Metadata-Version: 2.1
Name: complexity-science
Version: 0.0.3
Summary: A package for complexity science research
Home-page: https://github.com/complexity-science
Author: Krister Jazz Urog
Author-email: kristerjazz.urog@gmail.com
License: UNKNOWN
Description: # Complexity Science Package
        
        ## Download
        
        `pip install complexity-science`
        
        -------------------------------------------------------------------------------------
        
        ## Cellular Automata
        
        Basic usage:
        
        `import complexity_science.ca as cs`
        
        ### 1-D CA
        
        `ca = cs.wolfram(N, 20) #creates a 1-Dimensional CA of N cells with wolfram rule number 20`
        
        `ca.initialize([50]) #initializes the 50th cell of the CA`
        
        `ca.run(100) #returns the resulting state of the CA for 100 iterations following the rule and plots the result with a default colormap`
        
        
        
        ### 2-D CA
        
        `model = cs.brians_brain([128,128], toroidal=False)` 
        
        Initializes a CA based on brians brain with toroidal boundary conditions
        
        
        
        #### *Available models*
        
        `cs.game([dim], toroidal=True)`
        
        `cs.applause([dim],  alpha=1)`
        
        `cs.mpa([dim], percent_mpa=0)`
        
        The `dim` parameter is the only required parameter for all models, others are optional. Parameters are set to default value if not specified.
        
        `alpha` and `percent_mpa` are examples of model specific parameters. 
        
        **See model documentation for more information.**
        
        
        
        #### *Animation*  
        
        `model.animate(iteration=100)`
        
        If `iteration` is not set, animation will continue infinitely. 
        
        
        
        #### *Modifying Parameters* 
        
        Models can be initialized randomly, binary, by index, using different functions
        e.g.
        
        `model.initialize_random()`
        
        `model.initialize_random_bin(0.5)`
        
        `model.initialize_random_int(0,2)`
        
        
        
        Models with specific parameters can be modified by this function.
        
        `model.modify_rule(parameter = new_value)` 
        
        *See model documentation for more information*
        
        
        
        ### Adding rules and models
        
        Please contact the author for more information.
        
        
        
        
        
        
        
        -----------------------------------------------------------------------------------------
        
        ## Epidemics
        
        COMING SOON!
        
        ## Network Fragmentation
        
        COMING SOON!
        
        
        
        # Contributing:
        
        `git clone https://github.com/KristerJazz/complexity-science.git`
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Requires-Python: >3.6
Description-Content-Type: text/markdown
