further variable changes

.gitignore

@@ -174,3 +174,4 @@ cython_debug/
 # PyPI configuration file
 .pypirc
 
+/individual_images

changelog.md

@@ -1,3 +1,22 @@
+# Version 1.2.0
+
+- Added new parameters to the right side of the screen
+- These parameters include SHOW_OPTODE_NAMES, SECONDS_TO_STRIP_HR, MAX_LOW_HR, MAX_HIGH_HR, SMOOTHING_WINDOW_HR, HEART_RATE_WINDOW, BAD_CHANNELS_HANDLING, MAX_DIST, MIN_NEIGHBORS, L_TRANS_BANDWIDTH, H_TRANS_BANDWIDTH, RESAMPLE, RESAMPLE_FREQ, STIM_DUR, HRF_MODEL, HIGH_PASS, DRIFT_ORDER, FIR_DELAYS, MIN_ONSET, OVERSAMPLING, SHORT_CHANNEL_REGRESSION, NOISE_MODEL, BINS, and VERBOSITY.
+- All the new parameters have default values matching the underlying values in version 1.1.7
+- The order of the parameters have changed to match the order that the code runs when the Process button is clicked
+- Moved TIME_WINDOW_START and TIME_WINDOW_END to the 'Other' category
+- Fixed a bug causing SCI to not work when HEART_RATE was set to False
+- Bad channels can now be dealt with by taking no action, removing them completely, or interpolating them based on their neighbours. Interpolation remains the default option
+- Fixed an underlying deprecation warning
+- Fixed an issue causing some overlay elements to not render on the brain for certain devices
+- Fixed a crash when rendering some Inter-Group images with only one participant in a group
+- Fixed a crash when attempting to fOLD channels without the fOLD dataset installed
+- Lowered the number of rectangles in the progress bar to 24 after combining some actions
+- Fixed the User Guide window to properly display information about the 24 stages and added a link to the Git wiki page
+- MAX_WORKERS should now properly repect the value set
+- Added a new CSV export option to be used by other applications
+
+
 # Version 1.1.7
 
 - Fixed a bug where having both a L_FREQ and H_FREQ would cause only the L_FREQ to be used

flares.py

@@ -21,6 +21,7 @@ import os.path as op
 import re
 import traceback
 from concurrent.futures import ProcessPoolExecutor, as_completed
+from queue import Empty
 
 # External library imports
 import matplotlib.pyplot as plt
@@ -53,10 +54,10 @@ from scipy.signal import welch, butter, filtfilt # type: ignore
 import pywt # type: ignore
 import neurokit2 as nk # type: ignore
 
-# Backen visualization needed to be defined for pyinstaller
+# Backend visualization needed to be defined for pyinstaller
 import pyvistaqt # type: ignore
-#import vtkmodules.util.data_model
-#import vtkmodules.util.execution_model
+import vtkmodules.util.data_model
+import vtkmodules.util.execution_model
 
 # External library imports for mne
 from mne import (
@@ -89,9 +90,10 @@ from mne_nirs.io.fold import fold_channel_specificity  # type: ignore
 from mne_nirs.preprocessing import peak_power  # type: ignore
 from mne_nirs.statistics._glm_level_first import RegressionResults  # type: ignore
 
-
+# Needs to be set for mne
 os.environ["SUBJECTS_DIR"] = str(data_path()) + "/subjects" # type: ignore
 
+# TODO: Tidy this up
 FIXED_CATEGORY_COLORS = {
     "SCI only": "skyblue",
     "PSP only": "salmon",
@@ -112,10 +114,6 @@ FIXED_CATEGORY_COLORS = {
 }
 
 
-AGE: float
-GENDER: str
-
-# SECONDS_TO_STRIP: int
 DOWNSAMPLE: bool
 DOWNSAMPLE_FREQUENCY: int
 
@@ -123,21 +121,37 @@ TRIM: bool
 SECONDS_TO_KEEP: float
 
 OPTODE_PLACEMENT: bool
+SHOW_OPTODE_NAMES: bool
 
 HEART_RATE: bool
 
+SHORT_CHANNEL: bool
+SHORT_CHANNEL_THRESH: float
+LONG_CHANNEL_THRESH: float
+
+HEART_RATE: bool
+SECONDS_TO_STRIP_HR: int
+MAX_LOW_HR: int
+MAX_HIGH_HR: int
+SMOOTHING_WINDOW_HR: int
+HEART_RATE_WINDOW: int
+
 SCI: bool
 SCI_TIME_WINDOW: int
 SCI_THRESHOLD: float
 
 SNR: bool
-# SNR_TIME_WINDOW : int
+# SNR_TIME_WINDOW : int #TODO: is this needed?
 SNR_THRESHOLD: float
 
 PSP: bool
 PSP_TIME_WINDOW: int
 PSP_THRESHOLD: float
 
+BAD_CHANNELS_HANDLING: str
+MAX_DIST: float
+MIN_NEIGHBORS: int
+
 TDDR: bool
 
 WAVELET: bool
@@ -145,57 +159,41 @@ IQR: float
 WAVELET_TYPE: str
 WAVELET_LEVEL: int
 
-HEART_RATE = True                                                   # True if heart rate should be calculated. This helps the SCI, PSP, and SNR methods to be more accurate.
-SECONDS_TO_STRIP_HR =5                                             # Amount of seconds to temporarily strip from the data to calculate heart rate more effectively. Useful if participant removed cap while still recording.
-MAX_LOW_HR = 40                                                # Any heart rate values lower than this will be set to this value.
-MAX_HIGH_HR = 200                                                   # Any heart rate values higher than this will be set to this value.
-SMOOTHING_WINDOW_HR = 100                                               # Heart rate will be calculated as a rolling average over this many amount of samples.
-HEART_RATE_WINDOW = 25                                                 # Amount of BPM above and below the calculated average to use for a range of resting BPM.
-
 ENHANCE_NEGATIVE_CORRELATION: bool
 
 FILTER: bool
 L_FREQ: float
 H_FREQ: float
+L_TRANS_BANDWIDTH: float
+H_TRANS_BANDWIDTH: float
 
-SHORT_CHANNEL: bool
-SHORT_CHANNEL_THRESH: float
-LONG_CHANNEL_THRESH: float
-
+RESAMPLE: bool
+RESAMPLE_FREQ: int
+STIM_DUR: float
+HRF_MODEL: str
+DRIFT_MODEL: str
+HIGH_PASS: float
+DRIFT_ORDER: int
+FIR_DELAYS: range
+MIN_ONSET: int
+OVERSAMPLING: int
 REMOVE_EVENTS: list
+SHORT_CHANNEL_REGRESSION: bool
+
+NOISE_MODEL: str
+BINS: int
+N_JOBS: int
 
 TIME_WINDOW_START: int
 TIME_WINDOW_END: int
+MAX_WORKERS: int
+VERBOSITY: bool
 
-DRIFT_MODEL: str
-
-VERBOSITY = True
-
-# FIXME: Shouldn't need each ordering - just order it before checking
-FIXED_CATEGORY_COLORS = {
-    "SCI only": "skyblue",
-    "PSP only": "salmon",
-    "SNR only": "lightgreen",
-    "PSP + SCI": "orange",
-    "SCI + SNR": "violet",
-    "PSP + SNR": "gold",
-    "SCI + PSP": "orange",
-    "SNR + SCI": "violet",
-    "SNR + PSP": "gold",
-    "PSP + SNR + SCI": "gray",
-    "SCI + PSP + SNR": "gray",
-    "SCI + SNR + PSP": "gray",
-    "PSP + SCI + SNR": "gray",
-    "PSP + SNR + SCI": "gray",
-    "SNR + SCI + PSP": "gray",
-    "SNR + PSP + SCI": "gray",
-}
-
-
-AGE = 25
+AGE = 25    # Assume 25 if not set from the GUI. This will result in a reasonable PPF
 GENDER = ""
 GROUP = "Default"
 
