In following code samples, the prompt
>>> indicates a python interpreter session
started with the virtualenv activated. That gives access to the
Fast analysis with
Here we load a neuron and obtain some information from it:
>>> import neurom as nm >>> nrn = nm.load_neuron('some/data/path/morph_file.swc') >>> 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)
Morphology visualization with the
Here we visualize a neuronal morphology:
>>> # Initialize nrn as above >>> from neurom import viewer >>> fig, ax = viewer.draw(nrn) >>> fig.show() >>> >>> fig, ax = viewer.draw(nrn, mode='3d') # valid modes '2d', '3d', 'dendrogram' >>> fig.show()
Advanced iterator-based feature extraction example¶
These slightly more complex examples illustrate what can be done with the
module’s various generic iterators and simple morphometric functions.
The idea here is that there is a great deal of flexibility to build new analyses based
on some limited number of orthogonal iterator and morphometric components that can
be combined in many ways. Users with some knowledge of
neurom can easily
implement code to obtain new morphometrics.
All of the examples in the previous sections can be implemented in a similar way to those presented here.
"""Advanced analysis examples These examples highlight more advanced neurom morphometrics functionality using iterators. """ from __future__ import print_function from neurom.core.dataformat import COLS import neurom as nm from neurom import geom from neurom.features import sectionfunc from neurom.core import Section from neurom.core.types import tree_type_checker, NEURITES from neurom import morphmath as mm import numpy as np if __name__ == '__main__': filename = 'tests/data/swc/Neuron.swc' # load a neuron from an SWC file nrn = nm.load_neuron(filename) # Some examples of what can be done using iteration # instead of pre-packaged functions that return lists. # The iterations give us a lot of flexibility: we can map # any function that takes a segment or section. # Get of all neurites in cell by iterating over sections, # and summing the section lengths def sec_len(sec): """Return the length of a section.""" return mm.section_length(sec.points) print('Total neurite length (sections):', sum(sec_len(s) for s in nm.iter_sections(nrn))) # Get length of all neurites in cell by iterating over segments, # and summing the segment lengths. # This should yield the same result as iterating over sections. print('Total neurite length (segments):', sum(mm.segment_length(s) for s in nm.iter_segments(nrn))) # get volume of all neurites in cell by summing over segment # volumes print('Total neurite volume:', sum(mm.segment_volume(s) for s in nm.iter_segments(nrn))) # get area of all neurites in cell by summing over segment # areas print('Total neurite surface area:', sum(mm.segment_area(s) for s in nm.iter_segments(nrn))) # get total number of neurite points in cell. def n_points(sec): """number of points in a section.""" n = len(sec.points) # Non-root sections have duplicate first point return n if sec.parent is None else n - 1 print('Total number of points:', sum(n_points(s) for s in nm.iter_sections(nrn))) # get mean radius of neurite points in cell. # p[COLS.R] yields the radius for point p. # Note: this includes duplicated points at beginning of # non-trunk sections print('Mean radius of points:', np.mean([s.points[:, COLS.R] for s in nm.iter_sections(nrn)])) # get mean radius of neurite points in cell. # p[COLS.R] yields the radius for point p. # Note: this includes duplicated points at beginning of # non-trunk sections pts = [p[COLS.R] for s in nrn.sections[1:] for p in s.points] print('Mean radius of points:', np.mean(pts)) # get mean radius of segments print('Mean radius of segments:', np.mean(list(mm.segment_radius(s) for s in nm.iter_segments(nrn)))) # get stats for the segment taper rate, for different types of neurite for ttype in NEURITES: ttt = ttype seg_taper_rate = [mm.segment_taper_rate(s) for s in nm.iter_segments(nrn, neurite_filter=tree_type_checker(ttt))] print('Segment taper rate (', ttype, '):\n mean=', np.mean(seg_taper_rate), ', std=', np.std(seg_taper_rate), ', min=', np.min(seg_taper_rate), ', max=', np.max(seg_taper_rate), sep='') # Number of bifurcation points. print('Number of bifurcation points:', sum(1 for _ in nm.iter_sections(nrn, iterator_type=Section.ibifurcation_point))) # Number of bifurcation points for apical dendrites print('Number of bifurcation points (apical dendrites):', sum(1 for _ in nm.iter_sections(nrn, iterator_type=Section.ibifurcation_point, neurite_filter=tree_type_checker(nm.APICAL_DENDRITE)))) # Maximum branch order print('Maximum branch order:', max(sectionfunc.branch_order(s) for s in nm.iter_sections(nrn))) # Neuron's bounding box # Note: does not account for soma radius print('Bounding box ((min x, y, z), (max x, y, z))', geom.bounding_box(nrn))
Getting Log Information¶
neurom emits many logging statements during the course of its functioning.
They are emitted in the
neurom namespace, and can thus be filtered based
on this. An example of setting up a handler is:
>>> import logging >>> # setup which namespace will be examined, and at what level >>> # in this case we only want messages from 'neurom' and all messages >>> # (ie: DEBUG, INFO, etc) >>> logger = logging.getLogger('neurom') >>> logger.setLevel(logging.DEBUG) >>> # setup where the output will be saved, in this case the console >>> sh = logging.StreamHandler() >>> logger.addHandler(sh)
For more information on logging, it is recommended to read the official Python logging HOWTOs: Python 3.