Bruce Ford wrote:
> Below is the example script (sorry!). I've tried all three methods of
> establishing a colormap to no avail. The most promising looked like
> option 2, but that gave me the "AttributeError: 'module' object has no
> attribute 'register_cmap'" error.
>
> I'm getting this error with:
> Python 2.4 (user requirement because this application I'm building
> will live on a RHEL5 server)
> matplotlib 0.99.1.1
> numpy 1.3.0
>
> Could this be a versioning issue?
Yes, register_cmap is quite new--but it is just a convenience, and not
at all necessary. Use of a custom cmap without register_cmap is
illustrated in the first subplot of the example; you could modify the
example so that all of the subplots are made without register_cmap.
Eric
>
> Bruce
>
> #!/usr/bin/env python
>
> import numpy as np
> import matplotlib.pyplot as plt
> from matplotlib.colors import LinearSegmentedColormap
>
> """
>
> Example: suppose you want red to increase from 0 to 1 over the bottom
> half, green to do the same over the middle half, and blue over the top
> half. Then you would use:
>
> cdict = {'red': ((0.0, 0.0, 0.0),
> (0.5, 1.0, 1.0),
> (1.0, 1.0, 1.0)),
>
> 'green': ((0.0, 0.0, 0.0),
> (0.25, 0.0, 0.0),
> (0.75, 1.0, 1.0),
> (1.0, 1.0, 1.0)),
>
> 'blue': ((0.0, 0.0, 0.0),
> (0.5, 0.0, 0.0),
> (1.0, 1.0, 1.0))}
>
> If, as in this example, there are no discontinuities in the r, g, and b
> components, then it is quite simple: the second and third element of
> each tuple, above, is the same--call it "y". The first element ("x")
> defines interpolation intervals over the full range of 0 to 1, and it
> must span that whole range. In other words, the values of x divide the
> 0-to-1 range into a set of segments, and y gives the end-point color
> values for each segment.
>
> Now consider the green. cdict['green'] is saying that for
> 0 <= x <= 0.25, y is zero; no green.
> 0.25 < x <= 0.75, y varies linearly from 0 to 1.
> x > 0.75, y remains at 1, full green.
>
> If there are discontinuities, then it is a little more complicated.
> Label the 3 elements in each row in the cdict entry for a given color as
> (x, y0, y1). Then for values of x between x[i] and x[i+1] the color
> value is interpolated between y1[i] and y0[i+1].
>
> Going back to the cookbook example, look at cdict['red']; because y0 !=
> y1, it is saying that for x from 0 to 0.5, red increases from 0 to 1,
> but then it jumps down, so that for x from 0.5 to 1, red increases from
> 0.7 to 1. Green ramps from 0 to 1 as x goes from 0 to 0.5, then jumps
> back to 0, and ramps back to 1 as x goes from 0.5 to 1.
>
> row i: x y0 y1
> /
> /
> row i+1: x y0 y1
>
> Above is an attempt to show that for x in the range x[i] to x[i+1], the
> interpolation is between y1[i] and y0[i+1]. So, y0[0] and y1[-1] are
> never used.
>
> """
>
>
>
> cdict1 = {'red': ((0.0, 0.0, 0.0),
> (0.5, 0.0, 0.1),
> (1.0, 1.0, 1.0)),
>
> 'green': ((0.0, 0.0, 0.0),
> (1.0, 0.0, 0.0)),
>
> 'blue': ((0.0, 0.0, 1.0),
> (0.5, 0.1, 0.0),
> (1.0, 0.0, 0.0))
> }
>
> cdict2 = {'red': ((0.0, 0.0, 0.0),
> (0.5, 0.0, 1.0),
> (1.0, 0.1, 1.0)),
>
> 'green': ((0.0, 0.0, 0.0),
> (1.0, 0.0, 0.0)),
>
> 'blue': ((0.0, 0.0, 0.1),
> (0.5, 1.0, 0.0),
> (1.0, 0.0, 0.0))
> }
>
> cdict3 = {'red': ((0.0, 0.0, 0.0),
> (0.25,0.0, 0.0),
> (0.5, 0.8, 1.0),
> (0.75,1.0, 1.0),
> (1.0, 0.4, 1.0)),
>
> 'green': ((0.0, 0.0, 0.0),
> (0.25,0.0, 0.0),
> (0.5, 0.9, 0.9),
> (0.75,0.0, 0.0),
> (1.0, 0.0, 0.0)),
>
> 'blue': ((0.0, 0.0, 0.4),
> (0.25,1.0, 1.0),
> (0.5, 1.0, 0.8),
> (0.75,0.0, 0.0),
> (1.0, 0.0, 0.0))
> }
>
> # Now we will use this example to illustrate 3 ways of
> # handling custom colormaps.
