def run(profName=""):
global settings
global profile
global inputFaker
global bciDevice
global current
global keycodelistlabels
global niaDirectFeedbackApp
inputFaker = OutputManager.InputFaker()
if inputFaker == -1:
print "Import-Error: You need either Win32con (Windows) or XLib (Linux, Mac)"
else:
keycodelistlabels = sorted(
["Action: " + each for each in inputFaker.actions.keys()])
profile = ProfileSettings()
if (profName == ""):
if len(sys.argv) == 2:
profilefile = sys.argv[1]
if os.path.exists(profilefile + ".feedback"):
workfile = open(profilefile + ".feedback", "r")
profile = pickle.load(workfile)
workfile.close()
else:
print "no " + profilefile + ".feedback" + " file found"
selection = WXElements.selection(
"Select your Device",
InputManager.SupportedDevices.keys()[0],
InputManager.SupportedDevices.keys())
profile.deviceName = selection
else:
selection = WXElements.selection(
"Select your Device",
InputManager.SupportedDevices.keys()[0],
InputManager.SupportedDevices.keys())
profile.deviceName = selection
else:
profilefile = profName
if os.path.exists(profilefile + ".feedback"):
workfile = open(profilefile + ".feedback", "r")
profile = pickle.load(workfile)
workfile.close()
else:
profile.profileName = profilefile
bciDevice = InputManager.BCIDevice(profile.deviceName)
current = Current_Data()
niaDirectFeedbackApp = NiaDirectFeedbackApp()
niaDirectFeedbackApp.MainLoop()
bciDevice.quit()
def run(profName=""):
global settings
global neuralNetBreederApp
datafile = ""
if (profName==""):
if len(sys.argv)<2:
path = os.getcwd()
fileList = os.listdir(path)
profileList = []
for fileName in fileList:
if fileName[-5:] == "train":
profileList.append(fileName[:-6])
if len(profileList) > 0:
datafile = str(WXElements.selection("Select your Sample-set",profileList[0], profileList))
else:
print "Error: no profiles found"
else:
datafile = sys.argv[1]
if len(datafile)==0:
print ( "If you want to breed a neural net based on myProfile.train and myProfile.test,\n"+
"use: python Triathlon-Breeder.py myProfile")
else:
if os.path.exists(datafile+".train") and os.path.exists(datafile+".test"):
settings = AppSettings(datafile)
neuralNetBreederApp = NeuralNetBreederApp()
breedTimer = BreedingEventTimer()
neuralNetBreederApp.MainLoop()
else:
print "Error: no "+datafile+".train file\nor no "+datafile+".test file found."
else:
settings = AppSettings(profName)
neuralNetBreederApp = NeuralNetBreederApp()
breedTimer = BreedingEventTimer()
neuralNetBreederApp.MainLoop()
def run(profName=""):
global settings
global profile
global inputFaker
global bciDevice
global current
global keycodelistlabels
global niaDirectFeedbackApp
inputFaker = OutputManager.InputFaker()
if inputFaker == -1:
print "Import-Error: You need either Win32con (Windows) or XLib (Linux, Mac)"
else:
keycodelistlabels = sorted(["Action: "+each for each in inputFaker.actions.keys()])
profile = ProfileSettings()
if (profName==""):
if len(sys.argv)==2:
profilefile = sys.argv[1]
if os.path.exists(profilefile+".feedback"):
workfile = open(profilefile+".feedback", "r")
profile = pickle.load(workfile)
workfile.close()
else:
print "no "+profilefile+".feedback"+" file found"
selection = WXElements.selection("Select your Device",InputManager.SupportedDevices.keys()[0],InputManager.SupportedDevices.keys())
profile.deviceName = selection
else:
selection = WXElements.selection("Select your Device",InputManager.SupportedDevices.keys()[0],InputManager.SupportedDevices.keys())
profile.deviceName = selection
else:
profilefile = profName
if os.path.exists(profilefile+".feedback"):
workfile = open(profilefile+".feedback", "r")
profile = pickle.load(workfile)
workfile.close()
else:
profile.profileName = profilefile
bciDevice = InputManager.BCIDevice(profile.deviceName)
current = Current_Data()
niaDirectFeedbackApp = NiaDirectFeedbackApp()
niaDirectFeedbackApp.MainLoop()
bciDevice.quit()
def run(profName=""):
global settings
global neuralNetBreederApp
datafile = ""
if (profName == ""):
if len(sys.argv) < 2:
path = os.getcwd()
fileList = os.listdir(path)
profileList = []
for fileName in fileList:
if fileName[-5:] == "train":
profileList.append(fileName[:-6])
if len(profileList) > 0:
datafile = str(
WXElements.selection("Select your Sample-set",
profileList[0], profileList))
else:
print "Error: no profiles found"
else:
datafile = sys.argv[1]
if len(datafile) == 0:
print(
"If you want to breed a neural net based on myProfile.train and myProfile.test,\n"
+ "use: python Triathlon-Breeder.py myProfile")
else:
if os.path.exists(datafile +
".train") and os.path.exists(datafile + ".test"):
settings = AppSettings(datafile)
neuralNetBreederApp = NeuralNetBreederApp()
breedTimer = BreedingEventTimer()
neuralNetBreederApp.MainLoop()
else:
print "Error: no " + datafile + ".train file\nor no " + datafile + ".test file found."
