def tracePlot(dm, traceParams=trialParams, suffix='', err=True,
lumVar='cueLum', minSmp=200, diff=True):
"""
A pupil-trace plot for a single epoch.
Arguments:
dm -- A DataMatrix.
Keyword arguments:
traceParams -- The trace parameters. (default=trialParams)
suffix -- A suffix to identify the trace. (default='')
err -- Indicates whether error bars should be drawn.
(default=True)
lumVar -- The variable that contains the luminance information.
(default='cueLum')
diff -- Indicates whether the difference trace should be plotted
as well. (default=True)
"""
# At the moment we can only determine error bars for cueLum
assert(not err or lumVar == 'cueLum')
assert(lumVar in ['cueLum', 'targetLum'])
dmBright = dm.select('%s == "bright"' % lumVar)
dmDark = dm.select('%s == "dark"' % lumVar)
x1, y1, err1 = tk.getTraceAvg(dmBright, **traceParams)
x2, y2, err2 = tk.getTraceAvg(dmDark, **traceParams)
if err:
d = y2-y1
aErr = lmeTrace(dm, traceParams=traceParams, suffix=suffix, \
cacheId='lmeTrace%s' % suffix)
aT = aErr[:,0]
aLo = aErr[:,1]
aHi = aErr[:,2]
minErr = (d-aLo)/2
maxErr = (aHi-d)/2
y1min = y1 - minErr
y1max = y1 + maxErr
y2min = y2 - minErr
y2max = y2 + maxErr
plt.fill_between(x1, y1min, y1max, color=green[1], alpha=.25)
plt.fill_between(x2, y2min, y2max, color=blue[1], alpha=.25)
tk.markStats(plt.gca(), np.abs(aT), below=False, thr=2, minSmp=minSmp)
if diff:
plt.plot(x1, diffY+y2-y1, color=orange[1], label='Pupillary cuing effect')
if lumVar == 'cueLum':
plt.plot(x1, y1, color=green[1], label='Cue on bright (N=%d)' \
% len(dmBright))
plt.plot(x2, y2, color=blue[1], label='Cue on dark (N=%d)' \
% len(dmDark))
elif lumVar == 'targetLum':
plt.plot(x1, y1, color=green[1], label='Target on bright')
plt.plot(x2, y2, color=blue[1], label='Target on dark')
def tracePlot(dm, traceParams, suffix="", err=True):
"""
desc: |
A pupil-trace plot for a single epoch.
arguments:
dm: A DataMatrix.
traceParams: The trace parameters.
keywords:
suffix: A suffix to identify the trace.
err: Indicates whether error bars should be drawn.
"""
x1, y1, err1 = TraceKit.getTraceAvg(dm.select('saccCol == "white"'), **traceParams)
x2, y2, err2 = TraceKit.getTraceAvg(dm.select('saccCol == "black"'), **traceParams)
if err:
d = y1 - y2
aErr = lmeTrace(dm, traceParams=traceParams, suffix=suffix, cacheId="lmeTrace.%s%s" % (exp, suffix))
aT = aErr[:, 0]
aLo = aErr[:, 2]
aHi = aErr[:, 1]
minErr = (d - aLo) / 2
maxErr = (aHi - d) / 2
y1min = y1 - minErr
y1max = y1 + maxErr
y2min = y2 - minErr
y2max = y2 + maxErr
plt.fill_between(x1, y1min, y1max, color=brightColor, label="Bright")
plt.fill_between(x2, y2min, y2max, color=darkColor, label="Dark")
TraceKit.markStats(plt.gca(), np.abs(aT), below=False, thr=2, minSmp=200, loExt=True, hiExt=True)
plt.plot(x1, d, color="green")
else:
plt.plot(x1, y1, color=brightColor, label="Bright")
plt.plot(x2, y2, color=darkColor, label="Dark")
def corrPlot(dm, soaBehav, soaPupil, suffix=''):
"""
Plots and analyzes the correlation between the cuing effect in behavior and
pupil size.
Arguments:
dm -- A DataMatrix.
soaBehav -- The SOA to analyze for the behavioral effect.
soaPupil -- The SOA to analyze for the pupil effect.
Keyword arguments:
suffix -- A suffix for the plot filename. (default='')
"""
if stripCorr:
suffix += '.stripCorr'
Plot.new(size=Plot.ws)
plt.title('SOA: %d ms (behavior), %d ms (pupil)' % (soaBehav+55, \
soaPupil+55))
plt.ylim(-.2, 1)
plt.xlabel('Time since cue onset (ms)')
plt.ylabel('Behavior - pupil correlation (r)')
plt.axhline(0, linestyle='--', color='black')
# Cue shading
plt.axvspan(0, cueDur, color=blue[1], alpha=.2)
# Target shading
targetOnset = soaPupil+55
plt.axvspan(targetOnset, targetOnset+targetDur, color=blue[1], alpha=.2)
plt.xlim(0, 2550)
# Accuracy
a = corrTrace(dm, soaBehav, soaPupil, dv='correct', suffix='acc', \
cacheId='corrTrace.correct.%d.%d%s' % (soaBehav, soaPupil, suffix))
tk.markStats(plt.gca(), a[:,1])
plt.plot(a[:,0], label='Accuracy', color=blue[1])
# RTs
a = corrTrace(dm, soaBehav, soaPupil, dv='response_time', suffix='rt', \
cacheId='corrTrace.rt.%d.%d%s' % (soaBehav, soaPupil, suffix))
tk.markStats(plt.gca(), a[:,1])
tk.markStats(plt.gca(), a[:,1], color='red')
plt.plot(a[:,0], label='Response times', color=orange[1])
plt.legend(frameon=False, loc='upper left')
Plot.save('corrAnalysis.%d.%d%s' % (soaBehav, soaPupil, suffix),
'corrAnalysis', show=show)
def fitSwap(dm, suffix='all'):
"""
desc: |
Performs a mixture-model of the swap condition.
