def test_vol_std():
# We make a fake 4D image
shape_3d = (2, 3, 4)
V = np.prod(shape_3d)
T = 10 # The number of 3D volumes
# Make a 2D array that we will reshape to 4D
arr_2d = np.random.normal(size=(V, T))
expected_stds = np.std(arr_2d, axis=0)
# Reshape to 4D
arr_4d = np.reshape(arr_2d, shape_3d + (T,))
actual_stds = diagnostics.vol_std(arr_4d)
assert_almost_equal(expected_stds, actual_stds)
sub001_task_run
#Get the BOLD path
sub001_path_BOLD = path_bold('sub001')
#Get the each data of each task run
run_img_result = {
x: sub001_path_BOLD + "/" + x + '/bold.nii.gz'
for x in sub001_task_run
}
#Plot the standard deviation for each run
for i in run_img_result.keys():
img = nib.load(run_img_result[i])
data = img.get_data()
vol_stds = dg.vol_std(data)
outliers, thresholds = dg.iqr_outliers(vol_stds)
N = len(vol_stds)
x = np.arange(N)
plt.plot(vol_stds, label='vol std')
plt.plot(x[outliers], vol_stds[outliers], 'o', label='outliers')
plt.plot([0, N], [thresholds[0], thresholds[0]], ':', label='IQR lo')
plt.plot([0, N], [thresholds[1], thresholds[1]], ':', label='IQR hi')
plt.xlabel('Voxel')
plt.ylabel('Volume standard deviation')
plt.title('Volume Standard Deviation_' + i)
plt.legend(fontsize=8)
plt.savefig(i + '_vol_std.png')
plt.close()
# plot the RMS difference values
"""
* Load the image as an image object
* Load the image data from the image
* Drop the first four volumes, as we know these are outliers
"""
img = nib.load('ds114_sub009_t2r1.nii')
data = img.get_data()
data = data[..., 4:]
"""
Use your vol_std function to get the volume standard deviation values for the
remaining 169 volumes.
Write these 169 values out to a text file.
*IMPORTANT* - this text file MUST be called 'vol_std_values.txt'
"""
volstd = diagnostics.vol_std(data)
fobj = open('vol_std_values.txt', 'wt')
for i in volstd:
fobj.write(str(i) + '\n')
fobj.close()
"""
Use the iqr_outlier detection routine to get indices of outlier volumes.
Write these indices out to a text file.
*IMPORTANT* - this text file MUST be called 'vol_std_outliers.txt'
"""
outliers_index, thres = diagnostics.iqr_outliers(volstd)
fobj = open('vol_std_outliers.txt', 'wt')
for i in outliers_index:
fobj.write(str(i) + '\n')
"""take out the volumne outliers in an image"""
import nibabel as nib
import matplotlib.pyplot as plt
import numpy as np
import numpy.linalg as npl
from itertools import product
import diagnostics
reload(diagnostics)
img=nib.load('ds005/sub001/BOLD/task001_run001/bold.nii')
data=img.get_data()
"""get std"""
volstd = diagnostics.vol_std(data)
outliers_index, thres = diagnostics.iqr_outliers(volstd)
for image_path in images_paths:
name = image_path[0]
img = nib.load(image_path[1])
data_int = img.get_data()
data = data_int.astype(float)
vol_shape = data.shape[-1]
mean_vol = np.mean(data, axis=-1)
# Take the mean value over time and plot an histogram
mean_vol = np.mean(data, axis=-1)
plt.hist(np.ravel(mean_vol), bins=100)
plt.title('Histogram of mean values of voxels accross time\n' + str(name))
plt.savefig(project_path+'fig/histograms/' + str(name) + '_hist.png')
plt.xlabel('Mean value of voxels accross time')
plt.close()
# Outliers
vol_stds = diagnostics.vol_std(data)
assert len(vol_stds) == vol_shape
#Plot data to see outliers
np.savetxt(project_path+'txt_output/outliers/vol_std_' \
+ str(name) + '.txt', vol_stds)
outliers, thresholds = diagnostics.iqr_outliers(vol_stds)
np.savetxt(project_path+'txt_output/outliers/vol_std_outliers_' \
+ str(name) + '.txt', outliers)
N = len(vol_stds)
x = np.arange(N)
plt.plot(vol_stds, label='voxels std ' + str(name))
plt.plot(x[outliers], vol_stds[outliers], 'o', label='outliers')
plt.plot([0, N], [thresholds[0], thresholds[0]], ':', label='IQR lo')
plt.plot([0, N], [thresholds[1], thresholds[1]], ':', label='IQR hi')
plt.title('voxels std ' + str(name))
for image_path in images_paths:
name = image_path[0]
img = nib.load(image_path[1])
data_int = img.get_data()
data = data_int.astype(float)
vol_shape = data.shape[-1]
mean_vol = np.mean(data, axis=-1)
# Take the mean value over time and plot an histogram
mean_vol = np.mean(data, axis=-1)
plt.hist(np.ravel(mean_vol), bins=100)
plt.title('Histogram of mean values of voxels accross time\n' + str(name))
plt.savefig(project_path + 'fig/histograms/' + str(name) + '_hist.png')
plt.xlabel('Mean value of voxels accross time')
plt.close()
# Outliers
vol_stds = diagnostics.vol_std(data)
assert len(vol_stds) == vol_shape
#Plot data to see outliers
np.savetxt(project_path+'txt_output/outliers/vol_std_' \
+ str(name) + '.txt', vol_stds)
outliers, thresholds = diagnostics.iqr_outliers(vol_stds)
np.savetxt(project_path+'txt_output/outliers/vol_std_outliers_' \
+ str(name) + '.txt', outliers)
N = len(vol_stds)
x = np.arange(N)
plt.plot(vol_stds, label='voxels std ' + str(name))
plt.plot(x[outliers], vol_stds[outliers], 'o', label='outliers')
plt.plot([0, N], [thresholds[0], thresholds[0]], ':', label='IQR lo')
plt.plot([0, N], [thresholds[1], thresholds[1]], ':', label='IQR hi')
plt.title('voxels std ' + str(name))
#Extract each task
sub001_task_run = open_bold('sub001')
sub001_task_run
#Get the BOLD path
sub001_path_BOLD = path_bold('sub001')
#Get the each data of each task run
run_img_result = {x: sub001_path_BOLD + "/" + x + '/bold.nii.gz' for x in sub001_task_run}
#Plot the standard deviation for each run
for i in run_img_result.keys():
img = nib.load(run_img_result[i])
data = img.get_data()
vol_stds = dg.vol_std(data)
outliers, thresholds = dg.iqr_outliers(vol_stds)
N = len(vol_stds)
x = np.arange(N)
plt.plot(vol_stds, label='vol std')
plt.plot(x[outliers], vol_stds[outliers], 'o', label='outliers')
plt.plot([0, N], [thresholds[0], thresholds[0]], ':', label='IQR lo')
plt.plot([0, N], [thresholds[1], thresholds[1]], ':', label='IQR hi')
plt.xlabel('Voxel')
plt.ylabel('Volume standard deviation')
plt.title('Volume Standard Deviation_' + i)
plt.legend(fontsize = 8)
plt.savefig( i + '_vol_std.png')
plt.close()
# plot the RMS difference values