else:
settings = AppSettings(profName)
neuralNetBreederApp = NeuralNetBreederApp()
breedTimer = BreedingEventTimer()
neuralNetBreederApp.MainLoop()
def calibrateAll(self):
for i in range(len(self.devices)):
self.calibrate(i)
def working_Data(self, i):
return self.devices[i].Working_Data
def calibration(self, i):
return self.devices[i].Calibration
def setPoints(self, po):
for i in range(len(self.devices)):
self.devices[i].Points = po
def quit(self):
for i in range(len(self.devices)):
self.devices[i].timer.cancel()
sleep(0.5)
for i in range(len(self.devices)):
self.devices[i].close()
if __name__ == "__main__":
import WXElements
selection = WXElements.selection("Select your Device",
SupportedDevices.keys()[0],
SupportedDevices.keys())
bciDevice = BCIDevice(selection)
#print bciDevice.frequenciesCombined(10,30)
#bciDevice.quit()
self.devices.append(NIA_Data(25,1))
def frequencies(self,i,fromFreq,toFreq):
return self.devices[i].Frequencies[fromFreq:toFreq]
def frequenciesCombined(self,fromFreq,toFreq):
result = []
for i in range(len(self.devices)):
result.extend(self.frequencies(i,fromFreq,toFreq))
return result
def process(self,i):
self.devices[i].process()
def record(self,i):
self.devices[i].record()
def calibrate(self,i):
self.devices[i].calibrate()
def calibrateAll(self):
for i in range(len(self.devices)):
self.calibrate(i)
def working_Data(self,i):
return self.devices[i].Working_Data
def calibration(self,i):
return self.devices[i].Calibration
def setPoints(self,po):
for i in range(len(self.devices)):
self.devices[i].Points = po
if __name__ == "__main__":
import WXElements
selection = WXElements.selection("Select your Device",SupportedDevices.keys()[0],SupportedDevices.keys())
bciDevice = BCIDevice(selection)
print bciDevice.frequenciesCombined(10,30)
array[:,:,:] = color_gradient
image = wx.EmptyImage(width, height)
image.SetData(array.tostring())
return wx.BitmapFromImage(image)
if __name__ == "__main__":
datafile = ""
if len(sys.argv)<2:
path = os.getcwd()
fileList = os.listdir(path)
profileList = []
for fileName in fileList:
if fileName[-5:] == "train":
profileList.append(fileName[:-6])
if len(profileList) > 0:
datafile = str(WXElements.selection("Select your Sample-set",profileList[0], profileList))
else:
print "Error: no profiles found"
else:
datafile = sys.argv[1]
if not len(datafile):
print ( "If you want to breed a neural net based on myProfile.train and myProfile.test,\nuse: python Triathlon-Breeder.py myProfile")
else:
if os.path.exists(datafile+".train") and os.path.exists(datafile+".test"):
settings = AppSettings(datafile)
neuralNetBreederApp = NeuralNetBreederApp()
breedTimer = BreedingEventTimer()
neuralNetBreederApp.MainLoop()
else:
print "Error: no", datafile+".train file\nor no", datafile+".test file found."
