def test_calcBetaLme():
# Test data with large n = 1500
X = np.ones((2000, 4))
X[:, 0] = np.random.normal(0, 1, 2000)
X[:, 1] = np.random.normal(2, 2, 2000)
X[:, 2] = np.linspace(-1,1,2000)
X[:, 3] = X[:,2]**2
Y = np.random.normal(3,1,2000)
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(X, Y)
test_betas = regr.coef_
# My function, should produce same results if groups are all the same:
lme = calcBetaLme(Y, X[:,0], X[:,1], X[:,2], X[:,3], np.repeat(1,2000))
lme_thrs = calcBetaLme(Y, X[:,0], X[:,1], X[:,2], X[:,3], np.repeat(1,2000), -40000)
lme_thrs1 = calcBetaLme(Y, X[:,0], X[:,1], X[:,2], X[:,3], np.repeat(1,2000), 10)
# Compare betas
my_betas = lme.ravel()[[0,2]]
my_betas_thrs = lme_thrs.ravel()[[0,2]]
my_betas_thrs1 = lme_thrs1.ravel()[[0,2]]
assert max(abs(my_betas - test_betas[:2])) < 0.005
assert max(abs(my_betas_thrs - test_betas[:2])) < 0.005
assert (test_betas != my_betas_thrs1)
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)
