Getting started with mne.Report#

mne.Report is a way to create interactive HTML summaries of your data. These reports can show many different visualizations for one or multiple participants. A common use case is creating diagnostic summaries to check data quality at different stages in the processing pipeline. The report can show things like plots of data before and after each preprocessing step, epoch rejection statistics, MRI slices with overlaid BEM shells, all the way up to plots of estimated cortical activity.

Compared to a Jupyter notebook, mne.Report is easier to deploy (the HTML pages it generates are self-contained and do not require a running Python environment) but less flexible (you can’t change code and re-run something directly within the browser). This tutorial covers the basics of building a Report. As usual, we’ll start by importing the modules and data we need:

from pathlib import Path
import tempfile
import numpy as np
import scipy.ndimage
import matplotlib.pyplot as plt
import mne

data_path = Path(mne.datasets.sample.data_path(verbose=False))
sample_dir = data_path / 'MEG' / 'sample'
subjects_dir = data_path / 'subjects'

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(.gz), _eeg.fif(.gz), _ieeg.fif(.gz)

events

-eve.fif(.gz)

Epochs

-epo.fif(.gz)

Evoked

-ave.fif(.gz)

Covariance

-cov.fif(.gz)

Projection

-proj.fif(.gz)

Transform

-trans.fif(.gz)

Forward

-fwd.fif(.gz)

InverseOperator

-inv.fif(.gz)

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

The basic process for creating an HTML report is to instantiate the Report class and then use one or more of its many methods to add content, one element at a time.

You may also use the parse_folder() method to select particular files to include in the report. But more on that later.

Adding Raw data#

Raw data can be added via the mne.Report.add_raw() method. It can operate with a path to a raw file and Raw objects, and will produce – among other output – a slider that allows you to scrub through 10 equally-spaced 1-second segments of the data:

Warning

In the following example, we crop the raw data to 60 seconds merely to speed up processing; this is not usually recommended!

raw_path = sample_dir / 'sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw(raw_path)
raw.pick_types(eeg=True, eog=True, stim=True).crop(tmax=60).load_data()

report = mne.Report(title='Raw example')
# This method also accepts a path, e.g., raw=raw_path
report.add_raw(raw=raw, title='Raw', psd=False)  # omit PSD plot
report.save('report_raw.html', overwrite=True)
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.
Removing projector <Projection | PCA-v1, active : False, n_channels : 102>
Removing projector <Projection | PCA-v2, active : False, n_channels : 102>
Removing projector <Projection | PCA-v3, active : False, n_channels : 102>
Reading 0 ... 9009  =      0.000 ...    59.999 secs...
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Using matplotlib as 2D backend.
Using qt as 2D backend.
Saving report to : /home/circleci/project/tutorials/intro/report_raw.html

Adding events#

Events can be added via mne.Report.add_events(). You also need to supply the sampling frequency used during the recording; this information is used to generate a meaningful time axis.

events_path = sample_dir / 'sample_audvis_filt-0-40_raw-eve.fif'
events = mne.find_events(raw=raw)
sfreq = raw.info['sfreq']

report = mne.Report(title='Events example')
report.add_events(events=events_path, title='Events from Path', sfreq=sfreq)
report.add_events(events=events, title='Events from "events"', sfreq=sfreq)
report.save('report_events.html', overwrite=True)
86 events found
Event IDs: [ 1  2  3  4  5 32]
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Saving report to : /home/circleci/project/tutorials/intro/report_events.html

Adding Epochs#

Epochs can be added via mne.Report.add_epochs(). Note that although this method accepts a path to an epochs file too, in the following example we only add epochs that we create on the fly from raw data. To demonstrate the representation of epochs metadata, we’ll add some of that too.

