Note

Click here to download the full example code

Getting started with `mne.Report`¶

This tutorial covers making interactive HTML summaries with mne.Report.

Page contents

Basic reports
Adding custom plots to a report
Managing report sections
Editing a saved report

As usual we’ll start by importing the modules we need and loading some example data:

import os
import mne

Before getting started with mne.Report, make sure the files you want to render follow the filename conventions defined by MNE:

Data object	Filename convention (ends with)
raw	-raw.fif(.gz), -raw_sss.fif(.gz), -raw_tsss.fif(.gz), _meg.fif
events	-eve.fif(.gz)
epochs	-epo.fif(.gz)
evoked	-ave.fif(.gz)
covariance	-cov.fif(.gz)
SSP projectors	-proj.fif(.gz)
trans	-trans.fif(.gz)
forward	-fwd.fif(.gz)
inverse	-inv.fif(.gz)

Alternatively, the dash - in the filename may be replaced with an underscore _.

Basic reports ¶

The basic process for creating an HTML report is to instantiate the Report class, then use the parse_folder() method to select particular files to include in the report. Which files are included depends on both the pattern parameter passed to parse_folder() and also the subject and subjects_dir parameters provided to the Report constructor.

For our first example, we’ll generate a barebones report for all the .fif files containing raw data in the sample dataset, by passing the pattern *raw.fif to parse_folder(). We’ll omit the subject and subjects_dir parameters from the Report constructor, but we’ll also pass render_bem=False to the parse_folder() method — otherwise we would get a warning about not being able to render MRI and trans files without knowing the subject.

path = mne.datasets.sample.data_path(verbose=False)
report = mne.Report(verbose=True)
report.parse_folder(path, pattern='*raw.fif', render_bem=False)
report.save('report_basic.html', overwrite=True)

The HTML document written by mne.Report.save():

Out:

Embedding : jquery.js
Embedding : jquery-ui.min.js
Embedding : bootstrap.min.js
Embedding : jquery-ui.min.css
Embedding : bootstrap.min.css
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/ernoise_raw.fif...
Isotrak not found
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 19800 ... 85867 =     32.966 ...   142.965 secs
Ready.
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
Iterating over 3 potential files (this may take some time)
Rendering : /home/circleci/mne_data/MNE-sample-data/MEG/sample/ernoise_raw.fif
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/ernoise_raw.fif...
Isotrak not found
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 19800 ... 85867 =     32.966 ...   142.965 secs
Ready.
Rendering : /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Rendering : /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
Saving report to location /home/circleci/project/tutorials/misc/report_basic.html
Rendering : Table of Contents
raw
 ... ernoise_raw.fif
 ... sample_audvis_filt-0-40_raw.fif
 ... sample_audvis_raw.fif

This report yields a textual summary of the Raw files selected by the pattern. For a slightly more useful report, we’ll ask for the power spectral density of the Raw files, by passing raw_psd=True to the Report constructor. We’ll also visualize the SSP projectors stored in the raw data’s Info dictionary by setting projs=True. Lastly, let’s also refine our pattern to select only the filtered raw recording (omitting the unfiltered data and the empty-room noise recordings):

pattern = 'sample_audvis_filt-0-40_raw.fif'
report = mne.Report(raw_psd=True, projs=True, verbose=True)
report.parse_folder(path, pattern=pattern, render_bem=False)
report.save('report_raw_psd.html', overwrite=True)

The HTML document written by mne.Report.save():

Out:

Embedding : jquery.js
Embedding : jquery-ui.min.js
Embedding : bootstrap.min.js
Embedding : jquery-ui.min.css
Embedding : bootstrap.min.css
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Iterating over 1 potential files (this may take some time)
Rendering : /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Effective window size : 13.639 (s)
Effective window size : 13.639 (s)
Effective window size : 13.639 (s)
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
Saving report to location /home/circleci/project/tutorials/misc/report_raw_psd.html
Rendering : Table of Contents
raw
 ... sample_audvis_filt-0-40_raw.fif

