def plotBrain(objIn, how='full', thr=None, **kwargs):
"""
More complete brain plotting of a Brain_Data instance
Args:
obj: (Brain_Data) object to plot
how: (str) whether to plot a glass brain 'glass', 3 view-multi-slice mni 'mni', or both 'full'
thr: (str/float) thresholding of image. Can be string for percentage, or float for data units (see Brain_Data.threshold()
kwargs: optionals args to nilearn plot functions (e.g. vmax)
"""
if thr:
obj = objIn.threshold(thr)
else:
obj = objIn.copy()
views = ['x', 'y', 'z']
coords = [range(-50, 51, 8),
range(-80, 50, 10),
range(-40, 71, 9)] #[-88,-72,-58,-38,-26,8,20,34,46]
cmap = 'RdBu_r'
if thr is None:
print("Plotting unthresholded image")
elif type(thr) == str:
print("Plotting top %s of voxels" % thr)
elif type(thr) == float or type(thr) == int:
print("Plotting voxels with stat value >= %s" % thr)
if how == 'full':
plot_glass_brain(obj.to_nifti(),
display_mode='lzry',
colorbar=True,
cmap=cmap,
plot_abs=False,
**kwargs)
for v, c in zip(views, coords):
plot_stat_map(obj.to_nifti(),
cut_coords=c,
display_mode=v,
cmap=cmap,
bg_img=resolve_mni_path(MNI_Template)['brain'],
**kwargs)
elif how == 'glass':
plot_glass_brain(obj.to_nifti(),
display_mode='lzry',
colorbar=True,
cmap=cmap,
plot_abs=False,
**kwargs)
elif how == 'mni':
for v, c in zip(views, coords):
plot_stat_map(obj.to_nifti(),
cut_coords=c,
display_mode=v,
cmap=cmap,
bg_img=resolve_mni_path(MNI_Template)['brain'],
**kwargs)
del obj #save memory
return
def __init__(self, brain_mask=None, output_dir = None): #no scoring param
# self.resource_folder = os.path.join(os.getcwd(),'resources')
if output_dir is None:
self.output_dir = os.path.join(os.getcwd())
else:
self.output_dir = output_dir
if isinstance(brain_mask, str):
brain_mask = nib.load(brain_mask)
elif brain_mask is None:
brain_mask = nib.load(resolve_mni_path(MNI_Template)['mask'])
elif ~isinstance(brain_mask, nib.nifti1.Nifti1Image):
raise ValueError("brain_mask is not a string or a nibabel instance")
self.brain_mask = brain_mask
self.nifti_masker = NiftiMasker(mask_img=self.brain_mask)
def _viewer(objIn, x, y, z, stat, figsize):
"""
Generator function for ibrainViewer
"""
_, ax = plt.subplots(1, figsize=figsize)
plot_stat_map(objIn.to_nifti(),
display_mode='ortho',
bg_img=resolve_mni_path(MNI_Template)['brain'],
cut_coords=(x, y, z),
threshold=stat,
draw_cross=False,
black_bg=True,
dim=.25,
axes=ax)
return
def create_sphere(coordinates, radius=5, mask=None):
"""Generate a set of spheres in the brain mask space
Args:
radius: vector of radius. Will create multiple spheres if
len(radius) > 1
centers: a vector of sphere centers of the form [px, py, pz] or
[[px1, py1, pz1], ..., [pxn, pyn, pzn]]
"""
from nltools.data import Brain_Data
if mask is not None:
if not isinstance(mask, nib.Nifti1Image):
if isinstance(mask, str):
if os.path.isfile(mask):
mask = nib.load(mask)
else:
raise ValueError("mask is not a nibabel instance or a valid "
"file name")
else:
mask = nib.load(resolve_mni_path(MNI_Template)["mask"])
def sphere(r, p, mask):
"""create a sphere of given radius at some point p in the brain mask
Args:
r: radius of the sphere
p: point (in coordinates of the brain mask) of the center of the
sphere
"""
dims = mask.shape
m = [dims[0] / 2, dims[1] / 2, dims[2] / 2]
x, y, z = np.ogrid[-m[0]:dims[0] - m[0], -m[1]:dims[1] - m[1],
-m[2]:dims[2] - m[2]]
mask_r = x * x + y * y + z * z <= r * r
activation = np.zeros(dims)
activation[mask_r] = 1
translation_affine = np.array([
[1, 0, 0, p[0] - m[0]],
[0, 1, 0, p[1] - m[1]],
[0, 0, 1, p[2] - m[2]],
[0, 0, 0, 1],
])
return nib.Nifti1Image(activation, affine=translation_affine)
if any(isinstance(i, list) for i in coordinates):
if isinstance(radius, list):
if len(radius) != len(coordinates):
raise ValueError("Make sure length of radius list matches"
"length of coordinate list.")
