diff --git a/fsl/data/cifti.py b/fsl/data/cifti.py
index d615fb87a20a77c64b308521a545a77b6b7b1a81..96b8507adba63c9f0e1196a052fbbfa052eedd8b 100644
--- a/fsl/data/cifti.py
+++ b/fsl/data/cifti.py
@@ -7,9 +7,9 @@ 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
+from typing import Sequence, Optional, Union
import numpy as np
-from fsl.data.image import Image
+from fsl.data import image
import nibabel as nib
from fsl.utils.path import addExt
@@ -40,7 +40,7 @@ class Cifti:
- :py:class:`BrainModelAxis <cifti2_axes.BrainModelAxis>`
- :py:class:`ParcelsAxis <cifti2_axes.ParcelsAxis>`
"""
- def __init__(self, arr: np.ndarray, axes: Sequence[Optional[cifti2_axes.Axis], ...]):
+ def __init__(self, arr: np.ndarray, axes: Sequence[Optional[cifti2_axes.Axis]]):
"""
Defines a new dataset in greyordinate space
@@ -138,41 +138,6 @@ class Cifti:
"""
self.to_cifti(default_axis).to_filename(addExt(cifti_filename, defaultExt=self.extension))
- @classmethod
- def load(cls, filename, mask_values=(0, np.nan), writable=False):
- """
- Reads greyordinate data from the given file
-
- File can be:
-
- - NIFTI mask
- - GIFTI mask
- - 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: greyordinates object
- """
- if isinstance(filename, str):
- img = nib.load(filename)
- else:
- img = filename
-
- if isinstance(img, nib.Cifti2Image):
- return cls.from_cifti(img, writable=writable)
- if isinstance(img, nib.GiftiImage):
- if writable:
- raise ValueError("Can not open GIFTI file in writable mode")
- return cls.from_gifti(img, mask_values)
- try:
- vol_img = 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 cls.from_image(vol_img, mask_values)
-
@classmethod
def from_gifti(cls, filename, mask_values=(0, np.nan)):
"""
@@ -208,15 +173,15 @@ class Cifti:
return DenseCifti(data[..., mask], [bm_axes])
@classmethod
- def from_image(cls, image, mask_values=(np.nan, 0)):
+ def from_image(cls, input, mask_values=(np.nan, 0)):
"""
Creates a new greyordinate object from a NIFTI file
- :param image: FSL :class:`image.Image` object
+ :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)
+ img = image.Image(input)
mask = np.ones(img.data.shape, dtype='bool')
for value in mask_values:
@@ -226,10 +191,12 @@ class Cifti:
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.affine)
- return cifti2_axes.GreyOrdinates(inverted_data, [bm_axes])
+ bm_axes = cifti2_axes.BrainModelAxis.from_mask(mask, affine=img.nibImage.affine)
+ return DenseCifti(inverted_data, [bm_axes])
class DenseCifti(Cifti):
@@ -237,7 +204,7 @@ class DenseCifti(Cifti):
Represents sparse data defined for a subset of voxels and vertices (i.e., greyordinates)
"""
def __init__(self, *args, **kwargs):
- super().__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)}")
@@ -249,9 +216,9 @@ class DenseCifti(Cifti):
def extension(self, ):
return dense_extensions[type(self.axes[-2])]
- def to_image(self, fill=0) -> Image:
+ def to_image(self, fill=0) -> image.Image:
"""
- Get the volumetric data as an :class:`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}")
@@ -260,7 +227,7 @@ class DenseCifti(Cifti):
voxels = self.brain_model_axis.voxel[self.brain_model_axis.volume_mask]
data[tuple(voxels.T)] = np.transpose(self.data, (-1,) + tuple(range(self.data.ndim - 1)))[
self.brain_model_axis.volume_mask]
- return Image(data, xform=self.brain_model_axis.affine)
+ return image.Image(data, xform=self.brain_model_axis.affine)
def surface(self, anatomy, fill=np.nan, partial=False):
"""
@@ -326,7 +293,7 @@ class ParcelCifti(Cifti):
written[write_to] = True
if not written.any():
raise ValueError("Parcellation does not contain any volumetric data")
- return Image(data, xform=self.brain_model_axis.affine)
+ return image.Image(data, xform=self.brain_model_axis.affine)
def surface(self, anatomy, fill=np.nan, partial=False):
"""
@@ -467,7 +434,7 @@ class BrainStructure(object):
"""
primary_str = 'AnatomicalStructurePrimary'
secondary_str = 'AnatomicalStructureSecondary'
- primary = "unknown"
+ primary = "other"
secondary = None
for meta in [gifti_obj] + gifti_obj.darrays:
if primary_str in meta.meta.metadata:
@@ -477,3 +444,46 @@ class BrainStructure(object):
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..f61afe39395029ac1090cd47ee2d590d3b466fcd
--- /dev/null
+++ b/tests/test_cifti.py
@@ -0,0 +1,47 @@
+import pytest
+from fsl.data import cifti
+import os.path as op
+import numpy as np
+import nibabel as nib
+from numpy import testing
+import tests
+
+
+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