neurom

NeuroM neurom morphology analysis package.

Examples

Load a neuron

>>> import neurom as nm
>>> nrn = nm.load_neuron('some/data/path/morph_file.swc')

Obtain some morphometrics using the get function

>>> ap_seg_len = nm.get('segment_lengths', nrn, neurite_type=nm.APICAL_DENDRITE)
>>> ax_sec_len = nm.get('section_lengths', nrn, neurite_type=nm.AXON)

Load neurons from a directory. This loads all SWC, HDF5 or NeuroLucida .asc files it finds and returns a list of neurons

>>> import numpy as np  # For mean value calculation
>>> nrns = nm.load_neurons('some/data/directory')
>>> for nrn in nrns:
...     print 'mean section length', np.mean(nm.get('section_lengths', nrn))

Apply a function to a selection of neurites in a neuron or population. This example gets the number of points in each axon in a neuron population

>>> import neurom as nm
>>> filter = lambda n : n.type == nm.AXON
>>> mapping = lambda n : len(n.points)
>>> n_points = [n for n in nm.iter_neurites(nrns, mapping, filter)]

Functions

get	Obtain a feature from a set of morphology objects.
graft_neuron	Returns a neuron starting at root_section.
iter_neurites	Iterator to a neurite, neuron or neuron population.
iter_sections	Iterator to the sections in a neurite, neuron or neuron population.
iter_segments	Return an iterator to the segments in a collection of neurites.
load_neuron	Build section trees from an h5 or swc file.
load_neurons	Create a population object.

Classes

COLS	Column labels for internal data representation.
NeuriteType	Enum representing valid tree types.
Neuron	Class representing a simple neuron.
NeuronLoader	Caching morphology loader.

class neurom.COLS[source]

Bases: object

Column labels for internal data representation.

class neurom.NeuriteType(value)[source]

Bases: neurom.utils.OrderedEnum

Enum representing valid tree types.

class neurom.Neuron(soma=None, neurites=None, sections=None, name='Neuron')[source]

Bases: object

Class representing a simple neuron.

class neurom.NeuronLoader(directory, file_ext=None, cache_size=None)[source]

Bases: object

Caching morphology loader.

Parameters

• directory – path to directory with morphology files

• file_ext – file extension to look for (if not set, will pick any of .swc|.h5|.asc)

• cache_size – size of LRU cache (if not set, no caching done)

get(name)[source]

Get name morphology data.

neurom.get(feature, obj, **kwargs)[source]

Obtain a feature from a set of morphology objects.

Parameters

• feature (string) – feature to extract

• obj – a neuron, population or neurite tree

• kwargs – parameters to forward to underlying worker functions

Returns

features as a 1D or 2D numpy array.

Features:

Neurite features (neurite, neuron, neuron population):

• diameter_power_relation:

  Calculate the diameter power relation at a bifurcation point.

  Diameter power relation is defined in https://www.ncbi.nlm.nih.gov/pubmed/18568015

  This quantity gives an indication of how far the branching is from the Rall ratio (when =1).

• local_bifurcation_angles:

  Get a list of local bifurcation angles in a collection of neurites.

• neurite_lengths:

  Get the path length per neurite in a collection.

• neurite_volume_density:

  Get the volume density per neurite.

  The volume density is defined as the ratio of the neurite volume and the volume of the neurite’s enclosing convex hull

• neurite_volumes:

  Get the volume per neurite in a collection.

• number_of_bifurcations:

  Number of bifurcation points in a collection of neurites.

• number_of_forking_points:

  Number of forking points in a collection of neurites.

• number_of_neurites:

  Number of neurites in a collection of neurites.

• number_of_sections:

  Number of sections in a collection of neurites.

• number_of_sections_per_neurite:

  Get the number of sections per neurite in a collection of neurites.

• number_of_segments:

  Number of sections in a collection of neurites.

• number_of_terminations:

  Number of leaves points in a collection of neurites.

• partition:

  Partition at bifurcation points of a collection of neurites.

• partition_asymmetry:

  Partition asymmetry at bifurcation points of a collection of neurites.

  Variant: length is a different definition, as the absolute difference in downstream path lenghts, relative to the total neurite path length

• partition_asymmetry_length:

  Partition asymmetry at bifurcation points of a collection of neurites.

