small rewrite to impose dry principles

main.py

@@ -268,7 +268,13 @@ SECTIONS = [
             {"name": "N_JOBS", "default": 1, "type": int, "help": "The number of CPUs to use to do the GLM computation. -1 means 'all CPUs'."},
         ]
     },
-        {
+    {
+        "title": "Region of Interest",
+        "params": [
+            {"name": "JSON_LOCATION", "default": "", "type": str, "help": "Location of the JSON file containing region of interest results for significance calculations."},
+        ]
+    },
+    {
         "title": "Finishing Touches",
         "params": [
             # Intentionally empty (TODO)
@@ -287,6 +293,19 @@ SECTIONS = [
 
 
 
+DATA_SCHEMA = [
+    {"key": "raw_haemo_dict",        "help": "Dict[file_path, MNE RawArray]: Haemodynamic raw data"},
+    {"key": "epochs_dict",           "help": "Dict[file_path, MNE Epochs]: Time-locked epoch data"},
+    {"key": "cha_dict",              "help": "Dict[file_path, DataFrame]: Channel analysis results"},
+    {"key": "df_ind_dict",           "help": "Dict[file_path, DataFrame]: Individual-level data/ROI results"},
+    {"key": "design_matrix_dict",    "help": "Dict[file_path, DataFrame]: GLM design matrices"},
+    {"key": "config_dict",           "help": "Dict[file_path, dict]: Processing configuration parameters"},
+    {"key": "fig_bytes_dict",        "help": "Dict[file_path, dict]: Serialized figure data"},
+    {"key": "contrast_results_dict", "help": "Dict[file_path, dict]: Calculated contrast statistical results"},
+    {"key": "valid_dict",            "help": "Dict[file_path, bool]: Boolean validity status per file"}
+]
+
+
 
 
         
@@ -488,15 +507,9 @@ class MainApplication(QMainWindow):
 
 
         # Initialization to ensure that saving can occur
-        self.raw_haemo_dict = {}         # Processed Hemodynamic data
+        for item in DATA_SCHEMA:
-        self.config_dict = {}            # Analysis parameters/settings
+            setattr(self, item["key"], {})
-        self.epochs_dict = {}            # Timing/Event data
+
-        self.cha_dict = {}               # Channel configurations
-        self.contrast_results_dict = {}  # Statistical results
-        self.df_ind_dict = {}            # Individual dataframes
-        self.design_matrix_dict = {}     # GLM Design matrices
-        self.valid_dict = {}             # Quality control/Validity flags
-        self.fig_bytes_dict = {}         # Cached plot images (serialized)
         self.file_metadata = {}          # AGE, GENDER, GROUP
         self.metadata_cache = {}         # Internal file/path information metadata cache
         self.bubble_widgets = {}         # References to the UI "Bubble" objects
@@ -878,15 +891,8 @@ class MainApplication(QMainWindow):
         self.files_done = set()
         self.files_failed = set()
         
-        self.raw_haemo_dict = {}
+        for item in DATA_SCHEMA:
-        self.config_dict = {}
+            setattr(self, item["key"], {})
-        self.epochs_dict = {}
-        self.fig_bytes_dict = {}
-        self.cha_dict = {}
-        self.contrast_results_dict = {}
-        self.df_ind_dict = {}
-        self.design_matrix_dict = {}
-        self.valid_dict = {}
 
         self.metadata_cache = {}
 
@@ -1045,7 +1051,22 @@ class MainApplication(QMainWindow):
 
 
     def open_launcher_window(self):
-        self.launcher_window = ViewerLauncherWidget(self.raw_haemo_dict, self.config_dict, self.fig_bytes_dict, self.cha_dict, self.contrast_results_dict, self.df_ind_dict, self.design_matrix_dict, self.epochs_dict, self.folding_bypass)
+        data_map = {item["key"]: getattr(self, item["key"]) for item in DATA_SCHEMA}
+        
+        # 2. Extract values in the specific order the widget constructor expects
+        args = [
+            data_map["raw_haemo_dict"],
+            data_map["epochs_dict"],
+            data_map["cha_dict"],
+            data_map["df_ind_dict"],
+            data_map["design_matrix_dict"],
+            data_map["config_dict"],
+            data_map["fig_bytes_dict"],
+            data_map["contrast_results_dict"],
+            self.folding_bypass
+        ]
+
+        self.launcher_window = ViewerLauncherWidget(*args)
         self.launcher_window.show()
 
     def copy_text(self):
@@ -1188,7 +1209,7 @@ class MainApplication(QMainWindow):
     def open_folder_dialog(self):
         folder_path = QFileDialog.getExistingDirectory(self, "Select Folder", "")
         if folder_path:
-            snirf_files = [os.path.normpath(str(f)) for f in Path(folder_path).glob("*.snirf")]
+            snirf_files = [os.path.normpath(str(f)) for f in Path(folder_path).rglob("*.snirf")]
             self._load_files_into_pipeline(snirf_files)
 
 
@@ -1297,7 +1318,10 @@ class MainApplication(QMainWindow):
         has_param_changes = any(section.has_any_changes() for section in self.param_sections)
 
         # Check if there is processed data
-        has_processed_data = bool(getattr(self, 'raw_haemo_dict', None))
+        has_processed_data = any(
+            len(getattr(self, item["key"], {})) > 0 
+            for item in DATA_SCHEMA
+        )
 
         if not (has_processed_data or has_metadata or has_param_changes):
             if not onCrash: # Don't show popups during a crash/autosave
@@ -1368,23 +1392,17 @@ class MainApplication(QMainWindow):
                 current_params = self.config_dict[first_file]  
 
             version = CURRENT_VERSION
-            project_data = {
+
+            project_data = {item["key"]: getattr(self, item["key"]) for item in DATA_SCHEMA}
+
+            project_data.update({
                 "version": version,
                 "file_list": file_list,
                 "progress_states": progress_states,
-                "raw_haemo_dict": self.raw_haemo_dict,
                 "file_metadata": rel_metadata,
                 "file_parameters": rel_file_params,
-                "config_dict": self.config_dict,
-                "epochs_dict": self.epochs_dict,
-                "fig_bytes_dict": self.fig_bytes_dict,
-                "cha_dict": self.cha_dict,
                 "current_ui_params": current_params,
-                "contrast_results_dict": self.contrast_results_dict,
+            })
-                "df_ind_dict": self.df_ind_dict,
-                "design_matrix_dict": self.design_matrix_dict,
-                "valid_dict": self.valid_dict,
-            }
             
             def sanitize(obj):
                 if isinstance(obj, Path):
@@ -1472,15 +1490,9 @@ class MainApplication(QMainWindow):
                         return
 
 
-            self.raw_haemo_dict = data.get("raw_haemo_dict", {})
+            for item in DATA_SCHEMA:
-            self.config_dict = data.get("config_dict", {})
+                key = item["key"]
-            self.epochs_dict = data.get("epochs_dict", {})
+                setattr(self, key, data.get(key, {}))
-            self.fig_bytes_dict = data.get("fig_bytes_dict", {})
-            self.cha_dict = data.get("cha_dict", {})
-            self.contrast_results_dict = data.get("contrast_results_dict", {})
-            self.df_ind_dict = data.get("df_ind_dict", {})
-            self.design_matrix_dict = data.get("design_matrix_dict", {})
-            self.valid_dict = data.get("valid_dict", {})
 
             project_dir = Path(filename).parent
 
@@ -1530,7 +1542,7 @@ class MainApplication(QMainWindow):
                 first_file = next(iter(self.config_dict.keys()))
                 self.restore_sections_from_config(self.config_dict[first_file])
 
-            has_data = bool(self.raw_haemo_dict)
+            has_data = any(len(getattr(self, item["key"], {})) > 0 for item in DATA_SCHEMA)
             self.button1.setVisible(not has_data)
             self.button3.setVisible(has_data)
 
@@ -1955,15 +1967,8 @@ class MainApplication(QMainWindow):
 
             self.button3.setVisible(False)
     
-            self.raw_haemo_dict = {}
+            for item in DATA_SCHEMA:
-            self.config_dict = {}
+                setattr(self, item["key"], {})
-            self.epochs_dict = {}
-            self.fig_bytes_dict = {}
-            self.cha_dict = {}
-            self.contrast_results_dict = {}
-            self.df_ind_dict = {}
-            self.design_matrix_dict = {}
-            self.valid_dict = {}
             
         self.button1.clicked.disconnect(self.on_run_task)
         self.button1.setText("Cancel")
@@ -2091,27 +2096,13 @@ class MainApplication(QMainWindow):
                     # print(f"[DEBUG] Progress: {len(self.files_done)} / {self.files_total}")
                 
                     if msg.get("success"):
-                        # Unpack the massive tuple
-                        raw_haemo, config, epochs, fig_bytes, cha, contrast, df_ind, design, valid = msg["result"]
 
-                        # Initialize dictionaries once if needed
+                        results = msg["result"]
-                        if not hasattr(self, 'raw_haemo_dict') or self.raw_haemo_dict is None:
+                        self.files_results[file_path] = results
-                            attrs = ['raw_haemo_dict', 'config_dict', 'epochs_dict', 'fig_bytes_dict', 
-                                    'cha_dict', 'contrast_results_dict', 'df_ind_dict', 
-                                    'design_matrix_dict', 'valid_dict']
-                            for attr in attrs:
-                                setattr(self, attr, {})
                         
-                        self.files_results[file_path] = msg["result"]
+                        # Simple, clean assignment
-                        self.raw_haemo_dict[file_path] = raw_haemo
+                        for item, value in zip(DATA_SCHEMA, results):
-                        self.config_dict[file_path] = config
+                            getattr(self, item["key"])[file_path] = value
-                        self.epochs_dict[file_path] = epochs
-                        self.fig_bytes_dict[file_path] = fig_bytes
-                        self.cha_dict[file_path] = cha
-                        self.contrast_results_dict[file_path] = contrast
-                        self.df_ind_dict[file_path] = df_ind
-                        self.design_matrix_dict[file_path] = design
-                        self.valid_dict[file_path] = valid
 
