def extract(self, data, s_aligned):
s = int(s_aligned)
# Get block of given size around peak sample.
waveform = _get_padded(data,
s - self._extract_before - 1,
s + self._extract_after + 2)
return waveform
def extract(self, data, s_aligned):
s = int(s_aligned)
# Get block of given size around peak sample.
waveform = _get_padded(data,
s - self._extract_before - 1,
s + self._extract_after + 2)
return waveform
def _extract_wave(traces, start, mask, wave_len=None, mask_threshold=.5):
n_samples, n_channels = traces.shape
assert mask.shape == (n_channels,)
channels = np.nonzero(mask > mask_threshold)[0]
# There should be at least one non-masked channel.
if not len(channels):
return # pragma: no cover
i, j = start, start + wave_len
a, b = max(0, i), min(j, n_samples - 1)
data = traces[a:b, channels]
data = _get_padded(data, i - a, i - a + wave_len)
assert data.shape == (wave_len, len(channels))
return data, channels
def _extract_wave(traces, start, mask, wave_len=None, mask_threshold=.5):
n_samples, n_channels = traces.shape
assert mask.shape == (n_channels,)
channels = np.nonzero(mask > mask_threshold)[0]
# There should be at least one non-masked channel.
if not len(channels):
return # pragma: no cover
i, j = start, start + wave_len
a, b = max(0, i), min(j, n_samples - 1)
data = traces[a:b, channels]
data = _get_padded(data, i - a, i - a + wave_len)
assert data.shape == (wave_len, len(channels))
return data, channels