def corrExample(dm, soaBehav, soaPupil, dv='correct', suffix=''):
"""
Plots an example of the correlation between behavior and pupil size at the
most strongly correlating point.
Arguments:
dm -- A DataMatrix.
soaBehav -- The SOA to analyze for the behavioral effect.
soaPupil -- The SOA to analyze for the pupil effect.
Keyword arguments:
dv -- The dependent variable to use for the behavioral effect.
(default='correct')
suffix -- A filename suffix.
"""
assert(soaPupil in dm.unique('soa'))
assert(soaBehav in dm.unique('soa'))
if stripCorr:
suffix += '.stripCorr'
a = corrTrace(dm, soaBehav, soaPupil, dv='correct', suffix='acc', \
cacheId='corrTrace.correct.%d.%d%s' % (soaBehav, soaPupil, suffix))
bestSample = np.argmax(a[:,0])
dmBehav = dm.select('soa == %d' % soaBehav)
dmPupil = dm.select('soa == %d' % soaPupil)
xData = []
yData = []
print 'pp\tbehav\tpupil'
for subject_nr in dm.unique('subject_nr'):
ceb = cuingEffectBehav(dmBehav.select('subject_nr == %d' \
% subject_nr, verbose=False), dv=dv)
cep = cuingEffectPupil(dmPupil.select('subject_nr == %d' \
% subject_nr, verbose=False), epoch=(bestSample, \
bestSample+winSize))
print '%.2d\t %.4f\t%.4f' % (subject_nr, ceb, cep)
yData.append(100.*ceb)
xData.append(cep)
if stripCorr:
index, xData, yData = stripStd(np.array(xData), np.array(yData))
Plot.new(size=(3,3))
plt.title('SOA: %d ms (behavior), %d ms (pupil)' % (soaBehav+55, \
soaPupil+55))
Plot.regress(xData, yData)
plt.text(0.05, 0.90, 'Sample = %d' % bestSample, ha='left', \
va='top', transform=plt.gca().transAxes)
plt.axhline(0, linestyle='--', color='black')
plt.axvline(0, linestyle='--', color='black')
plt.plot(xData, yData, 'o', color='black')
plt.ylabel('Behav. cuing effect (%)')
plt.xlabel('Pupil cuing effect (norm.)')
Plot.save('corrExample.%d.%d%s' % (soaBehav, soaPupil, suffix),
'corrAnalysis', show=show)
def prepFit(dm, suffix=''):
"""
Perform a linear-regression fit on only the first 220 ms.
Arguments:
dm -- DataMatrix
Keyword arguments:
suffix -- A suffix for the output files. (default='')
"""
fig = newFig(size=bigWide)
plt.subplots_adjust(wspace=0, hspace=0)
i = 0
l = [['subjectNr', 'sConst', 'iConst', 'sOnset', 'iOnset', 'sSwap',
'iSwap']]
for dm in [dm] + dm.group('subject_nr'):
print 'Subject %s' % i
row = [i]
# We use a 6 x 2 plot grid
# XX X X X X
# XX X X X X
if i == 0:
# Overall plot
ax = plt.subplot2grid((2,6),(0,0), colspan=2, rowspan=2)
linewidth = 1
title = 'Full data (N=%d)' % len(dm.select('cond != "swap"'))
else:
# Individual plots
ax = plt.subplot2grid((2,6),((i-1)/4, 2+(i-1)%4))
linewidth = 1
title = '%d (N=%d)' % (i, len(dm.select('cond != "swap"')))
plt.text(0.9, 0.1, title, horizontalalignment='right', \
verticalalignment='bottom', transform=ax.transAxes)
if i == 0:
plt.xticks([0, 50, 100, 150, 200])
elif i > 0 and i < 5:
plt.xticks([])
else:
plt.xticks([0, 100])
if i > 0:
plt.yticks([])
else:
plt.yticks([0, -.01, -.02])
plt.ylim(-.025, .01)
plt.axhline(0, color='black', linestyle=':')
for cond in ('constant', 'onset', 'swap'):
_dm = dm.select("cond == '%s'" % cond, verbose=False)
dmWhite = _dm.select('saccCol == "white"', verbose=False)
xAvg, yAvg, errAvg= TraceKit.getTraceAvg(_dm, signal='pupil', \
phase='postSacc', traceLen=postTraceLen, baseline=baseline, \
baselineLock=baselineLock)
xWhite, yWhite, errWhite = TraceKit.getTraceAvg(dmWhite, \
signal='pupil', phase='postSacc', traceLen=postTraceLen, \
baseline=baseline, baselineLock=baselineLock)
yWhite -= yAvg
xWhite = xWhite[:prepWin]
yWhite = yWhite[:prepWin]
if cond == 'constant':
col = constColor
lineStyle = '-'
elif cond == 'onset':
col = onsetColor
lineStyle = '--'
else:
col = swapColor
lineStyle = '-.'
yWhite *= -1
opts = Plot.regress(xWhite, yWhite, lineColor=col, symbolColor=col,
label=cond, annotate=False, symbol=lineStyle, linestyle=':')
s = 1000.*opts[0]
_i = 1000.*opts[1]
print 's = %.4f, i = %.4f' % (s, _i)
if i > 0:
row += [s, _i]
else:
plt.legend(frameon=False, loc='upper right')
if i > 0:
l.append(row)
i += 1
saveFig('linFitPrepWin%s' % suffix, show=False)
dm = DataMatrix(l)
dm.save('output/%s/linFitPrepWin%s.csv' % (exp, suffix))
# Summarize the data and perform ttests on the model parameters
for dv in ['s', 'i']:
print'\nAnalyzing %s' % dv
aConst = dm['%sConst' % dv]
aOnset = dm['%sOnset' % dv]
aSwap = dm['%sSwap' % dv]
print 'Constant: M = %f, SE = %f' % (aConst.mean(), \
aConst.std() / np.sqrt(N))
print 'Onset: M = %f, SE = %f' % (aOnset.mean(), \
aOnset.std() / np.sqrt(N))
print 'Swap: M = %f, SE = %f' % (aSwap.mean(), \
aSwap.std() / np.sqrt(N))
# Standard t-tests
t, p = ttest_rel(aConst, aOnset)
print 'Const vs onset, t = %.4f, p = %.4f' % (t, p)
t, p = ttest_rel(aSwap, aOnset)
print 'Swap vs onset, t = %.4f, p = %.4f' % (t, p)
t, p = ttest_rel(aConst, aSwap)
print 'Const vs swap, t = %.4f, p = %.4f' % (t, p)