Add DWD group analysis and fix LDA/DWD stale results

Python/shapeworks/shapeworks/__init__.py

@@ -5,10 +5,10 @@
 from shapeworks_py import *
 from .conversion import sw2vtkImage, sw2vtkMesh
 from .plot import plot_meshes, plot_volumes, plot_meshes_volumes_mix, add_mesh_to_plotter, add_volume_to_plotter, plot_mesh_contour,plot_pca_metrics,\
-pca_loadings_violinplot,plot_mode_line,visualize_reconstruction,lda_plot
+pca_loadings_violinplot,plot_mode_line,visualize_reconstruction,lda_plot,dwd_plot
 from .utils import num_subplots, positive_factors, save_images, get_file_with_ext, find_reference_image_index, find_reference_mesh_index, load_mesh
 from .data import get_file_list, sample_images, sample_meshes
-from .stats import compute_pvalues_for_group_difference,lda
+from .stats import compute_pvalues_for_group_difference,lda,dwd_loadings
 from .network_analysis import NetworkAnalysis
 from .portal import download_dataset
 from .shape_scalars import run_mbpls

Python/shapeworks/shapeworks/plot.py

@@ -481,7 +481,7 @@ def lda_plot(group1_x,group2_x,group1_pdf,group2_pdf,group1_map,group2_map,lda_d
     group2_num = len(group2_map)
     plt.plot(group1_x, group1_pdf, label = labels[0] + ' PDF',linewidth=10)
     plt.plot(group2_x, group2_pdf, label = labels[1] + ' PDF',linewidth=10)
-    
+
     plt.scatter(group1_map, 0.01*np.ones((group1_num)), s=330, label = labels[0] + ' Shape Mappings', edgecolors='black',linewidths=5)
     plt.scatter(group2_map, 0.01*np.ones((group2_num)), s=330, label = labels[1] + ' Shape Mappings', edgecolors='black',linewidths=5)
     plt.ylabel("Probability Density")
@@ -491,4 +491,25 @@ def lda_plot(group1_x,group2_x,group1_pdf,group2_pdf,group1_map,group2_map,lda_d
     plt.close(fig)
 
     print("Figure saved in directory -" + lda_dir)
-    print()
+    print()
+
+def dwd_plot(group1_x,group2_x,group1_pdf,group2_pdf,group1_map,group2_map,dwd_dir,labels):
+
+    plt.figure(dpi=50,figsize=(14,14))
+    fig = plt.gcf()
+    plt.rcParams['font.size'] = '20'
+    group1_num = len(group1_map)
+    group2_num = len(group2_map)
+    plt.plot(group1_x, group1_pdf, label = labels[0] + ' PDF',linewidth=10)
+    plt.plot(group2_x, group2_pdf, label = labels[1] + ' PDF',linewidth=10)
+
+    plt.scatter(group1_map, 0.01*np.ones((group1_num)), s=330, label = labels[0] + ' Shape Mappings', edgecolors='black',linewidths=5)
+    plt.scatter(group2_map, 0.01*np.ones((group2_num)), s=330, label = labels[1] + ' Shape Mappings', edgecolors='black',linewidths=5)
+    plt.ylabel("Probability Density")
+    plt.xlabel('Shape mapping to DWD discrimination of variation between population means')
+    plt.legend(loc='upper right')
+    plt.savefig(dwd_dir+"/DWD.png")
+    plt.close(fig)
+
+    print("Figure saved in directory -" + dwd_dir)
+    print()

Python/shapeworks/shapeworks/stats.py

@@ -71,33 +71,49 @@ def compute_pvalues_for_group_difference_data(group_0_data, group_1_data, permut
 
 
 def normalize(subj_map, group1_mean_map, group2_mean_map):
-    slope = (2.0 / (group2_mean_map - group1_mean_map))
+    denom = group2_mean_map - group1_mean_map
+    if abs(denom) < 1e-12:
+        return 0.0
+    slope = 2.0 / denom
     subj_diff = subj_map - group1_mean_map
     subj_map_normalized = slope * subj_diff - 1
     return subj_map_normalized
 
 
 def lda_loadings(group1_data, group2_data):
-    group1_num = np.shape(group1_data)[1]
-    group2_num = np.shape(group2_data)[1]
-
     combined_data = np.concatenate((group1_data, group2_data), axis=1)
     group1_mean = np.mean(group1_data, axis=1)
     group2_mean = np.mean(group2_data, axis=1)
 
