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pycxsimulator-old.py 2013-09-14 3.2 kB 11 weekly downloads
pycxsimulator.pyc 2013-09-14 10.4 kB 11 weekly downloads
README.txt 2013-09-14 4.3 kB 33 weekly downloads
realtime-simulation-template-old.py 2013-09-14 1.4 kB 11 weekly downloads
realtime-simulation-template.py 2013-09-14 2.1 kB 11 weekly downloads
pycxsimulator.py 2013-09-14 10.7 kB 22 weekly downloads
net-voter.py 2013-09-14 1.0 kB 11 weekly downloads
net-synchronization.py 2013-09-14 1.8 kB 11 weekly downloads
net-networkxdemo2.py 2013-09-14 1.5 kB 11 weekly downloads
net-randomwalk.py 2013-09-14 950 Bytes 1818 weekly downloads
net-networkxdemo1.py 2013-09-14 2.9 kB 11 weekly downloads
net-networkgrowth.py 2013-09-14 1.6 kB 22 weekly downloads
net-majority.py 2013-09-14 1.5 kB 11 weekly downloads
net-epidemics-parametersweep.py 2013-09-14 2.6 kB 11 weekly downloads
net-epidemics-adaptive.py 2013-09-14 2.3 kB 22 weekly downloads
net-epidemics.py 2013-09-14 2.0 kB 11 weekly downloads
net-cascade-of-failure.py 2013-09-14 1.8 kB 11 weekly downloads
misc-fileio-csv.py 2013-09-14 440 Bytes 11 weekly downloads
misc-fileio.py 2013-09-14 392 Bytes 0
LICENSE.txt 2013-09-14 1.9 kB 33 weekly downloads
ds-logistic.py 2013-09-14 316 Bytes 22 weekly downloads
ds-lotka-volterra.py 2013-09-14 792 Bytes 11 weekly downloads
ds-euler-forward-method.py 2013-09-14 426 Bytes 11 weekly downloads
ds-cobwebplot.py 2013-09-14 684 Bytes 22 weekly downloads
ds-bifurcationdiagram.py 2013-09-14 597 Bytes 11 weekly downloads
ca-turing.py 2013-09-14 1.6 kB 11 weekly downloads
ca-schelling.py 2013-09-14 3.7 kB 22 weekly downloads
ca-rumor.py 2013-09-14 1.6 kB 11 weekly downloads
ca-majority.py 2013-09-14 1.6 kB 11 weekly downloads
ca-hostpathogen.py 2013-09-14 1.8 kB 11 weekly downloads
ca-GoL.py 2013-09-14 1.5 kB 44 weekly downloads
ca-forestfire.py 2013-09-14 1.5 kB 11 weekly downloads
ca-excitablemedia.py 2013-09-14 1.6 kB 11 weekly downloads
ca-droplet.py 2013-09-14 1.5 kB 11 weekly downloads
abm-predatorprey-withplot.py 2013-09-14 3.9 kB 11 weekly downloads
abm-randomwalk.py 2013-09-14 956 Bytes 11 weekly downloads
abm-predatorprey.py 2013-09-14 3.5 kB 11 weekly downloads
abm-DLA.py 2013-09-14 2.0 kB 11 weekly downloads
abm-ants-pheromone.py 2013-09-14 2.8 kB 11 weekly downloads
abm-ants.py 2013-09-14 2.0 kB 11 weekly downloads
pycx-0.31.zip 2013-09-14 38.9 kB 2424 weekly downloads
###################################################################### ###################################################################### ## ## PyCX 0.31 ## Complex Systems Simulation Sample Code Repository ## ## 2008-2013 (c) Copyright by Hiroki Sayama ## 2012 (c) Copyright by Chun Wong & Hiroki Sayama ## ("pycxsimulator-old.py", "realtime-simulation-template-old.py") ## 2013 (c) Copyright by Przemyslaw Szufel & Bogumil Kaminski ## Extensions to GUI module, some revisions ## All rights reserved. ## ## See LICENSE.txt for more details of license information. ## ## Send any correspondences to: ## Hiroki Sayama, D.Sc. ## Associate Professor, Departments of Bioengineering & ## Systems Science and Industrial Engineering ## Binghamton University, State University of New York ## P.O. Box 6000, Binghamton, NY 13902-6000, USA ## Tel: +1-607-777-4439 Fax: +1-607-777-5780 ## Email: sayama@binghamton.edu ## ## http://pycx.sf.net/ ## ###################################################################### ###################################################################### 1. What is PyCX? The PyCX Project aims to develop an online repository of simple, crude, yet easy-to-understand Python sample codes for dynamic complex systems simulations, including iterative maps, cellular automata, dynamical networks and agent-based models. You can run, read and modify any of its codes to learn the basics of complex systems simulation in Python. The target audiences of PyCX are researchers and students who are interested in developing their own complex systems simulation software using a general-purpose programming language but do not have much experience in computer programming. The core philosophy of PyCX is therefore placed on the simplicity, readability, generalizability and pedagogical values of simulation codes. This is often achieved even at the cost of computational speed, efficiency or maintainability. For example, PyCX does not use object-oriented programming paradigms, it does use global variables frequently, and so on. These choices were intentionally made based on our experience in teaching complex systems modeling and simulation to non-computer scientists. For more information, please see the following open-access article: Sayama, H. (2013) PyCX: A Python-based simulation code repository for complex systems education. Complex Adaptive Systems Modeling 1:2. http://www.casmodeling.com/content/1/1/2 2. What's new in version PyCX 0.3 / 0.31? * Przemyslaw Szufel & Bogumil Kaminski at the Warsaw School of Economics made a substantial improvement to the "pycxsimulator.py" GUI module, implementing interactive control of model and visualization parameters. This improvement is fully backward compatible, so you can run old PyCX 0.2 simulator codes with this new GUI module. * Several new sample simulation codes were added, including: Contributions by Przemyslaw Szufel & Bogumil Kaminski: - "ca-schelling.py" (Tom Schelling's segregation model) - "ca-rumor.py" (Spread of rumor) The above two codes show how to use the new interactive parameter setting feature. Other additions of dynamical network models: - "net-randomwalk.py" (Random walk on a network) - "net-voter.py" (Voter model of opinion formation on a network) - "net-epidemics-adaptive.py" (Epidemics on a network, with adaptive link cutting) - "misc-fileio-csv.py" (Example of how to read/write CSV files) * Revision made to 0.31: ttk is used as a graphics backend instead of Tix, so that Mac users can run the sample codes without installing Tix. 3. How to use it? (i) Install Python 2.7, NumPy, SciPy, matplotlib and NetworkX. Installers are available from the following websites: http://python.org/ http://scipy.org/ http://matplotlib.org/ http://networkx.github.io/ (ii) Choose a PyCX sample code of your interest. (iii) Run it. This should be just double clicking the file in most cases. (iv) Read the code to learn how the simulation was implemented. (v) Change the code as you like. Questions? Comments? Send them to sayama@binghamton.edu.
Source: README.txt, updated 2013-09-14