def test_anovStat():
# create a test dataset
t_data = np.reshape(np.array([2, 0.1, 3, 0.04, 2.1, 0.01, 0, 0, 1.2, 0.05, 2.2, 2]), (-1, 2))
# Significance level chosen to be 0.05, 0.05/5 = 0.01 after bonferroni correction
# Should give: 0.01 significant out of 0.1, 0.04, 0.01, 0.05, 2
test_prop = 1/5
# my function
my_prop = anovStat(t_data)
# Assert
assert_allclose(test_prop, my_prop)
parameters = merge_cond(behav_cond, task_cond1, task_cond2, task_cond3, task_cond4)
neural_prediction = events2neural_extend(parameters,TR, n_vols)
gain, loss, linear_dr, quad_dr = getRegressor(TR, n_vols, hrf_at_trs, neural_prediction)
data, gain, loss, linear_dr, quad_dr = deleteOutliers(data, gain, loss, linear_dr, quad_dr, i, run, dvars_out, fd_out)
run_count[j-1] = data.shape[3] ## dummy variable indicating the groups
data_full = np.concatenate((data_full,data),axis=3)
gain_full = np.concatenate((gain_full,gain),axis=0)
loss_full = np.concatenate((loss_full,loss),axis=0)
linear_full = np.concatenate((linear_full,linear_dr),axis=0)
quad_full = np.concatenate((quad_full,quad_dr),axis=0)
run_group = np.concatenate((np.repeat(1, run_count[0]),
np.repeat(2, run_count[1]), np.repeat(3, run_count[2])), axis=0)
thrshd = 400 ## set a threshold to idenfity the voxels inside the brain
print "calculating parameters of subject "+str(i)
beta = calcBetaLme(data_full, gain_full, loss_full, linear_full, quad_full, run_group, thrshd)
sig_level = 0.05
sig_gain_prop[i-1], sig_loss_prop[i-1] = calcSigProp(beta, sig_level)
write=pathtofolder + 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/sub0'+str(i).zfill(2)+'_lme_beta.txt'
np.savetxt(write, beta)
anov_test = calcAnov(data_full, run_group, thrshd)
anov_prop[i-1] = anovStat(anov_test)
write=pathtofolder + 'ds005/models/lme_sig_gain_prop.txt'
np.savetxt(write, sig_gain_prop)
write=pathtofolder + 'ds005/models/lme_sig_loss_prop.txt'
np.savetxt(write, sig_loss_prop)
write=pathtofolder + 'ds005/models/anova_prop.txt'
np.savetxt(write, anov_prop)
parameters = merge_cond(behav_cond, task_cond1, task_cond2, task_cond3, task_cond4)
neural_prediction = events2neural_extend(parameters,TR, n_vols)
gain, loss, linear_dr, quad_dr = getRegressor(TR, n_vols, hrf_at_trs, neural_prediction)
data, gain, loss, linear_dr, quad_dr = deleteOutliers(data, gain, loss, linear_dr, quad_dr, i, run, dvars_out, fd_out)
run_count[j-1] = data.shape[3] ## dummy variable indicating the groups
data_full = np.concatenate((data_full,data),axis=3)
gain_full = np.concatenate((gain_full,gain),axis=0)
loss_full = np.concatenate((loss_full,loss),axis=0)
linear_full = np.concatenate((linear_full,linear_dr),axis=0)
quad_full = np.concatenate((quad_full,quad_dr),axis=0)
run_group = np.concatenate((np.repeat(1, run_count[0]),
np.repeat(2, run_count[1]), np.repeat(3, run_count[2])), axis=0)
thrshd = 400 ## set a threshold to idenfity the voxels inside the brain
print "calculating parameters of subject "+str(i)
beta = calcBetaLme(data_full, gain_full, loss_full, linear_full, quad_full, run_group, thrshd)
sig_level = 0.05
sig_gain_prop[i-1], sig_loss_prop[i-1] = calcSigProp(beta, sig_level)
write=pathtofolder + 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/sub0'+str(i).zfill(2)+'_lme_beta.txt'
np.savetxt(write, beta)
anov_test = calcAnov(data_full, run_group)
anov_prop[i-1] = anovStat(anov_test)
write=pathtofolder + 'ds005/models/lme_sig_gain_prop.txt'
np.savetxt(write, sig_gain_prop)
write=pathtofolder + 'ds005/models/lme_sig_loss_prop.txt'
np.savetxt(write, sig_loss_prop)
write=pathtofolder + 'ds005/models/anova_prop.txt'
np.savetxt(write, anov_prop)
