# 'NoneType' object has no attribute '\_widget **URL:** \ **Category:** Support & Discussions\ **Tags:** visualization\ **Created:** [October 26, 2023, 1:48pm UTC](https://mne.discourse.group/t/nonetype-object-has-no-attribute-widget/7751 "2023-10-26T13:48:03Z")\ **Posts on this page:** 6\ **Page:** 1
**Author:** ![Jiahui](https://avatars.discourse-cdn.com/v4/letter/j/a88e57/32.png) [@Jiahui](https://mne.discourse.group/u/Jiahui)\ **Post date:** [October 26, 2023, 1:48pm UTC](https://mne.discourse.group/t/nonetype-object-has-no-attribute-widget/7751/1 "2023-10-26T13:48:03Z")
- MNE version: e.g. 1.3.0 - operating system: Windows 10 Hi there, I am now using the following code ```python brain = plot_glm_surface_projection(raw_haemo.copy().pick("hbo").pick(significant_channels), model_df, colorbar=True) brain.add_sensors(raw_haemo.info, trans='fsaverage', fnirs=['channels', 'pairs', 'sources', 'detectors']) ``` to plot my significant result on scalp, but I always get this error. I have tried different types of backend, including: qt, tk, webAgg, notebook etc., none of them are working, I got the same error message with this function. It would be super helpful if you could provide me the solution how to fix it! Thank you. ```python --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[47], line 3 1 # Plot the projection and sensor locations 2 get_ipython().run_line_magic('matplotlib', 'inline') ----> 3 brain = plot_glm_surface_projection(raw_haemo.copy().pick("hbo").pick(significant_channels), model_df, colorbar=True) 5 brain.add_sensors(raw_haemo.info, trans='fsaverage', fnirs=['channels', 'pairs', 'sources', 'detectors']) 7 # mark the premotor cortex in red File :10, in plot_glm_surface_projection(inst, statsmodel_df, picks, value, background, figure, clim, mode, colormap, surface, hemi, size, view, colorbar, distance, subjects_dir, src, verbose) File D:\Anaconda\envs\fnirs\lib\site-packages\mne_nirs\visualisation\_plot_GLM_surface_projection.py:97, in plot_glm_surface_projection(inst, statsmodel_df, picks, value, background, figure, clim, mode, colormap, surface, hemi, size, view, colorbar, distance, subjects_dir, src, verbose) 90 raise RuntimeError('MNE data structure does not match dataframe ' 91 f'results.\nMNE = {info.ch_names}.\n' 92 f'GLM = {list(statsmodel_df["ch_name"].values)}') 94 ea = EvokedArray(np.tile(statsmodel_df[value].values.T, (1, 1)).T, 95 info.copy()) ---> 97 return _plot_3d_evoked_array(inst, ea, picks=picks, 98 value=value, 99 background=background, figure=figure, 100 clim=clim, 101 mode=mode, colormap=colormap, 102 surface=surface, hemi=hemi, size=size, 103 view=view, colorbar=colorbar, 104 distance=distance, 105 subjects_dir=subjects_dir, src=src, 106 verbose=verbose) File D:\Anaconda\envs\fnirs\lib\site-packages\mne_nirs\visualisation\_plot_GLM_surface_projection.py:142, in _plot_3d_evoked_array(inst, ea, picks, value, background, figure, clim, mode, colormap, surface, hemi, size, view, colorbar, distance, subjects_dir, src, verbose) 139 stc = stc_near_sensors(picks=picks, **kwargs, verbose=verbose) 141 # Produce brain plot --> 142 brain = stc.plot(src=src, subjects_dir=subjects_dir, hemi=hemi, 143 surface=surface, initial_time=0, clim=clim, size=size, 144 colormap=colormap, figure=figure, background=background, 145 colorbar=colorbar, verbose=verbose) 146 if view is not None: 147 brain.show_view(view) File D:\Anaconda\envs\fnirs\lib\site-packages\mne\source_estimate.py:650, in _BaseSourceEstimate.plot(self, subject, surface, hemi, colormap, time_label, smoothing_steps, transparent, alpha, time_viewer, subjects_dir, figure, views, colorbar, clim, cortex, size, background, foreground, initial_time, time_unit, backend, spacing, title, show_traces, src, volume_options, view_layout, add_data_kwargs, brain_kwargs, verbose) 639 @copy_function_doc_to_method_doc(plot_source_estimates) 640 def plot(self, subject=None, surface='inflated', hemi='lh', 641 colormap='auto', time_label='auto', smoothing_steps=10, (...) 