class MouseTunnel(ShowBase):
def __init__(self):
# Initialize the ShowBase class from which we inherit, which will
# create a window and set up everything we need for rendering into it.
ShowBase.__init__(self)
self.stimtype = 'image_sequence'
#session_start
self.session_start_time = datetime.datetime.now()
self.accept("escape", sys.exit, [0])
self.accept('q', self.close)
self.accept('Q', self.close)
# disable mouse control so that we can place the camera
base.disableMouse()
camera.setPosHpr(0, 0, 10, 0, -90, 0)
mat = Mat4(camera.getMat())
mat.invertInPlace()
base.mouseInterfaceNode.setMat(mat)
# base.enableMouse()
props = WindowProperties()
# props.setFullscreen(True)
props.setCursorHidden(True)
props.setMouseMode(WindowProperties.M_relative)
base.win.requestProperties(props)
base.setBackgroundColor(0, 0, 0) # set the background color to black
#set up the textures
# we now get buffer thats going to hold the texture of our new scene
altBuffer = self.win.makeTextureBuffer("hello", 1524, 1024)
# altBuffer.getDisplayRegion(0).setDimensions(0.5,0.9,0.5,0.8)
# altBuffer = base.win.makeDisplayRegion()
# altBuffer.makeDisplayRegion(0,1,0,1)
# now we have to setup a new scene graph to make this scene
self.dr2 = base.win.makeDisplayRegion(0, 0.1, 0, 0.1)
altRender = NodePath("new render")
# this takes care of setting up ther camera properly
self.altCam = self.makeCamera(altBuffer)
self.dr2.setCamera(self.altCam)
self.altCam.reparentTo(altRender)
self.altCam.setPos(0, -10, 0)
self.bufferViewer.setPosition("llcorner")
# self.bufferViewer.position = (.1,.4,.1,.4)
self.bufferViewer.setCardSize(1.0, 0.0)
print(self.bufferViewer.position)
self.imagesTexture = MovieTexture("image_sequence")
# success = self.imagesTexture.read("models/natural_images.avi")
success = self.imagesTexture.read("models/movie_5hz.mpg")
self.imagesTexture.setPlayRate(1.0)
self.imagesTexture.setLoopCount(10)
# self.imageTexture =loader.loadTexture("models/NaturalImages/BSDs_8143.tiff")
# self.imagesTexture.reparentTo(altRender)
cm = CardMaker("stimwindow")
cm.setFrame(-4, 4, -3, 3)
# cm.setUvRange(self.imagesTexture)
self.card = NodePath(cm.generate())
self.card.reparentTo(altRender)
if self.stimtype == 'image_sequence':
self.card.setTexture(self.imagesTexture, 1)
# self.imagesTexture.play()
# self.bufferViewer.setPosition("lrcorner")
# self.bufferViewer.setCardSize(1.0, 0.0)
self.accept("v", self.bufferViewer.toggleEnable)
self.accept("V", self.bufferViewer.toggleEnable)
# Load the tunnel
self.initTunnel()
#initialize some things
# for the tunnel construction:
self.boundary_to_add_next_segment = -1 * TUNNEL_SEGMENT_LENGTH
self.current_number_of_segments = 8
#task flow booleans
self.in_waiting_period = False
self.stim_started = False
self.looking_for_a_cue_zone = True
self.in_reward_window = False
self.show_stimulus = False
#for task control
self.interval = 0
self.time_waiting_in_cue_zone = 0
self.wait_time = 1.0
self.stim_duration = 4.0 # in seconds
self.max_stim_duration = 6.0 # in seconds
self.stim_elapsed = 0.0 # in seconds
self.last_position = base.camera.getZ()
self.position_on_track = base.camera.getZ()
#for reward control
self.reward_window = 1.0 # in seconds
self.reward_elapsed = 0.0
self.reward_volume = 0.008 # in mL. this is for the hardcoded 0.1 seconds of reward time
self.reward_time = 0.1 # in sec, based on volume. hard coded right now but should be modified by the (1) calibration and (2) optionally by the main loop for dynamic reward scheduling
# self.lick_buffer = []
#INITIALIZE NIDAQ
self.nidevice = 'Dev1'
self.encodervinchannel = 0
self.encodervsigchannel = 1
self.invertdo = False
self.diport = 1
self.lickline = 1
self.doport = 0
self.rewardline = 0
self.rewardlines = [0]
self._setupDAQ()
self.do.WriteBit(1, 1)
self.do.WriteBit(
3, 1
) #set reward high, because the logic is flipped somehow. possibly by haphazard wiring of the circuit (12/24/2018 djd)
self.previous_encoder_position = self.ai.data[0][2]
self.encoder_gain = -20
#INITIALIZE LICK SENSOR
self._lickSensorSetup()
#INITIALIZE output data
self.lickData = []
self.x = []
self.t = []
self.trialData = []
self.rewardData = []
#INITIALIZE KEY SENSOR, for backup inputs and other user controls
self.keys = key.KeyStateHandler()
self.accept('r', self._give_reward, [self.reward_volume])
img_list = glob.glob('models/NaturalImages/*.tiff')[:10]
print(img_list)
self.imageTextures = [loader.loadTexture(img) for img in img_list]
if AUTO_MODE:
self.gameTask = taskMgr.add(self.autoLoop2, "autoLoop2")
self.rewardTask = taskMgr.add(self.rewardControl, "reward")
self.cue_zone = concatenate(
(self.cue_zone,
arange(
self.current_number_of_segments * -TUNNEL_SEGMENT_LENGTH -
10,
self.current_number_of_segments * -TUNNEL_SEGMENT_LENGTH -
TUNNEL_SEGMENT_LENGTH - 10, -1)))
self.auto_position_on_track = 0
self.auto_restart = False
self.auto_running = True
self.contTunnel()
else:
# Now we create the task. taskMgr is the task manager that actually
# calls the function each frame. The add method creates a new task.
# The first argument is the function to be called, and the second
# argument is the name for the task. It returns a task object which
# is passed to the function each frame.
self.gameTask = taskMgr.add(self.gameLoop, "gameLoop")
# self.stimulusTask = taskMgr.add(self.stimulusControl, "stimulus")
self.lickTask = taskMgr.add(self.lickControl, "lick")
self.rewardTask = taskMgr.add(self.rewardControl, "reward")
# Code to initialize the tunnel
def initTunnel(self):
self.tunnel = [None] * 8
for x in range(8):
# Load a copy of the tunnel
self.tunnel[x] = loader.loadModel('models/tunnel')
# The front segment needs to be attached to render
if x == 0:
self.tunnel[x].reparentTo(render)
# The rest of the segments parent to the previous one, so that by moving
# the front segement, the entire tunnel is moved
else:
self.tunnel[x].reparentTo(self.tunnel[x - 1])
# We have to offset each segment by its length so that they stack onto
# each other. Otherwise, they would all occupy the same space.
self.tunnel[x].setPos(0, 0, -TUNNEL_SEGMENT_LENGTH)
# Now we have a tunnel consisting of 4 repeating segments with a
# hierarchy like this:
# render<-tunnel[0]<-tunnel[1]<-tunnel[2]<-tunnel[3]
self.tunnel[0] = loader.loadModel('models/grating')
self.tunnel[0].reparentTo(render)
self.cue_zone = arange(0, TUNNEL_SEGMENT_LENGTH, -1)
# This function is called to snap the front of the tunnel to the back
# to simulate traveling through it
def contTunnel(self):
self.auto_position_on_track -= 50
position_on_track = self.auto_position_on_track
print(str(int(position_on_track)) + ' ' + str(self.cue_zone))
if int(position_on_track) in np.array(
self.cue_zone): #check for cue zone
if not self.auto_restart:
print('STOP!')
