def test_vol_rms_diff():
# 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))
differences = np.diff(arr_2d, axis=1)
exp_rms = np.sqrt(np.mean(differences ** 2, axis=0))
# Reshape to 4D and run function
arr_4d = np.reshape(arr_2d, shape_3d + (T,))
actual_rms = diagnostics.vol_rms_diff(arr_4d)
assert_almost_equal(actual_rms, exp_rms)
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
for i in run_img_result.keys():
img = nib.load(run_img_result[i])
data = img.get_data()
rms_dvals = dg.vol_rms_diff(data)
rms_outliers, rms_thresholds = dg.iqr_outliers(rms_dvals)
N = len(rms_dvals)
x = np.arange(N)
plt.plot(rms_dvals, label='vol RMS differences')
plt.plot(x[rms_outliers], rms_dvals[rms_outliers], 'o', label='outliers')
plt.plot([0, N], [rms_thresholds[0], rms_thresholds[0]],
':',
label='IQR lo')
plt.plot([0, N], [rms_thresholds[1], rms_thresholds[1]],
':',
label='IQR hi')
plt.xlabel('Voxel')
plt.ylabel('Volumn RMS difference values')
plt.title('Volume RMS difference values_' + i)
plt.legend(fontsize=8)
* Calculate the RMS difference values for the image data;
* Use the ``iqr_outlier`` function to return indices of possible outliers in
this RMS difference vector;
On the same plot, plot the following:
* The RMS vector;
* The identified outlier points marked with an `o` marker;
* A horizontal dashed line at the lower IRQ threshold;
* A horizontal dashed line at the higher IRQ threshold;
IMPORTANT - save this plot as ``vol_rms_outliers.png``
"""
rmsd = diagnostics.vol_rms_diff(data)
outliers_rms_index, thres_rms = diagnostics.iqr_outliers(rmsd)
plt.plot(rmsd, 'r', label='rms differences values')
for i in outliers_rms_index:
plt.plot(i, rmsd[i], 'o', color='blue')
plt.axhline(y=thres_rms[0], color='g', ls='dashed', label='low threshold')
plt.axhline(y=thres_rms[1], color='black', ls='dashed', label='high threshold')
plt.legend(loc=1)
plt.xlabel('volume')
plt.ylabel('rms difference')
plt.savefig('vol_rms_outliers.png')
plt.clf()
""" Use the ``extend_diff_outliers`` to label outliers
Use ``extend_diff_outliers`` on the output from ``iqr_outliers`` on the RMS
* Calculate the RMS difference values for the image data;
* Use the ``iqr_outlier`` function to return indices of possible outliers in
this RMS difference vector;
On the same plot, plot the following:
* The RMS vector;
* The identified outlier points marked with an `o` marker;
* A horizontal dashed line at the lower IRQ threshold;
* A horizontal dashed line at the higher IRQ threshold;
IMPORTANT - save this plot as ``vol_rms_outliers.png``
"""
rmsd = diagnostics.vol_rms_diff(data)
outliers_rms_index, thres_rms = diagnostics.iqr_outliers(rmsd)
plt.plot(rmsd, "r", label="rms differences values")
for i in outliers_rms_index:
plt.plot(i, rmsd[i], "o", color="blue")
plt.axhline(y=thres_rms[0], color="g", ls="dashed", label="low threshold")
plt.axhline(y=thres_rms[1], color="black", ls="dashed", label="high threshold")
plt.legend(loc=1)
plt.xlabel("volume")
plt.ylabel("rms difference")
plt.savefig("vol_rms_outliers.png")
plt.clf()
""" Use the ``extend_diff_outliers`` to label 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))
plt.xlabel('time')
plt.legend(fontsize=11, \
ncol=2, loc=9, borderaxespad=0.2)
plt.savefig(project_path+'fig/outliers/%s_vol_std.png' %str(name))
plt.close()
#RMS difference values
rms_dvals = diagnostics.vol_rms_diff(data)
rms_outliers, rms_thresholds = diagnostics.iqr_outliers(rms_dvals)
#Plot
N = len(rms_dvals)
x = np.arange(N)
plt.plot(rms_dvals, label='vol RMS differences ' + str(name))
plt.plot(x[rms_outliers], rms_dvals[rms_outliers], 'o', label='outliers')
plt.plot([0, N], [rms_thresholds[0], rms_thresholds[0]], ':', label='IQR lo')
plt.plot([0, N], [rms_thresholds[1], rms_thresholds[1]], ':', label='IQR hi')
plt.title('voxels rms difference ' + str(name))
plt.xlabel('time')
plt.legend(fontsize=11, \
ncol=2, loc=9, borderaxespad=0.2)
plt.savefig(project_path+'fig/outliers/%s_vol_rms_outliers.png'%str(name))
plt.close()
#Label the 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))
plt.xlabel('time')
plt.legend(fontsize=11, \
ncol=2, loc=9, borderaxespad=0.2)
plt.savefig(project_path + 'fig/outliers/%s_vol_std.png' % str(name))
plt.close()
#RMS difference values
rms_dvals = diagnostics.vol_rms_diff(data)
rms_outliers, rms_thresholds = diagnostics.iqr_outliers(rms_dvals)
#Plot
N = len(rms_dvals)
x = np.arange(N)
plt.plot(rms_dvals, label='vol RMS differences ' + str(name))
plt.plot(x[rms_outliers], rms_dvals[rms_outliers], 'o', label='outliers')
plt.plot([0, N], [rms_thresholds[0], rms_thresholds[0]],
':',
label='IQR lo')
plt.plot([0, N], [rms_thresholds[1], rms_thresholds[1]],
':',
label='IQR hi')
plt.title('voxels rms difference ' + str(name))
plt.xlabel('time')
plt.legend(fontsize=11, \
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
for i in run_img_result.keys():
img = nib.load(run_img_result[i])
data = img.get_data()
rms_dvals = dg.vol_rms_diff(data)
rms_outliers, rms_thresholds = dg.iqr_outliers(rms_dvals)
N = len(rms_dvals)
x = np.arange(N)
plt.plot(rms_dvals, label='vol RMS differences')
plt.plot(x[rms_outliers], rms_dvals[rms_outliers], 'o', label='outliers')
plt.plot([0, N], [rms_thresholds[0], rms_thresholds[0]], ':', label='IQR lo')
plt.plot([0, N], [rms_thresholds[1], rms_thresholds[1]], ':', label='IQR hi')
plt.xlabel('Voxel')
plt.ylabel('Volumn RMS difference values')
plt.title('Volume RMS difference values_' + i)
plt.legend(fontsize = 8)
plt.savefig( i + '_vol_rms_outliers.png')
plt.close()
# plot the extended_vol_rms_outliers