event_id = {
    'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3,
    'visual/right': 4, 'face': 5, 'buttonpress': 32
}

metadata, _, _ = mne.epochs.make_metadata(
    events=events,
    event_id=event_id,
    tmin=-0.2,
    tmax=0.5,
    sfreq=raw.info['sfreq']
)
epochs = mne.Epochs(
    raw=raw, events=events, event_id=event_id, metadata=metadata
)

report = mne.Report(title='Epochs example')
report.add_epochs(epochs=epochs, title='Epochs from "epochs"')
report.save('report_epochs.html', overwrite=True)
Adding metadata with 7 columns
86 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] sec
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 1)
1 projection items activated
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Using data from preloaded Raw for 86 events and 106 original time points ...
0 bad epochs dropped
    Using multitaper spectrum estimation with 7 DPSS windows
Saving report to : /home/circleci/project/tutorials/intro/report_epochs.html

Adding Evoked#

Evoked data can be added via mne.Report.add_evokeds(). By default, the Evoked.comment attribute of each evoked will be used as a title. We can specify custom titles via the titles parameter. Again, this method also accepts the path to an evoked file stored on disk; in the following example, however, we load the evokeds manually first, since we only want to add a subset of them to the report. The evokeds are not baseline-corrected, so we apply baseline correction, too. Lastly, by providing an (optional) noise covariance, we can add plots evokeds that were “whitened” using this covariance matrix.

By default, this method will produce snapshots at 21 equally-spaced time points (or fewer, if the data contains fewer time points). We can adjust this via the n_time_points parameter.

evoked_path = sample_dir / 'sample_audvis-ave.fif'
cov_path = sample_dir / 'sample_audvis-cov.fif'

evokeds = mne.read_evokeds(evoked_path, baseline=(None, 0))
evokeds_subset = evokeds[:2]  # The first two
for evoked in evokeds_subset:
    evoked.pick('eeg')  # just for speed of plotting

report = mne.Report(title='Evoked example')
report.add_evokeds(
    evokeds=evokeds_subset,
    titles=['evoked 1',  # Manually specify titles
            'evoked 2'],
    noise_cov=cov_path,
    n_time_points=5
)
report.save('report_evoked.html', overwrite=True)
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)
    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 (Right Auditory)
        0 CTF compensation matrices available
        nave = 61 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
    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 visual)
        0 CTF compensation matrices available
        nave = 67 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
    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 (Right visual)
        0 CTF compensation matrices available
        nave = 58 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
Applying baseline correction (mode: mean)
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>
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    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
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s remaining:    0.0s
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combining channels using "gfp"
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
    Created an SSP operator (subspace dimension = 1)
Computing rank from covariance with rank={'eeg': 58}
    Setting small EEG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 58 (1 small eigenvalues omitted)
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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combining channels using "gfp"
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
    Created an SSP operator (subspace dimension = 1)
Computing rank from covariance with rank={'eeg': 58}
    Setting small EEG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 58 (1 small eigenvalues omitted)
Saving report to : /home/circleci/project/tutorials/intro/report_evoked.html

Adding Covariance#

(Noise) covariance objects can be added via mne.Report.add_covariance(). The method accepts Covariance objects and the path to a file on disk. It also expects us to pass an Info object or the path to a file to read the measurement info from, as well as a title.

cov_path = sample_dir / 'sample_audvis-cov.fif'

report = mne.Report(title='Covariance example')
report.add_covariance(cov=cov_path, info=raw_path, title='Covariance')
report.save('report_cov.html', overwrite=True)
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    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
    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
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 2.6e-12 (2.2e-16 eps * 204 dim * 56  max singular value)
    Estimated rank (grad): 204
    GRAD: rank 204 computed from 204 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 4.8e-14 (2.2e-16 eps * 60 dim * 3.6  max singular value)
    Estimated rank (eeg): 60
    EEG: rank 60 computed from 60 data channels with 0 projectors
Saving report to : /home/circleci/project/tutorials/intro/report_cov.html

Adding Projection vectors#

Projection vectors can be added via mne.Report.add_projs(). The method requires an Info object (or the path to one) and a title. Projectors found in the Info will be visualized. You may also supply a list of Projection objects or a path to projectors stored on disk. In this case, the channel information is read from the Info, but projectors potentially included will be ignored; instead, only the explicitly passed projectors will be plotted.

