id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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24,900 | wonambi-python/wonambi | wonambi/widgets/spectrum.py | Spectrum.create | def create(self):
"""Create empty scene for power spectrum."""
self.idx_chan = QComboBox()
self.idx_chan.activated.connect(self.display_window)
self.idx_fig = QGraphicsView(self)
self.idx_fig.scale(1, -1)
layout = QVBoxLayout()
layout.addWidget(self.idx_chan)
layout.addWidget(self.idx_fig)
self.setLayout(layout)
self.resizeEvent(None) | python | def create(self):
self.idx_chan = QComboBox()
self.idx_chan.activated.connect(self.display_window)
self.idx_fig = QGraphicsView(self)
self.idx_fig.scale(1, -1)
layout = QVBoxLayout()
layout.addWidget(self.idx_chan)
layout.addWidget(self.idx_fig)
self.setLayout(layout)
self.resizeEvent(None) | [
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24,901 | wonambi-python/wonambi | wonambi/widgets/spectrum.py | Spectrum.update | def update(self):
"""Add channel names to the combobox."""
self.idx_chan.clear()
for chan_name in self.parent.traces.chan:
self.idx_chan.addItem(chan_name)
if self.selected_chan is not None:
self.idx_chan.setCurrentIndex(self.selected_chan)
self.selected_chan = None | python | def update(self):
self.idx_chan.clear()
for chan_name in self.parent.traces.chan:
self.idx_chan.addItem(chan_name)
if self.selected_chan is not None:
self.idx_chan.setCurrentIndex(self.selected_chan)
self.selected_chan = None | [
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24,902 | wonambi-python/wonambi | wonambi/widgets/spectrum.py | Spectrum.display_window | def display_window(self):
"""Read the channel name from QComboBox and plot its spectrum.
This function is necessary it reads the data and it sends it to
self.display. When the user selects a smaller chunk of data from the
visible traces, then we don't need to call this function.
"""
if self.idx_chan.count() == 0:
self.update()
chan_name = self.idx_chan.currentText()
lg.debug('Power spectrum for channel ' + chan_name)
if chan_name:
trial = 0
data = self.parent.traces.data(trial=trial, chan=chan_name)
self.display(data)
else:
self.scene.clear() | python | def display_window(self):
if self.idx_chan.count() == 0:
self.update()
chan_name = self.idx_chan.currentText()
lg.debug('Power spectrum for channel ' + chan_name)
if chan_name:
trial = 0
data = self.parent.traces.data(trial=trial, chan=chan_name)
self.display(data)
else:
self.scene.clear() | [
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24,903 | wonambi-python/wonambi | wonambi/widgets/spectrum.py | Spectrum.display | def display(self, data):
"""Make graphicsitem for spectrum figure.
Parameters
----------
data : ndarray
1D vector containing the data only
This function can be called by self.display_window (which reads the
data for the selected channel) or by the mouse-events functions in
traces (which read chunks of data from the user-made selection).
"""
value = self.config.value
self.scene = QGraphicsScene(value['x_min'], value['y_min'],
value['x_max'] - value['x_min'],
value['y_max'] - value['y_min'])
self.idx_fig.setScene(self.scene)
self.add_grid()
self.resizeEvent(None)
s_freq = self.parent.traces.data.s_freq
f, Pxx = welch(data, fs=s_freq,
nperseg=int(min((s_freq, len(data))))) # force int
freq_limit = (value['x_min'] <= f) & (f <= value['x_max'])
if self.config.value['log']:
Pxx_to_plot = log(Pxx[freq_limit])
else:
Pxx_to_plot = Pxx[freq_limit]
self.scene.addPath(Path(f[freq_limit], Pxx_to_plot),
QPen(QColor(LINE_COLOR), LINE_WIDTH)) | python | def display(self, data):
value = self.config.value
self.scene = QGraphicsScene(value['x_min'], value['y_min'],
value['x_max'] - value['x_min'],
value['y_max'] - value['y_min'])
self.idx_fig.setScene(self.scene)
self.add_grid()
self.resizeEvent(None)
s_freq = self.parent.traces.data.s_freq
f, Pxx = welch(data, fs=s_freq,
nperseg=int(min((s_freq, len(data))))) # force int
freq_limit = (value['x_min'] <= f) & (f <= value['x_max'])
if self.config.value['log']:
Pxx_to_plot = log(Pxx[freq_limit])
else:
Pxx_to_plot = Pxx[freq_limit]
self.scene.addPath(Path(f[freq_limit], Pxx_to_plot),
QPen(QColor(LINE_COLOR), LINE_WIDTH)) | [
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Parameters
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data : ndarray
1D vector containing the data only
This function can be called by self.display_window (which reads the
data for the selected channel) or by the mouse-events functions in
traces (which read chunks of data from the user-made selection). | [
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24,904 | wonambi-python/wonambi | wonambi/widgets/spectrum.py | Spectrum.add_grid | def add_grid(self):
"""Add axis and ticks to figure.
Notes
-----
I know that visvis and pyqtgraphs can do this in much simpler way, but
those packages create too large a padding around the figure and this is
pretty fast.
"""
value = self.config.value
# X-AXIS
# x-bottom
self.scene.addLine(value['x_min'], value['y_min'],
value['x_min'], value['y_max'],
QPen(QColor(LINE_COLOR), LINE_WIDTH))
# at y = 0, dashed
self.scene.addLine(value['x_min'], 0,
value['x_max'], 0,
QPen(QColor(LINE_COLOR), LINE_WIDTH, Qt.DashLine))
# ticks on y-axis
y_high = int(floor(value['y_max']))
y_low = int(ceil(value['y_min']))
x_length = (value['x_max'] - value['x_min']) / value['x_tick']
for y in range(y_low, y_high):
self.scene.addLine(value['x_min'], y,
value['x_min'] + x_length, y,
QPen(QColor(LINE_COLOR), LINE_WIDTH))
# Y-AXIS
# left axis
self.scene.addLine(value['x_min'], value['y_min'],
value['x_max'], value['y_min'],
QPen(QColor(LINE_COLOR), LINE_WIDTH))
# larger ticks on x-axis every 10 Hz
x_high = int(floor(value['x_max']))
x_low = int(ceil(value['x_min']))
y_length = (value['y_max'] - value['y_min']) / value['y_tick']
for x in range(x_low, x_high, 10):
self.scene.addLine(x, value['y_min'],
x, value['y_min'] + y_length,
QPen(QColor(LINE_COLOR), LINE_WIDTH))
# smaller ticks on x-axis every 10 Hz
y_length = (value['y_max'] - value['y_min']) / value['y_tick'] / 2
for x in range(x_low, x_high, 5):
self.scene.addLine(x, value['y_min'],
x, value['y_min'] + y_length,
QPen(QColor(LINE_COLOR), LINE_WIDTH)) | python | def add_grid(self):
value = self.config.value
# X-AXIS
# x-bottom
self.scene.addLine(value['x_min'], value['y_min'],
value['x_min'], value['y_max'],
QPen(QColor(LINE_COLOR), LINE_WIDTH))
# at y = 0, dashed
self.scene.addLine(value['x_min'], 0,
value['x_max'], 0,
QPen(QColor(LINE_COLOR), LINE_WIDTH, Qt.DashLine))
# ticks on y-axis
y_high = int(floor(value['y_max']))
y_low = int(ceil(value['y_min']))
x_length = (value['x_max'] - value['x_min']) / value['x_tick']
for y in range(y_low, y_high):
self.scene.addLine(value['x_min'], y,
value['x_min'] + x_length, y,
QPen(QColor(LINE_COLOR), LINE_WIDTH))
# Y-AXIS
# left axis
self.scene.addLine(value['x_min'], value['y_min'],
value['x_max'], value['y_min'],
QPen(QColor(LINE_COLOR), LINE_WIDTH))
# larger ticks on x-axis every 10 Hz
x_high = int(floor(value['x_max']))
x_low = int(ceil(value['x_min']))
y_length = (value['y_max'] - value['y_min']) / value['y_tick']
for x in range(x_low, x_high, 10):
self.scene.addLine(x, value['y_min'],
x, value['y_min'] + y_length,
QPen(QColor(LINE_COLOR), LINE_WIDTH))
# smaller ticks on x-axis every 10 Hz
y_length = (value['y_max'] - value['y_min']) / value['y_tick'] / 2
for x in range(x_low, x_high, 5):
self.scene.addLine(x, value['y_min'],
x, value['y_min'] + y_length,
QPen(QColor(LINE_COLOR), LINE_WIDTH)) | [
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24,905 | wonambi-python/wonambi | wonambi/widgets/spectrum.py | Spectrum.resizeEvent | def resizeEvent(self, event):
"""Fit the whole scene in view.
Parameters
----------
event : instance of Qt.Event
not important
"""
value = self.config.value
self.idx_fig.fitInView(value['x_min'],
value['y_min'],
value['x_max'] - value['x_min'],
value['y_max'] - value['y_min']) | python | def resizeEvent(self, event):
value = self.config.value
self.idx_fig.fitInView(value['x_min'],
value['y_min'],
value['x_max'] - value['x_min'],
value['y_max'] - value['y_min']) | [
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24,906 | wonambi-python/wonambi | wonambi/widgets/spectrum.py | Spectrum.reset | def reset(self):
"""Reset widget as new"""
self.idx_chan.clear()
if self.scene is not None:
self.scene.clear()
self.scene = None | python | def reset(self):
self.idx_chan.clear()
if self.scene is not None:
self.scene.clear()
self.scene = None | [
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24,907 | wonambi-python/wonambi | wonambi/detect/slowwave.py | detect_Massimini2004 | def detect_Massimini2004(dat_orig, s_freq, time, opts):
"""Slow wave detection based on Massimini et al., 2004.
Parameters
----------
dat_orig : ndarray (dtype='float')
vector with the data for one channel
s_freq : float
sampling frequency
time : ndarray (dtype='float')
vector with the time points for each sample
opts : instance of 'DetectSlowWave'
'det_filt' : dict
parameters for 'butter',
'duration' : tuple of float
min and max duration of SW
'min_ptp' : float
min peak-to-peak amplitude
'trough_duration' : tuple of float
min and max duration of first half-wave (trough)
Returns
-------
list of dict
list of detected SWs
float
SW density, per 30-s epoch
References
----------
Massimini, M. et al. J Neurosci 24(31) 6862-70 (2004).
"""
if opts.invert:
dat_orig = -dat_orig
dat_det = transform_signal(dat_orig, s_freq, 'double_butter',
opts.det_filt)
above_zero = detect_events(dat_det, 'above_thresh', value=0.)
sw_in_chan = []
if above_zero is not None:
troughs = within_duration(above_zero, time, opts.trough_duration)
#lg.info('troughs within duration: ' + str(troughs.shape))
if troughs is not None:
troughs = select_peaks(dat_det, troughs, opts.max_trough_amp)
#lg.info('troughs deep enough: ' + str(troughs.shape))
if troughs is not None:
events = _add_halfwave(dat_det, troughs, s_freq, opts)
#lg.info('SWs high enough: ' + str(events.shape))
if len(events):
events = within_duration(events, time, opts.duration)
events = remove_straddlers(events, time, s_freq)
#lg.info('SWs within duration: ' + str(events.shape))
sw_in_chan = make_slow_waves(events, dat_det, time, s_freq)
if len(sw_in_chan) == 0:
lg.info('No slow wave found')
return sw_in_chan | python | def detect_Massimini2004(dat_orig, s_freq, time, opts):
if opts.invert:
dat_orig = -dat_orig
dat_det = transform_signal(dat_orig, s_freq, 'double_butter',
opts.det_filt)
above_zero = detect_events(dat_det, 'above_thresh', value=0.)
sw_in_chan = []
if above_zero is not None:
troughs = within_duration(above_zero, time, opts.trough_duration)
#lg.info('troughs within duration: ' + str(troughs.shape))
if troughs is not None:
troughs = select_peaks(dat_det, troughs, opts.max_trough_amp)
#lg.info('troughs deep enough: ' + str(troughs.shape))
if troughs is not None:
events = _add_halfwave(dat_det, troughs, s_freq, opts)
#lg.info('SWs high enough: ' + str(events.shape))
if len(events):
events = within_duration(events, time, opts.duration)
events = remove_straddlers(events, time, s_freq)
#lg.info('SWs within duration: ' + str(events.shape))
sw_in_chan = make_slow_waves(events, dat_det, time, s_freq)
if len(sw_in_chan) == 0:
lg.info('No slow wave found')
return sw_in_chan | [
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Parameters
----------
dat_orig : ndarray (dtype='float')
vector with the data for one channel
s_freq : float
sampling frequency
time : ndarray (dtype='float')
vector with the time points for each sample
opts : instance of 'DetectSlowWave'
'det_filt' : dict
parameters for 'butter',
'duration' : tuple of float
min and max duration of SW
'min_ptp' : float
min peak-to-peak amplitude
'trough_duration' : tuple of float
min and max duration of first half-wave (trough)
Returns
-------
list of dict
list of detected SWs
float
SW density, per 30-s epoch
References
----------
Massimini, M. et al. J Neurosci 24(31) 6862-70 (2004). | [
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24,908 | wonambi-python/wonambi | wonambi/detect/slowwave.py | select_peaks | def select_peaks(data, events, limit):
"""Check whether event satisfies amplitude limit.
Parameters
----------
data : ndarray (dtype='float')
vector with data
events : ndarray (dtype='int')
N x 2+ matrix with peak/trough in second position
limit : float
low and high limit for spindle duration
Returns
-------
ndarray (dtype='int')
N x 2+ matrix with peak/trough in second position
"""
selected = abs(data[events[:, 1]]) >= abs(limit)
return events[selected, :] | python | def select_peaks(data, events, limit):
selected = abs(data[events[:, 1]]) >= abs(limit)
return events[selected, :] | [
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limit : float
low and high limit for spindle duration
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24,909 | wonambi-python/wonambi | wonambi/detect/slowwave.py | make_slow_waves | def make_slow_waves(events, data, time, s_freq):
"""Create dict for each slow wave, based on events of time points.
Parameters
----------
events : ndarray (dtype='int')
N x 5 matrix with start, trough, zero, peak, end samples
data : ndarray (dtype='float')
vector with the data
time : ndarray (dtype='float')
vector with time points
s_freq : float
sampling frequency
Returns
-------
list of dict
list of all the SWs, with information about start,
trough_time, zero_time, peak_time, end, duration (s), trough_val,
peak_val, peak-to-peak amplitude (signal units), area_under_curve
(signal units * s)
"""
slow_waves = []
for ev in events:
one_sw = {'start': time[ev[0]],
'trough_time': time[ev[1]],
'zero_time': time[ev[2]],
'peak_time': time[ev[3]],
'end': time[ev[4] - 1],
'trough_val': data[ev[1]],
'peak_val': data[ev[3]],
'dur': (ev[4] - ev[0]) / s_freq,
'ptp': abs(ev[3] - ev[1])
}
slow_waves.append(one_sw)
return slow_waves | python | def make_slow_waves(events, data, time, s_freq):
slow_waves = []
for ev in events:
one_sw = {'start': time[ev[0]],
'trough_time': time[ev[1]],
'zero_time': time[ev[2]],
'peak_time': time[ev[3]],
'end': time[ev[4] - 1],
'trough_val': data[ev[1]],
'peak_val': data[ev[3]],
'dur': (ev[4] - ev[0]) / s_freq,
'ptp': abs(ev[3] - ev[1])
}
slow_waves.append(one_sw)
return slow_waves | [
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Parameters
----------
events : ndarray (dtype='int')
N x 5 matrix with start, trough, zero, peak, end samples
data : ndarray (dtype='float')
vector with the data
time : ndarray (dtype='float')
vector with time points
s_freq : float
sampling frequency
Returns
-------
list of dict
list of all the SWs, with information about start,
trough_time, zero_time, peak_time, end, duration (s), trough_val,
peak_val, peak-to-peak amplitude (signal units), area_under_curve
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24,910 | wonambi-python/wonambi | wonambi/detect/slowwave.py | _add_halfwave | def _add_halfwave(data, events, s_freq, opts):
"""Find the next zero crossing and the intervening peak and add
them to events. If no zero found before max_dur, event is discarded. If
peak-to-peak is smaller than min_ptp, the event is discarded.
Parameters
----------
data : ndarray (dtype='float')
vector with the data
events : ndarray (dtype='int')
N x 3 matrix with start, trough, end samples
s_freq : float
sampling frequency
opts : instance of 'DetectSlowWave'
'duration' : tuple of float
min and max duration of SW
'min_ptp' : float
min peak-to-peak amplitude
Returns
-------
ndarray (dtype='int')
N x 5 matrix with start, trough, - to + zero crossing, peak,
and end samples
"""
max_dur = opts.duration[1]
if max_dur is None:
max_dur = MAXIMUM_DURATION
window = int(s_freq * max_dur)
peak_and_end = zeros((events.shape[0], 2), dtype='int')
events = concatenate((events, peak_and_end), axis=1)
selected = []
for ev in events:
zero_crossings = where(diff(sign(data[ev[2]:ev[0] + window])))[0]
if zero_crossings.any():
ev[4] = ev[2] + zero_crossings[0] + 1
#lg.info('0cross is at ' + str(ev[4]))
else:
selected.append(False)
#lg.info('no 0cross, rejected')
continue
ev[3] = ev[2] + argmin(data[ev[2]:ev[4]])
if abs(data[ev[1]] - data[ev[3]]) < opts.min_ptp:
selected.append(False)
#lg.info('ptp too low, rejected: ' + str(abs(data[ev[1]] - data[ev[3]])))
continue
selected.append(True)
#lg.info('SW checks out, accepted! ptp is ' + str(abs(data[ev[1]] - data[ev[3]])))
return events[selected, :] | python | def _add_halfwave(data, events, s_freq, opts):
max_dur = opts.duration[1]
if max_dur is None:
max_dur = MAXIMUM_DURATION
window = int(s_freq * max_dur)
peak_and_end = zeros((events.shape[0], 2), dtype='int')
events = concatenate((events, peak_and_end), axis=1)
selected = []
for ev in events:
zero_crossings = where(diff(sign(data[ev[2]:ev[0] + window])))[0]
if zero_crossings.any():
ev[4] = ev[2] + zero_crossings[0] + 1
#lg.info('0cross is at ' + str(ev[4]))
else:
selected.append(False)
#lg.info('no 0cross, rejected')
continue
ev[3] = ev[2] + argmin(data[ev[2]:ev[4]])
if abs(data[ev[1]] - data[ev[3]]) < opts.min_ptp:
selected.append(False)
#lg.info('ptp too low, rejected: ' + str(abs(data[ev[1]] - data[ev[3]])))
continue
selected.append(True)
#lg.info('SW checks out, accepted! ptp is ' + str(abs(data[ev[1]] - data[ev[3]])))
return events[selected, :] | [
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data : ndarray (dtype='float')
vector with the data
events : ndarray (dtype='int')
N x 3 matrix with start, trough, end samples
s_freq : float
sampling frequency
opts : instance of 'DetectSlowWave'
'duration' : tuple of float
min and max duration of SW
'min_ptp' : float
min peak-to-peak amplitude
Returns
-------
ndarray (dtype='int')
N x 5 matrix with start, trough, - to + zero crossing, peak,
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24,911 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.create | def create(self):
"""Create the widget layout with all the annotations."""
""" ------ MARKERS ------ """
tab0 = QTableWidget()
self.idx_marker = tab0
tab0.setColumnCount(3)
tab0.horizontalHeader().setStretchLastSection(True)
tab0.setSelectionBehavior(QAbstractItemView.SelectRows)
tab0.setEditTriggers(QAbstractItemView.NoEditTriggers)
go_to_marker = lambda r, c: self.go_to_marker(r, c, 'dataset')
tab0.cellDoubleClicked.connect(go_to_marker)
tab0.setHorizontalHeaderLabels(['Start', 'Duration', 'Text'])
""" ------ SUMMARY ------ """
tab1 = QWidget()
self.idx_eventtype = QComboBox(self)
self.idx_stage = QComboBox(self)
self.idx_stage.activated.connect(self.get_sleepstage)
self.idx_quality = QComboBox(self)
self.idx_quality.activated.connect(self.get_quality)
self.idx_annotations = QPushButton('Load Annotation File...')
self.idx_annotations.clicked.connect(self.load_annot)
self.idx_rater = QLabel('')
b0 = QGroupBox('Info')
form = QFormLayout()
b0.setLayout(form)
form.addRow('File:', self.idx_annotations)
form.addRow('Rater:', self.idx_rater)
b1 = QGroupBox('Staging')
b2 = QGroupBox('Signal quality')
layout = QVBoxLayout()
layout.addWidget(b0)
layout.addWidget(b1)
layout.addWidget(b2)
self.idx_summary = layout
tab1.setLayout(layout)
""" ------ ANNOTATIONS ------ """
tab2 = QWidget()
tab_annot = QTableWidget()
self.idx_annot_list = tab_annot
delete_row = QPushButton('Delete')
delete_row.clicked.connect(self.delete_row)
scroll = QScrollArea(tab2)
scroll.setWidgetResizable(True)
evttype_group = QGroupBox('Event Types')
scroll.setWidget(evttype_group)
self.idx_eventtype_scroll = scroll
tab_annot.setColumnCount(5)
tab_annot.setHorizontalHeaderLabels(['Start', 'Duration', 'Text',
'Type', 'Channel'])
tab_annot.horizontalHeader().setStretchLastSection(True)
tab_annot.setSelectionBehavior(QAbstractItemView.SelectRows)
tab_annot.setEditTriggers(QAbstractItemView.NoEditTriggers)
go_to_annot = lambda r, c: self.go_to_marker(r, c, 'annot')
tab_annot.cellDoubleClicked.connect(go_to_annot)
tab_annot.cellDoubleClicked.connect(self.reset_current_row)
layout = QVBoxLayout()
layout.addWidget(self.idx_eventtype_scroll, stretch=1)
layout.addWidget(self.idx_annot_list)
layout.addWidget(delete_row)
tab2.setLayout(layout)
""" ------ TABS ------ """
self.addTab(tab0, 'Markers')
self.addTab(tab1, 'Summary') # disable
self.addTab(tab2, 'Annotations') | python | def create(self):
""" ------ MARKERS ------ """
tab0 = QTableWidget()
self.idx_marker = tab0
tab0.setColumnCount(3)
tab0.horizontalHeader().setStretchLastSection(True)
tab0.setSelectionBehavior(QAbstractItemView.SelectRows)
tab0.setEditTriggers(QAbstractItemView.NoEditTriggers)
go_to_marker = lambda r, c: self.go_to_marker(r, c, 'dataset')
tab0.cellDoubleClicked.connect(go_to_marker)
tab0.setHorizontalHeaderLabels(['Start', 'Duration', 'Text'])
""" ------ SUMMARY ------ """
tab1 = QWidget()
self.idx_eventtype = QComboBox(self)
self.idx_stage = QComboBox(self)
self.idx_stage.activated.connect(self.get_sleepstage)
self.idx_quality = QComboBox(self)
self.idx_quality.activated.connect(self.get_quality)
self.idx_annotations = QPushButton('Load Annotation File...')
self.idx_annotations.clicked.connect(self.load_annot)
self.idx_rater = QLabel('')
b0 = QGroupBox('Info')
form = QFormLayout()
b0.setLayout(form)
form.addRow('File:', self.idx_annotations)
form.addRow('Rater:', self.idx_rater)
b1 = QGroupBox('Staging')
b2 = QGroupBox('Signal quality')
layout = QVBoxLayout()
layout.addWidget(b0)
layout.addWidget(b1)
layout.addWidget(b2)
self.idx_summary = layout
tab1.setLayout(layout)
""" ------ ANNOTATIONS ------ """
tab2 = QWidget()
tab_annot = QTableWidget()
self.idx_annot_list = tab_annot
delete_row = QPushButton('Delete')
delete_row.clicked.connect(self.delete_row)
scroll = QScrollArea(tab2)
scroll.setWidgetResizable(True)
evttype_group = QGroupBox('Event Types')
scroll.setWidget(evttype_group)
self.idx_eventtype_scroll = scroll
tab_annot.setColumnCount(5)
tab_annot.setHorizontalHeaderLabels(['Start', 'Duration', 'Text',
'Type', 'Channel'])
tab_annot.horizontalHeader().setStretchLastSection(True)
tab_annot.setSelectionBehavior(QAbstractItemView.SelectRows)
tab_annot.setEditTriggers(QAbstractItemView.NoEditTriggers)
go_to_annot = lambda r, c: self.go_to_marker(r, c, 'annot')
tab_annot.cellDoubleClicked.connect(go_to_annot)
tab_annot.cellDoubleClicked.connect(self.reset_current_row)
layout = QVBoxLayout()
layout.addWidget(self.idx_eventtype_scroll, stretch=1)
layout.addWidget(self.idx_annot_list)
layout.addWidget(delete_row)
tab2.setLayout(layout)
""" ------ TABS ------ """
self.addTab(tab0, 'Markers')
self.addTab(tab1, 'Summary') # disable
self.addTab(tab2, 'Annotations') | [
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24,912 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.update_notes | def update_notes(self, xml_file, new=False):
"""Update information about the sleep scoring.
