def descriptives(dm):
"""
Tabular analysis of the descriptive results.
Arguments:
dm -- A DataMatrix.
Returns:
A DataMatrix.
"""
dmc = dm.select('correct == 1')
pm = PivotMatrix(dm, ['soa'], ['subject_nr'], dv='ctoa')
pm._print('CTOA')
pm.save('output/ctoa.csv')
pm = PivotMatrix(dm, ['subject_nr'], ['subject_nr'], dv=lambda x: len(x))
pm._print('Cell count')
pm.save('output/cellcount.csv')
pm = PivotMatrix(dm, ['cueValidity', 'soa', 'cueLum'], ['subject_nr'], \
dv=lambda x: len(x))
pm._print('Cell count')
pm.save('output/cellcount.cueValidity.soa.cueLum.csv')
pm = PivotMatrix(dmc, ['cueValidity', 'soa'], ['subject_nr'], \
dv='response_time')
pm.save('output/rt.csv')
pm._print('Correct RT')
pm.linePlot(show=show)
pm = PivotMatrix(dm, ['cueValidity', 'soa'], ['subject_nr'], \
dv='correct')
pm.save('output/acc.csv')
pm._print('Accuracy')
pm.linePlot(show=show)
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 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 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 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 = "--")