self.tunnelMove.pause()
self.auto_presentation = True
# self.current_number_of_segments +=1
else:
self.auto_restart = True
self.tunnelMove.resume()
else:
self.in_waiting_period = False
self.auto_presentation = False
# base.setBackgroundColor([1,0 , 0])
if self.looking_for_a_cue_zone == False:
self.looking_for_a_cue_zone = True
if self.stim_started == True:
self.stop_a_presentation()
# This line uses slices to take the front of the list and put it on the
# back. For more information on slices check the Python manual
self.tunnel = self.tunnel[1:] + self.tunnel[0:1]
# Set the front segment (which was at TUNNEL_SEGMENT_LENGTH) to 0, which
# is where the previous segment started
self.tunnel[0].setZ(0)
# Reparent the front to render to preserve the hierarchy outlined above
self.tunnel[0].reparentTo(render)
# Set the scale to be apropriate (since attributes like scale are
# inherited, the rest of the segments have a scale of 1)
self.tunnel[0].setScale(.155, .155, .305)
# Set the new back to the values that the rest of the segments have
self.tunnel[3].reparentTo(self.tunnel[2])
self.tunnel[3].setZ(-TUNNEL_SEGMENT_LENGTH)
self.tunnel[3].setScale(1)
# Set up the tunnel to move one segment and then call contTunnel again
# to make the tunnel move infinitely
self.tunnelMove = Sequence(
LerpFunc(self.tunnel[0].setZ,
duration=TUNNEL_TIME,
fromData=0,
toData=TUNNEL_SEGMENT_LENGTH * .305),
Func(self.contTunnel))
self.tunnelMove.start()
def start_a_presentation(self):
print("start")
self.do.WriteBit(2, 1)
# self.bufferViewer.toggleEnable()
self.lick_buffer = []
if self.stimtype == 'random image':
for i in range(randint(len(self.imageTextures))):
self.card.setTexture(self.imageTextures[i], 1)
if self.stimtype == 'image_sequence':
self.imagesTexture.setTime(0.)
self.dr2.setDimensions(0.4, 0.8, 0.4,
0.70) #floats (left, right, bottom, top)
self.imagesTexture.play()
def stop_a_presentation(self):
if self.stim_started == True:
self.dr2.setDimensions(0, 0.1, 0, 0.1)
# self.bufferViewer.toggleEnable()
self.stim_started = False
self.stim_elapsed = 0.
self.stim_duration = exponential(self.max_stim_duration)
self.stim_off_time = globalClock.getFrameTime()
self.do.WriteBit(2, 0)
def _lickSensorSetup(self):
""" Attempts to set up lick sensor NI task. """
##TODO: Make lick sensor object if necessary. Let user select port and line.
if self.di:
self.lickSensor = self.di # just use DI for now
licktest = []
for i in range(30):
licktest.append(self.di.Read()[self.lickline])
time.sleep(0.01)
licktest = np.array(licktest, dtype=np.uint8)
if len(licktest[np.where(licktest > 0)]) > 25:
self.lickSensor = None
self.lickData = [np.zeros(len(self.rewardlines))]
print("Lick sensor failed startup test.")
else:
print(
"Could not initialize lick sensor. Ensure that NIDAQ is connected properly."
)
self.keycontrol = True
self.lickSensor = None
self.lickData = [np.zeros(len(self.rewardlines))]
self.keys = key.KeyStateHandler()
# self.window.winHandle.push_handlers(self.keys)
# def _read_licks(self): # not yet implemented; should be replaces with check to beam break
def _give_reward(self, volume):
print("reward!")
self.rewardData.extend([globalClock.getFrameTime()])
self.do.WriteBit(3, 0)
time.sleep(self.reward_time)
self.do.WriteBit(3, 1)
# put a TTL on a line to indicate that a reward was given
pass # not yet implemented
def autoLoop2(self, task):
dt = globalClock.getDt()
current_time = globalClock.getFrameTime()
self.x.extend([self.auto_position_on_track])
self.t.extend([globalClock.getFrameTime()])
if self.auto_presentation:
self.auto_running = False
if self.in_waiting_period:
self.time_waited += dt
else:
self.time_waited = 0
self.in_waiting_period = True
if self.time_waited > self.wait_time: #if in cue zone,see if we have been ther for long enough
#start a trial
self.start_position = self.auto_position_on_track
self.start_time = current_time
if not self.stim_started:
self.start_a_presentation()
# print(self.stim_duration)
self.stim_started = True
self.show_stimulus = True
else:
self.stim_elapsed += dt
if self.stim_elapsed > self.stim_duration:
self.show_stimulus = False
self.in_reward_window = True
self.stop_a_presentation()
self.auto_restart = False
# print(self.current_number_of_segments)
self.current_number_of_segments += 9
#redefine the cue zone as the next one
self.cue_zone = arange(
self.current_number_of_segments *
-TUNNEL_SEGMENT_LENGTH - 40,
self.current_number_of_segments *
-TUNNEL_SEGMENT_LENGTH - TUNNEL_SEGMENT_LENGTH -
40, -1)
#extend cue zone, keeping old ones
# self.cue_zone = concatenate((self.cue_zone,arange(self.current_number_of_segments*-TUNNEL_SEGMENT_LENGTH-40,
# self.current_number_of_segments*-TUNNEL_SEGMENT_LENGTH-TUNNEL_SEGMENT_LENGTH-40,
# -1)))
self.contTunnel()
self.time_waited = 0
self.looking_for_a_cue_zone = False
# base.setBackgroundColor([0, 0, 1])
else:
pass # base.setBackgroundColor([0, 1, 0])
else:
self.auto_running = True
return Task.cont # Since every return is Task.cont, the task will
def gameLoop(self, task):
# get the time elapsed since the next frame.
dt = globalClock.getDt()
current_time = globalClock.getFrameTime()
# get the camera position.
position_on_track = base.camera.getZ()
#get the encoder position from NIDAQ Analog Inputs channel 2
encoder_position = self.ai.data[0][
2] #zeroth sample in buffer [0], from ai2 [2]
#convert to track coordinates
encoder_position_diff = (encoder_position -
self.previous_encoder_position)
if encoder_position_diff > 4.5: encoder_position_diff -= 5.
