Python utility.assert_arrays_equal函数代码示例

def test__make_compatible_v():
    r = recording.Recorder('v')
    input_data = numpy.array([[0, 0.0, -65.0], [3, 0.0, -65.0],
                              [0, 0.1, -64.3], [3, 0.1, -65.1],
                              [0, 0.2, -63.7], [3, 0.2, -65.5]])
    output_data = r._make_compatible(input_data) # voltage id
    assert_arrays_equal(input_data[:,(2,0)], output_data) 

 def test_infinite_space_with_3D_distances(self):
     s = space.Space()
     self.assertEqual(s.distances(self.A, self.B), sqrt(3))
     self.assertEqual(s.distances(self.C, self.B), sqrt(12))
     self.assertArraysEqual(s.distances(self.A, self.ABCD), numpy.array([0.0, sqrt(3), sqrt(3), sqrt(29)]))
     self.assertArraysEqual(s.distances(self.A, self.ABCD), s.distances(self.ABCD, self.A).T)
     assert_arrays_equal(
         s.distances(self.ABCD, self.ABCD),
         numpy.array(
             [
                 0.0,
                 sqrt(3),
                 sqrt(3),
                 sqrt(29),
                 sqrt(3),
                 0.0,
                 sqrt(12),
                 sqrt(14),
                 sqrt(3),
                 sqrt(12),
                 0.0,
                 sqrt(50.0),
                 sqrt(29),
                 sqrt(14),
                 sqrt(50.0),
                 0.0,
             ]
         ),
     )
     self.assertArraysEqual(s.distances(self.ABCD, self.A), numpy.array([0.0, sqrt(3), sqrt(3), sqrt(29)]))

def test_get_positions():
    p = MockPopulation(11, MockStandardCell)
    ppos = numpy.random.uniform(size=(3,11))
    p._positions = ppos
    pv = common.PopulationView(parent=p,
                               selector=slice(3,9,2))
    assert_arrays_equal(pv.positions, numpy.array([ppos[:,3], ppos[:,5], ppos[:,7]]).T)

 def test_really_simple1(self):
     A = numpy.ones((3,))
     B = numpy.zeros((3,5))
     D = connectors.DistanceMatrix(B, space.Space())
     D.set_source(A)
     assert_arrays_equal(D.as_array(),
                         numpy.sqrt(3*numpy.ones((5,), float)))

def test_reset_recording(sim):
    """
    Check that record(None) resets the list of things to record.

    This test injects different levels of current into two neurons. In the
    first run, we record one of the neurons, in the second we record the other.
    The main point is to check that the first neuron is not recorded in the
    second run.
    """
    sim.setup()
    p = sim.Population(7, sim.IF_cond_exp())
    p[3].i_offset = 0.1
    p[4].i_offset = 0.2
    p[3:4].record('v')
    sim.run(10.0)
    sim.reset()
    p.record(None)
    p[4:5].record('v')
    sim.run(10.0)
    data = p.get_data()
    sim.end()
    ti = lambda i: data.segments[i].analogsignalarrays[0].times
    assert_arrays_equal(ti(0), ti(1))
    idx = lambda i: data.segments[i].analogsignalarrays[0].channel_index
    assert idx(0) == [3]
    assert idx(1) == [4]
    vi = lambda i: data.segments[i].analogsignalarrays[0]
    assert vi(0).shape == vi(1).shape == (101, 1)
    assert vi(0)[0, 0] == vi(1)[0, 0] == p.initial_values['v'].evaluate(simplify=True)*pq.mV # the first value should be the same
    assert not (vi(0)[1:, 0] == vi(1)[1:, 0]).any()            # none of the others should be, because of different i_offset

def test_columnwise_iteration_with_structured_array_and_mask():
    input = numpy.arange(12).reshape((4,3))
    m = LazyArray(input, shape=(4,3)) # 4 rows, 3 columns
    mask = numpy.array([False, True, True])
    cols = [col for col in m.by_column(mask=mask)]    
    assert_arrays_equal(cols[0], input[:,1])
    assert_arrays_equal(cols[1], input[:,2])

def test_apply_function_to_structured_array():
    f = lambda m: 2*m + 3
    input = numpy.arange(12).reshape((4,3))
    m0 = LazyArray(input, shape=(4,3))
    m1 = f(m0)
    assert isinstance(m1, larray)
    assert_arrays_equal(m1.evaluate(simplify=True), input*2 + 3)

def test_set_synaptic_parameters(sim):
    sim.setup()
    mpi_rank = sim.rank()
    p1 = sim.Population(4, sim.IF_cond_exp())
    p2 = sim.Population(2, sim.IF_cond_exp())
    syn = sim.TsodyksMarkramSynapse(U=0.5, weight=0.123, delay=0.1)
    prj = sim.Projection(p1, p2, sim.AllToAllConnector(), syn)

    expected = numpy.array([
        (0.0, 0.0, 0.123, 0.1, 0.5),
        (0.0, 1.0, 0.123, 0.1, 0.5),
        (1.0, 0.0, 0.123, 0.1, 0.5),
        (1.0, 1.0, 0.123, 0.1, 0.5),
        (2.0, 0.0, 0.123, 0.1, 0.5),
        (2.0, 1.0, 0.123, 0.1, 0.5),
        (3.0, 0.0, 0.123, 0.1, 0.5),
        (3.0, 1.0, 0.123, 0.1, 0.5),
    ])
    actual = numpy.array(prj.get(['weight', 'delay', 'U'], format='list'))
    if mpi_rank == 0:
        ind = numpy.lexsort((actual[:, 1], actual[:, 0]))
        assert_arrays_equal(actual[ind], expected)

