WheelockLab · pollaro · Jun 7, 2024 · Jun 7, 2024
diff --git a/+nla/+edge/+result/Base.m b/+nla/+edge/+result/Base.m
@@ -40,10 +40,7 @@ function output(obj, net_atlas, flags, prob_label)
             matrix_plot.displayImage();
             w = matrix_plot.image_dimensions("image_width");
             h = matrix_plot.image_dimensions("image_height");
-
-            if ~isfield(flags, 'display_sig')
-                flags.display_sig = true;
-            end
+%           
             if flags.display_sig
                 if ~exist('prob_label', 'var')
                     prob_label = [sprintf('Edge-level Significance (P < %g)', obj.prob_max), prob_label_appended];

diff --git a/+nla/+edge/+test/Precalculated.m b/+nla/+edge/+test/Precalculated.m
@@ -37,7 +37,7 @@
             inputs = {...
                 NumberWithoutDefault('coeff_min', 'Coeff minimum', -Inf, Inf),...
                 NumberWithoutDefault('coeff_max', 'Coeff maximum', -Inf, Inf),...
-                nla.inputField.NetworkAtlas(),...
+                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),...

diff --git a/+nla/+edge/+test/WelchT.m b/+nla/+edge/+test/WelchT.m
@@ -29,7 +29,7 @@
             x1 = input_struct.func_conn.v(:, group1);
             x2 = input_struct.func_conn.v(:, group2);
 
-            [p_vec, t_vec, dof_vec] = nla.welchT(x1, x2);
+            [p_vec, t_vec, dof_vec] = welchT(x1, x2);
 
             % Non-permuted
             group_names = {input_struct.group1_name, input_struct.group2_name};

diff --git a/+nla/+gfx/+plots/MatrixPlot.m b/+nla/+gfx/+plots/MatrixPlot.m
@@ -53,10 +53,6 @@
             "Bone", "Copper", "Pink"}; % Colorbar choices
     end
 
-    properties (SetAccess = immutable)
-        original_matrix % The original matrix for scaling purposes
-    end
-
     methods
         function obj = MatrixPlot(figure, name, matrix, networks, figure_size, varargin)
             % MatrixPlot constructor
@@ -116,7 +112,7 @@
                     end
                 end
             end
-            obj.original_matrix = matrix;
+
         end
 
         function displayImage(obj)
@@ -559,7 +555,7 @@ function createColorbar(obj, varargin)
         function openModal(obj, source, ~)
             % Callback for clicking on the colorbar.
             % This opens a modal with the upper and lower bounds along with a radio selector between linear and 
-            % log.
+            % log. This only works for a "regular" log scale, not the -log10 scale. Still working on that one
             import nla.gfx.ProbPlotMethod
 
             % source is the colorbar, not the figure
@@ -600,22 +596,20 @@ function openModal(obj, source, ~)
             uicontrol("Style", "text", "string", "Colormaps", "Units", "pixels",...
                 "Position", [10, scaleBaseButtons.Position(2) - 45, 80, 25]);
             color_map_select = uicontrol('Style', 'popupmenu',...
-                'Position', [100, scaleBaseButtons.Position(2) - 45, 242, 30]);
+                'Position', [100, scaleBaseButtons.Position(2) - 45, 250, 30]);
             initial_colors = 16;
             colormap_html = [];
             for colors = 1:numel(obj.colormap_choices)
                 colormap_function = str2func(strcat(strcat("@(x) ",lower(obj.colormap_choices{colors}), "(x)")));
                 CData = colormap_function(initial_colors);
-                new_html_start = '<HTML>';
-                new_html = '';
-                for color_iterator = initial_colors:-1:1
+                new_html = '<HTML>';
+                for color_iterator = 1:initial_colors
                     hex_code = nla.gfx.rgb2hex([CData(color_iterator, 1), CData(color_iterator, 2),...
                         CData(color_iterator, 3)]);
                     new_html = [new_html '<FONT bgcolor="' hex_code ' "color="' hex_code '">__</FONT>'];
                 end
                 %new_html = new_html(1:end-2);
-                new_html_end = [new_html '</HTML>'];
-                new_html = [new_html_start new_html new_html_end]
+                new_html = [new_html '</HTML>'];
                 colormap_html = [colormap_html; {new_html}];
             end
             set(color_map_select, "Value", 1, "String", colormap_html);
@@ -632,13 +626,12 @@ function openModal(obj, source, ~)
         end
 