if encoder_position_diff < -4.5: encoder_position_diff += 5.
encoder_position_diff *= self.encoder_gain
self.previous_encoder_position = encoder_position
position_on_track = base.camera.getZ() + encoder_position_diff
#reset the camera position
self.camera.setPos(base.camera.getX(), base.camera.getY(),
position_on_track)
self.x.extend([position_on_track])
self.t.extend([globalClock.getFrameTime()])
#first check if the mouse moved on the last frame.
if abs(self.last_position - position_on_track
) < .5: #the mouse didn't move more than 0.5 units on the track
self.moved = False
if int(position_on_track) in self.cue_zone: #check for cue zone
if self.looking_for_a_cue_zone: #make sure we transitioning from the tunnel to a cue zone
#increment how long we've been waiting in the cue zone.
if self.in_waiting_period:
self.time_waited += dt
else:
self.time_waited = 0
self.in_waiting_period = True
if self.time_waited > self.wait_time: #if in cue zone,see if we have been ther for long enough
#start a trial
self.start_position = position_on_track
self.start_time = current_time
if not self.stim_started:
self.start_a_presentation()
print(self.stim_duration)
self.stim_started = True
self.show_stimulus = True
else:
self.stim_elapsed += dt
if self.stim_elapsed > self.stim_duration:
self.show_stimulus = False
self.in_reward_window = True
self.stop_a_presentation()
self.time_waited = 0
self.looking_for_a_cue_zone = False
# base.setBackgroundColor([0, 0, 1])
else:
pass # base.setBackgroundColor([0, 1, 0])
else:
self.in_waiting_period = False
# base.setBackgroundColor([1,0 , 0])
if self.looking_for_a_cue_zone == False:
self.looking_for_a_cue_zone = True
if self.stim_started == True:
self.stop_a_presentation()
else: #the mouse did move
self.moved = True
if self.stim_started == True: #check if it moved during a presenation
self.stop_a_presentation()
self.time_waited = 0
self.looking_for_a_cue_zone = False
self.show_stimulus = False
#if we need to add another segment, do so
if position_on_track < self.boundary_to_add_next_segment:
self.tunnel.extend([None])
x = self.current_number_of_segments
if x % 8 == 0:
self.tunnel[x] = loader.loadModel('models/grating')
self.cue_zone = concatenate(
(self.cue_zone,
arange(
self.current_number_of_segments *
-TUNNEL_SEGMENT_LENGTH - 40,
self.current_number_of_segments *
-TUNNEL_SEGMENT_LENGTH - TUNNEL_SEGMENT_LENGTH - 40,
-1)))
else:
self.tunnel[x] = loader.loadModel('models/tunnel')
self.tunnel[x].setPos(0, 0, -TUNNEL_SEGMENT_LENGTH)
self.tunnel[x].reparentTo(self.tunnel[x - 1])
#increment
self.boundary_to_add_next_segment -= TUNNEL_SEGMENT_LENGTH
self.current_number_of_segments += 1
else:
pass #print('current:'+str(position_on_track) +' next boundary:' + str(self.boundary_to_add_next_segment))
self.last_position = position_on_track
# lick_times = self.
# self._read_licks()
return Task.cont # Since every return is Task.cont, the task will
# continue indefinitely, under control of the mouse
def stimulusControl(self, task):
if self.show_stimulus and not self.bufferViewer.isEnabled():
# self.bufferViewer.toggleEnable()
self.dr2.setDimensions(0.5, 0.9, 0.5, 0.8)
if not self.show_stimulus and self.bufferViewer.isEnabled():
# self.bufferViewer.toggleEnable()
self.dr2.setDimensions(0, 0.1, 0, 0.1)
return Task.cont
def lickControl(self, task):
""" Checks to see if a lick is occurring. """
##TODO: Let user select line for lick sensing.
if self.lickSensor:
if self.lickSensor.Read()[self.lickline]:
self.lickData.extend([globalClock.getFrameTime()])
# print(self.lickData)
elif self.keycontrol == True: #NO NI BOARD. KEY INPUT?
if self.keys[key.SPACE]:
data = [globalClock.getFrameTime()]
elif self.keys[key.NUM_1]:
# print(self.lickData)
# elif self.keys[key.NUM_3]:
# data = [0,1]
# else:
# data = [0,0]
self.lickData.extend(data)
return Task.cont
def rewardControl(self, task):
if self.in_reward_window:
self.reward_elapsed += globalClock.getDt()
if not AUTO_REWARD:
if len(
np.where(
np.array(self.lickData) > self.stim_off_time)[0]
) > 1: # this checks if there has been more than zero licks since the stimulus turned off
self._give_reward(self.reward_volume)
self.in_reward_window = False
else:
self._give_reward(self.reward_volume)
self.in_reward_window = False
# self.reward_elapsed=0.
# base.setBackgroundColor([1, 1, 0])
# if self.keys[key.NUM_1]:
# print('reward!')
if self.reward_elapsed > self.reward_window:
self.in_reward_window = False
self.reward_elapsed = 0.
return Task.cont
def _setupDAQ(self):
""" Sets up some digital IO for sync and tiggering. """
print('SETTING UP DAQ')
try:
if self.invertdo:
istate = 'low'
else:
istate = 'high'
self.do = DigitalOutput(self.nidevice,
self.doport,
initial_state='low')
self.do.StartTask()
except: # Exception, e:
print("Error starting DigitalOutput task:")
self.do = None
try:
self.di = DigitalInput(self.nidevice, self.diport)
self.di.StartTask()
except: # Exception, e:
print("Error starting DigitalInput task:")
self.di = None
try:
#set up 8 channels, only use 2 though for now
self.ai = AnalogInput(self.nidevice,
range(8),
buffer_size=25,
clock_speed=6000.0)
self.ai.StartTask()
except: # Exception, e:
print("Error starting AnalogInput task:")
self.ai = None
try:
self.ao = AnalogOutput(self.nidevice,
channels=[0, 1],
voltage_range=[0.0, 5.0])
self.ao.StartTask()
except: # Exception, e:
print("Error starting AnalogOutput task:")
self.ao = None
def close(self):
save_path = os.path.join(os.getcwd(),'data',str(MOUSE_ID)+'_'+\
str(self.session_start_time.year)+'_'+\
str(self.session_start_time.month)+'_'+\
str(self.session_start_time.day)+'-'+\
str(self.session_start_time.hour)+'_'+\
str(self.session_start_time.minute)+'_'+\
str(self.session_start_time.second))
if ~os.path.isdir(save_path):
os.mkdir(save_path)
print("saving data to " + save_path)
np.save(os.path.join(save_path, 'licks.npy'), self.lickData)
np.save(os.path.join(save_path, 'x.npy'), self.x)
np.save(os.path.join(save_path, 't.npy'), self.t)
np.save(os.path.join(save_path, 'trialData.npy'), self.trialData)
np.save(os.path.join(save_path, 'rewardData.npy'), self.rewardData)
sys.exit(0)
class MouseTunnel(ShowBase):
def __init__(self):
