def plot_orthogonal_views(data,
bbox_nii=None,
auto_brightness=False,
display_modes=['x', 'y', 'z'],
n_cuts=10,
plot_func=nplot.plot_anat,
figure_title=""):
if bbox_nii is None:
bbox_nii = nimg.threshold_img(data, 1e-3)
cuts = cuts_from_bbox(data, cuts=n_cuts)
if auto_brightness:
robust_params = robust_set_limits(bbox_nii.get_fdata().reshape(-1), {})
else:
robust_params = {}
svgs = []
for d in display_modes:
plot_params = {
"display_mode": d,
"cut_coords": cuts[d],
**robust_params
}
display = plot_func(data, **plot_params)
svg = extract_svg(display)
svg = svg.replace("figure_1", f"{figure_title}-{d}")
svgs.append(fromstring(svg))
display.close()
return svgs
def _generate_report(self):
anat_img = nib.load(self.inputs.in_file)
mask_img = nib.load(self.inputs.mask_file)
assert nvol(anat_img) == 1
assert nvol(mask_img) == 1
plot_params = robust_set_limits_in_mask(anat_img, mask_img)
template = self.inputs.template
parc_file = getresource(
f"tpl-{template}_RegistrationCheckOverlay.nii.gz")
assert parc_file is not None
parc_img = nib.load(parc_file)
levels = np.unique(np.asanyarray(parc_img.dataobj).astype(np.int32))
levels = (levels[levels > 0] - 0.5).tolist()
colors = color_palette("husl", len(levels))
label = None
if isdefined(self.inputs.label):
label = self.inputs.label
compress = self.inputs.compress_report
n_cuts = 7
cuts = cuts_from_bbox(mask_img, cuts=n_cuts)
outfiles = []
for dimension in ["z", "y", "x"]:
display = plot_anat(
anat_img,
draw_cross=False,
display_mode=dimension,
cut_coords=cuts[dimension],
title=label,
**plot_params,
)
display.add_contours(parc_img,
levels=levels,
colors=colors,
linewidths=0.25)
display.add_contours(mask_img,
levels=[0.5],
colors="r",
linewidths=0.5)
label = None # only on first
svg = extract_svg(display, compress=compress)
svg = svg.replace("figure_1", str(uuid4()), 1)
outfiles.append(fromstring(svg))
self._out_report = op.abspath(self.inputs.out_report)
compose_view(bg_svgs=outfiles, fg_svgs=None, out_file=self._out_report)
def _plot_anat_with_contours(image, segs=None, compress="auto", **plot_params):
'''
Patched version of _plot_anat_with_contours that will
accept
'''
from niworkflows.viz.utils import extract_svg
nsegs = len(segs or [])
plot_params = plot_params or {}
# plot_params' values can be None, however they MUST NOT
# be None for colors and levels from this point on.
colors = plot_params.pop("colors", None) or []
levels = plot_params.pop("levels", None) or []
filled = plot_params.pop("filled", False)
alpha = plot_params.pop("alpha", 1)
missing = nsegs - len(colors)
if missing > 0: # missing may be negative
from seaborn import color_palette
colors = colors + color_palette("husl", missing)
colors = [[c] if not isinstance(c, list) else c for c in colors]
# Handle when a sequence of RGB is given as colors
if not isinstance(colors[0], str):
from matplotlib.colors import to_hex
colors = [to_hex(c) for c in colors]
if not levels:
levels = [[0.5]] * nsegs
# anatomical
# this line is problematic
display = plot_anat(image, **plot_params)
# remove plot_anat -specific parameters
plot_params.pop("display_mode")
plot_params.pop("cut_coords")
plot_params["linewidths"] = 0.5
for i in reversed(range(nsegs)):
plot_params["colors"] = colors[i]
display.add_contours(segs[i],
levels=levels[i],
filled=filled,
alpha=alpha,
**plot_params)
svg = extract_svg(display, compress=compress)
display.close()
return svg
def _generate_report(self):
in_img = nib.load(self.inputs.in_file)
assert nvol(in_img) == 1
mask_img = nib.load(self.inputs.mask_file)
assert nvol(mask_img) == 1
label = None
if isdefined(self.inputs.label):
label = self.inputs.label
compress = self.inputs.compress_report
n_cuts = 7
cuts = cuts_from_bbox(mask_img, cuts=n_cuts)
img_vals = in_img.get_fdata()[np.asanyarray(mask_img.dataobj).astype(
np.bool)]
vmin = img_vals.min()
vmax = img_vals.max()
outfiles = []
for dimension in ["z", "y", "x"]:
display = plot_epi(
in_img,
draw_cross=False,
display_mode=dimension,
cut_coords=cuts[dimension],
title=label,
vmin=vmin,
vmax=vmax,
colorbar=(dimension == "z"),
cmap=plt.cm.gray,
)
display.add_contours(mask_img, levels=[0.5], colors="r")
label = None # only on first
svg = extract_svg(display, compress=compress)
svg = svg.replace("figure_1", str(uuid4()), 1)
outfiles.append(fromstring(svg))
self._out_report = op.abspath(self.inputs.out_report)
compose_view(bg_svgs=outfiles, fg_svgs=None, out_file=self._out_report)
def plot_montage(data,
orientation,
bbox_nii=None,
n_cuts=15,
n_cols=5,
auto_brightness=False,
plot_func=nplot.plot_anat,
figure_title="figure"):
'''
Plot a montage of cuts for a given orientation
for an image
'''
if bbox_nii is None:
bbox_nii = nimg.threshold_img(data, 1e-3)