                         self.statusbar.showMessage(f"Processed: {os.path.basename(file_path)}")
 

src/analysis/crossgroupbrainimage.py

@@ -0,0 +1,137 @@
+"""
+Filename: crossgroupbrainimage.py
+Description: Logic for the Cross-Group Brain & Image analysis window
+
+Author: Tyler de Zeeuw
+License: GPL-3.0
+"""
+
+# External library imports
+import pandas as pd
+
+from flares import aggregate_fnirs_group_geometry, plot_2d_3d_contrasts_between_groups
+from src.shared.flaresbasewidget import CrossGroupUIMixin, FlaresBaseWidget
+from src.shared.shareddata import APP_NAME
+
+
+PARAMETERIZED_INDEXES = {
+    0: [
+        {
+            "key": "show_optodes",
+            "label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
+            "default": "all",
+            "type": str,
+        },
+        {
+            "key": "t_or_theta",
+            "label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
+            "default": "theta",
+            "type": str,
+        },
+        {
+            "key": "show_text",
+            "label": "Display informative text on the top left corner about the contrast.",
+            "default": "True",
+            "type": bool,
+        },
+        {
+            "key": "brain_bounds",
+            "label": "Graph Upper/Lower Limit",
+            "default": "1.0",
+            "type": float,
+        },
+        {
+            "key": "is_3d",
+            "label": "Should we display the results in a 3D interactive window?",
+            "default": "True",
+            "type": bool,
+        }
+    ],
+}
+
+
+
+class CrossGroupBrainImageWidget(CrossGroupUIMixin, FlaresBaseWidget):
+    def __init__(self, haemo_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, group_dict):
+        super().__init__("CrossGroupBrainImage")
+        self.setWindowTitle(f"Cross-Group Brain & Image Viewer - {APP_NAME.upper()}")
+        self.haemo_dict = haemo_dict
+        self.df_ind_dict = df_ind_dict
+        self.design_matrix_dict = design_matrix_dict
+        self.contrast_results_dict = contrast_results_dict
+        self.group_dict = group_dict
+
+        self.setup_cross_group_ui(["0 (Contrast Image)"])
+
+
+    def proccess_request(self):
+              
+        request = self.get_common_request_data(PARAMETERIZED_INDEXES)
+        if request is None:
+            return
+
+        (selected_event, file_paths_a, file_paths_b, all_selected_paths, selected_indexes, param_values,) = request
+
+
+        # Build group-level contrast DataFrames
+        def concat_group_contrasts(file_paths: list[str], event: str | None) -> pd.DataFrame:
+            group_df = pd.DataFrame()
+            for fp in file_paths:
+                print(f"Looking up contrast for: {fp}")
+                event_con_dict = self.contrast_results_dict.get(fp, {})
+                print("Available events for this file:", list(event_con_dict.keys()))
+                if event and event in event_con_dict:
+                    df = event_con_dict[event]
+                    print(f"Appending contrast df for event: {event}")
+                    group_df = pd.concat([group_df, df], ignore_index=True)
+                else:
+                    print(f"Event '{event}' not found for {fp}")
+            return group_df
+
+        print("Selected event:", selected_event)
+        print("File paths A:", file_paths_a)
+        print("File paths B:", file_paths_b)
+
+        contrast_df_a = concat_group_contrasts(file_paths_a, selected_event)
+        contrast_df_b = concat_group_contrasts(file_paths_b, selected_event)
+
+        print("contrast_df_a empty?", contrast_df_a.empty)
+        print("contrast_df_b empty?", contrast_df_b.empty)
+
+        all_raw_objs = [self.haemo_dict.get(fp) for fp in all_selected_paths if self.haemo_dict.get(fp)]
+        
+        if len(all_raw_objs) > 1:
+            processed_raw = aggregate_fnirs_group_geometry(all_raw_objs)
+        else:
+            processed_raw = all_raw_objs[0].copy().pick(picks="hbo")
+
+        # Visualizations
+        for idx in selected_indexes:
+            if idx == 0:
+                params = param_values.get(idx, {})
+                show_optodes = params.get("show_optodes", None)
+                t_or_theta = params.get("t_or_theta", None)
+                show_text = params.get("show_text", None)
+                brain_bounds = params.get("brain_bounds", None)
+                is_3d = params.get("is_3d", None)
+
+                if show_optodes is None or t_or_theta is None or show_text is None or brain_bounds is None or is_3d is None:
+                    print(f"Missing parameters for index {idx}, skipping.")
+                    continue
+
+                if not contrast_df_a.empty and not contrast_df_b.empty and processed_raw:
+
+                    plot_2d_3d_contrasts_between_groups(
+                        contrast_df_a,
+                        contrast_df_b,
+                        raw_haemo=processed_raw,
+                        group_a_name=self.group_a_dropdown.currentText(),
+                        group_b_name=self.group_b_dropdown.currentText(),
+                        is_3d=is_3d,
+                        t_or_theta=t_or_theta,
+                        show_optodes=show_optodes,
+                        show_text=show_text,
+                        brain_bounds=brain_bounds
+                    )
+            else:
+                print(f"No method defined for index {idx}")

src/analysis/crossgroupstats.py

@@ -0,0 +1,114 @@
+"""
+Filename: crossgroupstats.py
+Description: Cross-Group stats analysis window
+
+Author: Tyler de Zeeuw
+License: GPL-3.0
+"""
+
+# External library imports
+import pandas as pd
+
+from flares import run_cross_group_second_level_analysis
+from src.shared.flaresbasewidget import CrossGroupUIMixin, FlaresBaseWidget
+from src.shared.shareddata import APP_NAME
+
+
+PARAMETERIZED_INDEXES = {
+    0: [
+        {
+            "key": "show_optodes",
+            "label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
+            "default": "all",
+            "type": str,
+        },
+        {
+            "key": "t_or_theta",
+            "label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
+            "default": "theta",
+            "type": str,
+        },
+        {
+            "key": "show_text",
+            "label": "Display informative text on the top left corner about the contrast.",
+            "default": "True",
+            "type": bool,
+        },
+        {
+            "key": "brain_bounds",
+            "label": "Graph Upper/Lower Limit",
+            "default": "1.0",
+            "type": float,
+        },
+        {
+            "key": "is_3d",
+            "label": "Should we display the results in a 3D interactive window?",
+            "default": "True",
+            "type": bool,
+        }
+    ],
+}
+        
+
+
+class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
+    def __init__(self, haemo_dict, cha_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, group_dict):
+        super().__init__("CrossGroupStats")
+        self.setWindowTitle(f"Cross-Group Stats Viewer - {APP_NAME.upper()}")
+        self.haemo_dict = haemo_dict
+        self.cha_dict = cha_dict
+        self.df_ind_dict = df_ind_dict
+        self.design_matrix_dict = design_matrix_dict
+        self.contrast_results_dict = contrast_results_dict
+        self.group_dict = group_dict
+
+        self.setup_cross_group_ui(["0 (Compute Statistics)"])
+        
+
+    def process_request(self):
+        request = self.get_common_request_data(PARAMETERIZED_INDEXES)
+        if request is None:
+            return
+
+        (selected_event, file_paths_a, file_paths_b, all_selected_paths, selected_indexes, param_values,) = request
+
+        if isinstance(self.df_ind_dict, dict):
+            # Filter out empty entries and concatenate
+            valid_dfs = [df for df in self.df_ind_dict.values() if isinstance(df, pd.DataFrame) and not df.empty]
+            if valid_dfs:
+                df_ind_combined = pd.concat(valid_dfs, ignore_index=True)
+            else:
+                df_ind_combined = pd.DataFrame()
+        else:
+            df_ind_combined = self.df_ind_dict
+        
+        if isinstance(self.cha_dict, dict):
+            valid_chas = [df for df in self.cha_dict.values() if isinstance(df, pd.DataFrame) and not df.empty]
+            cha_combined = pd.concat(valid_chas, ignore_index=True) if valid_chas else pd.DataFrame()
+        else:
+            cha_combined = self.cha_dict
+        
+        sample_path = file_paths_a[0]
+        p_haemo = self.haemo_dict.get(sample_path)
+
+        # Visualizations
+        for idx in selected_indexes:
+            if idx == 0:
+                run_cross_group_second_level_analysis(
+                    df_roi_all=df_ind_combined,  # Individual stats dataframe
+                    file_paths_a=file_paths_a,
+                    file_paths_b=file_paths_b,
+                    group_a_name=self.group_a_dropdown.currentText(),
+                    group_b_name=self.group_b_dropdown.currentText(),
+                    df_cha_all=cha_combined,
+                    raw_haemo=p_haemo,
+                    p_threshold=0.05,
+                    min_subjects=3,
+                    correction_method='fdr_bh',
+                    target_chroma='hbo',
+                    selected_event=selected_event,
+                    roi_config=r"C:\Users\tyler\Desktop\research\flares\regions.json",
+                    threshold_topo=False  # Shows the raw difference map (Unthresholded)
+                )
+            else:
+                print("no")

src/analysis/exporttocsv.py

@@ -1,27 +1,28 @@
 """
-Filename: exportcsv.py
+Filename: exporttocsv.py
-Description: Export data as csv analysis window for FLARES
+Description: Logic for the Export To CSV analysis window
 
 Author: Tyler de Zeeuw
 License: GPL-3.0
 """
 
+# Built-in imports
 import os
 
+# External library imports
 import numpy as np
 import pandas as pd
 
-from PySide6.QtWidgets import QFileDialog, QGridLayout, QHBoxLayout, QMessageBox, QPushButton, QScrollArea, QWidget, QVBoxLayout, QLabel
+from PySide6.QtWidgets import QFileDialog, QMessageBox
-from PySide6.QtCore import QSize
 
-from src.shared.flaresbasewidget import FlaresBaseWidget
+from src.shared.flaresbasewidget import CSVUIMixin, FlaresBaseWidget
 from src.shared.shareddata import APP_NAME
 
 
-class ExportDataAsCSVViewerWidget(FlaresBaseWidget):
+class ExportToCSVWidget(CSVUIMixin, FlaresBaseWidget):
     def __init__(self, haemo_dict, cha_dict, df_ind, design_matrix, group, contrast_results_dict):
-        super().__init__("ExportDataAsCSVViewer")
+        super().__init__("ExportToCSV")
-        self.setWindowTitle(f"Export Data As CSV Viewer - {APP_NAME.upper()}")
+        self.setWindowTitle(f"Export To CSV Viewer - {APP_NAME.upper()}")
         self.haemo_dict = haemo_dict
         self.cha_dict = cha_dict
         self.df_ind = df_ind
@@ -29,55 +30,11 @@ class ExportDataAsCSVViewerWidget(FlaresBaseWidget):
         self.group = group
         self.contrast_results_dict = contrast_results_dict
         
-        # Create mappings: file_path -> participant label and dropdown display text
+        self.setup_csv_ui(["0 (Export Data to CSV)", "1 (CSV for SPARKS)",])
-        self.participant_map = {}  # file_path -> "Participant 1"
-        self.participant_dropdown_items = []  # "Participant 1 (filename)"
-
-        for i, file_path in enumerate(self.haemo_dict.keys(), start=1):
-            short_label = f"Participant {i}"
-            display_label = f"{short_label} ({os.path.basename(file_path)})"
-            self.participant_map[file_path] = short_label
-            self.participant_dropdown_items.append(display_label)
-
-        self.layout = QVBoxLayout(self)
-        self.top_bar = QHBoxLayout()
-        self.layout.addLayout(self.top_bar)
-
-        self.participant_dropdown = self._create_multiselect_dropdown(self.participant_dropdown_items)
-        self.participant_dropdown.currentIndexChanged.connect(self.update_participant_dropdown_label)
-              
-        self.index_texts = [
-            "0 (Export Data to CSV)",
-            "1 (CSV for SPARKS)",
-            # "2 (third image)",
-            # "3 (fourth image)",
-            ]
-
-        self.image_index_dropdown = self._create_multiselect_dropdown(self.index_texts)
-        self.image_index_dropdown.currentIndexChanged.connect(self.update_image_index_dropdown_label)
-
-        self.submit_button = QPushButton("Submit")
-        self.submit_button.clicked.connect(self.generate_and_save_csv)
-
-        self.top_bar.addWidget(QLabel("Participants:"))
-        self.top_bar.addWidget(self.participant_dropdown)
-        self.top_bar.addWidget(QLabel("Export Type:"))
-        self.top_bar.addWidget(self.image_index_dropdown)
-        self.top_bar.addWidget(self.submit_button)
-
-        self.scroll = QScrollArea()
-        self.scroll.setWidgetResizable(True)
-        self.scroll_content = QWidget()
-        self.grid_layout = QGridLayout(self.scroll_content)
-        self.scroll.setWidget(self.scroll_content)
-        self.layout.addWidget(self.scroll)
-
-        self.thumb_size = QSize(280, 180)
-        self.showMaximized()
         