# Initialize the ShowBase class from which we inherit, which will
# create a window and set up everything we need for rendering into it.
ShowBase.__init__(self)
self.stimtype = 'random image'
# session_start
self.session_start_time = datetime.datetime.now()
# self.accept("escape", sys.exit, [0])#don't let the user do this, because then the data isn't saved.
self.accept('q', self.close)
self.accept('Q', self.close)
self.upArrowIsPressed = base.mouseWatcherNode.isButtonDown(KeyboardButton.up())
self.downArrowIsPressed = base.mouseWatcherNode.isButtonDown(KeyboardButton.down())
self.rightArrowIsPressed = base.mouseWatcherNode.isButtonDown(KeyboardButton.right())
self.leftArrowIsPressed = base.mouseWatcherNode.isButtonDown(KeyboardButton.left())
self.AUTO_REWARD = AUTO_REWARD
# disable mouse control so that we can place the camera
base.disableMouse()
camera.setPosHpr(0, 0, 10, 0, -90, 0)
mat = Mat4(camera.getMat())
mat.invertInPlace()
base.mouseInterfaceNode.setMat(mat)
# base.enableMouse()
props = WindowProperties()
# props.setOrigin(0, 0)
props.setFullscreen(True)
props.setCursorHidden(True)
props.setMouseMode(WindowProperties.M_relative)
base.win.requestProperties(props)
base.setBackgroundColor(0, 0, 0) # set the background color to black
print('FULSCREEN:')
print(props.getFullscreen())
print('=============')
# set up the textures
# we now get buffer thats going to hold the texture of our new scene
altBuffer = self.win.makeTextureBuffer("hello", 1524, 1024)
# altBuffer.getDisplayRegion(0).setDimensions(0.5,0.9,0.5,0.8)
# altBuffer = base.win.makeDisplayRegion()
# altBuffer.makeDisplayRegion(0,1,0,1)
# now we have to setup a new scene graph to make this scene
self.dr2 = base.win.makeDisplayRegion(0, 0.001, 0, 0.001)#make this really,really small so it's not seeable by the subject
altRender = NodePath("new render")
# this takes care of setting up ther camera properly
self.altCam = self.makeCamera(altBuffer)
self.dr2.setCamera(self.altCam)
self.altCam.reparentTo(altRender)
self.altCam.setPos(0, -10, 0)
self.bufferViewer.setPosition("lrcorner")
# self.bufferViewer.position = (-.1,-.4,-.1,-.4)
self.bufferViewer.setCardSize(1.0, 0.0)
print(self.bufferViewer.position)
self.imagesTexture = MovieTexture("image_sequence")
# success = self.imagesTexture.read("models/natural_images.avi")
# success = self.imagesTexture.read("models/movie_5hz.mpg")
self.imagesTexture.setPlayRate(1.0)
self.imagesTexture.setLoopCount(10)
# self.imageTexture =loader.loadTexture("models/NaturalImages/BSDs_8143.tiff")
# self.imagesTexture.reparentTo(altRender)
self.fixationPoint = OnscreenImage(image='models/fixationpoint.jpg', pos=(0, 0,0),scale=0.01)
cm = CardMaker("stimwindow")
cm.setFrame(-4, 4, -3, 3)
# cm.setUvRange(self.imagesTexture)
self.card = NodePath(cm.generate())
self.card.reparentTo(altRender)
if self.stimtype == 'image_sequence':
self.card.setTexture(self.imagesTexture, 1)
# add the score display
self.scoreLabel = OnscreenText(text='Current Score:', pos=(-1, 0.9), scale=0.1, fg=(0.8, 0.8, 0.8, 1))
self.scoreText = OnscreenText(text=str(0), pos=(-1, 0.76), scale=0.18, fg=(0, 1, 0, 1),
shadow=(0.1, 1, 0.1, 0.5))
self.feebackScoreText = OnscreenText(text='+ ' + str(0), pos=(-0.5, 0.5), scale=0.3, fg=(0, 1, 0, 1),
shadow=(0.1, 1, 0.1, 0.5))
self.feebackScoreText.setX(3.)
# self.imagesTexture.play()
# self.bufferViewer.setPosition("lrcorner")
# self.bufferViewer.setCardSize(1.0, 0.0)
self.accept("v", self.bufferViewer.toggleEnable)
self.accept("V", self.bufferViewer.toggleEnable)
# Load the tunnel
self.initTunnel()
# initialize some things
# for the tunnel construction:
self.boundary_to_add_next_segment = -1 * TUNNEL_SEGMENT_LENGTH
self.current_number_of_segments = 8
# task flow booleans
self.in_waiting_period = False
self.stim_started = False
self.looking_for_a_cue_zone = True
self.in_reward_window = False
self.show_stimulus = False
# for task control
self.interval = 0
self.time_waiting_in_cue_zone = 0
self.wait_time = 1.83
self.stim_duration = 0 # in seconds
# self.distribution_type = np.random.uniform#
# self.distribution_type_inputs = [0.016,0.4] #change the min & max stim duration times
#New lines EAS: set weights higher for faster image durations
self.durWeights = list()
a = np.linspace(0.016,0.4,10)
for i,j in enumerate(a):
if j<0.1:
p1 = 0.25
self.durWeights.append(p1)
elif j > 0.1 and j < 0.21:
p1 = 0.1
self.durWeights.append(p1)
elif j> 0.21:
p1 = 0.04
self.durWeights.append(p1)
self.rng = np.random.default_rng()
a = np.asarray(a)
self.distribution_type_inputs = a
#subset_size = len(p)
#End new lines
# self.distribution_type_inputs = [0.05,1.5] #can be anytong should match
# self.distribution_type_inputs = [0.016,0.4] #change the min & max stim duration times
# self.distribution_type_inputs = [0.016,0.4, 10] #change the min & max stim duration times
self.max_stim_duration = 1.0 # in seconds
self.stim_elapsed = 0.0 # in seconds
self.last_position = base.camera.getZ()
self.position_on_track = base.camera.getZ()
# for reward control
self.reward_window = REWARD_WINDOW # in seconds
self.reward_elapsed = 0.0
# self.new_dt = list()
# self.reward_volume = 0.008 # in mL. this is for the hardcoded 0.1 seconds of reward time
self.reward_volume = int(REWARD_VOLUME) # in uL, for the stepper motor
self.reward_time = 0.1 # in sec, based on volume. hard coded right now but should be modified by the (1) calibration and (2) optionally by the main loop for dynamic reward scheduling
# self.lick_buffer = []
self.current_score = 0
self.score = 0
self.feedback_score_startime = -2
# INITIALIZE NIDAQ
self.nidevice = 'Dev2'
self.encodervinchannel = 1
self.encodervsigchannel = 0
self.invertdo = False
self.diport = 1
self.lickline = 1
self.doport = 0
self.rewardline = 0
self.rewardlines = [0]
self.encoder_position_diff = 0
if have_nidaq:
self._setupDAQ()
self.do.WriteBit(1, 1)
self.do.WriteBit(3,
1) # set reward high, because the logic is flipped somehow. possibly by haphazard wiring of the circuit (12/24/2018 djd)
