def test_get_obliques():
'''Comparing results with the old repair launch with the following commands:
repair --dounravel 0 --inputdir /gpfs/bbp.cscs.ch/project/proj83/home/bcoste/release/out-new/01_ConvertMorphologies --input rp120430_P-2_idA --overlap=true --incremental=false --restrict=true --distmethod=mirror
The arguments are the one used in the legacy morphology workflow.
'''
neuron = load_neuron(DATA_PATH / 'compare-bbpsdk/rp120430_P-2_idA.h5')
apical_section = neuron.sections[apical_point_section_segment(neuron)[0]]
extended_types = repair_type_map(neuron, apical_section)
assert_equal([sec.id + OFFSET for sec in test_module.get_obliques(neuron, extended_types)],
[217])
def __init__(self,
inputfile: Path,
axons: Optional[Path] = None,
seed: Optional[int] = 0,
cut_leaves_coordinates: Optional[NDArray[(3, Any)]] = None,
legacy_detection: bool = False,
repair_flags: Optional[Dict[RepairType, bool]] = None):
'''Repair the input morphology
Args:
inputfile: the input neuron to repair
axons: donor axons whose section will be used to repair this axon
seed: the numpy seed
cut_leaves_coordinates: List of 3D coordinates from which to start the repair
legacy_detection: if True, use the legacy cut plane detection
(see neuror.legacy_detection)
repair_flags: a dict of flags where key is a RepairType and value is whether
it should be repaired or not. If not provided, all types will be repaired.
Note: based on https://bbpcode.epfl.ch/browse/code/platform/BlueRepairSDK/tree/BlueRepairSDK/src/repair.cpp#n469 # noqa, pylint: disable=line-too-long
'''
np.random.seed(seed)
self.legacy_detection = legacy_detection
self.inputfile = inputfile
self.axon_donors = axons or list()
self.donated_intact_axon_sections = list()
self.repair_flags = repair_flags or dict()
if legacy_detection:
self.cut_leaves = CutPlane.find_legacy(inputfile,
'z').cut_leaves_coordinates
elif cut_leaves_coordinates is None:
self.cut_leaves = CutPlane.find(
inputfile, bin_width=15).cut_leaves_coordinates
else:
self.cut_leaves = np.asarray(cut_leaves_coordinates)
self.neuron = load_neuron(inputfile)
self.repair_type_map = dict()
self.max_y_cylindrical_extent = _max_y_dendritic_cylindrical_extent(
self.neuron)
self.max_y_extent = max(
np.max(section.points[:, COLS.Y])
for section in self.neuron.iter())
self.info = dict()
apical_section_id, _ = apical_point_section_segment(self.neuron)
if apical_section_id:
self.apical_section = self.neuron.sections[apical_section_id]
else:
self.apical_section = None
def internal_cut_detection(neuron, axis):
'''As in:
https://bbpcode.epfl.ch/source/xref/platform/BlueRepairSDK/BlueRepairSDK/src/repair.cpp#263
Use cut_detect to get the side of the half space the points live in.
Then mark points which are children of the apical section.
'''
axis = {'x': COLS.X, 'y': COLS.Y, 'z': COLS.Z}[axis.lower()]
cut = defaultdict(lambda key: False)
side = cut_detect(neuron, cut, 0, axis)
# reclassify cut points in tuft,based on apical point position
apical_section_id, point_id = apical_point_section_segment(neuron)
if apical_section_id is not None:
apical_section = neuron.sections[apical_section_id]
apical_offset = apical_section.points[point_id, axis]
cut_mark(children_ids(apical_section), cut, apical_offset, side, axis)
else:
apical_section = None
extended_types = repair_type_map(neuron, apical_section)
oblique_roots = get_obliques(neuron, extended_types)
# reclassify points in obliques. based on the position of their root.
for root in oblique_roots:
offset = root.points[0]