+# These are parameters that are required for the analysis
 REQUIRED_KEYS: dict[str, Any] = {
         
     # "SECONDS_TO_STRIP": int,
@@ -262,7 +260,7 @@ PLATFORM_NAME = platform.system().lower()
 # Configure logging to file with timestamps and realtime flush
 if PLATFORM_NAME == 'darwin':
     logging.basicConfig(
-        filename=os.path.join(os.path.dirname(sys.executable), "../../../fnirs_analysis.log"),
+        filename=os.path.join(os.path.dirname(sys.executable), "../../../fnirs_analysis.log"),  # Needed to get out of the bundled application
         level=logging.INFO,
         format='%(asctime)s - %(processName)s - %(levelname)s - %(message)s',
         datefmt='%Y-%m-%d %H:%M:%S',
@@ -320,8 +318,6 @@ def set_metadata(file_path, metadata: dict[str, Any]) -> None:
             val = file_metadata.get(key, None)
             if val not in (None, '', [], {}, ()):  # check for "empty" values
                 globals()[key] = val
-from queue import Empty  # This works with multiprocessing.Manager().Queue()
-
 
 def gui_entry(config: dict[str, Any], gui_queue: Queue, progress_queue: Queue) -> None:
     def forward_progress():
@@ -825,7 +821,7 @@ def get_hbo_hbr_picks(raw):
     return hbo_picks, hbr_picks, hbo_wl, hbr_wl
 
 
-def interpolate_fNIRS_bads_weighted_average(raw, bad_channels, max_dist=0.03, min_neighbors=2):
+def interpolate_fNIRS_bads_weighted_average(raw, max_dist=0.03, min_neighbors=2):
     """
     Interpolate bad fNIRS channels using a distance-weighted average of nearby good channels.
 
@@ -1117,17 +1113,17 @@ def mark_bads(raw, bad_sci, bad_snr, bad_psp):
 
 
 def filter_the_data(raw_haemo):
-        # --- STEP 5: Filtering (0.01–0.2 Hz bandpass) ---
+        # --- STEP 5: Filtering (0.01-0.2 Hz bandpass) ---
     fig_filter = raw_haemo.compute_psd(fmax=3).plot(
         average=True, color="r", show=False, amplitude=True
     )
     
     if L_FREQ == 0 and H_FREQ != 0:
-        raw_haemo = raw_haemo.filter(l_freq=None, h_freq=H_FREQ, h_trans_bandwidth=0.02)
+        raw_haemo = raw_haemo.filter(l_freq=None, h_freq=H_FREQ, h_trans_bandwidth=H_TRANS_BANDWIDTH)
     elif L_FREQ != 0 and H_FREQ == 0:
-        raw_haemo = raw_haemo.filter(l_freq=L_FREQ, h_freq=None, l_trans_bandwidth=0.002)
+        raw_haemo = raw_haemo.filter(l_freq=L_FREQ, h_freq=None, l_trans_bandwidth=L_TRANS_BANDWIDTH)
     elif L_FREQ != 0 and H_FREQ != 0:
-        raw_haemo = raw_haemo.filter(l_freq=L_FREQ, h_freq=H_FREQ, l_trans_bandwidth=0.002, h_trans_bandwidth=0.02)
+        raw_haemo = raw_haemo.filter(l_freq=L_FREQ, h_freq=H_FREQ, l_trans_bandwidth=L_TRANS_BANDWIDTH, h_trans_bandwidth=H_TRANS_BANDWIDTH)
     else:
         print("No filter")
     #raw_haemo = raw_haemo.filter(l_freq=None, h_freq=0.4, h_trans_bandwidth=0.2)
@@ -1258,7 +1254,7 @@ def epochs_calculations(raw_haemo, events, event_dict):
                 continue
 
             data = evoked.data[picks_idx, :].mean(axis=0)
-            t_start, t_end = 0, 15
+            t_start, t_end = 0, 15 #TODO: Is this in seconds? or is it 1hz input that makes it 15s?
             times_mask = (evoked.times >= t_start) & (evoked.times <= t_end)
             data_segment = data[times_mask]
             times_segment = evoked.times[times_mask]
@@ -1307,33 +1303,53 @@ def epochs_calculations(raw_haemo, events, event_dict):
 
 def make_design_matrix(raw_haemo, short_chans):
 
-    raw_haemo.resample(1, npad="auto")
+    events_to_remove = REMOVE_EVENTS
+        
+    filtered_annotations = [ann for ann in raw_haemo.annotations if ann['description'] not in events_to_remove]
+    
+    new_annot = Annotations(
+        onset=[ann['onset'] for ann in filtered_annotations],
+        duration=[ann['duration'] for ann in filtered_annotations],
+        description=[ann['description'] for ann in filtered_annotations]
+    )
+    
+    # Set the new annotations
+    raw_haemo.set_annotations(new_annot)
+
+    if RESAMPLE:
+        raw_haemo.resample(RESAMPLE_FREQ, npad="auto")
         raw_haemo._data = raw_haemo._data * 1e6
+        try:
+            short_chans.resample(RESAMPLE_FREQ)
+        except:
+            pass
+
     # 2) Create design matrix
-    if SHORT_CHANNEL:
-        short_chans.resample(1)
+    if SHORT_CHANNEL_REGRESSION:
         design_matrix = make_first_level_design_matrix(
             raw=raw_haemo,
-            hrf_model='fir',
-            stim_dur=0.5,
-            fir_delays=range(15),
+            stim_dur=STIM_DUR,
+            hrf_model=HRF_MODEL,
             drift_model=DRIFT_MODEL,
-            high_pass=0.01,
-            oversampling=1,
-            min_onset=-125,
+            high_pass=HIGH_PASS,
+            drift_order=DRIFT_ORDER,
+            fir_delays=range(15),
             add_regs=short_chans.get_data().T,
-            add_reg_names=short_chans.ch_names
+            add_reg_names=short_chans.ch_names,
+            min_onset=MIN_ONSET,
+            oversampling=OVERSAMPLING
         )
     else:
         design_matrix = make_first_level_design_matrix(
             raw=raw_haemo,
-            hrf_model='fir',
-            stim_dur=0.5,
-            fir_delays=range(15),
+            stim_dur=STIM_DUR,
+            hrf_model=HRF_MODEL,
             drift_model=DRIFT_MODEL,
-            high_pass=0.01,
-            oversampling=1,
-            min_onset=-125,
+            high_pass=HIGH_PASS,
+            drift_order=DRIFT_ORDER,
+            fir_delays=range(15),
+            min_onset=MIN_ONSET,
+            oversampling=OVERSAMPLING
         )
 
     print(design_matrix.head())
@@ -2569,7 +2585,10 @@ def plot_fir_model_results(df, raw_haemo, dm, selected_event, l_bound, u_bound):
     dm_cols_activity = np.where([f"{selected_event}" in c for c in dm.columns])[0]
     dm = dm[[dm.columns[i] for i in dm_cols_activity]]
 
+    try:
         lme = smf.mixedlm("theta ~ -1 + delay:TidyCond:Chroma", df, groups=df["ID"]).fit()
+    except:
+        lme = smf.ols("theta ~ -1 + delay:TidyCond:Chroma", df, groups=df["ID"]).fit() # type: ignore
 
     df_sum = statsmodels_to_results(lme)
     df_sum["delay"] = [int(n) for n in df_sum["delay"]]
@@ -3310,18 +3329,20 @@ def hr_calc(raw):
     return fig, hr1, hr2, low, high
 
 
+
+
 def process_participant(file_path, progress_callback=None):
 
     fig_individual: dict[str, Figure] = {}
 
-    # Step 1: Load
+    # Step 1: Preprocessing
     raw = load_snirf(file_path)
     fig_raw = raw.plot(duration=raw.times[-1], n_channels=raw.info['nchan'], title="Loaded Raw", show=False)
     fig_individual["Loaded Raw"] = fig_raw
     if progress_callback: progress_callback(1)
-    logger.info("1")
-    
+    logger.info("Step 1 Completed.")
     
