def checkMissing(dm, checkCue=True, checkPostSacc=True):
"""
Checks whether there is missing data in the pupil traces, and filters trials
with missing data out.
Arguments:
dm -- DataMatrix
Keyword arguments:
checkCue -- Indicates whether the cue phase should be checked.
(default=True)
checkPostSacc -- Indicates whether the postSacc phase should be checked.
(default=True)
Returns:
The filtered DataMatrix.
"""
dm = dm.addField("missing", dtype=int, default=0)
count = 0
for i in dm.range():
missing = False
if checkCue:
a = TraceKit.getTrace(
dm[i],
signal="pupil",
phase="cue",
lock="end",
baseline=baseline,
baselineLock=baselineLock,
traceLen=preTraceLen,
nanPad=False,
)
if np.isnan(a.sum()):
missing = True
if checkPostSacc:
a = TraceKit.getTrace(
dm[i],
signal="pupil",
phase="postSacc",
lock="start",
baseline=baseline,
baselineLock=baselineLock,
traceLen=postTraceLen,
nanPad=False,
)
if np.isnan(a.sum()):
missing = True
if missing:
dm["missing"][i] = 1
count += 1
print "Found %d (of %d) trials with missing data" % (count, len(dm))
dm = dm.select("missing == 0")
return dm
def addFixStabInfo(dm, offset):
print "Determining fixation stability ..."
dm = dm.addField("fixStabX", dtype=float)
for i in dm.range():
a = TraceKit.getTrace(
dm[i],
signal="x",
phase="postSacc",
lock="start",
offset=offset,
traceLen=postTraceLen - offset,
nanPad=False,
)
print "%.4d\tM = %.2f, SD = %.2f" % (i, a.mean(), np.std(a))
dm["fixStabX"][i] = np.std(a)
print "Done"
return dm
def matchBias(dm):
"""
Matches trials based on a bias towards the saccade target side.
Arguments:
dm -- DataMatrix
Returns:
A matched DataMatrix.
"""
dm = dm.addField("gazeBias", dtype=float)
print "Adding gaze-bias information ..."
for i in dm.range():
a = TraceKit.getTrace(dm[i], signal="x", phase="cue", traceLen=preTraceLen, lock="end", nanPad=False)
saccSide = dm["saccSide"][i]
_a = a[:-50]
bias = (_a - 512).mean()
if saccSide == "left":
bias *= -1
dm["gazeBias"][i] = bias
print "All: M = %.4f, SD = %.4f" % (dm["gazeBias"].mean(), dm["gazeBias"].std())
newFig()
plt.subplot(2, 1, 1)
plt.title("Unbalanced: M = %.4f, SD = %.4f" % (dm["gazeBias"].mean(), dm["gazeBias"].std()))
plt.hist(dm["gazeBias"], bins=50, color=blue[1])
plt.axvline(dm["gazeBias"].mean())
# dm = dm.select('gazeBias < 0')
dm = dm.balance("gazeBias", 0.1, verbose=True)
# newDm = None
# for _dm in dm.group('subject_nr'):
# print 'Balancing subject_nr %d' % _dm['subject_nr'][0]
# if newDm == None:
# newDm = _dm.balance('gazeBias', 1)
# else:
# newDm += _dm.balance('gazeBias', 1)
# dm = newDm
dm = dm.select("__unbalanced__ == 0")
plt.subplot(2, 1, 2)
plt.title("Balanced: M = %.4f, SD = %.4f" % (dm["gazeBias"].mean(), dm["gazeBias"].std()))
plt.hist(dm["gazeBias"], bins=50, color=blue[1])
plt.axvline(dm["gazeBias"].mean())
print "Antibias: M = %.4f, SD = %.4f" % (dm["gazeBias"].mean(), dm["gazeBias"].std())
saveFig("gazeBias")
return dm
