neuror.main¶

Dendritic repair module

It is based on the BlueRepairSDK’s implementation

Functions

`_branching_angles`	Return a list of 2-tuples.
`_continuation`	Continue growing the section.
`_get_sholl_layer`	Returns this section sholl layer.
`_get_sholl_proba`	Return the probabilities of bifurcation, termination and bifurcation in a dictionnary for the given sholl layer and branch order.
`_grow_until_sholl_sphere`	Grow until reaching next sholl layer.
`_last_segment_vector`	Returns the vector formed by the last 2 points of the section
`_max_y_dendritic_cylindrical_extent`	Return the maximum distance of dendritic section ends and the origin in the XZ plane.
`_y_cylindrical_extent`	Returns the distance from the last section point to the origin in the XZ plane.
`is_branch_intact`	Does the branch have leaves belonging to the cut plane?
`is_cut_section`	Return true if the section is close from the cut plane.
`repair`	The repair function.

Classes

`Action`	To bifurcate or not to bifurcate?
`Repair`	Repair the input morphology.

class neuror.main.Action(value)[source]¶

Bases: Enum

To bifurcate or not to bifurcate?

class neuror.main.Repair(inputfile: Path, axons: Path | None = None, seed: int | None = 0, cut_leaves_coordinates: ndarray | None = None, legacy_detection: bool = False, repair_flags: Dict[RepairType, bool] | None = None, apical_point: ndarray | None = None, params: Dict | None = None, validate_params=False)[source]¶

Bases: object

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,  # resolution of the sholl 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
}

Parameters:

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.cut_plane.legacy_detection)
repair_flags – a dict of flags where key is a neuror.utils.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)
validate_params – if set to True, the given parameters are validated before processing

Note

This class is based on https://bbpgitlab.epfl.ch/nse/morphologyrepair/BlueRepairSDK/-/blob/main/BlueRepairSDK/src/repair.cpp#L469

run(outputfile: Path, plot_file: Path | None = None)[source]¶

Run repair and export the result.

Parameters:

outputfile – path to the output morphology
plot_file – path to the output figure

static validate_params(params)[source]¶: Validate the given parameters according to the JSON schema.

neuror.main._get_sholl_proba(sholl_data: dict, section_type: SectionType, sholl_layer: int, pseudo_order: int) → Dict[Action, float][source]¶

Return the probabilities of bifurcation, termination and bifurcation in a dictionnary for the given sholl layer and branch order.

If no data are available for this branch order, the action_counts are averaged on all branch orders for this sholl layer

Parameters:

sholl_data –
nested dict that stores the number of section per SectionType, sholl order, sholl layer and Action type

sholl_data[neurite_type][layer][order][action_type] = counts
section_type – section type
sholl_layer – sholl layer
pseudo_order – pseudo order

Returns:

Probability of each action

Note

This is based on https://bbpgitlab.epfl.ch/nse/morphologyrepair/BlueRepairSDK/-/blob/main/BlueRepairSDK/src/helper_dendrite.cpp#n398

Note

OrderedDict ensures the reproducibility of numpy.random.choice() outcome.

neuror.main.is_branch_intact(branch, cut_points)[source]¶: Does the branch have leaves belonging to the cut plane?

neuror.main.is_cut_section(section, cut_points)[source]¶: Return true if the section is close from the cut plane.

neuror.main.repair(inputfile: Path, outputfile: Path, axons: List[Path] | None = None, seed: int = 0, cut_leaves_coordinates: ndarray | None = None, legacy_detection: bool = False, plot_file: Path | None = None, repair_flags: Dict[RepairType, bool] | None = None, apical_point: List | None = None, params: Dict | None = None, validate_params=False)[source]¶

The repair function.

Parameters:

inputfile – the input morph
outputfile – the output morph
axons – the axons
seed – the numpy seed
cut_leaves_coordinates – List of 3D coordinates from which to start the repair
plot_file – the filename of the plot
repair_flags – a dict of flags where key is a neuror.utils.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
params – repair internal parameters, None will use defaults
validate_params – if set to True, the given parameters are validated before processing