FIX: Fix connectivity plots (#62)

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -10,17 +10,17 @@ body:
     attributes:
       label: What happened?
       description: Describe the bug
-      placeholder: terrible things happening
+      placeholder: <terrible things happening>
   - type: textarea
     attributes:
       label: Steps to reproduce
       description: What steps made the bug appear? Please describe the steps as detailed as possible
-      placeholder: things I did/buttons I clicked
+      placeholder: <things I did/buttons I clicked>
   - type: textarea
     attributes:
-      label: Error Traceback
-      description: Please copy the full error traceback into here (either from the dialog that popped up or the terminal)
-      placeholder: error-traceback
+      label: Error Traceback (if present)
+      description: Please copy the full error traceback into here if present (either from the dialog that popped up or the terminal)
+      placeholder: <error-traceback>
       render: python
   - type: markdown
     attributes:
@@ -29,5 +29,5 @@ body:
     attributes:
       label: System-Info
       description: Please paste the results from the aforementioned command in here.
-      placeholder: sys-info
+      placeholder: <sys-info>
       render: shell

.github/workflows/run_tests.yml

@@ -23,13 +23,11 @@ jobs:
             qt : 'PySide6'
     name: Run tests on ${{ matrix.os }} - ${{matrix.python}} - ${{ matrix.qt }}
     runs-on: ${{ matrix.os }}-latest
-    env:
-      DISPLAY: ':99.0'
     defaults:
       run:
         shell: bash
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       # Set up headless display for windows and linux
       - name: Setup headless display
         uses: pyvista/setup-headless-display-action@main
@@ -44,7 +42,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           python -m pip install ${{ matrix.qt }}
-          python -m pip install -ve .[tests]
+          python -m pip install -ve ".[tests]"
       - name: Get testing version
         run: |
           mkdir -p mne/datasets
@@ -56,8 +54,9 @@ jobs:
           key: ${{ env.TESTING_VERSION }}
           path: ~/mne_data
         name: 'Cache testing data'
-      - run: python -c 'import mne; print(mne.datasets.testing.data_path(verbose=True))'
-        name: 'Download sample data'
+      - name: 'Download testing, sample and fsaverage data'
+        run: |
+          python -c 'import mne; mne.datasets.testing.data_path(); mne.datasets.sample.data_path(); mne.datasets.fetch_fsaverage()'
       - name: Show system information
         run: mne sys_info
       - run: pytest

.pre-commit-config.yaml

@@ -15,7 +15,6 @@ repos:
     rev: 23.9.1
     hooks:
       - id: black
-        args: [ --quiet ]
 
