def plotRegr(exp):
"""
Plots data for one experiment.
Arguments:
exp --- {"004A", "004B"}
"""
src = 'selected_dm_%s_WITH_drift_corr_onlyControl_False.csv' % exp
dm = CsvReader(src).dataMatrix()
R = RBridge()
fig = plt.figure(figsize = (8,3))
plt.subplots_adjust(left=.2, bottom=.15)
# Plot hline:
plt.axhline(0, color = "black", linestyle = "--")
legend = True
for sacc in [1, 2]:
colList = [green[1], orange[1], blue[1]]
for contrast in dm.unique("contrast_side"):
print "contrast side = ", contrast
print "sacc = ", sacc
_dm = dm.select("contrast_side == '%s'" % contrast)
lmeRegr(R, _dm, sacc=sacc, corr=False, \
color = colList.pop(),stats=False, legend = contrast.strip("_"))
if legend:
plt.legend(frameon = False, loc='best')
legend = False
plt.savefig("Timecourse_contrast_per_cond%s.png" % exp)
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 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"))
sacc = "1"
dvNorm = "endX1CorrNormToHandle"
dvRaw = "endX1CorrNorm"
dm = onObject.onObject(dm, sacc)
print "ANOVA"
am = AnovaMatrix(dm, ["handle_side"], dvRaw, "file")._print(ret=True)
print am
print 'One-sample test scipy'
cm = dm.collapse(["file"], dvNorm)
ref = 0
t, p = scipy.stats.ttest_1samp(cm['mean'], ref)
print "t = ",t
print "p = ", p
# Paired-samples t-test:
print "paired samples t-test"
l = []
for handle in dm.unique("handle_side"):
handle_dm = dm.select("handle_side == '%s'" % handle)
cm = handle_dm.collapse(["file"], dvRaw)
l.append(cm["mean"])
t, p = scipy.stats.ttest_rel(l[0], l[1])
print "t = ",t
print "p = ", p
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])