles,
scale)
team2_avg = avg_interval_before_battle(team2_scouting_frames, battles,
scale)
return team1_avg, team2_avg
except:
raise
def control_group_stats(replay):
'''control_group_stats is the main function for this script. It takes in
a pre-loaded replay using sc2reader and returns the commands per second,
peacetime macro selection rate, and battletime macro selection rate
for each player.'''
r = replay
p1_cps, p2_cps = commandsPerSecond(r.game_events,
r.real_length.total_seconds())
battles, harassing = battle_detector.buildBattleList(r)
p1_peace_rate, p1_battle_rate, p2_peace_rate, p2_battle_rate = macroRates(
r.game_events, battles, r.frames, r.real_length.total_seconds())
return p1_cps, p1_peace_rate, p1_battle_rate, p2_cps, p2_peace_rate, p2_battle_rate
def scouting_analysis(replay):
'''scouting_analysis takes in a previously loaded replay and returns a
dictionary that contains verious metrics of scouting for each player.
These metrics include the scouting category, whether or not they
execute an initial scouting, whether they mostly scout their opponent's
main base, whether they consistently scout new areas of the map, and
whether they consistently scout between battles.'''
r = replay
try:
team1_scouting_states, team2_scouting_states = final_scouting_states(r)
battles, harassing = battle_detector.buildBattleList(r)
frames = r.frames
team1_initial = hasInitialScouting(team1_scouting_states, frames,
battles)
team2_initial = hasInitialScouting(team2_scouting_states, frames,
battles)
team1_frames = scouting_timeframe_list1(team1_scouting_states)
team2_frames = scouting_timeframe_list1(team2_scouting_states)
team1_cat = categorize_player(team1_frames, battles, harassing, frames)
team2_cat = categorize_player(team2_frames, battles, harassing, frames)
team1_base = scoutsMainBase(team1_scouting_states)
team2_base = scoutsMainBase(team2_scouting_states)
team1_newAreas = scoutNewAreas(team1_scouting_states)
team2_newAreas = scoutNewAreas(team2_scouting_states)
team1_betweenBattles = scoutBetweenBattles(team1_scouting_states,
battles, frames)
team2_betweenBattles = scoutBetweenBattles(team2_scouting_states,
battles, frames)
return {
1: [
team1_cat, team1_initial, team1_base, team1_newAreas,
team1_betweenBattles
],
2: [
team2_cat, team2_initial, team2_base, team2_newAreas,
team2_betweenBattles
]
}
except:
traceback.print_exc()
print(replay.filename, "contains errors within scouting_detector")
raise
def final_scouting_states(replay):
'''final_scouting_states is the backbone of scouting_detector.py. It does
all of the error checking needed, as well as combines all functions to
create completed scouting dictionaries - to then be used by other functions.
It takes in a previously loaded replay object from sc2reader and returns
completed scouting dictionaries for each player. This function is also
critical in understanding the order in which scouting dictionaries
must be built by using various functions in this file.'''
r = replay
if r.winner is None:
print(r.filename, "has no winner information")
raise RuntimeError()
try:
# some datafiles did not have a 'Controller' attribute
if r.attributes[1]["Controller"] == "Computer" or r.attributes[2][
"Controller"] == "Computer":
print(r.filename, "is a player vs. AI game")
raise RuntimeError()
except:
raise RuntimeError()
if r.length.seconds < 300:
print(r.filename, "is shorter than 5 minutes")
raise RuntimeError()
if len(r.players) != 2:
print(r.filename, "is not a 1v1 game")
raise RuntimeError()
tracker_events = r.tracker_events
game_events = r.game_events
frames = r.frames
# scale = 16*sc2reader.constants.GAME_SPEED_FACTOR[r.expansion][r.speed]
# it appears the scale is always 22.4 in our dataset, despite documentation to the contrary
scale = 22.4
allEvents = buildEventLists(tracker_events, game_events)
objects = r.objects.values()
team1_scouting_states, team2_scouting_states = buildScoutingDictionaries(
r, allEvents, objects, frames)
battles, harassing = battle_detector.buildBattleList(r)
team1_scouting_states = integrateEngagements(team1_scouting_states,
battles, scale,
"Engaged in Battle")
team2_scouting_states = integrateEngagements(team2_scouting_states,
battles, scale,
"Engaged in Battle")
team1_scouting_states = integrateEngagements(team1_scouting_states,
harassing, scale, "Harassing")
team2_scouting_states = integrateEngagements(team2_scouting_states,
harassing, scale, "Harassing")
team1_scouting_states = checkFirstInstance(team1_scouting_states, scale)
team2_scouting_states = checkFirstInstance(team2_scouting_states, scale)
team1_scouting_states = removeEmptyFrames(team1_scouting_states, frames)
team2_scouting_states = removeEmptyFrames(team2_scouting_states, frames)
return team1_scouting_states, team2_scouting_states
def generateFields(filename):
'''generateFields takes in a filename of a replay, loads it and gathers necessary
statistics, and returns the statistics in a tuple. It is to be used to write
these stats to a csv. It also takes in an integer (1 or 2), which indicates
which statistics will be gathered. In this case, generateFields gathers
each point in the game where a period of scouting is initiated. Inputting a
1 will return times as a fraction of the total game time, whereas inputting
a 2 will return absolute frames.'''
# loading the replay
try:
# skipping non-replay files in the directory
if filename[-9:] != "SC2Replay":
raise RuntimeError()
# extracting the game id and adding the correct tag
# pathname = "practice_replays/" + filename
pathname = "/Accounts/awb-data/replays/" + filename
game_id = filename.split("_")[1].split(".")[0]
if filename.startswith("ggg"):
game_id = "ggg-" + game_id
elif filename.startswith("spawningtool"):
game_id = "st-" + game_id
# loading the replay
try:
r = sc2reader.load_replay(pathname)
# print(r.filename, r.players[0].highest_league, r.players[1].highest_league)
if any(v != (0, {}) for v in r.plugin_result.values()):
print(pathname, r.plugin_result)
except:
print(filename,
"cannot load using sc2reader due to an internal ValueError")
traceback.print_exc()
raise
# team1_times, team2_times = scouting_detector.scouting_times(r, which)
team1_rank, team1_rel_rank, team2_rank, team2_rel_rank = scouting_stats.ranking_stats(
r)
battles, harassing = battle_detector.buildBattleList(r)
frames = r.frames
seconds = r.real_length.total_seconds()
battle_time = []
battle_duration = []
harassing_time = []
harassing_duration = []
for battle in battles:
starttime = (battle[0] / frames) * seconds
endtime = (battle[1] / frames) * seconds
duration = endtime - starttime
battle_duration.append(duration)
battle_time.append(starttime)
for harass in harassing:
starttime = (harass[0] / frames) * seconds
endtime = (harass[1] / frames) * seconds
duration = endtime - starttime
harassing_duration.append(duration)
harassing_time.append(starttime)
team1_uid = r.players[0].detail_data['bnet']['uid']
team2_uid = r.players[1].detail_data['bnet']['uid']
fields = [game_id, team1_uid, team1_rank, team2_uid, team2_rank,\
battle_time, battle_duration, harassing_time, harassing_duration]
return fields
except KeyboardInterrupt:
raise
except:
print("\nReturning none\n")
#traceback.print_exc()
return