self.previous_encoder_position = self.ai.data[0][self.encodervsigchannel]
else:
self.previous_encoder_position = 0
self.encoder_gain = 3
# INITIALIZE LICK SENSOR
self._lickSensorSetup()
# INITIALIZE output data
self.lickData = []
self.x = []
self.t = []
self.trialData = []
self.reactionTimeData = []
self.rewardData = []
self.rightKeyData = []
self.leftKeyData = []
self.imageData = []
self.imageTimeData = []
self.scoreData = []
self.trialDurationData = []
self.new_dt = []
# INITIALIZE KEY SENSOR, for backup inputs and other user controls
self.keys = key.KeyStateHandler()
self.accept('r', self._give_reward, [self.reward_volume])
self.accept('l', self._toggle_reward)
# initialize the image list and populate what images you want included
# self.img_list = glob.glob('models/2AFC_IMAGES_HUMAN/*.tif')
# self.img_list = glob.glob('models/2AFC_IMAGES_HUMAN2/*.tif')
# self.img_list = glob.glob('/Users/elizabethstubblefield/Desktop/cheetah_or_elephant/composite_images/masks/all_same_num_ea/*.tif') #Newest images
self.img_list = glob.glob('models/all_same_ea/*.tif') #Newest images
#No longer hard-coded:
self.original_indices = [0,0] #this was manually counted... first number must was the index of the first easy img; was [43, -18]
for ndx, name in enumerate(self.img_list):
if 'Cheetah255' in name:
self.original_indices[0] = ndx
elif 'Elephant0' in name:
self.original_indices[1] = ndx
# print(self.img_list)
# self.original_indices = [43,-18] #manually counted, grump #Problematic w/out at least 43 images in the folder
self.imageTextures =[loader.loadTexture(img) for img in self.img_list]
self.img_id = None #this variable is used so we know which stimulus is being presented
self.img_mask = None #this tells us what the image mask being presented is
# self._setupEyetracking()
# self._startEyetracking()
if AUTO_MODE:
self.gameTask = taskMgr.add(self.autoLoop2, "autoLoop2")
self.rewardTask = taskMgr.add(self.rewardControl, "reward")
self.cue_zone = concatenate((self.cue_zone, arange( \
self.current_number_of_segments * -TUNNEL_SEGMENT_LENGTH-50, \
self.current_number_of_segments * -TUNNEL_SEGMENT_LENGTH - TUNNEL_SEGMENT_LENGTH - 100, \
-1)))
self.auto_position_on_track = 0
self.auto_restart = False
self.auto_running = True
self.contTunnel()
else:
# Now we create the task. taskMgr is the task manager that actually
# calls the function each frame. The add method creates a new task.
# The first argument is the function to be called, and the second
# argument is the name for the task. It returns a task object which
# is passed to the function each frame.
self.gameTask = taskMgr.add(self.gameLoop, "gameLoop")
# self.stimulusTask = taskMgr.add(self.stimulusControl, "stimulus")
self.lickTask = taskMgr.add(self.lickControl, "lick")
self.rewardTask = taskMgr.add(self.rewardControl, "reward")
self.keyTask = taskMgr.add(self.keyControl, "Key press")
# Code to initialize the tunnel
def initTunnel(self):
self.tunnel = [None] * 8
for x in range(8):
# Load a copy of the tunnel
self.tunnel[x] = loader.loadModel('models/tunnel')
# The front segment needs to be attached to render
if x == 0:
self.tunnel[x].reparentTo(render)
# The rest of the segments parent to the previous one, so that by moving
# the front segement, the entire tunnel is moved
else:
self.tunnel[x].reparentTo(self.tunnel[x - 1])
# We have to offset each segment by its length so that they stack onto
# each other. Otherwise, they would all occupy the same space.
self.tunnel[x].setPos(0, 0, -TUNNEL_SEGMENT_LENGTH)
# Now we have a tunnel consisting of 4 repeating segments with a
# hierarchy like this:
# render<-tunnel[0]<-tunnel[1]<-tunnel[2]<-tunnel[3]
self.tunnel[0] = loader.loadModel('models/grating')
self.tunnel[0].reparentTo(render)
self.cue_zone = arange(0, TUNNEL_SEGMENT_LENGTH, -1)-280
# This function is called to snap the front of the tunnel to the back
# to simulate traveling through it
def contTunnel(self):
self.auto_position_on_track -= 50
position_on_track = self.auto_position_on_track
print(str(int(position_on_track)) + ' ' + str(self.cue_zone))
if int(position_on_track) in np.array(self.cue_zone): # check for cue zone
if not self.auto_restart:
print('STOP!')
self.tunnelMove.pause()
self.auto_presentation = True
# self.current_number_of_segments +=1
else:
self.auto_restart = True
self.tunnelMove.resume()
else:
self.in_waiting_period = False
self.auto_presentation = False
# base.setBackgroundColor([1,0 , 0])
if self.looking_for_a_cue_zone == False:
self.looking_for_a_cue_zone = True
if self.stim_started == True:
self.stop_a_presentation()
# This line uses slices to take the front of the list and put it on the
# back. For more information on slices check the Python manual
self.tunnel = self.tunnel[1:] + self.tunnel[0:1]
# Set the front segment (which was at TUNNEL_SEGMENT_LENGTH) to 0, which
# is where the previous segment started
self.tunnel[0].setZ(0)
# Reparent the front to render to preserve the hierarchy outlined above
self.tunnel[0].reparentTo(render)
# Set the scale to be apropriate (since attributes like scale are
# inherited, the rest of the segments have a scale of 1)
self.tunnel[0].setScale(.155, .155, .305)
# Set the new back to the values that the rest of the segments have
self.tunnel[3].reparentTo(self.tunnel[2])
self.tunnel[3].setZ(-TUNNEL_SEGMENT_LENGTH)
self.tunnel[3].setScale(1)
# Set up the tunnel to move one segment and then call contTunnel again
# to make the tunnel move infinitely
self.tunnelMove = Sequence(
LerpFunc(self.tunnel[0].setZ,
duration=TUNNEL_TIME,
fromData=0,
toData=TUNNEL_SEGMENT_LENGTH * .305),
Func(self.contTunnel)
)
self.tunnelMove.start()
def get_trial_duration(self): #EAS updated 10.5.20
self.stim_duration = self.rng.choice(self.distribution_type_inputs, 1, p=self.durWeights) #pull out one value in array a, w/ probability based on weights
return self.stim_duration[0]
# return self.distribution_type(*self.distribution_type_inputs)
def start_a_presentation(self):
self.save_data()
self.stim_duration = self.get_trial_duration()
self.fixationPoint.destroy()
