neuroglia.nwb.SpikeTablizer[source]¶Convert a dictionary of spike times to a dataframe of spike times.
It is common to store spike times as a dictionary where the keys are neuron IDs and the values are arrays of spike times for a given neuron.
This transformer converts a dictionary of spike times into a table of spike times.
Examples
>>> import numpy as np
>>> import pandas as pd
>>> from neuroglia.nwb import SpikeTablizer
>>> binner = SpikeTablizer()
>>> spike_dict = {0:[0.1,0.2,0.3],2:[0.11]}
>>> spikes = binner.fit_transform(spike_dict)
Notes
This estimator is stateless (besides constructor parameters), the fit method does nothing but is useful when used in a pipeline.
Methods
__init__() |
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) |
Convert a dictionary of spike times to a dataframe of spike times. |