# FIXME: z is hard coded in the original code as well. It's probably a bug.
cut_mark(root.children, cut, offset[COLS.Z], side, axis)
cut_leaves = np.array(
[sec.points[-1, COLS.XYZ] for sec, is_cut in cut.items() if is_cut])
return cut_leaves, side
def test__find_apical_section_segment():
neuron = Morphology(os.path.join(DATA, 'apical_test.swc'))
section, segment = apical_point_section_segment(neuron)
eq_(section, 1)
eq_(segment, 1)
def test__find_apical_section_segment():
neuron = Morphology(DATA / 'apical_test.swc')
section, segment = apical_point_section_segment(neuron)
assert section == 1
assert segment == 1
def __init__(
self, # pylint: disable=too-many-arguments
inputfile: Path,
axons: Optional[Path] = None,
seed: Optional[int] = 0,
cut_leaves_coordinates: Optional[NDArray[(3, Any)]] = None,
legacy_detection: bool = False,
repair_flags: Optional[Dict[RepairType, bool]] = None,
apical_point: NDArray[3, float] = None,
params: Dict = None):
'''Repair the input morphology
The repair algorithm uses sholl analysis of intact branches to grow new branches from cut
leaves. The algorithm is fairly complex, but can be controled via a few parameters in the
params dictionary. By default, they are:
_PARAMS = {
'seg_length': 5.0, # lenghts of new segments
'sholl_layer_size': 10, # resoluion of the shll profile
'noise_continuation': 0.5, # together with seg_length, this controls the tortuosity
'soma_repulsion': 0.2, # if 0, previous section direction, if 1, radial direction
'bifurcation_angle': 20, # noise amplitude in degree around mean bif angle on the cell
'path_length_ratio': 0.5, # a smaller value will make a strornger taper rate
'children_diameter_ratio': 0.8, # 1: child diam = parent diam, 0: child diam = tip diam
'tip_percentile': 25, # percentile of tip radius distributions to use as tip radius
}
Args:
inputfile: the input neuron to repair
axons: donor axons whose section will be used to repair this axon
seed: the numpy seed
cut_leaves_coordinates: List of 3D coordinates from which to start the repair
legacy_detection: if True, use the legacy cut plane detection
(see neuror.legacy_detection)
repair_flags: a dict of flags where key is a RepairType and value is whether
it should be repaired or not. If not provided, all types will be repaired.
apical_point: 3d vector for apical point, else, the automatic apical detection is used
if apical_point == -1, no automatic detection will be tried
params: repair internal parameters (see comments in code for details)
Note: based on https://bbpcode.epfl.ch/browse/code/platform/BlueRepairSDK/tree/BlueRepairSDK/src/repair.cpp#n469 # noqa, pylint: disable=line-too-long
'''
np.random.seed(seed)
self.legacy_detection = legacy_detection
self.inputfile = inputfile
self.axon_donors = axons or list()
self.donated_intact_axon_sections = list()
self.repair_flags = repair_flags or dict()
self.params = params if params is not None else _PARAMS
if legacy_detection:
self.cut_leaves = CutPlane.find_legacy(inputfile,
'z').cut_leaves_coordinates
elif cut_leaves_coordinates is None:
self.cut_leaves = CutPlane.find(
inputfile, bin_width=15).cut_leaves_coordinates
else:
self.cut_leaves = np.asarray(cut_leaves_coordinates)
self.neuron = load_neuron(inputfile)
self.repair_type_map = dict()
self.max_y_cylindrical_extent = _max_y_dendritic_cylindrical_extent(
self.neuron)
self.max_y_extent = max(
np.max(section.points[:, COLS.Y])
for section in self.neuron.iter())
self.info = dict()
apical_section_id = None
if apical_point != -1:
if apical_point:
# recall MorphIO ID = NeuroM ID - 1
apical_section_id = point_to_section_segment(
self.neuron, apical_point)[0] - 1
else:
apical_section_id, _ = apical_point_section_segment(
self.neuron)
if apical_section_id:
self.apical_section = self.neuron.sections[apical_section_id]
else:
self.apical_section = None
# record the tip radius as a lower bound for diameters with taper, excluding axons
# because they are treated separately, with thinner diameters
_diameters = [
np.mean(leaf.points[:, COLS.R]) for neurite in self.neuron.neurites
if neurite.type is not NeuriteType.axon
for leaf in iter_sections(neurite, iterator_type=Section.ileaf)
]
self.tip_radius = (np.percentile(
_diameters, self.params["tip_percentile"]) if _diameters else None)
self.current_trunk_radius = None
# estimate a global tapering rate of the morphology as a function of trunk radius,
# such that the tip radius is attained on average af max_path_length, defined as a fraction
# of the maximal path length via the parameter path_lengt_ratio. The smaller this parameter
# the faster the radii will convert to tip_radius.
# TODO: maybe we would need this per neurite_type
max_path_length = self.params["path_length_ratio"] * np.max(
nm.get("terminal_path_lengths_per_neurite", self.neuron))
self.taper = (lambda trunk_radius: (trunk_radius - self.tip_radius) *
self.params["seg_length"] / max_path_length)