matplotlib-users

[Matplotlib-users] defining a custom RGB colormap

[Michael R. <raw...@ya...>]

How does one define a range of colors for a custom user-defined colormap?  I'm fairly new to matplotlib and have been using standard colormaps.  Below is a sample program that makes a color bar based on the hot colormap.  I'd like to have a colormap like hot, but that starts at, say, orange (near 14%), and runs to black (40%).



'''
Make a colorbar as a separate figure.
'''

from matplotlib import pyplot, mpl
import sys,getopt
from mpl_toolkits.basemap import Basemap, shiftgrid, cm 
#from netCDF3 import Dataset as NetCDFFile 
from mpl_toolkits.basemap import  NetCDFFile
from pylab import *

usemaprev=True

# Make a figure and axes with dimensions as desired.
fig = pyplot.figure(figsize=(8,3))
ax1 = fig.add_axes([0.05, 0.4, 0.9, 0.14])

# Set the colormap and norm to correspond to the data for which
# the colorbar will be used.
cmap = mpl.cm.cool
norm = mpl.colors.Normalize(vmin=0, vmax=40)   # here set colorbar min/max

# alter a matplotlib color table, 
# cm.jet is very useful scheme, but reversed colors are better for drought 
colordict=cm.jet._segmentdata.copy() # dictionary ('blue', 'green', 'red') of nested tuples

#  autumn scheme is yellow to red
colordict=cm.hot._segmentdata.copy() 

#mycolormap=cm.jet
mycolormap=cm.hot

for k in colordict.keys():
	colordict[k]=[list(q) for q in colordict[k]] #convert nested tuples to nested list
	for a in colordict[k]: 
		a[0]=1.-a[0] #in inner list, change normalized value to 1 - value.
	colordict[k].reverse() #reverse order of outer list
maprev = cm.colors.LinearSegmentedColormap("maprev",  colordict)
#map = cm.colors.LinearSegmentedColormap("map", colordict)

if usemaprev:
	mycolormap=maprev
	print "using reverse of defined colormap"

#ax1 = fig.add_axes([0.05, 0.65, 0.9, 0.15])
#cax = axes([0.85, 0.1, 0.05, 0.7]) # setup colorbar axes
#colorbar(format='%d') # draw colorbar

# ColorbarBase derives from ScalarMappable and puts a colorbar
# in a specified axes, so it has everything needed for a
# standalone colorbar.  There are many more kwargs, but the
# following gives a basic continuous colorbar with ticks
# and labels.
#cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=jetrev,
#                                   norm=norm,
#                                   orientation='horizontal')
cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=mycolormap,
                                   norm=norm,
                                   orientation='horizontal')
cb1.set_label('percent')

#pyplot.show()
plt.savefig('colormap.png')
 


      

Re: [Matplotlib-users] defining a custom RGB colormap

[Paul I. <piv...@gm...>]

Michael Rawlins, on 2011-01-05 08:44,  wrote:
> How does one define a range of colors for a custom user-defined
> colormap?  I'm fairly new to matplotlib and have been using
> standard colormaps.  Below is a sample program that makes a
> color bar based on the hot colormap.  I'd like to have a
> colormap like hot, but that starts at, say, orange (near 14%),
> and runs to black (40%).
 
Hi Michael,

first a quick aside: your reversing of the colormap is
unnecessary - just change the colordict assignment line to be:

  colordict=cm.hot_r._segmentdata.copy()

and get rid of the two nested for loops. Or better still, get rid
of colordict altogether and just use

  maprev = cm.hot_r

Ok, as far as your question - I'm not certain that this is what
you're asking for - but if you want to construct a colormap that
"stretches" the portion of your plot that goes from 14 to 40
"percent" (as labeled), to be the entire range of a colormap -
use the following:

All colormaps take as input a value from 0.0-1.0, and give you
back an rgba tuple. You already have a normalization 'norm' in
there which maps an arbitrary interval (0-40 in your case) to the
0.0-1.0 interval. So you like the color value at 14 - let's find
out what the scalar value for 14 is, once it's mapped to the
0.0-1.0 interval.

In [68]: norm(14)
Out[68]: 0.34999999999999998

So that's the value we will pass to cm.hot_r to get the color at
14.  Let's verify that this is actually what's going on - I'll
create a new figure and plot just one big dot on there of what
should hopefully be the same color.

