Split apart pick event demo

In the docs, all the figures show up scrunched at the end after a large
block of code. But each figure is self-contained and already contains
some documentation, so split them up based on that.

Author: QuLogic

examples/event_handling/pick_event_demo.py

@@ -81,114 +81,125 @@ def pick_handler(event):
 np.random.seed(19680801)
 
 
-def pick_simple():
-    # simple picking, lines, rectangles and text
-    fig, (ax1, ax2) = plt.subplots(2, 1)
-    ax1.set_title('click on points, rectangles or text', picker=True)
-    ax1.set_ylabel('ylabel', picker=True, bbox=dict(facecolor='red'))
-    line, = ax1.plot(rand(100), 'o', picker=True, pickradius=5)
-
-    # pick the rectangle
-    ax2.bar(range(10), rand(10), picker=True)
-    for label in ax2.get_xticklabels():  # make the xtick labels pickable
-        label.set_picker(True)
-
-    def onpick1(event):
-        if isinstance(event.artist, Line2D):
-            thisline = event.artist
-            xdata = thisline.get_xdata()
-            ydata = thisline.get_ydata()
-            ind = event.ind
-            print('onpick1 line:', np.column_stack([xdata[ind], ydata[ind]]))
-        elif isinstance(event.artist, Rectangle):
-            patch = event.artist
-            print('onpick1 patch:', patch.get_path())
-        elif isinstance(event.artist, Text):
-            text = event.artist
-            print('onpick1 text:', text.get_text())
-
-    fig.canvas.mpl_connect('pick_event', onpick1)
-
-
-def pick_custom_hit():
-    # picking with a custom hit test function
-    # you can define custom pickers by setting picker to a callable
-    # function.  The function has the signature
-    #
-    #  hit, props = func(artist, mouseevent)
-    #
-    # to determine the hit test.  if the mouse event is over the artist,
-    # return hit=True and props is a dictionary of
-    # properties you want added to the PickEvent attributes
-
-    def line_picker(line, mouseevent):
-        """
-        Find the points within a certain distance from the mouseclick in
-        data coords and attach some extra attributes, pickx and picky
-        which are the data points that were picked.
-        """
-        if mouseevent.xdata is None:
-            return False, dict()
-        xdata = line.get_xdata()
-        ydata = line.get_ydata()
-        maxd = 0.05
-        d = np.sqrt(
-            (xdata - mouseevent.xdata)**2 + (ydata - mouseevent.ydata)**2)
-
-        ind, = np.nonzero(d <= maxd)
-        if len(ind):
-            pickx = xdata[ind]
-            picky = ydata[ind]
-            props = dict(ind=ind, pickx=pickx, picky=picky)
-            return True, props
-        else:
-            return False, dict()
-
-    def onpick2(event):
-        print('onpick2 line:', event.pickx, event.picky)
-
-    fig, ax = plt.subplots()
-    ax.set_title('custom picker for line data')
-    line, = ax.plot(rand(100), rand(100), 'o', picker=line_picker)
-    fig.canvas.mpl_connect('pick_event', onpick2)
-
-
-def pick_scatter_plot():
-    # picking on a scatter plot (matplotlib.collections.RegularPolyCollection)
-
-    x, y, c, s = rand(4, 100)
-
-    def onpick3(event):
+#############################################################################
+# Simple picking, lines, rectangles and text
+# ------------------------------------------
+
+fig, (ax1, ax2) = plt.subplots(2, 1)
+ax1.set_title('click on points, rectangles or text', picker=True)
+ax1.set_ylabel('ylabel', picker=True, bbox=dict(facecolor='red'))
+line, = ax1.plot(rand(100), 'o', picker=True, pickradius=5)
+
+# Pick the rectangle.
+ax2.bar(range(10), rand(10), picker=True)
+for label in ax2.get_xticklabels():  # Make the xtick labels pickable.
+    label.set_picker(True)
+
+
+def onpick1(event):
+    if isinstance(event.artist, Line2D):
+        thisline = event.artist
+        xdata = thisline.get_xdata()
+        ydata = thisline.get_ydata()
         ind = event.ind
-        print('onpick3 scatter:', ind, x[ind], y[ind])
