def windowPlot(dm, standalone=True, color=blue[1], label=None):
"""
desc:
Creates a graph in which the effect of pupil size on saliency is shown
separately for each temporal displacement. I.e. the effect of pupil size
on trial N on saliency on trial N+1, etc.
arguments:
dm:
type: DataMatrix
keywords:
standalone:
desc: Indicates whether this is a standalone plot, in which case
it will create and save the plot, or not.
type: bool
color:
desc: Plot color.
type: [str, unicode]
label:
desc: Line label.
type: [str, unicode]
"""
if standalone:
Plot.new(widePlot)
dm = dm.intertrialer(["file", "trialId", "saccNr"], "salFrom", _range=windowRange)
xData = []
yData = []
eData = []
for r in windowRange:
if r == 0:
_dv = "salFrom"
elif r < 0:
_dv = "salFrom_m%d" % (-1 * r)
else:
_dv = "salFrom_p%d" % r
print "dv = %s" % _dv
s, t, lo, up = stats.effectSlope(dm, dv=_dv)
print s
xData.append(r)
yData.append(s)
eData.append([lo, up])
xData = np.array(xData)
yData = np.array(yData)
eData = np.array(eData)
plt.fill_between(xData, eData[:, 0], eData[:, 1], color=color, alpha=0.1)
plt.plot(windowRange, yData, "o-", color=color, label=label)
plt.axhline(linestyle="--", color="black")
plt.xlabel("Pupil-size timepoint relative to saliency timepoint")
plt.xticks(windowRange)
plt.xlim(windowRange[0], windowRange[-1])
plt.ylabel("Partial slope")
plt.yticks(slopeTicks[exp])
plt.ylim(slopeLim[exp])
if standalone:
Plot.save("windowPlot", folder=exp, show=show)
def exp2InstructionPlot(dm):
"""
desc:
Plots the effect of pupil size on saliency for different task
instructions.
arguments:
dm:
type: DataMatrix
"""
assert(exp == 'exp2')
Plot.new(size=smallPlot)
for color, fmt, sceneType in [(fractalCol, 'o-', 'fractal'),
(sceneCol, 's:', 'scene')]:
lSlope = []
if sceneType == 'fractal':
x = -.1
else:
x = .1
lX = []
for inst in ['free', 'search', 'memory']:
lX.append(x)
_dm = dm.select('inst == "%s"' % inst).select(
'sceneType == "%s"' % sceneType)
s, t, lo, up = stats.effectSlope(_dm)
lSlope.append(s)
plt.plot([x, x], [lo, up], '-', color=color)
x += 1
plt.plot(lX, lSlope, fmt, color=color, label=sceneType.capitalize()+'s')
plt.xticks(range(0,3), ['Free', 'Search', 'Memory'])
plt.yticks([0, 2, 4, 6])
plt.ylabel('Partial slope')
plt.xlabel('Task')
plt.xlim(-.5, 2.5)
plt.ylim(slopeLim)
plt.axhline(0, color='black', linestyle='--')
plt.legend(frameon=False, loc='upper left')
Plot.save('instructionPlot', folder=exp, show=show)
def saccadePlot(dm, standalone=True, color=blue[1], label=None):
"""
desc:
Creates a graph in which the effect of pupil size on saliency is shown
separately for each saccade in a trial.
arguments:
dm:
desc: Data
type:
keywords:
standalone:
desc: Indicates whether this is a standalone plot, in which case
it will create and save the plot, or not.
type: bool
color:
desc: Plot color.
type: [str, unicode]
label:
desc: Line label.
type: [str, unicode]
"""
if standalone:
Plot.new(size=widePlot)
xData = []
yData = []
eData = []
for saccNr in [None] + range(1, maxSacc + 1):
if saccNr == None:
_dm = dm
else:
_dm = dm.select("saccNr == %d" % saccNr)
s, p, lo, up = stats.effectSlope(_dm)
if saccNr == None:
if standalone:
x = -1
elif exp == "exp1":
x = -1.2
else:
x = -0.8
plt.errorbar(x, s, yerr=[[s - lo], [up - s]], capsize=0, fmt="o-", color=color)
else:
xData.append(saccNr)
yData.append(s)
eData.append([lo, up])
xData = np.array(xData)
yData = np.array(yData)
eData = np.array(eData)
plt.fill_between(xData, eData[:, 0], eData[:, 1], color=color, alpha=0.1)
plt.plot(xData, yData, "o-", color=color, label=label)
plt.axhline(0, linestyle="--", color="black")
plt.xlabel("Saccade number")
plt.xlim(-2, maxSacc + 1)
plt.xticks([-1] + range(1, 21), ["Full"] + range(1, 21))
plt.ylabel("Partial slope")
plt.yticks(slopeTicks[exp])
plt.ylim(slopeLim[exp])
if standalone:
Plot.save("saccadePlot", folder=exp, show=show)
