group fc

changelog.md

@@ -9,6 +9,13 @@
 - Setting SHORT_CHANNEL to False will now grey out SHORT_CHANNEL_REGRESSION, as it is impossible to regress what does not exist. Sets SHORT_CHANNEL_REGRESSION to False under the hood when it is greyed out regardless of what is displayed. Fixes [Issue 47](https://git.research.dezeeuw.ca/tyler/flares/issues/47)
 - Projects can now be saves if files have different parent folders. Fixes [Issue 48](https://git.research.dezeeuw.ca/tyler/flares/issues/48)
 - It is no longer possible to attempt a save before any data has been processed. A popup will now display if a save is attempted with nothing to save
+- Fixed a bug where LONG_CHANNEL_THRESH was not being applied in the processing steps
+- Added a new option in the Analysis window for Group Functional Connectivity. Implements [Issue 50](https://git.research.dezeeuw.ca/tyler/flares/issues/50)
+- Group Functional connectivity is still in development and the results should currently be taken with a grain of salt
+- A warning is displayed when entering the Group Functional Connectivity Viewer disclosing this
+- Fixed a bug when updating optode positions that would prevent .txt files from being selected. Fixes [Issue 54](https://git.research.dezeeuw.ca/tyler/flares/issues/54)
+- Fixed a bug where the secondary download server would never get contacted if the primary failed
+- Automatic downloads will now ignore prerelease versions. Fixes [Issue 52](https://git.research.dezeeuw.ca/tyler/flares/issues/52)
 
 
 # Version 1.2.0
@@ -134,7 +141,7 @@
 - Added a group option when clicking on a participant's file
 - If no group is specified, the participant will be added to the "Default" group
 - Added option to update the optode positions in a snirf file from the Options menu (F6)
-- Fixed [Issue 3](https://git.research.dezeeuw.ca/tyler/flares/issues/3), [Issue 4](https://git.research.dezeeuw.ca/tyler/flares/issues/4), [Issue 17](https://git.research.dezeeuw.ca/tyler/flares/issues/17), [Issue 21](https://git.research.dezeeuw.ca/tyler/flares/issues/21), [Issue 22](https://git.research.dezeeuw.ca/tyler/flares/issues/22)
+- Fixed [Issue 3](https://git.research.dezeeuw.ca/tyler/flares/issues/3), [Issue 5](https://git.research.dezeeuw.ca/tyler/flares/issues/5), [Issue 17](https://git.research.dezeeuw.ca/tyler/flares/issues/17), [Issue 21](https://git.research.dezeeuw.ca/tyler/flares/issues/21), [Issue 22](https://git.research.dezeeuw.ca/tyler/flares/issues/22)
 
 
 # Version 1.0.1

flares.py

@@ -3403,7 +3403,7 @@ def process_participant(file_path, progress_callback=None):
         fig_individual["short"] = fig_short_chans
     else:
         short_chans = None
-    get_long_channels(raw, min_dist=SHORT_CHANNEL_THRESH, max_dist=LONG_CHANNEL_THRESH) # Don't update the existing raw
+    raw = get_long_channels(raw, min_dist=0, max_dist=LONG_CHANNEL_THRESH) # keep both short channels and all channels up to the threshold length
     if progress_callback: progress_callback(4)
     logger.info("Step 4 Completed.")
 