Parameters
----------
xml_file : str
file of the new or existing .xml file
new : bool
if the xml_file should be a new file or an existing one
"""
if new:
create_empty_annotations(xml_file, self.parent.info.dataset)
self.annot = Annotations(xml_file)
else:
self.annot = Annotations(xml_file)
self.enable_events()
self.parent.create_menubar()
self.idx_stage.clear()
for one_stage in STAGE_NAME:
self.idx_stage.addItem(one_stage)
self.idx_stage.setCurrentIndex(-1)
self.idx_quality.clear()
for one_qual in QUALIFIERS:
self.idx_quality.addItem(one_qual)
self.idx_quality.setCurrentIndex(-1)
w1 = self.idx_summary.takeAt(1).widget()
w2 = self.idx_summary.takeAt(1).widget()
self.idx_summary.removeWidget(w1)
self.idx_summary.removeWidget(w2)
w1.deleteLater()
w2.deleteLater()
b1 = QGroupBox('Staging')
layout = QFormLayout()
for one_stage in STAGE_NAME:
layout.addRow(one_stage, QLabel(''))
b1.setLayout(layout)
self.idx_summary.addWidget(b1)
self.idx_stage_stats = layout
b2 = QGroupBox('Signal quality')
layout = QFormLayout()
for one_qual in QUALIFIERS:
layout.addRow(one_qual, QLabel(''))
b2.setLayout(layout)
self.idx_summary.addWidget(b2)
self.idx_qual_stats = layout
self.display_notes() | python | def update_notes(self, xml_file, new=False):
if new:
create_empty_annotations(xml_file, self.parent.info.dataset)
self.annot = Annotations(xml_file)
else:
self.annot = Annotations(xml_file)
self.enable_events()
self.parent.create_menubar()
self.idx_stage.clear()
for one_stage in STAGE_NAME:
self.idx_stage.addItem(one_stage)
self.idx_stage.setCurrentIndex(-1)
self.idx_quality.clear()
for one_qual in QUALIFIERS:
self.idx_quality.addItem(one_qual)
self.idx_quality.setCurrentIndex(-1)
w1 = self.idx_summary.takeAt(1).widget()
w2 = self.idx_summary.takeAt(1).widget()
self.idx_summary.removeWidget(w1)
self.idx_summary.removeWidget(w2)
w1.deleteLater()
w2.deleteLater()
b1 = QGroupBox('Staging')
layout = QFormLayout()
for one_stage in STAGE_NAME:
layout.addRow(one_stage, QLabel(''))
b1.setLayout(layout)
self.idx_summary.addWidget(b1)
self.idx_stage_stats = layout
b2 = QGroupBox('Signal quality')
layout = QFormLayout()
for one_qual in QUALIFIERS:
layout.addRow(one_qual, QLabel(''))
b2.setLayout(layout)
self.idx_summary.addWidget(b2)
self.idx_qual_stats = layout
self.display_notes() | [
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Parameters
----------
xml_file : str
file of the new or existing .xml file
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if the xml_file should be a new file or an existing one | [
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24,913 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.enable_events | def enable_events(self):
"""enable slow wave and spindle detection if both
annotations and channels are active.
"""
if self.annot is not None and self.parent.channels.groups:
self.action['spindle'].setEnabled(True)
self.action['slow_wave'].setEnabled(True)
self.action['analyze'].setEnabled(True)
else:
self.action['spindle'].setEnabled(False)
self.action['slow_wave'].setEnabled(False)
self.action['analyze'].setEnabled(False) | python | def enable_events(self):
if self.annot is not None and self.parent.channels.groups:
self.action['spindle'].setEnabled(True)
self.action['slow_wave'].setEnabled(True)
self.action['analyze'].setEnabled(True)
else:
self.action['spindle'].setEnabled(False)
self.action['slow_wave'].setEnabled(False)
self.action['analyze'].setEnabled(False) | [
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24,914 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.display_notes | def display_notes(self):
"""Display information about scores and raters.
"""
if self.annot is not None:
short_xml_file = short_strings(basename(self.annot.xml_file))
self.idx_annotations.setText(short_xml_file)
# if annotations were loaded without dataset
if self.parent.overview.scene is None:
self.parent.overview.update()
if not self.annot.raters:
self.new_rater()
self.idx_rater.setText(self.annot.current_rater)
self.display_eventtype()
self.update_annotations()
self.display_stats()
self.epoch_length = self.annot.epoch_length | python | def display_notes(self):
if self.annot is not None:
short_xml_file = short_strings(basename(self.annot.xml_file))
self.idx_annotations.setText(short_xml_file)
# if annotations were loaded without dataset
if self.parent.overview.scene is None:
self.parent.overview.update()
if not self.annot.raters:
self.new_rater()
self.idx_rater.setText(self.annot.current_rater)
self.display_eventtype()
self.update_annotations()
self.display_stats()
self.epoch_length = self.annot.epoch_length | [
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24,915 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.display_stats | def display_stats(self):
"""Display summary statistics about duration in each stage."""
for i, one_stage in enumerate(STAGE_NAME):
second_in_stage = self.annot.time_in_stage(one_stage)
time_in_stage = str(timedelta(seconds=second_in_stage))
label = self.idx_stage_stats.itemAt(i,
QFormLayout.FieldRole).widget()
label.setText(time_in_stage)
for i, one_qual in enumerate(QUALIFIERS):
second_in_qual = self.annot.time_in_stage(one_qual, attr='quality')
time_in_qual = str(timedelta(seconds=second_in_qual))
label = self.idx_qual_stats.itemAt(i,
QFormLayout.FieldRole).widget()
label.setText(time_in_qual) | python | def display_stats(self):
for i, one_stage in enumerate(STAGE_NAME):
second_in_stage = self.annot.time_in_stage(one_stage)
time_in_stage = str(timedelta(seconds=second_in_stage))
label = self.idx_stage_stats.itemAt(i,
QFormLayout.FieldRole).widget()
label.setText(time_in_stage)
for i, one_qual in enumerate(QUALIFIERS):
second_in_qual = self.annot.time_in_stage(one_qual, attr='quality')
time_in_qual = str(timedelta(seconds=second_in_qual))
label = self.idx_qual_stats.itemAt(i,
QFormLayout.FieldRole).widget()
label.setText(time_in_qual) | [
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24,916 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.add_bookmark | def add_bookmark(self, time):
"""Run this function when user adds a new bookmark.
Parameters
----------
time : tuple of float
start and end of the new bookmark, in s
"""
if self.annot is None: # remove if buttons are disabled
msg = 'No score file loaded'
lg.debug(msg)
error_dialog = QErrorMessage()
error_dialog.setWindowTitle('Error adding bookmark')
error_dialog.showMessage(msg)
error_dialog.exec()
return
answer = QInputDialog.getText(self, 'New Bookmark',
'Enter bookmark\'s name')
if answer[1]:
name = answer[0]
self.annot.add_bookmark(name, time)
lg.info('Added Bookmark ' + name + 'at ' + str(time))
self.update_annotations() | python | def add_bookmark(self, time):
if self.annot is None: # remove if buttons are disabled
msg = 'No score file loaded'
lg.debug(msg)
error_dialog = QErrorMessage()
error_dialog.setWindowTitle('Error adding bookmark')
error_dialog.showMessage(msg)
error_dialog.exec()
return
answer = QInputDialog.getText(self, 'New Bookmark',
'Enter bookmark\'s name')
if answer[1]:
name = answer[0]
self.annot.add_bookmark(name, time)
lg.info('Added Bookmark ' + name + 'at ' + str(time))
self.update_annotations() | [
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Parameters
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time : tuple of float
start and end of the new bookmark, in s | [
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24,917 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.remove_bookmark | def remove_bookmark(self, time):
"""User removes bookmark.
Parameters
----------
time : tuple of float
start and end of the new bookmark, in s
"""
self.annot.remove_bookmark(time=time)
self.update_annotations() | python | def remove_bookmark(self, time):
self.annot.remove_bookmark(time=time)
self.update_annotations() | [
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time : tuple of float
start and end of the new bookmark, in s | [
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24,918 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.update_dataset_marker | def update_dataset_marker(self):
"""Update markers which are in the dataset. It always updates the list
of events. Depending on the settings, it might add the markers to
overview and traces.
"""
start_time = self.parent.overview.start_time
markers = []
if self.parent.info.markers is not None:
markers = self.parent.info.markers
self.idx_marker.clearContents()
self.idx_marker.setRowCount(len(markers))
for i, mrk in enumerate(markers):
abs_time = (start_time +
timedelta(seconds=mrk['start'])).strftime('%H:%M:%S')
dur = timedelta(seconds=mrk['end'] - mrk['start'])
duration = '{0:02d}.{1:03d}'.format(dur.seconds,
round(dur.microseconds / 1000))
item_time = QTableWidgetItem(abs_time)
item_duration = QTableWidgetItem(duration)
item_name = QTableWidgetItem(mrk['name'])
color = self.parent.value('marker_color')
item_time.setForeground(QColor(color))
item_duration.setForeground(QColor(color))
item_name.setForeground(QColor(color))
self.idx_marker.setItem(i, 0, item_time)
self.idx_marker.setItem(i, 1, item_duration)
self.idx_marker.setItem(i, 2, item_name)
# store information about the time as list (easy to access)
marker_start = [mrk['start'] for mrk in markers]
marker_end = [mrk['end'] for mrk in markers]
self.idx_marker.setProperty('start', marker_start)
self.idx_marker.setProperty('end', marker_end)
if self.parent.traces.data is not None:
self.parent.traces.display()
self.parent.overview.display_markers() | python | def update_dataset_marker(self):
start_time = self.parent.overview.start_time
markers = []
if self.parent.info.markers is not None:
markers = self.parent.info.markers
self.idx_marker.clearContents()
self.idx_marker.setRowCount(len(markers))
for i, mrk in enumerate(markers):
abs_time = (start_time +
timedelta(seconds=mrk['start'])).strftime('%H:%M:%S')
dur = timedelta(seconds=mrk['end'] - mrk['start'])
duration = '{0:02d}.{1:03d}'.format(dur.seconds,
round(dur.microseconds / 1000))
item_time = QTableWidgetItem(abs_time)
item_duration = QTableWidgetItem(duration)
item_name = QTableWidgetItem(mrk['name'])
color = self.parent.value('marker_color')
item_time.setForeground(QColor(color))
item_duration.setForeground(QColor(color))
item_name.setForeground(QColor(color))
self.idx_marker.setItem(i, 0, item_time)
self.idx_marker.setItem(i, 1, item_duration)
self.idx_marker.setItem(i, 2, item_name)
# store information about the time as list (easy to access)
marker_start = [mrk['start'] for mrk in markers]
marker_end = [mrk['end'] for mrk in markers]
self.idx_marker.setProperty('start', marker_start)
self.idx_marker.setProperty('end', marker_end)
if self.parent.traces.data is not None:
self.parent.traces.display()
self.parent.overview.display_markers() | [
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24,919 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.display_eventtype | def display_eventtype(self):
"""Read the list of event types in the annotations and update widgets.
"""
if self.annot is not None:
event_types = sorted(self.annot.event_types, key=str.lower)
else:
event_types = []
self.idx_eventtype.clear()
evttype_group = QGroupBox('Event Types')
layout = QVBoxLayout()
evttype_group.setLayout(layout)
self.check_all_eventtype = check_all = QCheckBox('All event types')
check_all.setCheckState(Qt.Checked)
check_all.clicked.connect(self.toggle_eventtype)
layout.addWidget(check_all)
self.idx_eventtype_list = []
for one_eventtype in event_types:
self.idx_eventtype.addItem(one_eventtype)
item = QCheckBox(one_eventtype)
layout.addWidget(item)
item.setCheckState(Qt.Checked)
item.stateChanged.connect(self.update_annotations)
item.stateChanged.connect(self.toggle_check_all_eventtype)
self.idx_eventtype_list.append(item)
self.idx_eventtype_scroll.setWidget(evttype_group) | python | def display_eventtype(self):
if self.annot is not None:
event_types = sorted(self.annot.event_types, key=str.lower)
else:
event_types = []
self.idx_eventtype.clear()
evttype_group = QGroupBox('Event Types')
layout = QVBoxLayout()
evttype_group.setLayout(layout)
self.check_all_eventtype = check_all = QCheckBox('All event types')
check_all.setCheckState(Qt.Checked)
check_all.clicked.connect(self.toggle_eventtype)
layout.addWidget(check_all)
self.idx_eventtype_list = []
for one_eventtype in event_types:
self.idx_eventtype.addItem(one_eventtype)
item = QCheckBox(one_eventtype)
layout.addWidget(item)
item.setCheckState(Qt.Checked)
item.stateChanged.connect(self.update_annotations)
item.stateChanged.connect(self.toggle_check_all_eventtype)
self.idx_eventtype_list.append(item)
self.idx_eventtype_scroll.setWidget(evttype_group) | [
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24,920 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.toggle_eventtype | def toggle_eventtype(self):
"""Check or uncheck all event types in event type scroll."""
check = self.check_all_eventtype.isChecked()
for btn in self.idx_eventtype_list:
btn.setChecked(check) | python | def toggle_eventtype(self):
check = self.check_all_eventtype.isChecked()
for btn in self.idx_eventtype_list:
btn.setChecked(check) | [
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24,921 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.toggle_check_all_eventtype | def toggle_check_all_eventtype(self):
"""Check 'All' if all event types are checked in event type scroll."""
checklist = asarray([btn.isChecked for btn in self.idx_eventtype_list])
if not checklist.all():
self.check_all_eventtype.setChecked(False) | python | def toggle_check_all_eventtype(self):
checklist = asarray([btn.isChecked for btn in self.idx_eventtype_list])
if not checklist.all():
self.check_all_eventtype.setChecked(False) | [
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24,922 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.get_selected_events | def get_selected_events(self, time_selection=None):
"""Returns which events are present in one time window.
Parameters
----------
time_selection : tuple of float
start and end of the window of interest
Returns
-------
list of dict
list of events in the window of interest
"""
events = []
for checkbox in self.idx_eventtype_list:
if checkbox.checkState() == Qt.Checked:
events.extend(self.annot.get_events(name=checkbox.text(),
time=time_selection))
return events | python | def get_selected_events(self, time_selection=None):
events = []
for checkbox in self.idx_eventtype_list:
if checkbox.checkState() == Qt.Checked:
events.extend(self.annot.get_events(name=checkbox.text(),
time=time_selection))
return events | [
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Parameters
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time_selection : tuple of float
start and end of the window of interest
Returns
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24,923 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.update_annotations | def update_annotations(self):
"""Update annotations made by the user, including bookmarks and events.
Depending on the settings, it might add the bookmarks to overview and
traces.
"""
start_time = self.parent.overview.start_time
if self.parent.notes.annot is None:
all_annot = []
else:
bookmarks = self.parent.notes.annot.get_bookmarks()
events = self.get_selected_events()
all_annot = bookmarks + events
all_annot = sorted(all_annot, key=lambda x: x['start'])
self.idx_annot_list.clearContents()
self.idx_annot_list.setRowCount(len(all_annot))
for i, mrk in enumerate(all_annot):
abs_time = (start_time +
timedelta(seconds=mrk['start'])).strftime('%H:%M:%S')
dur = timedelta(seconds=mrk['end'] - mrk['start'])
duration = '{0:02d}.{1:03d}'.format(dur.seconds,
round(dur.microseconds / 1000))
item_time = QTableWidgetItem(abs_time)
item_duration = QTableWidgetItem(duration)
item_name = QTableWidgetItem(mrk['name'])
if mrk in bookmarks:
item_type = QTableWidgetItem('bookmark')
color = self.parent.value('annot_bookmark_color')
else:
item_type = QTableWidgetItem('event')
color = convert_name_to_color(mrk['name'])
chan = mrk['chan']
if isinstance(chan, (tuple, list)):
chan = ', '.join(chan)
item_chan = QTableWidgetItem(chan)
item_time.setForeground(QColor(color))
item_duration.setForeground(QColor(color))
item_name.setForeground(QColor(color))
item_type.setForeground(QColor(color))
item_chan.setForeground(QColor(color))
self.idx_annot_list.setItem(i, 0, item_time)
self.idx_annot_list.setItem(i, 1, item_duration)
self.idx_annot_list.setItem(i, 2, item_name)
self.idx_annot_list.setItem(i, 3, item_type)
self.idx_annot_list.setItem(i, 4, item_chan)
# store information about the time as list (easy to access)
annot_start = [ann['start'] for ann in all_annot]
annot_end = [ann['end'] for ann in all_annot]
annot_name = [ann['name'] for ann in all_annot]
self.idx_annot_list.setProperty('start', annot_start)
self.idx_annot_list.setProperty('end', annot_end)
self.idx_annot_list.setProperty('name', annot_name)
if self.parent.traces.data is not None:
self.parent.traces.display_annotations()
self.parent.overview.display_annotations() | python | def update_annotations(self):
start_time = self.parent.overview.start_time
if self.parent.notes.annot is None:
all_annot = []
else:
bookmarks = self.parent.notes.annot.get_bookmarks()
events = self.get_selected_events()
all_annot = bookmarks + events
all_annot = sorted(all_annot, key=lambda x: x['start'])
self.idx_annot_list.clearContents()
self.idx_annot_list.setRowCount(len(all_annot))
for i, mrk in enumerate(all_annot):
abs_time = (start_time +
timedelta(seconds=mrk['start'])).strftime('%H:%M:%S')
dur = timedelta(seconds=mrk['end'] - mrk['start'])
duration = '{0:02d}.{1:03d}'.format(dur.seconds,
round(dur.microseconds / 1000))
item_time = QTableWidgetItem(abs_time)
item_duration = QTableWidgetItem(duration)
item_name = QTableWidgetItem(mrk['name'])
if mrk in bookmarks:
item_type = QTableWidgetItem('bookmark')
color = self.parent.value('annot_bookmark_color')
else:
item_type = QTableWidgetItem('event')
color = convert_name_to_color(mrk['name'])
chan = mrk['chan']
if isinstance(chan, (tuple, list)):
chan = ', '.join(chan)
item_chan = QTableWidgetItem(chan)
item_time.setForeground(QColor(color))
item_duration.setForeground(QColor(color))
item_name.setForeground(QColor(color))
item_type.setForeground(QColor(color))
item_chan.setForeground(QColor(color))
self.idx_annot_list.setItem(i, 0, item_time)
self.idx_annot_list.setItem(i, 1, item_duration)
self.idx_annot_list.setItem(i, 2, item_name)
self.idx_annot_list.setItem(i, 3, item_type)
self.idx_annot_list.setItem(i, 4, item_chan)
# store information about the time as list (easy to access)
annot_start = [ann['start'] for ann in all_annot]
annot_end = [ann['end'] for ann in all_annot]
annot_name = [ann['name'] for ann in all_annot]
self.idx_annot_list.setProperty('start', annot_start)
self.idx_annot_list.setProperty('end', annot_end)
self.idx_annot_list.setProperty('name', annot_name)
if self.parent.traces.data is not None:
self.parent.traces.display_annotations()
self.parent.overview.display_annotations() | [
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24,924 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.delete_row | def delete_row(self):
"""Delete bookmarks or event from annotations, based on row."""
sel_model = self.idx_annot_list.selectionModel()
for row in sel_model.selectedRows():
i = row.row()
start = self.idx_annot_list.property('start')[i]
end = self.idx_annot_list.property('end')[i]
name = self.idx_annot_list.item(i, 2).text()
marker_event = self.idx_annot_list.item(i, 3).text()
if marker_event == 'bookmark':
self.annot.remove_bookmark(name=name, time=(start, end))
else:
self.annot.remove_event(name=name, time=(start, end))
highlight = self.parent.traces.highlight
if highlight:
self.parent.traces.scene.removeItem(highlight)
highlight = None
self.parent.traces.event_sel = None
self.update_annotations() | python | def delete_row(self):
sel_model = self.idx_annot_list.selectionModel()
for row in sel_model.selectedRows():
i = row.row()
start = self.idx_annot_list.property('start')[i]
end = self.idx_annot_list.property('end')[i]
name = self.idx_annot_list.item(i, 2).text()
marker_event = self.idx_annot_list.item(i, 3).text()
if marker_event == 'bookmark':
self.annot.remove_bookmark(name=name, time=(start, end))
else:
self.annot.remove_event(name=name, time=(start, end))
highlight = self.parent.traces.highlight
if highlight:
self.parent.traces.scene.removeItem(highlight)
highlight = None
self.parent.traces.event_sel = None
self.update_annotations() | [
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24,925 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.go_to_marker | def go_to_marker(self, row, col, table_type):
"""Move to point in time marked by the marker.
Parameters
----------
row : QtCore.int
column : QtCore.int
table_type : str
'dataset' table or 'annot' table, it works on either
"""
if table_type == 'dataset':
marker_time = self.idx_marker.property('start')[row]
marker_end_time = self.idx_marker.property('end')[row]
else:
marker_time = self.idx_annot_list.property('start')[row]
marker_end_time = self.idx_annot_list.property('end')[row]
window_length = self.parent.value('window_length')
if self.parent.traces.action['centre_event'].isChecked():
window_start = (marker_time + marker_end_time - window_length) / 2
else:
window_start = floor(marker_time / window_length) * window_length
self.parent.overview.update_position(window_start)
if table_type == 'annot':
for annot in self.parent.traces.idx_annot:
if annot.marker.x() == marker_time:
self.parent.traces.highlight_event(annot)
break | python | def go_to_marker(self, row, col, table_type):
if table_type == 'dataset':
marker_time = self.idx_marker.property('start')[row]
marker_end_time = self.idx_marker.property('end')[row]
else:
marker_time = self.idx_annot_list.property('start')[row]
marker_end_time = self.idx_annot_list.property('end')[row]
window_length = self.parent.value('window_length')
if self.parent.traces.action['centre_event'].isChecked():
window_start = (marker_time + marker_end_time - window_length) / 2
else:
window_start = floor(marker_time / window_length) * window_length
self.parent.overview.update_position(window_start)
if table_type == 'annot':
for annot in self.parent.traces.idx_annot:
if annot.marker.x() == marker_time:
self.parent.traces.highlight_event(annot)
break | [
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24,926 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.get_sleepstage | def get_sleepstage(self, stage_idx=None):
"""Score the sleep stage, using shortcuts or combobox."""
if self.annot is None: # remove if buttons are disabled
error_dialog = QErrorMessage()
error_dialog.setWindowTitle('Error getting sleep stage')
error_dialog.showMessage('No score file loaded')
error_dialog.exec()
return
window_start = self.parent.value('window_start')
window_length = self.parent.value('window_length')
if window_length != self.epoch_length:
msg = ('Zoom to ' + str(self.epoch_length) + ' (epoch length) ' +
'for sleep scoring.')
error_dialog = QErrorMessage()
error_dialog.setWindowTitle('Error getting sleep stage')
error_dialog.showMessage(msg)
error_dialog.exec()
lg.debug(msg)
return
try:
self.annot.set_stage_for_epoch(window_start,
STAGE_NAME[stage_idx])
except KeyError:
msg = ('The start of the window does not correspond to any epoch ' +
'in sleep scoring file.\n\n'
'Switch to the appropriate window length in View, then use '
'Navigation --> Line Up with Epoch to line up the window.')
error_dialog = QErrorMessage()
error_dialog.setWindowTitle('Error getting sleep stage')
error_dialog.showMessage(msg)
error_dialog.exec()
lg.debug(msg)
else:
lg.debug('User staged ' + str(window_start) + ' as ' +
STAGE_NAME[stage_idx])
self.set_stage_index()
self.parent.overview.mark_stages(window_start, window_length,
STAGE_NAME[stage_idx])
self.display_stats()
self.parent.traces.page_next() | python | def get_sleepstage(self, stage_idx=None):
if self.annot is None: # remove if buttons are disabled
error_dialog = QErrorMessage()
error_dialog.setWindowTitle('Error getting sleep stage')
error_dialog.showMessage('No score file loaded')
error_dialog.exec()
return
window_start = self.parent.value('window_start')
window_length = self.parent.value('window_length')
if window_length != self.epoch_length:
msg = ('Zoom to ' + str(self.epoch_length) + ' (epoch length) ' +
'for sleep scoring.')
error_dialog = QErrorMessage()
error_dialog.setWindowTitle('Error getting sleep stage')
error_dialog.showMessage(msg)
error_dialog.exec()
lg.debug(msg)
return
try:
self.annot.set_stage_for_epoch(window_start,
STAGE_NAME[stage_idx])
except KeyError:
msg = ('The start of the window does not correspond to any epoch ' +
'in sleep scoring file.\n\n'
'Switch to the appropriate window length in View, then use '
'Navigation --> Line Up with Epoch to line up the window.')
error_dialog = QErrorMessage()
error_dialog.setWindowTitle('Error getting sleep stage')
error_dialog.showMessage(msg)
error_dialog.exec()
lg.debug(msg)
else:
lg.debug('User staged ' + str(window_start) + ' as ' +
STAGE_NAME[stage_idx])
self.set_stage_index()
self.parent.overview.mark_stages(window_start, window_length,
STAGE_NAME[stage_idx])
self.display_stats()
self.parent.traces.page_next() | [
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24,927 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.get_quality | def get_quality(self, qual_idx=None):
"""Get the signal qualifier, using shortcuts or combobox."""
if self.annot is None: # remove if buttons are disabled
msg = 'No score file loaded'
error_dialog = QErrorMessage()
error_dialog.setWindowTitle('Error getting quality')
error_dialog.showMessage(msg)
error_dialog.exec()
lg.debug(msg)
return
window_start = self.parent.value('window_start')
window_length = self.parent.value('window_length')
try:
self.annot.set_stage_for_epoch(window_start,
QUALIFIERS[qual_idx],
attr='quality')
except KeyError:
msg = ('The start of the window does not correspond to any epoch ' +
'in sleep scoring file')
error_dialog = QErrorMessage()
error_dialog.setWindowTitle('Error getting quality')
error_dialog.showMessage(msg)
error_dialog.exec()
lg.debug(msg)
else:
lg.debug('User staged ' + str(window_start) + ' as ' +
QUALIFIERS[qual_idx])
self.set_quality_index()
self.parent.overview.mark_quality(window_start, window_length,
QUALIFIERS[qual_idx])
self.display_stats()
self.parent.traces.page_next() | python | def get_quality(self, qual_idx=None):
if self.annot is None: # remove if buttons are disabled
msg = 'No score file loaded'
error_dialog = QErrorMessage()
error_dialog.setWindowTitle('Error getting quality')
error_dialog.showMessage(msg)
error_dialog.exec()
lg.debug(msg)
return
window_start = self.parent.value('window_start')
window_length = self.parent.value('window_length')
try:
self.annot.set_stage_for_epoch(window_start,
QUALIFIERS[qual_idx],
attr='quality')
except KeyError:
msg = ('The start of the window does not correspond to any epoch ' +
'in sleep scoring file')
error_dialog = QErrorMessage()
error_dialog.setWindowTitle('Error getting quality')
error_dialog.showMessage(msg)
error_dialog.exec()
lg.debug(msg)
else:
lg.debug('User staged ' + str(window_start) + ' as ' +
QUALIFIERS[qual_idx])
self.set_quality_index()
self.parent.overview.mark_quality(window_start, window_length,
QUALIFIERS[qual_idx])
self.display_stats()
self.parent.traces.page_next() | [
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24,928 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.get_cycle_mrkr | def get_cycle_mrkr(self, end=False):
"""Mark cycle start or end.