     
-    def generate_and_save_csv(self):
+    def process_request(self):
-        
+        # TODO: Move this into flares for the call?
         selected_display_names = self._get_checked_items(self.participant_dropdown)
         selected_file_paths = []
         for display_name in selected_display_names:

src/analysis/group.py

@@ -1,306 +0,0 @@
-"""
-Filename: group.py
-Description: Group analysis window for FLARES
-
-Author: Tyler de Zeeuw
-License: GPL-3.0
-"""
-
-import os
-
-import pandas as pd
-
-from PySide6.QtWidgets import QComboBox, QDialog, QGridLayout, QHBoxLayout, QPushButton, QScrollArea, QWidget, QVBoxLayout, QLabel
-from PySide6.QtCore import QSize
-
-from src.shared.flaresbasewidget import FlaresBaseWidget, ParameterInputDialog
-from src.shared.shareddata import APP_NAME
-
-
-class GroupViewerWidget(FlaresBaseWidget):
-    def __init__(self, haemo_dict, cha, df_ind, design_matrix, contrast_results, group):
-        super().__init__("GroupViewer")
-        self.setWindowTitle(f"Group 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.show_all_events = True
-        self._updating_checkstates = False
-        
-        # Create mappings: file_path -> participant label and dropdown display text
-        self.participant_map = {}  # file_path -> "Participant 1"
-        self.participant_dropdown_items = []  # "Participant 1 (filename)"
-
-        for i, file_path in enumerate(self.haemo_dict.keys(), start=1):
-            short_label = f"Participant {i}"
-            display_label = f"{short_label} ({os.path.basename(file_path)})"
-            self.participant_map[file_path] = short_label
-            self.participant_dropdown_items.append(display_label)
-
-        self.layout = QVBoxLayout(self)
-        self.top_bar = QHBoxLayout()
-        self.layout.addLayout(self.top_bar)
-
-        self.group_to_paths = {}
-        for file_path, group_name in self.group.items():
-            self.group_to_paths.setdefault(group_name, []).append(file_path)
-
-        self.group_names = sorted(self.group_to_paths.keys())
-
-        self.group_dropdown = QComboBox()
-        self.group_dropdown.addItem("<None Selected>")
-        self.group_dropdown.addItems(self.group_names)
-        self.group_dropdown.setCurrentIndex(0)
-        self.group_dropdown.currentIndexChanged.connect(self.update_participant_list_for_group)
-
-        self.participant_dropdown = self._create_multiselect_dropdown(self.participant_dropdown_items)
-        self.participant_dropdown.currentIndexChanged.connect(self.update_participant_dropdown_label)
-        self.participant_dropdown.setEnabled(False)
-
-        self.event_dropdown = QComboBox()
-        self.event_dropdown.addItem("<None Selected>")
-
-        self.index_texts = [
-            "0 (GLM Results)",
-            "1 (Significance)",
-            "2 (Brain Activity Visualization)",
-            # "3 (fourth image)",
-            ]
-
-        self.image_index_dropdown = self._create_multiselect_dropdown(self.index_texts)
-        self.image_index_dropdown.currentIndexChanged.connect(self.update_image_index_dropdown_label)
-
-        self.submit_button = QPushButton("Submit")
-        self.submit_button.clicked.connect(self.show_brain_images)
-
-        self.top_bar.addWidget(QLabel("Group:"))
-        self.top_bar.addWidget(self.group_dropdown)
-        self.top_bar.addWidget(QLabel("Participants:"))
-        self.top_bar.addWidget(self.participant_dropdown)
-        self.top_bar.addWidget(QLabel("Event:"))
-        self.top_bar.addWidget(self.event_dropdown)
-        self.top_bar.addWidget(QLabel("Image Indexes:"))
-        self.top_bar.addWidget(self.image_index_dropdown)
-        self.top_bar.addWidget(self.submit_button)
-
-        self.scroll = QScrollArea()
-        self.scroll.setWidgetResizable(True)
-        self.scroll_content = QWidget()
-        self.grid_layout = QGridLayout(self.scroll_content)
-        self.scroll.setWidget(self.scroll_content)
-        self.layout.addWidget(self.scroll)
-
-        self.thumb_size = QSize(280, 180)
-        self.showMaximized()
-
-
-
-    def show_brain_images(self):
-        import flares as flares
-
-        selected_event = self.event_dropdown.currentText()
-        if selected_event == "<None Selected>":
-            selected_event = None
-
-        selected_display_names = self._get_checked_items(self.participant_dropdown)
-        selected_file_paths = []
-        for display_name in selected_display_names:
-            for fp, short_label in self.participant_map.items():
-                expected_display = f"{short_label} ({os.path.basename(fp)})"
-                if display_name == expected_display:
-                    selected_file_paths.append(fp)
-                    break
-
-        if selected_event:
-            valid_paths = []
-            for fp in selected_file_paths:
-                raw = self.haemo_dict.get(fp)
-                # Check if this participant actually has the event in their annotations
-                if raw is not None and hasattr(raw, "annotations"):
-                    if selected_event in raw.annotations.description:
-                        valid_paths.append(fp)
-            
-            selected_file_paths = valid_paths
-
-        selected_indexes = [
-            int(s.split(" ")[0]) for s in self._get_checked_items(self.image_index_dropdown)
-        ]
-
-        if not selected_file_paths:
-            print("No participants selected.")
-            return
-
-        # Only keep indexes 0 and 1 that need parameters
-        parameterized_indexes = {
-            0: [
-                {
-                    "key": "lower_bound",
-                    "label": "Lower bound + <description>",
-                    "default": "-0.3",
-                    "type": float,  # specify int here
-                },
-                {
-                    "key": "upper_bound",
-                    "label": "Upper bound + <description>",
-                    "default": "0.8",
-                    "type": float,  # specify int here
-                }
-            ],
-            1: [
-                {
-                    "key": "p_value",
-                    "label": "Significance threshold P-value (e.g. 0.05)",
-                    "default": "0.05",
-                    "type": float,
-                },
-                {
-                    "key": "graph_bounds",
-                    "label": "Graph Upper/Lower Limit",
-                    "default": "3.0",
-                    "type": float,
-                }
-            ],
-            2: [
-                {
-                    "key": "show_optodes",
-                    "label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
-                    "default": "all",
-                    "type": str,
-                },
-                {
-                    "key": "t_or_theta",
-                    "label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
-                    "default": "theta",
-                    "type": str,
-                },
-                {
-                    "key": "show_text",
-                    "label": "Display informative text on the top left corner. THIS DOES NOT WORK AND SHOULD BE LEFT AT FALSE",
-                    "default": "False",
-                    "type": bool,
-                },
-                {
-                    "key": "brain_bounds",
-                    "label": "Graph Upper/Lower Limit",
-                    "default": "1.0",
-                    "type": float,
-                }
-            ],
-        }
-
-        # Inject full_text from index_texts
-        for idx, params_list in parameterized_indexes.items():
-            full_text = self.index_texts[idx] if idx < len(self.index_texts) else f"{idx} (No label found)"
-            for param_info in params_list:
-                param_info["full_text"] = full_text
-
-        indexes_needing_params = {idx: parameterized_indexes[idx] for idx in selected_indexes if idx in parameterized_indexes}
-
-        param_values = {}
-        if indexes_needing_params:
-            dialog = ParameterInputDialog(indexes_needing_params, parent=self)
-            if dialog.exec_() == QDialog.Accepted:
-                param_values = dialog.get_values()
-                if param_values is None:
-                    return
-            else:
-                return
-            
-
-        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)
-
-        # Pass the necessary arguments to each method
-        file_path = selected_file_paths[0]
-        p_haemo = self.haemo_dict.get(file_path)
-        p_design_matrix = self.design_matrix.get(file_path)
-
-        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, {})
-                lower_bound = params.get("lower_bound", None)
-                upper_bound = params.get("upper_bound", None)
-
-                if lower_bound is None or upper_bound is None:
-                    print(f"Missing parameters for index {idx}, skipping.")
-                    continue
-
-
-                flares.plot_fir_model_results(df_group, p_haemo, p_design_matrix, selected_event, lower_bound, upper_bound)
-
-            elif idx == 1:
-                params = param_values.get(idx, {})
-                p_val = params.get("p_value", None)
-                graph_bounds = params.get("graph_bounds", None)
-
-                if p_val is None or graph_bounds is None:
-                    print(f"Missing parameters for index {idx}, skipping.")
-                    continue
-
-                all_contrasts = []
-                for fp in selected_file_paths:
-                    condition_dfs = self.contrast_results.get(fp, {})
-                    if selected_event in condition_dfs:
-                        df = condition_dfs[selected_event].copy()
-                        df["ID"] = fp
-                        all_contrasts.append(df)
-
-                if not all_contrasts:
-                    print("No contrast data found for selected participants and event.")
-                    return
-
-                df_contrasts = pd.concat(all_contrasts, ignore_index=True)
-                flares.run_second_level_analysis(df_contrasts, p_haemo, p_val, graph_bounds)
-
-            elif idx == 2:
-                params = param_values.get(idx, {})
-                show_optodes = params.get("show_optodes", None)
-                t_or_theta = params.get("t_or_theta", None)
-                show_text = params.get("show_text", None)
-                brain_bounds = params.get("brain_bounds", None)
-
-                if show_optodes is None or t_or_theta is None or show_text is None or brain_bounds is None:
-                    print(f"Missing parameters for index {idx}, skipping.")
-                    continue
-
-                raw_list = [self.haemo_dict.get(fp) for fp in selected_file_paths]
-                
-                if len(selected_file_paths) > 1:
-                    print(f"Aggregating geometry for {len(selected_file_paths)} participants...")
-                    processed_raw = flares.aggregate_fnirs_group_geometry(raw_list)
-                else:
-                    processed_raw = raw_list[0].copy().pick(picks="hbo")
-
-                flares.brain_3d_visualization(processed_raw, all_cha, selected_event, t_or_theta=t_or_theta, show_optodes=show_optodes, show_text=show_text, brain_bounds=brain_bounds)
-                    
-            elif idx == 3:
-                pass
-
-            else:
-                print(f"No method defined for index {idx}")

