WheelockLab · pollaro · Apr 25, 2024 · Apr 22, 2024 · Apr 22, 2024 · Apr 22, 2024
diff --git a/+nla/+edge/+result/Base.m b/+nla/+edge/+result/Base.m
@@ -15,7 +15,8 @@
 
     methods
         function obj = Base(size, prob_max)
-            import nla.* % required due to matlab package system quirks
+            import nla.TriMatrix
+
             if nargin ~= 0
                 obj.coeff = TriMatrix(size);
                 obj.prob = TriMatrix(size);
@@ -25,7 +26,6 @@
         end
 
         function output(obj, net_atlas, flags, prob_label)
-            import nla.* % required due to matlab package system quirks
 
             coeff_label = sprintf('Edge-level %s', obj.coeff_name);
             prob_label_appended = '';
@@ -34,8 +34,8 @@ function output(obj, net_atlas, flags, prob_label)
                 prob_label_appended = sprintf(' (%s)', obj.behavior_name);
             end
 
-            fig = gfx.createFigure();
-            matrix_plot = gfx.plots.MatrixPlot(fig, coeff_label, obj.coeff, net_atlas.nets, gfx.FigSize.LARGE, 'lower_limit', obj.coeff_range(1),...
+            fig = nla.gfx.createFigure();
+            matrix_plot = nla.gfx.plots.MatrixPlot(fig, coeff_label, obj.coeff, net_atlas.nets, nla.gfx.FigSize.LARGE, 'lower_limit', obj.coeff_range(1),...
                 'upper_limit', obj.coeff_range(2));
             matrix_plot.displayImage();
             w = matrix_plot.image_dimensions("image_width");
@@ -45,7 +45,7 @@ function output(obj, net_atlas, flags, prob_label)
                 if ~exist('prob_label', 'var')
                     prob_label = [sprintf('Edge-level Significance (P < %g)', obj.prob_max), prob_label_appended];
                 end
-                matrix_plot2 = gfx.plots.MatrixPlot(fig, prob_label, obj.prob_sig, net_atlas.nets, gfx.FigSize.LARGE,...
+                matrix_plot2 = nla.gfx.plots.MatrixPlot(fig, prob_label, obj.prob_sig, net_atlas.nets, nla.gfx.FigSize.LARGE,...
                     'draw_legend', false, 'draw_colorbar', false, 'color_map', [[1,1,1];[0,0,0]], 'x_position', w, 'lower_limit', 0, 'upper_limit', 1);
                 w2 = matrix_plot2.image_dimensions("image_width");
                 h2 = matrix_plot2.image_dimensions("image_height");
@@ -58,7 +58,7 @@ function output(obj, net_atlas, flags, prob_label)
                 %prob_log.v = -1 * log10(obj.prob.v);
                 cm_base = parula(1000);
                 cm = flip(cm_base(ceil(logspace(-3, 0, 256) .* 1000), :));
-                matrix_plot2 = gfx.plots.MatrixPlot(fig, prob_label, obj.prob, net_atlas.nets, gfx.FigSize.LARGE,...
+                matrix_plot2 = nla.gfx.plots.MatrixPlot(fig, prob_label, obj.prob, net_atlas.nets, nla.gfx.FigSize.LARGE,...
                     'draw_legend', false, 'color_map', cm, 'x_position', w, 'lower_limit', 0, 'upper_limit', 1);
                 w2 = matrix_plot2.image_dimensions("image_width");
                 h2 = matrix_plot2.image_dimensions("image_height");

diff --git a/+nla/+edge/+result/Precalculated.m b/+nla/+edge/+result/Precalculated.m
@@ -3,8 +3,6 @@
 
     methods
         function obj = Precalculated(size, prob_max)
-            import nla.* % required due to matlab package system quirks
-            % hack because superclass constructor can't be optional??
             if nargin == 0
                 size = 2;
                 prob_max = -1;

diff --git a/+nla/+edge/+result/WelchT.m b/+nla/+edge/+result/WelchT.m
@@ -7,8 +7,6 @@
 