Parameters
----------
end : bool
If True, marks a cycle end; otherwise, it's a cycle start
"""
if self.annot is None: # remove if buttons are disabled
self.parent.statusBar().showMessage('No score file loaded')
return
window_start = self.parent.value('window_start')
window_length = self.parent.value('window_length')
try:
self.annot.set_cycle_mrkr(window_start, end=end)
except KeyError:
msg = ('The start of the window does not correspond to any epoch '
'in sleep scoring file')
self.parent.statusBar().showMessage(msg)
lg.debug(msg)
else:
bound = 'start'
if end:
bound = 'end'
lg.info('User marked ' + str(window_start) + ' as cycle ' +
bound)
self.parent.overview.mark_cycles(window_start, window_length,
end=end) | python | def get_cycle_mrkr(self, end=False):
if self.annot is None: # remove if buttons are disabled
self.parent.statusBar().showMessage('No score file loaded')
return
window_start = self.parent.value('window_start')
window_length = self.parent.value('window_length')
try:
self.annot.set_cycle_mrkr(window_start, end=end)
except KeyError:
msg = ('The start of the window does not correspond to any epoch '
'in sleep scoring file')
self.parent.statusBar().showMessage(msg)
lg.debug(msg)
else:
bound = 'start'
if end:
bound = 'end'
lg.info('User marked ' + str(window_start) + ' as cycle ' +
bound)
self.parent.overview.mark_cycles(window_start, window_length,
end=end) | [
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24,929 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.remove_cycle_mrkr | def remove_cycle_mrkr(self):
"""Remove cycle marker."""
window_start = self.parent.value('window_start')
try:
self.annot.remove_cycle_mrkr(window_start)
except KeyError:
msg = ('The start of the window does not correspond to any cycle '
'marker in sleep scoring file')
self.parent.statusBar().showMessage(msg)
lg.debug(msg)
else:
lg.debug('User removed cycle marker at' + str(window_start))
#self.trace
self.parent.overview.update(reset=False)
self.parent.overview.display_annotations() | python | def remove_cycle_mrkr(self):
window_start = self.parent.value('window_start')
try:
self.annot.remove_cycle_mrkr(window_start)
except KeyError:
msg = ('The start of the window does not correspond to any cycle '
'marker in sleep scoring file')
self.parent.statusBar().showMessage(msg)
lg.debug(msg)
else:
lg.debug('User removed cycle marker at' + str(window_start))
#self.trace
self.parent.overview.update(reset=False)
self.parent.overview.display_annotations() | [
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24,930 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.clear_cycle_mrkrs | def clear_cycle_mrkrs(self, test=False):
"""Remove all cycle markers."""
if not test:
msgBox = QMessageBox(QMessageBox.Question, 'Clear Cycle Markers',
'Are you sure you want to remove all cycle '
'markers for this rater?')
msgBox.setStandardButtons(QMessageBox.Yes | QMessageBox.No)
msgBox.setDefaultButton(QMessageBox.Yes)
response = msgBox.exec_()
if response == QMessageBox.No:
return
self.annot.clear_cycles()
self.parent.overview.display()
self.parent.overview.display_annotations() | python | def clear_cycle_mrkrs(self, test=False):
if not test:
msgBox = QMessageBox(QMessageBox.Question, 'Clear Cycle Markers',
'Are you sure you want to remove all cycle '
'markers for this rater?')
msgBox.setStandardButtons(QMessageBox.Yes | QMessageBox.No)
msgBox.setDefaultButton(QMessageBox.Yes)
response = msgBox.exec_()
if response == QMessageBox.No:
return
self.annot.clear_cycles()
self.parent.overview.display()
self.parent.overview.display_annotations() | [
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24,931 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.set_stage_index | def set_stage_index(self):
"""Set the current stage in combobox."""
window_start = self.parent.value('window_start')
window_length = self.parent.value('window_length')
stage = self.annot.get_stage_for_epoch(window_start, window_length)
#lg.info('winstart: ' + str(window_start) + ', stage: ' + str(stage))
if stage is None:
self.idx_stage.setCurrentIndex(-1)
else:
self.idx_stage.setCurrentIndex(STAGE_NAME.index(stage)) | python | def set_stage_index(self):
window_start = self.parent.value('window_start')
window_length = self.parent.value('window_length')
stage = self.annot.get_stage_for_epoch(window_start, window_length)
#lg.info('winstart: ' + str(window_start) + ', stage: ' + str(stage))
if stage is None:
self.idx_stage.setCurrentIndex(-1)
else:
self.idx_stage.setCurrentIndex(STAGE_NAME.index(stage)) | [
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24,932 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.set_quality_index | def set_quality_index(self):
"""Set the current signal quality in combobox."""
window_start = self.parent.value('window_start')
window_length = self.parent.value('window_length')
qual = self.annot.get_stage_for_epoch(window_start, window_length,
attr='quality')
#lg.info('winstart: ' + str(window_start) + ', quality: ' + str(qual))
if qual is None:
self.idx_quality.setCurrentIndex(-1)
else:
self.idx_quality.setCurrentIndex(QUALIFIERS.index(qual)) | python | def set_quality_index(self):
window_start = self.parent.value('window_start')
window_length = self.parent.value('window_length')
qual = self.annot.get_stage_for_epoch(window_start, window_length,
attr='quality')
#lg.info('winstart: ' + str(window_start) + ', quality: ' + str(qual))
if qual is None:
self.idx_quality.setCurrentIndex(-1)
else:
self.idx_quality.setCurrentIndex(QUALIFIERS.index(qual)) | [
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24,933 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.markers_to_events | def markers_to_events(self, keep_name=False):
"""Copy all markers in dataset to event type. """
markers = self.parent.info.markers
if markers is None:
self.parent.statusBar.showMessage('No markers in dataset.')
return
if not keep_name:
name, ok = self.new_eventtype()
if not ok:
return
else:
name = None
self.annot.add_events(markers, name=name, chan='')
if keep_name:
self.display_eventtype()
n_eventtype = self.idx_eventtype.count()
self.idx_eventtype.setCurrentIndex(n_eventtype - 1)
self.update_annotations() | python | def markers_to_events(self, keep_name=False):
markers = self.parent.info.markers
if markers is None:
self.parent.statusBar.showMessage('No markers in dataset.')
return
if not keep_name:
name, ok = self.new_eventtype()
if not ok:
return
else:
name = None
self.annot.add_events(markers, name=name, chan='')
if keep_name:
self.display_eventtype()
n_eventtype = self.idx_eventtype.count()
self.idx_eventtype.setCurrentIndex(n_eventtype - 1)
self.update_annotations() | [
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24,934 | wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.reset | def reset(self):
"""Remove all annotations from window."""
self.idx_annotations.setText('Load Annotation File...')
self.idx_rater.setText('')
self.annot = None
self.dataset_markers = None
# remove dataset marker
self.idx_marker.clearContents()
self.idx_marker.setRowCount(0)
# remove summary statistics
w1 = self.idx_summary.takeAt(1).widget()
w2 = self.idx_summary.takeAt(1).widget()
self.idx_summary.removeWidget(w1)
self.idx_summary.removeWidget(w2)
w1.deleteLater()
w2.deleteLater()
b1 = QGroupBox('Staging')
b2 = QGroupBox('Signal quality')
self.idx_summary.addWidget(b1)
self.idx_summary.addWidget(b2)
# remove annotations
self.display_eventtype()
self.update_annotations()
self.parent.create_menubar() | python | def reset(self):
self.idx_annotations.setText('Load Annotation File...')
self.idx_rater.setText('')
self.annot = None
self.dataset_markers = None
# remove dataset marker
self.idx_marker.clearContents()
self.idx_marker.setRowCount(0)
# remove summary statistics
w1 = self.idx_summary.takeAt(1).widget()
w2 = self.idx_summary.takeAt(1).widget()
self.idx_summary.removeWidget(w1)
self.idx_summary.removeWidget(w2)
w1.deleteLater()
w2.deleteLater()
b1 = QGroupBox('Staging')
b2 = QGroupBox('Signal quality')
self.idx_summary.addWidget(b1)
self.idx_summary.addWidget(b2)
# remove annotations
self.display_eventtype()
self.update_annotations()
self.parent.create_menubar() | [
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24,935 | wonambi-python/wonambi | wonambi/widgets/notes.py | MergeDialog.update_event_types | def update_event_types(self):
"""Update event types in event type box."""
self.idx_evt_type.clear()
self.idx_evt_type.setSelectionMode(QAbstractItemView.ExtendedSelection)
event_types = sorted(self.parent.notes.annot.event_types,
key=str.lower)
for ty in event_types:
item = QListWidgetItem(ty)
self.idx_evt_type.addItem(item) | python | def update_event_types(self):
self.idx_evt_type.clear()
self.idx_evt_type.setSelectionMode(QAbstractItemView.ExtendedSelection)
event_types = sorted(self.parent.notes.annot.event_types,
key=str.lower)
for ty in event_types:
item = QListWidgetItem(ty)
self.idx_evt_type.addItem(item) | [
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24,936 | wonambi-python/wonambi | wonambi/widgets/notes.py | ExportEventsDialog.update | def update(self):
"""Update the event types list, info, when dialog is opened."""
self.filename = self.parent.notes.annot.xml_file
self.event_types = self.parent.notes.annot.event_types
self.idx_evt_type.clear()
for ev in self.event_types:
self.idx_evt_type.addItem(ev) | python | def update(self):
self.filename = self.parent.notes.annot.xml_file
self.event_types = self.parent.notes.annot.event_types
self.idx_evt_type.clear()
for ev in self.event_types:
self.idx_evt_type.addItem(ev) | [
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24,937 | wonambi-python/wonambi | wonambi/widgets/notes.py | ExportEventsDialog.save_as | def save_as(self):
"""Dialog for getting name, location of dataset export."""
filename = splitext(self.filename)[0]
filename, _ = QFileDialog.getSaveFileName(self, 'Export events',
filename)
if filename == '':
return
self.filename = filename
short_filename = short_strings(basename(self.filename))
self.idx_filename.setText(short_filename) | python | def save_as(self):
filename = splitext(self.filename)[0]
filename, _ = QFileDialog.getSaveFileName(self, 'Export events',
filename)
if filename == '':
return
self.filename = filename
short_filename = short_strings(basename(self.filename))
self.idx_filename.setText(short_filename) | [
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24,938 | wonambi-python/wonambi | wonambi/attr/chan.py | _convert_unit | def _convert_unit(unit):
"""Convert different names into SI units.
Parameters
----------
unit : str
unit to convert to SI
Returns
-------
str
unit in SI format.
Notes
-----
SI unit such as mV (milliVolt, mVolt), μV (microVolt, muV).
"""
if unit is None:
return ''
prefix = None
suffix = None
if unit[:5].lower() == 'milli':
prefix = 'm'
unit = unit[5:]
elif unit[:5].lower() == 'micro':
prefix = mu
unit = unit[5:]
elif unit[:2].lower() == 'mu':
prefix = mu
unit = unit[2:]
if unit[-4:].lower() == 'volt':
suffix = 'V'
unit = unit[:-4]
if prefix is None and suffix is None:
unit = unit
elif prefix is None and suffix is not None:
unit = unit + suffix
elif prefix is not None and suffix is None:
unit = prefix + unit
else:
unit = prefix + suffix
return unit | python | def _convert_unit(unit):
if unit is None:
return ''
prefix = None
suffix = None
if unit[:5].lower() == 'milli':
prefix = 'm'
unit = unit[5:]
elif unit[:5].lower() == 'micro':
prefix = mu
unit = unit[5:]
elif unit[:2].lower() == 'mu':
prefix = mu
unit = unit[2:]
if unit[-4:].lower() == 'volt':
suffix = 'V'
unit = unit[:-4]
if prefix is None and suffix is None:
unit = unit
elif prefix is None and suffix is not None:
unit = unit + suffix
elif prefix is not None and suffix is None:
unit = prefix + unit
else:
unit = prefix + suffix
return unit | [
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Returns
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Notes
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24,939 | wonambi-python/wonambi | wonambi/attr/chan.py | detect_format | def detect_format(filename):
"""Detect file format of the channels based on extension.
Parameters
----------
filename : Path
name of the filename
Returns
-------
str
file format
"""
filename = Path(filename)
if filename.suffix == '.csv':
recformat = 'csv'
elif filename.suffix == '.sfp':
recformat = 'sfp'
else:
recformat = 'unknown'
return recformat | python | def detect_format(filename):
filename = Path(filename)
if filename.suffix == '.csv':
recformat = 'csv'
elif filename.suffix == '.sfp':
recformat = 'sfp'
else:
recformat = 'unknown'
return recformat | [
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filename : Path
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24,940 | wonambi-python/wonambi | wonambi/attr/chan.py | assign_region_to_channels | def assign_region_to_channels(channels, anat, parc_type='aparc', max_approx=3,
exclude_regions=None):
"""Assign a brain region based on the channel location.
Parameters
----------
channels : instance of wonambi.attr.chan.Channels
channels to assign regions to
anat : instance of wonambi.attr.anat.Freesurfer
anatomical information taken from freesurfer.
parc_type : str
'aparc', 'aparc.a2009s', 'BA', 'BA.thresh', or 'aparc.DKTatlas40'
'aparc.DKTatlas40' is only for recent freesurfer versions
max_approx : int, optional
approximation to define position of the electrode.
exclude_regions : list of str or empty list
do not report regions if they contain these substrings. None means
that it does not exclude any region. For example, to exclude white
matter regions and unknown regions you can use
exclude_regions=('White', 'WM', 'Unknown')
Returns
-------
instance of wonambi.attr.chan.Channels
same instance as before, now Chan have attr 'region'
"""
for one_chan in channels.chan:
one_region, approx = anat.find_brain_region(one_chan.xyz,
parc_type,
max_approx,
exclude_regions)
one_chan.attr.update({'region': one_region, 'approx': approx})
return channels | python | def assign_region_to_channels(channels, anat, parc_type='aparc', max_approx=3,
exclude_regions=None):
for one_chan in channels.chan:
one_region, approx = anat.find_brain_region(one_chan.xyz,
parc_type,
max_approx,
exclude_regions)
one_chan.attr.update({'region': one_region, 'approx': approx})
return channels | [
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channels to assign regions to
anat : instance of wonambi.attr.anat.Freesurfer
anatomical information taken from freesurfer.
parc_type : str
'aparc', 'aparc.a2009s', 'BA', 'BA.thresh', or 'aparc.DKTatlas40'
'aparc.DKTatlas40' is only for recent freesurfer versions
max_approx : int, optional
approximation to define position of the electrode.
exclude_regions : list of str or empty list
do not report regions if they contain these substrings. None means
that it does not exclude any region. For example, to exclude white
matter regions and unknown regions you can use
exclude_regions=('White', 'WM', 'Unknown')
Returns
-------
instance of wonambi.attr.chan.Channels
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24,941 | wonambi-python/wonambi | wonambi/attr/chan.py | find_chan_in_region | def find_chan_in_region(channels, anat, region_name):
"""Find which channels are in a specific region.
Parameters
----------
channels : instance of wonambi.attr.chan.Channels
channels, that have locations
anat : instance of wonambi.attr.anat.Freesurfer
anatomical information taken from freesurfer.
region_name : str
the name of the region, according to FreeSurferColorLUT.txt
Returns
-------
chan_in_region : list of str
list of the channels that are in one region.
"""
if 'region' not in channels.chan[0].attr.keys():
lg.info('Computing region for each channel.')
channels = assign_region_to_channels(channels, anat)
chan_in_region = []
for one_chan in channels.chan:
if region_name in one_chan.attr['region']:
chan_in_region.append(one_chan.label)
return chan_in_region | python | def find_chan_in_region(channels, anat, region_name):
if 'region' not in channels.chan[0].attr.keys():
lg.info('Computing region for each channel.')
channels = assign_region_to_channels(channels, anat)
chan_in_region = []
for one_chan in channels.chan:
if region_name in one_chan.attr['region']:
chan_in_region.append(one_chan.label)
return chan_in_region | [
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channels, that have locations
anat : instance of wonambi.attr.anat.Freesurfer
anatomical information taken from freesurfer.
region_name : str
the name of the region, according to FreeSurferColorLUT.txt
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24,942 | wonambi-python/wonambi | wonambi/attr/chan.py | create_sphere_around_elec | def create_sphere_around_elec(xyz, template_mri, distance=8, freesurfer=None):
"""Create an MRI mask around an electrode location,
Parameters
----------
xyz : ndarray
3x0 array
template_mri : path or str (as path) or nibabel.Nifti
(path to) MRI to be used as template
distance : float
distance in mm between electrode and selected voxels
freesurfer : instance of Freesurfer
to adjust RAS coordinates, see Notes
Returns
-------
3d bool ndarray
mask where True voxels are within selected distance to the electrode
Notes
-----
Freesurfer uses two coordinate systems: one for volumes ("RAS") and one for
surfaces ("tkReg", "tkRAS", and "Surface RAS"), so the electrodes might be
stored in one of the two systems. If the electrodes are in surface
coordinates (f.e. if you can plot surface and electrodes in the same space),
then you need to convert the coordinate system. This is done by passing an
instance of Freesurfer.
"""
if freesurfer is None:
shift = 0
else:
shift = freesurfer.surface_ras_shift
if isinstance(template_mri, str) or isinstance(template_mri, Path):
template_mri = nload(str(template_mri))
mask = zeros(template_mri.shape, dtype='bool')
for vox in ndindex(template_mri.shape):
vox_ras = apply_affine(template_mri.affine, vox) - shift
if norm(xyz - vox_ras) <= distance:
mask[vox] = True
return mask | python | def create_sphere_around_elec(xyz, template_mri, distance=8, freesurfer=None):
if freesurfer is None:
shift = 0
else:
shift = freesurfer.surface_ras_shift
if isinstance(template_mri, str) or isinstance(template_mri, Path):
template_mri = nload(str(template_mri))
mask = zeros(template_mri.shape, dtype='bool')
for vox in ndindex(template_mri.shape):
vox_ras = apply_affine(template_mri.affine, vox) - shift
if norm(xyz - vox_ras) <= distance:
mask[vox] = True
return mask | [
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template_mri : path or str (as path) or nibabel.Nifti
(path to) MRI to be used as template
distance : float
distance in mm between electrode and selected voxels
freesurfer : instance of Freesurfer
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Returns
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3d bool ndarray
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Notes
-----
Freesurfer uses two coordinate systems: one for volumes ("RAS") and one for
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stored in one of the two systems. If the electrodes are in surface
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24,943 | wonambi-python/wonambi | wonambi/attr/chan.py | Channels.return_attr | def return_attr(self, attr, labels=None):
"""return the attributes for each channels.
Parameters
----------
attr : str
attribute specified in Chan.attr.keys()
"""
all_labels = self.return_label()
if labels is None:
labels = all_labels
all_attr = []
for one_label in labels:
idx = all_labels.index(one_label)
try:
all_attr.append(self.chan[idx].attr[attr])
except KeyError:
possible_attr = ', '.join(self.chan[idx].attr.keys())
lg.debug('key "{}" not found, '.format(attr) +
'possible keys are {}'.format(possible_attr))
all_attr.append(None)
return all_attr | python | def return_attr(self, attr, labels=None):
all_labels = self.return_label()
if labels is None:
labels = all_labels
all_attr = []
for one_label in labels:
idx = all_labels.index(one_label)
try:
all_attr.append(self.chan[idx].attr[attr])
except KeyError:
possible_attr = ', '.join(self.chan[idx].attr.keys())
lg.debug('key "{}" not found, '.format(attr) +
'possible keys are {}'.format(possible_attr))
all_attr.append(None)
return all_attr | [
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24,944 | wonambi-python/wonambi | wonambi/attr/chan.py | Channels.export | def export(self, elec_file):
"""Export channel name and location to file.
Parameters
----------
elec_file : Path or str
path to file where to save csv
"""
elec_file = Path(elec_file)
if elec_file.suffix == '.csv':
sep = ', '
elif elec_file.suffix == '.sfp':
sep = ' '
with elec_file.open('w') as f:
for one_chan in self.chan:
values = ([one_chan.label, ] +
['{:.3f}'.format(x) for x in one_chan.xyz])
line = sep.join(values) + '\n'
f.write(line) | python | def export(self, elec_file):
elec_file = Path(elec_file)
if elec_file.suffix == '.csv':
sep = ', '
elif elec_file.suffix == '.sfp':
sep = ' '
with elec_file.open('w') as f:
for one_chan in self.chan:
values = ([one_chan.label, ] +
['{:.3f}'.format(x) for x in one_chan.xyz])
line = sep.join(values) + '\n'
f.write(line) | [
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24,945 | wonambi-python/wonambi | wonambi/trans/filter.py | filter_ | def filter_(data, axis='time', low_cut=None, high_cut=None, order=4,
ftype='butter', Rs=None, notchfreq=50, notchquality=25):
"""Design filter and apply it.
Parameters
----------
ftype : str
'butter', 'cheby1', 'cheby2', 'ellip', 'bessel', 'diff', or 'notch'
axis : str, optional
axis to apply the filter on.
low_cut : float, optional
(not for notch) low cutoff for high-pass filter
high_cut : float, optional
(not for notch) high cutoff for low-pass filter
order : int, optional
(not for notch) filter order
data : instance of Data
(not for notch) the data to filter.
notchfreq : float
(only for notch) frequency to apply notch filter to (+ harmonics)
notchquality : int
(only for notch) Quality factor (see scipy.signal.iirnotch)
Returns
-------
filtered_data : instance of DataRaw
filtered data
Notes
-----
You can specify any filter type as defined by iirfilter.