elif isinstance(radius, int):
radius = [radius] * len(coordinates)
out = Brain_Data(
nib.Nifti1Image(np.zeros_like(mask.get_data()),
affine=mask.affine),
mask=mask,
)
for r, c in zip(radius, coordinates):
out = out + Brain_Data(sphere(r, c, mask), mask=mask)
else:
out = Brain_Data(sphere(radius, coordinates, mask), mask=mask)
out = out.to_nifti()
out.get_data()[out.get_data() > 0.5] = 1
out.get_data()[out.get_data() < 0.5] = 0
return out
def plot_brain(objIn,
how="full",
thr_upper=None,
thr_lower=None,
save=False,
**kwargs):
"""
More complete brain plotting of a Brain_Data instance
Args:
obj: (Brain_Data) object to plot
how: (str) whether to plot a glass brain 'glass', 3 view-multi-slice mni 'mni', or both 'full'
thr_upper: (str/float) thresholding of image. Can be string for percentage, or float for data units (see Brain_Data.threshold()
thr_lower: (str/float) thresholding of image. Can be string for percentage, or float for data units (see Brain_Data.threshold()
save (str): if a string file name or path is provided plots will be saved into this directory appended with the orientation they belong to
kwargs: optionals args to nilearn plot functions (e.g. vmax)
"""
if thr_upper or thr_lower:
obj = objIn.threshold(upper=thr_upper, lower=thr_lower)
else:
obj = objIn.copy()
views = ["x", "y", "z"]
coords = [
range(-50, 51, 8),
range(-80, 50, 10),
range(-40, 71, 9),
] # [-88,-72,-58,-38,-26,8,20,34,46]
cmap = "RdBu_r"
if thr_upper is None and thr_lower is None:
print("Plotting unthresholded image")
else:
if isinstance(thr_upper, str):
print("Plotting top %s of voxels" % thr_upper)
elif isinstance(thr_upper, (float, int)):
print("Plotting voxels with stat value >= %s" % thr_upper)
if isinstance(thr_lower, str):
print("Plotting lower %s of voxels" % thr_lower)
elif isinstance(thr_lower, (float, int)):
print("Plotting voxels with stat value <= %s" % thr_lower)
if save:
path, filename = os.path.split(save)
filename, extension = filename.split(".")
glass_save = os.path.join(path, filename + "_glass." + extension)
x_save = os.path.join(path, filename + "_x." + extension)
y_save = os.path.join(path, filename + "_y." + extension)
z_save = os.path.join(path, filename + "_z." + extension)
else:
glass_save, x_save, y_save, z_save = None, None, None, None
saves = [x_save, y_save, z_save]
if how == "full":
plot_glass_brain(
obj.to_nifti(),
display_mode="lzry",
colorbar=True,
cmap=cmap,
plot_abs=False,
**kwargs,
)
if save:
plt.savefig(glass_save, bbox_inches="tight")
for v, c, savefile in zip(views, coords, saves):
plot_stat_map(
obj.to_nifti(),
cut_coords=c,
display_mode=v,
cmap=cmap,
bg_img=resolve_mni_path(MNI_Template)["brain"],
**kwargs,
)
if save:
plt.savefig(savefile, bbox_inches="tight")
elif how == "glass":
plot_glass_brain(
obj.to_nifti(),
display_mode="lzry",
colorbar=True,
cmap=cmap,
plot_abs=False,
**kwargs,
)
if save:
plt.savefig(glass_save, bbox_inches="tight")
elif how == "mni":
for v, c, savefile in zip(views, coords, saves):
plot_stat_map(
obj.to_nifti(),
cut_coords=c,
display_mode=v,
cmap=cmap,
bg_img=resolve_mni_path(MNI_Template)["brain"],
**kwargs,
)
if save:
plt.savefig(savefile, bbox_inches="tight")
del obj # save memory
return
def plot_t_brain(objIn,
how="full",
thr="unc",
alpha=None,
nperm=None,
cut_coords=[],
**kwargs):
"""
Takes a brain data object and computes a 1 sample t-test across it's first axis. If a list is provided will compute difference between brain data objects in list (i.e. paired samples t-test).
Args:
objIn:(list/Brain_Data) if list will compute difference map first
how: (list) whether to plot a glass brain 'glass', 3 view-multi-slice mni 'mni', or both 'full'
thr: (str) what method to use for multiple comparisons correction unc, fdr, or tfce
alpha: (float) p-value threshold
nperm: (int) number of permutations for tcfe; default 1000
cut_coords: (list) x,y,z coords to plot brain slice
kwargs: optionals args to nilearn plot functions (e.g. vmax)
"""
if thr not in ["unc", "fdr", "tfce"]:
raise ValueError("Acceptable threshold methods are 'unc','fdr','tfce'")
views = ["x", "y", "z"]
if len(cut_coords) == 0:
cut_coords = [
range(-40, 50, 10),
[-88, -72, -58, -38, -26, 8, 20, 34, 46],
[-34, -22, -10, 0, 16, 34, 46, 56, 66],
]
else:
if len(cut_coords) != 3:
raise ValueError(
"cut_coords must be a list of coordinates like [[xs],[ys],[zs]]"
)
cmap = "RdBu_r"
if isinstance(objIn, list):
if len(objIn) == 2:
obj = objIn[0] - objIn[1]
else:
raise ValueError("Contrasts should contain only 2 list items!")
