view +parametrization/old/curve_discretise.m @ 1037:2d7ba44340d0 feature/burgers1d

Pass scheme specific parameters as cell array. This will enabale constructDiffOps to be more general. In addition, allow for schemes returning function handles as diffOps, which is currently how non-linear schemes such as Burgers1d are implemented.
author Vidar Stiernström <vidar.stiernstrom@it.uu.se>
date Fri, 18 Jan 2019 09:02:02 +0100
parents 81e0ead29431
children
line wrap: on
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% Discretises the curve g with the smallest number of points such that all segments
% are shorter than h. If do_plot is true the points of the discretisation and
% the normals of the curve in those points are plotted.
%
%   [t,p,d] = curve_discretise(g,h,do_plot)
%
%   t is a vector of input values to g.
%   p is a cector of points.
%   d are the length of the segments.
function [t,p,d] = curve_discretise(g,h,do_plot)
    default_arg('do_plot',false)

    n = 10;

    [t,p,d] = curve_discretise_n(g,n);

    % ni = 0;
    while any(d>h)
        [t,p,d] = curve_discretise_n(g,n);
        n = ceil(n*d(1)/h);
        % ni = ni+1;
    end

    % nj = 0;
    while all(d<h)
        [t,p,d] = curve_discretise_n(g,n);
        n = n-1;
        % nj = nj+1;
    end
    [t,p,d] = curve_discretise_n(g,n+1);

    % fprintf('ni = %d, nj = %d\n',ni,nj);

    if do_plot
        fprintf('n:%d  max: %f min: %f\n', n, max(d),min(d));
        p = parametrization.map_curve(g,t);
        figure
        show(g,t,h);
    end

end

function [t,p,d] = curve_discretise_n(g,n)
    t = linspace(0,1,n);
    t = equalize_d(g,t);
    d = D(g,t);
    p = parametrization.map_curve(g,t);
end

function d = D(g,t)
    p = parametrization.map_curve(g,t);

    d = zeros(1,length(t)-1);
    for i = 1:length(d)
        d(i) = norm(p(:,i) - p(:,i+1));
    end
end

function t = equalize_d(g,t)
    d = D(g,t);
    v = d-mean(d);
    while any(abs(v)>0.01*mean(d))
        dt = t(2:end)-t(1:end-1);
        t(2:end) = t(2:end) - cumsum(dt.*v./d);

        t = t/t(end);
        d = D(g,t);
        v = d-mean(d);
    end
end


function show(g,t,hh)
    p = parametrization.map_curve(g,t);



    h = parametrization.plot_curve(g);
    h.LineWidth = 2;
    axis equal
    hold on
    h = plot(p(1,:),p(2,:),'.');
    h.Color = [0.8500 0.3250 0.0980];
    h.MarkerSize = 24;
    hold off

    n = parametrization.curve_normals(g,t);
    hold on
    for  i = 1:length(t)
        p0 = p(:,i);
        p1 = p0 + hh*n(:,i);
        l = [p0, p1];
        h = plot(l(1,:),l(2,:));
        h.Color = [0.8500 0.3250 0.0980];
    end

end