% 2D FFT Reconstruction For A Line Sensor Example
%
% This example demonstrates the use of k-Wave for the reconstruction of a
% two-dimensional photoacoustic wave-field recorded  over a linear array of
% sensor elements  The sensor data is simulated using kspaceFirstOrder2D
% and reconstructed using kspaceLineRecon. It builds on the Homogeneous
% Propagation Medium and Heterogeneous Propagation Medium examples. 
%
% author: Bradley Treeby
% date: 2nd July 2009
% last update: 4th June 2017
%  
% This function is part of the k-Wave Toolbox (http://www.k-wave.org)
% Copyright (C) 2009-2017 Bradley Treeby

% This file is part of k-Wave. k-Wave is free software: you can
% redistribute it and/or modify it under the terms of the GNU Lesser
% General Public License as published by the Free Software Foundation,
% either version 3 of the License, or (at your option) any later version.
% 
% k-Wave is distributed in the hope that it will be useful, but WITHOUT ANY
% WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
% FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for
% more details. 
% 
% You should have received a copy of the GNU Lesser General Public License
% along with k-Wave. If not, see <http://www.gnu.org/licenses/>. 

clearvars;

% =========================================================================
% SIMULATION
% =========================================================================

% create the computational grid
PML_size = 20;          % size of the PML in grid points
Nx = 128 - 2*PML_size;  % number of grid points in the x (row) direction
Ny = 256 - 2*PML_size;  % number of grid points in the y (column) direction
dx = 0.1e-3;            % grid point spacing in the x direction [m]
dy = 0.1e-3;            % grid point spacing in the y direction [m]
kgrid = kWaveGrid(Nx, dx, Ny, dy);

% define the properties of the propagation medium
medium.sound_speed = 1500;           % [m/s]

% create initial pressure distribution using makeDisc
disc_magnitude = 5; % [Pa]
disc_x_pos = 60;    % [grid points]
disc_y_pos = 140;  	% [grid points]
disc_radius = 5;    % [grid points]
disc_2 = disc_magnitude * makeDisc(Nx, Ny, disc_x_pos, disc_y_pos, disc_radius);

disc_x_pos = 30;    % [grid points]
disc_y_pos = 110; 	% [grid points]
disc_radius = 8;    % [grid points]
disc_1 = disc_magnitude * makeDisc(Nx, Ny, disc_x_pos, disc_y_pos, disc_radius);

source.p0 = disc_1 + disc_2;

% smooth the initial pressure distribution and restore the magnitude
source.p0 = smooth(kgrid, source.p0, true);

% define a binary line sensor
sensor.mask = zeros(Nx, Ny);
sensor.mask(1, :) = 1;

% create the time array
kgrid.makeTime(medium.sound_speed);

% set the input arguements: force the PML to be outside the computational
% grid; switch off p0 smoothing within kspaceFirstOrder2D
input_args = {'PMLInside', false, 'PMLSize', PML_size, 'PlotPML', false, 'Smooth', false};

% run the simulation
sensor_data = kspaceFirstOrder2D(kgrid, medium, source, sensor, input_args{:});

% reconstruct the initial pressure
p_xy = kspaceLineRecon(sensor_data.', dy, kgrid.dt, medium.sound_speed, ...
    'Plot', true, 'PosCond', true, 'Interp', '*linear');

% define a second k-space grid using the dimensions of p_xy
[Nx_recon, Ny_recon] = size(p_xy);
kgrid_recon = kWaveGrid(Nx_recon, kgrid.dt * medium.sound_speed, Ny_recon, dy);

% resample p_xy to be the same size as source.p0
p_xy_rs = interp2(kgrid_recon.y, kgrid_recon.x - min(kgrid_recon.x(:)), p_xy, kgrid.y, kgrid.x - min(kgrid.x(:)));

% =========================================================================
% VISUALISATION
% =========================================================================

% plot the initial pressure and sensor distribution
figure;
imagesc(kgrid.y_vec * 1e3, kgrid.x_vec * 1e3, source.p0 + sensor.mask * disc_magnitude, [-disc_magnitude, disc_magnitude]);
colormap(getColorMap);
ylabel('x-position [mm]');
xlabel('y-position [mm]');
axis image;
colorbar;
scaleFig(1, 0.65);

% plot the simulated sensor data
figure;
imagesc(sensor_data, [-1, 1]);
colormap(getColorMap);
ylabel('Sensor Position');
xlabel('Time Step');
colorbar;

% plot the reconstructed initial pressure 
figure;
imagesc(kgrid.y_vec * 1e3, kgrid.x_vec * 1e3, p_xy_rs, [-disc_magnitude, disc_magnitude]);
colormap(getColorMap);
ylabel('x-position [mm]');
xlabel('y-position [mm]');
axis image;
colorbar;
scaleFig(1, 0.65);

% plot a profile for comparison
figure;
plot(kgrid.y_vec * 1e3, source.p0(disc_x_pos, :), 'k-', kgrid.y_vec * 1e3, p_xy_rs(disc_x_pos, :), 'r--');
xlabel('y-position [mm]');
ylabel('Pressure');
legend('Initial Pressure', 'Reconstructed Pressure');
axis tight;
set(gca, 'YLim', [0, 5.1]);