-
-    fig, ax = plt.subplots()
-    ax.scatter(x, y, 100*s, c, picker=True)
-    fig.canvas.mpl_connect('pick_event', onpick3)
-
-
-def pick_image():
-    # picking images (matplotlib.image.AxesImage)
-    fig, ax = plt.subplots()
-    ax.imshow(rand(10, 5), extent=(1, 2, 1, 2), picker=True)
-    ax.imshow(rand(5, 10), extent=(3, 4, 1, 2), picker=True)
-    ax.imshow(rand(20, 25), extent=(1, 2, 3, 4), picker=True)
-    ax.imshow(rand(30, 12), extent=(3, 4, 3, 4), picker=True)
-    ax.set(xlim=(0, 5), ylim=(0, 5))
-
-    def onpick4(event):
-        artist = event.artist
-        if isinstance(artist, AxesImage):
-            im = artist
-            A = im.get_array()
-            print('onpick4 image', A.shape)
-
-    fig.canvas.mpl_connect('pick_event', onpick4)
-
-
-if __name__ == '__main__':
-    pick_simple()
-    pick_custom_hit()
-    pick_scatter_plot()
-    pick_image()
-    plt.show()
+        print('onpick1 line:', np.column_stack([xdata[ind], ydata[ind]]))
+    elif isinstance(event.artist, Rectangle):
+        patch = event.artist
+        print('onpick1 patch:', patch.get_path())
+    elif isinstance(event.artist, Text):
+        text = event.artist
+        print('onpick1 text:', text.get_text())
+
+
+fig.canvas.mpl_connect('pick_event', onpick1)
+
+
+#############################################################################
+# Picking with a custom hit test function
+# ---------------------------------------
+# You can define custom pickers by setting picker to a callable function. The
+# function has the signature::
+#
+#  hit, props = func(artist, mouseevent)
+#
+# to determine the hit test. If the mouse event is over the artist, return
+# ``hit=True`` and ``props`` is a dictionary of properties you want added to
+# the `.PickEvent` attributes.
+
+def line_picker(line, mouseevent):
+    """
+    Find the points within a certain distance from the mouseclick in
+    data coords and attach some extra attributes, pickx and picky
+    which are the data points that were picked.
+    """
+    if mouseevent.xdata is None:
+        return False, dict()
+    xdata = line.get_xdata()
+    ydata = line.get_ydata()
+    maxd = 0.05
+    d = np.sqrt(
+        (xdata - mouseevent.xdata)**2 + (ydata - mouseevent.ydata)**2)
+
+    ind, = np.nonzero(d <= maxd)
+    if len(ind):
+        pickx = xdata[ind]
+        picky = ydata[ind]
+        props = dict(ind=ind, pickx=pickx, picky=picky)
+        return True, props
+    else:
+        return False, dict()
+
+
+def onpick2(event):
+    print('onpick2 line:', event.pickx, event.picky)
+
+
+fig, ax = plt.subplots()
+ax.set_title('custom picker for line data')
+line, = ax.plot(rand(100), rand(100), 'o', picker=line_picker)
+fig.canvas.mpl_connect('pick_event', onpick2)
+
+
+#############################################################################
+# Picking on a scatter plot
+# -------------------------
+# A scatter plot is backed by a `~matplotlib.collections.PathCollection`.
+
+x, y, c, s = rand(4, 100)
+
+
+def onpick3(event):
+    ind = event.ind
+    print('onpick3 scatter:', ind, x[ind], y[ind])
+
+
+fig, ax = plt.subplots()
+ax.scatter(x, y, 100*s, c, picker=True)
+fig.canvas.mpl_connect('pick_event', onpick3)
+
+
+#############################################################################
+# Picking images
+# --------------
+# Images plotted using `.Axes.imshow` are `~matplotlib.image.AxesImage`
+# objects.
+
+fig, ax = plt.subplots()
+ax.imshow(rand(10, 5), extent=(1, 2, 1, 2), picker=True)
+ax.imshow(rand(5, 10), extent=(3, 4, 1, 2), picker=True)
+ax.imshow(rand(20, 25), extent=(1, 2, 3, 4), picker=True)
+ax.imshow(rand(30, 12), extent=(3, 4, 3, 4), picker=True)
+ax.set(xlim=(0, 5), ylim=(0, 5))
+
+
+def onpick4(event):
+    artist = event.artist
+    if isinstance(artist, AxesImage):
+        im = artist
+        A = im.get_array()
+        print('onpick4 image', A.shape)
+
+
+fig.canvas.mpl_connect('pick_event', onpick4)
+
+plt.show()