  Variant: length is a different definition, as the absolute difference in downstream path lenghts, relative to the total neurite path length

• partition_pairs:

  Partition pairs at bifurcation points of a collection of neurites.

  Partition pair is defined as the number of bifurcations at the two daughters of the bifurcating section

• principal_direction_extents:

  Principal direction extent of neurites in neurons.

• remote_bifurcation_angles:

  Get a list of remote bifurcation angles in a collection of neurites.

• section_areas:

  Section areas in a collection of neurites.

• section_bif_branch_orders:

  Bifurcation section branch orders in a collection of neurites.

• section_bif_lengths:

  Bifurcation section lengths in a collection of neurites.

• section_bif_radial_distances:

  Get the radial distances of the bifurcation sections for a collection of neurites.

• section_branch_orders:

  Section branch orders in a collection of neurites.

• section_end_distances:

  Section end to end distances in a collection of neurites.

• section_lengths:

  Section lengths in a collection of neurites.

• section_path_distances:

  Path lengths of a collection of neurites.

• section_radial_distances:

  Section radial distances in a collection of neurites.

  The iterator_type can be used to select only terminal sections (ileaf) or only bifurcations (ibifurcation_point).

• section_strahler_orders:

  Inter-segment opening angles in a section.

• section_taper_rates:

  Diameter taper rates of the sections in a collection of neurites from root to tip.

  Taper rate is defined here as the linear fit along a section. It is expected to be negative for neurons.

• section_term_branch_orders:

  Termination section branch orders in a collection of neurites.

• section_term_lengths:

  Termination section lengths in a collection of neurites.

• section_term_radial_distances:

  Get the radial distances of the termination sections for a collection of neurites.

• section_tortuosity:

  Section tortuosities in a collection of neurites.

• section_volumes:

  Section volumes in a collection of neurites.

• segment_areas:

  Areas of the segments in a collection of neurites.

• segment_lengths:

  Lengths of the segments in a collection of neurites.

• segment_meander_angles:

  Inter-segment opening angles in a section.

• segment_midpoints:

  Return a list of segment mid-points in a collection of neurites.

• segment_path_lengths:

  Returns pathlengths between all non-root points and their root point.

• segment_radial_distances:

  Returns the list of distances between all segment mid points and origin.

• segment_radii:

  Arithmetic mean of the radii of the points in segments in a collection of neurites.

• segment_taper_rates:

  Diameters taper rates of the segments in a collection of neurites.

  The taper rate is defined as the absolute radii differences divided by length of the section

• segment_volumes:

  Volumes of the segments in a collection of neurites.

• sibling_ratio:

  Sibling ratios at bifurcation points of a collection of neurites.

  The sibling ratio is the ratio between the diameters of the smallest and the largest child. It is a real number between 0 and 1. Method argument allows one to consider mean diameters along the child section instead of diameter of the first point.

• terminal_path_lengths_per_neurite:

  Get the path lengths to each terminal point per neurite in a collection.

• total_area_per_neurite:

  Surface area in a collection of neurites.

  The area is defined as the sum of the area of the sections.

• total_length:

  Get the total length of all sections in the group of neurons or neurites.

• total_length_per_neurite:

  Get the path length per neurite in a collection.

Neuron features (neuron, neuron population):

• sholl_frequency:

  Perform Sholl frequency calculations on a population of neurites.

  Args:

  nrn(morph): nrn or population neurite_type(NeuriteType): which neurites to operate on step_size(float): step size between Sholl radii

  Note:

  Given a neuron, the soma center is used for the concentric circles, which range from the soma radii, and the maximum radial distance in steps of step_size. When a population is given, the concentric circles range from the smallest soma radius to the largest radial neurite distance. Finally, each segment of the neuron is tested, so a neurite that bends back on itself, and crosses the same Sholl radius will get counted as having crossed multiple times.

• soma_radii:

  Get the radii of the somata of a population of neurons.

  Note:

  If a single neuron is passed, a single element list with the radius of its soma member is returned.

• soma_surface_areas:

  Get the surface areas of the somata in a population of neurons.

  Note:

  The surface area is calculated by assuming the soma is spherical.

  Note:

  If a single neuron is passed, a single element list with the surface area of its soma member is returned.

• soma_volumes:

  Get the volume of the somata in a population of neurons.

  Note:

  If a single neuron is passed, a single element list with the volume of its soma member is returned.

