note for updating optode placements

changelog.md

@@ -1,3 +1,18 @@
+# Version 1.4.2
+
+- Fixed AGE, GENDER, GROUP not visually appearing on a bubble after the metadata has been set. Fixes [Issue 42](https://git.research.dezeeuw.ca/tyler/flares/issues/42)
+- Fixed first stage of progress bar going yellow after loading from an unprocessed save
+- Fixed AGE, GENDER, GROUP not visually appearing on a bubble when loading from a save 
+- Group images involving an optode template will now be the average of all participants selected in the group and not the last processed participant. Fixes [Issue 62](https://git.research.dezeeuw.ca/tyler/flares/issues/62)
+- Group images will no longer crash if being made with participants that have a different number of channels
+- Changed CSV files to now save to the same folder rather than asking each time for each file. Fixes [Issue 39](https://git.research.dezeeuw.ca/tyler/flares/issues/39)
+
+
+# Version 1.4.1
+
+- Hotfix to fix a recursive child loop that would cause the MacOS version to not open
+
+
 # Version 1.4.0
 
 - This is potentially a save-changing release due to changes in how file paths and parameters are stored. Please update your project files to ensure compatibility

flares.py

@@ -2403,6 +2403,71 @@ def plot_3d_evoked_array(
     return brain
 
 
+def aggregate_fnirs_group_geometry(raw_list):
+    """
+    Averages fNIRS geometry across participants in two tiers:
+    1. Average by Channel Pairing (S_D).
+    2. Average by Individual Optode (S, D) across all averaged pairings.
+    Returns a unified MNE Raw object with exactly one dot per optode.
+    """
+    import mne
+    import numpy as np
+
+    channel_locs = {}
+    all_ch_names = []
+    
+    for raw in raw_list:
+        if raw is None: continue
+        raw_hbo = raw.copy().pick(picks="hbo")
+        
+        for i, ch_name in enumerate(raw_hbo.ch_names):
+            if ch_name not in channel_locs:
+                channel_locs[ch_name] = []
+                all_ch_names.append(ch_name)
+            
+            channel_locs[ch_name].append(raw_hbo.info['chs'][i]['loc'])
+
+    avg_pairings = {name: np.nanmean(locs, axis=0) for name, locs in channel_locs.items()}
+
+    optode_collections = {'sources': {}, 'detectors': {}}
+    
+    for ch_name, loc in avg_pairings.items():
+        parts = ch_name.split()[0].split('_')
+        s_name, d_name = parts[0], parts[1]
+        
+        optode_collections['sources'].setdefault(s_name, []).append(loc[3:6])
+        optode_collections['detectors'].setdefault(d_name, []).append(loc[6:9])
+
+    final_sources = {s: np.nanmean(coords, axis=0) for s, coords in optode_collections['sources'].items()}
+    final_detectors = {d: np.nanmean(coords, axis=0) for d, coords in optode_collections['detectors'].items()}
+
+    ref_raw = raw_list[0].copy().pick(picks="hbo")
+    template_lookup = {ch['ch_name']: ch for ch in ref_raw.info['chs']}
+    final_chs = []
+
+    for ch_name in all_ch_names:
+        unified_loc = avg_pairings[ch_name].copy()
+        parts = ch_name.split()[0].split('_')
+        s_name, d_name = parts[0], parts[1]
+        
+        unified_loc[3:6] = final_sources[s_name]
+        unified_loc[6:9] = final_detectors[d_name]
+        unified_loc[0:3] = (final_sources[s_name] + final_detectors[d_name]) / 2.0
+        
+        # Create the new channel object
+        new_ch = template_lookup.get(ch_name, ref_raw.info['chs'][0]).copy()
+        new_ch['ch_name'] = ch_name
+        new_ch['loc'] = unified_loc
+        final_chs.append(new_ch)
+
+    # Create the final MNE Info
+    fake_info = mne.create_info(ch_names=all_ch_names, sfreq=ref_raw.info['sfreq'], ch_types='hbo')
+    with fake_info._unlock():
+        fake_info['chs'] = final_chs
+    
+    return mne.io.RawArray(np.zeros((len(all_ch_names), 1)), fake_info)
+
+
 
 def brain_3d_visualization(raw_haemo, df_cha, selected_event, t_or_theta: Literal['t', 'theta'] = 'theta', show_optodes: Literal['sensors', 'labels', 'none', 'all'] = 'all', show_text: bool = True, brain_bounds: float = 1.0) -> None:
 
