Jae-Joon Lee wrote:
> Contour will work as expected if the axes is in log scale. See below.
>
> z = np.arange(100).reshape((10,10))
> x = np.logspace(0, 4, 10)
> y = np.logspace(0, 4, 10)
>
> ax1 = subplot(121)
> ax1.contour(np.log10(x), np.log10(y), z)
>
> ax2 = subplot(122)
> ax2.set_xscale("log")
> ax2.set_yscale("log")
> ax2.contour(x, y, z)
JJ,
Actually, I think your example illustrates that there is a problem with
the second approach--the first subplot generates straight lines, the
second does not. The contour calculation itself really needs to be done
in coordinates that are linear as displayed, because the contour
locations are determined by linear interpolation.
Adding support for log scales to make contour work right in your second
example would be easy; making it work with more general transforms, and
making it work when the transform changes after the call to contour,
would be harder.
I will have to look into this.
Eric
>
> Regards,
>
> -JJ
>
> p.s. good to see another astronomer begin to use matplotlib.
>
>
> On Mon, Jan 11, 2010 at 3:33 PM, Jonathan Slavin
> <jsla...@cfa.harvard.edu> wrote:
>> Is there any way to simply make a contour plot with logarithmic axes
>> using matplotlib? I found a workaround by plotting log10(x), log10(y),
>> but it'd be nicer if it was more direct.
>>
>> As someone new to matplotlib (experienced in IDL) I'm finding much to
>> like, but some things are more difficult for no clear reason. It would
>> seem to me that whether the axes are logarithmic or not would be a nice
>> thing to have as an attribute of the plot object. I'm not familiar yet
>> with the matplotlib internals to know how difficult that would be to
>> implement, but it sure would be convenient.
>>
>> Jon Slavin
>>
>>
>> ------------------------------------------------------------------------------
>> This SF.Net email is sponsored by the Verizon Developer Community
>> Take advantage of Verizon's best-in-class app development support
>> A streamlined, 14 day to market process makes app distribution fast and easy
>> Join now and get one step closer to millions of Verizon customers
>> http://p.sf.net/sfu/verizon-dev2dev
>> _______________________________________________
>> Matplotlib-users mailing list
>> Matplotlib-users@lists.sourceforge.net
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>
> ------------------------------------------------------------------------------
> This SF.Net email is sponsored by the Verizon Developer Community
> Take advantage of Verizon's best-in-class app development support
> A streamlined, 14 day to market process makes app distribution fast and easy
> Join now and get one step closer to millions of Verizon customers
> http://p.sf.net/sfu/verizon-dev2dev
> _______________________________________________
> Matplotlib-users mailing list
> Matplotlib-users@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
------------------------------------------------------------------------------
This SF.Net email is sponsored by the Verizon Developer Community
Take advantage of Verizon's best-in-class app development support
A streamlined, 14 day to market process makes app distribution fast and easy
Join now and get one step closer to millions of Verizon customers
http://p.sf.net/sfu/verizon-dev2dev
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users