ecg_proj_path = sample_dir / 'sample_audvis_ecg-proj.fif'
eog_proj_path = sample_dir / 'sample_audvis_eog-proj.fif'

report = mne.Report(title='Projectors example')
report.add_projs(info=raw_path, title='Projs from info')
report.add_projs(info=raw_path, projs=ecg_proj_path,
                 title='ECG projs from path')
report.add_projs(info=raw_path, projs=eog_proj_path,
                 title='EOG projs from path')
report.save('report_projs.html', overwrite=True)
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    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
    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 : /home/circleci/project/tutorials/intro/report_projs.html

Adding ICA#

ICA objects can be added via mne.Report.add_ica(). Aside from the parameters ica (that accepts an ICA instance or a path to an ICA object stored on disk) and the title, there is a third required parameter, inst. inst is used to specify a Raw or Epochs object for producing ICA property plots and overlay plots demonstrating the effects of ICA cleaning. If, instead, you only want to generate ICA component topography plots, explicitly pass inst=None.

Note

mne.Report.add_ica() only works with fitted ICAs.

You can optionally specify for which components to produce topography and properties plots by passing picks. By default, all components will be shown. It is also possible to pass evoked signals based on ECG and EOG events via ecg_evoked and eog_evoked. This allows you directly see the effects of ICA component removal on these artifactual signals. Artifact detection scores produced by find_bads_ecg() and find_bads_eog() can be passed via the ecg_scores and eog_scores parameters, respectively, producing visualizations of the scores for each ICA component.

Lastly, by passing n_jobs, you may largely speed up the generation of the properties plots by enabling parallel execution.

Warning

In the following example, we request a small number of ICA components to estimate, set the threshold for assuming ICA convergence to a very liberal value, and only visualize 2 of the components. All of this is done to largely reduce the processing time of this tutorial, and is usually not recommended for an actual data analysis.

ica = mne.preprocessing.ICA(
    n_components=5,  # fit 5 ICA components
    fit_params=dict(tol=0.01)  # assume very early on that ICA has converged
)

ica.fit(inst=raw)

# create epochs based on EOG events, find EOG artifacts in the data via pattern
# matching, and exclude the EOG-related ICA components
eog_epochs = mne.preprocessing.create_eog_epochs(raw=raw)
eog_components, eog_scores = ica.find_bads_eog(
    inst=eog_epochs,
    ch_name='EEG 001',  # a channel close to the eye
    threshold=1  # lower than the default threshold
)
ica.exclude = eog_components

report = mne.Report(title='ICA example')
report.add_ica(
    ica=ica,
    title='ICA cleaning',
    picks=[0, 1],  # only plot the first two components
    inst=raw,
    eog_evoked=eog_epochs.average(),
    eog_scores=eog_scores,
    n_jobs=None  # could be increased!
)
report.save('report_ica.html', overwrite=True)
Fitting ICA to data using 59 channels (please be patient, this may take a while)
Selecting by number: 5 components
Fitting ICA took 0.1s.
Using EOG channel: EOG 061
EOG channel index for this subject is: [68]
Filtering the data to remove DC offset to help distinguish blinks from saccades
Setting up band-pass filter from 1 - 10 Hz

FIR filter parameters
---------------------
Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:
- Windowed frequency-domain design (firwin2) method
- Hann window
- Lower passband edge: 1.00
- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)
- Upper passband edge: 10.00 Hz
- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)
- Filter length: 1502 samples (10.003 sec)

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
Now detecting blinks and generating corresponding events
Found 10 significant peaks
Number of EOG events detected: 10
Not setting metadata
10 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 1)
Using data from preloaded Raw for 10 events and 151 original time points ...
0 bad epochs dropped
Using EOG channel: EEG 001
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Applying ICA to Raw instance
    Transforming to ICA space (5 components)
    Zeroing out 1 ICA component
    Projecting back using 59 PCA components
Using matplotlib as 2D backend.
Using qt as 2D backend.
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
    Using multitaper spectrum estimation with 7 DPSS windows
Not setting metadata
30 matching events found
No baseline correction applied
0 projection items activated
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    Using multitaper spectrum estimation with 7 DPSS windows
Not setting metadata
30 matching events found
No baseline correction applied
0 projection items activated
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.9s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.9s finished
Saving report to : /home/circleci/project/tutorials/intro/report_ica.html

