def test_calcMRSS():
# Like above, create a test matrix of regressors
X = np.ones((6, 4))
X[:, 0] = np.array([1,2,3,4,5,6])
X[:, 1] = np.array([2,4,6,9,10,12])
X[:, 2] = np.linspace(-1,1,6)
X[:, 3] = X[:,2]**2
Y = np.array([10,12,14,15,17,20])
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(X, Y)
pred = regr.predict(X)
test_MRSS = np.mean(np.sum((pred - Y)**2/(Y.shape[-1]-4)))
#--------------------------------------------------------------------------#
# my function
my_MRSS = calcMRSS(Y, X[:,0], X[:,1], X[:,2], X[:,3])
#--------------------------------------------------------------------------#
# assert
assert (abs(test_MRSS-my_MRSS) < .0001)
#--------------------------------------------------------------------------#
# standard
X_s = np.ones((6, 2))
X_s[:, 0] = np.array([1,2,3,4,5,6])
X_s[:, 1] = np.array([2.5,4.1,6.3,9.7,10.1,12.3])
Y_s = np.array([10,12,14,15,17,20])
# Create linear regression object
regr_s = linear_model.LinearRegression()
# Train the model using the training sets
regr_s.fit(X_s, Y_s)
pred_s = regr_s.predict(X_s)
test_MRSS_s = np.mean(np.sum((pred_s - Y_s)**2/(Y_s.shape[-1]-3)))
# my function
my_MRSS_s = calcMRSS(Y_s, X_s[:,0], X_s[:,1])
#--------------------------------------------------------------------------#
# assert
assert (abs(test_MRSS_s-my_MRSS_s) < .0001)
def test_calcMRSS():
# Like above, create a test matrix of regressors
X = np.ones((6, 4))
X[:, 0] = np.array([1,2,3,4,5,6])
X[:, 1] = np.array([2,4,6,9,10,12])
X[:, 2] = np.linspace(-1,1,6)
X[:, 3] = X[:,2]**2
Y = np.array([10,12,14,15,17,20])
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(X, Y)
pred = regr.predict(X)
test_MRSS = np.mean(np.sum((pred - Y)**2/(Y.shape[-1]-4)))
#--------------------------------------------------------------------------#
# my function
my_MRSS = calcMRSS(Y, X[:,0], X[:,1], X[:,2], X[:,3])
#--------------------------------------------------------------------------#
# assert
assert (abs(test_MRSS-my_MRSS) < .0001)
def test_calcMRSS():
# Like above, create a test matrix of regressors
X = np.ones((6, 4))
X[:, 0] = np.array([1, 2, 3, 4, 5, 6])
X[:, 1] = np.array([2, 4, 6, 9, 10, 12])
X[:, 2] = np.linspace(-1, 1, 6)
X[:, 3] = X[:, 2] ** 2
Y = np.array([10, 12, 14, 15, 17, 20])
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(X, Y)
pred = regr.predict(X)
test_MRSS = np.mean(np.sum((pred - Y) ** 2 / (Y.shape[-1] - 4)))
# --------------------------------------------------------------------------#
# my function
my_MRSS = calcMRSS(Y, X[:, 0], X[:, 1], X[:, 2], X[:, 3])
# --------------------------------------------------------------------------#
# assert
assert abs(test_MRSS - my_MRSS) < 0.0001
+ ` 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)
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))
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)
