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 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 crossExpDescriptives(dm):
for exp in ['exp1', 'exp2', 'exp3']:
dm = CsvReader('data/%s.data.csv' % exp).dataMatrix()
if exp == 'exp1':
dm = dm.select('trialType == "control"')
elif exp == 'exp3':
print dm.collapse(['cond'], 'rt')
stats.R.load(dm)
lm = stats.R.lmer('rt ~ cond + (1+cond|subject_nr)')
print lm
rt = dm['rt']
print 'Exp = %s' % exp
print 'N = %d' % len(rt)
print 'RT = %.2f ms (%.2f)' % (rt.mean(), rt.std())
a = np.loadtxt('data/%s.fixdur.csv' % exp)
print 'Fixdur = %.2f ms (%.2f)' % (a.mean(), a.std())
def addCog(dm):
"""
Use NEW CoG calculation.
"""
# Get cog dictionary:
f = "compare cogs/cog_per_stim_004C.csv"
cogDm = CsvReader(f).dataMatrix()
dm = dm.addField("xCog", default = -1000)
for i in dm.range():
stimName = dm["object"][i]
flip = dm["handle_side"][i]
symm = dm["symm"][i]
if symm == "symm":
continue
xCogUnrot = cogDm.select("name == '%s'" % stimName, verbose = False)["xCog"][0]
if flip == "left":
xCog = xCogUnrot * -1
elif flip == "right":
xCog = xCogUnrot
dm["xCog"][i] = xCog
#print "object = ", stimName
#print "symm = ", symm
#print "flip = ", flip
#print "cog = ", xCog
return dm
# if int(xNorm) == 0:
# if fixNr == 2:
# if abs(dy) > constants.minSaccSize:
# print fix['t']
# print dy
# raw_input()
if fixNr > 3:
break
# Save the dm containing the simulation data:
dm.save('dm_004C_simulation.csv')
if printSum:
print '\nSummary:\n'
for gap in (None, 'overlap', 'zero'):
print 'Gap: %s' % gap
if gap == None:
_dm = dm
else:
_dm = dm.select('gap == "%s"' % gap, verbose=False)
fix1 = _dm['endX1NormToHandle'].mean()
fix2 = _dm['endX2NormToHandle'].mean()
fix3 = _dm['endX3NormToHandle'].mean()
t1 = _dm['saccLat1'].mean()
t2 = _dm['saccLat2'].mean()
t3 = _dm['saccLat3'].mean()
print '%.2f (%.2f)\t%.2f (%.2f)\t%.2f (%.2f)' % (fix1, t1, fix2, t2, fix3, \
t3)
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 13 16:11:45 2014
@author: lotje
"""
import onObject
import numpy as np
from matplotlib import pyplot as plt
from exparser.CsvReader import CsvReader
from exparser.PivotMatrix import PivotMatrix
exp = "004B"
src = 'selected_dm_%s_WITH_drift_corr_onlyControl_False.csv' % exp
main_dm = CsvReader(src).dataMatrix()
main_dm = main_dm.select("contrast_side != 'control'")
dv = "endX1NormToContrast"
dm = main_dm.select("%s != ''" % dv)
dm = dm.selectByStdDev(["file"], dv)
dm = dm.removeField("__dummyCond__")
dm = dm.removeField("__stdOutlier__")
dm = dm.selectByStdDev(["file"], "saccLat1")
fig = plt.figure()
pm = PivotMatrix(dm, cols = ["visual_field"], \
rows = ["file"], dv = dv, colsWithin=True)
pm.barPlot(fig = fig)
for vf in ["upper", "lower"]:
def plotRegr(exp, direction):
"""
Plots data for one experiment.
