diff --git a/fsl/data/cifti.py b/fsl/data/cifti.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1521d56f3c2fe798a8b8c5fc2830cc7c966c155
--- /dev/null
+++ b/fsl/data/cifti.py
@@ -0,0 +1,493 @@
+"""
+Provides a sparse representation of volumetric and/or surface data
+
+The data can be either defined per voxel/vertex (:class:`DenseCifti`) or per parcel (`class:`ParcelCifti`).
+
+The data can be read from NIFTI, GIFTI, or CIFTI files.
+Non-sparse volumetric or surface representations can be extracte.
+"""
+from nibabel.cifti2 import cifti2_axes
+from typing import Sequence, Optional, Union
+import numpy as np
+from fsl.data import image
+import nibabel as nib
+from fsl.utils.path import addExt
+
+
+dense_extensions = {
+ cifti2_axes.BrainModelAxis: '.dconn.nii',
+ cifti2_axes.ParcelsAxis: '.dpconn.nii',
+ cifti2_axes.SeriesAxis: '.dtseries.nii',
+ cifti2_axes.ScalarAxis: '.dscalar.nii',
+ cifti2_axes.LabelAxis: '.dlabel.nii',
+}
+
+parcel_extensions = {
+ cifti2_axes.BrainModelAxis: '.pdconn.nii',
+ cifti2_axes.ParcelsAxis: '.pconn.nii',
+ cifti2_axes.SeriesAxis: '.ptseries.nii',
+ cifti2_axes.ScalarAxis: '.pscalar.nii',
+ cifti2_axes.LabelAxis: '.plabel.nii',
+}
+
+
+class Cifti:
+ """
+ Parent class for the two types of CIFTI files.
+
+ The type of the CIFTI file is determined by the last axis, which can be one of:
+
+ - :py:class:`BrainModelAxis <cifti2_axes.BrainModelAxis>`
+ - :py:class:`ParcelsAxis <cifti2_axes.ParcelsAxis>`
+ """
+ def __init__(self, arr: np.ndarray, axes: Sequence[Optional[cifti2_axes.Axis]]):
+ """
+ Defines a new dataset in greyordinate space
+
+ :param data: (..., N) array for N greyordinates or parcels; can contain Nones for undefined axes
+ :param axes: sequence of CIFTI axes describing the data along each dimension
+ """
+ self.arr = arr
+ axes = tuple(axes)
+ while self.arr.ndim > len(axes):
+ axes = (None, ) + axes
+ self.axes = axes
+ if not all(ax is None or len(ax) == sz for ax, sz in zip(axes, self.arr.shape)):
+ raise ValueError(f"Shape of axes {tuple(-1 if ax is None else len(ax) for ax in axes)} does not "
+ f"match shape of array {self.arr.shape}")
+
+ def to_cifti(self, default_axis=None):
+ """
+ Create a CIFTI image from the data
+
+ :param default_axis: What to use as an axis along any undefined dimensions
+
+ - By default an error is raised
+ - if set to "scalar" a ScalarAxis is used with names of "default {index}"
+ - if set to "series" a SeriesAxis is used
+
+ :return: nibabel CIFTI image
+ """
+ if any(ax is None for ax in self.axes):
+ if default_axis is None:
+ raise ValueError("Can not store to CIFTI without defining what is stored along each dimension")
+ elif default_axis == 'scalar':
+ def get_axis(n: int):
+ return cifti2_axes.ScalarAxis([f'default {idx + 1}' for idx in range(n)])
+ elif default_axis == 'series':
+ def get_axis(n: int):
+ return cifti2_axes.SeriesAxis(0, 1, n)
+ else:
+ raise ValueError(f"default_axis should be set to None, 'scalar', or 'series', not {default_axis}")
+ new_axes = [
+ get_axis(sz) if ax is None else ax
+ for ax, sz in zip(self.axes, self.arr.shape)
+ ]
+ else:
+ new_axes = list(self.axes)
+
+ data = self.arr
+ if data.ndim == 1:
+ # CIFTI axes are always at least 2D
+ data = data[None, :]
+ new_axes.insert(0, cifti2_axes.ScalarAxis(['default']))
+
+ return nib.Cifti2Image(data, header=new_axes)
+
+ @classmethod
+ def from_cifti(cls, filename, writable=False):
+ """
+ Creates new greyordinate object from dense CIFTI file
+