Adding MRI with BEM#

MRI slices with superimposed traces of the boundary element model (BEM) surfaces can be added via mne.Report.add_bem(). All you need to pass is the FreeSurfer subject name and subjects directory, and a title. To reduce the resulting file size, you may pass the decim parameter to only include every n-th volume slice, and width to specify the width of the resulting figures in pixels.

report = mne.Report(title='BEM example')
report.add_bem(
    subject='sample', subjects_dir=subjects_dir, title='MRI & BEM',
    decim=20,
    width=256
)
report.save('report_mri_and_bem.html', overwrite=True)
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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
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.8s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.8s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.8s finished
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.2s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.2s finished
Saving report to : /home/circleci/project/tutorials/intro/report_mri_and_bem.html

Adding coregistration#

The sensor alignment (head -> mri transformation obtained by “coregistration”) can be visualized via mne.Report.add_trans(). The method expects the transformation either as a Transform object or as a path to a trans.fif file, the FreeSurfer subject name and subjects directory, and a title. The alpha parameter can be used to control the transparency of the head, where a value of 1 means fully opaque.

trans_path = sample_dir / 'sample_audvis_raw-trans.fif'

report = mne.Report(title='Coregistration example')
report.add_trans(
    trans=trans_path, info=raw_path, subject='sample',
    subjects_dir=subjects_dir, alpha=1.0, title='Coregistration'
)
report.save('report_coregistration.html', overwrite=True)
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    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
Using lh.seghead for head surface.
Getting helmet for system 306m
Channel types:: grad: 203, mag: 102, eeg: 59
Using surface from /home/circleci/mne_data/MNE-sample-data/subjects/sample/bem/sample-head.fif.
Saving report to : /home/circleci/project/tutorials/intro/report_coregistration.html

Adding a Forward solution#

Forward solutions (“leadfields”) can be added by passing a Forward object or the path to a forward solution stored on disk to meth:mne.Report.add_forward.

fwd_path = sample_dir / 'sample_audvis-meg-oct-6-fwd.fif'

report = mne.Report(title='Forward solution example')
report.add_forward(forward=fwd_path, title='Forward solution')
report.save('report_forward_sol.html', overwrite=True)
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Reading forward solution from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-fwd.fif...
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read
    Desired named matrix (kind = 3523) not available
    Read MEG forward solution (7498 sources, 306 channels, free orientations)
    Source spaces transformed to the forward solution coordinate frame
Saving report to : /home/circleci/project/tutorials/intro/report_forward_sol.html

Adding an InverseOperator#

An inverse operator can be added via mne.Report.add_inverse_operator(). The method expects an InverseOperator object or a path to one stored on disk, and a title.

inverse_op_path = sample_dir / 'sample_audvis-meg-oct-6-meg-inv.fif'

report = mne.Report(title='Inverse operator example')
report.add_inverse_operator(
    inverse_operator=inverse_op_path, title='Inverse operator'
)
report.save('report_inverse_op.html', overwrite=True)
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Reading inverse operator decomposition from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif...
    Reading inverse operator info...
    [done]
    Reading inverse operator decomposition...
    [done]
    305 x 305 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
    Noise covariance matrix read.
    22494 x 22494 diagonal covariance (kind = 2) found.
    Source covariance matrix read.
    22494 x 22494 diagonal covariance (kind = 6) found.
    Orientation priors read.
    22494 x 22494 diagonal covariance (kind = 5) found.
    Depth priors read.
    Did not find the desired covariance matrix (kind = 3)
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read
    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
    Source spaces transformed to the inverse solution coordinate frame
Saving report to : /home/circleci/project/tutorials/intro/report_inverse_op.html

Adding a SourceEstimate#

An inverse solution (also called source estimate or source time course, STC) can be added via mne.Report.add_stc(). The method expects an SourceEstimate, the corresponding FreeSurfer subject name and subjects directory, and a title. By default, it will produce snapshots at 51 equally-spaced time points (or fewer, if the data contains fewer time points). We can adjust this via the n_time_points parameter.

