more boris implementation

changelog.md

@@ -1,6 +1,22 @@
+# Version 1.1.1
+
+- Fixed the number of rectangles in the progress bar to 19
+- Fixed a crash when attempting to load a brain image on Windows
+- Removed hardcoded event annotations. Fixes [Issue 16](https://git.research.dezeeuw.ca/tyler/flares/issues/16)
+
+
 # Version 1.1.0
 
-- Changelog details coming soon
+- Revamped the Analysis window
+- 4 Options of Participant, Participant Brain, Inter-Group, and Cross Group Brain are available.
+- Customization is present to query different participants, images, events, brains, etc.
+- Removed preprocessing options and reorganized their order to correlate with the actual order.
+- Most preprocessing options removed will be coming back soon
+- 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)
+
 
 # Version 1.0.1
 

flares.py

@@ -48,9 +48,14 @@ from statsmodels.stats.multitest import multipletests
 from scipy import stats
 from scipy.spatial.distance import cdist
 
+# Backen visualization needed to be defined for pyinstaller
+import pyvistaqt # type: ignore
+# import vtkmodules.util.data_model
+# import vtkmodules.util.execution_model
+
 # External library imports for mne
 from mne import (
-    EvokedArray, SourceEstimate, Info, Epochs, Label,
+    EvokedArray, SourceEstimate, Info, Epochs, Label, Annotations,
     events_from_annotations, read_source_spaces,
     stc_near_sensors, pick_types, grand_average, get_config, set_config, read_labels_from_annot
 )  # type: ignore
@@ -125,6 +130,10 @@ TDDR: bool
 
 ENHANCE_NEGATIVE_CORRELATION: bool
 
+SHORT_CHANNEL: bool
+
+REMOVE_EVENTS: list
+
 VERBOSITY = True
 
 # FIXME: Shouldn't need each ordering - just order it before checking
@@ -171,6 +180,8 @@ REQUIRED_KEYS: dict[str, Any] = {
     "PSP_TIME_WINDOW": int,
     "PSP_THRESHOLD": float,
     
+    "SHORT_CHANNEL": bool,
+    "REMOVE_EVENTS": list,
     # "REJECT_PAIRS": bool,
     # "FORCE_DROP_ANNOTATIONS": list,
     # "FILTER_LOW_PASS": float,
@@ -1100,7 +1111,7 @@ def epochs_calculations(raw_haemo, events, event_dict):
     evokeds3 = []
     colors = []
     conditions = list(epochs.event_id.keys())
-    cmap = plt.cm.get_cmap("tab10", len(conditions))
+    cmap = plt.get_cmap("tab10", len(conditions))
 
     for idx, cond in enumerate(conditions):
         evoked = epochs[cond].average(picks="hbo")
@@ -1120,16 +1131,20 @@ def epochs_calculations(raw_haemo, events, event_dict):
     fig.legend(lines, conditions, loc="lower right")
     fig_epochs.append(("evoked_topo", help))  # Store with a unique name
 
-    # Evoked response for specific condition ("Reach")
-    evoked_stim1 = epochs['Reach'].average()
+    unique_annotations = set(raw_haemo.annotations.description)
 
-    fig_evoked_hbo = evoked_stim1.copy().pick(picks='hbo').plot(time_unit='s', show=False)
-    fig_evoked_hbr = evoked_stim1.copy().pick(picks='hbr').plot(time_unit='s', show=False)
-    
-    fig_epochs.append(("fig_evoked_hbo", fig_evoked_hbo))  # Store with a unique name
-    fig_epochs.append(("fig_evoked_hbr", fig_evoked_hbr))  # Store with a unique name
+    for cond in unique_annotations:
 
-    print("Evoked HbO peak amplitude:", evoked_stim1.copy().pick(picks='hbo').data.max())
+        # Evoked response for specific condition ("Activity")
+        evoked_stim1 = epochs[cond].average()
+
+        fig_evoked_hbo = evoked_stim1.copy().pick(picks='hbo').plot(time_unit='s', show=False)
+        fig_evoked_hbr = evoked_stim1.copy().pick(picks='hbr').plot(time_unit='s', show=False)
+        
+        fig_epochs.append((f"fig_evoked_hbo_{cond}", fig_evoked_hbo))  # Store with a unique name
+        fig_epochs.append((f"fig_evoked_hbr_{cond}", fig_evoked_hbr))  # Store with a unique name
+
+        print("Evoked HbO peak amplitude:", evoked_stim1.copy().pick(picks='hbo').data.max())
 
     evokeds = {}
     for condition in epochs2.event_id:
@@ -1200,26 +1215,36 @@ def epochs_calculations(raw_haemo, events, event_dict):
 
