def doRSVPStim(trial):
'''
ONLY DOES ONE CUE AT THE MOMENT
This function generates the stimuli for each trial. The word "frame" here refers to a set of simultaneous RSVP stimuli. I'll use "refresh" to refer to monitor refreshes.
Using the parameters for the current trial:
- Work out the temporal position of the cue(s)
- Shuffle the order of the letters
- Calculate the position and cortically-magnified size for each stream
- draw and buffer preCues
- Capture each stream's pixels on each frame using bufferImageStim
- Collate each frame's pixels so that all stimuli are represented by the same matrix of pixels. Put the cue in there if it's the cued frame
- pass the matrix of pixels to elementarraystim
'''
global cue
nStreams = trial['nStreams']
numTargets = trial['numToCue']
cuedFrame = trial['cue0temporalPos']
cuedStream = np.random.choice(np.arange(nStreams), 1)
cue.pos = calcStreamPos(
trial = trial,
cueOffsets = cueOffsets,
streami = cuedStream,
streamOrNoise = False
)
cue = corticalMagnification.corticalMagnification(cue, 0.9810000000000002, cue = True) #this is the cuesize from the original experiment
preCues = list()
preCues.append(cue)
if trial['cueSpatialPossibilities'] == 2:
preCue = visual.Circle(myWin,
radius=cueRadius,#Martini used circles with diameter of 12 deg
lineColorSpace = 'rgb',
lineColor=letterColor,
lineWidth=2.0, #in pixels
units = 'deg',
fillColorSpace = 'rgb',
fillColor=None, #beware, with convex shapes fill colors don't work
pos= [-5,-5], #the anchor (rotation and vertices are position with respect to this)
interpolate=True,
autoLog=False)#this stim changes too much for autologging to be useful
preCue.pos = calcStreamPos(
trial = trial,
cueOffsets = cueOffsets,
streami = cuedStream-4,
streamOrNoise = False
)
preCue = corticalMagnification.corticalMagnification(preCue, 0.9810000000000002, cue = True)
preCues.append(preCue)
elif trial['cueSpatialPossibilities'] == 8:
for i in range(1,8):
preCue = visual.Circle(myWin,
radius=cueRadius,#Martini used circles with diameter of 12 deg
lineColorSpace = 'rgb',
lineColor=letterColor,
lineWidth=2.0, #in pixels
units = 'deg',
fillColorSpace = 'rgb',
fillColor=None, #beware, with convex shapes fill colors don't work
pos= [-5,-5], #the anchor (rotation and vertices are position with respect to this)
interpolate=True,
autoLog=False)#this stim changes too much for autologging to be useful
preCue.pos = (calcStreamPos(
trial = trial,
cueOffsets = cueOffsets,
streami = cuedStream-i,
streamOrNoise = False
))
preCue = corticalMagnification.corticalMagnification(preCue, 0.9810000000000002, cue = True)
preCues.append(preCue)
print('cueFrame = ' + str(cuedFrame))
preCueStim = preTrial[int(baseAngleCWfromEast/anglesMustBeMultipleOf)] + preCues + [fixatnPoint]
preCueFrame = visual.BufferImageStim(
win = myWin,
stim = preCueStim)
preCueFrame = np.flipud(np.array(preCueFrame.image)[..., 0]) / 255.0 * 2.0 - 1.0 #Via djmannion. This converts the pixel values from [0,255] to [-1,1]. I think 0 is middle grey. I'll need to change this to match the background colour eventually
preCueFrame = np.pad(
array=preCueFrame,
pad_width=pad_amounts, #See 'Buffered image size dimensions' section
mode="constant",
constant_values=0.0
)
preCueFrame =visual.ElementArrayStim(
win = myWin,
units = 'pix',
nElements=1,
xys = [[0,0]],
sizes=preCueFrame.shape,
elementTex=preCueFrame,
elementMask = 'none'
)
streamPositions = list() #Might need to pass this to elementArrayStim as xys. Might not though
