Marijn van Vliet wmvanvliet

Academy Research Fellow.

wmvanvliet / EOG regression exploration.ipynb

Created August 25, 2022 10:46

Exploration of regression methods for correcting EOG artifacts in EEG data.

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wmvanvliet / aoc2021.py

Last active December 25, 2021 08:34

Solutions to Advent of Code 2021 using the Python ecosystem

	import numpy as np
	import pandas as pd
	import string
	from collections import deque, Counter, defaultdict
	from copy import deepcopy
	from itertools import chain
	from scipy.signal import convolve2d
	from scipy.spatial import distance
	from tqdm import tqdm
	import matplotlib.pyplot as plt

wmvanvliet / env.yml

Created May 27, 2021 10:48

wmvanvliet / test_docstring_highlighting.py

Created April 13, 2021 10:59

Test cases for python docstring highlighting

	"""
	This should be a docstring.

	Second line of the docstring.
	"""
	"""This should not be a docstring."""
	b"""This should not be a docstring."""
	r"""This should not be a docstring."""
	"This should not be a docstring."

wmvanvliet / test_batch_size.py

Created December 30, 2020 08:00

	"""
	Test whether modifying the batch size changes the result

	Authors: Marijn van Vliet <[email protected]>
	"""
	import torch
	from torch.utils.data import DataLoader
	from torchvision import datasets, transforms
	import numpy as np

wmvanvliet / dip_plot_test.py

Last active August 28, 2020 10:20

Testing plotting of dipoles using MNE-Python and PySurfer

	import mne
	import surfer
	from mayavi import mlab

	# Various filenames we'll be needing
	data_path = mne.datasets.sample.data_path()
	subjects_dir = f'{data_path}/subjects'
	fname_trans = f'{data_path}/MEG/sample/sample_audvis_raw-trans.fif'
	fname_cov = f'{data_path}/MEG/sample/sample_audvis-cov.fif'
	fname_evokeds = f'{data_path}/MEG/sample/sample_audvis-ave.fif'

wmvanvliet / pep8-toggle.vim

Last active May 5, 2020 19:40

Vim keybinding for toggling PEP8 linting

	" Linter (provided through ALE)
	let g:ale_lint_on_text_changed = 'never'
	let g:ale_lint_on_save = 1
	let g:ale_linters = {
	\ 'python': ['flake8'],
	\}
	" By default, don't worry about PEP8
	let g:ale_python_flake8_options = "--ignore=E2,E3,E4,E5,E7,W"
	let g:python_highlight_space_errors = 0

wmvanvliet / plot_rsa_result.py

Last active April 23, 2020 05:59

	import mne
	import numpy as np
	import mne_rsa

	dsms_file = np.load('pilot2_dsms.npz')
	meg_dsms = dsms_file['dsms']
	model_dsms = ... # Up to you to create

	# RSA: compare all MEG DSMs to all model DSMs
	rsa_results = mne_rsa.rsa(meg_dsms, model_dsms, metric='spearman')

wmvanvliet / without_mri.py

Created February 12, 2020 16:22

Performing MEG source estimation without an MRI, using the 'fsaverage' template MRI instead

	import mne

	# MEG data we take from the MNE-Sample dataset
	data_path = mne.datasets.sample.data_path()
	subjects_dir = f'{data_path}/subjects'
	evoked = mne.read_evokeds(f'{data_path}/MEG/sample/sample_audvis-ave.fif', condition='Left Auditory').apply_baseline()
	noise_cov = mne.read_cov(f'{data_path}/MEG/sample/sample_audvis-cov.fif')

	# MRI data we will take from the 'fsaverage' template brain
	fs_dir = mne.datasets.fetch_fsaverage(verbose=True) # Download the files

wmvanvliet / sphere_model.py

Last active February 12, 2020 16:13

Here's something you shouldn't do with MNE-Python

	import mne
	from mayavi import mlab

	data_path = mne.datasets.sample.data_path()
	subjects_dir = f'{data_path}/subjects'

	evoked = mne.read_evokeds(f'{data_path}/MEG/sample/sample_audvis-ave.fif', condition='Left Auditory').apply_baseline()
	noise_cov = mne.read_cov(f'{data_path}/MEG/sample/sample_audvis-cov.fif')
	bem = mne.make_sphere_model(r0='auto', head_radius='auto', info=evoked.info)
	src = mne.setup_volume_source_space(bem=bem)