 
 
-
-
 def make_design_matrix(raw_haemo, short_chans):
 
     raw_haemo.resample(1, npad="auto")
-    short_chans.resample(1)
     raw_haemo._data = raw_haemo._data * 1e6
     # 2) Create design matrix
-    design_matrix = make_first_level_design_matrix(
-        raw=raw_haemo,
-        hrf_model='fir',
-        stim_dur=0.5,
-        fir_delays=range(15),
-        drift_model='cosine',
-        high_pass=0.01,
-        oversampling=1,
-        min_onset=-125,
-        add_regs=short_chans.get_data().T,
-        add_reg_names=short_chans.ch_names
-    )
+    if SHORT_CHANNEL:
+        short_chans.resample(1)
+        design_matrix = make_first_level_design_matrix(
+            raw=raw_haemo,
+            hrf_model='fir',
+            stim_dur=0.5,
+            fir_delays=range(15),
+            drift_model='cosine',
+            high_pass=0.01,
+            oversampling=1,
+            min_onset=-125,
+            add_regs=short_chans.get_data().T,
+            add_reg_names=short_chans.ch_names
+        )
+    else:
+        design_matrix = make_first_level_design_matrix(
+            raw=raw_haemo,
+            hrf_model='fir',
+            stim_dur=0.5,
+            fir_delays=range(15),
+            drift_model='cosine',
+            high_pass=0.01,
+            oversampling=1,
+            min_onset=-125,
+        )
 
     print(design_matrix.head())
     print(design_matrix.columns)
@@ -1232,10 +1257,6 @@ def make_design_matrix(raw_haemo, short_chans):
 
 
 
-
-
-
-
 def generate_montage_locations():
     """Get standard MNI montage locations in dataframe.
 
@@ -1452,9 +1473,15 @@ def resource_path(relative_path):
 
 def fold_channels(raw: BaseRaw) -> None:
 
+    # if getattr(sys, 'frozen', False):
+    path = os.path.expanduser("~") + "/mne_data/fOLD/fOLD-public-master/Supplementary"
+    logger.info(path)
+    set_config('MNE_NIRS_FOLD_PATH', resource_path(path)) # type: ignore
 
-    # Locate the fOLD excel files
-    set_config('MNE_NIRS_FOLD_PATH', resource_path("../../mne_data/fOLD/fOLD-public-master/Supplementary")) # type: ignore
+    # # Locate the fOLD excel files
+    # else:
+    #     logger.info("yabba")
+    #     set_config('MNE_NIRS_FOLD_PATH', resource_path("../../mne_data/fOLD/fOLD-public-master/Supplementary")) # type: ignore
 
     output = None
 
@@ -1516,8 +1543,8 @@ def fold_channels(raw: BaseRaw) -> None:
             "Brain_Outside",
         ]
 
-        cmap1 = plt.cm.get_cmap('tab20')  # First 20 colors
-        cmap2 = plt.cm.get_cmap('tab20b')  # Next 20 colors
+        cmap1 = plt.get_cmap('tab20')  # First 20 colors
+        cmap2 = plt.get_cmap('tab20b')  # Next 20 colors
 
         # Combine the colors from both colormaps
         colors = [cmap1(i) for i in range(20)] + [cmap2(i) for i in range(20)]  # Total 40 colors
@@ -1593,6 +1620,7 @@ def fold_channels(raw: BaseRaw) -> None:
     for ax in axes[len(hbo_channel_names):]:
         ax.axis('off')
     
+    plt.show()
     return fig, legend_fig
 
                 
@@ -1600,153 +1628,158 @@ def fold_channels(raw: BaseRaw) -> None:
 
 def individual_significance(raw_haemo, glm_est):
 
