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Make examples great again #77

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Sep 4, 2019
Merged

Make examples great again #77

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85ff637
Merge pull request #74 from pyomeca/dev
romainmartinez Aug 24, 2019
0e708fc
Adjusted the name of an Analog in the example
pariterre Sep 2, 2019
3c2547f
made detect_onset compliant to always 3d data
pariterre Sep 2, 2019
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4 changes: 2 additions & 2 deletions examples/signal_processing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@
MARKERS_ANALOGS_C3D = DATA_FOLDER / 'markers_analogs.c3d'

# read an emg from a c3d file
a = Analogs3d.from_c3d(MARKERS_ANALOGS_C3D, names=['EMG1'])
a = Analogs3d.from_c3d(MARKERS_ANALOGS_C3D, names=['Delt_ant.EMG1'])
a.plot()
plt.show()

Expand Down Expand Up @@ -238,7 +238,7 @@
two_norm = Analogs3d(two_norm.reshape(1, 1, -1))

# threshold = mean during the first second
idx = two_norm[0, 0, :].detect_onset(
idx = two_norm.detect_onset(
threshold=np.nanmean(two_norm[..., :int(b.get_rate)]),
above=int(b.get_rate) / 2,
below=3,
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12 changes: 7 additions & 5 deletions pyomeca/frame_dependent.py
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Expand Up @@ -978,10 +978,12 @@ def detect_onset(self, threshold=0, above=1, below=0, threshold2=None, above2=1)
idx : np.ndarray
onset events
"""
if self.ndim > 1:
raise ValueError(f'detect_onset works only for vector (ndim < 2). Your data have {self.ndim} dimensions.')
self[np.isnan(self)] = -np.inf
idx = np.argwhere(self >= threshold).ravel()
if self.ndim != 3:
raise ValueError(f'detect_onset works only for vector (ndim == 3). Your data have {self.ndim} dimensions.')
if self.shape[0] != 1 or self.shape[1] != 1:
raise ValueError(f'detect_onset works on a single time-dependent value for instance: self[i, j, :].')
self[0, 0, np.squeeze(np.isnan(self))] = -np.inf
idx = np.argwhere(np.squeeze(self >= threshold)).ravel()

if np.any(idx):
# initial & final indexes of almost continuous data
Expand All @@ -996,7 +998,7 @@ def detect_onset(self, threshold=0, above=1, below=0, threshold2=None, above2=1)
# minimum amplitude of above2 values in x
ic = np.ones(idx.shape[0], dtype=bool)
for irow in range(idx.shape[0]):
if np.count_nonzero(self[idx[irow, 0]: idx[irow, 1] + 1] >= threshold2) < above2:
if np.count_nonzero(self[0, 0, idx[irow, 0]: idx[irow, 1] + 1] >= threshold2) < above2:
ic[irow] = False
idx = idx[ic, :]

Expand Down