def plotstatsimg(cbf,
ref_vol,
plot_params=None,
order=('z', 'x', 'y'),
estimate_brightness=False,
label=None,
compress='auto'):
"""
plot zsts
"""
plot_params = {} if plot_params is None else plot_params
image_nii = _3d_in_file(cbf)
data = image_nii.get_fdata()
from nilearn.image import threshold_img
bbox_nii = threshold_img(nb.load(cbf), 1)
cuts = cuts_from_bbox(bbox_nii, cuts=7)
out_files = []
if estimate_brightness:
plot_params = robust_set_limits(data, plot_params)
# Plot each cut axis
for i, mode in enumerate(list(order)):
plot_params['display_mode'] = mode
plot_params['cut_coords'] = cuts[mode]
plot_params['draw_cross'] = False
plot_params['symmetric_cbar'] = False
plot_params['threshold'] = 0.05
plot_params['vmax'] = 90
plot_params['colorbar'] = False
plot_params['cmap'] = 'gray'
if i == 0:
plot_params['title'] = label
plot_params['colorbar'] = False
else:
plot_params['title'] = None
# Generate nilearn figure
from nilearn.plotting import plot_stat_map
display = plot_stat_map(stat_map_img=cbf,
bg_img=ref_vol,
**plot_params)
svg = extract_svg(display, compress=compress)
display.close()
from lxml import etree
from .. import NIWORKFLOWS_LOG
# Find and replace the figure_1 id.
try:
xml_data = etree.fromstring(svg)
except etree.XMLSyntaxError as e:
NIWORKFLOWS_LOG.info(e)
return
svg_fig = SVGFigure()
svg_fig.root = xml_data
out_files.append(svg_fig)
return out_files
def plot_segs(image_nii,
seg_niis,
out_file,
bbox_nii=None,
masked=False,
colors=None,
compress='auto',
**plot_params):
""" plot segmentation as contours over the image (e.g. anatomical).
seg_niis should be a list of files. mask_nii helps determine the cut
coordinates. plot_params will be passed on to nilearn plot_* functions. If
seg_niis is a list of size one, it behaves as if it was plotting the mask.
"""
plot_params = {} if plot_params is None else plot_params
image_nii = _3d_in_file(image_nii)
data = image_nii.get_data()
plot_params = robust_set_limits(data, plot_params)
bbox_nii = nb.load(image_nii if bbox_nii is None else bbox_nii)
if masked:
bbox_nii = nlimage.threshold_img(bbox_nii, 1e-3)
cuts = cuts_from_bbox(bbox_nii, cuts=7)
plot_params['colors'] = colors or plot_params.get('colors', None)
out_files = []
for d in plot_params.pop('dimensions', ('z', 'x', 'y')):
plot_params['display_mode'] = d
plot_params['cut_coords'] = cuts[d]
svg = _plot_anat_with_contours(image_nii,
segs=seg_niis,
compress=compress,
**plot_params)
# Find and replace the figure_1 id.
try:
xml_data = etree.fromstring(svg)
except etree.XMLSyntaxError as e:
NIWORKFLOWS_LOG.info(e)
return
find_text = etree.ETXPath("//{%s}g[@id='figure_1']" % SVGNS)
find_text(xml_data)[0].set('id', 'segmentation-%s-%s' % (d, uuid4()))
svg_fig = SVGFigure()
svg_fig.root = xml_data
out_files.append(svg_fig)
return out_files
def plot_acpc(acpc_registered_img,
div_id,
plot_params=None,
order=('z', 'x', 'y'),
cuts=None,
estimate_brightness=False,
label=None,
compress='auto'):
"""
Plot the results of an AC-PC transformation.
"""
plot_params = plot_params or {}
# Do the low-b image first
out_files = []
if estimate_brightness:
plot_params = robust_set_limits(
acpc_registered_img.get_fdata(dtype='float32').reshape(-1),
plot_params)
# Plot each cut axis for low-b
for i, mode in enumerate(list(order)):
plot_params['display_mode'] = mode
plot_params['cut_coords'] = [-20.0, 0.0, 20.0]
if i == 0:
plot_params['title'] = label
else:
plot_params['title'] = None
# Generate nilearn figure
display = plot_anat(acpc_registered_img, **plot_params)
for coord, axis in display.axes.items():
axis.ax.axvline(0, lw=1)
axis.ax.axhline(0, lw=1)
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_segs(image_nii, seg_niis, out_file, bbox_nii=None, masked=False,
colors=None, compress='auto', **plot_params):
""" plot segmentation as contours over the image (e.g. anatomical).
seg_niis should be a list of files. mask_nii helps determine the cut
coordinates. plot_params will be passed on to nilearn plot_* functions. If
seg_niis is a list of size one, it behaves as if it was plotting the mask.