streamLetterIdxs = np.empty( #use an array so I can select columns (frames) for buffering. This is an empty array that will eventually have the
#letter indexes for each stream. Selecting a column gives us all streams at a particular frame. Selecting a row gives us
#all frames for a particular stream
shape = (nStreams,numLettersToPresent),
dtype = int
)
streamLetterIdentities = np.empty( #Letter identities for these streams
shape = (nStreams,numLettersToPresent),
dtype = str
)
for thisStream in xrange(nStreams):
thisSequence = np.arange(24)
np.random.shuffle(thisSequence)
theseIdentities = [potentialLetters[idx] for idx in thisSequence]
streamLetterIdxs[thisStream,:] = thisSequence
streamLetterIdentities[thisStream,:] = theseIdentities
#print('For stream %(streamN)d the letters are: %(theseLetters)s' % {'streamN':thisStream, 'theseLetters':''.join(theseIdentities)})
correctIdx = streamLetterIdxs[cuedStream,cuedFrame]
print('correctIdx')
print(correctIdx)
correctLetter = alphabetHelpers.numberToLetter(correctIdx, potentialLetters) #potentialLetters is global
frameStimuli = list() #A list of elementArrayStim objects, each represents a frame. Drawing one of these objects will draw the letters and the cue for that frame
for thisFrame in xrange(numLettersToPresent):
theseStimuli = streamLetterIdxs[:,thisFrame] #The alphabetical indexes of stimuli to be shown on this frame
stimuliToDraw = list() #Can pass a list to bufferimageStim!
stimuliToDraw.append(fixatn)
stimuliToDrawCounterPhase = list()
stimuliToDrawCounterPhase.append(fixatnCounterphase)
for thisStream in xrange(nStreams):
cueThisFrame = thisStream == cuedStream and thisFrame == cuedFrame #If true, draw the cue and capture that too
thisLetterIdx = theseStimuli[thisStream] #The letter index for this particular stream on this particular frame
thisStreamStimulus = streamTextObjects[thisStream,thisLetterIdx] #The text object for this stream
thisPos = calcStreamPos(
trial = trial,
cueOffsets = cueOffsets,
streami = thisStream,
streamOrNoise = False
)
thisStreamStimulus.pos = thisPos
stimuliToDraw.append(thisStreamStimulus)
stimuliToDrawCounterPhase.append(thisStreamStimulus)
if cueThisFrame and cueType == 'exogenousRing':
stimuliToDraw.append(cue)
stimuliToDrawCounterPhase.append(cue)
buff = visual.BufferImageStim( #Buffer these stimuli
win = myWin,
stim = stimuliToDraw
)
buff = np.flipud(np.array(buff.image)[..., 0]) / 255.0 * 2.0 - 1.0 #Via djmannion. This converts the pixel values from [0,255] to [-1,1]. I think 0 is middle grey. I'll need to change this to match the background colour eventually
buff = np.pad(
array=buff,
pad_width=pad_amounts, #See 'Buffered image size dimensions' section
mode="constant",
constant_values=0.0
)
thisFrameStimuli = visual.ElementArrayStim( #A stimulus representing this frame with the fixation at full luminance
win = myWin,
units = 'pix',
nElements=1,
xys = [[0,0]],
sizes=buff.shape,
elementTex=buff,
elementMask = 'none'
)
buff = visual.BufferImageStim( #Buffer these stimuli
win = myWin,
stim = stimuliToDrawCounterPhase
)
buff = np.flipud(np.array(buff.image)[..., 0]) / 255.0 * 2.0 - 1.0 #Via djmannion. This converts the pixel values from [0,255] to [-1,1]. I think 0 is middle grey. I'll need to change this to match the background colour eventually
buff = np.pad(
array=buff,
pad_width=pad_amounts, #See 'Buffered image size dimensions' section
mode="constant",
constant_values=0.0
)
thisFrameStimuliCounterPhase = visual.ElementArrayStim( #A stimulus representing this frame with the fixation phase reversed