@@ -3892,3 +3892,191 @@ def functional_connectivity_betas(raw_hbo, n_lines, vmin, event_name=None):
         vmax=1.0,
         colormap='hot' # Use 'hot' to make positive connections pop
     )
+
+
+
+
+def get_single_subject_beta_corr(raw_hbo, event_name=None, config=None):
+    """Processes one participant and returns their correlation matrix."""
+    raw_hbo = raw_hbo.copy().pick(picks="hbo")
+    ann = raw_hbo.annotations
+    
+    # Rename for trial-level GLM
+    new_desc = [f"{desc}__trial_{i:03d}" for i, desc in enumerate(ann.description)]
+    ann.description = np.array(new_desc)
+
+    if config == None:
+        print("no config")
+        design_matrix = make_first_level_design_matrix(
+            raw=raw_hbo, hrf_model='fir', 
+            fir_delays=np.arange(0, 12, 1),
+            drift_model='cosine', drift_order=1
+        )
+    else:
+        print("config")
+        if config.get("SHORT_CHANNEL_REGRESSION") == True:
+            short_chans = get_short_channels(raw_hbo, max_dist=config.get("SHORT_CHANNEL_THRESH"))
+
+            design_matrix = make_first_level_design_matrix(
+                raw=raw_hbo,
+                stim_dur=config.get("STIM_DUR"),
+                hrf_model=config.get("HRF_MODEL"),
+                drift_model=config.get("DRIFT_MODEL"),
+                high_pass=config.get("HIGH_PASS"),
+                drift_order=config.get("DRIFT_ORDER"),
+                fir_delays=config.get("FIR_DELAYS"),
+                add_regs=short_chans.get_data().T,
+                add_reg_names=short_chans.ch_names,
+                min_onset=config.get("MIN_ONSET"),
+                oversampling=config.get("OVERSAMPLING")
+            )
+            print("yep")
+        else:
+            design_matrix = make_first_level_design_matrix(
+                raw=raw_hbo,
+                stim_dur=config.get("STIM_DUR"),
+                hrf_model=config.get("HRF_MODEL"),
+                drift_model=config.get("DRIFT_MODEL"),
+                high_pass=config.get("HIGH_PASS"),
+                drift_order=config.get("DRIFT_ORDER"),
+                fir_delays=config.get("FIR_DELAYS"),
+                min_onset=config.get("MIN_ONSET"),
+                oversampling=config.get("OVERSAMPLING")
+            )
+
+
+    glm_results = run_glm(raw_hbo, design_matrix)
+    betas = np.array(glm_results.theta()) 
+    reg_names = list(design_matrix.columns)
+    n_channels = betas.shape[0]
+
+    # Filter trials by event name
+    trial_tags = sorted({
+        col.split("_delay")[0] for col in reg_names 
+        if "__trial_" in col and (event_name is None or col.startswith(event_name + "__"))
+    })
+
+    if not trial_tags:
+        return None, None
+
+    # Build Beta Series
+    beta_series = np.zeros((n_channels, len(trial_tags)))
+    for t, tag in enumerate(trial_tags):
+        idx = [i for i, col in enumerate(reg_names) if col.startswith(f"{tag}_delay")]
+        beta_series[:, t] = np.mean(betas[:, idx], axis=1).flatten()
+        #beta_series[:, t] = np.max(betas[:, idx], axis=1).flatten() #TODO: Figure out which one to use
+
+    # Z-score and GSR (Global Signal Regression)
+    beta_series = zscore(beta_series, axis=1)
+    global_signal = np.mean(beta_series, axis=0)
+    for i in range(n_channels):
+        slope, _ = np.polyfit(global_signal, beta_series[i, :], 1)
+        beta_series[i, :] -= (slope * global_signal)
+
+    # Correlation Matrix
+    corr_matrix = np.corrcoef(beta_series)
+    return corr_matrix, raw_hbo.ch_names
+
+
+def run_group_functional_connectivity(haemo_dict, config_dict, selected_paths, event_name, n_lines, vmin):
+    """Aggregates multiple participants and triggers the plot."""
+    all_z_matrices = []
+    common_names = None
+
+    for path in selected_paths:
+        raw = haemo_dict.get(path)
+        config = config_dict.get(path)
+        if raw is None: continue
+        print(config)
+
+        corr, names = get_single_subject_beta_corr(raw, event_name, config)
+        
+        if corr is not None:
+            # Fisher Z-transform for averaging
+            z_mat = np.arctanh(np.clip(corr, -0.99, 0.99))
+            all_z_matrices.append(z_mat)
+            common_names = names
+
+    from scipy.stats import ttest_1samp
+    # 1. Convert list to 3D array: (Participants, Channels, Channels)
+    group_z_data = np.array(all_z_matrices) 
+    
+    print("1")
+    # 2. Perform a T-Test across the participant dimension (axis 0)
+    # We test if the mean Z-score is different from 0
+    # C:\Users\tyler\Desktop\research\.venv\Lib\site-packages\scipy\stats\_axis_nan_policy.py:611: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.
+    # res = hypotest_fun_out(*samples, axis=axis, **kwds)
+
+    print("--- Variance Check ---")
+
+    # ADD THIS LINE: Define n_channels based on the data shape
+    # group_z_data.shape is (n_participants, n_channels, n_channels)
+    n_channels = group_z_data.shape[1]
+
+    variance_matrix = np.var(group_z_data, axis=0)
+
+    # Find where variance is exactly 0 (or very close to it)
+    zero_var_indices = np.where(variance_matrix < 1e-15)
+    coords = list(zip(zero_var_indices[0], zero_var_indices[1]))
+
+    diag_count = 0
+    non_diag_pairs = []
+
+    for r, c in coords:
+        if r == c:
+            diag_count += 1
+        else:
+            non_diag_pairs.append((r, c))
+
+    print(f"Total pairs with zero variance: {len(coords)}")
+    print(f"Identical diagonals: {diag_count}/{n_channels}")
+
+    if non_diag_pairs:
+        print(f"Warning: {len(non_diag_pairs)} non-diagonal pairs have zero variance!")
+        for r, c in non_diag_pairs[:10]: # Print first 10
+            print(f"  - Pair: Channel {r} & Channel {c}")
+    else:
+        print("Clean! Zero variance only exists on the diagonals.")
+    print("----------------------")
+
+    t_stats, p_values = ttest_1samp(group_z_data, popmean=0, axis=0)
+    print("2")
+
+    # 3. Multiple Comparisons Correction (FDR)
+    # We only care about the upper triangle (unique connections)
+    n_channels = p_values.shape[0]
+    triu_indices = np.triu_indices(n_channels, k=1)
+    flat_p = p_values[triu_indices]
+    
+    reject, corrected_p = multipletests(flat_p, method='fdr_bh', alpha=0.05)[:2]
+
+    # 4. Create the final "Significant" Matrix
+    avg_r = np.tanh(np.mean(group_z_data, axis=0))
+    sig_avg_r = np.zeros_like(avg_r)
+
+    # Only keep connections that are Significant AND above your VMIN (r-threshold)
+    for idx, is_sig in enumerate(reject):
+        row, col = triu_indices[0][idx], triu_indices[1][idx]
+        r_val = avg_r[row, col]
+        
+        if is_sig and abs(r_val) >= vmin:
+            sig_avg_r[row, col] = sig_avg_r[col, row] = r_val
+
+    # 5. Plot the significant results
+
+
+    # if not all_z_matrices:
+    #     return
+
+    # # Average and convert back to R
+    # avg_z = np.mean(all_z_matrices, axis=0)
+    # avg_r = np.tanh(avg_z)
+
+    # # Thresholding
+    # avg_r[np.abs(avg_r) < vmin] = 0
+
+    plot_connectivity_circle(
+        sig_avg_r, common_names, n_lines=n_lines, 
+        title=f"Group Connectivity: {event_name if event_name else 'All Events'}",
+        vmin=vmin, vmax=1.0, colormap='hot'
+    )

main.py

@@ -426,14 +426,18 @@ class UpdateCheckThread(QThread):
         for url in urls:
             try:
                     
-                response = requests.get(API_URL, timeout=5)
+                response = requests.get(url, timeout=5)
                 response.raise_for_status()
                 releases = response.json()
 
                 if not releases:
-                    return None, None
+                    continue
+
+                latest = next((r for r in releases if not r.get("prerelease") and not r.get("draft")), None)
+
+                if not latest:
+                    continue
                 
-                latest = releases[0]
                 tag = latest["tag_name"].lstrip("v")
 
                 for asset in latest.get("assets", []):
@@ -2775,6 +2779,182 @@ class ParticipantFunctionalConnectivityWidget(FlaresBaseWidget):
 
 
 
+class GroupFunctionalConnectivityWidget(FlaresBaseWidget):
+    def __init__(self, haemo_dict, group, config_dict):
+        super().__init__("GroupFunctionalConnectivityWidget")
+        self.setWindowTitle("FLARES Group Viewer")
+        self.haemo_dict = haemo_dict
+        self.group = group
+        self.config_dict = config_dict
+        self.show_all_events = True
+        self._updating_checkstates = False
+
+        QMessageBox.warning(self, "Warning - FLARES", f"Functional Connectivity is still in development and the results should currently be taken with a grain of salt. "
+                    "By clicking OK, you accept that the images generated may not be factual.")
+
+        
+        # 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 (Betas)",
+            #"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
+
+        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": "n_lines",
+                    "label": "<Description>",
+                    "default": "20",
+                    "type": int,
+                },
+                {
+                    "key": "vmin",
+                    "label": "<Description>",
+                    "default": "0.9",
+                    "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
+            
+        for idx in selected_indexes:
+            if idx == 0:
+                params = param_values.get(idx, {})
+                n_lines = params.get("n_lines", None)
+                vmin = params.get("vmin", None)
+
+                if n_lines is None or vmin is None:
+                    print(f"Missing parameters for index {idx}, skipping.")
+                    continue
+                flares.run_group_functional_connectivity(self.haemo_dict, self.config_dict, selected_file_paths, selected_event, 50, 0.5)            
+            elif idx == 1:
+                pass
+            elif idx == 2:
+                pass
+            elif idx == 3:
+                pass
+
+            else:
+                print(f"No method defined for index {idx}")
+
+
+
+
 class MultiProgressDialog(QDialog):
     def __init__(self, parent=None):
         super().__init__(parent)
@@ -4005,6 +4185,9 @@ class ViewerLauncherWidget(QWidget):
         btn7 = QPushButton("Open Functional Connectivity Viewer [BETA]")
         btn7.clicked.connect(lambda: self.open_participant_functional_connectivity_viewer(haemo_dict, epochs_dict))
 
+        btn8 = QPushButton("Open Group Functional Connectivity Viewer [BETA]")
+        btn8.clicked.connect(lambda: self.open_group_functional_connectivity_viewer(haemo_dict, group_dict, config_dict))
+
         btn4 = QPushButton("Open Inter-Group Viewer")
         btn4.clicked.connect(lambda: self.open_group_viewer(haemo_dict, cha_dict, df_ind, design_matrix, contrast_results_dict, group_dict))
         
@@ -4019,6 +4202,7 @@ class ViewerLauncherWidget(QWidget):
         layout.addWidget(btn2)
         layout.addWidget(btn3)
         layout.addWidget(btn7)
+        layout.addWidget(btn8)
         layout.addWidget(btn4)
         layout.addWidget(btn5)
         layout.addWidget(btn6)
@@ -4039,6 +4223,10 @@ class ViewerLauncherWidget(QWidget):
         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()