If you specify low_cut only, it generates a high-pass filter.
If you specify high_cut only, it generates a low-pass filter.
If you specify both, it generates a band-pass filter.
low_cut and high_cut should be given as ratio of the Nyquist. But if you
specify s_freq, then the ratio will be computed automatically.
Raises
------
ValueError
if the cutoff frequency is larger than the Nyquist frequency.
"""
nyquist = data.s_freq / 2.
btype = None
if low_cut is not None and high_cut is not None:
if low_cut > nyquist or high_cut > nyquist:
raise ValueError('cutoff has to be less than Nyquist '
'frequency')
btype = 'bandpass'
Wn = (low_cut / nyquist,
high_cut / nyquist)
elif low_cut is not None:
if low_cut > nyquist:
raise ValueError('cutoff has to be less than Nyquist '
'frequency')
btype = 'highpass'
Wn = low_cut / nyquist
elif high_cut is not None:
if high_cut > nyquist:
raise ValueError('cutoff has to be less than Nyquist '
'frequency')
btype = 'lowpass'
Wn = high_cut / nyquist
if btype is None and ftype != 'notch':
raise TypeError('You should specify at least low_cut or high_cut')
if Rs is None:
Rs = 40
if ftype == 'notch':
b_a = [iirnotch(w0 / nyquist, notchquality) for w0 in arange(notchfreq, nyquist, notchfreq)]
else:
lg.debug('order {0: 2}, Wn {1}, btype {2}, ftype {3}'
''.format(order, str(Wn), btype, ftype))
b_a = [iirfilter(order, Wn, btype=btype, ftype=ftype, rs=Rs), ]
fdata = data._copy()
for i in range(data.number_of('trial')):
x = data.data[i]
for b, a in b_a:
x = filtfilt(b, a, x, axis=data.index_of(axis))
fdata.data[i] = x
return fdata | python | def filter_(data, axis='time', low_cut=None, high_cut=None, order=4,
ftype='butter', Rs=None, notchfreq=50, notchquality=25):
nyquist = data.s_freq / 2.
btype = None
if low_cut is not None and high_cut is not None:
if low_cut > nyquist or high_cut > nyquist:
raise ValueError('cutoff has to be less than Nyquist '
'frequency')
btype = 'bandpass'
Wn = (low_cut / nyquist,
high_cut / nyquist)
elif low_cut is not None:
if low_cut > nyquist:
raise ValueError('cutoff has to be less than Nyquist '
'frequency')
btype = 'highpass'
Wn = low_cut / nyquist
elif high_cut is not None:
if high_cut > nyquist:
raise ValueError('cutoff has to be less than Nyquist '
'frequency')
btype = 'lowpass'
Wn = high_cut / nyquist
if btype is None and ftype != 'notch':
raise TypeError('You should specify at least low_cut or high_cut')
if Rs is None:
Rs = 40
if ftype == 'notch':
b_a = [iirnotch(w0 / nyquist, notchquality) for w0 in arange(notchfreq, nyquist, notchfreq)]
else:
lg.debug('order {0: 2}, Wn {1}, btype {2}, ftype {3}'
''.format(order, str(Wn), btype, ftype))
b_a = [iirfilter(order, Wn, btype=btype, ftype=ftype, rs=Rs), ]
fdata = data._copy()
for i in range(data.number_of('trial')):
x = data.data[i]
for b, a in b_a:
x = filtfilt(b, a, x, axis=data.index_of(axis))
fdata.data[i] = x
return fdata | [
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Parameters
----------
ftype : str
'butter', 'cheby1', 'cheby2', 'ellip', 'bessel', 'diff', or 'notch'
axis : str, optional
axis to apply the filter on.
low_cut : float, optional
(not for notch) low cutoff for high-pass filter
high_cut : float, optional
(not for notch) high cutoff for low-pass filter
order : int, optional
(not for notch) filter order
data : instance of Data
(not for notch) the data to filter.
notchfreq : float
(only for notch) frequency to apply notch filter to (+ harmonics)
notchquality : int
(only for notch) Quality factor (see scipy.signal.iirnotch)
Returns
-------
filtered_data : instance of DataRaw
filtered data
Notes
-----
You can specify any filter type as defined by iirfilter.
If you specify low_cut only, it generates a high-pass filter.
If you specify high_cut only, it generates a low-pass filter.
If you specify both, it generates a band-pass filter.
low_cut and high_cut should be given as ratio of the Nyquist. But if you
specify s_freq, then the ratio will be computed automatically.
Raises
------
ValueError
if the cutoff frequency is larger than the Nyquist frequency. | [
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24,946 | wonambi-python/wonambi | wonambi/trans/filter.py | convolve | def convolve(data, window, axis='time', length=1):
"""Design taper and convolve it with the signal.
Parameters
----------
data : instance of Data
the data to filter.
window : str
one of the windows in scipy, using get_window
length : float, optional
length of the window
axis : str, optional
axis to apply the filter on.
Returns
-------
instance of DataRaw
data after convolution
Notes
-----
Most of the code is identical to fftconvolve(axis=data.index_of(axis))
but unfortunately fftconvolve in scipy 0.13 doesn't take that argument
so we need to redefine it here. It's pretty slow too.
Taper is normalized such that the integral of the function remains the
same even after convolution.
See Also
--------
scipy.signal.get_window : function used to create windows
"""
taper = get_window(window, int(length * data.s_freq))
taper = taper / sum(taper)
fdata = data._copy()
idx_axis = data.index_of(axis)
for i in range(data.number_of('trial')):
orig_dat = data.data[i]
sel_dim = []
i_dim = []
dat = empty(orig_dat.shape, dtype=orig_dat.dtype)
for i_axis, one_axis in enumerate(data.list_of_axes):
if one_axis != axis:
i_dim.append(i_axis)
sel_dim.append(range(data.number_of(one_axis)[i]))
for one_iter in product(*sel_dim):
# create the numpy indices for one value per dimension,
# except for the dimension of interest
idx = [[x] for x in one_iter]
idx.insert(idx_axis, range(data.number_of(axis)[i]))
indices = ix_(*idx)
d_1dim = squeeze(orig_dat[indices], axis=i_dim)
d_1dim = fftconvolve(d_1dim, taper, 'same')
for to_squeeze in i_dim:
d_1dim = expand_dims(d_1dim, axis=to_squeeze)
dat[indices] = d_1dim
fdata.data[0] = dat
return fdata | python | def convolve(data, window, axis='time', length=1):
taper = get_window(window, int(length * data.s_freq))
taper = taper / sum(taper)
fdata = data._copy()
idx_axis = data.index_of(axis)
for i in range(data.number_of('trial')):
orig_dat = data.data[i]
sel_dim = []
i_dim = []
dat = empty(orig_dat.shape, dtype=orig_dat.dtype)
for i_axis, one_axis in enumerate(data.list_of_axes):
if one_axis != axis:
i_dim.append(i_axis)
sel_dim.append(range(data.number_of(one_axis)[i]))
for one_iter in product(*sel_dim):
# create the numpy indices for one value per dimension,
# except for the dimension of interest
idx = [[x] for x in one_iter]
idx.insert(idx_axis, range(data.number_of(axis)[i]))
indices = ix_(*idx)
d_1dim = squeeze(orig_dat[indices], axis=i_dim)
d_1dim = fftconvolve(d_1dim, taper, 'same')
for to_squeeze in i_dim:
d_1dim = expand_dims(d_1dim, axis=to_squeeze)
dat[indices] = d_1dim
fdata.data[0] = dat
return fdata | [
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Parameters
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data : instance of Data
the data to filter.
window : str
one of the windows in scipy, using get_window
length : float, optional
length of the window
axis : str, optional
axis to apply the filter on.
Returns
-------
instance of DataRaw
data after convolution
Notes
-----
Most of the code is identical to fftconvolve(axis=data.index_of(axis))
but unfortunately fftconvolve in scipy 0.13 doesn't take that argument
so we need to redefine it here. It's pretty slow too.
Taper is normalized such that the integral of the function remains the
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See Also
--------
scipy.signal.get_window : function used to create windows | [
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24,947 | wonambi-python/wonambi | wonambi/viz/base.py | normalize | def normalize(x, min_value, max_value):
"""Normalize value between min and max values.
It also clips the values, so that you cannot have values higher or lower
than 0 - 1."""
x = (x - min_value) / (max_value - min_value)
return clip(x, 0, 1) | python | def normalize(x, min_value, max_value):
x = (x - min_value) / (max_value - min_value)
return clip(x, 0, 1) | [
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24,948 | wonambi-python/wonambi | wonambi/viz/base.py | Viz._repr_png_ | def _repr_png_(self):
"""This is used by ipython to plot inline.
"""
app.process_events()
QApplication.processEvents()
img = read_pixels()
return bytes(_make_png(img)) | python | def _repr_png_(self):
app.process_events()
QApplication.processEvents()
img = read_pixels()
return bytes(_make_png(img)) | [
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24,949 | wonambi-python/wonambi | wonambi/viz/base.py | Viz.save | def save(self, png_file):
"""Save png to disk.
Parameters
----------
png_file : path to file
file to write to
Notes
-----
It relies on _repr_png_, so fix issues there.
"""
with open(png_file, 'wb') as f:
f.write(self._repr_png_()) | python | def save(self, png_file):
with open(png_file, 'wb') as f:
f.write(self._repr_png_()) | [
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----------
png_file : path to file
file to write to
Notes
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It relies on _repr_png_, so fix issues there. | [
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24,950 | wonambi-python/wonambi | wonambi/widgets/overview.py | _make_timestamps | def _make_timestamps(start_time, minimum, maximum, steps):
"""Create timestamps on x-axis, every so often.
Parameters
----------
start_time : instance of datetime
actual start time of the dataset
minimum : int
start time of the recording from start_time, in s
maximum : int
end time of the recording from start_time, in s
steps : int
how often you want a label, in s
Returns
-------
dict
where the key is the label and the value is the time point where the
label should be placed.
Notes
-----
This function takes care that labels are placed at the meaningful time, not
at random values.
"""
t0 = start_time + timedelta(seconds=minimum)
t1 = start_time + timedelta(seconds=maximum)
t0_midnight = t0.replace(hour=0, minute=0, second=0, microsecond=0)
d0 = t0 - t0_midnight
d1 = t1 - t0_midnight
first_stamp = ceil(d0.total_seconds() / steps) * steps
last_stamp = ceil(d1.total_seconds() / steps) * steps
stamp_label = []
stamp_time = []
for stamp in range(first_stamp, last_stamp, steps):
stamp_as_datetime = t0_midnight + timedelta(seconds=stamp)
stamp_label.append(stamp_as_datetime.strftime('%H:%M'))
stamp_time.append(stamp - d0.total_seconds())
return stamp_label, stamp_time | python | def _make_timestamps(start_time, minimum, maximum, steps):
t0 = start_time + timedelta(seconds=minimum)
t1 = start_time + timedelta(seconds=maximum)
t0_midnight = t0.replace(hour=0, minute=0, second=0, microsecond=0)
d0 = t0 - t0_midnight
d1 = t1 - t0_midnight
first_stamp = ceil(d0.total_seconds() / steps) * steps
last_stamp = ceil(d1.total_seconds() / steps) * steps
stamp_label = []
stamp_time = []
for stamp in range(first_stamp, last_stamp, steps):
stamp_as_datetime = t0_midnight + timedelta(seconds=stamp)
stamp_label.append(stamp_as_datetime.strftime('%H:%M'))
stamp_time.append(stamp - d0.total_seconds())
return stamp_label, stamp_time | [
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Parameters
----------
start_time : instance of datetime
actual start time of the dataset
minimum : int
start time of the recording from start_time, in s
maximum : int
end time of the recording from start_time, in s
steps : int
how often you want a label, in s
Returns
-------
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Notes
-----
This function takes care that labels are placed at the meaningful time, not
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24,951 | wonambi-python/wonambi | wonambi/widgets/overview.py | Overview.update | def update(self, reset=True):
"""Read full duration and update maximum.
Parameters
----------
reset: bool
If True, current window start time is reset to 0.
"""
if self.parent.info.dataset is not None:
# read from the dataset, if available
header = self.parent.info.dataset.header
maximum = header['n_samples'] / header['s_freq'] # in s
self.minimum = 0
self.maximum = maximum
self.start_time = self.parent.info.dataset.header['start_time']
elif self.parent.notes.annot is not None:
# read from annotations
annot = self.parent.notes.annot
self.minimum = annot.first_second
self.maximum = annot.last_second
self.start_time = annot.start_time
# make it time-zone unaware
self.start_time = self.start_time.replace(tzinfo=None)
if reset:
self.parent.value('window_start', 0) # the only value that is reset
self.display() | python | def update(self, reset=True):
if self.parent.info.dataset is not None:
# read from the dataset, if available
header = self.parent.info.dataset.header
maximum = header['n_samples'] / header['s_freq'] # in s
self.minimum = 0
self.maximum = maximum
self.start_time = self.parent.info.dataset.header['start_time']
elif self.parent.notes.annot is not None:
# read from annotations
annot = self.parent.notes.annot
self.minimum = annot.first_second
self.maximum = annot.last_second
self.start_time = annot.start_time
# make it time-zone unaware
self.start_time = self.start_time.replace(tzinfo=None)
if reset:
self.parent.value('window_start', 0) # the only value that is reset
self.display() | [
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reset: bool
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24,952 | wonambi-python/wonambi | wonambi/widgets/overview.py | Overview.display | def display(self):
"""Updates the widgets, especially based on length of recordings."""
lg.debug('GraphicsScene is between {}s and {}s'.format(self.minimum,
self.maximum))
x_scale = 1 / self.parent.value('overview_scale')
lg.debug('Set scene x-scaling to {}'.format(x_scale))
self.scale(1 / self.transform().m11(), 1) # reset to 1
self.scale(x_scale, 1)
self.scene = QGraphicsScene(self.minimum, 0,
self.maximum,
TOTAL_HEIGHT)
self.setScene(self.scene)
# reset annotations
self.idx_markers = []
self.idx_annot = []
self.display_current()
for name, pos in BARS.items():
item = QGraphicsRectItem(self.minimum, pos['pos0'],
self.maximum, pos['pos1'])
item.setToolTip(pos['tip'])
self.scene.addItem(item)
self.add_timestamps() | python | def display(self):
lg.debug('GraphicsScene is between {}s and {}s'.format(self.minimum,
self.maximum))
x_scale = 1 / self.parent.value('overview_scale')
lg.debug('Set scene x-scaling to {}'.format(x_scale))
self.scale(1 / self.transform().m11(), 1) # reset to 1
self.scale(x_scale, 1)
self.scene = QGraphicsScene(self.minimum, 0,
self.maximum,
TOTAL_HEIGHT)
self.setScene(self.scene)
# reset annotations
self.idx_markers = []
self.idx_annot = []
self.display_current()
for name, pos in BARS.items():
item = QGraphicsRectItem(self.minimum, pos['pos0'],
self.maximum, pos['pos1'])
item.setToolTip(pos['tip'])
self.scene.addItem(item)
self.add_timestamps() | [
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24,953 | wonambi-python/wonambi | wonambi/widgets/overview.py | Overview.add_timestamps | def add_timestamps(self):
"""Add timestamps at the bottom of the overview."""
transform, _ = self.transform().inverted()
stamps = _make_timestamps(self.start_time, self.minimum, self.maximum,
self.parent.value('timestamp_steps'))
for stamp, xpos in zip(*stamps):
text = self.scene.addSimpleText(stamp)
text.setFlag(QGraphicsItem.ItemIgnoresTransformations)
# set xpos and adjust for text width
text_width = text.boundingRect().width() * transform.m11()
text.setPos(xpos - text_width / 2, TIME_HEIGHT) | python | def add_timestamps(self):
transform, _ = self.transform().inverted()
stamps = _make_timestamps(self.start_time, self.minimum, self.maximum,
self.parent.value('timestamp_steps'))
for stamp, xpos in zip(*stamps):
text = self.scene.addSimpleText(stamp)
text.setFlag(QGraphicsItem.ItemIgnoresTransformations)
# set xpos and adjust for text width
text_width = text.boundingRect().width() * transform.m11()
text.setPos(xpos - text_width / 2, TIME_HEIGHT) | [
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24,954 | wonambi-python/wonambi | wonambi/widgets/overview.py | Overview.update_settings | def update_settings(self):
"""After changing the settings, we need to recreate the whole image."""
self.display()
self.display_markers()
if self.parent.notes.annot is not None:
self.parent.notes.display_notes() | python | def update_settings(self):
self.display()
self.display_markers()
if self.parent.notes.annot is not None:
self.parent.notes.display_notes() | [
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] | 1d8e3d7e53df8017c199f703bcab582914676e76 | https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/overview.py#L201-L206 |
24,955 | wonambi-python/wonambi | wonambi/widgets/overview.py | Overview.update_position | def update_position(self, new_position=None):
"""Update the cursor position and much more.
Parameters
----------
new_position : int or float
new position in s, for plotting etc.
Notes
-----
This is a central function. It updates the cursor, then updates
the traces, the scores, and the power spectrum. In other words, this
function is responsible for keep track of the changes every time
the start time of the window changes.
"""
if new_position is not None:
lg.debug('Updating position to {}'.format(new_position))
self.parent.value('window_start', new_position)
self.idx_current.setPos(new_position, 0)
current_time = (self.start_time +
timedelta(seconds=new_position))
msg = 'Current time: ' + current_time.strftime('%H:%M:%S')
msg2 = f' ({new_position} seconds from start)'
self.parent.statusBar().showMessage(msg + msg2)
lg.debug(msg)
else:
lg.debug('Updating position at {}'
''.format(self.parent.value('window_start')))
if self.parent.info.dataset is not None:
self.parent.traces.read_data()
if self.parent.traces.data is not None:
self.parent.traces.display()
self.parent.spectrum.display_window()
if self.parent.notes.annot is not None:
self.parent.notes.set_stage_index()
self.parent.notes.set_quality_index()
self.display_current() | python | def update_position(self, new_position=None):
if new_position is not None:
lg.debug('Updating position to {}'.format(new_position))
self.parent.value('window_start', new_position)
self.idx_current.setPos(new_position, 0)
current_time = (self.start_time +
timedelta(seconds=new_position))
msg = 'Current time: ' + current_time.strftime('%H:%M:%S')
msg2 = f' ({new_position} seconds from start)'
self.parent.statusBar().showMessage(msg + msg2)
lg.debug(msg)
else:
lg.debug('Updating position at {}'
''.format(self.parent.value('window_start')))
if self.parent.info.dataset is not None:
self.parent.traces.read_data()
if self.parent.traces.data is not None:
self.parent.traces.display()
self.parent.spectrum.display_window()
if self.parent.notes.annot is not None:
self.parent.notes.set_stage_index()
self.parent.notes.set_quality_index()
self.display_current() | [
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new_position : int or float
new position in s, for plotting etc.
Notes
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This is a central function. It updates the cursor, then updates
the traces, the scores, and the power spectrum. In other words, this
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24,956 | wonambi-python/wonambi | wonambi/widgets/overview.py | Overview.display_current | def display_current(self):
"""Create a rectangle showing the current window."""
if self.idx_current in self.scene.items():
self.scene.removeItem(self.idx_current)
item = QGraphicsRectItem(0,
CURR['pos0'],
self.parent.value('window_length'),
CURR['pos1'])
# it's necessary to create rect first, and then move it
item.setPos(self.parent.value('window_start'), 0)
item.setPen(QPen(Qt.lightGray))
item.setBrush(QBrush(Qt.lightGray))
item.setZValue(-10)
self.scene.addItem(item)
self.idx_current = item | python | def display_current(self):
if self.idx_current in self.scene.items():
self.scene.removeItem(self.idx_current)
item = QGraphicsRectItem(0,
CURR['pos0'],
self.parent.value('window_length'),
CURR['pos1'])
# it's necessary to create rect first, and then move it
item.setPos(self.parent.value('window_start'), 0)
item.setPen(QPen(Qt.lightGray))
item.setBrush(QBrush(Qt.lightGray))
item.setZValue(-10)
self.scene.addItem(item)
self.idx_current = item | [
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24,957 | wonambi-python/wonambi | wonambi/widgets/overview.py | Overview.display_markers | def display_markers(self):
"""Mark all the markers, from the dataset.
This function should be called only when we load the dataset or when
we change the settings.
"""
for rect in self.idx_markers:
self.scene.removeItem(rect)
self.idx_markers = []
markers = []
if self.parent.info.markers is not None:
if self.parent.value('marker_show'):
markers = self.parent.info.markers
for mrk in markers:
rect = QGraphicsRectItem(mrk['start'],
BARS['markers']['pos0'],
mrk['end'] - mrk['start'],
BARS['markers']['pos1'])
self.scene.addItem(rect)
color = self.parent.value('marker_color')
rect.setPen(QPen(QColor(color)))
rect.setBrush(QBrush(QColor(color)))
rect.setZValue(-5)
self.idx_markers.append(rect) | python | def display_markers(self):
for rect in self.idx_markers:
self.scene.removeItem(rect)
self.idx_markers = []
markers = []
if self.parent.info.markers is not None:
if self.parent.value('marker_show'):
markers = self.parent.info.markers
for mrk in markers:
rect = QGraphicsRectItem(mrk['start'],
BARS['markers']['pos0'],
mrk['end'] - mrk['start'],
BARS['markers']['pos1'])
self.scene.addItem(rect)
color = self.parent.value('marker_color')
rect.setPen(QPen(QColor(color)))
rect.setBrush(QBrush(QColor(color)))
rect.setZValue(-5)
self.idx_markers.append(rect) | [
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24,958 | wonambi-python/wonambi | wonambi/widgets/overview.py | Overview.mark_stages | def mark_stages(self, start_time, length, stage_name):
"""Mark stages, only add the new ones.
Parameters
----------
start_time : int
start time in s of the epoch being scored.
length : int
duration in s of the epoch being scored.
stage_name : str
one of the stages defined in global stages.
"""
y_pos = BARS['stage']['pos0']
current_stage = STAGES.get(stage_name, STAGES['Unknown'])
# the -1 is really important, otherwise we stay on the edge of the rect
old_score = self.scene.itemAt(start_time + length / 2,
y_pos +
current_stage['pos0'] +
current_stage['pos1'] - 1,
self.transform())
# check we are not removing the black border
if old_score is not None and old_score.pen() == NoPen:
lg.debug('Removing old score at {}'.format(start_time))
self.scene.removeItem(old_score)
self.idx_annot.remove(old_score)
rect = QGraphicsRectItem(start_time,
y_pos + current_stage['pos0'],
length,
current_stage['pos1'])
rect.setPen(NoPen)
rect.setBrush(current_stage['color'])
self.scene.addItem(rect)
self.idx_annot.append(rect) | python | def mark_stages(self, start_time, length, stage_name):
y_pos = BARS['stage']['pos0']
current_stage = STAGES.get(stage_name, STAGES['Unknown'])
# the -1 is really important, otherwise we stay on the edge of the rect
old_score = self.scene.itemAt(start_time + length / 2,
y_pos +
current_stage['pos0'] +
current_stage['pos1'] - 1,
self.transform())
# check we are not removing the black border
if old_score is not None and old_score.pen() == NoPen:
lg.debug('Removing old score at {}'.format(start_time))
self.scene.removeItem(old_score)
self.idx_annot.remove(old_score)
rect = QGraphicsRectItem(start_time,
y_pos + current_stage['pos0'],
length,
current_stage['pos1'])
rect.setPen(NoPen)
rect.setBrush(current_stage['color'])
self.scene.addItem(rect)
self.idx_annot.append(rect) | [
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start_time : int
start time in s of the epoch being scored.
length : int
duration in s of the epoch being scored.
stage_name : str
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24,959 | wonambi-python/wonambi | wonambi/widgets/overview.py | Overview.mark_quality | def mark_quality(self, start_time, length, qual_name):
"""Mark signal quality, only add the new ones.
Parameters
----------
start_time : int
start time in s of the epoch being scored.
length : int
duration in s of the epoch being scored.
qual_name : str
one of the stages defined in global stages.
