# Cellular automata¶

cromosim.ca.compute_exit_times(dt, results)[source]

To compute exit times for all the individuals from the results array

Parameters
dt: float

time step

results: numpy array

positions at each time

Returns
exit_times: all the exit times
cromosim.ca.exit(domain, people, people_ij)[source]

To update people and people_ij arrays in removing individuals who left the domain

Parameters
domain: Domain

contains everything for managing the domain

equal to 1 if the cell (i,j) is occupied, 0 elsewhere

people_ij: numpy array

(i,j) for each individual

Returns

new positions equal to 1 if the cell (i,j) is occupied, 0 elsewhere

new people index (i,j)

cromosim.ca.parallel_update(people, people_ij, weight, friction=0, randomstate=None)[source]

To move all individuals in parallel according to the following rule:  first, desired moves are precomputed and then the conflicts (two individuals at the same position) are resolved

Parameters

equal to 1 if the cell (i,j) is occupied, 0 elsewhere

people_ij: numpy array

(i,j) for each individual

weight: numpy array

weights for the probabilities in order to move in such a way like to reach the door

friction: float

to designate the effect induced by a modified handling of conflicts, friction is the probability that a conflict remains unresolved (no one moves)

randomstate: numpy randomstate

create a new one or reuse the given random state

Returns

new positions equal to 1 if the cell (i,j) is occupied, 0 elsewhere

new people index (i,j)

cromosim.ca.plot_people_according_to_current_door_distance(ifig, people, domain, axis=None, savefig=False, filename='fig.png')[source]

To draw occupied cells with colors depending on current door distances

Parameters
ifig: int

figure number

equal to 1 if the cell (i,j) is occupied, 0 elsewhere

domain: Domain

contains everything for managing the domain

axis: list

matplotlib axis

savefig: boolean

writes the figure as a png file if true

filename: string

png filename used to write the figure

cromosim.ca.plot_people_according_to_exit_times(ifig, dt, people, domain, results, axis=None, savefig=False, filename='fig.png')[source]

To draw occupied cells with colors depending on the exit times

Parameters
ifig: int

figure number

dt: float

time step

equal to 1 if the cell (i,j) is occupied, 0 elsewhere

domain: Domain

contains everything for managing the domain

results: ndarray

positions at each time

axis: list

matplotlib axis

savefig: boolean

writes the figure as a png file if true

filename: string

png filename used to write the figure

cromosim.ca.plot_people_according_to_initial_door_distance(ifig, people, domain, results, axis=None, savefig=False, filename='fig.png')[source]

To draw occupied cells with colors depending on initial (time=0) door distances

Parameters
ifig: int

figure number

equal to 1 if the cell (i,j) is occupied, 0 elsewhere

domain: Domain

contains everything for managing the domain

results: numpy array

contains the results for each iteration in time, used here to determine the initial distance to the door

axis: list

matplotlib axis

savefig: boolean

writes the figure as a png file if true

filename: string

png filename used to write the figure

cromosim.ca.plot_people_paths(ifig, dt, pixel_size, people, domain, results, axis=None, savefig=False, filename='fig.png')[source]

To draw all the individual paths from intial time to final time

Parameters
ifig: int

figure number

dt: float

time step

pixel_size: float

size of one pixel in meters

equal to 1 if the cell (i,j) is occupied, 0 elsewhere

domain: Domain

contains everything for managing the domain

results: numpy.ndarray

positions at each time

axis: list

matplotlib axis

savefig: boolean

writes the figure as a png file if true

filename: string

png filename used to write the figure

cromosim.ca.sequential_update(people, people_ij, weight, shuffle=None, randomstate=None)[source]

To move all individuals sequentially according to the following rule:  the update of individual i is determined in a stochastic way by computing transition probabilities on neighboring cells (including the current position of i).

Parameters

equal to 1 if the cell (i,j) is occupied, 0 elsewhere

people_ij: numpy array

(i,j) for each individual

weight: numpy array

weights for the probabilities in order to move in such a way like to reach the door

shuffle: string

shuffle kind (‘random’ or ‘random_frozen’): if the sequential order changes or not at each time

randomstate: numpy randomstate

create a new one or reuse the given random state

Returns