GhostiPy: An Efficient Signal Processing and Spectral Analysis Toolbox for Large Data

Chu, Joshua P.; Kemere, Caleb

doi:10.1523/eneuro.0202-21.2021

Cited by 4 publications

(6 citation statements)

References 27 publications

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“…Tutorials for all pipelines are available on the Spyglass documentation website (Table 1). Many pipelines are powered by community-developed, open-source packages, like GhostiPy 19 , SpikeInterface 1 and DeepLabCut 3 . These pipelines store a complete record of the analysis and simplify the application of these tools.…”

Section: Resultsmentioning

confidence: 99%

“…It uses DataJoint 6,18 to manage reproducible analysis pipelines with a relational database and incorporates novel software tools (Kachery and Figurl) for sharing data and web-based visualizations to enable collaboration within and across labs. It is Python-based and uses standard data types, and can thus include pipelines that use a wide array of analysis packages including SpikeInterface 1 , GhostiPy 19 , DeepLabCut 3 , and Pynapple 20 . Spyglass also offers ready-to-use pipelines for analyzing behavior and electrophysiological data, including spectral analysis of local field potential (LFP), spike sorting, video processing to extract position, and decoding neural data.…”

Section: Introductionmentioning

confidence: 99%

“…It uses DataJoint (Yatsenko et al, 2018, 2015) to manage reproducible analysis pipelines with a relational database and novel software tools such as Kachery and Figurl for sharing data and web-based visualizations to enable collaboration within and across labs. Being Python-based and using standard data types, it is compatible with a wide array of analysis packages including SpikeInterface (Buccino et al, 2020), GhostiPy (Chu and Kemere, 2021), and Pynapple (Viejo et al, 2023). Spyglass also offers ready-to-use pipelines for analyzing behavior and electrophysiology data, as well as extensive documentation and tutorials for training new users.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spyglass: a framework for reproducible and shareable neuroscience research

Lee,

Denovellis,

et al. 2024

Preprint

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Sharing data and reproducing scientific results are essential for progress in neuroscience, but the community lacks the tools to do this easily for large datasets and results obtained from intricate, multi-step analysis procedures. To address this issue, we created Spyglass, an open-source software framework designed to promote the shareability and reproducibility of data analysis in neuroscience. Spyglass integrates standardized formats with reliable open-source tools, offering a comprehensive solution for managing neurophysiological and behavioral data. It provides well-defined and reproducible pipelines for analyzing electrophysiology data, including core functions like spike sorting. In addition, Spyglass simplifies collaboration by enabling the sharing of final and intermediate results across custom, complex, multi-step pipelines as well as web-based visualizations. Here we demonstrate these features and showcase the potential of Spyglass to enable findable, accessible, interoperable, and reusable (FAIR) data management and analysis by applying advanced state space decoding algorithms to publicly available data from multiple laboratories.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spyglass: a framework for reproducible and shareable neuroscience research

Lee,

Denovellis,

et al. 2024

Preprint

Self Cite

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“…In addition, it will be essential to generate a well-documented implementation in the software mentioned above that help to perform reproducible results within areas or applications. Recently, Chu et al [101] presented GhostiPy, a package for Python that implements the GMWs; however, due to its recent publication, this package has yet to be widely used to assess the quality of the implementation.…”

Section: Discussionmentioning

confidence: 99%

Applications of the Generalized Morse Wavelets: A Review

et al. 2023

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The study of signals, processes, and systems has motivated the development of different representations that can be used to analyze and understand them. Classical ways of studying the behavior of signals are the time domain and frequency domain representations. For the analysis of non-stationary signals, time-frequency representations have become an essential tool to understand how the frequency content of signals changes with time. A common time-frequency technique employed in the literature is the wavelet transform. Nevertheless, selecting an adequate mother wavelet to perform the wavelet transform has become challenging due to the diverse available wavelet families. This paper reviews the applications and uses of a particular class of wavelet basis known as the Generalized Morse Wavelets. This class of wavelet family provides a systematic framework to choose and generate a wavelet for general-purpose use. This study reviews the application of Generalized Morse Wavelets in biomedical engineering, dynamical systems analysis, electrical engineering, geophysics, and communication systems. Moreover, the parameters of the Generalized Morse Wavelets used in each study are presented. The results of this study reveal that Generalized Morse Wavelets have proven helpful in studying signals, systems, and processes in areas ranging from biomedical engineering to geophysics. Nonetheless, the parameters of the Generalized Morse Wavelets are yet to be chosen through a rigorous methodology and argumentation. Therefore, there is an opportunity to generate methods for selecting the parameters of the Generalized Morse Wavelets based on the characteristics of the signals, systems, or processes under research.INDEX TERMS Generalized Morse wavelets, mother wavelet selection, continuous wavelet transform, applications, time-frequency analysis.

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“…Fortunately, these are well-known limitations that have solutions 4,54 . Moreover, the time-frequency landscape keeps growing, including new CWT implementations 58 . We therefore invite everyone to compare their implementations against fCWT's open source 59 , and, to extend its validity, we invite all to apply fCWT on more extensive and different specimens that fall outside this paper's scope.…”

Section: Discussionmentioning

confidence: 99%

The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time–frequency analysis

Arts

Broek

2022

Nat Comput Sci

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The spectral analysis of signals is currently either dominated by the speed–accuracy trade-off or ignores a signal’s often non-stationary character. Here we introduce an open-source algorithm to calculate the fast continuous wavelet transform (fCWT). The parallel environment of fCWT separates scale-independent and scale-dependent operations, while utilizing optimized fast Fourier transforms that exploit downsampled wavelets. fCWT is benchmarked for speed against eight competitive algorithms, tested on noise resistance and validated on synthetic electroencephalography and in vivo extracellular local field potential data. fCWT is shown to have the accuracy of CWT, to have 100 times higher spectral resolution than algorithms equal in speed, to be 122 times and 34 times faster than the reference and fastest state-of-the-art implementations and we demonstrate its real-time performance, as confirmed by the real-time analysis ratio. fCWT provides an improved balance between speed and accuracy, which enables real-time, wide-band, high-quality, time–frequency analysis of non-stationary noisy signals.

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GhostiPy: An Efficient Signal Processing and Spectral Analysis Toolbox for Large Data

Cited by 4 publications

References 27 publications

Spyglass: a framework for reproducible and shareable neuroscience research

Spyglass: a framework for reproducible and shareable neuroscience research

Applications of the Generalized Morse Wavelets: A Review

The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time–frequency analysis

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