Well, you can do this for imshow
by calling .set_data()
on the image, then fig.canvas.draw()
on the figure. I don't see any real performance advantage over just calling draw()
- both give me about 25FPS with the benchmark below (using WXAgg
as a backend).
import numpy as np
import matplotlib.pyplot as pp
import time
def animate_data(data):
fig,ax = pp.subplots(1,1)
# I'm not convinced that animated=True does anything either...
image = ax.imshow(data[0,:,:],animated=True)
# pp.draw()
fig.canvas.draw()
start = time.time()
tic = start
for ii in xrange(1,data.shape[0]):
if not(ii % 10):
toc = time.time()
print "FPS =%.6G" %(10./(toc-tic))
tic = time.time()
image.set_data(data[ii,:,:])
# pp.draw()
fig.canvas.draw()
print "Average FPS =%.6G" %(data.shape[0]/(time.time()-start))
fakedata = np.random.randn(200,512,512)
animate_data(fakedata)
In the case of quiver
, you can use .set_UVC()
to update the plot:
fig,ax = subplots(1,1)
u1 = np.random.rand(10,10)
v1 = np.random.rand(10,10)
c1 = np.random.rand(10,10)
q = ax.quiver(u1,v1,c1)
fig.canvas.draw()
u2 = np.random.rand(10,10)
v2 = np.random.rand(10,10)
c2 = np.random.rand(10,10)
q.set_UVC(u2,v2,c2)
fig.canvas.draw()
As far as I can tell, you can't update contour
plots in the same way. I'm not sure there would be all that much to gain anyway, since any solution would still require re-computing where the contour lines should go for a given array input. If I were you I would just call ax.contour()
and fig.canvas.draw()
.
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