"""
plot_params = {} if plot_params is None else plot_params
image_nii = _3d_in_file(image_nii)
data = image_nii.get_data()
plot_params = robust_set_limits(data, plot_params)
bbox_nii = nb.load(image_nii if bbox_nii is None else bbox_nii)
if masked:
bbox_nii = nlimage.threshold_img(bbox_nii, 1e-3)
cuts = cuts_from_bbox(bbox_nii, cuts=7)
plot_params['colors'] = colors or plot_params.get('colors', None)
out_files = []
for d in plot_params.pop('dimensions', ('z', 'x', 'y')):
plot_params['display_mode'] = d
plot_params['cut_coords'] = cuts[d]
svg = _plot_anat_with_contours(image_nii, segs=seg_niis, compress=compress,
**plot_params)
# Find and replace the figure_1 id.
try:
xml_data = etree.fromstring(svg)
except etree.XMLSyntaxError as e:
NIWORKFLOWS_LOG.info(e)
return
find_text = etree.ETXPath("//{%s}g[@id='figure_1']" % SVGNS)
find_text(xml_data)[0].set('id', 'segmentation-%s-%s' % (d, uuid4()))
svg_fig = SVGFigure()
svg_fig.root = xml_data
out_files.append(svg_fig)
return out_files
def plot_denoise(lowb_nii,
highb_nii,
div_id,
plot_params=None,
highb_plot_params=None,
order=('z', 'x', 'y'),
cuts=None,
estimate_brightness=False,
label=None,
lowb_contour=None,
highb_contour=None,
compress='auto',
overlay=None,
overlay_params=None):
"""
Plot the foreground and background views.
Default order is: axial, coronal, sagittal
Updated version from sdcflows
"""
plot_params = plot_params or {}
highb_plot_params = highb_plot_params or {}
# Use default MNI cuts if none defined
if cuts is None:
raise NotImplementedError
# Do the low-b image first
out_files = []
if estimate_brightness:
plot_params = robust_set_limits(
lowb_nii.get_fdata(dtype='float32').reshape(-1), plot_params)
# Plot each cut axis for low-b
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 + ": low-b"
else:
plot_params['title'] = None
# Generate nilearn figure
display = plot_anat(lowb_nii, **plot_params)
if lowb_contour is not None:
display.add_contours(lowb_contour, linewidths=1)
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)
# Plot each cut axis for high-b
if estimate_brightness:
highb_plot_params = robust_set_limits(
highb_nii.get_fdata(dtype='float32').reshape(-1),
highb_plot_params)
for i, mode in enumerate(list(order)):
highb_plot_params['display_mode'] = mode
highb_plot_params['cut_coords'] = cuts[mode]
if i == 0:
highb_plot_params['title'] = label + ': high-b'
else:
highb_plot_params['title'] = None
# Generate nilearn figure
display = plot_anat(highb_nii, **highb_plot_params)
if highb_contour is not None:
display.add_contours(highb_contour, linewidths=1)
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'):
"""
Plots the foreground and background views
Default order is: axial, coronal, sagittal
"""
plot_params = {} if plot_params is None else plot_params
# 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)
# FreeSurfer ribbon.mgz
ribbon = contour is not None and np.array_equal(
np.unique(contour.get_data()), [0, 2, 3, 41, 42])
if ribbon:
contour_data = contour.get_data() % 39
white = nlimage.new_img_like(contour, contour_data == 2)
pial = nlimage.new_img_like(contour, contour_data >= 2)
# dual mask
dual_mask = contour is not None and np.array_equal(
np.unique(contour.get_data().astype(np.uint8)), [0, 1, 2])
if dual_mask:
contour_data = contour.get_data()
outer_mask = nlimage.new_img_like(contour, contour_data == 1)
inner_mask = nlimage.new_img_like(contour, contour_data == 2)
all_mask = nlimage.new_img_like(contour, contour_data > 0)
# 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 ribbon:
kwargs = {'levels': [0.5], 'linewidths': 0.5}
display.add_contours(white, colors='b', **kwargs)
display.add_contours(pial, colors='r', **kwargs)
elif dual_mask:
kwargs = {'levels': [0.5], 'linewidths': 0.75}
display.add_contours(inner_mask, colors='b', **kwargs)
display.add_contours(outer_mask, colors='r', **kwargs)
display.add_contours(all_mask, colors='c', **kwargs)
elif contour is not None:
display.add_contours(contour,
colors='b',
levels=[0.5],
linewidths=0.5)
svg = extract_svg(display, compress=compress)
display.close()
# Find and replace the figure_1 id.
try:
xml_data = etree.fromstring(svg)
except etree.XMLSyntaxError as e:
NIWORKFLOWS_LOG.info(e)
return
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'):
"""
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 fromfile(fname):
fig = SVGFigure()
svg_file = etree.parse(fname)
fig.root = svg_file.getroot()
return fig
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
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'):
"""
Plots the foreground and background views
Default order is: axial, coronal, sagittal
"""
plot_params = {} if plot_params is None else plot_params
# 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)
# FreeSurfer ribbon.mgz
ribbon = contour is not None and np.array_equal(
np.unique(contour.get_data()), [0, 2, 3, 41, 42])
if ribbon:
contour_data = contour.get_data() % 39
white = nlimage.new_img_like(contour, contour_data == 2)
pial = nlimage.new_img_like(contour, contour_data >= 2)
# 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 ribbon:
kwargs = {'levels': [0.5], 'linewidths': 0.5}
display.add_contours(white, colors='b', **kwargs)
display.add_contours(pial, colors='r', **kwargs)
elif contour is not None:
display.add_contours(contour, colors='b', levels=[0.5],
linewidths=0.5)
svg = extract_svg(display, compress=compress)
display.close()
# Find and replace the figure_1 id.
try:
xml_data = etree.fromstring(svg)
except etree.XMLSyntaxError as e:
NIWORKFLOWS_LOG.info(e)
return
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