def filterEyeSamples(filter_type, xpix, ypix, pupil, invalid_data_mask,
**kwargs):
processSampleEventGaps(xpix, ypix, pupil, invalid_data_mask, 'linear')
vac = VisualAngleCalc(**calibration_area_info)
xdeg, ydeg = vac.pix2deg(xpix, ypix)
if filter_type == 'butter':
wn = kwargs.get('wn', 0.2)
order = kwargs.get('order', 2)
b, a = butter(order, wn, 'low')
x_filtered = filtfilt(b, a, xdeg)
y_filtered = filtfilt(b, a, ydeg)
elif filter_type == 'gauss':
sigma = kwargs.get('sigma', 2)
x_filtered = gaussian_filter1d(xdeg, sigma)
y_filtered = gaussian_filter1d(ydeg, sigma)
elif filter_type == 'median':
size = kwargs.get('size', 5)
x_filtered = medfilt(xdeg, size)
y_filtered = medfilt(ydeg, size)
elif filter_type == 'sg':
size = kwargs.get('size', 7)
order = kwargs.get('order', 2)
x_filtered = savitzky_golay(xdeg, window_size=size, order=order)
y_filtered = savitzky_golay(ydeg, window_size=size, order=order)
elif filter_type == 'average':
weights = np.asarray(kwargs.get('weights', [1., 2., 3., 2., 1.]))
weights = weights / np.sum(weights)
x_filtered = np.convolve(xdeg, weights, 'same')
y_filtered = np.convolve(ydeg, weights, 'same')
else:
raise ValueError(
'Unknown Filter Type: %s. Must be one of %s' %
(filter_type, str(['sg', 'butter', 'gauss', 'median'])))
xdeg[invalid_data_mask] = np.NaN
ydeg[invalid_data_mask] = np.NaN
x_filtered[invalid_data_mask] = np.NaN
y_filtered[invalid_data_mask] = np.NaN
return (xdeg, ydeg), (x_filtered, y_filtered)
def filterEyeSamples(filter_type,xpix,ypix,pupil,invalid_data_mask,**kwargs):
processSampleEventGaps(xpix,ypix,pupil,invalid_data_mask,'linear')
vac=VisualAngleCalc(**calibration_area_info)
xdeg,ydeg=vac.pix2deg(xpix,ypix)
if filter_type=='butter':
wn=kwargs.get('wn',0.2)
order=kwargs.get('order',2)
b, a = butter(order, wn, 'low')
x_filtered = filtfilt(b, a, xdeg)
y_filtered = filtfilt(b, a, ydeg)
elif filter_type=='gauss':
sigma=kwargs.get('sigma',2)
x_filtered = gaussian_filter1d(xdeg,sigma)
y_filtered = gaussian_filter1d(ydeg,sigma)
elif filter_type=='median':
size=kwargs.get('size',5)
x_filtered=medfilt(xdeg,size)
y_filtered=medfilt(ydeg,size)
elif filter_type=='sg':
size=kwargs.get('size',7)
order=kwargs.get('order',2)
x_filtered=savitzky_golay(xdeg,window_size=size, order=order)
y_filtered=savitzky_golay(ydeg,window_size=size, order=order)
elif filter_type=='average':
weights=np.asarray(kwargs.get('weights',[1.,2.,3.,2.,1.]))
weights=weights/np.sum(weights)
x_filtered=np.convolve(xdeg, weights,'same')
y_filtered=np.convolve(ydeg, weights,'same')
else:
raise ValueError('Unknown Filter Type: %s. Must be one of %s'%(filter_type,str(['sg','butter','gauss','median'])))
xdeg[invalid_data_mask]=np.NaN
ydeg[invalid_data_mask]=np.NaN
x_filtered[invalid_data_mask]=np.NaN
y_filtered[invalid_data_mask]=np.NaN
return (xdeg,ydeg),(x_filtered,y_filtered)
def createTrialDataStreams():
trial_data_streams=[]