In [69]: f, ax = plt.subplots(1,1)
In [70]: plt.scatter(0,0,c=cm.hot_r(norm(14)),s=1000) # c is color, s is size
Out[70]: <matplotlib.collections.CircleCollection object at 0xae9002c>

Ok, that looks good. We can repeat the procedure for the 40
"percent" case

In [89]: norm(40)
Out[89]: 1.0
In [90]: plt.scatter(0,0,c=cm.hot_r(norm(40)),s=1000)
Out[90]: <matplotlib.collections.CircleCollection object at 0xae97c4c>

No surprises there (it's black). Now let's create our own
segmented map. You can look at the documentation and an example:
http://matplotlib.sourceforge.net/api/colors_api.html#matplotlib.colors.LinearSegmentedColormap
http://matplotlib.sourceforge.net/examples/pylab_examples/custom_cmap.html

but a LinearSegmentedColormap just splits deals with the rgb
channels seperately, and for each color, defines transition
points in the 0.0-1.0 interval which are refered to as 'x' in the
links above, as well as the color to use before the transition
point ('y0'), and the color to use after the point ('y1'). Here's
a quote from the docs about this:

  Each row in the table for a given color is a sequence of x, y0,
  y1 tuples. In each sequence, x must increase monotonically from 0
  to 1. For any input value z falling between x[i] and x[i+1], the
  output value of a given color will be linearly interpolated
  between y1[i] and y0[i+1]:
  
    row i:   x  y0  y1
                   /
                  /
    row i+1: x  y0  y1
  
  Hence y0 in the first row and y1 in the last row are never used.

Armed with this knowledge, you can now use the color from
cm.hot_r(norm(14)) to get the entries for the first rows of your
new map (remember that you're doing red, green, and blue
seperately) - and then remap the remaining transition points (the
'x' portion of the tuple) to stretch portion of the colormap's
0.0-1.0 interval that you're interested in (i.e. map 0.345-1.0 to
0.0-1.0).

Now that's only if you want full control of the linear segments -
there's a quick and dirty way to get what you want by specifying
a ListedColormap using values taken from the colormap you're
using. I'll just get a whole bunch of values from the desired
interval of the colormap, map them through the colormap, and get
my new colormap out.

In [209]: vals = norm(np.linspace(14,40,1000))
In [210]: newcm = cm.colors.ListedColormap(cm.hot_r(vals))

Let's plot the original map (as in your email), and the new one
we created.

In [211]: f,(ax2,ax3) = plt.subplots(2,1)
In [212]: cb2 = mpl.colorbar.ColorbarBase(ax2, cmap=cm.hot_r,
   .....:                                  norm=norm,
   .....:                                  orientation='horizontal')
In [213]: cb2.set_label('"percent"')
In [214]: 
In [215]: cb3 = mpl.colorbar.ColorbarBase(ax3, cmap=newcm,
   .....:                                  orientation='horizontal')
In [216]: cb3.set_label("colormap interval 0.0-1.0")
In [217]: plt.draw()


And to further clarify that we did the right thing, let's adjust
the xlim on that original plot.

In [221]: ax2.set_xlim(norm(14),norm(40))
Out[221]: (0.34999999999999998, 1.0)
In [222]: plt.draw()


Hope this clears things up,
-- 
Paul Ivanov
314 address only used for lists,  off-list direct email at:
http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7 

Re: [Matplotlib-users] defining a custom RGB colormap

[Michael R. <raw...@ya...>]

Paul,

Thanks for the detailed tutorial. I'm getting errors when I attempt to use plt.subplots(1,1) and the newcm assignment.

Traceback (most recent call last):
  File "colorbar_Mytest2.py", line 17, in <module>
    f, ax = plt.subplots(1,1)
AttributeError: 'module' object has no attribute 'subplots'


Here are just a few of the errors I'm getting when executing colorbar command with newcm. Also, what does In and Out do, as in Out[68]: 0.34999999999999998 ?

    plt.draw()
  File "/usr/lib/pymodules/python2.6/matplotlib/pyplot.py", line 352, in draw
    get_current_fig_manager().canvas.draw()
  File "/usr/lib/pymodules/python2.6/matplotlib/backends/backend_tkagg.py", line 215, in draw
    FigureCanvasAgg.draw(self)
  File "/usr/lib/pymodules/python2.6/matplotlib/backends/backend_agg.py", line 314, in draw
    self.figure.draw(self.renderer)
  File "/usr/lib/pymodules/python2.6/matplotlib/artist.py", line 46, in draw_wrapper
    draw(artist, renderer, *kl)
  File "/usr/lib/pymodules/python2.6/matplotlib/figure.py", line 773, in draw
    for a in self.axes: a.draw(renderer)
  File "/usr/lib/pymodules/python2.6/matplotlib/artist.py", line 46, in draw_wrapper