win = myWin,
units = 'pix',
nElements=1,
xys = [[0,0]],
sizes=buff.shape,
elementTex=buff,
elementMask = 'none'
)
frameStimuli.append([thisFrameStimuli, thisFrameStimuliCounterPhase])
ts = []
waiting = True
myMouse.setVisible(waiting)
while waiting:
startTrialStimuli.draw()
startTrialBox.draw()
myWin.flip()
if myMouse.isPressedIn(startTrialBox):
waiting = False
myMouse.setVisible(waiting)
if eyetracking:
tracker.startEyeTracking(nDone,True,widthPix,heightPix) #start recording with eyetracker
myWin.flip(); myWin.flip()#Make sure raster at top of screen (unless not in blocking mode), and give CPU a chance to finish other tasks
preCueFrame.draw()
myWin.flip()
core.wait(.25)
myWin.flip
core.wait(.5)
fixatn.draw()
myWin.flip()
core.wait(1)
t0 = trialClock.getTime()
for n in xrange(trialDurFrames):
oneFrameOfStim(n, frameStimuli)
myWin.flip()
ts.append(trialClock.getTime() - t0)
if eyetracking:
tracker.stopEyeTracking()
print('stopped tracking')
return streamLetterIdxs, streamLetterIdentities, correctLetter, ts, cuedStream, cuedFrame
def doRSVPStim(trial):
'''
ONLY DOES ONE CUE AT THE MOMENT
This function generates the stimuli for each trial. The word "frame" here refers to a set of simultaneous RSVP stimuli. I'll use "refresh" to refer to monitor refreshes.
Using the parameters for the current trial:
- Work out the temporal position of the cue(s)
- Shuffle the order of the letters
- Calculate the position and cortically-magnified size for each stream
- Capture each stream's pixels on each frame using bufferImageStim
- Collate each frame's pixels so that all stimuli are represented by the same matrix of pixels. Put the cue in there if it's the cued frame
- pass the matrix of pixels to elementarraystim
'''
global cue
nStreams = trial['nStreams']
numTargets = trial['numToCue']
cuedFrame = trial['cue0temporalPos']
cuedStream = np.random.choice(np.arange(nStreams), 1)
print('cueFrame = ' + str(cuedFrame))
streamPositions = list(
) #Might need to pass this to elementArrayStim as xys. Might not though
streamLetterIdxs = np.empty( #use an array so I can select columns (frames) for buffering. This is an empty array that will eventually have the
#letter indexes for each stream. Selecting a column gives us all streams at a particular frame. Selecting a row gives us
#all frames for a particular stream
shape=(nStreams, numLettersToPresent),
dtype=int)
streamLetterIdentities = np.empty( #Letter identities for these streams
shape=(nStreams, numLettersToPresent),
dtype=str)
for thisStream in xrange(nStreams):
thisSequence = np.arange(24)
np.random.shuffle(thisSequence)
theseIdentities = [potentialLetters[idx] for idx in thisSequence]
streamLetterIdxs[thisStream, :] = thisSequence
streamLetterIdentities[thisStream, :] = theseIdentities
#print('For stream %(streamN)d the letters are: %(theseLetters)s' % {'streamN':thisStream, 'theseLetters':''.join(theseIdentities)})
correctIdx = streamLetterIdxs[cuedStream, cuedFrame]
correctLetter = alphabetHelpers.numberToLetter(
correctIdx, potentialLetters) #potentialLetters is global
frameStimuli = list(
) #A list of elementArrayStim objects, each represents a frame. Drawing one of these objects will draw the letters and the cue for that frame
for thisFrame in xrange(numLettersToPresent):
theseStimuli = streamLetterIdxs[:,
thisFrame] #The stimuli to be shown on this frame
thisFramePixels = np.zeros(shape=(screenValues['heightPix'],
screenValues['widthPix']))
stimuliToDraw = list() #Can pass a list to bufferimageStim!