"""
y_pos = BARS['quality']['pos0']
height = 10
# the -1 is really important, otherwise we stay on the edge of the rect
old_score = self.scene.itemAt(start_time + length / 2,
y_pos + height - 1,
self.transform())
# check we are not removing the black border
if old_score is not None and old_score.pen() == NoPen:
lg.debug('Removing old score at {}'.format(start_time))
self.scene.removeItem(old_score)
self.idx_annot.remove(old_score)
if qual_name == 'Poor':
rect = QGraphicsRectItem(start_time, y_pos, length, height)
rect.setPen(NoPen)
rect.setBrush(Qt.black)
self.scene.addItem(rect)
self.idx_annot.append(rect) | python | def mark_quality(self, start_time, length, qual_name):
y_pos = BARS['quality']['pos0']
height = 10
# the -1 is really important, otherwise we stay on the edge of the rect
old_score = self.scene.itemAt(start_time + length / 2,
y_pos + height - 1,
self.transform())
# check we are not removing the black border
if old_score is not None and old_score.pen() == NoPen:
lg.debug('Removing old score at {}'.format(start_time))
self.scene.removeItem(old_score)
self.idx_annot.remove(old_score)
if qual_name == 'Poor':
rect = QGraphicsRectItem(start_time, y_pos, length, height)
rect.setPen(NoPen)
rect.setBrush(Qt.black)
self.scene.addItem(rect)
self.idx_annot.append(rect) | [
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start_time : int
start time in s of the epoch being scored.
length : int
duration in s of the epoch being scored.
qual_name : str
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24,960 | wonambi-python/wonambi | wonambi/widgets/overview.py | Overview.mark_cycles | def mark_cycles(self, start_time, length, end=False):
"""Mark cycle bound, only add the new one.
Parameters
----------
start_time: int
start time in s of the bounding epoch
length : int
duration in s of the epoch being scored.
end: bool
If True, marker will be a cycle end marker; otherwise, it's start.
"""
y_pos = STAGES['cycle']['pos0']
height = STAGES['cycle']['pos1']
color = STAGES['cycle']['color']
# the -1 is really important, otherwise we stay on the edge of the rect
old_rect = self.scene.itemAt(start_time + length / 2,
y_pos + height - 1,
self.transform())
# check we are not removing the black border
if old_rect is not None and old_rect.pen() == NoPen:
lg.debug('Removing old score at {}'.format(start_time))
self.scene.removeItem(old_rect)
self.idx_annot.remove(old_rect)
rect = QGraphicsRectItem(start_time, y_pos, 30, height)
rect.setPen(NoPen)
rect.setBrush(color)
self.scene.addItem(rect)
self.idx_annot.append(rect)
if end:
start_time -= 120
kink_hi = QGraphicsRectItem(start_time, y_pos, 150, 1)
kink_hi.setPen(NoPen)
kink_hi.setBrush(color)
self.scene.addItem(kink_hi)
self.idx_annot.append(kink_hi)
kink_lo = QGraphicsRectItem(start_time, y_pos + height, 150, 1)
kink_lo.setPen(NoPen)
kink_lo.setBrush(color)
self.scene.addItem(kink_lo)
self.idx_annot.append(kink_lo) | python | def mark_cycles(self, start_time, length, end=False):
y_pos = STAGES['cycle']['pos0']
height = STAGES['cycle']['pos1']
color = STAGES['cycle']['color']
# the -1 is really important, otherwise we stay on the edge of the rect
old_rect = self.scene.itemAt(start_time + length / 2,
y_pos + height - 1,
self.transform())
# check we are not removing the black border
if old_rect is not None and old_rect.pen() == NoPen:
lg.debug('Removing old score at {}'.format(start_time))
self.scene.removeItem(old_rect)
self.idx_annot.remove(old_rect)
rect = QGraphicsRectItem(start_time, y_pos, 30, height)
rect.setPen(NoPen)
rect.setBrush(color)
self.scene.addItem(rect)
self.idx_annot.append(rect)
if end:
start_time -= 120
kink_hi = QGraphicsRectItem(start_time, y_pos, 150, 1)
kink_hi.setPen(NoPen)
kink_hi.setBrush(color)
self.scene.addItem(kink_hi)
self.idx_annot.append(kink_hi)
kink_lo = QGraphicsRectItem(start_time, y_pos + height, 150, 1)
kink_lo.setPen(NoPen)
kink_lo.setBrush(color)
self.scene.addItem(kink_lo)
self.idx_annot.append(kink_lo) | [
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start time in s of the bounding epoch
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duration in s of the epoch being scored.
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24,961 | wonambi-python/wonambi | wonambi/widgets/overview.py | Overview.mousePressEvent | def mousePressEvent(self, event):
"""Jump to window when user clicks on overview.
Parameters
----------
event : instance of QtCore.QEvent
it contains the position that was clicked.
"""
if self.scene is not None:
x_in_scene = self.mapToScene(event.pos()).x()
window_length = self.parent.value('window_length')
window_start = int(floor(x_in_scene / window_length) *
window_length)
if self.parent.notes.annot is not None:
window_start = self.parent.notes.annot.get_epoch_start(
window_start)
self.update_position(window_start) | python | def mousePressEvent(self, event):
if self.scene is not None:
x_in_scene = self.mapToScene(event.pos()).x()
window_length = self.parent.value('window_length')
window_start = int(floor(x_in_scene / window_length) *
window_length)
if self.parent.notes.annot is not None:
window_start = self.parent.notes.annot.get_epoch_start(
window_start)
self.update_position(window_start) | [
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24,962 | wonambi-python/wonambi | wonambi/widgets/overview.py | Overview.reset | def reset(self):
"""Reset the widget, and clear the scene."""
self.minimum = None
self.maximum = None
self.start_time = None # datetime, absolute start time
self.idx_current = None
self.idx_markers = []
self.idx_annot = []
if self.scene is not None:
self.scene.clear()
self.scene = None | python | def reset(self):
self.minimum = None
self.maximum = None
self.start_time = None # datetime, absolute start time
self.idx_current = None
self.idx_markers = []
self.idx_annot = []
if self.scene is not None:
self.scene.clear()
self.scene = None | [
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24,963 | wonambi-python/wonambi | wonambi/viz/plot_3d.py | _prepare_colors | def _prepare_colors(color, values, limits_c, colormap, alpha, chan=None):
"""Return colors for all the channels based on various inputs.
Parameters
----------
color : tuple
3-, 4-element tuple, representing RGB and alpha, between 0 and 1
values : ndarray
array with values for each channel
limits_c : tuple of 2 floats, optional
min and max values to normalize the color
colormap : str
one of the colormaps in vispy
alpha : float
transparency (0 = transparent, 1 = opaque)
chan : instance of Channels
use labels to create channel groups
Returns
-------
1d / 2d array
colors for all the channels or for each channel individually
tuple of two float or None
limits for the values
"""
if values is not None:
if limits_c is None:
limits_c = array([-1, 1]) * nanmax(abs(values))
norm_values = normalize(values, *limits_c)
cm = get_colormap(colormap)
colors = cm[norm_values]
elif color is not None:
colors = ColorArray(color)
else:
cm = get_colormap('hsl')
group_idx = _chan_groups_to_index(chan)
colors = cm[group_idx]
if alpha is not None:
colors.alpha = alpha
return colors, limits_c | python | def _prepare_colors(color, values, limits_c, colormap, alpha, chan=None):
if values is not None:
if limits_c is None:
limits_c = array([-1, 1]) * nanmax(abs(values))
norm_values = normalize(values, *limits_c)
cm = get_colormap(colormap)
colors = cm[norm_values]
elif color is not None:
colors = ColorArray(color)
else:
cm = get_colormap('hsl')
group_idx = _chan_groups_to_index(chan)
colors = cm[group_idx]
if alpha is not None:
colors.alpha = alpha
return colors, limits_c | [
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min and max values to normalize the color
colormap : str
one of the colormaps in vispy
alpha : float
transparency (0 = transparent, 1 = opaque)
chan : instance of Channels
use labels to create channel groups
Returns
-------
1d / 2d array
colors for all the channels or for each channel individually
tuple of two float or None
limits for the values | [
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24,964 | wonambi-python/wonambi | wonambi/viz/plot_3d.py | Viz3.add_surf | def add_surf(self, surf, color=SKIN_COLOR, vertex_colors=None,
values=None, limits_c=None, colormap=COLORMAP, alpha=1,
colorbar=False):
"""Add surfaces to the visualization.
Parameters
----------
surf : instance of wonambi.attr.anat.Surf
surface to be plotted
color : tuple or ndarray, optional
4-element tuple, representing RGB and alpha, between 0 and 1
vertex_colors : ndarray
ndarray with n vertices x 4 to specify color of each vertex
values : ndarray, optional
vector with values for each vertex
limits_c : tuple of 2 floats, optional
min and max values to normalize the color
colormap : str
one of the colormaps in vispy
alpha : float
transparency (1 = opaque)
colorbar : bool
add a colorbar at the back of the surface
"""
colors, limits = _prepare_colors(color=color, values=values,
limits_c=limits_c, colormap=colormap,
alpha=alpha)
# meshdata uses numpy array, in the correct dimension
vertex_colors = colors.rgba
if vertex_colors.shape[0] == 1:
vertex_colors = tile(vertex_colors, (surf.n_vert, 1))
meshdata = MeshData(vertices=surf.vert, faces=surf.tri,
vertex_colors=vertex_colors)
mesh = SurfaceMesh(meshdata)
self._add_mesh(mesh)
# adjust camera
surf_center = mean(surf.vert, axis=0)
if surf_center[0] < 0:
azimuth = 270
else:
azimuth = 90
self._view.camera.azimuth = azimuth
self._view.camera.center = surf_center
self._surf.append(mesh)
if colorbar:
self._view.add(_colorbar_for_surf(colormap, limits)) | python | def add_surf(self, surf, color=SKIN_COLOR, vertex_colors=None,
values=None, limits_c=None, colormap=COLORMAP, alpha=1,
colorbar=False):
colors, limits = _prepare_colors(color=color, values=values,
limits_c=limits_c, colormap=colormap,
alpha=alpha)
# meshdata uses numpy array, in the correct dimension
vertex_colors = colors.rgba
if vertex_colors.shape[0] == 1:
vertex_colors = tile(vertex_colors, (surf.n_vert, 1))
meshdata = MeshData(vertices=surf.vert, faces=surf.tri,
vertex_colors=vertex_colors)
mesh = SurfaceMesh(meshdata)
self._add_mesh(mesh)
# adjust camera
surf_center = mean(surf.vert, axis=0)
if surf_center[0] < 0:
azimuth = 270
else:
azimuth = 90
self._view.camera.azimuth = azimuth
self._view.camera.center = surf_center
self._surf.append(mesh)
if colorbar:
self._view.add(_colorbar_for_surf(colormap, limits)) | [
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surface to be plotted
color : tuple or ndarray, optional
4-element tuple, representing RGB and alpha, between 0 and 1
vertex_colors : ndarray
ndarray with n vertices x 4 to specify color of each vertex
values : ndarray, optional
vector with values for each vertex
limits_c : tuple of 2 floats, optional
min and max values to normalize the color
colormap : str
one of the colormaps in vispy
alpha : float
transparency (1 = opaque)
colorbar : bool
add a colorbar at the back of the surface | [
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24,965 | wonambi-python/wonambi | wonambi/viz/plot_3d.py | Viz3.add_chan | def add_chan(self, chan, color=None, values=None, limits_c=None,
colormap=CHAN_COLORMAP, alpha=None, colorbar=False):
"""Add channels to visualization
Parameters
----------
chan : instance of Channels
channels to plot
color : tuple
3-, 4-element tuple, representing RGB and alpha, between 0 and 1
values : ndarray
array with values for each channel
limits_c : tuple of 2 floats, optional
min and max values to normalize the color
colormap : str
one of the colormaps in vispy
alpha : float
transparency (0 = transparent, 1 = opaque)
colorbar : bool
add a colorbar at the back of the surface
"""
# reuse previous limits
if limits_c is None and self._chan_limits is not None:
limits_c = self._chan_limits
chan_colors, limits = _prepare_colors(color=color, values=values,
limits_c=limits_c,
colormap=colormap, alpha=alpha,
chan=chan)
self._chan_limits = limits
xyz = chan.return_xyz()
marker = Markers()
marker.set_data(pos=xyz, size=CHAN_SIZE, face_color=chan_colors)
self._add_mesh(marker)
if colorbar:
self._view.add(_colorbar_for_surf(colormap, limits)) | python | def add_chan(self, chan, color=None, values=None, limits_c=None,
colormap=CHAN_COLORMAP, alpha=None, colorbar=False):
# reuse previous limits
if limits_c is None and self._chan_limits is not None:
limits_c = self._chan_limits
chan_colors, limits = _prepare_colors(color=color, values=values,
limits_c=limits_c,
colormap=colormap, alpha=alpha,
chan=chan)
self._chan_limits = limits
xyz = chan.return_xyz()
marker = Markers()
marker.set_data(pos=xyz, size=CHAN_SIZE, face_color=chan_colors)
self._add_mesh(marker)
if colorbar:
self._view.add(_colorbar_for_surf(colormap, limits)) | [
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Parameters
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chan : instance of Channels
channels to plot
color : tuple
3-, 4-element tuple, representing RGB and alpha, between 0 and 1
values : ndarray
array with values for each channel
limits_c : tuple of 2 floats, optional
min and max values to normalize the color
colormap : str
one of the colormaps in vispy
alpha : float
transparency (0 = transparent, 1 = opaque)
colorbar : bool
add a colorbar at the back of the surface | [
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24,966 | wonambi-python/wonambi | wonambi/trans/select.py | select | def select(data, trial=None, invert=False, **axes_to_select):
"""Define the selection of trials, using ranges or actual values.
Parameters
----------
data : instance of Data
data to select from.
trial : list of int or ndarray (dtype='i'), optional
index of trials of interest
**axes_to_select, optional
Values need to be tuple or list. If the values in one axis are string,
then you need to specify all the strings that you want. If the values
are numeric, then you should specify the range (you cannot specify
single values, nor multiple values). To select only up to one point,
you can use (None, value_of_interest)
invert : bool
take the opposite selection
Returns
-------
instance, same class as input
data where selection has been applied.
"""
if trial is not None and not isinstance(trial, Iterable):
raise TypeError('Trial needs to be iterable.')
for axis_to_select, values_to_select in axes_to_select.items():
if (not isinstance(values_to_select, Iterable) or
isinstance(values_to_select, str)):
raise TypeError(axis_to_select + ' needs to be iterable.')
if trial is None:
trial = range(data.number_of('trial'))
else:
trial = trial
if invert:
trial = setdiff1d(range(data.number_of('trial')), trial)
# create empty axis
output = data._copy(axis=False)
for one_axis in output.axis:
output.axis[one_axis] = empty(len(trial), dtype='O')
output.data = empty(len(trial), dtype='O')
to_select = {}
for cnt, i in enumerate(trial):
lg.debug('Selection on trial {0: 6}'.format(i))
for one_axis in output.axis:
values = data.axis[one_axis][i]
if one_axis in axes_to_select.keys():
values_to_select = axes_to_select[one_axis]
if len(values_to_select) == 0:
selected_values = ()
elif isinstance(values_to_select[0], str):
selected_values = asarray(values_to_select, dtype='U')
else:
if (values_to_select[0] is None and
values_to_select[1] is None):
bool_values = ones(len(values), dtype=bool)
elif values_to_select[0] is None:
bool_values = values < values_to_select[1]
elif values_to_select[1] is None:
bool_values = values_to_select[0] <= values
else:
bool_values = ((values_to_select[0] <= values) &
(values < values_to_select[1]))
selected_values = values[bool_values]
if invert:
selected_values = setdiff1d(values, selected_values)
lg.debug('In axis {0}, selecting {1: 6} '
'values'.format(one_axis,
len(selected_values)))
to_select[one_axis] = selected_values
else:
lg.debug('In axis ' + one_axis + ', selecting all the '
'values')
selected_values = data.axis[one_axis][i]
output.axis[one_axis][cnt] = selected_values
output.data[cnt] = data(trial=i, **to_select)
return output | python | def select(data, trial=None, invert=False, **axes_to_select):
if trial is not None and not isinstance(trial, Iterable):
raise TypeError('Trial needs to be iterable.')
for axis_to_select, values_to_select in axes_to_select.items():
if (not isinstance(values_to_select, Iterable) or
isinstance(values_to_select, str)):
raise TypeError(axis_to_select + ' needs to be iterable.')
if trial is None:
trial = range(data.number_of('trial'))
else:
trial = trial
if invert:
trial = setdiff1d(range(data.number_of('trial')), trial)
# create empty axis
output = data._copy(axis=False)
for one_axis in output.axis:
output.axis[one_axis] = empty(len(trial), dtype='O')
output.data = empty(len(trial), dtype='O')
to_select = {}
for cnt, i in enumerate(trial):
lg.debug('Selection on trial {0: 6}'.format(i))
for one_axis in output.axis:
values = data.axis[one_axis][i]
if one_axis in axes_to_select.keys():
values_to_select = axes_to_select[one_axis]
if len(values_to_select) == 0:
selected_values = ()
elif isinstance(values_to_select[0], str):
selected_values = asarray(values_to_select, dtype='U')
else:
if (values_to_select[0] is None and
values_to_select[1] is None):
bool_values = ones(len(values), dtype=bool)
elif values_to_select[0] is None:
bool_values = values < values_to_select[1]
elif values_to_select[1] is None:
bool_values = values_to_select[0] <= values
else:
bool_values = ((values_to_select[0] <= values) &
(values < values_to_select[1]))
selected_values = values[bool_values]
if invert:
selected_values = setdiff1d(values, selected_values)
lg.debug('In axis {0}, selecting {1: 6} '
'values'.format(one_axis,
len(selected_values)))
to_select[one_axis] = selected_values
else:
lg.debug('In axis ' + one_axis + ', selecting all the '
'values')
selected_values = data.axis[one_axis][i]
output.axis[one_axis][cnt] = selected_values
output.data[cnt] = data(trial=i, **to_select)
return output | [
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Parameters
----------
data : instance of Data
data to select from.
trial : list of int or ndarray (dtype='i'), optional
index of trials of interest
**axes_to_select, optional
Values need to be tuple or list. If the values in one axis are string,
then you need to specify all the strings that you want. If the values
are numeric, then you should specify the range (you cannot specify
single values, nor multiple values). To select only up to one point,
you can use (None, value_of_interest)
invert : bool
take the opposite selection
Returns
-------
instance, same class as input
data where selection has been applied. | [
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24,967 | wonambi-python/wonambi | wonambi/trans/select.py | resample | def resample(data, s_freq=None, axis='time', ftype='fir', n=None):
"""Downsample the data after applying a filter.
Parameters
----------
data : instance of Data
data to downsample
s_freq : int or float
desired sampling frequency
axis : str
axis you want to apply downsample on (most likely 'time')
ftype : str
filter type to apply. The default here is 'fir', like Matlab but unlike
the default in scipy, because it works better
n : int
The order of the filter (1 less than the length for ‘fir’).
Returns
-------
instance of Data
downsampled data
"""
output = data._copy()
ratio = int(data.s_freq / s_freq)
for i in range(data.number_of('trial')):
output.data[i] = decimate(data.data[i], ratio,
axis=data.index_of(axis),
zero_phase=True)
n_samples = output.data[i].shape[data.index_of(axis)]
output.axis[axis][i] = linspace(data.axis[axis][i][0],
data.axis[axis][i][-1] +
1 / data.s_freq,
n_samples)
output.s_freq = s_freq
return output | python | def resample(data, s_freq=None, axis='time', ftype='fir', n=None):
output = data._copy()
ratio = int(data.s_freq / s_freq)
for i in range(data.number_of('trial')):
output.data[i] = decimate(data.data[i], ratio,
axis=data.index_of(axis),
zero_phase=True)
n_samples = output.data[i].shape[data.index_of(axis)]
output.axis[axis][i] = linspace(data.axis[axis][i][0],
data.axis[axis][i][-1] +
1 / data.s_freq,
n_samples)
output.s_freq = s_freq
return output | [
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Parameters
----------
data : instance of Data
data to downsample
s_freq : int or float
desired sampling frequency
axis : str
axis you want to apply downsample on (most likely 'time')
ftype : str
filter type to apply. The default here is 'fir', like Matlab but unlike
the default in scipy, because it works better
n : int
The order of the filter (1 less than the length for ‘fir’).
Returns
-------
instance of Data
downsampled data | [
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24,968 | wonambi-python/wonambi | wonambi/trans/select.py | fetch | def fetch(dataset, annot, cat=(0, 0, 0, 0), evt_type=None, stage=None,
cycle=None, chan_full=None, epoch=None, epoch_dur=30,
epoch_overlap=0, epoch_step=None, reject_epoch=False,
reject_artf=False, min_dur=0, buffer=0):
"""Create instance of Segments for analysis, complete with info about
stage, cycle, channel, event type. Segments contains only metadata until
.read_data is called.
Parameters
----------
dataset : instance of Dataset
info about record
annot : instance of Annotations
scoring info
cat : tuple of int
Determines where the signal is concatenated.
If cat[0] is 1, cycles will be concatenated.
If cat[1] is 1, different stages will be concatenated.
If cat[2] is 1, discontinuous signal within a same condition
(stage, cycle, event type) will be concatenated.
If cat[3] is 1, events of different types will be concatenated.
0 in any position indicates no concatenation.
evt_type: list of str, optional
Enter a list of event types to get events; otherwise, epochs will
be returned.
stage: list of str, optional
Stage(s) of interest. If None, stage is ignored.
cycle: list of tuple of two float, optional
Cycle(s) of interest, as start and end times in seconds from record
start. If None, cycles are ignored.
chan_full: list of str or None
Channel(s) of interest, only used for events (epochs have no
channel). Channel format is 'chan_name (group_name)'.
If used for epochs, separate segments will be returned for each
channel; this is necessary for channel-specific artefact removal (see
reject_artf below). If None, channel is ignored.
epoch : str, optional
If 'locked', returns epochs locked to staging. If 'unlocked', divides
signal (with specified concatenation) into epochs of duration epoch_dur
starting at first sample of every segment and discarding any remainder.
If None, longest run of signal is returned.
epoch_dur : float
only for epoch='unlocked'. Duration of epochs returned, in seconds.
epoch_overlap : float
only for epoch='unlocked'. Ratio of overlap between two consecutive
segments. Value between 0 and 1. Overriden by step.
epoch_step : float
only for epoch='unlocked'. Time between consecutive epoch starts, in
seconds. Overrides epoch_overlap/
reject_epoch: bool
If True, epochs marked as 'Poor' quality or staged as 'Artefact' will
be rejected (and the signal segmented in consequence). Has no effect on
event selection.
reject_artf : bool
If True, excludes events marked as 'Artefact'. If chan_full is
specified, only artefacts marked on a given channel are removed from
that channel. Signal is segmented in consequence.
If None, Artefact events are ignored.
min_dur : float
Minimum duration of segments returned, in seconds.
buffer : float
adds this many seconds of signal before and after each segment
Returns
-------
instance of Segments
metadata for all analysis segments
"""
bundles = get_times(annot, evt_type=evt_type, stage=stage, cycle=cycle,
chan=chan_full, exclude=reject_epoch, buffer=buffer)
# Remove artefacts
if reject_artf and bundles:
for bund in bundles:
bund['times'] = remove_artf_evts(bund['times'], annot,
bund['chan'], min_dur=0)
# Divide bundles into segments to be concatenated
if bundles:
if 'locked' == epoch:
bundles = _divide_bundles(bundles)
elif 'unlocked' == epoch:
if epoch_step is not None:
step = epoch_step
else:
step = epoch_dur - (epoch_dur * epoch_overlap)
bundles = _concat(bundles, cat)
bundles = _find_intervals(bundles, epoch_dur, step)
elif not epoch:
bundles = _concat(bundles, cat)
# Minimum duration
bundles = _longer_than(bundles, min_dur)
segments = Segments(dataset)
segments.segments = bundles
return segments | python | def fetch(dataset, annot, cat=(0, 0, 0, 0), evt_type=None, stage=None,
cycle=None, chan_full=None, epoch=None, epoch_dur=30,
epoch_overlap=0, epoch_step=None, reject_epoch=False,
reject_artf=False, min_dur=0, buffer=0):
bundles = get_times(annot, evt_type=evt_type, stage=stage, cycle=cycle,
chan=chan_full, exclude=reject_epoch, buffer=buffer)
# Remove artefacts
if reject_artf and bundles:
for bund in bundles:
bund['times'] = remove_artf_evts(bund['times'], annot,
bund['chan'], min_dur=0)
# Divide bundles into segments to be concatenated
if bundles:
if 'locked' == epoch:
bundles = _divide_bundles(bundles)
elif 'unlocked' == epoch:
if epoch_step is not None:
step = epoch_step
else:
step = epoch_dur - (epoch_dur * epoch_overlap)
bundles = _concat(bundles, cat)
bundles = _find_intervals(bundles, epoch_dur, step)
elif not epoch:
bundles = _concat(bundles, cat)
# Minimum duration
bundles = _longer_than(bundles, min_dur)
segments = Segments(dataset)
segments.segments = bundles
return segments | [
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stage, cycle, channel, event type. Segments contains only metadata until
.read_data is called.