+    fig_individual_significances = []  # List to store figures
+
     # TODO: BAD!
     cha = glm_est.to_dataframe()
 
-    ch_summary = cha.query("Condition.str.startswith('Reach_delay_') and Chroma == 'hbo'", engine='python')
+    unique_annotations = set(raw_haemo.annotations.description)
 
-    print(ch_summary.head())
+    for cond in unique_annotations:
 
-    channel_averages = ch_summary.groupby('ch_name')['theta'].mean().reset_index()
-    print(channel_averages.head())
+        ch_summary = cha.query(f"Condition.str.startswith('{cond}_delay_') and Chroma == 'hbo'", engine='python')
+
+        print(ch_summary.head())
+
+        channel_averages = ch_summary.groupby('ch_name')['theta'].mean().reset_index()
+        print(channel_averages.head())
 
 
-    reach_ch_summary = ch_summary.query(
-        "Chroma == 'hbo' and Condition.str.startswith('Reach_delay_')", engine='python'
-    )
+        activity_ch_summary = ch_summary.query(
+            f"Chroma == 'hbo' and Condition.str.startswith('{cond}_delay_')", engine='python'
+        )
 
-    # Function to correct p-values per channel
-    def fdr_correct_per_channel(df):
-        df = df.copy()
-        df['pval_fdr'] = multipletests(df['p_value'], method='fdr_bh')[1]
-        return df
+        # Function to correct p-values per channel
+        def fdr_correct_per_channel(df):
+            df = df.copy()
+            df['pval_fdr'] = multipletests(df['p_value'], method='fdr_bh')[1]
+            return df
 
-    # Apply FDR correction grouped by channel
-    corrected = reach_ch_summary.groupby("ch_name", group_keys=False).apply(fdr_correct_per_channel)
+        # Apply FDR correction grouped by channel
+        corrected = activity_ch_summary.groupby("ch_name", group_keys=False).apply(fdr_correct_per_channel)
 
-    # Determine which channels are significant across any delay
-    sig_channels = (
-        corrected.groupby('ch_name')
-        .apply(lambda df: (df['pval_fdr'] < 0.05).any())
-        .reset_index(name='significant')
-    )
+        # Determine which channels are significant across any delay
+        sig_channels = (
+            corrected.groupby('ch_name')
+            .apply(lambda df: (df['pval_fdr'] < 0.05).any())
+            .reset_index(name='significant')
+        )
 
-    # Merge with mean theta (optional for plotting)
-    mean_theta = reach_ch_summary.groupby('ch_name')['theta'].mean().reset_index()
-    sig_channels = sig_channels.merge(mean_theta, on='ch_name')
-    print(sig_channels)
+        # Merge with mean theta (optional for plotting)
+        mean_theta = activity_ch_summary.groupby('ch_name')['theta'].mean().reset_index()
+        sig_channels = sig_channels.merge(mean_theta, on='ch_name')
+        print(sig_channels)
 
 
-    # For example, take the minimum corrected p-value per channel
-    summary_pvals = corrected.groupby('ch_name')['pval_fdr'].min().reset_index()
-    print(summary_pvals)
+        # For example, take the minimum corrected p-value per channel
+        summary_pvals = corrected.groupby('ch_name')['pval_fdr'].min().reset_index()
+        print(summary_pvals)
 
 
-    def parse_ch_name(ch_name):
-        # Extract numbers after S and D in names like 'S10_D5 hbo'
-        match = re.match(r'S(\d+)_D(\d+)', ch_name)
-        if match:
-            return int(match.group(1)), int(match.group(2))
-        else:
-            return None, None
+        def parse_ch_name(ch_name):
+            # Extract numbers after S and D in names like 'S10_D5 hbo'
+            match = re.match(r'S(\d+)_D(\d+)', ch_name)
+            if match:
+                return int(match.group(1)), int(match.group(2))
+            else:
+                return None, None
 
 
-    min_pvals = corrected.groupby('ch_name')['pval_fdr'].min().reset_index()
+        min_pvals = corrected.groupby('ch_name')['pval_fdr'].min().reset_index()
 
-    # Merge the real p-values into sig_channels / avg_df
-    avg_df = sig_channels.merge(min_pvals, on='ch_name')
+        # Merge the real p-values into sig_channels / avg_df
+        avg_df = sig_channels.merge(min_pvals, on='ch_name')
 
