add pyinstaller to dependcies

.gitignore

@@ -175,3 +175,7 @@ cython_debug/
 .pypirc
 
 /individual_images
+*.xlsx
+*.csv
+*.snirf
+*.json

README.md

@@ -27,7 +27,7 @@ This only applies for the first time you attempt to run FLARES. Subsequent times
 
 Due to the cost of a code signing certificate, the application is not digitally signed. Once the application is extracted and attempted to be launched for the first time you will get a popup stating:
 
-"Windows protected your PC - Microsoft Defender SmartScreen prevented an unrecognized app from starting. Running this app might put your PC at risk.", with the options of" More info" or "Don't run".
+"Windows protected your PC - Microsoft Defender SmartScreen prevented an unrecognized app from starting. Running this app might put your PC at risk.", with the options of "More info" or "Don't run".
 
 The solution around this is to click "More info" and then select "Run anyway".
 
@@ -41,4 +41,4 @@ There are no conditions for Linux users at this time.
 
 FLARES is distributed under the GPL-3.0 license.
 
-Copyright (C) 2025 Tyler de Zeeuw
+Copyright (C) 2025-2026 Tyler de Zeeuw

changelog.md

@@ -1,7 +1,39 @@
+# Version 1.2.2
+
+- Added 'Update events in snirf file (BLAZES)...' and renamed 'Update events in snirf file...' to 'Update events in snirf file (BORIS)...'
+- The BLAZES option will assign events that are exported directly from the software [BLAZES](https://git.research.dezeeuw.ca/tyler/blazes)
+- Moved the updating logic to a seperate file for better reusability and generalization
+- Fixed 'Toggle Status Bar' having no effect on the visibility of the status bar
+- 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 missing dependency in the standalone application when attempting to use an .xlsx file to update optode positions
+
+
+# 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)
 - 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.
+- Certain parameters now have dependencies on other parameters and will now grey out if they are not used
 - 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
@@ -15,6 +47,20 @@
 - 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
+- Added support for updating optode positions directly from an .xlsx file from a Polhemius system
+- Fixed an issue where the dropdowns in the Viewer windows would immediately open and close when using a trackpad
+- glover and spm hrf models now function as intended without crashing. Currently, group analysis is still only supported by fir. Fixes [Issue 8](https://git.research.dezeeuw.ca/tyler/flares/issues/8)
+- Clicking 'Clear' should now properly clear all data. Fixes [Issue 9](https://git.research.dezeeuw.ca/tyler/flares/issues/9)
+- Revamped the fold channels viewer to not hang the application and to better process multiple participants at once. Fixes [Issue 34](https://git.research.dezeeuw.ca/tyler/flares/issues/34), [Issue 31](https://git.research.dezeeuw.ca/tyler/flares/issues/31)
+- Added a Preferences menu to the navigation bar
+- Two preferences have been added allowing to bypass the warning of 2D data detected and save files being from previous, potentially breaking versions
+- Fixed a typo when saving a CSV that stated a SNIRF was being saved
+- Loading a save file now properly restores AGE, GENDER, and GROUP. Fixes [Issue 40](https://git.research.dezeeuw.ca/tyler/flares/issues/40)
+- Saving a project now no longer makes the main window go not responding. Fixes [Issue 43](https://git.research.dezeeuw.ca/tyler/flares/issues/43)
+- Memory usage should no longer grow when generating lots of images multiple times. Fixes [Issue 36](https://git.research.dezeeuw.ca/tyler/flares/issues/36)
+- Added a new option in the Analysis window for Functional Connectivity
+- 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 Functional Connectivity Viewer disclosing this
 
 
 # Version 1.1.7
@@ -105,7 +151,7 @@
 - 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

@@ -47,9 +47,9 @@ from nilearn.glm.regression import OLSModel
 import statsmodels.formula.api as smf  # type: ignore
 from statsmodels.stats.multitest import multipletests
 
-from scipy import stats
 from scipy.spatial.distance import cdist
 from scipy.signal import welch, butter, filtfilt # type: ignore
+from scipy.stats import pearsonr, zscore, t
 
 import pywt # type: ignore
 import neurokit2 as nk # type: ignore
@@ -58,6 +58,7 @@ import neurokit2 as nk # type: ignore
 import pyvistaqt # type: ignore
 import vtkmodules.util.data_model
 import vtkmodules.util.execution_model
+import xlrd
 
 # External library imports for mne
 from mne import (
@@ -90,6 +91,9 @@ 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
 
+from mne_connectivity.viz import plot_connectivity_circle
+from mne_connectivity import envelope_correlation, spectral_connectivity_epochs,  spectral_connectivity_time
+
 # Needs to be set for mne
 os.environ["SUBJECTS_DIR"] = str(data_path()) + "/subjects" # type: ignore
 
@@ -123,8 +127,6 @@ 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
@@ -189,9 +191,9 @@ TIME_WINDOW_END: int
 MAX_WORKERS: int
 VERBOSITY: bool
 
-AGE = 25    # Assume 25 if not set from the GUI. This will result in a reasonable PPF
-GENDER = ""
-GROUP = "Default"
+AGE: int = 25  # Assume 25 if not set from the GUI. This will result in a reasonable PPF
+GENDER: str = ""
+GROUP: str = "Default"
 
 # These are parameters that are required for the analysis
 REQUIRED_KEYS: dict[str, Any] = {
@@ -928,11 +930,12 @@ def interpolate_fNIRS_bads_weighted_average(raw, max_dist=0.03, min_neighbors=2)
         raw.info['bads'] = [ch for ch in raw.info['bads'] if ch not in bad_ch_to_remove]
 
     print("\nInterpolation complete.\n")
+    print("Bads cleared:", raw.info['bads'])
+    raw.info['bads'] = []
 
     for ch in raw.info['bads']:
         print(f"Channel {ch} still marked as bad.")
 
