fix to stats when no json file is defined

changelog.md

@@ -1,8 +1,12 @@
-# Verison 1.5.3
+# Verison 1.6.0
 
+- This is potentially a save-changing release due to adding more data into the save file. Please update your project files to ensure compatibility
+- It is still possible to load older saves by enabling 'Incompatible Save Bypass' from the Preferences menu, but your mileage may vary
 - Optimized calculations being performed when calculating the heart rate to speed up step 5 by up to ~35% on a per-file basis
 - Optimized calculations being performed when running the General Linear Model to speed up step 5 by ~35% on a per-file basis
 - Fixed an issue where participants could be skipped when processing multiple particants at one which could prevent overall processing from completing
+- Fixed the two Group Stats Viewer windows crashing the application once opened when JSON_LOCATION was not set
+- The Group Stats Viewer windows will now properly load the Right/Left or Front/Back fallback ROIs if JSON_LOCATION is not set
 
 
 # Version 1.5.2

flares.py

@@ -2650,7 +2650,6 @@ def run_roi_second_level_analysis(
     correction_method: str | None = "fdr_bh",
     target_chroma: str = "hbo",
     graph_bounds: float | None = None,
-    roi_config: str | Path | None = None,
     threshold_topo: bool = False,
 ) -> DataFrame:
     
@@ -2805,35 +2804,13 @@ def run_roi_second_level_analysis(
             con_model_df = statsmodels_to_results(con_model, order=raw_picked.ch_names)
 
             # --- DYNAMIC ROI PARSING ---
-            roi_mapping = {}
+            if 'ROI' not in con_summary.columns or con_summary['ROI'].dropna().empty:
-            if roi_config is not None:
+                if df_roi_all is not None and 'ROI' in df_roi_all.columns and ch_col in df_roi_all.columns:
-                raw_json = None
+                    # Create channel -> ROI mapping from df_roi_all
-                if isinstance(roi_config, str) and os.path.exists(roi_config):
+                    ch_to_roi = df_roi_all.dropna(subset=['ROI', ch_col]).set_index(ch_col)['ROI'].to_dict()
-                    with open(roi_config, 'r') as f:
+                    con_summary['ROI'] = con_summary[ch_col].apply(
-                        raw_json = json.load(f)
+                        lambda x: ch_to_roi.get(x, ch_to_roi.get(x.split()[0], None) if isinstance(x, str) else None)
-                elif isinstance(roi_config, dict):
+                    )
-                    raw_json = roi_config
-
-                if raw_json:
-                    if "regions_of_interest" in raw_json:
-                        for roi_item in raw_json["regions_of_interest"]:
-                            roi_name = roi_item.get("name")
-                            channels = roi_item.get("channels", [])
-                            if roi_name and channels:
-                                roi_mapping[roi_name] = channels
-                    else:
-                        roi_mapping = raw_json
-
-            if roi_mapping:
-                ch_to_roi = {}
-                for roi_name, channels in roi_mapping.items():
-                    for ch in channels:
-                        ch_to_roi[ch] = roi_name
-                        ch_to_roi[ch.split()[0]] = roi_name
-
-                con_summary['ROI'] = con_summary[ch_col].apply(
-                    lambda x: ch_to_roi.get(x, ch_to_roi.get(x.split()[0], None) if isinstance(x, str) else None)
-                )
 
             unique_rois = []
             if 'ROI' in con_summary.columns:
@@ -2925,7 +2902,7 @@ def run_cross_group_second_level_analysis(
     target_chroma: str = "hbo",
     selected_event: str | None = None,
     graph_bounds: tuple[float, float] | list[float] | None = None,
-    roi_config: Path | str | None = None,
+    roi_channel_maps: dict[str, dict[str, str]] | None = None,
     threshold_topo: bool = False,
 ) -> DataFrame:
     
@@ -3129,21 +3106,13 @@ def run_cross_group_second_level_analysis(
             con_model_df = pd.DataFrame(contrast_data)
 
