Cmap creation

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@...2905...
http://www.ClearScienceInc.com
Phone/Fax: 904-379-9704
8241 Parkridge Circle N.
Jacksonville, FL 32211
Skype: bruce.w.ford
Google Talk: fordbw@...287...

You forgot about the attachment?

Friedrich

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@...2905...
http://www.ClearScienceInc.com
Phone/Fax: 904-379-9704
8241 Parkridge Circle N.
Jacksonville, FL 32211
Skype: bruce.w.ford
Google Talk: fordbw@...287...

------------------------------------------------------------------------------
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proactively, and fine-tune applications for parallel performance.
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http://p.sf.net/sfu/intel-sw-dev
_______________________________________________
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Chloe Lewis
Graduate student, Amundson Lab
Ecosystem Sciences
137 Mulford Hall
Berkeley, CA 94720-3114
http://nature.berkeley.edu/~chlewis

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@...2905...
bruce.w.ford.ctr@...2906...
http://www.ClearScienceInc.com
Phone/Fax: 904-379-9704
8241 Parkridge Circle N.
Jacksonville, FL 32211
Skype: bruce.w.ford
Google Talk: fordbw@...287...

On Thu, Apr 1, 2010 at 6:30 PM, Chloe Lewis <chlewis@...1016...> 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@...2905...
http://www.ClearScienceInc.com
Phone/Fax: 904-379-9704
8241 Parkridge Circle N.
Jacksonville, FL 32211
Skype: bruce.w.ford
Google Talk: fordbw@...287...

------------------------------------------------------------------------------
Download Intel&#174; 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@lists.sourceforge.net
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

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@...2905...
bruce.w.ford.ctr@...2906...
http://www.ClearScienceInc.com
Phone/Fax: 904-379-9704
8241 Parkridge Circle N.
Jacksonville, FL 32211
Skype: bruce.w.ford
Google Talk: fordbw@...287...

On Thu, Apr 1, 2010 at 6:30 PM, Chloe Lewis <chlewis@...1016...> 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@...2905...
http://www.ClearScienceInc.com
Phone/Fax: 904-379-9704
8241 Parkridge Circle N.
Jacksonville, FL 32211
Skype: bruce.w.ford
Google Talk: fordbw@...287...

------------------------------------------------------------------------------
Download Intel&#174; 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@lists.sourceforge.net
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&#174; 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@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users

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 <friedrichromstedt@...287...>:

···

You forgot about the attachment?

Friedrich