Introduction to the Modeling and Analysis of Complex Systems

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10.4. SIMULATION WITHOUT PYCX 181Code 10.10: random-walk-2D-pSetter.pydef num_particles (val = n):’’’Number of particles.Make sure you change this parameter while the simulation is not running,and reset the simulation before running it. Otherwise it causes an error!’’’global nn = int(val)return valimport pycxsimulatorpycxsimulator.GUI(parameterSetters = [num_particles]).start(func=[initialize, observe, update])Once you apply this modification to Code 10.6 and run it, the new parameter setter appearsunder the “Parameters” tab of the control window (Fig. 10.3). You can enter a newvalue into the input box, and then click either “Save parameters to the running model”or “Save parameters to the model and reset the model,” and the new parameter value isreflected in the model immediately.Exercise 10.2 To the code developed above, add one more parameter setter forsd (standard deviation of Gaussian noise for random motion of particles).10.4 Simulation without PyCXFinally, I would like to emphasize an important fact: The PyCX simulator file used in thischapter was used only for creating a GUI, while the core simulation model was still fullyimplemented in your own code. This means that your simulation model is completelyindependent of PyCX, and once the interactive exploration and model verification is over,your simulator can “graduate” from PyCX and run on its own.For example, here is a revised version of Code 10.6, which automatically generatesa series of image files without using PyCX at all. You can generate an animated moviefrom the saved image files using, e.g., Windows Movie Maker. This is a nice examplethat illustrates the main purpose of PyCX—to serve as a stepping stone for students and
182 CHAPTER 10. INTERACTIVE SIMULATION OF COMPLEX SYSTEMSFigure 10.3: New parameter setter for the number of particles implemented in Code10.10.
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Introduction to the Modeling and An
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iiiAbout the TextbookIntroduction t
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New York. He is also a Fellow of th
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PrefaceThis is an introductory text
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a comprehensive view of the related
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Chen, Hal Lewis, Vlad Miskovic, Chu
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xviCONTENTS5 Discrete-Time Models I
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xviiiCONTENTS15.3 Constructing Netw
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Chapter 1Introduction1.1 Complex Sy
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1.1. COMPLEX SYSTEMS IN A NUTSHELL
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1.2. TOPICAL CLUSTERS 7The possibil
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1.2. TOPICAL CLUSTERS 9these topica
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12 CHAPTER 2. FUNDAMENTALS OF MODEL
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Part IISystems with a Small Number
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30 CHAPTER 3. BASICS OF DYNAMICAL S
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36 CHAPTER 4. DISCRETE-TIME MODELS
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Chapter 6Continuous-Time Models I:
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6.2. CLASSIFICATIONS OF MODEL EQUAT
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6.3. CONNECTING CONTINUOUS-TIME MOD
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6.4. SIMULATING CONTINUOUS-TIME MOD
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6.4. SIMULATING CONTINUOUS-TIME MOD
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6.5. BUILDING YOUR OWN MODEL EQUATI
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112 CHAPTER 7. CONTINUOUS-TIME MODE
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Page 176 and 177: Next phase space9.3. LYAPUNOV EXPON
Page 178 and 179: 9.3. LYAPUNOV EXPONENT 159easily co
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OutIn232 CHAPTER 13. CONTINUOUS FIE
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234 CHAPTER 13. CONTINUOUS FIELD MO
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296 CHAPTER 15. BASICS OF NETWORKSg
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298 CHAPTER 15. BASICS OF NETWORKSi
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300 CHAPTER 15. BASICS OF NETWORKS5
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302 CHAPTER 15. BASICS OF NETWORKSE
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304 CHAPTER 15. BASICS OF NETWORKSC
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306 CHAPTER 15. BASICS OF NETWORKSC
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308 CHAPTER 15. BASICS OF NETWORKSt
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312 CHAPTER 15. BASICS OF NETWORKSs
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314 CHAPTER 15. BASICS OF NETWORKSn
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316 CHAPTER 15. BASICS OF NETWORKSL
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318 CHAPTER 15. BASICS OF NETWORKSJ
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320 CHAPTER 15. BASICS OF NETWORKSF
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322 CHAPTER 15. BASICS OF NETWORKSr
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326 CHAPTER 16. DYNAMICAL NETWORKS
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Chapter 17Dynamical Networks II: An
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17.1. NETWORK SIZE, DENSITY, AND PE
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17.1. NETWORK SIZE, DENSITY, AND PE
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17.2. SHORTEST PATH LENGTH 37717.2
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17.2. SHORTEST PATH LENGTH 379Eccen
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17.3. CENTRALITIES AND CORENESS 381
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17.4. CLUSTERING 387while the numer
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17.5. DEGREE DISTRIBUTION 3891.00.8
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17.5. DEGREE DISTRIBUTION 391er = n
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17.5. DEGREE DISTRIBUTION 393Pk = [
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17.5. DEGREE DISTRIBUTION 395logkda
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17.6. ASSORTATIVITY 397Assortativit
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17.6. ASSORTATIVITY 399>>> nx.degre
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17.7. COMMUNITY STRUCTURE AND MODUL
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406CHAPTER 18. DYNAMICAL NETWORKS I
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428 CHAPTER 19. AGENT-BASED MODELSE
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430 CHAPTER 19. AGENT-BASED MODELS
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432 CHAPTER 19. AGENT-BASED MODELST
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434 CHAPTER 19. AGENT-BASED MODELSI
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436 CHAPTER 19. AGENT-BASED MODELSt
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438 CHAPTER 19. AGENT-BASED MODELSF
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440 CHAPTER 19. AGENT-BASED MODELSY
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442 CHAPTER 19. AGENT-BASED MODELS3
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444 CHAPTER 19. AGENT-BASED MODELSn
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450 CHAPTER 19. AGENT-BASED MODELSF
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452 CHAPTER 19. AGENT-BASED MODELSe
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454 CHAPTER 19. AGENT-BASED MODELSf
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458 CHAPTER 19. AGENT-BASED MODELSi
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460 CHAPTER 19. AGENT-BASED MODELSw
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462 CHAPTER 19. AGENT-BASED MODELSi
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464 CHAPTER 19. AGENT-BASED MODELSB
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466 BIBLIOGRAPHY[12] “The Human C
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468 BIBLIOGRAPHY[39] H. Sayama, “
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470 BIBLIOGRAPHY[65] P. Holme and J
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Indexaction potential, 202actor, 29
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INDEX 475equilibrium point, 61, 111
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INDEX 477partial differential equat
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