# Get the filtered event data.
# We will use right eye data only for the testing..
#
dataAccessUtil=ExperimentDataAccessUtility('../hdf5_files','remote_data.hdf5',
experimentCode=None,sessionCodes=[])
event_type=EventConstants.BINOCULAR_EYE_SAMPLE
retrieve_attributes=('time','right_gaze_x','right_gaze_y','right_pupil_measure1','status')
trial_event_data=dataAccessUtil.getEventAttributeValues(event_type,
retrieve_attributes,
conditionVariablesFilter=None,
startConditions={'time':('>=','@TRIAL_START@')},
endConditions={'time':('<=','@TRIAL_END@')},
)
dataAccessUtil.close()
for t,trial_data in enumerate(trial_event_data):
#Create a mask to be used to define periods of missing data in a data trace (eye tracker dependent)
#
invalid_data_mask=trial_data.status%10>=2
time=trial_data.time
pupil=trial_data.right_pupil_measure1
# Get x, y eye position traces (in pixels), setting sample positions where there is track loss
# to NaN.
xpix_cleared=trial_data.right_gaze_x.copy()
ypix_cleared=trial_data.right_gaze_y.copy()
processSampleEventGaps(xpix_cleared,ypix_cleared,pupil,invalid_data_mask,'clear')
# Get x, y eye position traces (in pixels), setting sample positions
# where there is track loss to be linearly interpolated using each
# missing_sample_start-1 and missing_sample_end+1 as the points to
# interpolate between.
#
xpix_linear=trial_data.right_gaze_x.copy()
ypix_linear=trial_data.right_gaze_y.copy()
# valid_data_periods is a list of array slice objects giving the start,end index of each non missing
# period of in the data stream.
#
valid_data_periods=processSampleEventGaps(xpix_linear,ypix_linear,pupil,invalid_data_mask,'linear')
# Convert from pixels to visual angle coordinates
calibration_area_info=dict(display_size_mm=(340,280.0),
display_res_pix=(1280.0,1024.0),
eye_distance_mm=590.0)
vac=VisualAngleCalc(**calibration_area_info)
xdeg,ydeg=vac.pix2deg(xpix_linear,ypix_linear)
# Create Filtered versions of the x and y degree data traces
# We'll use the Median Filter...
#
xdeg_filtered = scipy.signal.medfilt(xdeg,SPATIAL_FILTER_WINDOW_SIZE)
ydeg_filtered = scipy.signal.medfilt(ydeg,SPATIAL_FILTER_WINDOW_SIZE)
# Create the velocity stream
#
xvel=calculateVelocity(time,xdeg_filtered)
yvel=calculateVelocity(time,ydeg_filtered)
# Filter the velocity data
#
FILTER_ORDER=2
Wn=0.3
b, a = scipy.signal.butter(FILTER_ORDER, Wn, 'low')
ffunc=scipy.signal.filtfilt
xvel_filtered = ffunc(b, a, xvel)
yvel_filtered = ffunc(b, a, yvel)
# xvel_filtered=savitzky_golay(xvel,window_size=VELOCITY_FILTER_WINDOW_SIZE,order=2)
# yvel_filtered=savitzky_golay(yvel,window_size=VELOCITY_FILTER_WINDOW_SIZE,order=2)
# xvel_filtered=gaussian_filter1d(xvel,VELOCITY_FILTER_WINDOW_SIZE)
# yvel_filtered=gaussian_filter1d(yvel,VELOCITY_FILTER_WINDOW_SIZE)
# xvel_filtered=scipy.signal.medfilt(xvel,VELOCITY_FILTER_WINDOW_SIZE)
# yvel_filtered=scipy.signal.medfilt(yvel,VELOCITY_FILTER_WINDOW_SIZE)
velocity=np.sqrt(xvel*xvel+yvel*yvel)
velocity_filtered=np.sqrt(xvel_filtered*xvel_filtered+yvel_filtered*yvel_filtered)
# Create a data trace dictionary for all the different types
# of data traces created for the trial
#
trial_data={}
trial_data['time']=time
trial_data['xpix_cleared']=xpix_cleared
trial_data['ypix_cleared']=ypix_cleared
trial_data['xpix_linear']=xpix_linear
trial_data['xpix_linear']=xpix_linear
trial_data['xdeg']=xdeg
trial_data['ydeg']=ydeg
trial_data['xdeg_filtered']=xdeg_filtered
trial_data['ydeg_filtered']=ydeg_filtered
trial_data['pupil']=pupil
trial_data['velocity']=velocity
trial_data['velocity_filtered']=velocity_filtered
trial_data['valid_data_periods']=valid_data_periods
trial_data['missing_data_mask']=invalid_data_mask
# Add the data trace dictionary to a list
#
trial_data_streams.append(trial_data)
