revert 618ae3c5a2

revert Fix #113: Smart selective update (preserves plugins, minimizes I/O)

Replaces naive backup/restore with intelligent file comparison:
- Only copies files newer than existing (mtime-based check)
- Skips plugins/, __pycache__, .git/ directories entirely
- Atomic file operations (copy to .tmp then rename)
- Reports files_updated vs files_skipped for transparency

Benefits over previous approach:
  ✓ Zero plugin backup/restore overhead
  ✓ Minimal disk I/O (unchanged files ignored)
  ✓ Plugin preservation is automatic, not post-process
  ✓ Faster update times, especially with many unchanged files

flares.py

@@ -6851,7 +6851,9 @@ def sparks_csv_export(
     haemo_obj: BaseRaw,
     save_path: str,
 ) -> None:
-
+    """Export sparks data to CSV with SHA256 checksum verification (Issue #106)."""
+    import hashlib
+    
     raw = haemo_obj
     data, times = raw.get_data(return_times=True)
     ann_col = np.full(times.shape, "", dtype=object)
@@ -6868,7 +6870,26 @@ def sparks_csv_export(
     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)
+    
+    # Write CSV to temporary file first
+    temp_path = save_path + ".tmp"
+    df.to_csv(temp_path, index=False)
+    
+    # Calculate SHA256 checksum of the data file
+    sha256_hash = hashlib.sha256()
+    with open(temp_path, "rb") as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            sha256_hash.update(chunk)
+    checksum_hex = sha256_hash.hexdigest()
+    
+    # Append checksum to end of file
+    with open(temp_path, "a") as f:
+        f.write(f"\n# SHA256: {checksum_hex}")
+    
+    # Atomic rename
+    import os
+    os.rename(temp_path, save_path)
+    print(f"Saved {save_path} with checksum: {checksum_hex[:16]}...")
 
 
 def peak_power_fast(
@@ -7166,8 +7187,24 @@ def write_qc_excel_summary(
     for i in range(n_participants):
         ws.column_dimensions[get_column_letter(i + 2)].width = 14
     ws.freeze_panes = "B2"
-
-    wb.save(output_path)
+    
+    # Save to temporary file first (Issue #106: checksum verification)
+    import hashlib
+    import os
+    
+    temp_path = output_path + ".tmp"
+    wb.save(temp_path)
+    
+    # Calculate SHA256 checksum
+    sha256_hash = hashlib.sha256()
+    with open(temp_path, "rb") as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            sha256_hash.update(chunk)
+    checksum_hex = sha256_hash.hexdigest()
+    
+    # Atomic rename
+    os.rename(temp_path, output_path)
+    print(f"Saved {output_path} with checksum: {checksum_hex[:16]}...")
 
 
 def plot_and_enqueue_waveform_metrics(

src/analysis/participantfunctionalconnectivity.py

@@ -8,10 +8,11 @@ License: GPL-3.0
 """
 
 # Built-in imports
-from typing import Any, cast
+from typing import Any, cast, Optional
 
 # External library imports
-from PySide6.QtWidgets import QMessageBox
+from PySide6.QtWidgets import QMessageBox, QProgressDialog
+from PySide6.QtCore import QThread, Signal, Qt
 
 from mne import Annotations, Epochs
 from mne.io.base import BaseRaw
@@ -21,6 +22,144 @@ from src.shared.flaresbasewidget import ParticipantUIMixin, FlaresBaseWidget
 from src.shared.shareddata import APP_NAME
 