+    # Step 2: Trimming
     if TRIM:
         if hasattr(raw, 'annotations') and len(raw.annotations) > 0:
             # Get time of first event
@@ -3329,17 +3350,16 @@ def process_participant(file_path, progress_callback=None):
             trim_time = max(0, first_event_time - SECONDS_TO_KEEP)  # Ensure we don't go negative
             raw.crop(tmin=trim_time)
             # Shift annotation onsets to match new t=0
-            import mne
 
             ann = raw.annotations
-            ann_shifted = mne.Annotations(
+            ann_shifted = Annotations(
                 onset=ann.onset - trim_time,  # shift to start at zero
                 duration=ann.duration,
                 description=ann.description
             )
             data = raw.get_data()
             info = raw.info.copy()
-            raw = mne.io.RawArray(data, info)
+            raw = RawArray(data, info)
             raw.set_annotations(ann_shifted)
 
             logger.info(f"Trimmed raw data: start at {trim_time}s (5s before first event), t=0 at new start")
@@ -3349,185 +3369,178 @@ def process_participant(file_path, progress_callback=None):
         fig_trimmed = raw.plot(duration=raw.times[-1], n_channels=raw.info['nchan'], title="Trimmed Raw", show=False)
         fig_individual["Trimmed Raw"] = fig_trimmed
     if progress_callback: progress_callback(2)
-    logger.info("2")
+    logger.info("Step 2 Completed.")
 
-    # Step 1.5: Verify optode positions
+    # Step 3: Verify Optode Placement
     if OPTODE_PLACEMENT:
-        fig_optodes = raw.plot_sensors(show_names=True, to_sphere=True, show=False) # type: ignore
+        fig_optodes = raw.plot_sensors(show_names=SHOW_OPTODE_NAMES, to_sphere=True, show=False) # type: ignore
         fig_individual["Plot Sensors"] = fig_optodes
     if progress_callback: progress_callback(3)
-    logger.info("3")
+    logger.info("Step 3 Completed.")
 
-    # Step 2: Bad from SCI
+    # Step 4: Short/Long Channels
+    if SHORT_CHANNEL:
+        short_chans = get_short_channels(raw, max_dist=SHORT_CHANNEL_THRESH)
+        fig_short_chans = short_chans.plot(duration=raw.times[-1], n_channels=raw.info['nchan'], title="Short Channels Only", show=False)
+        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
+    if progress_callback: progress_callback(4)
+    logger.info("Step 4 Completed.")
+
+    # Step 5: Heart Rate
     if HEART_RATE:
         fig, hr1, hr2, low, high = hr_calc(raw)
         fig_individual["PSD"] = fig
         fig_individual['HeartRate_PSD'] = hr1
         fig_individual['HeartRate_Time'] = hr2
-    if progress_callback: progress_callback(4)
-    logger.info("4")    
+    if progress_callback: progress_callback(5)
+    logger.info("Step 5 Completed.")    
         
+    # Step 6: Scalp Coupling Index    
     bad_sci = []
     if SCI:
+        if HEART_RATE:
             bad_sci, fig_sci_1, fig_sci_2 = calculate_scalp_coupling(raw, low, high)
+        else:
+            bad_sci, fig_sci_1, fig_sci_2 = calculate_scalp_coupling(raw)
         fig_individual["SCI1"] = fig_sci_1
         fig_individual["SCI2"] = fig_sci_2
-    if progress_callback: progress_callback(5)
-    logger.info("5")
+    if progress_callback: progress_callback(6)
+    logger.info("Step 6 Completed.")
 
-    # Step 2: Bad from SNR
+    # Step 7: Signal to Noise Ratio
     bad_snr = []
     if SNR:
         bad_snr, fig_snr = calculate_signal_noise_ratio(raw)
         fig_individual["SNR1"] = fig_snr
-    if progress_callback: progress_callback(6)
-    logger.info("6")
+    if progress_callback: progress_callback(7)
+    logger.info("Step 7 Completed.")
 
-    # Step 3: Bad from PSP
+    # Step 8: Peak Spectral Power
     bad_psp = []
     if PSP:
         bad_psp, fig_psp1, fig_psp2 = calculate_peak_power(raw)
         fig_individual["PSP1"] = fig_psp1
         fig_individual["PSP2"] = fig_psp2
-    if progress_callback: progress_callback(7)
-    logger.info("7")
+    if progress_callback: progress_callback(8)
+    logger.info("Step 8 Completed.")
 
-    # Step 4: Mark the bad channels
+    # Step 9: Bad Channels Handling
+    if BAD_CHANNELS_HANDLING != "None":
         raw, fig_dropped, fig_raw_before, bad_channels = mark_bads(raw, bad_sci, bad_snr, bad_psp)
         if fig_dropped and fig_raw_before is not None:
             fig_individual["fig2"] = fig_dropped
             fig_individual["fig3"] = fig_raw_before
-    if progress_callback: progress_callback(8)
-    logger.info("8")
-
-    # Step 5: Interpolate the bad channels
         if bad_channels:
-        raw, fig_raw_after = interpolate_fNIRS_bads_weighted_average(raw, bad_channels)
+            if BAD_CHANNELS_HANDLING == "Interpolate":
+                raw, fig_raw_after = interpolate_fNIRS_bads_weighted_average(raw, max_dist=MAX_DIST, min_neighbors=MIN_NEIGHBORS)
                 fig_individual["fig4"] = fig_raw_after
-    if progress_callback: progress_callback(9)
-    logger.info("9")
+            elif BAD_CHANNELS_HANDLING == "Remove":
+                pass
+                #TODO: Is there more needed here?
 
-    # Step 6: Optical Density
+    if progress_callback: progress_callback(9)
+    logger.info("Step 9 Completed.")
+
+    # Step 10: Optical Density
     raw_od = optical_density(raw)
     fig_raw_od = raw_od.plot(duration=raw.times[-1], n_channels=raw.info['nchan'], title="Optical Density", show=False)
     fig_individual["Optical Density"] = fig_raw_od
     if progress_callback: progress_callback(10)
-    logger.info("10")
+    logger.info("Step 10 Completed.")
 
-    # Step 7: TDDR
+    # Step 11: Temporal Derivative Distribution Repair Filtering
     if TDDR:
         raw_od = temporal_derivative_distribution_repair(raw_od)
         fig_raw_od_tddr = raw_od.plot(duration=raw.times[-1], n_channels=raw.info['nchan'], title="After TDDR (Motion Correction)", show=False)
         fig_individual["TDDR"] = fig_raw_od_tddr
     if progress_callback: progress_callback(11)
-    logger.info("11")
-    
+    logger.info("Step 11 Completed.")
     
+    # Step 12: Wavelet Filtering
     if WAVELET:
         raw_od, fig = calculate_and_apply_wavelet(raw_od)
         fig_individual["Wavelet"] = fig
     if progress_callback: progress_callback(12)
-    logger.info("12")
+    logger.info("Step 12 Completed.")
 
