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wonambi-python/wonambi | wonambi/detect/spindle.py | detect_UCSD | def detect_UCSD(dat_orig, s_freq, time, opts):
"""Spindle detection based on the UCSD method
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 'DetectSpindle'
det_wavelet : dict
parameters for 'wavelet_real',
det_thres' : float
detection threshold
sel_thresh : float
selection threshold
duration : tuple of float
min and max duration of spindles
frequency : tuple of float
low and high frequency of spindle band (for power ratio)
ratio_thresh : float
ratio between power inside and outside spindle band to accept them
Returns
-------
list of dict
list of detected spindles
dict
'det_value_lo' with detection value, 'det_value_hi' with nan,
'sel_value' with selection value
float
spindle density, per 30-s epoch
"""
dat_det = transform_signal(dat_orig, s_freq, 'wavelet_real',
opts.det_wavelet)
det_value = define_threshold(dat_det, s_freq, 'median+std',
opts.det_thresh)
events = detect_events(dat_det, 'maxima', det_value)
dat_sel = transform_signal(dat_orig, s_freq, 'wavelet_real',
opts.sel_wavelet)
sel_value = define_threshold(dat_sel, s_freq, 'median+std',
opts.sel_thresh)
events = select_events(dat_sel, events, 'above_thresh', sel_value)
events = _merge_close(dat_det, events, time, opts.tolerance)
events = within_duration(events, time, opts.duration)
events = _merge_close(dat_det, events, time, opts.min_interval)
events = remove_straddlers(events, time, s_freq)
events = power_ratio(events, dat_orig, s_freq, opts.frequency,
opts.ratio_thresh)
power_peaks = peak_in_power(events, dat_orig, s_freq, opts.power_peaks)
powers = power_in_band(events, dat_orig, s_freq, opts.frequency)
sp_in_chan = make_spindles(events, power_peaks, powers, dat_det,
dat_orig, time, s_freq)
values = {'det_value_lo': det_value, 'sel_value': sel_value}
density = len(sp_in_chan) * s_freq * 30 / len(dat_orig)
return sp_in_chan, values, density | python | def detect_UCSD(dat_orig, s_freq, time, opts):
"""Spindle detection based on the UCSD method
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 'DetectSpindle'
det_wavelet : dict
parameters for 'wavelet_real',
det_thres' : float
detection threshold
sel_thresh : float
selection threshold
duration : tuple of float
min and max duration of spindles
frequency : tuple of float
low and high frequency of spindle band (for power ratio)
ratio_thresh : float
ratio between power inside and outside spindle band to accept them
Returns
-------
list of dict
list of detected spindles
dict
'det_value_lo' with detection value, 'det_value_hi' with nan,
'sel_value' with selection value
float
spindle density, per 30-s epoch
"""
dat_det = transform_signal(dat_orig, s_freq, 'wavelet_real',
opts.det_wavelet)
det_value = define_threshold(dat_det, s_freq, 'median+std',
opts.det_thresh)
events = detect_events(dat_det, 'maxima', det_value)
dat_sel = transform_signal(dat_orig, s_freq, 'wavelet_real',
opts.sel_wavelet)
sel_value = define_threshold(dat_sel, s_freq, 'median+std',
opts.sel_thresh)
events = select_events(dat_sel, events, 'above_thresh', sel_value)
events = _merge_close(dat_det, events, time, opts.tolerance)
events = within_duration(events, time, opts.duration)
events = _merge_close(dat_det, events, time, opts.min_interval)
events = remove_straddlers(events, time, s_freq)
events = power_ratio(events, dat_orig, s_freq, opts.frequency,
opts.ratio_thresh)
power_peaks = peak_in_power(events, dat_orig, s_freq, opts.power_peaks)
powers = power_in_band(events, dat_orig, s_freq, opts.frequency)
sp_in_chan = make_spindles(events, power_peaks, powers, dat_det,
dat_orig, time, s_freq)
values = {'det_value_lo': det_value, 'sel_value': sel_value}
density = len(sp_in_chan) * s_freq * 30 / len(dat_orig)
return sp_in_chan, values, density | [
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Parameters
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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 'DetectSpindle'
det_wavelet : dict
parameters for 'wavelet_real',
det_thres' : float
detection threshold
sel_thresh : float
selection threshold
duration : tuple of float
min and max duration of spindles
frequency : tuple of float
low and high frequency of spindle band (for power ratio)
ratio_thresh : float
ratio between power inside and outside spindle band to accept them
Returns
-------
list of dict
list of detected spindles
dict
'det_value_lo' with detection value, 'det_value_hi' with nan,
'sel_value' with selection value
float
spindle density, per 30-s epoch | [
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wonambi-python/wonambi | wonambi/detect/spindle.py | detect_Concordia | def detect_Concordia(dat_orig, s_freq, time, opts):
"""Spindle detection, experimental Concordia method. Similar to Moelle 2011
and Nir2011.
Parameters
----------
dat_orig : ndarray (dtype='float')
vector with the data for one channel
s_freq : float
sampling frequency
opts : instance of 'DetectSpindle'
'det_butter' : dict
parameters for 'butter',
'moving_rms' : dict
parameters for 'moving_rms'
'smooth' : dict
parameters for 'smooth'
'det_thresh' : float
low detection threshold
'det_thresh_hi' : float
high detection threshold
'sel_thresh' : float
selection threshold
'duration' : tuple of float
min and max duration of spindles
Returns
-------
list of dict
list of detected spindles
dict
'det_value_lo', 'det_value_hi' with detection values, 'sel_value' with
selection value
float
spindle density, per 30-s epoch
"""
dat_det = transform_signal(dat_orig, s_freq, 'butter', opts.det_butter)
dat_det = transform_signal(dat_det, s_freq, 'moving_rms', opts.moving_rms)
dat_det = transform_signal(dat_det, s_freq, 'smooth', opts.smooth)
det_value_lo = define_threshold(dat_det, s_freq, 'mean+std',
opts.det_thresh)
det_value_hi = define_threshold(dat_det, s_freq, 'mean+std',
opts.det_thresh_hi)
sel_value = define_threshold(dat_det, s_freq, 'mean+std', opts.sel_thresh)
events = detect_events(dat_det, 'between_thresh',
value=(det_value_lo, det_value_hi))
if events is not None:
events = _merge_close(dat_det, events, time, opts.tolerance)
events = select_events(dat_det, events, 'above_thresh', sel_value)
events = within_duration(events, time, opts.duration)
events = _merge_close(dat_det, events, time, opts.min_interval)
events = remove_straddlers(events, time, s_freq)
power_peaks = peak_in_power(events, dat_orig, s_freq, opts.power_peaks)
powers = power_in_band(events, dat_orig, s_freq, opts.frequency)
sp_in_chan = make_spindles(events, power_peaks, powers, dat_det,
dat_orig, time, s_freq)
else:
lg.info('No spindle found')
sp_in_chan = []
values = {'det_value_lo': det_value_lo, 'sel_value': sel_value}
density = len(sp_in_chan) * s_freq * 30 / len(dat_orig)
return sp_in_chan, values, density | python | def detect_Concordia(dat_orig, s_freq, time, opts):
"""Spindle detection, experimental Concordia method. Similar to Moelle 2011
and Nir2011.
Parameters
----------
dat_orig : ndarray (dtype='float')
vector with the data for one channel
s_freq : float
sampling frequency
opts : instance of 'DetectSpindle'
'det_butter' : dict
parameters for 'butter',
'moving_rms' : dict
parameters for 'moving_rms'
'smooth' : dict
parameters for 'smooth'
'det_thresh' : float
low detection threshold
'det_thresh_hi' : float
high detection threshold
'sel_thresh' : float
selection threshold
'duration' : tuple of float
min and max duration of spindles
Returns
-------
list of dict
list of detected spindles
dict
'det_value_lo', 'det_value_hi' with detection values, 'sel_value' with
selection value
float
spindle density, per 30-s epoch
"""
dat_det = transform_signal(dat_orig, s_freq, 'butter', opts.det_butter)
dat_det = transform_signal(dat_det, s_freq, 'moving_rms', opts.moving_rms)
dat_det = transform_signal(dat_det, s_freq, 'smooth', opts.smooth)
det_value_lo = define_threshold(dat_det, s_freq, 'mean+std',
opts.det_thresh)
det_value_hi = define_threshold(dat_det, s_freq, 'mean+std',
opts.det_thresh_hi)
sel_value = define_threshold(dat_det, s_freq, 'mean+std', opts.sel_thresh)
events = detect_events(dat_det, 'between_thresh',
value=(det_value_lo, det_value_hi))
if events is not None:
events = _merge_close(dat_det, events, time, opts.tolerance)
events = select_events(dat_det, events, 'above_thresh', sel_value)
events = within_duration(events, time, opts.duration)
events = _merge_close(dat_det, events, time, opts.min_interval)
events = remove_straddlers(events, time, s_freq)
power_peaks = peak_in_power(events, dat_orig, s_freq, opts.power_peaks)
powers = power_in_band(events, dat_orig, s_freq, opts.frequency)
sp_in_chan = make_spindles(events, power_peaks, powers, dat_det,
dat_orig, time, s_freq)
else:
lg.info('No spindle found')
sp_in_chan = []
values = {'det_value_lo': det_value_lo, 'sel_value': sel_value}
density = len(sp_in_chan) * s_freq * 30 / len(dat_orig)
return sp_in_chan, values, density | [
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Parameters
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dat_orig : ndarray (dtype='float')
vector with the data for one channel
s_freq : float
sampling frequency
opts : instance of 'DetectSpindle'
'det_butter' : dict
parameters for 'butter',
'moving_rms' : dict
parameters for 'moving_rms'
'smooth' : dict
parameters for 'smooth'
'det_thresh' : float
low detection threshold
'det_thresh_hi' : float
high detection threshold
'sel_thresh' : float
selection threshold
'duration' : tuple of float
min and max duration of spindles
Returns
-------
list of dict
list of detected spindles
dict
'det_value_lo', 'det_value_hi' with detection values, 'sel_value' with
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float
spindle density, per 30-s epoch | [
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wonambi-python/wonambi | wonambi/detect/spindle.py | define_threshold | def define_threshold(dat, s_freq, method, value, nbins=120):
"""Return the value of the threshold based on relative values.
Parameters
----------
dat : ndarray (dtype='float')
vector with the data after selection-transformation
s_freq : float
sampling frequency
method : str
one of 'mean', 'median', 'std', 'mean+std', 'median+std', 'histmax'
value : float
value to multiply the values for
nbins : int
for histmax method, number of bins in the histogram
Returns
-------
float
threshold in useful units.
"""
if method == 'mean':
value = value * mean(dat)
elif method == 'median':
value = value * median(dat)
elif method == 'std':
value = value * std(dat)
elif method == 'mean+std':
value = mean(dat) + value * std(dat)
elif method == 'median+std':
value = median(dat) + value * std(dat)
elif method == 'histmax':
hist = histogram(dat, bins=nbins)
idx_maxbin = argmax(hist[0])
maxamp = mean((hist[1][idx_maxbin], hist[1][idx_maxbin + 1]))
value = value * maxamp
return value | python | def define_threshold(dat, s_freq, method, value, nbins=120):
"""Return the value of the threshold based on relative values.
Parameters
----------
dat : ndarray (dtype='float')
vector with the data after selection-transformation
s_freq : float
sampling frequency
method : str
one of 'mean', 'median', 'std', 'mean+std', 'median+std', 'histmax'
value : float
value to multiply the values for
nbins : int
for histmax method, number of bins in the histogram
Returns
-------
float
threshold in useful units.
