def ovpOtherFactor(dm, factor, rt = 'rtFromLanding',\
nRows = 2, nCols = 4, nBin = 5, ws = True):
"""
"""
# Determine whether or not to first split data per participant
# (i.e. to make within-subjects bins):
if ws:
keys = ['file', factor]
else:
keys = None
# Determine exp:
exp = dm["exp"][0]
# 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)
fig = plt.figure()
plt.subplots_adjust(wspace = .3, hspace = .4)
figName = "%s: OVP effects as a function of binned landing positions and %s - WS = %s" \
% (exp, factor, ws)
plt.suptitle(figName)
plotCount = 0
subjectID = ["file"]
if exp == "004A":
factorList = [[factor,"binned_endX1"], [factor,"binned_endX1Corr"]]
else:
factorList = [[factor,"binned_endX1"]]
for factors in factorList:
for dv in [rt, "durationFix1", "refixProb", "saccCount"]:
plotCount +=1
_dm = dm.select("%s != ''"%dv)
plt.subplot(nRows, nCols, plotCount)
plt.title(dv)
pm = PivotMatrix(_dm, factors, subjectID, dv, colsWithin = True)
pm.linePlot(fig = fig)
plt.savefig("%s.png"%figName)
def simon(dm):
"""
"""
pm = PivotMatrix(dm, ['response_hand', 'mask_side'], ['file'], dv='RT', colsWithin=False)
pm._print()
pm.barPlot(show=True)
plt.title("simon effect")
def affordance(dm):
"""
"""
dm = dm.select('symm == "asymm"')
pm = PivotMatrix(dm, ['response_hand', 'handle_side'],['file'], dv = "RT")
pm.barPlot(show=True)
plt.title("affordance effect")
def gapEffect(dm, dv = "saccLat"):
"""
Regardless of color change...
"""
pm = PivotMatrix(dm, ['gap'],['file'],dv =dv)
pm._print()
pm.barPlot(show=True)
plt.ylabel(dv)
plt.title("gap effect")
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 affCross(trim = True, exclY = False, dv = "RT"):
"""
Affordance effect as cross-over interaction between handle
and response hand ACROSS saccades
NOTE: on-object is difficult here, because we're not
looking across saccades.
"""
fig = plt.figure()
title = "Affordance effect per Exp - exclY = %s" % exclY
plt.suptitle(title)
plotNr = 1
for exp in ["004A", "004B"]:
if exp != "004B":
continue
plt.subplot(1,2,plotNr)
plt.title("Exp = %s" % exp)
dm = getDM.getDM(exp)
dm = onObject.onObject(dm, "1", exclY = exclY)
if trim:
trimmed_dm = dm.selectByStdDev(keys = \
["handle_side", "response_hand", "file"], dv = dv)
else:
trimmed_dm = dm
am = AnovaMatrix(trimmed_dm, ["handle_side", "response_hand"], dv = dv, \
subject = "file")._print(ret=True)
print am
# raw_input()
# Get descriptive statistics:
pm = PivotMatrix(trimmed_dm, ["comp"], ["file"], colsWithin = False, \
dv = dv, err = 'se')
print pm
raw_input()
pm = PivotMatrix(trimmed_dm, ["handle_side", "response_hand"], \
["file"], dv = dv, colsWithin = True)
pm.linePlot(fig = fig)
plotNr +=1
plt.savefig("%s.png"%title)
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 affLumCongr(dm):
dm = dm.select('symm == "asymm"')
xLabels = ["hande left", "handle right"]
lLabels = ["control", "more contrast right", "more contrast left"]
yLabel = "landing position: negative = left, positive = right"
pm = PivotMatrix(dm, ['handle_side', 'mask_side'], ['file'], \
dv='endX')
pm._print()
pm.barPlot(xLabels = xLabels, lLabels = lLabels, yLabel = yLabel, show=True)
plt.title("overlap between contrast and affordance")
def lumBinAn(dm, nBin = 8): """ """ dm = latBins(dm) pm = PivotMatrix(dm, ['mask_side', 'saccLatBin'], ['file'], dv='endX', colsWithin=False) pm._print() xLabels = range(1,nBin+1) xLabel = "binned latencies" lLabel = "contrast manipulation" lLabels = ["control", "more contrast right", "more contrast left"] #lLabels = None yLabel = "landing position - negative = left, positive = right" pm.linePlot(show=True,xLabels = xLabels, xLabel = xLabel, yLabel = yLabel, legendTitle = lLabel, lLabels = lLabels) plt.axhline(0, color = "grey", linestyle = "--")
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 affordanceEffect(dm, nRows = None, nCols = None, \
rowCount = None, fig = None):
"""
RT as a function of handle side and response handle
Note: this two-way interaction should be identical
to a main effect of compatibility.