for thisStream in xrange(nStreams):
cueThisFrame = thisStream == cuedStream and thisFrame == cuedFrame #If true, draw the cue and capture that too
thisLetterIdx = theseStimuli[
thisStream] #The letter index for this particular stream on this particular frame
thisLtr = alphabetHelpers.numberToLetter(thisLetterIdx,
potentialLetters)
thisStreamStimulus = streamTextObjects[
thisStream] #The text object for this stream
thisStreamStimulus.text = thisLtr
thisPos = calcStreamPos(trial=trial,
cueOffsets=cueOffsets,
streami=thisStream,
streamOrNoise=False)
thisStreamStimulus.pos = thisPos
stimuliToDraw.append(thisStreamStimulus)
if cueThisFrame and cueType == 'exogenousRing':
cue.setPos(thisPos)
cue = corticalMagnification.corticalMagnification(
cue, 0.9810000000000002, cue=True
) #this is the cuesize from the original experiment
stimuliToDraw.append(cue)
buff = visual.BufferImageStim( #Buffer these stimuli
win=myWin, stim=stimuliToDraw)
buff = np.flipud(
np.array(buff.image)[..., 0]
) / 255.0 * 2.0 - 1.0 #Via djmannion. This converts the pixel values from [0,255] to [-1,1]. I think 0 is middle grey. I'll need to change this to match the background colour eventually
new_size = max( #need to pad out the texture to a power of two to use it in elementArrayStim
[
int(np.power(2, np.ceil(np.log(dim_size) / np.log(2))))
for dim_size in buff.shape
])
pad_amounts = []
for i_dim in range(2):
first_offset = int((new_size - buff.shape[i_dim]) / 2.0)
second_offset = new_size - buff.shape[i_dim] - first_offset
pad_amounts.append([first_offset, second_offset])
buff = np.pad(array=buff,
pad_width=pad_amounts,
mode="constant",
constant_values=0.0)
thisFrameStimuli = visual.ElementArrayStim( #A stimulus representing this frame
win=myWin,
units='pix',
nElements=1,
xys=[[0, 0]],
sizes=buff.shape,
elementTex=buff,
elementMask='none')
frameStimuli.append(thisFrameStimuli)
ts = []
myWin.flip()
myWin.flip(
) #Make sure raster at top of screen (unless not in blocking mode), and give CPU a chance to finish other tasks
t0 = trialClock.getTime()
for n in xrange(trialDurFrames):
oneFrameOfStim(n, frameStimuli)
myWin.flip()
ts.append(trialClock.getTime() - t0)
return streamLetterIdxs, streamLetterIdentities, correctLetter, ts
def doRSVPStim(trial):
'''
ONLY DOES ONE CUE AT THE MOMENT
This function generates the stimuli for each trial. The word "frame" here refers to a set of simultaneous RSVP stimuli. I'll use "refresh" to refer to monitor refreshes.
Using the parameters for the current trial:
- Work out the temporal position of the cue(s)
- Shuffle the order of the letters
- Calculate the position and cortically-magnified size for each stream
- Capture each stream's pixels on each frame using bufferImageStim
- Collate each frame's pixels so that all stimuli are represented by the same matrix of pixels. Put the cue in there if it's the cued frame
- pass the matrix of pixels to elementarraystim
'''
global cue
global tracker
nStreams = trial['nStreams']
numTargets = trial['numToCue']
cuedFrame = trial['cue0temporalPos']
cuedStream = 0 #np.random.choice(np.arange(nStreams), 1)
maxCueCount = np.ceil(float(trials.nTotal) / float(nStreams) / 2.)
