roi from user provided file

changelog.md

@@ -22,6 +22,7 @@
 - Added a new parameter to the PSP section on the right side of the screen: PSP_USE_HEART_RATE_BAND. This functions similarly to the existing SCI_USE_HEART_RATE_BAND
 - Added description text to the Inter-Group and Intra-Group Brain and Image Viewers, as well as the Functional Connectivity windows to explain what output can be expected
 - Added a new Preference option of Theme. Allows from selecting Auto (System default), Light, or Dark. Fixes [Issue 7](https://git.research.dezeeuw.ca/tyler/flares/issues/7)
+- Optode updater can now update optode locations in multiple snirf files at once
 - Removed image index 1 (Significance) from the Intra-Group Brain and Image Viewer as it is now provided more in depth with the Stats viewers
 - Modified the timeout when waiting for the application to close while performing updates down to a reasonable number
 - Modified the help messages for parameters in the SCI and PSP areas to better reflect how the parameters are used
@@ -42,6 +43,7 @@
 - Fixed an issue where certain parameters would not enable or disable depending on other parameters when they should've
 - Fixed an issue where not all widgets would close when attempting to close the application causing the application to crash
 - Fixed an issue where events were not created correctly after the data had been resampled by the design matrix
+- Fixed an issue where progress bar colors would not reset if the data was reprocessed
 
 
 # Version 1.6.0

changelog_major.md

@@ -12,6 +12,7 @@
 - Added a new parameter to the PSP section on the right side of the screen: PSP_USE_HEART_RATE_BAND. This functions similarly to the existing SCI_USE_HEART_RATE_BAND
 - Added description text to the Inter-Group and Intra-Group Brain and Image Viewers, as well as the Functional Connectivity windows to explain what output can be expected
 - Added a new Preference option of Theme. Allows from selecting Auto (System default), Light, or Dark. Fixes [Issue 7](https://git.research.dezeeuw.ca/tyler/flares/issues/7)
+- Optode updater can now update optode locations in multiple snirf files at once
 - Removed image index 1 (Significance) from the Intra-Group Brain and Image Viewer as it is now provided more in depth with the Stats viewers
 - Modified the timeout when waiting for the application to close while performing updates down to a reasonable number
 - Modified the help messages for parameters in the SCI and PSP areas to better reflect how the parameters are used
@@ -32,3 +33,4 @@
 - Fixed an issue where certain parameters would not enable or disable depending on other parameters when they should've
 - Fixed an issue where not all widgets would close when attempting to close the application causing the application to crash
 - Fixed an issue where events were not created correctly after the data had been resampled by the design matrix
+- Fixed an issue where progress bar colors would not reset if the data was reprocessed

flares.py

@@ -162,8 +162,6 @@ QC_METRIC_LABELS = {
     "total_processing_seconds": "Processing Time (s)",
 }
 
-ROI_MAP = {} # TODO: Should be grabbed from the json file
-
 DOWNSAMPLE: bool
 DOWNSAMPLE_FREQUENCY: int
 
@@ -6059,21 +6057,22 @@ def process_participant(file_path, file_start, progress_callback=None):
     # Step 27.5: Extract FIR Waveform Features & Enqueue Metric Plots
     fir_feature_dict = {'features': np.array([]), 'feature_names': [], 'feature_channels': []}
 
-    # if HRF_MODEL.lower() == "fir":
+    if HRF_MODEL.lower() == "fir":
-    #     try:
+        try:
-    #         fir_feature_dict = extract_fir_features_real_data(
+            fir_feature_dict = extract_fir_features_real_data(
-    #             raw=raw_haemo,
+                raw=raw_haemo,
-    #             target_condition=None,  # e.g., 'reach'
+                target_condition=None,  # e.g., 'reach'
-    #             fir_delays=FIR_DELAYS,               # e.g., np.arange(0, 15)
+                fir_delays=FIR_DELAYS,               # e.g., np.arange(0, 15)
-    #             selected_metrics=tuple(METRIC_REGISTRY.keys()),   # e.g., ('Peak_Amp', 'TTP', 'AUC')
+                selected_metrics=tuple(METRIC_REGISTRY.keys()),   # e.g., ('Peak_Amp', 'TTP', 'AUC')
-    #             roi_map=ROI_MAP,
+                roi_map=JSON_LOCATION,
-    #             glm_est=glm_est,
+                chromophores=('hbo', 'hbr', 'hbt'),
-    #             df_design_matrix=df_design_matrix,
+                glm_est=glm_est,
-    #             png_queue=png_queue
+                df_design_matrix=df_design_matrix,
-    #         )
+                png_queue=png_queue
-    #         logger.info("Step 27.5: FIR features successfully extracted and metric images enqueued.")
+            )
-    #     except Exception as e:
+            logger.info("Step 27.5: FIR features successfully extracted and metric images enqueued.")
-    #         logger.warning(f"Step 27.5 Failed to extract FIR features: {e}")
+        except Exception as e:
+            logger.warning(f"Step 27.5 Failed to extract FIR features: {e}")
 
      
     # Step 28: Finishing Up
@@ -7197,8 +7196,8 @@ def _compute_roi_fir_curves(
     raw=None, 
     target_condition='reach', 
     fir_delays=np.arange(0, 15),
-    roi_map=ROI_MAP, 
+    roi_map={}, 
-    chromophores=('hbr',),
+    chromophores=('hbo', 'hbr', 'hbt'),
     glm_est=None,
     df_design_matrix=None
 ):
@@ -7207,6 +7206,7 @@ def _compute_roi_fir_curves(
     it reuses pre-calculated GLM results directly to avoid duplicate processing.
     """
     roi_curves = {}
+    active_roi_map = normalize_roi_map(roi_map)
 