data_thread2.start()
bciDevice.process(0)
bciDevice.process(1)
else:
data_thread = threading.Thread(target=bciDevice.record,args=([0]))
data_thread.start()
bciDevice.process(0)
self.rawvisualizationCanvas.newReading()
self.spectogramCanvas.newReading()
self.visualizationCanvas.newReading()
self.historyCanvas.newReading()
ev.Skip()
class NiaEEGApp(wx.App):
def __init__(self, redirect = False):
wx.App.__init__(self)
self.mainWindow = GUIMain()
self.mainWindow.Show(True)
bciDevice.setPoints(int(500.0/settings.niaFPS))
self.mainWindow.timer.Start(int(1000.0/settings.niaFPS))
if __name__ == "__main__":
settings = AppSettings()
selection = WXElements.selection("Select your Device",InputManager.SupportedDevices.keys()[0],InputManager.SupportedDevices.keys())
settings.deviceName = selection
bciDevice = InputManager.BCIDevice(settings.deviceName)
argcp = ''
glutInit(argcp, sys.argv)
niaEEGApp = NiaEEGApp()
niaEEGApp.MainLoop()
def run(profName=""):
global profile
global inputFaker
global bciDevice
global keycodelistlabels
global current
global fannToOutputApp
global ann
inputFaker = OutputManager.InputFaker()
if inputFaker == -1:
print "Import-Error: You need either Win32con (Windows) or XLib (Linux, Mac)"
else:
profilefile = ""
keycodelistlabels = sorted(["Action: "+each for each in inputFaker.actions.keys()])
if (profName==""):
if len(sys.argv)<2:
path = os.getcwd()
fileList = os.listdir(path)
profileList = []
for fileName in fileList:
if fileName[-7:] == "profile":
profileList.append(fileName[:-8])
if len(profileList) > 0:
profilefile = str(WXElements.selection("Select your Profile",profileList[0], profileList))
else:
print "Error: no profiles found"
else:
profilefile = sys.argv[1]
if len(profilefile)==0:
print "Error: no profile name given.\nExample: python nia-Triathlon-Player.py myProfile"
else:
if len(profilefile)==0:
profilefile = sys.argv[1]
profileLoaded = False
netLoaded = False
if os.path.exists(profilefile+".profile"):
workfile = open(profilefile+".profile", "r")
profile = pickle.load(workfile)
workfile.close()
profileLoaded = True
else:
print "no "+profilefile+".profile"+" file found"
ann = libfann.neural_net()
if os.path.exists(profilefile+".net"):
ann.create_from_file(profilefile+".net")
netLoaded = True
else:
print "no "+profilefile+".net"+" file found"
if (profileLoaded and netLoaded):
bciDevice = InputManager.BCIDevice(profile.deviceName)
current = Current_Data()
fannToOutputApp = FannToOutputApp()
fannToOutputApp.MainLoop()
bciDevice.quit()
else:
print "Cannot start without .profile and .net files"
else:
profilefile=profName
workfile = open(profilefile+".profile", "r")
profile = pickle.load(workfile)
workfile.close()
ann = libfann.neural_net()
ann.create_from_file(profilefile+".net")
bciDevice = InputManager.BCIDevice(profile.deviceName)
current = Current_Data()
fannToOutputApp = FannToOutputApp()
fannToOutputApp.MainLoop()
bciDevice.quit()
if __name__ == "__main__":
inputFaker = OutputManager.InputFaker()
if inputFaker == -1:
print "Import-Error: You need either SendKeys (Windows) or XLib (Linux, Mac)"
else:
profilefile = ""
keycodelistlabels = sorted(["Action: "+each for each in inputFaker.actions.keys()])
if len(sys.argv)<2:
path = os.getcwd()
fileList = os.listdir(path)
profileList = []
for fileName in fileList:
if fileName[-7:] == "profile":
profileList.append(fileName[:-8])
if len(profileList) > 0:
profilefile = str(WXElements.selection("Select your Profile",profileList[0], profileList))
else:
print "Error: no profiles found"
else:
profilefile = sys.argv[1]
if not len(profilefile):
print "Error: no profile name given.\nExample: python nia-Triathlon-Player.py myProfile"
else:
if not len(profilefile):
profilefile = sys.argv[1]
profileLoaded = False
netLoaded = False
if os.path.exists(profilefile+".profile"):
workfile = open(profilefile+".profile", "r")
profile = pickle.load(workfile)
