def selection_counts(filename):
with MWKFile(filename) as f:
r_codec = f.reverse_codec
red_code = r_codec['red_selected']
green_code = r_codec['green_selected']
blue_code = r_codec['blue_selected']
red_count = 0
green_count = 0
blue_count = 0
for evt in f.get_events_iter(codes=[red_code, green_code, blue_code]):
if evt.data:
if evt.code == red_code:
red_count += 1
elif evt.code == green_code:
green_count += 1
else:
assert evt.code == blue_code
blue_count += 1
index = numpy.arange(3)
pyplot.bar(index, [red_count, green_count, blue_count],
0.5,
color=['r', 'g', 'b'],
align='center')
pyplot.xticks(index, ['Red', 'Green', 'Blue'])
pyplot.title('Selection Counts')
pyplot.show()
def setUpClass(cls):
cls.event_counts = defaultdict(lambda: 0)
cls.event_times = []
for evt in cls.file_reader(cls.filename):
cls.event_counts[evt[0]] += 1
cls.event_times.append(evt[1])
cls.event_counts = dict(cls.event_counts)
cls.event_times = numpy.array(cls.event_times)
cls.fp = MWKFile(cls.filename)
def setUpClass(cls):
cls.event_counts = defaultdict(lambda: 0)
cls.event_times = []
with MWKStream.open_file(cls.filename) as stream:
for evt in stream:
cls.event_counts[evt.code] += 1
cls.event_times.append(evt.time)
cls.event_counts = dict(cls.event_counts)
cls.event_times = numpy.array(cls.event_times)
cls.fp = MWKFile(cls.filename)
def validate_dots_data(filename):
with MWKFile(filename) as f:
num_events = 0
for e in f.get_events_iter(codes=['#announceStimulus']):
value = e.value
if (isinstance(value, dict) and (value['type'] == 'moving_dots')
and ('dots' in value)):
data = numpy.fromstring(value['dots'], numpy.float32)
assert len(data) == (2 * value['num_dots'])
x = data[::2]
y = data[1::2]
assert (x * x + y * y).max() <= 1.0
num_events += 1
assert num_events > 0
print 'Processed %d events' % num_events
def receive(self):
try:
with MWKFile(self.filename) as fp:
evts = fp.get_events()
self.assertEqual(1, len(evts))
e = evts[0]
self.assertEqual(1, e.code)
self.assertEqual(2, e.time)
try:
return e.data
except Exception as e:
return e
finally:
try:
fp.unindex()
except mworks.data.IndexingException:
pass
os.remove(self.filename)
def dump_events(filename, photodiode_file, sample_on_file):
print(filename)
event_file = MWKFile(filename)
event_file.open()
# Variables we'd like to fetch data for
names = ['trial_start_line',
'correct_fixation',
'stimulus_presented',
'stim_on_time',
'stim_off_time',
'stim_on_delay',
'stimulus_size',
'fixation_window_size',
'fixation_point_size_min']
data = get_events(event_file=event_file, name=names)
# event_file.close()
###########################################################################
# Create a dict to store output information
###########################################################################
output = {
'stim_on_time_ms': data[data.name == 'stim_on_time']['data'].values[-1] / 1000.,
'stim_off_time_ms': data[data.name == 'stim_off_time']['data'].values[-1] / 1000.,
'stim_on_delay_ms': data[data.name == 'stim_on_delay']['data'].values[-1] / 1000.,
'stimulus_size_degrees': data[data.name == 'stimulus_size']['data'].values[-1],
'fixation_window_size_degrees': data[data.name == 'fixation_window_size']['data'].values[-1],
'fixation_point_size_degrees': data[data.name == 'fixation_point_size_min']['data'].values[-1],
}
###########################################################################
# Add column in data to indicate whether stimulus was first in trial or not
###########################################################################
data['first_in_trial'] = False
# Filter data to only get `trial_start_line` and `stimulus_presented` information
df = data[(data.name == 'trial_start_line') | ((data.name == 'stimulus_presented') & (data.data != -1))]
# Extract `time` for the first `stimulus_presented` (which is right after `trial_start_line` has been pulsed)
first_in_trial_times = [df.time.values[i] for i in range(1, len(df))
