netpyne.support.morphology

Module with support functions for morphology

Classes:

Cell([name, soma, apic, dend, axon])

Functions:

load(filename[, fileformat, cell, use_axon, ...])	Load an SWC from filename and instantiate inside cell.
sequential_spherical(xyz)	Converts sequence of cartesian coordinates into a sequence of line segments defined by spherical coordinates.
spherical_to_cartesian(r, theta, phi)	Simple conversion of spherical to cartesian coordinates
find_coord(targ_length, xyz, rcum, theta, phi)	Find (x,y,z) ending coordinate of segment path along section path.
interpolate_jagged(xyz, nseg)	Interpolates along a jagged path in 3D
get_section_path(h, sec)
get_section_diams(h, sec)
shapeplot(h, ax[, sections, order, cvals, ...])	Plots a 3D shapeplot
shapeplot_animate(v, lines[, nframes, ...])	Returns animate function which updates color of shapeplot
mark_locations(h, section, locs[, markspec])	Marks one or more locations on along a section.
root_sections(h)	Returns a list of all sections that have no parent.
leaf_sections(h)	Returns a list of all sections that have no children.
root_indices(sec_list)	Returns the index of all sections without a parent.
allsec_preorder(h)	Alternative to using h.allsec().
add_pre(h, sec_list, section[, order_list, ...])	A helper function that traverses a neuron's morphology (or a sub-tree) of the morphology in pre-order.
dist_between(h, seg1, seg2)	Calculates the distance between two segments.
all_branch_orders(h)	Produces a list branch orders for each section (following pre-order tree traversal)
branch_order(h, section[, path])	Returns the branch order of a section
dist_to_mark(h, section, secdict[, path])
branch_precedence(h)

class netpyne.support.morphology.Cell(name='neuron', soma=None, apic=None, dend=None, axon=None)

Bases: object

Methods:

delete()

delete()

netpyne.support.morphology.load(filename, fileformat=None, cell=None, use_axon=True, xshift=0, yshift=0, zshift=0)

Load an SWC from filename and instantiate inside cell. Code kindly provided by @ramcdougal.

Parameters:

• morphology (filename = .swc file containing)

• (Default (cell = Cell() object.) – None, creates new object)

• file (filename = the filename of the SWC)

• Default (use_axon = include the axon?) – True (yes)

• xshift

• yshift

• cell (zshift = use to position the)

Returns:

Cell() object with populated soma, axon, dend, & apic fields

Minimal example:

# pull the morphology for the demo from NeuroMorpho.Org from PyNeuronToolbox import neuromorphoorg with open(‘c91662.swc’, ‘w’) as f: f.write(neuromorphoorg.morphology(‘c91662’)) cell = load_swc(filename)

netpyne.support.morphology.sequential_spherical(xyz)

Converts sequence of cartesian coordinates into a sequence of line segments defined by spherical coordinates.

Parameters:

• array (xyz = 2d numpy) – cartesian coordinates (x,y,z) tracing out a path in 3D space.

• in (each row specifies a point) – cartesian coordinates (x,y,z) tracing out a path in 3D space.

Returns:

r = lengths of each line segment (1D array) theta = angles of line segments in XY plane (1D array) phi = angles of line segments down from Z axis (1D array)

netpyne.support.morphology.spherical_to_cartesian(r, theta, phi)

Simple conversion of spherical to cartesian coordinates

Parameters:

• r

• theta

• coordinates (phi = scalar spherical)

Returns:

x,y,z = scalar cartesian coordinates

netpyne.support.morphology.find_coord(targ_length, xyz, rcum, theta, phi)

Find (x,y,z) ending coordinate of segment path along section path.

Parameters:

• path (xyz = coordinates specifying the section) – from the begining of the section path

• starting – from the begining of the section path

• path

• xyz (phi = angles between each coordinate in)

• theta

• xyz

netpyne.support.morphology.interpolate_jagged(xyz, nseg)

Interpolates along a jagged path in 3D

Parameters:

• coordinates (xyz = section path specified in cartesian)

• path (nseg = number of segment paths in section)

Returns:

interp_xyz = interpolated path

netpyne.support.morphology.get_section_path(h, sec)

netpyne.support.morphology.get_section_diams(h, sec)

netpyne.support.morphology.shapeplot(h, ax, sections=None, order='pre', cvals=None, clim=None, cmap=<matplotlib.colors.LinearSegmentedColormap object>, legend=True, **kwargs)

Plots a 3D shapeplot

Parameters:

• neuron (h = hocObject to interface with)

• plotting (ax = matplotlib axis for)

• plotted (sections = list of h.Section() objects to be)

• sections (order = { None= use h.allsec() to get) – ‘pre’= pre-order traversal of morphology }

• useful (cvals = list/array with values mapped to color by cmap;) – for displaying voltage, calcium or some other state variable across the shapeplot.

• matplotlib (**kwargs passes on to)

Returns:

lines = list of line objects making up shapeplot

netpyne.support.morphology.shapeplot_animate(v, lines, nframes=None, tscale='linear', clim=[-80, 50], cmap=<matplotlib.colors.LinearSegmentedColormap object>)

Returns animate function which updates color of shapeplot

netpyne.support.morphology.mark_locations(h, section, locs, markspec='or', **kwargs)

Marks one or more locations on along a section. Could be used to mark the location of a recording or electrical stimulation.

Parameters:

• neuron (h = hocObject to interface with)

• section (section = reference to)

• 1 (locs = float between 0 and)

• floats (or array of)

• marker (optional arguments specify details of)

Returns:

line = reference to plotted markers

netpyne.support.morphology.root_sections(h)

Returns a list of all sections that have no parent.

netpyne.support.morphology.leaf_sections(h)

Returns a list of all sections that have no children.

netpyne.support.morphology.root_indices(sec_list)

Returns the index of all sections without a parent.

netpyne.support.morphology.allsec_preorder(h)

Alternative to using h.allsec(). This returns all sections in order from the root. Traverses the topology each neuron in “pre-order”

netpyne.support.morphology.add_pre(h, sec_list, section, order_list=None, branch_order=None)

A helper function that traverses a neuron’s morphology (or a sub-tree) of the morphology in pre-order. This is usually not necessary for the user to import.

netpyne.support.morphology.dist_between(h, seg1, seg2)

Calculates the distance between two segments. I stole this function from a post by Michael Hines on the NEURON forum (www.neuron.yale.edu/phpbb/viewtopic.php?f=2&t=2114)

netpyne.support.morphology.all_branch_orders(h)

Produces a list branch orders for each section (following pre-order tree traversal)

netpyne.support.morphology.branch_order(h, section, path=[])

Returns the branch order of a section

netpyne.support.morphology.dist_to_mark(h, section, secdict, path=[])

netpyne.support.morphology.branch_precedence(h)