@@ -2446,7 +2511,7 @@ def brain_3d_visualization(raw_haemo, df_cha, selected_event, t_or_theta: Litera
         brain = plot_3d_evoked_array(raw_for_plot.pick(picks="hbo"), model_df, view="dorsal", distance=0.02, colorbar=True, clim=clim, mode="weighted", size=(800, 700)) # type: ignore
         
         if show_optodes == 'all' or show_optodes == 'sensors':
-            brain.add_sensors(getattr(raw_for_plot, "info"), trans=Transform('head', 'mri', np.eye(4)), fnirs=["channels", "pairs", "sources", "detectors"], verbose=False) # type: ignore
+            brain.add_sensors(raw_for_plot.pick(picks="hbo").info, trans=Transform('head', 'mri', np.eye(4)), fnirs=["channels", "pairs", "sources", "detectors"], verbose=False) # type: ignore
         
         if True:
             display_text = ('Folder: '  + '\nGroup: ' + '\nCondition: '+ cond + '\nShort Channel Regression: ' 
@@ -4025,7 +4090,7 @@ def process_participant(file_path, progress_callback=None):
                 if num_bad > MAX_BAD_CHANNELS:
                     raise Exception(
                         f"Data Quality Error: {num_bad} channels flagged for removal, "
-                        f"which exceeds the limit of {MAX_BAD_CHANNELS}. To avoid this,"
+                        f"which exceeds the limit of {MAX_BAD_CHANNELS}. To avoid this, "
                         f"either lower your filtering parameters or increase MAX_BAD_CHANNELS."
                     )
 

main.py

@@ -46,7 +46,7 @@ from PySide6.QtGui import QAction, QKeySequence, QIcon, QIntValidator, QDoubleVa
 from PySide6.QtSvgWidgets import QSvgWidget # needed to show svgs when app is not frozen
 
 
-CURRENT_VERSION = "1.4.0"
+CURRENT_VERSION = "1.4.1"
 APP_NAME = "flares"
 API_URL = f"https://git.research.dezeeuw.ca/api/v1/repos/tyler/{APP_NAME}/releases"
 API_URL_SECONDARY = f"https://git.research2.dezeeuw.ca/api/v1/repos/tyler/{APP_NAME}/releases"
@@ -664,6 +664,7 @@ class UpdateOptodesWindow(QWidget):
 
         elif extension == '.xlsx':
 
+            # TODO: Bad! Why assume sheet1 has the contents?
             df = pd.read_excel(file_b, sheet_name='Sheet1')
 
             def _get_block_data(df, block_id, row_mapping, scale=0.001):
@@ -1685,13 +1686,14 @@ class ProgressBubble(QWidget):
             # Transition to a green checkmark
             self.setSuffixText(" <span style='color: green;'>✔</span>")
 
-    def update_progress(self, step_index):
+    def update_progress(self, step_index, active=True):
         self.current_step = step_index
         for i, rect in enumerate(self.rects):
             if i < step_index:
                 rect.setStyleSheet("background-color: green; border: 1px solid gray;")
             elif i == step_index:
-                rect.setStyleSheet("background-color: yellow; border: 1px solid gray;")
+                color = "yellow" if active else "white"
+                rect.setStyleSheet(f"background-color: {color}; border: 1px solid gray;")
             else:
                 rect.setStyleSheet("background-color: white; border: 1px solid gray;")
                 
@@ -1749,8 +1751,6 @@ class ParamSection(QWidget):
         self.dependencies = []
         self.selected_path = None
 
-        # Load the mne data in a seperate process
-        self.file_executor = concurrent.futures.ProcessPoolExecutor(max_workers=1)
 
