def test_labeltable():
img = GiftiImage()
assert_equal(len(img.labeltable.labels), 0)
new_table = GiftiLabelTable()
new_table.labels += ['test', 'me']
img.labeltable = new_table
assert_equal(len(img.labeltable.labels), 2)
def test_labeltable():
img = GiftiImage()
assert_equal(len(img.labeltable.labels), 0)
new_table = GiftiLabelTable()
new_table.labels += ['test', 'me']
img.labeltable = new_table
assert_equal(len(img.labeltable.labels), 2)
def write_mesh(cor, tri, filename):
from nibabel.gifti import GiftiImage, GiftiDataArray
nimg = GiftiImage()
intent = 'NIFTI_INTENT_POINTSET'
nimg.add_gifti_data_array(GiftiDataArray.from_array(cor,intent))
intent = 'NIFTI_INTENT_TRIANGLE'
nimg.add_gifti_data_array(GiftiDataArray.from_array(tri,intent))
gifti.write(nimg, filename)
def write(self, surface_obj, file_path):
image_metadata = GiftiMetaData().from_dict(
surface_obj.generic_metadata)
vertices_metadata = GiftiMetaData().from_dict(
surface_obj.vertices_metadata)
triangles_metadata = GiftiMetaData().from_dict(
surface_obj.triangles_metadata)
gifti_image = GiftiImage()
gifti_image.set_metadata(image_metadata)
data = GiftiDataArray(surface_obj.vertices,
datatype='NIFTI_TYPE_FLOAT32',
intent='NIFTI_INTENT_POINTSET')
data.meta = vertices_metadata
data.coordsys = surface_obj.vertices_coord_system
gifti_image.add_gifti_data_array(data)
data = GiftiDataArray(surface_obj.triangles,
datatype='NIFTI_TYPE_INT32',
intent='NIFTI_INTENT_TRIANGLE')
data.meta = triangles_metadata
data.coordsys = None
gifti_image.add_gifti_data_array(data)
nibabel.save(gifti_image, file_path)
def test_darray_dtype_coercion_failures():
dtypes = (np.uint8, np.int32, np.int64, np.float32, np.float64)
encodings = ('ASCII', 'B64BIN', 'B64GZ')
for data_dtype, darray_dtype, encoding in itertools.product(
dtypes, dtypes, encodings):
da = GiftiDataArray(np.arange(10).astype(data_dtype),
encoding=encoding,
intent='NIFTI_INTENT_NODE_INDEX',
datatype=darray_dtype)
gii = GiftiImage(darrays=[da])
gii_copy = GiftiImage.from_bytes(gii.to_bytes())
da_copy = gii_copy.darrays[0]
assert_equal(np.dtype(da_copy.data.dtype), np.dtype(darray_dtype))
assert_array_equal(da_copy.data, da.data)
def ply2gii(in_file, metadata, out_file=None):
"""Convert from ply to GIfTI"""
from pathlib import Path
from numpy import eye
from nibabel.gifti import (
GiftiMetaData,
GiftiCoordSystem,
GiftiImage,
GiftiDataArray,
)
from pyntcloud import PyntCloud
in_file = Path(in_file)
surf = PyntCloud.from_file(str(in_file))
# Update centroid metadata
metadata.update(
zip(
("SurfaceCenterX", "SurfaceCenterY", "SurfaceCenterZ"),
["%.4f" % c for c in surf.centroid],
))
# Prepare data arrays
da = (
GiftiDataArray(
data=surf.xyz.astype("float32"),
datatype="NIFTI_TYPE_FLOAT32",
intent="NIFTI_INTENT_POINTSET",
meta=GiftiMetaData.from_dict(metadata),
coordsys=GiftiCoordSystem(xform=eye(4), xformspace=3),
),
GiftiDataArray(
data=surf.mesh.values,
datatype="NIFTI_TYPE_INT32",
intent="NIFTI_INTENT_TRIANGLE",
coordsys=None,
),
)
surfgii = GiftiImage(darrays=da)
if out_file is None:
out_file = fname_presuffix(in_file.name,
suffix=".gii",
use_ext=False,
newpath=str(Path.cwd()))
surfgii.to_filename(str(out_file))
return out_file
def run_surface_glm(dmtx, contrasts, fmri_path, subject_session_output_dir):
""" """
from nibabel.gifti import read, write, GiftiDataArray, GiftiImage
from nilearn.glm.first_level import run_glm as run_glm_nl
from nilearn.glm import compute_contrast
Y = np.array([darrays.data for darrays in read(fmri_path).darrays])
labels, res = run_glm_nl(Y, dmtx.values)
# Estimate the contrasts
print('Computing contrasts...')
