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
# 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
# Create a subplot of eye position in degrees and velocity and a subplot
# of eye position in degrees and accelleration.
fig = plt.figure(figsize=(12, 8))
# 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
# Create a subplot of eye position in degrees and velocity and a subplot
# of eye position in degrees and accelleration.
fig = plt.figure(figsize=(12,8))
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