stc_path = sample_dir / 'sample_audvis-meg'

report = mne.Report(title='Source estimate example')
report.add_stc(
    stc=stc_path, subject='sample', subjects_dir=subjects_dir,
    title='Source estimate', n_time_points=2  # few for speed
)
report.save('report_inverse_sol.html', overwrite=True)
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Using control points [ 5.01632618  6.06303297 21.63565434]
Saving report to : /home/circleci/project/tutorials/intro/report_inverse_sol.html

Adding source code (e.g., a Python script)#

It is possible to add code or scripts (e.g., the scripts you used for analysis) to the report via mne.Report.add_code(). The code blocks will be automatically syntax-highlighted. You may pass a string with the respective code snippet, or the path to a file. If you pass a path, it must be a pathlib.Path object (and not a string), otherwise it will be treated as a code literal.

Optionally, you can specify which programming language to assume for syntax highlighting by passing the language parameter. By default, we’ll assume the provided code is Python.

mne_init_py_path = Path(mne.__file__)  # __init__.py in the MNE-Python root
mne_init_py_content = mne_init_py_path.read_text(encoding='utf-8')

report = mne.Report(title='Code example')
report.add_code(
    code=mne_init_py_path,
    title="Code from Path"
)
report.add_code(
    code=mne_init_py_content,
    title="Code from string"
)

report.save('report_code.html', overwrite=True)
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Saving report to : /home/circleci/project/tutorials/intro/report_code.html

Adding custom figures#

Custom Matplotlib figures can be added via add_figure(). Required parameters are the figure and a title. Optionally, may add a caption to appear below the figure. You can also specify the image format of the image file that will be generated from the figure, so it can be embedded in the HTML report.

x = np.linspace(start=0, stop=10, num=100)
y = x**2

fig, ax = plt.subplots()
ax.plot(x, y, ls='--', lw=2, color='blue', label='my function')
ax.set_xlabel('x')
ax.set_ylabel('f(x)')
ax.legend()

report = mne.Report(title='Figure example')
report.add_figure(
    fig=fig, title='A custom figure',
    caption='A blue dashed line reaches up into the sky …',
    image_format='PNG'
)
report.save('report_custom_figure.html', overwrite=True)
plt.close(fig)
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Multiple figures can be grouped into a single section via the section parameter.

fig_1, ax_1 = plt.subplots()
ax_1.plot([1, 2, 3])

fig_2, ax_2 = plt.subplots()
ax_2.plot([3, 2, 1])

section = 'Section example'

report = mne.Report(title='Figure section example')
report.add_figure(
    fig=fig_1,
    title='Figure 1',
    section=section,
    tags='fig-1'
)
report.add_figure(
    fig=fig_2,
    title='Figure 2',
    section=section,
    tags='fig-2'
)
report.save('report_custom_figure_sections.html', overwrite=True)
plt.close(fig_1)
plt.close(fig_2)
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The mne.Report.add_figure() method can also add multiple figures at once. In this case, a slider will appear, allowing users to intuitively browse the figures. To make this work, you need to provide a collection o figures, a title, and optionally a collection of captions.

In the following example, we will read the MNE logo as a Matplotlib figure and rotate it with different angles. Each rotated figure and its respective caption will be added to a list, which is then used to create the slider.

mne_logo_path = Path(mne.__file__).parent / 'icons' / 'mne_icon-cropped.png'
fig_array = plt.imread(mne_logo_path)
rotation_angles = np.linspace(start=0, stop=360, num=17)

figs = []
captions = []
for angle in rotation_angles:
    # Rotate and remove some rounding errors to avoid Matplotlib warnings
    fig_array_rotated = scipy.ndimage.rotate(input=fig_array, angle=angle)
    fig_array_rotated = fig_array_rotated.clip(min=0, max=1)

    # Create the figure
    fig, ax = plt.subplots()
    ax.imshow(fig_array_rotated)
    ax.set_axis_off()

    # Store figure and caption
    figs.append(fig)
    captions.append(f'Rotation angle: {round(angle, 1)}°')

# can also be a MNEQtBrowser instance
figs.append(raw.plot())
captions.append('... plus a raw data plot')

report = mne.Report(title='Multiple figures example')
report.add_figure(fig=figs, title='Fun with figures! 🥳', caption=captions)
report.save('report_custom_figures.html', overwrite=True)
for fig in figs[:-1]:
    plt.close(fig)
figs[-1].close()
del figs
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(768, 768, 4) 1.0 0.0 0.9104400302330122
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Adding image files#