Here's a simplified version that works for me:


from matplotlib import pyplot, mpl
import sys,getopt
from mpl_toolkits.basemap import Basemap, shiftgrid, cm 
#from netCDF3 import Dataset as NetCDFFile 
from mpl_toolkits.basemap import  NetCDFFile
from pylab import *

vals = norm(np.linspace(14,40,1000))
newcm = cm.colors.ListedColormap(cm.hot_r(vals))

# Make a figure and axes with dimensions as desired.
fig = pyplot.figure(figsize=(8,3))
#f, ax = plt.subplots(1,1)
ax1 = fig.add_axes([0.05, 0.4, 0.9, 0.14])
#ax2 = fig.add_axes([0.05, 0.8, 0.9, 0.6])

# Set the colormap and norm to correspond to the data for which
# the colorbar will be used.
cmap = mpl.cm.cool
norm = mpl.colors.Normalize(vmin=0, vmax=40)   # here set colorbar min/max

mycolormap=cm.hot
maprev = cm.hot_r

#f,(ax2,ax3) = plt.subplots(2,1)
cb2 = mpl.colorbar.ColorbarBase(ax1, cmap=cm.hot_r,
                                     norm=norm,
                                     orientation='horizontal')

#cb2.set_label('"percent"')
#cb3 = mpl.colorbar.ColorbarBase(ax1, cmap=newcm,
#                                     orientation='horizontal')

#cb3.set_label("colormap interval 0.0-1.0")

plt.draw()





      

Re: [Matplotlib-users] defining a custom RGB colormap

[Paul I. <piv...@gm...>]

Michael Rawlins, on 2011-01-05 14:42,  wrote:
> Thanks for the detailed tutorial. I'm getting errors when I
> attempt to use plt.subplots(1,1) and the newcm assignment.
> 
> Traceback (most recent call last):
>   File "colorbar_Mytest2.py", line 17, in <module>
>     f, ax = plt.subplots(1,1)
> AttributeError: 'module' object has no attribute 'subplots'

Ah, you must be using an older version of matplotlib - subplots
is a (recently added) convenience shortcut for:

  f = plt.figure()
  ax = plt.subplot(1,1,1)

It comes in handy when you're making lots of subplots by letting
you do it with one call, instead of doing that one by one (as I
have rewritten below, so you could run without having to upgrade
your matplotlib.

> Also, what does In and Out do, as in Out[68]: 0.34999?

That's just the prompts from IPython - I *highly* recommend using
IPython in place of the default python shell for interactive usage. 
In[10] is what I typed,  Out[10] is the result of my command at
In[10].

> Here are just a few of the errors I'm getting when executing
> colorbar command with newcm. 

> Here's a simplified version that works for me:

ouch! this code doesn't do quite what you want

> from pylab import *

Try to avoid doing this - because you will get unintended
consequences such as the one on the following line.
 
> vals = norm(np.linspace(14,40,1000))

This was meant to go *after* you initialize the 'norm' variable
with norm = mpl.colors.Normalize(...). That's the norm I
meant to be using. But because of the "from pylab import *" line,
the norm function from numpy was imported - which is what was being
used on that line as written in your code. 

so the vals=  line is equivalent to

  vals = numpy.norm(np.linspace(14,40,1000))

which meant vals got assigned the value 886.25397758173483, and
not at all what we wanted. We wanted it to get an array of 1000
numbers:

  vals = mpl.colors.Normalize(vmin=0, vmax=40)(np.linspace(14,40,1000))

That's where your trouble with newcm were coming from. Here's the
complete example again, I've renamed the 'norm' variable to
'rawlins_norm' for clarity.

import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm 
import numpy as np

# Make a figure and axes with dimensions as desired.
fig = plt.figure(figsize=(8,3))
ax1 = plt.subplot(2,1,1)
ax2 = plt.subplot(2,1,2)

# Set the colormap and norm to correspond to the data for which
# the colorbar will be used.
rawlins_norm = mpl.colors.Normalize(vmin=0, vmax=40)   # here set colorbar min/max
# the right place for vals
vals = rawlins_norm(np.linspace(14,40,1000))
newcm = cm.colors.ListedColormap(cm.hot_r(vals))

cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cm.hot_r,
                                     norm=rawlins_norm,
                                     orientation='horizontal')

cb1.set_label('"percent"')
cb2 = mpl.colorbar.ColorbarBase(ax2, cmap=newcm,
                                     orientation='horizontal')

cb2.set_label("colormap interval 0.0-1.0")
plt.subplots_adjust(hspace=.7, bottom=.2)

#comment out the next line to see the original (0-40 colormap)
ax1.set_xlim(rawlins_norm((14,40)))
plt.show()


best,
-- 
Paul Ivanov
314 address only used for lists,  off-list direct email at:
http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7 

Re: [Matplotlib-users] defining a custom RGB colormap

[Michael R. <raw...@ya...>]

Got it now.  Sorry about the confusion...by working for me I meant that  set of commands ran and made the standard colorbar.