-    # Rename columns for consistency
-    avg_df = avg_df.rename(columns={'theta': 't_or_theta', 'pval_fdr': 'p_value'})
+        # Rename columns for consistency
+        avg_df = avg_df.rename(columns={'theta': 't_or_theta', 'pval_fdr': 'p_value'})
 
-    # Add Source and Detector columns again
-    avg_df['Source'], avg_df['Detector'] = zip(*avg_df['ch_name'].map(parse_ch_name))
+        # Add Source and Detector columns again
+        avg_df['Source'], avg_df['Detector'] = zip(*avg_df['ch_name'].map(parse_ch_name))
 
-    # Keep relevant columns
-    avg_df = avg_df[['Source', 'Detector', 't_or_theta', 'p_value']].dropna()
+        # Keep relevant columns
+        avg_df = avg_df[['Source', 'Detector', 't_or_theta', 'p_value']].dropna()
 
-    ABS_SIGNIFICANCE_THETA_VALUE = 1
-    ABS_SIGNIFICANCE_T_VALUE = 1
-    P_THRESHOLD = 0.05
-    SOURCE_DETECTOR_SEPARATOR = "_"
-    Reach = "Reach"
+        ABS_SIGNIFICANCE_THETA_VALUE = 1
+        ABS_SIGNIFICANCE_T_VALUE = 1
+        P_THRESHOLD = 0.05
+        SOURCE_DETECTOR_SEPARATOR = "_"
 
+        t_or_theta = 'theta'
+        for _, row in avg_df.iterrows(): # type: ignore
+            print(f"Source {row['Source']} <-> Detector {row['Detector']}: "
+                f"Avg {t_or_theta}-value = {row['t_or_theta']:.3f}, Avg p-value = {row['p_value']:.3f}")
 
-    t_or_theta = 'theta'
-    for _, row in avg_df.iterrows(): # type: ignore
-        print(f"Source {row['Source']} <-> Detector {row['Detector']}: "
-            f"Avg {t_or_theta}-value = {row['t_or_theta']:.3f}, Avg p-value = {row['p_value']:.3f}")
+        # Extract the cource and detector positions from raw
+        src_pos: dict[int, tuple[float, float]] = {}
+        det_pos: dict[int, tuple[float, float]] = {}
+        for ch in getattr(raw_haemo, "info")["chs"]:
+            ch_name = ch['ch_name']
+            if not ch_name or not ch['loc'].any():
+                continue
+            parts = ch_name.split()[0]
+            src_str, det_str = parts.split(SOURCE_DETECTOR_SEPARATOR)
+            src_num = int(src_str[1:])
+            det_num = int(det_str[1:])
+            src_pos[src_num] = ch['loc'][3:5]
+            det_pos[det_num] = ch['loc'][6:8]
 
-    # Extract the cource and detector positions from raw
-    src_pos: dict[int, tuple[float, float]] = {}
-    det_pos: dict[int, tuple[float, float]] = {}
-    for ch in getattr(raw_haemo, "info")["chs"]:
-        ch_name = ch['ch_name']
-        if not ch_name or not ch['loc'].any():
-            continue
-        parts = ch_name.split()[0]
-        src_str, det_str = parts.split(SOURCE_DETECTOR_SEPARATOR)
-        src_num = int(src_str[1:])
-        det_num = int(det_str[1:])
-        src_pos[src_num] = ch['loc'][3:5]
-        det_pos[det_num] = ch['loc'][6:8]
+        # Set up the plot
+        fig, ax = plt.subplots(figsize=(8, 6)) # type: ignore
 
-    # Set up the plot
-    fig, ax = plt.subplots(figsize=(8, 6)) # type: ignore
+        # Plot the sources
+        for pos in src_pos.values():
+            ax.scatter(pos[0], pos[1], s=120, c='k', marker='o', edgecolors='white', linewidths=1, zorder=3) # type: ignore
 
