def initPanel(self):
_logger.info('initPanel')
self.addHandler()
g_statsCollector.eventHandlers += self.onEvent
g_statsCollector.start()
g_statsCollector.updateArenaInfo()
clientStatus = g_statsCollector.clientStatus
self.__panels = []
self.__keyHandlers = {}
for paneldef in self.__config.get('panelDefs', []):
if paneldef['channel'] == 'indicator':
panel = StatsIndicator(paneldef, clientStatus)
if 'events' in paneldef:
self.__eventHandlers += panel.onEvent
else:
self.__intervalHandlers += panel.update
if 'toggleKey' in paneldef['style']:
keyName = paneldef['style']['toggleKey']
keyId = getattr(Keys, keyName)
if keyId not in self.__keyHandlers:
self.__keyHandlers[keyId] = Event()
self.__keyHandlers[keyId] += panel.toggle
elif paneldef['channel'] == 'status':
panel = StatsLogger(paneldef, clientStatus)
self.__intervalHandlers += panel.update
elif paneldef['channel'] == 'event':
panel = EventLogger(paneldef, clientStatus)
self.__eventHandlers += panel.onEvent
self.__panels.append(panel)
session = dependency.instance(IBattleSessionProvider)
ctrl = session.shared.crosshair
self.changeView(ctrl.getViewID())
self.updateScreenPosition()
self.updateCrosshairPosition()
def __init__(self, nParticipant, from_file = False):
self.nParticipant = nParticipant
self.events = EventLogger()
self.ratings = ParticipantRatings(self.nParticipant)
self.featureVectors = {} # {1:{'Fp1_theta': 2453476, 'Fp1_slow_alpha': 482418, ... , 'avgSkinRes': 69'}}
self.featureDF = pd.DataFrame # use special function
self.Y = {}
for trial in range(1,41):
self.featureVectors[trial] = {}
self.Y[trial] = 0
# seed and variables for splitting data
self.randomSeed = random.randint(1, 1000000)
self.X_train = {}
self.X_test = {}
self.X_validation = {}
self.Y_train = {}
self.Y_test = {}
self.Y_validation = {}
# if we not use precomputed feature from file - compute it from raw signals
if not from_file:
self.ratings = ParticipantRatings(self.nParticipant)
self.physSignalsFeatures = ParticipantSignalsFeatures(self.nParticipant)
self.physSignalsFeatures.computeFeatures(range(1, 41), range(1, 33), BANDS, FREQ, 0, 8063,
ASYM_ELECTRODE_PAIRS, ASYM_BANDS)
else:
self.loadFeatureVectorsFromCSV()
self.convertFeatureVectorsToDataFrame()
def __init__(self, nParticipant, from_file = False):
self.nParticipant = nParticipant
self.events = EventLogger()
self.ratings = ParticipantRatings(self.nParticipant)
self.featureVectors = {} #
self.featureDF = pd.DataFrame # use special function
self.Y = {}
for trial in range(1, 41):
self.featureVectors[trial] = {}
self.Y[trial] = 0
# seed and variables for splitting data
self.randomSeed = random.randint(1, 1000000)
self.X_train = {}
self.X_test = {}
self.X_validation = {}
self.Y_train = {}
self.Y_test = {}
self.Y_validation = {}
# if we not use precomputed feature from file - compute it from raw signals
if not from_file:
self.featureVectors = self.unpackEEGDataToVectors()
else:
self.loadFeatureVectorsFromCSV()
self.convertFeatureVectorsToDataFrame()
