def test_commonsig_readonly():
"""Test that the common signals cannot be modified."""
net = nengo.Network(label="test_commonsig")
model = Model()
model.build(net)
signals = SignalDict()
for sig in itervalues(model.sig['common']):
signals.init(sig)
with pytest.raises((ValueError, RuntimeError)):
signals[sig] = np.array([-1])
with pytest.raises((ValueError, RuntimeError)):
signals[sig][...] = np.array([-1])
def test_commonsig_readonly():
"""Test that the common signals cannot be modified."""
net = nengo.Network(label="test_commonsig")
model = Model()
model.build(net)
signals = SignalDict()
for sig in itervalues(model.sig['common']):
signals.init(sig)
with pytest.raises((ValueError, RuntimeError)):
signals[sig] = np.array([-1])
with pytest.raises((ValueError, RuntimeError)):
signals[sig][...] = np.array([-1])
def test_signaldict(allclose):
"""Tests SignalDict's dict overrides."""
signaldict = SignalDict()
scalar = Signal(1.0)
# Both __getitem__ and __setitem__ raise KeyError
with pytest.raises(KeyError):
signaldict[scalar]
with pytest.raises(KeyError):
signaldict[scalar] = np.array(1.0)
signaldict.init(scalar)
# tests repeat init
with pytest.raises(SignalError, match="Cannot add signal twice"):
signaldict.init(scalar)
assert allclose(signaldict[scalar], np.array(1.0))
# __getitem__ handles scalars
assert signaldict[scalar].shape == ()
one_d = Signal([1.0])
signaldict.init(one_d)
assert allclose(signaldict[one_d], np.array([1.0]))
assert signaldict[one_d].shape == (1, )
two_d = Signal([[1.0], [1.0]])
signaldict.init(two_d)
assert allclose(signaldict[two_d], np.array([[1.0], [1.0]]))
assert signaldict[two_d].shape == (2, 1)
# __getitem__ handles views implicitly (note no .init)
two_d_view = two_d[0, :]
assert allclose(signaldict[two_d_view], np.array([1.0]))
assert signaldict[two_d_view].shape == (1, )
# __setitem__ ensures memory location stays the same
memloc = signaldict[scalar].__array_interface__["data"][0]
signaldict[scalar] = np.array(0.0)
assert allclose(signaldict[scalar], np.array(0.0))
assert signaldict[scalar].__array_interface__["data"][0] == memloc
memloc = signaldict[one_d].__array_interface__["data"][0]
signaldict[one_d] = np.array([0.0])
assert allclose(signaldict[one_d], np.array([0.0]))
assert signaldict[one_d].__array_interface__["data"][0] == memloc
memloc = signaldict[two_d].__array_interface__["data"][0]
signaldict[two_d] = np.array([[0.0], [0.0]])
assert allclose(signaldict[two_d], np.array([[0.0], [0.0]]))
assert signaldict[two_d].__array_interface__["data"][0] == memloc
# __str__ pretty-prints signals and current values
# Order not guaranteed for dicts, so we have to loop
for k in signaldict:
assert "%s %s" % (repr(k), repr(signaldict[k])) in str(signaldict)
def test_signal_reshape():
"""Tests Signal.reshape"""
# check proper shape after reshape
three_d = Signal(np.ones((2, 2, 2)))
assert three_d.reshape((8, )).shape == (8, )
assert three_d.reshape((4, 2)).shape == (4, 2)
assert three_d.reshape((2, 4)).shape == (2, 4)
assert three_d.reshape(-1).shape == (8, )
assert three_d.reshape((4, -1)).shape == (4, 2)
assert three_d.reshape((-1, 4)).shape == (2, 4)
assert three_d.reshape((2, -1, 2)).shape == (2, 2, 2)
assert three_d.reshape((1, 2, 1, 2, 2, 1)).shape == (1, 2, 1, 2, 2, 1)
# check with non-contiguous arrays (and with offset)
value = np.arange(20).reshape(5, 4)
s = Signal(np.array(value), name='s')
s0slice = slice(0, 3), slice(None, None, 2)
s0shape = 2, 3
s0 = s[s0slice].reshape(*s0shape)
assert s.offset == 0
assert np.array_equal(s0.initial_value, value[s0slice].reshape(*s0shape))
s1slice = slice(1, None), slice(None, None, 2)
s1shape = 2, 4
s1 = s[s1slice].reshape(s1shape)
assert s1.offset == 4 * s1.dtype.itemsize