The sample dataset also contains SSP projectors stored as individual files. To add them to a report, we also have to provide the path to a file containing an Info dictionary, from which the channel locations can be read.

info_fname = os.path.join(path, 'MEG', 'sample',
                          'sample_audvis_filt-0-40_raw.fif')
pattern = 'sample_audvis_*proj.fif'
report = mne.Report(info_fname=info_fname, verbose=True)
report.parse_folder(path, pattern=pattern, render_bem=False)
report.save('report_proj.html', overwrite=True)

The HTML document written by mne.Report.save():

Out:

Embedding : jquery.js
Embedding : jquery-ui.min.js
Embedding : bootstrap.min.js
Embedding : jquery-ui.min.css
Embedding : bootstrap.min.css
Iterating over 2 potential files (this may take some time)
Rendering : /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_ecg-proj.fif
    Read a total of 6 projection items:
        ECG-planar-999--0.200-0.400-PCA-01 (1 x 203)  idle
        ECG-planar-999--0.200-0.400-PCA-02 (1 x 203)  idle
        ECG-axial-999--0.200-0.400-PCA-01 (1 x 102)  idle
        ECG-axial-999--0.200-0.400-PCA-02 (1 x 102)  idle
        ECG-eeg-999--0.200-0.400-PCA-01 (1 x 59)  idle
        ECG-eeg-999--0.200-0.400-PCA-02 (1 x 59)  idle
Rendering : /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_eog-proj.fif
    Read a total of 6 projection items:
        EOG-planar-998--0.200-0.200-PCA-01 (1 x 203)  idle
        EOG-planar-998--0.200-0.200-PCA-02 (1 x 203)  idle
        EOG-axial-998--0.200-0.200-PCA-01 (1 x 102)  idle
        EOG-axial-998--0.200-0.200-PCA-02 (1 x 102)  idle
        EOG-eeg-998--0.200-0.200-PCA-01 (1 x 59)  idle
        EOG-eeg-998--0.200-0.200-PCA-02 (1 x 59)  idle
Saving report to location /home/circleci/project/tutorials/misc/report_proj.html
Rendering : Table of Contents
ssp
 ... sample_audvis_ecg-proj.fif
 ... sample_audvis_eog-proj.fif

This time we’ll pass a specific subject and subjects_dir (even though there’s only one subject in the sample dataset) and remove our render_bem=False parameter so we can see the MRI slices, with BEM contours overlaid on top if available. Since this is computationally expensive, we’ll also pass the mri_decim parameter for the benefit of our documentation servers, and skip processing the .fif files:

subjects_dir = os.path.join(path, 'subjects')
report = mne.Report(subject='sample', subjects_dir=subjects_dir, verbose=True)
report.parse_folder(path, pattern='', mri_decim=25)
report.save('report_mri_bem.html', overwrite=True)

The HTML document written by mne.Report.save():

Out:

Embedding : jquery.js
Embedding : jquery-ui.min.js
Embedding : bootstrap.min.js
Embedding : jquery-ui.min.css
Embedding : bootstrap.min.css
Iterating over 0 potential files (this may take some time)
Rendering BEM
Using surface: /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/inner_skull.surf
Using surface: /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/outer_skull.surf
Using surface: /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/outer_skin.surf
Saving report to location /home/circleci/project/tutorials/misc/report_mri_bem.html
Rendering : Table of Contents
bem
 ... bem

Now let’s look at how Report handles Evoked data (we’ll skip the MRIs to save computation time). The following code will produce butterfly plots, topomaps, and comparisons of the global field power (GFP) for different experimental conditions.

pattern = 'sample_audvis-no-filter-ave.fif'
report = mne.Report(verbose=True)
report.parse_folder(path, pattern=pattern, render_bem=False)
report.save('report_evoked.html', overwrite=True)

