Event manager

The event manager is the system which manage the different behavior of a simulation. It is composed of two lists :

  • current : list of actions to be executed during the current simulation step.

  • next : list of actions to be executed for the next simulation step, this list is filled during the current simulation step.

A behavior is a function which describes/contains the actions requested to realize an event. For example, the behavior which describe the cell division is composed of :

  • growing cell area until its reach a certain threshold

  • dividing cell into two daughter cell

Event manager principle

# Generate 2D tyssue

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

import ipywidgets as widgets
from IPython import display
import ipyvolume as ipv

# Core object
from tyssue import Sheet
# Simple 2D geometry
from tyssue import PlanarGeometry as sgeom
# Visualisation
from tyssue.draw import (

sheet = Sheet.planar_sheet_2d(
    'basic2D', # a name or identifier for this sheet
    nx=6, # approximate number of cells on the x axis
    ny=7, # approximate number of cells along the y axis
    distx=1, # distance between 2 cells along x
    disty=1, # distance between 2 cells along y
    noise=0 # some position noise

# Give the tissue a nice hear cut ;)
sheet.sanitize(trim_borders=True, order_edges=True)
# Visualisation of the tissue
fig, ax = sheet_view(sheet, mode="2D")
fig.set_size_inches(8, 8)
from tyssue.dynamics.planar_vertex_model import PlanarModel as smodel
from tyssue.solvers import QSSolver
from pprint import pprint

specs = {
    'edge': {
        'is_active': 1,
        'line_tension': 0.12,
        'ux': 0.0,
        'uy': 0.0,
        'uz': 0.0
   'face': {
       'area_elasticity': 1.0,
       'contractility': 0.04,
       'is_alive': 1,
       'prefered_area': 1.0},
   'settings': {
       'grad_norm_factor': 1.0,
       'nrj_norm_factor': 1.0
   'vert': {
       'is_active': 1

# Update the specs (adds / changes the values in the dataframes' columns)
# Check the tissue is at its equilibrium
solver = QSSolver()
res = solver.find_energy_min(sheet, sgeom, smodel)
# Visualisation of the tissue
fig, ax = sheet_view(sheet, mode="2D")

Write a behavior function

Behavior parameters function are composed of two parts :

  • signature part, which contains sheet and manager parameter

  • keywords part, which is specific to one behavior function

To add a behavior to the manager, append method has to be used, and it need as parameter the function name and the keyword part.

from tyssue.topology.sheet_topology import cell_division

def division(sheet, manager, cell_id=0, crit_area=2.0, growth_rate=0.1, dt=1.):
    """Defines a division behavior.
    sheet: a :class:`Sheet` object
    cell_id: int
        the index of the dividing cell
    crit_area: float
        the area at which 
    growth_rate: float
        increase in the prefered are per unit time
        A_0(t + dt) = A0(t) * (1 + growth_rate * dt)

    if sheet.face_df.loc[cell_id, "area"] > crit_area:
        # restore prefered_area
        sheet.face_df.loc[12, "prefered_area"] = 1.0
        # Do division
        daughter = cell_division(sheet, cell_id, sgeom)
        # Update the topology
        # update geometry
        print(f"cell n°{daughter} is born")
        sheet.face_df.loc[12, "prefered_area"] *= (1 + dt * growth_rate)
        manager.append(division, cell_id=cell_id)

When the manager is initialised, wait function is aded by default in the current event list. Any new event added to the manager are added to the next list.

from tyssue.behaviors import EventManager

# Initialisation of manager 
manager = EventManager("face")

# Add action/event to the manager
manager.append(division, cell_id=12)
print('manager.current :')
print('manager.next :')
manager.current :
deque([(<function wait at 0x7feac86b9ee0>, {'face_id': -1, 'n_steps': 1})])

manager.next :
deque([(<function division at 0x7feab9987c10>, {'cell_id': 12})])
from tyssue import History

t = 0
stop = 30

# The History object records all the time steps 
history = History(sheet)

while manager.current and t < stop:
    # Execute the event in the current list
    t += 1
    # Find energy min
    res = solver.find_energy_min(sheet, sgeom, smodel)
    # Switch event list from the next list to the current list
cell n°25 is born
draw_specs = {
    "edge": {
        "color": lambda sheet: sheet.edge_df.length
    "face": {
        "visible": True,
        "color": lambda sheet: sheet.face_df.area,
        "color_range": (0, 2)

create_gif(history, "growth.gif", num_frames=30, margin=5, **draw_specs)
<IPython.core.display.Image object>
# Visualisation of the tissue
fig, ax = sheet_view(sheet, mode="2D")
fig.set_size_inches(8, 8)