-    print("Bads cleared:", raw.info['bads'])
     fig_raw_after = raw.plot(duration=raw.times[-1], n_channels=raw.info['nchan'], title="After interpolation", show=False)
 
     return raw, fig_raw_after
@@ -1333,7 +1336,7 @@ def make_design_matrix(raw_haemo, short_chans):
             drift_model=DRIFT_MODEL,
             high_pass=HIGH_PASS,
             drift_order=DRIFT_ORDER,
-            fir_delays=range(15),
+            fir_delays=FIR_DELAYS,
             add_regs=short_chans.get_data().T,
             add_reg_names=short_chans.ch_names,
             min_onset=MIN_ONSET,
@@ -1347,7 +1350,7 @@ def make_design_matrix(raw_haemo, short_chans):
             drift_model=DRIFT_MODEL,
             high_pass=HIGH_PASS,
             drift_order=DRIFT_ORDER,
-            fir_delays=range(15),
+            fir_delays=FIR_DELAYS,
             min_onset=MIN_ONSET,
             oversampling=OVERSAMPLING
         )
@@ -1659,8 +1662,11 @@ def fold_channels(raw: BaseRaw) -> None:
         landmark_color_map = {landmark: colors[i % len(colors)] for i, landmark in enumerate(landmarks)}
 
     # Iterate over each channel
+    print(len(hbo_channel_names))
+
     for idx, channel_name in enumerate(hbo_channel_names):
 
+        print(idx, channel_name)
         # Run the fOLD on the selected channel
         channel_data = raw.copy().pick(picks=channel_name) # type: ignore
 
@@ -1703,6 +1709,7 @@ def fold_channels(raw: BaseRaw) -> None:
 
             landmark_specificity_data = []
 
+
     # TODO: Fix this
     if True:
         handles = [
@@ -1725,8 +1732,9 @@ def fold_channels(raw: BaseRaw) -> None:
     for ax in axes[len(hbo_channel_names):]:
         ax.axis('off')
     
-    plt.show()
-    return fig, legend_fig
+    #plt.show()
+    fig_dict = {"main": fig, "legend": legend_fig}
+    return convert_fig_dict_to_png_bytes(fig_dict)
 
                 
 
@@ -2246,8 +2254,14 @@ def brain_3d_visualization(raw_haemo, df_cha, selected_event, t_or_theta: Litera
     # Get all activity conditions
     for cond in [f'{selected_event}']:
 
-        if True:
-            ch_summary = df_cha.query(f"Condition.str.startswith('{cond}_delay_') and Chroma == 'hbo'", engine='python') # type: ignore
+        ch_summary = df_cha.query(f"Condition.str.startswith('{cond}_delay_') and Chroma == 'hbo'", engine='python') # type: ignore
+
+        print(ch_summary)
+        
+        if ch_summary.empty:
+            #not fir model
+            print("No data found for this condition.")
+            ch_summary = df_cha.query(f"Condition in [@cond] and Chroma == 'hbo'", engine='python') 
 
         # Use ordinary least squares (OLS) if only one participant
         # TODO: Fix.
@@ -2269,6 +2283,9 @@ def brain_3d_visualization(raw_haemo, df_cha, selected_event, t_or_theta: Litera
         valid_channels = ch_summary["ch_name"].unique().tolist() # type: ignore
         raw_for_plot = raw_haemo.copy().pick(picks=valid_channels) # type: ignore
 
+        print(f"DEBUG: Model DF rows: {len(model_df)}")
+        print(f"DEBUG: Raw channels: {len(raw_for_plot.ch_names)}")
+
         brain = plot_3d_evoked_array(raw_for_plot.pick(picks="hbo"), model_df, view="dorsal", distance=0.02, colorbar=True, clim=clim, mode="weighted", size=(800, 700)) # type: ignore
         
         if show_optodes == 'all' or show_optodes == 'sensors':
@@ -2804,7 +2821,7 @@ def run_second_level_analysis(df_contrasts, raw, p, bounds):
         result = model.fit(Y)
 
         t_val = result.t(0).item()
-        p_val = 2 * stats.t.sf(np.abs(t_val), df=result.df_model)
+        p_val = 2 * t.sf(np.abs(t_val), df=result.df_model)
         mean_beta = np.mean(Y)
 
         group_results.append({
@@ -3299,7 +3316,7 @@ def hr_calc(raw):
     # --- Parameters for PSD ---
     desired_bin_hz = 0.1
     nperseg = int(sfreq / desired_bin_hz)
-    hr_range = (30, 180)
+    hr_range = (30, 180) # TODO: SHould this not use the user defined values?
 
     # --- Function to find strongest local peak ---
     def find_hr_from_psd(ch_data):
@@ -3343,6 +3360,7 @@ def process_participant(file_path, progress_callback=None):
     logger.info("Step 1 Completed.")
     
     # Step 2: Trimming
+    # TODO: Clean this into a method
     if TRIM:
         if hasattr(raw, 'annotations') and len(raw.annotations) > 0:
             # Get time of first event
@@ -3385,7 +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.")
 