-
-    # Step 8: BLL
+    # Step 13: Haemoglobin Concentration
     raw_haemo = beer_lambert_law(raw_od, ppf=calculate_dpf(file_path))
     fig_raw_haemo_bll = raw_haemo.plot(duration=raw_haemo.times[-1], n_channels=raw_haemo.info['nchan'], title="HbO and HbR Signals", show=False)
     fig_individual["BLL"] = fig_raw_haemo_bll
     if progress_callback: progress_callback(13)
-    logger.info("13")
+    logger.info("Step 13 Completed.")
 
-    # Step 9: ENC
+    # Step 14: Enhance Negative Correlation
     if ENHANCE_NEGATIVE_CORRELATION:
         raw_haemo = enhance_negative_correlation(raw_haemo)
-        fig_raw_haemo_enc = raw_haemo.plot(duration=raw_haemo.times[-1], n_channels=raw_haemo.info['nchan'], title="HbO and HbR Signals", show=False)
+        fig_raw_haemo_enc = raw_haemo.plot(duration=raw_haemo.times[-1], n_channels=raw_haemo.info['nchan'], title="Enhance Negative Correlation", show=False)
         fig_individual["ENC"] = fig_raw_haemo_enc
     if progress_callback: progress_callback(14)
-    logger.info("14")
+    logger.info("Step 14 Completed.")
     
-    # Step 10: Filter
+    # Step 15: Filter
     if FILTER:
         raw_haemo, fig_filter, fig_raw_haemo_filter = filter_the_data(raw_haemo)
         fig_individual["filter1"] = fig_filter
         fig_individual["filter2"] = fig_raw_haemo_filter
     if progress_callback: progress_callback(15)
-    logger.info("15")
+    logger.info("Step 15 Completed.")
 
-    # Step 11: Get short / long channels
-    if SHORT_CHANNEL:
-        short_chans = get_short_channels(raw_haemo, max_dist=SHORT_CHANNEL_THRESH)
-        fig_short_chans = short_chans.plot(duration=raw_haemo.times[-1], n_channels=raw_haemo.info['nchan'], title="Short Channels Only", show=False)
-        fig_individual["short"] = fig_short_chans
-    else:
-        short_chans = None
-    raw_haemo = get_long_channels(raw_haemo, min_dist=SHORT_CHANNEL_THRESH, max_dist=LONG_CHANNEL_THRESH)
-    if progress_callback: progress_callback(16)
-    logger.info("16")
-
-    # Step 12: Events from annotations
+    # Step 16: Extracting Events
     events, event_dict = events_from_annotations(raw_haemo)
     fig_events = plot_events(events, event_id=event_dict, sfreq=raw_haemo.info["sfreq"], show=False)
     fig_individual["events"] = fig_events
-    if progress_callback: progress_callback(17)
-    logger.info("17")
+    if progress_callback: progress_callback(16)
+    logger.info("Step 16 Completed.")
 
-    # Step 13: Epoch calculations
+    # Step 17: Epoch Calculations
     epochs, fig_epochs = epochs_calculations(raw_haemo, events, event_dict)
-    for name, fig in fig_epochs:  # Unpack the tuple here
-        fig_individual[f"epochs_{name}"] = fig  # Store only the figure, not the name
-    if progress_callback: progress_callback(18)
-    logger.info("18")
-
-    # Step 14: Design Matrix
-    events_to_remove = REMOVE_EVENTS
-        
-    filtered_annotations = [ann for ann in raw.annotations if ann['description'] not in events_to_remove]
-    
-    new_annot = Annotations(
-        onset=[ann['onset'] for ann in filtered_annotations],
-        duration=[ann['duration'] for ann in filtered_annotations],
-        description=[ann['description'] for ann in filtered_annotations]
-    )
-    
-    # Set the new annotations
-    raw_haemo.set_annotations(new_annot)
+    for name, fig in fig_epochs:
+        fig_individual[f"epochs_{name}"] = fig
+    if progress_callback: progress_callback(17)
+    logger.info("Step 17 Completed.")
 
+    # Step 18: Design Matrix
     design_matrix, fig_design_matrix = make_design_matrix(raw_haemo, short_chans)
     fig_individual["Design Matrix"] = fig_design_matrix
-    if progress_callback: progress_callback(19)
-    logger.info("19")
+    if progress_callback: progress_callback(18)
+    logger.info("Step 18 Completed.")
 
-    # Step 15: Run GLM
-    glm_est = run_glm(raw_haemo, design_matrix)
+
+    # Step 19: Run GLM
+    glm_est = run_glm(raw_haemo, design_matrix, noise_model=NOISE_MODEL, bins=BINS, n_jobs=N_JOBS, verbose=VERBOSITY)
     # Not used AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\nilearn\glm\contrasts.py
     # Yes used AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\mne_nirs\utils\_io.py
 
-    # The p-value is calculated from this t-statistic using the Student’s t-distribution with appropriate degrees of freedom.
+    # The p-value is calculated from this t-statistic using the Student's t-distribution with appropriate degrees of freedom.
     # p_value = 2 * stats.t.cdf(-abs(t_statistic), df)
     # It is a two-tailed p-value.
     # It says how likely it is to observe the effect you did (or something more extreme) if the true effect was zero (null hypothesis).
-    # A small p-value (e.g., < 0.05) suggests the effect is unlikely to be zero — it’s "statistically significant."
+    # A small p-value (e.g., < 0.05) suggests the effect is unlikely to be zero — it's "statistically significant."
     # A large p-value means the data do not provide strong evidence that the effect is different from zero.
 
 
-    if progress_callback: progress_callback(20)
-    logger.info("20")
+    if progress_callback: progress_callback(19)
+    logger.info("19")
 
-    # Step 16: Plot GLM results
+    # Step 20: Generate GLM Results
     fig_glm_result = plot_glm_results(file_path, raw_haemo, glm_est, design_matrix)
     for name, fig in fig_glm_result:
         fig_individual[f"GLM {name}"] = fig
-    if progress_callback: progress_callback(21)
-    logger.info("21")
+    if progress_callback: progress_callback(20)
+    logger.info("20")
 
-    # Step 17: Plot channel significance
+    # Step 21: Generate Channel Significance
     fig_significance = individual_significance(raw_haemo, glm_est)
     for name, fig in fig_significance:
         fig_individual[f"Significance {name}"] = fig
-    if progress_callback: progress_callback(22)
-    logger.info("22")
+    if progress_callback: progress_callback(21)
+    logger.info("21")
 
-    # Step 18: cha, con, roi
+    # Step 22: Generate Channel, Region of Interest, and Contrast Results
     cha = glm_est.to_dataframe()
 
     # HACK: Comment out line 588 (self._renderer.show()) in _brain.py from MNE
@@ -3580,10 +3593,10 @@ def process_participant(file_path, progress_callback=None):
 
         contrast_dict[condition] = contrast_vector
 
-    if progress_callback: progress_callback(23)
-    logger.info("23")
+    if progress_callback: progress_callback(22)
+    logger.info("22")
 
-    # Compute contrast results
+    # Step 23: Compute Contrast Results
     contrast_results = {}
 
     for cond, contrast_vector in contrast_dict.items():
@@ -3594,10 +3607,10 @@ def process_participant(file_path, progress_callback=None):
 
     cha["ID"] = file_path
     
-    if progress_callback: progress_callback(24)
-    logger.info("24")
-        
+    if progress_callback: progress_callback(23)
+    logger.info("23")
         
+    # Step 24: Finishing Up
     fig_bytes = convert_fig_dict_to_png_bytes(fig_individual)
 
     sanitize_paths_for_pickle(raw_haemo, epochs)

main.py

@@ -22,11 +22,11 @@ import subprocess
 from pathlib import Path, PurePosixPath
 from datetime import datetime
 from multiprocessing import Process, current_process, freeze_support, Manager
-
-import numpy as np
-import pandas as pd
+from enum import Enum, auto
 