-    # Plot the sources
-    for pos in src_pos.values():
-        ax.scatter(pos[0], pos[1], s=120, c='k', marker='o', edgecolors='white', linewidths=1, zorder=3) # type: ignore
+        # Plot the detectors
+        for pos in det_pos.values():
+            ax.scatter(pos[0], pos[1], s=120, c='k', marker='s', edgecolors='white', linewidths=1, zorder=3) # type: ignore
 
-    # Plot the detectors
-    for pos in det_pos.values():
-        ax.scatter(pos[0], pos[1], s=120, c='k', marker='s', edgecolors='white', linewidths=1, zorder=3) # type: ignore
+        # Ensure that the colors stay within the boundaries even if they are over or under the max/min values
+        if t_or_theta == 't':
+            norm = mcolors.Normalize(vmin=-ABS_SIGNIFICANCE_T_VALUE, vmax=ABS_SIGNIFICANCE_T_VALUE)
+        elif t_or_theta == 'theta':
+            norm = mcolors.Normalize(vmin=-ABS_SIGNIFICANCE_THETA_VALUE, vmax=ABS_SIGNIFICANCE_THETA_VALUE)
 
-    # Ensure that the colors stay within the boundaries even if they are over or under the max/min values
-    if t_or_theta == 't':
-        norm = mcolors.Normalize(vmin=-ABS_SIGNIFICANCE_T_VALUE, vmax=ABS_SIGNIFICANCE_T_VALUE)
-    elif t_or_theta == 'theta':
-        norm = mcolors.Normalize(vmin=-ABS_SIGNIFICANCE_THETA_VALUE, vmax=ABS_SIGNIFICANCE_THETA_VALUE)
+        cmap: mcolors.Colormap = plt.get_cmap('seismic')
 
-    cmap: mcolors.Colormap = plt.get_cmap('seismic')
+        # Plot connections with avg t-values
+        for row in avg_df.itertuples():
+            src: int = cast(int, row.Source) # type: ignore
+            det: int = cast(int, row.Detector)  # type: ignore
+            tval: float = cast(float, row.t_or_theta) # type: ignore
+            pval: float = cast(float, row.p_value) # type: ignore
+            
 
-    # Plot connections with avg t-values
-    for row in avg_df.itertuples():
-        src: int = cast(int, row.Source) # type: ignore
-        det: int = cast(int, row.Detector)  # type: ignore
-        tval: float = cast(float, row.t_or_theta) # type: ignore
-        pval: float = cast(float, row.p_value) # type: ignore
+            if src in src_pos and det in det_pos:
+                x = [src_pos[src][0], det_pos[det][0]]
+                y = [src_pos[src][1], det_pos[det][1]]
+                style = '-' if pval <= P_THRESHOLD else '--'
+                ax.plot(x, y, linestyle=style, color=cmap(norm(tval)), linewidth=4, alpha=0.9, zorder=2) # type: ignore
+
+        # Format the Colorbar
+        sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+        sm.set_array([])
+        cbar = plt.colorbar(sm, ax=ax, shrink=0.85) # type: ignore
+        cbar.set_label(f'Average {cond} {t_or_theta} value (hbo)', fontsize=11) # type: ignore
+
+        # Formatting the subplots
+        ax.set_aspect('equal')
+        ax.set_title(f"Average {t_or_theta} values for {cond} (HbO)", fontsize=14) # type: ignore
+        ax.set_xlabel('X position (m)', fontsize=11) # type: ignore
+        ax.set_ylabel('Y position (m)', fontsize=11) # type: ignore
+        ax.grid(True, alpha=0.3) # type: ignore
+
+        # Set axis limits to be 1cm more than the optode positions
+        all_x = [pos[0] for pos in src_pos.values()] + [pos[0] for pos in det_pos.values()]
+        all_y = [pos[1] for pos in src_pos.values()] + [pos[1] for pos in det_pos.values()]
+        ax.set_xlim(min(all_x)-0.01, max(all_x)+0.01)
+        ax.set_ylim(min(all_y)-0.01, max(all_y)+0.01)
+
+        fig.tight_layout()
         
+        fig_individual_significances.append((f"Condition {cond}", fig))
 