> # First, the most direct and explicit:
>
> blue_red1 = LinearSegmentedColormap('BlueRed1', cdict1)
>
> # Second, create the map explicitly and register it.
> # Like the first method, this method works with any kind
> # of Colormap, not just
> # a LinearSegmentedColormap:
>
> blue_red2 = LinearSegmentedColormap('BlueRed2', cdict2)
> plt.register_cmap(cmap=blue_red2)
>
> # Third, for LinearSegmentedColormap only,
> # leave everything to register_cmap:
>
> plt.register_cmap(name='BlueRed3', data=cdict3) # optional lut kwarg
>
> x = np.arange(0, np.pi, 0.1)
> y = np.arange(0, 2*np.pi, 0.1)
> X, Y = np.meshgrid(x,y)
> Z = np.cos(X) * np.sin(Y)
>
> plt.figure(figsize=(10,4))
> plt.subplots_adjust(wspace=0.3)
>
> plt.subplot(1,3,1)
> plt.imshow(Z, interpolation='nearest', cmap=blue_red1)
> plt.colorbar()
>
> plt.subplot(1,3,2)
> cmap = plt.get_cmap('BlueRed2')
> plt.imshow(Z, interpolation='nearest', cmap=cmap)
> plt.colorbar()
>
> # Now we will set the third cmap as the default. One would
> # not normally do this in the middle of a script like this;
> # it is done here just to illustrate the method.
>
> plt.rcParams['image.cmap'] = 'BlueRed3'
>
> # Also see below for an alternative, particularly for
> # interactive use.
>
> plt.subplot(1,3,3)
> plt.imshow(Z, interpolation='nearest')
> plt.colorbar()
>
> # Or as yet another variation, we could replace the rcParams
> # specification *before* the imshow with the following *after*
> # imshow:
> #
> # plt.set_cmap('BlueRed3')
> #
> # This sets the new default *and* sets the colormap of the last
> # image-like item plotted via pyplot, if any.
>
>
> plt.suptitle('Custom Blue-Red colormaps')
>
> plt.show()
> ---------------------------------------
> Bruce W. Ford
> Clear Science, Inc.
> [email protected]
> [email protected]
> http://www.ClearScienceInc.com
> Phone/Fax: 904-379-9704
> 8241 Parkridge Circle N.
> Jacksonville, FL 32211
> Skype: bruce.w.ford
> Google Talk: [email protected]
>
>
>
> On Thu, Apr 1, 2010 at 6:30 PM, Chloe Lewis <[email protected]> wrote:
>> The example works for me; Python 2.6.4 (recent Enthought install).
>>
>> Can you use your new colormap without registering it?
>>
>> &C
>>
>> On Apr 1, 2010, at 1 Apr, 2:14 PM, Bruce Ford wrote:
>>
>>> I'm running into walls trying to create a custom cmap.
>>>
>>> Running the example custom_cmap.py unchanged, I get :
>>>
>>> AttributeError: 'module' object has no attribute 'register_cmap'
>>> args = ("'module' object has no attribute 'register_cmap'",)
>>>
>>> I've included custom_cmap.py below. It's a major shortcoming that
>>> there is not a suitable anomaly cmap (with white about the middle).
>>> Please consider this for an addition.
>>>
>>> Anyway, what am I missing with this error? Thanks so much!
>>>
>>> Bruce
>>> ---------------------------------------
>>> Bruce W. Ford
>>> Clear Science, Inc.
>>> [email protected]
>>> http://www.ClearScienceInc.com
>>> Phone/Fax: 904-379-9704
>>> 8241 Parkridge Circle N.
>>> Jacksonville, FL 32211
>>> Skype: bruce.w.ford
>>> Google Talk: [email protected]
>>>
>>>
>>> ------------------------------------------------------------------------------
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>>> Try the new software tools for yourself. Speed compiling, find bugs
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>>
>> Chloe Lewis
>> Graduate student, Amundson Lab
>> Ecosystem Sciences
>> 137 Mulford Hall
>> Berkeley, CA 94720-3114
>> http://nature.berkeley.edu/~chlewis
>>
>>
>>
>>
>>
>>
>>
>>
>
> ------------------------------------------------------------------------------
> Download Intel® Parallel Studio Eval
> Try the new software tools for yourself. Speed compiling, find bugs
> proactively, and fine-tune applications for parallel performance.
> See why Intel Parallel Studio got high marks during beta.
> http://p.sf.net/sfu/intel-sw-dev
> _______________________________________________
> Matplotlib-users mailing list
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
------------------------------------------------------------------------------
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Try the new software tools for yourself. Speed compiling, find bugs
proactively, and fine-tune applications for parallel performance.
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http://p.sf.net/sfu/intel-sw-dev
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