-    overall_mean = np.mean(combined_data, axis=1)
-
     diffVect = group1_mean - group2_mean
 
+    return _project_and_pdf(diffVect, group1_data, group2_data, combined_data)
+
+
+def _project_and_pdf(diffVect, group1_data, group2_data, combined_data):
+    """Shared logic for projecting groups onto a discriminant direction and fitting PDFs.
+
+    Args:
+        diffVect: Discriminant direction vector (features,)
+        group1_data: PCA loadings for group 1 (features x samples)
+        group2_data: PCA loadings for group 2 (features x samples)
+        combined_data: Concatenation of group1_data and group2_data (features x all_samples)
+
+    Returns: 6-tuple (group1_x, group2_x, group1_pdf, group2_pdf, group1_map, group2_map)
+    """
+    group1_num = group1_data.shape[1]
+    group2_num = group2_data.shape[1]
+
+    group1_mean = np.mean(group1_data, axis=1)
+    group2_mean = np.mean(group2_data, axis=1)
+    overall_mean = np.mean(combined_data, axis=1)
+
     group1_mean_diff = group1_mean - overall_mean
     group2_mean_diff = group2_mean - overall_mean
 
     group1_mean_map = np.dot(diffVect, group1_mean_diff)
     group2_mean_map = np.dot(diffVect, group2_mean_diff)
 
-    group1_mean_map_normalized = normalize(group1_mean_map, group1_mean_map, group2_mean_map)
-    group2_mean_map_normalized = normalize(group2_mean_map, group1_mean_map, group2_mean_map)
-
     group1_map = np.zeros((group1_num,))
     group2_map = np.zeros((group2_num,))
 
@@ -117,6 +133,13 @@ def lda_loadings(group1_data, group2_data):
     group1_map_std = group1_map.std()
     group2_map_std = group2_map.std()
 
+    # Guard against zero std (all samples project to same point)
+    min_std = 1e-6
+    if group1_map_std < min_std:
+        group1_map_std = min_std
+    if group2_map_std < min_std:
+        group2_map_std = min_std
+
     group1_x = np.linspace(group1_map_mean - 6, group1_map_mean + 6, num=300)
     group2_x = np.linspace(group2_map_mean - 6, group2_map_mean + 6, num=300)
 
@@ -125,6 +148,31 @@ def lda_loadings(group1_data, group2_data):
     return group1_x, group2_x, group1_pdf, group2_pdf, group1_map, group2_map
 
 
+def dwd_loadings(group1_data, group2_data):
+    from dwd.gen_dwd import GenDWD
+    group1_num = np.shape(group1_data)[1]
+    group2_num = np.shape(group2_data)[1]
+
+    if group1_num < 2 or group2_num < 2:
+        raise ValueError(f"DWD requires at least 2 samples per group (got {group1_num} and {group2_num})")
+
+    combined_data = np.concatenate((group1_data, group2_data), axis=1)
+
+    # Fit GenDWD (samples x features)
+    X = combined_data.T
+    y = np.array([1]*group1_num + [-1]*group2_num)
+
+    try:
+        model = GenDWD(lambd=1.0)
+        model.fit(X, y)
+    except Exception as e:
+        raise RuntimeError(f"DWD fitting failed: {e}") from e
+
+    diffVect = model.coef_.flatten()
+
+    return _project_and_pdf(diffVect, group1_data, group2_data, combined_data)
+
+
 def lda(data):
     group_id = data["group_ids"].unique()
     group1_idxs = data.index[data['group_ids'] == 0].tolist()

