paper: reword

paper/paper.md

@@ -37,7 +37,7 @@ Complete tutorials, examples, and API documentation are available on https://mpl
 
 <!-- A Statement of need section that clearly illustrates the research purpose of the software and places it in the context of related work. -->
 
-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. `mpl-interactions`' core goal is to make this aspect of interactive plotting easier when using Matplotlib. Other interactive functionalities such as zooming, panning, and selecting data points with the cursor are out of scope as they are provided by Matplotlib, or by other third party packages.
+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. `mpl-interactions`' core goal is to make this aspect of interactive plotting easier when using Matplotlib. Other interactive functionalities are out of scope as they are provided by Matplotlib (e.g. zooming and panning), or by other third party packages (e.g. point selection).
 
 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. While `matplotlib` and `ipywidgets` provide the tools for controlling plots with widgets, the overhead of implementing such control can overwhelm its utility. `mpl-interactions` fills this gap by making it easy for users to generate widgets that dynamically control plots.