         # Title label
         title_label = QLabel(section_data["title"])
@@ -3684,8 +3684,22 @@ class ExportDataAsCSVViewerWidget(FlaresBaseWidget):
             int(s.split(" ")[0]) for s in self._get_checked_items(self.image_index_dropdown)
         ]
 
+        if not selected_file_paths or not selected_indexes:
+            QMessageBox.warning(self, "Selection Missing", "Please select at least one participant and one export type.")
+            return
+
+        # 2. ASK ONCE: Select Output Directory
+        output_dir = QFileDialog.getExistingDirectory(self, "Select Output Folder for CSV Exports")
+        
+        if not output_dir:
+            print("Export cancelled: No folder selected.")
+            return
+
+        success_count = 0
+
         # Pass the necessary arguments to each method
         for file_path in selected_file_paths:
+            base_filename = os.path.splitext(os.path.basename(file_path))[0]
             haemo_obj = self.haemo_dict.get(file_path)
             if haemo_obj is None:
                 continue
@@ -3693,90 +3707,57 @@ class ExportDataAsCSVViewerWidget(FlaresBaseWidget):
             cha = self.cha_dict.get(file_path)
 
             for idx in selected_indexes:
-                if idx == 0:
+                try:
-                    try:
+                    if idx == 0:
-                        suggested_name = f"{file_path}.csv"
+                        save_path = os.path.join(output_dir, f"{base_filename}_exported.csv")
+                        if cha is not None:
+                            cha.to_csv(save_path)
+                            success_count += 1
 
-                        # Open save dialog
-                        save_path, _ = QFileDialog.getSaveFileName(
-                            self,
-                            "Save CSV File As",
-                            suggested_name,
-                            "CSV Files (*.csv)"
-                        )
 
-                        if not save_path:
+                    elif idx == 1:
-                            print("Save cancelled.")
+                            # SPARKS Export
-                            return
+                            save_path = os.path.join(output_dir, f"{base_filename}_sparks.csv")
+                            if haemo_obj is not None:
+                                raw = haemo_obj
+                                data, times = raw.get_data(return_times=True)
+                                ann_col = np.full(times.shape, "", dtype=object)
 
-                        if not save_path.lower().endswith(".csv"):
+                                if raw.annotations is not None and len(raw.annotations) > 0:
-                            save_path += ".csv"
+                                    for onset, duration, desc in zip(
-                        # Save the CSV here
+                                        raw.annotations.onset,
+                                        raw.annotations.duration,
+                                        raw.annotations.description
+                                    ):
+                                        mask = (times >= onset) & (times < onset + duration)
+                                        ann_col[mask] = desc
+
+                                df = pd.DataFrame(data.T, columns=raw.ch_names)
+                                df.insert(0, "annotation", ann_col)
+                                df.insert(0, "time", times)
+                                df.to_csv(save_path, index=False)
+                                success_count += 1
                         
-                        cha.to_csv(save_path)
+                    else:
-                        QMessageBox.information(self, "Success", "CSV file has been saved.")
+                        print(f"No method defined for index {idx}")
+                
+                except Exception as e:
+                    print(f"Failed to export {file_path} (Type {idx}): {e}")
 
-                    except Exception as e:
+        # 4. Final Notification
-                        QMessageBox.critical(self, "Error", f"Failed to update CSV file:\n{e}")
+        if success_count > 0:
+            QMessageBox.information(self, "Export Complete", f"Successfully saved {success_count} CSV files to:\n{output_dir}")
+            
+            # # If SPARKS export was included, show the Event Window once at the end
+            # if 1 in selected_indexes:
+            #     win = UpdateEventsWindow(
+            #         parent=self,
+            #         mode=EventUpdateMode.WRITE_JSON,
+            #         caller="Video Alignment Tool"
+            #     )
+            #     win.show()
 