+ :param filename: CIFTI filename or :class:`nib.Cifti2Image` object
+ :param writable: if True, opens data array in writable mode
+ """
+ if isinstance(filename, str):
+ img = nib.load(filename)
+ else:
+ img = filename
+
+ if not isinstance(img, nib.Cifti2Image):
+ raise ValueError(f"Input {filename} should be CIFTI filename or nibabel Cifti2Image")
+
+ if writable:
+ data = np.memmap(filename, img.dataobj.dtype, mode='r+',
+ offset=img.dataobj.offset, shape=img.shape, order='F')
+ else:
+ data = np.asanyarray(img.dataobj)
+
+ axes = [img.header.get_axis(idx) for idx in range(data.ndim)]
+
+ if isinstance(axes[-1], cifti2_axes.BrainModelAxis):
+ return DenseCifti(data, axes)
+ elif isinstance(axes[-1], cifti2_axes.ParcelsAxis):
+ return ParcelCifti(data, axes)
+ raise ValueError("Last axis of CIFTI object should be a BrainModelAxis or ParcelsAxis")
+
+ def save(self, cifti_filename, default_axis=None):
+ """
+ Writes this sparse representation to/from a filename
+
+ :param cifti_filename: output filename
+ :param default_axis: What to use as an axis along any undefined dimensions
+
+ - By default an error is raised
+ - if set to "scalar" a ScalarAxis is used with names of "default {index}"
+ - if set to "series" a SeriesAxis is used
+ :return:
+ """
+ self.to_cifti(default_axis).to_filename(addExt(cifti_filename, defaultExt=self.extension, mustExist=False))
+
+ @classmethod
+ def from_gifti(cls, filename, mask_values=(0, np.nan)):
+ """
+ Creates a new greyordinate object from a GIFTI file
+
+ :param filename: GIFTI filename
+ :param mask_values: values to mask out
+ :return: greyordinate object representing the unmasked vertices
+ """
+ if isinstance(filename, str):
+ img = nib.load(filename)
+ else:
+ img = filename
+ datasets = [darr.data for darr in img.darrays]
+ if len(datasets) == 1:
+ data = datasets[0]
+ else:
+ data = np.concatenate(
+ [np.atleast_2d(d) for d in datasets], axis=0
+ )
+ mask = np.ones(data.shape, dtype='bool')
+ for value in mask_values:
+ if value is np.nan:
+ mask &= ~np.isnan(data)
+ else:
+ mask &= ~(data == value)
+ while mask.ndim > 1:
+ mask = mask.any(0)
+
+ anatomy = BrainStructure.from_gifti(img)
+
+ bm_axes = cifti2_axes.BrainModelAxis.from_mask(mask, name=anatomy.cifti)
+ return DenseCifti(data[..., mask], [bm_axes])
+
+ @classmethod
+ def from_image(cls, input, mask_values=(np.nan, 0)):
+ """
+ Creates a new greyordinate object from a NIFTI file
+
+ :param input: FSL :class:`image.Image` object
+ :param mask_values: which values to mask out
+ :return: greyordinate object representing the unmasked voxels
+ """
+ img = image.Image(input)
+
+ mask = np.ones(img.data.shape, dtype='bool')
+ for value in mask_values:
+ if value is np.nan:
+ mask &= ~np.isnan(img.data)
+ else:
+ mask &= ~(img.data == value)
+ while mask.ndim > 3:
+ mask = mask.any(-1)
+ if np.sum(mask) == 0:
+ raise ValueError("No unmasked voxels found in NIFTI image")
+
+ inverted_data = np.transpose(img.data[mask], tuple(range(1, img.data.ndim - 2)) + (0, ))
+ bm_axes = cifti2_axes.BrainModelAxis.from_mask(mask, affine=img.nibImage.affine)
+ return DenseCifti(inverted_data, [bm_axes])
+
+
+class DenseCifti(Cifti):
+ """
+ Represents sparse data defined for a subset of voxels and vertices (i.e., greyordinates)
+ """
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ if not isinstance(self.brain_model_axis, cifti2_axes.BrainModelAxis):
+ raise ValueError(f"DenseCifti expects a BrainModelAxis as last axes object, not {type(self.brain_model_axis)}")
