def __init__(self, model, contrast, corrected=True,
stat_thresh=2.3, stat_range=(2, 5),
stat_cmap="OrRd_r", stat_alpha=.85,
sharp=False, label_slices=True):
"""Initialize the object but do not plot anything yet."""
anat_img = nib.load(op.join(data_dir, "average_anat.nii.gz"))
contrast_dir = op.join(analysis_dir, exp + "-" + model,
"group", "mni", contrast)
if corrected:
stat_file = "zstat1_threshold.nii.gz"
else:
stat_file = "zstat1.nii.gz"
stat_img = nib.load(op.join(contrast_dir, stat_file))
mask_img = nib.load(op.join(contrast_dir, "mask.nii.gz"))
self.anat_img = VolumeImg(anat_img.get_data(),
anat_img.get_affine(), "mni")
self.stat_img = VolumeImg(stat_img.get_data(),
stat_img.get_affine(), "mni")
self.mask_img = VolumeImg(mask_img.get_data(),
mask_img.get_affine(), "mni",
interpolation="nearest")
self.stat_thresh = stat_thresh
self.stat_cmap = stat_cmap
self.stat_alpha = stat_alpha
self.stat_vmin, self.stat_vmax = stat_range
self.label_slices = label_slices
self.sharp = sharp
def __init__(self,
model,
contrast,
corrected=True,
stat_thresh=2.3,
stat_range=(2, 5),
stat_cmap="OrRd_r",
stat_alpha=.85,
sharp=False,
label_slices=True):
"""Initialize the object but do not plot anything yet."""
anat_img = nib.load(op.join(data_dir, "average_anat.nii.gz"))
contrast_dir = op.join(analysis_dir, exp + "-" + model, "group", "mni",
contrast)
if corrected:
stat_file = "zstat1_threshold.nii.gz"
else:
stat_file = "zstat1.nii.gz"
stat_img = nib.load(op.join(contrast_dir, stat_file))
mask_img = nib.load(op.join(contrast_dir, "mask.nii.gz"))
self.anat_img = VolumeImg(anat_img.get_data(), anat_img.get_affine(),
"mni")
self.stat_img = VolumeImg(stat_img.get_data(), stat_img.get_affine(),
"mni")
self.mask_img = VolumeImg(mask_img.get_data(),
mask_img.get_affine(),
"mni",
interpolation="nearest")
self.stat_thresh = stat_thresh
self.stat_cmap = stat_cmap
self.stat_alpha = stat_alpha
self.stat_vmin, self.stat_vmax = stat_range
self.label_slices = label_slices
self.sharp = sharp
class SlicePlotter(object):
"""Object to generate single slice images or mosaics of volume results."""
def __init__(self, model, contrast, corrected=True,
stat_thresh=2.3, stat_range=(2, 5),
stat_cmap="OrRd_r", stat_alpha=.85,
sharp=False, label_slices=True):
"""Initialize the object but do not plot anything yet."""
anat_img = nib.load(op.join(data_dir, "average_anat.nii.gz"))
contrast_dir = op.join(analysis_dir, exp + "-" + model,
"group", "mni", contrast)
if corrected:
stat_file = "zstat1_threshold.nii.gz"
else:
stat_file = "zstat1.nii.gz"
stat_img = nib.load(op.join(contrast_dir, stat_file))
mask_img = nib.load(op.join(contrast_dir, "mask.nii.gz"))
self.anat_img = VolumeImg(anat_img.get_data(),
anat_img.get_affine(), "mni")
self.stat_img = VolumeImg(stat_img.get_data(),
stat_img.get_affine(), "mni")
self.mask_img = VolumeImg(mask_img.get_data(),
mask_img.get_affine(), "mni",
interpolation="nearest")
self.stat_thresh = stat_thresh
self.stat_cmap = stat_cmap
self.stat_alpha = stat_alpha
self.stat_vmin, self.stat_vmax = stat_range
self.label_slices = label_slices
self.sharp = sharp
def plot_slice(self, ax, y=None, z=None, stat_only=False, contour=None):
"""Draw a single slice image onto a matplotlib Axes object."""
x_vals = np.arange(-70, 74, 2)
y_vals = np.arange(-108, 76, 2)
z_vals = np.arange(-50, 80, 2)
if y is None:
label = "z = %d" % z
x, y = np.meshgrid(x_vals, y_vals)
z = np.ones_like(x) * z
elif z is None:
label = "y = %d" % y
x, z = np.meshgrid(x_vals, z_vals)
y = np.ones_like(x) * y
else:
raise ValueError
anat_slice = self.anat_img.values_in_world(x, y, z)
anat_slice = np.ma.masked_array(anat_slice, anat_slice < 30)
mask_slice = self.mask_img.values_in_world(x, y, z)
stat_slice = self.stat_img.values_in_world(x, y, z)
stat_mask = stat_slice < self.stat_thresh
stat_slice = np.ma.masked_array(stat_slice, stat_mask)
mask_mask = (mask_slice == 1) | (anat_slice < 30)
mask_slice = np.ma.masked_array(mask_slice, mask_mask)
interp = "spline16" if self.sharp else "bilinear"
im_kws = dict(origin="lower",
interpolation=interp,
rasterized=True)
if not stat_only:
ax.imshow(anat_slice, cmap="Greys_r",
vmin=20, vmax=120, **im_kws)
ax.imshow(mask_slice, cmap="bone",
vmin=-.25, vmax=1, alpha=.5, **im_kws)
if contour is None:
ax.imshow(stat_slice, cmap=self.stat_cmap, alpha=self.stat_alpha,
vmin=self.stat_vmin, vmax=self.stat_vmax, **im_kws)
else:
outline = stat_slice > contour
if outline.any():
ax.contour(outline, 1, cmap="Greys_r", vmin=0, vmax=5, lw=.3)
if self.label_slices:
ax.set_xlabel(label, size=7, labelpad=1.2)
sns.despine(ax=ax, left=True, bottom=True)
ax.set(xticks=[], yticks=[])
def plot_cmap(self, ax, vert=True):
"""Draw a colorbar to show the extent of the statistical colormap."""