side = fmri_path[-6:-4]
for index, contrast_id in enumerate(contrasts):
print(' Contrast % i out of %i: %s' %
(index + 1, len(contrasts), contrast_id))
# compute contrasts
con_ = contrasts[contrast_id]
contrast_ = compute_contrast(labels, res, con_)
stats = [
contrast_.z_score(), contrast_.stat_, contrast_.effect,
contrast_.variance
]
for map_type, out_map in zip(['z', 't', 'effects', 'variance'], stats):
map_dir = os.path.join(subject_session_output_dir,
'%s_surf' % map_type)
if not os.path.exists(map_dir):
os.makedirs(map_dir)
map_path = os.path.join(map_dir, '%s_%s.gii' % (contrast_id, side))
print("\t\tWriting %s ..." % map_path)
tex = GiftiImage(darrays=[
GiftiDataArray().from_array(out_map, intent='t test')
])
write(tex, map_path)
def elementary_contrasts_surf(con_imgs, var_imgs):
""" """
from nibabel.gifti import GiftiDataArray, GiftiImage
outputs = []
n_contrasts = 4
for i in range(n_contrasts):
con = nib.load(con_imgs[i]).darrays[0].data
var = nib.load(var_imgs[i]).darrays[0].data
effects = [
con - nib.load(con_imgs[j]).darrays[0].data
for j in range(n_contrasts) if j != i
]
variance = [
var + nib.load(var_imgs[j]).darrays[0].data
for j in range(n_contrasts) if j != i
]
fixed_con = np.array(effects).sum(0)
fixed_var = np.array(variance).sum(0)
stat = fixed_con / np.sqrt(fixed_var)
output = []
intents = ['NIFTI_INTENT_ESTIMATE', 'NIFTI_INTENT_ESTIMATE', 't test']
arrays = [fixed_con, fixed_var, stat]
for array, intent in zip(arrays, intents):
gii = GiftiImage(
darrays=[GiftiDataArray().from_array(array, intent)])
output.append(gii)
outputs.append(output)
return (outputs)
def test_data_array_round_trip():
# Test valid XML generated from new in-memory array
# See: https://github.com/nipy/nibabel/issues/469
verts = np.zeros((4, 3), np.float32)
verts[0, 0] = 10.5
verts[1, 1] = 20.5
verts[2, 2] = 30.5
vertices = GiftiDataArray(verts)
img = GiftiImage()
img.add_gifti_data_array(vertices)
bio = BytesIO()
fmap = dict(image=FileHolder(fileobj=bio))
bio.write(img.to_xml())
bio.seek(0)
gio = GiftiImage.from_file_map(fmap)
vertices = gio.darrays[0].data
assert_array_equal(vertices, verts)
def test_labeltable():
img = GiftiImage()
assert_equal(len(img.labeltable.labels), 0)
new_table = GiftiLabelTable()
new_table.labels += ['test', 'me']
img.labeltable = new_table
assert_equal(len(img.labeltable.labels), 2)
# Test deprecations
with clear_and_catch_warnings() as w:
warnings.filterwarnings('always', category=DeprecationWarning)
newer_table = GiftiLabelTable()
newer_table.labels += ['test', 'me', 'again']
img.set_labeltable(newer_table)
assert_equal(len(w), 1)
assert_equal(len(img.get_labeltable().labels), 3)
assert_equal(len(w), 2)
def extract_sub_mesh_with_files(input_mesh, center_node, radius,
output_mesh=None):
from nibabel import gifti
from nibabel.gifti import GiftiImage, GiftiDataArray
from pyhrf.tools._io import read_mesh
cor, tri, coord_sys = read_mesh(input_mesh)
sub_cor, sub_tri = extract_sub_mesh(cor, tri, center_node, radius)
#nimg = GiftiImage_fromTriangles(sub_cor, sub_tri)
nimg = GiftiImage()
intent = 'NIFTI_INTENT_POINTSET'
nimg.add_gifti_data_array(GiftiDataArray.from_array(sub_cor,intent))
intent = 'NIFTI_INTENT_TRIANGLE'
nimg.add_gifti_data_array(GiftiDataArray.from_array(sub_tri,intent))
if output_mesh is None:
output_mesh = non_existent_file(add_suffix(input_mesh, '_sub'))
pyhrf.verbose(1, 'Saving extracted mesh to %s' %output_mesh)
gifti.write(nimg, output_mesh)
return sub_cor, sub_tri, coord_sys
def smooth_data_as_texture(data, subject, hemi):
"""To smooth the data, save them as texture,
surfs2surf and extract the data """
from nibabel.gifti import read, write, GiftiImage, GiftiDataArray as gda
file_raw = '/tmp/data.gii'
file_smooth = '/tmp/smooth_data.gii'
write(GiftiImage(darrays=[gda(data=data.astype('float32'))]), file_raw)
os.system('$FREESURFER_HOME/bin/mri_surf2surf' +
' --srcsubject %s' % subject + ' --srcsurfval %s' % file_raw +
' --trgsurfval %s' % file_smooth + ' --trgsubject %s' % subject +
' --hemi %s' % hemi + ' --nsmooth-out 2')
return read(file_smooth).darrays[0].data
def mesh_from_arrays(coord, triangles, path=None):
""" Create a mesh object from two arrays
fixme: intent should be set !
