def printQueriedEventsDemo(): # Create an instance of the ExperimentDataAccessUtility class # for the selected DataStore file. This allows us to access data # in the file based on Device Event names and attributes. # experiment_data = ExperimentDataAccessUtility('..\hdf5_files', 'events.hdf5') # Retrieve the 'time','device_time','event_id','delay','category','text' # attributes from the Message Event table, where the event time is between # the associated trials condition variables TRIAL_START and TRIAL_END # value. # i.e. only get message events sent during each trial of the eperiment, not any # sent between trials. # event_results = experiment_data.getEventAttributeValues( EventConstants.MESSAGE, ['time', 'device_time', 'event_id', 'delay', 'category', 'text'], conditionVariablesFilter=None, startConditions={'time': ('>=', '@TRIAL_START@')}, endConditions={'time': ('<=', '@TRIAL_END@')}) for trial_events in event_results: print '==== TRIAL DATA START =======' print "Trial Condition Values:" for ck, cv in trial_events.condition_set._asdict().iteritems(): print "\t{ck} : {cv}".format(ck=ck, cv=cv) print trial_events.query_string print "Trial Query String:\t" print trial_events.query_string print event_value_arrays = [ (cv_name, cv_value) for cv_name, cv_value in trial_events._asdict().iteritems() if cv_name not in ('query_string', 'condition_set') ] print "Trial Event Field Data:" for field_name, field_data in event_value_arrays: print "\t" + field_name + ': ' + str(field_data) print print '===== TRIAL DATA END ========' experiment_data.close()
def printQueriedEventsDemo(): # Create an instance of the ExperimentDataAccessUtility class # for the selected DataStore file. This allows us to access data # in the file based on Device Event names and attributes. # experiment_data=ExperimentDataAccessUtility('..\hdf5_files' , 'events.hdf5') # Retrieve the 'time','device_time','event_id','delay','category','text' # attributes from the Message Event table, where the event time is between # the associated trials condition variables TRIAL_START and TRIAL_END # value. # i.e. only get message events sent during each trial of the eperiment, not any # sent between trials. # event_results=experiment_data.getEventAttributeValues(EventConstants.MESSAGE, ['time','device_time','event_id','delay','category','text'], conditionVariablesFilter=None, startConditions={'time':('>=','@TRIAL_START@')}, endConditions={'time':('<=','@TRIAL_END@')}) for trial_events in event_results: print '==== TRIAL DATA START =======' print "Trial Condition Values:" for ck,cv in trial_events.condition_set._asdict().iteritems(): print "\t{ck} : {cv}".format(ck=ck,cv=cv) print trial_events.query_string print "Trial Query String:\t" print trial_events.query_string print event_value_arrays=[(cv_name,cv_value) for cv_name,cv_value in trial_events._asdict().iteritems() if cv_name not in ('query_string','condition_set')] print "Trial Event Field Data:" for field_name,field_data in event_value_arrays: print "\t"+field_name+': '+str(field_data) print print '===== TRIAL DATA END ========' experiment_data.close()
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
experimentCode=None, sessionCodes=[]) ##### STEP A. ##### # Retrieve a subset of the BINOCULAR_EYE_SAMPLE event attributes, for events that occurred # between each time period defined by the TRIAL_START and TRIAL_END trial variables of each entry # in the trial_conditions data table. # event_type = EventConstants.BINOCULAR_EYE_SAMPLE retrieve_attributes = ('time', 'left_gaze_x', 'left_gaze_y', 'left_pupil_measure1', '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@')}, ) # No need to keep the hdf5 file open anymore... # dataAccessUtil.close() # Process and plot the sample data for each trial in the data file. # for trial_index, trial_samples in enumerate(trial_event_data): ##### STEP B. ##### # Find all samples that have missing eye position data and filter the eye position # and pupil size streams so that the eye track plot is more useful. In this case that # means setting position fields to NaN and pupil size to 0.
if __name__ == '__main__': # Select the hdf5 file to process. data_file_path = displayDataFileSelectionDialog( os.path.dirname(os.path.abspath(__file__))) if data_file_path is None: print("File Selection Cancelled, exiting...") sys.exit(0) data_file_path = data_file_path[0] dpath, dfile = os.path.split(data_file_path) datafile = ExperimentDataAccessUtility(dpath, dfile) events_by_trial = datafile.getEventAttributeValues( SAVE_EVENT_TYPE, SAVE_EVENT_FIELDS, startConditions={'time': ('>=', '@TRIAL_START@')}, endConditions={'time': ('<=', '@TRIAL_END@')}) ecount = 0 # Open a file to save the tab delimited output to. # output_file_name = "%s.txt" % (dfile[:-5]) with open(output_file_name, 'w') as output_file: print('Writing Data to %s:\n' % (output_file_name)) column_names = events_by_trial[0].condition_set._fields[ 2:] + events_by_trial[0]._fields[:-2] output_file.write('\t'.join(column_names)) output_file.write('\n')
# between each time period defined by the TRIAL_START and TRIAL_END trial variables of each entry # in the trial_conditions data table. # # Load an ioDataStore file containing 120 Hz sample data from a # remote eye tracker that was recording both eyes. In the plotting example # dataAccessUtil=ExperimentDataAccessUtility('../hdf5_files','remote_data.hdf5', experimentCode=None,sessionCodes=[]) # Get the filtered event data. # event_type=EventConstants.BINOCULAR_EYE_SAMPLE retrieve_attributes=('time','left_gaze_x','left_gaze_y','left_pupil_measure1', '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@')}, ) trial_data=trial_event_data[TRIAL_INDEX] time=trial_data.time status=trial_data.status if USE_RIGHT_EYE: pix_x=trial_data.right_gaze_x pix_y=trial_data.right_gaze_y pupil=trial_data.right_pupil_measure1 invalid_data_mask=trial_data.status%10>=2 else: pix_x=trial_data.left_gaze_x
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