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.. _mne_report_tutorial:
=================================================
Tutorial: Getting started with MNE report command
=================================================
This quick start will show you how to run the `mne report` command on the
sample data set provided with MNE.
First ensure that 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)
events -eve.fif(.gz)
epochs -epo.fif(.gz)
evoked -ave.fif(.gz)
covariance -cov.fif(.gz)
trans -trans.fif(.gz)
forward -fwd.fif(.gz)
inverse -inv.fif(.gz)
================== ====================================================
The command line interface
--------------------------
To generate a barebones report from all the \*.fif files in the sample dataset,
invoke the following command::
mne report --path MNE-sample-data/ --verbose
On successful creation of the report, it will open the html in a new tab in the browser.
To disable this, use the `--no-browser` option.
If the report is generated for a single subject, give the SUBJECT name and the
SUBJECTS_DIR and this will generate the MRI slices (with BEM contours overlaid on top
if available)::
mne report --path MNE-sample-data/ --subject sample --subjects-dir MNE-sample-data/subjects --verbose
To properly render `trans` and `covariance` files, add the measurement information::
mne report --path MNE-sample-data/ --info MNE-sample-data/MEG/sample/sample_audvis-ave.fif \
--subject sample --subjects_dir MNE-sample-data/subjects --verbose
To generate the report in parallel::
mne report --path MNE-sample-data/ --info MNE-sample-data/MEG/sample/sample_audvis-ave.fif \
--subject sample --subjects_dir MNE-sample-data/subjects --verbose --jobs 6
The Python interface
--------------------
The same functionality can also be achieved using the Python interface. Import
the required functions:
>>> from mne.report import Report
>>> from mne.datasets import sample
Generate the report:
>>> path = sample.data_path()
>>> report = Report()
Embedding : jquery-1.10.2.min.js
Embedding : jquery-ui.min.js
Embedding : bootstrap.min.js
Embedding : jquery-ui.min.css
Embedding : bootstrap.min.css
Only include \*-eve.fif files in the report:
>>> report.parse_folder(data_path=path, pattern='*-eve.fif') # doctest: +SKIP
Rendering : .../MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif
Rendering : .../MNE-sample-data/MEG/sample/sample_audvis_eog-eve.fif
Rendering : .../MNE-sample-data/MEG/sample/ernoise_raw-eve.fif
Rendering : .../MNE-sample-data/MEG/sample/sample_audvis_raw-eve.fif
Rendering : .../MNE-sample-data/MEG/sample/sample_audvis_ecg-eve.fif
Save the report as an html, but do not open the html in a browser:
>>> report.save('report.html', overwrite=True, open_browser=False) # doctest:+SKIP
Rendering : Table of Contents...
There is greater flexibility compared to the command line interface.
Custom plots can be added to the report. Let us first generate a custom plot:
>>> from mne import read_evokeds
>>> fname = path + '/MEG/sample/sample_audvis-ave.fif'
>>> evoked = read_evokeds(fname, condition='Left Auditory', baseline=(None, 0)) # doctest:+ELLIPSIS
Reading .../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. Doing nothing.
Applying baseline correction ... (mode: mean)
>>> fig = evoked.plot() # doctest: +SKIP
To add the custom plot to the report, do:
>>> report.add_section(fig, captions='Left Auditory', section='evoked') # doctest: +SKIP
>>> report.save('report.html', overwrite=True) # doctest: +SKIP
Rendering : Table of Contents...
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_section` command. Each section is identified by a toggle button in the navigation
bar of the report which can be used to show or hide the contents of the section.
That's it!
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