+
+ @property
+ def brain_model_axis(self, ) -> cifti2_axes.BrainModelAxis:
+ return self.axes[-1]
+
+ @property
+ def extension(self, ):
+ if self.arr.ndim == 1:
+ return dense_extensions[cifti2_axes.ScalarAxis]
+ return dense_extensions[type(self.axes[-2])]
+
+ def to_image(self, fill=0) -> image.Image:
+ """
+ Get the volumetric data as an :class:`image.Image`
+ """
+ if self.brain_model_axis.volume_mask.sum() == 0:
+ raise ValueError(f"Can not create volume without voxels in {self}")
+ data = np.full(self.brain_model_axis.volume_shape + self.arr.shape[:-1], fill,
+ dtype=self.arr.dtype)
+ voxels = self.brain_model_axis.voxel[self.brain_model_axis.volume_mask]
+ data[tuple(voxels.T)] = np.transpose(self.arr, (-1,) + tuple(range(self.arr.ndim - 1)))[
+ self.brain_model_axis.volume_mask]
+ return image.Image(data, xform=self.brain_model_axis.affine)
+
+ def surface(self, anatomy, fill=np.nan, partial=False):
+ """
+ Gets a specific surface
+
+ If `partial` is True a view of the data rather than a copy is returned.
+
+ :param anatomy: BrainStructure or string like 'CortexLeft' or 'CortexRight'
+ :param fill: which value to fill the array with if not all vertices are defined
+ :param partial: only return the part of the surface defined in the greyordinate file (ignores `fill` if set)
+ :return:
+ - if not partial: (..., n_vertices) array
+ - if partial: tuple with (N, ) int array with indices on the surface included in (..., N) array
+ """
+ if isinstance(anatomy, str):
+ anatomy = BrainStructure.from_string(anatomy, issurface=True)
+ if anatomy.cifti not in self.brain_model_axis.name:
+ raise ValueError(f"No surface data for {anatomy.cifti} found")
+ slc, bm = None, None
+ arr = np.full(self.arr.shape[:-1] + (self.brain_model_axis.nvertices[anatomy.cifti],), fill,
+ dtype=self.arr.dtype)
+ for name, slc_try, bm_try in self.brain_model_axis.iter_structures():
+ if name == anatomy.cifti:
+ if partial:
+ if bm is not None:
+ raise ValueError(f"Surface {anatomy} does not form a contiguous block")
+ slc, bm = slc_try, bm_try
+ else:
+ arr[..., bm_try.vertex] = self.arr[..., slc_try]
+ if not partial:
+ return arr
+ else:
+ return bm.vertex, self.arr[..., slc]
+
+
+class ParcelCifti(Cifti):
+ """
+ Represents sparse data defined at specific parcels
+ """
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ if not isinstance(self.parcel_axis, cifti2_axes.ParcelsAxis):
+ raise ValueError(f"ParcelCifti expects a ParcelsAxis as last axes object, not {type(self.parcel_axis)}")
+
+ @property
+ def extension(self, ):
+ if self.arr.ndim == 1:
+ return parcel_extensions[cifti2_axes.ScalarAxis]
+ return parcel_extensions[type(self.axes[-2])]
+
+ @property
+ def parcel_axis(self, ) -> cifti2_axes.ParcelsAxis:
+ return self.axes[-1]
+
+ def to_image(self, fill=0):
+ """
+ Get the volumetric data as an :class:`Image`
+ """
+ data = np.full(self.parcel_axis.volume_shape + self.arr.shape[:-1], fill, dtype=self.arr.dtype)
+ written = np.zeros(self.parcel_axis.volume_shape, dtype='bool')
+ for idx, write_to in enumerate(self.parcel_axis.voxels):
+ if written[tuple(write_to.T)].any():
+ raise ValueError("Duplicate voxels in different parcels")
+ data[tuple(write_to.T)] = self.arr[np.newaxis, ..., idx]
+ written[tuple(write_to.T)] = True
+ if not written.any():
+ raise ValueError("Parcellation does not contain any volumetric data")
+ return image.Image(data, xform=self.parcel_axis.affine)
+
