def test_t_stat():
psychopathy = [11.416, 4.514, 12.204, 14.835,
8.416, 6.563, 17.343, 13.02,
15.19 , 11.902, 22.721, 22.324]
clammy = [0.389, 0.2 , 0.241, 0.463,
4.585, 1.097, 1.642, 4.972,
7.957, 5.585, 5.527, 6.964]
age = [22.5, 25.3, 24.6, 21.4,
20.7, 23.3, 23.8, 21.7,
21.3, 25.2, 24.6, 21.8]
X = np.column_stack((np.ones(12), clammy))
Y = np.asarray(psychopathy)
B, t, df, p = t_stat(Y, X)
assert_array_equal((np.around(t[1][:6],6),np.around(p[1][:6],6)),
( [1.914389], [0.042295]))
def test_t_stat():
psychopathy = [
11.416, 4.514, 12.204, 14.835, 8.416, 6.563, 17.343, 13.02, 15.19,
11.902, 22.721, 22.324
]
clammy = [
0.389, 0.2, 0.241, 0.463, 4.585, 1.097, 1.642, 4.972, 7.957, 5.585,
5.527, 6.964
]
age = [
22.5, 25.3, 24.6, 21.4, 20.7, 23.3, 23.8, 21.7, 21.3, 25.2, 24.6, 21.8
]
X = np.column_stack((np.ones(12), clammy))
Y = np.asarray(psychopathy)
B, t, df, p = t_stat(Y, X)
assert_array_equal((np.around(t[1][:6], 6), np.around(p[1][:6], 6)),
([1.914389], [0.042295]))
X_matrix4 = np.loadtxt(txt_path[4])
X_matrix = np.ones((len(X_matrix1), p))
X_matrix[..., 1] = X_matrix1
X_matrix[..., 2] = X_matrix2
X_matrix[..., 3] = X_matrix3
X_matrix[..., 4] = X_matrix4
linear_drift = np.linspace(-1, 1, 240)
quadratic_drift = linear_drift**2
quadratic_drift -= np.mean(quadratic_drift)
X_matrix[..., 5] = linear_drift
X_matrix[..., 6] = quadratic_drift
# smooth the data
# use high resolution matrix and re-run the regression
data_smooth = smoothing(data, 1, range(data.shape[-1]))
beta_3d_smooth, t, df, p = t_stat(data_smooth, X_matrix)
beta_3d_smooth_task = beta_3d_smooth[..., 1]
beta_3d_smooth_gain = beta_3d_smooth[..., 2]
beta_3d_smooth_loss = beta_3d_smooth[..., 3]
beta_3d_smooth_dist = beta_3d_smooth[..., 4]
location_of_txt = dirs[0]
np.savetxt(location_of_txt + '/' + name[0:17] + "_beta_task.txt",
beta_3d_smooth_task.ravel())
np.savetxt(location_of_txt + '/' + name[0:17] + "_beta_gain.txt",
beta_3d_smooth_gain.ravel())
np.savetxt(location_of_txt + '/' + name[0:17] + "_beta_loss.txt",
beta_3d_smooth_loss.ravel())
np.savetxt(location_of_txt + '/' + name[0:17] + "_beta_dist.txt",
beta_3d_smooth_dist.ravel())
print("\nAll betas generated from the multi glm analysis")
X_matrix3 = np.loadtxt(txt_path[3])
X_matrix4 = np.loadtxt(txt_path[4])
X_matrix = np.ones((len(X_matrix1),p))
X_matrix[...,1] = X_matrix1
X_matrix[...,2] = X_matrix2
X_matrix[...,3] = X_matrix3
X_matrix[...,4] = X_matrix4
linear_drift = np.linspace(-1, 1, 240)
quadratic_drift = linear_drift ** 2
quadratic_drift -= np.mean(quadratic_drift)
X_matrix[...,5] = linear_drift
X_matrix[...,6] = quadratic_drift
# smooth the data
# use high resolution matrix and re-run the regression
data_smooth = smoothing(data,1,range(data.shape[-1]))
beta_3d_smooth, t, df, p = t_stat(data_smooth,X_matrix)
beta_3d_smooth_task = beta_3d_smooth[...,1]
beta_3d_smooth_gain = beta_3d_smooth[...,2]
beta_3d_smooth_loss = beta_3d_smooth[...,3]
beta_3d_smooth_dist = beta_3d_smooth[...,4]
location_of_txt= dirs[0]
np.savetxt(location_of_txt + '/' +name[0:17]+ "_beta_task.txt",beta_3d_smooth_task.ravel())