 # External library imports
+import numpy as np
+import pandas as pd
 import psutil
 import requests
 
@@ -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 = "1.0.0"
+CURRENT_VERSION = "1.2.0"
 
 API_URL = "https://git.research.dezeeuw.ca/api/v1/repos/tyler/flares/releases"
 API_URL_SECONDARY = "https://git.research2.dezeeuw.ca/api/v1/repos/tyler/flares/releases"
@@ -58,7 +58,6 @@ SECTIONS = [
     {
         "title": "Preprocessing",
         "params": [
-            # {"name": "SECONDS_TO_STRIP", "default": 0, "type": int, "help": "Seconds to remove from beginning of all loaded snirf files. Setting this to 0 will remove nothing from the files."},
             {"name": "DOWNSAMPLE", "default": True, "type": bool, "help": "Should the snirf files be downsampled? If this is set to True, DOWNSAMPLE_FREQUENCY will be used as the target frequency to downsample to."},
             {"name": "DOWNSAMPLE_FREQUENCY", "default": 25, "type": int, "help": "Frequency (Hz) to downsample to. If this is set higher than the input data, new data will be interpolated. Only used if DOWNSAMPLE is set to True"},
         ]
@@ -74,12 +73,26 @@ SECTIONS = [
         "title": "Verify Optode Placement",
         "params": [
             {"name": "OPTODE_PLACEMENT", "default": True, "type": bool, "help": "Generate an image for each participant outlining their optode placement."},
+            {"name": "SHOW_OPTODE_NAMES", "default": True, "type": bool, "help": "Should the optode names be written next to their location or not."},
+        ]
+    },
+    {
+        "title": "Short/Long Channels",
+        "params": [
+            {"name": "SHORT_CHANNEL", "default": True, "type": bool, "help": "This should be set to True if the data has a short channel present in the data."},
+            {"name": "SHORT_CHANNEL_THRESH", "default": 0.015, "type": float, "help": "The maximum distance the short channel can be in metres."},
+            {"name": "LONG_CHANNEL_THRESH", "default": 0.045, "type": float, "help": "The maximum distance the long channel can be in metres."},
         ]
     },
     {
         "title": "Heart Rate",
         "params": [
             {"name": "HEART_RATE", "default": True, "type": bool, "help": "Attempt to calculate the participants heart rate."},
+            {"name": "SECONDS_TO_STRIP_HR", "default": 5, "type": int, "help": "Will remove this many seconds from the start and end of the file. Useful if recording before cap is firmly placed, or participant removes cap while still recording."},
+            {"name": "MAX_LOW_HR", "default": 40, "type": int, "help": "Any heart rate windows that average below this value will be rounded up to this value."},
+            {"name": "MAX_HIGH_HR", "default": 200, "type": int, "help": "Any heart rate windows that average above this value will be rounded down to this value."},
+            {"name": "SMOOTHING_WINDOW_HR", "default": 100, "type": int, "help": "How many individual data points to smooth into a single window."},
+            {"name": "HEART_RATE_WINDOW", "default": 25, "type": int, "help": "Used for visualization. Shows the range of the calculated heart rate +- this value."},
         ]
     },
     {
@@ -94,14 +107,12 @@ SECTIONS = [
         "title": "Signal to Noise Ratio",
         "params": [
             {"name": "SNR", "default": True, "type": bool, "help": "Calculate and mark channels bad based on their Signal to Noise Ratio. This metric calculates how much of the observed signal was noise versus how much of it was a useful signal."},
-            # {"name": "SNR_TIME_WINDOW", "default": -1, "type": int, "help": "SNR time window."},
             {"name": "SNR_THRESHOLD", "default": 5.0, "type": float, "help": "SNR threshold (dB). A typical scale would be 0-25, but it is possible for values to be both above and below this range. Higher values correspond to a better signal. If SNR is True, any channels lower than this value will be marked as bad."},
         ]
     },
     {
         "title": "Peak Spectral Power",
         "params": [
-
             {"name": "PSP", "default": True, "type": bool, "help": "Calculate and mark channels bad based on their Peak Spectral Power. This metric calculates the amplitude or strength of a frequency component that is most prominent in a particular frequency range or spectrum."},
             {"name": "PSP_TIME_WINDOW", "default": 3, "type": int, "help": "Independent PSP calculations will be perfomed in a time window for the duration of the value provided, until the end of the file is reached."},
             {"name": "PSP_THRESHOLD", "default": 0.1, "type": float, "help": "PSP threshold. A typical scale would be 0-0.5, but it is possible for values to be above this range. Higher values correspond to a better signal. If PSP is True, any channels lower than this value will be marked as bad."},
@@ -110,15 +121,15 @@ SECTIONS = [
     {
         "title": "Bad Channels Handling",
         "params": [
-            # {"name": "NOT_IMPLEMENTED", "default": True, "type": bool, "help": "Calculate Peak Spectral Power."},
-            # {"name": "NOT_IMPLEMENTED", "default": 3, "type": int, "help": "PSP time window."},
-            # {"name": "NOT_IMPLEMENTED", "default": 0.1, "type": float, "help": "PSP threshold."},
+            {"name": "BAD_CHANNELS_HANDLING", "default": [], "type": list, "options": ["Interpolate", "Remove", "None"], "exclusive": True, "help": "How should we deal with the bad channels that occurred? Note: Some analysis options will only work when this is set to 'Interpolate'."},
+            {"name": "MAX_DIST", "default": 0.03, "type": float, "help": "The maximum distance to look for neighbours when interpolating. Used only when BAD_CHANNELS_HANDLING is set to 'Interpolate'."},
+            {"name": "MIN_NEIGHBORS", "default": 2, "type": int, "help": "The minimumn amount of neighbours needed within the MAX_DIST parameter. Used only when BAD_CHANNELS_HANDLING is set to 'Interpolate'."},
         ]
     },
     {
         "title": "Optical Density",
         "params": [
-            # Intentionally empty (TODO)
+            # NOTE: Intentionally empty
         ]
     },
     {
@@ -139,7 +150,7 @@ SECTIONS = [
     {
         "title": "Haemoglobin Concentration",
         "params": [
-            # Intentionally empty (TODO)
+            # NOTE: Intentionally empty
         ]
     },
     {
@@ -154,24 +165,18 @@ SECTIONS = [
             {"name": "FILTER", "default": True, "type": bool, "help": "Filter the data."},
             {"name": "L_FREQ", "default": 0.005, "type": float, "help": "Any frequencies lower than this value will be removed."},
             {"name": "H_FREQ", "default": 0.3, "type": float, "help": "Any frequencies higher than this value will be removed."},
+            {"name": "L_TRANS_BANDWIDTH", "default": 0.002, "type": float, "help": "How wide the transitional period should be so the data doesn't just drop off on the lower bound."},
+            {"name": "H_TRANS_BANDWIDTH", "default": 0.002, "type": float, "help": "How wide the transitional period should be so the data doesn't just drop off on the upper bound."},
         ]
     },
     {
-        "title": "Short/Long Channels",
-        "params": [
-            {"name": "SHORT_CHANNEL", "default": True, "type": bool, "help": "This should be set to True if the data has a short channel present in the data."},
-            {"name": "SHORT_CHANNEL_THRESH", "default": 0.015, "type": float, "help": "The maximum distance the short channel can be in metres."},
-            {"name": "LONG_CHANNEL_THRESH", "default": 0.045, "type": float, "help": "The maximum distance the long channel can be in metres."},
-        ]
-    },
-    {
-        "title": "Extracting Events",
+        "title": "Extracting Events*",
         "params": [
             #{"name": "EVENTS", "default": True, "type": bool, "help": "Calculate Peak Spectral Power."},
         ]
     },
     {
-        "title": "Epoch Calculations",
+        "title": "Epoch Calculations*",
         "params": [
             #{"name": "EVENTS", "default": True, "type": bool, "help": "Calculate Peak Spectral Power."},
         ]
@@ -179,18 +184,27 @@ SECTIONS = [
     {
         "title": "Design Matrix",
         "params": [
+            {"name": "RESAMPLE", "default": True, "type": bool, "help": "The length of your stimulus."},
+            {"name": "RESAMPLE_FREQ", "default": 1, "type": int, "help": "The length of your stimulus."},
+            
+            {"name": "STIM_DUR", "default": 0.5, "type": float, "help": "The length of your stimulus."},
+            {"name": "HRF_MODEL", "default": "fir", "type": str, "help": "Specifies the hemodynamic response function."},
+            {"name": "DRIFT_MODEL", "default": "cosine", "type": str, "help": "Specifies the desired drift model."},
+            {"name": "HIGH_PASS", "default": 0.01, "type": float, "help": "High-pass frequency in case of a cosine model (in Hz)."},
+            {"name": "DRIFT_ORDER", "default": 1, "type": int, "help": "Order of the drift model (in case it is polynomial)"},
+            {"name": "FIR_DELAYS", "default": "None", "type": range, "help": "In case of FIR design, yields the array of delays used in the FIR model (in scans)."},
+            {"name": "MIN_ONSET", "default": -24, "type": int, "help": "Minimal onset relative to frame times (in seconds)"},
+            {"name": "OVERSAMPLING", "default": 50, "type": int, "help": "Oversampling factor used in temporal convolutions."},
             {"name": "REMOVE_EVENTS", "default": "None", "type": list, "help": "Remove events matching the names provided before generating the Design Matrix"},
-            {"name": "DRIFT_MODEL", "default": "cosine", "type": str, "help": "Drift model for GLM."},
-            # {"name": "DURATION_BETWEEN_ACTIVITIES", "default": 35, "type": int, "help": "Time between activities (s)."},
-            # {"name": "SHORT_CHANNEL_REGRESSION", "default": True, "type": bool, "help": "Use short channel regression."},
+            {"name": "SHORT_CHANNEL_REGRESSION", "default": True, "type": bool, "help": "Whether to use short channel regression and regress out the short channels. Requires SHORT_CHANNELS to be True and at least one short channel to be found."},
         ]
     },
     {
         "title": "General Linear Model",
         "params": [
-            {"name": "TIME_WINDOW_START", "default": "0", "type": int, "help": "Where to start averaging the fir model bins. Only affects the significance and contrast images."},
-            {"name": "TIME_WINDOW_END", "default": "15", "type": int, "help": "Where to end averaging the fir model bins. Only affects the significance and contrast images."},
-            #{"name": "N_JOBS", "default": 1, "type": int, "help": "Number of jobs for GLM processing."},
+            {"name": "NOISE_MODEL", "default": "ar1", "type": str, "help": "Number of jobs for GLM processing."},
+            {"name": "BINS", "default": 0, "type": int, "help": "Number of jobs for GLM processing."},
+            {"name": "N_JOBS", "default": 1, "type": int, "help": "Number of jobs for GLM processing."},
         ]
     },
         {
@@ -202,7 +216,10 @@ SECTIONS = [
     {
         "title": "Other",
         "params": [
-            {"name": "MAX_WORKERS", "default": 4, "type": int, "help": "Number of files to be processed at once. Lowering this value may help on underpowered systems."},
+            {"name": "TIME_WINDOW_START", "default": 0, "type": int, "help": "Where to start averaging the fir model bins. Only affects the significance and contrast images."},
+            {"name": "TIME_WINDOW_END", "default": 15, "type": int, "help": "Where to end averaging the fir model bins. Only affects the significance and contrast images."},
+            {"name": "MAX_WORKERS", "default": 4, "type": str, "help": "Number of files to be processed at once. Setting this to a small integer value may help on underpowered systems. Remove the value to use an automatic amount."},
+            {"name": "VERBOSITY", "default": False, "type": bool, "help": "True will log lots of debugging information to the log file. False will only log required data."},
         ]
     },
 ]
@@ -483,29 +500,38 @@ class UserGuideWindow(QWidget):
 
