Revision: 6132
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=6132&view=rev
Author: efiring
Date: 2008-09-28 22:05:23 +0000 (Sun, 28 Sep 2008)
Log Message:
-----------
Fix mri_with_eeg.py; it was not rescaling or printing correctly.
Modified Paths:
--------------
trunk/matplotlib/examples/pylab_examples/mri_with_eeg.py
Modified: trunk/matplotlib/examples/pylab_examples/mri_with_eeg.py
===================================================================
--- trunk/matplotlib/examples/pylab_examples/mri_with_eeg.py 2008-09-28
13:32:55 UTC (rev 6131)
+++ trunk/matplotlib/examples/pylab_examples/mri_with_eeg.py 2008-09-28
22:05:23 UTC (rev 6132)
@@ -4,16 +4,18 @@
faster*
"""
from __future__ import division
-from pylab import *
-from matplotlib.lines import Line2D
-from matplotlib.transforms import Bbox, BboxTransform, BboxTransformTo,
Affine2D
+import numpy as np
+
+from matplotlib.pyplot import *
+from matplotlib.collections import LineCollection
+
# I use if 1 to break up the different regions of code visually
if 1: # load the data
# data are 256x256 16 bit integers
dfile = '../data/s1045.ima'
- im = fromstring(file(dfile, 'rb').read(), uint16).astype(float)
+ im = np.fromstring(file(dfile, 'rb').read(), np.uint16).astype(float)
im.shape = 256, 256
if 1: # plot the MRI in pcolor
@@ -23,8 +25,8 @@
if 1: # plot the histogram of MRI intensity
subplot(222)
- im = ravel(im)
- im = ravel(take(im, nonzero(im))) # ignore the background
+ im = np.ravel(im)
+ im = im[np.nonzero(im)] # ignore the background
im = im/(2.0**15) # normalize
hist(im, 100)
xticks([-1, -.5, 0, .5, 1])
@@ -36,52 +38,38 @@
# load the data
numSamples, numRows = 800,4
- data = fromstring(file('../data/eeg.dat', 'rb').read(), float)
+ data = np.fromstring(file('../data/eeg.dat', 'rb').read(), float)
data.shape = numSamples, numRows
- t = arange(numSamples)/float(numSamples)*10.0
+ t = 10.0 * np.arange(numSamples, dtype=float)/numSamples
ticklocs = []
ax = subplot(212)
xlim(0,10)
- xticks(arange(10))
+ xticks(np.arange(10))
+ dmin = data.min()
+ dmax = data.max()
+ dr = (dmax - dmin)*0.7 # Crowd them a bit.
+ y0 = dmin
+ y1 = (numRows-1) * dr + dmax
+ ylim(y0, y1)
- boxin = Bbox.from_extents(ax.viewLim.x0, -20, ax.viewLim.x1, 20)
-
- height = ax.bbox.height
- boxout = Bbox.from_extents(ax.bbox.x0, -1.0 * height,
- ax.bbox.x1, 1.0 * height)
-
- transOffset = BboxTransformTo(
- Bbox.from_extents(0.0, ax.bbox.y0, 1.0, ax.bbox.y1))
-
-
+ segs = []
for i in range(numRows):
- # effectively a copy of transData
- trans = BboxTransform(boxin, boxout)
- offset = (i+1)/(numRows+1)
+ segs.append(np.hstack((t[:,np.newaxis], data[:,i,np.newaxis])))
+ ticklocs.append(i*dr)
- trans += Affine2D().translate(*transOffset.transform_point((0,
offset)))
+ offsets = np.zeros((numRows,2), dtype=float)
+ offsets[:,1] = ticklocs
- thisLine = Line2D(
- t, data[:,i]-data[0,i],
- )
+ lines = LineCollection(segs, offsets=offsets,
+ transOffset=None,
+ )
- thisLine.set_transform(trans)
+ ax.add_collection(lines)
- ax.add_line(thisLine)
- ticklocs.append(offset)
-
- setp(gca(), 'yticklabels', ['PG3', 'PG5', 'PG7', 'PG9'])
-
# set the yticks to use axes coords on the y axis
ax.set_yticks(ticklocs)
- for tick in ax.yaxis.get_major_ticks():
- tick.label1.set_transform(ax.transAxes)
- tick.label2.set_transform(ax.transAxes)
- tick.tick1line.set_transform(ax.transAxes)
- tick.tick2line.set_transform(ax.transAxes)
- tick.gridline.set_transform(ax.transAxes)
+ ax.set_yticklabels(['PG3', 'PG5', 'PG7', 'PG9'])
-
xlabel('time (s)')
show()
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