DOC: fix clipped labels

jklymak · jklymak · commit 6d4c8dbc6712 · 2021-11-16T20:09:30.000+01:00
diff --git a/tutorials/introductory/usage.py b/tutorials/introductory/usage.py
@@ -113,14 +113,14 @@
 # `numpy.recarray`, or a `pandas.DataFrame`.  Matplotlib allows you provide
 # the ``data`` keyword argument and generate plots passing the strings
 # corresponding to the *x* and *y* variables.
-np.random.seed(19680801)
+np.random.seed(19680801)  # seed the random number generator.
 data = {'a': np.arange(50),
         'c': np.random.randint(0, 50, 50),
         'd': np.random.randn(50)}
 data['b'] = data['a'] + 10 * np.random.randn(50)
 data['d'] = np.abs(data['d']) * 100
 
-fig, ax = plt.subplots(figsize=(5, 2.7))
+fig, ax = plt.subplots(figsize=(5, 2.7), constrained_layout=True)
 ax.scatter('a', 'b', c='c', s='d', data=data)
 ax.set_xlabel('entry a')
 ax.set_ylabel('entry b')
@@ -146,7 +146,7 @@
 x = np.linspace(0, 2, 100)  # Sample data.
 
 # Note that even in the OO-style, we use `.pyplot.figure` to create the figure.
-fig, ax = plt.subplots(figsize=(5, 2.7))  # Create a figure and an axes.
+fig, ax = plt.subplots(figsize=(5, 2.7), constrained_layout=True)
 ax.plot(x, x, label='linear')  # Plot some data on the axes.
 ax.plot(x, x**2, label='quadratic')  # Plot more data on the axes...
 ax.plot(x, x**3, label='cubic')  # ... and some more.
@@ -160,7 +160,7 @@
 
 x = np.linspace(0, 2, 100)  # Sample data.
 
-plt.figure(figsize=(5, 2.7))
+plt.figure(figsize=(5, 2.7), constrained_layout=True)
 plt.plot(x, x, label='linear')  # Plot some data on the (implicit) axes.
 plt.plot(x, x**2, label='quadratic')  # etc.
 plt.plot(x, x**3, label='cubic')
@@ -287,7 +287,7 @@ def my_plotter(ax, data1, data2, param_dict):
 
 mu, sigma = 100, 15
 x = mu + sigma * np.random.randn(10000)
-fig, ax = plt.subplots(figsize=(5, 2.7))
+fig, ax = plt.subplots(figsize=(5, 2.7), constrained_layout=True)
 # the histogram of the data
 n, bins, patches = ax.hist(x, 50, density=1, facecolor='C0', alpha=0.75)
 
@@ -297,6 +297,7 @@ def my_plotter(ax, data1, data2, param_dict):
 ax.text(60, .025, r'$\mu=100,\ \sigma=15$')
 ax.axis([40, 160, 0, 0.03])
 ax.grid(True)
+plt.show()
 
 ###############################################################################
 # All of the `~.Axes.text` functions return a `matplotlib.text.Text`
@@ -414,7 +415,7 @@ def my_plotter(ax, data1, data2, param_dict):
 # Different scales can have different locators and formatters; for instance
 # the log-scale above uses `~.LogLocator` and `~.LogFormatter`. See
 # :doc:`/gallery/ticks/tick-locators` and
-# :doc:gallery/ticks/tick-formatters` for other formatters and
+# :doc:`/gallery/ticks/tick-formatters` for other formatters and
 # locators and information for writing your own.
 #
 # Plotting dates and strings