+ def surface(self, anatomy, fill=np.nan, partial=False):
+ """
+ Gets a specific surface
+
+ :param anatomy: BrainStructure or string like 'CortexLeft' or 'CortexRight'
+ :param fill: which value to fill the array with if not all vertices are defined
+ :param partial: only return the part of the surface defined in the greyordinate file (ignores `fill` if set)
+ :return:
+ - if not partial: (..., n_vertices) array
+ - if partial: tuple with (N, ) int array with indices on the surface included in (..., N) array
+ """
+ if isinstance(anatomy, str):
+ anatomy = BrainStructure.from_string(anatomy, issurface=True)
+ if anatomy.cifti not in self.parcel_axis.nvertices:
+ raise ValueError(f"No surface data for {anatomy.cifti} found")
+
+ arr = np.full(self.arr.shape[:-1] + (self.parcel_axis.nvertices[anatomy.cifti],), fill,
+ dtype=self.arr.dtype)
+ written = np.zeros(self.parcel_axis.nvertices[anatomy.cifti], dtype='bool')
+ for idx, vertices in enumerate(self.parcel_axis.vertices):
+ if anatomy.cifti not in vertices:
+ continue
+ write_to = vertices[anatomy.cifti]
+ if written[write_to].any():
+ raise ValueError("Duplicate vertices in different parcels")
+ arr[..., write_to] = self.arr[..., idx, np.newaxis]
+ written[write_to] = True
+
+ if not partial:
+ return arr
+ else:
+ return np.where(written)[0], arr[..., written]
+
+
+class BrainStructure(object):
+ """Which brain structure does the parent object describe?
+
+ Supports how brain structures are stored in both GIFTI and CIFTI files
+ """
+ def __init__(self, primary, secondary=None, hemisphere='both', geometry=None):
+ """Creates a new brain structure
+
+ :param primary: Name of the brain structure (e.g. cortex, thalamus)
+ :param secondary: Further specification of which part of the brain structure is described (e.g. 'white' or
+ 'pial' for the cortex)
+ :param hemisphere: which hemisphere is the brain structure in ('left', 'right', or 'both')
+ :param geometry: does the parent object describe the 'volume' or the 'surface'
+ """
+ self.primary = primary.lower()
+ self.secondary = None if secondary is None else secondary.lower()
+ self.hemisphere = hemisphere.lower()
+ if geometry not in (None, 'surface', 'volume'):
+ raise ValueError(f"Invalid value for geometry: {geometry}")
+ self.geometry = geometry
+
+ def __eq__(self, other):
+ """Two brain structures are equal if they could describe the same structure
+ """
+ if isinstance(other, str):
+ other = self.from_string(other)
+ match_primary = (self.primary == other.primary or self.primary == 'all' or other.primary == 'all' or
+ self.primary == other.geometry or self.geometry == other.primary)
+ match_hemisphere = self.hemisphere == other.hemisphere
+ match_secondary = (self.secondary is None or other.secondary is None or self.secondary == other.secondary)
+ match_geometry = (self.geometry is None or other.geometry is None or self.geometry == other.geometry)
+ return match_primary and match_hemisphere and match_secondary and match_geometry
+
+ @property
+ def gifti(self, ):
+ """Returns the keywords needed to define the surface in the meta information of a GIFTI file
+ """
+ main = self.primary.capitalize() + ('' if self.hemisphere == 'both' else self.hemisphere.capitalize())
+ res = {'AnatomicalStructurePrimary': main}
+ if self.secondary is not None:
+ res['AnatomicalStructureSecondary'] = self.secondary.capitalize()
+ return res
+
+ def __str__(self, ):
+ """Returns a short description of the brain structure