"""
carray = GiftiDataArray().from_array(coord.astype(np.float32),
"NIFTI_INTENT_POINTSET")
tarray = GiftiDataArray().from_array(triangles, "NIFTI_INTENT_TRIANGLE")
img = GiftiImage(darrays=[carray, tarray])
if path is not None:
write(img, path)
return img
def ply2gii(in_file, metadata, out_file=None):
"""Convert from ply to GIfTI"""
from pathlib import Path
from numpy import eye
from nibabel.gifti import (
GiftiMetaData, GiftiCoordSystem, GiftiImage, GiftiDataArray,
)
from pyntcloud import PyntCloud
in_file = Path(in_file)
surf = PyntCloud.from_file(str(in_file))
# Update centroid metadata
metadata.update(
zip(('SurfaceCenterX', 'SurfaceCenterY', 'SurfaceCenterZ'),
['%.4f' % c for c in surf.centroid])
)
# Prepare data arrays
da = (
GiftiDataArray(
data=surf.xyz.astype('float32'),
datatype='NIFTI_TYPE_FLOAT32',
intent='NIFTI_INTENT_POINTSET',
meta=GiftiMetaData.from_dict(metadata),
coordsys=GiftiCoordSystem(xform=eye(4), xformspace=3)),
GiftiDataArray(
data=surf.mesh.values,
datatype='NIFTI_TYPE_INT32',
intent='NIFTI_INTENT_TRIANGLE',
coordsys=None))
surfgii = GiftiImage(darrays=da)
if out_file is None:
out_file = fname_presuffix(
in_file.name, suffix='.gii', use_ext=False, newpath=str(Path.cwd()))
surfgii.to_filename(str(out_file))
return out_file
def write_mesh(cor, tri, filename):
from nibabel.gifti import GiftiImage, GiftiDataArray
nimg = GiftiImage()
intent = 'NIFTI_INTENT_POINTSET'
nimg.add_gifti_data_array(GiftiDataArray.from_array(cor, intent))
intent = 'NIFTI_INTENT_TRIANGLE'
nimg.add_gifti_data_array(GiftiDataArray.from_array(tri, intent))
gifti.write(nimg, filename)
def test_gifti_image_bad_inputs():
img = GiftiImage()
# Try to set a non-data-array
assert_raises(TypeError, img.add_gifti_data_array, 'not-a-data-array')
# Try to set to non-table
def assign_labeltable(val):
img.labeltable = val
assert_raises(TypeError, assign_labeltable, 'not-a-table')
# Try to set to non-table
def assign_metadata(val):
img.meta = val
assert_raises(TypeError, assign_metadata, 'not-a-meta')
def fixed_effects_surf(con_imgs, var_imgs):
"""Idem fixed_effects_img but for surfaces"""
from nibabel import load
from nibabel.gifti import GiftiDataArray, GiftiImage
con, var = [], []
for (con_img, var_img) in zip(con_imgs, var_imgs):
con.append(np.ravel([darrays.data for darrays in load(con_img).darrays]))
var.append(np.ravel([darrays.data for darrays in load(var_img).darrays]))
outputs = []
intents = ['NIFTI_INTENT_ESTIMATE', 'NIFTI_INTENT_ESTIMATE', 't test']
arrays = fixed_effects(con, var)
for array, intent in zip(arrays, intents):
gii = GiftiImage(
darrays=[GiftiDataArray().from_array(array, intent)])
outputs.append(gii)
return outputs
def test_gifti_image():
img = GiftiImage()
assert_true(img.darrays is not None)
assert_true(img.meta is not None)
assert_true(img.labeltable is not None)
# Try to set a non-data-array
assert_raises(TypeError, img.add_gifti_data_array, 'not-a-data-array')
# Try to set to non-table
def assign_labeltable(val):
img.labeltable = val
assert_raises(TypeError, assign_labeltable, 'not-a-table')
# Try to set to non-table
def assign_metadata(val):
img.meta = val
assert_raises(TypeError, assign_metadata, 'not-a-meta')
def write_gifti(self, surface, surface_path):
gifti_image = GiftiImage()
data_array = [0 for _ in xrange(2)]
data_array[0] = surface.vertices
data_array[1] = surface.triangles
image_metadata = GiftiMetaData().from_dict(surface.image_metadata)
vertices_metadata = GiftiMetaData().from_dict(surface.vertices_metadata)
triangles_metadata = GiftiMetaData().from_dict(surface.triangles_metadata)
# TODO We currently write metadata of the old surface
gifti_image.set_metadata(image_metadata)
data = GiftiDataArray(data_array[0], datatype='NIFTI_TYPE_FLOAT32', intent='NIFTI_INTENT_POINTSET')
data.meta = vertices_metadata
data.coordsys = surface.vertices_coord_system
gifti_image.add_gifti_data_array(data)
data = GiftiDataArray(data_array[1], datatype='NIFTI_TYPE_INT32', intent='NIFTI_INTENT_TRIANGLE')
data.meta = triangles_metadata
data.coordsys = None
gifti_image.add_gifti_data_array(data)
nibabel.save(gifti_image, surface_path)
def write(self, surface_obj, file_path):