return trial_data_streams
# Get the needed left eye sample arrays
#
left_gaze_x = trial_samples.left_gaze_x
left_gaze_y = trial_samples.left_gaze_y
left_pupil_size = trial_samples.left_pupil_measure1
# Process the left eye fields using the processSampleEventGaps function defined
# in the common_workshop_functions.py file. The last argument of 'clear'
# tells the function to set any x or y position missing data samples to NaN
# and to set the pupil size field to 0. The operations are preformed in-place
# on the numpy arrays passed to the function.
# The returned valid_data_periods is a list of each group of temporally adjacent
# samples that are valid, but providing a list where each element is the (start, stop)
# index for a given period of valid data.
#
left_valid_data_periods = processSampleEventGaps(
left_gaze_x, left_gaze_y, left_pupil_size, left_eye_invalid_data_masks,
'clear')
# Get the needed right eye sample field arrays
#
right_gaze_x = trial_samples.right_gaze_x
right_gaze_y = trial_samples.right_gaze_y
right_pupil_size = trial_samples.right_pupil_measure1
# Process the right eye fields
#
right_valid_data_periods = processSampleEventGaps(
right_gaze_x, right_gaze_y, right_pupil_size,
right_eye_invalid_data_masks, 'clear')
# get the array of sample times for the current trial
#
vac=VisualAngleCalc(**calibration_area_info)
# Calculate the visual degree position in x and y for the given pixel position arrays.
#
degree_x,degree_y=vac.pix2deg(pix_x,pix_y)
# Process the eye fields using the processSampleEventGaps function defined
# in the common_workshop_functions.py file. The last argument of 'clear'
# tells the function to set any x or y position missing data samples to NaN
# and to set the pupil size field to 0. The operations are preformed in-place
# on the numpy arrays passed to the function.
# The returned valid_data_periods is a list of each group of temporally adjacent
# samples that are valid, each element of the list is the (start, stop)
# index for a given period of valid data.
#
valid_data_periods=processSampleEventGaps(pix_x,pix_y,pupil,invalid_data_mask,
'clear')
#### STEP C. ####
# Create a plot of eye position in pixels and visual degrees
#
fig = plt.figure(figsize=(12,8))
fig.suptitle("Eye Sample Data For Trial Index %d"%(TRIAL_INDEX+1),fontsize=14)
# Get the range to use for the x axis
#
tmin=time.min()//1
tmax=time.max()//1+1
# Create the y axis (time)
ax=plt.gca()
px=ax.plot(time, pix_x,label='X Position (Pixels)',color=(.5,.5,1))
py=ax.plot(time, pix_y,label='Y Position (Pixels)',color=(1,.5,.5))
#
dataAccessUtil.close()
# Use the VisualAngleCalc class defined in the common_workshop_functions to
# generate an object that can convert data from pixels to visual angles based
# on the supplied calibration / display surface geometry and eye distance.
#
vac=VisualAngleCalc(**calibration_area_info)
# Calculate the visual degree position in x and y for the given pixel position arrays.
#
degree_x,degree_y=vac.pix2deg(pix_x,pix_y)
# Process the eye fields using the processSampleEventGaps function defined
# in the common_workshop_functions.py file.
#
valid_data_periods=processSampleEventGaps(degree_x,degree_y,pupil,invalid_data_mask,
'clear')
# calculate unfiltered velocity and accelleration streams
#
velocity=np.abs(calculateVelocity(time,degree_x,degree_y))
accelleration=calculateAccelleration(time,degree_x,degree_y)