+ """
+ if self.secondary is None:
+ return self.primary.capitalize() + self.hemisphere.capitalize()
+ else:
+ return "%s%s(%s)" % (self.primary.capitalize(), self.hemisphere.capitalize(), self.secondary)
+
+ @property
+ def cifti(self, ):
+ """Returns a description of the brain structure needed to define the surface in a CIFTI file
+ """
+ return 'CIFTI_STRUCTURE_' + self.primary.upper() + ('' if self.hemisphere == 'both' else ('_' + self.hemisphere.upper()))
+
+ @classmethod
+ def from_string(cls, value, issurface=None):
+ """Parses a string to find out which brain structure is being described
+
+ :param value: string to be parsed
+ :param issurface: defines whether the object describes the volume or surface of the brain structure (default: surface if the brain structure is the cortex volume otherwise)
+ """
+ if '_' in value:
+ items = [val.lower() for val in value.split('_')]
+ if items[-1] in ['left', 'right', 'both']:
+ hemisphere = items[-1]
+ others = items[:-1]
+ elif items[0] in ['left', 'right', 'both']:
+ hemisphere = items[0]
+ others = items[1:]
+ else:
+ hemisphere = 'both'
+ others = items
+ if others[0] in ['nifti', 'cifti', 'gifti']:
+ others = others[2:]
+ primary = '_'.join(others)
+ else:
+ low = value.lower()
+ if 'left' == low[-4:]:
+ hemisphere = 'left'
+ primary = low[:-4]
+ elif 'right' == low[-5:]:
+ hemisphere = 'right'
+ primary = low[:-5]
+ elif 'both' == low[-4:]:
+ hemisphere = 'both'
+ primary = low[:-4]
+ else:
+ hemisphere = 'both'
+ primary = low
+ if issurface is None:
+ issurface = primary == 'cortex'
+ if primary == '':
+ primary = 'all'
+ return cls(primary, None, hemisphere, 'surface' if issurface else 'volume')
+
+ @classmethod
+ def from_gifti(cls, gifti_obj):
+ """
+ Extracts the brain structure from a GIFTI object
+ """
+ primary_str = 'AnatomicalStructurePrimary'
+ secondary_str = 'AnatomicalStructureSecondary'
+ primary = "other"
+ secondary = None
+ for meta in [gifti_obj] + gifti_obj.darrays:
+ if primary_str in meta.meta.metadata:
+ primary = meta.meta.metadata[primary_str]
+ if secondary_str in meta.meta.metadata:
+ secondary = meta.meta.metadata[secondary_str]
+ anatomy = cls.from_string(primary, issurface=True)
+ anatomy.secondary = None if secondary is None else secondary.lower()
+ return anatomy
+
+
+def load(filename, mask_values=(0, np.nan), writable=False) -> Union[DenseCifti, ParcelCifti]:
+ """
+ Reads CIFTI data from the given file
+
+ File can be:
+
+ - NIFTI file
+ - GIFTI file
+ - CIFTI file
+
+ :param filename: input filename
+ :param mask_values: which values are outside of the mask for NIFTI or GIFTI input
+ :param writable: allow to write to disk
+ :return: appropriate CIFTI sub-class (parcellated or dense)
+ """
+ possible_extensions = (
+ tuple(dense_extensions.values()) +
+ tuple(parcel_extensions.values()) +
+ tuple(image.ALLOWED_EXTENSIONS) +
+ ('.shape.gii', '.gii')
+ )
+ if isinstance(filename, str):
+ filename = addExt(filename, possible_extensions, fileGroups=image.FILE_GROUPS)
+ img = nib.load(filename)
+ else:
+ img = filename
+
+ if isinstance(img, nib.Cifti2Image):
+ return Cifti.from_cifti(img, writable=writable)
+ if isinstance(img, nib.GiftiImage):
+ if writable:
+ raise ValueError("Can not open GIFTI file in writable mode")
+ return Cifti.from_gifti(img, mask_values)
+ try:
+ vol_img = image.Image(img)
+ except ValueError:
+ raise ValueError(f"I do not know how to convert {type(img)} into greyordinates (from {filename})")