             # --- DYNAMIC ROI PARSING ---
-            roi_mapping = {}
+            if roi_channel_maps:
-            if roi_config is not None and os.path.exists(roi_config):
+                def _lookup_roi(row):
-                with open(roi_config, 'r') as f:
+                    m = roi_channel_maps.get(row['clean_ID'], {})
-                    raw_json = json.load(f)
+                    ch = row[ch_col]
-                if "regions_of_interest" in raw_json:
+                    return m.get(ch, m.get(ch.split()[0]) if isinstance(ch, str) else None)
-                    for roi_item in raw_json["regions_of_interest"]:
-                        roi_mapping[roi_item.get("name")] = roi_item.get("channels", [])
 
-            if roi_mapping:
+                con_summary['ROI'] = con_summary.apply(_lookup_roi, axis=1)
-                ch_to_roi = {}
-                for roi_name, channels in roi_mapping.items():
-                    for ch in channels:
-                        ch_to_roi[ch] = roi_name
-                        ch_to_roi[ch.split()[0]] = roi_name
-                con_summary['ROI'] = con_summary[ch_col].apply(lambda x: ch_to_roi.get(x, ch_to_roi.get(x.split()[0], None) if isinstance(x, str) else None))
 
             unique_rois = [r for r in con_summary['ROI'].dropna().unique() if r != ""] if 'ROI' in con_summary.columns else ['All_Channels']
 
@@ -3454,7 +3423,8 @@ def run_cross_group_contrast_analysis(
     df_contrasts_a: DataFrame,
     df_contrasts_b: DataFrame,
     contrast_name: str,
-    roi_json_path: str | Path | None,
+    roi_channel_maps_a: dict[str, dict[str, str]],
+    roi_channel_maps_b: dict[str, dict[str, str]],
     group_a_name: str = "Group A",
     group_b_name: str = "Group B",
     target_chroma: str = "hbo",
@@ -3547,8 +3517,8 @@ def run_cross_group_contrast_analysis(
         print(f"[ERROR] Contrast '{contrast_name}' not found anywhere in {group_b_name}'s data.")
         return DataFrame()
 
-    roi_a = aggregate_channel_contrasts_to_roi(df_a_filt, roi_json_path, weighted=weighted)
+    roi_a = aggregate_channel_contrasts_to_roi(df_a_filt, roi_channel_maps_a, weighted=weighted)
-    roi_b = aggregate_channel_contrasts_to_roi(df_b_filt, roi_json_path, weighted=weighted)
+    roi_b = aggregate_channel_contrasts_to_roi(df_b_filt, roi_channel_maps_b, weighted=weighted)
 
     roi_a = roi_a[roi_a['Chroma'] == target_chroma]
     roi_b = roi_b[roi_b['Chroma'] == target_chroma]
@@ -3897,7 +3867,7 @@ def run_roi_paired_contrast_analysis(
 
 def aggregate_channel_contrasts_to_roi(
     df_contrasts: DataFrame,
-    roi_json_path: str | Path | None,
+    roi_channel_maps: dict[str, dict[str, str]],
     weighted: bool = True
 ) -> DataFrame:
     """
@@ -3924,11 +3894,14 @@ def aggregate_channel_contrasts_to_roi(
         ['ch_name', 'effect', 'stat', 'Chroma', 'contrast_name', 'ID']
         `stat` must be the t-statistic (ContrastType == 't'), since standard
         error is recovered as effect / stat.
-    roi_json_path : str
+    roi_channel_maps : dict[str, dict[str, str]]
-        Path to the same regions.json used elsewhere in the pipeline, with
+        Per-subject channel-to-ROI mapping, keyed by subject ID (the same
-        the structure: {"regions_of_interest": [{"name": ..., "channels": [...]}]}
+        ID values used in df_contrasts['ID']), e.g.
-        `channels` entries should be bare source-detector names (e.g. "S1_D1"),
+        {"sub-01": {"S1_D1 hbo": "Left", "S1_D1 hbr": "Left", ...}, ...}.
-        matching the convention already used for the GLM-level ROI loading.
+        This is the actual mapping generate_roi_results used for that
+        subject (whichever tier produced it — JSON, geometric split, or
+        per-channel fallback) — not re-derived here, so ROI assignments
+        stay consistent with df_ind_dict for the same subject.
     weighted : bool, default True
         If True, combine channels within an ROI using inverse-variance
         weighting (weight = 1 / se^2), matching MNE-NIRS's own default
@@ -3948,35 +3921,18 @@ def aggregate_channel_contrasts_to_roi(
     if not all(col in df_contrasts.columns for col in required_cols):
         raise ValueError(f"Input contrast DataFrame must include: {required_cols}")
  