648 src=None, volume_options=1., view_layout='vertical', 649 add_data_kwargs=None, brain_kwargs=None, verbose=None): --> 650 brain = plot_source_estimates( 651 self, subject, surface=surface, hemi=hemi, colormap=colormap, 652 time_label=time_label, smoothing_steps=smoothing_steps, 653 transparent=transparent, alpha=alpha, time_viewer=time_viewer, 654 subjects_dir=subjects_dir, figure=figure, views=views, 655 colorbar=colorbar, clim=clim, cortex=cortex, size=size, 656 background=background, foreground=foreground, 657 initial_time=initial_time, time_unit=time_unit, backend=backend, 658 spacing=spacing, title=title, show_traces=show_traces, 659 src=src, volume_options=volume_options, view_layout=view_layout, 660 add_data_kwargs=add_data_kwargs, brain_kwargs=brain_kwargs, 661 verbose=verbose) 662 return brain File :10, in plot_source_estimates(stc, subject, surface, hemi, colormap, time_label, smoothing_steps, transparent, alpha, time_viewer, subjects_dir, figure, views, colorbar, clim, cortex, size, background, foreground, initial_time, time_unit, backend, spacing, title, show_traces, src, volume_options, view_layout, add_data_kwargs, brain_kwargs, verbose) File D:\Anaconda\envs\fnirs\lib\site-packages\mne\viz\_3d.py:1987, in plot_source_estimates(stc, subject, surface, hemi, colormap, time_label, smoothing_steps, transparent, alpha, time_viewer, subjects_dir, figure, views, colorbar, clim, cortex, size, background, foreground, initial_time, time_unit, backend, spacing, title, show_traces, src, volume_options, view_layout, add_data_kwargs, brain_kwargs, verbose) 1985 else: 1986 with use_3d_backend(backend): -> 1987 return _plot_stc( 1988 stc, overlay_alpha=alpha, brain_alpha=alpha, 1989 vector_alpha=alpha, cortex=cortex, foreground=foreground, 1990 size=size, scale_factor=None, show_traces=show_traces, 1991 src=src, volume_options=volume_options, 1992 view_layout=view_layout, add_data_kwargs=add_data_kwargs, 1993 brain_kwargs=brain_kwargs, **kwargs) File D:\Anaconda\envs\fnirs\lib\site-packages\mne\viz\_3d.py:2110, in _plot_stc(stc, subject, surface, hemi, colormap, time_label, smoothing_steps, subjects_dir, views, clim, figure, initial_time, time_unit, background, time_viewer, colorbar, transparent, brain_alpha, overlay_alpha, vector_alpha, cortex, foreground, size, scale_factor, show_traces, src, volume_options, view_layout, add_data_kwargs, brain_kwargs) 2107 del kwargs 2109 if time_viewer: -> 2110 brain.setup_time_viewer(time_viewer=time_viewer, 2111 show_traces=show_traces) 2112 else: 2113 brain.show() File D:\Anaconda\envs\fnirs\lib\site-packages\mne\viz\_brain\_brain.py:523, in Brain.setup_time_viewer( ***failed resolving arguments*** ) 521 self._configure_menu() 522 self._configure_status_bar() --> 523 self._configure_playback() 524 self._configure_help() 525 # show everything at the end File D:\Anaconda\envs\fnirs\lib\site-packages\mne\viz\_brain\_brain.py:1004, in Brain._configure_playback(self) 1003 def _configure_playback(self): -> 1004 self._renderer._playback_initialize( 1005 func=self._play, 1006 timeout=self.refresh_rate_ms, 1007 value=self._data['time_idx'], 1008 rng=[0, len(self._data['time']) - 1], 1009 time_widget=self.widgets["time"], 1010 play_widget=self.widgets["play"], 1011 ) File D:\Anaconda\envs\fnirs\lib\site-packages\mne\viz\backends\_notebook.py:1227, in _IpyPlayback._playback_initialize(self, func, timeout, value, rng, time_widget, play_widget) 1225 play.max = rng[1] 1226 play.value = value -> 1227 slider = time_widget._widget 1228 jsdlink((play, 'value'), (slider, 'value')) 1229 jsdlink((slider, 'value'), (play, 'value')) AttributeError: 'NoneType' object has no attribute '_widget ``` ---