image_metadata = GiftiMetaData().from_dict(surface_obj.generic_metadata)
vertices_metadata = GiftiMetaData().from_dict(surface_obj.vertices_metadata)
triangles_metadata = GiftiMetaData().from_dict(surface_obj.triangles_metadata)
gifti_image = GiftiImage()
gifti_image.set_metadata(image_metadata)
data = GiftiDataArray(
surface_obj.vertices, datatype='NIFTI_TYPE_FLOAT32', intent='NIFTI_INTENT_POINTSET')
data.meta = vertices_metadata
data.coordsys = surface_obj.vertices_coord_system
gifti_image.add_gifti_data_array(data)
data = GiftiDataArray(
surface_obj.triangles, datatype='NIFTI_TYPE_INT32', intent='NIFTI_INTENT_TRIANGLE')
data.meta = triangles_metadata
data.coordsys = None
gifti_image.add_gifti_data_array(data)
nibabel.save(gifti_image, file_path)
def save_texture(path, data, intent='none', verbose=False):
"""
volume saving utility for textures
Parameters
----------
path, string, output image path
data, array of shape (nnode)
data to be put in the volume
intent: string, optional
intent
Fixme
-----
Missing checks
Handle the case where data is multi-dimensional ?
"""
from nibabel.gifti import write, GiftiDataArray, GiftiImage
if verbose:
print 'Warning: assuming a float32 gifti file'
darray = GiftiDataArray().from_array(data.astype(np.float32), intent)
img = GiftiImage(darrays=[darray])
write(img, path)
def test_data_array_round_trip():
# Test valid XML generated from new in-memory array
# See: https://github.com/nipy/nibabel/issues/469
verts = np.zeros((4, 3), np.float32)
verts[0, 0] = 10.5
verts[1, 1] = 20.5
verts[2, 2] = 30.5
vertices = GiftiDataArray(verts)
img = GiftiImage()
img.add_gifti_data_array(vertices)
bio = BytesIO()
fmap = dict(image=FileHolder(fileobj=bio))
bio.write(img.to_xml())
bio.seek(0)
gio = GiftiImage.from_file_map(fmap)
vertices = gio.darrays[0].data
assert_array_equal(vertices, verts)
def test_labeltable():
img = GiftiImage()
assert_equal(len(img.labeltable.labels), 0)
new_table = GiftiLabelTable()
new_table.labels += ['test', 'me']
img.labeltable = new_table
assert_equal(len(img.labeltable.labels), 2)
# Test deprecations
with clear_and_catch_warnings() as w:
warnings.filterwarnings('always', category=DeprecationWarning)
newer_table = GiftiLabelTable()
newer_table.labels += ['test', 'me', 'again']
img.set_labeltable(newer_table)
assert_equal(len(w), 1)
assert_equal(len(img.get_labeltable().labels), 3)
assert_equal(len(w), 2)
def extract_sub_mesh_with_files(input_mesh,
center_node,
radius,
output_mesh=None):
from nibabel import gifti
from nibabel.gifti import GiftiImage, GiftiDataArray
from pyhrf.tools._io import read_mesh
cor, tri, coord_sys = read_mesh(input_mesh)
sub_cor, sub_tri = extract_sub_mesh(cor, tri, center_node, radius)
#nimg = GiftiImage_fromTriangles(sub_cor, sub_tri)
nimg = GiftiImage()
intent = 'NIFTI_INTENT_POINTSET'
nimg.add_gifti_data_array(GiftiDataArray.from_array(sub_cor, intent))
intent = 'NIFTI_INTENT_TRIANGLE'
nimg.add_gifti_data_array(GiftiDataArray.from_array(sub_tri, intent))
if output_mesh is None:
output_mesh = non_existent_file(add_suffix(input_mesh, '_sub'))
logger.info('Saving extracted mesh to %s', output_mesh)
gifti.write(nimg, output_mesh)
return sub_cor, sub_tri, coord_sys
# Compute the fixed effects across sessions
# quick and dirty approach: sum z maps
if do_surface:
for hemi in ['lh', 'rh']:
z_maps = [
pjoin(work_dir, acq, 'z_surf',
'effects_interest_%s.gii' % hemi) for acq in acqs
]
mean_z = np.mean([
np.ravel([darrays.data for darrays in load(z_map).darrays])
for z_map in z_maps
], 0)
n_maps = len(z_maps)
fixed_effects = mean_z * np.sqrt(n_maps)
gii = GiftiImage(
darrays=[GiftiDataArray().from_array(fixed_effects, 't test')])
gii.to_filename(pjoin(write_dir, 'retinotopicity_%s.gii' % hemi))
fixed_effects[np.isnan(fixed_effects)] = 0
bh = fdr_threshold(fixed_effects, alpha)
print(bh)
mask = fixed_effects > bh
# todo: plot on a surface
"""
output_file = pjoin(write_dir, 'retinotopicity_%s.png' % hemi)
if hemi == 'lh':
plot_surf_stat_map(
lh_inflated, fixed_effects, bg_map=sulc_left, output_file=output_file,
hemi='left', view='medial', bg_on_data=True, darkness=1, alpha=1,
threshold=THRESHOLD)
else:
def test_gifti_image():
