data_base ❭ db_initializers ❭ synapse_activation_binning

synapse_activation_binning¶

Methods for fast binning of Synapse activation dataframes.

Example

>>> groupby  = ['EI']  # excitatory or inhibitory
>>> values = synapse_activation_postprocess_dask(
...    ddf = db['synapse_activation'],
...    groupby = groupby,
...    prefun = prefun,
...    applyfun = applyfun,
...    postfun = postfun)
>>> values = values.compute(scheduler=c.get)
>>> save_groupby(db, values, groupby)

Functions¶

`prefun`(df)	Augment a Synapse activation dataframe with additional columns.
`postfun`(s, maxtime)	Postprocess a column of the binned synapse activations.
`applyfun`(pdf, maxtime)	Bin the synapse activations using `universal()`.
`synapse_activation_postprocess_pandas`(pdf, groupby, prefun, applyfun, postfun)	Calculates bins of synapse activation per trial from a pandas dataframe.
`merge_results_together`(dicts)	Aggregate many dictionaries with the same keys.
`tree_reduction`(delayeds, aggregate_fun, length)	Recursively aggregate the results of a list of delayed objects.
`synapse_activation_postprocess_dask`(ddf, **kwargs)	Calculates bins of synapse activation per trial from a dask dataframe.
`save_groupby`(db, result, groupby)	Save the result of synapse_activation_postprocess_dask to a database.
`init`(db, groupby, scheduler, prefun, applyfun, postfun, maxtime)	Main pipeline to bin synapse activations from a Synapse activation dataframe.