bar = np.linspace(self.stat_thresh, self.stat_vmax, 100)
bar = np.atleast_2d(bar)
if vert:
bar = bar.T
ax.pcolormesh(bar, cmap=self.stat_cmap,
vmin=self.stat_vmin,
vmax=self.stat_vmax,
rasterized=True)
ax.set(xticks=[], yticks=[])
class SlicePlotter(object):
"""Object to generate single slice images or mosaics of volume results."""
def __init__(self,
model,
contrast,
corrected=True,
stat_thresh=2.3,
stat_range=(2, 5),
stat_cmap="OrRd_r",
stat_alpha=.85,
sharp=False,
label_slices=True):
"""Initialize the object but do not plot anything yet."""
anat_img = nib.load(op.join(data_dir, "average_anat.nii.gz"))
contrast_dir = op.join(analysis_dir, exp + "-" + model, "group", "mni",
contrast)
if corrected:
stat_file = "zstat1_threshold.nii.gz"
else:
stat_file = "zstat1.nii.gz"
stat_img = nib.load(op.join(contrast_dir, stat_file))
mask_img = nib.load(op.join(contrast_dir, "mask.nii.gz"))
self.anat_img = VolumeImg(anat_img.get_data(), anat_img.get_affine(),
"mni")
self.stat_img = VolumeImg(stat_img.get_data(), stat_img.get_affine(),
"mni")
self.mask_img = VolumeImg(mask_img.get_data(),
mask_img.get_affine(),
"mni",
interpolation="nearest")
self.stat_thresh = stat_thresh
self.stat_cmap = stat_cmap
self.stat_alpha = stat_alpha
self.stat_vmin, self.stat_vmax = stat_range
self.label_slices = label_slices
self.sharp = sharp
def plot_slice(self, ax, y=None, z=None, stat_only=False, contour=None):
"""Draw a single slice image onto a matplotlib Axes object."""
x_vals = np.arange(-70, 74, 2)
y_vals = np.arange(-108, 76, 2)
z_vals = np.arange(-50, 80, 2)
if y is None:
label = "z = %d" % z
x, y = np.meshgrid(x_vals, y_vals)
z = np.ones_like(x) * z
elif z is None:
label = "y = %d" % y
x, z = np.meshgrid(x_vals, z_vals)
y = np.ones_like(x) * y
else:
raise ValueError
anat_slice = self.anat_img.values_in_world(x, y, z)
anat_slice = np.ma.masked_array(anat_slice, anat_slice < 30)
mask_slice = self.mask_img.values_in_world(x, y, z)
stat_slice = self.stat_img.values_in_world(x, y, z)
stat_mask = stat_slice < self.stat_thresh
stat_slice = np.ma.masked_array(stat_slice, stat_mask)
mask_mask = (mask_slice == 1) | (anat_slice < 30)
mask_slice = np.ma.masked_array(mask_slice, mask_mask)
interp = "spline16" if self.sharp else "bilinear"
im_kws = dict(origin="lower", interpolation=interp, rasterized=True)
if not stat_only:
ax.imshow(anat_slice, cmap="Greys_r", vmin=20, vmax=120, **im_kws)
ax.imshow(mask_slice,
cmap="bone",
vmin=-.25,
vmax=1,
alpha=.5,
**im_kws)
if contour is None:
ax.imshow(stat_slice,
cmap=self.stat_cmap,
alpha=self.stat_alpha,
vmin=self.stat_vmin,
vmax=self.stat_vmax,
**im_kws)
else:
outline = stat_slice > contour
if outline.any():
ax.contour(outline, 1, cmap="Greys_r", vmin=0, vmax=5, lw=.3)
if self.label_slices:
ax.set_xlabel(label, size=7, labelpad=1.2)
sns.despine(ax=ax, left=True, bottom=True)
ax.set(xticks=[], yticks=[])
def plot_cmap(self, ax, vert=True):
"""Draw a colorbar to show the extent of the statistical colormap."""
bar = np.linspace(self.stat_thresh, self.stat_vmax, 100)
bar = np.atleast_2d(bar)
if vert:
bar = bar.T
ax.pcolormesh(bar,
cmap=self.stat_cmap,
vmin=self.stat_vmin,
vmax=self.stat_vmax,
rasterized=True)
ax.set(xticks=[], yticks=[])
