flins.construct package

Submodules

flins.construct.locations module

Functions that support the creation of new worlds

flins.construct.locations.location_end_cut(lengths, span)[source]

Randomly distribute along span, shorten end proteins if they protrude If a protein is poking out of the span, adjust its length so that it no longer does. This maintains a constant protein density across space at the cost of altering the distribution of lengths.

Parameters

  • lengths (array of floats) – Length of each protein

  • span (float) – Span of the space

flins.construct.locations.location_end_pushed(lengths, span)[source]

Randomly distribute along span, forcing end overlaps inwards If a protein is poking out of the span (negative or past the span value), push it back into the valid range. This maintains the length distribution but gives an uneven protein density across space.

Parameters

  • lengths (array of floats) – Length of each protein

  • span (float) – Span of the space

flins.construct.locations.normal_length_distribution(n, peak, scale)[source]

Normally distributed set of lengths, negative values thrown out

Parameters

  • n (int) – Number of lengths to return

  • peak (float) – Mean length value (assuming no rejected negative values)

  • scale (float) – Spread of length value

flins.construct.test module

Make a test world

This is the default world used to interactively test model components and structure.

flins.construct.test.create_test_world(radius, span, n_actin, n_actinin, n_motors)[source]

Create a world of given radius with n_actin and n_actinin per tract

flins.construct.world module

Make a world

Each world tracks the execution of a run.

class flins.construct.world.World(tractspace, random_state=None, **kwargs)[source]

Bases: object

Keep track of a tract space, simulation time, and metadata

Save the tractspace, initiate record keeping

Parameters

  • tractspace (flins.space.space.TrackSpace) – Spatial component of the world

  • random_state – The internal state of numpy’s Mersenne Twister implementation as given by numpy.random.get_state(). This allows us to recreate run trajectories.

step()[source]

Step forward one tick

Module contents