         layout = QVBoxLayout()
         label = QLabel("Progress Bar Stages:", self)
-        label2 = QLabel("Stage 1: Load the snirf file\n"
-                        "Stage 2: Check the optode positions\n"
-                        "Stage 3: Scalp Coupling Index\n"
-                        "Stage 4: Signal to Noise Ratio\n"
-                        "Stage 5: Peak Spectral Power\n"
-                        "Stage 6: Identify bad channels\n"
-                        "Stage 7: Interpolate bad channels\n"
-                        "Stage 8: Optical Density\n"
-                        "Stage 9: Temporal Derivative Distribution Repair\n"
-                        "Stage 10: Beer Lambert Law\n"
-                        "Stage 11: Heart Rate Filtering\n"
-                        "Stage 12: Get Short/Long Channels\n"
-                        "Stage 13: Calculate Events from Annotations\n"
-                        "Stage 14: Epoch Calculations\n"
-                        "Stage 15: Design Matrix\n"
-                        "Stage 16: General Linear Model\n"
-                        "Stage 17: Generate Plots from the GLM\n"
-                        "Stage 18: Individual Significance\n"
-                        "Stage 19: Channel, Region of Interest, and Contrast Results\n"
-                        "Stage 20: Image Conversion\n", self)
+        label2 = QLabel("Stage 1: Preprocessing\n"
+                        "Stage 2: Trimming\n"
+                        "Stage 3: Verify Optode Placement\n"
+                        "Stage 4: Short/Long Cannels\n"
+                        "Stage 5: Heart Rate\n"
+                        "Stage 6: Scalp Coupling Index\n"
+                        "Stage 7: Signal to Noise Ratio\n"
+                        "Stage 8: Peak Spectral Power\n"
+                        "Stage 9: Bad Channels Handling\n"
+                        "Stage 10: Optical Density\n"
+                        "Stage 11: Temporal Derivative Distribution Repair Filtering\n"
+                        "Stage 12: Wavelet Filtering\n"
+                        "Stage 13: Haemoglobin Concentration\n"
+                        "Stage 14: Enhance Negative Correlation\n"
+                        "Stage 15: Filter\n"
+                        "Stage 16: Extracting Events\n"
+                        "Stage 17: Epoch Calculations\n"
+                        "Stage 18: Design Matrix\n"
+                        "Stage 19: General Linear Model\n"
+                        "Stage 20: Generate GLM Results\n"
+                        "Stage 21: Generate Channel Significance\n"
+                        "Stage 22: Generate Channel, Region of Interest, and Contrast Results\n"
+                        "Stage 23: Compute Contrast Results\n"
+                        "Stage 24: Finishing Up\n", self)
 
+        label3 = QLabel("For more information, visit the Git wiki page <a href='https://git.research.dezeeuw.ca/tyler/flares/wiki'>here</a>.", self)
+        label3.setTextFormat(Qt.TextFormat.RichText)
+        label3.setTextInteractionFlags(Qt.TextInteractionFlag.TextBrowserInteraction)
+        label3.setOpenExternalLinks(True)
         layout.addWidget(label)
         layout.addWidget(label2)
+        layout.addWidget(label3)
 
         self.setLayout(layout)
 