@@ -3527,16 +3545,18 @@ def process_participant(file_path, progress_callback=None):
     logger.info("19")
 
     # 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 "derivative" not in HRF_MODEL.lower():
+        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(20)
     logger.info("20")
 
     # 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 HRF_MODEL == "fir":
+        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(21)
     logger.info("21")
 
@@ -3568,30 +3588,31 @@ def process_participant(file_path, progress_callback=None):
         [(column, contrast_matrix[i]) for i, column in enumerate(design_matrix.columns)]
     )
 
-    all_delay_cols = [col for col in design_matrix.columns if "_delay_" in col]
-    all_conditions = sorted({col.split("_delay_")[0] for col in all_delay_cols})
+    if HRF_MODEL == "fir":
+        all_delay_cols = [col for col in design_matrix.columns if "_delay_" in col]
+        all_conditions = sorted({col.split("_delay_")[0] for col in all_delay_cols})
 
-    if not all_conditions:
-        raise ValueError("No FIR regressors found in the design matrix.")
+        if not all_conditions:
+            raise ValueError("No FIR regressors found in the design matrix.")
 
-    # Build contrast vectors for each condition
-    contrast_dict = {}
+        # Build contrast vectors for each condition
+        contrast_dict = {}
 
-    for condition in all_conditions:
-        delay_cols = [
-            col for col in all_delay_cols
-            if col.startswith(f"{condition}_delay_") and
-            TIME_WINDOW_START <= int(col.split("_delay_")[-1]) <= TIME_WINDOW_END
-        ]
+        for condition in all_conditions:
+            delay_cols = [
+                col for col in all_delay_cols
+                if col.startswith(f"{condition}_delay_") and
+                TIME_WINDOW_START <= int(col.split("_delay_")[-1]) <= TIME_WINDOW_END
+            ]
 
-        if not delay_cols:
-            continue  # skip if no columns found (shouldn't happen?)
+            if not delay_cols:
+                continue  # skip if no columns found (shouldn't happen?)
 
-        # Average across all delay regressors for this condition
-        contrast_vector = np.sum([basic_conts[col] for col in delay_cols], axis=0)
-        contrast_vector /= len(delay_cols)
+            # Average across all delay regressors for this condition
+            contrast_vector = np.sum([basic_conts[col] for col in delay_cols], axis=0)
+            contrast_vector /= len(delay_cols)
 
-        contrast_dict[condition] = contrast_vector
+            contrast_dict[condition] = contrast_vector
 
     if progress_callback: progress_callback(22)
     logger.info("22")
@@ -3599,11 +3620,13 @@ def process_participant(file_path, progress_callback=None):
     # Step 23: Compute Contrast Results
     contrast_results = {}
 
-    for cond, contrast_vector in contrast_dict.items():
-        contrast = glm_est.compute_contrast(contrast_vector)  # type: ignore
-        df = contrast.to_dataframe()
-        df["ID"] = file_path
-        contrast_results[cond] = df
+    if HRF_MODEL == "fir":
+
+        for cond, contrast_vector in contrast_dict.items():
+            contrast = glm_est.compute_contrast(contrast_vector)  # type: ignore
+            df = contrast.to_dataframe()
+            df["ID"] = file_path
+            contrast_results[cond] = df
 
     cha["ID"] = file_path
     
@@ -3618,7 +3641,17 @@ def process_participant(file_path, progress_callback=None):
     if progress_callback: progress_callback(25)
     logger.info("25")
 
-    return raw_haemo, epochs, fig_bytes, cha, contrast_results, df_ind, design_matrix, AGE, GENDER, GROUP, True
+    # TODO: Tidy up
+    # Extract the parameters this file was ran with. No need to return age, gender, group?
+    config = {
+        k: globals()[k]
+        for k in __annotations__
+        if k in globals() and k != "REQUIRED_KEYS"
+    }
+
+    print(config)
+
+    return raw_haemo, config, epochs, fig_bytes, cha, contrast_results, df_ind, design_matrix, True
 