Arguments:
dm
Keyword arguments:
nRows
nCols
rowCount
fig
"""
# Select only handled objects:
dm = dm.select("symm == 'asymm'")
# Select only trials on which there was no mask applied:
dm = dm.select("contrast_side == 'control'")
#fig = plt.figure()
nPlot = 0
factors = ["response_hand", "handle_side"]
pp = ["file"]
dv = "rtFromLanding"
# Trim:
_dm = dm.selectByStdDev(keys = factors + pp, dv = dv)
am = AnovaMatrix(_dm, factors = factors, dv = dv, \
subject = pp[0])._print(maxLen=10, ret=True)
plt.subplot2grid((nRows,nCols), (rowCount, 0))
pm = PivotMatrix(_dm, factors, pp, dv = dv, colsWithin = True)
pm.linePlot(fig = fig, legendTitle = factors[0], xLabel = factors[1])
plt.subplot2grid((nRows,nCols), (rowCount, 1), colspan=2)
plt.text(0.1,0.1,am, family='monospace')
def contrastEffect(dm, saccCount):
"""
Plots dist hist of sacc latencies as a function of gap condition.
Arguments:
dm
saccCount
"""
# Define keys:
factors = ["contrast_side", "gap"]
pp = ["file"]
dv = "saccLat%s" % saccCount
exp = dm["exp"][0]
# Apply selections:
dm = dm.select("%s != ''" % dv)
dm = dm.selectByStdDev(keys = factors + pp, dv = dv)
# Create figure:
fig = plt.figure()
plt.subplots_adjust(hspace = .4)
figName = "%s: Latencies sacc %s as a function of Gap and Contrast" % (exp, saccCount)
plt.suptitle(figName)
plt.subplot2grid((2,2), (0, 0))
pm = PivotMatrix(dm, factors, pp, dv, colsWithin = True)
pm.linePlot(fig = fig,xLabel = factors[-1], legendTitle = factors[0], lLabels = ["left", "contrast", "right"])
# NOTE why I have to give the lLabels as a parameter: the underscore of "_left" gives problems for plotting
## the legend.??
plt.ylabel(dv)
am = AnovaMatrix(dm, factors = factors, dv = dv, \
subject = pp[0])._print(maxLen=10, ret=True)
plt.subplot2grid((2,2), (1, 0), colspan=2)
plt.text(0.1,0.1,am, family='monospace')
plt.savefig("%s.png"%figName)
return fig
def plotFullModel(dm, sacc):
"""
"""
fig = plt.figure(figsize = (15, 5))
plt.suptitle("Sacc = %s" % sacc)
ax1 = plt.subplot(141)
lCols = ["blue", "red", "orange", "yellow", "green", "pink"]
xVar = "sacc%s_ex" % sacc
yVar = "sacc%s_ey" % sacc
dm = dm.select("%s != ''" % xVar)
dm = dm.select("%s != ''" % yVar)
dm = dm.select("%s != -1000" % xVar)
dm = dm.select("%s != -1000" % yVar)
for a in dm.unique("realAngle"):
_dm = dm.select("realAngle == %s" % a)
col = lCols.pop()
plt.scatter(_dm[xVar], _dm[yVar], color = col, marker = ".", label = a)
plt.axvline(constants.xCen, color = gray[3], linestyle = '--')
plt.axhline(constants.yCen, color = gray[3], linestyle = '--')
ax2 = plt.subplot(142)
pm = PivotMatrix(dm, ["stim_type", "realAngle"], ["file"], "xNorm1", colsWithin =True)
pm.linePlot(fig = fig)
pm.save("PM.csv")
pm._print()
plt.axhline(0, color = gray[3], linestyle = "--")
ax3 = plt.subplot(143)
plt.title("object")
dmObj= dm.select("stim_type == 'object'")
#slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(stimObj["ecc"], stimObj["xNorm1"])
x = dmObj["ecc"]
y = dmObj["xNorm%s" % sacc]
fit = scipy.polyfit(x,y,1)
fit_fn = scipy.poly1d(fit)
plt.plot(x,y, 'yo', x, fit_fn(x), '--k')
ax4 = plt.subplot(144)
plt.title("non-object")
dmNo= dm.select("stim_type == 'non-object'")
#slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(stimObj["ecc"], stimObj["xNorm1"])
x = dmNo["ecc"]
y = dmNo["xNorm%s" % sacc]
fit = scipy.polyfit(x,y,1)
fit_fn = scipy.poly1d(fit)
plt.plot(x,y, 'yo', x, fit_fn(x), '--k')
plt.savefig("./plots/Full_model_004C_sacc%s.png" % sacc)
plt.show()
def affOtherFactor(dm, factor2, nRows = None, nCols = None, \
rowCount = None, fig = None):
"""
RT as a function of compatibility and something else (e.g. category).