   # Ruff
   - repo: https://github.com/astral-sh/ruff-pre-commit

mne_pipeline_hd/extra/functions.csv

@@ -28,7 +28,7 @@ source_estimate;;MEEG;Compute;Inverse;False;False;;operations;basic;meeg,inverse
 apply_morph;;MEEG;Compute;Inverse;False;False;;operations;basic;meeg,morph_to
 label_time_course;;MEEG;Compute;Inverse;False;False;;operations;basic;meeg,target_labels,target_parcellation,extract_mode
 ecd_fit;;MEEG;Compute;Inverse;False;False;;operations;basic;meeg,ecd_times,ecd_positions,ecd_orientations,t_epoch
-src_connectivity;;MEEG;Compute;Inverse;False;False;;operations;basic;meeg,target_labels,target_parcellation,inverse_method,lambda2,con_methods,con_fmin,con_fmax,n_jobs
+src_connectivity;;MEEG;Compute;Inverse;False;False;;operations;basic;meeg,target_labels,target_parcellation,inverse_method,lambda2,con_methods,con_frequencies,con_time_window,n_jobs
 grand_avg_evokeds;;Group;Compute;Grand-Average;False;False;;operations;basic;group,ga_interpolate_bads,ga_drop_bads
 grand_avg_tfr;;Group;Compute;Grand-Average;False;False;;operations;basic;group
 grand_avg_morphed;;Group;Compute;Grand-Average;False;False;;operations;basic;group,morph_to
@@ -61,21 +61,21 @@ plot_evoked_white;;MEEG;Plot;Evoked;True;False;;plot;basic;meeg,show_plots
 plot_evoked_image;;MEEG;Plot;Evoked;True;False;;plot;basic;meeg,show_plots
 plot_compare_evokeds;;MEEG;Plot;Evoked;True;False;;plot;basic;meeg,show_plots
 plot_gfp;;MEEG;Plot;Evoked;True;False;;plot;basic;meeg,show_plots
-plot_stc;Plot Source-Estimate;MEEG;Plot;Inverse;True;True;;plot;basic;meeg,target_labels,target_parcellation,label_colors,stc_surface,stc_hemi,stc_views,stc_time,stc_clim,stc_background,stc_roll,stc_azimuth,stc_elevation,backend_3d
-plot_stc_interactive;;MEEG;Plot;Inverse;True;True;;plot;basic;meeg,stc_surface,stc_hemi,stc_views,stc_time,stc_clim,stc_background,stc_roll,stc_azimuth,stc_elevation,backend_3d
+plot_stc;Plot Source-Estimate;MEEG;Plot;Inverse;True;True;;plot;basic;meeg,target_labels,target_parcellation,label_colors,stc_surface,stc_hemi,stc_views,stc_time,stc_clim,stc_roll,stc_azimuth,stc_elevation,backend_3d
+plot_stc_interactive;;MEEG;Plot;Inverse;True;True;;plot;basic;meeg,stc_surface,stc_hemi,stc_views,stc_time,stc_clim,stc_roll,stc_azimuth,stc_elevation,backend_3d
 plot_labels;;FSMRI;Plot;Inverse;True;True;;plot;basic;fsmri,target_labels,target_parcellation,label_colors,stc_hemi,stc_surface,stc_views,backend_3d
-plot_animated_stc;Plot Source-Estimate Video;MEEG;Plot;Inverse;True;True;;plot;basic;meeg,target_labels,target_parcellation,label_colors,stc_surface,stc_hemi,stc_views,stc_time,stc_clim,stc_background,stc_roll,stc_azimuth,stc_elevation,stc_animation_span,stc_animation_dilat,backend_3d
+plot_animated_stc;Plot Source-Estimate Video;MEEG;Plot;Inverse;True;True;;plot;basic;meeg,target_labels,target_parcellation,label_colors,stc_surface,stc_hemi,stc_views,stc_time,stc_clim,stc_roll,stc_azimuth,stc_elevation,stc_animation_span,stc_animation_dilat,backend_3d
 plot_snr;;MEEG;Plot;Inverse;True;False;;plot;basic;meeg,show_plots
 plot_label_time_course;;MEEG;Plot;Inverse;True;False;;plot;basic;meeg,show_plots
 plot_ecd;;MEEG;Plot;Inverse;True;True;;plot;basic;meeg
-plot_src_connectivity;;MEEG;Plot;Time-Frequency;True;False;;plot;basic;meeg,target_labels,target_parcellation,con_fmin,con_fmax,show_plots
+plot_src_connectivity;;MEEG;Plot;Time-Frequency;True;False;;plot;basic;meeg,show_plots
 plot_grand_avg_evokeds;;Group;Plot;Grand-Average;True;False;;plot;basic;group,show_plots
 plot_grand_avg_tfr;;Group;Plot;Grand-Average;True;False;;plot;basic;group,show_plots
-plot_grand_avg_stc;;Group;Plot;Grand-Average;True;True;;plot;basic;group,target_labels,target_parcellation,label_colors,stc_surface,stc_hemi,stc_views,stc_time,stc_clim,stc_background,stc_roll,stc_azimuth,stc_elevation,backend_3d
-plot_grand_avg_stc_anim;;Group;Plot;Grand-Average;True;True;;plot;basic;group,target_labels,target_parcellation,label_colors,stc_surface,stc_hemi,stc_views,stc_time,stc_clim,stc_background,stc_roll,stc_azimuth,stc_elevation,stc_animation_span,stc_animation_dilat,backend_3d
-plot_grand_average_stc_interactive;;Group;Plot;Grand-Average;True;True;;plot;basic;group,stc_surface,stc_hemi,stc_views,stc_time,stc_clim,stc_background,stc_roll,stc_azimuth,stc_elevation,backend_3d
+plot_grand_avg_stc;;Group;Plot;Grand-Average;True;True;;plot;basic;group,target_labels,target_parcellation,label_colors,stc_surface,stc_hemi,stc_views,stc_time,stc_clim,stc_roll,stc_azimuth,stc_elevation,backend_3d
+plot_grand_avg_stc_anim;;Group;Plot;Grand-Average;True;True;;plot;basic;group,target_labels,target_parcellation,label_colors,stc_surface,stc_hemi,stc_views,stc_time,stc_clim,stc_roll,stc_azimuth,stc_elevation,stc_animation_span,stc_animation_dilat,backend_3d
+plot_grand_average_stc_interactive;;Group;Plot;Grand-Average;True;True;;plot;basic;group,label_colors,stc_surface,stc_hemi,stc_views,stc_time,stc_clim,stc_roll,stc_azimuth,stc_elevation,backend_3d
 plot_grand_avg_ltc;;Group;Plot;Grand-Average;True;False;;plot;basic;group,show_plots
-plot_grand_avg_connect;;Group;Plot;Grand-Average;True;False;;plot;basic;group,con_fmin,con_fmax,target_labels,target_parcellation,morph_to,show_plots,connectivity_vmin,connectivity_vmax
+plot_grand_avg_connect;;Group;Plot;Grand-Average;True;False;;plot;basic;group,morph_to,show_plots,connectivity_vmin,connectivity_vmax
 plot_ica_components;Plot ICA-Components;MEEG;Plot;ICA;True;False;;plot;basic;meeg,show_plots,close_func
 plot_ica_sources;Plot ICA-Sources;MEEG;Plot;ICA;True;False;;plot;basic;meeg,ica_source_data,show_plots,close_func
 plot_ica_overlay;Plot ICA-Overlay;MEEG;Plot;ICA;True;False;;plot;basic;meeg,ica_overlay_data,show_plots