"""
if method == 'mean':
value = value * mean(dat)
elif method == 'median':
value = value * median(dat)
elif method == 'std':
value = value * std(dat)
elif method == 'mean+std':
value = mean(dat) + value * std(dat)
elif method == 'median+std':
value = median(dat) + value * std(dat)
elif method == 'histmax':
hist = histogram(dat, bins=nbins)
idx_maxbin = argmax(hist[0])
maxamp = mean((hist[1][idx_maxbin], hist[1][idx_maxbin + 1]))
value = value * maxamp
return value | [
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sampling frequency
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wonambi-python/wonambi | wonambi/detect/spindle.py | detect_events | def detect_events(dat, method, value=None):
"""Detect events using 'above_thresh', 'below_thresh' or
'maxima' method.
Parameters
----------
dat : ndarray (dtype='float')
vector with the data after transformation
method : str
'above_thresh', 'below_thresh' or 'maxima'
value : float or tuple of float
for 'above_thresh' or 'below_thresh', it's the value of threshold for
the event detection
for 'between_thresh', it's the lower and upper threshold as tuple
for 'maxima', it's the distance in s from the peak to find a minimum
Returns
-------
ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
"""
if 'thresh' in method or 'custom' == method:
if method == 'above_thresh':
above_det = dat >= value
detected = _detect_start_end(above_det)
if method == 'below_thresh':
below_det = dat < value
detected = _detect_start_end(below_det)
if method == 'between_thresh':
above_det = dat >= value[0]
below_det = dat < value[1]
between_det = logical_and(above_det, below_det)
detected = _detect_start_end(between_det)
if method == 'custom':
detected = _detect_start_end(dat)
if detected is None:
return None
if method in ['above_thresh', 'custom']:
# add the location of the peak in the middle
detected = insert(detected, 1, 0, axis=1)
for i in detected:
i[1] = i[0] + argmax(dat[i[0]:i[2]])
if method in ['below_thresh', 'between_thresh']:
# add the location of the trough in the middle
detected = insert(detected, 1, 0, axis=1)
for i in detected:
i[1] = i[0] + argmin(dat[i[0]:i[2]])
if method == 'maxima':
peaks = argrelmax(dat)[0]
detected = vstack((peaks, peaks, peaks)).T
if value is not None:
detected = detected[dat[peaks] > value, :]
return detected | python | def detect_events(dat, method, value=None):
"""Detect events using 'above_thresh', 'below_thresh' or
'maxima' method.
Parameters
----------
dat : ndarray (dtype='float')
vector with the data after transformation
method : str
'above_thresh', 'below_thresh' or 'maxima'
value : float or tuple of float
for 'above_thresh' or 'below_thresh', it's the value of threshold for
the event detection
for 'between_thresh', it's the lower and upper threshold as tuple
for 'maxima', it's the distance in s from the peak to find a minimum
Returns
-------
ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
"""
if 'thresh' in method or 'custom' == method:
if method == 'above_thresh':
above_det = dat >= value
detected = _detect_start_end(above_det)
if method == 'below_thresh':
below_det = dat < value
detected = _detect_start_end(below_det)
if method == 'between_thresh':
above_det = dat >= value[0]
below_det = dat < value[1]
between_det = logical_and(above_det, below_det)
detected = _detect_start_end(between_det)
if method == 'custom':
detected = _detect_start_end(dat)
if detected is None:
return None
if method in ['above_thresh', 'custom']:
# add the location of the peak in the middle
detected = insert(detected, 1, 0, axis=1)
for i in detected:
i[1] = i[0] + argmax(dat[i[0]:i[2]])
if method in ['below_thresh', 'between_thresh']:
# add the location of the trough in the middle
detected = insert(detected, 1, 0, axis=1)
for i in detected:
i[1] = i[0] + argmin(dat[i[0]:i[2]])
if method == 'maxima':
peaks = argrelmax(dat)[0]
detected = vstack((peaks, peaks, peaks)).T
if value is not None:
detected = detected[dat[peaks] > value, :]
return detected | [
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wonambi-python/wonambi | wonambi/detect/spindle.py | select_events | def select_events(dat, detected, method, value):
"""Select start sample and end sample of the events.
Parameters
----------
dat : ndarray (dtype='float')
vector with the data after selection-transformation
detected : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
method : str
'above_thresh', 'below_thresh'
value : float
for 'threshold', it's the value of threshold for the spindle selection.
Returns
-------
ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
"""
if method == 'above_thresh':
above_sel = dat >= value
detected = _select_period(detected, above_sel)
elif method == 'below_thresh':
below_sel = dat <= value
detected = _select_period(detected, below_sel)
return detected | python | def select_events(dat, detected, method, value):
"""Select start sample and end sample of the events.
Parameters
----------
dat : ndarray (dtype='float')
vector with the data after selection-transformation
detected : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
method : str
'above_thresh', 'below_thresh'
value : float
for 'threshold', it's the value of threshold for the spindle selection.
Returns
-------
ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
"""
if method == 'above_thresh':
above_sel = dat >= value
detected = _select_period(detected, above_sel)
elif method == 'below_thresh':
below_sel = dat <= value
detected = _select_period(detected, below_sel)
return detected | [
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wonambi-python/wonambi | wonambi/detect/spindle.py | merge_close | def merge_close(events, min_interval, merge_to_longer=False):
"""Merge events that are separated by a less than a minimum interval.
Parameters
----------
events : list of dict
events with 'start' and 'end' times, from one or several channels.
**Events must be sorted by their start time.**
min_interval : float
minimum delay between consecutive events, in seconds
merge_to_longer : bool (default: False)
If True, info (chan, peak, etc.) from the longer of the 2 events is
kept. Otherwise, info from the earlier onset spindle is kept.
Returns
-------
list of dict
original events list with close events merged.
"""
half_iv = min_interval / 2
merged = []
for higher in events:
if not merged:
merged.append(higher)
else:
lower = merged[-1]
if higher['start'] - half_iv <= lower['end'] + half_iv:
if merge_to_longer and (higher['end'] - higher['start'] >
lower['end'] - lower['start']):
start = min(lower['start'], higher['start'])
higher.update({'start': start})
merged[-1] = higher
else:
end = max(lower['end'], higher['end'])
merged[-1].update({'end': end})
else:
merged.append(higher)
return merged | python | def merge_close(events, min_interval, merge_to_longer=False):
"""Merge events that are separated by a less than a minimum interval.
Parameters
----------
events : list of dict
events with 'start' and 'end' times, from one or several channels.
**Events must be sorted by their start time.**
min_interval : float
minimum delay between consecutive events, in seconds
merge_to_longer : bool (default: False)
If True, info (chan, peak, etc.) from the longer of the 2 events is
kept. Otherwise, info from the earlier onset spindle is kept.
Returns
-------
list of dict
original events list with close events merged.
"""
half_iv = min_interval / 2
merged = []
for higher in events:
if not merged:
merged.append(higher)
else:
lower = merged[-1]
if higher['start'] - half_iv <= lower['end'] + half_iv:
if merge_to_longer and (higher['end'] - higher['start'] >
lower['end'] - lower['start']):
start = min(lower['start'], higher['start'])
higher.update({'start': start})
merged[-1] = higher
else:
end = max(lower['end'], higher['end'])
merged[-1].update({'end': end})
else:
merged.append(higher)
return merged | [
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events with 'start' and 'end' times, from one or several channels.
**Events must be sorted by their start time.**
min_interval : float
minimum delay between consecutive events, in seconds
merge_to_longer : bool (default: False)
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wonambi-python/wonambi | wonambi/detect/spindle.py | within_duration | def within_duration(events, time, limits):
"""Check whether event is within time limits.
Parameters
----------
events : ndarray (dtype='int')
N x M matrix with start sample first and end samples last on M
time : ndarray (dtype='float')
vector with time points
limits : tuple of float
low and high limit for spindle duration
Returns
-------
ndarray (dtype='int')
N x M matrix with start sample first and end samples last on M
"""
min_dur = max_dur = ones(events.shape[0], dtype=bool)
if limits[0] is not None:
min_dur = time[events[:, -1] - 1] - time[events[:, 0]] >= limits[0]
if limits[1] is not None:
max_dur = time[events[:, -1] - 1] - time[events[:, 0]] <= limits[1]
return events[min_dur & max_dur, :] | python | def within_duration(events, time, limits):
"""Check whether event is within time limits.
Parameters
----------
events : ndarray (dtype='int')
N x M matrix with start sample first and end samples last on M
time : ndarray (dtype='float')
vector with time points
limits : tuple of float
low and high limit for spindle duration
Returns
-------
ndarray (dtype='int')
N x M matrix with start sample first and end samples last on M
"""
min_dur = max_dur = ones(events.shape[0], dtype=bool)
if limits[0] is not None:
min_dur = time[events[:, -1] - 1] - time[events[:, 0]] >= limits[0]
if limits[1] is not None:
max_dur = time[events[:, -1] - 1] - time[events[:, 0]] <= limits[1]
return events[min_dur & max_dur, :] | [
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limits : tuple of float
low and high limit for spindle duration
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wonambi-python/wonambi | wonambi/detect/spindle.py | remove_straddlers | def remove_straddlers(events, time, s_freq, toler=0.1):
"""Reject an event if it straddles a stitch, by comparing its
duration to its timespan.
Parameters
----------
events : ndarray (dtype='int')
N x M matrix with start, ..., end samples
time : ndarray (dtype='float')
vector with time points
s_freq : float
sampling frequency
toler : float, def=0.1
maximum tolerated difference between event duration and timespan
Returns
-------
ndarray (dtype='int')
N x M matrix with start , ..., end samples
"""
dur = (events[:, -1] - 1 - events[:, 0]) / s_freq
continuous = time[events[:, -1] - 1] - time[events[:, 0]] - dur < toler
return events[continuous, :] | python | def remove_straddlers(events, time, s_freq, toler=0.1):
"""Reject an event if it straddles a stitch, by comparing its
duration to its timespan.
Parameters
----------
events : ndarray (dtype='int')
N x M matrix with start, ..., end samples
time : ndarray (dtype='float')
vector with time points
s_freq : float
sampling frequency
toler : float, def=0.1
maximum tolerated difference between event duration and timespan
Returns
-------
ndarray (dtype='int')
N x M matrix with start , ..., end samples
"""
dur = (events[:, -1] - 1 - events[:, 0]) / s_freq
continuous = time[events[:, -1] - 1] - time[events[:, 0]] - dur < toler
return events[continuous, :] | [
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N x M matrix with start, ..., end samples
time : ndarray (dtype='float')
vector with time points
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sampling frequency
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maximum tolerated difference between event duration and timespan
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ndarray (dtype='int')
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wonambi-python/wonambi | wonambi/detect/spindle.py | power_ratio | def power_ratio(events, dat, s_freq, limits, ratio_thresh):
"""Estimate the ratio in power between spindle band and lower frequencies.
Parameters
----------
events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
dat : ndarray (dtype='float')
vector with the original data
s_freq : float
sampling frequency
limits : tuple of float
high and low frequencies for spindle band
ratio_thresh : float
ratio between spindle vs non-spindle amplitude
Returns
-------
ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
Notes
-----
In the original matlab script, it uses amplitude, not power.
"""
ratio = empty(events.shape[0])
for i, one_event in enumerate(events):
x0 = one_event[0]
x1 = one_event[2]
if x0 < 0 or x1 >= len(dat):
ratio[i] = 0
else:
f, Pxx = periodogram(dat[x0:x1], s_freq, scaling='spectrum')
Pxx = sqrt(Pxx) # use amplitude
freq_sp = (f >= limits[0]) & (f <= limits[1])
freq_nonsp = (f <= limits[1])
ratio[i] = mean(Pxx[freq_sp]) / mean(Pxx[freq_nonsp])
events = events[ratio > ratio_thresh, :]
return events | python | def power_ratio(events, dat, s_freq, limits, ratio_thresh):
"""Estimate the ratio in power between spindle band and lower frequencies.