Arguments:
dm
factor2
"""
# Declare keys:
factors = ["comp"] + [factor2]
pp = ["file"]
dv = "rtFromLanding"
# Select only handled objects:
dm = dm.select("symm == 'asymm'")
# Trim:
dm = dm.selectByStdDev(keys = factors + pp, dv = dv)
# Create figure:
#fig = plt.figure()
nPlot = 0
# Stats:
am = AnovaMatrix(dm, factors = factors, dv = dv, \
subject = pp[0])._print(maxLen=10, ret=True)
# Plot:
plt.subplot2grid((nRows, nCols), (rowCount, 0))
pm = PivotMatrix(dm, factors, pp, dv = dv, colsWithin = True)
pm.linePlot(fig = fig, lLabels = None,legendTitle = factors[0], xLabel = factors[1])
plt.subplot2grid((nRows,nCols), (rowCount, 1), colspan=2)
plt.text(0.1,0.1,am, family='monospace')
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 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 vfDiff(dm,dv = "RT"):
pm = PivotMatrix(dm, ['visual_field'],['file'], dv = dv)
pm.barPlot(show=True)
plt.ylabel(dv)
plt.title("visual field difference")
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 desc(dm):
"""
Save descriptive statistics.
Arguments:
dm -- A DataMatrix.
"""
dm_cor = dm.select('correct == 1')
pm = PivotMatrix(dm, ['postRotDelay'], ['subject_nr'], \
'soa', colsWithin=False)
pm.save('output/soa.csv')
pm = PivotMatrix(dm, ['subject_nr'], ['subject_nr'], \
'correct', colsWithin=False)
pm.save('output/accuracy.csv')
pm = PivotMatrix(dm_cor, ['subject_nr'], ['subject_nr'], \
'response_time', colsWithin=False)
pm.save('output/rt.csv')
pm = PivotMatrix(dm, ['postRotDelay', 'targetRot'], ['subject_nr'], \
'correct', colsWithin=False)
pm.save('output/acc.targetrot.postRotDelay.csv')
pm = PivotMatrix(dm_cor, ['postRotDelay', 'targetRot'], ['subject_nr'], \
'response_time', colsWithin=False)
pm.save('output/rt.targetrot.postRotDelay.csv')
# Do the split analysis on RTs
pm = PivotMatrix(dm_cor, ['cond', 'postRotDelay', 'valid'], \
['subject_nr'], dv='response_time')
pm.save('output/rt.cond.postRotDelay.valid.pm.csv')
# Do the full split on RTs after applying 2.5 SD
pm = PivotMatrix(dm_cor.selectByStdDev(['subject_nr'], 'response_time'), \
['cond', 'postRotDelay', 'valid'], ['subject_nr'], dv='response_time')
pm.save('output/rt.2.5sd.cond.postRotDelay.valid.pm.csv')
# Do the full split on iRts
pm = PivotMatrix(dm_cor, ['cond', 'postRotDelay', 'valid'], \
['subject_nr'], dv='iRt')
pm.save('output/iRt.cond.postRotDelay.valid.pm.csv')
dm = dm.select("direction == 0")
dm = dm.addField("congruency", dtype = str)
dm = dm.addField("respHand", dtype = str)
dm["respHand"][dm.where("correct_response == 1")] = "left"
dm["respHand"][dm.where("correct_response == 2")] = "right"
for i in dm.range():
handle = dm["flip"][i]
respHand = dm["respHand"][i]
if handle == respHand:
dm["congruency"][i] = "congruent"
print "congruent"
else:
dm["congruency"][i] = "incongruent"
print "incongruent"
print "flip = ", dm["flip"][i]
print "correct response = ", dm["correct_response"][i]
print "response hand = ", dm["respHand"][i]
print "congruency = ", dm["congruency"][i]
pm = PivotMatrix(dm, ["congruency", "stim_type"], ["file"], "response_time", colsWithin = True)
pm.linePlot()
plt.show()
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 gapEffect(dm, saccCount = "1"):
"""
Plots dist hist of sacc latencies as a function of gap condition.
