def init_frontend():
nl = NeurolangPDL()
nl.add_symbol(
np.log,
name="log",
type_=Callable[[float], float],
)
return nl
def init_frontend():
nl = NeurolangPDL()
nl.add_symbol(
np.log,
name="log",
type_=Callable[[float], float],
)
@nl.add_symbol
def one_way_chi2(llr: float) -> float:
"""
2 * log(LR) follows a one-way chi2 distribution
"""
return scipy.special.chdtrc(1, 2 * llr)
return nl
def init_frontend():
nl = NeurolangPDL()
nl.add_symbol(
np.log,
name="log",
type_=Callable[[float], float],
)
nl.add_symbol(
lambda it: float(sum(it)),
name="agg_sum",
type_=Callable[[Iterable], float],
)
@nl.add_symbol
def agg_count(*iterables) -> int:
return len(next(iter(iterables)))
return nl
# ##############################################################################
# Data preparation
# ----------------
# ##############################################################################
# Load the MNI atlas and resample it to 4mm voxels
mni_t1 = nibabel.load(nilearn.datasets.fetch_icbm152_2009()["t1"])
mni_t1_4mm = nilearn.image.resample_img(mni_t1, np.eye(3) * 2)
# ##############################################################################
# Probabilistic Logic Programming in NeuroLang
# --------------------------------------------
nl = NeurolangPDL()
# ##############################################################################
# Adding new aggregation function to build a region overlay
# ----------------------------------
@nl.add_symbol
def agg_create_region_overlay(
i: Iterable, j: Iterable, k: Iterable, p: Iterable
) -> ExplicitVBR:
voxels = np.c_[i, j, k]
return ExplicitVBROverlay(
voxels, mni_t1_4mm.affine, p, image_dim=mni_t1_4mm.shape
)
if label == 0:
continue
voxels = np.transpose((image_data == label).nonzero())
if voxels.shape[0] == 0:
continue
r = ExplicitVBR(voxels, image.affine, image_dim=image.shape)
region_table.append((str(name.decode('utf8')), r))
##################################################
# Initialise the atlas to the Neurolang Engine
# add two symbols to split left and right structures
# in the Destrieux atlas and add the atlas.
nl = NeurolangPDL()
@nl.add_symbol
def lh(x: str) -> bool:
return x.startswith('L ')
@nl.add_symbol
def rh(x: str) -> bool:
return x.startswith('R ')
destrieux = nl.add_tuple_set(region_table, name='destrieux')
with nl.environment as e:
Uploading the Destrieux regions NeuroLang and
executing a simple query.
'''
from matplotlib import pyplot as plt
import nibabel as nib
from nilearn import datasets, plotting
from neurolang.frontend import NeurolangPDL
##################################################
# Initialise the NeuroLang probabilistic engine.
nl = NeurolangPDL()
###############################################################################
# Load the Destrieux example from nilearn as a fact list
atlas_destrieux = datasets.fetch_atlas_destrieux_2009()
atlas_labels = {
label: str(name.decode('utf8'))
for label, name in atlas_destrieux['labels']
}
nl.add_atlas_set('destrieux', atlas_labels, nib.load(atlas_destrieux['maps']))
###############################################################################
# Add utility functions to separate hemispheric regions
def init_frontend():
nl = NeurolangPDL()
return nl
# ----------------
data_dir = Path.home() / "neurolang_data"
# ##############################################################################
# Load the MNI atlas and resample it to 4mm voxels
mni_t1 = nibabel.load(
nilearn.datasets.fetch_icbm152_2009(data_dir=str(data_dir / "icbm"))["t1"])
mni_t1_2mm = nilearn.image.resample_img(mni_t1, np.eye(3) * 2)
# ##############################################################################
# Probabilistic Logic Programming in NeuroLang
# --------------------------------------------
nl = NeurolangPDL()
# ##############################################################################
# Adding new aggregation function to build a region overlay
# ----------------------------------
@nl.add_symbol
def agg_create_region_overlay(i: Iterable, j: Iterable, k: Iterable,
p: Iterable) -> ExplicitVBR:
voxels = np.c_[i, j, k]
return ExplicitVBROverlay(voxels,
mni_t1_2mm.affine,
p,
image_dim=mni_t1_2mm.shape)