def __init__(
self, filename, feature_filename=None, masker=None, r=6,
transform=True, target='MNI', **kwargs):
# Instance properties
self.r = r
# Set up transformations between different image spaces
if transform:
if not isinstance(transform, dict):
transform = {'T88': transformations.t88_to_mni(),
'TAL': transformations.t88_to_mni()
}
self.transformer = transformations.Transformer(transform, target)
else:
self.transformer = None
# Load mappables
self.mappables = self._load_mappables_from_txt(filename)
# Load the volume into a new Masker
if masker is None:
resource_dir = os.path.join(os.path.dirname(__file__),
os.path.pardir,
'resources')
masker = os.path.join(
resource_dir, 'MNI152_T1_2mm_brain.nii.gz')
self.masker = mask.Masker(masker)
# Create supporting tables for images and features
self.create_image_table()
if feature_filename is not None:
self.add_features(feature_filename, **kwargs)
def __init__(
self, filename, feature_filename=None, masker=None, r=6,
transform=True, target='MNI', **kwargs):
# Instance properties
self.r = r
# Set up transformations between different image spaces
if transform:
if not isinstance(transform, dict):
transform = {'T88': transformations.t88_to_mni(),
'TAL': transformations.t88_to_mni()
}
self.transformer = transformations.Transformer(transform, target)
else:
self.transformer = None
# Load and process activation data
self.activations = self._load_activations(filename)
# Load the volume into a new Masker
if masker is None:
resource_dir = os.path.join(os.path.dirname(__file__),
os.path.pardir,
'resources')
masker = os.path.join(
resource_dir, 'MNI152_T1_2mm_brain.nii.gz')
self.masker = mask.Masker(masker)
# Create supporting tables for images and features
self.create_image_table()
if feature_filename is not None:
self.add_features(feature_filename, **kwargs)
def __init__(
self, filename, feature_filename=None, masker=None, r=6, transform=True,
target='MNI'):
""" Initialize a new Dataset instance.
Creates a new Dataset instance from a text file containing activation data.
At minimum, the input file must contain tab-delimited columns named x, y, z,
id, and space (case-insensitive). The x/y/z columns indicate the coordinates
of the activation center or peak, the id column is used to group multiple
activations from a single Mappable (e.g. an article). Typically the id should
be a uniquely identifying field accessible to others, e.g., a doi in the case
of entire articles. The space column indicates the nominal atlas used to
produce each activation. Currently all values except 'TAL' (Talairach) will
be ignored. If space == TAL and the transform argument is True, all activations
reported in Talairach space will be converted to MNI space using the
Lancaster et al transform.
Args:
filename: The name of a database file containing a list of activations.
feature_filename: An optional filename to construct a FeatureTable from.
masker: An optional Nifti/Analyze image name defining the space to use for
all operations. If no image is passed, defaults to the MNI152 2 mm
template packaged with FSL.
r: An optional integer specifying the radius of the smoothing kernel, in mm.
Defaults to 6 mm.
transform: Optional argument specifying how to handle transformation between
coordinates reported in different stereotactic spaces. When True (default),
activations in Talairach (T88) space will be converted to MNI space using
the Lancaster et al (2007) transform; no other transformations will be
applied. When False, no transformation will be applied. Alternatively,
the user can pass their own dictionary of named transformations to apply,
in which case each activation will be checked against the dictionary
as it is read in and the specified transformation will be applied if
found (for further explanation, see transformations.Transformer).
target: The name of the target space within which activation coordinates
are represented. By default, MNI.
Returns:
A Dataset instance.
"""
# Instance properties
self.r = r
# Set up transformations between different image spaces
if transform:
if not isinstance(transform, dict):
transform = {'T88': transformations.t88_to_mni(),
'TAL': transformations.t88_to_mni()
}
self.transformer = transformations.Transformer(transform, target)
else:
self.transformer = None
# Load mappables
self.mappables = self._load_mappables_from_txt(filename)
# Load the volume into a new Masker
try:
if masker is None:
resource_dir = os.path.join(os.path.dirname(__file__),
os.path.pardir,
'resources')
masker = os.path.join(
resource_dir, 'MNI152_T1_2mm_brain.nii.gz')
self.masker = mask.Masker(masker)
except Exception as e:
logger.error("Error loading masker %s: %s" % (masker, e))
# yoh: TODO -- IMHO should re-raise or not even swallow the exception here
# raise e
# Create supporting tables for images and features
self.create_image_table()
if feature_filename is not None:
self.feature_table = FeatureTable(self, feature_filename)
def __init__(
self, filename, feature_filename=None, masker=None, r=6, transform=True,
target='MNI'):
""" Initialize a new Dataset instance.
Creates a new Dataset instance from a text file containing activation data.
At minimum, the input file must contain tab-delimited columns named x, y, z,
id, and space (case-insensitive). The x/y/z columns indicate the coordinates
of the activation center or peak, the id column is used to group multiple
activations from a single Mappable (e.g. an article). Typically the id should
be a uniquely identifying field accessible to others, e.g., a doi in the case
of entire articles. The space column indicates the nominal atlas used to
produce each activation. Currently all values except 'TAL' (Talairach) will
be ignored. If space == TAL and the transform argument is True, all activations
reported in Talairach space will be converted to MNI space using the
Lancaster et al transform.
Args:
filename: The name of a database file containing a list of activations.
feature_filename: An optional filename to construct a FeatureTable from.
masker: An optional Nifti/Analyze image name defining the space to use for
all operations. If no image is passed, defaults to the MNI152 2 mm
template packaged with FSL.
r: An optional integer specifying the radius of the smoothing kernel, in mm.
Defaults to 6 mm.
transform: Optional argument specifying how to handle transformation between
coordinates reported in different stereotactic spaces. When True (default),
activations in Talairach (T88) space will be converted to MNI space using
the Lancaster et al (2007) transform; no other transformations will be
applied. When False, no transformation will be applied. Alternatively,
the user can pass their own dictionary of named transformations to apply,
in which case each activation will be checked against the dictionary
as it is read in and the specified transformation will be applied if
found (for further explanation, see transformations.Transformer).
target: The name of the target space within which activation coordinates
are represented. By default, MNI.
Returns:
A Dataset instance.
"""
# Instance properties
self.r = r
# Set up transformations between different image spaces
if transform:
if not isinstance(transform, dict):
transform = {'T88': transformations.t88_to_mni(),
'TAL': transformations.t88_to_mni()
}
self.transformer = transformations.Transformer(transform, target)
else:
self.transformer = None
# Load mappables
self.mappables = self._load_mappables_from_txt(filename)
# Load the volume into a new Masker
if masker is None:
resource_dir = os.path.join(os.path.dirname(__file__),
os.path.pardir,
'resources')
masker = os.path.join(
resource_dir, 'MNI152_T1_2mm_brain.nii.gz')
self.masker = mask.Masker(masker)
# Create supporting tables for images and features
self.create_image_table()
if feature_filename is not None:
self.feature_table = FeatureTable(self, feature_filename)
