universal¶

data_base.analyze.temporal_binning.universal(df, bin_size=1, min_time=None, max_time=None, normalize=False, **kwargs)¶

Bin spike times for dask or pandas dataframes.

Infers the dataframe type and calls the appropriate binning function.

Parameters:¶

df : dask.dataframe.DataFrame¶

DataFrame with containing time values in columns whose name are integer-convertible.

bin_size : float, optional¶

Size of the bins. If not specified, bin_borders have to be specified.

min_time : float, optional¶

Minimum time to consider. If not specified, the minimum value in the DataFrame is used.

max_time : float, optional¶

Maximum time to consider. If not specified, the maximum value in the DataFrame is used.

normalize : bool, optional¶

If True, normalize the output to the total number of elements in the DataFrame.

kwargs : dict¶

Additional keyword arguments for pandas or dask, depending on the dataframe type. Refer to the corresponding methods below to check which additional keyword arguments these functions expect

See also

temporal_binning_pd() and temporal_binning_dask()

Returns:¶

Bin borders and bin frequencies.

Return type:¶

tuple