I'm wondering if there is a way of automating tick labeling on log-scale axes, so that labels do not overlap. Specifically, when the values get large, they overlap which makes the labels unreadable. I would expect them to automatically get more sparse with the axis value, as they do when you generate such plots in R, for example.
Thanks in advance,
cf
Christopher Fonnesbeck, on 2011-01-12 20:12, wrote:
I'm wondering if there is a way of automating tick labeling on
log-scale axes, so that labels do not overlap. Specifically,
when the values get large, they overlap which makes the labels
unreadable. I would expect them to automatically get more
sparse with the axis value, as they do when you generate such
plots in R, for example.
Hi Christopher,
can you provide a small code example that demonstrates the
problem you're having?
The ticks get placed by a ticklocator. You can change the number
of ticks that get placed by setting the appropriate locators
numticks parameter.
In [50]: plt.loglog(1,1)
Out[50]: [<matplotlib.lines.Line2D object at 0x108dde4c>]
In [51]: ax = plt.gca()
In [52]: loc = ax.xaxis.get_major_locator()
In [53]: loc.numticks
Out[53]: 15
In [54]: loc.numticks = 10
IIRC, the variable name changes slightly from locator to locator,
but you can quickly figure it out in ipython using tab completion
in IPython once you grab a given locator object.
best,
--
Paul Ivanov
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http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7
Also, this approach does not seem to work in general for me. As an example:
In [49]: loglog([1.341, 0.1034, 0.6076, 1.4278, 0.0374],
....: [0.37, 0.12, 0.22, 0.4, 0.08], 'o')
In [50]: loc = ax.xaxis.get_minor_locator()
In [51]: loc.numticks=10
In [52]: loc.numticks=5
This does nothing to change the number of minor ticks, and I tried the same thing for get_major_locator(), with the same result. The plot does not change at all.
Thanks,
cf
On Jan 12, 2011, at 10:33 PM, Paul Ivanov wrote:
In [50]: plt.loglog(1,1)
Out[50]: [<matplotlib.lines.Line2D object at 0x108dde4c>]In [51]: ax = plt.gca()
In [52]: loc = ax.xaxis.get_major_locator()
In [53]: loc.numticks
Out[53]: 15In [54]: loc.numticks = 10