<img src="https://raw.githubusercontent.com/BlueBrain/eFEL/master/docs/source/logo/eFELBanner.png" alt="eFEL banner" />
<table>
<tr>
<td>Latest Release</td>
<td>
<a href="https://pypi.org/project/efel/">
<img src="https://img.shields.io/pypi/v/efel.svg" alt="latest release" />
</a>
</td>
</tr>
<tr>
<td>Documentation</td>
<td>
<a href="https://efel.readthedocs.io/">
<img src="https://readthedocs.org/projects/efel/badge/?version=latest" alt="latest documentation" />
</a>
</td>
</tr>
<tr>
<td>License</td>
<td>
<a href="https://github.com/BlueBrain/efel/blob/master/LICENSE.txt">
<img src="https://img.shields.io/pypi/l/efel.svg" alt="license" />
</a>
</td>
</tr>
<tr>
<td>Build Status</td>
<td>
<a href="https://github.com/BlueBrain/eFEL/actions">
<img src="https://github.com/BlueBrain/eFEL/workflows/Build/badge.svg?branch=master" alt="actions build status" />
</a>
</td>
</tr>
<tr>
<td>Coverage</td>
<td>
<a href="https://codecov.io/gh/BlueBrain/efel">
<img src="https://codecov.io/github/BlueBrain/eFEL/coverage.svg?branch=master" alt="coverage" />
</a>
</td>
</tr>
<tr>
<td>Gitter</td>
<td>
<a href="https://gitter.im/bluebrain/efel">
<img src="https://badges.gitter.im/Join%20Chat.svg" />
</a>
</td>
</tr>
<tr>
<td>Citation</td>
<td>
<a href="https://doi.org/10.5281/zenodo.593869">
<img src="https://zenodo.org/badge/DOI/10.5281/zenodo.8146607.svg" alt="DOI"/>
</a>
</td>
</tr>
</table>
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](http://efel.readthedocs.io/en/latest/installation.html#installing-the-c-standalone-library).
Citation
========
When 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}
}
```
Requirements
============
* [Python 3.9+](https://www.python.org/downloads/)
* [Pip](https://pip.pypa.io) (installed by default in newer versions of Python)
* C++ compiler that can be used by pip
* [Numpy](http://www.numpy.org) (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](https://pip.pypa.io)
```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](https://virtualenv.pypa.io)
```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()
```
The python function to extract features is get_feature_values(...).
Below is a short example on how to use this function. The code and example
trace are available
[here](https://github.com/BlueBrain/eFEL/blob/master/examples/basic/basic_example1.py)
```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]
# 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 'AP_amplitude' feature.
The voltage before the start of the stimulus is measured by 'voltage_base'.
Results are in mV.
Full documentation
==================
The full documentation can be found [here](http://efel.readthedocs.io)
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.
Copyright (c) 2009-2024 Blue Brain Project/EPFL
Raw data
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"keywords": "feature, extraction, electrophysiology, BlueBrainProject",
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"description": "<img src=\"https://raw.githubusercontent.com/BlueBrain/eFEL/master/docs/source/logo/eFELBanner.png\" alt=\"eFEL banner\" />\n\n<table>\n<tr>\n <td>Latest Release</td>\n <td>\n <a href=\"https://pypi.org/project/efel/\">\n <img src=\"https://img.shields.io/pypi/v/efel.svg\" alt=\"latest release\" />\n </a>\n </td>\n</tr>\n<tr>\n <td>Documentation</td>\n <td>\n <a href=\"https://efel.readthedocs.io/\">\n <img src=\"https://readthedocs.org/projects/efel/badge/?version=latest\" alt=\"latest documentation\" />\n </a>\n </td>\n</tr>\n<tr>\n <td>License</td>\n <td>\n <a href=\"https://github.com/BlueBrain/efel/blob/master/LICENSE.txt\">\n <img src=\"https://img.shields.io/pypi/l/efel.svg\" alt=\"license\" />\n </a>\n</td>\n</tr>\n<tr>\n <td>Build Status</td>\n <td>\n <a href=\"https://github.com/BlueBrain/eFEL/actions\">\n <img src=\"https://github.com/BlueBrain/eFEL/workflows/Build/badge.svg?branch=master\" alt=\"actions build status\" />\n </a>\n </td>\n</tr>\n<tr>\n <td>Coverage</td>\n <td>\n <a href=\"https://codecov.io/gh/BlueBrain/efel\">\n <img src=\"https://codecov.io/github/BlueBrain/eFEL/coverage.svg?branch=master\" alt=\"coverage\" />\n </a>\n </td>\n</tr>\n<tr>\n <td>Gitter</td>\n <td>\n <a href=\"https://gitter.im/bluebrain/efel\">\n <img src=\"https://badges.gitter.im/Join%20Chat.svg\" />\n </a>\n </td>\n</tr>\n<tr>\n <td>Citation</td>\n <td>\n <a href=\"https://doi.org/10.5281/zenodo.593869\">\n <img src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.8146607.svg\" alt=\"DOI\"/>\n </a>\n </td>\n</tr>\n</table>\n\nIntroduction\n============\n\nThe Electrophys Feature Extraction Library (eFEL) allows neuroscientists\nto automatically extract features from time series data recorded from neurons\n(both in vitro and in silico).\nExamples are the action potential width and amplitude in voltage traces recorded\nduring whole-cell patch clamp experiments.\nThe user of the library provides a set of traces and selects the features to\nbe calculated. The library will then extract the requested features and return\nthe values to the user.\n\nThe core of the library is written in C++, and a Python wrapper is included.\nAt the moment we provide a way to automatically compile and install the library\nas a Python module. Instructions on how to compile the eFEL as a standalone C++\nlibrary can be found [here](http://efel.readthedocs.io/en/latest/installation.html#installing-the-c-standalone-library).