for j in range(nCategory):
for i in range(nTrials):
if (trialsName[i] == categoryName[j]):
whichCat[i,j] = 1
whichCat = np.array(whichCat, dtype='i')
catName1 = categoryName[0]; catName2 = categoryName[1] ;
print('Two sample T-test: %s vs %s' %(catName1, catName2))
X1 = s[:,:, (whichCat[:,0]>0)]; X2 = s[:,:, (whichCat[:,1]>0)];
nX1 = X1.shape[2]
SigLevel = 0.05
XMean1 = np.mean(X1, axis=2); XSigma1 = np.std(X1, axis=2);
statOut1 = permustat_fun.one_sample_t(X1, SigLevel/2)
Gfp1 = np.std(XMean1,axis=0)
fig = plt.figure()
plt.plot(msSamples, Gfp1)
plt.axvline(43, color='black', lw=2)
fig = plt.figure()
plt.imshow(statOut1['Sig'])
tsid = np.where(msSamples == 43)[0]
statOut1['Sig'][:, tsid]
up = XMean1[:, tsid] + 3* XSigma1[:, tsid] / np.sqrt(nX1)
bt = XMean1[:, tsid] - 3* XSigma1[:, tsid] / np.sqrt(nX1)
def pbRunStat_Callback(self):
import numpy as np
import config
from permustat import permustat_fun, perm_plot
msSamples = config.msSamples
s = config.s
sden = config.sden
categoryName = config.categoryName
whichCat = config.whichCat
nCategory = config.nCategory
#---------------------------------------------------------------------
SigLevel = self.editSigLevel.text()
SigLevel = float(SigLevel)
print("Significant level is %3.2f" % SigLevel)
NumSim = self.editNumSim.text()
NumSim = int(NumSim)
if self.rbPara.isChecked():
config.ParaVal = 0
print("Parametric is selected")
else:
config.ParaVal = 1
print("Non-Parametric is selected and Num Sim is %d" % NumSim)
##---------------------------------------------------------------------
if self.rbOneT.isChecked():
catName = categoryName[config.OneTCatVal]
print("One sample T-test:%s" % catName)
if self.rbScalp.isChecked():
X = s[:, :, (whichCat[:, config.OneTCatVal] > 0)]
else:
X = sden[:, :, (whichCat[:, config.OneTCatVal] > 0)]
XMean = np.mean(X, axis=2)
XSigma = np.std(X, axis=2)
statOut = permustat_fun.one_sample_t(X, SigLevel)
perm_plot.oneT(XMean, XSigma, statOut['T'], statOut['nSigS'],
catName, msSamples)
config.Stat = statOut['T']
elif self.rbTwoT.isChecked():
catName1 = categoryName[config.TwoTCatVal1]
catName2 = categoryName[config.TwoTCatVal2]
print('Two sample T-test: %s vs %s' % (catName1, catName2))
if self.rbScalp.isChecked():
X1 = s[:, :, (whichCat[:, config.TwoTCatVal1] > 0)]
X2 = s[:, :, (whichCat[:, config.TwoTCatVal2] > 0)]
else:
X1 = sden[:, :, (whichCat[:, config.TwoTCatVal1] > 0)]
X2 = sden[:, :, (whichCat[:, config.TwoTCatVal2] > 0)]
XMean1 = np.mean(X1, axis=2)
XSigma1 = np.std(X1, axis=2)
XMean2 = np.mean(X2, axis=2)
XSigma2 = np.std(X2, axis=2)
statOut1 = permustat_fun.one_sample_t(X1, SigLevel / 2)
statOut2 = permustat_fun.one_sample_t(X2, SigLevel / 2)
if self.rbPara.isChecked():
statOut = permustat_fun.two_sample_t(X1, X2, SigLevel)
perm_plot.twoT(XMean1, XSigma1, statOut1['T'],
statOut1['nSigS'], catName1, XMean2, XSigma2,
statOut2['T'], statOut2['nSigS'], catName2,
statOut['T'], statOut['nSigS'], msSamples)
else:
statOut = permustat_fun.two_sample_permu(
X1, X2, SigLevel / 2, NumSim)
logPvalST = np.log(statOut['Pval'])
