larsoner

larsoner

"""Visual stimulus design.

Tools for drawing shapes and text on the screen.
"""

# Authors: Dan McCloy <drmccloy@uw.edu>
#          Eric Larson <larsoner@uw.edu>
#          Ross Maddox <rkmaddox@uw.edu>
#
# License: BSD (3-clause)

larsoner

import numpy as np
import mne
import time

data_path = mne.datasets.sample.data_path()
subjects_dir = data_path / 'subjects'
n_dips = 10
radius = 0.05
rr = np.random.default_rng(0).normal(size=(n_dips, 3))
rr /= np.linalg.norm(rr, axis=1)[:, np.newaxis] / radius * 0.9

larsoner

import numpy as np

import mne
from mne.datasets import sample
from mne.inverse_sparse import make_stc_from_dipoles, mixed_norm
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.viz import (
    plot_dipole_amplitudes,
    plot_dipole_locations,
    plot_sparse_source_estimates,

larsoner

.PHONY: all clean openmeeg_wrap.cxx _example.so test1 test2 test3 test4 test5
all : clean example_wrap.cxx _example.so test1 test2 test3 test4 test5

clean:
	rm -f *.o
	rm -f *.so
	rm -f *wrap*.c*
	rm -f *wrapper.py
	rm -Rf build
	rm -Rf __pycache__

larsoner

import mne
data_path = mne.datasets.sample.data_path()
sample_data_raw_file = data_path / "MEG" / "sample" / "sample_audvis_raw.fif"
subjects_dir = data_path / "subjects"
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False).crop(tmax=60)
events = mne.find_events(raw, stim_channel="STI 014")
epochs = mne.Epochs(raw, events, event_id = {"auditory/left": 1}, tmin=-0.3, tmax=0.7)[:10]
inverse_operator_file = data_path / "MEG" / "sample" / "sample_audvis-meg-oct-6-meg-inv.fif"
inv_operator = mne.minimum_norm.read_inverse_operator(inverse_operator_file)
stcs = mne.minimum_norm.apply_inverse_epochs(epochs, inv_operator, lambda2=1/3, pick_ori=None)

larsoner

"""BIDSify and trim the KIT phantom data."""
from pathlib import Path

import numpy as np
from scipy.signal import find_peaks
import matplotlib.pyplot as plt

import mne
import mne_bids
from mne.io.constants import FIFF

larsoner

"""Attempt to build a mne.viz.Brain-like GUI using magicgui."""

from magicgui import widgets, use_app
import numpy as np
import re
import mne
from matplotlib.figure import Figure
import pyvista
import pyvista.plotting

larsoner

# Check 1-tailed t-test bias when selecting ROIs using different methods
import numpy as np
from scipy.stats import ttest_ind

rng = np.random.default_rng(0)

n_subjects = 20  # number of subjects to simulate
n_sensors = 100  # number of "sensors" (could be source points, etc.)
n_run = 5000  # number of times to simulate (n_subjects, n_sensors) values
prop_false = np.zeros((n_run, 3, 2))  # proportion of false alarms

larsoner