     methods
         function obj = WelchT(size, prob_max, group_names)
-            import nla.* % required due to matlab package system quirks
-            % hack because superclass constructor can't be optional??
             if nargin == 0
                 size = 2;
                 prob_max = -1;
@@ -18,7 +16,7 @@
             obj@nla.edge.result.Base(size, prob_max);
 
             if nargin ~= 0
-                obj.dof = TriMatrix(size);
+                obj.dof = nla.TriMatrix(size);
                 obj.behavior_name = sprintf("%s > %s", group_names{1}, group_names{2});
                 obj.coeff_range = [-3 3];
             end

diff --git a/+nla/+edge/+test/KendallB.m b/+nla/+edge/+test/KendallB.m
@@ -7,13 +7,11 @@
 
     methods
         function obj = KendallB()
-            import nla.* % required due to matlab package system quirks
             obj@nla.edge.BaseTest();
         end
 
         function result = run(obj, input_struct)
-            import nla.* % required due to matlab package system quirks
-            [tau_vec, p_vec] = mex.run('kendallTauB', input_struct.behavior, input_struct.func_conn.v');
+            [tau_vec, p_vec] = nla.mex.run('kendallTauB', input_struct.behavior, input_struct.func_conn.v');
             result = obj.composeResult(input_struct.net_atlas, tau_vec', p_vec', input_struct.prob_max);
         end
     end

diff --git a/+nla/+edge/+test/Pearson.m b/+nla/+edge/+test/Pearson.m
@@ -7,7 +7,6 @@
 
     methods
         function obj = Pearson()
-            import nla.* % required due to matlab package system quirks
             obj@nla.edge.BaseTest();
         end
 

diff --git a/+nla/+edge/+test/Precalculated.m b/+nla/+edge/+test/Precalculated.m
@@ -7,7 +7,6 @@
 
     methods
         function obj = Precalculated()
-            import nla.* % required due to matlab package system quirks
             obj@nla.edge.BaseTest();
         end
 
@@ -31,10 +30,19 @@
 
     methods (Static)
         function inputs = requiredInputs()
-            import nla.* % required due to matlab package system quirks
-            npairs_x_nperms = [inputField.DimensionType.NROIPAIRS, inputField.DimensionType.NPERMS];
-            npairs_x_1 = [inputField.DimensionType.NROIPAIRS, 1];
-            inputs = {inputField.NumberWithoutDefault('coeff_min', 'Coeff minimum', -Inf, Inf), inputField.NumberWithoutDefault('coeff_max', 'Coeff maximum', -Inf, Inf), inputField.NetworkAtlas(), inputField.EdgeLevelMatrix('precalc_obs_p', 'Precalculated observed significance (thresholded p-value)', npairs_x_1), inputField.EdgeLevelMatrix('precalc_obs_coeff', 'Precalculated observed coeff', npairs_x_1), inputField.EdgeLevelMatrix('precalc_perm_p', 'Precalculated permuted significance (thresholded p-value)', npairs_x_nperms), inputField.EdgeLevelMatrix('precalc_perm_coeff', 'Precalculated permuted coeff', npairs_x_nperms)};
+            import nla.inputField.DimensionType nla.inputField.NumberWithoutDefault nla.inputField.EdgeLevelMatrix
+
+            npairs_x_nperms = [DimensionType.NROIPAIRS,DimensionType.NPERMS];
+            npairs_x_1 = [DimensionType.NROIPAIRS, 1];
+            inputs = {...
+                NumberWithoutDefault('coeff_min', 'Coeff minimum', -Inf, Inf),...
+                NumberWithoutDefault('coeff_max', 'Coeff maximum', -Inf, Inf),...
+                inputField.NetworkAtlas(),...
+                EdgeLevelMatrix('precalc_obs_p', 'Precalculated observed significance (thresholded p-value)', npairs_x_1),...
+                EdgeLevelMatrix('precalc_obs_coeff', 'Precalculated observed coeff', npairs_x_1),...
+                EdgeLevelMatrix('precalc_perm_p', 'Precalculated permuted significance (thresholded p-value)', npairs_x_nperms),...
+                EdgeLevelMatrix('precalc_perm_coeff', 'Precalculated permuted coeff', npairs_x_nperms)...
+            };
         end
     end
 end