 
-                elif idx == 1:
-                    try:
-                        suggested_name = f"{file_path}_sparks.csv"
-
-                        # Open save dialog
-                        save_path, _ = QFileDialog.getSaveFileName(
-                            self,
-                            "Save CSV File As",
-                            suggested_name,
-                            "CSV Files (*.csv)"
-                        )
-
-                        if not save_path:
-                            print("Save cancelled.")
-                            return
-
-                        if not save_path.lower().endswith(".csv"):
-                            save_path += ".csv"
-                        # Save the CSV here
-                        
-                        raw = haemo_obj
-
-                        data, times = raw.get_data(return_times=True)
-
-
-                        ann_col = np.full(times.shape, "", dtype=object)
-
-                        if raw.annotations is not None and len(raw.annotations) > 0:
-                            for onset, duration, desc in zip(
-                                raw.annotations.onset,
-                                raw.annotations.duration,
-                                raw.annotations.description
-                            ):
-                                mask = (times >= onset) & (times < onset + duration)
-                                ann_col[mask] = desc
-
-                        df = pd.DataFrame(data.T, columns=raw.ch_names)
-                        df.insert(0, "annotation", ann_col)
-
-                        df.insert(0, "time", times)
-                        df.to_csv(save_path, index=False)
-                        QMessageBox.information(self, "Success", "CSV file has been saved.")
-
-                        win = UpdateEventsWindow(
-                            parent=self,
-                            mode=EventUpdateMode.WRITE_JSON,
-                            caller="Video Alignment Tool"
-                        )
-                        win.show()
-
-                    except Exception as e:
-                        QMessageBox.critical(self, "Error", f"Failed to update CSV file:\n{e}")
-
-
-                else:
-                    print(f"No method defined for index {idx}")
-
 class ClickableLabel(QLabel):
     def __init__(self, full_pixmap: QPixmap, thumbnail_pixmap: QPixmap):
         super().__init__()
@@ -4206,9 +4187,16 @@ class GroupViewerWidget(FlaresBaseWidget):
                     print(f"Missing parameters for index {idx}, skipping.")
                     continue
 
-                flares.brain_3d_visualization(haemo_obj, all_cha, selected_event, t_or_theta=t_or_theta, show_optodes=show_optodes, show_text=show_text, brain_bounds=brain_bounds)
+                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
 
@@ -4385,6 +4373,11 @@ class GroupBrainViewerWidget(FlaresBaseWidget):
             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: [
@@ -4465,14 +4458,12 @@ class GroupBrainViewerWidget(FlaresBaseWidget):
         print("contrast_df_a empty?", contrast_df_a.empty)
         print("contrast_df_b empty?", contrast_df_b.empty)
 
-        # Get one person for their layout
+        all_raw_objs = [self.haemo_dict.get(fp) for fp in all_selected_paths if self.haemo_dict.get(fp)]
-        rep_raw = None
+        
-        for fp in file_paths_a + file_paths_b:
+        if len(all_raw_objs) > 1:
-            rep_raw = self.haemo_dict.get(fp)
+            processed_raw = flares.aggregate_fnirs_group_geometry(all_raw_objs)
-            if rep_raw:
+        else:
-                break
+            processed_raw = all_raw_objs[0].copy().pick(picks="hbo")
-
-        print(rep_raw)
 
         # Visualizations
         for idx in selected_indexes:
@@ -4488,12 +4479,12 @@ class GroupBrainViewerWidget(FlaresBaseWidget):
                     print(f"Missing parameters for index {idx}, skipping.")
                     continue
 
-                if not contrast_df_a.empty and not contrast_df_b.empty and rep_raw:
+                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=rep_raw,
+                        raw_haemo=processed_raw,
                         group_a_name=self.group_a_dropdown.currentText(),
                         group_b_name=self.group_b_dropdown.currentText(),
                         is_3d=is_3d,
@@ -4616,6 +4607,9 @@ class MainApplication(QMainWindow):
         self.setWindowTitle(f"{APP_NAME.upper()}")
         self.setGeometry(100, 100, 1280, 720)
 