The HTML document written by mne.Report.save():

Out:

Embedding : jquery.js
Embedding : jquery-ui.min.js
Embedding : bootstrap.min.js
Embedding : jquery-ui.min.css
Embedding : bootstrap.min.css
Iterating over 1 potential files (this may take some time)
Rendering : /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-no-filter-ave.fif
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Multiple channel types selected, returning one figure per type.
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
Saving report to location /home/circleci/project/tutorials/misc/report_evoked.html
Rendering : Table of Contents
evoked
 ... sample_audvis-no-filter-ave.fif

You have probably noticed that the EEG recordings look particularly odd. This is because by default, Report does not apply baseline correction before rendering evoked data. So if the dataset you wish to add to the report has not been baseline-corrected already, you can request baseline correction here. The MNE sample dataset we’re using in this example has not been baseline-corrected; so let’s do this now for the report!

To request baseline correction, pass a baseline argument to Report, which should be a tuple with the starting and ending time of the baseline period. For more details, see the documentation on apply_baseline. Here, we will apply baseline correction for a baseline period from the beginning of the time interval to time point zero.

baseline = (None, 0)
pattern = 'sample_audvis-no-filter-ave.fif'
report = mne.Report(baseline=baseline, verbose=True)
report.parse_folder(path, pattern=pattern, render_bem=False)
report.save('report_evoked_baseline.html', overwrite=True)

The HTML document written by mne.Report.save():

Out:

Embedding : jquery.js
Embedding : jquery-ui.min.js
Embedding : bootstrap.min.js
Embedding : jquery-ui.min.css
Embedding : bootstrap.min.css
Iterating over 1 potential files (this may take some time)
Rendering : /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-no-filter-ave.fif
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Multiple channel types selected, returning one figure per type.
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
Saving report to location /home/circleci/project/tutorials/misc/report_evoked_baseline.html
Rendering : Table of Contents
evoked
 ... sample_audvis-no-filter-ave.fif

To render whitened Evoked files with baseline correction, pass the baseline argument we just used, and add the noise covariance file. This will display ERP/ERF plots for both the original and whitened Evoked objects, but scalp topomaps only for the original.

cov_fname = os.path.join(path, 'MEG', 'sample', 'sample_audvis-cov.fif')
baseline = (None, 0)
report = mne.Report(cov_fname=cov_fname, baseline=baseline, verbose=True)
report.parse_folder(path, pattern=pattern, render_bem=False)
report.save('report_evoked_whitened.html', overwrite=True)

The HTML document written by mne.Report.save():

Out:

Embedding : jquery.js
Embedding : jquery-ui.min.js
Embedding : bootstrap.min.js
Embedding : jquery-ui.min.css
Embedding : bootstrap.min.css
    366 x 366 full covariance (kind = 1) found.
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
Iterating over 1 potential files (this may take some time)
Rendering : /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-no-filter-ave.fif
Computing rank from covariance with rank=None
    Using tolerance 4.7e-14 (2.2e-16 eps * 59 dim * 3.6  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
Computing rank from covariance with rank=None
    Using tolerance 1.8e-13 (2.2e-16 eps * 203 dim * 3.9  max singular value)
    Estimated rank (grad): 203
    GRAD: rank 203 computed from 203 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 2.5e-14 (2.2e-16 eps * 102 dim * 1.1  max singular value)
    Estimated rank (mag): 99
    MAG: rank 99 computed from 102 data channels with 3 projectors
    Created an SSP operator (subspace dimension = 4)
Computing rank from covariance with rank={'eeg': 58, 'grad': 203, 'mag': 99, 'meg': 302}
    Setting small MEG eigenvalues to zero (without PCA)
    Setting small EEG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)
Computing rank from covariance with rank=None
    Using tolerance 4.7e-14 (2.2e-16 eps * 59 dim * 3.6  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
Computing rank from covariance with rank=None
    Using tolerance 1.8e-13 (2.2e-16 eps * 203 dim * 3.9  max singular value)
    Estimated rank (grad): 203
    GRAD: rank 203 computed from 203 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 2.5e-14 (2.2e-16 eps * 102 dim * 1.1  max singular value)
    Estimated rank (mag): 99
    MAG: rank 99 computed from 102 data channels with 3 projectors
    Created an SSP operator (subspace dimension = 4)
Computing rank from covariance with rank={'eeg': 58, 'grad': 203, 'mag': 99, 'meg': 302}
    Setting small MEG eigenvalues to zero (without PCA)
    Setting small EEG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)
Computing rank from covariance with rank=None
    Using tolerance 4.7e-14 (2.2e-16 eps * 59 dim * 3.6  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
Computing rank from covariance with rank=None
    Using tolerance 1.8e-13 (2.2e-16 eps * 203 dim * 3.9  max singular value)
    Estimated rank (grad): 203
    GRAD: rank 203 computed from 203 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 2.5e-14 (2.2e-16 eps * 102 dim * 1.1  max singular value)
    Estimated rank (mag): 99
    MAG: rank 99 computed from 102 data channels with 3 projectors
    Created an SSP operator (subspace dimension = 4)
Computing rank from covariance with rank={'eeg': 58, 'grad': 203, 'mag': 99, 'meg': 302}
    Setting small MEG eigenvalues to zero (without PCA)
    Setting small EEG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)
Computing rank from covariance with rank=None
    Using tolerance 4.7e-14 (2.2e-16 eps * 59 dim * 3.6  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
Computing rank from covariance with rank=None
    Using tolerance 1.8e-13 (2.2e-16 eps * 203 dim * 3.9  max singular value)
    Estimated rank (grad): 203
    GRAD: rank 203 computed from 203 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 2.5e-14 (2.2e-16 eps * 102 dim * 1.1  max singular value)
    Estimated rank (mag): 99
    MAG: rank 99 computed from 102 data channels with 3 projectors
    Created an SSP operator (subspace dimension = 4)
Computing rank from covariance with rank={'eeg': 58, 'grad': 203, 'mag': 99, 'meg': 302}
    Setting small MEG eigenvalues to zero (without PCA)
    Setting small EEG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Multiple channel types selected, returning one figure per type.
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
Saving report to location /home/circleci/project/tutorials/misc/report_evoked_whitened.html
Rendering : Table of Contents
evoked
 ... sample_audvis-no-filter-ave.fif (whitened)
 ... sample_audvis-no-filter-ave.fif

If you want to actually view the noise covariance in the report, make sure it is captured by the pattern passed to parse_folder(), and also include a source for an Info object (any of the Raw, Epochs or Evoked .fif files that contain subject data also contain the measurement information and should work):

pattern = 'sample_audvis-cov.fif'
info_fname = os.path.join(path, 'MEG', 'sample', 'sample_audvis-ave.fif')
report = mne.Report(info_fname=info_fname, verbose=True)
report.parse_folder(path, pattern=pattern, render_bem=False)
report.save('report_cov.html', overwrite=True)

The HTML document written by mne.Report.save():

Out:

Embedding : jquery.js
Embedding : jquery-ui.min.js
Embedding : bootstrap.min.js
Embedding : jquery-ui.min.css
Embedding : bootstrap.min.css
Iterating over 1 potential files (this may take some time)
Rendering : /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-cov.fif
    366 x 366 full covariance (kind = 1) found.
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
Computing rank from covariance with rank=None
    Using tolerance 2.5e-14 (2.2e-16 eps * 102 dim * 1.1  max singular value)
    Estimated rank (mag): 102
    MAG: rank 102 computed from 102 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 1.8e-13 (2.2e-16 eps * 203 dim * 3.9  max singular value)
    Estimated rank (grad): 203
    GRAD: rank 203 computed from 203 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 4.7e-14 (2.2e-16 eps * 59 dim * 3.6  max singular value)
    Estimated rank (eeg): 59
    EEG: rank 59 computed from 59 data channels with 0 projectors
Saving report to location /home/circleci/project/tutorials/misc/report_cov.html
Rendering : Table of Contents
covariance
 ... sample_audvis-cov.fif