Arguments:
dm
Keyword arguments:
saccCount --- Default = "1"
"""
# Define keys:
factors = ["visual_field", "gap"]
pp = ["file"]
dv = "saccLat%s" % saccCount
exp = dm["exp"][0]
# Apply selections:
dm = dm.select("%s != ''" % dv)
dm = dm.selectByStdDev(keys = factors + pp, dv = dv)
# Create figure:
fig = plt.figure()
plt.subplots_adjust(hspace = .4)
figName = "%s: Latencies sacc %s as a function of Gap and Visual Field" % (exp, saccCount)
plt.suptitle(figName)
nPlots = 0
for vf in ["upper", "lower"]:
vf_dm = dm.select("visual_field == '%s'" % vf)
#plt.subplot(nRows, nCols, nPlots)
plt.subplot2grid((3,2), (nPlots, 0))
for gap in ["zero", "overlap"]:
if gap == "zero":
col = "#ef2929"
else:
col = "#8ae234"
plt.title("%s visual field" % vf)
gap_dm = vf_dm.select("gap == '%s'" % gap)
plt.hist(gap_dm[dv], bins = 30, color = col, alpha = .3)
plt.xlim(0, 400)
plt.ylabel('freq')
plt.xlabel('initial saccade latencies')
if vf == "lower":
plt.legend(["zero", "overlap"], loc = 'best')
nPlots +=1
plt.subplot2grid((3,2), (0, 1), rowspan=2)
pm = PivotMatrix(dm, factors, pp, dv, colsWithin = True)
pm.linePlot(fig = fig)
plt.ylabel("saccade latency")
am = AnovaMatrix(dm, factors = factors, dv = dv, \
subject = pp[0])._print(maxLen=10, ret=True)
plt.subplot2grid((3,2), (2, 0), colspan=2)
plt.text(0.1,0.1,am, family='monospace')
plt.savefig("%s.png"%figName)
def landWithoutContrast(factor = ["handle_side"], nRows = 3, nCols = 4, saccNr = "1", \
yLim = [-.1, .1], pp = ["file"], stats = True, colsWithin = True, sepPlots = True, exclY = True):
"""
Plots effect of handle side on landing-position variable. Only trials without
contrast manipulation are included.
