def test_variableview_calculations():
# Check that you can directly calculate with "variable views"
G = NeuronGroup(10, '''x : 1
y : volt''')
G.x = np.arange(10)
G.y = np.arange(10)[::-1]*mV
assert_allclose(G.x * G.y, np.arange(10)*np.arange(10)[::-1]*mV)
assert_allclose(-G.x, -np.arange(10))
assert_allclose(-G.y, -np.arange(10)[::-1]*mV)
assert_allclose(3 * G.x, 3 * np.arange(10))
assert_allclose(3 * G.y, 3 *np.arange(10)[::-1]*mV)
assert_allclose(G.x * 3, 3 * np.arange(10))
assert_allclose(G.y * 3, 3 *np.arange(10)[::-1]*mV)
assert_allclose(G.x / 2.0, np.arange(10)/2.0)
assert_allclose(G.y / 2, np.arange(10)[::-1]*mV/2)
assert_allclose(G.x + 2, 2 + np.arange(10))
assert_allclose(G.y + 2*mV, 2*mV + np.arange(10)[::-1]*mV)
assert_allclose(2 + G.x, 2 + np.arange(10))
assert_allclose(2*mV + G.y, 2*mV + np.arange(10)[::-1]*mV)
assert_allclose(G.x - 2, np.arange(10) - 2)
assert_allclose(G.y - 2*mV, np.arange(10)[::-1]*mV - 2*mV)
assert_allclose(2 - G.x, 2 - np.arange(10))
assert_allclose(2*mV - G.y, 2*mV - np.arange(10)[::-1]*mV)
# incorrect units
assert_raises(DimensionMismatchError, lambda: G.x + G.y)
assert_raises(DimensionMismatchError, lambda: G.x[:] + G.y)
assert_raises(DimensionMismatchError, lambda: G.x + G.y[:])
assert_raises(DimensionMismatchError, lambda: G.x + 3*mV)
assert_raises(DimensionMismatchError, lambda: 3*mV + G.x)
assert_raises(DimensionMismatchError, lambda: G.y + 3)
assert_raises(DimensionMismatchError, lambda: 3 + G.y)
def test_variableview_calculations():
# Check that you can directly calculate with "variable views"
G = NeuronGroup(10, '''x : 1
y : volt''')
G.x = np.arange(10)
G.y = np.arange(10)[::-1]*mV
assert_allclose(G.x * G.y, np.arange(10)*np.arange(10)[::-1]*mV)
assert_allclose(-G.x, -np.arange(10))
assert_allclose(-G.y, -np.arange(10)[::-1]*mV)
assert_allclose(3 * G.x, 3 * np.arange(10))
assert_allclose(3 * G.y, 3 *np.arange(10)[::-1]*mV)
assert_allclose(G.x * 3, 3 * np.arange(10))
assert_allclose(G.y * 3, 3 *np.arange(10)[::-1]*mV)
assert_allclose(G.x / 2.0, np.arange(10)/2.0)
assert_allclose(G.y / 2, np.arange(10)[::-1]*mV/2)
assert_allclose(G.x + 2, 2 + np.arange(10))
assert_allclose(G.y + 2*mV, 2*mV + np.arange(10)[::-1]*mV)
assert_allclose(2 + G.x, 2 + np.arange(10))
assert_allclose(2*mV + G.y, 2*mV + np.arange(10)[::-1]*mV)
assert_allclose(G.x - 2, np.arange(10) - 2)
assert_allclose(G.y - 2*mV, np.arange(10)[::-1]*mV - 2*mV)
assert_allclose(2 - G.x, 2 - np.arange(10))
assert_allclose(2*mV - G.y, 2*mV - np.arange(10)[::-1]*mV)
# incorrect units
assert_raises(DimensionMismatchError, lambda: G.x + G.y)
assert_raises(DimensionMismatchError, lambda: G.x[:] + G.y)
assert_raises(DimensionMismatchError, lambda: G.x + G.y[:])
assert_raises(DimensionMismatchError, lambda: G.x + 3*mV)
assert_raises(DimensionMismatchError, lambda: 3*mV + G.x)
assert_raises(DimensionMismatchError, lambda: G.y + 3)
assert_raises(DimensionMismatchError, lambda: 3 + G.y)
def test_linked_subgroup2():
'''
Test linking a variable from a subgroup with indexing
'''
G1 = NeuronGroup(10, 'x : 1')
G1.x = np.arange(10) * 0.1
G2 = G1[3:8]
G3 = NeuronGroup(10, 'y:1 (linked)')
G3.y = linked_var(G2.x, index=np.arange(5).repeat(2))
assert_equal(G3.y[:], (np.arange(5)+3).repeat(2)*0.1)
def test_linked_subgroup():
'''
Test linking a variable from a subgroup
'''
G1 = NeuronGroup(10, 'x : 1')
G1.x = np.arange(10) * 0.1
G2 = G1[3:8]
G3 = NeuronGroup(5, 'y:1 (linked)')
G3.y = linked_var(G2.x)
assert_equal(G3.y[:], (np.arange(5)+3)*0.1)
def test_linked_variable_indexed():
'''
Test linking a variable with an index specified as an array
'''
G = NeuronGroup(10, '''x : 1
y : 1 (linked)''')
G.x = np.arange(10)*0.1
G.y = linked_var(G.x, index=np.arange(10)[::-1])
# G.y should refer to an inverted version of G.x
assert_equal(G.y[:], np.arange(10)[::-1]*0.1)
def test_linked_var_in_reset_size_1():
G1 = NeuronGroup(1, 'x:1')
G2 = NeuronGroup(1, '''x_linked : 1 (linked)
y:1''',
threshold='y>1', reset='y=0; x_linked += 1')
G2.x_linked = linked_var(G1, 'x')
G2.y = 1.1
