Matplotlib works with a number of user interface toolkits (wxpython, tkinter, qt, gtk, and macOS) and in order to support features like interactive panning and zooming of figures, it is helpful to the developers to have an API for interacting with the figure via key presses and mouse movements that is "GUI neutral" so we don't have to repeat a lot of code across the different user interfaces. Although the event handling API is GUI neutral, it is based on the GTK model, which was the first user interface Matplotlib supported. The events that are triggered are also a bit richer vis-a-vis Matplotlib than standard GUI events, including information like which Axes the event occurred in. The events also understand the Matplotlib coordinate system, and report event locations in both pixel and data coordinates.
To receive events, you need to write a callback function and then connect your function to the event manager, which is part of the FigureCanvasBase. Here is a simple example that prints the location of the mouse click and which button was pressed:
fig, ax = plt.subplots()
ax.plot(np.random.rand(10))
def onclick(event):
print('%s click: button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %
('double' if event.dblclick else 'single', event.button,
event.x, event.y, event.xdata, event.ydata))
cid = fig.canvas.mpl_connect('button_press_event', onclick)
The FigureCanvasBase.mpl_connect method returns a connection id (an integer), which can be used to disconnect the callback via
fig.canvas.mpl_disconnect(cid)
Note
The canvas retains only weak references to instance methods used as callbacks. Therefore, you need to retain a reference to instances owning such methods. Otherwise the instance will be garbage-collected and the callback will vanish.
This does not affect free functions used as callbacks.
Here are the events that you can connect to, the class instances that are sent back to you when the event occurs, and the event descriptions:
Note
When connecting to 'key_press_event' and 'key_release_event' events, you may encounter inconsistencies between the different user interface toolkits that Matplotlib works with. This is due to inconsistencies/limitations of the user interface toolkit. The following table shows some basic examples of what you may expect to receive as key(s) (using a QWERTY keyboard layout) from the different user interface toolkits, where a comma separates different keys:
Matplotlib attaches some keypress callbacks by default for interactivity; they are documented in the Navigation keyboard shortcuts section.
Event attributes#All Matplotlib events inherit from the base class matplotlib.backend_bases.Event, which stores the attributes:
name
the event name
canvas
the FigureCanvas instance generating the event
guiEvent
the GUI event that triggered the Matplotlib event
The most common events that are the bread and butter of event handling are key press/release events and mouse press/release and movement events. The KeyEvent and MouseEvent classes that handle these events are both derived from the LocationEvent, which has the following attributes
x, y
mouse x and y position in pixels from left and bottom of canvas
inaxes
the Axes instance over which the mouse is, if any; else None
xdata, ydata
mouse x and y position in data coordinates, if the mouse is over an axes
Let's look a simple example of a canvas, where a simple line segment is created every time a mouse is pressed:
from matplotlib import pyplot as plt
class LineBuilder:
def __init__(self, line):
self.line = line
self.xs = list(line.get_xdata())
self.ys = list(line.get_ydata())
self.cid = line.figure.canvas.mpl_connect('button_press_event', self)
def __call__(self, event):
print('click', event)
if event.inaxes != self.line.axes:
return
self.xs.append(event.xdata)
self.ys.append(event.ydata)
self.line.set_data(self.xs, self.ys)
self.line.figure.canvas.draw()
fig, ax = plt.subplots()
ax.set_title('click to build line segments')
line, = ax.plot([0], [0]) # empty line
linebuilder = LineBuilder(line)
plt.show()
The MouseEvent that we just used is a LocationEvent, so we have access to the data and pixel coordinates via (event.x, event.y) and (event.xdata, event.ydata). In addition to the LocationEvent attributes, it also has:
button
the button pressed: None, MouseButton, 'up', or 'down' (up and down are used for scroll events)
key
the key pressed: None, any character, 'shift', 'win', or 'control'
Write a draggable rectangle class that is initialized with a Rectangle instance but will move its xy location when dragged.
Hint: You will need to store the original xy location of the rectangle which is stored as rect.xy and connect to the press, motion and release mouse events. When the mouse is pressed, check to see if the click occurs over your rectangle (see Rectangle.contains) and if it does, store the rectangle xy and the location of the mouse click in data coordinates. In the motion event callback, compute the deltax and deltay of the mouse movement, and add those deltas to the origin of the rectangle you stored, then redraw the figure. On the button release event, just reset all the button press data you stored as None.
