neuroglia.spike.Smoother(sample_times, kernel='gaussian', tau=0.005)[source]¶Smooth a population of spike events into an array.
This transformer converts a table of spike times into a trace of smoothed spike values. Spikes are binned according to the spike_times argument.
| Parameters: | sample_times (array-like) – The samples times that will be used to bin spikes. |
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Examples
>>> import numpy as np
>>> import pandas as pd
>>> from neuroglia.spike import Smoother
>>> smoother = Smoother(np.arange(0,1.0,0.001))
>>> spikes = pd.DataFrame({'times':np.random.rand})
>>> X = binner.fit_transform(spikes)
Notes
This estimator is stateless (besides constructor parameters), the fit method does nothing but is useful when used in a pipeline.
__init__(sample_times, kernel='gaussian', tau=0.005)[source]¶x.__init__(…) initializes x; see help(type(x)) for signature
Methods
__init__(sample_times[, kernel, tau]) |
x.__init__(…) initializes x; see help(type(x)) for signature |
fit(X[, y]) |
Do nothing an return the estimator unchanged. |
fit_transform(X[, y]) |
Fit to data, then transform it. |
get_params([deep]) |
Get parameters for this estimator. |
set_params(**params) |
Set the parameters of this estimator. |
transform(X) |
Smooth each neuron’s spikes into a trace of smoothed spikes. |