perm_plot.permT(statOut['T'], logPvalST, msSamples)
config.Stat = statOut['T']
elif self.rbPairT.isChecked():
catName1 = categoryName[config.PairTCatVal1]
catName2 = categoryName[config.PairTCatVal2]
print('Pair T-test: %s vs %s' % (catName1, catName2))
if self.rbScalp.isChecked():
X1 = s[:, :, (whichCat[:, config.PairTCatVal1] > 0)]
X2 = s[:, :, (whichCat[:, config.PairTCatVal2] > 0)]
else:
X1 = sden[:, :, (whichCat[:, config.PairTCatVal1] > 0)]
X2 = sden[:, :, (whichCat[:, config.PairTCatVal2] > 0)]
XMean1 = np.mean(X1, axis=2)
XSigma1 = np.std(X1, axis=2)
XMean2 = np.mean(X2, axis=2)
XSigma2 = np.std(X2, axis=2)
statOut1 = permustat_fun.one_sample_t(X1, SigLevel / 2)
statOut2 = permustat_fun.one_sample_t(X2, SigLevel / 2)
if self.rbPara.isChecked():
statOut = permustat_fun.pairt(X1, X2, SigLevel)
perm_plot.pairT(XMean1, XSigma1, statOut1['T'],
statOut1['nSigS'], catName1, XMean2, XSigma2,
statOut2['T'], statOut2['nSigS'], catName2,
statOut['T'], statOut['nSigS'], msSamples)
else:
statOut = permustat_fun.pairt_permu(X1, X2, SigLevel / 2,
NumSim)
logPvalST = np.log(statOut['Pval'])
perm_plot.permPairT(statOut['T'], logPvalST, msSamples)
config.Stat = statOut['T']
elif self.rbAnova.isChecked():
if self.rbScalp.isChecked():
statOut = permustat_fun.anova_f(s, whichCat, nCategory,
SigLevel)
else:
statOut = permustat_fun.anova_f(sden, whichCat, nCategory,
SigLevel)
perm_plot.anova(statOut['F'], statOut['Pval'], statOut['Sig'],
msSamples)
config.Stat = statOut['F']
config.Pval = statOut['Pval']
config.Sig = statOut['Sig']
config.StatOut = statOut
return statOut
def pbRunStat_Callback(self):
import numpy as np
import config
from permustat import permustat_fun, perm_plot
msSamples = config.msSamples
s = config.s
sden = config.sden
categoryName = config.categoryName
whichCat = config.whichCat
nCategory = config.nCategory
#---------------------------------------------------------------------
SigLevel = self.editSigLevel.text()
SigLevel = float(SigLevel)
print("Significant level is %3.2f" %SigLevel)
NumSim = self.editNumSim.text()
NumSim = int(NumSim)
if self.rbPara.isChecked():
config.ParaVal = 0
print("Parametric is selected")
else:
config.ParaVal = 1
print("Non-Parametric is selected and Num Sim is %d" %NumSim)
##---------------------------------------------------------------------
if self.rbOneT.isChecked():
catName = categoryName[config.OneTCatVal]
print("One sample T-test:%s" %catName)
if self.rbScalp.isChecked():
X = s[:,:, (whichCat[:, config.OneTCatVal]>0)]
else:
X = sden[:,:, (whichCat[:, config.OneTCatVal]>0)]
XMean = np.mean(X, axis=2)
XSigma = np.std(X, axis=2)
statOut = permustat_fun.one_sample_t(X, SigLevel)
perm_plot.oneT(XMean, XSigma, statOut['T'], statOut['nSigS'], catName, msSamples)
config.Stat = statOut['T']
elif self.rbTwoT.isChecked():
catName1 = categoryName[config.TwoTCatVal1]
catName2 = categoryName[config.TwoTCatVal2]
print('Two sample T-test: %s vs %s' %(catName1, catName2))
if self.rbScalp.isChecked():
X1 = s[:,:, (whichCat[:, config.TwoTCatVal1]>0)]
X2 = s[:,:, (whichCat[:, config.TwoTCatVal2]>0)]