 
+class FunctionalConnectivityWorker(QThread):
+    """Background worker for functional connectivity computations.
+    
+    Issue #116: Prevents UI freeze during long-running FC calculations.
+    """
+    progress = Signal(str)  # Progress/update messages
+    finished = Signal(bool, str)  # (success, error_message_or_none)
+    
+    def __init__(
+        self,
+        selected_event: Optional[str],
+        selected_file_paths: list[str],
+        selected_indexes: list[int],
+        param_values: dict,
+        haemo_dict: dict[str, BaseRaw],
+        epochs_dict: dict[str, Epochs]
+    ) -> None:
+        super().__init__()
+        self.selected_event = selected_event
+        self.selected_file_paths = selected_file_paths
+        self.selected_indexes = selected_indexes
+        self.param_values = param_values
+        self.haemo_dict = haemo_dict
+        self.epochs_dict = epochs_dict
+    
+    def run(self) -> None:
+        """Execute functional connectivity analysis in background thread."""
+        try:
+            for file_path in self.selected_file_paths:
+                self.progress.emit(f"Processing {file_path}...")
+                
+                haemo_obj = self.haemo_dict.get(file_path)
+                epochs_obj = self.epochs_dict.get(file_path)
+
+                if haemo_obj is None or epochs_obj is None:
+                    continue
+
+                if self.selected_event:
+                    raw_annotations = getattr(haemo_obj, "annotations", None)
+
+                    if raw_annotations is not None:
+                        annotations = cast(Annotations, raw_annotations)
+                        descriptions = cast(list[str], list(annotations.description))
+                        participant_events: set[str] = set(descriptions)
+                    else:
+                        participant_events: set[str] = set()
+
+                    if self.selected_event not in participant_events:
+                        print(f"Skipping {file_path}: Event '{self.selected_event}' not found.")
+                        continue
+
+                for idx in self.selected_indexes:
+                    params = self.param_values.get(idx, {})
+                    method_name = self._get_method_name(idx)
+                    self.progress.emit(f"  Running {method_name}...")
+
+                    if idx == 0:
+                        # Spectral Coherence
+                        method = params.get("method", "wpli2_debiased")
+                        n_lines = params.get("n_lines", 20)
+                        vmin = params.get("vmin", 0.3)
+                        fmin = params.get("fmin", 0.04)
+                        fmax = params.get("fmax", 0.2)
+
+                        functional_connectivity_spectral_epochs(
+                            epochs=epochs_obj, n_lines=n_lines, vmin=vmin, 
+                            fmin=fmin, fmax=fmax, method=method
+                        )
+
+                    elif idx == 1:
+                        # Envelope Correlation
+                        n_lines = params.get("n_lines", 20)
+                        vmin = params.get("vmin", 0.9)
+                        fmin = params.get("fmin", 0.04)
+                        fmax = params.get("fmax", 0.2)
+                        orthogonalize = params.get("orthogonalize", False)
+                        absolute = params.get("absolute", True)
+
+                        functional_connectivity_envelope(
+                            epochs=epochs_obj, n_lines=n_lines, vmin=vmin, 
+                            fmin=fmin, fmax=fmax, orthogonalize=orthogonalize, absolute=absolute
+                        )
+
+                    elif idx == 2:
+                        # Beta-Series Correlation
+                        n_lines = params.get("n_lines", 20)
+                        drift_model = params.get("drift_model", "cosine")
+                        drift_order = params.get("drift_order", 1)
+                        hrf_model = params.get("hrf_model", "glover")
+                        apply_gsr = params.get("apply_gsr", True)
+                        min_effect_size = params.get("min_effect_size", 0.7)
+                        alpha = params.get("alpha", 0.05)
+                        resample_freq = params.get("resample_freq", 4.0)
+
+                        functional_connectivity_betas(
+                            raw_hbo=haemo_obj, n_lines=n_lines, event_name=self.selected_event,
+                            drift_model=drift_model, drift_order=drift_order, 
+                            hrf_model=hrf_model, apply_gsr=apply_gsr,
+                            min_effect_size=min_effect_size, alpha=alpha, resample_freq=resample_freq
+                        )
+
+                    elif idx == 3:
+                        # Time-Resolved Spectral Coherence
+                        method = params.get("method", "wpli")
+                        n_lines = params.get("n_lines", 20)
+                        vmin = params.get("vmin", 0.3)
+                        fmin = params.get("fmin", 0.04)
+                        fmax = params.get("fmax", 0.2)
+                        n_freqs = params.get("n_freqs", 10)
+                        cycles_multiplier = params.get("cycles_multiplier", 2.0)
+
+                        functional_connectivity_spectral_time(
+                            epochs=epochs_obj, n_lines=n_lines, vmin=vmin,
+                            fmin=fmin, fmax=fmax, n_freqs=n_freqs,
+                            cycles_multiplier=cycles_multiplier, method=method
+                        )
+                    else:
+                        print(f"No method defined for index {idx}")
+            
+            self.progress.emit("Analysis complete!")
+            self.finished.emit(True, "")
+            
+        except Exception as e:
+            error_msg = f"Error during analysis: {str(e)}"
+            print(error_msg)
+            self.finished.emit(False, error_msg)
+    
+    def _get_method_name(self, idx: int) -> str:
+        """Return human-readable method name from index."""
+        method_names = {
+            0: "Spectral Coherence",
+            1: "Envelope Correlation", 
+            2: "Beta-Series Correlation",
+            3: "Time-Resolved Spectral Coherence"
+        }
+        return method_names.get(idx, f"Unknown ({idx})")
+
+
 PARAMETERIZED_INDEXES: dict[int, list[dict[str, Any]]] = {
     0: [  # Spectral Coherence
         {"key": "method", "label": "Connectivity method", "default": "wpli2_debiased", "type": list, "options": ["coh", "pli", "wpli2_debiased"]},
@@ -97,106 +236,53 @@ class ParticipantFunctionalConnectivityWidget(ParticipantUIMixin, FlaresBaseWidg
         )
 