Parameters
----------
events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
dat : ndarray (dtype='float')
vector with the original data
s_freq : float
sampling frequency
limits : tuple of float
high and low frequencies for spindle band
ratio_thresh : float
ratio between spindle vs non-spindle amplitude
Returns
-------
ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
Notes
-----
In the original matlab script, it uses amplitude, not power.
"""
ratio = empty(events.shape[0])
for i, one_event in enumerate(events):
x0 = one_event[0]
x1 = one_event[2]
if x0 < 0 or x1 >= len(dat):
ratio[i] = 0
else:
f, Pxx = periodogram(dat[x0:x1], s_freq, scaling='spectrum')
Pxx = sqrt(Pxx) # use amplitude
freq_sp = (f >= limits[0]) & (f <= limits[1])
freq_nonsp = (f <= limits[1])
ratio[i] = mean(Pxx[freq_sp]) / mean(Pxx[freq_nonsp])
events = events[ratio > ratio_thresh, :]
return events | [
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Parameters
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events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
dat : ndarray (dtype='float')
vector with the original data
s_freq : float
sampling frequency
limits : tuple of float
high and low frequencies for spindle band
ratio_thresh : float
ratio between spindle vs non-spindle amplitude
Returns
-------
ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
Notes
-----
In the original matlab script, it uses amplitude, not power. | [
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wonambi-python/wonambi | wonambi/detect/spindle.py | peak_in_power | def peak_in_power(events, dat, s_freq, method, value=None):
"""Define peak in power of the signal.
Parameters
----------
events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
dat : ndarray (dtype='float')
vector with the original data
s_freq : float
sampling frequency
method : str or None
'peak' or 'interval'. If None, values will be all NaN
value : float
size of the window around peak, or nothing (for 'interval')
Returns
-------
ndarray (dtype='float')
vector with peak frequency
"""
dat = diff(dat) # remove 1/f
peak = empty(events.shape[0])
peak.fill(nan)
if method is not None:
for i, one_event in enumerate(events):
if method == 'peak':
x0 = one_event[1] - value / 2 * s_freq
x1 = one_event[1] + value / 2 * s_freq
elif method == 'interval':
x0 = one_event[0]
x1 = one_event[2]
if x0 < 0 or x1 >= len(dat):
peak[i] = nan
else:
f, Pxx = periodogram(dat[x0:x1], s_freq)
idx_peak = Pxx[f < MAX_FREQUENCY_OF_INTEREST].argmax()
peak[i] = f[idx_peak]
return peak | python | def peak_in_power(events, dat, s_freq, method, value=None):
"""Define peak in power of the signal.
Parameters
----------
events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
dat : ndarray (dtype='float')
vector with the original data
s_freq : float
sampling frequency
method : str or None
'peak' or 'interval'. If None, values will be all NaN
value : float
size of the window around peak, or nothing (for 'interval')
Returns
-------
ndarray (dtype='float')
vector with peak frequency
"""
dat = diff(dat) # remove 1/f
peak = empty(events.shape[0])
peak.fill(nan)
if method is not None:
for i, one_event in enumerate(events):
if method == 'peak':
x0 = one_event[1] - value / 2 * s_freq
x1 = one_event[1] + value / 2 * s_freq
elif method == 'interval':
x0 = one_event[0]
x1 = one_event[2]
if x0 < 0 or x1 >= len(dat):
peak[i] = nan
else:
f, Pxx = periodogram(dat[x0:x1], s_freq)
idx_peak = Pxx[f < MAX_FREQUENCY_OF_INTEREST].argmax()
peak[i] = f[idx_peak]
return peak | [
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N x 3 matrix with start, peak, end samples
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vector with the original data
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sampling frequency
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size of the window around peak, or nothing (for 'interval')
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wonambi-python/wonambi | wonambi/detect/spindle.py | power_in_band | def power_in_band(events, dat, s_freq, frequency):
"""Define power of the signal within frequency band.
Parameters
----------
events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
dat : ndarray (dtype='float')
vector with the original data
s_freq : float
sampling frequency
frequency : tuple of float
low and high frequency of spindle band, for window
Returns
-------
ndarray (dtype='float')
vector with power
"""
dat = diff(dat) # remove 1/f
pw = empty(events.shape[0])
pw.fill(nan)
for i, one_event in enumerate(events):
x0 = one_event[0]
x1 = one_event[2]
if x0 < 0 or x1 >= len(dat):
pw[i] = nan
else:
sf, Pxx = periodogram(dat[x0:x1], s_freq)
# find nearest frequencies in sf
b0 = asarray([abs(x - frequency[0]) for x in sf]).argmin()
b1 = asarray([abs(x - frequency[1]) for x in sf]).argmin()
pw[i] = mean(Pxx[b0:b1])
return pw | python | def power_in_band(events, dat, s_freq, frequency):
"""Define power of the signal within frequency band.
Parameters
----------
events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
dat : ndarray (dtype='float')
vector with the original data
s_freq : float
sampling frequency
frequency : tuple of float
low and high frequency of spindle band, for window
Returns
-------
ndarray (dtype='float')
vector with power
"""
dat = diff(dat) # remove 1/f
pw = empty(events.shape[0])
pw.fill(nan)
for i, one_event in enumerate(events):
x0 = one_event[0]
x1 = one_event[2]
if x0 < 0 or x1 >= len(dat):
pw[i] = nan
else:
sf, Pxx = periodogram(dat[x0:x1], s_freq)
# find nearest frequencies in sf
b0 = asarray([abs(x - frequency[0]) for x in sf]).argmin()
b1 = asarray([abs(x - frequency[1]) for x in sf]).argmin()
pw[i] = mean(Pxx[b0:b1])
return pw | [
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dat : ndarray (dtype='float')
vector with the original data
s_freq : float
sampling frequency
frequency : tuple of float
low and high frequency of spindle band, for window
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wonambi-python/wonambi | wonambi/detect/spindle.py | make_spindles | def make_spindles(events, power_peaks, powers, dat_det, dat_orig, time,
s_freq):
"""Create dict for each spindle, based on events of time points.
Parameters
----------
events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples, and peak frequency
power_peaks : ndarray (dtype='float')
peak in power spectrum for each event
powers : ndarray (dtype='float')
average power in power spectrum for each event
dat_det : ndarray (dtype='float')
vector with the data after detection-transformation (to compute peak)
dat_orig : ndarray (dtype='float')
vector with the raw data on which detection was performed
time : ndarray (dtype='float')
vector with time points
s_freq : float
sampling frequency
Returns
-------
list of dict
list of all the spindles, with information about start_time, peak_time,
end_time (s), peak_val (signal units), area_under_curve
(signal units * s), peak_freq (Hz)
"""
i, events = _remove_duplicate(events, dat_det)
power_peaks = power_peaks[i]
spindles = []
for i, one_peak, one_pwr in zip(events, power_peaks, powers):
one_spindle = {'start': time[i[0]],
'end': time[i[2] - 1],
'peak_time': time[i[1]],
'peak_val_det': dat_det[i[1]],
'peak_val_orig': dat_orig[i[1]],
'dur': (i[2] - i[0]) / s_freq,
'auc_det': sum(dat_det[i[0]:i[2]]) / s_freq,
'auc_orig': sum(dat_orig[i[0]:i[2]]) / s_freq,
'rms_det': sqrt(mean(square(dat_det[i[0]:i[2]]))),
'rms_orig': sqrt(mean(square(dat_orig[i[0]:i[2]]))),
'power_orig': one_pwr,
'peak_freq': one_peak,
'ptp_det': ptp(dat_det[i[0]:i[2]]),
'ptp_orig': ptp(dat_orig[i[0]:i[2]])
}
spindles.append(one_spindle)
return spindles | python | def make_spindles(events, power_peaks, powers, dat_det, dat_orig, time,
s_freq):
"""Create dict for each spindle, based on events of time points.
Parameters
----------
events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples, and peak frequency
power_peaks : ndarray (dtype='float')
peak in power spectrum for each event
powers : ndarray (dtype='float')
average power in power spectrum for each event
dat_det : ndarray (dtype='float')
vector with the data after detection-transformation (to compute peak)
dat_orig : ndarray (dtype='float')
vector with the raw data on which detection was performed
time : ndarray (dtype='float')
vector with time points
s_freq : float
sampling frequency
Returns
-------
list of dict
list of all the spindles, with information about start_time, peak_time,
end_time (s), peak_val (signal units), area_under_curve
(signal units * s), peak_freq (Hz)
"""
i, events = _remove_duplicate(events, dat_det)
power_peaks = power_peaks[i]
spindles = []
for i, one_peak, one_pwr in zip(events, power_peaks, powers):
one_spindle = {'start': time[i[0]],
'end': time[i[2] - 1],
'peak_time': time[i[1]],
'peak_val_det': dat_det[i[1]],
'peak_val_orig': dat_orig[i[1]],
'dur': (i[2] - i[0]) / s_freq,
'auc_det': sum(dat_det[i[0]:i[2]]) / s_freq,
'auc_orig': sum(dat_orig[i[0]:i[2]]) / s_freq,
'rms_det': sqrt(mean(square(dat_det[i[0]:i[2]]))),
'rms_orig': sqrt(mean(square(dat_orig[i[0]:i[2]]))),
'power_orig': one_pwr,
'peak_freq': one_peak,
'ptp_det': ptp(dat_det[i[0]:i[2]]),
'ptp_orig': ptp(dat_orig[i[0]:i[2]])
}
spindles.append(one_spindle)
return spindles | [
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Parameters
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events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples, and peak frequency
power_peaks : ndarray (dtype='float')
peak in power spectrum for each event
powers : ndarray (dtype='float')
average power in power spectrum for each event
dat_det : ndarray (dtype='float')
vector with the data after detection-transformation (to compute peak)
dat_orig : ndarray (dtype='float')
vector with the raw data on which detection was performed
time : ndarray (dtype='float')
vector with time points
s_freq : float
sampling frequency
Returns
-------
list of dict
list of all the spindles, with information about start_time, peak_time,
end_time (s), peak_val (signal units), area_under_curve
(signal units * s), peak_freq (Hz) | [
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wonambi-python/wonambi | wonambi/detect/spindle.py | _remove_duplicate | def _remove_duplicate(old_events, dat):
"""Remove duplicates from the events.
Parameters
----------
old_events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
dat : ndarray (dtype='float')
vector with the data after detection-transformation (to compute peak)
Returns
-------
ndarray (dtype='int')
vector of indices of the events to keep
ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
Notes
-----
old_events is assumed to be sorted. It only checks for the start time and
end time. When two (or more) events have the same start time and the same
end time, then it takes the largest peak.
There is no tolerance, indices need to be identical.
"""
diff_events = diff(old_events, axis=0)
dupl = where((diff_events[:, 0] == 0) & (diff_events[:, 2] == 0))[0]
dupl += 1 # more convenient, it copies old_event first and then compares
n_nondupl_events = old_events.shape[0] - len(dupl)
new_events = zeros((n_nondupl_events, old_events.shape[1]), dtype='int')
if len(dupl):
lg.debug('Removing ' + str(len(dupl)) + ' duplicate events')
i = 0
indices = []
for i_old, one_old_event in enumerate(old_events):
if i_old not in dupl:
new_events[i, :] = one_old_event
i += 1
indices.append(i_old)
else:
peak_0 = new_events[i - 1, 1]
peak_1 = one_old_event[1]
if dat[peak_0] >= dat[peak_1]:
new_events[i - 1, 1] = peak_0
else:
new_events[i - 1, 1] = peak_1
return indices, new_events | python | def _remove_duplicate(old_events, dat):
"""Remove duplicates from the events.
