small rewrite to impose dry principles

2026-07-15 21:21:35 -07:00
parent ffa14693b3
commit 12afc5d3bc
14 changed files with 1825 additions and 1716 deletions
@@ -0,0 +1,130 @@
+"""
+Filename: intergroupstats.py
+Description: Logic for the Inter-Group Stats analysis window
+
+Author: Tyler de Zeeuw
+License: GPL-3.0
+"""
+
+# External library imports
+import pandas as pd
+
+from flares import run_roi_second_level_analysis
+from src.shared.flaresbasewidget import InterGroupUIMixin, FlaresBaseWidget
+from src.shared.shareddata import APP_NAME
+
+
+PARAMETERIZED_INDEXES = {
+    0: [
+        {
+            "key": "p_value",
+            "label": "Significance threshold P-value (e.g. 0.05)",
+            "default": "0.05",
+            "type": float,
+        },
+        {
+            "key": "graph_bounds",
+            "label": "Graph Y-Limit (Optional, e.g. 1e-5)",
+            "default": "0.0",  # Set to 0.0 to auto-scale
+            "type": float,
+        }
+    ],
+}
+
+
+
+class InterGroupStatsWidget(InterGroupUIMixin, FlaresBaseWidget):
+    def __init__(self, haemo_dict, cha, df_ind, design_matrix, contrast_results, group):
+        super().__init__("InterGroupStats")
+        self.setWindowTitle(f"Inter-Group Stats Viewer - {APP_NAME.upper()}")
+        self.haemo_dict = haemo_dict
+        self.cha = cha
+        self.df_ind = df_ind
+        self.design_matrix = design_matrix
+        self.contrast_results = contrast_results
+        self.group = group
+
+        self.setup_inter_group_ui(["0 (Significance)",])
+
+
+    def process_request(self):
+        request = self.get_common_request_data(PARAMETERIZED_INDEXES)
+        if request is None:
+            return
+
+        (selected_event, selected_file_paths, selected_indexes, param_values,) = request     
+
+        all_cha = pd.DataFrame()
+        for file_path in selected_file_paths:
+            haemo_obj = self.haemo_dict.get(file_path)
+
+            if selected_event:
+                participant_events = set(haemo_obj.annotations.description)
+                if selected_event not in participant_events:
+                    print(f"Skipping {self.participant_map[file_path]}: Event '{selected_event}' not found.")
+                    continue
+
+            if haemo_obj is None:
+                continue
+            
+            cha_df = self.cha.get(file_path)
+            if cha_df is not None:
+                all_cha = pd.concat([all_cha, cha_df], ignore_index=True)
+
+        file_path = selected_file_paths[0]
+        p_haemo = self.haemo_dict.get(file_path)
+
+        # Concatenate individual ROI stats (df_ind) for all chosen subjects
+        df_group = pd.DataFrame()
+        if selected_file_paths:
+            for file_path in selected_file_paths:
+                df = self.df_ind.get(file_path)
+                if df is not None:
+                    df_group = pd.concat([df_group, df], ignore_index=True)
+
+        for idx in selected_indexes:
+            if idx == 0:
+                params = param_values.get(idx, {})
+                p_val = params.get("p_value", 0.05)
+                graph_bounds = params.get("graph_bounds", 0.0)
+
+                if df_group.empty:
+                    print("No ROI data (df_ind) found for selected participants.")
+                    continue
+
+                # Filter down to the selected experimental event/condition
+                if selected_event:
+                    if 'Condition' in df_group.columns:
+                        df_filtered = df_group[df_group['Condition'] == selected_event]
+                    else:
+                        print("Warning: 'Condition' column not found in ROI data.")
+                        df_filtered = df_group
+                else:
+                    df_filtered = df_group
+
+                if df_filtered.empty:
+                    print(f"No ROI data matches the condition '{selected_event}'.")
+                    continue
+
+                all_cha_filtered = pd.DataFrame()
+                if not all_cha.empty:
+                    if selected_event and 'Condition' in all_cha.columns:
+                        all_cha_filtered = all_cha[all_cha['Condition'] == selected_event]
+                    else:
+                        all_cha_filtered = all_cha
+
+                # Call your new custom group ROI method!
+                run_roi_second_level_analysis(
+                    df_roi_all=df_filtered,
+                    df_cha_all=all_cha_filtered,
+                    raw_haemo=p_haemo,
+                    p_threshold=p_val,
+                    min_subjects=len(selected_file_paths),
+                    correction_method='fdr_bh',
+                    target_chroma='hbo',
+                    graph_bounds=graph_bounds if graph_bounds > 0.0 else None,
+                    roi_config=r"C:\Users\tyler\Desktop\research\flares\regions.json"
+                )
+
+            else:
+                print(f"No method defined for index {idx}")