address easun comments

Author: ianhi

paper/paper.md

@@ -23,7 +23,7 @@ bibliography: paper.bib
 
 <!-- A summary describing the high-level functionality and purpose of the software for a diverse, non-specialist audience. -->
 
-Data exploration, model building and pedagogy all benefit from the ability to interactively update elements in Matplotlib 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 generated widgets are easy to reuse 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.
+Data exploration, model building and pedagogy all benefit from the ability to interactively update elements in Matplotlib 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.
 
 Complete Tutorials, Examples, and API documentation are available on https://mpl-interactions.readthedocs.io/en/stable/.
 
@@ -134,6 +134,8 @@ plt.legend()
 
 ![Generated figure and sliders after running above example in jupyter lab.\label{fig:logistic}](imgs/logistic_growth.png){ width=75% }
 
+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.
+
 # Acknowledgements
 
 We thank Dr. K. Dalton for stimulating discussions, and [CONTRIBUTIONS]. This work was supported by a National Defense Science and Engineering Graduate Fellowship (IHI, add award number), the George W. Merck Fund of the New York Community Trust (award 338034, to DRH), and funds from Harvard University.