Add detect_oscillatory_events method
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jacobsimon
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pynapple/process/filtering.py
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| Parameters | ||
| ---------- | ||
| lfp : Tsd | ||
| Should be a single channel raw lfp |
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Not necessary raw lfp. I would just say
| Should be a single channel raw lfp | |
| 1-dimensional time series |
pynapple/process/filtering.py
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| min_inter_duration : float | ||
| The minimum duration between two events otherwise they are merged (in seconds) | ||
| wsize : int, optional | ||
| The size of the window for digitial filtering |
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| The size of the window for digitial filtering | |
| The size of the window for digital filtering |
pynapple/process/filtering.py
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| the power, amplitude, and peak_time | ||
| """ | ||
| lfp = lfp.restrict(epoch) | ||
| frequency = lfp.rate |
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This should be passed as a parameter 'fs' with default None.
If it's None, use tsd.rate. If it's a Number, use the passed fs.
pynapple/process/filtering.py
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| The interval set of detected events with metadata containing | ||
| the power, amplitude, and peak_time | ||
| """ | ||
| lfp = lfp.restrict(epoch) |
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| lfp = lfp.restrict(epoch) | |
| tsd = tsd.restrict(epoch) |
It can be anything
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Changed to data in 84fc658
pynapple/process/filtering.py
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| peak_times = [] | ||
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| for s, e in osc_ep.values: | ||
| seg = signal.restrict(nap.IntervalSet(s, e)) |
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Here you can be faster with :
| seg = signal.restrict(nap.IntervalSet(s, e)) | |
| seg = signal.get(s, e) |
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Fixed in 84fc658
tests/test_filtering.py
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| "freq_band, thresh_band, start, end", | ||
| [ | ||
| ((10, 30), (1, 10), 0, 2), | ||
| ((40, 60), (1, 10), 3, 5), |
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There should be one more case which is the absence of detected events.
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Added! I've noticed certain freq ranges seem to produce false positives though - going to keep testing a bit
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Could you try to merge |
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Summary
Adds a new method
detect_oscillatory_eventsto thefilteringmodule that allows for detection of ripple-like intervals in signals. Based on this EEG processing example.Example
Todo
Open questions
apply_bandpassinstead offiltfiltbut I think as written, both may be necessary unless we think it's ok to skip the smoothing step—or could useTsd.smoothhere?