function [ok]= t_general_noisy_jump(draw_plot, conVar, conFreq, proc_ind, meas_ind)

noise = 'normal';

if(nargin < 5)
    conFreq = 0;
    conVar = 0;
    proc_ind = 1:2;
    meas_ind = [];
end
% --------------------------------------------------------

N     = 100;        % number of measurement time points

dt    = 4*pi / N;  % time between measurement points

%dt = 1;

gamma =  1;        % transition covariance multiplier

sigma =  0.5;       % standard deviation of measurement noise

if (strcmp(noise,'student'))
    sigma = sqrt(2);
end


max_itr = 100;      % maximum number of iterations

epsilon = 1e-3;    % convergence criteria

h_min   = 0;       % minimum horizontal value in plots

h_max   = 14;       % maximum horizontal value in plots

v_min   = -10.0;    % minimum vertical value in plots

v_max   = 10.0;    % maximum vertical value in plots

% ---------------------------------------------------------


if nargin < 1

     draw_plot = false;

end


% number of measurements per time point
m     = 1;
% number of state vector components per time point
n     = 2;

params.level = 1;



% simulate the true trajectory and measurement noise

t1       =  (1 : N/2 - 2) * dt;
t2       =  (N/2 - 1: N/2 + 1)*dt;
t3       =  (N/2 +2 : N)*dt;

t = [t1, t2, t3];

% alpha = 8;
% beta = 4;
% gammaOne = 20;

jump = 10;
derivs = jump/(4*dt) * ones(1,3);

    t2 = [-cos(t1), derivs, -cos(t3)];
    t1 = [-jump/2-sin(t1), -jump/2-sin(N/2 - 2) + cumsum(derivs)*dt, jump/2-sin(t3)];

   x1_true = t1(1:N);
   x2_true = t2(1:N);



x_true  = [ x1_true ; x2_true ];

%
params.direct_h_index = 1;
h_fun = @(k,x) direct_h(k,x,params);

params.pos_vel_g_dt = .01;
params.pos_vel_g_initial = x_true(:,1); % initialize to true values for now
g_fun = @(k,x) pos_vel_g(k,x,params);

df_meas = 4;
df_proc = 4;
params.df_proc = df_proc;
params.df_meas = df_meas;
params.inds_proc_st = proc_ind;
params.inds_meas_st = meas_ind;

% measurement values and model


%exp_true = random('exponential', 1/sigma, 1, N);
%bin = 2*random('binomial', 1, 0.5, 1, N) - 1;
%exp_true = exp_true .* bin;
%z        = x2_true + exp_true;


% Normal Noise

v_true = zeros(1,N);
%temp = zeros(1,N);
%bin = zeros(1,N);

randn('state', sum(100*clock))
rand('twister', sum(100*clock));


if (strcmp(noise,  'normal'))
    v_true  = sigma * randn(1, N);
end


%Laplace Noise
if (strcmp(noise,'laplace'))
    temp = sigma*exprnd(1/sigma, 1,100);
    bin  = 2*(binornd(1, 0.5, 1, 100)-0.5);
    v_true = temp.*bin/sqrt(2);
end

if (strcmp(noise,'student'))
  v_true = trnd(4,1,100);
end

nI= binornd(1, conFreq, 1, N);
newNoise = sqrt(conVar)*randn(1,N);


z = x1_true + v_true + nI.*newNoise;

rk      = sigma * sigma;

rinvk   = 1 / rk;

rinv    = zeros(m, m, N);


for k = 1 : N
    if (mod(k, 2) == 0)
        rinv(:, :, k) = rinvk;
   end
end

% transition model

qinv    = zeros(n, n, N);
qk      = gamma * [ dt , dt^2/2 ; dt^2/2 , dt^3/3 ];
%qk       = gamma * [ dt^3/3 , dt^2/2 ; dt^2/2 , dt ];
qinvk   = inv(qk);

for k = 2 : N
     qinv(:,:, k)  = qinvk;
end

qinv(:,:,1) = 100*eye(n);
%


xZero = zeros(n, N);
% L1 Kalman-Bucy Smoother

w = 1;
[xOut, info] = ckbs_t_general(g_fun, h_fun, max_itr, epsilon, xZero, z, qinv, w*rinv, params);

ok = info(end, 3) < epsilon || info(1) < 1e-8;


% % --------------------------------------------------------------------------
% 
if draw_plot

     figure(1);

     clf

     hold on

     plot(t', x_true(1,:)', '-k', 'Linewidth', 2, 'MarkerEdgeColor', 'k' );

     meas = z(1,:);
     meas(meas > v_max) = v_max;
     meas(meas < v_min) = v_min;
     rvec = rinv(1,:);
     meas(rvec==0) = v_max + 5; % take non-measurements out
     plot(t', meas, 'bd',  'MarkerFaceColor', [.49 1 .63], 'MarkerSize', 6);

     %zWrong = z.*nI;
     %i = 0;
     %for(i = 1:N)
     %    if (zWrong(1,i) ~=0)
     %       plot(t(i)', zWrong(1,i)', 'rs',  'MarkerFaceColor', [.49 1 .63], 'MarkerSize', 4);
     %    end
     %end

     %plot(t', xOut(2,:)', 'r-' );
      
     
   %plot(t', xfilt(2,:)', '-b', 'Linewidth', 2, 'MarkerEdgeColor', 'k');
     
   plot(t', xOut(1,:)', '-m', 'Linewidth', 2, 'MarkerEdgeColor', 'k' );

%   plot(t', xOut2(2,:)', '-r', 'Linewidth', 2, 'MarkerEdgeColor', 'k');
     
%   plot(t', xOut3(2,:)', '-y', 'Linewidth', 2, 'MarkerEdgeColor', 'k');
 
%   plot(t', xOut4(2,:)', '-g', 'Linewidth', 2, 'MarkerEdgeColor', 'k');
 
%   plot(t', xOut5(2,:)', '-b', 'Linewidth', 2, 'MarkerEdgeColor', 'k');
 
   
   %plot(t', xsmooth(2,:)', '-m', 'Linewidth', 2, 'MarkerEdgeColor', 'k');
  
     %plot(t', xfilt(2,:)', '-b', 'Linewidth', 2, 'MarkerEdgeColor', 'k');
 
%     plot(t', - ones(N,1), 'b-');

%     plot(t', ones(N,1), 'b-');

%    xlabel('Time', 14);
 %   ylabel('Position', 'Fontsize', 14);
  %  set(gca,'FontSize',14);
     

  axis([h_min, h_max, v_min, v_max]);

  res_x = zeros(n, N);
  xk = zeros(n, 1);
  for k = 1 : N
      xkm   = xk;
      xk    = xOut(:, k);
      gk    = feval(g_fun, k, xkm);
      res_x(:,k)  = xk - gk;
  end
  
%   hold off
%   figure(2);
%   clf
%   hold on
%   plot(t', x_true(2,:)', '-k', 'Linewidth', 2, 'MarkerEdgeColor', 'k' );
%   plot(t', xOut(2,:), '-m', 'Linewidth', 2, 'MarkerEdgeColor', 'k' );
%   hold off
%   
%   figure(3);
%   clf 
%   hold on
%   
%   plot(t', res_x(1,:)', '-m', 'Linewidth', 2, 'MarkerEdgeColor', 'k' );
% 
%   hold off
% 
%   figure(4)
%   clf
%   hold on
%   plot(t', res_x(2,:)', '-b', 'Linewidth', 2, 'MarkerEdgeColor', 'k' );
%   hold off
  
  
end