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 ovpOtherFactor(dm, factor, rt = 'rtFromLanding',\
nRows = 2, nCols = 4, nBin = 5, ws = True):
"""
"""
# Determine whether or not to first split data per participant
# (i.e. to make within-subjects bins):
if ws:
keys = ['file', factor]
else:
keys = None
# Determine exp:
exp = dm["exp"][0]
# Add factors containing bins:
dm = dm.addField("binned_endX1")
dm = dm.calcPerc("endX1Norm", "binned_endX1", \
keys = keys, nBin = nBin)
if exp == "004A":
dm = dm.addField("binned_endX1Corr")
dm = dm.calcPerc("endX1CorrNorm", "binned_endX1Corr", \
keys = keys, nBin = nBin)
fig = plt.figure()
plt.subplots_adjust(wspace = .3, hspace = .4)
figName = "%s: OVP effects as a function of binned landing positions and %s - WS = %s" \
% (exp, factor, ws)
plt.suptitle(figName)
plotCount = 0
subjectID = ["file"]
if exp == "004A":
factorList = [[factor,"binned_endX1"], [factor,"binned_endX1Corr"]]
else:
factorList = [[factor,"binned_endX1"]]
for factors in factorList:
for dv in [rt, "durationFix1", "refixProb", "saccCount"]:
plotCount +=1
_dm = dm.select("%s != ''"%dv)
plt.subplot(nRows, nCols, plotCount)
plt.title(dv)
pm = PivotMatrix(_dm, factors, subjectID, dv, colsWithin = True)
pm.linePlot(fig = fig)
plt.savefig("%s.png"%figName)
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 affCross(trim = True, exclY = False, dv = "RT"):
"""
Affordance effect as cross-over interaction between handle
and response hand ACROSS saccades
NOTE: on-object is difficult here, because we're not
looking across saccades.
"""
fig = plt.figure()
title = "Affordance effect per Exp - exclY = %s" % exclY
plt.suptitle(title)
plotNr = 1
for exp in ["004A", "004B"]:
if exp != "004B":
continue
plt.subplot(1,2,plotNr)
plt.title("Exp = %s" % exp)
dm = getDM.getDM(exp)
dm = onObject.onObject(dm, "1", exclY = exclY)
if trim:
trimmed_dm = dm.selectByStdDev(keys = \
["handle_side", "response_hand", "file"], dv = dv)
else:
trimmed_dm = dm
am = AnovaMatrix(trimmed_dm, ["handle_side", "response_hand"], dv = dv, \
subject = "file")._print(ret=True)
print am
# raw_input()
# Get descriptive statistics:
pm = PivotMatrix(trimmed_dm, ["comp"], ["file"], colsWithin = False, \
dv = dv, err = 'se')
print pm
raw_input()
pm = PivotMatrix(trimmed_dm, ["handle_side", "response_hand"], \
["file"], dv = dv, colsWithin = True)
pm.linePlot(fig = fig)
plotNr +=1
plt.savefig("%s.png"%title)
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 = "--")
def affordanceEffect(dm, nRows = None, nCols = None, \
rowCount = None, fig = None):
"""
RT as a function of handle side and response handle
Note: this two-way interaction should be identical
to a main effect of compatibility.