@@ -742,15 +768,23 @@ class UpdateOptodesWindow(QWidget):
         write_raw_snirf(raw, save_path)
 
 
+class EventUpdateMode(Enum):
+    WRITE_SNIRF = auto()   # destructive
+    WRITE_JSON = auto()    # non-destructive
 
 
 class UpdateEventsWindow(QWidget):
 
-    def __init__(self, parent=None):
+    def __init__(self, parent=None, mode=EventUpdateMode.WRITE_SNIRF, caller=None):
         super().__init__(parent, Qt.WindowType.Window)
+
+        self.mode = mode
+        self.caller = caller or self.__class__.__name__
         self.setWindowTitle("Update event markers")
         self.resize(760, 200)
 
+        print("INIT MODE:", mode)
+
         self.label_file_a = QLabel("SNIRF file:")
         self.line_edit_file_a = QLineEdit()
         self.line_edit_file_a.setReadOnly(True)
@@ -1051,46 +1085,55 @@ class UpdateEventsWindow(QWidget):
             QMessageBox.warning(self, "No SNIRF file", "Please select a SNIRF file.")
             return
 
+
+        boris_obs = self.boris_data["observations"][selected_obs]
+
+        # --- Extract videos + delays ---
+        files = boris_obs.get("file", {})
+        offsets = boris_obs.get("media_info", {}).get("offset", {})
+
+        videos = {}
+        for key, path in files.items():
+            if path:  # only include videos that exist
+                delay = offsets.get(key, 0.0)  # default 0 if missing
+                videos[key] = {"file": path, "delay": delay}
+
+        base_name = os.path.splitext(os.path.basename(file_a))[0]
+
+        if self.mode == EventUpdateMode.WRITE_SNIRF:
+            # Open save dialog for SNIRF
             base_name = os.path.splitext(os.path.basename(file_a))[0]
             suggested_name = f"{base_name}_{suffix}.snirf"
-
-        # Open save dialog
             save_path, _ = QFileDialog.getSaveFileName(
                 self,
                 "Save SNIRF File As",
                 suggested_name,
                 "SNIRF Files (*.snirf)"
             )
-
             if not save_path:
-            print("Save cancelled.")
+                print("SNIRF save cancelled.")
                 return
-
             if not save_path.lower().endswith(".snirf"):
                 save_path += ".snirf"
 
             try:
-            raw = read_raw_snirf(snirf_path, preload=True)
-
-            onsets = []
-            durations = []
-            descriptions = []
+                raw = read_raw_snirf(file_a, preload=True)
 
+                # --- Align BORIS events to SNIRF ---
+                boris_events = boris_obs.get("events", [])
+                onsets, durations, descriptions = [], [], []
                 open_events = {}  # label -> list of start times
                 label_counts = {}
-
                 used_times = set()
-            sfreq = raw.info['sfreq']  # sampling frequency in Hz
+                sfreq = raw.info['sfreq']
                 min_shift = 1.0 / sfreq
                 max_attempts = 10
 
                 for event in boris_events:
                     if not isinstance(event, list) or len(event) < 3:
                         continue
-
                     event_time = event[0]
                     label = event[2]
-
                     count = label_counts.get(label, 0) + 1
                     label_counts[label] = count
 
@@ -1098,74 +1141,84 @@ class UpdateEventsWindow(QWidget):
                         open_events[label] = []
 
                     if count % 2 == 1:
-                    # Odd occurrence = start event
                         open_events[label].append(event_time)
                     else:
-                    # Even occurrence = end event
                         if open_events[label]:
-                        matched_start = open_events[label].pop(0)
-                        duration = event_time - matched_start
-
+                            start_time = open_events[label].pop(0)
+                            duration = event_time - start_time
                             if duration <= 0:
-                            print(f"Warning: Duration for {label} is non-positive ({duration}). Skipping.")
                                 continue
 
-                        shifted_start = matched_start + time_shift
-
-                        adjusted_time = shifted_start
+                            adjusted_time = start_time + time_shift
                             attempts = 0
                             while round(adjusted_time, 6) in used_times and attempts < max_attempts:
                                 adjusted_time += min_shift
                                 attempts += 1
-
                             if attempts == max_attempts:
-                            print(f"Warning: Couldn't find unique time for {label} @ {matched_start}s. Skipping.")
                                 continue
 
                             adjusted_time = round(adjusted_time, 6)
                             used_times.add(adjusted_time)
-
-                        print(f"Adding event: {label} @ {adjusted_time:.3f}s for {duration:.3f}s")
-
                             onsets.append(adjusted_time)
                             durations.append(duration)
                             descriptions.append(label)
-                    else:
-                        print(f"Warning: Unmatched end for label '{label}' at {event_time:.3f}s. Skipping.")
-
-            # Optionally warn about any unmatched starts left open
+                # Handle unmatched starts
                 for label, starts in open_events.items():
                     for start_time in starts:
-                    shifted_start = start_time + time_shift
-
-                    adjusted_time = shifted_start
+                        adjusted_time = start_time + time_shift
                         attempts = 0
                         while round(adjusted_time, 6) in used_times and attempts < max_attempts:
                             adjusted_time += min_shift
                             attempts += 1
-
                         if attempts == max_attempts:
-                        print(f"Warning: Couldn't find unique time for unmatched start {label} @ {start_time}s. Skipping.")
                             continue
-
                         adjusted_time = round(adjusted_time, 6)
                         used_times.add(adjusted_time)
-
-                    print(f"Warning: Unmatched start for label '{label}' at {start_time:.3f}s. Adding with duration 0.")
                         onsets.append(adjusted_time)
                         durations.append(0.0)
                         descriptions.append(label)
 
                 new_annotations = Annotations(onset=onsets, duration=durations, description=descriptions)
-
                 raw.set_annotations(new_annotations)
                 write_raw_snirf(raw, save_path)
-
                 QMessageBox.information(self, "Success", "SNIRF file updated with aligned BORIS events.")
 
             except Exception as e:
                 QMessageBox.critical(self, "Error", f"Failed to update SNIRF file:\n{e}")
 
+        elif self.mode == EventUpdateMode.WRITE_JSON:
+            # Open save dialog for JSON
+            base_name = os.path.splitext(os.path.basename(file_a))[0]
+            suggested_name = f"{base_name}_{suffix}_alignment.json"
+            save_path, _ = QFileDialog.getSaveFileName(
+                self,
+                "Save Event Alignment JSON As",
+                suggested_name,
+                "JSON Files (*.json)"
+            )
+            if not save_path:
+                print("JSON save cancelled.")
+                return
+            if not save_path.lower().endswith(".json"):
+                save_path += ".json"
+
+            # Build JSON dict
+            json_data = {
+                "observation": selected_obs,
+                "snirf_anchor": {"label": snirf_label, "time": snirf_anchor_time},
+                "boris_anchor": {"label": boris_label, "time": boris_anchor_time},
+                "time_shift": time_shift,
+                "videos": videos
+            }
+
+            # Write JSON
+            try:
+                with open(save_path, "w", encoding="utf-8") as f:
+                    json.dump(json_data, f, indent=4)
+                QMessageBox.information(self, "Success", f"Event alignment saved to:\n{save_path}")
+            except Exception as e:
+                QMessageBox.critical(self, "Error", f"Failed to write JSON:\n{e}")
+
 
     def update_optode_positions(self, file_a, file_b, save_path):
                 
@@ -1197,6 +1250,47 @@ class UpdateEventsWindow(QWidget):
         write_raw_snirf(raw, save_path)
 