Existing images (e.g., photos, screenshots, sketches etc.) can be added to the report via mne.Report.add_image(). Supported image formats include JPEG, PNG, GIF, and SVG (and possibly others). Like with Matplotlib figures, you can specify a caption to appear below the image.

report = mne.Report(title='Image example')
report.add_image(
    image=mne_logo_path, title='MNE',
    caption='Powered by 🧠 🧠 🧠 around the world!'
)
report.save('report_custom_image.html', overwrite=True)
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Working with tags#

Each add_* method accepts a keyword parameter tags, which can be used to pass one or more tags to associate with the respective content elements. By default, each add_* method adds a tag describing the data type, e.g., evoked or source-estimate. When viewing the HTML report, the Filter by tags dropdown menu can be used to interactively show or hide content with specific tags. This allows you e.g. to only view evoked or participant-001 data, should you have added those tags. Visible tags will appear with blue, and hidden tags with gray background color.

To toggle the visibility of all tags, use the respective checkbox in the Filter by tags dropdown menu, or press T.

report = mne.Report(title='Tags example')
report.add_image(
    image=mne_logo_path,
    title='MNE Logo',
    tags=('image', 'mne', 'logo', 'open-source')
)
report.save('report_tags.html', overwrite=True)
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Saving report to : /home/circleci/project/tutorials/intro/report_tags.html

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 = mne.Report(title='Saved report example', verbose=True)
report.add_image(image=mne_logo_path, title='MNE 1')
report.save('report_partial.hdf5', overwrite=True)
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The saved report can be read back and modified or amended. This allows the possibility to e.g. run multiple scripts in a processing pipeline, where each script adds new content to an existing report.

report_from_disk = mne.open_report('report_partial.hdf5')
report_from_disk.add_image(image=mne_logo_path, title='MNE 2')
report_from_disk.save('report_partial.hdf5', overwrite=True)
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Overwriting existing file.
Saving report to : /home/circleci/project/tutorials/intro/report_partial.hdf5

To make this even easier, mne.Report can be used as a context manager (note the with statement)`):

with mne.open_report('report_partial.hdf5') as report:
    report.add_image(image=mne_logo_path, title='MNE 3')
    report.save('report_final.html', overwrite=True)
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Saving report to : /home/circleci/project/tutorials/intro/report_final.html
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Saving report to : /home/circleci/project/tutorials/intro/report_partial.hdf5

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

Adding an entire folder of files#

We also provide a way to add an entire folder of files to the report at once, without having to invoke the individual add_* methods outlined above for each file. This approach, while convenient, provides less flexibility with respect to content ordering, tags, titles, etc.

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. To save some processing time in this tutorial, we’re also going to disable rendering of the butterfly plots for the Raw data by passing raw_butterfly=False.

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.

report = mne.Report(title='parse_folder example')
report.parse_folder(
    data_path=data_path, pattern='*raw.fif', render_bem=False,
    raw_butterfly=False
)
report.save('report_parse_folder_basic.html', overwrite=True)
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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
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished
Saving report to : /home/circleci/project/tutorials/intro/report_parse_folder_basic.html

By default, the power spectral density and SSP projectors of the Raw files are not shown to speed up report generation. You can add them by passing raw_psd=True and projs=True to the Report constructor. Like in the previous example, we’re going to omit the butterfly plots by passing raw_butterfly=False. 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(title='parse_folder example 2', raw_psd=True, projs=True)
report.parse_folder(
    data_path=data_path, pattern=pattern, render_bem=False, raw_butterfly=False
)
report.save('report_parse_folder_raw_psd_projs.html', overwrite=True)
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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
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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)
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
Effective window size : 13.639 (s)
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s finished
Effective window size : 13.639 (s)
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.7s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.7s finished
Saving report to : /home/circleci/project/tutorials/intro/report_parse_folder_raw_psd_projs.html

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.

report = mne.Report(
    title='parse_folder example 3', subject='sample', subjects_dir=subjects_dir
)
report.parse_folder(data_path=data_path, pattern='', mri_decim=25)
report.save('report_parse_folder_mri_bem.html', overwrite=True)
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Iterating over 0 potential files (this may take some
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished
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 : /home/circleci/project/tutorials/intro/report_parse_folder_mri_bem.html

Now let’s look at how Report handles Evoked data (we will skip the MRIs to save computation time).