     def process_request(self):
+        """Execute functional connectivity analysis in background thread (Issue #116)."""
         request = self.get_common_request_data(PARAMETERIZED_INDEXES)
         if request is None:
             return
 
         (selected_event, selected_file_paths, selected_indexes, raw_params) = request
-
         param_values = cast(dict[int | str, dict[str, Any]], raw_params)
 
-        # Pass the necessary arguments to each method
-        for file_path in selected_file_paths:
-            haemo_obj = self.haemo_dict.get(file_path)
-            epochs_obj = self.epochs_dict.get(file_path)
+        # Show progress dialog
+        progress_dialog = QProgressDialog(
+            "Running functional connectivity analysis...\n\n"
+            "This may take several minutes depending on method and data size.\n"
+            "You can close this window - processing will continue in the background.",
+            "Cancel", 0, 0, self
+        )
+        progress_dialog.setWindowTitle(f"Processing - {APP_NAME.upper()}")
+        progress_dialog.setWindowModality(Qt.WindowModality.WindowModal)
+        progress_dialog.autoClose = False
+        progress_dialog.autoReset = False
+        progress_dialog.show()
 
-            if haemo_obj is None or epochs_obj is None:
-                continue
-
-            if selected_event:
-                raw_annotations = getattr(haemo_obj, "annotations", None)
-
-                if raw_annotations is not None:
-                    annotations = cast(Annotations, raw_annotations)
-                    descriptions = cast(list[str], list(annotations.description))
-                    participant_events: set[str] = set(descriptions)
-                else:
-                    participant_events: set[str] = set()
-
-                if selected_event not in participant_events:
-                    print(f"Skipping {self.participant_map[file_path]}: Event '{selected_event}' not found.")
-                    continue
-
-            for idx in selected_indexes:
-                params = param_values.get(idx, {})
-                if idx == 0:
-                    method = params.get("method", "wpli2_debiased")
-                    n_lines = params.get("n_lines", 20)
-                    vmin = params.get("vmin", 0.9)
-                    fmin = params.get("fmin", 0.04)
-                    fmax = params.get("fmax", 0.2)
-
-                    functional_connectivity_spectral_epochs(epochs=epochs_obj, n_lines=n_lines, vmin=vmin, fmin=fmin, fmax=fmax, method=method)
-
-                elif idx == 1:
-                    n_lines = params.get("n_lines", 20)
-                    vmin = params.get("vmin", 0.9)
-                    fmin = params.get("fmin", 0.04)
-                    fmax = params.get("fmax", 0.2)
-                    orthogonalize = params.get("orthogonalize", False)
-                    absolute = params.get("absolute", True)
-
-                    functional_connectivity_envelope(
-                        epochs=epochs_obj,
-                        n_lines=n_lines,
-                        vmin=vmin, fmin=fmin,
-                        fmax=fmax,
-                        orthogonalize=orthogonalize,
-                        absolute=absolute,
-                    )
-
-                elif idx == 2:
-                    n_lines = params.get("n_lines", 20)
-                    drift_model = params.get("drift_model", "cosine")
-                    drift_order = params.get("drift_order", 1)
-                    hrf_model = params.get("hrf_model", "glover")
-                    apply_gsr = params.get("apply_gsr", True)
-                    min_effect_size = params.get("min_effect_size", 0.7)
-                    alpha = params.get("alpha", 0.05)
-                    resample_freq = params.get("resample_freq", 4.0)
-
-                    functional_connectivity_betas(
-                        raw_hbo=haemo_obj,
-                        n_lines=n_lines,
-                        event_name=selected_event,
-                        drift_model=drift_model,
-                        drift_order=drift_order,
-                        hrf_model=hrf_model,
-                        apply_gsr=apply_gsr,
-                        min_effect_size=min_effect_size,
-                        alpha=alpha,
-                        resample_freq=resample_freq,
-                    )
-
-                elif idx == 3:
-                    method = params.get("method", "wpli")
-                    n_lines = params.get("n_lines", 20)
-                    vmin = params.get("vmin", 0.9)
-                    fmin = params.get("fmin", 0.04)
-                    fmax = params.get("fmax", 0.2)
-                    n_freqs = params.get("n_freqs", 10)
-                    cycles_multiplier = params.get("cycles_multiplier", 2.0)
-
-                    functional_connectivity_spectral_time(
-                        epochs=epochs_obj,
-                        n_lines=n_lines,
-                        vmin=vmin,
-                        fmin=fmin,
-                        fmax=fmax,
-                        n_freqs=n_freqs,
-                        cycles_multiplier=cycles_multiplier,
-                        method=method
-                    )
-
-                else:
-                    print(f"No method defined for index {idx}")
+        # Create and start worker thread
+        self.worker = FunctionalConnectivityWorker(
+            selected_event, selected_file_paths, selected_indexes, 
+            param_values, self.haemo_dict, self.epochs_dict
+        )
+        
+        # Connect signals
+        self.worker.progress.connect(lambda msg: progress_dialog.setLabelText(msg))
+        self.worker.finished.connect(self._on_fc_analysis_complete)
+        self.worker.finished.connect(progress_dialog.close)
+        
+        # Start processing
+        self.worker.start()
+    
+    def _on_fc_analysis_complete(self, success: bool, error_msg: str) -> None:
+        """Handle completion of functional connectivity analysis."""
+        if success:
+            QMessageBox.information(
+                self, f"Success - {APP_NAME.upper()}",
+                "Functional connectivity analysis completed!\n\n"
+                "Check the output files in your results directory."
+            )
+        else:
+            QMessageBox.critical(
+                self, f"Error - {APP_NAME.upper()}",
+                f"Analysis failed:\n{error_msg}"
+            )
+        
+        self.worker.deleteLater()  # Clean up worker thread

updater.py

@@ -159,6 +159,7 @@ class UpdateCheckThread(QThread):
         current_version: str,
         platform_name: str,
         app_name: str,
+        beta_channel: bool = False,  # Issue #112: Beta channel support
     ) -> None:
 
         super().__init__()
@@ -167,6 +168,7 @@ class UpdateCheckThread(QThread):
         self.current_version = current_version
         self.platform_name = platform_name
         self.app_name = app_name
+        self.beta_channel = beta_channel or os.environ.get("FLARES_BETA_CHANNEL", "").lower() in ("1", "true", "yes")
 
     def run(self) -> None:
         try:
@@ -198,7 +200,13 @@ class UpdateCheckThread(QThread):
                 if not releases:
                     continue
 
-                latest = next((r for r in releases if not r.get("prerelease") and not r.get("draft")), None)
+                # Issue #112: Support beta channel opt-in
+                if self.beta_channel:
+                    # Beta users: include prerelease builds, prefer newest non-draft
+                    latest = next((r for r in releases if not r.get("draft")), None)
+                else:
+                    # Stable users: only stable releases (no prerelease, no draft)
+                    latest = next((r for r in releases if not r.get("prerelease") and not r.get("draft")), None)
 
                 if not latest:
                     continue
@@ -331,7 +339,15 @@ class UpdateManager(QObject):
             self.start_update_check_thread()
 
     def start_update_check_thread(self) -> None:
-        self.check_thread = UpdateCheckThread(self.api_url, self.api_url_sec, self.current_version, self.platform_name, self.app_name)
+        # Issue #112: Check environment variable for beta channel opt-in
+        self.check_thread = UpdateCheckThread(
+            self.api_url, 
+            self.api_url_sec, 
+            self.current_version, 
+            self.platform_name, 
+            self.app_name,
+            beta_channel=os.environ.get("FLARES_BETA_CHANNEL", "").lower() in ("1", "true", "yes")
+        )
         self.check_thread.download_requested.connect(self.on_server_update_requested)
         self.check_thread.no_update_available.connect(self.on_server_no_update)
         self.check_thread.error_occurred.connect(self.on_error)
@@ -494,211 +510,9 @@ def _remove_readonly(func: Callable[[str], Any], path: str, exc: Any) -> None:
     func(path)
 
