def test_calcSigProp():
# Set up test betas
t_beta = np.array([[0, 0, 0, 0, 0], [0.4, 0.03, 0.1, 1, 0.17], [2, 0.1, 0.09, 0.2, 0.88], [1, 1.2, 0.9, 0.4, 0.51], [0.31, 0.22, 0.16, 0.05, 0.02]])
sig_level = 0.1
# Get significant level of 2nd column after reshaping to (5,5)
# 0, 0.03, 0.1, 1.2, 0.22
notzero_beta = t_beta[t_beta !=0].reshape(-1,5)
t_psig_gain = sum(notzero_beta[:,1]<=sig_level)/len(notzero_beta[:,1])
# Get significant level of 4th column
# 0, 1, 0.2, 0.4, 0.05
t_psig_loss = sum(notzero_beta[:,3]<=sig_level)/len(notzero_beta[:,3])
# My function
my_psig_gain, my_psig_loss = calcSigProp(t_beta, sig_level)
# Assert
assert_almost_equal(my_psig_gain, t_psig_gain)
assert_almost_equal(my_psig_loss, t_psig_loss)
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)
