@author: Sol
"""
import string
import random
from psychopy import visual, core, event
from psychopy.iohub.util import NumPyRingBuffer
import pyglet.gl as gl
# Variables to control text string length etc.
text_length=160
chng_txt_each_flips=5
max_flip_count=60*10
display_resolution=1920,1080
# Circular buffers to store timing measures
stim1_txt_change_draw_times=NumPyRingBuffer(6000)
stim1_no_change_draw_times=NumPyRingBuffer(6000)
stim2_txt_change_draw_times=NumPyRingBuffer(6000)
stim2_no_change_draw_times=NumPyRingBuffer(6000)
# Some utility functions >>>
#
char_choices=string.ascii_uppercase+u"ùéèàç^ùèàç髼±£¢¤¬¦²³½¾°µ¯±√∞≤≥±≠"
def getRandomString(slength):
"""
Create a random text string of length 'slength' from the unichar values
in char_choices; then split the random text into 'words' of random length
from [1,3,5,7,9].
"""
s=u''.join(random.choice(char_choices) for i in range(slength))
Values can be appended to the ring buffer and accessed using slice notation.
Any method of the numpy.array class can be called by a NumPyRingBuffer instance,
however numpy.array module funtions will not accept a NumPyRingBuffer as input.
@author: Sol
"""
from psychopy.iohub.util import NumPyRingBuffer
# Create a ring buffer with a maximum size of 10 elements. AS more than 10 elements
# are added using append(x), each new element removed the oldeest element in
# the buffer. The default data type is numpy.float32. To change the type to a
# different numpy scalar value (for example numpy.uint, numpy ubyte, etc),
# use the dtype parameter of NumPyRingBuffer.
ring_buffer = NumPyRingBuffer(10)
# Add 25 values to the ring buffer, between 1 and 25 inclusive.
# Print out info about the ring buffer and ring buffer contents state
# after each element is added.
#
for i in xrange(1, 26):
ring_buffer.append(i)
print '-------'
print 'Ring Buffer Stats:'
print '\tWindow size: ', len(ring_buffer)
print '\tMin Value: ', ring_buffer.min()
print '\tMax Value: ', ring_buffer.max()
print '\tMean Value: ', ring_buffer.mean()
print '\tStandard Deviation: ', ring_buffer.std()
print '\tFirst 3 Elements: ', ring_buffer[:3]
@author: Sol
"""
import string
import random
from psychopy import visual, core, event
from psychopy.iohub.util import NumPyRingBuffer
import pyglet.gl as gl
# Variables to control text string length etc.
text_length=160
chng_txt_each_flips=5
max_flip_count=60*10
display_resolution=1920,1080
# Circular buffers to store timing measures
stim1_txt_change_draw_times=NumPyRingBuffer(6000)
stim1_no_change_draw_times=NumPyRingBuffer(6000)
stim2_txt_change_draw_times=NumPyRingBuffer(6000)
stim2_no_change_draw_times=NumPyRingBuffer(6000)
# Some utility functions >>>
#
char_choices=string.ascii_uppercase+u"ùéèàç^ùèàç髼±£¢¤¬¦²³½¾°µ¯±√∞≤≥±≠"
def getRandomString(slength):
"""
Create a random text string of length 'slength' from the unichar values
in char_choices; then split the random text into 'words' of random length
from [1,3,5,7,9].
"""
s=u''.join(random.choice(char_choices) for i in range(slength))
from psychopy import visual, core, event
from psychopy.visual import textbox
from psychopy.iohub.util import NumPyRingBuffer
import pyglet.gl as gl
fm = textbox.getFontManager()
print(dir(fm))
print(fm.getFontFamilyNames())
# Variables to control text string length etc.
text_length=160
chng_txt_each_flips=5
max_flip_count=60*10
display_resolution=1920,1080
# Circular buffers to store timing measures
stim1_txt_change_draw_times=NumPyRingBuffer(6000)
stim1_no_change_draw_times=NumPyRingBuffer(6000)
stim2_txt_change_draw_times=NumPyRingBuffer(6000)
stim2_no_change_draw_times=NumPyRingBuffer(6000)
# Some utility functions >>>
#
char_choices=string.ascii_uppercase+u"ùéèàç^ùèàç髼±£¢¤¬¦²³½¾°µ¯±√∞≤≥±≠"
def getRandomString(slength):
"""
Create a random text string of length 'slength' from the unichar values
in char_choices; then split the random text into 'words' of random length
from [1,3,5,7,9].
"""
s=u''.join(random.choice(char_choices) for i in range(slength))
Values can be appended to the ring buffer and accessed using slice notation.
Any method of the numpy.array class can be called by a NumPyRingBuffer instance,
however numpy.array module funtions will not accept a NumPyRingBuffer as input.