src/analysis/groupbrain.py

@@ -1,311 +0,0 @@
-"""
-Filename: groupbrain.py
-Description: Group brain analysis window for FLARES
-
-Author: Tyler de Zeeuw
-License: GPL-3.0
-"""
-
-import os
-
-import pandas as pd
-
-from PySide6.QtWidgets import QComboBox, QDialog, QGridLayout, QHBoxLayout, QPushButton, QScrollArea, QWidget, QVBoxLayout, QLabel
-from PySide6.QtCore import QSize
-
-from src.shared.flaresbasewidget import FlaresBaseWidget, ParameterInputDialog
-from src.shared.shareddata import APP_NAME
-
-
-class GroupBrainViewerWidget(FlaresBaseWidget):
-    def __init__(self, haemo_dict, df_ind, design_matrix, group, contrast_results_dict):
-        super().__init__("GroupBrainViewer")
-        self.setWindowTitle(f"Group Brain Viewer - {APP_NAME.upper()}")
-        self.haemo_dict = haemo_dict
-        self.df_ind = df_ind
-        self.design_matrix = design_matrix
-        self.group = group
-        self.contrast_results_dict = contrast_results_dict
-
-        self.group_to_paths = {}
-        for file_path, group_name in self.group.items():
-            self.group_to_paths.setdefault(group_name, []).append(file_path)
-
-        self.group_names = sorted(self.group_to_paths.keys())
-
-        self.layout = QVBoxLayout(self)
-        self.top_bar = QHBoxLayout()
-        self.layout.addLayout(self.top_bar)
-
-
-        self.group_a_dropdown = QComboBox()
-        self.group_a_dropdown.addItem("<None Selected>")
-        self.group_a_dropdown.addItems(self.group_names)
-        self.group_a_dropdown.currentIndexChanged.connect(self._update_group_a_options)
-
-
-        self.group_b_dropdown = QComboBox()
-        self.group_b_dropdown.addItem("<None Selected>")
-        self.group_b_dropdown.addItems(self.group_names)
-        self.group_b_dropdown.currentIndexChanged.connect(self._update_group_b_options)
-
-
-        self.event_dropdown = QComboBox()
-        self.event_dropdown.addItem("<None Selected>")
-
-        self.participant_dropdown_a = self._create_multiselect_dropdown([])
-        self.participant_dropdown_a.lineEdit().setPlaceholderText("Select participants (Group A)")
-        self.participant_dropdown_a.model().itemChanged.connect(self._on_participants_changed)
-
-
-        self.participant_dropdown_b = self._create_multiselect_dropdown([])
-        self.participant_dropdown_b.lineEdit().setPlaceholderText("Select participants (Group B)")
-        self.participant_dropdown_b.model().itemChanged.connect(self._on_participants_changed)
-
-
-        self.index_texts = [
-            "0 (Contrast Image)",
-            # "1 (3D Brain Contrast)",
-            # "2 (third image)",
-            # "3 (fourth image)",
-        ]
-        self.image_index_dropdown = self._create_multiselect_dropdown(self.index_texts)
-        self.image_index_dropdown.currentIndexChanged.connect(self.update_image_index_dropdown_label)
-
-
-        self.submit_button = QPushButton("Submit")
-        self.submit_button.clicked.connect(self.show_brain_images)
-
-
-        self.top_bar.addWidget(QLabel("Group A:"))
-        self.top_bar.addWidget(self.group_a_dropdown)
-        self.top_bar.addWidget(QLabel("Participants (Group A):"))
-        self.top_bar.addWidget(self.participant_dropdown_a)
-        self.top_bar.addWidget(QLabel("Group B:"))
-        self.top_bar.addWidget(self.group_b_dropdown)
-        self.top_bar.addWidget(QLabel("Participants (Group B):"))
-        self.top_bar.addWidget(self.participant_dropdown_b)
-        self.top_bar.addWidget(QLabel("Event:"))
-        self.top_bar.addWidget(self.event_dropdown)
-        self.top_bar.addWidget(QLabel("Image Indexes:"))
-        self.top_bar.addWidget(self.image_index_dropdown)
-        self.top_bar.addWidget(self.submit_button)
-
-        self.scroll = QScrollArea()
-        self.scroll.setWidgetResizable(True)
-        self.scroll_content = QWidget()
-        self.grid_layout = QGridLayout(self.scroll_content)
-        self.scroll.setWidget(self.scroll_content)
-        self.layout.addWidget(self.scroll)
-
-        self.thumb_size = QSize(280, 180)
-        self.showMaximized()
-
-    def _update_group_b_options(self):
-        """Triggered when Group B changes: Update Group A to exclude B's choice"""
-        selected_b = self.group_b_dropdown.currentText()
-        
-        # Refresh Group A and exclude what was just picked in Group B
-        self._refresh_group_dropdown(self.group_a_dropdown, exclude=selected_b)
-        
-        # Update the participants for Group B
-        self.update_participant_list_for_group(selected_b, self.participant_dropdown_b)
-        self._update_event_dropdown()
-
-    def _update_group_a_options(self):
-        """Triggered when Group A changes: Update Group B to exclude A's choice"""
-        selected_a = self.group_a_dropdown.currentText()
-        
-        # Refresh Group B and exclude what was just picked in Group A
-        self._refresh_group_dropdown(self.group_b_dropdown, exclude=selected_a)
-        
-        # Update the participants for Group A
-        self.update_participant_list_for_group(selected_a, self.participant_dropdown_a)
-        self._update_event_dropdown()
-
-    def _on_participants_changed(self, item=None):
-        self._update_event_dropdown()
-
-
-    def _refresh_group_dropdown(self, dropdown, exclude):
-        current = dropdown.currentText()
-        dropdown.blockSignals(True)
-        dropdown.clear()
-        dropdown.addItem("<None Selected>")
-        for group in self.group_names:
-            if group != exclude:
-                dropdown.addItem(group)
-        # Restore previous selection if still valid
-        if current != "<None Selected>" and current != exclude and dropdown.findText(current) != -1:
-            dropdown.setCurrentText(current)
-        else:
-            dropdown.setCurrentIndex(0)  # Reset to "<None Selected>"
-        dropdown.blockSignals(False)
-
-
-    def _get_file_paths_from_labels(self, labels, group_name):
-        file_paths = []
-
-        if group_name == self.group_a_dropdown.currentText():
-            participant_map = self.participant_map_a
-        elif group_name == self.group_b_dropdown.currentText():
-            participant_map = self.participant_map_b
-        else:
-            return []
-
-        # Reverse map: display label -> file path
-        reverse_map = {
-            f"{label} ({os.path.basename(fp)})": fp
-            for fp, label in participant_map.items()
-        }
-
-        for label in labels:
-            file_path = reverse_map.get(label)
-            if file_path:
-                file_paths.append(file_path)
-
-        return file_paths
-
-    def show_brain_images(self):
-        import flares as flares
-
-        selected_event = self.event_dropdown.currentText()
-        if selected_event == "<None Selected>":
-            selected_event = None
-
-        # Group A
-        participants_a = self._get_checked_items(self.participant_dropdown_a)
-        file_paths_a = self._get_file_paths_from_labels(participants_a, self.group_a_dropdown.currentText())
-
-        # Group B
-        participants_b = self._get_checked_items(self.participant_dropdown_b)
-        file_paths_b = self._get_file_paths_from_labels(participants_b, self.group_b_dropdown.currentText())
-
-        selected_indexes = [
-            int(s.split(" ")[0]) for s in self._get_checked_items(self.image_index_dropdown)
-        ]
-
-        all_selected_paths = list(set(file_paths_a + file_paths_b))
-        
-        if not all_selected_paths:
-            print("No participants selected.")
-            return
-        
-        parameterized_indexes = {
-            0: [
-                {
-                    "key": "show_optodes",
-                    "label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
-                    "default": "all",
-                    "type": str,
-                },
-                {
-                    "key": "t_or_theta",
-                    "label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
-                    "default": "theta",
-                    "type": str,
-                },
-                {
-                    "key": "show_text",
-                    "label": "Display informative text on the top left corner about the contrast.",
-                    "default": "True",
-                    "type": bool,
-                },
-                {
-                    "key": "brain_bounds",
-                    "label": "Graph Upper/Lower Limit",
-                    "default": "1.0",
-                    "type": float,
-                },
-                {
-                    "key": "is_3d",
-                    "label": "Should we display the results in a 3D interactive window?",
-                    "default": "True",
-                    "type": bool,
-                }
-            ],
-        }
-        
-
-        # Inject full_text from index_texts
-        for idx, params_list in parameterized_indexes.items():
-            full_text = self.index_texts[idx] if idx < len(self.index_texts) else f"{idx} (No label found)"
-            for param_info in params_list:
-                param_info["full_text"] = full_text
-
-        indexes_needing_params = {idx: parameterized_indexes[idx] for idx in selected_indexes if idx in parameterized_indexes}
-
-        param_values = {}
-        if indexes_needing_params:
-            dialog = ParameterInputDialog(indexes_needing_params, parent=self)
-            if dialog.exec_() == QDialog.Accepted:
-                param_values = dialog.get_values()
-                if param_values is None:
-                    return
-            else:
-                return
-
-        # Build group-level contrast DataFrames
-        def concat_group_contrasts(file_paths: list[str], event: str | None) -> pd.DataFrame:
-            group_df = pd.DataFrame()
-            for fp in file_paths:
-                print(f"Looking up contrast for: {fp}")
-                event_con_dict = self.contrast_results_dict.get(fp, {})
-                print("Available events for this file:", list(event_con_dict.keys()))
-                if event and event in event_con_dict:
-                    df = event_con_dict[event]
-                    print(f"Appending contrast df for event: {event}")
-                    group_df = pd.concat([group_df, df], ignore_index=True)
-                else:
-                    print(f"Event '{event}' not found for {fp}")
-            return group_df
-
-        print("Selected event:", selected_event)
-        print("File paths A:", file_paths_a)
-        print("File paths B:", file_paths_b)
-
-        contrast_df_a = concat_group_contrasts(file_paths_a, selected_event)
-        contrast_df_b = concat_group_contrasts(file_paths_b, selected_event)
-
-        print("contrast_df_a empty?", contrast_df_a.empty)
-        print("contrast_df_b empty?", contrast_df_b.empty)
-
-        all_raw_objs = [self.haemo_dict.get(fp) for fp in all_selected_paths if self.haemo_dict.get(fp)]
-        
-        if len(all_raw_objs) > 1:
-            processed_raw = flares.aggregate_fnirs_group_geometry(all_raw_objs)
-        else:
-            processed_raw = all_raw_objs[0].copy().pick(picks="hbo")
-
-        # Visualizations
-        for idx in selected_indexes:
-            if idx == 0:
-                params = param_values.get(idx, {})
-                show_optodes = params.get("show_optodes", None)
-                t_or_theta = params.get("t_or_theta", None)
-                show_text = params.get("show_text", None)
-                brain_bounds = params.get("brain_bounds", None)
-                is_3d = params.get("is_3d", None)
-
-                if show_optodes is None or t_or_theta is None or show_text is None or brain_bounds is None or is_3d is None:
-                    print(f"Missing parameters for index {idx}, skipping.")
-                    continue
-
-                if not contrast_df_a.empty and not contrast_df_b.empty and processed_raw:
-
-                    flares.plot_2d_3d_contrasts_between_groups(
-                        contrast_df_a,
-                        contrast_df_b,
-                        raw_haemo=processed_raw,
-                        group_a_name=self.group_a_dropdown.currentText(),
-                        group_b_name=self.group_b_dropdown.currentText(),
-                        is_3d=is_3d,
-                        t_or_theta=t_or_theta,
-                        show_optodes=show_optodes,
-                        show_text=show_text,
-                        brain_bounds=brain_bounds
-                    )
-                else:
-                    print("no")
-           
-

