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Copy file name to clipboardExpand all lines: paper/paper.bib
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year = {2019},
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pages = {195--203},
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file = {Full Text:C\:\\Users\\au646069\\Zotero\\storage\\UBP697JI\\Peirce et al. - 2019 - PsychoPy2 Experiments in behavior made easy.pdf:application/pdf},
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}
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@article{SciPy:2020,
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author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
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Haberland, Matt and Reddy, Tyler and Cournapeau, David and
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Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
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Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
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Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
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Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
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Kern, Robert and Larson, Eric and Carey, C J and
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Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
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{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
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Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
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Harris, Charles R. and Archibald, Anne M. and
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Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
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{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
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title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
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Computing in Python}},
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journal = {Nature Methods},
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year = {2020},
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volume = {17},
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pages = {261--272},
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adsurl = {https://rdcu.be/b08Wh},
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doi = {10.1038/s41592-019-0686-2},
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}
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@article{harris:2020,
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title = {Array programming with {NumPy}},
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author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J.
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van der Walt and Ralf Gommers and Pauli Virtanen and David
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Cournapeau and Eric Wieser and Julian Taylor and Sebastian
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Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
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and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
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Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del
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R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre
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G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and
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Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
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Travis E. Oliphant},
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year = {2020},
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month = sep,
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journal = {Nature},
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volume = {585},
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number = {7825},
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pages = {357--362},
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doi = {10.1038/s41586-020-2649-2},
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publisher = {Springer Science and Business Media {LLC}},
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url = {https://doi.org/10.1038/s41586-020-2649-2}
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}
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@software{reback2020pandas,
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author = {The pandas development team},
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title = {pandas-dev/pandas: Pandas},
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month = feb,
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year = 2020,
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publisher = {Zenodo},
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version = {latest},
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doi = {10.5281/zenodo.3509134},
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url = {https://doi.org/10.5281/zenodo.3509134}
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}
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@InProceedings{mckinney-proc-scipy-2010,
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author = { {W}es {M}c{K}inney },
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title = { {D}ata {S}tructures for {S}tatistical {C}omputing in {P}ython },
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booktitle = { {P}roceedings of the 9th {P}ython in {S}cience {C}onference },
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pages = { 56 - 61 },
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year = { 2010 },
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editor = { {S}t\'efan van der {W}alt and {J}arrod {M}illman },
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index: 2
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- name: Cambridge Psychiatry, University of Cambridge, United Kingdom
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index: 3
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date: 4 October 2021
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date: 5 January 2022
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bibliography: paper.bib
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---
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@@ -31,11 +31,11 @@ for recording cardiac signals via electrocardiography (ECG) or photoplethysmogra
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(PPG), as well as both online and offline data analysis methods extracting cardiac
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features, synchronizing experimental stimuli with different phases of the heart,
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removing artefacts at different levels and generating plots for data quality check and
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publication. Systole is built on the top of Numpy, Pandas and Scipy, and can use both
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Matplotlib and Bokeh to generate visualisations. It is designed to build modular
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publication. Systole is built on the top of Numpy[@harris:2020], Pandas [@reback2020pandas; mckinney-proc-scipy-2010]and Scipy[@SciPy:2020], and can use both
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Matplotlib [@hunter:2007]and Bokeh[@bokeh] to generate visualisations. It is designed to build modular
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pipelines that can interface easily with other signal processing or heart rate
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variability toolboxes, with a focus on data quality checks. Several parts of the
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toolbox utilize Numba to offer more competitive performances with classic processing
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toolbox utilize Numba [@numba]to offer more competitive performances with classic processing
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