diff --git a/+nla/+edge/+test/Spearman.m b/+nla/+edge/+test/Spearman.m
@@ -7,7 +7,6 @@
 
     methods
         function obj = Spearman()
-            import nla.* % required due to matlab package system quirks
             obj@nla.edge.BaseTest();
         end
 

diff --git a/+nla/+edge/+test/SpearmanEstimator.m b/+nla/+edge/+test/SpearmanEstimator.m
@@ -21,12 +21,10 @@
 
     methods
         function obj = SpearmanEstimator()
-            import nla.* % required due to matlab package system quirks
             obj@nla.edge.BaseTest();
         end
 
         function result = run(obj, input_struct)
-            import nla.* % required due to matlab package system quirks
             %% input
             y = input_struct.func_conn.v';
 
@@ -78,7 +76,7 @@
             ok = (abs(rho_vec) < 1);
             t(ok) = rho_vec(ok) .* sqrt((n - 2) ./ (1 - rho_vec(ok) .^ 2));
 
-            result = obj.composeResult(input_struct.net_atlas, fisherR2Z(rho_vec), (2 * tcdf(-abs(t), n - 2)), input_struct.prob_max);
+            result = obj.composeResult(input_struct.net_atlas, nla.fisherR2Z(rho_vec), (2 * tcdf(-abs(t), n - 2)), input_struct.prob_max);
         end
     end
 end

diff --git a/+nla/+edge/+test/WelchT.m b/+nla/+edge/+test/WelchT.m
@@ -9,12 +9,10 @@
 
     methods
         function obj = WelchT()
-            import nla.* % required due to matlab package system quirks
             obj@nla.edge.BaseTest();
         end
 
         function result = run(obj, input_struct)
-            import nla.* % required due to matlab package system quirks
             % This function calculates the t-test between 2 sets of data using the
             % Welch method that does not assume equal mean or variance or samples. The
             % function returns the t-statistic, p-value, and degrees of freedom.
@@ -48,17 +46,16 @@
 
     methods (Static)
         function inputs = requiredInputs()
-            import nla.* % required due to matlab package system quirks
             inputs = requiredInputs@nla.edge.BaseTest();
 
             % disable adding/modifying covariates in behavior
-            behavior_handle = helpers.firstInstanceOfClass(inputs, 'nla.inputField.Behavior');
+            behavior_handle = nla.helpers.firstInstanceOfClass(inputs, 'nla.inputField.Behavior');
             behavior_handle.covariates_enabled = nla.inputField.CovariatesEnabled.ONLY_FC;
 
-            inputs{end + 1} = inputField.String('group1_name', 'Group 1 name:', 'Group1');
-            inputs{end + 1} = inputField.Number('group1_val', 'Group 1 behavior value:', -Inf, 1, Inf);
-            inputs{end + 1} = inputField.String('group2_name', 'Group 2 name:', 'Group2');
-            inputs{end + 1} = inputField.Number('group2_val', 'Group 2 behavior value:', -Inf, 0, Inf);
+            inputs{end + 1} = nla.inputField.String('group1_name', 'Group 1 name:', 'Group1');
+            inputs{end + 1} = nla.inputField.Number('group1_val', 'Group 1 behavior value:', -Inf, 1, Inf);
+            inputs{end + 1} = nla.inputField.String('group2_name', 'Group 2 name:', 'Group2');
+            inputs{end + 1} = nla.inputField.Number('group2_val', 'Group 2 behavior value:', -Inf, 0, Inf);
         end
     end
 end

diff --git a/+nla/+edge/BaseTest.m b/+nla/+edge/BaseTest.m
@@ -7,7 +7,6 @@
 
     methods
         function obj = BaseTest()
-            import nla.* % required due to matlab package system quirks
         end
     end
 
