def affordancePerSacc(trim = True, exclY = True, dv = "RT"):
"""
"""
fig = plt.figure()
title = "Affordance effect per sacc - dv = %s" % dv
plt.suptitle(title)
plotNr = 1
for exp in ["004A", "004B"]:
colList = ["red", "blue"]
plt.subplot(1,2,plotNr)
plt.title("Exp = %s" % exp)
dm = getDM.getDM(exp)
for sacc in ["1", "2"]:
_dm = dm.select("endX%sNorm != ''" % sacc)
_dm = _dm.select("endX%sNorm > -.5" % sacc)
_dm = _dm.select("endX%sNorm < .5" % sacc)
if exclY:
_dm = _dm.select("endY%sNorm != ''" % sacc)
_dm = _dm.select("endY%sNorm > -.5" % sacc)
_dm = _dm.select("endY%sNorm < .5" % sacc)
trimmed_dm = _dm.selectByStdDev(keys =["comp", "file"], dv = dv)
for val in trimmed_dm.unique("comp"):
print val
print trimmed_dm["RT"][trimmed_dm.where("comp == '%s'" % val)].mean()
pm = PivotMatrix(trimmed_dm, ["comp"], ["file"], dv = dv, colsWithin = True)
am = AnovaMatrix(trimmed_dm, ["comp"], dv = dv, \
subject = "file")._print(ret=True)
print am
col = colList.pop()
pm.plot(nLvl1=1, fig = fig, colors = [col])
plt.show()
plt.legend(["data sacc 1", "data sacc 2"])
plotNr +=1
plt.show()
def perHandle(trim = True, exclY = True):
"""
"""
fig = plt.figure()
title = "Per handle side - exclY = %s" % exclY
for sacc in ["1", "2", "3"]:
if sacc == "3":
continue
colList = ["#3465a4","#73d216", "#f57900"]
plt.subplot(1,3,int(sacc))
for exp in ["004A", "004B"]:
dm = getDM.getDM(exp = exp)
# Only include on-object saccades:
dm = dm.select("endX%sNorm != ''" % sacc)
dm = dm.select("endX%sNorm > -.5" % sacc)
dm = dm.select("endX%sNorm < .5" % sacc)
if exclY:
dm = dm.select("endY%sNorm != ''" % sacc)
dm = dm.select("endY%sNorm > -.5" % sacc)
dm = dm.select("endY%sNorm < .5" % sacc)
if exp == "004A":
dvList = ["endX%sNorm" % sacc, "endX%sCorrNorm" % sacc]
else:
dvList = ["endX%sNorm" % sacc]
for dv in dvList:
print dv
if trim:
dm = dm.selectByStdDev(keys = ["file"], dv = dv)
pm = PivotMatrix(dm, ["handle_side"], ["file"], dv=dv, colsWithin=True)
pm._print()
sys.exit()
col = colList.pop()
pm.plot(nLvl1 = 1,fig = fig, colors = [col])
# Modify plot:
plt.xlabel("handle side")
plt.ylabel("normalised landing position")
plt.axhline(0, linestyle = "--", color = "#888a85")
plt.savefig("%s.png" % title)
def affPerSacc(trim = True, exclY = False, dv = "RT", \
colList = ["#73d216", "#f57900"]):
"""
Affordance effect as t-test between levels of the factor
compatibility, per saccade.