 
+    def _apply_events_to_snirf(self, raw, new_annotations, save_path):
+        raw.set_annotations(new_annotations)
+        write_raw_snirf(raw, save_path)
+        
+    def _write_event_mapping_json(
+        self,
+        file_a,
+        file_b,
+        selected_obs,
+        snirf_anchor,
+        boris_anchor,
+        time_shift,
+        mapped_events,
+        save_path
+    ):
+        import json
+        from datetime import datetime
+        import os
+
+        payload = {
+            "source": {
+                "called_from": self.caller,
+                "snirf_file": os.path.basename(file_a),
+                "boris_file": os.path.basename(file_b),
+                "observation": selected_obs
+            },
+            "alignment": {
+                "snirf_anchor": snirf_anchor,
+                "boris_anchor": boris_anchor,
+                "time_shift_seconds": time_shift
+            },
+            "events": mapped_events,
+            "created_at": datetime.utcnow().isoformat() + "Z"
+        }
+
+        with open(save_path, "w", encoding="utf-8") as f:
+            json.dump(payload, f, indent=2)
+
+        return save_path
+
+
 class ProgressBubble(QWidget):
     """
     A clickable widget displaying a progress bar made of colored rectangles and a label.
@@ -1229,7 +1323,7 @@ class ProgressBubble(QWidget):
         self.progress_layout = QHBoxLayout()
 
         self.rects = []
-        for _ in range(25):
+        for _ in range(24):
             rect = QFrame()
             rect.setFixedSize(10, 18)
             rect.setStyleSheet("background-color: white; border: 1px solid gray;")
@@ -1358,6 +1452,11 @@ class ParamSection(QWidget):
                 widget.setValidator(QDoubleValidator())
                 widget.setText(str(param["default"]))
             elif param["type"] == list:
+                if param.get("exclusive", True):
+                    widget = QComboBox()
+                    widget.addItems(param.get("options", []))
+                    widget.setCurrentText(str(param.get("default", "<None Selected>")))
+                else:
                     widget = self._create_multiselect_dropdown(None)
             else: 
                 widget = QLineEdit()
@@ -1466,7 +1565,10 @@ class ParamSection(QWidget):
             if expected_type == bool:
                 values[name] = widget.currentText() == "True"
             elif expected_type == list:
+                if isinstance(widget, FullClickComboBox):
                     values[name] = [x.strip() for x in widget.lineEdit().text().split(",") if x.strip()]
+                elif isinstance(widget, QComboBox):
+                    values[name] = widget.currentText()
             else:
                 raw_text = widget.text()
                 try:
@@ -2422,9 +2524,23 @@ class ParticipantFoldChannelsWidget(QWidget):
 
             for idx in selected_indexes:
                 if idx == 0:
-
+                    try:
                         flares.fold_channels(haemo_obj)
-
+                    except:
+                        msg_box = QMessageBox()
+                        msg_box.setIcon(QMessageBox.Icon.Critical)
+                        msg_box.setWindowTitle("Something went wrong!")
+                        message = (
+                            "Unable to locate the fOLD files!<br><br>"
+                            f"Please download the 'Supplementary' folder from <a href='https://github.com/nirx/fOLD-public'>here</a>. "
+                            "Once the folder is downloaded, place it in C:/Users/your username/mne_data/fOLD/fOLD-public-master/Supplementary.<br><br>"
+                            "If you are not using Windows, please go to the FLARES Git page for more information."
+                        )
+                        msg_box.setTextFormat(Qt.TextFormat.RichText)
+                        msg_box.setText(message)
+                        msg_box.setTextInteractionFlags(Qt.TextInteractionFlag.TextBrowserInteraction)
+                        msg_box.setStandardButtons(QMessageBox.StandardButton.Ok)
+                        msg_box.exec()
                 else:
                     print(f"No method defined for index {idx}")
 
@@ -2460,7 +2576,7 @@ class ExportDataAsCSVViewerWidget(QWidget):
               
         self.index_texts = [
             "0 (Export Data to CSV)",
-            # "1 (second image)",
+            "1 (CSV for SPARKS)",
             # "2 (third image)",
             # "3 (fourth image)",
             ]
@@ -2612,7 +2728,6 @@ class ExportDataAsCSVViewerWidget(QWidget):
         # Pass the necessary arguments to each method
         for file_path in selected_file_paths:
             haemo_obj = self.haemo_dict.get(file_path)
-
             if haemo_obj is None:
                 continue
 
@@ -2646,10 +2761,63 @@ class ExportDataAsCSVViewerWidget(QWidget):
                         QMessageBox.critical(self, "Error", f"Failed to update SNIRF file:\n{e}")
 
 
+                elif idx == 1:
+                    try:
+                        suggested_name = f"{file_path}_sparks.csv"
+
+                        # Open save dialog
+                        save_path, _ = QFileDialog.getSaveFileName(
+                            self,
+                            "Save SNIRF 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 SNIRF 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__()
@@ -4275,7 +4443,7 @@ class MainApplication(QMainWindow):
 
     def update_event_markers(self):
         if self.events is None or not self.events.isVisible():
-            self.events = UpdateEventsWindow(self)
+            self.events = UpdateEventsWindow(self, EventUpdateMode.WRITE_SNIRF, "Manual SNIRF Edit")
             self.events.show()
 
     def open_file_dialog(self):

mne/transforms.py

@@ -12,7 +12,7 @@ from pathlib import Path
 import numpy as np
 from scipy import linalg
 from scipy.spatial.distance import cdist
-from scipy.special import sph_harm
+from scipy.special import sph_harm_y
 
 from ._fiff.constants import FIFF
 from ._fiff.open import fiff_open

mne/viz/_3d.py

@@ -1025,7 +1025,7 @@ def _handle_sensor_types(meg, eeg, fnirs):
         fnirs=dict(channels="fnirs", pairs="fnirs_pairs"),
     )
     sensor_alpha = {
-        key: dict(meg_helmet=0.25, meg=0.25).get(key, 0.8)
+        key: dict(meg_helmet=0.25, meg=0.25).get(key, 1.0)
         for ch_dict in alpha_map.values()
         for key in ch_dict.values()
     }

mne/viz/backends/_pyvista.py

@@ -586,7 +586,7 @@ class _PyVistaRenderer(_AbstractRenderer):
                 color = None
             else:
                 scalars = None
-            tube = line.tube(radius, n_sides=self.tube_n_sides)
+            tube = line.tube(radius=radius, n_sides=self.tube_n_sides)
             actor = _add_mesh(
                 plotter=self.plotter,
                 mesh=tube,

version_main.txt

@@ -18,7 +18,7 @@ VSVersionInfo(
            StringStruct('FileDescription', 'FLARES main application'),
            StringStruct('FileVersion', '1.0.0.0'),
            StringStruct('InternalName', 'flares.exe'),
-           StringStruct('LegalCopyright', '© 2025 Tyler de Zeeuw'),
+           StringStruct('LegalCopyright', '© 2025-2026 Tyler de Zeeuw'),
            StringStruct('OriginalFilename', 'flares.exe'),
            StringStruct('ProductName', 'FLARES'),
            StringStruct('ProductVersion', '1.0.0.0')])

version_updater.txt

@@ -18,7 +18,7 @@ VSVersionInfo(
            StringStruct('FileDescription', 'FLARES updater application'),
            StringStruct('FileVersion', '1.0.0.0'),
            StringStruct('InternalName', 'main.exe'),
-           StringStruct('LegalCopyright', '© 2025 Tyler de Zeeuw'),
+           StringStruct('LegalCopyright', '© 2025-2026 Tyler de Zeeuw'),
            StringStruct('OriginalFilename', 'flares_updater.exe'),
            StringStruct('ProductName', 'FLARES Updater'),
            StringStruct('ProductVersion', '1.0.0.0')])