# import pickle
# with open('objs.pkl', 'wb') as f: # Python 3: open(..., 'wb')
# pickle.dump([self.stim_duration,self.distribution_type_inputs], f)
# print('variables saved!!!')
self.in_reward_window = True
print("start")
if have_nidaq:
self.do.WriteBit(2, 1)
# self.bufferViewer.toggleEnable()
self.lick_buffer = []
self.trialData.extend([globalClock.getFrameTime()])
self.reactionTimeData.extend([-1])
if self.stimtype == 'random image':
if len(self.trialData) < 4:
i=self.original_indices[int(np.round(np.random.random()))] #commented out due to <43 images [see l. 290ish]
self.stim_duration = 2.0
else: #commented out due to <43 images
i = randint(len(self.imageTextures))
self.card.setTexture(self.imageTextures[i],0)
self.dr2.setDimensions(0.25, 0.75, 0.25, 0.75) # floats (left, right, bottom, top)
self.img_id = self.img_list[i] #this assigns the current presented image to self.image_id
print(self.img_id)
self.imageData.extend(self.img_id)
if self.stimtype == 'image_sequence':
self.imagesTexture.setTime(0.)
self.dr2.setDimensions(0.4, 0.8, 0.4, 0.70) # floats (left, right, bottom, top)
self.imagesTexture.play()
self.imageTimeData.extend([globalClock.getFrameTime()])
self.imageData.extend(self.img_id)
def check_arrows(self):
# this function will report which arrow was pressed. right = 1, left = 2, no press = 0
if self.rightArrowIsPressed:
#print('check arrows RIGHT')
return 1
# time.sleep(2)
elif self.leftArrowIsPressed:
#print('check arrows LEFT')
return 2
# time.sleep(2)
else:
return 0
def stop_a_presentation(self):
if self.stim_started == True:
if self.stim_elapsed > self.stim_duration:
self.trialDurationData.append(self.stim_duration)
else: self.trialDurationData.append(self.stim_duration)
self.dr2.setDimensions(0, 0.001, 0, 0.001)#make this really,really small so it's not seeable by the subject
# self.bufferViewer.toggleEnable()
self.stim_started = False
self.stim_elapsed = 0.
self.stim_duration = 0.
self.stim_off_time = globalClock.getFrameTime()
if have_nidaq:
self.do.WriteBit(2, 0)
# if globalClock.getFrameTime() > self.feedback_score_startime + 1.5: # arbitrary wait to make sure this isn't after a correct trial
# self.feebackScoreText.setText('+' + str(0))
# self.feebackScoreText.setFg((1, 0, 0, 1))
# self.feebackScoreText.setX(.5)
# self.feedback_score_startime = globalClock.getFrameTime()
self.scoreData.append(self.current_score)
self.fixationPoint = OnscreenImage(image='models/fixationpoint.jpg', pos=(0, 0,0),scale=0.01)
# def keySensorSetup(self):
def show_the_score(self):
self.feebackScoreText.setText('+' + str(self.score))
if self.score == 0:
self.feebackScoreText.setFg((1, 0, 0, 1))
else:
self.feebackScoreText.setFg((0, 1, 0, 1))
self.feebackScoreText.setX(.5)
self.feedback_score_startime = globalClock.getFrameTime()
# sets up the task to listen for user input from the keys
def _lickSensorSetup(self):
""" Attempts to set up lick sensor NI task. """
##TODO: Make lick sensor object if necessary. Let user select port and line.
if have_nidaq:
if self.di:
self.lickSensor = self.di # just use DI for now
licktest = []
for i in range(30):
licktest.append(self.rightArrowIsPressed.Read()[self.lickline])
time.sleep(0.01)
licktest = np.array(licktest, dtype=np.uint8)
if len(licktest[np.where(licktest > 0)]) > 25:
self.lickSensor = None
self.lickData = [np.zeros(len(self.rewardlines))]
print("Lick sensor failed startup test.")
else:
print('lick sensor setup succeeded.')
self.keycontrol = True
else:
print("Could not initialize lick sensor. Ensure that NIDAQ is connected properly.")
self.keycontrol = True
self.lickSensor = None
self.lickData = [np.zeros(len(self.rewardlines))]
self.keys = key.KeyStateHandler()
# self.window.winHandle.push_handlers(self.keys)
else:
print("Could not initialize lick sensor. Ensure that NIDAQ is connected properly.")
self.keycontrol = True
self.lickSensor = None
self.lickData = [np.zeros(len(self.rewardlines))]
self.keys = key.KeyStateHandler()
def reactiontime_to_score(self):
rt = globalClock.getFrameTime() - self.trialData[-1]
self.reactionTimeData[-1] = rt
score = 100. / rt # scale the reaction time, with faster producing more points
return int(score / 20.)
# def _read_licks(self): # not yet implemented; should be replaces with check to beam break
def _give_reward(self, volume):
print("reward!")
self.rewardData.extend([globalClock.getFrameTime()])
# for humans
self.score = self.reactiontime_to_score()
self.current_score += self.score
self.show_the_score()
self.scoreText.setText(str(self.current_score))
# for mice
if have_nidaq:
self.do.WriteBit(3, 0)
time.sleep(self.reward_time)
self.do.WriteBit(3, 1) # put a TTL on a line to indicate that a reward was given
s.dispense(volume) # pass # not yet implemented
def _toggle_reward(self):
if self.AUTO_REWARD:
self.AUTO_REWARD = False
print('switched to lick sensing for reward.')
else:
self.AUTO_REWARD = True
print('switched to automatic rewards after stimuli.')
def autoLoop2(self, task):
dt = globalClock.getDt()
current_time = globalClock.getFrameTime()
self.x.extend([self.auto_position_on_track])
self.t.extend([globalClock.getFrameTime()])
if self.auto_presentation:
self.auto_running = False
if self.in_waiting_period:
self.time_waited += dt
else:
self.time_waited = 0
self.in_waiting_period = True
if self.time_waited > self.wait_time: # if in cue zone,see if we have been ther for long enough
# start a trial
self.start_position = self.auto_position_on_track
self.start_time = current_time
if not self.stim_started:
self.start_a_presentation()
# print(self.stim_duration)
self.stim_started = True
self.show_stimulus = True
else:
self.stim_elapsed += dt
if self.stim_elapsed > self.stim_duration:
self.show_stimulus = False
self.in_reward_window = True
self.stop_a_presentation()
self.auto_restart = False
# print(self.current_number_of_segments)
self.current_number_of_segments += 9
# redefine the cue zone as the next one
self.cue_zone = arange(self.current_number_of_segments * -TUNNEL_SEGMENT_LENGTH,
self.current_number_of_segments * -TUNNEL_SEGMENT_LENGTH - TUNNEL_SEGMENT_LENGTH - 280,
-1)
# extend cue zone, keeping old ones
# self.cue_zone = concatenate((self.cue_zone,arange(self.current_number_of_segments*-TUNNEL_SEGMENT_LENGTH-40,
# self.current_number_of_segments*-TUNNEL_SEGMENT_LENGTH-TUNNEL_SEGMENT_LENGTH-40,
# -1)))
self.contTunnel()
self.time_waited = 0
self.looking_for_a_cue_zone = False
# base.setBackgroundColor([0, 0, 1])
else:
pass # base.setBackgroundColor([0, 1, 0])
else:
self.auto_running = True
return Task.cont # Since every return is Task.cont, the task will
def gameLoop(self, task):
# get the time elapsed since the next frame.
dt = globalClock.getDt()
self.new_dt.append(dt) #Store the append here for dt
current_time = globalClock.getFrameTime() #This is for the key press
if current_time > self.feedback_score_startime + 1.5:
self.feebackScoreText.setX(3.)