**Author:** ![PuddleJumper2018](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/puddlejumper2018/32/2645_2.png) [@PuddleJumper2018](https://mne.discourse.group/u/PuddleJumper2018)\ **Post date:** [December 1, 2023, 6:17pm UTC](https://mne.discourse.group/t/nonetype-object-has-no-attribute-widget/7751/2 "2023-12-01T18:17:09Z")
Hi, I wonder if you’ve found the solution to this issue? I’m having the same problem working on Mac Sonoma with MNE 1.5.1. ---
**Author:** ![drammock](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/drammock/32/4_2.png) [@drammock](https://mne.discourse.group/u/drammock)\ **Post date:** [December 1, 2023, 7:36pm UTC](https://mne.discourse.group/t/nonetype-object-has-no-attribute-widget/7751/3 "2023-12-01T19:36:36Z")
hi @Jiahui, I’ve edited your post to format the code and traceback as “preformatted text” so it’s easier to read. If possible, can you (or @PuddleJumper2018) post a _complete_ reproducible example (i.e., one that any user could copy-paste into a python terminal and generate the same error you’re seeing)? Ideally it would use one of our [example datasets](https://mne.tools/stable/documentation/datasets.html) and include all necessary imports. Given the nature of @Jiahui’s example code above, I’m guessing the [fnirs motor](https://mne.tools/stable/documentation/datasets.html#fnirs-motor) dataset would be the place to start. ---
**Author:** ![PuddleJumper2018](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/puddlejumper2018/32/2645_2.png) [@PuddleJumper2018](https://mne.discourse.group/u/PuddleJumper2018)\ **Post date:** [December 6, 2023, 4:15pm UTC](https://mne.discourse.group/t/nonetype-object-has-no-attribute-widget/7751/4 "2023-12-06T16:15:08Z")
Hi @drammock , I’ll do my best. I tried on tapping example from [here](https://mne.tools/mne-nirs/stable/auto_examples/general/plot_70_visualise_brain.html). My OS: Sonoma 14.1.1 MNE-NIRS version: 0.5.0 MNE version: 1.5.1 Python: 3.10.12 Should probably add I work in conda environment. If there’s something I missed let me know. Thank you! ```python import numpy as np import pandas as pd import mne from mne.preprocessing.nirs import optical_density, beer_lambert_law import statsmodels.formula.api as smf from mne_bids import BIDSPath, read_raw_bids, get_entity_vals import mne_nirs from mne_nirs.experimental_design import make_first_level_design_matrix from mne_nirs.statistics import run_glm, statsmodels_to_results from mne_nirs.channels import get_long_channels, get_short_channels from mne_nirs.io.fold import fold_landmark_specificity from mne_nirs.visualisation import plot_nirs_source_detector, plot_glm_surface_projection from mne_nirs.datasets import fnirs_motor_group def individual_analysis(bids_path, ID): raw_intensity = read_raw_bids(bids_path=bids_path, verbose=False) raw_intensity.annotations.delete(raw_intensity.annotations.description == '15.0') # sanitize event names raw_intensity.annotations.description[:] = [ d.replace('/', '_') for d in raw_intensity.annotations.description] # Convert signal to haemoglobin and resample raw_od = optical_density(raw_intensity) raw_haemo = beer_lambert_law(raw_od, ppf=0.1) raw_haemo.resample(0.3) # Cut out just the short channels for creating a GLM repressor sht_chans = get_short_channels(raw_haemo) raw_haemo = get_long_channels(raw_haemo) # Create a design matrix design_matrix = make_first_level_design_matrix(raw_haemo, stim_dur=5.0) # Append