-    # --- Load ROI definitions and build a channel-base -> ROI lookup ---
-    # Channel base names (e.g. "S1_D1") map to both hbo/hbr rows via the
-    # ch_name column ("S1_D1 hbo" / "S1_D1 hbr"), so we key on the base name.
-    with open(roi_json_path, 'r') as f:
-        roi_data = json.load(f)
- 
-    ch_base_to_roi = {}
-    for region in roi_data.get("regions_of_interest", []):
-        roi_name = region["name"]
-        for ch_base in region["channels"]:
-            if ch_base in ch_base_to_roi:
-                logger.warning(
-                    f"Channel '{ch_base}' assigned to multiple ROIs "
-                    f"('{ch_base_to_roi[ch_base]}' and '{roi_name}') — "
-                    f"using '{roi_name}' (last one wins)."
-                )
-            ch_base_to_roi[ch_base] = roi_name
- 
     df = df_contrasts.copy()
-    df['ch_base'] = df['ch_name'].str.split().str[0]  # "S1_D1 hbo" -> "S1_D1"
+    df['ch_base'] = df['ch_name'].str.split().str[0]
-    df['ROI'] = df['ch_base'].map(ch_base_to_roi)
+
- 
+    def lookup(row):
-    n_unassigned = df['ROI'].isna().sum()
+        m = roi_channel_maps.get(row['ID'], {})
-    if n_unassigned:
+        return m.get(row['ch_name'], m.get(row['ch_base']))
-        logger.warning(
+
-            f"{n_unassigned} channel-rows did not match any ROI in "
+    df['ROI'] = df.apply(lookup, axis=1)
-            f"'{roi_json_path}' and will be excluded."
-        )
     df = df.dropna(subset=['ROI'])
+    
+    if df.empty:
+        raise ValueError("No channel contrasts matched any subject's ROI mapping.")
  
     # Recover standard error from the t-statistic: t = effect / se -> se = effect / t
     with np.errstate(divide='ignore', invalid='ignore'):
@@ -5029,6 +4985,12 @@ def generate_roi_results(raw_haemo, df_design_matrix, glm_est, file_path, json_l
  
     subject_id = sub['ID'].iloc[0] if 'ID' in sub.columns else ''
     n_conditions = sub['Condition'].nunique()
+
+    roi_channel_map = {
+        raw_haemo.ch_names[idx]: roi_name
+        for roi_name, indices in rois_formatted.items()
+        for idx in indices
+    }
  
     sns.set_theme(style="whitegrid")
     fig, ax = plt.subplots(figsize=(max(6, 1.5 * sub['ROI'].nunique()), 5))
@@ -5048,7 +5010,7 @@ def generate_roi_results(raw_haemo, df_design_matrix, glm_est, file_path, json_l
     plt.tight_layout()
     plt.close(fig)
 
-    return df_roi, fig
+    return df_roi, roi_channel_map, fig
 
 
 
@@ -5495,7 +5457,7 @@ def process_participant(file_path, file_start, progress_callback=None):
     step_start = lap(step_start, timings, "Step 25")
 
     # Step 26: Generate Region of Interest Results
-    df_roi, fig_roi = generate_roi_results(raw_haemo, df_design_matrix, glm_est, file_path, json_location=JSON_LOCATION)
+    df_roi, roi_channel_map, fig_roi = generate_roi_results(raw_haemo, df_design_matrix, glm_est, file_path, json_location=JSON_LOCATION)
     _enqueue("Region of Interest", fig_roi, png_queue)
     if progress_callback: progress_callback(26)
     logger.info("26")
@@ -5523,7 +5485,7 @@ def process_participant(file_path, file_start, progress_callback=None):
         logger.info(f"  {name:<25} {elapsed:7.3f}s")
 
     logger.info(f"Total processing time: {sum(timings.values()):.3f}s")
-    return raw_haemo, epochs, df_cha, df_roi, df_design_matrix, config_dict, fig_bytes_dict, contrast_results_dict, True
+    return raw_haemo, epochs, df_cha, df_roi, df_design_matrix, config_dict, fig_bytes_dict, contrast_results_dict, roi_channel_map, True
 
