#!/usr/bin/env python
"""
You can enable picking by setting the"picker" property of an artist
(eg 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)
boolean - if True then picking will be enabled and the
artist will fire a pick event if the mouse event is over
the artist
float - if picker is a number it is interpreted as an
epsilon tolerance in points and the the artist will fire
off an event if it's 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, eg 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. Eg,
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 coords in
display space, eg pixels from left, bottom) and xdata, ydata (the
coords in data space). Additionaly, 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 meta data like the indices into the data that meet
the picker criteria (eg all the points in the line that are within the
specified epsilon tolerance)
The examples below illustrate each of these methods.
"""
from matplotlib.pyplot import figure,show
from matplotlib.lines import Line2D
from matplotlib.patches import Patch,Rectangle
from matplotlib.text import Text
from matplotlib.image import AxesImage
import numpy as npy
from numpy.random import rand
if 1: # simple picking, lines, rectangles and text
fig = figure()
ax1 = fig.add_subplot(211)
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=5) # 5 points tolerance
# pick the rectangle
ax2 = fig.add_subplot(212)
bars = 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:', zip(npy.take(xdata, ind), npy.take(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)
if 1: # 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 = npy.sqrt((xdata-mouseevent.xdata)**2. + (ydata-mouseevent.ydata)**2.)
ind = npy.nonzero(npy.less_equal(d, maxd))
if len(ind):
pickx = npy.take(xdata, ind)
picky = npy.take(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 = figure()
ax1 = fig.add_subplot(111)
ax1.set_title('custom picker for line data')
line, = ax1.plot(rand(100), rand(100), 'o', picker=line_picker)
fig.canvas.mpl_connect('pick_event', onpick2)
if 1: # picking on a scatter plot (matplotlib.collections.RegularPolyCollection)
x, y, c, s = rand(4, 100)
def onpick3(event):
ind = event.ind
print 'onpick3 scatter:', ind, npy.take(x, ind), npy.take(y, ind)
fig = figure()
ax1 = fig.add_subplot(111)
col = ax1.scatter(x, y, 100*s, c, picker=True)
#fig.savefig('pscoll.eps')
fig.canvas.mpl_connect('pick_event', onpick3)
if 1: # picking images (matplotlib.image.AxesImage)
fig = figure()
ax1 = fig.add_subplot(111)
im1 = ax1.imshow(rand(10,5), extent=(1,2,1,2), picker=True)
im2 = ax1.imshow(rand(5,10), extent=(3,4,1,2), picker=True)
im3 = ax1.imshow(rand(20,25), extent=(1,2,3,4), picker=True)
im4 = ax1.imshow(rand(30,12), extent=(3,4,3,4), picker=True)
ax1.axis([0,5,0,5])
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)
show()
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