Arguments:
exp --- {"004A", "004B"}
"""
if direction == "ToHandle":
src = 'selected_dm_%s_WITH_drift_corr_onlyControl_True.csv' % exp
elif direction == "ToContrast":
src = 'selected_dm_%s_WITH_drift_corr_onlyControl_False.csv' % exp
if direction == "ToHandle":
yLim = [-.30,.15]
elif direction == "ToContrast":
yLim = [-.15,.1]
dm = CsvReader(src).dataMatrix()
if direction == "ToContrast":
dm = dm.select("contrast_side != 'control'")
R = RBridge()
fig = plt.figure(figsize = (8,3))
plt.subplots_adjust(left=.2, bottom=.15)
# Plot hline:
plt.axhline(0, color = "black", linestyle = "--")
lmeRegr(R, dm, sacc=1, direction=direction, corr=False, color=blue[1])
if exp == "004A":
lmeRegr(R, dm, sacc=1, direction=direction, corr=True, color=orange[1])
lmeRegr(R, dm, sacc=2, direction=direction, corr=False, color=blue[1])
if exp == "004A":
lmeRegr(R, dm, direction=direction, sacc=2, corr=True, color=orange[1])
# H line indicating landing position simulated saccades:
xmin1 = 60
xmax1 = xmin2 = 250
xmax2 = 550
if direction == "ToHandle" and exp == "004B":
lSal = salMeans()
plt.hlines(lSal[0], xmin = xmin1, xmax= xmax1, color = red[1], \
linestyle = "--")
plt.hlines(lSal[1], xmin = xmin2, xmax = xmax2, color = red[1],\
linestyle = "--")
# Fake legend:
if exp == "004A":
lLabels = ["Relative to center", "Relative to CoG"]
col1 = orange[1]
col2 = blue[1]
linestyle2 = "-"
if exp == "004B":
lLabels = ["Relative to CoG", "Simulated landing positions"]
col1 = blue[1]
col2 = red[1]
linestyle2="--"
line1= plt.Line2D((0,1),(0,0), marker = "o", color = col1, \
markerfacecolor = "white", markeredgecolor = col1, \
markeredgewidth = 1)
line2= plt.Line2D((0,1),(0,0), marker = "o", color = col2, \
markerfacecolor = "white", markeredgecolor = col2, \
markeredgewidth = 1, linestyle = "--")
if direction == "ToHandle":
plt.legend([line1, line2], lLabels,\
frameon = False, loc=3) # TODO!!
plt.xlabel("Saccade latency since stimulus onset")
plt.xlim([80,660])
plt.ylim(yLim)
plt.ylabel("Normalised landing position")
figName = "Timecourse_%s_%s.png" % (direction, exp)
plt.savefig(os.path.join(dst,figName))
plt.show()
print
print
print "Done!"
print figName, "is saved!"
print
print
"""
# Import modules:
from exparser.CsvReader import CsvReader
# Constants:
xc = 1024/2
yc = 768/2
ythr = 100
if __name__ == "__main__":
dm = CsvReader('data.csv').dataMatrix()
dm = dm.select('mask_side == "control"')
dm = dm.select('symm == "asymm"')
#dm = dm.select('gap == "overlap"')
#dm = dm.select('handle_side == "right"')
#dm = dm.select('object == "screwdriver"')
# X coordinates, where positive values indicate a deviation towards the handle
dm = dm.addField('refix1', dtype=float)
dm = dm.addField('refix2', dtype=float)
dm = dm.addField('refix3', dtype=float)
# Timestamps for each fixation
dm = dm.addField('tfix1', dtype=float)
dm = dm.addField('tfix2', dtype=float)
dm = dm.addField('tfix3', dtype=float)
dm = CsvReader(f).dataMatrix()
dm = dm.addField("trial_dur")
for i in dm.range():
# Skip the very first trial because it doesn't have a previous trial:
if i == 0:
dm["trial_dur"][i] = -1000
else:
s = dm["time_correct_response"][i-1]
e = dm["time_correct_response"][i]
dur = e-s
dm["trial_dur"][i] = dur
# Exclude first trial, which was skipped:
dm = dm.select("trial_dur > 0")
dm["trial_dur"] = dm["trial_dur"]/1000/60
#import analyse
#analyse.plotDist(dm, "trial_dur", bins = 100)
#plt.show()
plt.hist(dm["trial_dur"], bins=100)
plt.show()
# Exclude trials that took longer than 1 minute, because this was probably a
# break:
#dm = dm.select("trial_dur < 60000")
#ms = dm["trial_dur"].sum()
#sec = ms/1000.
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()
#!/usr/bin/env python
#-*- coding:utf-8 -*-
"""
DESCRIPTION:
Plots landing position as a function of sacc latency (and other predictors, if
wanted), using LMM.