Keyword arguments:
"""
fig = plt.figure()
title = "Affordance effect per sacc - dv = %s - exclY = %s" % (dv, exclY)
plt.suptitle(title)
plotNr = 1
for exp in ["004A", "004B"]:
copyColList = colList[:]
plt.subplot(1,2,plotNr)
plt.title("Exp = %s" % exp)
dm = getDM.getDM(exp)
for sacc in ["1", "2"]:
# Only on-object saccades:
_dm = onObject.onObject(dm, sacc, exclY = exclY, verbose = False)
# Trim:
if trim:
trimmed_dm = _dm.selectByStdDev(keys =["comp", "file"], dv = dv)
else:
trimmed_dm = _dm
pm = PivotMatrix(trimmed_dm, ["comp"], ["file"], dv = dv, colsWithin = True)
am = AnovaMatrix(trimmed_dm, ["comp"], dv = dv, \
subject = "file")._print(ret=True)
print am
col = copyColList.pop()
pm.plot(nLvl1=1, fig = fig, colors = [col])
plt.legend(["data sacc 1", "data sacc 2"])
plotNr +=1
plt.savefig("%s.png"%title)
plt.savefig("%s.svg"%title)
def affordances(trim = True, exclY = True, dv = "RT"):
"""
"""
fig = plt.figure()
title = "Affordance effect per Exp"
plt.suptitle(title)
plotNr = 1
for exp in ["004A", "004B"]:
plt.subplot(1,2,plotNr)
plt.title("Exp = %s" % exp)
dm = getDM.getDM(exp)
trimmed_dm = dm.selectByStdDev(keys =["handle_side", "response_hand", "file"], dv = dv)
for val in trimmed_dm.unique("comp"):
print val
print trimmed_dm["RT"][trimmed_dm.where("comp == '%s'" % val)].mean()
pm = PivotMatrix(trimmed_dm, ["handle_side", "response_hand"], ["file"], dv = dv, colsWithin = True)
pm.linePlot(fig = fig)
plt.show()
pm = PivotMatrix(trimmed_dm, ["comp"], ["file"], dv = dv, colsWithin = True)
pm.plot(nLvl1 = 1, fig = fig)
plt.show()
plotNr +=1
###am = AnovaMatrix(trimmed_dm, ["comp"], dv = dv, \
###subject = "file")._print(ret=True)
###print am
plt.show()
def fixChange(dm, nBin = 4):
dmGap = dm.select('gap == "zero"')
dmNoGap = dm.select('gap == "overlap"')
dmFixGray = dmNoGap.select('large_fix_col == "change"')
dmFixBlack = dmNoGap.select('large_fix_col == "no_change"')
fig = plt.figure()
dmFixGray = dmFixGray.addField('fixSizeBin')
dmFixGray = dmFixGray.calcPerc('large_fix_size', 'fixSizeBin', keys=['file'], nBin=nBin)
pm = PivotMatrix(dmFixGray, ["fixSizeBin"], ["file"], dv = "saccLat")
pm.plot(fig=fig, nLvl1 = 1,colors = ["blue"])
dmFixBlack = dmFixBlack.addField('fixSizeBin')
dmFixBlack = dmFixBlack.calcPerc('large_fix_size', 'fixSizeBin', keys=['file'], nBin=nBin)
pm = PivotMatrix(dmFixBlack, ["fixSizeBin"], ["file"], dv = "saccLat")
pm.plot(fig=fig, nLvl1 = 1,colors = ["red"])
plt.legend(["color change", "no color change"])
plt.show()
def ovpSingleFix(dm, counter = 'fixCount', \
rt = 'rtFromLanding', ws = True, nRows = 2, nCols = 4, nBin = 4):
"""
Plots OVP effects (only the ones that are suitable) for single-fixation
cases only
"""
# Select only single fixations:
dm = dm.select('%s == 1' % counter)
# Determine exp:
exp = dm['exp'][0]
if ws:
keys = ['file']
else:
keys = None
# Add factors containing bins:
dm = dm.addField("binned_endX1")
dm = dm.calcPerc("endX1Norm", "binned_endX1", \
keys = keys, nBin = nBin)
if exp == "004A":
dm = dm.addField("binned_endX1Corr")
dm = dm.calcPerc("endX1CorrNorm", "binned_endX1Corr", \
keys = keys, nBin = nBin)
# Create figure:
fig = plt.figure()
#fig = plt.figure(figsize = (10,5))
plt.subplots_adjust(wspace = .4, hspace = 0)
figName = "%s: OVP effects for single fixations only - counter = %s - WS = %s" % (exp, counter, ws)
plt.suptitle(figName)
plotCount = 0
# Define keys:
factors = ["binned_endX1"]
subjectID = ["file"]
dvList = [rt, "durationFix1", "gazeDur", "saccLat1"]
if exp == "004A":
factorList = ["binned_endX1", "binned_endX1Corr"]
else:
factorList = ["binned_endX1"]
for factors in factorList:
for dv in dvList:
plotCount +=1
# Exclude trials on which the dv does not exist:
_dm = dm.select("%s != ''"%dv)
plt.subplot(nRows, nCols, plotCount)
#if factors == factorList[0]:
#plt.title(dv)
pm = PivotMatrix(_dm, factors, subjectID, dv, colsWithin = True)
