pyEDFlib

library to read/write EDF+/BDF+ files

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Project description

What is pyEDFlib

pyEDFlib is a python library to read/write EDF+/BDF+ files based on EDFlib.

EDF means European Data Format and was firstly published Kemp1992. In 2003, an improved version of the file protocol named EDF+ has been published and can be found at Kemp2003.

The EDF/EDF+ format saves all data with 16 Bit. A version which saves all data with 24 Bit, was introduces by the compony BioSemi.

The definition of the EDF/EDF+/BDF/BDF+ format can be found under edfplus.info.

This Python toolbox is a fork of the toolbox from Christopher Lee-Messer and uses the EDFlib from Teunis van Beelen. The EDFlib is able to read and write EDF/EDF+/BDF/BDF+ files.

Documentation

Documentation is available online at http://pyedflib.readthedocs.org.

pyEDFlib can be used with Python >=3.5. It depends on the Numpy package. To use the newest source code from git, you have to download the source code. You need a C compiler and a recent version of Cython. Go then to the source directory and type:

python setup.py build
python setup.py install

There are binary wheels which can be installed by (use pip3 when available):

pip install pyEDFlib

Users of the Anaconda Python distribution can directly obtain pre-built Windows, Intel Linux or macOS / OSX binaries from the conda-forge channel. This can be done via:

conda install -c conda-forge pyedflib

The most recent development version can be found on GitHub at https://github.com/holgern/pyedflib.

The latest release, including source and binary packages for Linux, macOS and Windows, is available for download from the Python Package Index. You can find source releases at the Releases Page.

Highlevel interface

pyEDFlib includes an highlevel interface for easy access to read and write edf files. Additionally functionality as anonymizing, dropping or renaming channels can be found there.

from pyedflib import highlevel

# write an edf file
signals = np.random.rand(5, 256*300)*200 # 5 minutes of random signal
channel_names = ['ch1', 'ch2', 'ch3', 'ch4', 'ch5']
signal_headers = highlevel.make_signal_headers(channel_names, sample_frequency=256)
header = highlevel.make_header(patientname='patient_x', gender='Female')
highlevel.write_edf('edf_file.edf', signals, signal_headers, header)

# read an edf file
signals, signal_headers, header = highlevel.read_edf('edf_file.edf', ch_names=['ch1', 'ch2'])
print(signal_headers[0]['sample_frequency']) # prints 256

# drop a channel from the file or anonymize edf
highlevel.drop_channels('edf_file.edf', to_drop=['ch2', 'ch4'])
highlevel.anonymize_edf('edf_file.edf', new_file='anonymized.edf'
                             to_remove=['patientname', 'birthdate'],
                             new_values=['anonymized', ''])
# check if the two files have the same content
highlevel.compare_edf('edf_file.edf', 'anonymized.edf')
# change polarity of certain channels
highlevel.change_polarity('file.edf', channels=[1,3])
# rename channels within a file
highlevel.rename_channels('file.edf', mapping={'C3-M1':'C3'})

License

pyEDFlib is a free Open Source software released under the BSD 2-clause license.

Releases can be cited via Zenodo.

https://zenodo.org/badge/DOI/10.5281/zenodo.5678481.svg

Project details

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Release history Release notifications | RSS feed

0.1.38

Aug 7, 2024

0.1.37

Mar 14, 2024

0.1.36

Oct 9, 2023

0.1.35

Oct 7, 2023

0.1.34

Jul 20, 2023

0.1.33

Jun 26, 2023

0.1.32

Apr 19, 2023

0.1.30

Jul 11, 2022

0.1.29

Jun 1, 2022

This version

0.1.28

Feb 21, 2022

0.1.26

Feb 21, 2022

0.1.25

Jan 21, 2022

0.1.24

Jan 21, 2022

0.1.23

Nov 11, 2021

0.1.22

Apr 16, 2021

0.1.21

Apr 16, 2021

0.1.20

Dec 9, 2020

0.1.19

Oct 12, 2020

0.1.18

Jul 1, 2020

0.1.17

Feb 18, 2020

0.1.15

Oct 17, 2019

0.1.14

Oct 25, 2018

0.1.13

Jul 18, 2018

0.1.12

Feb 12, 2018

0.1.11

Nov 16, 2017

0.1.10

Sep 5, 2017

0.1.9

Jun 13, 2017

0.1.8

May 10, 2017

0.1.7

Apr 28, 2017

0.1.6

Oct 17, 2016

0.1.5

Oct 6, 2016

0.1.4

Jun 7, 2016

0.1.3

Mar 15, 2016

0.1.1

Nov 16, 2015

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pyEDFlib 0.1.28

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BLAKE2b-256	`a483878a72779c24984891834b51dadfde286193b719f4f6583f62af73ce3383`

Algorithm	Hash digest
SHA256	`bc5de169d716dd89bd88c6c9b685a37224cd66c3e48374fe2120ebc21d7a0cfd`
MD5	`0e9752bfb11394a3654eadf6e8bb4fc8`
BLAKE2b-256	`8a04ade2f472ed40eb44ea3fa6d0b19ea37ebfc47923b41cba432c6c828f8940`

Algorithm	Hash digest
SHA256	`e2e0ec33f4795d4276cd1079d7954478c0a9c0bd81ff851b64c47590b58ca55f`
MD5	`2718897e09dd32ae85240e668d64f2fc`
BLAKE2b-256	`52c198d0fda286ab132cbb4a05a59eb2cf4821e10616ea3cdb66d85dc93a2ff2`

Algorithm	Hash digest
SHA256	`27ea51d6f8d3a9e2f31a904ba550be62786619938e05bd89ff21174f5f79f368`
MD5	`2a7374de07051bbd82fd68b9efad144c`
BLAKE2b-256	`d4bb3fc003d72900dc3853fc48f98794c58347d012fd1877ab5406a0f2d62564`

Algorithm	Hash digest
SHA256	`dbf111fd818db28513b32f3931f84a7ef0e8b9668bf44f8fa1a350f7e2ee3e36`
MD5	`ed638aecda1a9ab28135366d9bdeafb7`
BLAKE2b-256	`5487aca524ada7e23f300654b600f726d0aa19a50149fd8a1fe88b4ab9d880e9`

Algorithm	Hash digest
SHA256	`714d91c9895734a81ff88b4b5a5bf895599d84fd46d96544c89af19652ca630e`
MD5	`2ca6c33aeb56d941fb6d82de84361807`
BLAKE2b-256	`707911298b1f76fd646e7684ca459abfedca9f05ec2b831a9397096b3073130e`

Algorithm	Hash digest
SHA256	`a2976d885032ed39b892273e5b4259b742f43c8faff5462da6604f4f676887e4`
MD5	`22468afac107277d6d468d19b4aa05e8`
BLAKE2b-256	`915720542c8c7cf0afdb03e5d8687fa5951c0a7e4768420eadd866c22ca6a8b2`