On 6/11/07, Trevis Crane <[EMAIL PROTECTED]> wrote:
I've coded (with help from John) a plot of mine to allow a user to select a
data point, and when they click on it a new plot pops up. It works great.
However, some of the points are very close together and if I'm not extremely
careful in selecting the point, then multiple graphs pop up or I get the
wrong one entirely.
You can always write a custom hit testing function -- see
http://matplotlib.sourceforge.net/examples/pick_event_demo.py for an
example.
Probably easiest here is to just find the index which is closest to
one of your data points and just plot that data set.
BTW, numpy gurus, is there a better way to find the index in an array
that is minimal than
indmin = int(numpy.nonzero(distances.min()==distances)[0])
import numpy
from pylab import figure, show
X = numpy.random.rand(100, 200)
xs = numpy.mean(X, axis=1)
ys = numpy.std(X, axis=1)
fig = figure()
ax = fig.add_subplot(211)
ax.set_title('click on point to plot time series')
line, = ax.plot(xs, ys, 'o', picker=5) # 5 points tolerance
ax2 = fig.add_subplot(212)
def onpick(event):
if event.artist!=line: return True
N = len(event.ind)
if not N: return True
# the click locations
x = event.mouseevent.xdata
y = event.mouseevent.ydata
distances = numpy.array(numpy.sqrt((x-xs[event.ind])**2. +
(y-ys[event.ind])**2))
indmin = int(numpy.nonzero(distances.min()==distances)[0])
dataind = event.ind[indmin]
print event.ind, distances, indmin, dataind, X[dataind][:5]
ax2.cla()
ax2.plot(X[dataind])
ax2.text(0.05, 0.9, 'mu=%1.3f\nsigma=%1.3f'%(xs[dataind], ys[dataind]),
transform=ax2.transAxes, va='top')
ax2.set_ylim(-0.5, 1.5)
fig.canvas.draw()
return True
fig.canvas.mpl_connect('pick_event', onpick)
show()
"""
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
from pylab import figure, show
X = numpy.random.rand(100, 200)
xs = numpy.mean(X, axis=1)
ys = numpy.std(X, axis=1)
fig = figure()
ax = fig.add_subplot(211)
ax.set_title('click on point to plot time series')
line, = ax.plot(xs, ys, 'o', picker=5) # 5 points tolerance
ax2 = fig.add_subplot(212)
def onpick(event):
if event.artist!=line: return True
N = len(event.ind)
if not N: return True
# the click locations
x = event.mouseevent.xdata
y = event.mouseevent.ydata
distances = numpy.array(numpy.sqrt((x-xs[event.ind])**2. + (y-ys[event.ind])**2))
indmin = int(numpy.nonzero(distances.min()==distances)[0])
dataind = event.ind[indmin]
print event.ind, distances, indmin, dataind, X[dataind][:5]
ax2.cla()
ax2.plot(X[dataind])
ax2.text(0.05, 0.9, 'mu=%1.3f\nsigma=%1.3f'%(xs[dataind], ys[dataind]),
transform=ax2.transAxes, va='top')
ax2.set_ylim(-0.5, 1.5)
fig.canvas.draw()
return True
fig.canvas.mpl_connect('pick_event', onpick)
show()
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