def __init__(
self,
traces=None,
sample_rate=None,
spike_samples=None,
masks=None,
mask_threshold=None,
filter_order=None,
n_samples_waveforms=None,
):
# Traces.
if traces is not None:
self.traces = traces
self.n_samples_trace, self.n_channels = traces.shape
else:
self._traces = None
self.n_samples_trace = self.n_channels = 0
assert spike_samples is not None
self._spike_samples = spike_samples
self.n_spikes = len(spike_samples)
self._masks = masks
if masks is not None:
assert self._masks.shape == (self.n_spikes, self.n_channels)
self._mask_threshold = mask_threshold
# Define filter.
if filter_order:
filter_margin = filter_order * 3
b_filter = bandpass_filter(
rate=sample_rate,
low=500.,
high=sample_rate * .475,
order=filter_order,
)
self._filter = lambda x, axis=0: apply_filter(
x, b_filter, axis=axis)
else:
filter_margin = 0
self._filter = lambda x, axis=0: x
# Number of samples to return, can be an int or a
# tuple (before, after).
assert n_samples_waveforms is not None
self.n_samples_before_after = _before_after(n_samples_waveforms)
self.n_samples_waveforms = sum(self.n_samples_before_after)
# Number of additional samples to use for filtering.
self._filter_margin = _before_after(filter_margin)
# Number of samples in the extracted raw data chunk.
self._n_samples_extract = (self.n_samples_waveforms +
sum(self._filter_margin))
self.dtype = np.float32
self.shape = (self.n_spikes, self._n_samples_extract, self.n_channels)
self.ndim = 3
def __init__(self,
traces=None,
sample_rate=None,
spike_samples=None,
filter_order=None,
n_samples_waveforms=None,
):
# Traces.
if traces is not None:
self.traces = traces
self.n_samples_trace, self.n_channels = traces.shape
else:
self._traces = None
self.n_samples_trace = self.n_channels = 0
assert spike_samples is not None
self._spike_samples = spike_samples
self.n_spikes = len(spike_samples)
# Define filter.
if filter_order:
filter_margin = filter_order * 3
b_filter = bandpass_filter(rate=sample_rate,
low=500.,
high=sample_rate * .475,
order=filter_order,
)
self._filter = lambda x, axis=0: apply_filter(x, b_filter,
axis=axis)
else:
filter_margin = 0
self._filter = lambda x, axis=0: x
# Number of samples to return, can be an int or a
# tuple (before, after).
assert n_samples_waveforms is not None
self.n_samples_before_after = _before_after(n_samples_waveforms)
self.n_samples_waveforms = sum(self.n_samples_before_after)
# Number of additional samples to use for filtering.
self._filter_margin = _before_after(filter_margin)
# Number of samples in the extracted raw data chunk.
self._n_samples_extract = (self.n_samples_waveforms +
sum(self._filter_margin))
self.dtype = np.float32
self.shape = (self.n_spikes, self._n_samples_extract, self.n_channels)
self.ndim = 3
def the_filter(x, axis=0):
return apply_filter(x, b_filter, axis=axis)
def filter(x):
return apply_filter(x, b_filter)
def the_filter(x, axis=0):
return apply_filter(x, b_filter, axis=axis)
