Thanks for your response to my request, Ray. I had looked at this
approach earlier; but, what I really need is something like is
produced by the following code for the cax object:
But, for a colorline object, which was referenced in the link given
in my earlier email.
–V
···
On 16-Jun-14 00:46, Raymond Smith
wrote:
Hi Virgil,
I did something very much like this recently by simply adding
an axes to my figure and using it to show a linspace of the
data range off which the line color was based. See http://matplotlib.org/examples/color/colormaps_reference.html.
Best,
Ray
On Sun, Jun 15, 2014 at 6:17 PM,
Virgil Stokes <vs@…4541…>
wrote:
There are some
rather nice and useful matplotlib examples for colormaps
that are shown at:
[http://nbviewer.ipython.org/github/dpsanders/matplotlib-examples/blob/master/colorline.ipynb](http://nbviewer.ipython.org/github/dpsanders/matplotlib-examples/blob/master/colorline.ipynb)
In ** Example 1. Sine wave colored by time (uses the
defaults for colorline)** , how can one add a
colorbar?
--V
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`“”"Produce custom labelling for a colorbar.``
``
`` Original Script: Scott Sinclair``
`` Modification: V. Stokes``
``"""``
``
``import matplotlib.pyplot as plt``
``import numpy as np``
``import matplotlib.colors as col``
``from matplotlib import cm``
``from numpy.random import randn``
``
``def register_cmap():``
`` """``
`` Purpose: define colormap using the from_List() method
as a ``
`` segmented list and register it.``
`` """``
`` cmap_Name = 'reyegr' # my colormap name``
`` startcolor = '#00AF33' # truegreen ``
`` midcolor = '#FFE600' # yolk (a medium dark
yellow) ``
`` endcolor = '#FF0033' # bright red``
`` cmap2 =
col.LinearSegmentedColormap.from_list(cmap_Name,``
``
[startcolor,midcolor,endcolor])``
`` cm.register_cmap(cmap=cmap2)``
`` return cm.get_cmap(cmap_Name) # my new cmap for
‘reylgr’``
``
``#-----------------------------------------------------------------------------``
``my_cmap = register_cmap()``
``
``## Vertical colorbar-1``
``fig, ax = plt.subplots()``
``
``data = np.clip(randn(250, 250), -1, 1)``
``
`` cax = ax.imshow(data, interpolation='nearest',
cmap=my_cmap)``
``ax.set_title('Gaussian noise with vertical colorbar')``
`` # Add colorbar, make sure to specify tick locations to
match desired ticklabels``
``cbar = fig.colorbar(cax, ticks=[-1, 0, 1])``
`` cbar.ax.set_yticklabels(['<-1', '0', '> 1'])#
vertically oriented colorbar``
``
``## Vertical colorbar-2``
``fig, ax = plt.subplots()``
``
``data = np.clip(randn(50, 50), -1, 1)``
``
`` #cax = ax.imshow(data, interpolation='nearest',
cmap=cm.coolwarm)``
`` cax = ax.imshow(data, interpolation='nearest',
cmap=my_cmap)``
``ax.set_title('Gaussian noise with vertical colorbar')``
``
`` # Add colorbar, make sure to specify tick locations to
match desired ticklabels``
``cbar = fig.colorbar(cax, ticks=[-1, 0, 1])``
``# Vertically oriented (by default) colorbar``
``cbar.ax.set_yticklabels(['Low', 'Medium', 'High'])``
``
``## Horizontal colorbar``
``fig, ax = plt.subplots()``
``# ``
`` cax = ax.imshow(data, interpolation='nearest',
cmap=my_cmap)``
``ax.set_title('Gaussian noise with horizontal colorbar')``
``
`` cbar = fig.colorbar(cax, ticks=[-1, 0, 1],
orientation=‘horizontal’)``
`` cbar.ax.set_xticklabels(['Low', 'Medium', 'High'])#
horizontal colorbar``
``
``plt.show()`