Parameters
----------
old_events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
dat : ndarray (dtype='float')
vector with the data after detection-transformation (to compute peak)
Returns
-------
ndarray (dtype='int')
vector of indices of the events to keep
ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
Notes
-----
old_events is assumed to be sorted. It only checks for the start time and
end time. When two (or more) events have the same start time and the same
end time, then it takes the largest peak.
There is no tolerance, indices need to be identical.
"""
diff_events = diff(old_events, axis=0)
dupl = where((diff_events[:, 0] == 0) & (diff_events[:, 2] == 0))[0]
dupl += 1 # more convenient, it copies old_event first and then compares
n_nondupl_events = old_events.shape[0] - len(dupl)
new_events = zeros((n_nondupl_events, old_events.shape[1]), dtype='int')
if len(dupl):
lg.debug('Removing ' + str(len(dupl)) + ' duplicate events')
i = 0
indices = []
for i_old, one_old_event in enumerate(old_events):
if i_old not in dupl:
new_events[i, :] = one_old_event
i += 1
indices.append(i_old)
else:
peak_0 = new_events[i - 1, 1]
peak_1 = one_old_event[1]
if dat[peak_0] >= dat[peak_1]:
new_events[i - 1, 1] = peak_0
else:
new_events[i - 1, 1] = peak_1
return indices, new_events | [
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vector with the data after detection-transformation (to compute peak)
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wonambi-python/wonambi | wonambi/detect/spindle.py | _detect_start_end | def _detect_start_end(true_values):
"""From ndarray of bool values, return intervals of True values.
Parameters
----------
true_values : ndarray (dtype='bool')
array with bool values
Returns
-------
ndarray (dtype='int')
N x 2 matrix with starting and ending times.
"""
neg = zeros((1), dtype='bool')
int_values = asarray(concatenate((neg, true_values[:-1], neg)),
dtype='int')
# must discard last value to avoid axis out of bounds
cross_threshold = diff(int_values)
event_starts = where(cross_threshold == 1)[0]
event_ends = where(cross_threshold == -1)[0]
if len(event_starts):
events = vstack((event_starts, event_ends)).T
else:
events = None
return events | python | def _detect_start_end(true_values):
"""From ndarray of bool values, return intervals of True values.
Parameters
----------
true_values : ndarray (dtype='bool')
array with bool values
Returns
-------
ndarray (dtype='int')
N x 2 matrix with starting and ending times.
"""
neg = zeros((1), dtype='bool')
int_values = asarray(concatenate((neg, true_values[:-1], neg)),
dtype='int')
# must discard last value to avoid axis out of bounds
cross_threshold = diff(int_values)
event_starts = where(cross_threshold == 1)[0]
event_ends = where(cross_threshold == -1)[0]
if len(event_starts):
events = vstack((event_starts, event_ends)).T
else:
events = None
return events | [
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wonambi-python/wonambi | wonambi/detect/spindle.py | _merge_close | def _merge_close(dat, events, time, min_interval):
"""Merge together events separated by less than a minimum interval.
Parameters
----------
dat : ndarray (dtype='float')
vector with the data after selection-transformation
events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
time : ndarray (dtype='float')
vector with time points
min_interval : float
minimum delay between consecutive events, in seconds
Returns
-------
ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
"""
if not events.any():
return events
no_merge = time[events[1:, 0] - 1] - time[events[:-1, 2]] >= min_interval
if no_merge.any():
begs = concatenate([[events[0, 0]], events[1:, 0][no_merge]])
ends = concatenate([events[:-1, 2][no_merge], [events[-1, 2]]])
new_events = vstack((begs, ends)).T
else:
new_events = asarray([[events[0, 0], events[-1, 2]]])
# add the location of the peak in the middle
new_events = insert(new_events, 1, 0, axis=1)
for i in new_events:
if i[2] - i[0] >= 1:
i[1] = i[0] + argmax(dat[i[0]:i[2]])
return new_events | python | def _merge_close(dat, events, time, min_interval):
"""Merge together events separated by less than a minimum interval.
Parameters
----------
dat : ndarray (dtype='float')
vector with the data after selection-transformation
events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
time : ndarray (dtype='float')
vector with time points
min_interval : float
minimum delay between consecutive events, in seconds
Returns
-------
ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
"""
if not events.any():
return events
no_merge = time[events[1:, 0] - 1] - time[events[:-1, 2]] >= min_interval
if no_merge.any():
begs = concatenate([[events[0, 0]], events[1:, 0][no_merge]])
ends = concatenate([events[:-1, 2][no_merge], [events[-1, 2]]])
new_events = vstack((begs, ends)).T
else:
new_events = asarray([[events[0, 0], events[-1, 2]]])
# add the location of the peak in the middle
new_events = insert(new_events, 1, 0, axis=1)
for i in new_events:
if i[2] - i[0] >= 1:
i[1] = i[0] + argmax(dat[i[0]:i[2]])
return new_events | [
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vector with the data after selection-transformation
events : ndarray (dtype='int')
N x 3 matrix with start, peak, end samples
time : ndarray (dtype='float')
vector with time points
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minimum delay between consecutive events, in seconds
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ndarray (dtype='int')
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wonambi-python/wonambi | wonambi/detect/spindle.py | _wmorlet | def _wmorlet(f0, sd, sampling_rate, ns=5):
"""
adapted from nitime
returns a complex morlet wavelet in the time domain
Parameters
----------
f0 : center frequency
sd : standard deviation of frequency
sampling_rate : samplingrate
ns : window length in number of standard deviations
"""
st = 1. / (2. * pi * sd)
w_sz = float(int(ns * st * sampling_rate)) # half time window size
t = arange(-w_sz, w_sz + 1, dtype=float) / sampling_rate
w = (exp(-t ** 2 / (2. * st ** 2)) * exp(2j * pi * f0 * t) /
sqrt(sqrt(pi) * st * sampling_rate))
return w | python | def _wmorlet(f0, sd, sampling_rate, ns=5):
"""
adapted from nitime
returns a complex morlet wavelet in the time domain
Parameters
----------
f0 : center frequency
sd : standard deviation of frequency
sampling_rate : samplingrate
ns : window length in number of standard deviations
"""
st = 1. / (2. * pi * sd)
w_sz = float(int(ns * st * sampling_rate)) # half time window size
t = arange(-w_sz, w_sz + 1, dtype=float) / sampling_rate
w = (exp(-t ** 2 / (2. * st ** 2)) * exp(2j * pi * f0 * t) /
sqrt(sqrt(pi) * st * sampling_rate))
return w | [
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f0 : center frequency
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wonambi-python/wonambi | wonambi/detect/spindle.py | _realwavelets | def _realwavelets(s_freq, freqs, dur, width):
"""Create real wavelets, for UCSD.
Parameters
----------
s_freq : int
sampling frequency
freqs : ndarray
vector with frequencies of interest
dur : float
duration of the wavelets in s
width : float
parameter controlling gaussian shape
Returns
-------
ndarray
wavelets
"""
x = arange(-dur / 2, dur / 2, 1 / s_freq)
wavelets = empty((len(freqs), len(x)))
g = exp(-(pi * x ** 2) / width ** 2)
for i, one_freq in enumerate(freqs):
y = cos(2 * pi * x * one_freq)
wavelets[i, :] = y * g
return wavelets | python | def _realwavelets(s_freq, freqs, dur, width):
"""Create real wavelets, for UCSD.
Parameters
----------
s_freq : int
sampling frequency
freqs : ndarray
vector with frequencies of interest
dur : float
duration of the wavelets in s
width : float
parameter controlling gaussian shape
Returns
-------
ndarray
wavelets
"""
x = arange(-dur / 2, dur / 2, 1 / s_freq)
wavelets = empty((len(freqs), len(x)))
g = exp(-(pi * x ** 2) / width ** 2)
for i, one_freq in enumerate(freqs):
y = cos(2 * pi * x * one_freq)
wavelets[i, :] = y * g
return wavelets | [
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vector with frequencies of interest
dur : float
duration of the wavelets in s
width : float
parameter controlling gaussian shape
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wonambi-python/wonambi | wonambi/widgets/detect_dialogs.py | SpindleDialog.count_channels | def count_channels(self):
"""If more than one channel selected, activate merge checkbox."""
merge = self.index['merge']
if len(self.idx_chan.selectedItems()) > 1:
if merge.isEnabled():
return
else:
merge.setEnabled(True)
else:
self.index['merge'].setCheckState(Qt.Unchecked)
self.index['merge'].setEnabled(False) | python | def count_channels(self):
"""If more than one channel selected, activate merge checkbox."""
merge = self.index['merge']
if len(self.idx_chan.selectedItems()) > 1:
if merge.isEnabled():
return
else:
merge.setEnabled(True)
else:
self.index['merge'].setCheckState(Qt.Unchecked)
self.index['merge'].setEnabled(False) | [
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wonambi-python/wonambi | wonambi/trans/math.py | math | def math(data, operator=None, operator_name=None, axis=None):
"""Apply mathematical operation to each trial and channel individually.
Parameters
----------
data : instance of DataTime, DataFreq, or DataTimeFreq
operator : function or tuple of functions, optional
function(s) to run on the data.
operator_name : str or tuple of str, optional
name of the function(s) to run on the data.
axis : str, optional
for functions that accept it, which axis you should run it on.
Returns
-------
instance of Data
data where the trials underwent operator.
Raises
------
TypeError
If you pass both operator and operator_name.
ValueError
When you try to operate on an axis that has already been removed.
Notes
-----
operator and operator_name are mutually exclusive. operator_name is given
as shortcut for most common operations.
If a function accepts an 'axis' argument, you need to pass 'axis' to the
constructor. In this way, it'll apply the function to the correct
dimension.
The possible point-wise operator_name are:
'absolute', 'angle', 'dB' (=10 * log10), 'exp', 'log', 'sqrt', 'square',
'unwrap'
The operator_name's that need an axis, but do not remove it:
'hilbert', 'diff', 'detrend'
The operator_name's that need an axis and remove it:
'mean', 'median', 'mode', 'std'
Examples
--------
You can pass a single value or a tuple. The order starts from left to
right, so abs of the hilbert transform, should be:
>>> rms = math(data, operator_name=('hilbert', 'abs'), axis='time')
If you want to pass the power of three, use lambda (or partial):
>>> p3 = lambda x: power(x, 3)
>>> data_p3 = math(data, operator=p3)
Note that lambdas are fine with point-wise operation, but if you want them
to operate on axis, you need to pass ''axis'' as well, so that:
>>> std_ddof = lambda x, axis: std(x, axis, ddof=1)
>>> data_std = math(data, operator=std_ddof)
If you don't pass 'axis' in lambda, it'll never know on which axis the
function should be applied and you'll get unpredictable results.
