def test_deleteOutliers():
# Create some test arrays/dictionaries
t_data = np.arange(8).reshape((1, 1, 1, 8))
t_gain = np.arange(8) + 2
t_loss = np.arange(8) + 4
t_lin = np.linspace(-1, 1, 8)
t_quad = t_lin ** 2
t_quad -= np.mean(t_quad)
dvars_out = {"sub1run1": [2, 3], "sub2run2": [1, 2]}
fd_out = {"sub1run1": [1, 2, 3], "sub2run2": [4, 5, 6]}
# sub 1 run 1
sub1 = 1
run1 = 1
# t_outliers = [2,3]
t_nonoutliers1 = [0, 4, 5, 6, 7]
t_data1 = t_data[:, :, :, t_nonoutliers1]
t_gain1 = t_gain[t_nonoutliers1]
t_loss1 = t_loss[t_nonoutliers1]
t_lin1 = t_lin[t_nonoutliers1]
t_quad1 = t_quad[t_nonoutliers1]
# --------------------------------------------------------------------------#
# my function
my_data1, my_gain1, my_loss1, my_lin1, my_quad1 = deleteOutliers(
t_data, t_gain, t_loss, t_lin, t_quad, sub1, run1, dvars_out, fd_out
)
# --------------------------------------------------------------------------#
# asssert 1
assert_allclose(my_data1, t_data1)
assert_allclose(my_gain1, t_gain1)
assert_allclose(my_loss1, t_loss1)
assert_allclose(my_lin1, t_lin1)
assert_allclose(my_quad1, t_quad1)
# --------------------------------------------------------------------------#
# sub 2 run 2
sub2 = 2
run2 = 2
# t_outliers = [1,2,4,5,6]
t_nonoutliers2 = [0, 3, 7]
t_data2 = t_data[:, :, :, t_nonoutliers2]
t_gain2 = t_gain[t_nonoutliers2]
t_loss2 = t_loss[t_nonoutliers2]
t_lin2 = t_lin[t_nonoutliers2]
t_quad2 = t_quad[t_nonoutliers2]
# --------------------------------------------------------------------------#
# my function
my_data2, my_gain2, my_loss2, my_lin2, my_quad2 = deleteOutliers(
t_data, t_gain, t_loss, t_lin, t_quad, sub2, run2, dvars_out, fd_out
)
# --------------------------------------------------------------------------#
# assert 2
assert_allclose(my_data2, t_data2)
assert_allclose(my_gain2, t_gain2)
assert_allclose(my_loss2, t_loss2)
assert_allclose(my_lin2, t_lin2)
assert_allclose(my_quad2, t_quad2)
def test_deleteOutliers():
# Create some test arrays/dictionaries
t_data = np.arange(8).reshape((1,1,1,8))
t_gain = np.arange(8)+2
t_loss = np.arange(8)+4
t_lin = np.linspace(-1,1, 8)
t_quad = t_lin ** 2
t_quad -= np.mean(t_quad)
dvars_out= {'sub1run1': [2,3],'sub2run2':[1,2]}
fd_out= {'sub1run1': [1,2,3], 'sub2run2':[4,5,6]}
# sub 1 run 1
sub1 = 1
run1 = 1
# t_outliers = [2,3]
t_nonoutliers1 = [0,4,5,6,7]
t_data1= t_data[:,:,:,t_nonoutliers1]
t_gain1 = t_gain[t_nonoutliers1]
t_loss1 = t_loss[t_nonoutliers1]
t_lin1 = t_lin[t_nonoutliers1]
t_quad1 = t_quad[t_nonoutliers1]
#--------------------------------------------------------------------------#
# my function
my_data1, my_gain1, my_loss1, my_lin1, my_quad1 = deleteOutliers(t_data, t_gain, t_loss, t_lin, t_quad, sub1, run1, dvars_out, fd_out)
#--------------------------------------------------------------------------#
# asssert 1
assert_allclose(my_data1, t_data1)
assert_allclose(my_gain1, t_gain1)
assert_allclose(my_loss1, t_loss1)
assert_allclose(my_lin1, t_lin1)
assert_allclose(my_quad1, t_quad1)
#--------------------------------------------------------------------------#
# sub 2 run 2
sub2 = 2
run2 = 2
# t_outliers = [1,2,4,5,6]
t_nonoutliers2 = [0,3,7]
t_data2= t_data[:,:,:,t_nonoutliers2]
t_gain2 = t_gain[t_nonoutliers2]
t_loss2 = t_loss[t_nonoutliers2]
t_lin2 = t_lin[t_nonoutliers2]
t_quad2 = t_quad[t_nonoutliers2]
#--------------------------------------------------------------------------#
# my function
my_data2, my_gain2, my_loss2 , my_lin2, my_quad2 = deleteOutliers(t_data, t_gain, t_loss,t_lin, t_quad, sub2, run2, dvars_out, fd_out)
#--------------------------------------------------------------------------#
# assert 2
assert_allclose(my_data2, t_data2)
assert_allclose(my_gain2, t_gain2)
assert_allclose(my_loss2, t_loss2)
assert_allclose(my_lin2, t_lin2)
assert_allclose(my_quad2, t_quad2)
+ ` j `
+ "/cond003.txt"
)
task_cond4 = (
pathtodata
+ "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)
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)
run_count = np.zeros(3)
for j in range(1,4):
direct='ds005/sub0'+str(i).zfill(2)+'/BOLD/task001_run00'+`j`+'/'
boldname = pathtofolder + direct+'bold.nii.gz'
img=nib.load(boldname)
data=img.get_data()
run = j
behav_cond = pathtofolder + 'ds005/sub0'+str(i).zfill(2)+'/behav/task001_run00'+`j`+'/behavdata.txt'
task_cond1 = pathtofolder + 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond001.txt'
task_cond2 = pathtofolder + 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond002.txt'
task_cond3 = pathtofolder + 'ds005/sub0'+str(i).zfill(2)+'/model/model001/onsets/task001_run00'+`j`+'/cond003.txt'
task_cond4 = pathtofolder + '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)
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'