# Check that we're not modifying the default empty list in the default
# arguments.
gi = GiftiImage()
assert_equal(gi.darrays, [])
assert_equal(gi.meta.metadata, {})
assert_equal(gi.labeltable.labels, [])
arr = np.zeros((2, 3))
gi.darrays.append(arr)
# Now check we didn't overwrite the default arg
gi = GiftiImage()
assert_equal(gi.darrays, [])
# Test darrays / numDA
gi = GiftiImage()
assert_equal(gi.numDA, 0)
# Test from numpy numeric array
data = np.random.random((5, ))
da = GiftiDataArray(data)
gi.add_gifti_data_array(da)
assert_equal(gi.numDA, 1)
assert_array_equal(gi.darrays[0].data, data)
# Test removing
gi.remove_gifti_data_array(0)
assert_equal(gi.numDA, 0)
# Remove from empty
gi = GiftiImage()
gi.remove_gifti_data_array_by_intent(0)
assert_equal(gi.numDA, 0)
# Remove one
gi = GiftiImage()
da = GiftiDataArray(np.zeros((5, )), intent=0)
gi.add_gifti_data_array(da)
gi.remove_gifti_data_array_by_intent(3)
assert_equal(gi.numDA, 1, "data array should exist on 'missed' remove")
gi.remove_gifti_data_array_by_intent(da.intent)
assert_equal(gi.numDA, 0)
def test_gifti_round_trip():
# From section 14.4 in GIFTI Surface Data Format Version 1.0
# (with some adaptations)
test_data = b'''<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE GIFTI SYSTEM "http://www.nitrc.org/frs/download.php/1594/gifti.dtd">
<GIFTI
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:noNamespaceSchemaLocation="http://www.nitrc.org/frs/download.php/1303/GIFTI_Caret.xsd"
Version="1.0"
NumberOfDataArrays="2">
<MetaData>
<MD>
<Name><![CDATA[date]]></Name>
<Value><![CDATA[Thu Nov 15 09:05:22 2007]]></Value>
</MD>
</MetaData>
<LabelTable/>
<DataArray Intent="NIFTI_INTENT_POINTSET"
DataType="NIFTI_TYPE_FLOAT32"
ArrayIndexingOrder="RowMajorOrder"
Dimensionality="2"
Dim0="4"
Dim1="3"
Encoding="ASCII"
Endian="LittleEndian"
ExternalFileName=""
ExternalFileOffset="">
<CoordinateSystemTransformMatrix>
<DataSpace><![CDATA[NIFTI_XFORM_TALAIRACH]]></DataSpace>
<TransformedSpace><![CDATA[NIFTI_XFORM_TALAIRACH]]></TransformedSpace>
<MatrixData>
1.000000 0.000000 0.000000 0.000000
0.000000 1.000000 0.000000 0.000000
0.000000 0.000000 1.000000 0.000000
0.000000 0.000000 0.000000 1.000000
</MatrixData>
</CoordinateSystemTransformMatrix>
<Data>
10.5 0 0
0 20.5 0
0 0 30.5
0 0 0
</Data>
</DataArray>
<DataArray Intent="NIFTI_INTENT_TRIANGLE"
DataType="NIFTI_TYPE_INT32"
ArrayIndexingOrder="RowMajorOrder"
Dimensionality="2"
Dim0="4"
Dim1="3"
Encoding="ASCII"
Endian="LittleEndian"
ExternalFileName="" ExternalFileOffset="">
<Data>
0 1 2
1 2 3
0 1 3
0 2 3
</Data>
</DataArray>
</GIFTI>'''
exp_verts = np.zeros((4, 3))
exp_verts[0, 0] = 10.5
exp_verts[1, 1] = 20.5
exp_verts[2, 2] = 30.5
exp_faces = np.asarray([[0, 1, 2], [1, 2, 3], [0, 1, 3], [0, 2, 3]],
dtype=np.int32)
def _check_gifti(gio):
vertices = gio.get_arrays_from_intent('NIFTI_INTENT_POINTSET')[0].data
faces = gio.get_arrays_from_intent('NIFTI_INTENT_TRIANGLE')[0].data
assert_array_equal(vertices, exp_verts)
assert_array_equal(faces, exp_faces)
bio = BytesIO()
fmap = dict(image=FileHolder(fileobj=bio))
bio.write(test_data)
bio.seek(0)
gio = GiftiImage.from_file_map(fmap)
_check_gifti(gio)
# Write and read again
bio.seek(0)
gio.to_file_map(fmap)
bio.seek(0)
gio2 = GiftiImage.from_file_map(fmap)
_check_gifti(gio2)
def test_gifti_image():