Parameters
----------
dataset : instance of Dataset
info about record
annot : instance of Annotations
scoring info
cat : tuple of int
Determines where the signal is concatenated.
If cat[0] is 1, cycles will be concatenated.
If cat[1] is 1, different stages will be concatenated.
If cat[2] is 1, discontinuous signal within a same condition
(stage, cycle, event type) will be concatenated.
If cat[3] is 1, events of different types will be concatenated.
0 in any position indicates no concatenation.
evt_type: list of str, optional
Enter a list of event types to get events; otherwise, epochs will
be returned.
stage: list of str, optional
Stage(s) of interest. If None, stage is ignored.
cycle: list of tuple of two float, optional
Cycle(s) of interest, as start and end times in seconds from record
start. If None, cycles are ignored.
chan_full: list of str or None
Channel(s) of interest, only used for events (epochs have no
channel). Channel format is 'chan_name (group_name)'.
If used for epochs, separate segments will be returned for each
channel; this is necessary for channel-specific artefact removal (see
reject_artf below). If None, channel is ignored.
epoch : str, optional
If 'locked', returns epochs locked to staging. If 'unlocked', divides
signal (with specified concatenation) into epochs of duration epoch_dur
starting at first sample of every segment and discarding any remainder.
If None, longest run of signal is returned.
epoch_dur : float
only for epoch='unlocked'. Duration of epochs returned, in seconds.
epoch_overlap : float
only for epoch='unlocked'. Ratio of overlap between two consecutive
segments. Value between 0 and 1. Overriden by step.
epoch_step : float
only for epoch='unlocked'. Time between consecutive epoch starts, in
seconds. Overrides epoch_overlap/
reject_epoch: bool
If True, epochs marked as 'Poor' quality or staged as 'Artefact' will
be rejected (and the signal segmented in consequence). Has no effect on
event selection.
reject_artf : bool
If True, excludes events marked as 'Artefact'. If chan_full is
specified, only artefacts marked on a given channel are removed from
that channel. Signal is segmented in consequence.
If None, Artefact events are ignored.
min_dur : float
Minimum duration of segments returned, in seconds.
buffer : float
adds this many seconds of signal before and after each segment
Returns
-------
instance of Segments
metadata for all analysis segments | [
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24,969 | wonambi-python/wonambi | wonambi/trans/select.py | get_times | def get_times(annot, evt_type=None, stage=None, cycle=None, chan=None,
exclude=False, buffer=0):
"""Get start and end times for selected segments of data, bundled
together with info.
Parameters
----------
annot: instance of Annotations
The annotation file containing events and epochs
evt_type: list of str, optional
Enter a list of event types to get events; otherwise, epochs will
be returned.
stage: list of str, optional
Stage(s) of interest. If None, stage is ignored.
cycle: list of tuple of two float, optional
Cycle(s) of interest, as start and end times in seconds from record
start. If None, cycles are ignored.
chan: list of str or tuple of None
Channel(s) of interest. Channel format is 'chan_name (group_name)'.
If None, channel is ignored.
exclude: bool
Exclude epochs by quality. If True, epochs marked as 'Poor' quality
or staged as 'Artefact' will be rejected (and the signal segmented
in consequence). Has no effect on event selection.
buffer : float
adds this many seconds of signal before and after each segment
Returns
-------
list of dict
Each dict has times (the start and end times of each segment, as
list of tuple of float), stage, cycle, chan, name (event type,
if applicable)
Notes
-----
This function returns epoch or event start and end times, bundled
together according to the specified parameters.
Presently, setting exclude to True does not exclude events found in Poor
signal epochs. The rationale is that events would never be marked in Poor
signal epochs. If they were automatically detected, these epochs would
have been left out during detection. If they were manually marked, then
it must have been Good signal. At the moment, in the GUI, the exclude epoch
option is disabled when analyzing events, but we could fix the code if we
find a use case for rejecting events based on the quality of the epoch
signal.
"""
getter = annot.get_epochs
last = annot.last_second
if stage is None:
stage = (None,)
if cycle is None:
cycle = (None,)
if chan is None:
chan = (None,)
if evt_type is None:
evt_type = (None,)
elif isinstance(evt_type[0], str):
getter = annot.get_events
if chan != (None,):
chan.append('') # also retrieve events marked on all channels
else:
lg.error('Event type must be list/tuple of str or None')
qual = None
if exclude:
qual = 'Good'
bundles = []
for et in evt_type:
for ch in chan:
for cyc in cycle:
for ss in stage:
st_input = ss
if ss is not None:
st_input = (ss,)
evochs = getter(name=et, time=cyc, chan=(ch,),
stage=st_input, qual=qual)
if evochs:
times = [(
max(e['start'] - buffer, 0),
min(e['end'] + buffer, last)) for e in evochs]
times = sorted(times, key=lambda x: x[0])
one_bundle = {'times': times,
'stage': ss,
'cycle': cyc,
'chan': ch,
'name': et}
bundles.append(one_bundle)
return bundles | python | def get_times(annot, evt_type=None, stage=None, cycle=None, chan=None,
exclude=False, buffer=0):
getter = annot.get_epochs
last = annot.last_second
if stage is None:
stage = (None,)
if cycle is None:
cycle = (None,)
if chan is None:
chan = (None,)
if evt_type is None:
evt_type = (None,)
elif isinstance(evt_type[0], str):
getter = annot.get_events
if chan != (None,):
chan.append('') # also retrieve events marked on all channels
else:
lg.error('Event type must be list/tuple of str or None')
qual = None
if exclude:
qual = 'Good'
bundles = []
for et in evt_type:
for ch in chan:
for cyc in cycle:
for ss in stage:
st_input = ss
if ss is not None:
st_input = (ss,)
evochs = getter(name=et, time=cyc, chan=(ch,),
stage=st_input, qual=qual)
if evochs:
times = [(
max(e['start'] - buffer, 0),
min(e['end'] + buffer, last)) for e in evochs]
times = sorted(times, key=lambda x: x[0])
one_bundle = {'times': times,
'stage': ss,
'cycle': cyc,
'chan': ch,
'name': et}
bundles.append(one_bundle)
return bundles | [
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Parameters
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annot: instance of Annotations
The annotation file containing events and epochs
evt_type: list of str, optional
Enter a list of event types to get events; otherwise, epochs will
be returned.
stage: list of str, optional
Stage(s) of interest. If None, stage is ignored.
cycle: list of tuple of two float, optional
Cycle(s) of interest, as start and end times in seconds from record
start. If None, cycles are ignored.
chan: list of str or tuple of None
Channel(s) of interest. Channel format is 'chan_name (group_name)'.
If None, channel is ignored.
exclude: bool
Exclude epochs by quality. If True, epochs marked as 'Poor' quality
or staged as 'Artefact' will be rejected (and the signal segmented
in consequence). Has no effect on event selection.
buffer : float
adds this many seconds of signal before and after each segment
Returns
-------
list of dict
Each dict has times (the start and end times of each segment, as
list of tuple of float), stage, cycle, chan, name (event type,
if applicable)
Notes
-----
This function returns epoch or event start and end times, bundled
together according to the specified parameters.
Presently, setting exclude to True does not exclude events found in Poor
signal epochs. The rationale is that events would never be marked in Poor
signal epochs. If they were automatically detected, these epochs would
have been left out during detection. If they were manually marked, then
it must have been Good signal. At the moment, in the GUI, the exclude epoch
option is disabled when analyzing events, but we could fix the code if we
find a use case for rejecting events based on the quality of the epoch
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24,970 | wonambi-python/wonambi | wonambi/trans/select.py | _longer_than | def _longer_than(segments, min_dur):
"""Remove segments longer than min_dur."""
if min_dur <= 0.:
return segments
long_enough = []
for seg in segments:
if sum([t[1] - t[0] for t in seg['times']]) >= min_dur:
long_enough.append(seg)
return long_enough | python | def _longer_than(segments, min_dur):
if min_dur <= 0.:
return segments
long_enough = []
for seg in segments:
if sum([t[1] - t[0] for t in seg['times']]) >= min_dur:
long_enough.append(seg)
return long_enough | [
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24,971 | wonambi-python/wonambi | wonambi/trans/select.py | _concat | def _concat(bundles, cat=(0, 0, 0, 0)):
"""Prepare event or epoch start and end times for concatenation."""
chan = sorted(set([x['chan'] for x in bundles]))
cycle = sorted(set([x['cycle'] for x in bundles]))
stage = sorted(set([x['stage'] for x in bundles]))
evt_type = sorted(set([x['name'] for x in bundles]))
all_cycle = None
all_stage = None
all_evt_type = None
if cycle[0] is not None:
all_cycle = ', '.join([str(c) for c in cycle])
if stage[0] is not None:
all_stage = ', '.join(stage)
if evt_type[0] is not None:
all_evt_type = ', '.join(evt_type)
if cat[0]:
cycle = [all_cycle]
if cat[1]:
stage = [all_stage]
if cat[3]:
evt_type = [all_evt_type]
to_concat = []
for ch in chan:
for cyc in cycle:
for st in stage:
for et in evt_type:
new_times = []
for bund in bundles:
chan_cond = ch == bund['chan']
cyc_cond = cyc in (bund['cycle'], all_cycle)
st_cond = st in (bund['stage'], all_stage)
et_cond = et in (bund['name'], all_evt_type)
if chan_cond and cyc_cond and st_cond and et_cond:
new_times.extend(bund['times'])
new_times = sorted(new_times, key=lambda x: x[0])
new_bund = {'times': new_times,
'chan': ch,
'cycle': cyc,
'stage': st,
'name': et
}
to_concat.append(new_bund)
if not cat[2]:
to_concat_new = []
for bund in to_concat:
last = None
bund['times'].append((inf,inf))
start = 0
for i, j in enumerate(bund['times']):
if last is not None:
if not isclose(j[0], last, abs_tol=0.01):
new_times = bund['times'][start:i]
new_bund = bund.copy()
new_bund['times'] = new_times
to_concat_new.append(new_bund)
start = i
last = j[1]
to_concat = to_concat_new
to_concat = [x for x in to_concat if x['times']]
return to_concat | python | def _concat(bundles, cat=(0, 0, 0, 0)):
chan = sorted(set([x['chan'] for x in bundles]))
cycle = sorted(set([x['cycle'] for x in bundles]))
stage = sorted(set([x['stage'] for x in bundles]))
evt_type = sorted(set([x['name'] for x in bundles]))
all_cycle = None
all_stage = None
all_evt_type = None
if cycle[0] is not None:
all_cycle = ', '.join([str(c) for c in cycle])
if stage[0] is not None:
all_stage = ', '.join(stage)
if evt_type[0] is not None:
all_evt_type = ', '.join(evt_type)
if cat[0]:
cycle = [all_cycle]
if cat[1]:
stage = [all_stage]
if cat[3]:
evt_type = [all_evt_type]
to_concat = []
for ch in chan:
for cyc in cycle:
for st in stage:
for et in evt_type:
new_times = []
for bund in bundles:
chan_cond = ch == bund['chan']
cyc_cond = cyc in (bund['cycle'], all_cycle)
st_cond = st in (bund['stage'], all_stage)
et_cond = et in (bund['name'], all_evt_type)
if chan_cond and cyc_cond and st_cond and et_cond:
new_times.extend(bund['times'])
new_times = sorted(new_times, key=lambda x: x[0])
new_bund = {'times': new_times,
'chan': ch,
'cycle': cyc,
'stage': st,
'name': et
}
to_concat.append(new_bund)
if not cat[2]:
to_concat_new = []
for bund in to_concat:
last = None
bund['times'].append((inf,inf))
start = 0
for i, j in enumerate(bund['times']):
if last is not None:
if not isclose(j[0], last, abs_tol=0.01):
new_times = bund['times'][start:i]
new_bund = bund.copy()
new_bund['times'] = new_times
to_concat_new.append(new_bund)
start = i
last = j[1]
to_concat = to_concat_new
to_concat = [x for x in to_concat if x['times']]
return to_concat | [
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24,972 | wonambi-python/wonambi | wonambi/trans/select.py | _divide_bundles | def _divide_bundles(bundles):
"""Take each subsegment inside a bundle and put it in its own bundle,
copying the bundle metadata."""
divided = []
for bund in bundles:
for t in bund['times']:
new_bund = bund.copy()
new_bund['times'] = [t]
divided.append(new_bund)
return divided | python | def _divide_bundles(bundles):
divided = []
for bund in bundles:
for t in bund['times']:
new_bund = bund.copy()
new_bund['times'] = [t]
divided.append(new_bund)
return divided | [
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24,973 | wonambi-python/wonambi | wonambi/trans/select.py | _find_intervals | def _find_intervals(bundles, duration, step):
"""Divide bundles into segments of a certain duration and a certain step,
discarding any remainder."""
segments = []
for bund in bundles:
beg, end = bund['times'][0][0], bund['times'][-1][1]
if end - beg >= duration:
new_begs = arange(beg, end - duration, step)
for t in new_begs:
seg = bund.copy()
seg['times'] = [(t, t + duration)]
segments.append(seg)
return segments | python | def _find_intervals(bundles, duration, step):
segments = []
for bund in bundles:
beg, end = bund['times'][0][0], bund['times'][-1][1]
if end - beg >= duration:
new_begs = arange(beg, end - duration, step)
for t in new_begs:
seg = bund.copy()
seg['times'] = [(t, t + duration)]
segments.append(seg)
return segments | [
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24,974 | wonambi-python/wonambi | wonambi/trans/select.py | _create_data | def _create_data(data, active_chan, ref_chan=[], grp_name=None):
"""Create data after montage.
Parameters
----------
data : instance of ChanTime
the raw data
active_chan : list of str
the channel(s) of interest, without reference or group
ref_chan : list of str
reference channel(s), without group
grp_name : str
name of channel group, if applicable
Returns
-------
instance of ChanTime
the re-referenced data
"""
output = ChanTime()
output.s_freq = data.s_freq
output.start_time = data.start_time
output.axis['time'] = data.axis['time']
output.axis['chan'] = empty(1, dtype='O')
output.data = empty(1, dtype='O')
output.data[0] = empty((len(active_chan), data.number_of('time')[0]),
dtype='f')
sel_data = _select_channels(data, active_chan + ref_chan)
data1 = montage(sel_data, ref_chan=ref_chan)
data1.data[0] = nan_to_num(data1.data[0])
all_chan_grp_name = []
for i, chan in enumerate(active_chan):
chan_grp_name = chan
if grp_name:
chan_grp_name = chan + ' (' + grp_name + ')'
all_chan_grp_name.append(chan_grp_name)
dat = data1(chan=chan, trial=0)
output.data[0][i, :] = dat
output.axis['chan'][0] = asarray(all_chan_grp_name, dtype='U')
return output | python | def _create_data(data, active_chan, ref_chan=[], grp_name=None):
output = ChanTime()
output.s_freq = data.s_freq
output.start_time = data.start_time
output.axis['time'] = data.axis['time']
output.axis['chan'] = empty(1, dtype='O')
output.data = empty(1, dtype='O')
output.data[0] = empty((len(active_chan), data.number_of('time')[0]),
dtype='f')
sel_data = _select_channels(data, active_chan + ref_chan)
data1 = montage(sel_data, ref_chan=ref_chan)
data1.data[0] = nan_to_num(data1.data[0])
all_chan_grp_name = []
for i, chan in enumerate(active_chan):
chan_grp_name = chan
if grp_name:
chan_grp_name = chan + ' (' + grp_name + ')'
all_chan_grp_name.append(chan_grp_name)
dat = data1(chan=chan, trial=0)
output.data[0][i, :] = dat
output.axis['chan'][0] = asarray(all_chan_grp_name, dtype='U')
return output | [
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data : instance of ChanTime
the raw data
active_chan : list of str
the channel(s) of interest, without reference or group
ref_chan : list of str
reference channel(s), without group
grp_name : str
name of channel group, if applicable
Returns
-------
instance of ChanTime
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24,975 | wonambi-python/wonambi | wonambi/trans/select.py | _select_channels | def _select_channels(data, channels):
"""Select channels.
Parameters
----------
data : instance of ChanTime
data with all the channels
channels : list
channels of interest
Returns
-------
instance of ChanTime
data with only channels of interest
Notes
-----
This function does the same as wonambi.trans.select, but it's much faster.
wonambi.trans.Select needs to flexible for any data type, here we assume
that we have one trial, and that channel is the first dimension.
"""
output = data._copy()
chan_list = list(data.axis['chan'][0])
idx_chan = [chan_list.index(i_chan) for i_chan in channels]
output.data[0] = data.data[0][idx_chan, :]
output.axis['chan'][0] = asarray(channels)
return output | python | def _select_channels(data, channels):
output = data._copy()
chan_list = list(data.axis['chan'][0])
idx_chan = [chan_list.index(i_chan) for i_chan in channels]
output.data[0] = data.data[0][idx_chan, :]
output.axis['chan'][0] = asarray(channels)
return output | [
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Parameters
----------
data : instance of ChanTime
data with all the channels
channels : list
channels of interest
Returns
-------
instance of ChanTime
data with only channels of interest
Notes
-----
This function does the same as wonambi.trans.select, but it's much faster.
wonambi.trans.Select needs to flexible for any data type, here we assume
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24,976 | wonambi-python/wonambi | wonambi/widgets/creation.py | create_widgets | def create_widgets(MAIN):
"""Create all the widgets and dockwidgets. It also creates actions to
toggle views of dockwidgets in dockwidgets.
"""
""" ------ CREATE WIDGETS ------ """
MAIN.labels = Labels(MAIN)
MAIN.channels = Channels(MAIN)
MAIN.notes = Notes(MAIN)
MAIN.merge_dialog = MergeDialog(MAIN)
MAIN.export_events_dialog = ExportEventsDialog(MAIN)
MAIN.export_dataset_dialog = ExportDatasetDialog(MAIN)
MAIN.spindle_dialog = SpindleDialog(MAIN)
MAIN.slow_wave_dialog = SWDialog(MAIN)
MAIN.analysis_dialog = AnalysisDialog(MAIN)
#MAIN.plot_dialog = PlotDialog(MAIN)
MAIN.overview = Overview(MAIN)
MAIN.spectrum = Spectrum(MAIN)
MAIN.traces = Traces(MAIN)
MAIN.video = Video(MAIN)
MAIN.settings = Settings(MAIN) # depends on all widgets apart from Info
MAIN.info = Info(MAIN) # this has to be the last, it depends on settings
MAIN.setCentralWidget(MAIN.traces)
""" ------ LIST DOCKWIDGETS ------ """
new_docks = [{'name': 'Information',
'widget': MAIN.info,
'main_area': Qt.LeftDockWidgetArea,
'extra_area': Qt.RightDockWidgetArea,
},
{'name': 'Labels',
'widget': MAIN.labels,
'main_area': Qt.RightDockWidgetArea,
'extra_area': Qt.LeftDockWidgetArea,
},
{'name': 'Channels',
'widget': MAIN.channels,
'main_area': Qt.RightDockWidgetArea,
'extra_area': Qt.LeftDockWidgetArea,
},
{'name': 'Spectrum',
'widget': MAIN.spectrum,
'main_area': Qt.RightDockWidgetArea,
'extra_area': Qt.LeftDockWidgetArea,
},
{'name': 'Annotations',
'widget': MAIN.notes,
'main_area': Qt.LeftDockWidgetArea,
'extra_area': Qt.RightDockWidgetArea,
},
{'name': 'Video',
'widget': MAIN.video,
'main_area': Qt.LeftDockWidgetArea,
'extra_area': Qt.RightDockWidgetArea,
},
{'name': 'Overview',
'widget': MAIN.overview,
'main_area': Qt.BottomDockWidgetArea,
'extra_area': Qt.TopDockWidgetArea,
},
]
""" ------ CREATE DOCKWIDGETS ------ """
idx_docks = {}
actions = MAIN.action
actions['dockwidgets'] = []
for dock in new_docks:
dockwidget = QDockWidget(dock['name'], MAIN)
dockwidget.setWidget(dock['widget'])
dockwidget.setAllowedAreas(dock['main_area'] | dock['extra_area'])
dockwidget.setObjectName(dock['name']) # savestate
idx_docks[dock['name']] = dockwidget
MAIN.addDockWidget(dock['main_area'], dockwidget)
dockwidget_action = dockwidget.toggleViewAction()
dockwidget_action.setIcon(QIcon(ICON['widget']))
actions['dockwidgets'].append(dockwidget_action)
""" ------ ORGANIZE DOCKWIDGETS ------ """
MAIN.tabifyDockWidget(idx_docks['Information'],
idx_docks['Video'])
MAIN.tabifyDockWidget(idx_docks['Channels'],
idx_docks['Labels'])
idx_docks['Information'].raise_() | python | def create_widgets(MAIN):
""" ------ CREATE WIDGETS ------ """
MAIN.labels = Labels(MAIN)
MAIN.channels = Channels(MAIN)
MAIN.notes = Notes(MAIN)
MAIN.merge_dialog = MergeDialog(MAIN)
MAIN.export_events_dialog = ExportEventsDialog(MAIN)
MAIN.export_dataset_dialog = ExportDatasetDialog(MAIN)
MAIN.spindle_dialog = SpindleDialog(MAIN)
MAIN.slow_wave_dialog = SWDialog(MAIN)
MAIN.analysis_dialog = AnalysisDialog(MAIN)
#MAIN.plot_dialog = PlotDialog(MAIN)
MAIN.overview = Overview(MAIN)
MAIN.spectrum = Spectrum(MAIN)
MAIN.traces = Traces(MAIN)
MAIN.video = Video(MAIN)
MAIN.settings = Settings(MAIN) # depends on all widgets apart from Info
MAIN.info = Info(MAIN) # this has to be the last, it depends on settings
MAIN.setCentralWidget(MAIN.traces)
""" ------ LIST DOCKWIDGETS ------ """
new_docks = [{'name': 'Information',
'widget': MAIN.info,
'main_area': Qt.LeftDockWidgetArea,
'extra_area': Qt.RightDockWidgetArea,
},
{'name': 'Labels',
'widget': MAIN.labels,
'main_area': Qt.RightDockWidgetArea,
'extra_area': Qt.LeftDockWidgetArea,
},
{'name': 'Channels',
'widget': MAIN.channels,
'main_area': Qt.RightDockWidgetArea,
'extra_area': Qt.LeftDockWidgetArea,
},
{'name': 'Spectrum',
'widget': MAIN.spectrum,
'main_area': Qt.RightDockWidgetArea,
'extra_area': Qt.LeftDockWidgetArea,
},
{'name': 'Annotations',
'widget': MAIN.notes,
'main_area': Qt.LeftDockWidgetArea,
'extra_area': Qt.RightDockWidgetArea,
},
{'name': 'Video',
'widget': MAIN.video,
'main_area': Qt.LeftDockWidgetArea,
'extra_area': Qt.RightDockWidgetArea,
},
{'name': 'Overview',
'widget': MAIN.overview,
'main_area': Qt.BottomDockWidgetArea,
'extra_area': Qt.TopDockWidgetArea,
},
]
""" ------ CREATE DOCKWIDGETS ------ """
idx_docks = {}
actions = MAIN.action
actions['dockwidgets'] = []
for dock in new_docks:
dockwidget = QDockWidget(dock['name'], MAIN)
dockwidget.setWidget(dock['widget'])
dockwidget.setAllowedAreas(dock['main_area'] | dock['extra_area'])
dockwidget.setObjectName(dock['name']) # savestate
idx_docks[dock['name']] = dockwidget
MAIN.addDockWidget(dock['main_area'], dockwidget)
dockwidget_action = dockwidget.toggleViewAction()
dockwidget_action.setIcon(QIcon(ICON['widget']))
actions['dockwidgets'].append(dockwidget_action)
""" ------ ORGANIZE DOCKWIDGETS ------ """
MAIN.tabifyDockWidget(idx_docks['Information'],
idx_docks['Video'])
MAIN.tabifyDockWidget(idx_docks['Channels'],
idx_docks['Labels'])
idx_docks['Information'].raise_() | [
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24,977 | wonambi-python/wonambi | wonambi/widgets/creation.py | create_actions | def create_actions(MAIN):
"""Create all the possible actions."""
