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"""
============
MRI with EEG
============
Displays a set of subplots with an MRI image, its intensity
histogram and some EEG traces.
.. redirect-from:: /gallery/specialty_plots/mri_demo
"""
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cbook as cbook
fig, axd = plt.subplot_mosaic(
[["image", "density"],
["EEG", "EEG"]],
layout="constrained",
# "image" will contain a square image. We fine-tune the width so that
# there is no excess horizontal or vertical margin around the image.
width_ratios=[1.05, 2],
)
# Load the MRI data (256x256 16-bit integers)
with cbook.get_sample_data('s1045.ima.gz') as dfile:
im = np.frombuffer(dfile.read(), np.uint16).reshape((256, 256))
# Plot the MRI image
axd["image"].imshow(im, cmap="gray")
axd["image"].axis('off')
# Plot the histogram of MRI intensity
im = im[im.nonzero()] # Ignore the background
axd["density"].hist(im, bins=np.arange(0, 2**16+1, 512))
axd["density"].set(xlabel='Intensity (a.u.)', xlim=(0, 2**16),
ylabel='MRI density', yticks=[])
axd["density"].minorticks_on()
# Load the EEG data
n_samples, n_rows = 800, 4
with cbook.get_sample_data('eeg.dat') as eegfile:
data = np.fromfile(eegfile, dtype=float).reshape((n_samples, n_rows))
t = 10 * np.arange(n_samples) / n_samples
# Plot the EEG
axd["EEG"].set_xlabel('Time (s)')
axd["EEG"].set_xlim(0, 10)
dy = (data.min() - data.max()) * 0.7 # Crowd them a bit.
axd["EEG"].set_ylim(-dy, n_rows * dy)
axd["EEG"].set_yticks([0, dy, 2*dy, 3*dy], labels=['PG3', 'PG5', 'PG7', 'PG9'])
for i, data_col in enumerate(data.T):
axd["EEG"].plot(t, data_col + i*dy, color="C0")
plt.show()
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