def anova(self, model1, model2, anovaVar="aov1"):
"""
Performs a model comparison using the R `anova()` function. The models
should be fitted and given a name first, using `RBridge.lmer()`.
Arguments:
model1 -- The name of the first model.
model2 -- The name of the first model.
Keyword arguments:
anovaVar -- The R variable to store the `anova()` output in.
(default='aov1')
Returns:
A DataMatrix with the output for the model comparison.
"""
if os.path.exists(".rbridge-anova.csv"):
os.remove(".rbridge-anova.csv")
self.write("%s <- anova(%s, %s)" % (anovaVar, model1, model2))
self.call('write.csv(%s, ".rbridge-anova.csv")' % anovaVar)
while not os.path.exists(".rbridge-anova.csv"):
time.sleep(0.1)
# Try this a few times, because sometimes the csv hasn't been written
# yet
for i in range(10):
try:
dm = CsvReader(".rbridge-anova.csv").dataMatrix()
break
except:
time.sleep(1)
dm.rename("f0", "model")
return dm
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 __init__(self, path='data', ext='.csv', delimiter=',', quote='"',
maxN=None):
"""
desc:
Constructor. Reads all csv files from a specific folder.
keywords:
path:
desc: The folder containing the csv files.
type: [str, unicode]
ext:
desc: The extension of the csv files, or None to parse all
files.
type: [str, unicode, NoneType]
delimiter:
desc: The column delimiter.
type: [str, unicode]
quote:
desc: The character used for quoting columns.
type: [str, unicode, NoneType]
maxN:
desc: The maximum number of files to process, or `None` to
process all.
type: [int, NoneType]
"""
print 'Scanning \'%s\'' % path
self.dm = None
l = sorted(os.listdir(path))
if maxN != None:
l = l[:maxN]
for fname in l:
if ext == None or os.path.splitext(fname)[1] == ext:
print 'Reading %s ...' % fname,
try:
cr = CsvReader(os.path.join(path, fname),
delimiter=delimiter, quote=quote)
except:
warnings.warn('Failed to read %s' % fname)
continue
dm = cr.dataMatrix()
while '__src__' not in dm.columns():
try:
# Apparently this can go wrong sometimes, presumably
# due to a low-level bug. It appears to be safe to just
# try again.
dm = dm.addField('__src__', dtype=str)
except:
print 'Trying again ...'
dm['__src__'] = fname
if self.dm == None:
self.dm = dm
else:
self.dm += dm
print '(%d rows)' % len(dm)
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
# Constants:
fName = "/home/lotje/Documents/PhD Marseille/Studies/004 - Single-object experiment - Handle-orientation effect/analysis 004/simulation/data.csv"
heavyHandle = ["chisel", "screwdriver", "sharpeningsteel"]
printSum = False
if __name__ == "__main__":
# Get dictionary containing w and h of imaginary boxes around the stimuli:
boxDict = drawBox.boxDict()
# OpenSesame output file is opened as dm. The fixation values will
# be added to this dm.
dm = CsvReader(fName, delimiter = ",").dataMatrix()
# The varialbe 'corrDirection' is determined in ascParser,
# and doesn't exist for the simulation data.
# Therefore we add it manually:
dm = dm.addField("corrDirection", dtype = str)
# Make new column headers:
for sacc in range(1,4):
# Landing position:
dm = dm.addField('endX%s' % sacc, dtype=str)
dm = dm.addField('endY%s' % sacc, dtype = str)
# Normalised landing position:
dm = dm.addField('endX%sNorm' % sacc, dtype = str)
the analyses of experiment 1 and 2.
"""
# 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)
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()