 
 def sanitize_paths_for_pickle(raw_haemo, epochs):
@@ -3629,3 +3662,421 @@ def sanitize_paths_for_pickle(raw_haemo, epochs):
     # Fix epochs._raw._filenames
     if hasattr(epochs, '_raw') and hasattr(epochs._raw, '_filenames'):
         epochs._raw._filenames = [str(p) for p in epochs._raw._filenames]
+
+
+def functional_connectivity_spectral_epochs(epochs, n_lines, vmin):
+
+    # will crash without this load
+    epochs.load_data()
+    hbo_epochs = epochs.copy().pick(picks="hbo")
+    data = hbo_epochs.get_data()
+    names = hbo_epochs.ch_names
+    sfreq = hbo_epochs.info["sfreq"]
+    con = spectral_connectivity_epochs(
+        data,
+        method=["coh", "plv"],
+        mode="multitaper",
+        sfreq=sfreq,
+        fmin=0.04,
+        fmax=0.2,
+        faverage=True,
+        verbose=True
+    )
+
+    con_coh, con_plv = con
+
+    coh = con_coh.get_data(output="dense").squeeze()
+    plv = con_plv.get_data(output="dense").squeeze()
+
+    np.fill_diagonal(coh, 0)
+    np.fill_diagonal(plv, 0)
+
+    plot_connectivity_circle(
+        coh,
+        names,
+        title="fNIRS Functional Connectivity (HbO - Coherence)",
+        n_lines=n_lines,
+        vmin=vmin
+    )
+
+
+
+
+
+def functional_connectivity_spectral_time(epochs, n_lines, vmin):
+
+    # will crash without this load
+    epochs.load_data()
+    hbo_epochs = epochs.copy().pick(picks="hbo")
+    data = hbo_epochs.get_data()
+    names = hbo_epochs.ch_names
+    sfreq = hbo_epochs.info["sfreq"]
+
+    freqs = np.linspace(0.04, 0.2, 10)
+    n_cycles = freqs * 2
+
+    con = spectral_connectivity_time(
+        data,
+        freqs=freqs,
+        method=["coh", "plv"],
+        mode="multitaper",
+        sfreq=sfreq,
+        fmin=0.04,
+        fmax=0.2,
+        n_cycles=n_cycles,
+        faverage=True,
+        verbose=True
+    )
+
+    con_coh, con_plv = con
+
+    coh = con_coh.get_data(output="dense").squeeze()
+    plv = con_plv.get_data(output="dense").squeeze()
+
+    np.fill_diagonal(coh, 0)
+    np.fill_diagonal(plv, 0)
+
+    plot_connectivity_circle(
+        coh,
+        names,
+        title="fNIRS Functional Connectivity (HbO - Coherence)",
+        n_lines=n_lines,
+        vmin=vmin
+    )
+
+
+
+
+def functional_connectivity_envelope(epochs, n_lines, vmin):
+    # will crash without this load
+
+    epochs.load_data()
+    hbo_epochs = epochs.copy().pick(picks="hbo")
+    data = hbo_epochs.get_data()
+
+
+    env = envelope_correlation(
+        data,
+        orthogonalize=False,
+        absolute=True
+    )
+    env_data = env.get_data(output="dense")
+
+    env_corr = env_data.mean(axis=0)
+
+    env_corr = np.squeeze(env_corr)
+
+    np.fill_diagonal(env_corr, 0)
+
+    plot_connectivity_circle(
+        env_corr,
+        hbo_epochs.ch_names,
+        title="fNIRS HbO Envelope Correlation (Task Connectivity)",
+        n_lines=n_lines,
+        vmin=vmin
+    )
+
+
+def functional_connectivity_betas(raw_hbo, n_lines, vmin, event_name=None):
+
+    raw_hbo = raw_hbo.copy().pick(picks="hbo")
+    onsets = raw_hbo.annotations.onset
+    
+    # CRITICAL: Update the Raw object's annotations so the GLM sees unique events
+    ann = raw_hbo.annotations
+    new_desc = []
+
+    for i, desc in enumerate(ann.description):
+        new_desc.append(f"{desc}__trial_{i:03d}")
+
+    ann.description = np.array(new_desc)
+
+
+    # shoudl use user defiuned!!!!
+    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
+    )
+
+
+    # 3. Run GLM & Extract Betas
+    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]
+
+    # ------------------------------------------------------------------
+    # 5. Find unique trial tags (optionally filtered by event)
+    # ------------------------------------------------------------------
+    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 len(trial_tags) == 0:
+        raise ValueError(f"No trials found for event_name={event_name}")
+
+    # ------------------------------------------------------------------
+    # 6. Build beta series (average across FIR delays per trial)
+    # ------------------------------------------------------------------
+    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()
+
+
+
+    # n_channels, n_trials = betas.shape[0], len(onsets)
+    # beta_series = np.zeros((n_channels, n_trials))
+
+    # for t in range(n_trials):
+    #     trial_indices = [i for i, col in enumerate(reg_names) if col.startswith(f"trial_{t:03d}_delay")]
+    #     if trial_indices:
+    #         beta_series[:, t] = np.mean(betas[:, trial_indices], axis=1).flatten()
+
+    # Normalize each channel so they are on the same scale
+    # Without this, everything is connected to everything. Apparently this is a big issue in fNIRS?
+    beta_series = zscore(beta_series, axis=1)
+
+    global_signal = np.mean(beta_series, axis=0) 
+    beta_series_clean = np.zeros_like(beta_series)
+    for i in range(n_channels):
+        slope, _ = np.polyfit(global_signal, beta_series[i, :], 1)
+        beta_series_clean[i, :] = beta_series[i, :] - (slope * global_signal)
+
+    # 4. Correlation & Strict Filtering
+    corr_matrix = np.zeros((n_channels, n_channels))
+    p_matrix = np.ones((n_channels, n_channels))
+
+    for i in range(n_channels):
+        for j in range(i + 1, n_channels):
+            r, p = pearsonr(beta_series_clean[i, :], beta_series_clean[j, :])
+            corr_matrix[i, j] = corr_matrix[j, i] = r
+            p_matrix[i, j] = p_matrix[j, i] = p
+
+    # 5. High-Bar Thresholding
+    reject, _ = multipletests(p_matrix[np.triu_indices(n_channels, k=1)], method='fdr_bh', alpha=0.05)[:2]
+    sig_corr_matrix = np.zeros_like(corr_matrix)
+    triu = np.triu_indices(n_channels, k=1)
+
+    for idx, is_sig in enumerate(reject):
+        r_val = corr_matrix[triu[0][idx], triu[1][idx]]
+        # Only keep the absolute strongest connections
+        if is_sig and abs(r_val) > 0.7: 
+            sig_corr_matrix[triu[0][idx], triu[1][idx]] = r_val
+            sig_corr_matrix[triu[1][idx], triu[0][idx]] = r_val
+
+    # 6. Plot
+    plot_connectivity_circle(
+        sig_corr_matrix, 
+        raw_hbo.ch_names,
+        title="Strictly Filtered Connectivity (TDDR + GSR + Z-Score)",
+        n_lines=None, 
+        vmin=0.7, 
+        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'
+    )

flares_updater.py

@@ -18,11 +18,12 @@ import subprocess
 from datetime import datetime
 
 PLATFORM_NAME = platform.system().lower()
+APP_NAME = "flares"
 