 
 

main.py

@@ -329,6 +329,7 @@ DATA_SCHEMA = [
     {"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": "roi_channel_map_dict",  "help": "Dict[file_path, dict]: Calculated contrast statistical results"},
     {"key": "valid_dict",            "help": "Dict[file_path, bool]: Boolean validity status per file"}
 ]
 
@@ -677,7 +678,6 @@ class MainApplication(QMainWindow):
         self.analysis_clearing_bypass = False
         self.folding_bypass = False
         self.advanced_parameters = False
-        self.json_location = ""
 
 
         # Initialization to ensure that saving can occur
@@ -1268,8 +1268,8 @@ class MainApplication(QMainWindow):
             data_map["config_dict"],
             data_map["fig_bytes_dict"],
             data_map["contrast_results_dict"],
+            data_map["roi_channel_map_dict"],
             self.folding_bypass,
-            self.json_location
         ]
 
         self.launcher_window = ViewerLauncherWidget(*args)
@@ -2349,8 +2349,6 @@ class MainApplication(QMainWindow):
         if self.folding_bypass:
             all_params['FOLDING_BYP'] = True
 
-        self.json_location = all_params['JSON_LOCATION']
-
         collected_data = {
             "SNIRF_FILES": snirf_files,
             "PARAMS": all_params,  # add this line

src/analysis/crossgroupstats.py

@@ -152,8 +152,8 @@ class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
         df_ind_dict: dict[str, DataFrame],
         design_matrix_dict: dict[str, DataFrame],
         contrast_results_dict: dict[str, dict[str, Any]],
+        roi_channel_map_dict: dict[str, dict[str, str]],
         group_dict: dict[str, str],
-        json_location: str | Path
     ) -> None:
         
         super().__init__("CrossGroupStats")
@@ -163,14 +163,14 @@ class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
         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.roi_channel_map_dict = roi_channel_map_dict
-        self.json_location = json_location
+        self.group_dict = group_dict
 
         self.setup_cross_group_ui(["0 (Raw ROI Comparison)", "1 (Laterality Comparison)", "2 (Contrast Comparison)",], placeholder_text=DESCRIPTION)
         
 
     def process_request(self):
-        request = self.get_common_request_data(PARAMETERIZED_INDEXES, self.json_location, self.contrast_results_dict)
+        request = self.get_common_request_data(PARAMETERIZED_INDEXES, self.df_ind_dict, self.contrast_results_dict)
         if request is None:
             return
 
@@ -200,6 +200,12 @@ class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
                 target_chroma = params.get("target_chroma", "hbo")
                 threshold_topo = params.get("threshold_topo", False)
 
+                selected_roi_maps = {
+                    fp: self.roi_channel_map_dict[fp]
+                    for fp in (file_paths_a + file_paths_b)
+                    if fp in self.roi_channel_map_dict
+                }
+
                 run_cross_group_second_level_analysis(
                     df_roi_all=df_ind_combined,  # Individual stats dataframe
                     file_paths_a=file_paths_a,
@@ -213,7 +219,7 @@ class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
                     correction_method=correction_method,
                     target_chroma=target_chroma,
                     selected_event=selected_event,
-                    roi_config=self.json_location,
+                    roi_channel_maps=selected_roi_maps,
                     threshold_topo=threshold_topo  # Shows the raw difference map (Unthresholded)
                 )
             elif idx == 1:
@@ -317,12 +323,27 @@ class CrossGroupStatsWidget(CrossGroupUIMixin, FlaresBaseWidget):
                 if df_contrasts_a.empty or df_contrasts_b.empty:
                     print("No contrast data found for one or both groups.")
                     continue
- 
+
+                roi_maps_a = {
+                    fp: self.roi_channel_map_dict[fp]
+                    for fp in file_paths_a
+                    if fp in self.roi_channel_map_dict
+                }
+                roi_maps_b = {
+                    fp: self.roi_channel_map_dict[fp]
+                    for fp in file_paths_b
+                    if fp in self.roi_channel_map_dict
+                }
+                if not roi_maps_a or not roi_maps_b:
+                    print("No channel-to-ROI mapping available for one or both groups.")
+                    continue
+
                 run_cross_group_contrast_analysis(
                     df_contrasts_a=df_contrasts_a,
                     df_contrasts_b=df_contrasts_b,
                     contrast_name=contrast_name,
-                    roi_json_path=self.json_location,
+                    roi_channel_maps_a=roi_maps_a,
+                    roi_channel_maps_b=roi_maps_b,
                     group_a_name=self.group_a_dropdown.currentText(),
                     group_b_name=self.group_b_dropdown.currentText(),
                     target_chroma=target_chroma,