• trunk_angles:

  Calculates the angles between all the trunks of the neuron.

  The angles are defined on the x-y plane and the trees are sorted from the y axis and anticlock-wise.

• trunk_origin_azimuths:

  Get a list of all the trunk origin azimuths of a neuron or population.

  The azimuth is defined as Angle between x-axis and the vector defined by (initial tree point - soma center) on the x-z plane.

  The range of the azimuth angle [-pi, pi] radians

• trunk_origin_elevations:

  Get a list of all the trunk origin elevations of a neuron or population.

  The elevation is defined as the angle between x-axis and the vector defined by (initial tree point - soma center) on the x-y half-plane.

  The range of the elevation angle [-pi/2, pi/2] radians

• trunk_origin_radii:

  Radii of the trunk sections of neurites in a neuron.

• trunk_section_lengths:

  List of lengths of trunk sections of neurites in a neuron.

• trunk_vectors:

  Calculates the vectors between all the trunks of the neuron and the soma center.

neurom.graft_neuron(root_section)[source]

Returns a neuron starting at root_section.

neurom.iter_neurites(obj, mapfun=None, filt=None, neurite_order=<NeuriteIter.FileOrder: 1>)[source]

Iterator to a neurite, neuron or neuron population.

Applies optional neurite filter and mapping functions.

Parameters

• obj – a neurite, neuron or neuron population.

• mapfun – optional neurite mapping function.

• filt – optional neurite filter function.

• neurite_order (NeuriteIter) – order upon which neurites should be iterated - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical

Examples

Get the number of points in each neurite in a neuron population

>>> from neurom.core import iter_neurites
>>> n_points = [n for n in iter_neurites(pop, lambda x : len(x.points))]

Get the number of points in each axon in a neuron population

>>> import neurom as nm
>>> from neurom.core import iter_neurites
>>> filter = lambda n : n.type == nm.AXON
>>> mapping = lambda n : len(n.points)
>>> n_points = [n for n in iter_neurites(pop, mapping, filter)]

neurom.iter_sections(neurites, iterator_type=<function Tree.ipreorder>, neurite_filter=None, neurite_order=<NeuriteIter.FileOrder: 1>)[source]

Iterator to the sections in a neurite, neuron or neuron population.

Parameters

• neurites – neuron, population, neurite, or iterable containing neurite objects

• iterator_type – section iteration order within a given neurite. Must be one of: Tree.ipreorder: Depth-first pre-order iteration of tree nodes Tree.ipostorder: Depth-first post-order iteration of tree nodes Tree.iupstream: Iterate from a tree node to the root nodes Tree.ibifurcation_point: Iterator to bifurcation points Tree.ileaf: Iterator to all leaves of a tree

• neurite_filter – optional top level filter on properties of neurite neurite objects.

• neurite_order (NeuriteIter) – order upon which neurites should be iterated - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical

Examples

Get the number of points in each section of all the axons in a neuron population

>>> import neurom as nm
>>> from neurom.core import ites_sections
>>> filter = lambda n : n.type == nm.AXON
>>> n_points = [len(s.points) for s in iter_sections(pop,  neurite_filter=filter)]

neurom.iter_segments(obj, neurite_filter=None, neurite_order=<NeuriteIter.FileOrder: 1>)[source]

Return an iterator to the segments in a collection of neurites.

Parameters

• obj – neuron, population, neurite, section, or iterable containing neurite objects

• neurite_filter – optional top level filter on properties of neurite neurite objects

• neurite_order – order upon which neurite should be iterated. Values: - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical

Note

This is a convenience function provided for generic access to neuron segments. It may have a performance overhead WRT custom-made segment analysis functions that leverage numpy and section-wise iteration.

neurom.load_neuron(handle, reader=None)[source]

Build section trees from an h5 or swc file.

neurom.load_neurons(neurons, neuron_loader=<function load_neuron>, name=None, population_class=<class 'neurom.core.population.Population'>, ignored_exceptions=())[source]

Create a population object.

From all morphologies in a directory of from morphologies in a list of file names.

Parameters

• neurons – directory path or list of neuron file paths

• neuron_loader – function taking a filename and returning a neuron

• population_class – class representing populations

• name (str) – optional name of population. By default ‘Population’ or filepath basename depending on whether neurons is list or directory path respectively.

Returns

neuron population object