On 06/03/2010 10:43 AM, Jim Vickroy wrote:
> Eric Firing wrote:
>> On 06/03/2010 10:00 AM, Jim Vickroy wrote:
>>
>>> I want to generate a 2-d figure with a (fixed) color scale that does
>>> not vary with the range of the data being plotted.
>>>
>>> How do I do this? Attempts to specify vimin and vmax appear to be ignored.
>>>
>>> The following example:
>>>
>>> #<code>
>>> import numpy
>>> data = numpy.zeros(shape=(240,240),dtype=int)
>>> data[ 0: 80] = -1
>>> data[ 80:160] = 0
>>> data[160:] = 1
>>>
>>> import matplotlib.pyplot as plot
>>> figure = plot.figure()
>>> ax = figure.add_subplot(111)
>>> cax = ax.imshow(data, interpolation='bilinear')
>>> ax.set_title('test data with fixed colorbar')
>>>
>>
>> Adding to what JJ said, note that setting the ticks on the colorbar has
>> no effect on the norm used in color mapping. The vmin and vmax kwargs
>> to imshow get passed to the norm, so they do set the mapping range.
>>
>>
>>> colorbar = figure.colorbar(cax, ticks=[-1, 0, 1])
>>> colorbar.ax.set_yticklabels(['-1', '0', '1'])
>>>
>>
>> Please avoid setting the ticklabels directly--it is almost always
>> unnecessary, and it is too easy to shoot yourself in the foot. If the
>> default tick label formatting is inadequate, you can use the format
>> kwarg in colorbar.
>>
>> From the docstring:
>>
>> *ticks* [ None | list of ticks | Locator object ]
>> If None, ticks are determined automatically from the
>> input.
>> *format* [ None | format string | Formatter object ]
>> If None, the
>> :class:`~matplotlib.ticker.ScalarFormatter` is used.
>> If a format string is given, e.g. '%.3f', that is
>> used. An alternative
>> :class:`~matplotlib.ticker.Formatter` object may be
>> given instead.
>>
>>
>> Eric
>>
>
> Thanks for this advice.
>
> In my case, the data being plotted is in the range 0-255, but the
> color-bar labels are to be in the range 1-4095. So I have the following
> code snippet:
>
> colorbar = figure.colorbar(image, cax, orientation='vertical', ticks=(0,
> 64, 128, 192, 254))
> colorbar.ax.set_yticklabels(('1','8','64','512','4095')) # colorbar
> labels (which are to be in units of DN/sec on a log10 scale)
>
> Is there a better way to do this?
The advantage of using a custom formatter is that it formats actual tick
values, so if you decide to use a different set of tick locations, you
don't have to remember to change the labels. A formatter for a
complicated case such as the above could use a dictionary, which would
at least generate a KeyError if you changed a tick without adding the
new location to the dictionary, or, better, it could calculate the label
numbers. Suppose you have a function to do the translation:
def to_DNpersec(x):
dn = ... whatever function of x
return dn
import matplotlib as mpl
class DNpersecFormatter(mpl.ticker.Formatter):
def __call__(self, val, pos=None):
dn = to_DNpersec(val)
return "%d" % round(dn)
...
colorbar = figure.colorbar(image, cax, orientation='vertical',
ticks=(0, 64, 128, 192, 254),
format=DNpersecFormatter())
Eric
>
> -- jv
>
>
>>
>>
>>> plot.show()
>>> #</code>
>>>
>>> produces a figure with 3 color bands (blue,green,red) and matching color
>>> bar with labels (-1,0,1) as expected.
>>>
>>> if the data[160:]=1 specification is deleted, in the above code, the
>>> resulting figure has 2 color bands (blue,red) and the associated color
>>> bar is identical to the original, but the labels are (-1,0).
>>>
>>> What I want, in this second case, is a blue-green figure and a color bar
>>> with labels identical to the original example.
>>>
>>> -- jv
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