Adding custom plots to a report ¶

The python interface has greater flexibility compared to the command line interface. For example, custom plots can be added via the add_figs_to_section() method:

# generate a custom plot:
fname_evoked = os.path.join(path, 'MEG', 'sample', 'sample_audvis-ave.fif')
evoked = mne.read_evokeds(fname_evoked,
                          condition='Left Auditory',
                          baseline=(None, 0),
                          verbose=True)
fig = evoked.plot(show=False)

# add the custom plot to the report:
report.add_figs_to_section(fig, captions='Left Auditory', section='evoked')
report.save('report_custom.html', overwrite=True)

The HTML document written by mne.Report.save():

Out:

Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left Auditory)
        0 CTF compensation matrices available
        nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
Saving report to location /home/circleci/project/tutorials/misc/report_custom.html
Rendering : Table of Contents
covariance
 ... sample_audvis-cov.fif
evoked
 ... Left Auditory

Managing report sections ¶

The MNE report command internally manages the sections so that plots belonging to the same section are rendered consecutively. Within a section, the plots are ordered in the same order that they were added using the add_figs_to_section() command. Each section is identified by a toggle button in the top navigation bar of the report which can be used to show or hide the contents of the section. To toggle the show/hide state of all sections in the HTML report, press t.

Note

Although we’ve been generating separate reports in each example, you could easily create a single report for all .fif files (raw, evoked, covariance, etc) by passing pattern='*.fif'.

Editing a saved report ¶

Saving to HTML is a write-only operation, meaning that we cannot read an .html file back as a Report object. In order to be able to edit a report once it’s no longer in-memory in an active Python session, save it as an HDF5 file instead of HTML:

report.save('report.h5', overwrite=True)
report_from_disk = mne.open_report('report.h5')
print(report_from_disk)

Out:

Saving report to location /home/circleci/project/tutorials/misc/report.h5
Embedding : jquery.js
Embedding : jquery-ui.min.js
Embedding : bootstrap.min.js
Embedding : jquery-ui.min.css
Embedding : bootstrap.min.css
<Report | 2 items | MNE Report for ...data/MNE-sample-data
 ... sample_audvis-cov.fif
 ... Left Auditory
>

This allows the possibility of multiple scripts adding figures to the same report. To make this even easier, mne.Report can be used as a context manager:

with mne.open_report('report.h5') as report:
    report.add_figs_to_section(fig,
                               captions='Left Auditory',
                               section='evoked',
                               replace=True)
    report.save('report_final.html', overwrite=True)

Out:

Embedding : jquery.js
Embedding : jquery-ui.min.js
Embedding : bootstrap.min.js
Embedding : jquery-ui.min.css
Embedding : bootstrap.min.css
Saving report to location /home/circleci/project/tutorials/misc/report_final.html
Rendering : Table of Contents
covariance
 ... sample_audvis-cov.fif
evoked
 ... Left Auditory
Saving report to location /home/circleci/project/tutorials/misc/report.h5

With the context manager, the updated report is also automatically saved back to report.h5 upon leaving the block.

Total running time of the script: ( 0 minutes 57.873 seconds)

Estimated memory usage: 136 MB

Gallery generated by Sphinx-Gallery

Getting started with mne.Report¶

Basic reports¶

Adding custom plots to a report¶

Managing report sections¶

Editing a saved report¶

Getting started with `mne.Report`¶

Basic reports ¶

Adding custom plots to a report ¶

Managing report sections ¶

Editing a saved report ¶