def delete_peaks(data, remove):
"""
Deletes peaks in `remove` from peaks stored in `data`
Parameters
----------
data : Physio_like
remove : array_like
Returns
-------
data : Physio_like
"""
data = utils.check_physio(data, ensure_fs=False, copy=True)
data._metadata['peaks'] = np.setdiff1d(data._metadata['peaks'], remove)
data._metadata['troughs'] = utils.check_troughs(data, data.peaks)
return data
def reject_peaks(data, remove):
"""
Marks peaks in `remove` as rejected artifacts in `data`
Parameters
----------
data : Physio_like
remove : array_like
Returns
-------
data : Physio_like
"""
data = utils.check_physio(data, ensure_fs=False, copy=True)
data._metadata['reject'] = np.append(data._metadata['reject'], remove)
data._metadata['troughs'] = utils.check_troughs(data, data.peaks)
return data
def peakfind_physio(data, *, thresh=0.2, dist=None):
"""
Performs peak and trough detection on `data`
Parameters
----------
data : Physio_like
Input data in which to find peaks
thresh : float [0,1], optional
Relative height threshold a data point must surpass to be classified as
a peak. Default: 0.2
dist : int, optional
Distance in indices that peaks must be separated by in `data`. If None,
this is estimated. Default: None
Returns
-------
peaks : :class:`peakdet.Physio`
Input `data` with detected peaks and troughs
"""
ensure_fs = True if dist is None else False
data = utils.check_physio(data, ensure_fs=ensure_fs, copy=True)
# first pass peak detection to get approximate distance between peaks
cdist = data.fs // 4 if dist is None else dist
thresh = np.squeeze(np.diff(np.percentile(data, [5, 95]))) * thresh
locs, heights = signal.find_peaks(data[:], distance=cdist, height=thresh)
# second, more thorough peak detection
cdist = np.diff(locs).mean() // 2
heights = np.percentile(heights['peak_heights'], 1)
locs, heights = signal.find_peaks(data[:], distance=cdist, height=heights)
data._metadata['peaks'] = locs
# perform trough detection based on detected peaks
data._metadata['troughs'] = utils.check_troughs(data, data.peaks)
return data
def undo(self):
""" Resets last span select peak removal """
# check if last history entry was a manual reject / delete
if self.data._history[-1][0] not in ['reject_peaks', 'delete_peaks']:
return
# pop off last edit and delete
func, peaks = self.data._history.pop()
if func == 'reject_peaks':
self.data._metadata['reject'] = np.setdiff1d(
self.data._metadata['reject'], peaks['remove'])
self.rejected.difference_update(peaks['remove'])
elif func == 'delete_peaks':
self.data._metadata['peaks'] = np.insert(
self.data._metadata['peaks'],
np.searchsorted(self.data._metadata['peaks'], peaks['remove']),
peaks['remove'])
self.deleted.difference_update(peaks['remove'])
self.data._metadata['troughs'] = utils.check_troughs(
self.data, self.data.peaks)
self.plot_signals()
def test_check_troughs():
true = np.array([9, 21])
assert_array_equal(utils.check_troughs(DATA, PEAKS), true)