src/analysis/intergroupstats.py

@@ -165,8 +165,8 @@ class InterGroupStatsWidget(InterGroupUIMixin, FlaresBaseWidget):
         df_ind_dict: dict[str, DataFrame],
         design_matrix_dict: dict[str, DataFrame],
         contrast_results_dict: dict[str, dict[str, Any]],
+        roi_channel_map_dict: dict[str, dict[str, str]],
         group_dict: dict[str, str],
-        json_location: str | Path
     ) -> None:
         
         super().__init__("InterGroupStats")
@@ -176,14 +176,14 @@ class InterGroupStatsWidget(InterGroupUIMixin, FlaresBaseWidget):
         self.df_ind_dict = df_ind_dict
         self.design_matrix_dict = design_matrix_dict
         self.contrast_results_dict = contrast_results_dict
+        self.roi_channel_map_dict = roi_channel_map_dict
         self.group_dict = group_dict
-        self.json_location = json_location
 
         self.setup_inter_group_ui(["0 (ROI vs. Zero)", "1 (Paired ROI Contrast)", "2 (Joint Contrast, ROI-Aggregated)"], placeholder_text=DESCRIPTION)
 
 
     def process_request(self):
-        request = self.get_common_request_data(PARAMETERIZED_INDEXES, self.json_location, self.contrast_results_dict)
+        request = self.get_common_request_data(PARAMETERIZED_INDEXES, self.df_ind_dict, self.contrast_results_dict)
         if request is None:
             return
 
@@ -276,7 +276,6 @@ class InterGroupStatsWidget(InterGroupUIMixin, FlaresBaseWidget):
                     correction_method=correction_method,
                     target_chroma=target_chroma,
                     graph_bounds=graph_bounds if graph_bounds > 0.0 else None,
-                    roi_config=self.json_location
                 )
 
             elif idx == 1:
@@ -358,11 +357,20 @@ class InterGroupStatsWidget(InterGroupUIMixin, FlaresBaseWidget):
                     continue
  
                 df_contrasts = pd.concat(all_contrasts, ignore_index=True)
- 
+
+                selected_roi_maps = {
+                    fp: self.roi_channel_map_dict[fp]
+                    for fp in selected_file_paths
+                    if fp in self.roi_channel_map_dict
+                }
+                if not selected_roi_maps:
+                    print("No channel-to-ROI mapping available for selected participants.")
+                    continue
+
                 try:
                     roi_theta = aggregate_channel_contrasts_to_roi(
                         df_contrasts,
-                        roi_json_path=self.json_location,
+                        roi_channel_maps=selected_roi_maps,
                         weighted=weighted,
                     )
 

src/shared/flaresbasewidget.py

@@ -11,6 +11,8 @@ import json
 
 from pathlib import Path
 from typing import Sequence, Any
+import pandas as pd
+from pandas import DataFrame
 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, QSize, Qt
@@ -1380,7 +1382,7 @@ class CrossGroupUIMixin:
     def get_common_request_data(
         self,
         parameterized_indexes: dict[int, list[dict[str, Any]]],
-        json_location: str | Path | None = None,
+        df_ind_dict: dict[str, DataFrame] | None = None,
         contrast_dfs: dict[str, dict[str, Any]] | None = None,
     ) -> tuple[str | None, list[str], list[str], list[str], list[int], dict[str, Any]] | None:
         
@@ -1423,19 +1425,15 @@ class CrossGroupUIMixin:
 
         dynamic_rois = []
         