The MNE sample dataset we’re using in this example has not been baseline-corrected; so let’s apply baseline correction 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.

Lastly, we want to render the “whitened” evoked data, too. Whitening requires us to specify the path to a covariance matrix file via the cov_fname parameter of Report.

Now, let’s put all of this together! Here we use a temporary directory for speed so we can render a single Evoked instance, using just EEG channels.

baseline = (None, 0)
cov_fname = sample_dir / 'sample_audvis-cov.fif'
pattern = 'sample_audvis-no-filter-ave.fif'
evoked = mne.read_evokeds(sample_dir / pattern)[0]
report = mne.Report(
    title='parse_folder example 4', baseline=baseline, cov_fname=cov_fname
)
with tempfile.TemporaryDirectory() as path:
    evoked.save(Path(path) / pattern)
    report.parse_folder(
        path, pattern=pattern, render_bem=False, n_time_points_evokeds=5
    )
report.save('report_parse_folder_evoked.html', overwrite=True)
Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-no-filter-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.
No baseline correction applied
    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 (Right Auditory)
        0 CTF compensation matrices available
        nave = 59 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
    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 visual)
        0 CTF compensation matrices available
        nave = 64 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
    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 (Right visual)
        0 CTF compensation matrices available
        nave = 56 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
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    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
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
Rendering : /tmp/tmpsbnfig7l/sample_audvis-no-filter-ave.fif
Reading /tmp/tmpsbnfig7l/sample_audvis-no-filter-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.
No baseline correction applied
Applying baseline correction (mode: mean)
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.6s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    1.5s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    2.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    2.6s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    3.2s finished
combining channels using "gfp"
combining channels using "gfp"
combining channels using "gfp"
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)
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.2s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.2s finished
Saving report to : /home/circleci/project/tutorials/intro/report_parse_folder_evoked.html

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 = sample_dir / 'sample_audvis-ave.fif'
report = mne.Report(title='parse_folder example 5', info_fname=info_fname)
report.parse_folder(
    data_path, pattern=pattern, render_bem=False, n_time_points_evokeds=5
)
report.save('report_parse_folder_cov.html', overwrite=True)
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Iterating over 1 potential files (this may take some
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
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
    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 2.6e-12 (2.2e-16 eps * 204 dim * 56  max singular value)
    Estimated rank (grad): 204
    GRAD: rank 204 computed from 204 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 4.8e-14 (2.2e-16 eps * 60 dim * 3.6  max singular value)
    Estimated rank (eeg): 60
    EEG: rank 60 computed from 60 data channels with 0 projectors
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.4s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.4s finished
Saving report to : /home/circleci/project/tutorials/intro/report_parse_folder_cov.html

Adding custom HTML (e.g., a description text)#

The add_html() method allows you to add custom HTML to your report. This feature can be very convenient to add short descriptions, lists, or reminders to your report (among many other things you can think of encoding in HTML).

report = mne.Report(title='Report on hypothesis 1')

my_html = """
<p>We have the following hypothesis:</p>
<ol>
<li>There is a difference between images showing man-made vs. natural
environments</li>
<li>This difference manifests itself most strongly in the amplitude of the
N1 ERP component</li>
</ol>
<p>Below we show several plots and tests of the data.</p>
"""

report.add_html(title='Hypothesis', html=my_html)
report.save('report_add_html.html', overwrite=True)
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Saving report to : /home/circleci/project/tutorials/intro/report_add_html.html

Total running time of the script: ( 1 minutes 1.035 seconds)

Estimated memory usage: 271 MB

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