mne_pipeline_hd/extra/parameters.csv

@@ -72,17 +72,17 @@ stc_time;;Inverse;0;;Initial time for Source Estimate Plots;FloatGui;
 stc_clim;;Inverse;'auto';;Colorbar Limits for Source Estimate Plots;MultiTypeGui;{'type_selection': True, 'types': ['str', 'dict']}
 stc_background;;Inverse;'black';;Background for Source Estimate Plots;MultiTypeGui;{'type_selection': False}
 stc_roll;;Inverse;0;;Roll for view in for Source Estimate Plots;IntGui;{'max_val': 360}
-stc_azimuth;;Inverse;0;;Azimuth for view for Source Estimate Plots;IntGui;{'max_val': 360}
-stc_elevation;;Inverse;0;;Elevation for view for Source Estimate Plots;IntGui;{'max_val': 360}
+stc_azimuth;;Inverse;70;;Azimuth for view for Source Estimate Plots;IntGui;{'max_val': 360}
+stc_elevation;;Inverse;60;;Elevation for view for Source Estimate Plots;IntGui;{'max_val': 360}
 stc_animation_span;;Inverse;(0,0.5);s;time-span for stc-animation[s];TupleGui;
 stc_animation_dilat;;Inverse;20;;time-dilation for stc-animation;IntGui;
 target_parcellation;Target Parcellation;Inverse;aparc;;The parcellation to use.;StringGui;
 target_labels;Target Labels;Inverse;[];;;LabelGui;
 label_colors;;Inverse;{};;Set custom colors for labels.;ColorGui;{'keys': 'target_labels', 'none_select':True}
-extract_mode;Label-Extraction-Mode;Inverse;mean_flip;;mode for extracting label-time-course from Source-Estimate;ComboGui;{'options': ['max', 'mean', 'mean_flip', 'pca_flip']}
-con_methods;;Connectivity;['coh'];;methods for connectivity plots;CheckListGui;{'options': ['coh', 'cohy', 'imcoh', 'plv', 'ciplv', 'ppc', 'pli', 'pli2_unbiased', 'wpli', 'wpli2_debiased']}
-con_fmin;;Connectivity;30;;fmin for connectivity plot;IntGui;
-con_fmax;;Connectivity;80;;fmax for connectivity plot;IntGui;
+extract_mode;Label-Extraction-Mode;Inverse;auto;;mode for extracting label-time-course from Source-Estimate;ComboGui;{'options': ['auto', 'max', 'mean', 'mean_flip', 'pca_flip']}
+con_methods;;Connectivity;['coh'];;methods for connectivity;CheckListGui;{'options': ['coh', 'cohy', 'imcoh', 'plv', 'ciplv', 'ppc', 'pli', 'pli2_unbiased', 'wpli', 'wpli2_debiased']}
+con_frequencies;;Connectivity;(30, 80);;frequencies for connectivity;TupleGui;{'none_select': True, 'step': 1}
+con_time_window;;Connectivity;(0, 0.5);;time-window for connectivity;TupleGui;{'none_select': True, 'step': 0.001}
 ecd_times;;Inverse;{};;;DictGui;
 ecd_positions;;Inverse;{};;;DictGui;
 ecd_orientations;;Inverse;{};;;DictGui;

mne_pipeline_hd/functions/operations.py

@@ -1473,8 +1473,8 @@ def src_connectivity(
     inverse_method,
     lambda2,
     con_methods,
-    con_fmin,
-    con_fmax,
+    con_frequencies,
+    con_time_window,
     n_jobs,
 ):
     info = meeg.load_info()
@@ -1483,54 +1483,27 @@ def src_connectivity(
     src = inverse_operator["src"]
     labels = meeg.fsmri.get_labels(target_labels, target_parcellation)
 