\n\n\nCitation\n========\n\nWhen you use this eFEL software for your research, we ask you to cite the following publications (this includes poster presentations):\n\n```\n @article{efel,\n title={eFEL},\n DOI={10.5281/zenodo.593869},\n url={https://doi.org/10.5281/zenodo.593869}\n 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.},\n publisher={Zenodo},\n author={Ranjan, Rajnish and\n Van Geit, Werner and\n Moor, Ruben and\n R\u00f6ssert, Christian and\n Riquelme, Juan Luis and\n Damart, Tanguy and\n Jaquier, Aur\u00e9lien and\n Tuncel, Anil},\n year={2023},\n month={Jul}\n }\n```\n\nRequirements\n============\n\n* [Python 3.9+](https://www.python.org/downloads/)\n* [Pip](https://pip.pypa.io) (installed by default in newer versions of Python)\n* C++ compiler that can be used by pip\n* [Numpy](http://www.numpy.org) (will be installed automatically by pip)\n* The instruction below are written assuming you have access to a command shell\non Linux / UNIX / MacOSX / Cygwin\n\nInstallation\n============\n\nThe easiest way to install eFEL is to use [pip](https://pip.pypa.io)\n\n```bash\npip install efel\n```\n\nIn case you don't have administrator access this command might fail with a\npermission error. In that case you could install eFEL in your home directory\n\n```bash\npip install efel --user\n```\n\nOr you could use a [python virtual environment](https://virtualenv.pypa.io)\n\n```bash\nvirtualenv pythonenv\n. ./pythonenv/bin/activate\n# If you use csh or tcsh, you should use:\n# source ./pythonenv/bin/activate.csh\npip install efel\n```\n\nIf you want to install straight from the github repository you can use\n\n```bash\npip install git+git://github.com/BlueBrain/eFEL\n```\n\nQuick Start\n===========\n\nFirst you need to import the module\n\n```python\nimport efel\n```\n\nTo get a list with all the available feature names\n\n```python\nefel.get_feature_names()\n```\n\nThe python function to extract features is get_feature_values(...).\nBelow is a short example on how to use this function. The code and example\ntrace are available\n[here](https://github.com/BlueBrain/eFEL/blob/master/examples/basic/basic_example1.py)\n\n```python\n\"\"\"Basic example 1 for eFEL\"\"\"\n\nimport efel\nimport numpy\n\ndef main():\n \"\"\"Main\"\"\"\n\n # Use numpy to read the trace data from the txt file\n data = numpy.loadtxt('example_trace1.txt')\n\n # Time is the first column\n time = data[:, 0]\n # Voltage is the second column\n voltage = data[:, 1]\n\n # Now we will construct the datastructure that will be passed to eFEL\n\n # A 'trace' is a dictionary\n trace1 = {}\n\n # Set the 'T' (=time) key of the trace\n trace1['T'] = time\n\n # Set the 'V' (=voltage) key of the trace\n trace1['V'] = voltage\n\n # Set the 'stim_start' (time at which a stimulus starts, in ms)\n # key of the trace\n # Warning: this need to be a list (with one element)\n trace1['stim_start'] = [700]\n\n # Set the 'stim_end' (time at which a stimulus end) key of the trace\n # Warning: this need to be a list (with one element)\n trace1['stim_end'] = [2700]\n\n # Multiple traces can be passed to the eFEL at the same time, so the\n # argument should be a list\n traces = [trace1]\n\n # Now we pass 'traces' to the efel and ask it to calculate the feature\n # values\n traces_results = efel.get_feature_values(traces,\n ['AP_amplitude', 'voltage_base'])\n\n # The return value is a list of trace_results, every trace_results\n # corresponds to one trace in the 'traces' list above (in same order)\n for trace_results in traces_results:\n # trace_result is a dictionary, with as keys the requested features\n for feature_name, feature_values in trace_results.items():\n print(\"Feature %s has the following values: %s\" %\n (feature_name, ', '.join([str(x) for x in feature_values])))\n\n\nif __name__ == '__main__':\n main()\n```\n\nThe output of this example is\n```\nFeature AP_amplitude has the following values: 72.5782441262, 46.3672552618, 41.1546679158, 39.7631750953, 36.1614653031, 37.8489295737\nFeature voltage_base has the following values: -75.446665721\n```\nThis means that the eFEL found 5 action potentials in the voltage trace. The\namplitudes of these APs are the result of the 'AP_amplitude' feature.\nThe voltage before the start of the stimulus is measured by 'voltage_base'.\nResults are in mV.\n\nFull documentation\n==================\nThe full documentation can be found [here](http://efel.readthedocs.io)\n\nFunding\n=======\nThis work has been partially funded by the European Union Seventh Framework Program (FP7/2007\u00ad2013) under grant agreement no. 604102 (HBP),\nthe European Union\u2019s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270, 785907\n(Human Brain Project SGA1/SGA2) and by the EBRAINS research infrastructure, funded from the European Union\u2019s Horizon 2020 Framework\nProgramme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).\nThis project/research was supported by funding to the Blue Brain Project, a research center of the \u00c9cole polytechnique f\u00e9d\u00e9rale de\nLausanne (EPFL), from the Swiss government\u2019s ETH Board of the Swiss Federal Institutes of Technology.\n\nCopyright (c) 2009-2024 Blue Brain Project/EPFL\n\n",
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