 if PLATFORM_NAME == 'darwin':
-    LOG_FILE = os.path.join(os.path.dirname(sys.executable), "../../../flares_updater.log")
+    LOG_FILE = os.path.join(os.path.dirname(sys.executable), f"../../../{APP_NAME}_updater.log")
 else:
-    LOG_FILE = os.path.join(os.getcwd(), "flares_updater.log")
+    LOG_FILE = os.path.join(os.getcwd(), f"{APP_NAME}_updater.log")
 
 
 def log(msg):
@@ -147,7 +148,7 @@ def copy_update_files_darwin(src_folder, dest_folder, updater_name):
 
 def remove_quarantine(app_path):
     script = f'''
-    do shell script "xattr -d -r com.apple.quarantine {shlex.quote(app_path)}" with administrator privileges with prompt "FLARES needs privileges to finish the update. (1/2)"
+    do shell script "xattr -d -r com.apple.quarantine {shlex.quote(app_path)}" with administrator privileges with prompt "{APP_NAME} needs privileges to finish the update. (1/2)"
     '''
     try:
         subprocess.run(['osascript', '-e', script], check=True)
@@ -162,7 +163,7 @@ def main():
         log(f"[Updater] sys.argv: {sys.argv}")
 
         if len(sys.argv) != 3:
-            log("Invalid arguments. Usage: flares_updater <update_folder> <main_app_executable>")
+            log(f"Invalid arguments. Usage: {APP_NAME}_updater <update_folder> <main_app_executable>")
             sys.exit(1)
 
         update_folder = sys.argv[1]
@@ -215,7 +216,7 @@ def main():
         if PLATFORM_NAME == 'darwin':
             log(f'Attempting to delete {ppparent_dir}')
             delete_path(ppparent_dir)
-            update_folder = os.path.join(sys.argv[1], "flares-darwin")
+            update_folder = os.path.join(sys.argv[1], f"{APP_NAME}-darwin")
             copy_update_files_darwin(update_folder, pppparent_dir, updater_name)
 
         else:

updater.py

@@ -0,0 +1,539 @@
+"""
+Filename: updater.py
+Description: Generic updater file
+
+Author: Tyler de Zeeuw
+License: GPL-3.0
+"""
+
+# Built-in imports
+import os
+import re
+import sys
+import time
+import shlex
+import shutil
+import zipfile
+import traceback
+import subprocess
+
+# External library imports
+import psutil
+import requests
+
+from PySide6.QtWidgets import QMessageBox
+from PySide6.QtCore import QThread, Signal, QObject
+
+
+class UpdateDownloadThread(QThread):
+    """
+    Thread that downloads and extracts an update package and emits a signal on completion or error.
+
+    Args:
+        download_url (str): URL of the update zip file to download.
+        latest_version (str): Version string of the latest update.
+    """
+        
+    update_ready = Signal(str, str)
+    error_occurred = Signal(str)
+
+    def __init__(self, download_url, latest_version, platform_name, app_name):
+        super().__init__()
+        self.download_url = download_url
+        self.latest_version = latest_version
+        self.platform_name = platform_name
+        self.app_name = app_name
+
+    def run(self):
+        try:
+            local_filename = os.path.basename(self.download_url)
+
+            if self.platform_name == 'darwin':
+                tmp_dir = f'/tmp/{self.app_name}tempupdate'
+                os.makedirs(tmp_dir, exist_ok=True)
+                local_path = os.path.join(tmp_dir, local_filename)
+            else:
+                local_path = os.path.join(os.getcwd(), local_filename)
+
+            # Download the file
+            with requests.get(self.download_url, stream=True, timeout=15) as r:
+                r.raise_for_status()
+                with open(local_path, 'wb') as f:
+                    for chunk in r.iter_content(chunk_size=8192):
+                        if chunk:
+                            f.write(chunk)
+
+            # Extract folder name (remove .zip)
+            if self.platform_name == 'darwin':
+                extract_folder = os.path.splitext(local_filename)[0]
+                extract_path = os.path.join(tmp_dir, extract_folder)
+
+            else:
+                extract_folder = os.path.splitext(local_filename)[0]
+                extract_path = os.path.join(os.getcwd(), extract_folder)
+
+            # Create the folder if not exists
+            os.makedirs(extract_path, exist_ok=True)
+
+            # Extract the zip file contents
+            if self.platform_name == 'darwin':
+                subprocess.run(['ditto', '-xk', local_path, extract_path], check=True)
+            else:
+                with zipfile.ZipFile(local_path, 'r') as zip_ref:
+                    zip_ref.extractall(extract_path)
+
+            # Remove the zip once extracted and emit a signal
+            os.remove(local_path)
+            self.update_ready.emit(self.latest_version, extract_path)
+
+        except Exception as e:
+            # Emit a signal signifying failure
+            self.error_occurred.emit(str(e))
+
+
+
+class UpdateCheckThread(QThread):
+    """
+    Thread that checks for updates by querying the API and emits a signal based on the result.
+
+    Signals:
+        download_requested(str, str): Emitted with (download_url, latest_version) when an update is available.
+        no_update_available(): Emitted when no update is found or current version is up to date.
+        error_occurred(str): Emitted with an error message if the update check fails.
+    """
+
+    download_requested = Signal(str, str)
+    no_update_available = Signal()
+    error_occurred = Signal(str)
+
+    def __init__(self, api_url, api_url_sec, current_version, platform_name, app_name):
+        super().__init__()
+        self.api_url = api_url
+        self.api_url_sec = api_url_sec
+        self.current_version = current_version
+        self.platform_name = platform_name
+        self.app_name = app_name
+
+    def run(self):
+        # if not getattr(sys, 'frozen', False):
+        #     self.error_occurred.emit("Application is not frozen (Development mode).")
+        #     return
+        try:
+            latest_version, download_url = self.get_latest_release_for_platform()
+            if not latest_version:
+                self.no_update_available.emit()
+                return
+
+            if not download_url:
+                self.error_occurred.emit(f"No download available for platform '{self.platform_name}'")
+                return
+
+            if self.version_compare(latest_version, self.current_version) > 0:
+                self.download_requested.emit(download_url, latest_version)
+            else:
+                self.no_update_available.emit()
+
+        except Exception as e:
+            self.error_occurred.emit(f"Update check failed: {e}")
+
+    def version_compare(self, v1, v2):
+        def normalize(v): return [int(x) for x in v.split(".")]
+        return (normalize(v1) > normalize(v2)) - (normalize(v1) < normalize(v2))
+
+    def get_latest_release_for_platform(self):
+        urls = [self.api_url, self.api_url_sec]
+        for url in urls:
+            try:
+                    
+                response = requests.get(url, timeout=5)
+                response.raise_for_status()
+                releases = response.json()
+
+                if not releases:
+                    continue
+
+                latest = next((r for r in releases if not r.get("prerelease") and not r.get("draft")), None)
+
+                if not latest:
+                    continue
+                
+                tag = latest["tag_name"].lstrip("v")
+
+                for asset in latest.get("assets", []):
+                    if self.platform_name in asset["name"].lower():
+                        return tag, asset["browser_download_url"]
+
+                return tag, None
+            except (requests.RequestException, ValueError) as e:
+                continue
+        return None, None
+
+
+class LocalPendingUpdateCheckThread(QThread):
+    """
+    Thread that checks for locally pending updates by scanning the download directory and emits a signal accordingly.
+
+    Args:
+        current_version (str): Current application version.
+        platform_suffix (str): Platform-specific suffix to identify update folders.
+    """
+
+    pending_update_found = Signal(str, str)
+    no_pending_update = Signal()
+
+    def __init__(self, current_version, platform_suffix, platform_name, app_name):
+        super().__init__()
+        self.current_version = current_version
+        self.platform_suffix = platform_suffix
+        self.platform_name = platform_name
+        self.app_name = app_name
+
+    def version_compare(self, v1, v2):
+        def normalize(v): return [int(x) for x in v.split(".")]
+        return (normalize(v1) > normalize(v2)) - (normalize(v1) < normalize(v2))
+
+    def run(self):
+        if self.platform_name == 'darwin':
+            cwd = f'/tmp/{self.app_name}tempupdate'
+        else:
+            cwd = os.getcwd()
+
+        pattern = re.compile(r".*-(\d+\.\d+\.\d+)" + re.escape(self.platform_suffix) + r"$")
+        found = False
+
+        try:
+            for item in os.listdir(cwd):
+                folder_path = os.path.join(cwd, item)
+                if os.path.isdir(folder_path) and item.endswith(self.platform_suffix):
+                    match = pattern.match(item)
+                    if match:
+                        folder_version = match.group(1)
