more boris implementation

flares.py

@@ -50,12 +50,12 @@ 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.data_model
-import vtkmodules.util.execution_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
@@ -132,6 +132,8 @@ ENHANCE_NEGATIVE_CORRELATION: bool
 
 SHORT_CHANNEL: bool
 
+REMOVE_EVENTS: list
+
 VERBOSITY = True
 
 # FIXME: Shouldn't need each ordering - just order it before checking
@@ -179,6 +181,7 @@ REQUIRED_KEYS: dict[str, Any] = {
     "PSP_THRESHOLD": float,
     
     "SHORT_CHANNEL": bool,
+    "REMOVE_EVENTS": list,
     # "REJECT_PAIRS": bool,
     # "FORCE_DROP_ANNOTATIONS": list,
     # "FILTER_LOW_PASS": float,
@@ -1108,7 +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")
@@ -1470,9 +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
+    # # Locate the fOLD excel files
-    set_config('MNE_NIRS_FOLD_PATH', resource_path("../../mne_data/fOLD/fOLD-public-master/Supplementary")) # type: ignore
+    # else:
+    #     logger.info("yabba")
+    #     set_config('MNE_NIRS_FOLD_PATH', resource_path("../../mne_data/fOLD/fOLD-public-master/Supplementary")) # type: ignore
 
     output = None
 
@@ -1534,8 +1543,8 @@ def fold_channels(raw: BaseRaw) -> None:
             "Brain_Outside",
         ]
 
-        cmap1 = plt.cm.get_cmap('tab20')  # First 20 colors
+        cmap1 = plt.get_cmap('tab20')  # First 20 colors
-        cmap2 = plt.cm.get_cmap('tab20b')  # Next 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
@@ -1611,6 +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
 
                 
@@ -2935,6 +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)