@author: Sol
"""
from psychopy.iohub.util import NumPyRingBuffer
# Create a ring buffer with a maximum size of 10 elements. AS more than 10 elements
# are added using append(x), each new element removed the oldeest element in
# the buffer. The default data type is numpy.float32. To change the type to a
# different numpy scalar value (for example numpy.uint, numpy ubyte, etc),
# use the dtype parameter of NumPyRingBuffer.
ring_buffer=NumPyRingBuffer(10)
# Add 25 values to the ring buffer, between 1 and 25 inclusive.
# Print out info about the ring buffer and ring buffer contents state
# after each element is added.
#
for i in xrange(1,26):
ring_buffer.append(i)
print '-------'
print 'Ring Buffer Stats:'
print '\tWindow size: ',len(ring_buffer)
print '\tMin Value: ',ring_buffer.min()
print '\tMax Value: ',ring_buffer.max()
print '\tMean Value: ',ring_buffer.mean()
print '\tStandard Deviation: ',ring_buffer.std()
print '\tFirst 3 Elements: ',ring_buffer[:3]
import pyglet.gl as gl
# Variables to control text string length etc.
text_length = 160
chng_txt_each_flips = 5
max_flip_count = 60 * 10
text_stim_types = [visual.TextBox, visual.TextBox2, visual.TextStim]
text_stim = []
stim_init_durations = {}
txt_change_draw_times = {}
no_change_draw_times = {}
for stype in text_stim_types:
# Circular buffers to store timing measures
cname = stype.__name__
txt_change_draw_times[cname] = NumPyRingBuffer(max_flip_count)
no_change_draw_times[cname] = NumPyRingBuffer(max_flip_count)
# Some utility functions >>>
#
char_choices = string.ascii_uppercase + "ùéèàç^ùèàç髼±£¢¤¬¦²³½¾°µ¯±√∞≤≥±≠"
def getRandomString(slength):
"""
Create a random text string of length 'slength' from the unichar values
in char_choices; then split the random text into 'words' of random length
from [1,3,5,7,9].
"""
s = u''.join(random.choice(char_choices) for i in range(slength))
ns = u''
def processEyeTrackerData(self, dev_data):
current_et_data = self.data_collection_state.get('eyetracker')
if current_et_data is None:
current_et_data = dev_data
new_fields = {
"eye_sample_type": [None, ''],
"proportion_valid_samples": [None, ' %'],
"time": [None, ' sec'], # time of last sample received
"rms_noise": [None, ' RMS'],
"stdev_noise": [None, ' STDEV'],
}
current_et_data.update(new_fields)
self.data_collection_state['eyetracker'] = current_et_data
self.data_collection_state[
'proportion_valid_samples'] = NumPyRingBuffer(int(
current_et_data["sampling_rate"][0]),
dtype=np.int8)
else:
current_et_data.update(dev_data)
dcapp_config = self.data_collection_state['data_collection_config']
new_samples = current_et_data.get('samples')[0]
sampling_rate = float(current_et_data["sampling_rate"][0])
if new_samples:
latest_sample_event = new_samples[-1]
current_et_data['time'][0] = latest_sample_event['time']
tracking_eyes = dev_data.get('track_eyes')[0]
eyename = 'BOTH'
sample_type = current_et_data.get('eye_sample_type')[0]
noise_win_size = dcapp_config.get('noise_calculation',
{}).get("win_size", 0.2)
noise_sample_count = int(sampling_rate * noise_win_size)
if tracking_eyes == 'BINOCULAR_AVERAGED':
sample_type = "Monocular"
eyename = 'AVERAGED'
if self.data_collection_state.get('right_eye') is None:
self.data_collection_state['right_eye'] = dict(
x=NumPyRingBuffer(noise_sample_count,
dtype=np.float64),
y=NumPyRingBuffer(noise_sample_count,
dtype=np.float64),
pupil=NumPyRingBuffer(noise_sample_count,
dtype=np.float64))
createAveragedEyeInfo(current_et_data)
#----
elif tracking_eyes == EyeTrackerConstants.LEFT_EYE:
if self.data_collection_state.get('left_eye') is None:
createLeftEyeInfo(current_et_data)
self.data_collection_state['left_eye'] = dict(
x=NumPyRingBuffer(noise_sample_count,
dtype=np.float64),
y=NumPyRingBuffer(noise_sample_count,
dtype=np.float64),
pupil=NumPyRingBuffer(noise_sample_count,
dtype=np.float64))
eyename = 'LEFT'
if sample_type is None:
sample_type = "Monocular"
elif tracking_eyes <= EyeTrackerConstants.MONOCULAR:
if self.data_collection_state.get('right_eye') is None:
self.data_collection_state['right_eye'] = dict(
x=NumPyRingBuffer(noise_sample_count,
dtype=np.float64),
y=NumPyRingBuffer(noise_sample_count,
dtype=np.float64),
pupil=NumPyRingBuffer(noise_sample_count,
dtype=np.float64))
createRightEyeInfo(current_et_data)
eyename = 'RIGHT'
if sample_type is None:
sample_type = "Monocular"
else:
