# test positive bounding box x = arange(-1.0001, 1.0, 0.01) bpos = Bound1D(.1, 1, isPos=True) bpos.update(x) assert (bpos.min() > 0) bpos.is_positive(False) bpos.update(x) assert (bpos.min() == -1.0001) print 'passed positive bounding box tests ...' # testing transforms i1 = Bound1D(0, 1) i2 = Bound1D(-6, 6) identityTrans = Transform() linearTrans = Transform(i1, i2) logTrans = Transform(Bound1D(0.1, 1), i2, funcs=(log10, pow10)) scalar = 1 tup = 1, 2, 3 a = arange(0.0, 2.0, 0.1) assert (identityTrans.positions(scalar) == scalar) assert (identityTrans.positions(tup) == tup) assert (identityTrans.positions(a) == a) assert (identityTrans.scale(scalar) == scalar) assert (identityTrans.scale(tup) == tup) assert (identityTrans.scale(a) == a) assert (linearTrans.positions(1) == 6)
set(gca(), 'yticks', [])
xlabel('intensity')
ylabel('MRI density')
if 1: # plot the EEG
# load the data
numSamples, numRows = 800, 4
data = fromstring(file('data/eeg.dat', 'rb').read(), Float)
data.shape = numSamples, numRows
t = arange(numSamples) / float(numSamples) * 10.0
ticklocs = []
ax = subplot(212)
height = 72 # height of one EEG in pixels
# transform data to axes coord (0,1)
transy = Transform(Bound1D(-.05, .05), Bound1D(-.2, .2))
for i in range(numRows):
thisLine = Line2D(ax.dpi,
ax.bbox,
t,
data[:, i] - data[0, i],
transx=ax.xaxis.transData,
transy=transy)
offset = (i + 1) / (numRows + 1)
thisLine.set_vertical_offset(offset, ax.yaxis.transAxis)
ax.add_line(thisLine)
ticklocs.append(offset)
set(gca(), 'xlim', [0, 10])
set(gca(), 'xticks', arange(10))