if ((df.name.values[i - 1] == 'trial_start_line') and
(df.name.values[i] == 'stimulus_presented'))]
data['first_in_trial'] = data['time'].apply(lambda x: True if x in first_in_trial_times else False)
###########################################################################
# Extract stimulus presentation order and fixation information
###########################################################################
stimulus_presented_df = data[data.name == 'stimulus_presented'].reset_index(drop=True)
correct_fixation_df = data[data.name == 'correct_fixation'].reset_index(drop=True)
# stimulus_presented_df = stimulus_presented_df[:len(correct_fixation_df)] # If you have one extra stimulus event but not fixation, use this
assert len(stimulus_presented_df) == len(correct_fixation_df)
# Drop `empty` data (i.e. -1) before the experiment actually began and after it had already ended
correct_fixation_df = correct_fixation_df[stimulus_presented_df.data != -1].reset_index(drop=True)
stimulus_presented_df = stimulus_presented_df[stimulus_presented_df.data != -1].reset_index(drop=True)
# Add `first_in_trial` info to other data frame too
correct_fixation_df['first_in_trial'] = stimulus_presented_df['first_in_trial']
###########################################################################
# Add column to indicate order in trial (1 2 3 1 2 3 etc.)
###########################################################################
assert stimulus_presented_df.iloc[0].first_in_trial
stimulus_presented_df['stimulus_order_in_trial'] = ''
counter = 1
for index, row in stimulus_presented_df.iterrows():
if row['first_in_trial']:
counter = 1
stimulus_presented_df.at[index, 'stimulus_order_in_trial'] = counter
counter += 1
correct_fixation_df['stimulus_order_in_trial'] = stimulus_presented_df['stimulus_order_in_trial']
###########################################################################
# Read sample on file
###########################################################################
fid = open(sample_on_file, 'r')
filesize = os.path.getsize(filename) # in bytes
num_samples = filesize // 2 # uint16 = 2 bytes
digital_in = np.fromfile(fid, 'uint16', num_samples)
fid.close()
samp_on, = np.nonzero(digital_in[:-1] < digital_in[1:]) # Look for 0->1 transitions
samp_on = samp_on + 1 # Previous line returns indexes of 0s seen before spikes, but we want indexes of first spikes
if len(stimulus_presented_df) > len(samp_on):
print(f'Warning: Trimming MWorks files as ({len(stimulus_presented_df)} > {len(samp_on)})')
stimulus_presented_df = stimulus_presented_df[:len(samp_on)]
correct_fixation_df = correct_fixation_df[:len(samp_on)]
# samp_on = samp_on[:len(correct_fixation_df)] # If you have one extra stimulus event but not fixation, use this
assert len(samp_on) == len(stimulus_presented_df)
###########################################################################
# Read photodiode file
###########################################################################
fid = open(photodiode_file, 'r')
filesize = os.path.getsize(photodiode_file) # in bytes
num_samples = filesize // 2 # uint16 = 2 bytes
v = np.fromfile(fid, 'uint16', num_samples)
fid.close()
# Convert to volts (use this if the data file was generated by Recording Controller)
v = (v - 32768) * 0.0003125
# Detect rises in the oscillating photodiode signal
peaks, _ = find_peaks(v, height=0) # Find all peaks
peaks = np.asarray([p for p in peaks if v[p] > THRESHOLD]) # Apply threshold
photodiode_on = np.asarray([min(peaks[(peaks >= s) & (peaks < (s + 100_000))]) for s in samp_on])
def setUp(self):
with warnings.catch_warnings():
warnings.simplefilter('ignore', RuntimeWarning)
self.filename = tempfile.mktemp(suffix=self.file_extension)
self.fp = MWKFile(self.filename)
def setUp(self):
with warnings.catch_warnings():
warnings.simplefilter('ignore', RuntimeWarning)
self.filename = os.tempnam() + '.mwk'
self.fp = MWKFile(self.filename)
