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?
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.
br...@clearscienceinc.com
bruce.w.ford....@navy.smil.mil
http://www.ClearScienceInc.com
Phone/Fax: 904-379-9704
8241 Parkridge Circle N.
Jacksonville, FL 32211
Skype: bruce.w.ford
Google Talk: for...@gmail.com
On Thu, Apr 1, 2010 at 6:30 PM, Chloe Lewis <chle...@berkeley.edu> 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.
>> br...@clearscienceinc.com
>> http://www.ClearScienceInc.com
>> Phone/Fax: 904-379-9704
>> 8241 Parkridge Circle N.
>> Jacksonville, FL 32211
>> Skype: bruce.w.ford
>> Google Talk: for...@gmail.com
>>
>>
>> ------------------------------------------------------------------------------
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>> _______________________________________________
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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
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