indexOfThisNStreams = nStreamsPossibilities.index(nStreams)
theseCueCounts = countsList[
indexOfThisNStreams] #Get the counts of the number of times each position has been cued
theseCueCountsNotComplete = [
index for index, value in enumerate(theseCueCounts)
if value < maxCueCount
] #Find those counts less than the max possible
print('theseCueCountsNotComplete')
print(theseCueCountsNotComplete)
cuedStream = np.random.choice(theseCueCountsNotComplete,
1)[0] #sample a position, cue it
countsList[indexOfThisNStreams][cuedStream] += 1 #Increment the count
print('cuedStream is ' + str(cuedStream))
print(theseCueCounts)
print('cueFrame = ' + str(cuedFrame))
streamPositions = list(
) #Might need to pass this to elementArrayStim as xys. Might not though
streamLetterIdxs = np.empty( #use an array so I can select columns (frames) for buffering. This is an empty array that will eventually have the
#letter indexes for each stream. Selecting a column gives us all streams at a particular frame. Selecting a row gives us
#all frames for a particular stream
shape=(nStreams, numLettersToPresent),
dtype=int)
streamLetterIdentities = np.empty( #Letter identities for these streams
shape=(nStreams, numLettersToPresent),
dtype=str)
for thisStream in xrange(nStreams):
thisSequence = np.arange(24)
np.random.shuffle(thisSequence)
theseIdentities = [potentialLetters[idx] for idx in thisSequence]
streamLetterIdxs[thisStream, :] = thisSequence
streamLetterIdentities[thisStream, :] = theseIdentities
#print('For stream %(streamN)d the letters are: %(theseLetters)s' % {'streamN':thisStream, 'theseLetters':''.join(theseIdentities)})
print(streamLetterIdentities)
correctIdx = streamLetterIdxs[cuedStream, cuedFrame]
print('correctIdx')
print(correctIdx)
correctLetter = alphabetHelpers.numberToLetter(
correctIdx, potentialLetters) #potentialLetters is global
frameStimuli = list(
) #A list of elementArrayStim objects, each represents a frame. Drawing one of these objects will draw the letters and the cue for that frame
for thisFrame in xrange(numLettersToPresent):
theseStimuli = streamLetterIdxs[:,
thisFrame] #The alphabetical indexes of stimuli to be shown on this frame
### IN DO RSVP STIM ###
stimuliToDraw = list() #Can pass a list to bufferimageStim!
stimuliToDraw.append(fixatn)
stimuliToDrawCounterPhase = list()
stimuliToDrawCounterPhase.append(fixatnCounterphase)
for thisStream in xrange(nStreams):
thisPos = calcStreamPos(trial=trial,
cueOffsets=cueOffsets,
streami=thisStream,
streamOrNoise=False)
cueThisFrame = thisStream == cuedStream and thisFrame == cuedFrame #If true, draw the cue and capture that too
thisLetterIdx = theseStimuli[
thisStream] #The letter index for this particular stream on this particular frame
if nStreams == 2 and max(nStreamsPossibilities) > 2:
#print('Stream was' + str(thisStream) +', but is now' + str(trial['ring']*streamsPerRing+thisStream))
thisStreamStimulus = streamTextObjects[trial['ring'] *
streamsPerRing +
thisStream,
thisLetterIdx]
else:
thisStreamStimulus = streamTextObjects[thisStream,
thisLetterIdx]
thisStreamStimulus.pos = thisPos
#print('For stream %(thisStream)d the height is: %(letterHeight)s' % {'thisStream':thisStream, 'letterHeight':thisStreamStimulus.height})
stimuliToDraw.append(thisStreamStimulus)
stimuliToDrawCounterPhase.append(thisStreamStimulus)
if cueThisFrame and cueType == 'exogenousRing':
cue.setPos(thisPos)
cue = corticalMagnification.corticalMagnification(
cue, 0.9810000000000002, cue=True
) #this is the cuesize from the original experiment
stimuliToDraw.append(cue)
stimuliToDrawCounterPhase.append(cue)