src/analysis/intergroupbrainimage.py

@@ -0,0 +1,190 @@
+"""
+Filename: intergroupbrainimage.py
+Description: Logic for the Inter-Group Brain & Image analysis window
+
+Author: Tyler de Zeeuw
+License: GPL-3.0
+"""
+
+# External library imports
+import pandas as pd
+
+from flares import aggregate_fnirs_group_geometry, plot_fir_model_results, brain_3d_visualization
+from src.shared.flaresbasewidget import InterGroupUIMixin, FlaresBaseWidget
+from src.shared.shareddata import APP_NAME
+
+
+PARAMETERIZED_INDEXES = {
+    0: [
+        {
+            "key": "lower_bound",
+            "label": "Lower bound + <description>",
+            "default": "-0.3",
+            "type": float,  # specify int here
+        },
+        {
+            "key": "upper_bound",
+            "label": "Upper bound + <description>",
+            "default": "0.8",
+            "type": float,  # specify int here
+        }
+    ],
+    1: [
+        {
+            "key": "p_value",
+            "label": "Significance threshold P-value (e.g. 0.05)",
+            "default": "0.05",
+            "type": float,
+        },
+        {
+            "key": "graph_bounds",
+            "label": "Graph Upper/Lower Limit",
+            "default": "3.0",
+            "type": float,
+        }
+    ],
+    2: [
+        {
+            "key": "show_optodes",
+            "label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
+            "default": "all",
+            "type": str,
+        },
+        {
+            "key": "t_or_theta",
+            "label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
+            "default": "theta",
+            "type": str,
+        },
+        {
+            "key": "show_text",
+            "label": "Display informative text on the top left corner. THIS DOES NOT WORK AND SHOULD BE LEFT AT FALSE",
+            "default": "False",
+            "type": bool,
+        },
+        {
+            "key": "brain_bounds",
+            "label": "Graph Upper/Lower Limit",
+            "default": "1.0",
+            "type": float,
+        }
+    ],
+}
+
+
+
+class InterGroupBrainImageWidget(InterGroupUIMixin, FlaresBaseWidget):
+    def __init__(self, haemo_dict, cha, df_ind, design_matrix, contrast_results, group):
+        super().__init__("InterGroupBrainImage")
+        self.setWindowTitle(f"Inter-Group Brain & Image 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 (GLM Results)", "1 (Significance)", "2 (Brain Activity Visualization)",])
+
+
+    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)
+
+        # Pass the necessary arguments to each method
+        file_path = selected_file_paths[0]
+        p_haemo = self.haemo_dict.get(file_path)
+        p_design_matrix = self.design_matrix.get(file_path)
+
+        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, {})
+                lower_bound = params.get("lower_bound", None)
+                upper_bound = params.get("upper_bound", None)
+
+                if lower_bound is None or upper_bound is None:
+                    print(f"Missing parameters for index {idx}, skipping.")
+                    continue
+
+
+                plot_fir_model_results(df_group, p_haemo, p_design_matrix, selected_event, lower_bound, upper_bound)
+
+            elif idx == 1:
+                params = param_values.get(idx, {})
+                p_val = params.get("p_value", None)
+                graph_bounds = params.get("graph_bounds", None)
+
+                if p_val is None or graph_bounds is None:
+                    print(f"Missing parameters for index {idx}, skipping.")
+                    continue
+
+                all_contrasts = []
+                for fp in selected_file_paths:
+                    condition_dfs = self.contrast_results.get(fp, {})
+                    if selected_event in condition_dfs:
+                        df = condition_dfs[selected_event].copy()
+                        df["ID"] = fp
+                        all_contrasts.append(df)
+
+                if not all_contrasts:
+                    print("No contrast data found for selected participants and event.")
+                    return
+
+                df_contrasts = pd.concat(all_contrasts, ignore_index=True)
+                #flares.run_second_level_analysis(df_contrasts, p_haemo, p_val, graph_bounds)
+
+            elif idx == 2:
+                params = param_values.get(idx, {})
+                show_optodes = params.get("show_optodes", None)
+                t_or_theta = params.get("t_or_theta", None)
+                show_text = params.get("show_text", None)
+                brain_bounds = params.get("brain_bounds", None)
+
+                if show_optodes is None or t_or_theta is None or show_text is None or brain_bounds is None:
+                    print(f"Missing parameters for index {idx}, skipping.")
+                    continue
+
+                raw_list = [self.haemo_dict.get(fp) for fp in selected_file_paths]
+                
+                if len(selected_file_paths) > 1:
+                    print(f"Aggregating geometry for {len(selected_file_paths)} participants...")
+                    processed_raw = aggregate_fnirs_group_geometry(raw_list)
+                else:
+                    processed_raw = raw_list[0].copy().pick(picks="hbo")
+
+                brain_3d_visualization(processed_raw, all_cha, selected_event, t_or_theta=t_or_theta, show_optodes=show_optodes, show_text=show_text, brain_bounds=brain_bounds)
+                    
+            elif idx == 3:
+                pass
+
+            else:
+                print(f"No method defined for index {idx}")

src/analysis/intergroupstats.py

@@ -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}")

src/analysis/participantbrain.py

@@ -1,161 +1,78 @@
 """
 Filename: participantbrain.py
-Description: Participant brain analysis window for FLARES
+Description: Logic for the Participant Brain analysis window
 
 Author: Tyler de Zeeuw
 License: GPL-3.0
 """
 
-import os
+# External library imports
-
+from flares import brain_3d_visualization, brain_landmarks_3d
-from PySide6.QtWidgets import QComboBox, QDialog, QGridLayout, QHBoxLayout, QPushButton, QScrollArea, QWidget, QVBoxLayout, QLabel
+from src.shared.flaresbasewidget import ParticipantUIMixin, FlaresBaseWidget
-from PySide6.QtCore import QSize
-
-from src.shared.flaresbasewidget import FlaresBaseWidget, ParameterInputDialog
 from src.shared.shareddata import APP_NAME
 
 
-class ParticipantBrainViewerWidget(FlaresBaseWidget):
+PARAMETERIZED_INDEXES = {
+    0: [
+        {
+            "key": "show_optodes",
+            "label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
+            "default": "all",
+            "type": str,
+        },
+        {
+            "key": "show_brodmann",
+            "label": "Show common brodmann areas on the brain.",
+            "default": "True",
+            "type": bool,
+        }
+    ],
+    1: [
+        {
+            "key": "show_optodes",
+            "label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
+            "default": "all",
+            "type": str,
+        },
+        {
+            "key": "t_or_theta",
+            "label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
+            "default": "theta",
+            "type": str,
+        },
+        {
+            "key": "show_text",
+            "label": "Display informative text on the top left corner. THIS DOES NOT WORK AND SHOULD BE LEFT AT FALSE",
+            "default": "False",
+            "type": bool,
+        },
+        {
+            "key": "brain_bounds",
+            "label": "Graph Upper/Lower Limit",
+            "default": "1.0",
+            "type": float,
+        }
+    ],
+}
+
+
+class ParticipantBrainViewerWidget(ParticipantUIMixin, FlaresBaseWidget):
     def __init__(self, haemo_dict, cha_dict):
         super().__init__("ParticipantBrainViewer")
         self.setWindowTitle(f"Participant Brain Viewer - {APP_NAME.upper()}")
         self.haemo_dict = haemo_dict
         self.cha_dict = cha_dict
 
-        # Create mappings: file_path -> participant label and dropdown display text
+        self.setup_participant_ui(["0 (Brain Landmarks)", "1 (Brain Activity Visualization)",])
-        self.participant_map = {}  # file_path -> "Participant 1"
-        self.participant_dropdown_items = []  # "Participant 1 (filename)"
-
-        for i, file_path in enumerate(self.haemo_dict.keys(), start=1):
-            short_label = f"Participant {i}"
-            display_label = f"{short_label} ({os.path.basename(file_path)})"
-            self.participant_map[file_path] = short_label
-            self.participant_dropdown_items.append(display_label)
-
-        self.layout = QVBoxLayout(self)
-        self.top_bar = QHBoxLayout()
-        self.layout.addLayout(self.top_bar)
-
-        self.participant_dropdown = self._create_multiselect_dropdown(self.participant_dropdown_items)
-        self.participant_dropdown.currentIndexChanged.connect(self.update_participant_dropdown_label)
-
-        self.event_dropdown = QComboBox()
-        self.event_dropdown.addItem("<None Selected>")
 
 
-        self.index_texts = [
+    def process_request(self):
-            "0 (Brain Landmarks)",
-            "1 (Brain Activity Visualization)",
-            # "2 (third image)",
-            # "3 (fourth image)",
-            ]
 
-        self.image_index_dropdown = self._create_multiselect_dropdown(self.index_texts)
+        request = self.get_common_request_data(PARAMETERIZED_INDEXES)
-        self.image_index_dropdown.currentIndexChanged.connect(self.update_image_index_dropdown_label)
+        if request is None:
+            return
 
