Python numpy.longfloat() Examples

def _setup_unsigned_graph():
    edges, signed_edges, edge_beliefs, all_ns = _digraph_setup()
    dg = nx.DiGraph()
    dg.add_edges_from(edges)

    # Add belief
    for e in dg.edges:
        dg.edges[e]['belief'] = edge_beliefs[e]
        dg.edges[e]['weight'] = -np.log(edge_beliefs[e], dtype=np.longfloat)

    # Add namespaces
    nodes1, nodes2 = list(zip(*edges))
    nodes = set(nodes1).union(nodes2)
    for node in nodes:
        ns = node[0]
        _id = node[1]
        dg.nodes[node]['ns'] = ns
        dg.nodes[node]['id'] = _id
    return dg, all_ns 

def test_to_digraph():
    ia = IndraNetAssembler([ab1, ab2, ab3, ab4, bc1, bc2, bc3, bc4])
    df = ia.make_df()
    net = IndraNet.from_df(df)
    assert len(net.nodes) == 3
    assert len(net.edges) == 8
    digraph = net.to_digraph(weight_mapping=_weight_mapping)
    assert len(digraph.nodes) == 3
    assert len(digraph.edges) == 2
    assert set([
        stmt['stmt_type'] for stmt in digraph['a']['b']['statements']]) == {
            'Activation', 'Phosphorylation', 'Inhibition', 'IncreaseAmount'}
    assert all(digraph.edges[e].get('belief', False) for e in digraph.edges)
    assert all(isinstance(digraph.edges[e]['belief'],
                          (float, np.longfloat)) for e in digraph.edges)
    assert all(digraph.edges[e].get('weight', False) for e in digraph.edges)
    assert all(isinstance(digraph.edges[e]['weight'],
                          (float, np.longfloat)) for e in digraph.edges)
    digraph_from_df = IndraNet.digraph_from_df(df)
    assert nx.is_isomorphic(digraph, digraph_from_df) 

def test_arange_endian(self,level=rlevel):
        """Ticket #111"""
        ref = np.arange(10)
        x = np.arange(10, dtype='<f8')
        assert_array_equal(ref, x)
        x = np.arange(10, dtype='>f8')
        assert_array_equal(ref, x)

#    Longfloat support is not consistent enough across
#     platforms for this test to be meaningful.
#    def test_longfloat_repr(self,level=rlevel):
#        """Ticket #112"""
#        if np.longfloat(0).itemsize > 8:
#            a = np.exp(np.array([1000],dtype=np.longfloat))
#            assert_(str(a)[1:9] == str(a[0])[:8]) 

def test_to_signed_graph():
    ia = IndraNetAssembler([ab1, ab2, ab3, ab4, bc1, bc2, bc3, bc4])
    df = ia.make_df()
    net = IndraNet.from_df(df)
    signed_graph = net.to_signed_graph(
        sign_dict=default_sign_dict,
        weight_mapping=_weight_mapping)
    assert len(signed_graph.nodes) == 3
    assert len(signed_graph.edges) == 4
    assert set([stmt['stmt_type'] for stmt in
                signed_graph['a']['b'][0]['statements']]) == {
                    'Activation', 'IncreaseAmount'}
    assert set([stmt['stmt_type'] for stmt in
                signed_graph['a']['b'][1]['statements']]) == {'Inhibition'}
    assert set([stmt['stmt_type'] for stmt in
                signed_graph['b']['c'][0]['statements']]) == {
                    'Activation', 'IncreaseAmount'}
    assert set([stmt['stmt_type'] for stmt in
                signed_graph['b']['c'][1]['statements']]) == {
                    'Inhibition', 'DecreaseAmount'}
    assert all(signed_graph.edges[e].get('belief', False) for e in
               signed_graph.edges)
    assert all(isinstance(signed_graph.edges[e]['belief'],
                          (float, np.longfloat)) for e in signed_graph.edges)
    assert all(signed_graph.edges[e].get('weight', False) for e in
               signed_graph.edges)
    assert all(isinstance(signed_graph.edges[e]['weight'],
                          (float, np.longfloat)) for e in signed_graph.edges) 

def _complementary_belief(G, edge):
    # Aggregate belief score: 1-prod(1-belief_i)
    np.seterr(all='raise')
    NP_PRECISION = 10 ** -np.finfo(np.longfloat).precision  # Numpy precision
    belief_list = [s['belief'] for s in G.edges[edge]['statements']]
    try:
        ag_belief = np.longfloat(1.0) - np.prod(np.fromiter(
            map(lambda belief: np.longfloat(1.0) - belief, belief_list),
            dtype=np.longfloat))
    except FloatingPointError as err:
        logger.warning('%s: Resetting ag_belief to 10*np.longfloat precision '
                       '(%.0e)' % (err, Decimal(NP_PRECISION * 10)))
        ag_belief = NP_PRECISION * 10
    return ag_belief 

def test_arange_endian(self,level=rlevel):
        """Ticket #111"""
        ref = np.arange(10)
        x = np.arange(10, dtype='<f8')
        assert_array_equal(ref, x)
        x = np.arange(10, dtype='>f8')
        assert_array_equal(ref, x)

#    Longfloat support is not consistent enough across
#     platforms for this test to be meaningful.
#    def test_longfloat_repr(self,level=rlevel):
#        """Ticket #112"""
#        if np.longfloat(0).itemsize > 8:
#            a = np.exp(np.array([1000],dtype=np.longfloat))
#            assert_(str(a)[1:9] == str(a[0])[:8]) 

def forward(self, weighted_input):
        return np.longfloat(1.0 / (1.0 + np.exp(-weighted_input)))