def plot_brainrsa_regions(img,
threshold=None,
background=get_bg_ch2(),
type='r'):
"""
Plot the RSA-result regions by 3 cuts (frontal, axial & lateral)
Parameters
----------
img : string
The file path of the .nii file of the RSA results.
threshold : None or int. Default is None.
The threshold of the number of voxels used in correction.
If threshold=n, only the similarity clusters consisting more than threshold voxels will be visible. If it is
None, the threshold-correction will not work.
background : Niimg-like object or string. Default is stuff.get_bg_ch2()
The background image that the RSA results will be plotted on top of.
type : string 'r' or 't'
The type of result (r-values or t-values).
"""
imgarray = nib.load(img).get_fdata()
if (imgarray == np.nan).all() == True:
print("No Valid Results")
else:
if threshold != None:
imgarray = nib.load(img).get_fdata()
affine = get_affine(img)
imgarray = correct_by_threshold(imgarray, threshold)
img = nib.Nifti1Image(imgarray, affine)
if type == 'r':
plotting.plot_roi(roi_img=img,
bg_img=background,
threshold=0,
vmin=0.1,
vmax=1,
title="Similarity",
resampling_interpolation="continuous")
if type == 't':
plotting.plot_roi(roi_img=img,
bg_img=background,
threshold=0,
vmin=-7,
vmax=7,
title="Similarity",
resampling_interpolation="continuous")
plt.show()
return 0
def plot_brainrsa_rlts(img, threshold=None, slice=[6, 6, 6], background=None, type='r'):
"""
Plot the RSA-result by a set of images
Parameters
----------
img : string
The file path of the .nii file of the RSA results.
threshold : None or int. Default is None.
The threshold of the number of voxels used in correction.
If threshold=n, only the similarity clusters consisting more than threshold voxels will be visible. If it is
None, the threshold-correction will not work.
background : Niimg-like object or string. Default is None.
The background image that the RSA results will be plotted on top of.
type : string 'r' or 't'
The type of result (r-values or t-values).
"""
imgarray = nib.load(img).get_data()
if (imgarray == np.nan).all() == True:
print("No Valid Results")
else:
if threshold != None:
imgarray = nib.load(img).get_data()
affine = get_affine(img)
imgarray = correct_by_threshold(imgarray, threshold)
img = nib.Nifti1Image(imgarray, affine)
if background == None:
plot_brainrsa_regions(img, threshold=threshold, type=type)
plot_brainrsa_montage(img, threshold=threshold, slice=slice, type=type)
plot_brainrsa_glass(img, threshold=threshold, type=type)
plot_brainrsa_surface(img, threshold=threshold, type=type)
else:
plot_brainrsa_regions(img, threshold=threshold, background=background, type=type)
plot_brainrsa_montage(img, threshold=threshold, slice=slice, background=background, type=type)
plot_brainrsa_surface(img, threshold=threshold, type=type)
def plot_brainrsa_glass(img, threshold=None, type='r'):
"""
Plot the 2-D projection of the RSA-result
Parameters
----------
img : string
The file path of the .nii file of the RSA results.
threshold : None or int. Default is None.
The threshold of the number of voxels used in correction.
If threshold=n, only the similarity clusters consisting more than threshold voxels will be visible. If it is
None, the threshold-correction will not work.
type : string 'r' or 't'
The type of result (r-values or t-values).
"""
imgarray = nib.load(img).get_fdata()
if (imgarray == np.nan).all() == True:
print("No Valid Results")
else:
if threshold != None:
imgarray = nib.load(img).get_fdata()
affine = get_affine(img)
imgarray = correct_by_threshold(imgarray, threshold)
img = nib.Nifti1Image(imgarray, affine)
if type == 'r':
plotting.plot_glass_brain(img,
colorbar=True,
title="Similarity",
black_bg=True,
draw_cross=True,
vmax=1)
if type == 't':
plotting.plot_glass_brain(img,
colorbar=True,
title="Similarity",
black_bg=True,
draw_cross=True,
vmax=7)
plt.show()
return 0
def plot_brainrsa_surface(img, threshold=None, type='r'):
"""
Plot the RSA-result into a brain surface
Parameters
----------
img : string
The file path of the .nii file of the RSA results.
threshold : None or int. Default is None.
The threshold of the number of voxels used in correction.
If threshold=n, only the similarity clusters consisting more than threshold voxels will be visible. If it is
None, the threshold-correction will not work.
type : string 'r' or 't'
The type of result (r-values or t-values).