+ if writable:
+ raise ValueError("Can not open NIFTI file in writable mode")
+ return Cifti.from_image(vol_img, mask_values)
+
diff --git a/tests/test_cifti.py b/tests/test_cifti.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0b758f2bd1ff6baec7c9eece4023795583e5340
--- /dev/null
+++ b/tests/test_cifti.py
@@ -0,0 +1,264 @@
+from fsl.data import cifti
+import os.path as op
+import numpy as np
+import nibabel as nib
+from numpy import testing
+import tests
+from nibabel.cifti2 import cifti2_axes
+
+
+def volumetric_brain_model():
+ mask = np.random.randint(2, size=(10, 10, 10)) > 0
+ return cifti2_axes.BrainModelAxis.from_mask(mask, affine=np.eye(4))
+
+
+def surface_brain_model():
+ mask = np.random.randint(2, size=100) > 0
+ return cifti2_axes.BrainModelAxis.from_mask(mask, name='cortex')
+
+
+def volumetric_parcels(return_mask=False):
+ mask = np.random.randint(5, size=(10, 10, 10))
+ axis = cifti2_axes.ParcelsAxis(
+ [f'vol_{idx}' for idx in range(1, 5)],
+ voxels=[np.stack(np.where(mask == idx), axis=-1) for idx in range(1, 5)],
+ vertices=[{} for _ in range(1, 5)],
+ volume_shape=mask.shape,
+ affine=np.eye(4),
+ )
+ if return_mask:
+ return axis, mask
+ else:
+ return axis
+
+
+def surface_parcels(return_mask=False):
+ mask = np.random.randint(5, size=100)
+ axis = cifti2_axes.ParcelsAxis(
+ [f'surf_{idx}' for idx in range(1, 5)],
+ voxels=[np.zeros((0, 3), dtype=int) for _ in range(1, 5)],
+ vertices=[{'CIFTI_STRUCTURE_CORTEX': np.where(mask == idx)[0]} for idx in range(1, 5)],
+ nvertices={'CIFTI_STRUCTURE_CORTEX': 100},
+ )
+ if return_mask:
+ return axis, mask
+ else:
+ return axis
+
+
+def gen_data(axes):
+ return np.random.randn(*(5 if ax is None else len(ax) for ax in axes))
+
+
+def test_read_gifti():
+ testdir = op.join(op.dirname(__file__), 'testdata')
+
+ shapefile = op.join(testdir, 'example.shape.gii')
+ ref_data = nib.load(shapefile).darrays[0].data
+
+ data = cifti.load(shapefile)
+ assert isinstance(data, cifti.DenseCifti)
+ assert data.arr.shape == (642, )
+ testing.assert_equal(data.arr, ref_data)
+ testing.assert_equal(data.brain_model_axis.vertex, np.arange(642))
+ assert len(data.brain_model_axis.nvertices) == 1
+ assert data.brain_model_axis.nvertices['CIFTI_STRUCTURE_OTHER'] == 642
+
+ data = cifti.load(shapefile, mask_values=(ref_data[0], ))
+ assert isinstance(data, cifti.DenseCifti)
+ assert data.arr.shape == (np.sum(ref_data != ref_data[0]), )
+ testing.assert_equal(data.arr, ref_data[ref_data != ref_data[0]])
+ testing.assert_equal(data.brain_model_axis.vertex, np.where(ref_data != ref_data[0])[0])
+ assert len(data.brain_model_axis.nvertices) == 1
+ assert data.brain_model_axis.nvertices['CIFTI_STRUCTURE_OTHER'] == 642
+
+ cifti.load(op.join(testdir, 'example'))
+
+
+def test_read_nifti():
+ mask = np.random.randint(2, size=(10, 10, 10)) > 0
+ values = np.random.randn(10, 10, 10)
+ for mask_val in (0, np.nan):
+ values[~mask] = mask_val
+ affine = np.concatenate((np.random.randn(3, 4), np.array([0, 0, 0, 1])[None, :]), axis=0)
+ with tests.testdir():
+ nib.Nifti1Image(values, affine).to_filename("masked_image.nii.gz")
+ data = cifti.load("masked_image")
+ assert isinstance(data, cifti.DenseCifti)
+ testing.assert_equal(data.arr, values[mask])
+ testing.assert_allclose(data.brain_model_axis.affine, affine)
+ assert len(data.brain_model_axis.nvertices) == 0
+
+
+def check_io(data: cifti.Cifti, extension):
+ with tests.testdir():
+ data.save("test")
+ assert op.isfile(f'test.{extension}.nii')