I just installed ipython (Ubuntu OS).  Will try the interactive way as well.  All very new.  I've used PGPLOT for ~15 years.  

Thanks again.
Mike

--- On Wed, 1/5/11, Paul Ivanov <piv...@gm...> wrote:

> From: Paul Ivanov <piv...@gm...>
> Subject: Re: [Matplotlib-users] defining a custom RGB colormap
> To: mat...@li...
> Date: Wednesday, January 5, 2011, 7:15 PM
> Michael Rawlins, on 2011-01-05
> 14:42,  wrote:
> > Thanks for the detailed tutorial. I'm getting errors
> when I
> > attempt to use plt.subplots(1,1) and the newcm
> assignment.
> > 
> > Traceback (most recent call last):
> >   File "colorbar_Mytest2.py", line 17,
> in <module>
> >     f, ax = plt.subplots(1,1)
> > AttributeError: 'module' object has no attribute
> 'subplots'
> 
> Ah, you must be using an older version of matplotlib -
> subplots
> is a (recently added) convenience shortcut for:
> 
>   f = plt.figure()
>   ax = plt.subplot(1,1,1)
> 
> It comes in handy when you're making lots of subplots by
> letting
> you do it with one call, instead of doing that one by one
> (as I
> have rewritten below, so you could run without having to
> upgrade
> your matplotlib.
> 
> > Also, what does In and Out do, as in Out[68]:
> 0.34999?
> 
> That's just the prompts from IPython - I *highly* recommend
> using
> IPython in place of the default python shell for
> interactive usage. 
> In[10] is what I typed,  Out[10] is the result of my
> command at
> In[10].
> 
> > Here are just a few of the errors I'm getting when
> executing
> > colorbar command with newcm. 
> 
> > Here's a simplified version that works for me:
> 
> ouch! this code doesn't do quite what you want
> 
> > from pylab import *
> 
> Try to avoid doing this - because you will get unintended
> consequences such as the one on the following line.
>  
> > vals = norm(np.linspace(14,40,1000))
> 
> This was meant to go *after* you initialize the 'norm'
> variable
> with norm = mpl.colors.Normalize(...). That's the norm I
> meant to be using. But because of the "from pylab import *"
> line,
> the norm function from numpy was imported - which is what
> was being
> used on that line as written in your code. 
> 
> so the vals=  line is equivalent to
> 
>   vals = numpy.norm(np.linspace(14,40,1000))
> 
> which meant vals got assigned the value 886.25397758173483,
> and
> not at all what we wanted. We wanted it to get an array of
> 1000
> numbers:
> 
>   vals = mpl.colors.Normalize(vmin=0,
> vmax=40)(np.linspace(14,40,1000))
> 
> That's where your trouble with newcm were coming from.
> Here's the
> complete example again, I've renamed the 'norm' variable
> to
> 'rawlins_norm' for clarity.
> 
> import matplotlib as mpl
> import matplotlib.pyplot as plt
> from matplotlib import cm 
> import numpy as np
> 
> # Make a figure and axes with dimensions as desired.
> fig = plt.figure(figsize=(8,3))
> ax1 = plt.subplot(2,1,1)
> ax2 = plt.subplot(2,1,2)
> 
> # Set the colormap and norm to correspond to the data for
> which
> # the colorbar will be used.
> rawlins_norm = mpl.colors.Normalize(vmin=0,
> vmax=40)   # here set colorbar min/max
> # the right place for vals
> vals = rawlins_norm(np.linspace(14,40,1000))
> newcm = cm.colors.ListedColormap(cm.hot_r(vals))
> 
> cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cm.hot_r,
>                
>                
>      norm=rawlins_norm,
>                
>                
>      orientation='horizontal')
> 
> cb1.set_label('"percent"')
> cb2 = mpl.colorbar.ColorbarBase(ax2, cmap=newcm,
>                
>                
>      orientation='horizontal')
> 
> cb2.set_label("colormap interval 0.0-1.0")
> plt.subplots_adjust(hspace=.7, bottom=.2)
> 
> #comment out the next line to see the original (0-40
> colormap)
> ax1.set_xlim(rawlins_norm((14,40)))
> plt.show()
> 
> 
> best,
> -- 
> Paul Ivanov
> 314 address only used for lists,  off-list direct
> email at:
> http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7 
> 
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