# get the camera position.
position_on_track = base.camera.getZ()
# get the encoder position from NIDAQ Analog Inputs channel 2
if have_nidaq:
encoder_position = self.ai.data[0][self.encodervsigchannel] # zeroth sample in buffer [0], from ai2 [2]
# convert to track coordinates
encoder_position_diff = (encoder_position - self.previous_encoder_position)
if encoder_position_diff > 4.5: encoder_position_diff -= 5.
if encoder_position_diff < -4.5: encoder_position_diff += 5.
self.encoder_position_diff = encoder_position_diff * self.encoder_gain
self.previous_encoder_position = encoder_position
else:
self.read_keys()
if self.upArrowIsPressed:
self.encoder_position_diff = -1 * self.encoder_gain
if self.downArrowIsPressed:
self.encoder_position_diff = 1 * self.encoder_gain
if not self.downArrowIsPressed:
if not self.upArrowIsPressed:
self.encoder_position_diff = 0
position_on_track = base.camera.getZ() + self.encoder_position_diff
# reset the camera position
self.camera.setPos(base.camera.getX(), base.camera.getY(), position_on_track)
self.x.extend([position_on_track])
self.t.extend([globalClock.getFrameTime()])
# first check if the mouse moved on the last frame.
if abs(self.last_position - position_on_track) < 1.5: # the mouse didn't move more than 0.5 units on the track
self.moved = False
if int(position_on_track) in self.cue_zone: # check for cue zone
if self.looking_for_a_cue_zone: # make sure we transitioning from the tunnel to a cue zone
# increment how long we've been waiting in the cue zone.
if self.in_waiting_period:
self.time_waited += dt
else:
self.time_waited = 0
self.in_waiting_period = True
if self.time_waited > self.wait_time: # if in cue zone,see if we have been ther for long enough
# start a trial
self.start_position = position_on_track
self.start_time = current_time
if not self.stim_started:
self.start_a_presentation()
print(self.stim_duration)
self.stim_started = True
self.show_stimulus = True
else:
self.stim_elapsed += dt
if self.stim_elapsed > self.stim_duration:
self.show_stimulus = False
self.stop_a_presentation()
self.time_waited = 0
self.looking_for_a_cue_zone = False
# base.setBackgroundColor([0, 0, 1])
else:
pass # base.setBackgroundColor([0, 1, 0])
else:
self.in_waiting_period = False
# base.setBackgroundColor([1,0 , 0])
if self.looking_for_a_cue_zone == False:
self.looking_for_a_cue_zone = True
if self.stim_started == True:
self.stop_a_presentation()
else: # the mouse did move
self.moved = True
if self.stim_started == True: # check if it moved during a presenation
self.stop_a_presentation()
self.time_waited = 0
self.looking_for_a_cue_zone = False
self.show_stimulus = False
# if we need to add another segment, do so
if position_on_track < self.boundary_to_add_next_segment:
self.tunnel.extend([None])
x = self.current_number_of_segments
if x % 8 == 0:
self.tunnel[x] = loader.loadModel('models/grating')
self.cue_zone = concatenate((self.cue_zone, arange( \
self.current_number_of_segments * -TUNNEL_SEGMENT_LENGTH - 50, \
self.current_number_of_segments * -TUNNEL_SEGMENT_LENGTH - TUNNEL_SEGMENT_LENGTH - 100, \
-1)))
else:
self.tunnel[x] = loader.loadModel('models/tunnel')
self.tunnel[x].setPos(0, 0, -TUNNEL_SEGMENT_LENGTH)
self.tunnel[x].reparentTo(self.tunnel[x - 1])
# increment
self.boundary_to_add_next_segment -= TUNNEL_SEGMENT_LENGTH
self.current_number_of_segments += 1
else:
pass # print('current:'+str(position_on_track) +' next boundary:' + str(self.boundary_to_add_next_segment))
self.last_position = position_on_track
# lick_times = self.
# self._read_licks()
return Task.cont # Since every return is Task.cont, the task will
# continue indefinitely, under control of the mouse (animal)
def stimulusControl(self, task):
if self.show_stimulus and not self.bufferViewer.isEnabled():
# self.bufferViewer.toggleEnable()
self.dr2.setDimensions(0.5, 0.9, 0.5, 0.8)
if not self.show_stimulus and self.bufferViewer.isEnabled():
# self.bufferViewer.toggleEnable()
self.dr2.setDimensions(0, 0.1, 0, 0.1)
return Task.cont
def lickControl(self, task):
""" Checks to see if a lick is occurring. """
##TODO: Let user select line for lick sensing.
if self.lickSensor:
if self.lickSensor.Read()[self.lickline]:
self.lickData.extend([globalClock.getFrameTime()])
print('lick happened at: ' + str(self.lickData[-1]))
elif self.keycontrol == True: # NO NI BOARD. KEY INPUT?
if self.keys[key.SPACE]:
data = [globalClock.getFrameTime()]
elif self.keys[key.NUM_1]:
# print(self.lickData)
# elif self.keys[key.NUM_3]:
# data = [0,1]
# else:
# data = [0,0]
self.lickData.extend(data)
return Task.cont
def keyControl(self, task):
# listen to and record when the arrows are pressed
self.read_keys()
if self.rightArrowIsPressed:
self.rightKeyData.extend([globalClock.getFrameTime()])
print('right arrow at: ' + str(self.rightKeyData[-1]))
# time.sleep(.2) #EAS changed this only allows for one keystroke to be recorded every 0.5s
elif self.leftArrowIsPressed:
self.leftKeyData.extend([globalClock.getFrameTime()])
print('left arrow at: ' + str(self.leftKeyData[-1]))
# time.sleep(.2) #EAS changed this only allows for one keystroke to be recorded every 0.5s
return Task.cont
def rewardControl(self, task):
# print(self.in_reward_window)
if self.in_reward_window == True:
if self.reward_elapsed < self.reward_window:
self.reward_elapsed += globalClock.getDt()
# self.new_reward_elapsed.append(self.reward_elapsed)
# import pickle
# with open('objs.pkl', 'wb') as f: # Python 3: open(..., 'wb')
# pickle.dump([self.stim_duration,self.distribution_type_inputs, self.new_rew_control], f)
# print('variables saved!!!')
self.check_arrows()
if not self.AUTO_REWARD:
if self.check_arrows() == 1: # if check arrows returns a 1 the right arrow was pressed during stimulus presentation
# note reaction time
if self.img_id.find("Elephant") != -1: #if the current image file contains the string in () the value is not -1
self._give_reward(self.reward_volume)
print('correct: image was mostly elephant')
self.in_reward_window = False;
self.reward_elapsed = 0. # reset
elif 'Same' in self.img_id: #updated 8.24.20: reward 50/50 images 1/2 the time
if round(np.random.random(1)[0]):
self._give_reward(self.reward_volume)
print('correct: image was same')
self.in_reward_window = False;
self.reward_elapsed = 0. # reset
else:
print(self.img_mask)
print('incorrect: image was same')
self.in_reward_window = False
self.reward_elapsed = 0. # reset
self.score=0
self.show_the_score()
else:
print(self.img_mask)
print('image was not mostly elephant!')
