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)
def landOnHandle(trim = True, spacing = .5, exclOverlap = False, \
exclY = False, colList = ["#ef2929", "#3465a4", "#f57900", "#73d216"],\
lLegend = ["Exp1: relative to center", "Exp1: relative to CoG", \
"Exp2: relative to CoG", "Exp3: relative to CoG"], yLim = [-.3, .3],\
xLabels = ["1", "2", "3"], xTitle = "saccade", \
yTitle = "normalised landings towards handle"):
"""
Landing position as a funciton of orientation, across or per object group.
Arguments:
Keyword arguments:
trim ---
exclOverlap --- indicates whether or not to exclude gap-overlap trials
in the simulation.
"""
fig = plt.figure(figsize = (5,10))
title = "Average towards-handle landings - exclOverlap = %s - exclY = %s" \
% (exclOverlap, exclY)
plt.suptitle(title)
copyColList = colList[:]
dm_sim = getDM.getDM("004C")
## for handle in ["right", "left"]:
## handle_dm = dm_sim.select("handle_side == '%s'" % handle)
## print handle_dm["endX1NormToHandle"].mean()
#
# cm = dm_sim.collapse(['object'], 'endX1NormToHandle')
# cm._print(sign=5)
#
# dm_sim = dm_sim.select('object == "fork"')
# for _dm in dm_sim:
# print '%d: %.4f' % (_dm['count_trial_sequence'], \
# _dm['endX1NormToHandle'])
# cm = dm_sim.collapse(['gap', 'handle_side'], 'endX1NormToHandle')
# cm._print(sign=5)
# sys.exit()
for exp in ["004A", "004B", "004C"]:
if exp != "004B":
continue
# l = []
# if exp != "004A":
# continue
dm = getDM.getDM(exp)
for vf in dm.unique("visual_field"):
_dm = dm.select("visual_field == '%s'" % vf)
plt.hist(_dm["y_stim"], bins = 50)
plt.show()
sys.exit()
if exp == "004A":
dvList = ["abs", "corr"]
else:
dvList = ["abs"]
for dvType in dvList:
lMeans = []
for sacc in ["1", "2", "3"]:
sim_avg = float(dm_sim["endX%sNormToHandle" % sacc].mean())
print sim_avg
#raw_input()
if dvType == "corr":
dv = "endX%sCorrNormToHandle" % sacc
else:
dv = "endX%sNormToHandle" % sacc
# dv must not contain ''s:
on_dm = onObject.onObject(dm, sacc)
if trim:
trim_dm = on_dm.selectByStdDev(keys = ["file"], dv = dv)
else:
trim_dm = on_dm
pm = PivotMatrix(trim_dm, ["catch_trial"], ["file"], dv, \
colsWithin = False, err = 'se')
a = pm.asArray()
# print pm
M = float(a[-2][2])
SE = float(a[-1][2])
# print M
# print SE
#sys.exit()
#M = cdm["mean"].mean()
#SE = cdm["se"].mean()
#print M
#print SE
print "exp ", exp
print "sacc = ", sacc
print "dv = ", dv
am = AnovaMatrix(trim_dm, ["handle_side"], "endX%sNorm" % \
sacc, "file")#._print(ret=True)
# print am
p = am.asArray()[2][3]
p_corr = float(p) * 6
# print p_corr
print 'T test scipy'
cm = trim_dm.collapse(["file"], dv)
M = cm["mean"].mean()
SE = cm['mean'].std() / np.sqrt(len(cm))
ref = 0
t, p = scipy.stats.ttest_1samp(cm['mean'], ref)
p_corr = min(1, float(p * 6.))
# print p_corr
print "M = %.3f, SE = %.3f, t(17) = %.2f, p = %6.4f" % \
(M, SE, t, p_corr)
raw_input()