Here is the solution:
import numpy as np
import matplotlib.pyplot as plt
class DraggableRectangle:
def __init__(self, rect):
self.rect = rect
self.press = None
def connect(self):
"""Connect to all the events we need."""
self.cidpress = self.rect.figure.canvas.mpl_connect(
'button_press_event', self.on_press)
self.cidrelease = self.rect.figure.canvas.mpl_connect(
'button_release_event', self.on_release)
self.cidmotion = self.rect.figure.canvas.mpl_connect(
'motion_notify_event', self.on_motion)
def on_press(self, event):
"""Check whether mouse is over us; if so, store some data."""
if event.inaxes != self.rect.axes:
return
contains, attrd = self.rect.contains(event)
if not contains:
return
print('event contains', self.rect.xy)
self.press = self.rect.xy, (event.xdata, event.ydata)
def on_motion(self, event):
"""Move the rectangle if the mouse is over us."""
if self.press is None or event.inaxes != self.rect.axes:
return
(x0, y0), (xpress, ypress) = self.press
dx = event.xdata - xpress
dy = event.ydata - ypress
# print(f'x0={x0}, xpress={xpress}, event.xdata={event.xdata}, '
# f'dx={dx}, x0+dx={x0+dx}')
self.rect.set_x(x0+dx)
self.rect.set_y(y0+dy)
self.rect.figure.canvas.draw()
def on_release(self, event):
"""Clear button press information."""
self.press = None
self.rect.figure.canvas.draw()
def disconnect(self):
"""Disconnect all callbacks."""
self.rect.figure.canvas.mpl_disconnect(self.cidpress)
self.rect.figure.canvas.mpl_disconnect(self.cidrelease)
self.rect.figure.canvas.mpl_disconnect(self.cidmotion)
fig, ax = plt.subplots()
rects = ax.bar(range(10), 20*np.random.rand(10))
drs = []
for rect in rects:
dr = DraggableRectangle(rect)
dr.connect()
drs.append(dr)
plt.show()
Extra credit: Use blitting to make the animated drawing faster and smoother.
Extra credit solution:
# Draggable rectangle with blitting.
import numpy as np
import matplotlib.pyplot as plt
class DraggableRectangle:
lock = None # only one can be animated at a time
def __init__(self, rect):
self.rect = rect
self.press = None
self.background = None
def connect(self):
"""Connect to all the events we need."""
self.cidpress = self.rect.figure.canvas.mpl_connect(
'button_press_event', self.on_press)
self.cidrelease = self.rect.figure.canvas.mpl_connect(
'button_release_event', self.on_release)
self.cidmotion = self.rect.figure.canvas.mpl_connect(
'motion_notify_event', self.on_motion)
def on_press(self, event):
"""Check whether mouse is over us; if so, store some data."""
if (event.inaxes != self.rect.axes
or DraggableRectangle.lock is not None):
return
contains, attrd = self.rect.contains(event)
if not contains:
return
print('event contains', self.rect.xy)
self.press = self.rect.xy, (event.xdata, event.ydata)
DraggableRectangle.lock = self
# draw everything but the selected rectangle and store the pixel buffer
canvas = self.rect.figure.canvas
axes = self.rect.axes
self.rect.set_animated(True)
canvas.draw()
self.background = canvas.copy_from_bbox(self.rect.axes.bbox)
# now redraw just the rectangle
axes.draw_artist(self.rect)
# and blit just the redrawn area
canvas.blit(axes.bbox)
def on_motion(self, event):
"""Move the rectangle if the mouse is over us."""
if (event.inaxes != self.rect.axes
or DraggableRectangle.lock is not self):
return
(x0, y0), (xpress, ypress) = self.press
dx = event.xdata - xpress
dy = event.ydata - ypress
self.rect.set_x(x0+dx)
self.rect.set_y(y0+dy)
canvas = self.rect.figure.canvas
axes = self.rect.axes
# restore the background region
canvas.restore_region(self.background)
# redraw just the current rectangle
axes.draw_artist(self.rect)
# blit just the redrawn area
canvas.blit(axes.bbox)
def on_release(self, event):
"""Clear button press information."""
if DraggableRectangle.lock is not self:
return
self.press = None
DraggableRectangle.lock = None
# turn off the rect animation property and reset the background
self.rect.set_animated(False)
self.background = None
# redraw the full figure
self.rect.figure.canvas.draw()
def disconnect(self):
"""Disconnect all callbacks."""
