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Implementation of some known cellular automata. Use for academic purposes only.
Work in progress ...
# Simulation results
# Floor Field Model (cellular_automaton.py)
## Reference
Cellular Automaton. Floor Field Model [Burstedde2001] Simulation of
pedestriandynamics using a two-dimensional cellular automaton Physica A, 295,
507-525, 2001
## Usage
```
python cellular_automaton.py <optional arguments>
```
## Optional arguments:
| option | value | descrption |
|-----------------|:------------------------:|------------------------------------------------------------------------------ |
|`-h, --help` | | show this help message and exit |
| `-s, --ks` | KS | sensitivity parameter for the Static Floor Field (*default 2*) |
| `-d, --kd` | KD | sensitivity parameter for the Dynamic Floor Field (*default 1*) |
| `-n, --numPeds`| NUMPEDS | Number of agents (*default 10*) |
| `-p, --plotS` | | plot Static Floor Field |
| `--plotD` | | plot Dynamic Floor Field |
| `--plotAvgD` | | plot average Dynamic Floor Field |
| `-P, --plotP` | | plot Pedestrians |
| `-r, --shuffle`| | random shuffle |
| `-v, --reverse`| | reverse sequential update |
| `-l, --log` | LOG | log file (*default log.dat*) |
| `--decay` | DECAY | the decay probability of the Dynamic Floor Field (*default 0.2*) |
| `--diffusion` | DIFFUSION | the diffusion probability of the Dynamic Floor Field (*default 0.2*) |
| `-W, --width` | WIDTH | the width of the simulation area in meter, excluding walls |
| `-H, --height` | HEIGHT | the height of the simulation room in meter, excluding walls |
| `-c, --clean` | | remove files from directories dff/ sff/ and peds/ |
| `-N, --nruns` | NRUNS | repeat the simulation N times (*default 1*) |
| `--parallel` | | use multithreading |
| `--moore` | | use moore neighborhood (*default Von Neumann*) |
| `--box` | from_x to_x from_y to_y | Rectangular box defined by 4 numbers, where agents will be distributed. (*default the whole room*) |
## Simulation results
With the following parameter:
- Width of room: 30 m
......@@ -26,7 +65,7 @@ python cellular_automaton.py -W 30 -H 30 -N 1 -n 2000 --diffusion 2 --plotAvgD
- Dynamics floor field (averaged over time)
![](figs/DFF-avg_S2.00_D5.00.png)
# Different neighborhoods
## Comparison of different neighborhoods
Call the script with the option `-moore` to use the moore neighborhood. Otherwise, von Neumann neighborhood will be used as default.
......@@ -34,19 +73,15 @@ The choice of the neighborhood has an influence on the evacuation time, as can s
![evactime](./figs/moore_neumann_tevac.png)
## Moore neighborhood (video)
### Moore neighborhood (video)
[![./figs/SFF_moore.png](http://img.youtube.com/vi/DAzu7GkUjHc/0.jpg)](https://youtu.be/DAzu7GkUjHc)
## von Neumann neighborhood (video)
### von Neumann neighborhood (video)
[![./figs/SFF_neumann.png](http://img.youtube.com/vi/tnQegJcclu0/0.jpg)](https://youtu.be/tnQegJcclu0)
# Models
Two models are implemented:
## Floor field model:
## Todos:
- Different update schemes: sequential, shuffle sequential, reverse sequential.
- visualisation of the cell states.
- ~~todo~~: make a video from the png's
......@@ -55,24 +90,46 @@ Two models are implemented:
- *todo*: implement the parallel update.
- todo: implement the conflict friction `mu`
- *todo*: read geometry from a png file. See [read_png.py](geometry/read_png.py).
## ASEP model
- the theoretical fundamental diagram can be reproduced, see [figure](figs/asep_fd.png). The size of the system should be reasonably high and the simulation time also.
- *todo*: implement TASEP
- *todo*: implement sequential update with all its variants.
- Remarque: There are two implementations of the asep. One it optimized using vector-operations from `numpy` (`asep_fast.py`) and the other implementation is using explicit loops (`asep_slow.py`). The naming of the two variations is justified when measuring their execution time:
# ASEP model (asep_fast.py)
the Asymmetric Simple Exclusion Process (ASEP)
## Reference
Rajewsky, N. and Santen, L. and Schadschneider, A. and Schreckenberg, M.
The asymmetric exclusion process: Comparison of update procedures
Journal of Statistical Physics, 1998
## Usage
```
python make_fd.py asep_fast.py: 0:56.71 real, 52.12 user, 4.03 sys
python make_fd.py asep_slow.py: 1:15.42 real, 70.55 user, 4.23 sys
python asep_slow.py <optional arguments>
```
## Optional arguments:
| option | value | descrption |
|-----------------|:-------------------------:|------------|
| -`h, --help` | | show this help message and exit |
| `-n, --np` | NUMPEDS | number of agents (*default 10*)|
| `-N, --nr` | NRUNS | number of runs (*default 1*) |
| `-m, --ms` | MS | max simulation steps (*default 100*) |
| `-w, --width` | WIDTH | width of the system (*default 50*) |
| `-p, --plotP` | | plot Pedestrians |
| `-r, --shuffle` | | random shuffle|
| `-v, --reverse` | | reverse sequential update|
| `-l, --log` | LOG | log file (default *log.dat*) |
# How to use
## simulation results
run
the theoretical fundamental diagram can be reproduced, see [figure](figs/asep_fd.png). The size of the system should be reasonably high and the simulation time also.
## Todos
- *todo*: implement TASEP
- *todo*: implement sequential update with all its variants.
- Remarque: There are two implementations of the asep. One it optimized using vector-operations from `numpy` (`asep_fast.py`) and the other implementation is using explicit loops (`asep_slow.py`). The naming of the two variations is justified when measuring their execution time:
```
python one_of_the_scripts -h
python make_fd.py asep_fast.py: 0:56.71 real, 52.12 user, 4.03 sys
python make_fd.py asep_slow.py: 1:15.42 real, 70.55 user, 4.23 sys
```
to see the options.
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