def refixLaunch(exp,corr=False, trim=True, bins=8):
"""
"""
src = 'selected_dm_%s_WITH_drift_corr_onlyControl_True.csv' % exp
dm = CsvReader(src).dataMatrix()
lat1 = "saccLat1"
lat2 = "saccLat2"
if corr:
fix1 = "endX1CorrNormToHandle"
fix2 = "endX2CorrNormToHandle"
else:
fix1 = "endX1NormToHandle"
fix2 = "endX2NormToHandle"
for var in [lat1, lat2, fix1, fix2]:
dm = dm.select("%s != ''" % var)
# Trim the data
if trim:
dm = dm.selectByStdDev(["file"],lat2,
verbose=False)
dm = dm.removeField("__dummyCond__")
dm = dm.removeField("__stdOutlier__")
dm = dm.selectByStdDev(["file"], fix2,
verbose=False)
dm = dm.addField('saccLat2_perc', dtype=float)
dm = dm.calcPerc(lat2, 'saccLat2_perc', nBin=bins)
cmLat1 = dm.collapse(['saccLat2_perc'], lat1)
cmX = cmLat2 = dm.collapse(['saccLat2_perc'], lat2)
cmFix1 = dm.collapse(['saccLat2_perc'], fix1)
cmFix2 = dm.collapse(['saccLat2_perc'], fix2)
fig = plt.figure(figsize = (3,6))
plt.subplots_adjust(left=.2, bottom=.15)
colList = [orange[1], blue[1]]
nRows = 3
nCols = 1
nPlot = 0
lTitles = ["Landing pos 2", "Landing pos 1"]
lTitles.reverse()
for cmY in [cmFix2, cmFix1]:#, cmLat1]:
#nPlot +=1
#plt.subplot(nRows, nCols, nPlot)
color = colList.pop()
plt.plot(cmX['mean'], cmY['mean'], marker = 'o', color=color, \
markerfacecolor='white', markeredgecolor=color, \
markeredgewidth=1)
plt.xlabel("Sacc lat 2")
plt.legend(lTitles, frameon=False, loc='best')
plt.axhline(0, linestyle = "--", color = gray[3])
plt.savefig("Launch_site_refixations_%s_corr_%s.png" % (exp, corr))
def plotContrast(exp = "004B", trim=True, inclSim=True,stats=True,norm=False):
"""
Plots landing positions as a function of contrats manipulation per saccade,
for Exp 1 and Exp 2, relative to CoG.
Keyword arguments:
trim --- (default=True)
"""
# Get dm:
src = 'selected_dm_%s_WITH_drift_corr_onlyControl_False.csv' % exp
dm = CsvReader(src).dataMatrix()
colList = ["#f57900", "#3465a4"]
lLegend = ["Saccade 1", "Saccade 2"]
fig = plt.figure(figsize = (3,4))
plt.subplots_adjust(left=.2, bottom=.15)
yLim = [-.07, .12]
for sacc in ["1", "2"]:
dv = "endX%sNorm" % sacc
saccVar = "saccLat%s" % sacc
# Get dm:
# Only on-object:
dm = onObject.onObject(dm, sacc,verbose=False)
if trim:
dm = dm.removeField("__dummyCond__")
dm = dm.removeField("__stdOutlier__")
dm = dm.selectByStdDev(keys = ["file"], \
dv = dv,verbose=False)
dm = dm.removeField("__dummyCond__")
dm = dm.removeField("__stdOutlier__")
dm = dm.selectByStdDev(keys = ["file"], dv = saccVar,\
verbose=False)
# Collect mean and error bar per saccade:
lM = []
lErr = []
if norm:
# Normalize across handle side:
dm = dm.removeField("normDV")
dm= dm.addField("normDV", dtype = float)
dm= dm.withinize(dv, "normDV", \
["handle_side"], whiten=False)
dv = "normDV"
for contrast in dm.unique("contrast_side"):
contrast_dm = dm.select("contrast_side == '%s'" % contrast,\
verbose = False)
cm = contrast_dm.collapse(["file"], dv)
M = cm["mean"].mean()
SE = cm['mean'].std() / np.sqrt(len(cm))
CI = SE * critVal
lM.append(M)
lErr.append(CI)
if stats:
# Run a full LME
print "Exp = ", exp
print "DV = ", dv
print "trim = ", trim
print "norm = ", norm
print "saccVar = ", saccVar
lmeContrast(R, dm, dv, saccVar, exp=exp)
#raw_input()
xData = range(len(lM))
yData = lM
yErr = lErr
col = colList.pop()
plt.errorbar(xData, yData, yerr=yErr, fmt='o-', marker = "o", \
color = col, markerfacecolor='white', markeredgecolor=col, \
markeredgewidth=1)
plt.axhline(0, linestyle = "--")
plt.ylim(yLim)
plt.ylabel(yTitle)
#ax.yaxis.set_ticklabels([])
plt.legend(lLegend, frameon = False)
plt.axhline(0, color = "black", linestyle = "--")
plt.xlabel("High-contrast side")
spacing = 0.5
xTicks = range(0,3)
xLabels = ["Left", "Control", "Right"]
plt.xticks(xTicks, xLabels, rotation = .5)
plt.xlim(min(xTicks)-spacing, max(xTicks)+spacing)
plt.savefig(os.path.join(dst,"Contrast_Effect_%s_trim_%s_norm_%s.png") \
% (exp, trim, norm))
plt.show()
def ovp(exp,corr=False, trim=True, bins=8):
"""
"""
src = 'selected_dm_%s_WITH_drift_corr_onlyControl_True.csv' % exp
dm = CsvReader(src).dataMatrix()
lat1 = "saccLat1"
lat2 = "saccLat2"
prob = "saccCount"
dur1 = "durationFix1"
dur2 = "durationFix2"
total = "gazeDur"
rt = "rtFromStim"
if corr:
fix1 = "endX1CorrNormToHandle"
fix2 = "endX2CorrNormToHandle"
else:
fix1 = "endX1NormToHandle"
fix2 = "endX2NormToHandle"
for var in [lat1, lat2, fix1, fix2, dur1, dur2, total, rt]:
dm = dm.select("%s != ''" % var)
# Trim the data
if trim:
dm = dm.selectByStdDev(["file"],lat2,
verbose=False)
dm = dm.removeField("__dummyCond__")
dm = dm.removeField("__stdOutlier__")
dm = dm.selectByStdDev(["file"], fix2,
verbose=False)
dm = dm.addField('land2_perc', dtype=float)
dm = dm.calcPerc(fix2, 'land2_perc', nBin=bins)
cmLat1 = dm.collapse(['land2_perc'], lat1)
cmLat2 = dm.collapse(['land2_perc'], lat2)
cmFix1 = dm.collapse(['land2_perc'], fix1)
cmX = cmFix2 = dm.collapse(['land2_perc'], fix2)
cmProb = dm.collapse(['land2_perc'], prob)
cmDur1 = dm.collapse(['land2_perc'], dur1)
cmDur2 = dm.collapse(['land2_perc'], dur2)
cmTotal = dm.collapse(['land2_perc'], total)
cmRt = dm.collapse(['land2_perc'], rt)
fig = plt.figure(figsize = (3,10))
plt.subplots_adjust(left=.2, bottom=.15, hspace = .2)
lTitles = ["dur 1", "prob", "land 1", "rt", "total", "lat 1", "lat 2", "dur 2"]
lTitles.reverse()
nRows = len(lTitles)
nCols = 1
nPlot = 0
for cmY in [cmDur1, cmProb, cmFix1, cmRt, cmTotal, \
cmLat1, cmLat2, cmDur2]:
nPlot +=1
ax = plt.subplot(nRows, nCols, nPlot)
#color = colList.pop()
color = blue[1]
plt.plot(cmX['mean'], cmY['mean'], marker = 'o', color=color, \
markerfacecolor='white', markeredgecolor=color, \
markeredgewidth=1)
plt.ylabel(lTitles.pop())
plt.axvline(0, linestyle = "--", color = gray[3])
if nPlot == nRows:
plt.xlabel("Second landing position")
else:
ax.xaxis.set_ticklabels([])
plt.savefig("ovp2_%s_corr_%s.png" % (exp, corr))
def plotAff(rtVar, trim=True,errBar = False,stats=True):
"""
"""
fig = plt.figure(figsize = (5, 2.5))
plt.subplots_adjust(left=.2, bottom=.15, wspace=.4)
nRows = 1
nCols = 2
nPlot = 0
