Merge pull request #209 from QuantEcon/style-sheet

Chapter 10: Adding a Section on Matplotlib Style Sheets

Author: jstac

lectures/matplotlib.md

@@ -268,6 +268,92 @@ The custom `subplots` function
 1. makes the desired customizations to `ax`, and
 1. passes the `fig, ax` pair back to the calling code.
 
+### Style Sheets
+
+Another useful feature in Matplotlib is [style sheets](https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html).
+
+We can use style sheets to create plots with uniform styles.
+
+We can find a list of available styles by printing the attribute `plt.style.available`
+
+
+```{code-cell} python3
+print(plt.style.available)
+```
+
+We can now use the `plt.style.use()` method to set the style sheet.
+
+Let's write a function that takes the name of a style sheet and draws different plots with the style
+
+```{code-cell} python3
+
+def draw_graphs(style='default'):
+
+    # Setting a style sheet
+    plt.style.use(style)
+
+    fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))
+    x = np.linspace(-13, 13, 150)
+
+    # Set seed values to replicate results of random draws
+    np.random.seed(9)
+
+    for i in range(3):
+
+        # Draw mean and standard deviation from uniform distributions
+        m, s = np.random.uniform(-8, 8), np.random.uniform(2, 2.5)
+
+        # Generate a normal density plot
+        y = norm.pdf(x, loc=m, scale=s)
+        axes[0].plot(x, y, linewidth=3, alpha=0.7)
+
+        # Create a scatter plot with random X and Y values 
+        # from normal distributions
+        rnormX = norm.rvs(loc=m, scale=s, size=150)
+        rnormY = norm.rvs(loc=m, scale=s, size=150)
+        axes[1].plot(rnormX, rnormY, ls='none', marker='o', alpha=0.7)
+
+        # Create a histogram with random X values
+        axes[2].hist(rnormX, alpha=0.7)
+
+        # and a line graph with random Y values
+        axes[3].plot(x, rnormY, linewidth=2, alpha=0.7)
+
+    plt.suptitle(f'Style: {style}', fontsize=13)
+    plt.show()
+
+```
+
+Let's see what some of the styles look like.
+
+First, we draw graphs with the style sheet `seaborn`
+
+```{code-cell} python3
+draw_graphs(style='seaborn')
+```
+
+We can use `grayscale` to remove colors in plots
+
+```{code-cell} python3
+draw_graphs(style='grayscale')
+```
+
+Here is what `ggplot` looks like
+
+```{code-cell} python3
+draw_graphs(style='ggplot')
+```
+
+We can also use the style `dark_background`
+
+```{code-cell} python3
+draw_graphs(style='dark_background')
+```
+
+You can use the function to experiment with other styles in the list.
+
+If you are interested, you can even create [your own style sheets](https://matplotlib.org/stable/tutorials/introductory/customizing.html#defining-your-own-style).
+
 ## Further Reading
 
 * The [Matplotlib gallery](http://matplotlib.org/gallery.html) provides many examples.