Arguments:
dm
Keyword arguments:
saccNr --- nth saccade of interest (default = first, "1")
sepPlots --- save and return as separate plots or as subplots of one main figure
"""
# Create figure:
# Experiment 004A:
exp = "004A"
dm1 = getDM.getDM(exp = exp, driftCorr = True)
# Only use objects without contrast manipulation
dm1 = dm1.select("mask_side == 'control'", verbose = False)
figList = []
if not sepPlots:
fig = plt.figure(figsize = (10,2))
nRows = 1
nCols = 3
plotNr = 0
for dv in ["endX%sNorm" % saccNr, "endX%sCorrNorm" % saccNr]:
if sepPlots:
fig = plt.figure(figsize = (10,3))
figName = "exp %s dv %s colsWithin = %s - exclY = %s" % (exp, dv, colsWithin, exclY)
else:
plotNr +=1
plt.subplot(nRows,nCols,plotNr)
_dm = dm1
# Only trials where variable has a value:
_dm = _dm.select("%s != ''"%dv)
# Only on-object EM's:
_dm = _dm.select('endX%sNorm < .5' % saccNr)
_dm = _dm.select('endX%sNorm > -.5' % saccNr)
if exclY:
_dm = _dm.select('endY%sNorm < .5' % saccNr)
_dm = _dm.select('endY%sNorm > -.5' % saccNr)
# Trim:
_dm = _dm.selectByStdDev(keys = factor + pp, dv = dv)
#plt.subplot2grid((nRows,nCols), (plotCount, 0))
pm = PivotMatrix(_dm, factor, pp, dv, colsWithin=colsWithin, err='se')
#plt.axhline(0, linestyle = "--", color = "#555753")
#plt.ylim(yLim)
#pm.barPlot(fig=fig)
pm.barPlot(fig = fig, _dir='right')
plt.axvline(0, linestyle = "--", color = "#555753")
plt.axhline(0.5, linestyle = "-", color = "#555753")
plt.xlim(yLim)
if sepPlots:
if "Corr" in dv:
plt.xlabel("corrected horizontal landing position")
else:
plt.xlabel("horizontal landing position")
else:
plt.xticks([])
if stats:
am = AnovaMatrix(_dm, factors = factor, dv = dv, \
subject = pp[0])._print(maxLen=7, ret=True)
print am
#plt.subplot2grid((nRows,nCols), (plotCount, 1), colspan=3)
#plt.title("Exp = %s DV = %s" % (exp, dv))
#plt.text(0.05,0.1,am, family='monospace')
#plt.xticks([])
#plt.yticks([])
#plotCount +=1
if sepPlots:
plt.savefig("%s.svg"%figName)
plt.savefig("%s.png"%figName)
#plt.show()
figList.append(fig)
# For experiment 004B we plot only the normalised landing position.
# Correcting for CoG doesn't make sense here!
exp = "004B"
# Get dm:
dm2 = getDM.getDM(exp = exp, driftCorr = True)
# Only trials without contrast manipulation:
dm2 = dm2.select("mask_side == 'control'")
# Only trials where variable has a value:
dm2 = dm2.select("%s != ''"%dv)
# Only on-object eye movements:
dm2 = dm2.select('endX%sNorm < .5' % saccNr)
dm2= dm2.select('endX%sNorm > -.5' % saccNr)
if exclY:
dm2 = dm2.select('endY%sNorm < .5' % saccNr)
dm2= dm2.select('endY%sNorm > -.5' % saccNr)
# Pivot matrix and stats:
dv = "endX%sNorm" % saccNr
# Trim:
dm2 = dm2.selectByStdDev(keys = factor + pp, dv = dv)
if sepPlots:
fig = plt.figure(figsize = (10,3))
figName = "exp %s dv %s colsWithin = %s - exclY = %s" % (exp, dv, colsWithin, exclY)
else:
plt.subplot(nRows,nCols,3)
pm = PivotMatrix(dm2, factor, pp, dv = dv, colsWithin = colsWithin, err = 'se')
#plt.subplot2grid((nRows,nCols), (plotCount, 0))
pm.barPlot(fig = fig, _dir='right')
plt.axvline(0, linestyle = "--", color = "#555753")
plt.axhline(0.5, linestyle = "-", color = "#555753")
plt.xlim(yLim)
if sepPlots:
plt.xlabel("horizontal landing position")
else:
plt.xticks([])
if stats:
am = AnovaMatrix(dm2, factors = factor, dv = dv, \
subject = pp[0])._print(maxLen=7, ret=True)
print am
#plt.subplot2grid((nRows,nCols), (plotCount, 1), colspan=3)
#plt.title("Exp = %s DV = %s" % (exp, dv))
#plt.text(0.05,0.1,am, family='monospace')
#plt.xticks([])
#plt.yticks([])
if sepPlots:
plt.savefig("%s.png"%figName)
plt.savefig("%s.svg"%figName)
figList.append(fig)
return figList
else:
return fig
def allSaccLats(dm, direction, figTitle = True, trim = True, cousineau = True, nBin = 15, usePm = True, err = 'se'):
"""
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:
Returns fig list
"""
figList = []
