def descriptives(dm):
"""Prints descriptive statistics and perform some basic anovas."""
pm = PivotMatrix(dm, ["cond"], ["subject_nr"], dv="saccEndX", func="size")
pm.save("output/%s/cellCount.cond.csv" % exp)
print pm
quit()
pm = PivotMatrix(dm, ["saccSide"], ["subject_nr"], dv="saccEndX")
pm.save("output/%s/saccEndX.saccSide.csv" % exp)
pm = PivotMatrix(dm, ["saccSide"], ["subject_nr"], dv="saccNormX")
pm.save("output/%s/saccNormX.saccSide.csv" % exp)
pm = PivotMatrix(dm, ["cond", "saccSide"], ["subject_nr"], dv="saccLat2")
pm.save("output/%s/saccLat.cond.saccSide.csv" % exp)
pm = PivotMatrix(dm, ["saccCol"], ["subject_nr"], dv="saccLat2")
pm.save("output/%s/saccLat.saccCol.csv" % exp)
pm = PivotMatrix(dm, ["cond"], ["subject_nr"], dv="saccLat2")
pm.save("output/%s/saccLat.cond.csv" % exp)
aov = AnovaMatrix(dm, ["cond"], "saccLat2", subject="subject_nr")
aov.save("output/%s/aov.cond.saccLat.csv" % exp)
print "Saccade latency: %.4f (%.4f)" % (dm["saccLat2"].mean(), dm["saccLat2"].std())
def latencyCorr(dm):
"""
Determines the correlation between saccade latency and the latency of the
PLR.
Arguments:
dm -- DataMatrix
"""
dm = dm.addField("saccLat2Bin", dtype=float)
dm = dm.calcPerc("saccLat2", "saccLat2Bin", keys=["subject_nr"], nBin=10)
# dm = dm.select('cond == "onset"')
l = [["subject_nr", "cond", "bin", "saccLat", "t0"]]
colors = TangoPalette.brightColors[:] * 10
for _dm in dm.group(["subject_nr", "saccLat2Bin"]):
for cond in ("constant", "onset"):
__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
opts = fitCurve(xWhite, yWhite, plot=False)
t0 = opts[0]
subject = __dm["subject_nr"][0]
col = colors[int(subject)]
saccLat = __dm["saccLat2"].mean()
_bin = __dm["saccLat2Bin"][0]
plt.plot(saccLat, t0, "o", color=col)
l.append([subject, cond, _bin, saccLat, t0])
print subject, t0, saccLat
dm = DataMatrix(l)
dm.save("output/%s/latencyCorr.csv" % exp)
print dm
# plt.plot(dm['saccLat'], dm['t0'], '.')
s, i, r, p, se = linregress(dm["saccLat"], dm["t0"])
print "r = %.4f, p = %.4f" % (r, p)
plt.plot(dm["saccLat"], i + s * dm["saccLat"])
plt.show()
newFig(size=(4.8, 2.4))
plt.subplots_adjust(bottom=0.2)
pmX = PivotMatrix(dm, ["cond", "bin"], ["subject_nr"], "saccLat", colsWithin=True, err="se")
print pmX
pmY = PivotMatrix(dm, ["cond", "bin"], ["subject_nr"], "t0", colsWithin=True, err="se")
print pmY
xM = np.array(pmX.m[-2, 2:-2], dtype=float)
xErr = np.array(pmX.m[-1, 2:-2], dtype=float)
xMConst = xM[: len(xM) / 2]
xMOnset = xM[len(xM) / 2 :]
xErrConst = xErr[: len(xErr) / 2]
xErrOnset = xErr[len(xErr) / 2 :]
yM = np.array(pmY.m[-2, 2:-2], dtype=float)
yErr = np.array(pmY.m[-1, 2:-2], dtype=float)
yMConst = yM[: len(yM) / 2]
yMOnset = yM[len(yM) / 2 :]
yErrConst = yErr[: len(yErr) / 2]
yErrOnset = yErr[len(yErr) / 2 :]
plt.errorbar(
xMConst, yMConst, xerr=xErrConst, yerr=yErrConst, label="Constant", capsize=0, color=constColor, fmt="o-"
)
plt.errorbar(xMOnset, yMOnset, xerr=xErrOnset, yerr=yErrOnset, label="Onset", capsize=0, color=onsetColor, fmt="o-")
plt.legend(frameon=False)
plt.xlabel("Saccade latency (ms)")
plt.ylabel("PLR latency (ms)")
saveFig("latencyCorr")
aov = AnovaMatrix(dm, ["cond", "bin"], "t0", "subject_nr")
print aov
aov.save("output/%s/aov.latencyCorr.csv" % exp)
