def test_calcBeta():
# X, Y are constructed like the way in calcBeta(data, gain, loss).
# Create a simple linear model based on Y = 2X1 + 5X2 + e
X = np.ones((6, 4))
X[:, 0] = np.array([1,2,3,4,5,6])
X[:, 1] = np.array([2,4,6,8,10,12])
X[:, 2] = np.linspace(-1,1,6)
t_quad = X[:,2] ** 2
t_quad -= np.mean(t_quad)
X[:, 3] = t_quad
Y = X[:,0] + X[:,1]*2 + X[:,2] + X[:,3] + 1
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(X, Y)
test_beta = np.append(regr.coef_ , regr.intercept_)
#--------------------------------------------------------------------------#
# my function
design, t_by_v, my_beta = calcBeta(Y, X[:,0], X[:,1], X[:,2], X[:,3])
#--------------------------------------------------------------------------#
# assert betas: the threshold here is a bit high since 2 methods of
# implementation for a small sample size gives some variation in betas
assert all(my_beta.ravel()-test_beta < 0.1)
# assert time_by_voxel (predictions)
assert_allclose(t_by_v.ravel(), regr.predict(X))
# assert design
assert_allclose(X, design[:,:4])
#--------------------------------------------------------------------------#
Y = X[:,0] + X[:,1]*2 + X[:,2] + X[:,3] + 1
# myfunction when thrs != None
design1, t_by_v1, my_beta1 = calcBeta(Y, X[:,0], X[:,1], X[:,2], X[:,3], 1)
# assert the threshold values are produce different betas and tbyv
assert (t_by_v.ravel() != t_by_v1.ravel()).any()
assert (my_beta.ravel() != my_beta1.ravel()).all()
# assert design still the same
assert_allclose(X, design1[:,:4])
#--------------------------------------------------------------------------#
# Standard Template test
X_s = np.ones((6, 2))
X_s[:, 0] = np.array([1,2,3,4,5,6])
X_s[:, 1] = np.array([2,4,6,8,10,12])
Y_s = X_s[:,0] + X_s[:,1]*2 + 1
# Create linear regression object
regr_s = linear_model.LinearRegression()
# Train the model using the training sets
regr_s.fit(X_s, Y_s)
# my function standard
design_s, t_by_v_s, my_beta_s = calcBeta(Y_s, X_s[:,0], X_s[:,1])
# assert the threshold values are produce different betas and tbyv
assert_allclose(t_by_v_s.ravel(), regr_s.predict(X_s))
# assert design
assert_allclose(X_s, design_s[:,:2])
def test_calcBeta():
# X, Y are constructed like the way in calcBeta(data, gain, loss).
# Create a simple linear model based on Y = 2X1 + 5X2 + e
X = np.ones((6, 4))
X[:, 0] = np.array([1,2,3,4,5,6])
X[:, 1] = np.array([2,4,6,8,10,12])
X[:, 2] = np.linspace(-1,1,6)
t_quad = X[:,2] ** 2
t_quad -= np.mean(t_quad)
X[:, 3] = t_quad
Y = X[:,0] + X[:,1]*2 + X[:,2] + X[:,3] + 1
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(X, Y)
test_beta = np.append(regr.coef_ , regr.intercept_)
#--------------------------------------------------------------------------#
# my function
design, t_by_v, my_beta = calcBeta(Y, X[:,0], X[:,1], X[:,2], X[:,3])
#--------------------------------------------------------------------------#
# assert betas: the threshold here is a bit high since 2 methods of
# implementation for a small sample size gives some variation in betas
assert all(my_beta.ravel()-test_beta < 0.1)
# assert time_by_voxel (predictions)
assert_allclose(t_by_v.ravel(), regr.predict(X))
# assert design
assert_allclose(X, design[:,:4])
+ "/cond004.txt"
)
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
)
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)
d_shape = data_full.shape[:3]
mea = calcMRSS(data_full, gain_full, loss_full, linear_full, quad_full)
X, Y, beta = calcBeta(data_full, gain_full, loss_full, linear_full, quad_full)
# ------------------------------------------------------------------------------#
# Take the 40,000 voxel
fitted = X.dot(beta[:, 40000])
residuals = Y[:, 40000] - fitted
qqplot(residuals, saveit=True)
res_var(fitted, residuals, name="fitted", saveit=True)
# possibly transform data:
X_log, Y_log, beta_log = calcBeta(np.log(data_full + 1), gain_full, loss_full, linear_full, quad_full)
residuals_log = Y_log[:, 40000] - X_log.dot(beta_log[:, 40000])
qqplot(residuals_log, saveit=True)
res_var(X_log.dot(beta_log[:, 40000]), residuals_log, name="fitted_log", saveit=True)
vox_pos = np.unravel_index(40000, d_shape)
print("Voxel used: " + str(vox_pos))
boldname='ds005/sub0'+str(i).zfill(2)+'/model/model001/task001_run00'+`j`+'.feat/filtered_func_data_mni.nii.gz'
img=nib.load(boldname)
data=img.get_data()
data=smooth_spatial(data)
run = j
behav_cond = 'ds005/sub0'+str(i).zfill(2)+'/behav/task001_run00'+`j`+'/behavdata.txt'
task_cond1 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond001.txt'
task_cond2 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond002.txt'
task_cond3 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond003.txt'
task_cond4 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond004.txt'
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, standard = True)
data, gain, loss, linear_dr, quad_dr = deleteOutliers(data, gain, loss, i, run, dvars_out, fd_out)
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)
# mea=calcMRSS(data_full, gain_full, loss_full, None, None, threshold)
X, Y, beta=calcBeta(data_full, gain_full, loss_full, None, None, threshold)
# calculate t values
t_val=np.zeros((2,902629))
for k in range(Y.shape[1]):
t_val[:,k] = significant(X,Y[:,k], beta[:,k])
# file names for beta and t
beta_file='../results/texts/sub0'+str(i).zfill(2)+'_standard_beta.txt'
t_file='../results/texts/sub0'+str(i).zfill(2)+'_standard_tvals.txt'
# save beta and t values to file
np.savetxt(beta_file, beta)
np.savetxt(t_file, t_val)
gain_full = np.empty([0,])
loss_full = np.empty([0,])
linear_full = np.empty([0,])
quad_full = np.empty([0,])
for j in range(1,4):
direct='ds005/sub0'+str(i).zfill(2)+'/BOLD/task001_run00'+`j`+'/'
boldname = direct+'bold.nii.gz'
img=nib.load(boldname)
data=img.get_data()
data=smooth_spatial(data)
run = j
behav_cond = 'ds005/sub0'+str(i).zfill(2)+'/behav/task001_run00'+`j`+'/behavdata.txt'
task_cond1 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond001.txt'
task_cond2 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond002.txt'
task_cond3 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond003.txt'
task_cond4 = 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond004.txt'
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)
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)
mea=calcMRSS(data_full, gain_full, loss_full, linear_full, quad_full, threshold)
X, Y, beta=calcBeta(data_full, gain_full, loss_full, linear_full, quad_full, threshold)
# write='ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/sub'+`i`+'_beta.txt'
# np.savetxt(write, beta)
