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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