    positional_weights = numpy.array([[0, 1], [2, 3], [4, 5], [6, 7]], dtype=float)
    prj.set(weight=positional_weights)
    expected = positional_weights
    actual = prj.get('weight', format='array')
    if mpi_rank == 0:
        assert_arrays_equal(actual, expected)

    u_list = [0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2]
    prj.set(U=u_list)
    expected = numpy.array([[0.9, 0.8], [0.7, 0.6], [0.5, 0.4], [0.3, 0.2]])
    actual = prj.get('U', format='array')
    if mpi_rank == 0:
        assert_arrays_equal(actual, expected)

    f_delay = lambda d: 0.5+d
    prj.set(delay=f_delay)
    expected = numpy.array([[0.5, 1.5], [1.5, 0.5], [2.5, 1.5], [3.5, 2.5]])
    actual = prj.get('delay', format='array')
    if mpi_rank == 0:
        assert_arrays_equal(actual, expected)

    # final sanity check
    expected = numpy.array([
        (0.0, 0.0, 0.0, 0.5, 0.9),
        (0.0, 1.0, 1.0, 1.5, 0.8),
        (1.0, 0.0, 2.0, 1.5, 0.7),
        (1.0, 1.0, 3.0, 0.5, 0.6),
        (2.0, 0.0, 4.0, 2.5, 0.5),
        (2.0, 1.0, 5.0, 1.5, 0.4),
        (3.0, 0.0, 6.0, 3.5, 0.3),
        (3.0, 1.0, 7.0, 2.5, 0.2),
    ])
    actual = numpy.array(prj.get(['weight', 'delay', 'U'], format='list'))
    if mpi_rank == 0:
        ind = numpy.lexsort((actual[:, 1], actual[:, 0]))
        assert_arrays_equal(actual[ind], expected)

def test__record():
    p = MockPopulation()
    p.recorders = {'v': Mock()}
    p._record('v')
    meth, args, kwargs = p.recorders['v'].method_calls[0]
    id_arr, = args
    assert_equal(meth, 'record')
    assert_arrays_equal(id_arr, p.all_cells)

def test_evaluate_with_functional_array():
    input = lambda i,j: 2*i + j
    m = LazyArray(input, shape=(4,3))
    assert_arrays_equal(m.evaluate(),
                        numpy.array([[0, 1, 2],
                                     [2, 3, 4],
                                     [4, 5, 6],
                                     [6, 7, 8]]))

def issue241(sim):
    spike_train1 = sim.Population(1, sim.SpikeSourcePoisson, {'rate' : [5], 'start' : [1000], 'duration': [1234]})
    spike_train2 = sim.Population(2, sim.SpikeSourcePoisson, {'rate' : [5, 6], 'start' : [1000, 1001], 'duration': [1234, 2345]})
    spike_train3 = sim.Population(1, sim.SpikeSourcePoisson, {'rate' : [5], 'start' : [1000], 'duration': 1234})
    spike_train4 = sim.Population(1, sim.SpikeSourcePoisson, {'rate' : [5], 'start' : [1000]})
    spike_train5 = sim.Population(2, sim.SpikeSourcePoisson, {'rate' : [5, 6], 'start' : [1000, 1001]})
    assert_arrays_equal(spike_train2.get('duration'), numpy.array([1234, 2345]))
    assert_equal(spike_train3.get(['rate', 'start', 'duration']), [5, 1000, 1234])

def test_tset_with_array_values():
    p = MockPopulation()
    p._set_array = Mock()
    spike_times = numpy.linspace(0.0, 1000.0, num=10*p.size).reshape((p.size,10))
    p.tset("spike_times", spike_times)
    call_args = p._set_array.call_args[1]['spike_times']
    assert_equal(call_args.shape, spike_times[p._mask_local].shape)
    assert_arrays_equal(call_args.flatten(),
                        spike_times[p._mask_local].flatten())

def test_get__zero_offset():
    r = recording.Recorder('spikes')
    fake_data = numpy.array([
                    (3, 12.3),
                    (4, 14.5),
                    (7, 19.8)
                ])
    r._get = Mock(return_value=fake_data)
    assert_arrays_equal(r.get(), fake_data)

def test_sample():
    orig_pv = populations.PopulationView
    populations.PopulationView = Mock()
    p = MockPopulation()
    rng = Mock()
    rng.permutation = Mock(return_value=numpy.array([7,4,8,12,0,3,9,1,2,11,5,10,6]))
    pv = p.sample(5, rng=rng)
    assert_arrays_equal(populations.PopulationView.call_args[0][1], numpy.array([7,4,8,12,0]))
    populations.PopulationView = orig_pv

def test_initialize_random_distribution():
    p = MockPopulation()
    p.initial_values = {}
    p._set_initial_value_array = Mock()
    class MockRandomDistribution(random.RandomDistribution):
        def next(self, n, mask_local):
            return 42*numpy.ones(n)[mask_local]
    p.initialize('v', MockRandomDistribution())
    assert_arrays_equal(p.initial_values['v'].evaluate(simplify=True), 42*numpy.ones(p.local_size))