short channels mean to design matrix design_matrix["ShortHbO"] = np.mean(sht_chans.copy().pick(picks="hbo").get_data(), axis=0) design_matrix["ShortHbR"] = np.mean(sht_chans.copy().pick(picks="hbr").get_data(), axis=0) # Run GLM glm_est = run_glm(raw_haemo, design_matrix) # Extract channel metrics cha = glm_est.to_dataframe() # Add the participant ID to the dataframes cha["ID"] = ID # Convert to uM for nicer plotting below. cha["theta"] = [t * 1.e6 for t in cha["theta"]] return raw_haemo, cha # Get dataset details root = fnirs_motor_group.data_path() dataset = BIDSPath(root=root, task="tapping", datatype="nirs", suffix="nirs", extension=".snirf") subjects = get_entity_vals(root, 'subject') df_cha = pd.DataFrame() # To store channel level results for sub in subjects: # Loop from first to fifth subject # Create path to file based on experiment info bids_path = dataset.update(subject=sub) # Analyse data and return both ROI and channel results raw_haemo, channel = individual_analysis(bids_path, sub) # Append individual results to all participants df_cha = pd.concat([df_cha, channel], ignore_index=True) ch_summary = df_cha.query("Condition in ['Tapping_Right']") assert len(ch_summary) ch_summary = ch_summary.query("Chroma in ['hbo']") ch_model = smf.mixedlm("theta ~ -1 + ch_name", ch_summary, groups=ch_summary["ID"]).fit(method='nm') model_df = statsmodels_to_results(ch_model, order=raw_haemo.copy().pick("hbo").ch_names) # Plot the projection and sensor locations brain = plot_glm_surface_projection(raw_haemo.copy().pick("hbo"), model_df, colorbar=True) brain.add_sensors(raw_haemo.info, trans='fsaverage', fnirs=['channels', 'pairs', 'sources', 'detectors']) # mark the premotor cortex in green aud_label = [label for label in labels_combined if label.name == 'Premotor Cortex-lh'][0] brain.add_label(aud_label, borders=True, color='green') # mark the auditory association cortex in blue aud_label = [label for label in labels_combined if label.name == 'Auditory Association Cortex-lh'][0] brain.add_label(aud_label, borders=True, color='blue') brain.show_view(azimuth=160, elevation=60, distance=400) ``` Which seems to work: ```python Using default location ~/mne_data for fnirs_motor_group... Downloading file 'BIDS-NIRS-Tapping-master.zip' from 'https://github.com/rob-luke/BIDS-NIRS-Tapping/archive/v0.1.0.zip' to '/Users/xxxx/mne_data'. 0.00B [00:00, ?B/s] Unzipping contents of '/Users/xxxxx/mne_data/BIDS-NIRS-Tapping-master.zip' to '/Users/xxxxx/mne_data/./fNIRS-motor-group' Download complete in 06s (41.7 MB) Reading 0 ... 23238 = 0.000 ... 2974.464 secs... Reading 0 ... 18877 = 0.000 ... 2416.256 secs... Reading 0 ... 18874 = 0.000 ... 2415.872 secs... Reading 0 ... 23120 = 0.000 ... 2959.360 secs... Reading 0 ... 23006 = 0.000 ... 2944.768 secs... /Users/xxxxx/anaconda3/envs/svwm/lib/python3.10/site-packages/mne_nirs/statistics/_statsmodels.py:114: FutureWarning: Series. __getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` sdf[jj, 1] = np.sqrt(model.scale) * model.bse[model.k_fe + jj] ``` Until I get error on plot\_glm\_surface\_projection here’s the error I get ```python --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Untitled-1.ipynb Cell 5 line 2 1 # Plot the projection and sensor locations ----> 2 brain = plot_glm_surface_projection(raw_haemo.copy().pick("hbo"), model_df, colorbar=True) 3 brain.add_sensors(raw_haemo.info, trans='fsaverage', fnirs=['channels', 'pairs', 'sources', 'detectors']) 5 # mark the premotor cortex in green File :10, in plot_glm_surface_projection(inst, statsmodel_df, picks, value, background, figure, clim, mode, colormap, surface, hemi, size, view, colorbar, distance, subjects_dir, src, verbose) File ~/anaconda3/envs/svwm/lib/python3.10/site-packages/mne_nirs/visualisation/_plot_GLM_surface_projection.py:97, in plot_glm_surface_projection(inst, statsmodel_df, picks, value, background, figure, clim, mode, colormap, surface, hemi, size, view, colorbar, distance, subjects_dir, src, verbose) 90 raise RuntimeError('MNE data structure does not match dataframe ' 91 f'results.