#!/usr/bin/env python
from nose.tools import *
from nose import SkipTest
from nose.plugins.attrib import attr
import random
import networkx

# Example from
# A. Langville and C. Meyer, "A survey of eigenvector methods of web
# information retrieval."  http://citeseer.ist.psu.edu/713792.html


class TestPageRank:

    def setUp(self):
        G=networkx.DiGraph()
        edges=[(1,2),(1,3),\
           (3,1),(3,2),(3,5),\
           (4,5),(4,6),\
           (5,4),(5,6),\
           (6,4)]
        G.add_edges_from(edges)
        self.G=G
        self.G.pagerank=dict(zip(G,
                                 [0.03721197,0.05395735,0.04150565,
                                  0.37508082,0.20599833, 0.28624589]))

    def test_pagerank(self):
        G=self.G
        p=networkx.pagerank(G,alpha=0.9,tol=1.e-08)
        for n in G:
            assert_almost_equal(p[n],G.pagerank[n],places=4)

        nstart = dict((n,random.random()) for n in G)
        p=networkx.pagerank(G,alpha=0.9,tol=1.e-08, nstart=nstart)
        for n in G:
            assert_almost_equal(p[n],G.pagerank[n],places=4)

        assert_raises(networkx.NetworkXError,networkx.pagerank,G,
                      max_iter=0)


    @attr('numpy')
    def test_numpy_pagerank(self):
        try:
            import numpy
        except ImportError:
            raise SkipTest('numpy not available.')
        G=self.G
        p=networkx.pagerank_numpy(G,alpha=0.9)
        for n in G:
            assert_almost_equal(p[n],G.pagerank[n],places=4)
        personalize = dict((n,random.random()) for n in G)
        p=networkx.pagerank_numpy(G,alpha=0.9, personalization=personalize)



    @attr('numpy')
    def test_google_matrix(self):
        try:
            import numpy.linalg
        except ImportError:
            raise SkipTest('numpy not available.')
        G=self.G
        M=networkx.google_matrix(G,alpha=0.9)
        e,ev=numpy.linalg.eig(M.T)
        p=numpy.array(ev[:,0]/ev[:,0].sum())[:,0]
        for (a,b) in zip(p,self.G.pagerank.values()):
            assert_almost_equal(a,b)

        personalize = dict((n,random.random()) for n in G)
        M=networkx.google_matrix(G,alpha=0.9, personalization=personalize)
        _ = personalize.pop(1)
        assert_raises(networkx.NetworkXError,networkx.google_matrix,G,
                      personalization=personalize)

    def test_scipy_pagerank(self):
        G=self.G
        try:
            import scipy
        except ImportError:
            raise SkipTest('scipy not available.')
        p=networkx.pagerank_scipy(G,alpha=0.9,tol=1.e-08)
        for n in G:
            assert_almost_equal(p[n],G.pagerank[n],places=4)
        personalize = dict((n,random.random()) for n in G)
        p=networkx.pagerank_scipy(G,alpha=0.9,tol=1.e-08,
                                  personalization=personalize)

        assert_raises(networkx.NetworkXError,networkx.pagerank_scipy,G,
                      max_iter=0)

    def test_personalization(self):
        G=networkx.complete_graph(4)
        personalize={0:1,1:1,2:4,3:4}
        answer={0:0.1,1:0.1,2:0.4,3:0.4}
        p=networkx.pagerank(G,alpha=0.0,personalization=personalize)
        for n in G:
            assert_almost_equal(p[n],answer[n],places=4)
        _ = personalize.pop(0)
        assert_raises(networkx.NetworkXError,networkx.pagerank,G,
                      personalization=personalize)


    @attr('numpy')
    def test_empty(self):
        try:
            import numpy
        except ImportError:
            raise SkipTest('numpy not available.')
        G=networkx.Graph()
        assert_equal(networkx.pagerank(G),{})
        assert_equal(networkx.pagerank_numpy(G),{})
        assert_equal(networkx.pagerank_scipy(G),{})
        assert_equal(networkx.google_matrix(G).shape,(0,0))