arguments:
dm: A DataMatrix,
keywords:
suffix:
desc: A suffix.
type: [str, unicode]
returns:
desc: |
- None if suffix is 'all'
- Otherwise An (i1, i2) tuple indicating the end of the preparation
period and the start of the non-preparation period.
type:
[tuple, None]
"""
dmSwap = dm.select('cond == "swap"', verbose=False)
ySwap = getDiffTrace(dmSwap, cacheId='swapDiff%s.%s' % (exp, suffix))
dmOnset = dm.select('cond == "onset"', verbose=False)
yOnset = getDiffTrace(dmOnset, cacheId='onsetDiff%s.%s' % (exp, suffix))
dmConst= dm.select('cond == "constant"', verbose=False)
yConst = getDiffTrace(dmConst, cacheId='constDiff%s.%s' % (exp, suffix))
lP = []
lErr = []
for i in np.arange(1000):
y1 = yConst[i]
y2 = yOnset[i]
y3 = ySwap[i]
pBest = None
errBest = None
for p in np.linspace(0, 1, 1000):
y3est = p*-y1 + (1-p)*y2
err = abs(y3est-y3)
if errBest == None or err < errBest:
errBest = err
pBest = p
lP.append(pBest)
lErr.append(errBest)
aP = np.array(lP)
fig = newFig(size=flat)
plt.ylim(-.1, 1.1)
plt.axhline(0, linestyle=':', color='black')
plt.axhline(1, linestyle=':', color='black')
plt.yticks(np.linspace(0,1,6))
plt.ylabel('Preparation index')
plt.xlabel('Time relative to display change (ms)')
if suffix == 'all':
# Values hard-coded based on fitSwapAll() output
plt.axvspan(352.50-1.96*13.22, 352.50+1.96*13.22, color=brown[1],
alpha=.1)
plt.axvline(352.50, color=brown[1], linestyle='--')
else:
lRoi = TraceKit.markStats(plt.gca(), aP, thr=.5, below=False,
color=green[0])
if len(lRoi) != 1:
i1 = np.nan
else:
i1 = lRoi[0][1]
lRoi = TraceKit.markStats(plt.gca(), aP, thr=.5, below=True, color=red[0])
if len(lRoi) != 1:
i2 = np.nan
else:
i2 = lRoi[0][0]
plt.plot(aP, color=blue[1])
saveFig('fitSwap/%s' % suffix)
if suffix != 'all':
return i1, i2
def posTracePlots(dm, signal="x"):
"""
Analyzes the position traces. Most useful to see whether the eyes gravitate
towards the saccade side before the actual saccade.
Arguments:
dm -- DataMatrix
Keyword arguments:
signal -- Indicates the trace signal. (default='x')
"""
for subjectNr in ["all"] + dm.unique("subject_nr"):
print baseline, subjectNr
if subjectNr == "all":
_dm = dm
else:
_dm = dm.select("subject_nr == %d" % subjectNr, verbose=False)
fig = newFig(size=(12, 4))
plt.subplots_adjust(wspace=0)
# Select DataMatrices
_dmLeft = _dm.select('saccSide == "left"', verbose=False)
_dmRight = _dm.select('saccSide == "right"', verbose=False)
# Get stats
if subjectNr == "all":
aStatsPre = np.load("stats/%s/x.pre.npy" % exp)
print "First significant: %s" % np.where(aStatsPre[:, 0] < 0.05)[0]
aErrLeft, aErrRight, aDiff = errorTrace(aStatsPre, _dmLeft, _dmRight, phase="cue", signal="x")
else:
aErrLeft, aErrRight, aDiff = None, None, None
# Pre-saccade
ax = plt.subplot2grid((1, 7), (0, 0), colspan=1)
plt.axhline(384, linestyle="--", color="black")
plt.ylim(0, 1024)
plt.xlim(0, preTraceLen)
plt.xticks(range(0, preTraceLen, 50), range(-preTraceLen, 1, 50))
if subjectNr == "all":
TraceKit.markStats(ax, aStatsPre[:, 0], minSmp=1)
TraceKit.plotTraceAvg(
ax,
_dmLeft,
signal=signal,
aErr=aErrLeft,
phase="cue",
lock="end",
traceLen=preTraceLen,
lineColor=brightColor,
errColor=brightColor,
)
TraceKit.plotTraceAvg(
ax,
_dmRight,
signal=signal,
aErr=aErrRight,
phase="cue",
lock="end",
traceLen=preTraceLen,
lineColor=darkColor,
errColor=darkColor,
)
# Post-saccade
ax = plt.subplot2grid((1, 7), (0, 1), colspan=6)
plt.axhline(384, linestyle="--", color="black")
plt.ylim(0, 1024)
plt.xlim(0, postTraceLen)
plt.xticks(range(0, postTraceLen, 50))
plt.yticks([])
TraceKit.plotTraceAvg(
ax,
_dmLeft,
signal=signal,
phase="postSacc",
traceLen=postTraceLen,
lineColor=brightColor,
errColor=brightColor,
)
TraceKit.plotTraceAvg(
ax,
_dmRight,
signal=signal,
phase="postSacc",
traceLen=postTraceLen,
lineColor=darkColor,
errColor=darkColor,
)
saveFig("%s.posTrace.%s" % (signal, subjectNr), show=True)