@@ -33,8 +32,8 @@
 
     methods (Static)
         function inputs = requiredInputs()
-            import nla.* % required due to matlab package system quirks
-            inputs = {inputField.Number('prob_max', 'Edge-level P threshold <', 0, 0.05, 1), inputField.NetworkAtlasFuncConn(), inputField.Behavior()};
+            inputs = {nla.inputField.Number('prob_max', 'Edge-level P threshold <', 0, 0.05, 1),...
+                nla.inputField.NetworkAtlasFuncConn(), nla.inputField.Behavior()};
         end
     end
 end
diff --git a/+nla/+gfx/anatToMesh.m b/+nla/+gfx/anatToMesh.m
@@ -1,20 +1,21 @@
 function [mesh_l, mesh_r] = anatToMesh(anat, ctx, view_pos)
+    import nla.gfx.ViewPos nla.gfx.MeshType
+
     %ANATTOMESH generate cortex hemisphere mesh from anatomy
     %   anat: anatomy struct, contained in cortex mesh files
     %   ctx: MeshType value, what inflation level of mesh to use
     %   view_pos: ViewPos value, which standard view to use
     %   mesh_l: mesh of left hemisphere of brain
     %   mesh_r: mesh of right hemisphere of brain
-    import nla.* % required due to matlab package system quirks
     %% Choose inflation
     switch ctx
-        case gfx.MeshType.STD
+        case MeshType.STD
             mesh_l = anat.hemi_l.nodes;
             mesh_r = anat.hemi_r.nodes;
-        case gfx.MeshType.INF
+        case MeshType.INF
             mesh_l = anat.hemi_l.Inodes;
             mesh_r = anat.hemi_r.Inodes;
-        case gfx.MeshType.VINF
+        case MeshType.VINF
             mesh_l = anat.hemi_l.VInodes;
             mesh_r = anat.hemi_r.VInodes;
     end
@@ -24,18 +25,18 @@
     mesh_r(:,1) = mesh_r(:,1) - min(mesh_r(:,1));
 
     %% Rotate if necessary
-    if view_pos == gfx.ViewPos.LAT || view_pos == gfx.ViewPos.MED
+    if view_pos == ViewPos.LAT || view_pos == ViewPos.MED
         dy = -5;
         % rotate right hemi around and move to position for visualization
         cmL = mean(mesh_l, 1);
         cmR = mean(mesh_r, 1);
-        rm = helpers.rotationMatrix(Dir.Z, pi);
+        rm = nla.helpers.rotationMatrix(Dir.Z, pi);
 
         % Rotate
         switch view_pos
-            case gfx.ViewPos.LAT
+            case ViewPos.LAT
                 mesh_r = (mesh_r - (repmat(cmR, size(mesh_r, 1), 1))) * rm + (repmat(cmR, size(mesh_r, 1), 1));             
-            case gfx.ViewPos.MED
+            case ViewPos.MED
                 mesh_l = (mesh_l - (repmat(cmL, size(mesh_l, 1), 1))) * rm + (repmat(cmL, size(mesh_l, 1), 1));              
         end
         mesh_r(:, 1) = mesh_r(:, 1) + (cmL(:, 1) - cmR(:, 1));    % Shift over to same YZ plane

diff --git a/+nla/+gfx/colorChunk.m b/+nla/+gfx/colorChunk.m
@@ -1,5 +1,4 @@
 function chunk = colorChunk(color, h, w)
-    import nla.* % required due to matlab package system quirks
     %COLORCHUNK create a matrix of size h * w * 3, filled with color
     chunk = repmat(reshape(color,1,1,[]), h, w);
 end

diff --git a/+nla/+gfx/createFigure.m b/+nla/+gfx/createFigure.m
@@ -3,7 +3,7 @@
     %toolbar, etc. settings.
     %   w: width in pixels
     %   h: height in pixels
-    import nla.* % required due to matlab package system quirks
+
     fig = figure('Color', 'w', 'Resize', 'off', 'Name', 'NLA Figure', 'NumberTitle','off');
     %fig.Icon = [findRootPath() 'thumb.png'];
     if exist('w', 'var') && exist('h', 'var') 

diff --git a/+nla/+gfx/drawBrainVis.m b/+nla/+gfx/drawBrainVis.m
@@ -13,8 +13,6 @@ function drawBrainVis(edge_input_struct, input_struct, net_atlas, ctx, mesh_alph
     %   net2: Network to view correlations between
     %   sig_based: whether the net-level test is based on p-values
     %       thresholded by significance (for example, Chi2, HG)
-
-    import nla.* % required due to matlab package system quirks
 
     fc_exists = isfield(edge_input_struct, 'func_conn');
 
