Update to v1.0.1

+merit/+visualize/display_3D_scan.m → +merit/+display/as_3D_scan.m

@@ -1,4 +1,4 @@
-function display_3D_scan(grid_, options)
+function as_3D_scan(grid_, options)
 % Display a 3D colormap render of a (:, :, :) grid.
 % The density is relative to the highest and lowest numbers.
 % Higher density means higher opacity.
@@ -8,7 +8,7 @@ function display_3D_scan(grid_, options)
 
     % --- Optional arguments---
 
-    % Specify figure to display. "0" means don't specify
+    % Specify figure to display. "0" means create new figure.
     options.figure_number (1, 1) {mustBeGreaterThanOrEqual(options.figure_number, 0)} ...
         = 0
 
@@ -68,7 +68,7 @@ function display_3D_scan(grid_, options)
 xlabel('X-axis');
 ylabel('Y-axis');
 zlabel('Z-axis');
-title('3D Scan Density Visualization');
+title('3D Scan');
 view(3); % Set the view to 3D
 axis tight; % Fit the axes to the data
 grid on; % Turn on the grid

+merit/+beamform/+windows/rectangular.m → +merit/+domain/+windows/rectangular.m

@@ -1,5 +1,5 @@
 function [get_signals] = rectangular(window_length),
   get_signals = @(signals) ...
-    merit.utility.reshape2d(@(signals) signals(1:window_length, :)...
+    merit.process.reshape2d(@(signals) signals(1:window_length, :)...
       , signals);
 end

+merit/+beamform/imaging_domain.m → +merit/+domain/get_points.m

@@ -1,4 +1,4 @@
-function [points] = imaging_domain(domain, varargin)
+function [points] = get_points(domain, varargin)
   if numel(varargin) < 1
     ndim = ndims(domain);
   else

+merit/+domain/hemisphere.m

@@ -84,7 +84,7 @@
 end
 
 % use area and axes to get the set of points in the imaging domain only
-points = merit.beamform.imaging_domain(area_, axes_{:});
+points = merit.domain.get_points(area_, axes_{:});
 end
 
 function mustBeNumericOrRealVectorOrEmpty(a)

+merit/+example_scripts/create_ummid_render.m

@@ -79,21 +79,21 @@
 number_antennas = size(scan_data, 3); % The number of antenna locations.
 starting_antenna_angle = deg2rad(-130);
 
-antenna_locations = merit.domain.create_circumference(...
+antenna_locations = merit.domain.circumference(...
     ant_phase_rad, ...
     number_antennas, ...
     starting_antenna_angle...
     );
 
 %% Calculate delays
-c_0 = 299792458; % Vaccuum speed, taken from "merit.beamform.get_delays.m"
+c_0 = 299792458; % Vaccuum speed, taken from "merit.get_delay.m"
 %{
 The relative permittivity was measured by calculating the speed for every
 s11 scan in "gen-three/clean". Then dividing the mean of that by...
 the speed of a vaccuum to get relative permittivity.
 %}
 relative_permittivity = 1.0932; 
-delays_temp = merit.beamform.get_delays([1:number_antennas; 1:number_antennas]', antenna_locations, relative_permittivity=relative_permittivity);
+delays_temp = merit.get_delay([1:number_antennas; 1:number_antennas]', antenna_locations, relative_permittivity=relative_permittivity);
 
 %{
 Apply extra time delay for monostatic. Constant taken from T. Reimer's measurements.

+merit/+example_scripts/measure_performance.m

@@ -35,7 +35,7 @@
 
     [points, ~] = merit.domain.hemisphere(7e-2, pixel_dim=50);
 
-    delays = merit.beamform.get_delays(channel_names, antenna_locations, relative_permittivity=8);
+    delays = merit.get_delay(channel_names, antenna_locations, relative_permittivity=8);
     loaded_delays = delays(points);
 end
 

plot_mag_and_phase.m → +merit/+example_scripts/plot_mag_and_phase.m

@@ -1,7 +1,27 @@
 % A basic guide to loading and visualising the sample data;
 
-% Load example data
-load_example_data()
+%% Load example data
+% Details of the breast phantoms used to collect the sample data
+% are given in "Microwave Breast Imaging: experimental
+% tumour phantoms for the evaluation of new breast cancer diagnosis
+% systems", 2018 Biomed. Phys. Eng. Express 4 025036.
+% The antenna locations, frequency points and scattered signals
+% are given in the "example_data/" folder:
+
+% frequencies.csv: the frequency points in Hertz;
+frequencies = dlmread('example_data/frequencies.csv');
+% antenna_locations.csv: the antenna locations in metres;
+antenna_locations = dlmread('example_data/antenna_locations.csv');
+% channel_names.csv: the descriptions of the channels in the scattered data;
+channel_names = dlmread('example_data/channel_names.csv');
+
+% Select the signal data from a range of different scans.
+ph = "B0"; % Choose breast phantom from the following: {'B0','B10E','B15E','B20E','B30E'}.
+pl = 3; % Choose tumour from list of 1 to 22.
+[scan1, scan2] = load_example_scan(ph, pl);
+
+% Perform rotation subtraction
+signals = scan1-scan2;
 