-        if src in src_pos and det in det_pos:
-            x = [src_pos[src][0], det_pos[det][0]]
-            y = [src_pos[src][1], det_pos[det][1]]
-            style = '-' if pval <= P_THRESHOLD else '--'
-            ax.plot(x, y, linestyle=style, color=cmap(norm(tval)), linewidth=4, alpha=0.9, zorder=2) # type: ignore
-
-    # Format the Colorbar
-    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
-    sm.set_array([])
-    cbar = plt.colorbar(sm, ax=ax, shrink=0.85) # type: ignore
-    cbar.set_label(f'Average {Reach} {t_or_theta} value (hbo)', fontsize=11) # type: ignore
-
-    # Formatting the subplots
-    ax.set_aspect('equal')
-    ax.set_title(f"Average {t_or_theta} values for {Reach} (HbO)", fontsize=14) # type: ignore
-    ax.set_xlabel('X position (m)', fontsize=11) # type: ignore
-    ax.set_ylabel('Y position (m)', fontsize=11) # type: ignore
-    ax.grid(True, alpha=0.3) # type: ignore
-
-    # Set axis limits to be 1cm more than the optode positions
-    all_x = [pos[0] for pos in src_pos.values()] + [pos[0] for pos in det_pos.values()]
-    all_y = [pos[1] for pos in src_pos.values()] + [pos[1] for pos in det_pos.values()]
-    ax.set_xlim(min(all_x)-0.01, max(all_x)+0.01)
-    ax.set_ylim(min(all_y)-0.01, max(all_y)+0.01)
-
-    fig.tight_layout()
-    
-
-    return fig
+    return fig_individual_significances
 
 # TODO: Hardcoded
 def group_significance(
@@ -1761,7 +1794,7 @@ def group_significance(
     Args:
         raw_haemo: Raw haemoglobin MNE object (used for optode positions)
         all_cha: DataFrame with columns including 'ID', 'Condition', 'p_value', 'theta', 'df', 'ch_name', 'Chroma'
-        condition: condition prefix, e.g., 'Reach'
+        condition: condition prefix, e.g., 'Activity'
         correction: p-value correction method ('fdr_bh' or 'bonferroni')
 
     Returns:
@@ -1919,7 +1952,12 @@ def group_significance(
 
 def plot_glm_results(file_path, raw_haemo, glm_est, design_matrix):
     
+    fig_glms = []  # List to store figures
+
     dm = design_matrix.copy()
+    logger.info(design_matrix.shape)
+    logger.info(design_matrix.columns)
+    logger.info(design_matrix.head())
 
     rois = dict(AllChannels=range(len(raw_haemo.ch_names)))
     conditions = design_matrix.columns
@@ -1928,72 +1966,83 @@ def plot_glm_results(file_path, raw_haemo, glm_est, design_matrix):
     df_individual["ID"] = file_path
     # df_individual["theta"] = [t * 1.0e6 for t in df_individual["theta"]]
 
-    condition_of_interest="Reach"
+    first_onset_for_cond = {}
+    for onset, desc in zip(raw_haemo.annotations.onset, raw_haemo.annotations.description):
+        if desc not in first_onset_for_cond:
+            first_onset_for_cond[desc] = onset
 
-    # Filter for the condition of interest and FIR delays
-    df_individual["isCondition"] = [condition_of_interest in n for n in df_individual["Condition"]]
-    df_individual["isDelay"] = ["delay" in n for n in df_individual["Condition"]]
-    df_individual = df_individual.query("isDelay and isCondition")
-    
-    # Remove other conditions from design matrix
-    dm_condition_cols = [col for col in dm.columns if condition_of_interest in col]
-    dm_cond = dm[dm_condition_cols]
+    # Get unique condition names from annotations (descriptions)
+    unique_annotations = set(raw_haemo.annotations.description)
+
+    for cond in unique_annotations:
+        logger.info(cond)
+        df_individual_filtered = df_individual.copy()
+
+        # Filter for the condition of interest and FIR delays
+        df_individual_filtered["isCondition"] = [cond in n for n in df_individual_filtered["Condition"]]
+        df_individual_filtered["isDelay"] = ["delay" in n for n in df_individual_filtered["Condition"]]
+        df_individual_filtered = df_individual_filtered.query("isDelay and isCondition")
         
-    # Add a numeric delay column
-    def extract_delay_number(condition_str):
-        # Extracts the number at the end of a string like 'Reach_delay_5'
-        return int(condition_str.split("_")[-1])
+        # Remove other conditions from design matrix
+        dm_condition_cols = [col for col in dm.columns if cond in col]
+        dm_cond = dm[dm_condition_cols]
+            
+        # Add a numeric delay column
+        def extract_delay_number(condition_str):
+            # Extracts the number at the end of a string like 'Activity_delay_5'
+            return int(condition_str.split("_")[-1])
 
-    df_individual["DelayNum"] = df_individual["Condition"].apply(extract_delay_number)
+        df_individual_filtered["DelayNum"] = df_individual_filtered["Condition"].apply(extract_delay_number)
 
-    # Now separate and sort using numeric delay
-    df_hbo = df_individual[df_individual["Chroma"] == "hbo"].sort_values("DelayNum")
-    df_hbr = df_individual[df_individual["Chroma"] == "hbr"].sort_values("DelayNum")
+        # Now separate and sort using numeric delay
+        df_hbo = df_individual_filtered[df_individual_filtered["Chroma"] == "hbo"].sort_values("DelayNum")
+        df_hbr = df_individual_filtered[df_individual_filtered["Chroma"] == "hbr"].sort_values("DelayNum")
 
-    vals_hbo = df_hbo["theta"].values
-    vals_hbr = df_hbr["theta"].values
-    
-    # Create the plot
-    fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(19, 10))
-    
-    # Scale design matrix components using numpy arrays instead of pandas operations
-    dm_cond_values = dm_cond.values
-    dm_cond_scaled_hbo = dm_cond_values * vals_hbo.reshape(1, -1)
-    dm_cond_scaled_hbr = dm_cond_values * vals_hbr.reshape(1, -1)
-    
-    # Create time axis relative to stimulus onset
-    time = dm_cond.index - np.ceil(raw_haemo.annotations.onset[1])
-    
-    # Plot
-    axes[0].plot(time, dm_cond_values)
-    axes[1].plot(time, dm_cond_scaled_hbo)
-    axes[2].plot(time, np.sum(dm_cond_scaled_hbo, axis=1), 'r')
-    axes[2].plot(time, np.sum(dm_cond_scaled_hbr, axis=1), 'b')
-    
-    # Format plots
-    for ax in range(3):
-        axes[ax].set_xlim(-5, 25)
-        axes[ax].set_xlabel("Time (s)")
-    axes[0].set_ylim(-0.2, 1.2)
-    axes[1].set_ylim(-0.5, 1)
-    axes[2].set_ylim(-0.5, 1)
-    axes[0].set_title(f"FIR Model (Unscaled)")
-    axes[1].set_title(f"FIR Components (Scaled by {condition_of_interest} GLM Estimates)")
-    axes[2].set_title(f"Evoked Response ({condition_of_interest})")
-    axes[0].set_ylabel("FIR Model")
-    axes[1].set_ylabel("Oxyhaemoglobin (ΔμMol)")
-    axes[2].set_ylabel("Haemoglobin (ΔμMol)")
-    axes[2].legend(["Oxyhaemoglobin", "Deoxyhaemoglobin"])
+        vals_hbo = df_hbo["theta"].values
+        vals_hbr = df_hbr["theta"].values
+        
+        # Create the plot
+        fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(19, 10))
+        
+        # Scale design matrix components using numpy arrays instead of pandas operations
+        dm_cond_values = dm_cond.values
+        dm_cond_scaled_hbo = dm_cond_values * vals_hbo.reshape(1, -1)
+        dm_cond_scaled_hbr = dm_cond_values * vals_hbr.reshape(1, -1)
+        
+        # Create time axis relative to stimulus onset
+        time = dm_cond.index - np.ceil(first_onset_for_cond.get(cond, 0))
+        
+        # Plot
+        axes[0].plot(time, dm_cond_values)
+        axes[1].plot(time, dm_cond_scaled_hbo)
+        axes[2].plot(time, np.sum(dm_cond_scaled_hbo, axis=1), 'r')
+        axes[2].plot(time, np.sum(dm_cond_scaled_hbr, axis=1), 'b')
+        
+        # Format plots
+        for ax in range(3):
+            axes[ax].set_xlim(-5, 25)
+            axes[ax].set_xlabel("Time (s)")
+        axes[0].set_ylim(-0.2, 1.2)
+        axes[1].set_ylim(-0.5, 1)
+        axes[2].set_ylim(-0.5, 1)
+        axes[0].set_title(f"FIR Model (Unscaled)")
+        axes[1].set_title(f"FIR Components (Scaled by {cond} GLM Estimates)")
+        axes[2].set_title(f"Evoked Response ({cond})")
+        axes[0].set_ylabel("FIR Model")
+        axes[1].set_ylabel("Oxyhaemoglobin (ΔμMol)")
+        axes[2].set_ylabel("Haemoglobin (ΔμMol)")
+        axes[2].legend(["Oxyhaemoglobin", "Deoxyhaemoglobin"])
 