print '\n\tdirection = %s \n' % direction
if direction == "ToHandle":
start_dm = dm.select("contrast_side == 'control'")
if direction == "ToContrast":
start_dm = dm.select("contrast_side != 'control'")
exp = start_dm['exp'][0]
lDm = []
if exp == "004A":
# Max sacc count = 4
lSacc = range(1,5)
if exp == "004B":
# Max sacc count = 3
lSacc = range(1,4)
for saccCount in lSacc:
# 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
corrList = ["corrected", "uncorrected"]
for corr in corrList:
fig = plt.figure(figsize = (3,7))
title = "Landing (%s) %s as a function of ALL binned sacc lats exp %s cousineau = %s trim = %s usePm = %s" \
% (corr, direction, exp, cousineau, trim, usePm)
if figTitle:
plt.title(title)
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 = onObject(merged_dm,dv)
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)
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 = True, 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)
if not usePm:
lX = []
lY = []
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()
lX.append(xMean)
lY.append(yMean)
plt.plot(lX, lY, color = "#3465a4", marker = 'o')
plt.xlabel("binned saccade latencies from stimulus onset")
plt.ylabel("landing position (%s) %s" % (corr,direction))
plt.ylim(-.2, .2)
plt.axhline(0, color = "#555753", linestyle = "--", linewidth = 2)
plt.savefig(os.path.join(dst,'%s.png' % title))
if usePm:
pm = PivotMatrix(binned_dm, binnedVar, "file", dv, colsWithin = True, err = 'se')
#pm.plot(nLvl1=1, fig = fig, xLabel = "binned saccade latencies from stimulus onset", \
# yLabel = "landing position (%s) %s" % (corr,direction))
pm.barPlot(fig = fig, xLabel = "binned saccade latencies from stimulus onset", \
yLabel = "landing position (%s) %s" % (corr,direction))
plt.ylim(-.2, .2)
plt.axhline(0, color = "#555753", linestyle = "--", linewidth = 2)
plt.savefig(os.path.join(dst,'%s.png' % title))
plt.savefig(os.path.join(dst,'%s.png' % title))
figList.append(fig)
return figList
def twoWayHandle(dm, dv, factor2, onlyControl = True, addStats = True, figTitle = True):
"""
Plots landing positions as a function of gap condition.
Arguments:
dm
dv --- dv, should be a landing variable
factor2 -- second IV
"""
if onlyControl and factor2 == "contrast_side":
print "You can't exclude contrast manipulation if one of the IV's is contrat_side"
return
if onlyControl:
dm = dm.select("contrast_side == 'control'")
# Define keys:
factors = [factor2, "handle_side"]
pp = ["file"]
exp = dm["exp"][0]
if addStats:
nCols = nRows = 2
fig = plt.figure()
else:
nCols = nRows = 1
fig = plt.figure(figsize = (3,5))
# Apply selections:
dm = dm.select("%s != ''" % dv)
dm = dm.selectByStdDev(keys = factors + pp, dv = dv)
# Create figure:
plt.subplots_adjust(hspace = .4)
figName = "%s: %s as a function of %s and Handle Side onlyControl = %s" % (exp, dv, factor2, onlyControl)
if figTitle:
plt.suptitle(figName)
plt.subplot2grid((nRows,nCols), (0, 0))
pm = PivotMatrix(dm, factors, pp, dv, colsWithin = True)
# Note why I have to give the lLabels as a parameter: the underscore of "_left" gives problems for plotting
# the legend.??
if factor2 == "contrast_side":
lLabels = ["left", "contrast", "right"]
else:
lLabels = None
pm.linePlot(fig = fig,xLabel = factors[-1], legendTitle = factors[0], lLabels = lLabels)
plt.ylabel(dv)
plt.axhline(0, color = "#555753", linestyle = "--", linewidth = 2)
if addStats:
am = AnovaMatrix(dm, factors = factors, dv = dv, \
subject = pp[0])._print(maxLen=10, ret=True)
plt.subplot2grid((nRows,nCols), (1, 0), colspan=2)
plt.text(0.1,0.1,am, family='monospace')
plt.savefig("%s.png"%figName)
return fig
def ovp(dm, rt = "rtFromLanding", ws = True, nRows = 1, nCols = 8, nBin = 5, \
binVar = "endX1Norm", binVar2 = None, figSize = (15,4), onlyControl = True):
"""
Plots OVP effects as a function of binned initial landing position.
