How do I set the colorbar range for custom colormaps in matplotlib?

Set Colorbar Range for Custom Colormaps

When creating plots using custom colormaps, it is often desirable to set the range of the colorbar independently from the data values. This allows for consistent color interpretation across multiple plots with different data ranges.

In the provided code snippet, the pcolor function uses a custom colormap defined by the cdict dictionary. The colorbar's range is determined by the maximum and minimum values of the data array (v) by default. However, in some cases, it is necessary to force the colorbar range to a specific interval, such as 0 to 1.

The vmin and vmax parameters of the pcolor function can be used to set the minimum and maximum values for the colorbar, respectively. By specifying these values, the colormap will be stretched or compressed to fit the specified range.

Here's an updated example:

<code class="python">import matplotlib as m
import matplotlib.pyplot as plt
import numpy as np

cdict = {
  'red'  :  ( (0.0, 0.25, .25), (0.02, .59, .59), (1., 1., 1.)),
  'green':  ( (0.0, 0.0, 0.0), (0.02, .45, .45), (1., .97, .97)),
  'blue' :  ( (0.0, 1.0, 1.0), (0.02, .75, .75), (1., 0.45, 0.45))
}

cm = m.colors.LinearSegmentedColormap('my_colormap', cdict, 1024)

x = np.arange(0, 10, .1)
y = np.arange(0, 10, .1)
X, Y = np.meshgrid(x,y)

data = 2*( np.sin(X) + np.sin(3*Y) )

plt.pcolor(X, Y, data, cmap=cm, vmin=0, vmax=1)
plt.colorbar()
plt.show()</code>

In this example, the vmin and vmax parameters are set to 0 and 1, respectively. This forces the colorbar to range from 0 to 1, regardless of the data values.

By specifying vmin and vmax, you can ensure consistent color interpretation across different plots, even when the data ranges vary. This can be particularly useful when comparing multiple data sets or when creating interactive visualizations where the data range may change dynamically.

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