# In this context, x_linked should not be considered as a scalar variable
# and therefore the reset statement should be allowed
run(3*defaultclock.dt)
assert_equal(G1.x[:], 1)
def test_linked_var_in_reset_size_1():
G1 = NeuronGroup(1, 'x:1')
G2 = NeuronGroup(1, '''x_linked : 1 (linked)
y:1''',
threshold='y>1', reset='y=0; x_linked += 1')
G2.x_linked = linked_var(G1, 'x')
G2.y = 1.1
# In this context, x_linked should not be considered as a scalar variable
# and therefore the reset statement should be allowed
run(3*defaultclock.dt)
assert_equal(G1.x[:], 1)
def test_linked_double_linked4():
'''
Linked to a linked variable, both use indices
'''
G1 = NeuronGroup(5, 'x : 1')
G2 = NeuronGroup(10, 'y : 1 (linked)')
G2.y = linked_var(G1.x, index=np.arange(5).repeat(2))
G3 = NeuronGroup(10, 'z: 1 (linked)')
G3.z = linked_var(G2.y, index=np.arange(10)[::-1])
G1.x = np.arange(5)*0.1
assert_equal(G3.z[:], np.arange(5).repeat(2)[::-1]*0.1)
def test_linked_double_linked3():
'''
Linked to a linked variable, first with indices, second without indices
'''
G1 = NeuronGroup(5, 'x : 1')
G2 = NeuronGroup(10, 'y : 1 (linked)')
G2.y = linked_var(G1.x, index=np.arange(5).repeat(2))
G3 = NeuronGroup(10, 'z: 1 (linked)')
G3.z = linked_var(G2.y)
G1.x = np.arange(5)*0.1
assert_equal(G3.z[:], np.arange(5).repeat(2)*0.1)
def test_linked_double_linked1():
'''
Linked to a linked variable, without indices
'''
G1 = NeuronGroup(10, 'x : 1')
G2 = NeuronGroup(10, 'y : 1 (linked)')
G2.y = linked_var(G1.x)
G3 = NeuronGroup(10, 'z: 1 (linked)')
G3.z = linked_var(G2.y)
G1.x = np.arange(10)
assert_equal(G3.z[:], np.arange(10))
def test_linked_variable_scalar():
'''
Test linked variable from a size 1 group.
'''
G1 = NeuronGroup(1, 'dx/dt = -x / (10*ms) : 1')
G2 = NeuronGroup(10, '''dy/dt = (-y + x) / (20*ms) : 1
x : 1 (linked)''')
G1.x = 1
G2.y = np.linspace(0, 1, 10)
G2.x = linked_var(G1.x)
mon = StateMonitor(G2, 'y', record=True)
net = Network(G1, G2, mon)
net.run(10*ms)
def test_linked_var_in_reset_incorrect():
# Raise an error if a scalar variable (linked variable from a group of size
# 1 is set in a reset statement of a group with size > 1)
G1 = NeuronGroup(1, 'x:1')
G2 = NeuronGroup(2, '''x_linked : 1 (linked)
y:1''',
threshold='y>1', reset='y=0; x_linked += 1')
G2.x_linked = linked_var(G1, 'x')
G2.y = 1.1
net = Network(G1, G2)
# It is not well-defined what x_linked +=1 means in this context
# (as for any other shared variable)
assert_raises(SyntaxError, lambda: net.run(0*ms))
def test_linked_var_in_reset_incorrect():
# Raise an error if a scalar variable (linked variable from a group of size
# 1 is set in a reset statement of a group with size > 1)
G1 = NeuronGroup(1, 'x:1')
G2 = NeuronGroup(2, '''x_linked : 1 (linked)
y:1''',
threshold='y>1', reset='y=0; x_linked += 1')
G2.x_linked = linked_var(G1, 'x')
G2.y = 1.1
net = Network(G1, G2)
# It is not well-defined what x_linked +=1 means in this context
# (as for any other shared variable)
assert_raises(SyntaxError, lambda: net.run(0*ms))
def test_linked_variable_scalar():
'''
Test linked variable from a size 1 group.
'''
G1 = NeuronGroup(1, 'dx/dt = -x / (10*ms) : 1')
G2 = NeuronGroup(10, '''dy/dt = (-y + x) / (20*ms) : 1
x : 1 (linked)''')
G1.x = 1
G2.y = np.linspace(0, 1, 10)
G2.x = linked_var(G1.x)
mon = StateMonitor(G2, 'y', record=True)
# We don't test anything for now, except that it runs without raising an
# error
run(10*ms)
# Make sure that printing the variable values works
assert len(str(G2.x)) > 0
assert len(repr(G2.x)) > 0
assert len(str(G2.x[:])) > 0
assert len(repr(G2.x[:])) > 0
def test_linked_variable_scalar():
'''
Test linked variable from a size 1 group.
'''
G1 = NeuronGroup(1, 'dx/dt = -x / (10*ms) : 1')
G2 = NeuronGroup(10, '''dy/dt = (-y + x) / (20*ms) : 1
x : 1 (linked)''')
G1.x = 1
G2.y = np.linspace(0, 1, 10)
G2.x = linked_var(G1.x)
mon = StateMonitor(G2, 'y', record=True)
# We don't test anything for now, except that it runs without raising an
# error
run(10*ms)
# Make sure that printing the variable values works
assert len(str(G2.x)) > 0
assert len(repr(G2.x)) > 0
assert len(str(G2.x[:])) > 0
assert len(repr(G2.x[:])) > 0