Studio/Analysis/AnalysisTool.cpp

@@ -11,6 +11,7 @@
 #include <Job/NetworkAnalysisJob.h>
 #include <Job/ParticleNormalEvaluationJob.h>
 #include <Job/StatsGroupLDAJob.h>
+#include <Job/StatsGroupDWDJob.h>
 #include <Libs/Application/Job/PythonWorker.h>
 #include <Groom/GroomParameters.h>
 #include <Logging.h>
@@ -168,6 +169,12 @@ AnalysisTool::AnalysisTool(Preferences& prefs) : preferences_(prefs) {
   connect(group_lda_job_.data(), &StatsGroupLDAJob::progress, this, &AnalysisTool::handle_lda_progress);
   connect(group_lda_job_.data(), &StatsGroupLDAJob::finished, this, &AnalysisTool::handle_lda_complete);
 
+  ui_->dwd_graph->hide();
+  ui_->dwd_hint_label->hide();
+  group_dwd_job_ = QSharedPointer<StatsGroupDWDJob>::create();
+  connect(group_dwd_job_.data(), &StatsGroupDWDJob::progress, this, &AnalysisTool::handle_dwd_progress);
+  connect(group_dwd_job_.data(), &StatsGroupDWDJob::finished, this, &AnalysisTool::handle_dwd_complete);
+
   connect(ui_->show_difference_to_mean, &QPushButton::clicked, this, &AnalysisTool::show_difference_to_mean_clicked);
 
   connect(ui_->group_analysis_combo, qOverload<int>(&QComboBox::currentIndexChanged), this,
@@ -1620,6 +1627,24 @@ void AnalysisTool::update_lda_graph() {
   }
 }
 
+//---------------------------------------------------------------------------
+void AnalysisTool::update_dwd_graph() {
+  if (groups_active()) {
+    if (!dwd_computed_ && !group_dwd_job_running_) {
+      group_dwd_job_running_ = true;
+      ui_->dwd_label->show();
+      ui_->dwd_progress->setValue(0);
+      ui_->dwd_progress->setMaximum(0);
+      ui_->dwd_progress->update();
+      group_dwd_job_->set_stats(stats_);
+      app_->get_py_worker()->run_job(group_dwd_job_);
+    }
+  } else {
+    ui_->dwd_graph->setVisible(false);
+    ui_->dwd_hint_label->setVisible(false);
+  }
+}
+
 //---------------------------------------------------------------------------
 void AnalysisTool::update_difference_particles() {
   if (!stats_ready_) {
@@ -1679,7 +1704,10 @@ void AnalysisTool::group_changed() {
   stats_ready_ = false;
   group_pvalue_job_ = nullptr;
   lda_computed_ = false;
+  dwd_computed_ = false;
   compute_stats();
+  // Re-trigger LDA/DWD if currently visible
+  group_analysis_combo_changed();
 }
 
 //---------------------------------------------------------------------------
@@ -1909,12 +1937,64 @@ void AnalysisTool::handle_lda_complete() {
   QString left_group = ui_->group_left->currentText();
   QString right_group = ui_->group_right->currentText();
 
+  if (!group_lda_job_->succeeded()) {
+    ui_->lda_graph->setVisible(false);
+    if (left_group == right_group) {
+      ui_->lda_hint_label->setText("LDA requires two distinct groups.");
+    } else {
+      ui_->lda_hint_label->setText("LDA computation failed. Check log for details.");
+    }
+    ui_->lda_hint_label->setVisible(true);
+    QTimer::singleShot(0, this, &AnalysisTool::resize_tab_to_current);
+    return;
+  }
+
   group_lda_job_->plot(ui_->lda_graph, left_group, right_group);
   ui_->lda_graph->setVisible(true);
   ui_->lda_hint_label->setVisible(true);
   QTimer::singleShot(0, this, &AnalysisTool::resize_tab_to_current);
 }
 
