matplotlib-users

[Matplotlib-users] sensible tick labels for log scale?

[Christopher F. <sta...@me...>]

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

Re: [Matplotlib-users] sensible tick labels for log scale?

[Paul I. <piv...@gm...>]

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
314 address only used for lists,  off-list direct email at:
http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7 

Re: [Matplotlib-users] sensible tick labels for log scale?

[Chris F. <sta...@me...>]

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]: 15
> 
> In [54]: loc.numticks = 10

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