+ loaded = cifti.load("test")
+ if data.arr.ndim == 1:
+ testing.assert_equal(data.arr, loaded.arr[0])
+ assert data.axes == loaded.axes[1:]
+ else:
+ testing.assert_equal(data.arr, loaded.arr)
+ assert data.axes == loaded.axes
+
+
+def test_io_cifti():
+ for cifti_class, cifti_type, main_axis_options in (
+ (cifti.DenseCifti, 'd', (volumetric_brain_model(), surface_brain_model(),
+ volumetric_brain_model() + surface_brain_model())),
+ (cifti.ParcelCifti, 'p', (volumetric_parcels(), surface_parcels(),
+ volumetric_parcels() + surface_parcels())),
+ ):
+ for main_axis in main_axis_options:
+ with tests.testdir():
+ data_1d = cifti_class(gen_data([main_axis]), [main_axis])
+ check_io(data_1d, f'{cifti_type}scalar')
+
+ connectome = cifti_class(gen_data([main_axis, main_axis]), (main_axis, main_axis))
+ check_io(connectome, f'{cifti_type}conn')
+
+ scalar_axis = cifti2_axes.ScalarAxis(['A', 'B', 'C'])
+ scalar = cifti_class(gen_data([scalar_axis, main_axis]), (scalar_axis, main_axis))
+ check_io(scalar, f'{cifti_type}scalar')
+
+ label_axis = cifti2_axes.LabelAxis(['A', 'B', 'C'], {1: ('some parcel', (1, 0, 0, 1))})
+ label = cifti_class(gen_data([label_axis, main_axis]), (label_axis, main_axis))
+ check_io(label, f'{cifti_type}label')
+
+ series_axis = cifti2_axes.SeriesAxis(10, 3, 50, unit='HERTZ')
+ series = cifti_class(gen_data([series_axis, main_axis]), (series_axis, main_axis))
+ check_io(series, f'{cifti_type}tseries')
+
+ if cifti_type == 'd':
+ parcel_axis = surface_parcels()
+ dpconn = cifti_class(gen_data([parcel_axis, main_axis]), (parcel_axis, main_axis))
+ check_io(dpconn, 'dpconn')
+ else:
+ dense_axis = surface_brain_model()
+ pdconn = cifti_class(gen_data([dense_axis, main_axis]), (dense_axis, main_axis))
+ check_io(pdconn, 'pdconn')
+
+
+def test_extract_dense():
+ vol_bm = volumetric_brain_model()
+ surf_bm = surface_brain_model()
+ for bm in (vol_bm + surf_bm, surf_bm + vol_bm):
+ for ndim, no_other_axis in ((1, True), (2, False), (2, True)):
+ if ndim == 1:
+ data = cifti.DenseCifti(gen_data([bm]), [bm])
+ else:
+ scl = cifti2_axes.ScalarAxis(['A', 'B', 'C'])
+ data = cifti.DenseCifti(gen_data([scl, bm]),
+ [None if no_other_axis else scl, bm])
+
+ # extract volume
+ ref_arr = data.arr[..., data.brain_model_axis.volume_mask]
+ vol_image = data.to_image(fill=np.nan)
+ if ndim == 1:
+ assert vol_image.shape == data.brain_model_axis.volume_shape
+ else:
+ assert vol_image.shape == data.brain_model_axis.volume_shape + (3, )
+ assert np.isfinite(vol_image.data).sum() == len(vol_bm) * (3 if ndim == 2 else 1)
+ testing.assert_equal(vol_image.data[tuple(vol_bm.voxel.T)], ref_arr.T)
+
+ from_image = cifti.DenseCifti.from_image(vol_image)
+ assert from_image.brain_model_axis == vol_bm
+ testing.assert_equal(from_image.arr, ref_arr)
+
+ # extract surface
+ ref_arr = data.arr[..., data.brain_model_axis.surface_mask]
+ mask, surf_data = data.surface('cortex', partial=True)
+ assert surf_data.shape[-1] < 100
+ testing.assert_equal(ref_arr, surf_data)
+ testing.assert_equal(surf_bm.vertex, mask)
+
+ surf_data_full = data.surface('cortex', fill=np.nan)
+ assert surf_data_full.shape[-1] == 100
+ mask_full = np.isfinite(surf_data_full)
+ if ndim == 2:
+ assert (mask_full.any(0) == mask_full.all(0)).all()
+ mask_full = mask_full[0]
+ assert mask_full.sum() == len(surf_bm)
+ assert mask_full[..., mask].sum() == len(surf_bm)
+ testing.assert_equal(surf_data_full[..., mask_full], ref_arr)
+
+
