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get_spike_model Qualitative test for presence of spike in model trajectory data using events to identify spike times. |
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get_spike_data Qualitative test for presence of spike in noisy data. |
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get_burst_duration | |||
get_burst_active_phase | |||
get_burst_dc_offset | |||
get_burst_passive_extent | |||
burst_feature Embed the following sub-features, if desired: get_burst_X, where X is a number of feature types defined in this module. |
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get_burst_spikes Requires a get_spike_data and get_spike_model instance to be the only sub-features (supplied as a dict with keys 'is_spike_data' and 'is_spike_model'). |
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get_burst_peak_env Requires tol and num_samples parameters. |
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get_burst_trough_env Requires tol and num_samples parameters. |
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get_burst_isi_env Requires tol and num_samples parameters. |
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get_burst_upsweep | |||
get_burst_downsweep | |||
get_burst_num_spikes | |||
get_burst_period_info | |||
spike_metric Measures the distance between spike time and height, using an inherent weighting of height suited to neural voltage signals (0.05 of time distance). |
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spike_feature pars keys: tol |
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geom_feature Measures the residual between two 1D parameterized geometric curves (given as Trajectory objects). |
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estimate_spiking Estimate pattern of spiking in tonic or burst patterns. |
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spike_envelope Find an amplitude envelope over a smooth 1D signal that features roughly periodic spikes. |
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Find an interior (local) maximum and minimum values of a 1D pointset, away from the endpoints. Returns a dictionary mapping 'local_max' -> (index_max, xmax), 'local_min' -> (index_min, xmin), whose values are None if the pointset is monotonic or is close enough so that the global extrema are at the endpoints. Use noise_tol > 0 to avoid getting a local extremum right next to an endpoint because of noise. Also returned in the dictionary for reference: 'first' -> (0, <start_endpoint_value>), 'last' -> (last_index, <last_endpoint_value>), 'global_max' -> (index, value), 'global_min' -> (index, value) Assumes there is only one interior (max, min) pair in pts, otherwise will return an arbitrary choice from multiple maxima and minima. |
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