Arguments:
dm
Keyword arguments:
nRows
nCols
rowCount
fig
"""
# Select only handled objects:
dm = dm.select("symm == 'asymm'")
# Select only trials on which there was no mask applied:
dm = dm.select("contrast_side == 'control'")
#fig = plt.figure()
nPlot = 0
factors = ["response_hand", "handle_side"]
pp = ["file"]
dv = "rtFromLanding"
# Trim:
_dm = dm.selectByStdDev(keys = factors + pp, dv = dv)
am = AnovaMatrix(_dm, factors = factors, dv = dv, \
subject = pp[0])._print(maxLen=10, ret=True)
plt.subplot2grid((nRows,nCols), (rowCount, 0))
pm = PivotMatrix(_dm, factors, pp, dv = dv, colsWithin = True)
pm.linePlot(fig = fig, legendTitle = factors[0], xLabel = factors[1])
plt.subplot2grid((nRows,nCols), (rowCount, 1), colspan=2)
plt.text(0.1,0.1,am, family='monospace')
def affordances(trim = True, exclY = True, dv = "RT"):
"""
"""
fig = plt.figure()
title = "Affordance effect per Exp"
plt.suptitle(title)
plotNr = 1
for exp in ["004A", "004B"]:
plt.subplot(1,2,plotNr)
plt.title("Exp = %s" % exp)
dm = getDM.getDM(exp)
trimmed_dm = dm.selectByStdDev(keys =["handle_side", "response_hand", "file"], dv = dv)
for val in trimmed_dm.unique("comp"):
print val
print trimmed_dm["RT"][trimmed_dm.where("comp == '%s'" % val)].mean()
pm = PivotMatrix(trimmed_dm, ["handle_side", "response_hand"], ["file"], dv = dv, colsWithin = True)
pm.linePlot(fig = fig)
plt.show()
pm = PivotMatrix(trimmed_dm, ["comp"], ["file"], dv = dv, colsWithin = True)
pm.plot(nLvl1 = 1, fig = fig)
plt.show()
plotNr +=1
###am = AnovaMatrix(trimmed_dm, ["comp"], dv = dv, \
###subject = "file")._print(ret=True)
###print am
plt.show()
def contrastEffect(dm, saccCount):
"""
Plots dist hist of sacc latencies as a function of gap condition.
Arguments:
dm
saccCount
"""
# Define keys:
factors = ["contrast_side", "gap"]
pp = ["file"]
dv = "saccLat%s" % saccCount
exp = dm["exp"][0]
# Apply selections:
dm = dm.select("%s != ''" % dv)
dm = dm.selectByStdDev(keys = factors + pp, dv = dv)
# Create figure:
fig = plt.figure()
plt.subplots_adjust(hspace = .4)
figName = "%s: Latencies sacc %s as a function of Gap and Contrast" % (exp, saccCount)
plt.suptitle(figName)
plt.subplot2grid((2,2), (0, 0))
pm = PivotMatrix(dm, factors, pp, dv, colsWithin = True)
pm.linePlot(fig = fig,xLabel = factors[-1], legendTitle = factors[0], lLabels = ["left", "contrast", "right"])
# NOTE why I have to give the lLabels as a parameter: the underscore of "_left" gives problems for plotting
## the legend.??
plt.ylabel(dv)
am = AnovaMatrix(dm, factors = factors, dv = dv, \
subject = pp[0])._print(maxLen=10, ret=True)
plt.subplot2grid((2,2), (1, 0), colspan=2)
plt.text(0.1,0.1,am, family='monospace')
plt.savefig("%s.png"%figName)
return fig
def affOtherFactor(dm, factor2, nRows = None, nCols = None, \
rowCount = None, fig = None):
"""
RT as a function of compatibility and something else (e.g. category).
Arguments:
dm
factor2
"""
# Declare keys:
factors = ["comp"] + [factor2]
pp = ["file"]
dv = "rtFromLanding"
# Select only handled objects:
dm = dm.select("symm == 'asymm'")
# Trim:
dm = dm.selectByStdDev(keys = factors + pp, dv = dv)
# Create figure:
#fig = plt.figure()
nPlot = 0
# Stats:
am = AnovaMatrix(dm, factors = factors, dv = dv, \
subject = pp[0])._print(maxLen=10, ret=True)
# Plot:
plt.subplot2grid((nRows, nCols), (rowCount, 0))
pm = PivotMatrix(dm, factors, pp, dv = dv, colsWithin = True)
pm.linePlot(fig = fig, lLabels = None,legendTitle = factors[0], xLabel = factors[1])
plt.subplot2grid((nRows,nCols), (rowCount, 1), colspan=2)
plt.text(0.1,0.1,am, family='monospace')
def gapEffect(dm, saccCount = "1"):
"""
Plots dist hist of sacc latencies as a function of gap condition.