pm.plot(nLvl1 = 1, fig = fig)
if dv == 'endX2Norm':
plt.axhline(0, linestyle = '--', color = 'gray')
if factors == factorList[0]:
plt.xticks([])
plt.savefig("%s RT = %s.png"%(figName,rt))
def timeCourse(yLim = [-.25,.25], exclY = False, nRows = 1, nCols = 3, \
colList = ["#ef2929", "#3465a4","#73d216", "#f57900"],\
lLegend = ["exp1: relative to center", "exp1: relative to CoG",\
"exp2: relative to CoG"], binMax = 10, binMin = 2):
"""
Time course per saccade
"""
if exclY:
binMax = 3
binMin = 1
fig = plt.figure()#figsize = (9,7))
plt.subplots_adjust(wspace = 0)
title = "Time course per saccade - exclY = %s -binMax = %s binMin = %s" \
% (exclY, binMax, binMin)
#plt.suptitle(title)
plotNr = 0
lLegend = ["Experiment 1 (relative to center)", "Experiment 1 (relative to CoG)", \
"Experiment 2 (relative to CoG)", "Saliency-model simulation"]
dm_sim = getDM.getDM("004C")
for sacc in ["1", "2", "3"]:
plotNr +=1
copyColList = ["#73d216","#f57900","#3465a4","#3465a4"]
markerList = ["s","^","o", "o"]
sim_avg = dm_sim["endX%sNormToHandle" % sacc].mean()
for exp in ["004A", "004B"]:
if exp == "004A":
dvList = ["endX%sNormToHandle" % sacc, "endX%sCorrNormToHandle" % sacc]
else:
dvList = ["endX%sNormToHandle" % sacc]
for dv in dvList:
if sacc== "1":
nBin = binMax
if sacc== "2":
nBin = binMax
if sacc== "3":
nBin = binMin
dm = getDM.getDM(exp = exp)
#dm = dm.select("corrDirection == 'correction awayHandle'")
# Only on-object:
_dm = onObject.onObject(dm, sacc, exclY = exclY)
# Make bins, only for the first dv (for the second dv, the binned
# variable is the same and therefore already exists:
saccLat = "saccLat%s" % sacc
varToBin = saccLat
binnedVar = "binnend%s" % varToBin
binned_dm = _dm.addField(binnedVar)
binned_dm = binned_dm.calcPerc(varToBin, binnedVar ,keys = ["file"], nBin = nBin)
plt.subplot(nRows,nCols,plotNr)
col = copyColList.pop()
plt.title("sacc = %s" % sacc)
pm = PivotMatrix(binned_dm, binnedVar, "file", dv, colsWithin = True, err = 'se')
pm.plot(nLvl1=1, fig = fig, colors = [col])
# Modify plot:
if sacc != "1":
plt.yticks([])
if sacc == "3":
plt.legend(lLegend)
else:
plt.legend([])
plt.xticks([])
plt.ylim(yLim)
# Indicate reference point:
plt.axhline(0, color = "#555753", linestyle = "--", linewidth = 2)
# Indicate saliency peak:
plt.axhline(sim_avg, color = "#ef2929", linestyle = "--", linewidth = 2)
for ext in [".png", ".svg"]:
plt.savefig(os.path.join(dst, "Figure2%s" % ext))
def binPerSacc(direction = "ToHandle", cousineau = True,nBin = 8,trim = False, \
figTitle = True, usePm = True, yLim = [-.25,.25], onlyExp2 = False):
"""
Argument:
dm --- data matrix
Keyword argument:
cousineau --- withinize or not. Default = True.
direction --- {"toHandle", "toContrast"
"""
titleList = ["Exp1 absolute", "Exp1 corrected", "Exp2"]
if not onlyExp2:
fig = plt.figure(figsize = (9,7))
title = "Landing %s as a function of bin PER SACC cousineau = %s trim = %s onlyExp2 = %s" % \
(direction, cousineau,trim, onlyExp2)
if figTitle:
plt.title(title)
nRows = 1
nCols = 3
plotNr = 0
for exp in ["004A", "004B"]:
if onlyExp2:
if exp != "004B":
continue
start_dm = getDM.getDM(exp = exp, driftCorr = True)
if direction == "ToHandle":
start_dm = start_dm.select("contrast_side == 'control'", verbose = False)
if direction == "ToContrast":
start_dm = start_dm.select("contrast_side != 'control'", verbose = False)
if exp == "004A":
corrList = ["uncorrected", "corrected"]
if onlyExp2:
nCols = 1
continue
if exp == "004B":
corrList = ["uncorrected"]
for corr in corrList:
subTitle = titleList[plotNr]
plotNr +=1
colList = [["#f57900"], ["#73d216"], ["#3465a4"]]
if onlyExp2:
fig = plt.figure(figsize = (3,7))
title = "Landing %s as a function of bin PER SACC ONLY EXP2 cousineau = %s trim = %s onlyExp2 = %s" % \
(direction, cousineau,trim, onlyExp2)
for saccCount in ["1", "2", "3"]:
if saccCount == "1":
nBin = 10
if saccCount == "2":
nBin = 10
if saccCount == "3":