buff = visual.BufferImageStim( #Buffer these stimuli
win=myWin, stim=stimuliToDraw)
buff = np.flipud(
np.array(buff.image)[..., 0]
) / 255.0 * 2.0 - 1.0 #Via djmannion. This converts the pixel values from [0,255] to [-1,1]. I think 0 is middle grey. I'll need to change this to match the background colour eventually
buff = np.pad(
array=buff,
pad_width=pad_amounts, #See 'Buffered image size dimensions' section
mode="constant",
constant_values=0.0)
thisFrameStimuli = visual.ElementArrayStim( #A stimulus representing this frame with the fixation at full luminance
win=myWin,
units='pix',
nElements=1,
xys=[[0, 0]],
sizes=buff.shape,
elementTex=buff,
elementMask='none')
buff = visual.BufferImageStim( #Buffer these stimuli
win=myWin, stim=stimuliToDrawCounterPhase)
buff = np.flipud(
np.array(buff.image)[..., 0]
) / 255.0 * 2.0 - 1.0 #Via djmannion. This converts the pixel values from [0,255] to [-1,1]. I think 0 is middle grey. I'll need to change this to match the background colour eventually
buff = np.pad(
array=buff,
pad_width=pad_amounts, #See 'Buffered image size dimensions' section
mode="constant",
constant_values=0.0)
thisFrameStimuliCounterPhase = visual.ElementArrayStim( #A stimulus representing this frame with the fixation phase reversed
win=myWin,
units='pix',
nElements=1,
xys=[[0, 0]],
sizes=buff.shape,
elementTex=buff,
elementMask='none')
frameStimuli.append([thisFrameStimuli, thisFrameStimuliCounterPhase])
ts = []
waiting = True
myMouse.setVisible(waiting)
while waiting:
startTrialStimuli.draw()
startTrialBox.draw()
myWin.flip()
if myMouse.isPressedIn(startTrialBox):
waiting = False
myMouse.setVisible(waiting)
if eyetracking:
tracker.startEyeTracking(nDone, True, widthPix,
heightPix) #start recording with eyetracker
ts = []
myWin.flip()
myWin.flip(
) #Make sure raster at top of screen (unless not in blocking mode), and give CPU a chance to finish other tasks
fixatn.draw()
myWin.flip()
core.wait(1)
t0 = trialClock.getTime()
for n in xrange(trialDurFrames):
oneFrameOfStim(n, frameStimuli)
myWin.flip()
ts.append(trialClock.getTime() - t0)
if eyetracking:
tracker.stopEyeTracking()
print('stopped tracking')
return streamLetterIdxs, streamLetterIdentities, correctLetter, ts, cuedStream, cuedFrame
shape = (nStreams,numLettersToPresent),
dtype = str
)
for thisStream in xrange(nStreams):
thisSequence = np.arange(24)
np.random.shuffle(thisSequence)
theseIdentities = [potentialLetters[idx] for idx in thisSequence]
streamLetterIdxs[thisStream,:] = thisSequence
streamLetterIdentities[thisStream,:] = theseIdentities
#print('For stream %(streamN)d the letters are: %(theseLetters)s' % {'streamN':thisStream, 'theseLetters':''.join(theseIdentities)})
correctIdx = streamLetterIdxs[cuedStream,cuedFrame]
print('correctIdx')
print(correctIdx)
correctLetter = alphabetHelpers.numberToLetter(correctIdx, potentialLetters) #potentialLetters is global
frameStimuli = list() #A list of elementArrayStim objects, each represents a frame. Drawing one of these objects will draw the letters and the cue for that frame
for thisFrame in xrange(numLettersToPresent):
theseStimuli = streamLetterIdxs[:,thisFrame] #The alphabetical indexes of stimuli to be shown on this frame
stimuliToDraw = list() #Can pass a list to bufferimageStim!
stimuliToDraw.append(fixatn)
for thisStream in xrange(nStreams):
cueThisFrame = thisStream == cuedStream and thisFrame == cuedFrame #If true, draw the cue and capture that too
thisLetterIdx = theseStimuli[thisStream] #The letter index for this particular stream on this particular frame
thisStreamStimulus = streamTextObjects[thisStream,thisLetterIdx] #The text object for this stream