if self.data_collection_state.get('right_eye') is None:
createLeftEyeInfo(current_et_data)
createRightEyeInfo(current_et_data)
self.data_collection_state['left_eye'] = dict(
x=NumPyRingBuffer(noise_sample_count,
dtype=np.float64),
y=NumPyRingBuffer(noise_sample_count,
dtype=np.float64),
pupil=NumPyRingBuffer(noise_sample_count,
dtype=np.float64))
self.data_collection_state['right_eye'] = dict(
x=NumPyRingBuffer(noise_sample_count,
dtype=np.float64),
y=NumPyRingBuffer(noise_sample_count,
dtype=np.float64),
pupil=NumPyRingBuffer(noise_sample_count,
dtype=np.float64))
if sample_type is None:
sample_type = "Binocular"
#----
if sample_type is None:
current_et_data['eye_sample_type'][0] = sample_type
new_sample_count = len(new_samples)
if tracking_eyes == 'BINOCULAR_AVERAGED':
valid_samples = [v for v in new_samples if v['status'] != 22]
else:
valid_samples = [v for v in new_samples if v['status'] == 0]
valid_sample_count = len(valid_samples)
invalid_sample_count = new_sample_count - valid_sample_count
#----
s = None
right_eye_buffers = None
left_eye_buffers = None
for s in valid_samples:
if eyename == 'AVERAGED':
right_eye_buffers = self.data_collection_state['right_eye']
right_eye_buffers['x'].append(s['gaze_x'])
right_eye_buffers['y'].append(s['gaze_y'])
right_eye_buffers['pupil'].append(s['pupil_measure1'])
elif eyename == 'BOTH':
rgx, rgy = s['right_gaze_x'], s['right_gaze_y']
lgx, lgy = s['left_gaze_x'], s['left_gaze_y']
right_eye_buffers = self.data_collection_state['right_eye']
right_eye_buffers['x'].append(rgx)
right_eye_buffers['y'].append(rgy)
right_eye_buffers['pupil'].append(
s['right_pupil_measure1'])
left_eye_buffers = self.data_collection_state['left_eye']
left_eye_buffers['x'].append(lgx)
left_eye_buffers['y'].append(lgx)
left_eye_buffers['pupil'].append(s['left_pupil_measure1'])
elif eyename == 'RIGHT':
right_eye_buffers = self.data_collection_state['right_eye']
if sample_type == 'Binocular':
right_eye_buffers['x'].append(s['right_gaze_x'])
right_eye_buffers['y'].append(s['right_gaze_y'])
right_eye_buffers['pupil'].append(
s['right_pupil_measure1'])
else:
right_eye_buffers['x'].append(s['gaze_x'])
right_eye_buffers['y'].append(s['gaze_y'])
right_eye_buffers['pupil'].append(s['pupil_measure1'])
else: # LEFT
left_eye_buffers = self.data_collection_state['left_eye']
if sample_type == 'Binocular':
left_eye_buffers['x'].append(s['left_gaze_x'])
left_eye_buffers['y'].append(s['left_gaze_y'])
left_eye_buffers['pupil'].append(
s['left_pupil_measure1'])
else:
left_eye_buffers['x'].append(s['gaze_x'])
left_eye_buffers['y'].append(s['gaze_y'])
left_eye_buffers['pupil'].append(s['pupil_measure1'])
def calcrms(x, axis=None):
return float(np.sqrt(np.mean(np.power(x, 2.0),
axis=axis))) / len(x)
rms = None
stdev = None
if right_eye_buffers is not None and right_eye_buffers['x'].isFull(
):
right_x = right_eye_buffers['x'].getElements()
right_y = right_eye_buffers['y'].getElements()
stdev = (right_x.std() + right_y.std()) / 2.0
rms = (calcrms(right_x) + calcrms(right_y)) / 2.0
if left_eye_buffers is not None and left_eye_buffers['x'].isFull():
left_x = left_eye_buffers['x'].getElements()
left_y = left_eye_buffers['y'].getElements()
left_rms = (calcrms(left_x) + calcrms(left_y)) / 2.0
left_stdev = (left_x.std() + left_y.std()) / 2.0
if rms is not None:
rms = (rms + left_rms) / 2.0
else:
rms = left_rms
if stdev is not None:
stdev = (stdev + left_stdev) / 2.0
else:
stdev = left_rms
prop_vsamples = self.data_collection_state[
'proportion_valid_samples']
for i in range(valid_sample_count):
prop_vsamples.append(1)
for i in range(invalid_sample_count):
prop_vsamples.append(0)
prop_valid_samples = prop_vsamples.sum() / float(sampling_rate)
current_et_data.get('samples')[0] = None
if rms:
current_et_data['rms_noise'][0] = rms
if stdev:
current_et_data['stdev_noise'][0] = stdev
if prop_valid_samples:
current_et_data['proportion_valid_samples'][0] = int(
prop_valid_samples * 100.0)
if valid_samples:
s = valid_samples[-1]
status = 'TBC'
if eyename == 'AVERAGED':
setAveragedEyeInfo(current_et_data, status, s)
elif eyename == 'BOTH':
setLeftEyeInfo(current_et_data, status, s)
setRightEyeInfo(current_et_data, status, s)
elif eyename == 'LEFT':
setLeftEyeInfo(current_et_data, status, s)
else:
setRightEyeInfo(current_et_data, status, s)
assert self.data_collection_state['eyetracker'] == current_et_data