nBin = 3
print '\n\tsacc count = %s\n' % saccCount
if corr == "uncorrected":
dv = "endX%sNorm%s"%(saccCount, direction)
elif corr == "corrected":
dv = "endX%sCorrNorm%s"%(saccCount, direction)
print "\tDV = %s\n" % dv
dv_dm = timecourse.onObject(start_dm,dv, verbose = False)
saccLat = "saccLat%s" % saccCount
# Trim the data matrix such that the most extreme latencies are excluded:
if trim:
trimmed_dm = dv_dm.selectByStdDev(keys = ["file"], dv = saccLat, verbose = False)
else:
trimmed_dm = dv_dm
# Withinize sacc latencies, if wanted:
print "\n\tcousineau = %s\n" % cousineau
if cousineau:
_dm = trimmed_dm.addField("cousineau_%s"%saccLat, dtype = None)
_dm = _dm.withinize(saccLat, "cousineau_%s"%saccLat, "file", verbose = False, whiten=False)
saccLat = "cousineau_%s"%saccLat
else:
_dm = dv_dm
col = colList.pop()
# Make bins, only for the first dv (for the second dv, the binned variable is the same and
# therefore already exists:
varToBin = saccLat
binnedVar = "binnend%s" % varToBin
binned_dm = _dm.addField(binnedVar)
binned_dm = binned_dm.calcPerc(varToBin, binnedVar ,keys = ["file"], nBin = nBin)
# Determine x and y means per bin by hand:
if not usePm:
lX = []
lY = []
binCount = 0
for _bin in binned_dm.unique(binnedVar):
# Filter out all but one bin
dm_one_bin = binned_dm.select('%s == %f' % (binnedVar, _bin))
# Get the mean sacc lat and the mean landing position (for x and y axis, respectively):
# NOTE: withinising doesn't make any real difference for the overall pattern.
yMean = dm_one_bin[dv].mean()
xMean = dm_one_bin[varToBin].mean()
binCount +=1
print binCount, xMean
lX.append(xMean)
lY.append(yMean)
if exp == "004B":
print 'XXXXXXXXX'
print saccCount
i = 0
for x in lX:
i +=1
print "Bin %s: " % i, x
print
print 'xxxxxxxxxxx'
if not onlyExp2:
plt.subplot(nRows,nCols,plotNr)
plt.title(subTitle)
plt.plot(lX, lY, color = col[0], marker = 'o')
plt.xlabel("binned saccade latencies from stimulus onset")
plt.ylabel("landing position (%s) %s" % (corr,direction))
# Disadvantages of pm:
# - There SHOULD be enough observations per participant to be able to plot
# - bin number (instead of bin mean) on x axis
if usePm:
if not onlyExp2:
plt.subplot(nRows,nCols,plotNr)
plt.title(subTitle)
pm = PivotMatrix(binned_dm, binnedVar, "file", dv, colsWithin = True, err = 'se')
pm.plot(nLvl1=1, fig = fig, colors = col)
if plotNr != "1":
plt.yticks([])
plt.xticks([])
plt.ylim(yLim)
plt.legend(["initial saccade", "refixation 1", "refixation 2"])
plt.axhline(0, color = "#555753", linestyle = "--", linewidth = 2)
plt.savefig(os.path.join('%s.png' % title))
plt.savefig(os.path.join('%s.svg' % title))
def binAllSaccs(direction = "ToHandle", figTitle = False, trim = True, cousineau = True, nBin = 15, usePm = True, err = 'se',\
onlySacc1 = False, onlyExp2 = False):
"""
Throw all saccades latencies (since stim onset) in for bin analysis, irrespective of saccade count
Arguments:
dm
direction --- {"ToHandle", "ToContrast"}, variable on the y axis
Keyword arguments:
onlyFirstSacc --- True means only bin the FIRST sacc lateny
Returns fig list
"""
lLabels = ["Exp1 absolute", "Exp1 corrected", "Exp2"]
colList = [["#f57900"], ["#73d216"], ["#3465a4"]]
fig = plt.figure(figsize = (3,7))
title = "Landing %s as a function of ALL binned sacc lats cousineau = %s trim = %s usePm = %s onlyExp2 = %s onlySacc1 = %s" \
% (direction, cousineau, trim, usePm, onlyExp2, onlySacc1)
if figTitle:
plt.title(title)
for exp in ["004A", "004B"]:
dm = getDM.getDM(exp = exp, driftCorr = True)
if direction == "ToHandle":
dm = dm.select("contrast_side == 'control'", verbose = False)
if direction == "ToContrast":
dm = dm.select("contrast_side != 'control'", verbose = False)
lDm = []
if exp == "004A":
if onlyExp2:
continue
# Max sacc count = 4
lSacc = range(1,5)
if exp == "004B":
# Max sacc count = 3
lSacc = range(1,4)
for saccCount in lSacc:
if onlySacc1:
if saccCount != 1:
continue
# Create dms containing all latencies (i.e., the to-be-binned variable)
# of a given saccade count, e.g.
# almost all trials for the first saccade, and less and less for the
# subsequent saccades.
# Do the same for the landing positions (dv's on the y-axis):
# Select one saccade:
dm_sacc = dm.select("saccLat%s != ''" % str(saccCount))
# Create new column containing sacc count:
dm_sacc = dm_sacc.addField("saccNr", dtype = str)
dm_sacc["saccNr"] = "sacc%s" % saccCount
# Create new column containing IV and DV's, but without the
# sacc count in the column header:
dm_sacc = dm_sacc.addField("absSaccLat", dtype = float)
dm_sacc["absSaccLat"] = dm_sacc["saccLat%s" % str(saccCount)]
dm_sacc = dm_sacc.addField("abs%s" % direction, dtype = float)
dm_sacc["abs%s" % direction] = dm_sacc["endX%sNorm%s"%(str(saccCount), direction)]
dm_sacc = dm_sacc.addField("absCorr%s" % direction, dtype = float)
dm_sacc["absCorr%s" % direction] = dm_sacc["endX%sCorrNorm%s"%(str(saccCount), direction)]
# Add the dm to the list of dm's
lDm.append(dm_sacc)
# Combine all dm's into one big dm:
merged_dm = lDm[0]
for dm in lDm[1:]:
merged_dm = merged_dm + dm
if exp == "004A":
corrList = ["uncorrected", "corrected"]
continue
if exp == "004B":
corrList = ["uncorrected"]
for corr in corrList:
if corr == "uncorrected":
dv = "abs%s" % direction
elif corr == "corrected":
dv = "absCorr%s" % direction
print "\tDV = %s\n" % dv
# There should be no ''s in the dm anymore, but off-object saccades are still
# possible, so this filtering remains necessary:
dv_dm = timecourse.onObject(merged_dm,dv, verbose = False)
saccLat = "absSaccLat"
# Trim the data matrix such that the most extreme latencies are excluded:
print "\n\ttrim = %s\n" % trim
if trim:
trimmed_dm = dv_dm.selectByStdDev(keys = ["file"], dv = saccLat, verbose = False)
else:
trimmed_dm = dv_dm
# Withinize sacc latencies, if wanted:
print "\n\tcousineau = %s\n" % cousineau
if cousineau:
_dm = trimmed_dm.addField("cousineau_%s"%saccLat, dtype = None)
_dm = _dm.withinize(saccLat, "cousineau_%s"%saccLat, "file", verbose = False, whiten=False)
saccLat = "cousineau_%s"%saccLat
else:
_dm = dv_dm
# Make bins, only for the first dv (for the second dv, the binned variable is the same and
# therefore already exists:
varToBin = saccLat
binnedVar = "binnend%s" % varToBin
binned_dm = _dm.addField(binnedVar)
binned_dm = binned_dm.calcPerc(varToBin, binnedVar ,keys = ["file"], nBin = nBin)
col = colList.pop()
print
print "EXP = ", exp
print "DV = ", dv
print
if not usePm:
lX = []
lY = []
binCount = 0
for _bin in binned_dm.unique(binnedVar):
# Filter out all but one bin
dm_one_bin = binned_dm.select('%s == %f' % (binnedVar, _bin))
# Get the mean sacc lat and the mean landing position (for x and y axis, respectively):
# NOTE: withinising doesn't make any real difference for the overall pattern.
yMean = dm_one_bin[dv].mean()
xMean = dm_one_bin[varToBin].mean()
binCount +=1
print binCount,xMean
lX.append(xMean)
lY.append(yMean)
i = 0
for x in lX:
i +=1
print "Bin %s: " % i, x
plt.plot(lX, lY, color = col[0], marker = 'o')
if usePm:
pm = PivotMatrix(binned_dm, binnedVar, "file", dv, colsWithin = True, err = err)
pm.plot(nLvl1=1, fig = fig, colors = col)
#pm.barPlot(fig = fig, xLabel = "binned saccade latencies from stimulus onset", \
#yLabel = "landing position (%s) %s" % (corr,direction))
plt.xlabel("Binned stimulus-saccade interval")
plt.ylabel("Landing positiontowards handle")
plt.legend(lLabels)
plt.ylim(-.2, .2)
plt.axhline(0, color = "#555753", linestyle = "--", linewidth = 2)
plt.savefig("%s.png"%title)
plt.savefig("%s.svg"%title)
return fig
def contrastSide(trim = True, exclOverlap = False):
"""
Landing position as a function of contrast.