thrDict = {}
if thr == "tfce":
thrDict["permutation"] = thr
if nperm is None:
nperm = 1000
thrDict["n_permutations"] = nperm
print("1-sample t-test corrected using: TFCE w/ %s permutations" %
nperm)
else:
if thr == "unc":
if alpha is None:
alpha = 0.001
thrDict[thr] = alpha
print("1-sample t-test uncorrected at p < %.3f " % alpha)
elif thr == "fdr":
if alpha is None:
alpha = 0.05
thrDict[thr] = alpha
print("1-sample t-test corrected at q < %.3f " % alpha)
else:
thrDict = None
print("1-sample test unthresholded")
out = objIn.ttest(threshold_dict=thrDict)
if thrDict is not None:
obj = out["thr_t"]
else:
obj = out["t"]
if how == "full":
plot_glass_brain(
obj.to_nifti(),
display_mode="lzry",
colorbar=True,
cmap=cmap,
plot_abs=False,
**kwargs,
)
for v, c in zip(views, cut_coords):
plot_stat_map(
obj.to_nifti(),
cut_coords=c,
display_mode=v,
cmap=cmap,
bg_img=resolve_mni_path(MNI_Template)["brain"],
**kwargs,
)
elif how == "glass":
plot_glass_brain(
obj.to_nifti(),
display_mode="lzry",
colorbar=True,
cmap=cmap,
plot_abs=False,
**kwargs,
)
elif how == "mni":
for v, c in zip(views, cut_coords):
plot_stat_map(
obj.to_nifti(),
cut_coords=c,
display_mode=v,
cmap=cmap,
bg_img=resolve_mni_path(MNI_Template)["brain"],
**kwargs,
)
del obj
del out
return
def plotTBrain(objIn,
how='full',
thr='unc',
alpha=None,
nperm=None,
cut_coords=[],
**kwargs):
"""
Takes a brain data object and computes a 1 sample t-test across it's first axis. If a list is provided will compute difference between brain data objects in list (i.e. paired samples t-test).
Args:
objIn:(list/Brain_Data) if list will compute difference map first
how: (list) whether to plot a glass brain 'glass', 3 view-multi-slice mni 'mni', or both 'full'
thr: (str) what method to use for multiple comparisons correction unc, fdr, or tfce
alpha: (float) p-value threshold
nperm: (int) number of permutations for tcfe; default 1000
cut_coords: (list) x,y,z coords to plot brain slice
kwargs: optionals args to nilearn plot functions (e.g. vmax)
"""
assert thr in ['unc', 'fdr', 'tfce'
], "Acceptable threshold methods are 'unc','fdr','tfce'"
views = ['x', 'y', 'z']
if len(cut_coords) == 0:
cut_coords = [
range(-40, 50, 10), [-88, -72, -58, -38, -26, 8, 20, 34, 46],
[-34, -22, -10, 0, 16, 34, 46, 56, 66]
]
else:
assert (
len(cut_coords) == 3
), 'cut_coords must be a list of coordinates like [[xs],[ys],[zs]]'
cmap = 'RdBu_r'
if type(objIn) == list:
if len(objIn) == 2:
obj = objIn[0] - objIn[1]
else:
raise ValueError('Contrasts should contain only 2 list items!')
thrDict = {}
if thr == 'tfce':
thrDict['permutation'] = thr
if nperm is None:
nperm = 1000
thrDict['n_permutations'] = nperm
print("1-sample t-test corrected using: TFCE w/ %s permutations" %
nperm)
else:
if thr == 'unc':
if alpha is None:
alpha = .001
thrDict[thr] = alpha
print("1-sample t-test uncorrected at p < %.3f " % alpha)
elif thr == 'fdr':
if alpha is None:
alpha = .05
thrDict[thr] = alpha
print("1-sample t-test corrected at q < %.3f " % alpha)
else:
thrDict = None
print("1-sample test unthresholded")
out = objIn.ttest(threshold_dict=thrDict)
if thrDict is not None:
obj = out['thr_t']
else:
obj = out['t']
if how == 'full':
plot_glass_brain(obj.to_nifti(),
display_mode='lzry',
colorbar=True,
cmap=cmap,
plot_abs=False,
**kwargs)
for v, c in zip(views, cut_coords):
plot_stat_map(obj.to_nifti(),
cut_coords=c,
display_mode=v,
cmap=cmap,
bg_img=resolve_mni_path(MNI_Template)['brain'],
**kwargs)
elif how == 'glass':
plot_glass_brain(obj.to_nifti(),
display_mode='lzry',
colorbar=True,
cmap=cmap,
plot_abs=False,
**kwargs)
elif how == 'mni':
for v, c in zip(views, cut_coords):
plot_stat_map(obj.to_nifti(),
cut_coords=c,
display_mode=v,
cmap=cmap,
bg_img=resolve_mni_path(MNI_Template)['brain'],
**kwargs)
del obj
del out
return
