https://github.com/bluebrain/efel

Electrophys Feature Extraction Library

Science Score: 72.0%

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Links to: zenodo.org
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16 of 28 committers (57.1%) from academic institutions
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Organization bluebrain has institutional domain (portal.bluebrain.epfl.ch)
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Low similarity (17.4%) to scientific vocabulary

Keywords

computational-neuroscience electrophysiology neurons neuroscience python

Keywords from Contributors

optimisations parameter neuron biological-simulations evolutionary-algorithms genetic-algorithm build morphology brain circuit

Last synced: 6 months ago · JSON representation ·

Repository

Electrophys Feature Extraction Library

Basic Info

Host: GitHub
Owner: BlueBrain
License: other
Language: Python
Default Branch: master
Homepage: http://efel.readthedocs.io
Size: 61.1 MB

Statistics

Stars: 69
Watchers: 18
Forks: 40
Open Issues: 23
Releases: 100

Archived

Topics

computational-neuroscience electrophysiology neurons neuroscience python

Created almost 11 years ago · Last pushed 12 months ago

Metadata Files

Readme Changelog License Citation Authors

README.md

[!WARNING] The Blue Brain Project concluded in December 2024, so development has ceased under the BlueBrain GitHub organization. Future development will take place at: https://github.com/openbraininstitute/eFEL

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Introduction

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.

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 found here.

How to cite

When you use this eFEL software for your research, we ask you to cite it (this includes poster presentations) by referring to the "Cite this repository" button at the top of the repository page to get various citation formats, including APA and BibTeX.

For detailed citation information, please refer to the CITATION.cff file.

Requirements

Python 3.9+
Pip (installed by default in newer versions of Python)
C++ compiler that can be used by pip
Numpy (will be installed automatically by pip)
The instruction below are written assuming you have access to a command shell on Linux / UNIX / MacOSX / Cygwin

Installation

The easiest way to install eFEL is to use pip

bash pip install efel

In 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 directory

bash pip install efel --user

Or you could use a python virtual environment

```bash virtualenv pythonenv . ./pythonenv/bin/activate

If you use csh or tcsh, you should use:

source ./pythonenv/bin/activate.csh

pip install efel ```

If you want to install straight from the github repository you can use

bash pip install git+git://github.com/BlueBrain/eFEL

Quick Start

First you need to import the module

python import efel

To get a list with all the available feature names

python efel.get_feature_names()

Note that the extra-cellular features, the bpapattenuation feature and the checkaisinitiation feature are not listed above because they have to be used in a special way, as described here for extra-cellular features, here for bpapattenuation feature and here for checkaisinitiation feature.

To change the spike detection threshold setting (default is -20 mV)

python efel.set_setting('Threshold', -30) For a full list of available settings, please refer to the Setting class

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 available here

```python """Basic example 1 for eFEL"""

import efel import numpy

def main(): """Main"""

# Use numpy to read the trace data from the txt file
data = numpy.loadtxt('example_trace1.txt')

# Time is the first column
time = data[:, 0]
# Voltage is the second column
voltage = data[:, 1]

# Now we will construct the datastructure that will be passed to eFEL

# A 'trace' is a dictionary
trace1 = {}

# Set the 'T' (=time) key of the trace
trace1['T'] = time

# Set the 'V' (=voltage) key of the trace
trace1['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 list
traces = [trace1]

# set the threshold for spike detection to -20 mV
efel.set_setting('Threshold', -20)

# Now we pass 'traces' to the efel and ask it to calculate the feature
# values
traces_results = efel.get_feature_values(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)
for trace_results in traces_results:
    # trace_result is a dictionary, with as keys the requested features
    for feature_name, feature_values in trace_results.items():
        print("Feature %s has the following values: %s" %
            (feature_name, ', '.join([str(x) for x in feature_values])))

if name == 'main': main() ```

The output of this example is Feature 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: -75.446665721 This means that the eFEL found 5 action potentials in the voltage trace. The amplitudes of these APs are the result of the 'APamplitude' feature. The voltage before the start of the stimulus is measured by 'voltagebase'. Results are in mV.

Full documentation

The full documentation can be found here

Funding

This 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.