Positive values indicate landing position to the right,
negative values indicate landing pos to the left
"""
fig = plt.figure()
title = "Effect of contrast - exclOverlap = %s" % exclOverlap
plt.suptitle(title)
for sacc in ["1", "2", "3"]:
colList = ["#ef2929", "#3465a4","#73d216", "#f57900"]
plt.subplot(1,3, int(sacc))
plt.title("sacc = %s"% (sacc))
# Exp 1:
exp = "004A"
dm_004A = getDM.getDM(exp = exp, driftCorr = True, onlyControl = False)
# This is the same for corrected landing positions (the saccade
# doesn't change; only the reference point does)
dm_004A = dm_004A.select("endX%sNorm != ''" % sacc, verbose = False)
dm_004A = dm_004A.select("endX%sNorm > -.5" % sacc, verbose = False)
dm_004A = dm_004A.select("endX%sNorm < .5" % sacc, verbose = False)
for dv in ["endX%sNorm" % sacc, "endX%sCorrNorm" % sacc]:
#If wanted, trim the data
if trim:
_dm = dm_004A.selectByStdDev(keys = ["contrast_side", "file"], dv = dv)
# For experiment 1 there are not enough third fixations:
if exp == "004A" and sacc == "3":
colList = ["#ef2929", "#3465a4"]
continue
# Get pivot matrix:
pm = PivotMatrix(_dm, ["contrast_side"], ["file"], dv=dv, colsWithin=True)#, xLabels = ["left", "control", "right"])
col = colList.pop()
pm.plot(fig = fig, nLvl1 = 1, colors = [col])
# Experiment 2 and 3:
dv = "endX%sNorm" % sacc
for exp in ["004B", "004C"]:
if exp == "004C" and exclOverlap:
dm = dm.select("gap == 'zero'")
print "EXP = ", exp
dm = getDM.getDM(exp = exp, driftCorr = True, onlyControl = False)
# This is the same for corrected landing positions (the saccade
# doesn't change; only the reference point does)
dm = dm.select("endX%sNorm != ''" % sacc, verbose = False)
dm = dm.select("endX%sNorm > -.5" % sacc, verbose = False)
dm = dm.select("endX%sNorm < .5" % sacc, verbose = False)
#If wanted, trim the data
if trim:
_dm = dm.selectByStdDev(keys = ["contrast_side", "file"], dv = dv)
# Get pivot matrix:
pm = PivotMatrix(_dm, ["contrast_side"], ["file"], dv=dv, colsWithin=True)
col = colList.pop()
pm.plot(fig = fig, nLvl1 = 1, colors = [col])
# Modify plot:
plt.ylim(-.2, .2)
#if sacc == "1":
plt.legend(["Exp1 (abs)", "Exp1 (corr)", "Exp2 (abs)", "Exp2 (sim)"])
if sacc == "3":
plt.legend(["Exp2 (abs)", "Exp2 (sim)"])
plt.axhline(0, color = "#888a85", linestyle = "--", linewidth = 2)
plt.savefig("%s.png" % title)
def timeCourse(direction = "ToHandle", cousineau = True,nBin = 8,trim = False, \
usePm = True, yLim = [-.25,.25], onlyExp2 = False, exclY = True):
"""
"""
if direction != "ToHandle":
print "Only ToHandle at the moment!!"