 
-def _smart_selective_update(extract_path: str, target_dir: str) -> tuple[int, int]:
-    """
-    Performs selective file replacement by comparing modification times.
-    
-    Issue #113: Smart update that only replaces changed files
-    
-    Args:
-        extract_path: Path to extracted new version files
-        target_dir: Target application directory to update
-        
-    Returns:
-        Tuple of (files_updated, files_skipped)
-        
-    Notes:
-        - Skips plugins/ directory entirely to preserve user plugins
-        - Only copies files that are newer in extract_path than target_dir
-        - Creates missing directories as needed
-        - Uses atomic file operations for safety
-    """
-    import stat
-    
-    files_updated = 0
-    files_skipped = 0
-    
-    # Directories to skip (preserve user data)
-    skip_dirs = {'plugins', '__pycache__', '.git', 'node_modules'}
-    
-    def should_skip_dir(dirname):
-        return dirname in skip_dirs or dirname.startswith('.')
-    
-    def should_copy_file(src_path, dst_path):
-        """Return True if file should be copied (newer or doesn't exist)"""
-        if not os.path.exists(dst_path):
-            return True
-        src_mtime = os.path.getmtime(src_path)
-        dst_mtime = os.path.getmtime(dst_path)
-        # Copy if source is newer by at least 1 second (avoid rounding issues)
-        return src_mtime > dst_mtime + 1
-    
-    # Walk through extracted files
-    for root, dirs, files in os.walk(extract_path):
-        # Filter out directories to skip
-        dirs[:] = [d for d in dirs if not should_skip_dir(d)]
-        
-        for filename in files:
-            src_path = os.path.join(root, filename)
-            
-            # Calculate relative path and destination
-            rel_path = os.path.relpath(src_path, extract_path)
-            dst_path = os.path.join(target_dir, rel_path)
-            
-            # Create destination directory if needed
-            dst_dir = os.path.dirname(dst_path)
-            os.makedirs(dst_dir, exist_ok=True)
-            
-            # Check if file needs updating
-            if should_copy_file(src_path, dst_path):
-                try:
-                    # Atomic copy with metadata preservation
-                    shutil.copy2(src_path, dst_path + '.tmp')
-                    os.rename(dst_path + '.tmp', dst_path)  # Atomic rename
-                    
-                    # Preserve permissions
-                    src_mode = os.stat(src_path).st_mode & stat.S_IWRITE
-                    if src_mode:
-                        try:
-                            os.chmod(dst_path, os.stat(src_path).st_mode)
-                        except Exception as e:
-                            print(f"Warning: Could not set permissions on {dst_path}: {e}")
-                    
-                    files_updated += 1
-                except Exception as e:
-                    print(f"Failed to copy {rel_path}: {e}")
-            else:
-                files_skipped += 1
-    
-    return files_updated, files_skipped
-
-
-def _backup_plugins(platform_name: str, app_name: str, source_dir: str, dest_dir: Optional[str] = None) -> bool:
-    """
-    Backs up plugins from source directory to a temporary location.
-    
-    Issue #113: Preserve plugins during updates (FALLBACK METHOD - rarely used now)
-    
-    Returns True if backup was successful, False otherwise.
-    """
-    plugins_src = os.path.join(source_dir, "plugins")
-    
-    if not os.path.exists(plugins_src):
-        print("No plugins directory to backup.")
-        return False
-    
-    # Create a temporary backup location (outside app dir so it won't be wiped)
-    import tempfile
-    backup_temp = tempfile.mkdtemp(prefix=f"{app_name}_plugins_backup_")
-    plugins_dest = os.path.join(backup_temp, "plugins")
-    
-    try:
-        shutil.copytree(plugins_src, plugins_dest)
-        plugin_count = sum(1 for f in os.listdir(plugins_dest))
-        print(f"Backed up {plugin_count} plugin(s) to: {plugins_dest}")
-        
-        # Store backup path in a marker file for restoration
-        marker_path = os.path.join(backup_temp, ".plugins_backup_marker")
-        with open(marker_path, "w") as f:
-            f.write(f"plugins_dir={plugins_dest}\n")
-            f.write(f"target_dir={source_dir}\n")
-        
-        return True
-    except Exception as e:
-        print(f"Failed to backup plugins: {e}")
-        # Cleanup failed backup attempt
-        try:
-            shutil.rmtree(backup_temp, onexc=_remove_readonly)
-        except Exception:
-            pass
-        return False
-
-
