## Re: [Matplotlib-users] Cmap creation

 Re: [Matplotlib-users] Cmap creation From: Bruce Ford - 2010-04-02 14:41:42 ```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. bruce@... bruce.w.ford.ctr@... http://www.ClearScienceInc.com Phone/Fax: 904-379-9704 8241 Parkridge Circle N. Jacksonville, FL 32211 Skype: bruce.w.ford Google Talk: fordbw@... On Thu, Apr 1, 2010 at 6:30 PM, Chloe Lewis 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. >> bruce@... >> http://www.ClearScienceInc.com >> Phone/Fax: 904-379-9704 >> 8241 Parkridge Circle N. >> Jacksonville, FL  32211 >> Skype:  bruce.w.ford >> Google Talk: fordbw@... >> >> >> ------------------------------------------------------------------------------ >> 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 >> Matplotlib-users@... >> https://lists.sourceforge.net/lists/listinfo/matplotlib-users > > > Chloe Lewis > Graduate student, Amundson Lab > Ecosystem Sciences > 137 Mulford Hall > Berkeley, CA  94720-3114 > http://nature.berkeley.edu/~chlewis > > > > > > > > ```

 [Matplotlib-users] Cmap creation From: Bruce Ford - 2010-04-01 21:14:43 ```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. bruce@... http://www.ClearScienceInc.com Phone/Fax: 904-379-9704 8241 Parkridge Circle N. Jacksonville, FL 32211 Skype: bruce.w.ford Google Talk: fordbw@... ```
 Re: [Matplotlib-users] Cmap creation From: Friedrich Romstedt - 2010-04-01 21:41:19 ```You forgot about the attachment? Friedrich ```
 Re: [Matplotlib-users] Cmap creation From: Friedrich Romstedt - 2010-04-03 21:14:09 ```Oh, sorry, it was late at night, and so on, but in fact you said it's a standard example, so well ... I was wrong. Friedrich 2010/4/1 Friedrich Romstedt : > You forgot about the attachment? > > Friedrich > ```
 Re: [Matplotlib-users] Cmap creation From: Chloe Lewis - 2010-04-01 22:30:15 ```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. > bruce@... > http://www.ClearScienceInc.com > Phone/Fax: 904-379-9704 > 8241 Parkridge Circle N. > Jacksonville, FL 32211 > Skype: bruce.w.ford > Google Talk: fordbw@... > > ------------------------------------------------------------------------------ > 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 > Matplotlib-users@... > https://lists.sourceforge.net/lists/listinfo/matplotlib-users Chloe Lewis Graduate student, Amundson Lab Ecosystem Sciences 137 Mulford Hall Berkeley, CA 94720-3114 http://nature.berkeley.edu/~chlewis ```
 Re: [Matplotlib-users] Cmap creation From: Bruce Ford - 2010-04-02 14:41:42 ```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. bruce@... bruce.w.ford.ctr@... http://www.ClearScienceInc.com Phone/Fax: 904-379-9704 8241 Parkridge Circle N. Jacksonville, FL 32211 Skype: bruce.w.ford Google Talk: fordbw@... On Thu, Apr 1, 2010 at 6:30 PM, Chloe Lewis 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. >> bruce@... >> http://www.ClearScienceInc.com >> Phone/Fax: 904-379-9704 >> 8241 Parkridge Circle N. >> Jacksonville, FL  32211 >> Skype:  bruce.w.ford >> Google Talk: fordbw@... >> >> >> ------------------------------------------------------------------------------ >> 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 >> Matplotlib-users@... >> https://lists.sourceforge.net/lists/listinfo/matplotlib-users > > > Chloe Lewis > Graduate student, Amundson Lab > Ecosystem Sciences > 137 Mulford Hall > Berkeley, CA  94720-3114 > http://nature.berkeley.edu/~chlewis > > > > > > > > ```
 Re: [Matplotlib-users] Cmap creation From: Eric Firing - 2010-04-02 17:01:35 ```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. > bruce@... > bruce.w.ford.ctr@... > http://www.ClearScienceInc.com > Phone/Fax: 904-379-9704 > 8241 Parkridge Circle N. > Jacksonville, FL 32211 > Skype: bruce.w.ford > Google Talk: fordbw@... > > > > On Thu, Apr 1, 2010 at 6:30 PM, Chloe Lewis 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. >>> bruce@... >>> http://www.ClearScienceInc.com >>> Phone/Fax: 904-379-9704 >>> 8241 Parkridge Circle N. >>> Jacksonville, FL 32211 >>> Skype: bruce.w.ford >>> Google Talk: fordbw@... >>> >>> >>> ------------------------------------------------------------------------------ >>> 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 >>> Matplotlib-users@... >>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users >> >> 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 > Matplotlib-users@... > https://lists.sourceforge.net/lists/listinfo/matplotlib-users ```