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Commit b1bab75

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tacaswelltacaswell
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Merge pull request #7192 from afvincent/doc_pylab_examples_mri_with_eeg
DOC: switch pylab example `mri_with_eeg.py` to OO interface + cosmetic fixes
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‎examples/pylab_examples/mri_demo.py

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+11-5Lines changed: 11 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -1,13 +1,19 @@
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from __future__ import print_function
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"""Displays an MRI image."""
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import matplotlib.pyplot as plt
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import matplotlib.cbook as cbook
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import matplotlib.cm as cm
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import numpy as np
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# data are 256x256 16 bit integers
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fig, ax = plt.subplots(num="MRI_demo")
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# Data are 256x256 16 bit integers
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dfile = cbook.get_sample_data('s1045.ima.gz')
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im = np.fromstring(dfile.read(), np.uint16).astype(float)
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im.shape = 256, 256
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im.shape = (256, 256)
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dfile.close()
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plt.imshow(im, cmap=plt.cm.gray)
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plt.axis('off')
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ax.imshow(im, cmap=cm.gray)
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ax.axis('off')
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plt.show()

‎examples/pylab_examples/mri_with_eeg.py

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+72-70Lines changed: 72 additions & 70 deletions
Original file line numberDiff line numberDiff line change
@@ -1,77 +1,79 @@
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"""
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This now uses the imshow command instead of pcolor which *is much
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faster*
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"""Displays a set of subplots with an MRI image, its intensity histogram and
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some EEG traces.
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"""
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from __future__ import division, print_function
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.cbook as cbook
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import matplotlib.cm as cm
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from matplotlib.pyplot import *
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from matplotlib.collections import LineCollection
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import matplotlib.cbook as cbook
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# I use if 1 to break up the different regions of code visually
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if 1: # load the data
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# data are 256x256 16 bit integers
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dfile = cbook.get_sample_data('s1045.ima.gz')
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im = np.fromstring(dfile.read(), np.uint16).astype(float)
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im.shape = 256, 256
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if 1: # plot the MRI in pcolor
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subplot(221)
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imshow(im, cmap=cm.gray)
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axis('off')
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if 1: # plot the histogram of MRI intensity
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subplot(222)
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im = np.ravel(im)
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im = im[np.nonzero(im)] # ignore the background
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im = im/(2.0**15) # normalize
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hist(im, 100)
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xticks([-1, -.5, 0, .5, 1])
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yticks([])
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xlabel('intensity')
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ylabel('MRI density')
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if 1: # plot the EEG
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# load the data
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numSamples, numRows = 800, 4
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eegfile = cbook.get_sample_data('eeg.dat', asfileobj=False)
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print('loading eeg %s' % eegfile)
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data = np.fromstring(open(eegfile, 'rb').read(), float)
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data.shape = numSamples, numRows
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t = 10.0 * np.arange(numSamples, dtype=float)/numSamples
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ticklocs = []
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ax = subplot(212)
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xlim(0, 10)
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xticks(np.arange(10))
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dmin = data.min()
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dmax = data.max()
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dr = (dmax - dmin)*0.7 # Crowd them a bit.
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y0 = dmin
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y1 = (numRows - 1) * dr + dmax
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ylim(y0, y1)
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segs = []
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for i in range(numRows):
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segs.append(np.hstack((t[:, np.newaxis], data[:, i, np.newaxis])))
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ticklocs.append(i*dr)
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offsets = np.zeros((numRows, 2), dtype=float)
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offsets[:, 1] = ticklocs
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lines = LineCollection(segs, offsets=offsets,
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transOffset=None,
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)
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ax.add_collection(lines)
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# set the yticks to use axes coords on the y axis
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ax.set_yticks(ticklocs)
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ax.set_yticklabels(['PG3', 'PG5', 'PG7', 'PG9'])
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xlabel('time (s)')
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show()
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from matplotlib.ticker import MultipleLocator
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fig = plt.figure("MRI_with_EEG")
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# Load the MRI data (256x256 16 bit integers)
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dfile = cbook.get_sample_data('s1045.ima.gz')
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im = np.fromstring(dfile.read(), np.uint16).astype(float)
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im.shape = (256, 256)
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dfile.close()
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# Plot the MRI image
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ax0 = fig.add_subplot(2, 2, 1)
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ax0.imshow(im, cmap=cm.gray)
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ax0.axis('off')
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# Plot the histogram of MRI intensity
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ax1 = fig.add_subplot(2, 2, 2)
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im = np.ravel(im)
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im = im[np.nonzero(im)] # Ignore the background
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im = im / (2**16 - 1) # Normalize
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ax1.hist(im, bins=100)
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ax1.xaxis.set_major_locator(MultipleLocator(0.4))
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ax1.minorticks_on()
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ax1.set_yticks([])
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ax1.set_xlabel('Intensity (a.u.)')
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ax1.set_ylabel('MRI density')
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# Load the EEG data
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numSamples, numRows = 800, 4
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eegfile = cbook.get_sample_data('eeg.dat', asfileobj=False)
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print('Loading EEG %s' % eegfile)
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data = np.fromfile(eegfile, dtype=float)
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data.shape = (numSamples, numRows)
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t = 10.0 * np.arange(numSamples) / numSamples
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# Plot the EEG
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ticklocs = []
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ax2 = fig.add_subplot(2, 1, 2)
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ax2.set_xlim(0, 10)
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ax2.set_xticks(np.arange(10))
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dmin = data.min()
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dmax = data.max()
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dr = (dmax - dmin) * 0.7 # Crowd them a bit.
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y0 = dmin
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y1 = (numRows - 1) * dr + dmax
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ax2.set_ylim(y0, y1)
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segs = []
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for i in range(numRows):
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segs.append(np.hstack((t[:, np.newaxis], data[:, i, np.newaxis])))
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ticklocs.append(i * dr)
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offsets = np.zeros((numRows, 2), dtype=float)
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offsets[:, 1] = ticklocs
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lines = LineCollection(segs, offsets=offsets, transOffset=None)
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ax2.add_collection(lines)
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# Set the yticks to use axes coordinates on the y axis
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ax2.set_yticks(ticklocs)
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ax2.set_yticklabels(['PG3', 'PG5', 'PG7', 'PG9'])
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ax2.set_xlabel('Time (s)')
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plt.tight_layout()
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plt.show()

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