+                        if self.version_compare(folder_version, self.current_version) > 0:
+                            self.pending_update_found.emit(folder_version, folder_path)
+                            found = True
+                            break
+        except:
+            pass
+        
+        if not found:
+            self.no_pending_update.emit()
+
+
+
+
+
+class UpdateManager(QObject):
+    """
+    Orchestrates the update process. 
+    Main apps should instantiate this and call check_for_updates().
+    """
+    
+    def __init__(self, main_window, api_url, api_url_sec, current_version, platform_name, platform_suffix, app_name):
+        super().__init__()
+        self.parent = main_window
+        self.api_url = api_url
+        self.api_url_sec = api_url_sec
+        self.current_version = current_version
+        self.platform_name = platform_name
+        self.platform_suffix = platform_suffix
+        self.app_name = app_name
+        
+        self.pending_update_version = None
+        self.pending_update_path = None
+
+
+    def manual_check_for_updates(self):
+        self.local_check_thread = LocalPendingUpdateCheckThread(self.current_version, self.platform_suffix, self.platform_name, self.app_name)
+        self.local_check_thread.pending_update_found.connect(self.on_pending_update_found)
+        self.local_check_thread.no_pending_update.connect(self.on_no_pending_update)
+        self.local_check_thread.start()
+
+    def on_pending_update_found(self, version, folder_path):
+        self.parent.statusBar().showMessage(f"Pending update found: version {version}")
+        self.pending_update_version = version
+        self.pending_update_path = folder_path
+        self.show_pending_update_popup()
+
+    def on_no_pending_update(self):
+        # No pending update found locally, start server check directly
+        self.parent.statusBar().showMessage("No pending local update found. Checking server...")
+        self.start_update_check_thread()
+
+    def show_pending_update_popup(self):
+        msg_box = QMessageBox(self.parent)
+        msg_box.setWindowTitle("Pending Update Found")
+        msg_box.setText(f"A previously downloaded update for {self.app_name.upper()} (version {self.pending_update_version}) is available at:\n{self.pending_update_path}\nWould you like to install it now?")
+        install_now_button = msg_box.addButton("Install Now", QMessageBox.ButtonRole.AcceptRole)
+        install_later_button = msg_box.addButton("Install Later", QMessageBox.ButtonRole.RejectRole)
+        msg_box.exec()
+
+        if msg_box.clickedButton() == install_now_button:
+            self.install_update(self.pending_update_path)
+        else:
+            self.parent.statusBar().showMessage("Pending update available. Install later.")
+            # After user dismisses, still check the server for new updates
+            self.start_update_check_thread()
+
+    def start_update_check_thread(self):
+        self.check_thread = UpdateCheckThread(self.api_url, self.api_url_sec, self.current_version, self.platform_name, self.app_name)
+        self.check_thread.download_requested.connect(self.on_server_update_requested)
+        self.check_thread.no_update_available.connect(self.on_server_no_update)
+        self.check_thread.error_occurred.connect(self.on_error)
+        self.check_thread.start()
+
+    def on_server_no_update(self):
+        self.parent.statusBar().showMessage("No new updates found on server.", 5000)
+
+    def on_server_update_requested(self, download_url, latest_version):
+        if self.pending_update_version:
+            cmp = self.version_compare(latest_version, self.pending_update_version)
+            if cmp > 0:
+                # Server version is newer than pending update
+                self.parent.statusBar().showMessage(f"Newer version {latest_version} available on server. Removing old pending update...")
+                try:
+                    shutil.rmtree(self.pending_update_path)
+                    self.parent.statusBar().showMessage(f"Deleted old update folder: {self.pending_update_path}")
+                except Exception as e:
+                    self.parent.statusBar().showMessage(f"Failed to delete old update folder: {e}")
+                    
+                # Clear pending update info so new download proceeds
+                self.pending_update_version = None
+                self.pending_update_path = None
+
+                # Download the new update
+                self.download_update(download_url, latest_version)
+            elif cmp == 0:
+                # Versions equal, no download needed
+                self.parent.statusBar().showMessage(f"Pending update version {self.pending_update_version} is already latest. No download needed.")
+            else:
+                # Server version older than pending? Unlikely but just keep pending update
+                self.parent.statusBar().showMessage(f"Pending update version {self.pending_update_version} is newer than server version. No action.")
+        else:
+            # No pending update, just download
+            self.download_update(download_url, latest_version)
+
+    def download_update(self, download_url, latest_version):
+        self.parent.statusBar().showMessage("Downloading update...")
+        self.download_thread = UpdateDownloadThread(download_url, latest_version, self.platform_name, self.app_name)
+        self.download_thread.update_ready.connect(self.on_update_ready)
+        self.download_thread.error_occurred.connect(self.on_error)
+        self.download_thread.start()
+
+    def on_update_ready(self, latest_version, extract_folder):
+        self.parent.statusBar().showMessage("Update downloaded and extracted.")
+
+        msg_box = QMessageBox(self.parent)
+        msg_box.setWindowTitle("Update Ready")
+        msg_box.setText(f"Version {latest_version} has been downloaded and extracted to:\n{extract_folder}\nWould you like to install it now?")
+        install_now_button = msg_box.addButton("Install Now", QMessageBox.ButtonRole.AcceptRole)
+        install_later_button = msg_box.addButton("Install Later", QMessageBox.ButtonRole.RejectRole)
+
+        msg_box.exec()
+
+        if msg_box.clickedButton() == install_now_button:
+            self.install_update(extract_folder)
+        else:
+            self.parent.statusBar().showMessage("Update ready. Install later.")
+
+
+    def install_update(self, extract_folder):
+        # Path to updater executable
+
+        if self.platform_name == 'windows':
+            updater_path = os.path.join(os.getcwd(), f"{self.app_name}_updater.exe")
+        elif self.platform_name == 'darwin':
+            if getattr(sys, 'frozen', False):
+                updater_path = os.path.join(os.path.dirname(sys.executable), f"../../../{self.app_name}_updater.app")
+            else:
+                updater_path = os.path.join(os.getcwd(), f"../{self.app_name}_updater.app")
+
+        elif self.platform_name == 'linux':
+            updater_path = os.path.join(os.getcwd(), f"{self.app_name}_updater")
+        else:
+            updater_path = os.getcwd()
+
+        if not os.path.exists(updater_path):
+            QMessageBox.critical(self.parent, "Error", f"Updater not found at:\n{updater_path}. The absolute path was {os.path.abspath(updater_path)}")
+            return
+
+        # Launch updater with extracted folder path as argument