+def test_extract_parcel():
+ vol_parcel, vol_mask = volumetric_parcels(return_mask=True)
+ surf_parcel, surf_mask = surface_parcels(return_mask=True)
+ parcel = vol_parcel + surf_parcel
+ for ndim, no_other_axis in ((1, True), (2, False), (2, True)):
+ if ndim == 1:
+ data = cifti.ParcelCifti(gen_data([parcel]), [parcel])
+ else:
+ scl = cifti2_axes.ScalarAxis(['A', 'B', 'C'])
+ data = cifti.ParcelCifti(gen_data([scl, parcel]),
+ [None if no_other_axis else scl, parcel])
+
+ # extract volume
+ vol_image = data.to_image(fill=np.nan)
+ if ndim == 1:
+ assert vol_image.shape == data.parcel_axis.volume_shape
+ else:
+ assert vol_image.shape == data.parcel_axis.volume_shape + (3, )
+ assert np.isfinite(vol_image.data).sum() == np.sum(vol_mask != 0) * (3 if ndim == 2 else 1)
+ if ndim == 1:
+ testing.assert_equal(vol_mask != 0, np.isfinite(vol_image.data))
+ for idx in range(1, 5):
+ testing.assert_allclose(vol_image.data[vol_mask == idx], data.arr[..., idx - 1])
+ else:
+ for idx in range(3):
+ testing.assert_equal(vol_mask != 0, np.isfinite(vol_image.data[..., idx]))
+ for idx2 in range(1, 5):
+ testing.assert_allclose(vol_image.data[vol_mask == idx2, idx], data.arr[idx, idx2 - 1])
+
+ # extract surface
+ mask, surf_data = data.surface('cortex', partial=True)
+ assert surf_data.shape[-1] == (surf_mask != 0).sum()
+ assert (surf_mask[mask] != 0).all()
+ print(data.arr)
+ for idx in range(1, 5):
+ if ndim == 1:
+ testing.assert_equal(surf_data.T[surf_mask[mask] == idx], data.arr[idx + 3])
+ else:
+ for idx2 in range(3):
+ testing.assert_equal(surf_data.T[surf_mask[mask] == idx, idx2], data.arr[idx2, idx + 3])
+
+ surf_data_full = data.surface('cortex', partial=False)
+ assert surf_data_full.shape[-1] == 100
+ if ndim == 1:
+ testing.assert_equal(np.isfinite(surf_data_full), surf_mask != 0)
+ for idx in range(1, 5):
+ testing.assert_equal(surf_data_full.T[surf_mask == idx], data.arr[idx + 3])
+ else:
+ for idx2 in range(3):
+ testing.assert_equal(np.isfinite(surf_data_full)[idx2], (surf_mask != 0))
+ for idx in range(1, 5):
+ testing.assert_equal(surf_data_full.T[surf_mask == idx, idx2], data.arr[idx2, idx + 3])
+
+
+def test_brainstructure():
+ for primary in ['cortex', 'cerebellum']:
+ for secondary in [None, 'white', 'pial']:
+ for gtype in [None, 'volume', 'surface']:
+ for orientation in ['left', 'right', 'both']:
+ bst = cifti.BrainStructure(primary, secondary, orientation, gtype)
+ print(bst.cifti)
+ assert bst.cifti == 'CIFTI_STRUCTURE_%s%s' % (primary.upper(), '' if orientation == 'both' else '_' + orientation.upper())
+ assert bst.gifti['AnatomicalStructurePrimary'][:len(primary)] == primary.capitalize()
+ assert len(bst.gifti) == (1 if secondary is None else 2)
+ if secondary is not None:
+ assert bst.gifti['AnatomicalStructureSecondary'] == secondary.capitalize()
+ assert bst == cifti.BrainStructure(primary, secondary, orientation, gtype)
+ assert bst == bst
+ assert bst != cifti.BrainStructure('Thalamus', secondary, orientation, gtype)
+ if secondary is None:
+ assert bst == cifti.BrainStructure(primary, 'midplane', orientation, gtype)
+ else:
+ assert bst != cifti.BrainStructure(primary, 'midplane', orientation, gtype)
+ if (gtype == 'volume' and primary == 'cortex') or (gtype == 'surface' and primary != 'cortex'):
+ assert cifti.BrainStructure.from_string(bst.cifti) != bst
+ else:
+ assert cifti.BrainStructure.from_string(bst.cifti) == bst
+ assert cifti.BrainStructure.from_string(bst.cifti).secondary is None