Merge pull request #398 from nsryan2/plot_guide

Create plotting file and update guides page

Author: RhysMacMillan

manual/guides/guides.md

@@ -66,6 +66,7 @@ twitter accounts, and whatnot that tie us together.
 # Writing
 
 - [How to Write](/manual/guides/writing/)
+- [How to Plot](/manual/guides/plots/)
 - [Conference Abstract](http://arfc.github.io/manual/guides/writing/conf-abs/)
 - [Conference Paper](http://arfc.github.io/manual/guides/writing/conf-paper/)
 - [Poster](http://arfc.github.io/manual/guides/writing/poster/)

manual/guides/plots.md

@@ -0,0 +1,103 @@
+---
+layout: manual
+title: Making Plots
+subtitle: "Tips and tricks for making better plots."
+permalink: /manual/guides/plots/
+---
+
+
+This guide provides best practices for ensuring that plots created by ARFC are
+accessible and visually clear. By following these simple recommendations, you
+will make your work more inclusive, readable, and aligned with accessibility
+standards.
+
+
+## 1. Using Color
+
+Color combinations like red, green, brown, orange, and yellow can be difficult
+to distinguish for people. Instead, use distinguishable patterns and contrast;
+different line styles, shapes, and markers can help differentiate elements
+without relying on color.
+
+This does not mean you cannot employ a color pallette, **Sequential palettes**
+should be used for data with a natural order (e.g., temperature), while
+**diverging palettes** are better suited for data with two distinct extremes.
+Popular accessible color palettes include **Cividis**, **Viridis**, and
+**Inferno**, which are perceptually uniform. Avoid the Jet (rainbow)
+colormap, as it is not perceptually uniform. It often causes difficulty in
+interpreting data, as colors that are close to each other can look almost
+identical, while distant colors may appear too different.
+
+
+## 2. Choosing Font
+
+Choose sans-serif fonts (like **Arial**, **Helvetica**, **Verdana**), as they
+are generally easier to read compared to serif fonts (e.g., Times New Roman).In
+addition to a good font, your text should also have enough contrast against the
+background for readability.
+- For headings, the contrast ratio should be at least 3:1.
+- For body text, the contrast ratio should be at least 4.5:1.
+
+Avoid fonts with narrow letter spacing or uneven height (between lowercase and
+uppercase) as they exacerbate all of the above problems. Keep text, whitespace,
+and figures evenly spaced, allowing for easy zooming and reading.
+
+
+## 3. Use of Markers and Line Styles
+
+To ensure that your plots will stand the test of time by using non-color-based
+visual cues like:
+- Markers (e.g., circles, squares, triangles) to differentiate data points.
+- Line styles (e.g., dashed, dotted, solid) to distinguish data in a line plot.
+
+This will help ensure that even if a user has difficulty distinguishing certain
+colors, they can still interpret the plot accurately.
+
+
+## 4. Plot Size and Resolution
+
+Make sure your plots are high resolution so that details are not lost when
+zooming in. In addition to a high resolution your plots should be high-quality
+formats (e.g., **SVG**, **PDF**).
+
+
+## 5. Matplotlib Style File for ARFC
+
+To standardize the style of your plots, you can use a **Matplotlib style file**
+that reflects ARFC's standards. This will help ensure that all plots follow the
+same formatting conventions, making them visually consistent and aligned with
+our standards.
+
+You can use this style file to configure:
+- font,
+- color palettes,
+- plot dimensions,
+- and figure size.
+
+A Matplotlib style file is available here:
+https://gist.github.com/nsryan2/456490df118ab3318ec07c7610432e69.
+It uses Paul Tol's muted qualitative color scheme as the default.
+
+### Example:
+
+Here's a minimal example of how you can apply the style file to Python:
+
+```python
+import matplotlib.pyplot as plt
+
+# Load the ARFC style file
+plt.style.use('path/to/plotting.mplstyle')
+
+# Create a sample plot
+x = [1, 2, 3, 4, 5]
+y1 = [1, 4, 9, 16, 25]
+y2 = [2, 3, 9, 16, 26]
+
+plt.plot(x, y1, label='Sample 1', marker='o', linestyle='-')
+plt.plot(x, y2, label='Sample 2', marker='+', linestyle='--')
+
+plt.xlabel('X Axis')
+plt.ylabel('Y Axis')
+plt.legend()
+plt.show()
+```