         function applyScale(obj, ~, ~, upper_limit_box, lower_limit_box, button_group, color_map_select)
+
             % This callback gets the colormap/scale and then applies the new bounds to the data.
             % Only works with APPLY button, will not work with only CLOSE
 
             import nla.net.result.NetworkResultPlotParameter nla.gfx.ProbPlotMethod
 
-            obj.matrix = obj.original_matrix;
-
             button_group_value = get(get(button_group, "SelectedObject"), "String");
 
             if ismember(obj.plot_scale, [ProbPlotMethod.NEG_LOG_10, ProbPlotMethod.NEG_LOG_STATISTIC]) &&...
@@ -652,32 +645,29 @@ function applyScale(obj, ~, ~, upper_limit_box, lower_limit_box, button_group, c
             discrete_colors = NetworkResultPlotParameter().default_discrete_colors;
             color_map = get(color_map_select, "Value");
             if button_group_value == "Linear"
-                new_color_map = NetworkResultPlotParameter.getColormap(discrete_colors, get(upper_limit_box, "String"),...
+                obj.color_map = NetworkResultPlotParameter.getColormap(discrete_colors, get(upper_limit_box, "String"),...
                     obj.colormap_choices{color_map});
                 obj.plot_scale = ProbPlotMethod.DEFAULT;
             elseif button_group_value == "Log"
-                new_color_map = NetworkResultPlotParameter.getLogColormap(discrete_colors, obj.matrix, get(upper_limit_box, "String"), obj.colormap_choices{color_map});
+                obj.color_map = NetworkResultPlotParameter.getLogColormap(discrete_colors, obj.matrix, get(upper_limit_box, "String"), obj.colormap_choices{color_map});
                 obj.plot_scale = ProbPlotMethod.LOG;
             else
                 color_map_name = str2func(lower(obj.colormap_choices{color_map}));
-                new_color_map = color_map_name(discrete_colors);
+                obj.color_map = color_map_name(discrete_colors);
                 obj.plot_scale = ProbPlotMethod.NEG_LOG_10;
             end
-            obj.color_map = new_color_map;
             obj.embiggenMatrix(get(lower_limit_box, "String"), get(upper_limit_box, "String"));
             obj.createColorbar(get(lower_limit_box, "String"), get(upper_limit_box, "String"));
         end
 
         function chunk_color = getChunkColor(obj, chunk_raw, upper_value, lower_value)
             % Get color for the chunk (square)
-
             chunk_color = nla.gfx.valToColor(chunk_raw, lower_value, upper_value, obj.color_map);
             chunk_color(isnan(chunk_raw)) = NaN; % puts all NaNs back removed with valToColor
         end
 
         function applyColorToData(obj, position_x, position_y, chunk_height, chunk_width, chunk_color)
             % Fill in the chunks (squares) with color
-
             obj.image_display.CData(position_y:position_y + chunk_height - 1, position_x:position_x + chunk_width - 1, :) =...
                 repelem(chunk_color, obj.elementSize(), obj.elementSize());
             obj.image_display.CData(position_y + chunk_height, position_x:position_x + chunk_width - 1, :) =...