@@ -26,7 +24,7 @@ function drawBrainVis(edge_input_struct, input_struct, net_atlas, ctx, mesh_alph
     end
 
     %% Display figures 
-    fig = gfx.createFigure(1550, 750);
+    fig = nla.gfx.createFigure(1550, 750);
     figure_title = sprintf('Brain Visualization: Average of edge-level correlations between nets in [%s - %s] Network Pair', net_atlas.nets(net1).name, net_atlas.nets(net2).name);
 
     if sig_based
@@ -98,13 +96,15 @@ function drawBrainVis(edge_input_struct, input_struct, net_atlas, ctx, mesh_alph
     end
 
     function cols = valsToColor(ROI_vals, fc_vals, color_map, color_map_p, color_map_n, color_fc, llimit, ulimit)
+        import nla.gfx.valToColor
+
         if color_fc
-            cols_p = gfx.valToColor(fc_vals, -0.5, 0.5, color_map_p);
-            cols_n = gfx.valToColor(fc_vals, -0.5, 0.5, color_map_n);
+            cols_p = valToColor(fc_vals, -0.5, 0.5, color_map_p);
+            cols_n = valToColor(fc_vals, -0.5, 0.5, color_map_n);
             cols(ROI_vals > 0, :) = cols_p(ROI_vals > 0, :);
             cols(ROI_vals <= 0, :) = cols_n(ROI_vals <= 0, :);
         else
-            cols = gfx.valToColor(ROI_vals, llimit, ulimit, color_map);
+            cols = valToColor(ROI_vals, llimit, ulimit, color_map);
         end
     end
 
@@ -121,7 +121,7 @@ function drawROISpheres(ROI_pos, ax, net_atlas, net1, net2, ROI_radius, conn_map
 
                 if conn_map(j)
                     % render a sphere at each ROI location
-                    gfx.drawSphere(ax, ROI_pos(j, :), net_atlas.nets(n).color, ROI_radius);
+                    nla.gfx.drawSphere(ax, ROI_pos(j, :), net_atlas.nets(n).color, ROI_radius);
                 end
             end
         end
@@ -148,19 +148,23 @@ function drawEdges(ROI_pos, ax, net_atlas, net1, net2, color_map, color_map_p, c
                 if ~isempty(val)
                     col = valsToColor(val, fc_val_avg, color_map, color_map_p, color_map_n, color_fc && fc_exists, llimit, ulimit);
                     col = [reshape(col, [1, 3]), 0.5];
-                    p = plot3(ax, [ROI_pos(n1, 1), ROI_pos(n2, 1)], [ROI_pos(n1, 2), ROI_pos(n2, 2)], [ROI_pos(n1, 3), ROI_pos(n2, 3)], 'Color', col, 'LineWidth', 5);
+                    p = plot3(ax, [ROI_pos(n1, 1), ROI_pos(n2, 1)], [ROI_pos(n1, 2), ROI_pos(n2, 2)],...
+                        [ROI_pos(n1, 3), ROI_pos(n2, 3)], 'Color', col, 'LineWidth', 5);
                     p.Annotation.LegendInformation.IconDisplayStyle = 'off';
                 end
             end
         end
     end
 
     function onePlot(ax, pos, color_mode, color_mat)
-        if color_mode == gfx.BrainColorMode.NONE
-            ROI_final_pos = gfx.drawROIsOnCortex(ax, net_atlas, ctx, mesh_alpha, ROI_radius, pos, surface_parcels, gfx.BrainColorMode.NONE);
-            drawEdges(ROI_final_pos, ax, net_atlas, net1, net2, color_map, color_map_p, color_map_n, color_fc, fc_exists, llimit, ulimit, edge_input_struct, edge_result, sig_based);
+        if color_mode == nla.gfx.BrainColorMode.NONE
+            ROI_final_pos = nla.gfx.drawROIsOnCortex(ax, net_atlas, ctx, mesh_alpha, ROI_radius, pos, surface_parcels,...
+                nla.gfx.BrainColorMode.NONE);
+            drawEdges(ROI_final_pos, ax, net_atlas, net1, net2, color_map, color_map_p, color_map_n, color_fc, fc_exists,...
+                llimit, ulimit, edge_input_struct, edge_result, sig_based);
         else
-            ROI_final_pos = gfx.drawROIsOnCortex(ax, net_atlas, ctx, 1, ROI_radius, pos, surface_parcels, gfx.BrainColorMode.COLOR_ROIS, color_mat);
+            ROI_final_pos = nla.gfx.drawROIsOnCortex(ax, net_atlas, ctx, 1, ROI_radius, pos, surface_parcels,...
+                nla.gfx.BrainColorMode.COLOR_ROIS, color_mat);
         end
 
