python-igraph API reference

List of all classes, functions and methods in python-igraph

class documentation

class FittedPowerLaw:

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Result of fitting a power-law to a vector of samples

Example:

>>> result = power_law_fit([1, 2, 3, 4, 5, 6])
>>> result                   # doctest:+ELLIPSIS
FittedPowerLaw(continuous=False, alpha=2.42..., xmin=3.0, L=-7.54..., D=0.21..., p=0.993...)
>>> print(result)            # doctest:+ELLIPSIS
Fitted power-law distribution on discrete data
<BLANKLINE>
Exponent (alpha)  = 2.42...
Cutoff (xmin)     = 3.000000
<BLANKLINE>
Log-likelihood    = -7.54...
<BLANKLINE>
H0: data was drawn from the fitted distribution
<BLANKLINE>
KS test statistic = 0.21...
p-value           = 0.993...
<BLANKLINE>
H0 could not be rejected at significance level 0.05
>>> result.alpha             # doctest:+ELLIPSIS
2.42...
>>> result.xmin
3.0
>>> result.continuous
False
Method __init__ Undocumented
Instance Variable continuous Undocumented
Instance Variable xmin Undocumented
Instance Variable alpha Undocumented
Instance Variable L Undocumented
Instance Variable D Undocumented
Instance Variable p Undocumented
Method __repr__ Undocumented
Method __str__ Undocumented
Method summary Returns the summary of the power law fit.
def __init__(self, continuous, alpha, xmin, L, D, p):

Undocumented

continuous =

Undocumented

xmin =

Undocumented

alpha =

Undocumented

L =

Undocumented

D =

Undocumented

p =

Undocumented

def __repr__(self):

Undocumented

def __str__(self):

Undocumented

def summary(self, significance=0.05):

Returns the summary of the power law fit.

Parameterssignificancethe significance level of the Kolmogorov-Smirnov test used to decide whether the input data could have come from the fitted distribution
Returnsthe summary as a string
API Documentation for python-igraph, generated by pydoctor 21.2.2 at 2021-10-18 16:23:19.