If you want to pass a function that operates on an axis and removes it (for
example, if you want the max value over time), you need to add an argument
in your function called ''keepdims'' (the values won't be used):
>>> def func(x, axis, keepdims=None):
>>> return nanmax(x, axis=axis)
"""
if operator is not None and operator_name is not None:
raise TypeError('Parameters "operator" and "operator_name" are '
'mutually exclusive')
# turn input into a tuple of functions in operators
if operator_name is not None:
if isinstance(operator_name, str):
operator_name = (operator_name, )
operators = []
for one_operator_name in operator_name:
operators.append(eval(one_operator_name))
operator = tuple(operators)
# make it an iterable
if callable(operator):
operator = (operator, )
operations = []
for one_operator in operator:
on_axis = False
keepdims = True
try:
args = getfullargspec(one_operator).args
except TypeError:
lg.debug('func ' + str(one_operator) + ' is not a Python '
'function')
else:
if 'axis' in args:
on_axis = True
if axis is None:
raise TypeError('You need to specify an axis if you '
'use ' + one_operator.__name__ +
' (which applies to an axis)')
if 'keepdims' in args or one_operator in NOKEEPDIM:
keepdims = False
operations.append({'name': one_operator.__name__,
'func': one_operator,
'on_axis': on_axis,
'keepdims': keepdims,
})
output = data._copy()
if axis is not None:
idx_axis = data.index_of(axis)
first_op = True
for op in operations:
#lg.info('running operator: ' + op['name'])
func = op['func']
if func == mode:
func = lambda x, axis: mode(x, axis=axis)[0]
for i in range(output.number_of('trial')):
# don't copy original data, but use data if it's the first operation
if first_op:
x = data(trial=i)
else:
x = output(trial=i)
if op['on_axis']:
lg.debug('running ' + op['name'] + ' on ' + str(idx_axis))
try:
if func == diff:
lg.debug('Diff has one-point of zero padding')
x = _pad_one_axis_one_value(x, idx_axis)
output.data[i] = func(x, axis=idx_axis)
except IndexError:
raise ValueError('The axis ' + axis + ' does not '
'exist in [' +
', '.join(list(data.axis.keys())) + ']')
else:
lg.debug('running ' + op['name'] + ' on each datapoint')
output.data[i] = func(x)
first_op = False
if op['on_axis'] and not op['keepdims']:
del output.axis[axis]
return output | python | def math(data, operator=None, operator_name=None, axis=None):
"""Apply mathematical operation to each trial and channel individually.
Parameters
----------
data : instance of DataTime, DataFreq, or DataTimeFreq
operator : function or tuple of functions, optional
function(s) to run on the data.
operator_name : str or tuple of str, optional
name of the function(s) to run on the data.
axis : str, optional
for functions that accept it, which axis you should run it on.
Returns
-------
instance of Data
data where the trials underwent operator.
Raises
------
TypeError
If you pass both operator and operator_name.
ValueError
When you try to operate on an axis that has already been removed.
Notes
-----
operator and operator_name are mutually exclusive. operator_name is given
as shortcut for most common operations.
If a function accepts an 'axis' argument, you need to pass 'axis' to the
constructor. In this way, it'll apply the function to the correct
dimension.
The possible point-wise operator_name are:
'absolute', 'angle', 'dB' (=10 * log10), 'exp', 'log', 'sqrt', 'square',
'unwrap'
The operator_name's that need an axis, but do not remove it:
'hilbert', 'diff', 'detrend'
The operator_name's that need an axis and remove it:
'mean', 'median', 'mode', 'std'
Examples
--------
You can pass a single value or a tuple. The order starts from left to
right, so abs of the hilbert transform, should be:
>>> rms = math(data, operator_name=('hilbert', 'abs'), axis='time')
If you want to pass the power of three, use lambda (or partial):
>>> p3 = lambda x: power(x, 3)
>>> data_p3 = math(data, operator=p3)
Note that lambdas are fine with point-wise operation, but if you want them
to operate on axis, you need to pass ''axis'' as well, so that:
>>> std_ddof = lambda x, axis: std(x, axis, ddof=1)
>>> data_std = math(data, operator=std_ddof)
If you don't pass 'axis' in lambda, it'll never know on which axis the
function should be applied and you'll get unpredictable results.
If you want to pass a function that operates on an axis and removes it (for
example, if you want the max value over time), you need to add an argument
in your function called ''keepdims'' (the values won't be used):
>>> def func(x, axis, keepdims=None):
>>> return nanmax(x, axis=axis)
"""
if operator is not None and operator_name is not None:
raise TypeError('Parameters "operator" and "operator_name" are '
'mutually exclusive')
# turn input into a tuple of functions in operators
if operator_name is not None:
if isinstance(operator_name, str):
operator_name = (operator_name, )
operators = []
for one_operator_name in operator_name:
operators.append(eval(one_operator_name))
operator = tuple(operators)
# make it an iterable
if callable(operator):
operator = (operator, )
operations = []
for one_operator in operator:
on_axis = False
keepdims = True
try:
args = getfullargspec(one_operator).args
except TypeError:
lg.debug('func ' + str(one_operator) + ' is not a Python '
'function')
else:
if 'axis' in args:
on_axis = True
if axis is None:
raise TypeError('You need to specify an axis if you '
'use ' + one_operator.__name__ +
' (which applies to an axis)')
if 'keepdims' in args or one_operator in NOKEEPDIM:
keepdims = False
operations.append({'name': one_operator.__name__,
'func': one_operator,
'on_axis': on_axis,
'keepdims': keepdims,
})
output = data._copy()
if axis is not None:
idx_axis = data.index_of(axis)
first_op = True
for op in operations:
#lg.info('running operator: ' + op['name'])
func = op['func']
if func == mode:
func = lambda x, axis: mode(x, axis=axis)[0]
for i in range(output.number_of('trial')):
# don't copy original data, but use data if it's the first operation
if first_op:
x = data(trial=i)
else:
x = output(trial=i)
if op['on_axis']:
lg.debug('running ' + op['name'] + ' on ' + str(idx_axis))
try:
if func == diff:
lg.debug('Diff has one-point of zero padding')
x = _pad_one_axis_one_value(x, idx_axis)
output.data[i] = func(x, axis=idx_axis)
except IndexError:
raise ValueError('The axis ' + axis + ' does not '
'exist in [' +
', '.join(list(data.axis.keys())) + ']')
else:
lg.debug('running ' + op['name'] + ' on each datapoint')
output.data[i] = func(x)
first_op = False
if op['on_axis'] and not op['keepdims']:
del output.axis[axis]
return output | [
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operator : function or tuple of functions, optional
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operator_name : str or tuple of str, optional
name of the function(s) to run on the data.
axis : str, optional
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Returns
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data where the trials underwent operator.
Raises
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If you pass both operator and operator_name.
ValueError
When you try to operate on an axis that has already been removed.
Notes
-----
operator and operator_name are mutually exclusive. operator_name is given
as shortcut for most common operations.
If a function accepts an 'axis' argument, you need to pass 'axis' to the
constructor. In this way, it'll apply the function to the correct
dimension.
The possible point-wise operator_name are:
'absolute', 'angle', 'dB' (=10 * log10), 'exp', 'log', 'sqrt', 'square',
'unwrap'
The operator_name's that need an axis, but do not remove it:
'hilbert', 'diff', 'detrend'
The operator_name's that need an axis and remove it:
'mean', 'median', 'mode', 'std'
Examples
--------
You can pass a single value or a tuple. The order starts from left to
right, so abs of the hilbert transform, should be:
>>> rms = math(data, operator_name=('hilbert', 'abs'), axis='time')
If you want to pass the power of three, use lambda (or partial):
>>> p3 = lambda x: power(x, 3)
>>> data_p3 = math(data, operator=p3)
Note that lambdas are fine with point-wise operation, but if you want them
to operate on axis, you need to pass ''axis'' as well, so that:
>>> std_ddof = lambda x, axis: std(x, axis, ddof=1)
>>> data_std = math(data, operator=std_ddof)
If you don't pass 'axis' in lambda, it'll never know on which axis the
function should be applied and you'll get unpredictable results.
If you want to pass a function that operates on an axis and removes it (for
example, if you want the max value over time), you need to add an argument
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wonambi-python/wonambi | wonambi/trans/math.py | get_descriptives | def get_descriptives(data):
"""Get mean, SD, and mean and SD of log values.
Parameters
----------
data : ndarray
Data with segment as first dimension
and all other dimensions raveled into second dimension.
Returns
-------
dict of ndarray
each entry is a 1-D vector of descriptives over segment dimension
"""
output = {}
dat_log = log(abs(data))
output['mean'] = nanmean(data, axis=0)
output['sd'] = nanstd(data, axis=0)
output['mean_log'] = nanmean(dat_log, axis=0)
output['sd_log'] = nanstd(dat_log, axis=0)
return output | python | def get_descriptives(data):
"""Get mean, SD, and mean and SD of log values.
Parameters
----------
data : ndarray
Data with segment as first dimension
and all other dimensions raveled into second dimension.
Returns
-------
dict of ndarray
each entry is a 1-D vector of descriptives over segment dimension
"""
output = {}
dat_log = log(abs(data))
output['mean'] = nanmean(data, axis=0)
output['sd'] = nanstd(data, axis=0)
output['mean_log'] = nanmean(dat_log, axis=0)
output['sd_log'] = nanstd(dat_log, axis=0)
return output | [
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data : ndarray
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Returns
-------
dict of ndarray
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wonambi-python/wonambi | wonambi/ioeeg/abf.py | _read_info_as_dict | def _read_info_as_dict(fid, values):
"""Convenience function to read info in axon data to a nicely organized
dict.
"""
output = {}
for key, fmt in values:
val = unpack(fmt, fid.read(calcsize(fmt)))
if len(val) == 1:
output[key] = val[0]
else:
output[key] = val
return output | python | def _read_info_as_dict(fid, values):
"""Convenience function to read info in axon data to a nicely organized
dict.
"""
output = {}
for key, fmt in values:
val = unpack(fmt, fid.read(calcsize(fmt)))
if len(val) == 1:
output[key] = val[0]
else:
output[key] = val
return output | [
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wonambi-python/wonambi | wonambi/widgets/settings.py | read_settings | def read_settings(widget, value_name):
"""Read Settings information, either from INI or from default values.
Parameters
----------
widget : str
name of the widget
value_name : str
name of the value of interest.
Returns
-------
multiple types
type depends on the type in the default values.
"""
setting_name = widget + '/' + value_name
default_value = DEFAULTS[widget][value_name]
default_type = type(default_value)
if default_type is list:
default_type = type(default_value[0])
val = settings.value(setting_name, default_value, type=default_type)
return val | python | def read_settings(widget, value_name):
"""Read Settings information, either from INI or from default values.
Parameters
----------
widget : str
name of the widget
value_name : str
name of the value of interest.
Returns
-------
multiple types
type depends on the type in the default values.
"""
setting_name = widget + '/' + value_name
default_value = DEFAULTS[widget][value_name]
default_type = type(default_value)
if default_type is list:
default_type = type(default_value[0])
val = settings.value(setting_name, default_value, type=default_type)
return val | [
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value_name : str
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wonambi-python/wonambi | wonambi/widgets/settings.py | Settings.create_settings | def create_settings(self):
"""Create the widget, organized in two parts.
Notes
-----
When you add widgets in config, remember to update show_settings too
"""
bbox = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Apply |
QDialogButtonBox.Cancel)
self.idx_ok = bbox.button(QDialogButtonBox.Ok)
self.idx_apply = bbox.button(QDialogButtonBox.Apply)
self.idx_cancel = bbox.button(QDialogButtonBox.Cancel)
bbox.clicked.connect(self.button_clicked)
page_list = QListWidget()
page_list.setSpacing(1)
page_list.currentRowChanged.connect(self.change_widget)
pages = ['General', 'Overview', 'Signals', 'Channels', 'Spectrum',
'Notes', 'Video']
for one_page in pages:
page_list.addItem(one_page)
self.stacked = QStackedWidget()
self.stacked.addWidget(self.config)
self.stacked.addWidget(self.parent.overview.config)
self.stacked.addWidget(self.parent.traces.config)
self.stacked.addWidget(self.parent.channels.config)
self.stacked.addWidget(self.parent.spectrum.config)
self.stacked.addWidget(self.parent.notes.config)
self.stacked.addWidget(self.parent.video.config)
hsplitter = QSplitter()
hsplitter.addWidget(page_list)
hsplitter.addWidget(self.stacked)
btnlayout = QHBoxLayout()
btnlayout.addStretch(1)
btnlayout.addWidget(bbox)
vlayout = QVBoxLayout()
vlayout.addWidget(hsplitter)
vlayout.addLayout(btnlayout)
self.setLayout(vlayout) | python | def create_settings(self):
"""Create the widget, organized in two parts.