pix_x[invalid_data_mask]=np.NaN
pix_y[invalid_data_mask]=np.NaN
degree_x[invalid_data_mask]=np.NaN
degree_y[invalid_data_mask]=np.NaN
# Get the range to use for the x axis
#
tmin=time.min()//1
tmax=time.max()//1+1
# Get the needed left eye sample arrays
#
gaze_x = trial_data.gaze_x
gaze_y = trial_data.gaze_y
pupil_size = trial_data.pupil_measure1
# Process the eye fields using the processSampleEventGaps function defined
# in the common_workshop_functions.py file. The last arguement of 'clear'
# tells the function to set any x or y position missing data samples to NaN
# and to set the pupil size field to 0. The operations are preformed in-place
# on the numpy arrays passed to the function.
# The returned valid_data_periods is a list of each group of temporally adjacent
# samples that are valid, but providing a list where each element is the (start, stop)
# index for a given period of valid data.
#
valid_data_periods = processSampleEventGaps(gaze_x, gaze_y, pupil_size,
invalid_data_mask, 'clear')
# get the array of sample times for the current trial
#
time = trial_data.time
# Start plotting for the trial
plt.figure(figsize=(12, 8))
##### STEP C. #####
# Create Image background for each trial scanpath
# Get the condition variable set used for the current trial
#
condition_set = trial_data.condition_set
# Get the image name and trial_id from the condition data for
# the trial.
# Get the needed left eye sample arrays
#
left_gaze_x=trial_samples.left_gaze_x
left_gaze_y=trial_samples.left_gaze_y
left_pupil_size=trial_samples.left_pupil_measure1
# Process the left eye fields using the processSampleEventGaps function defined
# in the common_workshop_functions.py file. The last argument of 'clear'
# tells the function to set any x or y position missing data samples to NaN
# and to set the pupil size field to 0. The operations are preformed in-place
# on the numpy arrays passed to the function.
# The returned valid_data_periods is a list of each group of temporally adjacent
# samples that are valid, but providing a list where each element is the (start, stop)
# index for a given period of valid data.
#
left_valid_data_periods=processSampleEventGaps(left_gaze_x,left_gaze_y,
left_pupil_size,
left_eye_invalid_data_masks,
'clear')
# Get the needed right eye sample field arrays
#
right_gaze_x=trial_samples.right_gaze_x
right_gaze_y=trial_samples.right_gaze_y
right_pupil_size=trial_samples.right_pupil_measure1
# Process the right eye fields
#
right_valid_data_periods=processSampleEventGaps(right_gaze_x,right_gaze_y,
right_pupil_size,
right_eye_invalid_data_masks,
'clear')
#
dataAccessUtil.close()
# Use the VisualAngleCalc class defined in the common_workshop_functions to
# generate an object that can convert data from pixels to visual angles based
# on the supplied calibration / display surface geometry and eye distance.
#
vac = VisualAngleCalc(**calibration_area_info)
# Calculate the visual degree position in x and y for the given pixel position arrays.
#
degree_x, degree_y = vac.pix2deg(pix_x, pix_y)
# Process the eye fields using the processSampleEventGaps function defined
# in the common_workshop_functions.py file.
#
valid_data_periods = processSampleEventGaps(degree_x, degree_y, pupil,
invalid_data_mask, 'clear')
# calculate unfiltered velocity and accelleration streams
#
velocity = np.abs(calculateVelocity(time, degree_x, degree_y))
accelleration = calculateAccelleration(time, degree_x, degree_y)
pix_x[invalid_data_mask] = np.NaN
pix_y[invalid_data_mask] = np.NaN
degree_x[invalid_data_mask] = np.NaN
degree_y[invalid_data_mask] = np.NaN
# Get the range to use for the x axis
#
tmin = time.min() // 1
tmax = time.max() // 1 + 1
def createTrialDataStreams():
trial_data_streams = []
# Get the filtered event data.
# We will use right eye data only for the testing..
#
dataAccessUtil = ExperimentDataAccessUtility(
"../hdf5_files", "remote_data.hdf5", experimentCode=None, sessionCodes=[]
)
event_type = EventConstants.BINOCULAR_EYE_SAMPLE
retrieve_attributes = ("time", "right_gaze_x", "right_gaze_y", "right_pupil_measure1", "status")
trial_event_data = dataAccessUtil.getEventAttributeValues(
event_type,
retrieve_attributes,
conditionVariablesFilter=None,
startConditions={"time": (">=", "@TRIAL_START@")},
endConditions={"time": ("<=", "@TRIAL_END@")},
)
dataAccessUtil.close()
for t, trial_data in enumerate(trial_event_data):
# Create a mask to be used to define periods of missing data in a data trace (eye tracker dependent)
#
invalid_data_mask = trial_data.status % 10 >= 2
time = trial_data.time
pupil = trial_data.right_pupil_measure1
# Get x, y eye position traces (in pixels), setting sample positions where there is track loss
# to NaN.
xpix_cleared = trial_data.right_gaze_x.copy()
ypix_cleared = trial_data.right_gaze_y.copy()
processSampleEventGaps(xpix_cleared, ypix_cleared, pupil, invalid_data_mask, "clear")