cuts = cuts_from_bbox(bbox_nii, cuts=n_cuts)
if auto_brightness:
robust_params = robust_set_limits(bbox_nii.get_fdata().reshape(-1), {})
else:
robust_params = {}
svgs = []
for i in range(n_cuts // n_cols):
start = i * n_cols
end = min(i * n_cols + n_cols, n_cuts)
row_cuts = cuts[orientation][start:end]
plot_params = {
"display_mode": orientation,
"cut_coords": row_cuts,
**robust_params
}
display = plot_func(data, **plot_params)
svg = extract_svg(display)
svg = svg.replace("figure_1", f"{figure_title}:{start}-{end}")
svgs.append(fromstring(svg))
return svgs
def _generate_report(self):
epi_img = nib.load(self.inputs.in_file)
mask_img = nib.load(self.inputs.mask_file)
assert nvol(epi_img) == 1
assert nvol(mask_img) == 1
label = None
if isdefined(self.inputs.label):
label = self.inputs.label
compress = self.inputs.compress_report
n_cuts = 7
cuts = cuts_from_bbox(mask_img, cuts=n_cuts)
plot_params = robust_set_limits_in_mask(epi_img, mask_img)
outfiles = []
for dimension in ["z", "y", "x"]:
display = plot_epi(
epi_img,
draw_cross=False,
display_mode=dimension,
cut_coords=cuts[dimension],
title=label,
colorbar=(dimension == "z"),
cmap=plt.get_cmap("gray"),
**plot_params,
)
display.add_contours(mask_img, levels=[0.5], colors="r")
label = None # only on first
svg = extract_svg(display, compress=compress)
svg = svg.replace("figure_1", str(uuid4()), 1)
outfiles.append(fromstring(svg))
self._out_report = op.abspath(self.inputs.out_report)
compose_view(bg_svgs=outfiles, fg_svgs=None, out_file=self._out_report)
def plot_registration(anat_nii,
div_id,
plot_params=None,
order=('z', 'x', 'y'),
cuts=None,
estimate_brightness=False,
label=None,
contour=None,
compress='auto'):
"""
Plot the foreground and background views.
Default order is: axial, coronal, sagittal
"""
from uuid import uuid4
from lxml import etree
from nilearn.plotting import plot_anat
from svgutils.transform import SVGFigure
from niworkflows.viz.utils import robust_set_limits, extract_svg, SVGNS
plot_params = plot_params or {}
# Use default MNI cuts if none defined
if cuts is None:
raise NotImplementedError # TODO
out_files = []
if estimate_brightness:
plot_params = robust_set_limits(anat_nii.get_data().reshape(-1),
plot_params)
# Plot each cut axis
for i, mode in enumerate(list(order)):
plot_params['display_mode'] = mode
plot_params['cut_coords'] = cuts[mode]
if i == 0:
plot_params['title'] = label
else:
plot_params['title'] = None
# Generate nilearn figure
display = plot_anat(anat_nii, **plot_params)
if contour is not None:
display.add_contours(contour,
colors='g',
levels=[0.5],
linewidths=0.5)
svg = extract_svg(display, compress=compress)
display.close()
# Find and replace the figure_1 id.
xml_data = etree.fromstring(svg)
find_text = etree.ETXPath("//{%s}g[@id='figure_1']" % SVGNS)
find_text(xml_data)[0].set('id', '%s-%s-%s' % (div_id, mode, uuid4()))
svg_fig = SVGFigure()
svg_fig.root = xml_data
out_files.append(svg_fig)
return out_files
def plot_registration(
anat_nii,
div_id,
plot_params=None,
order=("z", "x", "y"),
cuts=None,
estimate_brightness=False,
label=None,
contour=None,
compress="auto",
overlay=None,
overlay_params=None,
):
"""
Plot the foreground and background views.
Default order is: axial, coronal, sagittal
"""
from uuid import uuid4
from lxml import etree
import matplotlib.pyplot as plt
from nilearn.plotting import plot_anat
from svgutils.transform import SVGFigure
from niworkflows.viz.utils import robust_set_limits, extract_svg, SVGNS
plot_params = plot_params or {}
# Use default MNI cuts if none defined
if cuts is None:
raise NotImplementedError # TODO
out_files = []
if estimate_brightness:
plot_params = robust_set_limits(
anat_nii.get_fdata(dtype="float32").reshape(-1), plot_params)
# Plot each cut axis
for i, mode in enumerate(list(order)):
plot_params["display_mode"] = mode
plot_params["cut_coords"] = cuts[mode]
if i == 0:
plot_params["title"] = label
else:
plot_params["title"] = None
# Generate nilearn figure
display = plot_anat(anat_nii, **plot_params)
if overlay is not None:
_overlay_params = {
"vmin": overlay.get_fdata(dtype="float32").min(),
"vmax": overlay.get_fdata(dtype="float32").max(),
"cmap": plt.cm.gray,
"interpolation": "nearest",
}
_overlay_params.update(overlay_params)
display.add_overlay(overlay, **_overlay_params)
if contour is not None:
display.add_contours(contour,
colors="g",
levels=[0.5],
linewidths=0.5)
svg = extract_svg(display, compress=compress)
display.close()
# Find and replace the figure_1 id.
xml_data = etree.fromstring(svg)
find_text = etree.ETXPath("//{%s}g[@id='figure_1']" % SVGNS)
find_text(xml_data)[0].set("id", "%s-%s-%s" % (div_id, mode, uuid4()))
svg_fig = SVGFigure()
svg_fig.root = xml_data
out_files.append(svg_fig)
return out_files