The mfng module

This is the reference of the mfng module. The references have been created mainly from the documentation strings of the program, so it is quite up-to-date. If you want to learn the basics of the mfng, see the tutorial.

class mfng.Generator(T0, steps, Tlimit, m=2, K=3, n=200, divexponent=2, project='base', verbose=False)

Generate a network with given properties.

T: float
initial temperature
steps: int
the number of steps
Tlimit: float
the temperature when the generation stops
m: integer
the probmeasure will be mxm type
K: integer
it will use K iteration to average the energy
divexponent: int
the exponent in the formula for adjusting division points
project: string
The values will be stored in the directory named with 'project_' + project E.g if project is 'base' the directory will be 'project_base'.

The generation of a generator measure - an example:

generator = mfng.Generator(T0=0.2, steps=10000, Tlimit=0.00002,
        m=3, K=3,
        divexponent = 7,
        project = 'base',
        bigfloat = False
        division = True,

Appends the property prop to the list of properties.


Starts the generation.

Classes for target properties

class mfng.DistributionFunction(function, maxdeg, mindeg=0, **kwargs)

The main class to give a degree distribution as a function to the Generator.

funtion: string
A valid Python expression with the only variable ‘k’. This describes the target degree distribution. The function will be normalized. numpy functions are allowed. E.g.: “5*numpy.exp(-4*k)”
maxdeg: integer
the maximal degree we compare it the degree distribution of the probability measure with.
mindeg: int, default 0, 0 <= mindeg < maxdeg
like maxdeg, but for minimal degree
class mfng.AverageDegree(value)

The class to give an average degree to the Generator.

value: float, value >= 0
The target average degree.

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