"""
imgarray = nib.load(img).get_data()
if (imgarray == np.nan).all() == True:
print("No Valid Results")
else:
if threshold != None:
imgarray = nib.load(img).get_data()
affine = get_affine(img)
imgarray = correct_by_threshold(imgarray, threshold)
img = nib.Nifti1Image(imgarray, affine)
fsaverage = datasets.fetch_surf_fsaverage(mesh='fsaverage')
texture_left = surface.vol_to_surf(img, fsaverage.pial_left)
texture_right = surface.vol_to_surf(img, fsaverage.pial_right)
# type='r'
if type == 'r':
plotting.plot_surf_stat_map(fsaverage.pial_left, texture_left, hemi='left', threshold=0.1,
bg_map=fsaverage.sulc_right, colorbar=False, vmax=0.8, darkness=0.7)
plotting.plot_surf_stat_map(fsaverage.pial_right, texture_right, hemi='right', threshold=0.1,
bg_map=fsaverage.sulc_right, colorbar=True, vmax=0.8, darkness=0.7)
plotting.plot_surf_stat_map(fsaverage.pial_right, texture_left, hemi='left', threshold=0.1,
bg_map=fsaverage.sulc_right, colorbar=False, vmax=0.8, darkness=0.7)
plotting.plot_surf_stat_map(fsaverage.pial_left, texture_right, hemi='right', threshold=0.1,
bg_map=fsaverage.sulc_right, colorbar=True, vmax=0.8, darkness=0.7)
plt.show()
# type='t'
if type == 't':
plotting.plot_surf_stat_map(fsaverage.pial_left, texture_left, hemi='left', threshold=0.8,
bg_map=fsaverage.sulc_right, colorbar=False, darkness=0.7)
plotting.plot_surf_stat_map(fsaverage.pial_right, texture_right, hemi='right', threshold=0.8,
bg_map=fsaverage.sulc_right, colorbar=True, darkness=0.7)
plotting.plot_surf_stat_map(fsaverage.pial_right, texture_left, hemi='left', threshold=0.8,
bg_map=fsaverage.sulc_right, colorbar=False, darkness=0.7)
plotting.plot_surf_stat_map(fsaverage.pial_left, texture_right, hemi='right', threshold=0.8,
bg_map=fsaverage.sulc_right, colorbar=True, darkness=0.7)
plt.show()
def plot_brainrsa_montage(img, threshold=None, slice=[6, 6, 6], background=get_bg_ch2bet(), type='r'):
"""
Plot the RSA-result by different cuts
Parameters
----------
img : string
The file path of the .nii file of the RSA results.
threshold : None or int. Default is None.
The threshold of the number of voxels used in correction.
If threshold=n, only the similarity clusters consisting more than threshold voxels will be visible. If it is
None, the threshold-correction will not work.
slice : array
The point where the cut is performed.
If slice=[slice_x, slice_y, slice_z], slice_x, slice_y, slice_z represent the coordinates of each cut in the x,
y, z direction. If slice=[[slice_x1, slice_x2], [slice_y1, slice_y2], [slice_z1, slice_z2]], slice_x1 & slice_x2
represent the coordinates of each cut in the x direction, slice_y1 & slice_y2 represent the coordinates of each
cut in the y direction, slice_z1 & slice_z2 represent the coordinates of each cut in the z direction.
background : Niimg-like object or string. Default is stuff.get_bg_ch2bet()
The background image that the RSA results will be plotted on top of.
type : string 'r' or 't'
The type of result (r-values or t-values).
"""
imgarray = nib.load(img).get_data()
if (imgarray == np.nan).all() == True:
print("No Valid Results")
else:
if threshold != None:
imgarray = nib.load(img).get_data()
affine = get_affine(img)
imgarray = correct_by_threshold(imgarray, threshold)
img = nib.Nifti1Image(imgarray, affine)
slice_x = slice[0]
slice_y = slice[1]
slice_z = slice[2]
if type == 'r':
vmax = 1
if type == 't':
vmax = 7
if slice_x != 0:
plotting.plot_stat_map(stat_map_img=img, bg_img=background, display_mode='x', cut_coords=slice_x,
title="Similarity -sagittal", draw_cross=True, vmax=vmax)
if slice_y != 0:
plotting.plot_stat_map(stat_map_img=img, bg_img=background, display_mode='y', cut_coords=slice_y,
title="Similarity -coronal", draw_cross=True, vmax=vmax)
if slice_z != 0:
plotting.plot_stat_map(stat_map_img=img, bg_img=background, display_mode='z', cut_coords=slice_z,
title="Similarity -axial", draw_cross=True, vmax=vmax)
plt.show()
def test_correct_by_threshold(self):
img = np.random.rand(20, 22, 21)
threshold = 25
output = correct_by_threshold(img, threshold)
self.assertEqual(output.shape[0], 20)