# Get x, y eye position traces (in pixels), setting sample positions
# where there is track loss to be linearly interpolated using each
# missing_sample_start-1 and missing_sample_end+1 as the points to
# interpolate between.
#
xpix_linear = trial_data.right_gaze_x.copy()
ypix_linear = trial_data.right_gaze_y.copy()
# valid_data_periods is a list of array slice objects giving the start,end index of each non missing
# period of in the data stream.
#
valid_data_periods = processSampleEventGaps(xpix_linear, ypix_linear, pupil, invalid_data_mask, "linear")
# Convert from pixels to visual angle coordinates
calibration_area_info = dict(
display_size_mm=(340, 280.0), display_res_pix=(1280.0, 1024.0), eye_distance_mm=590.0
)
vac = VisualAngleCalc(**calibration_area_info)
xdeg, ydeg = vac.pix2deg(xpix_linear, ypix_linear)
# Create Filtered versions of the x and y degree data traces
# We'll use the Median Filter...
#
xdeg_filtered = scipy.signal.medfilt(xdeg, SPATIAL_FILTER_WINDOW_SIZE)
ydeg_filtered = scipy.signal.medfilt(ydeg, SPATIAL_FILTER_WINDOW_SIZE)
# Create the velocity stream
#
xvel = calculateVelocity(time, xdeg_filtered)
yvel = calculateVelocity(time, ydeg_filtered)
# Filter the velocity data
#
FILTER_ORDER = 2
Wn = 0.3
b, a = scipy.signal.butter(FILTER_ORDER, Wn, "low")
ffunc = scipy.signal.filtfilt
xvel_filtered = ffunc(b, a, xvel)
yvel_filtered = ffunc(b, a, yvel)
# xvel_filtered=savitzky_golay(xvel,window_size=VELOCITY_FILTER_WINDOW_SIZE,order=2)
# yvel_filtered=savitzky_golay(yvel,window_size=VELOCITY_FILTER_WINDOW_SIZE,order=2)
# xvel_filtered=gaussian_filter1d(xvel,VELOCITY_FILTER_WINDOW_SIZE)
# yvel_filtered=gaussian_filter1d(yvel,VELOCITY_FILTER_WINDOW_SIZE)
# xvel_filtered=scipy.signal.medfilt(xvel,VELOCITY_FILTER_WINDOW_SIZE)
# yvel_filtered=scipy.signal.medfilt(yvel,VELOCITY_FILTER_WINDOW_SIZE)
velocity = np.sqrt(xvel * xvel + yvel * yvel)
velocity_filtered = np.sqrt(xvel_filtered * xvel_filtered + yvel_filtered * yvel_filtered)
# Create a data trace dictionary for all the different types
# of data traces created for the trial
#
trial_data = {}
trial_data["time"] = time
trial_data["xpix_cleared"] = xpix_cleared
trial_data["ypix_cleared"] = ypix_cleared
trial_data["xpix_linear"] = xpix_linear
trial_data["xpix_linear"] = xpix_linear
trial_data["xdeg"] = xdeg
trial_data["ydeg"] = ydeg
trial_data["xdeg_filtered"] = xdeg_filtered
trial_data["ydeg_filtered"] = ydeg_filtered
trial_data["pupil"] = pupil
trial_data["velocity"] = velocity
trial_data["velocity_filtered"] = velocity_filtered
trial_data["valid_data_periods"] = valid_data_periods
trial_data["missing_data_mask"] = invalid_data_mask
# Add the data trace dictionary to a list
#
trial_data_streams.append(trial_data)
return trial_data_streams
#
gaze_x=trial_data.gaze_x
gaze_y=trial_data.gaze_y
pupil_size=trial_data.pupil_measure1
# Process the eye fields using the processSampleEventGaps function defined
# in the common_workshop_functions.py file. The last arguement of 'clear'
# tells the function to set any x or y position missing data samples to NaN
# and to set the pupil size field to 0. The operations are preformed in-place
# on the numpy arrays passed to the function.
# The returned valid_data_periods is a list of each group of temporally adjacent
# samples that are valid, but providing a list where each element is the (start, stop)
# index for a given period of valid data.
#
valid_data_periods=processSampleEventGaps(gaze_x,gaze_y,
pupil_size,
invalid_data_mask,
'clear')
# get the array of sample times for the current trial
#
time=trial_data.time
# Start plotting for the trial
plt.figure(figsize=(12,8))
##### STEP C. #####
# Create Image background for each trial scanpath
# Get the condition variable set used for the current trial
#
condition_set=trial_data.condition_set
# Get the image name and trial_id from the condition data for