 %% Plot the acquired scans.
 figure()

+merit/+metrics/G_GSS.m → +merit/+metric/G_GSS.m

@@ -1,10 +1,10 @@
 function [f] = G_GSS(imgs, points, axes_),
-  %merit.beamform.un_imaging_domain(img, points, axes_)
   window_size = 15;
   nPoints = size(points, 1);
 
   inds = (1:nPoints)';
-  grid_ = merit.beamform.un_imaging_domain(inds, points, axes_{:});
+  % There is/was never an un_imaging_domain.m, so this is broken.
+  grid_ = merit.un_imaging_domain(inds, points, axes_{:});
   if isa(imgs, 'single')
     grid_ = single(grid_);
   end

+merit/+process/delay.m

@@ -21,26 +21,28 @@
     padding = @nan;
   end
 
+  [signals, delays] = merit.process.expand2(signals, delays);
+
   if isreal(signals)
     %% Time domain
     if nargin == 2
       %% time domain, delay in samples
       validateattributes(delays, {'numeric'}, {'integer'});
-      signals_ = merit.utility.reshape2d(@delay_sample, signals, delays);
+      signals_ = merit.process.reshape2d(@delay_sample, signals, delays);
     elseif nargin >= 3 & numel(axis_) == size(signals, 1)
       %% Time axis provided
       validateattributes(axis_, {'numeric'},...
         {'vector', 'increasing', 'real'});
-      if ~merit.utility.linearlysampled(axis_)
+      if ~merit.process.linearlysampled(axis_)
         error('merit:process:delay', 'Time axis needs to be linearly sampled');
       end
       dt = diff(axis_(1:2));
-      signals_ = merit.utility.reshape2d(@delay_sample, signals, round(delays./dt));
+      signals_ = merit.process.reshape2d(@delay_sample, signals, round(delays./dt));
     elseif nargin == 3 & isa(axis_, 'function_handle')
       % Padding provided
       padding = axis_;
       validateattributes(delays, {'numeric'}, {'integer'});
-      signals_ = merit.utility.reshape2d(@delay_sample, signals, delays);
+      signals_ = merit.process.reshape2d(@delay_sample, signals, delays);
     end
   elseif ~isreal(signals) && nargin == 3
     %% Frequency domain

+merit/+utility/expand.m → +merit/+process/expand.m

@@ -8,7 +8,7 @@
   for c = 1:size(sizes, 2)
     u_sizes = unique(sizes(:, c));
     if numel(u_sizes) ~= 1 && u_sizes(1) ~= 1
-      error('merit:utility:expand', 'Dimensions need to be the same or one for all inputs.');
+      error('Dimensions need to be the same or one for all inputs.');
     end
   end
 

+merit/+process/expand2.m

@@ -0,0 +1,21 @@
+function [varargout] = expand2(varargin)
+  dims = cellfun(@ndims, varargin);
+  sizes = ones(numel(varargin), max(dims));
+  for v = 1:numel(varargin)
+    sizes(v, 1:dims(v)) = size(varargin{v});
+  end
+
+  for c = 2:size(sizes, 2)
+    u_sizes = unique(sizes(:, c));
+    if numel(u_sizes) ~= 1 && u_sizes(1) ~= 1
+      error('merit:utility:expand', 'Dimensions need to be the same or one for all inputs.');
+    end
+  end
+
+  varargout = varargin(1:nargout);
+  repeats = max(sizes)./sizes;
+  repeats(:, 1) = 1;
+  for v = 1:numel(varargout)
+    varargout{v} = repmat(varargout{v}, repeats(v, :));
+  end
+end

+merit/+process/fd2td.m

@@ -20,15 +20,15 @@
   validateattributes(time_axis, {'numeric'},...
     {'vector', 'increasing', 'real'});
 
-  if ~merit.utility.linearlysampled(time_axis)
+  if ~merit.process.linearlysampled(time_axis)
     error('merit:process:fd2td', 'Time axis needs to be linearly sampled');
   end
-  if ~merit.utility.linearlysampled(frequency_axis)
+  if ~merit.process.linearlysampled(frequency_axis)
     error('merit:process:fd2td', 'Frequency axis needs to be linearly sampled');
   end
 