# Check that we're not modifying the default empty list in the default
# arguments.
gi = GiftiImage()
assert_equal(gi.darrays, [])
arr = np.zeros((2, 3))
gi.darrays.append(arr)
# Now check we didn't overwrite the default arg
gi = GiftiImage()
assert_equal(gi.darrays, [])
# Test darrays / numDA
gi = GiftiImage()
assert_equal(gi.numDA, 0)
da = GiftiDataArray(data='data')
gi.add_gifti_data_array(da)
assert_equal(gi.numDA, 1)
assert_equal(gi.darrays[0].data, 'data')
gi.remove_gifti_data_array(0)
assert_equal(gi.numDA, 0)
# Remove from empty
gi = GiftiImage()
gi.remove_gifti_data_array_by_intent(0)
assert_equal(gi.numDA, 0)
# Remove one
gi = GiftiImage()
da = GiftiDataArray(data='data')
gi.add_gifti_data_array(da)
gi.remove_gifti_data_array_by_intent(0)
assert_equal(gi.numDA, 1)
gi.darrays[0].intent = 0
gi.remove_gifti_data_array_by_intent(0)
assert_equal(gi.numDA, 0)
def test_data_tag_deprecated():
img = GiftiImage()
with clear_and_catch_warnings() as w:
warnings.filterwarnings('once', category=DeprecationWarning)
data_tag(np.array([]), 'ASCII', '%i', 1)
assert_equal(len(w), 1)
def test_gifti_image():
# Check that we're not modifying the default empty list in the default
# arguments.
gi = GiftiImage()
assert_equal(gi.darrays, [])
arr = np.zeros((2, 3))
gi.darrays.append(arr)
# Now check we didn't overwrite the default arg
gi = GiftiImage()
assert_equal(gi.darrays, [])
# Test darrays / numDA
gi = GiftiImage()
assert_equal(gi.numDA, 0)
da = GiftiDataArray(data='data')
gi.add_gifti_data_array(da)
assert_equal(gi.numDA, 1)
assert_equal(gi.darrays[0].data, 'data')
gi.remove_gifti_data_array(0)
assert_equal(gi.numDA, 0)
# Remove from empty
gi = GiftiImage()
gi.remove_gifti_data_array_by_intent(0)
assert_equal(gi.numDA, 0)
# Remove one
gi = GiftiImage()
da = GiftiDataArray(data='data')
gi.add_gifti_data_array(da)
gi.remove_gifti_data_array_by_intent(0)
assert_equal(gi.numDA, 1)
gi.darrays[0].intent = 0
gi.remove_gifti_data_array_by_intent(0)
assert_equal(gi.numDA, 0)
def test_gifti_round_trip():
# From section 14.4 in GIFTI Surface Data Format Version 1.0
# (with some adaptations)
test_data = b'''<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE GIFTI SYSTEM "http://www.nitrc.org/frs/download.php/1594/gifti.dtd">
<GIFTI
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:noNamespaceSchemaLocation="http://www.nitrc.org/frs/download.php/1303/GIFTI_Caret.xsd"
Version="1.0"
NumberOfDataArrays="2">
<MetaData>
<MD>
<Name><![CDATA[date]]></Name>
<Value><![CDATA[Thu Nov 15 09:05:22 2007]]></Value>
</MD>
</MetaData>
<LabelTable/>
<DataArray Intent="NIFTI_INTENT_POINTSET"
DataType="NIFTI_TYPE_FLOAT32"
ArrayIndexingOrder="RowMajorOrder"
Dimensionality="2"
Dim0="4"
Dim1="3"
Encoding="ASCII"
Endian="LittleEndian"
ExternalFileName=""
ExternalFileOffset="">
<CoordinateSystemTransformMatrix>
<DataSpace><![CDATA[NIFTI_XFORM_TALAIRACH]]></DataSpace>
<TransformedSpace><![CDATA[NIFTI_XFORM_TALAIRACH]]></TransformedSpace>
<MatrixData>
1.000000 0.000000 0.000000 0.000000
0.000000 1.000000 0.000000 0.000000
0.000000 0.000000 1.000000 0.000000
0.000000 0.000000 0.000000 1.000000
</MatrixData>
</CoordinateSystemTransformMatrix>
<Data>
10.5 0 0
0 20.5 0
0 0 30.5
0 0 0
</Data>
</DataArray>
<DataArray Intent="NIFTI_INTENT_TRIANGLE"
DataType="NIFTI_TYPE_INT32"
ArrayIndexingOrder="RowMajorOrder"