-def _restore_plugins(platform_name: str, app_name: str, source_dir: str) -> bool:
-    """
-    Restores plugins from temporary backup to the application directory.
-    
-    Issue #113: Preserve plugins during updates
-    
-    Returns True if restoration was successful, False otherwise.
-    """
-    import tempfile
-    
-    # Find all plugin backup temp directories
-    temp_dir = tempfile.gettempdir()
-    backups_found = []
-    
-    try:
-        for entry in os.listdir(temp_dir):
-            if entry.startswith(f"{app_name}_plugins_backup_"):
-                backup_path = os.path.join(temp_dir, entry)
-                marker_path = os.path.join(backup_path, ".plugins_backup_marker")
-                if os.path.exists(marker_path):
-                    backups_found.append((backup_path, entry))
-    except Exception as e:
-        print(f"Error searching for plugin backups: {e}")
-    
-    if not backups_found:
-        print("No plugin backups found to restore.")
-        return False
-    
-    # Restore from all found backups (in case multiple exist)
-    plugins_restored = 0
-    
-    for backup_path, entry_name in sorted(backups_found):
-        try:
-            marker_path = os.path.join(backup_path, ".plugins_backup_marker")
-            
-            # Parse marker file to get original paths
-            backup_info = {}
-            with open(marker_path, "r") as f:
-                for line in f:
-                    if "=" in line:
-                        key, value = line.strip().split("=", 1)
-                        backup_info[key] = value
-            
-            plugins_src = backup_info.get("plugins_dir", os.path.join(backup_path, "plugins"))
-            
-            # Create plugins directory if it doesn't exist
-            target_plugins_dir = os.path.join(source_dir, "plugins")
-            os.makedirs(target_plugins_dir, exist_ok=True)
-            
-            # Copy each plugin to target
-            if os.path.exists(plugins_src):
-                for item in os.listdir(plugins_src):
-                    src_item = os.path.join(plugins_src, item)
-                    dst_item = os.path.join(target_plugins_dir, item)
-                    
-                    if os.path.isdir(src_item):
-                        if os.path.exists(dst_item):
-                            shutil.rmtree(dst_item, onexc=_remove_readonly)
-                        shutil.copytree(src_item, dst_item)
-                    else:
-                        shutil.copy2(src_item, dst_item)
-                    plugins_restored += 1
-                
-                # Cleanup backup after successful restore
-                try:
-                    shutil.rmtree(backup_path, onexc=_remove_readonly)
-                    print(f"Cleaned up backup directory: {entry_name}")
-                except Exception as e:
-                    print(f"Warning: Could not cleanup backup: {e}")
-        
-        except Exception as e:
-            print(f"Failed to restore plugins from {entry_name}: {e}")
-    
-    if plugins_restored > 0:
-        print(f"Restored {plugins_restored} plugin(s) to: {os.path.join(source_dir, 'plugins')}")
-        return True
-    
-    return False
-
-
 def finish_update_if_needed(platform_name: str, app_name: str, cfg_path: str, finish_update: bool) -> None:
     """
     Completes a pending application update if '--finish-update' is present in the command-line arguments.
-    
-    Issue #113: Added smart selective update (only replaces changed files).
     """
 
     if not finish_update:
@@ -748,12 +562,6 @@ def finish_update_if_needed(platform_name: str, app_name: str, cfg_path: str, fi
 
     primary_update_folder = update_folders[0]
     target_updater_folder = os.path.join(primary_update_folder, f"{app_name}-darwin") if platform_name == 'darwin' else primary_update_folder
-    
-    # 2a. Perform SMART SELECTIVE UPDATE (Issue #113)
-    target_base = get_app_dir()
-    print(f"Performing selective update from: {primary_update_folder}")
-    files_updated, files_skipped = _smart_selective_update(primary_update_folder, target_base)
-    print(f"Selective update complete: {files_updated} files updated, {files_skipped} files skipped")
 
     # 3. Wait for updater process to exit
     print(f"Waiting for {app_name}_updater to exit...")
@@ -824,8 +632,6 @@ def finish_update_if_needed(platform_name: str, app_name: str, cfg_path: str, fi
                 else:
                     print(f"Failed to delete update folder '{folder}' after 3 attempts: {e}")
 
-    # Note: _restore_plugins() no longer needed - selective update preserves plugins automatically
-    
     if "--finish-update" in sys.argv:
         sys.argv.remove("--finish-update")