def test_state_variables():
'''
Test the setting and accessing of state variables.
'''
G = NeuronGroup(10, 'v : volt')
G.v = -70 * mV
assert_raises(DimensionMismatchError, lambda: G.__setattr__('v', -70))
G.v_ = float(-70 * mV)
# Numpy methods should be able to deal with state variables
# (discarding units)
assert_allclose(np.mean(G.v), float(-70 * mV))
# Getting the content should return a Quantity object which then natively
# supports numpy functions that access a method
assert_allclose(np.mean(G.v[:]), -70 * mV)
# You should also be able to set variables with a string
G.v = '-70*mV + i*mV'
assert_allclose(G.v[0], -70 * mV)
assert_allclose(G.v[9], -61 * mV)
assert_allclose(G.v[:], -70 * mV + np.arange(10) * mV)
# Calculating with state variables should work too
assert all(G.v - G.v == 0)
# And in-place modification should work as well
G.v += 10 * mV
G.v *= 2
# with unit checking
assert_raises(DimensionMismatchError, lambda: G.v.__iadd__(3 * second))
assert_raises(DimensionMismatchError, lambda: G.v.__iadd__(3))
assert_raises(DimensionMismatchError, lambda: G.v.__imul__(3 * second))
def test_linked_variable_correct():
'''
Test correct uses of linked variables.
'''
tau = 10*ms
G1 = NeuronGroup(10, 'dv/dt = -v / tau : volt')
G1.v = np.linspace(0*mV, 20*mV, 10)
G2 = NeuronGroup(10, 'v : volt (linked)')
G2.v = linked_var(G1.v)
mon1 = StateMonitor(G1, 'v', record=True)
mon2 = StateMonitor(G2, 'v', record=True)
net = Network(G1, G2, mon1, mon2)
net.run(10*ms)
assert_equal(mon1.v[:, :], mon2.v[:, :])
def test_state_variable_access():
G = NeuronGroup(10, 'v:volt')
G.v = np.arange(10) * volt
assert_equal(np.asarray(G.v[:]), np.arange(10))
assert have_same_dimensions(G.v[:], volt)
assert_equal(np.asarray(G.v[:]), G.v_[:])
# Accessing single elements, slices and arrays
assert G.v[5] == 5 * volt
assert G.v_[5] == 5
assert_equal(G.v[:5], np.arange(5) * volt)
assert_equal(G.v_[:5], np.arange(5))
assert_equal(G.v[[0, 5]], [0, 5] * volt)
assert_equal(G.v_[[0, 5]], np.array([0, 5]))
# Illegal indexing
assert_raises(IndexError, lambda: G.v[0, 0])
assert_raises(IndexError, lambda: G.v_[0, 0])
assert_raises(TypeError, lambda: G.v[object()])
assert_raises(TypeError, lambda: G.v_[object()])
# Indexing with strings
assert G.v['i==2'] == G.v[2]
assert G.v_['i==2'] == G.v_[2]
assert_equal(G.v['v >= 3*volt'], G.v[3:])
assert_equal(G.v_['v >= 3*volt'], G.v_[3:])
# Should also check for units
assert_raises(DimensionMismatchError, lambda: G.v['v >= 3'])
assert_raises(DimensionMismatchError, lambda: G.v['v >= 3*second'])
def test_state_variable_access():
for codeobj_class in codeobj_classes:
G = NeuronGroup(10, 'v:volt', codeobj_class=codeobj_class)
G.v = np.arange(10) * volt
assert_equal(np.asarray(G.v[:]), np.arange(10))
assert have_same_dimensions(G.v[:], volt)
assert_equal(np.asarray(G.v[:]), G.v_[:])
# Accessing single elements, slices and arrays
assert G.v[5] == 5 * volt
assert G.v_[5] == 5
assert_equal(G.v[:5], np.arange(5) * volt)
assert_equal(G.v_[:5], np.arange(5))
assert_equal(G.v[[0, 5]], [0, 5] * volt)