Owner

Name: The Blue Brain Project
Login: BlueBrain
Kind: organization
Email: bbp.opensource@epfl.ch
Location: Geneva, Switzerland

Website: https://portal.bluebrain.epfl.ch/
Repositories: 226
Profile: https://github.com/BlueBrain

Open Source Software produced and used by the Blue Brain Project

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
title: "eFEL"
doi: 10.5281/zenodo.593869
url: https://doi.org/10.5281/zenodo.593869
abstract: "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."
authors:
  - family-names: "Ranjan"
    given-names: "Rajnish"
  - family-names: "Van Geit"
    given-names: "Werner"
  - family-names: "Moor"
    given-names: "Ruben"
  - family-names: "Roessert"
    given-names: "Christian"
  - family-names: "Riquelme"
    given-names: "Luis"
  - family-names: "Damart"
    given-names: "Tanguy"
  - family-names: "Jaquier"
    given-names: "Aurélien"
  - family-names: "Tuncel"
    given-names: "Anil"
  - family-names: "Mandge"
    given-names: "Darshan"
  - family-names: "Kilic"
    given-names: "Ilkan"
date-released: 2020-03-01
publisher: "Zenodo"

GitHub Events

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Watch event: 4
Delete event: 1
Issue comment event: 1
Push event: 2
Pull request event: 4
Pull request review event: 8
Pull request review comment event: 6
Gollum event: 5
Fork event: 3

Last Year

Create event: 6
Release event: 3
Watch event: 4
Delete event: 1
Issue comment event: 1
Push event: 2
Pull request event: 4
Pull request review event: 8
Pull request review comment event: 6
Gollum event: 5
Fork event: 3

Committers

Last synced: 12 months ago

All Time

Total Commits: 799
Total Committers: 28
Avg Commits per committer: 28.536
Development Distribution Score (DDS): 0.523

Past Year

Commits: 57
Committers: 8
Avg Commits per committer: 7.125
Development Distribution Score (DDS): 0.632

Top Committers

Name	Email	Commits
Werner Van Geit	w**t@e**h	381
Jaquier Aurélien Tristan	a**r@e**h	134
Anil Tuncel	a**l@e**h	101
Mike Gevaert	m**t@e**h	44
Aurélien Jaquier	7****r	25
Kiliç Ilkan Fabrice	i**c@b**h	15
Jaquier Aurélien Tristan	a**r@b**h	15
arnaudon	a**n@e**h	12
dependabot[bot]	4****]	10
ilkilic	1****c	9
Tanguy Pierre Louis Damart	t**t@e**h	7
Joffrey Gonin	J**n@g**m	7
Ani Nandi	a**8@g**m	6
Damart Tanguy Pierre Louis	d**t@b**h	5
Luis Riquelme	l**e@e**h	4
mariarv	j**a@g**m	4
croessert	c**a@r**e	4
Rössert Christian Andreas	c**t@e**h	4
Sandro	d**l@p**l	2
Benoît Coste	b**e@e**h	2
Andrew Davison	a**n@u**r	1
Darshan Mandge	8****e	1
Erik Heeren	e**n@o**g	1
Liesbeth Vanherpe	l**e@e**h	1
Oren Amsalem	o**1@m**l	1
Pietro Monticone	3****e	1
The Gitter Badger	b**r@g**m	1
adietz	a**z@e**h	1

Committer Domains (Top 20 + Academic)

epfl.ch: 11 gitter.im: 1 mail.huji.ac.il: 1 openbraininstitute.org: 1 unic.cnrs-gif.fr: 1 penguinpee.nl: 1 roessert.de: 1 bb-c02xw077jhd5.epfl.ch: 1 bb-fvfcv295mnhx.epfl.ch: 1 bb-fvff870aq05n.epfl.ch: 1

Issues and Pull Requests

Last synced: 12 months ago

All Time

Total issues: 53
Total pull requests: 170
Average time to close issues: 6 months
Average time to close pull requests: 20 days
Total issue authors: 23
Total pull request authors: 13
Average comments per issue: 3.3
Average comments per pull request: 1.72
Merged pull requests: 157
Bot issues: 0
Bot pull requests: 11

Past Year

Issues: 4
Pull requests: 41
Average time to close issues: 24 days
Average time to close pull requests: 4 days
Issue authors: 3
Pull request authors: 6
Average comments per issue: 3.0
Average comments per pull request: 1.2
Merged pull requests: 39
Bot issues: 0
Bot pull requests: 5

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Sverreg (2)
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coder2003lucky (1)
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Helveg (1)
KeremKurban (1)
PolinaL (1)
alexyonk (1)

Pull Request Authors

AurelienJaquier (103)
anilbey (51)
ilkilic (23)
dependabot[bot] (21)
arnaudon (11)
DrTaDa (6)
wvangeit (4)
darshanmandge (2)
mgeplf (2)
penguinpee (1)
pitmonticone (1)
mariarv (1)
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Packages

Total packages: 1
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Total dependent packages: 3
Total dependent repositories: 0
Total versions: 1
Total maintainers: 1

spack.io: py-efel

The Electrophys Feature Extract 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.

Homepage: https://github.com/BlueBrain/eFEL
License: []
Latest release: 5.2.0
published over 2 years ago

Versions: 1
Dependent Packages: 3
Dependent Repositories: 0

Rankings

Dependent repos count: 0.0%

Average: 29.3%

Dependent packages count: 58.6%

Maintainers (1)

adamjstewart

Last synced: 6 months ago

Dependencies

setup.py pypi

numpy >=1.6
six *

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