self.rect.figure.canvas.mpl_disconnect(self.cidpress)
self.rect.figure.canvas.mpl_disconnect(self.cidrelease)
self.rect.figure.canvas.mpl_disconnect(self.cidmotion)
fig, ax = plt.subplots()
rects = ax.bar(range(10), 20*np.random.rand(10))
drs = []
for rect in rects:
dr = DraggableRectangle(rect)
dr.connect()
drs.append(dr)
plt.show()
Mouse enter and leave#
If you want to be notified when the mouse enters or leaves a figure or axes, you can connect to the figure/axes enter/leave events. Here is a simple example that changes the colors of the axes and figure background that the mouse is over:
"""
Illustrate the figure and axes enter and leave events by changing the
frame colors on enter and leave
"""
import matplotlib.pyplot as plt
def enter_axes(event):
print('enter_axes', event.inaxes)
event.inaxes.patch.set_facecolor('yellow')
event.canvas.draw()
def leave_axes(event):
print('leave_axes', event.inaxes)
event.inaxes.patch.set_facecolor('white')
event.canvas.draw()
def enter_figure(event):
print('enter_figure', event.canvas.figure)
event.canvas.figure.patch.set_facecolor('red')
event.canvas.draw()
def leave_figure(event):
print('leave_figure', event.canvas.figure)
event.canvas.figure.patch.set_facecolor('grey')
event.canvas.draw()
fig1, axs = plt.subplots(2)
fig1.suptitle('mouse hover over figure or axes to trigger events')
fig1.canvas.mpl_connect('figure_enter_event', enter_figure)
fig1.canvas.mpl_connect('figure_leave_event', leave_figure)
fig1.canvas.mpl_connect('axes_enter_event', enter_axes)
fig1.canvas.mpl_connect('axes_leave_event', leave_axes)
fig2, axs = plt.subplots(2)
fig2.suptitle('mouse hover over figure or axes to trigger events')
fig2.canvas.mpl_connect('figure_enter_event', enter_figure)
fig2.canvas.mpl_connect('figure_leave_event', leave_figure)
fig2.canvas.mpl_connect('axes_enter_event', enter_axes)
fig2.canvas.mpl_connect('axes_leave_event', leave_axes)
plt.show()
Object picking#
You can enable picking by setting the picker property of an Artist (such as Line2D, Text, Patch, Polygon, AxesImage, etc.)
The picker property can be set using various types:
None
Picking is disabled for this artist (default).
boolean
If True, then picking will be enabled and the artist will fire a pick event if the mouse event is over the artist.
callable
If picker is a callable, it is a user supplied function which determines whether the artist is hit by the mouse event. The signature is hit, props = picker(artist, mouseevent) to determine the hit test. If the mouse event is over the artist, return hit = True; props is a dictionary of properties that become additional attributes on the PickEvent.
The artist's pickradius property can additionally be set to a tolerance value in points (there are 72 points per inch) that determines how far the mouse can be and still trigger a mouse event.
After you have enabled an artist for picking by setting the picker property, you need to connect a handler to the figure canvas pick_event to get pick callbacks on mouse press events. The handler typically looks like
def pick_handler(event):
mouseevent = event.mouseevent
artist = event.artist
# now do something with this...
The PickEvent passed to your callback always has the following attributes:
mouseevent
The MouseEvent that generate the pick event. See event-attributes for a list of useful attributes on the mouse event.
artist
The Artist that generated the pick event.
Additionally, certain artists like Line2D and PatchCollection may attach additional metadata, like the indices of the data that meet the picker criteria (e.g., all the points in the line that are within the specified pickradius tolerance).
In the example below, we enable picking on the line and set a pick radius tolerance in points. The onpick callback function will be called when the pick event it within the tolerance distance from the line, and has the indices of the data vertices that are within the pick distance tolerance. Our onpick callback function simply prints the data that are under the pick location. Different Matplotlib Artists can attach different data to the PickEvent. For example, Line2D attaches the ind property, which are the indices into the line data under the pick point. See Line2D.pick for details on the PickEvent properties of the line.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.set_title('click on points')
line, = ax.plot(np.random.rand(100), 'o',
picker=True, pickradius=5) # 5 points tolerance
def onpick(event):
thisline = event.artist
xdata = thisline.get_xdata()
ydata = thisline.get_ydata()
ind = event.ind
points = tuple(zip(xdata[ind], ydata[ind]))
print('onpick points:', points)
fig.canvas.mpl_connect('pick_event', onpick)
plt.show()
Picking exercise#
Create a data set of 100 arrays of 1000 Gaussian random numbers and compute the sample mean and standard deviation of each of them (hint: NumPy arrays have a mean and std method) and make a xy marker plot of the 100 means vs. the 100 standard deviations. Connect the line created by the plot command to the pick event, and plot the original time series of the data that generated the clicked on points. If more than one point is within the tolerance of the clicked on point, you can use multiple subplots to plot the multiple time series.
Exercise solution:
"""
Compute the mean and stddev of 100 data sets and plot mean vs. stddev.
When you click on one of the (mean, stddev) points, plot the raw dataset
that generated that point.
"""
import numpy as np
import matplotlib.pyplot as plt
X = np.random.rand(100, 1000)
xs = np.mean(X, axis=1)
ys = np.std(X, axis=1)
fig, ax = plt.subplots()
ax.set_title('click on point to plot time series')
line, = ax.plot(xs, ys, 'o', picker=True, pickradius=5) # 5 points tolerance
def onpick(event):
if event.artist != line:
return
n = len(event.ind)
if not n:
return
fig, axs = plt.subplots(n, squeeze=False)
for dataind, ax in zip(event.ind, axs.flat):
ax.plot(X[dataind])
ax.text(0.05, 0.9,
f"$\\mu$={xs[dataind]:1.3f}\n$\\sigma$={ys[dataind]:1.3f}",
transform=ax.transAxes, verticalalignment='top')
ax.set_ylim(-0.5, 1.5)
fig.show()
return True
fig.canvas.mpl_connect('pick_event', onpick)
plt.show()
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