lTitles = ["b) Experiment 2", "a) Experiment 1"]
for exp in ["004A", "004B"]:
nPlot +=1
ax = plt.subplot(nRows, nCols, nPlot)
plt.title(lTitles.pop())
# Get dm:
src = 'selected_dm_%s_WITH_drift_corr_onlyControl_True.csv' % exp
dm = CsvReader(src).dataMatrix()
# Determine the names of the dependent variables in the datamatrix
if trim:
dm = dm.selectByStdDev(["file"], \
rtVar, verbose=False)
colList = [orange[1], blue[1]]
for hand in dm.unique("response_hand"):
_dm = dm.select("response_hand == '%s'" % hand)
lM = []
lErr = []
for handle in dm.unique("handle_side"):
__dm = _dm.select("handle_side == '%s'" % handle)
cm = __dm.collapse(["file"], rtVar)
M = cm["mean"].mean()
SE = cm['mean'].std() / np.sqrt(len(cm))
lM.append(M)
lErr.append(SE)
col = colList.pop()
xData = range(len(lM))
yData = lM
if errBar:
plt.errorbar(xData, yData, yerr=lErr, fmt='o-', marker = "o", \
color = col, markerfacecolor='white', markeredgecolor=col, \
markeredgewidth=1)
else:
plt.plot(yData, marker = "o", \
color = col, markerfacecolor='white', markeredgecolor=col, \
markeredgewidth=1)
if exp == "004A":
plt.legend(dm.unique("handle_side"), loc = 'best', frameon=False, \
title="Handle Orientation")
if exp == "004A":
plt.ylim([700, 750])
if exp == "004B":
plt.ylim([600,650])
plt.xlabel("Response Hand")
spacing = 0.5
xTicks = range(0,2)
xLabels = dm.unique("response_hand")
plt.xticks(xTicks, xLabels, rotation = .5)
plt.xlim(min(xTicks)-spacing, max(xTicks)+spacing)
plt.ylabel("Response time")
if stats:
print "Exp = ", exp
print "rtVar = ", rtVar
lmeAff(R, dm, rtVar,exp=exp)
raw_input()
plt.savefig(os.path.join(dst,"Affordances_both_exp.png"))
def plotGap(exp, dv = "saccLat1", trim = False, norm=True, nBins = 50, \
exclFastSacc = False):
"""
"""
src = 'selected_dm_%s_WITH_drift_corr_onlyControl_True.csv' % exp
dm = CsvReader(src).dataMatrix()
# Determine sacc:
sacc = [int(x.group()) for x in re.finditer(r'\d+', dv)][0]
dm = onObject.onObject(dm, sacc)
# Exclude very fast saccades:
dm = dm.select("%s > 80" % dv)
colList = [orange[1], blue[1]]
# Trim the data
if trim:
dm = dm.removeField("__dummyCond__")
dm = dm.removeField("__stdOutlier__")
dm = dm.selectByStdDev(["file"], dv, \
verbose=False)
dm = dm.removeField("__dummyCond__")
dm = dm.removeField("__stdOutlier__")
dm = dm.selectByStdDev(["file"], "endX%sNormToHandle" % sacc, \
verbose=False)
if exp == "004A":
dm = dm.removeField("__dummyCond__")
dm = dm.removeField("__stdOutlier__")
dm = dm.selectByStdDev(["file"], "endX%sCorrNormToHandle" % sacc, \
verbose=False)
else:
dm = dm.select("%s < 1000" % dv)
# Normalize saccade latencies
if norm:
dm= dm.addField("normSacc", dtype = float)
dm= dm.withinize(dv, "normSacc", \
["file"], whiten=False)
dv = "normSacc"
for gap in dm.unique("gap"):
_dm = dm.select("gap == '%s'" % gap)
samp = _dm[dv]
y, edges = np.histogram(samp, bins = nBins)
y = normY(y)
x = .5*edges[1:] + .5*edges[:-1]
col = colList.pop()
plt.plot(x, y, color = col)
plt.fill_between(x, 0, y, alpha=.3, color=col)
plt.legend(dm.unique("gap"))
plt.ylim([0,1.1])