assert np.array_equal(s1.initial_value, value[s1slice].reshape(s1shape))
# check error if non-contiguous array cannot be reshaped without copy
s2slice = slice(None, None, 2), slice(None, None, 2)
s2shape = 2, 3
s2 = s[s2slice]
with pytest.raises(SignalError):
s2.reshape(s2shape)
# check that views are working properly (incrementing `s` effects views)
values = SignalDict()
values.init(s)
values.init(s0)
values.init(s1)
values[s] += 1
assert np.array_equal(values[s0], value[s0slice].reshape(s0shape) + 1)
assert np.array_equal(values[s1], value[s1slice].reshape(s1shape) + 1)
def test_signal_reshape():
"""Tests Signal.reshape"""
# check proper shape after reshape
three_d = Signal(np.ones((2, 2, 2)))
assert three_d.reshape((8,)).shape == (8,)
assert three_d.reshape((4, 2)).shape == (4, 2)
assert three_d.reshape((2, 4)).shape == (2, 4)
assert three_d.reshape(-1).shape == (8,)
assert three_d.reshape((4, -1)).shape == (4, 2)
assert three_d.reshape((-1, 4)).shape == (2, 4)
assert three_d.reshape((2, -1, 2)).shape == (2, 2, 2)
assert three_d.reshape((1, 2, 1, 2, 2, 1)).shape == (1, 2, 1, 2, 2, 1)
# check with non-contiguous arrays (and with offset)
value = np.arange(20).reshape(5, 4)
s = Signal(np.array(value), name='s')
s0slice = slice(0, 3), slice(None, None, 2)
s0shape = 2, 3
s0 = s[s0slice].reshape(*s0shape)
assert s.offset == 0
assert np.array_equal(s0.initial_value, value[s0slice].reshape(*s0shape))
s1slice = slice(1, None), slice(None, None, 2)
s1shape = 2, 4
s1 = s[s1slice].reshape(s1shape)
assert s1.offset == 4 * s1.dtype.itemsize
assert np.array_equal(s1.initial_value, value[s1slice].reshape(s1shape))
# check error if non-contiguous array cannot be reshaped without copy
s2slice = slice(None, None, 2), slice(None, None, 2)
s2shape = 2, 3
s2 = s[s2slice]
with pytest.raises(SignalError):
s2.reshape(s2shape)
# check that views are working properly (incrementing `s` effects views)
values = SignalDict()
values.init(s)
values.init(s0)
values.init(s1)
values[s] += 1
assert np.array_equal(values[s0], value[s0slice].reshape(s0shape) + 1)
assert np.array_equal(values[s1], value[s1slice].reshape(s1shape) + 1)
def test_signal_init(sig_type):
if sig_type == "sparse_scipy":
pytest.importorskip("scipy.sparse")
sig, dense = make_signal(
sig_type,
shape=(3, 3),
indices=np.asarray([[0, 0], [0, 2], [1, 1], [2, 2]]),
data=[1.0, 2.0, 1.0, 1.5],
)
signals = SignalDict()
signals.init(sig)
assert np.all(signals[sig] == dense)
sig.readonly = True
signals = SignalDict()
signals.init(sig)
with pytest.raises((ValueError, RuntimeError, TypeError)):
signals[sig].data[0] = -1
def test_signaldict():
"""Tests SignalDict's dict overrides."""
signaldict = SignalDict()
scalar = Signal(1.)
# Both __getitem__ and __setitem__ raise KeyError
with pytest.raises(KeyError):
signaldict[scalar]
with pytest.raises(KeyError):
signaldict[scalar] = np.array(1.)
signaldict.init(scalar)
assert np.allclose(signaldict[scalar], np.array(1.))
# __getitem__ handles scalars
assert signaldict[scalar].shape == ()
one_d = Signal([1.])
signaldict.init(one_d)
assert np.allclose(signaldict[one_d], np.array([1.]))
assert signaldict[one_d].shape == (1,)
two_d = Signal([[1.], [1.]])
signaldict.init(two_d)
assert np.allclose(signaldict[two_d], np.array([[1.], [1.]]))
assert signaldict[two_d].shape == (2, 1)
# __getitem__ handles views
two_d_view = two_d[0, :]
signaldict.init(two_d_view)
assert np.allclose(signaldict[two_d_view], np.array([1.]))
assert signaldict[two_d_view].shape == (1,)
# __setitem__ ensures memory location stays the same
memloc = signaldict[scalar].__array_interface__['data'][0]
signaldict[scalar] = np.array(0.)
assert np.allclose(signaldict[scalar], np.array(0.))