Arguments:
dm
Keyword arguments:
saccCount --- Default = "1"
"""
# Define keys:
factors = ["visual_field", "gap"]
pp = ["file"]
dv = "saccLat%s" % saccCount
exp = dm["exp"][0]
# Apply selections:
dm = dm.select("%s != ''" % dv)
dm = dm.selectByStdDev(keys = factors + pp, dv = dv)
# Create figure:
fig = plt.figure()
plt.subplots_adjust(hspace = .4)
figName = "%s: Latencies sacc %s as a function of Gap and Visual Field" % (exp, saccCount)
plt.suptitle(figName)
nPlots = 0
for vf in ["upper", "lower"]:
vf_dm = dm.select("visual_field == '%s'" % vf)
#plt.subplot(nRows, nCols, nPlots)
plt.subplot2grid((3,2), (nPlots, 0))
for gap in ["zero", "overlap"]:
if gap == "zero":
col = "#ef2929"
else:
col = "#8ae234"
plt.title("%s visual field" % vf)
gap_dm = vf_dm.select("gap == '%s'" % gap)
plt.hist(gap_dm[dv], bins = 30, color = col, alpha = .3)
plt.xlim(0, 400)
plt.ylabel('freq')
plt.xlabel('initial saccade latencies')
if vf == "lower":
plt.legend(["zero", "overlap"], loc = 'best')
nPlots +=1
plt.subplot2grid((3,2), (0, 1), rowspan=2)
pm = PivotMatrix(dm, factors, pp, dv, colsWithin = True)
pm.linePlot(fig = fig)
plt.ylabel("saccade latency")
am = AnovaMatrix(dm, factors = factors, dv = dv, \
subject = pp[0])._print(maxLen=10, ret=True)
plt.subplot2grid((3,2), (2, 0), colspan=2)
plt.text(0.1,0.1,am, family='monospace')
plt.savefig("%s.png"%figName)
def twoWayHandle(dm, dv, factor2, onlyControl = True, addStats = True, figTitle = True):
"""
Plots landing positions as a function of gap condition.
Arguments:
dm
dv --- dv, should be a landing variable
factor2 -- second IV
"""
if onlyControl and factor2 == "contrast_side":
print "You can't exclude contrast manipulation if one of the IV's is contrat_side"
return
if onlyControl:
dm = dm.select("contrast_side == 'control'")
# Define keys:
factors = [factor2, "handle_side"]
pp = ["file"]
exp = dm["exp"][0]
if addStats:
nCols = nRows = 2
fig = plt.figure()
else:
nCols = nRows = 1
fig = plt.figure(figsize = (3,5))
# Apply selections:
dm = dm.select("%s != ''" % dv)
dm = dm.selectByStdDev(keys = factors + pp, dv = dv)
# Create figure:
plt.subplots_adjust(hspace = .4)
figName = "%s: %s as a function of %s and Handle Side onlyControl = %s" % (exp, dv, factor2, onlyControl)
if figTitle:
plt.suptitle(figName)
plt.subplot2grid((nRows,nCols), (0, 0))
pm = PivotMatrix(dm, factors, pp, dv, colsWithin = True)
# Note why I have to give the lLabels as a parameter: the underscore of "_left" gives problems for plotting
# the legend.??
if factor2 == "contrast_side":
lLabels = ["left", "contrast", "right"]
else:
lLabels = None
pm.linePlot(fig = fig,xLabel = factors[-1], legendTitle = factors[0], lLabels = lLabels)
plt.ylabel(dv)
plt.axhline(0, color = "#555753", linestyle = "--", linewidth = 2)
if addStats:
am = AnovaMatrix(dm, factors = factors, dv = dv, \
subject = pp[0])._print(maxLen=10, ret=True)
plt.subplot2grid((nRows,nCols), (1, 0), colspan=2)
plt.text(0.1,0.1,am, family='monospace')
plt.savefig("%s.png"%figName)
return fig
dm = dm.select("direction == 0")
dm = dm.addField("congruency", dtype = str)
dm = dm.addField("respHand", dtype = str)
dm["respHand"][dm.where("correct_response == 1")] = "left"
dm["respHand"][dm.where("correct_response == 2")] = "right"
for i in dm.range():
handle = dm["flip"][i]
respHand = dm["respHand"][i]
if handle == respHand:
dm["congruency"][i] = "congruent"
print "congruent"
else:
dm["congruency"][i] = "incongruent"
print "incongruent"
print "flip = ", dm["flip"][i]
print "correct response = ", dm["correct_response"][i]
print "response hand = ", dm["respHand"][i]
print "congruency = ", dm["congruency"][i]
pm = PivotMatrix(dm, ["congruency", "stim_type"], ["file"], "response_time", colsWithin = True)
pm.linePlot()
plt.show()
def threeFactor(dm, dv, factors, subjectID = ["file"], trim = True, printStats = True,
showFig=False, saveFig = True, figName = None,
saveExt = ".jpg", legendTitle = None, lLabels = None, xLabel = None,
xLabels = None, yLabel = None,exControlMask = False, exFiller = False, \
yLim = None):
"""
Performs and plots a three-factorial ANOVA
dm --- Data matrix.
dv --- Dependent variable.
factors --- List containing three factor identifiers. Note that the first
factor in the list is used to split the effects per subplot.