return
titleList = ["Exp1: relative to center", "Exp1: relative to CoG", \
"Exp2: relative to CoG"]
fig = plt.figure(figsize = (9,7))
title = "Time course per saccade - cousineau = %s trim = %s - exclY = %s" % \
(direction, cousineau,exclY)
plt.title(title)
nRows = 1
nCols = 3
plotNr = 0
for exp in ["004A", "004B"]:
if exp == "004A":
corrList = ["uncorrected", "corrected"]
if exp == "004B":
corrList = ["uncorrected"]
for corr in corrList:
subTitle = titleList[plotNr]
plotNr +=1
colList = [["#f57900"], ["#73d216"], ["#3465a4"]]
for sacc in ["1", "2", "3"]:
dm = getDM.getDM(exp = exp)
#if sacc == "3":
# continue
if sacc== "1":
nBin = 3
if sacc== "2":
nBin = 3
if sacc== "3":
nBin = 2
if exp == "004B":
nBin = 1
print '\n\tsacc count = %s\n' % sacc
if corr == "uncorrected":
dv = "endX%sNorm%s"%(sacc, direction)
elif corr == "corrected":
dv = "endX%sCorrNorm%s"%(sacc, direction)
print "\tDV = %s\n" % dv
# This is the same for corrected landing positions (the saccade
# doesn't change; only the reference point does)
dm = dm.select("endX%sNorm != ''" % sacc)
dm = dm.select("endX%sNorm > -.5" % sacc)
dm = dm.select("endX%sNorm < .5" % sacc)
if exclY:
dm = dm.select("endY%sNorm != ''" % sacc)
dm = dm.select("endY%sNorm > -.5" % sacc)
dm = dm.select("endY%sNorm < .5" % sacc)
saccLat = "saccLat%s" % sacc
# Trim the data matrix such that the most extreme latencies are excluded:
if trim:
_dm = dm.selectByStdDev(keys = ["file"], dv = saccLat, verbose = False)
else:
_dm = dm
# Withinize sacc latencies, if wanted:
print "\n\tcousineau = %s\n" % cousineau
if cousineau:
_dm = _dm.addField("cousineau_%s"%saccLat, dtype = None)
_dm = _dm.withinize(saccLat, "cousineau_%s"%saccLat, "file", verbose = False, whiten=False)
saccLat = "cousineau_%s"%saccLat
col = colList.pop()
# Make bins, only for the first dv (for the second dv, the binned variable is the same and
# therefore already exists:
varToBin = saccLat
binnedVar = "binnend%s" % varToBin
binned_dm = _dm.addField(binnedVar)
binned_dm = binned_dm.calcPerc(varToBin, binnedVar ,keys = ["file"], nBin = nBin)
# Disadvantages of pm:
# - There SHOULD be enough observations per participant to be able to plot
# - bin number (instead of bin mean) on x axis
plt.subplot(nRows,nCols,plotNr)
plt.title(subTitle)
pm = PivotMatrix(binned_dm, binnedVar, "file", dv, colsWithin = True, err = 'se')
pm.plot(nLvl1=1, fig = fig, colors = col)
if plotNr != "1":
plt.yticks([])
plt.xticks([])
plt.ylim(yLim)
plt.legend(["initial saccade", "refixation 1", "refixation 2"])
plt.axhline(0, color = "#555753", linestyle = "--", linewidth = 2)
#plt.show()
plt.savefig(os.path.join('%s.png' % title))
plt.savefig(os.path.join('%s.svg' % title))
def contrastSide(trim = True, exclOverlap = False, exclY = True):
"""
Landing position as a function of contrast.
Positive values indicate landing position to the right,
negative values indicate landing pos to the left
NOTE: be careful with using --shortcut, because
for the contrast analyses the contrast manipulations
should not be excluded.
TODO: make a ToContrast variable, such that we have more observations
per cell and can do the on-object filter on the y axis as well?
The disadvantage is that we can't use the no-contrast-trials in this case
#(because 'ToContrast' should be a difference score between two bins).
"""
fig = plt.figure()
title = "Effect of contrast - exclOverlap = %s - exclY = %s" % (exclOverlap, exclY)
plt.suptitle(title)
for sacc in ["1", "2", "3"]:
colList = ["#ef2929", "#3465a4","#73d216", "#f57900"]
plt.subplot(1,3, int(sacc))
plt.title("sacc = %s"% (sacc))
# Exp 1:
exp = "004A"
dm1 = getDM.getDM(exp = exp, driftCorr = True, onlyControl = False)
# This is the same for corrected landing positions (the saccade
# doesn't change; only the reference point does)
dm1 = dm1.select("endX%sNorm != ''" % sacc, verbose = False)
dm1 = dm1.select("endX%sNorm > -.5" % sacc, verbose = False)
dm1 = dm1.select("endX%sNorm < .5" % sacc, verbose = False)
if exclY:
dm1 = dm1.select("endY%sNorm != ''" % sacc)
dm1 = dm1.select("endY%sNorm > -.5" % sacc)
dm1 = dm1.select("endY%sNorm < .5" % sacc)
for dv in ["endX%sNorm" % sacc, "endX%sCorrNorm" % sacc]:
#If wanted, trim the data
if trim:
_dm1 = dm1.selectByStdDev(keys = ["contrast_side", "file"], dv = dv)