\nMNE = {info.ch_names}.\n' 92 f'GLM = {list(statsmodel_df["ch_name"].values)}') 94 ea = EvokedArray(np.tile(statsmodel_df[value].values.T, (1, 1)).T, 95 info.copy()) ---> 97 return _plot_3d_evoked_array(inst, ea, picks=picks, 98 value=value, 99 background=background, figure=figure, 100 clim=clim, 101 mode=mode, colormap=colormap, 102 surface=surface, hemi=hemi, size=size, 103 view=view, colorbar=colorbar, 104 distance=distance, 105 subjects_dir=subjects_dir, src=src, 106 verbose=verbose) File ~/anaconda3/envs/svwm/lib/python3.10/site-packages/mne_nirs/visualisation/_plot_GLM_surface_projection.py:142, in _plot_3d_evoked_array(inst, ea, picks, value, background, figure, clim, mode, colormap, surface, hemi, size, view, colorbar, distance, subjects_dir, src, verbose) 139 stc = stc_near_sensors(picks=picks, **kwargs, verbose=verbose) 141 # Produce brain plot --> 142 brain = stc.plot(src=src, subjects_dir=subjects_dir, hemi=hemi, 143 surface=surface, initial_time=0, clim=clim, size=size, 144 colormap=colormap, figure=figure, background=background, 145 colorbar=colorbar, verbose=verbose) 146 if view is not None: 147 brain.show_view(view) File ~/anaconda3/envs/svwm/lib/python3.10/site-packages/mne/source_estimate.py:740, in _BaseSourceEstimate.plot(self, subject, surface, hemi, colormap, time_label, smoothing_steps, transparent, alpha, time_viewer, subjects_dir, figure, views, colorbar, clim, cortex, size, background, foreground, initial_time, time_unit, backend, spacing, title, show_traces, src, volume_options, view_layout, add_data_kwargs, brain_kwargs, verbose) 706 @copy_function_doc_to_method_doc(plot_source_estimates) 707 def plot( 708 self, (...) 738 verbose=None, 739 ): --> 740 brain = plot_source_estimates( 741 self, 742 subject, 743 surface=surface, 744 hemi=hemi, 745 colormap=colormap, 746 time_label=time_label, 747 smoothing_steps=smoothing_steps, 748 transparent=transparent, 749 alpha=alpha, 750 time_viewer=time_viewer, 751 subjects_dir=subjects_dir, 752 figure=figure, 753 views=views, 754 colorbar=colorbar, 755 clim=clim, 756 cortex=cortex, 757 size=size, 758 background=background, 759 foreground=foreground, 760 initial_time=initial_time, 761 time_unit=time_unit, 762 backend=backend, 763 spacing=spacing, 764 title=title, 765 show_traces=show_traces, 766 src=src, 767 volume_options=volume_options, 768 view_layout=view_layout, 769 add_data_kwargs=add_data_kwargs, 770 brain_kwargs=brain_kwargs, 771 verbose=verbose, 772 ) 773 return brain File :10, in plot_source_estimates(stc, subject, surface, hemi, colormap, time_label, smoothing_steps, transparent, alpha, time_viewer, subjects_dir, figure, views, colorbar, clim, cortex, size, background, foreground, initial_time, time_unit, backend, spacing, title, show_traces, src, volume_options, view_layout, add_data_kwargs, brain_kwargs, verbose) File ~/anaconda3/envs/svwm/lib/python3.10/site-packages/mne/viz/_3d.py:2467, in plot_source_estimates(stc, subject, surface, hemi, colormap, time_label, smoothing_steps, transparent, alpha, time_viewer, subjects_dir, figure, views, colorbar, clim, cortex, size, background, foreground, initial_time, time_unit, backend, spacing, title, show_traces, src, volume_options, view_layout, add_data_kwargs, brain_kwargs, verbose) 2465 else: 2466 with use_3d_backend(backend): -> 2467 return _plot_stc( 2468 stc, 