self.in_reward_window = False
self.reward_elapsed = 0. # reset
self.score=0
self.show_the_score()
if self.check_arrows() == 2: #if check arrows returns a 2 the left arrow was pressed during stimulus presentation
if self.img_id.find("Cheetah") != -1: #if the current image file contains the string in () the value is not -1
self._give_reward(self.reward_volume)
print('correct: image was mostly cheetah')
self.in_reward_window = False;
self.reward_elapsed = 0. # reset
elif 'Same' in self.img_id: #updated 8.24.20: reward 50/50 images 1/2 the time
if round(np.random.random(1)[0]):
self._give_reward(self.reward_volume)
print('correct: image was same')
self.in_reward_window = False;
self.reward_elapsed = 0. # reset
else:
print(self.img_mask)
print('incorrect: image was same')
self.in_reward_window = False
self.reward_elapsed = 0. # reset
self.score=0
self.show_the_score()
else:
print(self.img_mask)
print('image was not mostly cheetah!')
self.in_reward_window = False
self.reward_elapsed = 0. # reset
self.score=0
self.show_the_score()
else:
self._give_reward(self.reward_volume)
self.in_reward_window = False;
self.reward_elapsed = 0. # reset
# base.setBackgroundColor([1, 1, 0])
# # if self.keys[key.NUM_1]:
# # print('reward!')
else:
self.score=0
self.show_the_score()
self.in_reward_window = False
self.reward_elapsed = 0.
else:
pass#print('not listening for reward'+str(globalClock.getFrameTime()))
return Task.cont
def _setupEyetracking(self):
""" sets up eye tracking"""
try:
eyetrackerip = "DESKTOP-EE5KKDO"
eyetrackerport = 10000
trackeyepos = False
from aibs.Eyetracking.EyetrackerClient import Client
self.eyetracker = Client(outgoing_ip=eyetrackerip,
outgoing_port=eyetrackerport,
output_filename=str(datetime.datetime.now()).replace(':', '').replace('.',
'').replace(
' ', '-'))
self.eyetracker.setup()
# eyedatalog = []
# if trackeyepos:
# eyeinitpos = None
except:
print("Could not initialize eyetracker:")
self.eyetracker = None
def _startEyetracking(self):
if self.eyetracker:
self.eyetracker.recordStart()
def _setupDAQ(self):
""" Sets up some digital IO for sync and tiggering. """
print('SETTING UP DAQ')
try:
if self.invertdo:
istate = 'low'
else:
istate = 'high'
self.do = DigitalOutput(self.nidevice, self.doport,
initial_state='low')
self.do.StartTask()
except: # Exception, e:
print("Error starting DigitalOutput task:")
self.do = None
try:
self.di = DigitalInput(self.nidevice, self.diport)
self.di.StartTask()
except: # Exception, e:
print("Error starting DigitalInput task:")
self.di = None
# try:
# set up 8 channels, only use 2 though for now
self.ai = AnalogInput(self.nidevice, range(8), buffer_size=25, terminal_config='RSE',
clock_speed=6000.0, voltage_range=[-5.0, 5.0])
self.ai.StartTask()
# except:# Exception, e:
# print("Error starting AnalogInput task:")
# self.ai = None
try:
self.ao = AnalogOutput(self.nidevice, channels=[0, 1], terminal_config='RSE',
voltage_range=[0.0, 5.0])
self.ao.StartTask()
except: # Exception, e:
print("Error starting AnalogOutput task:")
self.ao = None
def read_keys(self):
self.upArrowIsPressed = base.mouseWatcherNode.isButtonDown(KeyboardButton.up())
self.downArrowIsPressed = base.mouseWatcherNode.isButtonDown(KeyboardButton.down())
self.rightArrowIsPressed = base.mouseWatcherNode.isButtonDown(KeyboardButton.right())
self.leftArrowIsPressed = base.mouseWatcherNode.isButtonDown(KeyboardButton.left())
def save_data(self):
if not os.path.isdir(os.path.join(os.getcwd(), 'data')):
os.mkdir(os.path.join(os.getcwd(), 'data'))
save_path = os.path.join(os.getcwd(), 'data', str(MOUSE_ID) + '_' + \
str(self.session_start_time.year) + '_' + \
str(self.session_start_time.month) + '_' + \
str(self.session_start_time.day) + '-' + \
str(self.session_start_time.hour) + '_' + \
str(self.session_start_time.minute) + '_' + \
str(self.session_start_time.second))
if not os.path.isdir(save_path):
os.mkdir(save_path)
print("saving data to " + save_path)
np.save(os.path.join(save_path, 'licks.npy'), self.lickData)
np.save(os.path.join(save_path, 'x.npy'), self.x)
np.save(os.path.join(save_path, 't.npy'), self.t)
np.save(os.path.join(save_path, 'trialData.npy'), self.trialData)
np.save(os.path.join(save_path, 'rewardData.npy'), self.rewardData)
np.save(os.path.join(save_path, 'rtData.npy'), self.reactionTimeData)
np.save(os.path.join(save_path, 'rightKeyData.npy'), self.rightKeyData)
np.save(os.path.join(save_path, 'leftKeyData.npy'), self.leftKeyData)
np.save(os.path.join(save_path, 'imageData.npy'), self.imageData)
np.save(os.path.join(save_path, 'imageTimeData.npy'), self.imageTimeData)
np.save(os.path.join(save_path, 'scoreData.npy'), self.scoreData)
np.save(os.path.join(save_path, 'trialDurationData.npy'), self.trialDurationData)
np.save(os.path.join(save_path, 'dT.npy'), self.new_dt)
def close(self):
self.save_data()
print('rewardData:')
print(np.shape(self.rewardData))
try:
#push anonymized data to Denman Lab Open Science Framework project for human psychophysics
subprocess.call('osf -p 7xruh -u [email protected] upload -r '+save_path+' data/'+os.path.basename(save_path),shell=True) #commented these lines out to test if saving to osf is the hangup; it's not
except:pass
sys.exit(0)