group fc

changelog.md

@@ -1,3 +1,23 @@
+# Version 1.2.1
+
+- Added a requirements.txt file to ensure compatibility
+- Added new options 'Missing Events Bypass' and 'Analysis Clearing Bypass' to the Preferences Menu
+- Missing Events Bypass allows comparing events in the Group Viewers even if not all participants in the group have the event present. Fixes [Issue 28](https://git.research.dezeeuw.ca/tyler/flares/issues/28)
+- Clicking Process after an analysis has been performed will now clear the existing analysis by default with a popup warning that the analysis will be cleared
+- Analysis Clearing Bypass will prevent the popup and will not clear the existing analysis data. Fixes [Issue 41](https://git.research.dezeeuw.ca/tyler/flares/issues/41)
+- Clicking 'Clear' should now actually properly clear all data. Hopefully fixes [Issue 9](https://git.research.dezeeuw.ca/tyler/flares/issues/9) for good
+- 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
 
 - This is a save-breaking release due to a new save file format. Please update your project files to ensure compatibility. Fixes [Issue 30](https://git.research.dezeeuw.ca/tyler/flares/issues/30)
@@ -121,7 +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'
+    )