2469 overlay_alpha=alpha, 2470 brain_alpha=alpha, 2471 vector_alpha=alpha, 2472 cortex=cortex, 2473 foreground=foreground, 2474 size=size, 2475 scale_factor=None, 2476 show_traces=show_traces, 2477 src=src, 2478 volume_options=volume_options, 2479 view_layout=view_layout, 2480 add_data_kwargs=add_data_kwargs, 2481 brain_kwargs=brain_kwargs, 2482 **kwargs, 2483 ) File ~/anaconda3/envs/svwm/lib/python3.10/site-packages/mne/viz/_3d.py:2637, in _plot_stc(stc, subject, surface, hemi, colormap, time_label, smoothing_steps, subjects_dir, views, clim, figure, initial_time, time_unit, background, time_viewer, colorbar, transparent, brain_alpha, overlay_alpha, vector_alpha, cortex, foreground, size, scale_factor, show_traces, src, volume_options, view_layout, add_data_kwargs, brain_kwargs) 2634 del kwargs 2636 if time_viewer: -> 2637 brain.setup_time_viewer(time_viewer=time_viewer, show_traces=show_traces) 2638 else: 2639 brain.show() File ~/anaconda3/envs/svwm/lib/python3.10/site-packages/mne/viz/_brain/_brain.py:600, in Brain.setup_time_viewer( ***failed resolving arguments*** ) 598 self._configure_menu() 599 self._configure_status_bar() --> 600 self._configure_playback() 601 self._configure_help() 602 # show everything at the end File ~/anaconda3/envs/svwm/lib/python3.10/site-packages/mne/viz/_brain/_brain.py:1101, in Brain._configure_playback(self) 1100 def _configure_playback(self): -> 1101 self._renderer._playback_initialize( 1102 func=self._play, 1103 timeout=self.refresh_rate_ms, 1104 value=self._data["time_idx"], 1105 rng=[0, len(self._data["time"]) - 1], 1106 time_widget=self.widgets["time"], 1107 play_widget=self.widgets["play"], 1108 ) File ~/anaconda3/envs/svwm/lib/python3.10/site-packages/mne/viz/backends/_notebook.py:1342, in _IpyPlayback._playback_initialize(self, func, timeout, value, rng, time_widget, play_widget) 1340 play.max = rng[1] 1341 play.value = value -> 1342 slider = time_widget._widget 1343 jsdlink((play, "value"), (slider, "value")) 1344 jsdlink((slider, "value"), (play, "value")) AttributeError: 'NoneType' object has no attribute '_widget' ``` ---
**Author:** ![Sam54000](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/sam54000/32/2152_2.png) [@Sam54000](https://mne.discourse.group/u/Sam54000)\ **Post date:** [December 7, 2023, 1:36pm UTC](https://mne.discourse.group/t/nonetype-object-has-no-attribute-widget/7751/5 "2023-12-07T13:36:50Z")
Hi! I tried to reproduce the error but I got another error related to [freesurfer BEM dataset](https://mne.tools/stable/generated/mne.datasets.fetch_fsaverage.html#mne.datasets.fetch_fsaverage). The solution was to get this dataset. Can you try to add this line ```python mne.datasets.fetch_fsaverage(subjects_dir=None,verbose=None) ``` below `# Get dataset details` in your example? Tell me what you get ---
**Author:** ![drammock](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/drammock/32/4_2.png) [@drammock](https://mne.discourse.group/u/drammock)\ **Post date:** [December 7, 2023, 11:18pm UTC](https://mne.discourse.group/t/nonetype-object-has-no-attribute-widget/7751/6 "2023-12-07T23:18:27Z")
I also tried to reproduce (with current dev versions of MNE and MNE-NIRS) but I get a different error (about color limits not being monotonically increasing). But if I run this line instead: ```python brain = plot_glm_surface_projection( raw_haemo.copy().pick("hbo"), model_df, colorbar=True, clim=dict(kind="percent", pos_lims=(90, 95, 99)), # <--- new line ) ``` then I get a plot: ![nirs](https://global.discourse-cdn.com/free1/uploads/mne/original/2X/e/e8a05d4d7010cf2fb004262243ab3505e962f09c.jpeg) so… maybe the bug you’re seeing is already fixed in MNE-NIRS 0.6, or in MNE-Python \> 1.5.1?