-    con_dict = {}
-
-    for trial in [t for t in all_epochs.event_id if t]:
-        con_dict[trial.split("/")[0]] = {}
-        epochs = all_epochs[trial.split("/")[0]]
-        # Compute inverse solution and for each epoch.
-        # By using "return_generator=True" stcs will be a generator object
-        # instead of a list.
-        stcs = mne.minimum_norm.apply_inverse_epochs(
-            epochs,
-            inverse_operator,
-            lambda2,
-            inverse_method,
-            pick_ori="normal",
-            return_generator=True,
-        )
-
-        label_ts = mne.extract_label_time_course(
-            stcs, labels, src, mode="mean_flip", return_generator=True
+    if len(labels) == 0:
+        raise RuntimeError(
+            "No labels found, check your target_labels and target_parcellation"
         )
-
-        sfreq = info["sfreq"]  # the sampling frequency
-        con = mne_connectivity.spectral_connectivity_epochs(
-            label_ts,
-            method=con_methods,
-            mode="multitaper",
-            sfreq=sfreq,
-            fmin=con_fmin,
-            fmax=con_fmax,
-            faverage=True,
-            mt_adaptive=True,
-            n_jobs=n_jobs,
+    if len(meeg.sel_trials) == 0:
+        raise RuntimeError(
+            "No trials selected, check your Selected IDs in Preparation/"
         )
 
-        if not isinstance(con, list):
-            con = [con]
+    con_dict = {}
 
-        # con is a 3D array, get the connectivity for the first (and only)
-        # freq. band for each con_method
-        for method, c in zip(con_methods, con):
-            con_dict[trial.split("/")[0]][method] = c.get_data(output="dense")[:, :, 0]
+    for trial in meeg.sel_trials:
+        con_dict[trial] = {}
+        epochs = all_epochs[trial]
 
-    meeg.save_connectivity(con_dict)
+        # Crop if necessary
+        if con_time_window is not None:
+            epochs = epochs.copy().crop(
+                tmin=con_time_window[0], tmax=con_time_window[1]
+            )
 
-    for trial in all_epochs.event_id:
-        con_dict[trial.split("/")[1]] = {}
-        # epochs = all_epochs[trial.split('/')[1]][:2]
-        epochs = all_epochs[trial.split("/")[1]]
         # Compute inverse solution and for each epoch.
         # By using "return_generator=True" stcs will be a generator object
         # instead of a list.
@@ -1543,22 +1516,18 @@ def src_connectivity(
             return_generator=True,
         )
 
-        # Average the source estimates within each label using
-        # sign-flips to reduce signal cancellations, also here we return a
-        # generator
-
         label_ts = mne.extract_label_time_course(
             stcs, labels, src, mode="mean_flip", return_generator=True
         )
 
         sfreq = info["sfreq"]  # the sampling frequency
-        con = mne_connectivity.spectral_connectivity(
+        con = mne_connectivity.spectral_connectivity_epochs(
             label_ts,
             method=con_methods,
             mode="multitaper",
             sfreq=sfreq,
-            fmin=con_fmin,
-            fmax=con_fmax,
+            fmin=con_frequencies[0],
+            fmax=con_frequencies[1],
             faverage=True,
             mt_adaptive=True,
             n_jobs=n_jobs,
@@ -1567,10 +1536,17 @@ def src_connectivity(
         if not isinstance(con, list):
             con = [con]
 
-        # con is a 3D array, get the connectivity for the first
-        # (and only) freq. band for each con_method
+        # con is a 3D array, get the connectivity for the first (and only)
+        # freq. band for each con_method
         for method, c in zip(con_methods, con):
-            con_dict[trial.split("/")[1]][method] = c.get_data(output="dense")[:, :, 0]
+            con_dict[trial][method] = c.get_data(output="dense")[:, :, 0]
+
+    # Add target_labels for later identification
+    con_dict["__info__"] = {
+        "labels": target_labels,
+        "parcellation": target_parcellation,
+        "frequencies": con_frequencies,
+    }
 
     meeg.save_connectivity(con_dict)
 
@@ -1682,6 +1658,7 @@ def grand_avg_connect(group):
         meeg = MEEG(name, group.ct)
         print(f"Add {name} to grand_average")
         con_dict = meeg.load_connectivity()
+        con_info = con_dict.pop("__info__")
         for trial in con_dict:
             if trial not in con_average_dict:
                 con_average_dict[trial] = {}
@@ -1693,7 +1670,7 @@ def grand_avg_connect(group):
                 else:
                     con_average_dict[trial][con_method] = [con_dict[trial][con_method]]
 
-    ga_con = {}
+    ga_con = {"__info__": con_info}
     for trial in con_average_dict:
         ga_con[trial] = {}
         for con_method in con_average_dict[trial]: