import numpy as np import matplotlib.pyplot as plt import matplotlib.cbook as cbook import matplotlib.cm as cm
from matplotlib.collections import LineCollection from matplotlib.ticker import MultipleLocator
fig = plt.figure("MRI_with_EEG")
# Load the MRI data (256x256 16 bit integers) with cbook.get_sample_data('s1045.ima.gz') as dfile: im = np.fromstring(dfile.read(), np.uint16).reshape((256, 256))
# Plot the histogram of MRI intensity ax1 = fig.add_subplot(2, 2, 2) im = np.ravel(im) im = im[np.nonzero(im)] # Ignore the background im = im / (2**16 - 1) # Normalize ax1.hist(im, bins=100) ax1.xaxis.set_major_locator(MultipleLocator(0.4)) ax1.minorticks_on() ax1.set_yticks([]) ax1.set_xlabel('Intensity (a.u.)') ax1.set_ylabel('MRI density')
# 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 ticklocs = [] ax2 = fig.add_subplot(2, 1, 2) ax2.set_xlim(0, 10) ax2.set_xticks(np.arange(10)) dmin = data.min() dmax = data.max() dr = (dmax - dmin) * 0.7# Crowd them a bit. y0 = dmin y1 = (n_rows - 1) * dr + dmax ax2.set_ylim(y0, y1)
segs = [] for i inrange(n_rows): segs.append(np.column_stack((t, data[:, i]))) ticklocs.append(i * dr)