+        try:
+            # Pass current app's executable path for updater to relaunch
+            main_app_executable = os.path.abspath(sys.argv[0])
+
+            print(f'Launching updater with: "{updater_path}" "{extract_folder}" "{main_app_executable}"')
+
+            if self.platform_name == 'darwin':
+                subprocess.Popen(['open', updater_path, '--args', extract_folder, main_app_executable])
+            else:
+                subprocess.Popen([updater_path, f'{extract_folder}', f'{main_app_executable}'], cwd=os.path.dirname(updater_path))
+
+            # Close the current app so updater can replace files
+            sys.exit(0)
+
+        except Exception as e:
+            QMessageBox.critical(self.parent, "Error", f"[Updater Launch Failed]\n{str(e)}\n{traceback.format_exc()}")
+
+    def on_error(self, message):
+        # print(f"Error: {message}")
+        self.parent.statusBar().showMessage(f"Error occurred during update process. {message}")
+
+    def version_compare(self, v1, v2):
+        def normalize(v): return [int(x) for x in v.split(".")]
+        return (normalize(v1) > normalize(v2)) - (normalize(v1) < normalize(v2))
+
+
+def wait_for_process_to_exit(process_name, timeout=10):
+    """
+    Waits for a process with the specified name to exit within a timeout period.
+
+    Args:
+        process_name (str): Name (or part of the name) of the process to wait for.
+        timeout (int, optional): Maximum time to wait in seconds. Defaults to 10.
+
+    Returns:
+        bool: True if the process exited before the timeout, False otherwise.
+    """
+
+    print(f"Waiting for {process_name} to exit...")
+    deadline = time.time() + timeout
+    while time.time() < deadline:
+        still_running = False
+        for proc in psutil.process_iter(['name']):
+            try:
+                if proc.info['name'] and process_name.lower() in proc.info['name'].lower():
+                    still_running = True
+                    print(f"Still running: {proc.info['name']} (PID: {proc.pid})")
+                    break
+            except (psutil.NoSuchProcess, psutil.AccessDenied):
+                continue
+        if not still_running:
+            print(f"{process_name} has exited.")
+            return True
+        time.sleep(0.5)
+    print(f"{process_name} did not exit in time.")
+    return False
+
+
+def finish_update_if_needed(platform_name, app_name):
+    """
+    Completes a pending application update if '--finish-update' is present in the command-line arguments.
+    """
+
+    if "--finish-update" in sys.argv:
+        print("Finishing update...")
+
+        if platform_name == 'darwin':
+            app_dir = f'/tmp/{app_name}tempupdate'
+        else:
+            app_dir = os.getcwd()
+
+        # 1. Find update folder
+        update_folder = None
+        for entry in os.listdir(app_dir):
+            entry_path = os.path.join(app_dir, entry)
+            if os.path.isdir(entry_path) and entry.startswith(f"{app_name}-") and entry.endswith("-" + platform_name):
+                update_folder = os.path.join(app_dir, entry)
+                break
+
+        if update_folder is None:
+            print("No update folder found. Skipping update steps.")
+            return
+        
+        if platform_name == 'darwin':
+            update_folder = os.path.join(update_folder, f"{app_name}-darwin")
+
+        # 2. Wait for updater to exit
+        print(f"Waiting for {app_name}_updater to exit...")
+        for proc in psutil.process_iter(['pid', 'name']):
+            if proc.info['name'] and f"{app_name}_updater" in proc.info['name'].lower():
+                try:
+                    proc.wait(timeout=5)
+                except psutil.TimeoutExpired:
+                    print(f"Force killing lingering {app_name}_updater")
+                    proc.kill()
+
+        # 3. Replace the updater
+        if platform_name == 'windows':
+            new_updater = os.path.join(update_folder, f"{app_name}_updater.exe")
+            dest_updater = os.path.join(app_dir, f"{app_name}_updater.exe")
+
+        elif platform_name == 'darwin':
+            new_updater = os.path.join(update_folder, f"{app_name}_updater.app")
+            dest_updater = os.path.abspath(os.path.join(sys.executable, f"../../../../{app_name}_updater.app"))
+
+        elif platform_name == 'linux':
+            new_updater = os.path.join(update_folder, f"{app_name}_updater")
+            dest_updater = os.path.join(app_dir, f"{app_name}_updater")
+
+        else:
+            print("Unknown Platform")
+            new_updater = os.getcwd()
+            dest_updater = os.getcwd()
+
+        print(f"New updater is {new_updater}")
+        print(f"Dest updater is {dest_updater}")
+
+        print("Writable?", os.access(dest_updater, os.W_OK))
+        print("Executable path:", sys.executable)
+        print("Trying to copy:", new_updater, "->", dest_updater)
+
+        if os.path.exists(new_updater):
+            try:
+                if os.path.exists(dest_updater):
+                    if platform_name == 'darwin':
+                        try:
+                            if os.path.isdir(dest_updater):
+                                shutil.rmtree(dest_updater)
+                                print(f"Deleted directory: {dest_updater}")
+                            else:
+                                os.remove(dest_updater)
+                                print(f"Deleted file: {dest_updater}")
+                        except Exception as e:
+                            print(f"Error deleting {dest_updater}: {e}")
+                    else:
+                        os.remove(dest_updater)
+
+                if platform_name == 'darwin':
+                    wait_for_process_to_exit(f"{app_name}_updater", timeout=10)
+                    subprocess.check_call(["ditto", new_updater, dest_updater])
+                else:
+                    shutil.copy2(new_updater, dest_updater)
+
+                if platform_name in ('linux', 'darwin'):
+                    os.chmod(dest_updater, 0o755)
+
+                if platform_name == 'darwin':
+                    remove_quarantine(dest_updater, app_name)
+
+                print(f"{app_name}_updater replaced.")
+            except Exception as e:
+                print(f"Failed to replace {app_name}_updater: {e}")
+
+        # 4. Delete the update folder
+        try:
+            if platform_name == 'darwin':
+                shutil.rmtree(app_dir)
+            else:
+                shutil.rmtree(update_folder)
+        except Exception as e:
+            print(f"Failed to delete update folder: {e}")
+
+        QMessageBox.information(None, "Update Complete", "The application has been successfully updated.")
+        sys.argv.remove("--finish-update")
+
+
+def remove_quarantine(app_path, app_name):
+    """
+    Removes the macOS quarantine attribute from the specified application path.
+    """
+
+    script = f'''
+    do shell script "xattr -d -r com.apple.quarantine {shlex.quote(app_path)}" with administrator privileges with prompt "{app_name.upper()} needs privileges to finish the update. (2/2)"
+    '''
+    try:
+        subprocess.run(['osascript', '-e', script], check=True)
+        print("✅ Quarantine attribute removed.")
+    except subprocess.CalledProcessError as e:
+        print("❌ Failed to remove quarantine attribute.")
+        print(e)