Notes
-----
When you add widgets in config, remember to update show_settings too
"""
bbox = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Apply |
QDialogButtonBox.Cancel)
self.idx_ok = bbox.button(QDialogButtonBox.Ok)
self.idx_apply = bbox.button(QDialogButtonBox.Apply)
self.idx_cancel = bbox.button(QDialogButtonBox.Cancel)
bbox.clicked.connect(self.button_clicked)
page_list = QListWidget()
page_list.setSpacing(1)
page_list.currentRowChanged.connect(self.change_widget)
pages = ['General', 'Overview', 'Signals', 'Channels', 'Spectrum',
'Notes', 'Video']
for one_page in pages:
page_list.addItem(one_page)
self.stacked = QStackedWidget()
self.stacked.addWidget(self.config)
self.stacked.addWidget(self.parent.overview.config)
self.stacked.addWidget(self.parent.traces.config)
self.stacked.addWidget(self.parent.channels.config)
self.stacked.addWidget(self.parent.spectrum.config)
self.stacked.addWidget(self.parent.notes.config)
self.stacked.addWidget(self.parent.video.config)
hsplitter = QSplitter()
hsplitter.addWidget(page_list)
hsplitter.addWidget(self.stacked)
btnlayout = QHBoxLayout()
btnlayout.addStretch(1)
btnlayout.addWidget(bbox)
vlayout = QVBoxLayout()
vlayout.addWidget(hsplitter)
vlayout.addLayout(btnlayout)
self.setLayout(vlayout) | [
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wonambi-python/wonambi | wonambi/widgets/settings.py | Config.create_values | def create_values(self, value_names):
"""Read original values from the settings or the defaults.
Parameters
----------
value_names : list of str
list of value names to read
Returns
-------
dict
dictionary with the value names as keys
"""
output = {}
for value_name in value_names:
output[value_name] = read_settings(self.widget, value_name)
return output | python | def create_values(self, value_names):
"""Read original values from the settings or the defaults.
Parameters
----------
value_names : list of str
list of value names to read
Returns
-------
dict
dictionary with the value names as keys
"""
output = {}
for value_name in value_names:
output[value_name] = read_settings(self.widget, value_name)
return output | [
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wonambi-python/wonambi | wonambi/widgets/settings.py | Config.get_values | def get_values(self):
"""Get values from the GUI and save them in preference file."""
for value_name, widget in self.index.items():
self.value[value_name] = widget.get_value(self.value[value_name])
setting_name = self.widget + '/' + value_name
settings.setValue(setting_name, self.value[value_name]) | python | def get_values(self):
"""Get values from the GUI and save them in preference file."""
for value_name, widget in self.index.items():
self.value[value_name] = widget.get_value(self.value[value_name])
setting_name = self.widget + '/' + value_name
settings.setValue(setting_name, self.value[value_name]) | [
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wonambi-python/wonambi | wonambi/widgets/settings.py | Config.put_values | def put_values(self):
"""Put values to the GUI.
Notes
-----
In addition, when one small widget has been changed, it calls
set_modified, so that we know that the preference widget was modified.
"""
for value_name, widget in self.index.items():
widget.set_value(self.value[value_name])
widget.connect(self.set_modified) | python | def put_values(self):
"""Put values to the GUI.
Notes
-----
In addition, when one small widget has been changed, it calls
set_modified, so that we know that the preference widget was modified.
"""
for value_name, widget in self.index.items():
widget.set_value(self.value[value_name])
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wonambi-python/wonambi | wonambi/datatype.py | _get_indices | def _get_indices(values, selected, tolerance):
"""Get indices based on user-selected values.
Parameters
----------
values : ndarray (any dtype)
values present in the axis.
selected : ndarray (any dtype) or tuple or list
values selected by the user
tolerance : float
avoid rounding errors.
Returns
-------
idx_data : list of int
indices of row/column to select the data
idx_output : list of int
indices of row/column to copy into output
Notes
-----
This function is probably not very fast, but it's pretty robust. It keeps
the order, which is extremely important.
If you use values in the self.axis, you don't need to specify tolerance.
However, if you specify arbitrary points, floating point errors might
affect the actual values. Of course, using tolerance is much slower.
Maybe tolerance should be part of Select instead of here.
"""
idx_data = []
idx_output = []
for idx_of_selected, one_selected in enumerate(selected):
if tolerance is None or values.dtype.kind == 'U':
idx_of_data = where(values == one_selected)[0]
else:
idx_of_data = where(abs(values - one_selected) <= tolerance)[0] # actual use min
if len(idx_of_data) > 0:
idx_data.append(idx_of_data[0])
idx_output.append(idx_of_selected)
return idx_data, idx_output | python | def _get_indices(values, selected, tolerance):
"""Get indices based on user-selected values.
Parameters
----------
values : ndarray (any dtype)
values present in the axis.
selected : ndarray (any dtype) or tuple or list
values selected by the user
tolerance : float
avoid rounding errors.
Returns
-------
idx_data : list of int
indices of row/column to select the data
idx_output : list of int
indices of row/column to copy into output
Notes
-----
This function is probably not very fast, but it's pretty robust. It keeps
the order, which is extremely important.
If you use values in the self.axis, you don't need to specify tolerance.
However, if you specify arbitrary points, floating point errors might
affect the actual values. Of course, using tolerance is much slower.
Maybe tolerance should be part of Select instead of here.
"""
idx_data = []
idx_output = []
for idx_of_selected, one_selected in enumerate(selected):
if tolerance is None or values.dtype.kind == 'U':
idx_of_data = where(values == one_selected)[0]
else:
idx_of_data = where(abs(values - one_selected) <= tolerance)[0] # actual use min
if len(idx_of_data) > 0:
idx_data.append(idx_of_data[0])
idx_output.append(idx_of_selected)
return idx_data, idx_output | [
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values : ndarray (any dtype)
values present in the axis.
selected : ndarray (any dtype) or tuple or list
values selected by the user
tolerance : float
avoid rounding errors.
Returns
-------
idx_data : list of int
indices of row/column to select the data
idx_output : list of int
indices of row/column to copy into output
Notes
-----
This function is probably not very fast, but it's pretty robust. It keeps
the order, which is extremely important.
If you use values in the self.axis, you don't need to specify tolerance.
However, if you specify arbitrary points, floating point errors might
affect the actual values. Of course, using tolerance is much slower.
Maybe tolerance should be part of Select instead of here. | [
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wonambi-python/wonambi | wonambi/datatype.py | Data.number_of | def number_of(self, axis):
"""Return the number of in one axis, as generally as possible.
Parameters
----------
axis : str
Name of the axis (such as 'trial', 'time', etc)
Returns
-------
int or ndarray (dtype='int')
number of trial (as int) or number of element in the selected
axis (if any of the other axiss) as 1d array.
Raises
------
KeyError
If the requested axis is not in the data.
Notes
-----
or is it better to catch the exception?
"""
if axis == 'trial':
return len(self.data)
else:
n_trial = self.number_of('trial')
output = empty(n_trial, dtype='int')
for i in range(n_trial):
output[i] = len(self.axis[axis][i])
return output | python | def number_of(self, axis):
"""Return the number of in one axis, as generally as possible.
Parameters
----------
axis : str
Name of the axis (such as 'trial', 'time', etc)
Returns
-------
int or ndarray (dtype='int')
number of trial (as int) or number of element in the selected
axis (if any of the other axiss) as 1d array.
Raises
------
KeyError
If the requested axis is not in the data.
Notes
-----
or is it better to catch the exception?
"""
if axis == 'trial':
return len(self.data)
else:
n_trial = self.number_of('trial')
output = empty(n_trial, dtype='int')
for i in range(n_trial):
output[i] = len(self.axis[axis][i])
return output | [
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axis : str
Name of the axis (such as 'trial', 'time', etc)
Returns
-------
int or ndarray (dtype='int')
number of trial (as int) or number of element in the selected
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------
KeyError
If the requested axis is not in the data.
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-----
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wonambi-python/wonambi | wonambi/datatype.py | Data._copy | def _copy(self, axis=True, attr=True, data=False):
"""Create a new instance of Data, but does not copy the data
necessarily.
Parameters
----------
axis : bool, optional
deep copy the axes (default: True)
attr : bool, optional
deep copy the attributes (default: True)
data : bool, optional
deep copy the data (default: False)
Returns
-------
instance of Data (or ChanTime, ChanFreq, ChanTimeFreq)
copy of the data, but without the actual data
Notes
-----
It's important that we copy all the relevant information here. If you
add new attributes, you should add them here.
Remember that it deep-copies all the information, so if you copy data
the size might become really large.
"""
cdata = type(self)() # create instance of the same class
cdata.s_freq = self.s_freq
cdata.start_time = self.start_time
if axis:
cdata.axis = deepcopy(self.axis)
else:
cdata_axis = OrderedDict()
for axis_name in self.axis:
cdata_axis[axis_name] = array([], dtype='O')
cdata.axis = cdata_axis
if attr:
cdata.attr = deepcopy(self.attr)
if data:
cdata.data = deepcopy(self.data)
else:
# empty data with the correct number of trials
cdata.data = empty(self.number_of('trial'), dtype='O')
return cdata | python | def _copy(self, axis=True, attr=True, data=False):
"""Create a new instance of Data, but does not copy the data
necessarily.
Parameters
----------
axis : bool, optional
deep copy the axes (default: True)
attr : bool, optional
deep copy the attributes (default: True)
data : bool, optional
deep copy the data (default: False)
Returns
-------
instance of Data (or ChanTime, ChanFreq, ChanTimeFreq)
copy of the data, but without the actual data
Notes
-----
It's important that we copy all the relevant information here. If you
add new attributes, you should add them here.
Remember that it deep-copies all the information, so if you copy data
the size might become really large.
"""
cdata = type(self)() # create instance of the same class
cdata.s_freq = self.s_freq
cdata.start_time = self.start_time
if axis:
cdata.axis = deepcopy(self.axis)
else:
cdata_axis = OrderedDict()
for axis_name in self.axis:
cdata_axis[axis_name] = array([], dtype='O')
cdata.axis = cdata_axis
if attr:
cdata.attr = deepcopy(self.attr)
if data:
cdata.data = deepcopy(self.data)
else:
# empty data with the correct number of trials
cdata.data = empty(self.number_of('trial'), dtype='O')
return cdata | [
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axis : bool, optional
deep copy the axes (default: True)
attr : bool, optional
deep copy the attributes (default: True)
data : bool, optional
deep copy the data (default: False)
Returns
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instance of Data (or ChanTime, ChanFreq, ChanTimeFreq)
copy of the data, but without the actual data
Notes
-----
It's important that we copy all the relevant information here. If you
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Remember that it deep-copies all the information, so if you copy data
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wonambi-python/wonambi | wonambi/datatype.py | Data.export | def export(self, filename, export_format='FieldTrip', **options):
"""Export data in other formats.