actions = MAIN.action # actions was already taken
""" ------ OPEN SETTINGS ------ """
actions['open_settings'] = QAction(QIcon(ICON['settings']), 'Settings',
MAIN)
actions['open_settings'].triggered.connect(MAIN.show_settings)
""" ------ CLOSE WINDOW ------ """
actions['close_wndw'] = QAction(QIcon(ICON['quit']), 'Quit', MAIN)
actions['close_wndw'].triggered.connect(MAIN.close)
""" ------ ABOUT ------ """
actions['about'] = QAction('About WONAMBI', MAIN)
actions['about'].triggered.connect(MAIN.about)
actions['aboutqt'] = QAction('About Qt', MAIN)
actions['aboutqt'].triggered.connect(lambda: QMessageBox.aboutQt(MAIN)) | python | def create_actions(MAIN):
actions = MAIN.action # actions was already taken
""" ------ OPEN SETTINGS ------ """
actions['open_settings'] = QAction(QIcon(ICON['settings']), 'Settings',
MAIN)
actions['open_settings'].triggered.connect(MAIN.show_settings)
""" ------ CLOSE WINDOW ------ """
actions['close_wndw'] = QAction(QIcon(ICON['quit']), 'Quit', MAIN)
actions['close_wndw'].triggered.connect(MAIN.close)
""" ------ ABOUT ------ """
actions['about'] = QAction('About WONAMBI', MAIN)
actions['about'].triggered.connect(MAIN.about)
actions['aboutqt'] = QAction('About Qt', MAIN)
actions['aboutqt'].triggered.connect(lambda: QMessageBox.aboutQt(MAIN)) | [
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24,978 | wonambi-python/wonambi | wonambi/widgets/creation.py | create_toolbar | def create_toolbar(MAIN):
"""Create the various toolbars."""
actions = MAIN.action
toolbar = MAIN.addToolBar('File Management')
toolbar.setObjectName('File Management') # for savestate
toolbar.addAction(MAIN.info.action['open_dataset'])
toolbar.addSeparator()
toolbar.addAction(MAIN.channels.action['load_channels'])
toolbar.addAction(MAIN.channels.action['save_channels'])
toolbar.addSeparator()
toolbar.addAction(MAIN.notes.action['new_annot'])
toolbar.addAction(MAIN.notes.action['load_annot'])
""" ------ SCROLL ------ """
actions = MAIN.traces.action
toolbar = MAIN.addToolBar('Scroll')
toolbar.setObjectName('Scroll') # for savestate
toolbar.addAction(actions['step_prev'])
toolbar.addAction(actions['step_next'])
toolbar.addAction(actions['page_prev'])
toolbar.addAction(actions['page_next'])
toolbar.addSeparator()
toolbar.addAction(actions['X_more'])
toolbar.addAction(actions['X_less'])
toolbar.addSeparator()
toolbar.addAction(actions['Y_less'])
toolbar.addAction(actions['Y_more'])
toolbar.addAction(actions['Y_wider'])
toolbar.addAction(actions['Y_tighter'])
""" ------ ANNOTATIONS ------ """
actions = MAIN.notes.action
toolbar = MAIN.addToolBar('Annotations')
toolbar.setObjectName('Annotations')
toolbar.addAction(actions['new_bookmark'])
toolbar.addSeparator()
toolbar.addAction(actions['new_event'])
toolbar.addWidget(MAIN.notes.idx_eventtype)
toolbar.addSeparator()
toolbar.addWidget(MAIN.notes.idx_stage)
toolbar.addWidget(MAIN.notes.idx_quality) | python | def create_toolbar(MAIN):
actions = MAIN.action
toolbar = MAIN.addToolBar('File Management')
toolbar.setObjectName('File Management') # for savestate
toolbar.addAction(MAIN.info.action['open_dataset'])
toolbar.addSeparator()
toolbar.addAction(MAIN.channels.action['load_channels'])
toolbar.addAction(MAIN.channels.action['save_channels'])
toolbar.addSeparator()
toolbar.addAction(MAIN.notes.action['new_annot'])
toolbar.addAction(MAIN.notes.action['load_annot'])
""" ------ SCROLL ------ """
actions = MAIN.traces.action
toolbar = MAIN.addToolBar('Scroll')
toolbar.setObjectName('Scroll') # for savestate
toolbar.addAction(actions['step_prev'])
toolbar.addAction(actions['step_next'])
toolbar.addAction(actions['page_prev'])
toolbar.addAction(actions['page_next'])
toolbar.addSeparator()
toolbar.addAction(actions['X_more'])
toolbar.addAction(actions['X_less'])
toolbar.addSeparator()
toolbar.addAction(actions['Y_less'])
toolbar.addAction(actions['Y_more'])
toolbar.addAction(actions['Y_wider'])
toolbar.addAction(actions['Y_tighter'])
""" ------ ANNOTATIONS ------ """
actions = MAIN.notes.action
toolbar = MAIN.addToolBar('Annotations')
toolbar.setObjectName('Annotations')
toolbar.addAction(actions['new_bookmark'])
toolbar.addSeparator()
toolbar.addAction(actions['new_event'])
toolbar.addWidget(MAIN.notes.idx_eventtype)
toolbar.addSeparator()
toolbar.addWidget(MAIN.notes.idx_stage)
toolbar.addWidget(MAIN.notes.idx_quality) | [
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24,979 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.update_evt_types | def update_evt_types(self):
"""Update the event types list when dialog is opened."""
self.event_types = self.parent.notes.annot.event_types
self.idx_evt_type.clear()
self.frequency['norm_evt_type'].clear()
for ev in self.event_types:
self.idx_evt_type.addItem(ev)
self.frequency['norm_evt_type'].addItem(ev) | python | def update_evt_types(self):
self.event_types = self.parent.notes.annot.event_types
self.idx_evt_type.clear()
self.frequency['norm_evt_type'].clear()
for ev in self.event_types:
self.idx_evt_type.addItem(ev)
self.frequency['norm_evt_type'].addItem(ev) | [
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24,980 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.toggle_concatenate | def toggle_concatenate(self):
"""Enable and disable concatenation options."""
if not (self.chunk['epoch'].isChecked() and
self.lock_to_staging.get_value()):
for i,j in zip([self.idx_chan, self.idx_cycle, self.idx_stage,
self.idx_evt_type],
[self.cat['chan'], self.cat['cycle'],
self.cat['stage'], self.cat['evt_type']]):
if len(i.selectedItems()) > 1:
j.setEnabled(True)
else:
j.setEnabled(False)
j.setChecked(False)
if not self.chunk['event'].isChecked():
self.cat['evt_type'].setEnabled(False)
if not self.cat['discontinuous'].get_value():
self.cat['chan'].setEnabled(False)
self.cat['chan'].setChecked(False)
self.update_nseg() | python | def toggle_concatenate(self):
if not (self.chunk['epoch'].isChecked() and
self.lock_to_staging.get_value()):
for i,j in zip([self.idx_chan, self.idx_cycle, self.idx_stage,
self.idx_evt_type],
[self.cat['chan'], self.cat['cycle'],
self.cat['stage'], self.cat['evt_type']]):
if len(i.selectedItems()) > 1:
j.setEnabled(True)
else:
j.setEnabled(False)
j.setChecked(False)
if not self.chunk['event'].isChecked():
self.cat['evt_type'].setEnabled(False)
if not self.cat['discontinuous'].get_value():
self.cat['chan'].setEnabled(False)
self.cat['chan'].setChecked(False)
self.update_nseg() | [
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24,981 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.toggle_pac | def toggle_pac(self):
"""Enable and disable PAC options."""
if Pac is not None:
pac_on = self.pac['pac_on'].get_value()
self.pac['prep'].setEnabled(pac_on)
self.pac['box_metric'].setEnabled(pac_on)
self.pac['box_complex'].setEnabled(pac_on)
self.pac['box_surro'].setEnabled(pac_on)
self.pac['box_opts'].setEnabled(pac_on)
if not pac_on:
self.pac['prep'].set_value(False)
if Pac is not None and pac_on:
pac = self.pac
hilb_on = pac['hilbert_on'].isChecked()
wav_on = pac['wavelet_on'].isChecked()
for button in pac['hilbert'].values():
button[0].setEnabled(hilb_on)
if button[1] is not None:
button[1].setEnabled(hilb_on)
pac['wav_width'][0].setEnabled(wav_on)
pac['wav_width'][1].setEnabled(wav_on)
if pac['metric'].get_value() in [
'Kullback-Leibler Distance',
'Heights ratio']:
pac['nbin'][0].setEnabled(True)
pac['nbin'][1].setEnabled(True)
else:
pac['nbin'][0].setEnabled(False)
pac['nbin'][1].setEnabled(False)
if pac['metric'] == 'ndPac':
for button in pac['surro'].values():
button[0].setEnabled(False)
if button[1] is not None:
button[1].setEnabled(False)
pac['surro']['pval'][0].setEnabled(True)
ndpac_on = pac['metric'].get_value() == 'ndPac'
surro_on = logical_and(pac['surro_method'].get_value() != ''
'No surrogates', not ndpac_on)
norm_on = pac['surro_norm'].get_value() != 'No normalization'
blocks_on = 'across time' in pac['surro_method'].get_value()
pac['surro_method'].setEnabled(not ndpac_on)
for button in pac['surro'].values():
button[0].setEnabled(surro_on and norm_on)
if button[1] is not None:
button[1].setEnabled(surro_on and norm_on)
pac['surro']['nblocks'][0].setEnabled(blocks_on)
pac['surro']['nblocks'][1].setEnabled(blocks_on)
if ndpac_on:
pac['surro_method'].set_value('No surrogates')
pac['surro']['pval'][0].setEnabled(True) | python | def toggle_pac(self):
if Pac is not None:
pac_on = self.pac['pac_on'].get_value()
self.pac['prep'].setEnabled(pac_on)
self.pac['box_metric'].setEnabled(pac_on)
self.pac['box_complex'].setEnabled(pac_on)
self.pac['box_surro'].setEnabled(pac_on)
self.pac['box_opts'].setEnabled(pac_on)
if not pac_on:
self.pac['prep'].set_value(False)
if Pac is not None and pac_on:
pac = self.pac
hilb_on = pac['hilbert_on'].isChecked()
wav_on = pac['wavelet_on'].isChecked()
for button in pac['hilbert'].values():
button[0].setEnabled(hilb_on)
if button[1] is not None:
button[1].setEnabled(hilb_on)
pac['wav_width'][0].setEnabled(wav_on)
pac['wav_width'][1].setEnabled(wav_on)
if pac['metric'].get_value() in [
'Kullback-Leibler Distance',
'Heights ratio']:
pac['nbin'][0].setEnabled(True)
pac['nbin'][1].setEnabled(True)
else:
pac['nbin'][0].setEnabled(False)
pac['nbin'][1].setEnabled(False)
if pac['metric'] == 'ndPac':
for button in pac['surro'].values():
button[0].setEnabled(False)
if button[1] is not None:
button[1].setEnabled(False)
pac['surro']['pval'][0].setEnabled(True)
ndpac_on = pac['metric'].get_value() == 'ndPac'
surro_on = logical_and(pac['surro_method'].get_value() != ''
'No surrogates', not ndpac_on)
norm_on = pac['surro_norm'].get_value() != 'No normalization'
blocks_on = 'across time' in pac['surro_method'].get_value()
pac['surro_method'].setEnabled(not ndpac_on)
for button in pac['surro'].values():
button[0].setEnabled(surro_on and norm_on)
if button[1] is not None:
button[1].setEnabled(surro_on and norm_on)
pac['surro']['nblocks'][0].setEnabled(blocks_on)
pac['surro']['nblocks'][1].setEnabled(blocks_on)
if ndpac_on:
pac['surro_method'].set_value('No surrogates')
pac['surro']['pval'][0].setEnabled(True) | [
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24,982 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.update_nseg | def update_nseg(self):
"""Update the number of segments, displayed in the dialog."""
self.nseg = 0
if self.one_grp:
segments = self.get_segments()
if segments is not None:
self.nseg = len(segments)
self.show_nseg.setText('Number of segments: ' + str(self.nseg))
times = [t for seg in segments for t in seg['times']]
self.parent.overview.mark_poi(times)
else:
self.show_nseg.setText('No valid segments')
self.toggle_freq() | python | def update_nseg(self):
self.nseg = 0
if self.one_grp:
segments = self.get_segments()
if segments is not None:
self.nseg = len(segments)
self.show_nseg.setText('Number of segments: ' + str(self.nseg))
times = [t for seg in segments for t in seg['times']]
self.parent.overview.mark_poi(times)
else:
self.show_nseg.setText('No valid segments')
self.toggle_freq() | [
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24,983 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.check_all_local | def check_all_local(self):
"""Check or uncheck all local event parameters."""
all_local_chk = self.event['global']['all_local'].isChecked()
for buttons in self.event['local'].values():
buttons[0].setChecked(all_local_chk)
buttons[1].setEnabled(buttons[0].isChecked()) | python | def check_all_local(self):
all_local_chk = self.event['global']['all_local'].isChecked()
for buttons in self.event['local'].values():
buttons[0].setChecked(all_local_chk)
buttons[1].setEnabled(buttons[0].isChecked()) | [
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24,984 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.check_all_local_prep | def check_all_local_prep(self):
"""Check or uncheck all enabled event pre-processing."""
all_local_pp_chk = self.event['global']['all_local_prep'].isChecked()
for buttons in self.event['local'].values():
if buttons[1].isEnabled():
buttons[1].setChecked(all_local_pp_chk) | python | def check_all_local_prep(self):
all_local_pp_chk = self.event['global']['all_local_prep'].isChecked()
for buttons in self.event['local'].values():
if buttons[1].isEnabled():
buttons[1].setChecked(all_local_pp_chk) | [
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24,985 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.uncheck_all_local | def uncheck_all_local(self):
"""Uncheck 'all local' box when a local event is unchecked."""
for buttons in self.event['local'].values():
if not buttons[0].get_value():
self.event['global']['all_local'].setChecked(False)
if buttons[1].isEnabled() and not buttons[1].get_value():
self.event['global']['all_local_prep'].setChecked(False) | python | def uncheck_all_local(self):
for buttons in self.event['local'].values():
if not buttons[0].get_value():
self.event['global']['all_local'].setChecked(False)
if buttons[1].isEnabled() and not buttons[1].get_value():
self.event['global']['all_local_prep'].setChecked(False) | [
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24,986 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.get_segments | def get_segments(self):
"""Get segments for analysis. Creates instance of trans.Segments."""
# Chunking
chunk = {k: v.isChecked() for k, v in self.chunk.items()}
lock_to_staging = self.lock_to_staging.get_value()
epoch_dur = self.epoch_param['dur'].get_value()
epoch_overlap = self.epoch_param['overlap_val'].value()
epoch_step = None
epoch = None
if chunk['epoch']:
if lock_to_staging:
epoch = 'locked'
else:
epoch = 'unlocked'
if self.epoch_param['step'].isChecked():
epoch_step = self.epoch_param['step_val'].get_value()
if epoch_step <= 0:
epoch_step = 0.1
# Which channel(s)
self.chan = self.get_channels() # chan name without group
if not self.chan:
return
# Which event type(s)
chan_full = None
evt_type = None
if chunk['event']:
if self.evt_chan_only.get_value():
chan_full = [i + ' (' + self.idx_group.currentText() + ''
')' for i in self.chan]
evt_type = self.idx_evt_type.selectedItems()
if not evt_type:
return
else:
evt_type = [x.text() for x in evt_type]
# Which cycle(s)
cycle = self.cycle = self.get_cycles()
# Which stage(s)
stage = self.idx_stage.selectedItems()
if not stage:
stage = self.stage = None
else:
stage = self.stage = [
x.text() for x in self.idx_stage.selectedItems()]
# Concatenation
cat = {k: v.get_value() for k, v in self.cat.items()}
cat = (int(cat['cycle']), int(cat['stage']),
int(cat['discontinuous']), int(cat['evt_type']))
# Artefact event rejection
reject_event = self.reject_event.get_value()
if reject_event == 'channel-specific':
chan_full = [i + ' (' + self.idx_group.currentText() + ''
')' for i in self.chan]
reject_artf = True
elif reject_event == 'from any channel':
reject_artf = True
else:
reject_artf = False
# Other options
min_dur = self.min_dur.get_value()
reject_epoch = self.reject_epoch.get_value()
# Generate title for summary plot
self.title = self.make_title(chan_full, cycle, stage, evt_type)
segments = fetch(self.parent.info.dataset,
self.parent.notes.annot, cat=cat,
evt_type=evt_type, stage=stage, cycle=cycle,
chan_full=chan_full, epoch=epoch,
epoch_dur=epoch_dur, epoch_overlap=epoch_overlap,
epoch_step=epoch_step, reject_epoch=reject_epoch,
reject_artf=reject_artf, min_dur=min_dur)
return segments | python | def get_segments(self):
# Chunking
chunk = {k: v.isChecked() for k, v in self.chunk.items()}
lock_to_staging = self.lock_to_staging.get_value()
epoch_dur = self.epoch_param['dur'].get_value()
epoch_overlap = self.epoch_param['overlap_val'].value()
epoch_step = None
epoch = None
if chunk['epoch']:
if lock_to_staging:
epoch = 'locked'
else:
epoch = 'unlocked'
if self.epoch_param['step'].isChecked():
epoch_step = self.epoch_param['step_val'].get_value()
if epoch_step <= 0:
epoch_step = 0.1
# Which channel(s)
self.chan = self.get_channels() # chan name without group
if not self.chan:
return
# Which event type(s)
chan_full = None
evt_type = None
if chunk['event']:
if self.evt_chan_only.get_value():
chan_full = [i + ' (' + self.idx_group.currentText() + ''
')' for i in self.chan]
evt_type = self.idx_evt_type.selectedItems()
if not evt_type:
return
else:
evt_type = [x.text() for x in evt_type]
# Which cycle(s)
cycle = self.cycle = self.get_cycles()
# Which stage(s)
stage = self.idx_stage.selectedItems()
if not stage:
stage = self.stage = None
else:
stage = self.stage = [
x.text() for x in self.idx_stage.selectedItems()]
# Concatenation
cat = {k: v.get_value() for k, v in self.cat.items()}
cat = (int(cat['cycle']), int(cat['stage']),
int(cat['discontinuous']), int(cat['evt_type']))
# Artefact event rejection
reject_event = self.reject_event.get_value()
if reject_event == 'channel-specific':
chan_full = [i + ' (' + self.idx_group.currentText() + ''
')' for i in self.chan]
reject_artf = True
elif reject_event == 'from any channel':
reject_artf = True
else:
reject_artf = False
# Other options
min_dur = self.min_dur.get_value()
reject_epoch = self.reject_epoch.get_value()
# Generate title for summary plot
self.title = self.make_title(chan_full, cycle, stage, evt_type)
segments = fetch(self.parent.info.dataset,
self.parent.notes.annot, cat=cat,
evt_type=evt_type, stage=stage, cycle=cycle,
chan_full=chan_full, epoch=epoch,
epoch_dur=epoch_dur, epoch_overlap=epoch_overlap,
epoch_step=epoch_step, reject_epoch=reject_epoch,
reject_artf=reject_artf, min_dur=min_dur)
return segments | [
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24,987 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.transform_data | def transform_data(self, data):
"""Apply pre-processing transformation to data, and add it to data
dict.
Parameters
---------
data : instance of Segments
segments including 'data' (ChanTime)
Returns
-------
instance of Segments
same object with transformed data as 'trans_data' (ChanTime)
"""
trans = self.trans
differ = trans['diff'].get_value()
bandpass = trans['bandpass'].get_value()
notch1 = trans['notch1'].get_value()
notch2 = trans['notch2'].get_value()
for seg in data:
dat = seg['data']
if differ:
dat = math(dat, operator=diff, axis='time')
if bandpass != 'none':
order = trans['bp']['order'][1].get_value()
f1 = trans['bp']['f1'][1].get_value()
f2 = trans['bp']['f2'][1].get_value()
if f1 == '':
f1 = None
if f2 == '':
f2 = None
dat = filter_(dat, low_cut=f1, high_cut=f2, order=order,
ftype=bandpass)
if notch1 != 'none':
order = trans['n1']['order'][1].get_value()
cf = trans['n1']['cf'][1].get_value()
hbw = trans['n1']['bw'][1].get_value() / 2.0
lo_pass = cf - hbw
hi_pass = cf + hbw
dat = filter_(dat, low_cut=hi_pass, order=order, ftype=notch1)
dat = filter_(dat, high_cut=lo_pass, order=order, ftype=notch1)
if notch2 != 'none':
order = trans['n2']['order'][1].get_value()
cf = trans['n2']['cf'][1].get_value()
hbw = trans['n2']['bw'][1].get_value() / 2.0
lo_pass = cf - hbw
hi_pass = cf + hbw
dat = filter_(dat, low_cut=hi_pass, order=order, ftype=notch1)
dat = filter_(dat, high_cut=lo_pass, order=order, ftype=notch1)
seg['trans_data'] = dat
return data | python | def transform_data(self, data):
trans = self.trans
differ = trans['diff'].get_value()
bandpass = trans['bandpass'].get_value()
notch1 = trans['notch1'].get_value()
notch2 = trans['notch2'].get_value()
for seg in data:
dat = seg['data']
if differ:
dat = math(dat, operator=diff, axis='time')
if bandpass != 'none':
order = trans['bp']['order'][1].get_value()
f1 = trans['bp']['f1'][1].get_value()
f2 = trans['bp']['f2'][1].get_value()
if f1 == '':
f1 = None
if f2 == '':
f2 = None
dat = filter_(dat, low_cut=f1, high_cut=f2, order=order,
ftype=bandpass)
if notch1 != 'none':
order = trans['n1']['order'][1].get_value()
cf = trans['n1']['cf'][1].get_value()
hbw = trans['n1']['bw'][1].get_value() / 2.0
lo_pass = cf - hbw
hi_pass = cf + hbw
dat = filter_(dat, low_cut=hi_pass, order=order, ftype=notch1)
dat = filter_(dat, high_cut=lo_pass, order=order, ftype=notch1)
if notch2 != 'none':
order = trans['n2']['order'][1].get_value()
cf = trans['n2']['cf'][1].get_value()
hbw = trans['n2']['bw'][1].get_value() / 2.0
lo_pass = cf - hbw
hi_pass = cf + hbw
dat = filter_(dat, low_cut=hi_pass, order=order, ftype=notch1)
dat = filter_(dat, high_cut=lo_pass, order=order, ftype=notch1)
seg['trans_data'] = dat
return data | [
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24,988 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.save_as | def save_as(self):
"""Dialog for getting name, location of data export file."""
filename = splitext(
self.parent.notes.annot.xml_file)[0] + '_data'
filename, _ = QFileDialog.getSaveFileName(self, 'Export analysis data',
filename,
'CSV (*.csv)')
if filename == '':
return
self.filename = filename
short_filename = short_strings(basename(self.filename))
self.idx_filename.setText(short_filename) | python | def save_as(self):
filename = splitext(
self.parent.notes.annot.xml_file)[0] + '_data'
filename, _ = QFileDialog.getSaveFileName(self, 'Export analysis data',
filename,
'CSV (*.csv)')
if filename == '':
return
self.filename = filename
short_filename = short_strings(basename(self.filename))
self.idx_filename.setText(short_filename) | [
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24,989 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.plot_freq | def plot_freq(self, x, y, title='', ylabel=None, scale='semilogy'):
"""Plot mean frequency spectrum and display in dialog.
