#!/usr/bin/env python
# -*- coding: utf-8 -*-
# "THE WISKEY-WARE LICENSE":
# <utn_kdd@googlegroups.com> wrote this file. As long as you retain this notice
# you can do whatever you want with this stuff. If we meet some day, and you
# think this stuff is worth it, you can buy us a WISKEY us return.
#===============================================================================
# DOCS
#===============================================================================
"""Statistic functions to calculate weight statistics over Yatel enviroments.
"""
#===============================================================================
# IMPORT
#===============================================================================
import collections
import numpy as np
from scipy import stats
from yatel import db
#===============================================================================
# BASIC POSITION STATS
#===============================================================================
[docs]def average(nw, env=None, **kwargs):
"""Compute the weighted average on a network.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
return np.average(arr)
[docs]def percentile(nw, q, env=None, **kwargs):
"""Compute the q-th percentile of the network.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
return np.percentile(arr, q)
[docs]def min(nw, env=None, **kwargs):
"""Return the minimum in a network.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
return np.min(arr)
[docs]def max(nw, env=None, **kwargs):
"""Return the maximum in a network.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
return np.max(arr)
[docs]def amin(nw, env=None, **kwargs):
"""Return the minimum in a network.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
return np.amin(arr)
[docs]def amax(nw, env=None, **kwargs):
"""Return the maximum in a network.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
return np.amax(arr)
[docs]def sum(nw, env=None, **kwargs):
"""Sum of the elements on the network.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
return np.sum(arr)
[docs]def mode(nw, env=None, **kwargs):
"""Calculates mode on a network.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
cnt = collections.Counter(arr)
value = np.max(cnt.values())
n = cnt.values().count(value)
return np.array(tuple(v[0] for v in cnt.most_common(n)))
#===============================================================================
# DISPERTION STATS
#===============================================================================
[docs]def var(nw, env=None, **kwargs):
"""Compute the variance of the network.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
return np.var(arr)
[docs]def std(nw, env=None, **kwargs):
"""Compute the standard deviation of the network.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
return np.std(arr)
[docs]def variation(nw, env=None, **kwargs):
"""Computes the coefficient of variation.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
return stats.variation(arr)
[docs]def range(nw, env=None, **kwargs):
"""Computes the distance between the maximum and minimum.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
return np.amax(arr) - np.amin(arr)
#===============================================================================
# KURTOSIS
#===============================================================================
[docs]def kurtosis(nw, env=None, **kwargs):
"""Computes the kurtosis (Fisher’s definition) of a network.
Parameters
----------
nw : :py:class:`yatel.db.YatelNetwork`
Network to which apply the operation.
env : :py:class:`yatel.dom.Enviroment` or dict like
Environment for filtering.
"""
arr = env2weightarray(nw, env=env, **kwargs)
return stats.kurtosis(arr)
#===============================================================================
# SUPPORT
#===============================================================================
[docs]def weights2array(edges):
"""Create a **numpy.ndarray** with all the weights of
:py:class:`yatel.dom.Edge`
"""
generator = (e.weight for e in edges)
return np.fromiter(generator, float)
[docs]def env2weightarray(nw, env=None, **kwargs):
"""This function always return a **numpy.ndarray** with this conditions:
- If ``nw`` is instance of **numpy.ndarray** the same array is returned.
- If ``nw`` is instance of :py:class:`yatel.db.YatelNetwork` and an
environment is given return all the edges in this environment.
- If ``nw`` is instance of :py:class:`yatel.db.YatelNetwork` and no
environment is given then return all edges.
- In the last case the function tries to convert ``nw`` to
**numpy.ndarray** instance.
"""
env = dict(env) if env else {}
env.update(kwargs)
if isinstance(nw, np.ndarray):
if env:
msg = "if nw is numpy.ndarray you can't use environments"
raise ValueError(msg)
return nw
elif isinstance(nw, db.YatelNetwork):
if not env:
return weights2array(nw.edges())
return weights2array(nw.edges_by_environment(env=env))
else:
return np.array(nw)
#===============================================================================
# MAIN
#===============================================================================
if __name__ == "__main__":
print(__doc__)