Dimensionality="2"
Dim0="4"
Dim1="3"
Encoding="ASCII"
Endian="LittleEndian"
ExternalFileName="" ExternalFileOffset="">
<Data>
0 1 2
1 2 3
0 1 3
0 2 3
</Data>
</DataArray>
</GIFTI>'''
exp_verts = np.zeros((4, 3))
exp_verts[0, 0] = 10.5
exp_verts[1, 1] = 20.5
exp_verts[2, 2] = 30.5
exp_faces = np.asarray([[0, 1, 2], [1, 2, 3], [0, 1, 3], [0, 2, 3]],
dtype=np.int32)
def _check_gifti(gio):
vertices = gio.get_arrays_from_intent('NIFTI_INTENT_POINTSET')[0].data
faces = gio.get_arrays_from_intent('NIFTI_INTENT_TRIANGLE')[0].data
assert_array_equal(vertices, exp_verts)
assert_array_equal(faces, exp_faces)
bio = BytesIO()
fmap = dict(image=FileHolder(fileobj=bio))
bio.write(test_data)
bio.seek(0)
gio = GiftiImage.from_file_map(fmap)
_check_gifti(gio)
# Write and read again
bio.seek(0)
gio.to_file_map(fmap)
bio.seek(0)
gio2 = GiftiImage.from_file_map(fmap)
_check_gifti(gio2)
def test_gifti_image():
# Check that we're not modifying the default empty list in the default
# arguments.
gi = GiftiImage()
assert_equal(gi.darrays, [])
assert_equal(gi.meta.metadata, {})
assert_equal(gi.labeltable.labels, [])
arr = np.zeros((2, 3))
gi.darrays.append(arr)
# Now check we didn't overwrite the default arg
gi = GiftiImage()
assert_equal(gi.darrays, [])
# Test darrays / numDA
gi = GiftiImage()
assert_equal(gi.numDA, 0)
# Test from numpy numeric array
data = np.random.random((5,))
da = GiftiDataArray(data)
gi.add_gifti_data_array(da)
assert_equal(gi.numDA, 1)
assert_array_equal(gi.darrays[0].data, data)
# Test removing
gi.remove_gifti_data_array(0)
assert_equal(gi.numDA, 0)
# Remove from empty
gi = GiftiImage()
gi.remove_gifti_data_array_by_intent(0)
assert_equal(gi.numDA, 0)
# Remove one
gi = GiftiImage()
da = GiftiDataArray(np.zeros((5,)), intent=0)
gi.add_gifti_data_array(da)
gi.remove_gifti_data_array_by_intent(3)
assert_equal(gi.numDA, 1, "data array should exist on 'missed' remove")
gi.remove_gifti_data_array_by_intent(da.intent)
assert_equal(gi.numDA, 0)
def smooth_texture(mesh,
input_texture,
output_texture=None,
sigma=1,
lsigma=1.,
mask=None):
""" Smooth a texture along some mesh
parameters
----------
mesh: string,
path to gii mesh
input_texture: string,
texture path
ouput_texture: string,
smooth texture path
sigma: float,
desired amount of smoothing
lsigma: float,
approximate smoothing in one iteration
mask: string,
path of a mask texture
"""
import nipy.algorithms.graph.field as ff
G = mesh_to_graph(mesh)
if mask is not None:
mask = read(mask).darrays[0].data > 0
G = G.subgraph(mask)
add_edges = np.vstack((np.arange(G.V), np.arange(G.V))).T
edges = np.vstack((G.edges, add_edges))
weights = np.concatenate((G.weights, np.zeros(G.V)))
weights = np.maximum(np.exp(-weights**2 / (2 * lsigma**2)), 1.e-15)
f = ff.Field(G.V, edges, weights)
# need to re-order the edges
order = np.argsort(f.edges.T[0] * f.V + f.edges.T[1])
f.edges, f.weights = f.edges[order], f.weights[order]
f.normalize(0)
niter = (sigma * 1. / lsigma)**2
if input_texture[-4:] == '.tex':
import tio as tio
data = tio.Texture("").read(input_texture).data
else:
data = read(input_texture).darrays[0].data
if mask is not None:
data = data[mask]
dtype = data.dtype
data[np.isnan(data)] = 0
f.set_field(data.T)
f.diffusion(niter)
data = f.get_field().astype(dtype)
if output_texture is not None:
if output_texture[-4:] == '.tex':
import tio as tio