Parameters
----------
x : list
vector with frequencies
y : ndarray
vector with amplitudes
title : str
plot title
ylabel : str
plot y label
scale : str
semilogy, loglog or linear
"""
freq = self.frequency
scaling = freq['scaling'].get_value()
if ylabel is None:
if freq['complex'].get_value():
ylabel = 'Amplitude (uV)'
elif 'power' == scaling:
ylabel = 'Power spectral density (uV ** 2 / Hz)'
elif 'energy' == scaling:
ylabel = 'Energy spectral density (uV ** 2)'
self.parent.plot_dialog = PlotDialog(self.parent)
self.parent.plot_dialog.canvas.plot(x, y, title, ylabel, scale=scale)
self.parent.show_plot_dialog() | python | def plot_freq(self, x, y, title='', ylabel=None, scale='semilogy'):
freq = self.frequency
scaling = freq['scaling'].get_value()
if ylabel is None:
if freq['complex'].get_value():
ylabel = 'Amplitude (uV)'
elif 'power' == scaling:
ylabel = 'Power spectral density (uV ** 2 / Hz)'
elif 'energy' == scaling:
ylabel = 'Energy spectral density (uV ** 2)'
self.parent.plot_dialog = PlotDialog(self.parent)
self.parent.plot_dialog.canvas.plot(x, y, title, ylabel, scale=scale)
self.parent.show_plot_dialog() | [
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x : list
vector with frequencies
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ylabel : str
plot y label
scale : str
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24,990 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.export_pac | def export_pac(self, xpac, fpha, famp, desc):
"""Write PAC analysis data to CSV."""
filename = splitext(self.filename)[0] + '_pac.csv'
heading_row_1 = ['Segment index',
'Start time',
'End time',
'Duration',
'Stitch',
'Stage',
'Cycle',
'Event type',
'Channel',
]
spacer = [''] * (len(heading_row_1) - 1)
heading_row_2 = []
for fp in fpha:
fp_str = str(fp[0]) + '-' + str(fp[1])
for fa in famp:
fa_str = str(fa[0]) + '-' + str(fa[1])
heading_row_2.append(fp_str + '_' + fa_str + '_pac')
if 'pval' in xpac[list(xpac.keys())[0]].keys():
heading_row_3 = [x[:-4] + '_pval' for x in heading_row_2]
heading_row_2.extend(heading_row_3)
with open(filename, 'w', newline='') as f:
lg.info('Writing to ' + str(filename))
csv_file = writer(f)
csv_file.writerow(['Wonambi v{}'.format(__version__)])
csv_file.writerow(heading_row_1 + heading_row_2)
csv_file.writerow(['Mean'] + spacer + list(desc['mean']))
csv_file.writerow(['SD'] + spacer + list(desc['sd']))
csv_file.writerow(['Mean of ln'] + spacer + list(desc['mean_log']))
csv_file.writerow(['SD of ln'] + spacer + list(desc['sd_log']))
idx = 0
for chan in xpac.keys():
for i, j in enumerate(xpac[chan]['times']):
idx += 1
cyc = None
if xpac[chan]['cycle'][i] is not None:
cyc = xpac[chan]['cycle'][i][2]
data_row = list(ravel(xpac[chan]['data'][i, :, :]))
pval_row = []
if 'pval' in xpac[chan]:
pval_row = list(ravel(xpac[chan]['pval'][i, :, :]))
csv_file.writerow([idx,
j[0],
j[1],
xpac[chan]['duration'][i],
xpac[chan]['n_stitch'][i],
xpac[chan]['stage'][i],
cyc,
xpac[chan]['name'][i],
chan,
] + data_row + pval_row) | python | def export_pac(self, xpac, fpha, famp, desc):
filename = splitext(self.filename)[0] + '_pac.csv'
heading_row_1 = ['Segment index',
'Start time',
'End time',
'Duration',
'Stitch',
'Stage',
'Cycle',
'Event type',
'Channel',
]
spacer = [''] * (len(heading_row_1) - 1)
heading_row_2 = []
for fp in fpha:
fp_str = str(fp[0]) + '-' + str(fp[1])
for fa in famp:
fa_str = str(fa[0]) + '-' + str(fa[1])
heading_row_2.append(fp_str + '_' + fa_str + '_pac')
if 'pval' in xpac[list(xpac.keys())[0]].keys():
heading_row_3 = [x[:-4] + '_pval' for x in heading_row_2]
heading_row_2.extend(heading_row_3)
with open(filename, 'w', newline='') as f:
lg.info('Writing to ' + str(filename))
csv_file = writer(f)
csv_file.writerow(['Wonambi v{}'.format(__version__)])
csv_file.writerow(heading_row_1 + heading_row_2)
csv_file.writerow(['Mean'] + spacer + list(desc['mean']))
csv_file.writerow(['SD'] + spacer + list(desc['sd']))
csv_file.writerow(['Mean of ln'] + spacer + list(desc['mean_log']))
csv_file.writerow(['SD of ln'] + spacer + list(desc['sd_log']))
idx = 0
for chan in xpac.keys():
for i, j in enumerate(xpac[chan]['times']):
idx += 1
cyc = None
if xpac[chan]['cycle'][i] is not None:
cyc = xpac[chan]['cycle'][i][2]
data_row = list(ravel(xpac[chan]['data'][i, :, :]))
pval_row = []
if 'pval' in xpac[chan]:
pval_row = list(ravel(xpac[chan]['pval'][i, :, :]))
csv_file.writerow([idx,
j[0],
j[1],
xpac[chan]['duration'][i],
xpac[chan]['n_stitch'][i],
xpac[chan]['stage'][i],
cyc,
xpac[chan]['name'][i],
chan,
] + data_row + pval_row) | [
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24,991 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.compute_evt_params | def compute_evt_params(self):
"""Compute event parameters."""
ev = self.event
glob = {k: v.get_value() for k, v in ev['global'].items()}
params = {k: v[0].get_value() for k, v in ev['local'].items()}
prep = {k: v[1].get_value() for k, v in ev['local'].items()}
slopes = {k: v.get_value() for k, v in ev['sw'].items()}
f1 = ev['f1'].get_value()
f2 = ev['f2'].get_value()
if not f2:
f2 = None
band = (f1, f2)
if not (slopes['avg_slope'] or slopes['max_slope']):
slopes = None
evt_dat = event_params(self.data, params, band=band, slopes=slopes,
prep=prep, parent=self)
count = None
density = None
if glob['count']:
count = len(self.data)
if glob['density']:
epoch_dur = glob['density_per']
# get period of interest based on stage and cycle selection
poi = get_times(self.parent.notes.annot, stage=self.stage,
cycle=self.cycle, exclude=True)
total_dur = sum([x[1] - x[0] for y in poi for x in y['times']])
density = len(self.data) / (total_dur / epoch_dur)
return evt_dat, count, density | python | def compute_evt_params(self):
ev = self.event
glob = {k: v.get_value() for k, v in ev['global'].items()}
params = {k: v[0].get_value() for k, v in ev['local'].items()}
prep = {k: v[1].get_value() for k, v in ev['local'].items()}
slopes = {k: v.get_value() for k, v in ev['sw'].items()}
f1 = ev['f1'].get_value()
f2 = ev['f2'].get_value()
if not f2:
f2 = None
band = (f1, f2)
if not (slopes['avg_slope'] or slopes['max_slope']):
slopes = None
evt_dat = event_params(self.data, params, band=band, slopes=slopes,
prep=prep, parent=self)
count = None
density = None
if glob['count']:
count = len(self.data)
if glob['density']:
epoch_dur = glob['density_per']
# get period of interest based on stage and cycle selection
poi = get_times(self.parent.notes.annot, stage=self.stage,
cycle=self.cycle, exclude=True)
total_dur = sum([x[1] - x[0] for y in poi for x in y['times']])
density = len(self.data) / (total_dur / epoch_dur)
return evt_dat, count, density | [
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24,992 | wonambi-python/wonambi | wonambi/widgets/analysis.py | AnalysisDialog.make_title | def make_title(self, chan, cycle, stage, evt_type):
"""Make a title for plots, etc."""
cyc_str = None
if cycle is not None:
cyc_str = [str(c[2]) for c in cycle]
cyc_str[0] = 'cycle ' + cyc_str[0]
title = [' + '.join([str(x) for x in y]) for y in [chan, cyc_str,
stage, evt_type] if y is not None]
return ', '.join(title) | python | def make_title(self, chan, cycle, stage, evt_type):
cyc_str = None
if cycle is not None:
cyc_str = [str(c[2]) for c in cycle]
cyc_str[0] = 'cycle ' + cyc_str[0]
title = [' + '.join([str(x) for x in y]) for y in [chan, cyc_str,
stage, evt_type] if y is not None]
return ', '.join(title) | [
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24,993 | wonambi-python/wonambi | wonambi/widgets/analysis.py | PlotCanvas.plot | def plot(self, x, y, title, ylabel, scale='semilogy', idx_lim=(1, -1)):
"""Plot the data.
Parameters
----------
x : ndarray
vector with frequencies
y : ndarray
vector with amplitudes
title : str
title of the plot, to appear above it
ylabel : str
label for the y-axis
scale : str
'log y-axis', 'log both axes' or 'linear', to set axis scaling
idx_lim : tuple of (int or None)
indices of the data to plot. by default, the first value is left
out, because of assymptotic tendencies near 0 Hz.
"""
x = x[slice(*idx_lim)]
y = y[slice(*idx_lim)]
ax = self.figure.add_subplot(111)
ax.set_title(title)
ax.set_xlabel('Frequency (Hz)')
ax.set_ylabel(ylabel)
if 'semilogy' == scale:
ax.semilogy(x, y, 'r-')
elif 'loglog' == scale:
ax.loglog(x, y, 'r-')
elif 'linear' == scale:
ax.plot(x, y, 'r-') | python | def plot(self, x, y, title, ylabel, scale='semilogy', idx_lim=(1, -1)):
x = x[slice(*idx_lim)]
y = y[slice(*idx_lim)]
ax = self.figure.add_subplot(111)
ax.set_title(title)
ax.set_xlabel('Frequency (Hz)')
ax.set_ylabel(ylabel)
if 'semilogy' == scale:
ax.semilogy(x, y, 'r-')
elif 'loglog' == scale:
ax.loglog(x, y, 'r-')
elif 'linear' == scale:
ax.plot(x, y, 'r-') | [
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x : ndarray
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y : ndarray
vector with amplitudes
title : str
title of the plot, to appear above it
ylabel : str
label for the y-axis
scale : str
'log y-axis', 'log both axes' or 'linear', to set axis scaling
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24,994 | wonambi-python/wonambi | wonambi/widgets/analysis.py | PlotDialog.create_dialog | def create_dialog(self):
"""Create the basic dialog."""
self.bbox = QDialogButtonBox(QDialogButtonBox.Close)
self.idx_close = self.bbox.button(QDialogButtonBox.Close)
self.idx_close.pressed.connect(self.reject)
btnlayout = QHBoxLayout()
btnlayout.addStretch(1)
btnlayout.addWidget(self.bbox)
layout = QVBoxLayout()
layout.addWidget(self.toolbar)
layout.addWidget(self.canvas)
layout.addLayout(btnlayout)
layout.addStretch(1)
self.setLayout(layout) | python | def create_dialog(self):
self.bbox = QDialogButtonBox(QDialogButtonBox.Close)
self.idx_close = self.bbox.button(QDialogButtonBox.Close)
self.idx_close.pressed.connect(self.reject)
btnlayout = QHBoxLayout()
btnlayout.addStretch(1)
btnlayout.addWidget(self.bbox)
layout = QVBoxLayout()
layout.addWidget(self.toolbar)
layout.addWidget(self.canvas)
layout.addLayout(btnlayout)
layout.addStretch(1)
self.setLayout(layout) | [
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24,995 | wonambi-python/wonambi | wonambi/detect/arousal.py | make_arousals | def make_arousals(events, time, s_freq):
"""Create dict for each arousal, based on events of time points.
Parameters
----------
events : ndarray (dtype='int')
N x 5 matrix with start, end samples
data : ndarray (dtype='float')
vector with the data
time : ndarray (dtype='float')
vector with time points
s_freq : float
sampling frequency
Returns
-------
list of dict
list of all the arousals, with information about start, end,
duration (s),
"""
arousals = []
for ev in events:
one_ar = {'start': time[ev[0]],
'end': time[ev[1] - 1],
'dur': (ev[1] - ev[0]) / s_freq,
}
arousals.append(one_ar)
return arousals | python | def make_arousals(events, time, s_freq):
arousals = []
for ev in events:
one_ar = {'start': time[ev[0]],
'end': time[ev[1] - 1],
'dur': (ev[1] - ev[0]) / s_freq,
}
arousals.append(one_ar)
return arousals | [
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N x 5 matrix with start, end samples
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vector with the data
time : ndarray (dtype='float')
vector with time points
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sampling frequency
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24,996 | wonambi-python/wonambi | wonambi/dataset.py | _convert_time_to_sample | def _convert_time_to_sample(abs_time, dataset):
"""Convert absolute time into samples.
Parameters
----------
abs_time : dat
if it's int or float, it's assumed it's s;
if it's timedelta, it's assumed from the start of the recording;
if it's datetime, it's assumed it's absolute time.
dataset : instance of wonambi.Dataset
dataset to get sampling frequency and start time
Returns
-------
int
sample (from the starting of the recording).
"""
if isinstance(abs_time, datetime):
abs_time = abs_time - dataset.header['start_time']
if not isinstance(abs_time, timedelta):
try:
abs_time = timedelta(seconds=float(abs_time))
except TypeError as err:
if isinstance(abs_time, int64):
# timedelta and int64: http://bugs.python.org/issue5476
abs_time = timedelta(seconds=int(abs_time))
else:
raise err
sample = int(ceil(abs_time.total_seconds() * dataset.header['s_freq']))
return sample | python | def _convert_time_to_sample(abs_time, dataset):
if isinstance(abs_time, datetime):
abs_time = abs_time - dataset.header['start_time']
if not isinstance(abs_time, timedelta):
try:
abs_time = timedelta(seconds=float(abs_time))
except TypeError as err:
if isinstance(abs_time, int64):
# timedelta and int64: http://bugs.python.org/issue5476
abs_time = timedelta(seconds=int(abs_time))
else:
raise err
sample = int(ceil(abs_time.total_seconds() * dataset.header['s_freq']))
return sample | [
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if it's datetime, it's assumed it's absolute time.
dataset : instance of wonambi.Dataset
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Returns
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24,997 | wonambi-python/wonambi | wonambi/dataset.py | detect_format | def detect_format(filename):
"""Detect file format.
Parameters
----------
filename : str or Path
name of the filename or directory.
Returns
-------
class used to read the data.
"""
filename = Path(filename)
if filename.is_dir():
if list(filename.glob('*.stc')) and list(filename.glob('*.erd')):
return Ktlx
elif (filename / 'patient.info').exists():
return Moberg
elif (filename / 'info.xml').exists():
return EgiMff
elif list(filename.glob('*.openephys')):
return OpenEphys
elif list(filename.glob('*.txt')):
return Text
else:
raise UnrecognizedFormat('Unrecognized format for directory ' +
str(filename))
else:
if filename.suffix == '.won':
return Wonambi
if filename.suffix.lower() == '.trc':
return Micromed
if filename.suffix == '.set':
return EEGLAB
if filename.suffix == '.edf':
return Edf
if filename.suffix == '.abf':
return Abf
if filename.suffix == '.vhdr' or filename.suffix == '.eeg':
return BrainVision
if filename.suffix == '.dat': # very general
try:
_read_header_length(filename)
except (AttributeError, ValueError): # there is no HeaderLen
pass
else:
return BCI2000
with filename.open('rb') as f:
file_header = f.read(8)
if file_header in (b'NEURALCD', b'NEURALSG', b'NEURALEV'):
return BlackRock
elif file_header[:6] == b'MATLAB': # we might need to read more
return FieldTrip
if filename.suffix.lower() == '.txt':
with filename.open('rt') as f:
first_line = f.readline()
if '.rr' in first_line[-4:]:
return LyonRRI
else:
raise UnrecognizedFormat('Unrecognized format for file ' +
str(filename)) | python | def detect_format(filename):
filename = Path(filename)
if filename.is_dir():
if list(filename.glob('*.stc')) and list(filename.glob('*.erd')):
return Ktlx
elif (filename / 'patient.info').exists():
return Moberg
elif (filename / 'info.xml').exists():
return EgiMff
elif list(filename.glob('*.openephys')):
return OpenEphys
elif list(filename.glob('*.txt')):
return Text
else:
raise UnrecognizedFormat('Unrecognized format for directory ' +
str(filename))
else:
if filename.suffix == '.won':
return Wonambi
if filename.suffix.lower() == '.trc':
return Micromed
if filename.suffix == '.set':
return EEGLAB
if filename.suffix == '.edf':
return Edf
if filename.suffix == '.abf':
return Abf
if filename.suffix == '.vhdr' or filename.suffix == '.eeg':
return BrainVision
if filename.suffix == '.dat': # very general
try:
_read_header_length(filename)
except (AttributeError, ValueError): # there is no HeaderLen
pass
else:
return BCI2000
with filename.open('rb') as f:
file_header = f.read(8)
if file_header in (b'NEURALCD', b'NEURALSG', b'NEURALEV'):
return BlackRock
elif file_header[:6] == b'MATLAB': # we might need to read more
return FieldTrip
if filename.suffix.lower() == '.txt':
with filename.open('rt') as f:
first_line = f.readline()
if '.rr' in first_line[-4:]:
return LyonRRI
else:
raise UnrecognizedFormat('Unrecognized format for file ' +
str(filename)) | [
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24,998 | wonambi-python/wonambi | wonambi/dataset.py | Dataset.read_videos | def read_videos(self, begtime=None, endtime=None):
"""Return list of videos with start and end times for a period.
Parameters
----------
begtime : int or datedelta or datetime or list
start of the data to read;
if it's int, it's assumed it's s;
if it's datedelta, it's assumed from the start of the recording;
if it's datetime, it's assumed it's absolute time.
It can also be a list of any of the above type.
endtime : int or datedelta or datetime
end of the data to read;
if it's int, it's assumed it's s;
if it's datedelta, it's assumed from the start of the recording;
if it's datetime, it's assumed it's absolute time.
It can also be a list of any of the above type.
Returns
-------
list of path
list of absolute paths (as str) to the movie files
float
time in s from the beginning of the first movie when the part of
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float
time in s from the beginning of the last movie when the part of
interest ends
Raises
------
OSError
when there are no video files at all
IndexError
when there are video files, but the interval of interest is not in
the list of files.
"""
if isinstance(begtime, datetime):
begtime = begtime - self.header['start_time']
if isinstance(begtime, timedelta):
begtime = begtime.total_seconds()
if isinstance(endtime, datetime):
endtime = endtime - self.header['start_time']
if isinstance(endtime, timedelta):
endtime = endtime.total_seconds()
videos = self.dataset.return_videos(begtime, endtime)
"""
try
except AttributeError:
lg.debug('This format does not have video')
videos = None
"""
return videos | python | def read_videos(self, begtime=None, endtime=None):
if isinstance(begtime, datetime):
begtime = begtime - self.header['start_time']
if isinstance(begtime, timedelta):
begtime = begtime.total_seconds()
if isinstance(endtime, datetime):
endtime = endtime - self.header['start_time']
if isinstance(endtime, timedelta):
endtime = endtime.total_seconds()
videos = self.dataset.return_videos(begtime, endtime)
"""
try
except AttributeError:
lg.debug('This format does not have video')
videos = None
"""
return videos | [
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if it's datedelta, it's assumed from the start of the recording;
if it's datetime, it's assumed it's absolute time.
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time in s from the beginning of the last movie when the part of
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Raises
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when there are no video files at all
IndexError
when there are video files, but the interval of interest is not in
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24,999 | wonambi-python/wonambi | wonambi/dataset.py | Dataset.read_data | def read_data(self, chan=None, begtime=None, endtime=None, begsam=None,
endsam=None, s_freq=None):
"""Read the data and creates a ChanTime instance
Parameters
----------
chan : list of strings
names of the channels to read
begtime : int or datedelta or datetime or list
start of the data to read;
if it's int or float, it's assumed it's s;
if it's timedelta, it's assumed from the start of the recording;
if it's datetime, it's assumed it's absolute time.
It can also be a list of any of the above type.
endtime : int or datedelta or datetime
end of the data to read;
if it's int or float, it's assumed it's s;
if it's timedelta, it's assumed from the start of the recording;
if it's datetime, it's assumed it's absolute time.
It can also be a list of any of the above type.
begsam : int
first sample (this sample will be included)
endsam : int
last sample (this sample will NOT be included)
s_freq : int
sampling frequency of the data
Returns
-------
An instance of ChanTime
Notes
-----
begsam and endsam follow Python convention, which starts at zero,
includes begsam but DOES NOT include endsam.
If begtime and endtime are a list, they both need the exact same
length and the data will be stored in trials.
If neither begtime or begsam are specified, it starts from the first
sample. If neither endtime or endsam are specified, it reads until the
end.
"""
data = ChanTime()
data.start_time = self.header['start_time']
data.s_freq = s_freq = s_freq if s_freq else self.header['s_freq']
if chan is None:
chan = self.header['chan_name']
if not (isinstance(chan, list) or isinstance(chan, tuple)):
raise TypeError('Parameter "chan" should be a list')
add_ref = False
if '_REF' in chan:
add_ref = True
chan[:] = [x for x in chan if x != '_REF']
idx_chan = [self.header['chan_name'].index(x) for x in chan]
if begtime is None and begsam is None:
begsam = 0
if endtime is None and endsam is None:
endsam = self.header['n_samples']
if begtime is not None:
if not isinstance(begtime, list):
begtime = [begtime]
begsam = []
for one_begtime in begtime:
begsam.append(_convert_time_to_sample(one_begtime, self))
if endtime is not None:
if not isinstance(endtime, list):
endtime = [endtime]
endsam = []
for one_endtime in endtime:
endsam.append(_convert_time_to_sample(one_endtime, self))
if not isinstance(begsam, list):
begsam = [begsam]
if not isinstance(endsam, list):
endsam = [endsam]
if len(begsam) != len(endsam):
raise ValueError('There should be the same number of start and ' +
'end point')
n_trl = len(begsam)
data.axis['chan'] = empty(n_trl, dtype='O')
data.axis['time'] = empty(n_trl, dtype='O')
data.data = empty(n_trl, dtype='O')
for i, one_begsam, one_endsam in zip(range(n_trl), begsam, endsam):
dataset = self.dataset
lg.debug('begsam {0: 6}, endsam {1: 6}'.format(one_begsam,
one_endsam))
dat = dataset.return_dat(idx_chan, one_begsam, one_endsam)
chan_in_dat = chan
if add_ref:
zero_ref = zeros((1, one_endsam - one_begsam))
dat = concatenate((dat, zero_ref), axis=0)
chan_in_dat.append('_REF')
data.data[i] = dat
data.axis['chan'][i] = asarray(chan_in_dat, dtype='U')
data.axis['time'][i] = (arange(one_begsam, one_endsam) / s_freq)
return data | python | def read_data(self, chan=None, begtime=None, endtime=None, begsam=None,
endsam=None, s_freq=None):
data = ChanTime()
data.start_time = self.header['start_time']
data.s_freq = s_freq = s_freq if s_freq else self.header['s_freq']
if chan is None:
chan = self.header['chan_name']
if not (isinstance(chan, list) or isinstance(chan, tuple)):
raise TypeError('Parameter "chan" should be a list')
add_ref = False
if '_REF' in chan:
add_ref = True
chan[:] = [x for x in chan if x != '_REF']
idx_chan = [self.header['chan_name'].index(x) for x in chan]
if begtime is None and begsam is None:
begsam = 0
if endtime is None and endsam is None:
endsam = self.header['n_samples']
if begtime is not None:
if not isinstance(begtime, list):
begtime = [begtime]
begsam = []
for one_begtime in begtime:
begsam.append(_convert_time_to_sample(one_begtime, self))
if endtime is not None:
if not isinstance(endtime, list):
endtime = [endtime]
endsam = []
for one_endtime in endtime:
endsam.append(_convert_time_to_sample(one_endtime, self))
if not isinstance(begsam, list):
begsam = [begsam]
if not isinstance(endsam, list):
endsam = [endsam]
if len(begsam) != len(endsam):
raise ValueError('There should be the same number of start and ' +
'end point')
n_trl = len(begsam)
data.axis['chan'] = empty(n_trl, dtype='O')
data.axis['time'] = empty(n_trl, dtype='O')
data.data = empty(n_trl, dtype='O')
for i, one_begsam, one_endsam in zip(range(n_trl), begsam, endsam):
dataset = self.dataset
lg.debug('begsam {0: 6}, endsam {1: 6}'.format(one_begsam,
one_endsam))
dat = dataset.return_dat(idx_chan, one_begsam, one_endsam)
chan_in_dat = chan
if add_ref:
zero_ref = zeros((1, one_endsam - one_begsam))
dat = concatenate((dat, zero_ref), axis=0)
chan_in_dat.append('_REF')
data.data[i] = dat
data.axis['chan'][i] = asarray(chan_in_dat, dtype='U')
data.axis['time'][i] = (arange(one_begsam, one_endsam) / s_freq)
return data | [
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... | Read the data and creates a ChanTime instance
Parameters
----------
chan : list of strings
names of the channels to read
begtime : int or datedelta or datetime or list
start of the data to read;
if it's int or float, it's assumed it's s;
if it's timedelta, it's assumed from the start of the recording;
if it's datetime, it's assumed it's absolute time.
It can also be a list of any of the above type.
endtime : int or datedelta or datetime
end of the data to read;
if it's int or float, it's assumed it's s;
if it's timedelta, it's assumed from the start of the recording;
if it's datetime, it's assumed it's absolute time.
It can also be a list of any of the above type.
begsam : int
first sample (this sample will be included)
endsam : int
last sample (this sample will NOT be included)
s_freq : int
sampling frequency of the data
Returns
-------
An instance of ChanTime
Notes
-----
begsam and endsam follow Python convention, which starts at zero,
includes begsam but DOES NOT include endsam.
If begtime and endtime are a list, they both need the exact same
length and the data will be stored in trials.
If neither begtime or begsam are specified, it starts from the first
sample. If neither endtime or endsam are specified, it reads until the
end. | [
"Read",
"the",
"data",
"and",
"creates",
"a",
"ChanTime",
"instance"
] | 1d8e3d7e53df8017c199f703bcab582914676e76 | https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/dataset.py#L274-L379 |
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