"""
from exparser.CsvReader import CsvReader
from exparser.RBridge import RBridge
src = "/home/lotje/Documents/PhD Marseille/Studies/004 - Single-object experiment - Handle-orientation effect/analysis 004/selected_dm_004B_WITH_drift_corr_onlyControl_True.csv"
dm = CsvReader(src).dataMatrix()
dm = dm.select("endX1NormToHandle == ''")
dvRaw = "endX1Norm"
dvNorm = "endX1NormToHandle"
nsim = 10
R = RBridge()
R.load(dm)
print "Condition in DV"
# Run a full LME
lmerDm = R.lmer(\
'%s ~ (1|file) + (1|object)'\
% (dvNorm), nsim=nsim, printLmer=True)
lmerDm._print(sign=5)
lmerDm.save('lme_condition_in_dv.csv')
print "Condition as factor"
srcDm = "/home/lotje/Documents/PhD Marseille/Studies/004/004C/stimuli/cog_per_stim.csv"
dm = CsvReader(srcDm).dataMatrix()
stimList = ["chisel", "chisel2", "mallet", "paintbrush", "screwdriver", \
"wrench", "fork", "knife", "peeler", "sharpeningsteel", "spoon", \
"spoon2", "washingbrush", "hammer"]
f = open("blockloop.csv", "w")
for direction in [-20, 0, 20]:
for stimType in ["object", "non-object"]:
for flip in ["left", "right"]:
for vf in ["upper","lower"]:
for stim in stimList:
_dm = dm.select("stim_type == '%s'" % stimType)
_dm = _dm.select("name == '%s'" % stim)
assert(len(_dm)==1)
cog = _dm["xCoG"][0]
if flip == "left":
cog = cog * -1
print "object = ", stim
print "flip = ", flip
print "cog = ", cog
#raw_input()
f.write(",".join([str(direction), \
stimType, flip, vf, stim, str(cog)]) + "\n")
f.close()
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])
def plotPerExp(exp, direction, norm=True):
"""
"""
print "exp = ", exp
print "direction = ", direction
legend = True
# Get dm:
if direction == "ToHandle":
src = 'selected_dm_%s_WITH_drift_corr_onlyControl_True.csv' % exp
#dm = getDM.getDM(exp, onlyControl=True)
elif direction == "ToContrast":
src = 'selected_dm_%s_WITH_drift_corr_onlyControl_False.csv' % exp
#dm = getDM.getDM(exp, onlyControl=False)
#
main_dm = CsvReader(src).dataMatrix()
if direction == "ToContrast":
main_dm = main_dm.select("contrast_side != 'control'")
nCols = 2
if exp == "004B":
nRows = 1
fig = plt.figure(figsize = (8, 3))
plt.subplots_adjust(wspace=0, hspace = 0.2, left = .2, bottom = .15)
#plt.suptitle("Experiment 2", s)
lTitles = ["a) Saccade 1 relative to CoG", \
"b) Saccade 2 relative to CoG"]
lTitles.reverse()
if exp == "004A":
nRows = 2
fig = plt.figure(figsize = (8,6))
plt.subplots_adjust(wspace=0, hspace = 0.2,\
left = .2, bottom = .15)
#plt.suptitle("Experiment 1")
lTitles = ["a) Saccade 1 relative to absolute center", \
"b) Saccade 2 relative to absolute center",
"c) Saccade 1 relative to CoG", "d) Saccade 2 relative to CoG"]
lTitles.reverse()
nPlot = 0
if exp == "004A":
varList = ["absolute center", "CoG"]
elif exp == "004B":
varList = ["absolute center"]
for dvType in varList:
for sacc in ["1", "2"]:
if dvType == "absolute center":
dv = "endX%sNorm%s" % (sacc, direction)
elif dvType == "CoG":
dv = "endX%sCorrNorm%s" % (sacc, direction)
# Plot distr per saccade bin:
nPlot +=1
ax = plt.subplot(nRows, nCols, nPlot)
plt.title(lTitles.pop(), size = 10)
distr(main_dm, dv, "saccLat%s" % sacc, norm=norm)
if legend:
plt.legend(["Fast", "Medium", "Slow"],loc='best')
legend=False
if sacc == "2":
ax.yaxis.set_ticklabels([])
elif sacc == "1":
plt.ylabel("Normalized frequency")
if exp == "004A":
if dvType == "absolute center":
ax.xaxis.set_ticklabels([])
elif dvType == "CoG":
plt.xlabel("Normalized landing position")
if exp == "004B":
plt.xlabel("Normalized landing position")
plt.axvline(0, linestyle = "--", color = gray[3])
figName = "Distr_%s_%s_norm_%s.svg" % \
(exp, direction, norm)
plt.savefig(os.path.join(dst,figName))
print
print
print "Done!"
print figName, "is saved"
print
print
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 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"))
