.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/04_glm_first_level/plot_write_events_file.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_04_glm_first_level_plot_write_events_file.py: Generate an events.tsv file for the NeuroSpin localizer task ============================================================ Create a :term:`BIDS`-compatible events.tsv file from onset/trial-type information. The protocol described is the so-called "ARCHI Standard" functional localizer task. For details on the task, please see: Pinel, P., Thirion, B., Meriaux, S. et al. Fast reproducible identification and large-scale databasing of individual functional cognitive networks. BMC Neurosci 8, 91 (2007). https://doi.org/10.1186/1471-2202-8-91 .. GENERATED FROM PYTHON SOURCE LINES 18-21 .. code-block:: default print(__doc__) .. GENERATED FROM PYTHON SOURCE LINES 22-25 Define the onset times in seconds. These are typically extracted from the stimulation software used, but we will use hardcoded values in this example. .. GENERATED FROM PYTHON SOURCE LINES 25-38 .. code-block:: default import numpy as np onsets = np.array([ 0., 2.4, 8.7, 11.4, 15., 18., 20.7, 23.7, 26.7, 29.7, 33., 35.4, 39., 41.7, 44.7, 48., 56.4, 59.7, 62.4, 69., 71.4, 75., 83.4, 87., 89.7, 96., 108., 116.7, 119.4, 122.7, 125.4, 131.4, 135., 137.7, 140.4, 143.4, 146.7, 149.4, 153., 156., 159., 162., 164.4, 167.7, 170.4, 173.7, 176.7, 188.4, 191.7, 195., 198., 201., 203.7, 207., 210., 212.7, 215.7, 218.7, 221.4, 224.7, 227.7, 230.7, 234., 236.7, 246., 248.4, 251.7, 254.7, 257.4, 260.4, 264., 266.7, 269.7, 275.4, 278.4, 284.4, 288., 291., 293.4, 296.7, ]) .. GENERATED FROM PYTHON SOURCE LINES 39-41 Associated trial types: these are numbered between 0 and 9, hence corresponding to 10 different conditions. .. GENERATED FROM PYTHON SOURCE LINES 41-48 .. code-block:: default trial_type_idx = np.array([ 7, 7, 0, 2, 9, 4, 9, 3, 5, 9, 1, 6, 8, 8, 6, 6, 8, 0, 3, 4, 5, 8, 6, 2, 9, 1, 6, 5, 9, 1, 7, 8, 6, 6, 1, 2, 9, 0, 7, 1, 8, 2, 7, 8, 3, 6, 0, 0, 6, 8, 7, 7, 1, 1, 1, 5, 5, 0, 7, 0, 4, 2, 7, 9, 8, 0, 6, 3, 3, 7, 1, 0, 0, 4, 1, 9, 8, 4, 9, 9, ]) .. GENERATED FROM PYTHON SOURCE LINES 49-51 We may want to map these indices to explicit condition names. For that, we define a list of 10 strings. .. GENERATED FROM PYTHON SOURCE LINES 51-66 .. code-block:: default condition_ids = [ 'horizontal checkerboard', 'vertical checkerboard', 'right button press, auditory instructions', 'left button press, auditory instructions', 'right button press, visual instructions', 'left button press, visual instructions', 'mental computation, auditory instructions', 'mental computation, visual instructions', 'visual sentence', 'auditory sentence', ] trial_types = [condition_ids[i] for i in trial_type_idx] .. GENERATED FROM PYTHON SOURCE LINES 67-69 We must also define a duration (required by :term:`BIDS` conventions). In this case, all trials lasted one second. .. GENERATED FROM PYTHON SOURCE LINES 69-71 .. code-block:: default durations = np.ones_like(onsets) .. GENERATED FROM PYTHON SOURCE LINES 72-73 Form a pandas DataFrame from this information. .. GENERATED FROM PYTHON SOURCE LINES 73-81 .. code-block:: default import pandas as pd events = pd.DataFrame({ 'trial_type': trial_types, 'onset': onsets, 'duration': durations, }) .. GENERATED FROM PYTHON SOURCE LINES 82-83 Take a look at the new DataFrame .. GENERATED FROM PYTHON SOURCE LINES 83-85 .. code-block:: default events .. raw:: html
trial_type onset duration
0 mental computation, visual instructions 0.0 1.0
1 mental computation, visual instructions 2.4 1.0
2 horizontal checkerboard 8.7 1.0
3 right button press, auditory instructions 11.4 1.0
4 auditory sentence 15.0 1.0
... ... ... ...
75 auditory sentence 284.4 1.0
76 visual sentence 288.0 1.0
77 right button press, visual instructions 291.0 1.0
78 auditory sentence 293.4 1.0
79 auditory sentence 296.7 1.0

80 rows × 3 columns



.. GENERATED FROM PYTHON SOURCE LINES 86-87 Export them to a tsv file. .. GENERATED FROM PYTHON SOURCE LINES 87-91 .. code-block:: default tsvfile = 'localizer_events.tsv' events.to_csv(tsvfile, sep='\t', index=False) print(f'The event information has been saved to {tsvfile}') .. rst-class:: sphx-glr-script-out Out: .. code-block:: none The event information has been saved to localizer_events.tsv .. GENERATED FROM PYTHON SOURCE LINES 92-94 Optionally, the events can be visualized using the :func:`~nilearn.plotting.plot_event` function. .. GENERATED FROM PYTHON SOURCE LINES 94-99 .. code-block:: default import matplotlib.pyplot as plt from nilearn.plotting import plot_event plot_event(events, figsize=(15, 5)) plt.show() .. image-sg:: /auto_examples/04_glm_first_level/images/sphx_glr_plot_write_events_file_001.png :alt: plot write events file :srcset: /auto_examples/04_glm_first_level/images/sphx_glr_plot_write_events_file_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 4.708 seconds) **Estimated memory usage:** 9 MB .. _sphx_glr_download_auto_examples_04_glm_first_level_plot_write_events_file.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/main?filepath=examples/auto_examples/04_glm_first_level/plot_write_events_file.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_write_events_file.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_write_events_file.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_