-        self.submit_button = QPushButton("Submit")
+        (selected_event, selected_file_paths, selected_indexes, param_values,) = request  
-        self.submit_button.clicked.connect(self.show_brain_images)
-
-        self.top_bar.addWidget(QLabel("Participants:"))
-        self.top_bar.addWidget(self.participant_dropdown)
-        self.top_bar.addWidget(QLabel("Event:"))
-        self.top_bar.addWidget(self.event_dropdown)
-        self.top_bar.addWidget(QLabel("Image Indexes:"))
-        self.top_bar.addWidget(self.image_index_dropdown)
-        self.top_bar.addWidget(self.submit_button)
-
-        self.scroll = QScrollArea()
-        self.scroll.setWidgetResizable(True)
-        self.scroll_content = QWidget()
-        self.grid_layout = QGridLayout(self.scroll_content)
-        self.scroll.setWidget(self.scroll_content)
-        self.layout.addWidget(self.scroll)
-
-        self.thumb_size = QSize(280, 180)
-        self.showMaximized()
-    
-
-    def show_brain_images(self):
-        import flares as flares
-
-        selected_event = self.event_dropdown.currentText()
-        if selected_event == "<None Selected>":
-            selected_event = None
-
-        selected_display_names = self._get_checked_items(self.participant_dropdown)
-        selected_file_paths = []
-        for display_name in selected_display_names:
-            for fp, short_label in self.participant_map.items():
-                expected_display = f"{short_label} ({os.path.basename(fp)})"
-                if display_name == expected_display:
-                    selected_file_paths.append(fp)
-                    break
-
-        selected_indexes = [
-            int(s.split(" ")[0]) for s in self._get_checked_items(self.image_index_dropdown)
-        ]
-
-
-        parameterized_indexes = {
-            0: [
-                {
-                    "key": "show_optodes",
-                    "label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
-                    "default": "all",
-                    "type": str,
-                },
-                {
-                    "key": "show_brodmann",
-                    "label": "Show common brodmann areas on the brain.",
-                    "default": "True",
-                    "type": bool,
-                }
-            ],
-            1: [
-                {
-                    "key": "show_optodes",
-                    "label": "Determine what is rendered above the brain. Valid values are 'sensors', 'labels', 'none', 'all'.",
-                    "default": "all",
-                    "type": str,
-                },
-                {
-                    "key": "t_or_theta",
-                    "label": "Specify if t values or theta values should be plotted. Valid values are 't', 'theta'",
-                    "default": "theta",
-                    "type": str,
-                },
-                {
-                    "key": "show_text",
-                    "label": "Display informative text on the top left corner. THIS DOES NOT WORK AND SHOULD BE LEFT AT FALSE",
-                    "default": "False",
-                    "type": bool,
-                },
-                {
-                    "key": "brain_bounds",
-                    "label": "Graph Upper/Lower Limit",
-                    "default": "1.0",
-                    "type": float,
-                }
-            ],
-        }
-
-        # Inject full_text from index_texts
-        for idx, params_list in parameterized_indexes.items():
-            full_text = self.index_texts[idx] if idx < len(self.index_texts) else f"{idx} (No label found)"
-            for param_info in params_list:
-                param_info["full_text"] = full_text
-
-        indexes_needing_params = {idx: parameterized_indexes[idx] for idx in selected_indexes if idx in parameterized_indexes}
-
-        param_values = {}
-        if indexes_needing_params:
-            dialog = ParameterInputDialog(indexes_needing_params, parent=self)
-            if dialog.exec_() == QDialog.Accepted:
-                param_values = dialog.get_values()
-                if param_values is None:
-                    return
-            else:
-                return
 
         # Pass the necessary arguments to each method
         for file_path in selected_file_paths:
@@ -183,7 +100,7 @@ class ParticipantBrainViewerWidget(FlaresBaseWidget):
                         print(f"Missing parameters for index {idx}, skipping.")
                         continue
 
-                    flares.brain_landmarks_3d(haemo_obj, show_optodes, show_brodmann)
+                    brain_landmarks_3d(haemo_obj, show_optodes, show_brodmann)
 
                 elif idx == 1:
                     params = param_values.get(idx, {})
@@ -196,7 +113,7 @@ class ParticipantBrainViewerWidget(FlaresBaseWidget):
                         print(f"Missing parameters for index {idx}, skipping.")
                         continue
 
-                    flares.brain_3d_visualization(haemo_obj, cha, selected_event, t_or_theta=t_or_theta, show_optodes=show_optodes, show_text=show_text, brain_bounds=brain_bounds)
+                    brain_3d_visualization(haemo_obj, cha, selected_event, t_or_theta=t_or_theta, show_optodes=show_optodes, show_text=show_text, brain_bounds=brain_bounds)
 
                 else:
                     print(f"No method defined for index {idx}")

src/analysis/participantimage.py

@@ -18,9 +18,9 @@ from src.shared.flaresbasewidget import ClickableLabel, FlaresBaseWidget
 from src.shared.shareddata import APP_NAME
 
 
-class ParticipantViewerWidget(FlaresBaseWidget):
+class ParticipantImageViewerWidget(FlaresBaseWidget):
     def __init__(self, haemo_dict, fig_bytes_dict):
-        super().__init__("ParticipantViewer")
+        super().__init__("ParticipantImage")
         self.setAttribute(Qt.WidgetAttribute.WA_DeleteOnClose)
         self.setWindowTitle(f"Participant Viewer - {APP_NAME.upper()}")
         self.haemo_dict = haemo_dict

src/shared/flaresbasewidget.py

@@ -8,9 +8,9 @@ License: GPL-3.0
 
 import os
 
-from PySide6.QtWidgets import QApplication, QComboBox, QDialog, QHBoxLayout, QLabel, QLineEdit, QListView, QMessageBox, QPushButton, QVBoxLayout, QWidget, QFrame, QSpinBox
+from PySide6.QtWidgets import QApplication, QComboBox, QDialog, QGridLayout, QHBoxLayout, QLabel, QLineEdit, QListView, QMessageBox, QPushButton, QScrollArea, QVBoxLayout, QWidget, QFrame, QSpinBox
 from PySide6.QtGui import QStandardItemModel, QStandardItem, QPixmap, QIntValidator, QDoubleValidator
-from PySide6.QtCore import QEvent, Qt
+from PySide6.QtCore import QEvent, QSize, Qt
 
 from src.shared.shareddata import APP_NAME
 
@@ -836,9 +836,9 @@ class FlaresBaseWidget(QWidget):
         # 3. Conditional trigger for event updates
         # We only update events if we aren't in one of the excluded viewers
         excluded_viewers = {
-            "ParticipantViewer", 
+            "ParticipantImage", 
             "ParticipantFoldChannels", 
-            "ExportDataAsCSVViewer", 
+            "ExportToCSV", 
         }
         
         if getattr(self, "caller", None) not in excluded_viewers:
@@ -1051,3 +1051,479 @@ class FlaresBaseWidget(QWidget):
 