         if show_ROI_centroids
@@ -169,13 +173,13 @@ function onePlot(ax, pos, color_mode, color_mat)
     end
 
     if surface_parcels && ~islogical(net_atlas.parcels)
-        onePlot(subplot('Position',[.45,0.505,.53,.45]), gfx.ViewPos.LAT, gfx.BrainColorMode.COLOR_ROIS, color_mat);
-        onePlot(subplot('Position',[.45,0.055,.53,.45]), gfx.ViewPos.MED, gfx.BrainColorMode.COLOR_ROIS, color_mat);
+        onePlot(subplot('Position',[.45,0.505,.53,.45]), nla.gfx.ViewPos.LAT, nla.gfx.BrainColorMode.COLOR_ROIS, color_mat);
+        onePlot(subplot('Position',[.45,0.055,.53,.45]), nla.gfx.ViewPos.MED, nla.gfx.BrainColorMode.COLOR_ROIS, color_mat);
     else
-        onePlot(subplot('Position',[.45,0.505,.26,.45]), gfx.ViewPos.BACK, gfx.BrainColorMode.NONE);
-        onePlot(subplot('Position',[.73,0.505,.26,.45]), gfx.ViewPos.FRONT, gfx.BrainColorMode.NONE);
-        onePlot(subplot('Position',[.45,0.055,.26,.45]), gfx.ViewPos.LEFT, gfx.BrainColorMode.NONE);
-        onePlot(subplot('Position',[.73,0.055,.26,.45]), gfx.ViewPos.RIGHT, gfx.BrainColorMode.NONE);
+        onePlot(subplot('Position',[.45,0.505,.26,.45]), nla.gfx.ViewPos.BACK, nla.gfx.BrainColorMode.NONE);
+        onePlot(subplot('Position',[.73,0.505,.26,.45]), nla.gfx.ViewPos.FRONT, nla.gfx.BrainColorMode.NONE);
+        onePlot(subplot('Position',[.45,0.055,.26,.45]), nla.gfx.ViewPos.LEFT, nla.gfx.BrainColorMode.NONE);
+        onePlot(subplot('Position',[.73,0.055,.26,.45]), nla.gfx.ViewPos.RIGHT, nla.gfx.BrainColorMode.NONE);
     end
 
     if color_fc
@@ -185,9 +189,9 @@ function onePlot(ax, pos, color_mode, color_mat)
     end
 
     if surface_parcels && ~islogical(net_atlas.parcels)
-        onePlot(ax, gfx.ViewPos.DORSAL, gfx.BrainColorMode.COLOR_ROIS, color_mat);
+        onePlot(ax, nla.gfx.ViewPos.DORSAL, nla.gfx.BrainColorMode.COLOR_ROIS, color_mat);
     else
-        onePlot(ax, gfx.ViewPos.DORSAL, gfx.BrainColorMode.NONE);
+        onePlot(ax, nla.gfx.ViewPos.DORSAL, nla.gfx.BrainColorMode.NONE);
     end
 
     light('Position',[0,100,100],'Style','local');
@@ -206,7 +210,7 @@ function onePlot(ax, pos, color_mode, color_mat)
         end
     end
     hold(ax, 'off'); 
-    gfx.hideAxes(ax);
+    nla.gfx.hideAxes(ax);
 
     %% Display colormap
     if color_fc
-Original file line number
+Diff line change
@@ Expand Up / @@ -7,7 +7,6 @@ @@
         methods
             function obj = Pearson()
-                import nla.* % required due to matlab package system quirks
                 obj@nla.edge.BaseTest();
             end
@@ Expand Down @@