Commit 1f416716 authored by Edward Andò's avatar Edward Andò
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[skip-ci] small details

parent 82b24ebb
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......@@ -267,8 +267,8 @@ author = "Sindre Nordmark Olufsen and Marius Endre Andersen and Egil Fagerholt"
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = 2020,
url = {https://www.R-project.org/}
year = {2018},
url = {https://www.R-project.org/},
}
@article{roubin2015meso,
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......@@ -196,10 +196,10 @@ Spam has a number of different use cases:
Given the large data volumes often encountered in 3D analysis, critical parts of the code are written in C/C++ wrapped with appropriate Python calling functions which are responsible for checking input sanity.
The current wrapping method is with pybind11 [@pybind11].
Building on the large number of functions already available in NumPy [@numpy], SciPy [@SciPy2020], scikit-image [@scikitimage] and making use of tifffile [@tifffile] and meshio [@meshio], `spam` adds a large amount of functionality, which means that a number of advanced forms of data analysis can be chained together in ways that are otherwise complex, requiring the combination of many different tools.
Building on the large number of functions already available in NumPy [@numpy], SciPy [@SciPy2020], scikit-image [@scikitimage] and making use of tifffile [@tifffile] and meshio [@meshio], spam adds a large amount of functionality, which means that a number of advanced forms of data analysis can be chained together in ways that are otherwise complex, requiring the combination of many different tools.
Spam uses `unittest` to check each commit with a coverage of more than 90\% of lines of code covered as of June 2020.
Details of coverage are available [at this address](https://ttk.gricad-pages.univ-grenoble-alpes.fr/spam/coverage/).
[Details of the coverage](https://ttk.gricad-pages.univ-grenoble-alpes.fr/spam/coverage/) are available on the GitLab repository.
# Online documentation
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