assert signaldict[scalar].__array_interface__['data'][0] == memloc
memloc = signaldict[one_d].__array_interface__['data'][0]
signaldict[one_d] = np.array([0.])
assert np.allclose(signaldict[one_d], np.array([0.]))
assert signaldict[one_d].__array_interface__['data'][0] == memloc
memloc = signaldict[two_d].__array_interface__['data'][0]
signaldict[two_d] = np.array([[0.], [0.]])
assert np.allclose(signaldict[two_d], np.array([[0.], [0.]]))
assert signaldict[two_d].__array_interface__['data'][0] == memloc
# __str__ pretty-prints signals and current values
# Order not guaranteed for dicts, so we have to loop
for k in signaldict:
assert "%s %s" % (repr(k), repr(signaldict[k])) in str(signaldict)
def test_signaldict():
"""Tests SignalDict's dict overrides."""
signaldict = SignalDict()
scalar = Signal(1.)
# Both __getitem__ and __setitem__ raise KeyError
with pytest.raises(KeyError):
signaldict[scalar]
with pytest.raises(KeyError):
signaldict[scalar] = np.array(1.)
signaldict.init(scalar)
assert np.allclose(signaldict[scalar], np.array(1.))
# __getitem__ handles scalars
assert signaldict[scalar].shape == ()
one_d = Signal([1.])
signaldict.init(one_d)
assert np.allclose(signaldict[one_d], np.array([1.]))
assert signaldict[one_d].shape == (1, )
two_d = Signal([[1.], [1.]])
signaldict.init(two_d)
assert np.allclose(signaldict[two_d], np.array([[1.], [1.]]))
assert signaldict[two_d].shape == (2, 1)
# __getitem__ handles views
two_d_view = two_d[0, :]
signaldict.init(two_d_view)
assert np.allclose(signaldict[two_d_view], np.array([1.]))
assert signaldict[two_d_view].shape == (1, )
# __setitem__ ensures memory location stays the same
memloc = signaldict[scalar].__array_interface__['data'][0]
signaldict[scalar] = np.array(0.)
assert np.allclose(signaldict[scalar], np.array(0.))
assert signaldict[scalar].__array_interface__['data'][0] == memloc
memloc = signaldict[one_d].__array_interface__['data'][0]
signaldict[one_d] = np.array([0.])
assert np.allclose(signaldict[one_d], np.array([0.]))
assert signaldict[one_d].__array_interface__['data'][0] == memloc
memloc = signaldict[two_d].__array_interface__['data'][0]
signaldict[two_d] = np.array([[0.], [0.]])
assert np.allclose(signaldict[two_d], np.array([[0.], [0.]]))
assert signaldict[two_d].__array_interface__['data'][0] == memloc
# __str__ pretty-prints signals and current values
# Order not guaranteed for dicts, so we have to loop
for k in signaldict:
assert "%s %s" % (repr(k), repr(signaldict[k])) in str(signaldict)
def test_signaldict_reset():
"""Tests SignalDict's reset function."""
signaldict = SignalDict()
two_d = Signal([[1], [1]])
signaldict.init(two_d)
two_d_view = two_d[0, :]
signaldict[two_d_view] = -0.5
assert np.allclose(signaldict[two_d], np.array([[-0.5], [1]]))
signaldict[two_d] = np.array([[-1], [-1]])
assert np.allclose(signaldict[two_d], np.array([[-1], [-1]]))
assert np.allclose(signaldict[two_d_view], np.array([-1]))
signaldict.reset(two_d_view)
assert np.allclose(signaldict[two_d_view], np.array([1]))
assert np.allclose(signaldict[two_d], np.array([[1], [-1]]))
signaldict.reset(two_d)
assert np.allclose(signaldict[two_d], np.array([[1], [1]]))
def test_signaldict_reset():
"""Tests SignalDict's reset function."""
signaldict = SignalDict()
two_d = Signal([[1], [1]])
signaldict.init(two_d)
two_d_view = two_d[0, :]
signaldict[two_d_view] = -0.5
assert np.allclose(signaldict[two_d], np.array([[-0.5], [1]]))
signaldict[two_d] = np.array([[-1], [-1]])
assert np.allclose(signaldict[two_d], np.array([[-1], [-1]]))
assert np.allclose(signaldict[two_d_view], np.array([-1]))
signaldict.reset(two_d_view)
assert np.allclose(signaldict[two_d_view], np.array([1]))
assert np.allclose(signaldict[two_d], np.array([[1], [-1]]))
signaldict.reset(two_d)
assert np.allclose(signaldict[two_d], np.array([[1], [1]]))