created demo file

pm3512 · pm3512 · commit 2c859ee68b37 · 2023-05-07T23:08:04.000-04:00
diff --git a/galleries/examples/event_handling/hover_event_demo.py b/galleries/examples/event_handling/hover_event_demo.py
@@ -0,0 +1,208 @@
+"""
+===============
+Pick event demo
+===============
+
+You can enable picking by setting the "picker" property of an artist
+(for example, a Matplotlib Line2D, Text, Patch, Polygon, AxesImage,
+etc.)
+
+There are a variety of meanings of the picker property:
+
+* *None* - picking is disabled for this artist (default)
+
+* bool - if *True* then picking will be enabled and the artist will fire a pick
+  event if the mouse event is over the artist.
+
+  Setting ``pickradius`` will add an epsilon tolerance in points and the artist
+  will fire off an event if its data is within epsilon of the mouse event.  For
+  some artists like lines and patch collections, the artist may provide
+  additional data to the pick event that is generated, for example, the indices
+  of the data within epsilon of the pick event
+
+* function - if picker is callable, it is a user supplied function which
+  determines whether the artist is hit by the mouse event. ::
+
+     hit, props = picker(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.
+
+After you have enabled an artist for picking by setting the "picker"
+property, you need to connect to the figure canvas pick_event to get
+pick callbacks on mouse press events.  For example, ::
+
+  def pick_handler(event):
+      mouseevent = event.mouseevent
+      artist = event.artist
+      # now do something with this...
+
+
+The pick event (matplotlib.backend_bases.PickEvent) which is passed to
+your callback is always fired with two attributes:
+
+mouseevent
+  the mouse event that generate the pick event.
+
+  The mouse event in turn has attributes like x and y (the coordinates in
+  display space, such as pixels from left, bottom) and xdata, ydata (the
+  coords in data space).  Additionally, you can get information about
+  which buttons were pressed, which keys were pressed, which Axes
+  the mouse is over, etc.  See matplotlib.backend_bases.MouseEvent
+  for details.
+
+artist
+  the matplotlib.artist that generated the pick event.
+
+Additionally, certain artists like Line2D and PatchCollection may
+attach additional metadata like the indices into the data that meet
+the picker criteria (for example, all the points in the line that are within
+the specified epsilon tolerance)
+
+The examples below illustrate each of these methods.
+
+.. note::
+    These examples exercises the interactive capabilities of Matplotlib, and
+    this will not appear in the static documentation. Please run this code on
+    your machine to see the interactivity.
+
+    You can copy and paste individual parts, or download the entire example
+    using the link at the bottom of the page.
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from numpy.random import rand
+
+from matplotlib.image import AxesImage
+from matplotlib.lines import Line2D
+from matplotlib.patches import Rectangle
+from matplotlib.text import Text
+
+# Fixing random state for reproducibility
+np.random.seed(19680801)
+
+
+# %%
+# 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)
+
+
+# %%
+# 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()