   %% Accommodate trailing dimensions
-  signals_ = merit.utility.reshape2d(@fd2td_, signals);
+  signals_ = merit.process.reshape2d(@fd2td_, signals);
 
   function [signals_] = fd2td_(signals)
     time_axis = time_axis(time_axis >= 0);
@@ -40,7 +40,7 @@
     w = exp(-1j*2*pi*dt*df);
     a = exp(1j*2*pi*min(time_axis(:))*df);
 
-    iczt = @(z) merit.utility.under(@(z) czt(z, m, w, a), @conj, @conj, z);
+    iczt = @(z) merit.process.under(@(z) czt(z, m, w, a), @conj, @conj, z);
 
     % Phase compensation
     phase = exp(1j*2*pi*min(frequency_axis(:)).*time_axis(:));

+merit/+utility/reshape2d.m → +merit/+process/reshape2d.m

@@ -11,12 +11,12 @@
   for v = 2:numel(varargin)
     size_ = size(varargin{v});
     if ~isequal(size_(2:end), original_size(2:end)),
-      error('merit:utility:reshape2d', 'Input arrays must have the same trailing dimension sizes');
+      error('Input arrays must have the same trailing dimension sizes');
     end
   end
 
   flatten = @(a) reshape(a, size(a, 1), []);
   restore = @(a) reshape(a, [size(a, 1), original_size(2:end)]);
 
-  result = merit.utility.under(f, flatten, restore, varargin{:});
+  result = merit.process.under(f, flatten, restore, varargin{:});
 end

+merit/+process/td2fd.m

@@ -20,15 +20,15 @@
   validateattributes(frequency_axis, {'numeric'},...
     {'vector', 'increasing', 'real'});
 
-  if ~merit.utility.linearlysampled(time_axis)
+  if ~merit.process.linearlysampled(time_axis)
     error('merit:process:td2fd', 'Time axis needs to be linearly sampled');
   end
-  if ~merit.utility.linearlysampled(frequency_axis)
+  if ~merit.process.linearlysampled(frequency_axis)
     error('merit:process:td2fd', 'Frequency axis needs to be linearly sampled');
   end
 
   %% Accommodate trailing dimensions
-  signals_ = merit.utility.reshape2d(@td2fd_, signals);
+  signals_ = merit.process.reshape2d(@td2fd_, signals);
 
   function [signals_] = td2fd_(signals)
     dt = diff(time_axis(1:2));

+merit/+process/txrx2ind.m

@@ -8,12 +8,12 @@
   if ~exist('rx', 'var')
     rx = tx;
   else
-    [tx, rx] = merit.utility.expand(tx, rx);
+    [tx, rx] = merit.process.expand(tx, rx);
   end
 
   nChannels = size(channels, 1);
 
-  inds = merit.utility.under(@get_inds, @(a) a(:), @(a) reshape(a, size(tx)), tx, rx);
+  inds = merit.process.under(@get_inds, @(a) a(:), @(a) reshape(a, size(tx)), tx, rx);
 
   function [inds] = get_inds(tx, rx)
     s = [nChannels, numel(tx)];

+merit/+test/+from_list/+beamform/test_beamform.m

@@ -20,9 +20,9 @@
       [pulse_td, pulse_fd] = DG(3e9, 1/3e9, time_axis, frequencies);
       signals = single(merit.process.shape(data, pulse_fd, frequencies, time_axis));
 
-      delay_func = merit.beamform.get_delays(channels, antenna_locations, relative_permittivity=8);
+      delay_func = merit.get_delay(channels, antenna_locations, relative_permittivity=8);
 
-      img = merit.beamform(signals, time_axis, points, delay_func, merit.beamformers.DAS, window= merit.beamform.windows.rectangular(150));
+      img = merit.beamform(signals, time_axis, points, delay_func, merit.beamformer.DAS, window=merit.domain.windows.rectangular(150));
       [~, i] = max(img);
       [t, r, z] = cart2pol(points(i, 1), points(i, 2), points(i, 3));
 
@@ -40,9 +40,9 @@
       data = single(data(F, :));
       frequencies = frequencies(F);
 
-      delay_func = merit.beamform.get_delays(channels, antenna_locations, relative_permittivity=8);
+      delay_func = merit.get_delay(channels, antenna_locations, relative_permittivity=8);
 
-      img = abs(merit.beamform(data, frequencies, points, delay_func, merit.beamformers.DAS));
+      img = abs(merit.beamform(data, frequencies, points, delay_func, merit.beamformer.DAS));
       [~, i] = max(img);
       [t, r, z] = cart2pol(points(i, 1), points(i, 2), points(i, 3));
       testCase.verifyLessThan(abs(rad2deg(t)-location(1)), 9);