Package: efel
Latest ReleaseDocumentationLicenseBuild StatusCoverageGitterCitationIntroductionThe Electrophys Feature Extraction Library (eFEL) allows neuroscientists
to automatically extract features from time series data recorded from neurons
(both in vitro and in silico).
Examples are the action potential width and amplitude in voltage traces recorded
during whole-cell patch clamp experiments.
The user of the library provides a set of traces and selects the features to
be calculated. The library will then extract the requested features and return
the values to the user.The core of the library is written in C++, and a Python wrapper is included.
At the moment we provide a way to automatically compile and install the library
as a Python module. Instructions on how to compile the eFEL as a standalone C++
library can be foundhere.CitationWhen you use this eFEL software for your research, we ask you to cite the following publications (this includes poster presentations):@article{efel,
        title={eFEL},
        DOI={10.5281/zenodo.593869},
        url={https://doi.org/10.5281/zenodo.593869}
        abstractNote={The Electrophys Feature Extraction Library (eFEL) allows neuroscientists to automatically extract features from time series data recorded from neurons (both in vitro and in silico). Examples are the action potential width and amplitude in voltage traces recorded during whole-cell patch clamp experiments. The user of the library provides a set of traces and selects the features to be calculated. The library will then extract the requested features and return the values to the user.},
        publisher={Zenodo},
        author={Ranjan, Rajnish and
                Van Geit, Werner and
                Moor, Ruben and
                Rössert, Christian and
                Riquelme, Juan Luis and
                Damart, Tanguy and
                Jaquier, Aurélien and
                Tuncel, Anil},
        year={2023},
        month={Jul}
    }RequirementsPython 3.9+Pip(installed by default in newer versions of Python)C++ compiler that can be used by pipNumpy(will be installed automatically by pip)The instruction below are written assuming you have access to a command shell
on Linux / UNIX / MacOSX / CygwinInstallationThe easiest way to install eFEL is to usepippipinstallefelIn case you don't have administrator access this command might fail with a
permission error. In that case you could install eFEL in your home directorypipinstallefel--userOr you could use apython virtual environmentvirtualenvpythonenv
../pythonenv/bin/activate# If you use csh or tcsh, you should use:# source ./pythonenv/bin/activate.cshpipinstallefelIf you want to install straight from the github repository you can usepipinstallgit+git://github.com/BlueBrain/eFELQuick StartFirst you need to import the moduleimportefelTo get a list with all the available feature namesefel.getFeatureNames()The python function to extract features is getFeatureValues(...).
Below is a short example on how to use this function. The code and example
trace are availablehere"""Basic example 1 for eFEL"""importefelimportnumpydefmain():"""Main"""# Use numpy to read the trace data from the txt filedata=numpy.loadtxt('example_trace1.txt')# Time is the first columntime=data[:,0]# Voltage is the second columnvoltage=data[:,1]# Now we will construct the datastructure that will be passed to eFEL# A 'trace' is a dictionarytrace1={}# Set the 'T' (=time) key of the tracetrace1['T']=time# Set the 'V' (=voltage) key of the tracetrace1['V']=voltage# Set the 'stim_start' (time at which a stimulus starts, in ms)# key of the trace# Warning: this need to be a list (with one element)trace1['stim_start']=[700]# Set the 'stim_end' (time at which a stimulus end) key of the trace# Warning: this need to be a list (with one element)trace1['stim_end']=[2700]# Multiple traces can be passed to the eFEL at the same time, so the# argument should be a listtraces=[trace1]# Now we pass 'traces' to the efel and ask it to calculate the feature# valuestraces_results=efel.getFeatureValues(traces,['AP_amplitude','voltage_base'])# The return value is a list of trace_results, every trace_results# corresponds to one trace in the 'traces' list above (in same order)fortrace_resultsintraces_results:# trace_result is a dictionary, with as keys the requested featuresforfeature_name,feature_valuesintrace_results.items():print("Feature%shas the following values:%s"%(feature_name,', '.join([str(x)forxinfeature_values])))if__name__=='__main__':main()The output of this example isFeature AP_amplitude has the following values: 72.5782441262, 46.3672552618, 41.1546679158, 39.7631750953, 36.1614653031, 37.8489295737
Feature voltage_base has the following values: -75.446665721This means that the eFEL found 5 action potentials in the voltage trace. The
amplitudes of these APs are the result of the 'AP_amplitude' feature.
The voltage before the start of the stimulus is measured by 'voltage_base'.
Results are in mV.Full documentationThe full documentation can be foundhereFundingThis work has been partially funded by the European Union Seventh Framework Program (FP7/2007­2013) under grant agreement no. 604102 (HBP),
the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270, 785907
(Human Brain Project SGA1/SGA2) and by the EBRAINS research infrastructure, funded from the European Union’s Horizon 2020 Framework
Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).
This project/research was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de
Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.Copyright (c) 2009-2024 Blue Brain Project/EPFL