tio.Texture("", data=data.T).write(output_texture)
print 'tex'
else:
intent = 0
wdata = data
if mask is not None:
wdata = mask.astype(np.float)
wdata[mask > 0] = data
darray = GiftiDataArray().from_array(wdata.astype(np.float32),
intent)
img = GiftiImage(darrays=[darray])
write(img, output_texture)
return data
def project_volume(work_dir, subject, do_bbr=True):
# first find the session where T1w and T2w files could be
ref_file = sorted(
glob.glob(
os.path.join(work_dir, subject, 'ses-*', 'anat',
'*-highres_T1w.nii*')))[-1]
# session = ref_file.split('/')[-3]
anat_dir = os.path.dirname(ref_file)
write_dir = os.path.join(anat_dir, 'analysis')
if not os.path.exists(write_dir):
os.mkdir(write_dir)
os.environ['SUBJECTS_DIR'] = anat_dir
data = {}
for modality in ['T1w', 'T2w']:
if modality == ['T1w']:
image = ref_file
else:
image = sorted(
glob.glob(
os.path.join(work_dir, subject, 'ses-*', 'anat',
'*-highres_T2w.nii*')))[-1]
image_ = closing(image)
# --------------------------------------------------------------------
# run the projection using freesurfer
print("image", image)
basename = os.path.basename(image).split('.')[0]
if modality == 'T1w':
bbreg = BBRegister(subject_id=subject,
source_file=image,
init='header',
contrast_type='t1')
else:
# use BBR registration to finesse the coregistration
bbreg = BBRegister(subject_id=subject,
source_file=image,
init='header',
contrast_type='t2')
regheader = os.path.join(anat_dir,
basename + '_bbreg_%s.dat' % subject)
bbreg.run()
if 1:
# output names
# the .gii files will be put in the same directory as the input
left_tex = os.path.join(write_dir, basename + '_lh.gii')
right_tex = os.path.join(write_dir, basename + '_rh.gii')
# run freesrufer command for projection
os.system(
'$FREESURFER_HOME/bin/mri_vol2surf --src %s --o %s '
'--out_type gii --srcreg %s --hemi lh --projfrac-avg 0 1 0.1' %
(image_, left_tex, regheader))
os.system(
'$FREESURFER_HOME/bin/mri_vol2surf --src %s --o %s '
'--out_type gii --srcreg %s --hemi rh --projfrac-avg 0 1 0.1' %
(image_, right_tex, regheader))
# resample to fsaverage
left_smooth_tex = os.path.join(write_dir,
basename + '_fsaverage_lh.gii')
right_smooth_tex = os.path.join(write_dir,
basename + '_fsaverage_rh.gii')
os.system('$FREESURFER_HOME/bin/mri_surf2surf --srcsubject %s '
'--srcsurfval %s --trgsurfval %s --trgsubject ico '
'--trgicoorder 7 --hemi lh' %
(subject, left_tex, left_smooth_tex))
os.system('$FREESURFER_HOME/bin/mri_surf2surf --srcsubject %s '
'--srcsurfval %s --trgsubject ico --trgicoorder 7 '
'--trgsurfval %s --hemi rh' %
(subject, right_tex, right_smooth_tex))
data[modality] = {
'lh': nib.load(left_smooth_tex).darrays[0].data,
'rh': nib.load(right_smooth_tex).darrays[0].data
}
else:
from surfer import project_volume_data
data[modality] = {}
for hemi in ['lh', 'rh']:
data_ = project_volume_data(image_,
hemi,
regheader,
projarg=[0, 1., .1],
smooth_fwhm=0)
data[modality][hemi] = data_
# reset subject_dir to set fsaverage
os.environ['SUBJECTS_DIR'] = os.path.join(work_dir, subject, 'ses-00',
'anat')
for hemi in ['lh', 'rh']:
ratio = data['T1w'][hemi] / data['T2w'][hemi]
from nibabel.gifti import write, GiftiImage, GiftiDataArray as gda
file_ratio = os.path.join(write_dir, 't1_t2_ratio_%s.gii' % hemi)
write(GiftiImage(darrays=[gda(data=ratio.astype('float32'))]),
file_ratio)
"""