     # --- SHORT-CIRCUIT: Reuse pre-calculated GLM estimation if available ---
     if glm_est is not None and df_design_matrix is not None:
@@ -7225,25 +7225,36 @@ def _compute_roi_fir_curves(
         fir_df = glm_df[glm_df[cond_col].astype(str).str.lower().str.contains(target_condition.lower())].copy()
         print(f"Matched rows for '{target_condition}': {len(fir_df)}")
 
+        active_roi_map = normalize_roi_map(roi_map)
+
         if fir_df.empty:
             logger.warning(f"Condition '{target_condition}' not found in precalculated GLM estimates.")
             return roi_curves
 
-        for chromo in chromophores:
+        # 1. Extract base measured chromophores (e.g., hbo, hbr) directly from GLM data
-            chromo_df = fir_df[fir_df[ch_col].str.lower().str.contains(chromo.lower())] if ch_col in fir_df.columns else fir_df
+        base_chromos = [c.lower() for c in chromophores if c.lower() != 'hbt']
+        
+        for chromo in base_chromos:
+            chromo_df = fir_df[fir_df[ch_col].str.lower().str.contains(chromo)] if ch_col in fir_df.columns else fir_df
             
             ch_curves = {}
             for ch_name, ch_group in chromo_df.groupby(ch_col):
                 pair = ch_name.split(' ')[0]
                 ch_curves[pair] = ch_group['theta'].values if 'theta' in ch_group.columns else ch_group['beta'].values
 
-            print("Extracted channel keys:", list(ch_curves.keys())[:5])
+            for roi_name, channels in active_roi_map.items():
-
-            for roi_name, channels in roi_map.items():
                 matching_curves = [ch_curves[ch] for ch in channels if ch in ch_curves]
                 if matching_curves:
                     roi_curves[(chromo, roi_name)] = np.mean(matching_curves, axis=0)
 
+        # 2. Derive HbT (HbO + HbR) dynamically if requested
+        if 'hbt' in [c.lower() for c in chromophores]:
+            for roi_name in active_roi_map.keys():
+                hbo_key = ('hbo', roi_name)
+                hbr_key = ('hbr', roi_name)
+                if hbo_key in roi_curves and hbr_key in roi_curves:
+                    roi_curves[('hbt', roi_name)] = roi_curves[hbo_key] + roi_curves[hbr_key]
+
         return roi_curves
 
 
@@ -7252,7 +7263,8 @@ def extract_fir_features_real_data(
     target_condition=None,
     fir_delays=np.arange(0, 15), 
     selected_metrics=('Peak_Amp',), 
-    roi_map=ROI_MAP,
+    roi_map={},
+    chromophores=('hbo', 'hbr', 'hbt'),
     glm_est=None,
     df_design_matrix=None,
     png_queue=None
@@ -7285,7 +7297,7 @@ def extract_fir_features_real_data(
                 target_condition=cond, 
                 fir_delays=fir_delays,
                 roi_map=roi_map, 
-                chromophores=('hbr',),
+                chromophores=chromophores,
                 glm_est=glm_est,
                 df_design_matrix=df_design_matrix
             )
@@ -7300,9 +7312,10 @@ def extract_fir_features_real_data(
             for (chromo, roi_name), roi_fir_curve in roi_curves.items():
                 metrics = compute_waveform_metrics(roi_fir_curve, fir_delays=fir_delays, selected_metrics=selected_metrics)
                 collapsed_features.extend(metrics)
-                # Prefix feature names with condition
+                
-                feature_names.extend([f"{cond}_{roi_name}_{m}" for m in metric_labels])
+                # Prefix feature names with condition AND chromophore
-                feature_channels.extend([roi_name] * len(metric_labels))
+                feature_names.extend([f"{cond}_{chromo.upper()}_{roi_name}_{m}" for m in metric_labels])
+                feature_channels.extend([f"{roi_name} ({chromo.upper()})"] * len(metric_labels))
 
             # Enqueue plots for this specific condition
             plot_and_enqueue_waveform_metrics(
@@ -7366,6 +7379,43 @@ def compute_waveform_metrics(fir_curve, fir_delays, selected_metrics=('Peak_Amp'
     return [calculated_metrics[m] for m in selected_metrics]
 
 
+def normalize_roi_map(roi_map):
+    """
+    Normalizes ROI mapping inputs into a flat {roi_name: [channel_list]} dict.
+    
+    Accepts:
+    1. String or Path pointing to a JSON file (JSON_LOCATION).
+    2. Dict with 'regions_of_interest' list (loaded JSON).
+    3. Standard flat dict {roi_name: [channels]}.
+    """
+    # 1. If roi_map is a file path string or Path, load JSON from disk
+    if isinstance(roi_map, (str, Path)):
+        json_path = Path(roi_map)
+        if json_path.is_file():
+            try:
+                with open(json_path, 'r', encoding='utf-8') as f:
+                    roi_map = json.load(f)
+            except Exception as e:
+                logger.error(f"Failed to load ROI JSON file from {json_path}: {e}")
+                return {}
+        else:
+            logger.error(f"ROI JSON file path does not exist: {json_path}")
+            return {}
+
+    # 2. Handle nested JSON structure with 'regions_of_interest'
+    if isinstance(roi_map, dict) and 'regions_of_interest' in roi_map:
+        return {
+            roi['name']: roi['channels'] 
+            for roi in roi_map['regions_of_interest']
+        }
+
+    # 3. Fallback for flat dictionary {roi_name: [channels]}
+    if isinstance(roi_map, dict):
+        return roi_map
+
+    return {}
+
+
 if __name__ == "__main__":
     print("This file has no functionality when not used in tandem with the FLARES application.")