matplotlib · timhoffm · Jan 11, 2021 · Jan 9, 2021 · Jan 10, 2021 · Jan 10, 2021
diff --git a/examples/widgets/slider_demo.py b/examples/widgets/slider_demo.py
@@ -5,63 +5,70 @@
 
 Using the slider widget to control visual properties of your plot.
 
-In this example, a slider is used to choose the frequency of a sine
-wave. You can control many continuously-varying properties of your plot in
-this way.
+In this example, sliders are used to control the frequency and amplitude of
+a sine wave. You can control many continuously-varying properties of your plot
+in this way.
+
+For a more detailed example of value snapping see
+:doc:`/gallery/widgets/slider_snap_demo`.
+
+For an example of using a `matplotlib.widgets.RangeSlider` to define a range
+of values see :doc:`/gallery/widgets/range_slider`.
 """
 import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.widgets import Slider, Button, RadioButtons
+from matplotlib.widgets import Slider, Button
+
+
+def fxn(t, amp, freq):
+    return amp * np.sin(2 * np.pi * freq * t)
 
-fig, ax = plt.subplots()
-plt.subplots_adjust(left=0.25, bottom=0.25)
 t = np.arange(0.0, 1.0, 0.001)
+
+# Define initial parameters
 a0 = 5
 f0 = 3
-delta_f = 5.0
-s = a0 * np.sin(2 * np.pi * f0 * t)
-l, = plt.plot(t, s, lw=2)
-ax.margins(x=0)
+
+# Create the figure and the `~.Line2D` that we will manipulate
+fig, ax = plt.subplots()
+line, = plt.plot(t, fxn(t, a0, f0), lw=2)
 
 axcolor = 'lightgoldenrodyellow'
+ax.margins(x=0)
+
+# adjust the main plot to make room for the sliders
+plt.subplots_adjust(left=0.25, bottom=0.25)
+
+# Make a horizontal slider to control the frequency.
+# This slider will snap to discrete values as defind by ``valstep``.
 axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)
-axamp = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)
+freq_slider = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0, valstep=5.0)
 
-sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0, valstep=delta_f)
-samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)
+# Make a vertically oriented slider to control the amplitude
+axamp = plt.axes([0.1, 0.15, 0.03, 0.65], facecolor=axcolor)
+amp_slider = Slider(
+    axamp, "Amp", 0.1, 10.0, valinit=a0, orientation="vertical"
+)
 
 
 def update(val):
-    amp = samp.val
-    freq = sfreq.val
-    l.set_ydata(amp*np.sin(2*np.pi*freq*t))
+    line.set_ydata(fxn(t, amp_slider.val, freq_slider.val))
     fig.canvas.draw_idle()
 
 
-sfreq.on_changed(update)
-samp.on_changed(update)
+freq_slider.on_changed(update)
+amp_slider.on_changed(update)
 
+# Create a `matplotlib.widgets.Button` to reset the sliders to initial values.
 resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
 button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
 
 
 def reset(event):
-    sfreq.reset()
-    samp.reset()
+    freq_slider.reset()
+    amp_slider.reset()
 button.on_clicked(reset)
 
-rax = plt.axes([0.025, 0.5, 0.15, 0.15], facecolor=axcolor)
-radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)
-
-
-def colorfunc(label):
-    l.set_color(label)
-    fig.canvas.draw_idle()
-radio.on_clicked(colorfunc)
-
-# Initialize plot with correct initial active value
-colorfunc(radio.value_selected)
-
 plt.show()
 
 #############################################################################
@@ -76,5 +83,4 @@ def colorfunc(label):
 
 import matplotlib
 matplotlib.widgets.Button
-matplotlib.widgets.RadioButtons
 matplotlib.widgets.Slider