Keyword arguments:
subjectID --- Default = ["file"]
showFig --- Boolean indicating whether or not to show the plots. Default
= False.
saveFig --- Boolean indicating whehter or not to save the plot. Default =
True.
printStats --- TODO Boolean indicating whether or not to print stats.
figName --- Will be used as suptitle and save path, of provided.
Default = None.
saveExt ---
legendTitle ---
lLabels ---
xLabel ---
xLabels ---
yLabel ---
"""
# Check whether 'factor' and 'dv' are column headers in the spread sheet:
vNames = dm.asArray()[0]
for factor in factors:
if not factor in vNames:
msg.userMsg.userMsg("The factor %s is not a column header."%factor,\
factor, __file__)
return
if not dv in vNames:
msg.userMsg.userMsg("The dv %s is not a column header."%dv, __file__)
return
# Apply selection criteria:
if exControlMask:
dm = dm.select("mask_side != 'control'")
if exFiller:
dm = dm.select("symm == 'asymm'")
driftCorr = dm["driftCorr"][0]
if trim:
dm = dm.selectByStdDev(keys = factors + subjectID, dv = dv)
# Get anova matrix:
am = AnovaMatrix(dm, factors = factors, dv = dv, subject = \
subjectID[0])._print(maxLen=8,ret=True)
if printStats:
print am
# One of the factors is used to split the dm (for plotting purposes only,
# the anova is carried out 'correctly').
splitFactor = factors[0]
twoFactors = factors[1:]
lvlList = np.unique(dm[splitFactor])
# Create the figure:
fig = plt.figure()
name = "%s by %s by %s effect on %s - Trim = %s exControlMask = %s exFiller = %s driftCorr = %s" %(factors[0], factors[1], \
factors[2], dv, trim, exControlMask, exFiller, driftCorr)
plt.suptitle(name)
nRows = 1
nCols = len(lvlList)+1 # 1 for the stats
plotNr = 0
# Change the labels if one of the factors is 'mask_side':
if "mask_side" == factors[1]:
if "control" in np.unique(dm["mask_side"]):
lLabels = ["control", "more contrast right", "more contrast left"]
else:
lLabels = ["more contrast right", "more contrast left"]
if "mask_side" == factors[2]:
if "control" in np.unique(dm["mask_side"]):
xLabels = ["control", "more contrast right", "more contrast left"]
else:
xLabels = ["more contrast right", "more contrast left"]
if "mask_side" == factors[0]:
if "control" in np.unique(dm["mask_side"]):
titles = ["control", "more contrast right", "more contrast left"]
else:
titles = ["more contrast right", "more contrast left"]
else:
titles = lvlList
# Convert the list of titles to a list instead of a np.array:
#titles = list(titles)
# Reverse (for popping)
popTitles = list(titles)[::-1]
# Create the subplots:
for lvl in lvlList:
# Select one level:
_dm = dm.select('%s == "%s"' % (splitFactor, lvl))
# Get pivot matrix:
pm = PivotMatrix(_dm, twoFactors, subjectID, dv = dv, colsWithin=True)
# Plot:
plotNr +=1
if legendTitle == None:
legendTitle = twoFactors[0]
if xLabel == None:
xLabel = twoFactors[1]
if yLabel == None:
yLabel = dv
plt.subplot(nRows, nCols, plotNr)
#title = popTitles.pop()
#print title
plt.title(popTitles.pop())
if lvl != lvlList[-1]:
legendPos = None
else:
legendPos = 'best'
pm.linePlot(fig = fig, xLabel = xLabel, xLabels = xLabels, \
legendTitle = legendTitle, lLabels = lLabels, yLabel = yLabel, \
legendPos = legendPos)
plt.ylim(yLim)
if dv in ["endX", "endXCorr", "endXCorrMask", "endXDegr","endXCorrDegr", "endXCorrMaskDegr"]:
plt.axhline(0, linestyle = "--", color = "#555753")
# Plot stats:
statSubplot(am, nRows = nRows, nCols = nCols, plotNr = nCols)
if showFig:
plt.show()
saveFig = False
if saveFig:
plt.savefig(os.path.join(outputFolder,"%s%s" % (name, saveExt)))
def twoFactor(dm, dv, factors, subjectID = ["file"], trim = True, printStats = True,
showFig=False, saveFig = True, figName = None,
saveExt = ".jpg", legendTitle = None, lLabels = None, xLabel = None,
xLabels = None, yLabel = None,exControlMask = False, exFiller = False, \
mixedEffects = False, exMask = False, yLim = None, errBar = True):
"""
Performs and plots a two-factorial ANOVA.
