fix: writing comments

paper/paper.md

@@ -28,15 +28,15 @@ bibliography: paper.bib
 
 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.
 
-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/en/stable/.
 
 # Statement of Need
 
 <!-- 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.
 
-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 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.
 
@@ -107,7 +107,9 @@ def logistic_growth(t, L, k, t0):
 # create a synthetic dataset of logistic growth
 rng = np.random.default_rng(seed=1995)
 t_data = np.sort(rng.uniform(0, 10, size=50))
-y_data = logistic_growth(t_data, L=5, k=1, t0=1) + rng.normal(size=t_data.size, scale=0.1)
+y_data = logistic_growth(
+  t_data, L=5, k=1, t0=1) + rng.normal(size=t_data.size, scale=0.1
+)
 
 
 # You can use the `logistic_growth` function to curve_fit
@@ -137,7 +139,7 @@ plt.legend()
 
 ![Generated figure and sliders after running above example in jupyter lab.\label{fig:logistic}](imgs/logistic_growth-dark.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.
+This framework makes it easy to 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