## COLORMAP Image Colormap Function

Section: Handle-Based Graphics

### Usage

Changes the colormap for the current figure. The generic syntax for its use is

      colormap(map)

where `map` is a an array organized as `3 \times N`), which defines the RGB (Red Green Blue) coordinates for each color in the colormap. You can also use the function with no arguments to recover the current colormap

      map = colormap

### Function Internals

Assuming that the contents of the colormap function argument `c` are labeled as:

then these columns for the RGB coordinates of pixel in the mapped image. Assume that the image occupies the range $[a,b]$. Then the RGB color of each pixel depends on the value $x$ via the following integer

so that a pixel corresponding to image value $x$ will receive RGB color $[r_k,g_k,b_k]$. Colormaps are generally used to pseudo color images to enhance visibility of features, etc.

### Examples

We start by creating a smoothly varying image of a 2D Gaussian pulse.

    --> x = linspace(-1,1,512)'*ones(1,512);
    --> y = x';
    --> Z = exp(-(x.^2+y.^2)/0.3);
    --> image(Z);

which we display with the default (grayscale) colormap here.

Next we switch to the `copper` colormap, and redisplay the image.

    --> colormap(copper);
    --> image(Z);

which results in the following image.

If we capture the output of the `copper` command and plot it, we obtain the following result:

    --> a = copper;
    --> plot(a);

Note that in the output that each of the color components are linear functions of the index, with the ratio between the red, blue and green components remaining constant as a function of index. The result is an intensity map with a copper tint. We can similarly construct a colormap of our own by defining the three components seperately. For example, suppose we take three gaussian curves, one for each color, centered on different parts of the index space:

    --> t = linspace(0,1,256);
    --> A = [exp(-(t-1.0).^2/0.1);exp(-(t-0.5).^2/0.1);exp(-t.^2/0.1)]';
    --> plot(A);

The resulting image has dark bands in it near the color transitions.

    --> image(Z);
    --> colormap(A);

These dark bands are a result of the nonuniform color intensity, which we can correct for by renormalizing each color to have the same norm.

    --> w = sqrt(sum(A'.^2));
    --> sA = diag(1./w)*A;
    --> plot(A);

The resulting image has no more dark bands.

    --> image(Z);
    --> colormap(A);