Pooch: A friend to fetch your data files

Uieda, Leonardo; Soler, Santiago R.; Rampin, Rémi; Kemenade, Hugo van; Turk, Matthew J.; Shapero, Daniel; Banihirwe, Anderson; Leeman, J. R.

doi:10.21105/joss.01943

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…• Third, specialised neuroimaging libraries-nibabel (for reading and writing common neuroimaging data formats), templateflow (for accessing template geometries and atlases; Ciric, Thompson, et al, 2022), and lytemaps (a pooch-based fork of neuromaps, for resampling data and accessing certain maps not available in templateflow; Markello et al, 2022;Uieda et al, 2020); and…”

Section: Installation and Dependenciesmentioning

confidence: 99%

hyve, a compositional visualisation engine for brain imaging data

Ciric,

Xu,

Poldrack

2024

Preprint

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Neuroimaging visualisations facilitate the interpretation of geometrically structured data and results. However, heterogeneous geometries--such as volumes, surfaces, and networks--have traditionally mandated different software approaches. We introduce hyve, a Python library that uses a compositional functional framework to enable parametric implementation of custom visualisations for different brain geometries. Under this framework, users compose a reusable visualisation protocol from geometric primitives for representing data geometries, input primitives for common data formats and research objectives, and output primitives for producing interactive displays or configurable snapshots. hyve also writes documentation for user-constructed protocols, automates serial production of multiple visualisations, and includes an API for semantically organising an editable multi-panel figure. Through the seamless composition of input, output, and geometric primitives, hyve supports creating visualisations for a range of neuroimaging research objectives.

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Section: Installation and Dependenciesmentioning

confidence: 99%

hyve, a compositional visualisation engine for brain imaging data

Ciric,

Xu,

Poldrack

2024

Preprint

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“…The FARSFetcher class provides an interface to download and unzip selected years from the NHTSA FARS FTP server. The class uses pooch (Uieda et al, 2020) to download and unzip the selected files. By default, files are unzipped to your OS's cache directory.…”

Section: Usage Downloading Fars Datamentioning

confidence: 99%

fars_cleaner: A Python package for downloading and pre-processing vehicle fatality data in the US

Abrams¹,

Bass²

2022

JOSS

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Historical vehicle safety analysis in the United States leans heavily on public datasets to determine factors leading to crash fatality. The Fatality Analysis Reporting System (FARS) is one such database collected by the National Highway Traffic Safety Administration (NHTSA) documenting all vehicle fatalities in the United States since 1975. The FARS dataset is used to inform safety decisions at the local, state and national levels, and provides key insights into the efficacy of changing vehicle and trafficway safety standards (National Highway Traffic Safety Administration, 2022). The FARS dataset is frequently used by vehicle safety researchers to track long-term trends in fatality outcomes. As the FARS data have evolved over time, the variable coding has changed, sometimes dramatically, in some cases making it difficult to compare and analyze data across decades.

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“…The gradient-boosted equivalent sources implementation is based on the equivalent source code in the Harmonica library (Uieda et al, 2020b). Other software used in this study includes: Pooch (Uieda et al, 2020a) for downloading and caching datasets, Verde (Uieda, 2018) for block reductions and coordinate manipulations, Boule (Uieda and Soler, 2020) for normal gravity calculations, xarray (Hoyer and Hamman, 2017) and Numpy (Harris et al, 2020) for handling multidimensional arrays and numerical computations, Numba (Lam et al, 2015) for justin-time compilation and parallelization, Matplotlib (Hunter, 2007) and PyGMT (Uieda et al, 2020c) for generating the figures and maps, and the Jupyter notebook programming environment (Kluyver et al, 2016). Harmonica, Boule, Pooch, and Verde are part of the Fatiando a Terra project (Uieda et al, 2013).…”

Section: Data and Code Availabilitymentioning

confidence: 99%

Gradient-boosted equivalent sources

Soler¹,

Uieda²

2021

Preprint

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The equivalent source technique is a powerful and widely used method for processing gravity and magnetic data. Nevertheless, its major drawback is the large computational cost in terms of processing time and computer memory. We present two techniques for reducing the computational cost of equivalent source processing: block-averaging source locations and the gradient-boosted equivalent source algorithm. Through block-averaging, we reduce the number of source coefficients that must be estimated while retaining the minimum desired resolution in the final processed data. With the gradient boosting method, we estimate the sources coefficients in small batches along overlapping windows, allowing us to reduce the computer memory requirements arbitrarily to conform to the constraints of the available hardware. We show that the combination of block-averaging and gradient-boosted equivalent sources is capable of producing accurate interpolations through tests against synthetic data. Moreover, we demonstrate the feasibility of our method by gridding a gravity dataset covering Australia with over 1.7 million observations using a modest personal computer.

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Pooch: A friend to fetch your data files

Cited by 10 publications

References 7 publications

hyve, a compositional visualisation engine for brain imaging data

hyve, a compositional visualisation engine for brain imaging data

fars_cleaner: A Python package for downloading and pre-processing vehicle fatality data in the US

Gradient-boosted equivalent sources

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