Hello,
I am testing the newly added subplots function in ipython -pylab with the following code:
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, sharey=True)
ax1.plot(np.random.random(20))
ax2.plot(np.random.random(20))
ax3.plot(np.random.random(20))
ax4.plot(np.random.random(20))
For some reason scaling the y-axes logaritmically works only on the focused figure canvas, the rest of the subplots are scaled in a distorted fashion. Axes labels change to proper notation but the scaling stays as if linear along with the data. See for better description: http://img408.imageshack.us/img408/7149/logscale.png
Any ideas?
···
–
Gökhan
In the current implementation, sharing the axis does not mean sharing its scale.
This is not a subplots-specific issue, but applies to all kind of axes sharing.
So you need to change the scale of all the axes even though they have
shared axis.
What seems to be a better approach to me is to initialize "subplots"
with proper scale.
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, sharey=True,
subplot_kw=dict(yscale="log"))
Regards,
-JJ
···
On Mon, Mar 22, 2010 at 1:18 PM, Gökhan Sever <gokhansever@...287...> wrote:
Hello,
I am testing the newly added subplots function in ipython -pylab with the
following code:
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, sharey=True)
ax1.plot(np.random.random(20))
ax2.plot(np.random.random(20))
ax3.plot(np.random.random(20))
ax4.plot(np.random.random(20))
For some reason scaling the y-axes logaritmically works only on the focused
figure canvas, the rest of the subplots are scaled in a distorted fashion.
Axes labels change to proper notation but the scaling stays as if linear
along with the data. See for better description:
http://img408.imageshack.us/img408/7149/logscale.png
Any ideas?
--
Gökhan
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