8000 Split apart pick event demo · matplotlib/matplotlib@46c44b6 · GitHub
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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.
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‎examples/event_handling/pick_event_demo.py

Lines changed: 121 additions & 110 deletions
Original file line numberDiff line numberDiff line change
@@ -81,114 +81,125 @@ def pick_handler(event):
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np.random.seed(19680801)
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def pick_simple():
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# simple picking, lines, rectangles and text
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fig, (ax1, ax2) = plt.subplots(2, 1)
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ax1.set_title('click on points, rectangles or text', picker=True)
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ax1.set_ylabel('ylabel', picker=True, bbox=dict(facecolor='red'))
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line, = ax1.plot(rand(100), 'o', picker=True, pickradius=5)
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# pick the rectangle
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ax2.bar(range(10), rand(10), picker=True)
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for label in ax2.get_xticklabels(): # make the xtick labels pickable
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label.set_picker(True)
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def onpick1(event):
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if isinstance(event.artist, Line2D):
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thisline = event.artist
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xdata = thisline.get_xdata()
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ydata = thisline.get_ydata()
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ind = event.ind
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print('onpick1 line:', np.column_stack([xdata[ind], ydata[ind]]))
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elif isinstance(event.artist, Rectangle):
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patch = event.artist
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print('onpick1 patch:', patch.get_path())
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elif isinstance(event.artist, Text):
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text = event.artist
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print('onpick1 text:', text.get_text())
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fig.canvas.mpl_connect('pick_event', onpick1)
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def pick_custom_hit():
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# picking with a custom hit test function
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# you can define custom pickers by setting picker to a callable
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# function. The function has the signature
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#
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# hit, props = func(artist, mouseevent)
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#
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# to determine the hit test. if the mouse event is over the artist,
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# return hit=True and props is a dictionary of
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# properties you want added to the PickEvent attributes
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def line_picker(line, mouseevent):
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"""
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Find the points within a certain distance from the mouseclick in
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data coords and attach some extra attributes, pickx and picky
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which are the data points that were picked.
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"""
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if mouseevent.xdata is None:
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return False, dict()
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xdata = line.get_xdata()
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ydata = line.get_ydata()
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maxd = 0.05
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d = np.sqrt(
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(xdata - mouseevent.xdata)**2 + (ydata - mouseevent.ydata)**2)
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ind, = np.nonzero(d <= maxd)
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if len(ind):
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pickx = xdata[ind]
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picky = ydata[ind]
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props = dict(ind=ind, pickx=pickx, picky=picky)
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return True, props
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else:
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return False, dict()
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def onpick2(event):
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print('onpick2 line:', event.pickx, event.picky)
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fig, ax = plt.subplots()
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ax.set_title('custom picker for line data')
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line, = ax.plot(rand(100), rand(100), 'o', picker=line_picker)
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fig.canvas.mpl_connect('pick_event', onpick2)
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def pick_scatter_plot():
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# picking on a scatter plot (matplotlib.collections.RegularPolyCollection)
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x, y, c, s = rand(4, 100)
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def onpick3(event):
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#############################################################################
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# Simple picking, lines, rectangles and text
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# ------------------------------------------
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fig, (ax1, ax2) = plt.subplots(2, 1)
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ax1.set_title('click on points, rectangles or text', picker=True)
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ax1.set_ylabel('ylabel', picker=True, bbox=dict(facecolor='red'))
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line, = ax1.plot(rand(100), 'o', picker=True, pickradius=5)
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# Pick the rectangle.
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ax2.bar(range(10), rand(10), picker=True)
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for label in ax2.get_xticklabels(): # Make the xtick labels pickable.