-        # 1. Check for the JSON file and parse ROI names
+        if df_ind_dict:
-        if os.path.exists(json_location):
+            roi_set = set()
-            try:
+            for fp in all_selected_paths:
-                with open(json_location, 'r', encoding='utf-8') as f:
+                df_roi = df_ind_dict.get(fp)
-                    regions_data = json.load(f)
+                if isinstance(df_roi, pd.DataFrame) and "ROI" in df_roi.columns:
-                    
+                    roi_set.update(df_roi["ROI"].dropna().unique())
-                    # Extract "name" from each region under "regions_of_interest"
+
-                    regions_list = regions_data.get("regions_of_interest", [])
+            if roi_set:
-                    dynamic_rois = [region["name"] for region in regions_list if "name" in region]
+                dynamic_rois = sorted(list(roi_set))
-                    
-            except Exception as e:
-                # Safe log if JSON is corrupted or unreadable
-                print(f"Error reading ROI configurations from {json_location}: {e}")
                 
         # Fallback to prevent UI crashes if JSON file doesn't exist or is empty
         if not dynamic_rois:
@@ -1580,7 +1578,7 @@ class InterGroupUIMixin:
         self.layout.addLayout(self.top_bar)
 
         self.group_to_paths = {}
-        for file_path, group_name in self.group.items():
+        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())
@@ -1632,7 +1630,7 @@ class InterGroupUIMixin:
     def get_common_request_data(
         self,
         parameterized_indexes: dict[int, list[dict[str, Any]]],
-        json_location: str | Path | None = None,
+        df_ind_dict: dict[str, DataFrame] | None = None,
         contrast_dfs: dict[str, dict[str, Any]] | None = None,
     ) -> tuple[str | None, list[str], list[int], dict[str, Any]] | None:
         
@@ -1679,20 +1677,16 @@ class InterGroupUIMixin:
 
         dynamic_rois = []
         
-        # 1. Check for the JSON file and parse ROI names
+        if df_ind_dict:
-        if json_location is not None and os.path.exists(json_location):
+            roi_set = set()
-            try:
+            for fp in selected_file_paths:
-                with open(json_location, 'r', encoding='utf-8') as f:
+                df_roi = df_ind_dict.get(fp)
-                    regions_data = json.load(f)
+                if isinstance(df_roi, pd.DataFrame) and "ROI" in df_roi.columns:
-                    
+                    roi_set.update(df_roi["ROI"].dropna().unique())
-                    # Extract "name" from each region under "regions_of_interest"
+
-                    regions_list = regions_data.get("regions_of_interest", [])
+            if roi_set:
-                    dynamic_rois = [region["name"] for region in regions_list if "name" in region]
+                dynamic_rois = sorted(list(roi_set))
-                    
+
-            except Exception as e:
-                # Safe log if JSON is corrupted or unreadable
-                print(f"Error reading ROI configurations from {json_location}: {e}")
-                
         # Fallback to prevent UI crashes if JSON file doesn't exist or is empty
         if not dynamic_rois:
             dynamic_rois = ["Option 1", "Option 2"]

src/window/viewerlauncher.py

@@ -24,7 +24,7 @@ from src.shared.shareddata import APP_NAME
 
 
 class ViewerLauncherWidget(QWidget):
-    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, json_location):
+    def __init__(self, haemo_dict, epochs_dict, cha_dict, df_ind_dict, design_matrix_dict, config_dict, fig_bytes_dict, contrast_results_dict, roi_channel_map_dict, folding_bypass):
         super().__init__()
         self.setWindowTitle(f"Viewer Launcher - {APP_NAME.upper()}")
 
@@ -36,8 +36,8 @@ class ViewerLauncherWidget(QWidget):
             ("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, json_location], True),
+            ("Inter-Group Stats Viewer", InterGroupStatsWidget, [haemo_dict, cha_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, roi_channel_map_dict, group_dict], True),
-            ("Cross-Group Stats Viewer", CrossGroupStatsWidget, [haemo_dict, cha_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, group_dict, json_location], True),
+            ("Cross-Group Stats Viewer", CrossGroupStatsWidget, [haemo_dict, cha_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, roi_channel_map_dict, group_dict], True),
             ("Inter-Group Brain and Image Viewer", InterGroupBrainImageWidget, [haemo_dict, cha_dict, df_ind_dict, design_matrix_dict, contrast_results_dict, group_dict], True),
             ("Cross-Group Brain and 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, contrast_results_dict, group_dict, config_dict], True)