function tim_demo
%TIM_DEMO show how a few functions that are vector-based can do a lot of work
%   with minimal overhead.

% Preallocate memory for the three pictures we'll be imaging.  Replace this with
% your own (JPG) image to see how the speed scales with size.
im = imread('large.jpg', 'JPG');
im2 = im;
im3 = im;

% from MATLAB help on 'figure'
scrsz = get(0, 'ScreenSize');
[im_y im_x unused] = size(im);
aspect_ratio = im_y / im_x;
figure_box = [1, 1, (scrsz(4) - 100) / (aspect_ratio * 3), scrsz(4) - 100];
figure('Position', figure_box);

% Display the original image
subplot(3,1,1); imagesc(im); axis off;

% Make radial gaussian profile.  The middle element of the matrix will have
% value 1, and the values of values 'farther away' from the center value die off
% until they reach 1.5 standard deviations at the nearest edge point.
x = (1:im_x) - ceil(im_x / 2);
y = (1:im_y) - ceil(im_y / 2);
r = (ones(im_y, 1) * x .^ 2 + y' .^ 2 * ones(1, im_x)) .^ 0.5;
z = normpdf(r, 0, min(im_x, im_y) / 3);
z = z / max(max(z));

% Scale red channel by multiplying it with our gaussian envelope.  uint8() and
% double() are matching the datatype between the matrix we made and the image.
im2(:,:,1) = uint8(double(im2(:,:,1)) .* z);
subplot(3,1,2); imagesc(im2); axis off;

% Now reuse our matrix by shifting the center around for each channel.  Also,
% this time use a 3D array so that we can alter all three channels with one
% multiplication command.
sep = 100;
z(:,:,2) = circshift(z, [sep sep]);
z(:,:,3) = circshift(z(:,:,1), [sep, -sep]);
z(:,:,1) = circshift(z(:,:,1), [-floor(sep / sqrt(3)), 0]);

% Scale all channels at once with 3D matrix
im3 = uint8(double(im3) .* z);

subplot(3,1,3); imagesc(im3); axis off;