else:
X1 = sden[:,:, (whichCat[:, config.TwoTCatVal1]>0)]
X2 = sden[:,:, (whichCat[:, config.TwoTCatVal2]>0)]
XMean1 = np.mean(X1, axis=2); XSigma1 = np.std(X1, axis=2);
XMean2 = np.mean(X2, axis=2); XSigma2 = np.std(X2, axis=2);
statOut1 = permustat_fun.one_sample_t(X1, SigLevel/2)
statOut2 = permustat_fun.one_sample_t(X2, SigLevel/2)
if self.rbPara.isChecked():
statOut = permustat_fun.two_sample_t(X1, X2, SigLevel)
perm_plot.twoT(XMean1, XSigma1, statOut1['T'], statOut1['nSigS'], catName1,
XMean2, XSigma2, statOut2['T'], statOut2['nSigS'], catName2, statOut['T'], statOut['nSigS'], msSamples)
else:
statOut = permustat_fun.two_sample_permu(X1, X2, SigLevel/2, NumSim)
logPvalST = np.log(statOut['Pval'])
perm_plot.permT(statOut['T'], logPvalST, msSamples)
config.Stat = statOut['T']
elif self.rbPairT.isChecked():
catName1 = categoryName[config.PairTCatVal1]
catName2 = categoryName[config.PairTCatVal2]
print('Pair T-test: %s vs %s' %(catName1, catName2))
if self.rbScalp.isChecked():
X1 = s[:,:, (whichCat[:, config.PairTCatVal1]>0)]
X2 = s[:,:, (whichCat[:, config.PairTCatVal2]>0)]
else:
X1 = sden[:,:, (whichCat[:, config.PairTCatVal1]>0)]
X2 = sden[:,:, (whichCat[:, config.PairTCatVal2]>0)]
XMean1 = np.mean(X1, axis=2); XSigma1 = np.std(X1, axis=2);
XMean2 = np.mean(X2, axis=2); XSigma2 = np.std(X2, axis=2);
statOut1 = permustat_fun.one_sample_t(X1, SigLevel/2)
statOut2 = permustat_fun.one_sample_t(X2, SigLevel/2)
if self.rbPara.isChecked():
statOut = permustat_fun.pairt(X1, X2, SigLevel)
perm_plot.pairT(XMean1, XSigma1, statOut1['T'], statOut1['nSigS'], catName1,
XMean2, XSigma2, statOut2['T'], statOut2['nSigS'], catName2, statOut['T'], statOut['nSigS'], msSamples)
else:
statOut = permustat_fun.pairt_permu(X1, X2, SigLevel/2, NumSim)
logPvalST = np.log(statOut['Pval'])
perm_plot.permPairT(statOut['T'], logPvalST, msSamples)
config.Stat = statOut['T']
elif self.rbAnova.isChecked():
if self.rbScalp.isChecked():
statOut = permustat_fun.anova_f(s, whichCat, nCategory, SigLevel)
else:
statOut = permustat_fun.anova_f(sden, whichCat, nCategory, SigLevel)
perm_plot.anova(statOut['F'], statOut['Pval'], statOut['Sig'], msSamples)
config.Stat = statOut['F']
config.Pval = statOut['Pval']
config.Sig = statOut['Sig']
config.StatOut = statOut
return statOut
if (trialsName[i] == categoryName[j]):
whichCat[i, j] = 1
whichCat = np.array(whichCat, dtype='i')
catName1 = categoryName[0]
catName2 = categoryName[1]
print('Two sample T-test: %s vs %s' % (catName1, catName2))
X1 = s[:, :, (whichCat[:, 0] > 0)]
X2 = s[:, :, (whichCat[:, 1] > 0)]
nX1 = X1.shape[2]
SigLevel = 0.05
XMean1 = np.mean(X1, axis=2)
XSigma1 = np.std(X1, axis=2)
statOut1 = permustat_fun.one_sample_t(X1, SigLevel / 2)
Gfp1 = np.std(XMean1, axis=0)
fig = plt.figure()
plt.plot(msSamples, Gfp1)
plt.axvline(43, color='black', lw=2)
fig = plt.figure()
plt.imshow(statOut1['Sig'])
tsid = np.where(msSamples == 43)[0]
statOut1['Sig'][:, tsid]
up = XMean1[:, tsid] + 3 * XSigma1[:, tsid] / np.sqrt(nX1)
bt = XMean1[:, tsid] - 3 * XSigma1[:, tsid] / np.sqrt(nX1)