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label.set_picker(True)
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def onpick1(event):
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if isinstance(event.artist, Line2D):
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thisline = event.artist
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xdata = thisline.get_xdata()
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ydata = thisline.get_ydata()
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ind = event.ind
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print('onpick3 scatter:', ind, x[ind], y[ind])
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fig, ax = plt.subplots()
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ax.scatter(x, y, 100*s, c, picker=True)
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fig.canvas.mpl_connect('pick_event', onpick3)
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def pick_image():
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# picking images (matplotlib.image.AxesImage)
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fig, ax = plt.subplots()
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ax.imshow(rand(10, 5), extent=(1, 2, 1, 2), picker=True)
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ax.imshow(rand(5, 10), extent=(3, 4, 1, 2), picker=True)
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ax.imshow(rand(20, 25), extent=(1, 2, 3, 4), picker=True)
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ax.imshow(rand(30, 12), extent=(3, 4, 3, 4), picker=True)
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ax.set(xlim=(0, 5), ylim=(0, 5))
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def onpick4(event):
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artist = event.artist
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if isinstance(artist, AxesImage):
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im = artist
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A = im.get_array()
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print('onpick4 image', A.shape)
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fig.canvas.mpl_connect('pick_event', onpick4)
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if __name__ == '__main__':
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pick_simple()
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pick_custom_hit()
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pick_scatter_plot()
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pick_image()
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plt.show()
105+
print('onpick1 line:', np.column_stack([xdata[ind], ydata[ind]]))
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elif isinstance(event.artist, Rectangle):
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patch = event.artist
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print('onpick1 patch:', patch.get_path())
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elif isinstance(event.artist, Text):
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text = event.artist
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print('onpick1 text:', text.get_text())
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fig.canvas.mpl_connect('pick_event', onpick1)
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#############################################################################
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# Picking with a custom hit test function
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# ---------------------------------------
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# You can define custom pickers by setting picker to a callable function. The
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# function has the signature::
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#
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# hit, props = func(artist, mouseevent)
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#
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# to determine the hit test. If the mouse event is over the artist, return
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# ``hit=True`` and ``props`` is a dictionary of properties you want added to
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# the `.PickEvent` attributes.
128+
129+
def line_picker(line, mouseevent):
130+
"""
131+
Find the points within a certain distance from the mouseclick in
132+
data coords and attach some extra attributes, pickx and picky
133+
which are the data points that were picked.
134+
"""
135+
if mouseevent.xdata is None:
136+
return False, dict()
137+
xdata = line.get_xdata()
138+
ydata = line.get_ydata()
139+
maxd = 0.05
140+
d = np.sqrt(
141+
(xdata - mouseevent.xdata)**2 + (ydata - mouseevent.ydata)**2)
142+
143+
ind, = np.nonzero(d <= maxd)
144+
if len(ind):
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pickx = xdata[ind]
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picky = ydata[ind]
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props = dict(ind=ind, pickx=pickx, picky=picky)
148+
return True, props
149+
else:
150+
return False, dict()
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152+
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def onpick2(event):
154+
print('onpick2 line:', event.pickx, event.picky)
155+
156+
157+
fig, ax = plt.subplots()
158+
ax.set_title('custom picker for line data')
159+
line, = ax.plot(rand(100), rand(100), 'o', picker=line_picker)
160+
fig.canvas.mpl_connect('pick_event', onpick2)
161+
162+
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#############################################################################
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# Picking on a scatter plot
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# -------------------------
166+
# A scatter plot is backed by a `~matplotlib.collections.PathCollection`.
167+
168+
x, y, c, s = rand(4, 100)
169+
170+
171+
def onpick3(event):
172+
ind = event.ind
173+
print('onpick3 scatter:', ind, x[ind], y[ind])
174+
175+
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fig, ax = plt.subplots()
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ax.scatter(x, y, 100*s, c, picker=True)
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fig.canvas.mpl_connect('pick_event', onpick3)
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#############################################################################
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# Picking images
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# --------------
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# Images plotted using `.Axes.imshow` are `~matplotlib.image.AxesImage`
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# objects.
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fig, ax = plt.subplots()
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ax.imshow(rand(10, 5), extent=(1, 2, 1, 2), picker=True)
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ax.imshow(rand(5, 10), extent=(3, 4, 1, 2), picker=True)
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ax.imshow(rand(20, 25), extent=(1, 2, 3, 4), picker=True)
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ax.imshow(rand(30, 12), extent=(3, 4, 3, 4), picker=True)
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ax.set(xlim=(0, 5), ylim=(0, 5))
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def onpick4(event):
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artist = event.artist
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if isinstance(artist, AxesImage):
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im = artist
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A = im.get_array()
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print('onpick4 image', A.shape)
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fig.canvas.mpl_connect('pick_event', onpick4)
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plt.show()

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