diff --git a/+nla/+gfx/ProbPlotMethod.m b/+nla/+gfx/ProbPlotMethod.m
@@ -1,5 +1,5 @@
 classdef ProbPlotMethod
     enumeration
-        DEFAULT, LOG, NEG_LOG_10, STATISTIC, LOG_STATISTIC, NEG_LOG_STATISTIC
+        DEFAULT, LOG, NEG_LOG_10, STATISITC, LOG_STATISTIC, NEG_LOG_STATISTIC
     end
 end
diff --git a/+nla/+gfx/anatToMesh.m b/+nla/+gfx/anatToMesh.m
@@ -30,7 +30,7 @@
         % rotate right hemi around and move to position for visualization
         cmL = mean(mesh_l, 1);
         cmR = mean(mesh_r, 1);
-        rm = nla.helpers.rotationMatrix(nla.Dir.Z, pi);
+        rm = nla.helpers.rotationMatrix(Dir.Z, pi);
 
         % Rotate
         switch view_pos

diff --git a/+nla/+gfx/drawBrainVis.m b/+nla/+gfx/drawBrainVis.m
@@ -16,8 +16,7 @@ function drawBrainVis(edge_input_struct, input_struct, net_atlas, ctx, mesh_alph
 
     fc_exists = isfield(edge_input_struct, 'func_conn');
 
-    color_fc = false; % short term fix for NET-167 
-    % color_fc = fc_exists;
+    color_fc = fc_exists;
 
     show_ROI_centroids = true;
     if isfield(input_struct, 'show_ROI_centroids')

diff --git a/+nla/+helpers/rotationMatrix.m b/+nla/+helpers/rotationMatrix.m
@@ -1,8 +1,6 @@
 function mat = rotationMatrix(dir, theta)
     % Generate a rotation matrix for the direction given
     % an angle (in radians).
-    import nla.Dir
-
     mat = zeros(3);
 
     switch dir

diff --git a/+nla/+inputField/NetworkAtlas.m b/+nla/+inputField/NetworkAtlas.m
@@ -24,7 +24,7 @@
         end
 
         function [w, h] = draw(obj, x, y, parent, fig)
-            import nla.inputField.LABEL_GAP nla.inputField.LABEL_H nla.inputField.widthOfString
+            import nla.inputField.LABEL_GAP nla.inputField.LABEL_H nla.inputFieldwidthOfString
             import nla.gfx.MeshType
 
             obj.fig = fig;

diff --git a/+nla/+net/+result/+chord/ChordPlotter.m b/+nla/+net/+result/+chord/ChordPlotter.m
@@ -32,7 +32,7 @@
 
         function generateChordFigure(obj, parameters, chord_type)
             % generateChordFigure plots chords for a network test
-            import nla.gfx.SigType nla.net.result.plot.PermutationTestPlotter nla.gfx.EdgeChordPlotMethod
+            import nla.gfx.SigType nla.net.result.plot.NoPermutationPlotter nla.gfx.EdgeChordPlotMethod
 
             coefficient_bounds = [0, parameters.p_value_plot_max];
             if parameters.significance_type == SigType.INCREASING && parameters.p_value_plot_max < 1
@@ -79,7 +79,7 @@ function generateChordFigure(obj, parameters, chord_type)
             end
 
             % Plot Trimatrix with the chord plots
-            plotter = PermutationTestPlotter(obj.network_atlas);
+            plotter = NoPermutationPlotter(obj.network_atlas);
             plotter.plotProbability(plot_figure, parameters, 25, obj.bottom_text_height);
 
             obj.generatePlotText(plot_figure, chord_type);
@@ -185,7 +185,7 @@ function generateEdgeChordFigure(obj, plot_figure, parameters, chord_type)
             end
 
             chord_plotter = nla.gfx.chord.ChordPlot(obj.network_atlas, plot_axis, 450, clipped_values, 'chord_type', chord_type,...
-                'direction', significance_type, 'color_map', color_map, 'lower_limit', coefficient_min, 'upper_limit', coefficient_max);
+                'direction', significance_type, 'color_map', color_map, 'lower_limit', coefficient_min, 'upper_limit', coefficient_min);
             chord_plotter.drawChords();
             setTitle(plot_axis, main_title);
 