# For experiment 1 there are not enough third fixations anyway,
# not even when not filtering on-object on the y-axis.
if exp == "004A" and sacc == "3":
colList = ["#ef2929", "#3465a4"]
continue
# Get pivot matrix:
pm = PivotMatrix(_dm1, ["contrast_side"], ["file"], dv=dv, colsWithin=True)#, xLabels = ["left", "control", "right"])
col = colList.pop()
pm.plot(fig = fig, nLvl1 = 1, colors = [col])
# Experiment 2 and 3:
dv = "endX%sNorm" % sacc
for exp in ["004B", "004C"]:
if exclY and exp == "004B" and sacc == "3":
colList = ["#ef2929"]
continue
if exp == "004C" and exclOverlap:
dm = dm.select("gap == 'zero'")
print "EXP = ", exp
dm = getDM.getDM(exp = exp, driftCorr = True, onlyControl = False)
# This is the same for corrected landing positions (the saccade
# doesn't change; only the reference point does)
dm = dm.select("endX%sNorm != ''" % sacc, verbose = False)
dm = dm.select("endX%sNorm > -.5" % sacc, verbose = False)
dm = dm.select("endX%sNorm < .5" % sacc, verbose = False)
if exclY:
dm = dm.select("endY%sNorm != ''" % sacc)
dm = dm.select("endY%sNorm > -.5" % sacc)
dm = dm.select("endY%sNorm < .5" % sacc)
#If wanted, trim the data
if trim:
_dm = dm.selectByStdDev(keys = ["contrast_side", "file"], dv = dv)
# Get pivot matrix:
pm = PivotMatrix(_dm, ["contrast_side"], ["file"], dv=dv, colsWithin=True)
col = colList.pop()
pm.plot(fig = fig, nLvl1 = 1, colors = [col])
# Modify plot:
plt.ylim(-.2, .2)
plt.legend(["Exp1 (abs)", "Exp1 (corr)", "Exp2 (abs)", "Exp2 (sim)"])
if sacc == "3":
plt.legend(["Exp2 (abs)", "Exp2 (sim)"])
if exclY:
plt.legend(["Exp2 (sim)"])
plt.axhline(0, color = "#888a85", linestyle = "--", linewidth = 2)
plt.savefig("%s.png" % title)
def contrastSide(trim = True):
"""
Landing position as a function of contrast.
Keyword arguments:
"""
colList = ["#ef2929","#73d216","#f57900","#3465a4"]
lLegend = ["Exp. 1 (relative to center)", "Exp. 1 (relative to CoG)", \
"Experiment 2 (relative to CoG)", "Saliency-model simulation"]
yLim = [-.3, .3]
yTitle = "normalised landing position"
fig = plt.figure()
title = "Effect of contrast"
plt.suptitle(title)
plt.subplots_adjust(wspace = 0)
for sacc in ["1", "2"]:#, "3"]:
copyColList = colList[:]
plt.subplot(1,2, int(sacc))
plt.title("sacc = %s"% (sacc))
#linestyles = ['o-', ',--', 'x:' #TODO
for exp in ["004A", "004B", "004C"]:
dm = getDM.getDM(exp, onlyControl = False)
# Only on-object:
on_dm = onObject.onObject(dm, sacc)
if exp == "004A":
dvList = ["endX%sNorm" % sacc, "endX%sCorrNorm" % sacc]
else:
dvList = ["endX%sNorm" % sacc]
for dv in dvList:
#If wanted, trim the data
if trim:
trim_dm = on_dm.selectByStdDev(keys = ["contrast_side", "file"], dv = dv)
else:
trim_dm = on_dm
# Get pivot matrix:
pm = PivotMatrix(trim_dm, ["contrast_side"], ["file"], dv=dv, colsWithin=True)
col = copyColList.pop()
#Constants.plotLineSymbols = [linestyles.pop()]
pm.plot(fig = fig, nLvl1 = 1, colors = [col])
if exp != "004C":
am = AnovaMatrix(trim_dm, ["contrast_side"], dv, subject = "file")
print exp, dv
print am
#raw_input()
plt.ylim(yLim)
if sacc == "1":
plt.ylabel(yTitle)
if sacc == "2":
plt.legend(lLegend)
plt.yticks([])
plt.axhline(0, color = "#888a85", linewidth = 2)
plt.xlabel("Contrast manipulation")
for ext in [".png", ".svg"]:
plt.savefig("Figure_S1%s" % ext)