Parameters
----------
filename : path to file
file to write
export_format : str, optional
supported export format is currently FieldTrip, EDF, FIFF, Wonambi,
BrainVision
Notes
-----
'edf' takes an optional argument "physical_max", see write_edf.
'wonambi' takes an optional argument "subj_id", see write_wonambi.
wonambi format creates two files, one .won with the dataset info as json
file and one .dat with the memmap recordings.
'brainvision' takes an additional argument ("markers") which is a list
of dictionaries with fields:
"name" : str (name of the marker),
"start" : float (start time in seconds)
"end" : float (end time in seconds)
'bids' has an optional argument "markers", like in 'brainvision'
"""
filename = Path(filename)
filename.parent.mkdir(parents=True, exist_ok=True)
export_format = export_format.lower()
if export_format == 'edf':
from .ioeeg import write_edf # avoid circular import
write_edf(self, filename, **options)
elif export_format == 'fieldtrip':
from .ioeeg import write_fieldtrip # avoid circular import
write_fieldtrip(self, filename)
elif export_format == 'mnefiff':
from .ioeeg import write_mnefiff
write_mnefiff(self, filename)
elif export_format == 'wonambi':
from .ioeeg import write_wonambi
write_wonambi(self, filename, **options)
elif export_format == 'brainvision':
from .ioeeg import write_brainvision
write_brainvision(self, filename, **options)
elif export_format == 'bids':
from .ioeeg import write_bids
write_bids(self, filename, **options)
else:
raise ValueError('Cannot export to ' + export_format) | python | def export(self, filename, export_format='FieldTrip', **options):
"""Export data in other formats.
Parameters
----------
filename : path to file
file to write
export_format : str, optional
supported export format is currently FieldTrip, EDF, FIFF, Wonambi,
BrainVision
Notes
-----
'edf' takes an optional argument "physical_max", see write_edf.
'wonambi' takes an optional argument "subj_id", see write_wonambi.
wonambi format creates two files, one .won with the dataset info as json
file and one .dat with the memmap recordings.
'brainvision' takes an additional argument ("markers") which is a list
of dictionaries with fields:
"name" : str (name of the marker),
"start" : float (start time in seconds)
"end" : float (end time in seconds)
'bids' has an optional argument "markers", like in 'brainvision'
"""
filename = Path(filename)
filename.parent.mkdir(parents=True, exist_ok=True)
export_format = export_format.lower()
if export_format == 'edf':
from .ioeeg import write_edf # avoid circular import
write_edf(self, filename, **options)
elif export_format == 'fieldtrip':
from .ioeeg import write_fieldtrip # avoid circular import
write_fieldtrip(self, filename)
elif export_format == 'mnefiff':
from .ioeeg import write_mnefiff
write_mnefiff(self, filename)
elif export_format == 'wonambi':
from .ioeeg import write_wonambi
write_wonambi(self, filename, **options)
elif export_format == 'brainvision':
from .ioeeg import write_brainvision
write_brainvision(self, filename, **options)
elif export_format == 'bids':
from .ioeeg import write_bids
write_bids(self, filename, **options)
else:
raise ValueError('Cannot export to ' + export_format) | [
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filename : path to file
file to write
export_format : str, optional
supported export format is currently FieldTrip, EDF, FIFF, Wonambi,
BrainVision
Notes
-----
'edf' takes an optional argument "physical_max", see write_edf.
'wonambi' takes an optional argument "subj_id", see write_wonambi.
wonambi format creates two files, one .won with the dataset info as json
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'brainvision' takes an additional argument ("markers") which is a list
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"name" : str (name of the marker),
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"end" : float (end time in seconds)
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wonambi-python/wonambi | wonambi/widgets/channels.py | ChannelsGroup.highlight_channels | def highlight_channels(self, l, selected_chan):
"""Highlight channels in the list of channels.
Parameters
----------
selected_chan : list of str
channels to indicate as selected.
"""
for row in range(l.count()):
item = l.item(row)
if item.text() in selected_chan:
item.setSelected(True)
else:
item.setSelected(False) | python | def highlight_channels(self, l, selected_chan):
"""Highlight channels in the list of channels.
Parameters
----------
selected_chan : list of str
channels to indicate as selected.
"""
for row in range(l.count()):
item = l.item(row)
if item.text() in selected_chan:
item.setSelected(True)
else:
item.setSelected(False) | [
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wonambi-python/wonambi | wonambi/widgets/channels.py | ChannelsGroup.rereference | def rereference(self):
"""Automatically highlight channels to use as reference, based on
selected channels."""
selectedItems = self.idx_l0.selectedItems()
chan_to_plot = []
for selected in selectedItems:
chan_to_plot.append(selected.text())
self.highlight_channels(self.idx_l1, chan_to_plot) | python | def rereference(self):
"""Automatically highlight channels to use as reference, based on
selected channels."""
selectedItems = self.idx_l0.selectedItems()
chan_to_plot = []
for selected in selectedItems:
chan_to_plot.append(selected.text())
self.highlight_channels(self.idx_l1, chan_to_plot) | [
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wonambi-python/wonambi | wonambi/widgets/channels.py | ChannelsGroup.get_info | def get_info(self):
"""Get the information about the channel groups.
Returns
-------
dict
information about this channel group
Notes
-----
The items in selectedItems() are ordered based on the user's selection
(which appears pretty random). It's more consistent to use the same
order of the main channel list. That's why the additional for-loop
is necessary. We don't care about the order of the reference channels.
"""
selectedItems = self.idx_l0.selectedItems()
selected_chan = [x.text() for x in selectedItems]
chan_to_plot = []
for chan in self.chan_name + ['_REF']:
if chan in selected_chan:
chan_to_plot.append(chan)
selectedItems = self.idx_l1.selectedItems()
ref_chan = []
for selected in selectedItems:
ref_chan.append(selected.text())
hp = self.idx_hp.value()
if hp == 0:
low_cut = None
else:
low_cut = hp
lp = self.idx_lp.value()
if lp == 0:
high_cut = None
else:
high_cut = lp
scale = self.idx_scale.value()
group_info = {'name': self.group_name,
'chan_to_plot': chan_to_plot,
'ref_chan': ref_chan,
'hp': low_cut,
'lp': high_cut,
'scale': float(scale),
'color': self.idx_color
}
return group_info | python | def get_info(self):
"""Get the information about the channel groups.
Returns
-------
dict
information about this channel group
Notes
-----
The items in selectedItems() are ordered based on the user's selection
(which appears pretty random). It's more consistent to use the same
order of the main channel list. That's why the additional for-loop
is necessary. We don't care about the order of the reference channels.
"""
selectedItems = self.idx_l0.selectedItems()
selected_chan = [x.text() for x in selectedItems]
chan_to_plot = []
for chan in self.chan_name + ['_REF']:
if chan in selected_chan:
chan_to_plot.append(chan)
selectedItems = self.idx_l1.selectedItems()
ref_chan = []
for selected in selectedItems:
ref_chan.append(selected.text())
hp = self.idx_hp.value()
if hp == 0:
low_cut = None
else:
low_cut = hp
lp = self.idx_lp.value()
if lp == 0:
high_cut = None
else:
high_cut = lp
scale = self.idx_scale.value()
group_info = {'name': self.group_name,
'chan_to_plot': chan_to_plot,
'ref_chan': ref_chan,
'hp': low_cut,
'lp': high_cut,
'scale': float(scale),
'color': self.idx_color
}
return group_info | [
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wonambi-python/wonambi | wonambi/widgets/channels.py | Channels.create | def create(self):
"""Create Channels Widget"""
add_button = QPushButton('New')
add_button.clicked.connect(self.new_group)
color_button = QPushButton('Color')
color_button.clicked.connect(self.color_group)
del_button = QPushButton('Delete')
del_button.clicked.connect(self.del_group)
apply_button = QPushButton('Apply')
apply_button.clicked.connect(self.apply)
self.button_add = add_button
self.button_color = color_button
self.button_del = del_button
self.button_apply = apply_button
buttons = QGridLayout()
buttons.addWidget(add_button, 0, 0)
buttons.addWidget(color_button, 1, 0)
buttons.addWidget(del_button, 0, 1)
buttons.addWidget(apply_button, 1, 1)
self.tabs = QTabWidget()
layout = QVBoxLayout()
layout.addLayout(buttons)
layout.addWidget(self.tabs)
self.setLayout(layout)
self.setEnabled(False)
self.button_color.setEnabled(False)
self.button_del.setEnabled(False)
self.button_apply.setEnabled(False) | python | def create(self):
"""Create Channels Widget"""
add_button = QPushButton('New')
add_button.clicked.connect(self.new_group)
color_button = QPushButton('Color')
color_button.clicked.connect(self.color_group)
del_button = QPushButton('Delete')
del_button.clicked.connect(self.del_group)
apply_button = QPushButton('Apply')
apply_button.clicked.connect(self.apply)
self.button_add = add_button
self.button_color = color_button
self.button_del = del_button
self.button_apply = apply_button
buttons = QGridLayout()
buttons.addWidget(add_button, 0, 0)
buttons.addWidget(color_button, 1, 0)
buttons.addWidget(del_button, 0, 1)
buttons.addWidget(apply_button, 1, 1)
self.tabs = QTabWidget()
layout = QVBoxLayout()
layout.addLayout(buttons)
layout.addWidget(self.tabs)
self.setLayout(layout)
self.setEnabled(False)
self.button_color.setEnabled(False)
self.button_del.setEnabled(False)
self.button_apply.setEnabled(False) | [
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wonambi-python/wonambi | wonambi/widgets/channels.py | Channels.create_action | def create_action(self):
"""Create actions related to channel selection."""
actions = {}
act = QAction('Load Montage...', self)
act.triggered.connect(self.load_channels)
act.setEnabled(False)
actions['load_channels'] = act
act = QAction('Save Montage...', self)
act.triggered.connect(self.save_channels)
act.setEnabled(False)
actions['save_channels'] = act
self.action = actions | python | def create_action(self):
"""Create actions related to channel selection."""
actions = {}
act = QAction('Load Montage...', self)
act.triggered.connect(self.load_channels)
act.setEnabled(False)
actions['load_channels'] = act
act = QAction('Save Montage...', self)
act.triggered.connect(self.save_channels)
act.setEnabled(False)
actions['save_channels'] = act
self.action = actions | [
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wonambi-python/wonambi | wonambi/widgets/channels.py | Channels.new_group | def new_group(self, checked=False, test_name=None):
"""Create a new channel group.
Parameters
----------
checked : bool
comes from QAbstractButton.clicked
test_name : str
used for testing purposes to avoid modal window
Notes
-----
Don't call self.apply() just yet, only if the user wants it.
"""
chan_name = self.parent.labels.chan_name
if chan_name is None:
msg = 'No dataset loaded'
self.parent.statusBar().showMessage(msg)
lg.debug(msg)
else:
if test_name is None:
new_name = QInputDialog.getText(self, 'New Channel Group',
'Enter Name')
else:
new_name = [test_name, True] # like output of getText
if new_name[1]:
s_freq = self.parent.info.dataset.header['s_freq']
group = ChannelsGroup(chan_name, new_name[0],
self.config.value, s_freq)
self.tabs.addTab(group, new_name[0])
self.tabs.setCurrentIndex(self.tabs.currentIndex() + 1)
# activate buttons
self.button_color.setEnabled(True)
self.button_del.setEnabled(True)
self.button_apply.setEnabled(True) | python | def new_group(self, checked=False, test_name=None):
"""Create a new channel group.
Parameters
----------
checked : bool
comes from QAbstractButton.clicked
test_name : str
used for testing purposes to avoid modal window
Notes
-----
Don't call self.apply() just yet, only if the user wants it.