Arguments:
dm --- data matrix
rt --- variable for RT
ws --- Vincentize or not.
binVar --- variable for first row of OVP plots
binVar2 --- variable for second row of OVP plots (optional)
onlyControl --- exclude contrast manipulation. Default = True
"""
# If wanted, exclude the contrast manipulation:
if onlyControl:
dm = dm.select("mask_side == 'control'")
# Determine whether or not to first split data per participant
# (i.e. to make within-subjects bins):
if ws:
keys = ['file']
else:
keys = None
# Determine exp:
exp = dm["exp"][0]
# Add factors containing bins:
binnedVar = "binned_%s"%binVar
dm = dm.addField(binnedVar)
dm = dm.calcPerc(binVar, binnedVar,keys = keys, nBin = nBin)
factorList = [binnedVar]
if binVar2 != None:
binnedVar2 = "binned_%s" % binVar2
dm = dm.addField(binnedVar2)
dm = dm.calcPerc(binVar2, binnedVar2, keys = keys, nBin = nBin)
factorList.append(binnedVar2)
# Create figure:
fig = plt.figure(figsize = figSize)
plt.subplots_adjust(wspace = .5)
figName = "%s: OVP effects as a function of binned %s - WS = %s" % (exp, factorList, ws)
plt.suptitle(figName)
plotCount = 0
# Define keys:
subjectID = ["file"]
dvList = [rt, "durationFix1", "refixProb", "saccCount", "gazeDur", "durationFix2", \
"endX2Norm", "saccLat1"]
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)
plt.title(dv)
pm = PivotMatrix(_dm, factors, subjectID, dv, colsWithin = True)
pm.barPlot(fig = fig)
if dv == 'endX2Norm':
plt.axhline(0, linestyle = '--', color = 'gray')
# Determine ylim's:
if exp == "004A":
if dv == "rtFromLanding":
plt.ylim(500,550)
if dv == "durationFix1":
plt.ylim(260, 300)
if dv == "durationFix2":
plt.ylim(315, 355)
if dv == "refixProb":
plt.ylim(0.58, 0.72)
if dv == "saccCount":
plt.ylim(1.75, 1.94)
if dv == "endX2Norm":
plt.ylim(-.2, .2)
if dv == "gazeDur":
plt.ylim(500, 600)
if dv == "saccLat1":
plt.ylim(150, 200)
if exp == "004B":
if dv == "rtFromLanding":
plt.ylim(400,500)
if dv == "durationFix1":
plt.ylim(260, 300)
if dv == "durationFix2":
plt.ylim(270, 300)
if dv == "refixProb":
plt.ylim(0.4, 0.65)
if dv == "saccCount":
plt.ylim(1.6, 2.)
if dv == "endX2Norm":
plt.ylim(-.2, .2)
if dv == "gazeDur":
plt.ylim(450, 510)
if dv == "saccLat1":
plt.ylim(150, 250)
#plt.ylabel(dv)
plt.xticks([])
plt.savefig("%s RT = %s nBin = %s.png"%(figName,rt, nBin))
def splitHeavy(factor = ["corrDirection", "handle_side"], nRows = 3, nCols = 4, saccNr = "1", \
yLim = [-.5, .5], pp = ["file"], stats = True, colsWithin = True, sepPlots = True, exclY = True):
# Create figure:
# Experiment 004A:
exp = "004A"
dm1 = getDM.getDM(exp = exp, driftCorr = True)
dm1 = dm1.select("corrDirection != 'correction smaller than 8'")
for corr in dm1.unique("corrDirection"):
print corr
tmp = dm1.select("corrDirection == '%s'" % corr)
print "Corr direction = ", corr
print tmp.unique("object")
# Only use objects without contrast manipulation
dm1 = dm1.select("mask_side == 'control'", verbose = False)
figList = []
if not sepPlots:
fig = plt.figure(figsize = (10,2))
nRows = 1
nCols = 3
plotNr = 0
for dv in ["endX%sNorm" % saccNr, "endX%sCorrNorm" % saccNr]:
if sepPlots:
fig = plt.figure(figsize = (10,3))
