Geometry module#

This module is a direct copy of the SENeC package PyNCulture. Therefore, in the examples below, you will have to import nngt instead of PyNCulture and replace pnc by nngt.geometry.

Overview#

`nngt.geometry.Area`([shell, holes])	Specialized `Shape` that stores additional properties regarding the interactions with the neurons.
`nngt.geometry.Shape`([shell, holes])	Class containing the shape of the area where neurons will be distributed to form a network.
`nngt.geometry.culture_from_file`(filename[, ...])	Generate a culture from an SVG, a DXF, or a WKT/WKB file.
`nngt.geometry.plot_shape`(shape[, axis, m, ...])	Plot a shape (you should set the axis aspect to 1 to respect the proportions).
`nngt.geometry.pop_largest`(shapes)	Returns the largest shape, removing it from the list.
`nngt.geometry.shapes_from_file`(filename[, ...])	Generate a set of `Shape` objects from an SVG, a DXF, or a WKT/WKB file.

Principle#

Module dedicated to the description of the spatial boundaries of neuronal cultures. This allows for the generation of neuronal networks that are embedded in space.

The shapely library is used to generate and deal with the spatial environment of the neurons.

Examples

Basic features#

The module provides a backup Shape object, which can be used with only the numpy and scipy libraries. It allows for the generation of simple rectangle, disk and ellipse shapes.

import matplotlib.pyplot as plt

import PyNCulture as nc


fig, ax = plt.subplots()

''' Choose a shape (uncomment the desired line) '''
# culture = nc.Shape.rectangle(15, 20, (5, 0))
culture = nc.Shape.disk(20, (5, 0))
# culture = nc.Shape.ellipse((20, 5), (5, 0))

''' Generate the neurons inside '''
pos = culture.seed_neurons(neurons=1000, xmax=0., ymax=0.)

''' Plot '''
nc.plot_shape(culture, ax, show=False)
ax.scatter(pos[:, 0], pos[:, 1], s=2, zorder=2)

plt.show()

All these features are of course still available with the more advanced Shape object which inherits from shapely.geometry.Polygon.

Complex shapes from files#

import matplotlib.pyplot as plt

import PyNCulture as nc


''' Choose a file '''
culture_file = "culture_from_filled_polygons.svg"
# culture_file = "culture_with_holes.svg"
# culture_file = "culture.dxf"

shapes = nc.shapes_from_file(culture_file, min_x=-5000., max_x=5000.)

''' Plot the shapes '''
fig, ax = plt.subplots()
fig.suptitle("shapes")

for p in shapes:
    nc.plot_shape(p, ax, show=False)

plt.show()

''' Make a culture '''
fig2, ax2 = plt.subplots()
plt.title("culture")

culture = nc.culture_from_file(culture_file, min_x=-5000., max_x=5000.)

nc.plot_shape(culture, ax2)

''' Add neurons '''
fig3, ax3 = plt.subplots()
plt.title("culture with neurons")

culture_bis = nc.culture_from_file(culture_file, min_x=-5000., max_x=5000.)
pos = culture_bis.seed_neurons(neurons=1000, xmax=0)

nc.plot_shape(culture_bis, ax3, show=False)
ax3.scatter(pos[:, 0], pos[:, 1], s=2, zorder=3)

plt.show()

Content#

class nngt.geometry.Area(shell=None, holes=None, **kwargs)[source]#

Specialized Shape that stores additional properties regarding the interactions with the neurons.

Each Area is characteristic of a given substrate and height. These two properties are homogeneous over the whole area, meaning that the neurons interact in the same manner with an Area reagardless of their position inside.

The substrate is described through its modulation of the neuronal properties compared to their default behavior. Thus, a given area will modulate the speed, wall affinity, etc, of the growth cones that are growing above it.

Initialize the Shape object and the underlying shapely.geometry.Polygon.

Parameters:

shell (array-like object of shape (N, 2)) – List of points defining the external border of the shape.
holes (array-like, optional (default: None)) – List of array-like objects of shape (M, 2), defining empty regions inside the shape.
unit (string (default: ‘um’)) – Unit in the metric system among ‘um’ ( $\mu m$ ), ‘mm’, ‘cm’, ‘dm’, ‘m’.
height (float, optional (default: 0.)) – Height of the area.
name (str, optional (default: “area”)) – The name of the area.
properties (dict, optional (default: default neuronal properties)) – Dictionary containing the list of the neuronal properties that are modified by the substrate. Since this describes how the default property is modulated, all values must be positive reals or NaN.

property areas#: Returns the dictionary containing the Shape’s areas.

copy()[source]#: Create a copy of the current Area.

classmethod from_shape(shape, height=0.0, name='area', properties=None, unit='um', min_x=None, max_x=None)[source]#

Create an Area from a Shape object.

Parameters:: shape (Shape) – Shape that should be converted to an Area.
Returns:: Area object.

class nngt.geometry.Shape(shell=None, holes=None, **kwargs)[source]#

Class containing the shape of the area where neurons will be distributed to form a network.

area#

Area of the shape in the Shape’s Shape.unit() squared ( $\mu m^2$ , $mm^2$ , $cm^2$ , $dm^2$ or $m^2$ ).

Type:: double

centroid#

Position of the center of mass of the current shape in unit.

Type:: tuple of doubles