+//---------------------------------------------------------------------------
+void AnalysisTool::handle_dwd_progress(double progress) {
+  if (progress > 0) {
+    ui_->dwd_progress->setMaximum(100);
+  } else {
+    ui_->dwd_progress->setMaximum(0);
+  }
+  ui_->dwd_progress_widget->setVisible(progress < 1);
+  ui_->dwd_progress->setValue(progress * 100);
+  ui_->dwd_progress->update();
+}
+
+//---------------------------------------------------------------------------
+void AnalysisTool::handle_dwd_complete() {
+  ui_->dwd_progress_widget->setVisible(false);
+  ui_->dwd_label->setVisible(false);
+  group_dwd_job_running_ = false;
+  dwd_computed_ = true;
+
+  QString left_group = ui_->group_left->currentText();
+  QString right_group = ui_->group_right->currentText();
+
+  if (!group_dwd_job_->succeeded()) {
+    ui_->dwd_graph->setVisible(false);
+    if (left_group == right_group) {
+      ui_->dwd_hint_label->setText("DWD requires two distinct groups.");
+    } else {
+      ui_->dwd_hint_label->setText("DWD computation failed. Check log for details.");
+    }
+    ui_->dwd_hint_label->setVisible(true);
+    QTimer::singleShot(0, this, &AnalysisTool::resize_tab_to_current);
+    return;
+  }
+
+  group_dwd_job_->plot(ui_->dwd_graph, left_group, right_group);
+  ui_->dwd_graph->setVisible(true);
+  ui_->dwd_hint_label->setVisible(true);
+  QTimer::singleShot(0, this, &AnalysisTool::resize_tab_to_current);
+}
+
 void AnalysisTool::handle_network_analysis_progress(int progress) {
   if (progress > 0) {
     ui_->network_progress->setMaximum(100);
@@ -1960,6 +2040,9 @@ void AnalysisTool::group_analysis_combo_changed() {
     if (ui_->group_analysis_stacked_widget->currentWidget() == ui_->lda_page) {
       update_lda_graph();
     }
+    if (ui_->group_analysis_stacked_widget->currentWidget() == ui_->dwd_page) {
+      update_dwd_graph();
+    }
   }
   // Recalculate tab height since analysis content changed
   QTimer::singleShot(0, this, &AnalysisTool::resize_tab_to_current);

Studio/Analysis/AnalysisTool.h

@@ -28,6 +28,7 @@ class ShapeWorksStudioApp;
 class GroupPvalueJob;
 class NetworkAnalysisJob;
 class StatsGroupLDAJob;
+class StatsGroupDWDJob;
 class ParticleAreaPanel;
 class ShapeScalarPanel;
 
@@ -37,7 +38,7 @@ class AnalysisTool : public QWidget {
  public:
   using AlignmentType = Analyze::AlignmentType;
 
-  enum GroupAnalysisType { None = 0, Pvalues = 1, NetworkAnalysis = 2, LDA = 3 };
+  enum GroupAnalysisType { None = 0, Pvalues = 1, NetworkAnalysis = 2, LDA = 3, DWD = 4 };
 
   enum McaMode { Vanilla, Within, Between };
 
@@ -187,6 +188,9 @@ class AnalysisTool : public QWidget {
   void handle_lda_progress(double progress);
   void handle_lda_complete();
 
+  void handle_dwd_progress(double progress);
+  void handle_dwd_complete();
+
   void handle_network_analysis_progress(int progress);
   void handle_network_analysis_complete();
 
@@ -243,6 +247,7 @@ class AnalysisTool : public QWidget {
   void handle_pca_group_list_item_changed();
 
   void update_lda_graph();
+  void update_dwd_graph();
 
   void update_difference_particles();
 
@@ -294,10 +299,13 @@ class AnalysisTool : public QWidget {
 
   QSharedPointer<GroupPvalueJob> group_pvalue_job_;
   QSharedPointer<StatsGroupLDAJob> group_lda_job_;
+  QSharedPointer<StatsGroupDWDJob> group_dwd_job_;
   QSharedPointer<NetworkAnalysisJob> network_analysis_job_;
 
   bool group_lda_job_running_ = false;
   bool lda_computed_ = false;
+  bool group_dwd_job_running_ = false;
+  bool dwd_computed_ = false;
   bool block_group_change_ = false;
 
   ParticleAreaPanel* particle_area_panel_{nullptr};