figName = "split by heavy side exp %s dv %s colsWithin = %s - exclY = %s" % (exp, dv, colsWithin, exclY)
else:
plotNr +=1
plt.subplot(nRows,nCols,plotNr)
_dm = dm1
# Only trials where variable has a value:
_dm = _dm.select("%s != ''"%dv)
# Only on-object EM's
_dm = _dm.select('endX%sNorm < .5' % saccNr)
_dm = _dm.select('endX%sNorm > -.5' % saccNr)
if exclY:
_dm = _dm.select('endY%sNorm < .5' % saccNr)
_dm = _dm.select('endY%sNorm > -.5' % saccNr)
# Trim:
_dm = _dm.selectByStdDev(keys = factor + pp, dv = dv)
#plt.subplot2grid((nRows,nCols), (plotCount, 0))
pm = PivotMatrix(_dm, factor, pp, dv, colsWithin=colsWithin, err='se')
#plt.axhline(0, linestyle = "--", color = "#555753")
#plt.ylim(yLim)
#pm.barPlot(fig=fig)
pm.barPlot(fig = fig, _dir='right', legendTitle = "handle side")
plt.axvline(0, linestyle = "--", color = "#555753")
plt.axhline(0.5, linestyle = "-", color = "#555753")
plt.xlim(yLim)
if sepPlots:
if "Corr" in dv:
plt.xlabel("corrected horizontal landing position")
else:
plt.xlabel("horizontal landing position")
else:
plt.xticks([])
if stats:
am = AnovaMatrix(_dm, factors = factor, dv = dv, \
subject = pp[0])._print(maxLen=7, ret=True)
print am
#plt.subplot2grid((nRows,nCols), (plotCount, 1), colspan=3)
#plt.title("Exp = %s DV = %s" % (exp, dv))
#plt.text(0.05,0.1,am, family='monospace')
#plt.xticks([])
#plt.yticks([])
#plotCount +=1
if sepPlots:
plt.savefig("%s.svg"%figName)
plt.savefig("%s.png"%figName)
#plt.show()
figList.append(fig)
# For experiment 004B we plot only the normalised landing position.
# Correcting for CoG doesn't make sense here!
exp = "004B"
# Get dm:
dm2 = getDM.getDM(exp = exp, driftCorr = True)
# Only trials without contrast manipulation:
dm2 = dm2.select("mask_side == 'control'")
dm2 = dm2.select("corrDirection != 'correction smaller than 8'")
# Only trials where variable has a value:
dm2 = dm2.select("%s != ''"%dv)
# Only on-object eye movements:
dm2 = dm2.select('endX%sNorm < .5' % saccNr)
dm2= dm2.select('endX%sNorm > -.5' % saccNr)
if exclY:
dm2 = dm2.select('endY%sNorm < .5' % saccNr)
dm2= dm2.select('endY%sNorm > -.5' % saccNr)
# Pivot matrix and stats:
dv = "endX%sNorm" % saccNr
# Trim:
dm2 = dm2.selectByStdDev(keys = factor + pp, dv = dv)
if sepPlots:
fig = plt.figure(figsize = (10,3))
figName = "split by heavy side exp %s dv %s colsWithin = %s" % (exp, dv, colsWithin)
else:
plt.subplot(nRows,nCols,3)
pm = PivotMatrix(dm2, factor, pp, dv = dv, colsWithin = colsWithin, err = 'se')
#plt.subplot2grid((nRows,nCols), (plotCount, 0))
pm.barPlot(fig = fig, _dir='right')
plt.axvline(0, linestyle = "--", color = "#555753")
plt.axhline(0.5, linestyle = "-", color = "#555753")
plt.xlim(yLim)
if sepPlots:
plt.xlabel("horizontal landing position")
else:
plt.xticks([])
if stats:
am = AnovaMatrix(dm2, factors = factor, dv = dv, \
subject = pp[0])._print(maxLen=7, ret=True)
print am
#plt.subplot2grid((nRows,nCols), (plotCount, 1), colspan=3)
#plt.title("Exp = %s DV = %s" % (exp, dv))
#plt.text(0.05,0.1,am, family='monospace')
#plt.xticks([])
#plt.yticks([])
if sepPlots:
plt.savefig("%s.png"%figName)
plt.savefig("%s.svg"%figName)
figList.append(fig)
return figList
else:
plt.savefig('per corrDir.png')
return fig