         self._connect_select_all_toggle(toggle_ref, model)
         self.update_participant_dropdown_label(combo=target_combo)
+
+
+class CrossGroupUIMixin:
+
+    def setup_cross_group_ui(self, index_texts):
+
+        self.group_to_paths = {}
+        for file_path, group_name in self.group_dict.items():
+            self.group_to_paths.setdefault(group_name, []).append(file_path)
+
+        self.group_names = sorted(self.group_to_paths.keys())
+
+        self.main_layout = QVBoxLayout(self)
+        self.top_bar = QHBoxLayout()
+        self.main_layout.addLayout(self.top_bar)
+
+
+        self.group_a_dropdown = QComboBox()
+        self.group_a_dropdown.addItem("<None Selected>")
+        self.group_a_dropdown.addItems(self.group_names)
+        self.group_a_dropdown.currentIndexChanged.connect(self._update_group_a_options)
+
+
+        self.group_b_dropdown = QComboBox()
+        self.group_b_dropdown.addItem("<None Selected>")
+        self.group_b_dropdown.addItems(self.group_names)
+        self.group_b_dropdown.currentIndexChanged.connect(self._update_group_b_options)
+
+
+        self.event_dropdown = QComboBox()
+        self.event_dropdown.addItem("<None Selected>")
+
+        
+        self.participant_dropdown_a = self._create_multiselect_dropdown([])
+        line_edit = self.participant_dropdown_a.lineEdit()
+        assert line_edit is not None, "Dropdown A must be editable to have a lineEdit"
+        line_edit.setPlaceholderText("Select participants (Group A)")   
+        model = self.participant_dropdown_a.model()
+        assert isinstance(model, QStandardItemModel), "Model must be QStandardItemModel"
+        model.itemChanged.connect(self._on_participants_changed)
+
+
+        self.participant_dropdown_b = self._create_multiselect_dropdown([])
+        line_edit = self.participant_dropdown_b.lineEdit()
+        assert line_edit is not None, "Dropdown B must be editable to have a lineEdit"
+        line_edit.setPlaceholderText("Select participants (Group B)")   
+        model = self.participant_dropdown_b.model()
+        assert isinstance(model, QStandardItemModel), "Model must be QStandardItemModel"
+        model.itemChanged.connect(self._on_participants_changed)
+
+
+        self.index_texts = index_texts
+        self.image_index_dropdown = self._create_multiselect_dropdown(self.index_texts)
+        self.image_index_dropdown.currentIndexChanged.connect(self.update_image_index_dropdown_label)
+
+
+        self.submit_button = QPushButton("Submit")
+        self.submit_button.clicked.connect(self.proccess_request)
+
+
+        self.top_bar.addWidget(QLabel("Group A:"))
+        self.top_bar.addWidget(self.group_a_dropdown)
+        self.top_bar.addWidget(QLabel("Participants (Group A):"))
+        self.top_bar.addWidget(self.participant_dropdown_a)
+        self.top_bar.addWidget(QLabel("Group B:"))
+        self.top_bar.addWidget(self.group_b_dropdown)
+        self.top_bar.addWidget(QLabel("Participants (Group B):"))
+        self.top_bar.addWidget(self.participant_dropdown_b)
+        self.top_bar.addWidget(QLabel("Event:"))
+        self.top_bar.addWidget(self.event_dropdown)
+        self.top_bar.addWidget(QLabel("Image Indexes:"))
+        self.top_bar.addWidget(self.image_index_dropdown)
+        self.top_bar.addWidget(self.submit_button)
+
+        self.scroll_area = QScrollArea()
+        self.scroll_area.setWidgetResizable(True)
+        self.scroll_content = QWidget()
+        self.grid_layout = QGridLayout(self.scroll_content)
+        self.scroll_area.setWidget(self.scroll_content)
+        self.main_layout.addWidget(self.scroll_area)
+
+        self.thumb_size = QSize(280, 180)
+        self.showMaximized()
+
+    def _update_group_b_options(self):
+        """Triggered when Group B changes: Update Group A to exclude B's choice"""
+        selected_b = self.group_b_dropdown.currentText()
+        
+        # Refresh Group A and exclude what was just picked in Group B
+        self._refresh_group_dropdown(self.group_a_dropdown, exclude=selected_b)
+        
+        # Update the participants for Group B
+        self.update_participant_list_for_group(selected_b, self.participant_dropdown_b)
+        self._update_event_dropdown()
+
+    def _update_group_a_options(self):
+        """Triggered when Group A changes: Update Group B to exclude A's choice"""
+        selected_a = self.group_a_dropdown.currentText()
+        
+        # Refresh Group B and exclude what was just picked in Group A
+        self._refresh_group_dropdown(self.group_b_dropdown, exclude=selected_a)
+        
+        # Update the participants for Group A
+        self.update_participant_list_for_group(selected_a, self.participant_dropdown_a)
+        self._update_event_dropdown()
+
+    def _on_participants_changed(self, item=None):
+        self._update_event_dropdown()
+
+
+    def _refresh_group_dropdown(self, dropdown, exclude):
+        current = dropdown.currentText()
+        dropdown.blockSignals(True)
+        dropdown.clear()
+        dropdown.addItem("<None Selected>")
+        for group in self.group_names:
+            if group != exclude:
+                dropdown.addItem(group)
+        # Restore previous selection if still valid
+        if current != "<None Selected>" and current != exclude and dropdown.findText(current) != -1:
+            dropdown.setCurrentText(current)
+        else:
+            dropdown.setCurrentIndex(0)  # Reset to "<None Selected>"
+        dropdown.blockSignals(False)
+
+    
+
+
+    def _get_file_paths_from_labels(self, labels, group_name):
+        file_paths = []
+
+        if group_name == self.group_a_dropdown.currentText():
+            participant_map = self.participant_map_a
+        elif group_name == self.group_b_dropdown.currentText():
+            participant_map = self.participant_map_b
+        else:
+            return []
+
+        # Reverse map: display label -> file path
+        reverse_map = {
+            f"{label} ({os.path.basename(fp)})": fp
+            for fp, label in participant_map.items()
+        }
+
+        for label in labels:
+            file_path = reverse_map.get(label)
+            if file_path:
+                file_paths.append(file_path)
+
+        return file_paths
+    
+    def get_common_request_data(self, parameterized_indexes):
+        selected_event = self.event_dropdown.currentText()
+        if selected_event == "<None Selected>":
+            selected_event = None
+
+        participants_a = self._get_checked_items(self.participant_dropdown_a)
+        file_paths_a = self._get_file_paths_from_labels(
+            participants_a, self.group_a_dropdown.currentText()
+        )
+
+        participants_b = self._get_checked_items(self.participant_dropdown_b)
+        file_paths_b = self._get_file_paths_from_labels(
+            participants_b, self.group_b_dropdown.currentText()
+        )
+
+        selected_indexes = [
+            int(s.split(" ")[0])
+            for s in self._get_checked_items(self.image_index_dropdown)
+        ]
+
+        all_selected_paths = list(set(file_paths_a + file_paths_b))
+
+        if not all_selected_paths:
+            print("No participants selected.")
+            return None
+
+        # Inject full_text
+        for idx, params_list in parameterized_indexes.items():
+            full_text = self.index_texts[idx]
+            for param in params_list:
+                param["full_text"] = full_text
+
+        indexes_needing_params = {
+            idx: parameterized_indexes[idx]
+            for idx in selected_indexes
+            if idx in parameterized_indexes
+        }
+
+        param_values = {}
+        if indexes_needing_params:
+            dialog = ParameterInputDialog(indexes_needing_params, parent=self)
+            if dialog.exec() != QDialog.DialogCode.Accepted:
+                return None
+
+            param_values = dialog.get_values()
+            if param_values is None:
+                return None
+
+        return (
+            selected_event,
+            file_paths_a,
+            file_paths_b,
+            all_selected_paths,
+            selected_indexes,
+            param_values,
+        )
+    
+
+class CSVUIMixin:
+
+    def setup_csv_ui(self, index_texts):
+
+        # Create mappings: file_path -> participant label and dropdown display text
+        self.participant_map = {}  # file_path -> "Participant 1"
+        self.participant_dropdown_items = []  # "Participant 1 (filename)"
+
+        for i, file_path in enumerate(self.haemo_dict.keys(), start=1):
+            short_label = f"Participant {i}"
+            display_label = f"{short_label} ({os.path.basename(file_path)})"
+            self.participant_map[file_path] = short_label
+            self.participant_dropdown_items.append(display_label)
+
+        self.layout = QVBoxLayout(self)
+        self.top_bar = QHBoxLayout()
+        self.layout.addLayout(self.top_bar)
+
+        self.participant_dropdown = self._create_multiselect_dropdown(self.participant_dropdown_items)
+        self.participant_dropdown.currentIndexChanged.connect(self.update_participant_dropdown_label)
+              
+        self.index_texts = index_texts
+
+        self.image_index_dropdown = self._create_multiselect_dropdown(self.index_texts)
+        self.image_index_dropdown.currentIndexChanged.connect(self.update_image_index_dropdown_label)
+
+        self.submit_button = QPushButton("Submit")
+        self.submit_button.clicked.connect(self.process_request)
+
+        self.top_bar.addWidget(QLabel("Participants:"))
+        self.top_bar.addWidget(self.participant_dropdown)
+        self.top_bar.addWidget(QLabel("Export Type:"))
+        self.top_bar.addWidget(self.image_index_dropdown)
+        self.top_bar.addWidget(self.submit_button)
+
+        self.scroll = QScrollArea()
+        self.scroll.setWidgetResizable(True)
+        self.scroll_content = QWidget()
+        self.grid_layout = QGridLayout(self.scroll_content)
+        self.scroll.setWidget(self.scroll_content)
+        self.layout.addWidget(self.scroll)
+
+        self.thumb_size = QSize(280, 180)
+        self.showMaximized()
+
+
+class InterGroupUIMixin:
+    def setup_inter_group_ui(self, index_texts):
+        self.show_all_events = True
+        self._updating_checkstates = False
+        
+        # Create mappings: file_path -> participant label and dropdown display text
+        self.participant_map = {}  # file_path -> "Participant 1"
+        self.participant_dropdown_items = []  # "Participant 1 (filename)"
+
+        for i, file_path in enumerate(self.haemo_dict.keys(), start=1):
+            short_label = f"Participant {i}"
+            display_label = f"{short_label} ({os.path.basename(file_path)})"
+            self.participant_map[file_path] = short_label
+            self.participant_dropdown_items.append(display_label)
+
+        self.layout = QVBoxLayout(self)
+        self.top_bar = QHBoxLayout()
+        self.layout.addLayout(self.top_bar)
+
+        self.group_to_paths = {}
+        for file_path, group_name in self.group.items():
+            self.group_to_paths.setdefault(group_name, []).append(file_path)
+
+        self.group_names = sorted(self.group_to_paths.keys())
+
+        self.group_dropdown = QComboBox()
+        self.group_dropdown.addItem("<None Selected>")
+        self.group_dropdown.addItems(self.group_names)
+        self.group_dropdown.setCurrentIndex(0)
+        self.group_dropdown.currentIndexChanged.connect(self.update_participant_list_for_group)
+
+        self.participant_dropdown = self._create_multiselect_dropdown(self.participant_dropdown_items)
+        self.participant_dropdown.currentIndexChanged.connect(self.update_participant_dropdown_label)
+        self.participant_dropdown.setEnabled(False)
+
+        self.event_dropdown = QComboBox()
+        self.event_dropdown.addItem("<None Selected>")
+
+        self.index_texts = index_texts
+        self.image_index_dropdown = self._create_multiselect_dropdown(self.index_texts)
+        self.image_index_dropdown.currentIndexChanged.connect(self.update_image_index_dropdown_label)
+
+        self.submit_button = QPushButton("Submit")
+        self.submit_button.clicked.connect(self.process_request)
+
+        self.top_bar.addWidget(QLabel("Group:"))
+        self.top_bar.addWidget(self.group_dropdown)
+        self.top_bar.addWidget(QLabel("Participants:"))
+        self.top_bar.addWidget(self.participant_dropdown)
+        self.top_bar.addWidget(QLabel("Event:"))
+        self.top_bar.addWidget(self.event_dropdown)
+        self.top_bar.addWidget(QLabel("Image Indexes:"))
+        self.top_bar.addWidget(self.image_index_dropdown)
+        self.top_bar.addWidget(self.submit_button)
+
+        self.scroll = QScrollArea()
+        self.scroll.setWidgetResizable(True)
+        self.scroll_content = QWidget()
+        self.grid_layout = QGridLayout(self.scroll_content)
+        self.scroll.setWidget(self.scroll_content)
+        self.layout.addWidget(self.scroll)
+
+        self.thumb_size = QSize(280, 180)
+        self.showMaximized()
+
+    def get_common_request_data(self, parameterized_indexes):
+        selected_event = self.event_dropdown.currentText()
+        if selected_event == "<None Selected>":
+            selected_event = None
+
+        selected_display_names = self._get_checked_items(self.participant_dropdown)
+        selected_file_paths = []
+        for display_name in selected_display_names:
+            for fp, short_label in self.participant_map.items():
+                expected_display = f"{short_label} ({os.path.basename(fp)})"
+                if display_name == expected_display:
+                    selected_file_paths.append(fp)
+                    break
+
+        if selected_event:
+            valid_paths = []
+            for fp in selected_file_paths:
+                raw = self.haemo_dict.get(fp)
+                # Check if this participant actually has the event in their annotations
+                if raw is not None and hasattr(raw, "annotations"):
+                    if selected_event in raw.annotations.description:
+                        valid_paths.append(fp)
+            
+            selected_file_paths = valid_paths
+
+        selected_indexes = [
+            int(s.split(" ")[0]) for s in self._get_checked_items(self.image_index_dropdown)
+        ]
+
+        if not selected_file_paths:
+            print("No participants selected.")
+            return
+
+        # Only keep indexes 0 and 1 that need parameters
+        
+
+        # Inject full_text from index_texts
+        for idx, params_list in parameterized_indexes.items():
+            full_text = self.index_texts[idx] if idx < len(self.index_texts) else f"{idx} (No label found)"
+            for param_info in params_list:
+                param_info["full_text"] = full_text
+
+        indexes_needing_params = {idx: parameterized_indexes[idx] for idx in selected_indexes if idx in parameterized_indexes}
+
+        param_values = {}
+        if indexes_needing_params:
+            dialog = ParameterInputDialog(indexes_needing_params, parent=self)
+            if dialog.exec_() == QDialog.Accepted:
+                param_values = dialog.get_values()
+                if param_values is None:
+                    return
+            else:
+                return
+            
+        return (
+            selected_event,
+            selected_file_paths,
+            selected_indexes,
+            param_values,
+        )
+    
+class ParticipantUIMixin:
+    def setup_participant_ui(self, index_texts):
+        # Create mappings: file_path -> participant label and dropdown display text
+        self.participant_map = {}  # file_path -> "Participant 1"
+        self.participant_dropdown_items = []  # "Participant 1 (filename)"
+
+        for i, file_path in enumerate(self.haemo_dict.keys(), start=1):
+            short_label = f"Participant {i}"
+            display_label = f"{short_label} ({os.path.basename(file_path)})"
+            self.participant_map[file_path] = short_label
+            self.participant_dropdown_items.append(display_label)
+
+        self.layout = QVBoxLayout(self)
+        self.top_bar = QHBoxLayout()
+        self.layout.addLayout(self.top_bar)
+
+        self.participant_dropdown = self._create_multiselect_dropdown(self.participant_dropdown_items)
+        self.participant_dropdown.currentIndexChanged.connect(self.update_participant_dropdown_label)
+
+        self.event_dropdown = QComboBox()
+        self.event_dropdown.addItem("<None Selected>")
+
+                
+        self.index_texts = index_texts
+
+        self.image_index_dropdown = self._create_multiselect_dropdown(self.index_texts)
+        self.image_index_dropdown.currentIndexChanged.connect(self.update_image_index_dropdown_label)
+
+        self.submit_button = QPushButton("Submit")
+        self.submit_button.clicked.connect(self.process_request)
+
+        self.top_bar.addWidget(QLabel("Participants:"))
+        self.top_bar.addWidget(self.participant_dropdown)
+        self.top_bar.addWidget(QLabel("Event:"))
+        self.top_bar.addWidget(self.event_dropdown)
+        self.top_bar.addWidget(QLabel("Image Indexes:"))
+        self.top_bar.addWidget(self.image_index_dropdown)
+        self.top_bar.addWidget(self.submit_button)
+
+        self.scroll = QScrollArea()
+        self.scroll.setWidgetResizable(True)
+        self.scroll_content = QWidget()
+        self.grid_layout = QGridLayout(self.scroll_content)
+        self.scroll.setWidget(self.scroll_content)
+        self.layout.addWidget(self.scroll)
+
+        self.thumb_size = QSize(280, 180)
+        self.showMaximized()
+
+
+    def get_common_request_data(self, parameterized_indexes):
+        selected_event = self.event_dropdown.currentText()
+        if selected_event == "<None Selected>":
+            selected_event = None
+
+        selected_display_names = self._get_checked_items(self.participant_dropdown)
+        selected_file_paths = []
+        for display_name in selected_display_names:
+            for fp, short_label in self.participant_map.items():
+                expected_display = f"{short_label} ({os.path.basename(fp)})"
+                if display_name == expected_display:
+                    selected_file_paths.append(fp)
+                    break
+
+        selected_indexes = [
+            int(s.split(" ")[0]) for s in self._get_checked_items(self.image_index_dropdown)
+        ]
+
+
+        
+
+        # Inject full_text from index_texts
+        for idx, params_list in parameterized_indexes.items():
+            full_text = self.index_texts[idx] if idx < len(self.index_texts) else f"{idx} (No label found)"
+            for param_info in params_list:
+                param_info["full_text"] = full_text
+
+        indexes_needing_params = {idx: parameterized_indexes[idx] for idx in selected_indexes if idx in parameterized_indexes}
+
+        param_values = {}
+        if indexes_needing_params:
+            dialog = ParameterInputDialog(indexes_needing_params, parent=self)
+            if dialog.exec_() == QDialog.Accepted:
+                param_values = dialog.get_values()
+                if param_values is None:
+                    return
+            else:
+                return
+            
+        return (
+        selected_event,
+        selected_file_paths,
+        selected_indexes,
+        param_values,
+        )