"""
chan_name = self.parent.labels.chan_name
if chan_name is None:
msg = 'No dataset loaded'
self.parent.statusBar().showMessage(msg)
lg.debug(msg)
else:
if test_name is None:
new_name = QInputDialog.getText(self, 'New Channel Group',
'Enter Name')
else:
new_name = [test_name, True] # like output of getText
if new_name[1]:
s_freq = self.parent.info.dataset.header['s_freq']
group = ChannelsGroup(chan_name, new_name[0],
self.config.value, s_freq)
self.tabs.addTab(group, new_name[0])
self.tabs.setCurrentIndex(self.tabs.currentIndex() + 1)
# activate buttons
self.button_color.setEnabled(True)
self.button_del.setEnabled(True)
self.button_apply.setEnabled(True) | [
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wonambi-python/wonambi | wonambi/widgets/channels.py | Channels.color_group | def color_group(self, checked=False, test_color=None):
"""Change the color of the group."""
group = self.tabs.currentWidget()
if test_color is None:
newcolor = QColorDialog.getColor(group.idx_color)
else:
newcolor = test_color
group.idx_color = newcolor
self.apply() | python | def color_group(self, checked=False, test_color=None):
"""Change the color of the group."""
group = self.tabs.currentWidget()
if test_color is None:
newcolor = QColorDialog.getColor(group.idx_color)
else:
newcolor = test_color
group.idx_color = newcolor
self.apply() | [
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wonambi-python/wonambi | wonambi/widgets/channels.py | Channels.del_group | def del_group(self):
"""Delete current group."""
idx = self.tabs.currentIndex()
self.tabs.removeTab(idx)
self.apply() | python | def del_group(self):
"""Delete current group."""
idx = self.tabs.currentIndex()
self.tabs.removeTab(idx)
self.apply() | [
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wonambi-python/wonambi | wonambi/widgets/channels.py | Channels.apply | def apply(self):
"""Apply changes to the plots."""
self.read_group_info()
if self.tabs.count() == 0:
# disactivate buttons
self.button_color.setEnabled(False)
self.button_del.setEnabled(False)
self.button_apply.setEnabled(False)
else:
# activate buttons
self.button_color.setEnabled(True)
self.button_del.setEnabled(True)
self.button_apply.setEnabled(True)
if self.groups:
self.parent.overview.update_position()
self.parent.spectrum.update()
self.parent.notes.enable_events()
else:
self.parent.traces.reset()
self.parent.spectrum.reset()
self.parent.notes.enable_events() | python | def apply(self):
"""Apply changes to the plots."""
self.read_group_info()
if self.tabs.count() == 0:
# disactivate buttons
self.button_color.setEnabled(False)
self.button_del.setEnabled(False)
self.button_apply.setEnabled(False)
else:
# activate buttons
self.button_color.setEnabled(True)
self.button_del.setEnabled(True)
self.button_apply.setEnabled(True)
if self.groups:
self.parent.overview.update_position()
self.parent.spectrum.update()
self.parent.notes.enable_events()
else:
self.parent.traces.reset()
self.parent.spectrum.reset()
self.parent.notes.enable_events() | [
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wonambi-python/wonambi | wonambi/widgets/channels.py | Channels.read_group_info | def read_group_info(self):
"""Get information about groups directly from the widget."""
self.groups = []
for i in range(self.tabs.count()):
one_group = self.tabs.widget(i).get_info()
# one_group['name'] = self.tabs.tabText(i)
self.groups.append(one_group) | python | def read_group_info(self):
"""Get information about groups directly from the widget."""
self.groups = []
for i in range(self.tabs.count()):
one_group = self.tabs.widget(i).get_info()
# one_group['name'] = self.tabs.tabText(i)
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wonambi-python/wonambi | wonambi/widgets/channels.py | Channels.load_channels | def load_channels(self, checked=False, test_name=None):
"""Load channel groups from file.
Parameters
----------
test_name : path to file
when debugging the function, you can open a channels file from the
command line
"""
chan_name = self.parent.labels.chan_name
if self.filename is not None:
filename = self.filename
elif self.parent.info.filename is not None:
filename = (splitext(self.parent.info.filename)[0] +
'_channels.json')
else:
filename = None
if test_name is None:
filename, _ = QFileDialog.getOpenFileName(self,
'Open Channels Montage',
filename,
'Channels File (*.json)')
else:
filename = test_name
if filename == '':
return
self.filename = filename
with open(filename, 'r') as outfile:
groups = load(outfile)
s_freq = self.parent.info.dataset.header['s_freq']
no_in_dataset = []
for one_grp in groups:
no_in_dataset.extend(set(one_grp['chan_to_plot']) -
set(chan_name))
chan_to_plot = set(chan_name) & set(one_grp['chan_to_plot'])
ref_chan = set(chan_name) & set(one_grp['ref_chan'])
group = ChannelsGroup(chan_name, one_grp['name'], one_grp, s_freq)
group.highlight_channels(group.idx_l0, chan_to_plot)
group.highlight_channels(group.idx_l1, ref_chan)
self.tabs.addTab(group, one_grp['name'])
if no_in_dataset:
msg = 'Channels not present in the dataset: ' + ', '.join(no_in_dataset)
self.parent.statusBar().showMessage(msg)
lg.debug(msg)
self.apply() | python | def load_channels(self, checked=False, test_name=None):
"""Load channel groups from file.
Parameters
----------
test_name : path to file
when debugging the function, you can open a channels file from the
command line
"""
chan_name = self.parent.labels.chan_name
if self.filename is not None:
filename = self.filename
elif self.parent.info.filename is not None:
filename = (splitext(self.parent.info.filename)[0] +
'_channels.json')
else:
filename = None
if test_name is None:
filename, _ = QFileDialog.getOpenFileName(self,
'Open Channels Montage',
filename,
'Channels File (*.json)')
else:
filename = test_name
if filename == '':
return
self.filename = filename
with open(filename, 'r') as outfile:
groups = load(outfile)
s_freq = self.parent.info.dataset.header['s_freq']
no_in_dataset = []
for one_grp in groups:
no_in_dataset.extend(set(one_grp['chan_to_plot']) -
set(chan_name))
chan_to_plot = set(chan_name) & set(one_grp['chan_to_plot'])
ref_chan = set(chan_name) & set(one_grp['ref_chan'])
group = ChannelsGroup(chan_name, one_grp['name'], one_grp, s_freq)
group.highlight_channels(group.idx_l0, chan_to_plot)
group.highlight_channels(group.idx_l1, ref_chan)
self.tabs.addTab(group, one_grp['name'])
if no_in_dataset:
msg = 'Channels not present in the dataset: ' + ', '.join(no_in_dataset)
self.parent.statusBar().showMessage(msg)
lg.debug(msg)
self.apply() | [
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wonambi-python/wonambi | wonambi/widgets/channels.py | Channels.save_channels | def save_channels(self, checked=False, test_name=None):
"""Save channel groups to file."""
self.read_group_info()
if self.filename is not None:
filename = self.filename
elif self.parent.info.filename is not None:
filename = (splitext(self.parent.info.filename)[0] +
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else:
filename = None
if test_name is None:
filename, _ = QFileDialog.getSaveFileName(self,
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else:
filename = test_name
if filename == '':
return
self.filename = filename
groups = deepcopy(self.groups)
for one_grp in groups:
one_grp['color'] = one_grp['color'].rgba()
with open(filename, 'w') as outfile:
dump(groups, outfile, indent=' ') | python | def save_channels(self, checked=False, test_name=None):
"""Save channel groups to file."""
self.read_group_info()
if self.filename is not None:
filename = self.filename
elif self.parent.info.filename is not None:
filename = (splitext(self.parent.info.filename)[0] +
'_channels.json')
else:
filename = None
if test_name is None:
filename, _ = QFileDialog.getSaveFileName(self,
'Save Channels Montage',
filename,
'Channels File (*.json)')
else:
filename = test_name
if filename == '':
return
self.filename = filename
groups = deepcopy(self.groups)
for one_grp in groups:
one_grp['color'] = one_grp['color'].rgba()
with open(filename, 'w') as outfile:
dump(groups, outfile, indent=' ') | [
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wonambi-python/wonambi | wonambi/widgets/channels.py | Channels.reset | def reset(self):
"""Reset all the information of this widget."""
self.filename = None
self.groups = []
self.tabs.clear()
self.setEnabled(False)
self.button_color.setEnabled(False)
self.button_del.setEnabled(False)
self.button_apply.setEnabled(False)
self.action['load_channels'].setEnabled(False)
self.action['save_channels'].setEnabled(False) | python | def reset(self):
"""Reset all the information of this widget."""
self.filename = None
self.groups = []
self.tabs.clear()
self.setEnabled(False)
self.button_color.setEnabled(False)
self.button_del.setEnabled(False)
self.button_apply.setEnabled(False)
self.action['load_channels'].setEnabled(False)
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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):
"""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) | [
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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):
"""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)
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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):
"""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() | [
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] | 1d8e3d7e53df8017c199f703bcab582914676e76 | https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/spectrum.py#L136-L154 | train | 23,445 |
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):
"""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)) | [
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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):
"""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)) | [
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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):
"""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']) | [
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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):
"""Reset widget as new"""
self.idx_chan.clear()
if self.scene is not None:
self.scene.clear()
self.scene = None | [
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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):
"""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 | [
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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'
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parameters for 'butter',
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min and max duration of SW
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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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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):
"""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, :] | [
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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):
"""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 | [
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vector with the data
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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):
"""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, :] | [
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sampling frequency
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min and max duration of SW
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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):
"""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') | [
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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):
"""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() | [
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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):
"""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) | [
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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):
"""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 | [
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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):
"""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) | [
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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):
"""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() | [
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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):
"""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() | [
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Parameters
----------
time : tuple of float
start and end of the new bookmark, in s | [
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] | 1d8e3d7e53df8017c199f703bcab582914676e76 | https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/notes.py#L638-L647 | train | 23,460 |
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):
"""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() | [
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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):
"""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) | [
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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 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) | [
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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):
"""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) | [
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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):
"""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 | [
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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):
"""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() | [
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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):
"""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() | [
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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):
"""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 | [
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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):
"""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() | [
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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):
"""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()
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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):
"""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) | [
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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 '
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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):
"""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() | [
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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):
"""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()
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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):
"""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)) | [
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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):
"""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)) | [
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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):
"""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)
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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):
"""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()
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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):
"""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) | [
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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):
"""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) | [
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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):
"""Dialog for getting name, location of dataset export."""
filename = splitext(self.filename)[0]
filename, _ = QFileDialog.getSaveFileName(self, 'Export events',
filename)
if filename == '':
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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):
"""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 | [
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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):
"""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 | [
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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):
"""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 | [
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anatomical information taken from freesurfer.
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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):
"""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 | [
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anatomical information taken from freesurfer.
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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):
"""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 | [
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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):
"""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 | [
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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):
"""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) | [
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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):
"""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 | [
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Parameters
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ftype : str
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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
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Raises
------
ValueError
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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):
"""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 | [
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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
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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
--------
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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):
"""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) | [
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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):
"""This is used by ipython to plot inline.
"""
app.process_events()
QApplication.processEvents()
img = read_pixels()
return bytes(_make_png(img)) | [
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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):
"""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_()) | [
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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):
"""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 | [
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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
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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):
"""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() | [
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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):
"""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() | [
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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):
"""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) | [
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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):
"""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() | [
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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):
"""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() | [
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Parameters
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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
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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):
"""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 | [
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] | 1d8e3d7e53df8017c199f703bcab582914676e76 | https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/overview.py#L250-L265 | train | 23,499 |
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