def test_dynamic_array_1d_shrink():
for numpy_resize in [True, False]:
da = DynamicArray1D(10, use_numpy_resize=numpy_resize)
da[:] = np.arange(10)
da.shrink(5)
assert len(da) == 5
assert all(da[:] == np.arange(5))
# After using shrink, the underlying array should have changed
assert len(da._data) == 5
def test_dynamic_array_2d_access():
da = DynamicArray1D((10, 20))
da[:, :] = np.arange(200).reshape((10, 20))
assert da[5, 10] == 5 * 20 + 10
assert da.shape == (10, 20)
assert len(str(da))
assert len(repr(da))
da[:] += 1
da.data[:] += 1
assert_equal(da[:, :], np.arange(200).reshape((10, 20)) + 2)
def create_all_to_all(N, dt):
'''
Return a tuple containing `synapses` and `delays` in the form that is needed
for the `SpikeQueue` initializer.
Every synapse has a delay depending on the presynaptic neuron.
'''
data = np.repeat(np.arange(N, dtype=np.int32), N)
delays = DynamicArray1D(data.shape, dtype=np.float64)
delays[:] = data * dt
synapses = data
return synapses, delays
def add_array(self, var):
# This creates the actual numpy arrays (or DynamicArrayVariable objects)
if isinstance(var, DynamicArrayVariable):
if var.ndim == 1:
arr = DynamicArray1D(var.size, dtype=var.dtype)
else:
arr = DynamicArray(var.size, dtype=var.dtype)
else:
arr = np.empty(var.size, dtype=var.dtype)
self.arrays[var] = arr
def test_dynamic_array_1d_access():
da = DynamicArray1D(10)
da[:] = np.arange(10)
assert da[7] == 7
assert len(da) == 10
assert da.shape == (10, )
assert len(str(da))
assert len(repr(da))
da[:] += 1
da.data[:] += 1
assert all(da[:] == (np.arange(10) + 2))
def test_dynamic_array_1d_resize_down_up():
for numpy_resize in [True, False]:
da = DynamicArray1D(10, use_numpy_resize=numpy_resize)
da[:] = np.arange(10)
da.resize(5)
assert len(da) == 5
assert da.shape == (5, )
assert all(da[:5] == np.arange(5))
da.resize(10)
assert len(da) == 10
assert da.shape == (10, )
assert all(da[:5] == np.arange(5))
assert all(da[5:] == 0)