-    
-    print(f"Number of FIR bins: {len(vals_hbo)}")
-    print(f"Mean theta (HbO): {np.mean(vals_hbo):.4f}")
-    print(f"Sum of theta (HbO): {np.sum(vals_hbo):.4f}")
-    print(f"Mean theta (HbR): {np.mean(vals_hbr):.4f}")
-    print(f"Sum of theta (HbR): {np.sum(vals_hbr):.4f}")
-    
-    return fig
+        
+        print(f"Number of FIR bins: {len(vals_hbo)}")
+        print(f"Mean theta (HbO): {np.mean(vals_hbo):.4f}")
+        print(f"Sum of theta (HbO): {np.sum(vals_hbo):.4f}")
+        print(f"Mean theta (HbR): {np.mean(vals_hbr):.4f}")
+        print(f"Sum of theta (HbR): {np.sum(vals_hbr):.4f}")
+        
+        fig_glms.append((f"Condition {cond}", fig))
 
+    return fig_glms
 
 
 def plot_3d_evoked_array(
@@ -2871,9 +2920,12 @@ def process_participant(file_path, progress_callback=None):
     logger.info("11")
 
     # Step 11: Get short / long channels
-    short_chans = get_short_channels(raw_haemo, max_dist=0.015)
-    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
+    if SHORT_CHANNEL:
+        short_chans = get_short_channels(raw_haemo, max_dist=0.015)
+        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)
     if progress_callback: progress_callback(12)
     logger.info("12")
@@ -2893,6 +2945,19 @@ def process_participant(file_path, progress_callback=None):
     logger.info("14")
 
     # 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)
+
     design_matrix, fig_design_matrix = make_design_matrix(raw_haemo, short_chans)
     fig_individual["Design Matrix"] = fig_design_matrix
     if progress_callback: progress_callback(15)
@@ -2916,13 +2981,15 @@ def process_participant(file_path, progress_callback=None):
 
     # Step 16: Plot GLM results
     fig_glm_result = plot_glm_results(file_path, raw_haemo, glm_est, design_matrix)
-    fig_individual["GLM"] = fig_glm_result
+    for name, fig in fig_glm_result:
+        fig_individual[f"GLM {name}"] = fig
     if progress_callback: progress_callback(17)
     logger.info("17")
 
     # Step 17: Plot channel significance
     fig_significance = individual_significance(raw_haemo, glm_est)
-    fig_individual["Significance"] = fig_significance
+    for name, fig in fig_significance:
+        fig_individual[f"Significance {name}"] = fig
     if progress_callback: progress_callback(18)
     logger.info("18")
 
@@ -2975,6 +3042,9 @@ def process_participant(file_path, progress_callback=None):
 
         contrast_dict[condition] = contrast_vector
 
+    if progress_callback: progress_callback(19)
+    logger.info("19")
+
     # Compute contrast results
     contrast_results = {}
 
@@ -2988,7 +3058,7 @@ def process_participant(file_path, progress_callback=None):
     
     fig_bytes = convert_fig_dict_to_png_bytes(fig_individual)
 
+    if progress_callback: progress_callback(20)
+    logger.info("20")
 
-    return raw_haemo, epochs, fig_bytes, cha, contrast_results, df_ind, design_matrix, AGE, GENDER, GROUP, True
-
-# Not 3000 lines yay!
+    return raw_haemo, epochs, fig_bytes, cha, contrast_results, df_ind, design_matrix, AGE, GENDER, GROUP, True