Learn what to expect in the new updates 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 matplotlib.axes.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 = plt.figure() ax = fig.add_subplot(111) ax.plot(np.random.rand(10)) def onclick(event): print 'button=%d, x=%d, y=%d, xdata=%f, ydata=%f'%( event.button, event.x, event.y, event.xdata, event.ydata) cid = fig.canvas.mpl_connect('button_press_event', onclick)
The FigureCanvas method mpl_connect() returns a connection id which is simply an integer. When you want to disconnect the callback, just call:
fig.canvas.mpl_disconnect(cid)
Note
The canvas retains only weak references to the callbacks. Therefore if a callback is a method of a class instance, you need to retain a reference to that instance. Otherwise the instance will be garbage-collected and the callback will vanish.
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
All matplotlib events inherit from the base class matplotlib.backend_bases.Event, which store 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
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 = plt.figure() ax = fig.add_subplot(111) 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 in event.x and event.xdata. In addition to the LocationEvent attributes, it has
- button
- button pressed None, 1, 2, 3, ‘up’, ‘down’ (up and down are used for scroll events)
- key
- the key pressed: None, any character, ‘shift’, ‘win’, or ‘control’
Write draggable rectangle class that is initialized with a Rectangle instance but will move its x,y 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 matplotlib.patches.Rectangle.contains()) and if it does, store the rectangle xy and the location of the mouse click in data coords. 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. The 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 = contains, attrd = self.rect.contains(event) if not contains: return print 'event contains', self.rect.xy x0, y0 = self.rect.xy self.press = x0, y0, event.xdata, event.ydata def on_motion(self, event): 'on motion we will move the rect if the mouse is over us' if self.press is None: return if event.inaxes != self.rect.axes: return x0, y0, xpress, ypress = self.press dx = event.xdata - xpress dy = event.ydata - ypress #print 'x0=%f, xpress=%f, event.xdata=%f, dx=%f, x0+dx=%f'%(x0, xpress, event.xdata, 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): 'on release we reset the press data' self.press = None()
Extra credit: use the animation blit techniques discussed in the animations recipe to make the animated drawing faster and smoother.
Extra credit solution:
# draggable rectangle with the animation blit techniques; see # 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 = if DraggableRectangle.lock is not None: return contains, attrd = self.rect.contains(event) if not contains: return print 'event contains', self.rect.xy x0, y0 = self.rect.xy self.press = x0, y0, 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): 'on motion we will move the rect if the mouse is over us' if DraggableRectangle.lock is not self: return if event.inaxes != self.rect.axes: 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): 'on release we reset the press data' 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()
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 = plt.figure() fig1.suptitle('mouse hover over figure or axes to trigger events') ax1 = fig1.add_subplot(211) ax2 = fig1.add_subplot(212) 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 = plt.figure() fig2.suptitle('mouse hover over figure or axes to trigger events') ax1 = fig2.add_subplot(211) ax2 = fig2.add_subplot(212) 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()
You can enable picking by setting the picker property of an Artist (e.g., 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, e.g., 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. The signature is. e.g.:
def pick_handler(event): mouseevent = event.mouseevent artist = event.artist # now do something with this...
The 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 coords in display space, e.g., 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 Artist that generated the pick event.
Additionally, certain artists like Line2D and PatchCollection may attach additional meta data like the indices into the data that meet the picker criteria (e.g., all the points in the line that are within the specified epsilon tolerance)
In the example below, we set the line picker property to a scalar, so it represents a tolerance in points (72 points per inch). 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 pick() for details on the PickEvent properties of the line. Here is the code:
import numpy as np import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111) ax.set_title('click on points') line, = ax.plot(np.random.rand(100), 'o', picker=5) # 5 points tolerance def onpick(event): thisline = event.artist xdata = thisline.get_xdata() ydata = thisline.get_ydata() ind = event.ind print 'onpick points:', zip(xdata[ind], ydata[ind]) fig.canvas.mpl_connect('pick_event', onpick) plt.show()
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 mu, sigma points, plot the raw data from the dataset that generated the mean and stddev """ 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 = plt.figure() ax = fig.add_subplot(111) ax.set_title('click on point to plot time series') line, = ax.plot(xs, ys, 'o', picker=5) # 5 points tolerance def onpick(event): if event.artist!=line: return True N = len(event.ind) if not N: return True figi = plt.figure() for subplotnum, dataind in enumerate(event.ind): ax = figi.add_subplot(N,1,subplotnum+1) ax.plot(X[dataind]) ax.text(0.05, 0.9, 'mu=%1.3f\nsigma=%1.3f'%(xs[dataind], ys[dataind]), transform=ax.transAxes, va='top') ax.set_ylim(-0.5, 1.5) figi.show() return True fig.canvas.mpl_connect('pick_event', onpick) plt.show()