Arguments:
dm --- Data matrix.
dv --- Dependent variable.
factors --- List containing two factor identifiers.
Keyword arguments:
subjectID --- Default = ["file"]
showFig --- Boolean indicating whether or not to show the plots. Default
= False.
saveFig --- Boolean indicating whehter or not to save the plot. Default =
True.
printStats --- TODO Boolean indicating whether or not to print stats.
figName --- Will be used as suptitle and save path, of provided.
Default = None.
saveExt ---
legendTitle ---
lLabels ---
xLabel ---
xLabels ---
yLabel ---
"""
# Check whether 'factor' and 'dv' are column headers in the spread sheet:
vNames = dm.asArray()[0]
for factor in factors:
if not factor in vNames:
msg.userMsg.userMsg("The factor %s is not a column header."%factor,\
factor, __file__)
return
if not dv in vNames:
msg.userMsg.userMsg("The dv %s is not a column header."%dv, __file__)
return
driftCorr = dm["driftCorr"][0]
# If wanted, trim the data:
if trim:
dm = dm.selectByStdDev(keys = factors + subjectID, dv = dv)
# Apply selection criteria:
if exControlMask:
dm = dm.select("mask_side != 'control'")
if exMask:
dm = dm.select("mask_side == 'control'")
if exFiller:
dm = dm.select("symm == 'asymm'")
# Get pivot matrix:
pm = PivotMatrix(dm, factors, subjectID, dv=dv, colsWithin=True)
# Get anova matrix:
am = AnovaMatrix(dm, factors = factors, dv = dv, \
subject = subjectID[0])._print(maxLen=10, ret=True)
if mixedEffects:
mem = MixedEffectsMatrix(dm, dv, factors, subjectID)._print(maxLen=10, ret=True)
if printStats:
if mixedEffects:
print "MIXED EFFECTS:"
print mem
print "ANOVA:"
print am
# Create figure:
fig = plt.figure()
if figName == None:
name = "%s by %s effect on %s - trim = %s exControlMask = %s exMask = %s exFiller = %s driftCorr = %s"%(\
factors[0], factors[1], dv, trim, exControlMask, exMask, exFiller, driftCorr)
else:
name = figName
# Determine some plot properties:
if yLabel == None:
yLabel = dv
if legendTitle == None:
legendTitle = factors[0]
if xLabel == None:
xLabel = factors[1]
# Change the labels if one of the factors is 'mask_side':
if "mask_side" == factors[0]:
if "control" in np.unique(dm["mask_side"]):
lLabels = ["control", "more contrast right", "more contrast left"]
else:
lLabels = ["more contrast right", "more contrast left"]
if "mask_side" == factors[1]:
if "control" in np.unique(dm["mask_side"]):
xLabels = ["control", "more contrast right", "more contrast left"]
else:
xLabels = ["more contrast right", "more contrast left"]
# Plot effect:
plt.subplot(121)
plt.suptitle(name)
pm.linePlot(fig = fig, legendTitle = legendTitle, xLabel = xLabel, \
lLabels = lLabels, xLabels = xLabels, yLabel = yLabel, yLim = yLim, \
errBar = errBar)
if dv in ["endX", "endXCorr", "endXCorrMask", "endXDegr","endXCorrDegr", "endXCorrMaskDegr"]:
plt.axhline(0, linestyle = "--", color = "#555753")
# Print the stats:
if mixedEffects:
statSubplot(mem)
else:
statSubplot(am)
if showFig:
plt.show()
saveFig = False
if saveFig:
plt.savefig(os.path.join(outputFolder,"%s%s"%(name,saveExt)))