diff --git a/+nla/+net/+result/+plot/PermutationTestPlotter.m b/+nla/+net/+result/+plot/PermutationTestPlotter.m
diff --git a/+nla/+net/+result/NetworkResultPlotParameter.m b/+nla/+net/+result/NetworkResultPlotParameter.m
@@ -9,7 +9,7 @@
 
     properties (Dependent)
         test_methods
-        noncorrelation_input_tests
+        noncorrelation_input_test_names
         number_of_networks
     end
 
@@ -29,9 +29,9 @@
         function result = plotProbabilityParameters(obj, edge_test_options, edge_test_result, test_method, plot_statistic,...
                 plot_title, fdr_correction, significance_filter)
             % plot_title - this will be a string
-            % plot_statistic - this is the stat that will be plotted
+            % plot_statistic - this is the stat that will be plotted, string
             % significance filter - this will be a boolean or some sort of object (like Cohen's D > D-value)
-            % fdr_correction - a struct of fdr_correction (found in nla.net.mcc)
+            % fdr_correction - a struct of fdr_correction (found in nla.net.mcc) or None
             % test_method - 'no permutations', 'within network pair', 'full connectome'
 
             import nla.TriMatrix nla.TriMatrixDiag
@@ -73,8 +73,7 @@
             % determine colormap and operate on values if it's -log10
             switch obj.updated_test_options.prob_plot_method
                 case nla.gfx.ProbPlotMethod.LOG
-                    color_map = nla.net.result.NetworkResultPlotParameter.getLogColormap(obj.default_discrete_colors,...
-                        statistic_input, p_value_max);
+                    color_map = nla.net.result.NetworkResultPlotParameter.getLogColormap(obj.default_discrete_colors, statistic_input, p_value_max);
                 % Here we take a -log10 and change the maximum value to show on the plot
                 case nla.gfx.ProbPlotMethod.NEG_LOG_10
                     color_map = parula(obj.default_discrete_colors);
@@ -89,8 +88,7 @@
                     end
                     significance_type = nla.gfx.SigType.INCREASING;
                 otherwise
-                    color_map = nla.net.result.NetworkResultPlotParameter.getColormap(obj.default_discrete_colors,...
-                        p_value_max);
+                    color_map = nla.net.result.NetworkResultPlotParameter.getColormap(obj.default_discrete_colors, p_value_max);
             end
 
             % callback function for brain image. 
@@ -102,7 +100,7 @@ function brainFigureButtonCallback(network1, network2)
                 wait_popup = waitbar(0.05, wait_text);
                 nla.gfx.drawBrainVis(edge_test_options, obj.updated_test_options, obj.network_atlas,...
                     nla.gfx.MeshType.STD, 0.25, 3, true, edge_test_result, network1, network2,...
-                    any(strcmp(obj.noncorrelation_input_tests, obj.network_test_results.test_name)));
+                    any(strcmp(obj.noncorrelation_input_test_names, obj.network_test_results.test_name)));
                 waitbar(0.95);
                 close(wait_popup);
             end
@@ -142,8 +140,8 @@ function brainFigureButtonCallback(network1, network2)
             value = obj.network_test_results.test_methods;
         end
 
-        function value = get.noncorrelation_input_tests(obj)
-            value = obj.network_test_results.noncorrelation_input_tests;
+        function value = get.noncorrelation_input_test_names(obj)
+            value = obj.network_test_results.noncorrelation_input_test_names;
         end
 
         function value = get.number_of_networks(obj)
@@ -201,7 +199,6 @@ function brainFigureButtonCallback(network1, network2)
                 color_map_name = str2func(lower(color_map));
                 color_map_base = color_map_name(default_discrete_colors);
             end
-
             default_color_map = [1 1 1];
             if p_value_max == 0
                 color_map = default_color_map;