[WIP] joss paper #260
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[WIP] joss paper #260
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ianhi
commented
May 31, 2023
paper/paper.md
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| WThis work was supported by a National Defense Science and Engineering Graduate Fellowship (FA9550-19-F-0008, to IHI), the George W. Merck Fund of the New York Community Trust (award 338034, to DRH), and funds from Harvard University. | ||
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| We thank Dr. K. Dalton from stimulating discussion, and Easun Arunachalam for feedback on drafts of this paper. In addition, many users have contributed features and bug fixes. Of particular note are Remco de Boer, John Russell, and Samantha Hamilton who made contributions to documentation and code, code, and documentation respectively. A full list of coding contributors can be found here: https://github.com/mpl-extensions/mpl-interactions/graphs/contributors |
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attention: @redeboer @samanthahamilton I hope you don't mind getting a shout out here :)
The other people I know in real life so will check with them offline
redeboer
approved these changes
May 31, 2023
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attention: @redeboer @samanthahamilton I hope you don't mind getting a shout out here :)
Nice, you're publishing a JOSS paper? Congrats! 🎉
Thanks for the shoutout, appreciated ;)
Also great to see the package is under mpl-extensions now 🙌
Paper draft reads well, just added some tiny suggestions while reading through.
paper/paper.md
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| This framework makes it easy generate complex interactive visualizations. It also enables `mpl-interactions` to manage generating GIFs. Any parameter controlled through `mpl-interactions` can be used to automatically generate a gif of the plot changing as a function of that parameter ([Animation Documentation](https://mpl-interactions.readthedocs.io/en/stable/examples/animations.html)). Thus `mpl-interactions` can assist across the data visualization process, from initial exploration to the creation of a final animated plot as a GIF. |
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For the paper, I would use pinned URLs, e.g. of the latest version:
[Animation Documentation](https://mpl-interactions.readthedocs.io/en/0.23.0/examples/animations.html)
paper/paper.md
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| Data exploration, model building and pedagogy all benefit from the ability to interactively update elements in Matplotlib [@Hunter:2007] figures. `mpl-interactions` enables this by making it easy for users to create matplotlib figures in which the displayed data can be dynamically controlled through widgets. These widgets can be automatically generated by passing arguments such as arrays or shorthands (such as a tuple of numbers to generate a slider) to modified pyplot functions. After creation of these widgets, `mpl-interactions` updates plot elements without further user intervention. For ease of use, it adds these features while otherwise staying close to the `matplotlib.pyplot` interface. `mpl-interactions` is built such that parameters controlled by the generated widgets are easy to re-use for multiple plot elements, while not interfering with static elements. This design allows for building any figure that `matplotlib` can produce, while adding interactivity to specific parts as desired. | ||
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| Complete Tutorials, Examples, and API documentation are available on https://mpl-interactions.readthedocs.io/en/stable/. |
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Suggested change
| Complete Tutorials, Examples, and API documentation are available on https://mpl-interactions.readthedocs.io/en/stable/. | |
| Complete Tutorials, Examples, and API documentation are available on https://mpl-interactions.readthedocs.io. |
(here no pinned URL, see other comment)
paper/paper.md
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| The ability to interact dynamically with plots through widgets such as sliders can be a powerful tool in the scientific process and in pedagogy. For instance, varying a parameter of a mathematical model plotted on top of data helps to understand the relationship between the model and the data. Similarly, exploratory data analysis can be enhanced by interactively modifying aspects of the plot such as which points are displayed, or the threshold level of a displayed image. | ||
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| Matplotlib provides mechanisms for updating elements (artists) in figures. However, the APIs for these artists are not consistent and some are under- or undocumented. Furthermore, the creation and positioning of the native Matplotlib widgets is nontrivial. While the `ipywidgets` [@interactive_Jupyter_widgets] library makes widget creation and positioning easier, it is difficult to integrate with matplotlib in a performant manner. The easiest way to do so is to use the `ipywidgets`' `interact()` function, which automatically generates sliders and other widgets to control arguments to arbitrary python functions. However, this can result in completely regenerating the figure which can be slow. Alternatively, the user needs to remember the specifics of how to update each individual artist. The final issue is that `ipywidgets` is a general framework, and thus constrained in its choices of how to interpret shorthands for widget generation -- as such, the choices it makes are not always optimal for scientific plotting. |
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Suggested change
| Matplotlib provides mechanisms for updating elements (artists) in figures. However, the APIs for these artists are not consistent and some are under- or undocumented. Furthermore, the creation and positioning of the native Matplotlib widgets is nontrivial. While the `ipywidgets` [@interactive_Jupyter_widgets] library makes widget creation and positioning easier, it is difficult to integrate with matplotlib in a performant manner. The easiest way to do so is to use the `ipywidgets`' `interact()` function, which automatically generates sliders and other widgets to control arguments to arbitrary python functions. However, this can result in completely regenerating the figure which can be slow. Alternatively, the user needs to remember the specifics of how to update each individual artist. The final issue is that `ipywidgets` is a general framework, and thus constrained in its choices of how to interpret shorthands for widget generation -- as such, the choices it makes are not always optimal for scientific plotting. | |
| Matplotlib provides mechanisms for updating elements (artists) in figures. However, the APIs for these artists can be inconsistent and some are not well documented. Furthermore, the creation and positioning of the native Matplotlib widgets is nontrivial. While the `ipywidgets` [@interactive_Jupyter_widgets] library makes widget creation and positioning easier, it is difficult to integrate with matplotlib in a performant manner. The easiest way to do so is to use the `ipywidgets`' `interact()` function, which automatically generates sliders and other widgets to control arguments to arbitrary python functions. However, this can result in completely regenerating the figure, which can be slow. Alternatively, the user needs to remember the specifics of how to update each individual artist. The final issue is that `ipywidgets` is a general framework, and thus constrained in its choices of how to interpret shorthands for widget generation. As such, the choices it makes are not always optimal for scientific plotting. |
Just some minor suggestions here.
Btw, matplotlib or Matplotlib? 🤔 Same for some other package names.
DHekstra
reviewed
Sep 7, 2023
DHekstra
reviewed
Sep 7, 2023
paper: clarify out of scope paper: reword spelling: typo
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