+        # Load the mne data in a seperate process
+        self.file_executor = concurrent.futures.ProcessPoolExecutor(max_workers=1)
+
         self.about = None
         self.help = None
         self.optodes = None
@@ -4712,6 +4706,7 @@ class MainApplication(QMainWindow):
             label = QLabel(key.capitalize())
             right_column_layout.addWidget(label)
             right_column_layout.addWidget(field)
+            field.textChanged.connect(self.sync_bubble_data)
 
         label_desc = QLabel('<a href="#">Why are these useful?</a>')
         label_desc.setTextInteractionFlags(Qt.TextInteractionFlag.TextBrowserInteraction)
@@ -5404,8 +5399,6 @@ class MainApplication(QMainWindow):
                 for rel_path, step in raw_progress.items()
             }
 
-            self.show_files_as_bubbles_from_list(file_list, progress_states, filename)
-
             for rel_path in data["file_list"]:
                 abs_path = str((project_dir / Path(rel_path)).resolve())
                 
@@ -5424,14 +5417,7 @@ class MainApplication(QMainWindow):
                     # Scenario C: Empty default
                     self.file_metadata[abs_path] = {"AGE": "", "GENDER": "", "GROUP": ""}
 
-            if file_list:
+            self.show_files_as_bubbles_from_list(file_list, progress_states, filename)
-                self.current_file = file_list[0]
-                self.right_column_widget.show()
-                
-                # Update Metadata fields (Age/Gender/Group) for the selected file
-                curr_meta = self.file_metadata.get(self.current_file, {"AGE": "", "GENDER": "", "GROUP": ""})
-                for key, field in self.meta_fields.items():
-                    field.setText(curr_meta.get(key, ""))
 
             if "current_ui_params" in data:
                 self.restore_sections_from_config(data["current_ui_params"])
@@ -5588,6 +5574,18 @@ class MainApplication(QMainWindow):
             bubble.clicked.connect(self.on_bubble_clicked)
             bubble.rightClicked.connect(self.on_bubble_right_clicked)
 
+            if hasattr(self, 'file_metadata') and file_path in self.file_metadata:
+                meta = self.file_metadata[file_path]
+                
+                parts = []
+                for key in ["AGE", "GENDER", "GROUP"]:
+                    value = meta.get(key, "").strip()
+                    if value:
+                        parts.append(f"{key}: {value}")
+                
+                suffix = f"{', '.join(parts)}" if parts else ""
+                bubble.setSuffixText(suffix)
+
             # Track it
             self.bubble_widgets[file_path] = bubble
             if file_path not in self.selected_paths:
@@ -5595,7 +5593,7 @@ class MainApplication(QMainWindow):
 
             # Restore saved progress but keep loading state active
             step = progress_states.get(file_path, 0)
-            bubble.update_progress(step)
+            bubble.update_progress(step, active=False)
 
             # Add to layout
             self.bubble_layout.addWidget(bubble, index, 1)
@@ -5756,21 +5754,15 @@ class MainApplication(QMainWindow):
             if getattr(self, 'last_clicked_bubble', None) is bubble:
                 self.last_clicked_bubble = None
             
-    def eventFilter(self, watched, event):
+    def sync_bubble_data(self):
-        if event.type() == QEvent.Type.MouseButtonPress:
+        """Refreshes the bubble and saves data in real-time."""
-            widget = self.childAt(event.pos())
+        if self.current_file and self.last_clicked_bubble:
-            if isinstance(widget, ProgressBubble):
+            # Save the current state of all fields
-                pass
+            self.save_metadata(self.current_file)
-            else:
+            
-                if self.last_clicked_bubble:
+            # Grab the updated suffix and apply it immediately
-                    if not self.last_clicked_bubble.isAncestorOf(widget):
+            suffix = self.get_suffix_from_meta_fields()
-                        if self.current_file:
+            self.last_clicked_bubble.setSuffixText(suffix)
-                            self.save_metadata(self.current_file)
-                        suffix = self.get_suffix_from_meta_fields()
-                        self.last_clicked_bubble.setSuffixText(suffix)
-                        self.last_clicked_bubble = None
-
-        return super().eventFilter(watched, event)
     
     def placeholder(self):
         QMessageBox.information(self, "Placeholder", "This feature is not implemented yet.")