Thanks! I'll just add that if you want to use the ticker example it needs
to be:
......
zmin, zmax = 160, 500
locator = ticker.MaxNLocator(10) # if you want no more than 10 contours
locator.create_dummy_axis()
locator.set_bounds(zmin, zmax)
levs = locator()
....
On Mon, May 18, 2009 at 7:29 PM, Eric Firing <efir...@hawaii.edu> wrote:
> william ratcliff wrote:
>
>> Thanks! I think that explains a lot. In the full range of my dataset, I
>> do have some rather high values. Instead of masking them out, I was hoping
>> that I could just set a minimum and maximum value using clim to only display
>> values within that range--it sounds like I need to mask these values out if
>> I want the contours to be generated automatically. What I'd really like to
>> be able to do is set zlow and zhigh and renormalize the colorbar based on
>> that range (for example, blue the lowest value and red the highest value)
>> for display purposes without touching the underlying data...It might
>> complicate the signature of contour even more, but would it be possible to
>> define a zlow, zhigh, such that the contour levels are determined based on
>> that zlow and zhigh instead of the actual ranges in the data? If they are
>> undefined, the behavior would default to the actual range in the data? That
>> way, one could further separate the display of the data from the actual
>> data...
>>
>>
> William,
>
> Yes, it would be possible to put in zlow and zhigh kwargs--but why? Why
> not just generate the contours you want over the range you want, as I
> originally suggested? You can use linspace, or arange, or a Ticker. You
> *don't* need to mask out values that are out of range. You can supply a
> colormap that uses a color you specify for over-range and under-range
> values, if you like; see
> http://matplotlib.sourceforge.net/examples/pylab_examples/image_masked.html
> for an example of this (although most of the rest of the example is much
> more complicated than what you need for contourf.)
>
> Ticker example (untested):
>
> import matplotlib.ticker as ticker
> zmin, zmax = 160, 500
> locator = ticker.MaxNLocator(10) # if you want no more than 10 contours
> locator.set_bounds(zmin, zmax)
> levs = locator()
> # ... set up x, y, z ...
> contourf(x, y, z, levs)
>
>
> There was an earlier proposal that mappables, like ContourSet, accept norm
> kwargs vmin and vmax to be passed on to the norm when the default norm is
> used. I should look at this idea again. I'm not sure offhand whether it is
> a good idea, or whether it would take care of what you are asking for.
>
> Eric
>
> Thanks,
>> William
>>
>>
>> On Mon, May 18, 2009 at 6:18 PM, Eric Firing <efir...@hawaii.edu <mailto:
>> efir...@hawaii.edu>> wrote:
>>
>> william ratcliff wrote:
>>
>> Here, I've changed the number of contours to 15 and 45
>> respectively--and the problem still remains. Do I need to
>> manually set the ranges of the segments on the colorbar or
>> something? It would seem to me that somehow the new limits are
>> not being used in determining the boundaries of which color is
>> used for which set of values. I should also mention that I am
>> using a masked array for z (which is what gives rise to the
>> white square in the bottom right corner).
>>
>>
>> I was not suggesting just changing the *number* of contours, I was
>> suggesting explicitly setting the boundaries. This is almost always
>> a better strategy; the signature in which a number of contours is
>> specified is intended only for quick and dirty exploration (and
>> Matlab compatibility, which led to the overly complex set of
>> possible signatures for contour in mpl). When contour is called as
>> you called it, it doesn't know or care anything about clim; it finds
>> the range of your input data and linearly spreads the requested
>> number of values over approximately that range. (Actually, it uses
>> a ticker to do this, so contour values will fall on reasonably nice
>> numbers.) I suspect there is an unmasked high value that is making
>> the auto-detected range too large. What do you get from
>>
>> print z.min(), z.max()
>>
>>
>> If you don't set the clim, it is set automatically based on the
>> contour levels; so if you set the levels, you don't need to set the
>> clim.
>>
>> Colorbar gets its information from the ContourSet object.
>>
>>
>> Eric
>>
>>
>> Thanks,
>> William
>>
>>
>> On Mon, May 18, 2009 at 5:16 PM, Eric Firing <efir...@hawaii.edu
>> <mailto:efir...@hawaii.edu> <mailto:efir...@hawaii.edu
>>
>> <mailto:efir...@hawaii.edu>>> wrote:
>>
>> william ratcliff wrote:
>>
>> Hi! I have a question about contours and clim within
>> matplotlib. I load in some files and do some processing and
>> generate a contour plot using:
>>
>>
>> cmap=pylab.cm.jet
>> mycontour=pylab.contourf(x,y,z,95)#,
>>
>>
>>
>>
>> You don't really want 95 contour levels, do you?
>> Instead of using set_clim, set the contour levels you want, and
>> I
>> suspect everything will come out OK. E.g.,
>>
>> pylab.contourf(x,y,z,pylab.linspace(160, 500, 18))
>>
>> Eric
>>
>> mycontour.set_clim(vmin=160, vmax=500)
>> mycbar=pylab.colorbar()
>> mycbar.set_clim(vmin=160, vmax=500)
>> pylab.xlim((17,19.6))
>> pylab.show()
>>
>>
>> However, the behavior is rather unexpected for me. I
>> find that
>> the colorbar has values rather stretched for values above
>> the
>> cutoff specified in clim. Also, are the values
>> normalized between
>> the limits set in clim, or am I loosing a lot of dynamic
>> range?
>>
>>
>> Thanks,
>> William
>>
>>
>>
>
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