np.savetxt(location_of_txt + '/' +name[0:17]+ "_beta_gain.txt",beta_3d_smooth_gain.ravel())
np.savetxt(location_of_txt + '/' +name[0:17]+ "_beta_loss.txt",beta_3d_smooth_loss.ravel())
np.savetxt(location_of_txt + '/' +name[0:17]+ "_beta_dist.txt",beta_3d_smooth_dist.ravel())
print("\nAll betas generated from the multi glm analysis")
print("See project-epsilon/" + location_of_txt + " to for the txt files containing the betas")
from __future__ import division, print_function, absolute_import
import numpy as np
import sys
sys.path.append(".././utils")
import nibabel as nib
import matplotlib.pyplot as plt
from load_BOLD import *
from t_stat import *
from find_activated_voxel_functions import *
from convolution_normal_script import X_matrix
from convolution_high_res_script import X_matrix_high_res
location_of_data = "../../data/ds005/sub001/BOLD/task001_run001/"
data = load_img(1, 1)
beta, t, df, p = t_stat(data, X_matrix_high_res)
shape = data.shape[:3]
lst = find_activated_voxel(shape, p)
location_of_txt = "../txt_files/"
for i in range(1, len(lst) + 1):
np.savetxt(location_of_txt + 'ds005_sub001_t1r1_position%s.txt' % (str(i)),
lst[i - 1].ravel())
#smooth the data set
smooth_data = smoothing(data, 1, range(n_trs))
#initialize design matrix for t test
p = 7
X_matrix = np.ones((data.shape[-1], p))
#build our design matrix
for cond in range(1,5):
convolved = np.loadtxt(txt_path + name + '_conv_' + str(cond).zfill(3) + '_high_res.txt')
#convolved = np.loadtxt(txt_path + name + '_conv_' + str(cond).zfill(3) + '_canonical.txt')
X_matrix[:,cond] = convolved
linear_drift = np.linspace(-1, 1, n_trs)
X_matrix[:,5] = linear_drift
quadratic_drift = linear_drift ** 2
quadratic_drift -= np.mean(quadratic_drift)
X_matrix[:,6] = quadratic_drift
beta, t, df, p = t_stat(smooth_data, X_matrix)
for cond in range(0,4):
print("Starting test for condition " + str(cond+1))
t_newshape = np.reshape(t[cond,:],vol_shape)
t_newshape[~in_brain_mask]=np.nan
t_T = np.zeros(vol_shape)
for z in range(vol_shape[2]):
t_T[:, :, z] = t_newshape[:,:, z].T
t_plot = plot_mosaic(t_T)
plt.imshow(t_plot,interpolation='nearest', cmap='seismic')
zero_out=max(abs(np.nanmin(t_T)),np.nanmax(t_T))
plt.title(name+'_t_statistics'+'_cond_'+'_%s'%(cond+1))
plt.clim(-zero_out,zero_out)
plt.colorbar()
plt.savefig(dirs[1]+'/'+ name +'_t-test_'+'cond'+str(cond+1)+'.png')
plt.close()
from __future__ import division, print_function, absolute_import
import numpy as np
import sys
sys.path.append(".././utils")
import nibabel as nib
import matplotlib.pyplot as plt
from load_BOLD import *
from t_stat import *
from find_activated_voxel_functions import *
from convolution_normal_script import X_matrix
from convolution_high_res_script import X_matrix_high_res
location_of_data = "../../data/ds005/sub001/BOLD/task001_run001/"
data = load_img(1,1)
beta, t, df,p=t_stat(data,X_matrix_high_res)
shape = data.shape[:3]
lst = find_activated_voxel(shape, p)
location_of_txt="../txt_files/"
for i in range(1,len(lst)+1):
np.savetxt(location_of_txt+'ds005_sub001_t1r1_position%s.txt'%(str(i)),lst[i-1].ravel())