src/window/viewerlauncher.py

@@ -1,19 +1,22 @@
 """
 Filename: viewerlauncher.py
-Description: Analysis options launcher for FLARES
+Description: Viewer launcher window
 
 Author: Tyler de Zeeuw
 License: GPL-3.0
 """
 
+# External library imports
 from PySide6.QtWidgets import  QPushButton, QWidget, QVBoxLayout
 from PySide6.QtCore import QTimer
 
-from src.analysis.exportcsv import ExportDataAsCSVViewerWidget
+from src.analysis.exporttocsv import ExportToCSVWidget
-from src.analysis.group import GroupViewerWidget
+from src.analysis.intergroupbrainimage import InterGroupBrainImageWidget
-from src.analysis.groupbrain import GroupBrainViewerWidget
+from src.analysis.crossgroupbrainimage import CrossGroupBrainImageWidget
-from src.analysis.groupfunctionalconnectivity import GroupFunctionalConnectivityWidget
+from src.analysis.intergroupfunctionalconnectivity import InterGroupFunctionalConnectivityWidget
-from src.analysis.participant import ParticipantViewerWidget
+from src.analysis.intergroupstats import InterGroupStatsWidget
+from src.analysis.crossgroupstats import CrossGroupStatsWidget
+from src.analysis.participantimage import ParticipantImageViewerWidget
 from src.analysis.participantbrain import ParticipantBrainViewerWidget
 from src.analysis.participantfoldchannels import ParticipantFoldChannelsWidget
 from src.analysis.participantfunctionalconnectivity import ParticipantFunctionalConnectivityWidget
@@ -21,92 +24,42 @@ from src.shared.shareddata import APP_NAME
 
 
 class ViewerLauncherWidget(QWidget):
-    def __init__(self, haemo_dict, config_dict, fig_bytes_dict, cha_dict, contrast_results_dict, df_ind, design_matrix, epochs_dict, folding_bypass):
+    def __init__(self, haemo_dict, epochs_dict, cha_dict, df_ind_dict, design_matrix_dict, config_dict, fig_bytes_dict, contrast_results_dict, folding_bypass):
         super().__init__()
         self.setWindowTitle(f"Viewer Launcher - {APP_NAME.upper()}")
 
-        group_dict = {
+        group_dict = {f: c.get("GROUP", "Unknown") for f, c in config_dict.items()}
-            file_path: config.get("GROUP", "Unknown")
-            for file_path, config in config_dict.items()
-        }
         
-        def launch(func, btn, *args):
+        btn_data = [
-            func(*args)
+            ("Participant Image Viewer", ParticipantImageViewerWidget, [haemo_dict, fig_bytes_dict], True),
-            self._trigger_success(btn)
+            ("Participant Brain Viewer", ParticipantBrainViewerWidget, [haemo_dict, cha_dict], True),
+            ("Participant Fold Channels Viewer", ParticipantFoldChannelsWidget, [haemo_dict, cha_dict], False),
+            ("Participant Functional Connectivity Viewer [BETA]", ParticipantFunctionalConnectivityWidget, [haemo_dict, epochs_dict], True),
+            ("Inter-Group Functional Connectivity Viewer [BETA]", InterGroupFunctionalConnectivityWidget, [haemo_dict, group_dict, config_dict], True),
+            ("Inter-Group Stats Viewer", InterGroupStatsWidget, [haemo_dict, cha_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, group_dict], True),
+            ("Cross-Group Stats Viewer", CrossGroupStatsWidget, [haemo_dict, cha_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, group_dict], True),
+            ("Inter-Group Brain & Image Viewer", InterGroupBrainImageWidget, [haemo_dict, cha_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, group_dict], True),
+            ("Cross-Group Brain & Image Viewer", CrossGroupBrainImageWidget, [haemo_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, group_dict], True),
+            ("Export To CSV Viewer", ExportToCSVWidget, [haemo_dict, cha_dict, df_ind_dict, design_matrix_dict, group_dict, contrast_results_dict], True)
+        ]
 
         layout = QVBoxLayout(self)
+        for label, widget_class, args, requires_bypass in btn_data:
+            btn = QPushButton(f"Open {label}")
+            # Connect directly to the generic opener
+            btn.clicked.connect(lambda _, c=widget_class, b=btn, a=args: self._open_viewer(c, b, *a))
+            btn.setEnabled(not (requires_bypass and folding_bypass))
+            layout.addWidget(btn)
 
-        btn1 = QPushButton("Open Participant Viewer")
+    def _open_viewer(self, widget_class, btn, *args):
-        btn1.clicked.connect(lambda: launch(self.open_participant_viewer, btn1, haemo_dict, fig_bytes_dict))
+        # Instantiate and show dynamically
-        btn1.setEnabled(not folding_bypass)
+        self.active_viewer = widget_class(*args)
+        self.active_viewer.show()
+        self._trigger_success(btn)
 
-        btn2 = QPushButton("Open Participant Brain Viewer")
+    def _launch(self, func, btn, *args):
-        btn2.clicked.connect(lambda: launch(self.open_participant_brain_viewer, btn2, haemo_dict, cha_dict))
+        func(*args)
-        btn2.setEnabled(not folding_bypass)
+        self._trigger_success(btn)
-
-        btn3 = QPushButton("Open Participant Fold Channels Viewer")
-        btn3.clicked.connect(lambda: launch(self.open_participant_fold_channels_viewer, btn3, haemo_dict, cha_dict))
-
-        btn7 = QPushButton("Open Functional Connectivity Viewer [BETA]")
-        btn7.clicked.connect(lambda: launch(self.open_participant_functional_connectivity_viewer, btn7, haemo_dict, epochs_dict))
-        btn7.setEnabled(not folding_bypass)
-
-        btn8 = QPushButton("Open Group Functional Connectivity Viewer [BETA]")
-        btn8.clicked.connect(lambda: launch(self.open_group_functional_connectivity_viewer, btn8, haemo_dict, group_dict, config_dict))
-        btn8.setEnabled(not folding_bypass)
-
-        btn4 = QPushButton("Open Inter-Group Viewer")
-        btn4.clicked.connect(lambda: launch(self.open_group_viewer, btn4, haemo_dict, cha_dict, df_ind, design_matrix, contrast_results_dict, group_dict))
-        btn4.setEnabled(not folding_bypass)
-        
-        btn5 = QPushButton("Open Cross Group Brain Viewer")
-        btn5.clicked.connect(lambda: launch(self.open_group_brain_viewer, btn5, haemo_dict, df_ind, design_matrix, group_dict, contrast_results_dict))
-        btn5.setEnabled(not folding_bypass)
-        
-        btn6 = QPushButton("Open Export Data As CSV Viewer")
-        btn6.clicked.connect(lambda: launch(self.open_export_data_as_csv_viewer, btn6, haemo_dict, cha_dict, df_ind, design_matrix, group_dict, contrast_results_dict))
-        btn6.setEnabled(not folding_bypass)
-
-        layout.addWidget(btn1)
-        layout.addWidget(btn2)
-        layout.addWidget(btn3)
-        layout.addWidget(btn7)
-        layout.addWidget(btn8)
-        layout.addWidget(btn4)
-        layout.addWidget(btn5)
-        layout.addWidget(btn6)
-
-    def open_participant_viewer(self, haemo_dict, fig_bytes_dict):
-        self.participant_viewer = ParticipantViewerWidget(haemo_dict, fig_bytes_dict)
-        self.participant_viewer.show()
-
-    def open_participant_brain_viewer(self, haemo_dict, cha_dict):
-        self.participant_brain_viewer = ParticipantBrainViewerWidget(haemo_dict, cha_dict)
-        self.participant_brain_viewer.show()
-        
-    def open_participant_fold_channels_viewer(self, haemo_dict, cha_dict):
-        self.participant_fold_channels_viewer = ParticipantFoldChannelsWidget(haemo_dict, cha_dict)
-        self.participant_fold_channels_viewer.show()
-
-    def open_participant_functional_connectivity_viewer(self, haemo_dict, epochs_dict):
-        self.participant_brain_viewer = ParticipantFunctionalConnectivityWidget(haemo_dict, epochs_dict)
-        self.participant_brain_viewer.show()
-
-    def open_group_functional_connectivity_viewer(self, haemo_dict, group, config_dict):
-        self.participant_brain_viewer = GroupFunctionalConnectivityWidget(haemo_dict, group, config_dict)
-        self.participant_brain_viewer.show()
-
-    def open_group_viewer(self, haemo_dict, cha_dict, df_ind, design_matrix, contrast_results_dict, group):
-        self.participant_brain_viewer = GroupViewerWidget(haemo_dict, cha_dict, df_ind, design_matrix, contrast_results_dict, group)
-        self.participant_brain_viewer.show()
-
-    def open_group_brain_viewer(self, haemo_dict, df_ind, design_matrix, group, contrast_results_dict):
-        self.participant_brain_viewer = GroupBrainViewerWidget(haemo_dict, df_ind, design_matrix, group, contrast_results_dict)
-        self.participant_brain_viewer.show()
-
-    def open_export_data_as_csv_viewer(self, haemo_dict, cha_dict, df_ind, design_matrix, group, contrast_results_dict):
-        self.export_data_as_csv_viewer = ExportDataAsCSVViewerWidget(haemo_dict, cha_dict, df_ind, design_matrix, group, contrast_results_dict)
-        self.export_data_as_csv_viewer.show()
 
     def _trigger_success(self, button):
         """Temporarily adds a green checkmark to the button text."""