assert_equal(G.v_[[0, 5]], np.array([0, 5]))
# Illegal indexing
assert_raises(IndexError, lambda: G.v[0, 0])
assert_raises(IndexError, lambda: G.v_[0, 0])
assert_raises(TypeError, lambda: G.v[object()])
assert_raises(TypeError, lambda: G.v_[object()])
# A string representation should not raise any error
assert len(str(G.v))
assert len(repr(G.v))
assert len(str(G.v_))
assert len(repr(G.v_))
def test_get_states():
G = NeuronGroup(10, '''v : volt
x : 1
subexpr = x + v/volt : 1
subexpr2 = x*volt + v : volt''')
G.v = 'i*volt'
G.x = '10*i'
states_units = G.get_states(['v', 'x', 'subexpr', 'subexpr2'], units=True)
states = G.get_states(['v', 'x', 'subexpr', 'subexpr2'], units=False)
assert len(states_units.keys()) == len(states.keys()) == 4
assert_equal(states_units['v'], np.arange(10)*volt)
assert_equal(states_units['x'], 10*np.arange(10))
assert_equal(states_units['subexpr'], 11*np.arange(10))
assert_equal(states_units['subexpr2'], 11*np.arange(10)*volt)
assert_equal(states['v'], np.arange(10))
assert_equal(states['x'], 10*np.arange(10))
assert_equal(states['subexpr'], 11*np.arange(10))
assert_equal(states['subexpr2'], 11*np.arange(10))
all_states = G.get_states(units=True)
assert set(all_states.keys()) == {'v', 'x', 'N', 't', 'dt', 'i'}
all_states = G.get_states(units=True, subexpressions=True)
assert set(all_states.keys()) == {'v', 'x', 'N', 't', 'dt', 'i',
'subexpr', 'subexpr2'}
def test_get_states():
G = NeuronGroup(10, '''v : volt
x : 1
subexpr = x + v/volt : 1
subexpr2 = x*volt + v : volt''')
G.v = 'i*volt'
G.x = '10*i'
states_units = G.get_states(['v', 'x', 'subexpr', 'subexpr2'], units=True)
states = G.get_states(['v', 'x', 'subexpr', 'subexpr2'], units=False)
assert len(states_units.keys()) == len(states.keys()) == 4
assert_equal(states_units['v'], np.arange(10)*volt)
assert_equal(states_units['x'], 10*np.arange(10))
assert_equal(states_units['subexpr'], 11*np.arange(10))
assert_equal(states_units['subexpr2'], 11*np.arange(10)*volt)
assert_equal(states['v'], np.arange(10))
assert_equal(states['x'], 10*np.arange(10))
assert_equal(states['subexpr'], 11*np.arange(10))
assert_equal(states['subexpr2'], 11*np.arange(10))
all_states = G.get_states(units=True)
assert set(all_states.keys()) == {'v', 'x', 'N', 't', 'dt', 'i'}
all_states = G.get_states(units=True, subexpressions=True)
assert set(all_states.keys()) == {'v', 'x', 'N', 't', 'dt', 'i',
'subexpr', 'subexpr2'}
def test_get_set_random_generator_state():
group = NeuronGroup(10,
'dv/dt = -v/(10*ms) + (10*ms)**-0.5*xi : 1',
method='euler')
group.v = 'rand()'
run(10 * ms)
assert np.var(group.v) > 0 # very basic test for randomness ;)
old_v = np.array(group.v)
random_state = get_device().get_random_state()
group.v = 'rand()'
run(10 * ms)
assert np.var(group.v - old_v) > 0 # just checking for *some* difference
old_v = np.array(group.v)
get_device().set_random_state(random_state)
group.v = 'rand()'
run(10 * ms)
assert_equal(group.v, old_v)
def test_indices():
G = NeuronGroup(10, 'v : 1')
G.v = 'i'
ext_var = 5
assert_equal(G.indices[:], G.i[:])
assert_equal(G.indices[5:], G.indices['i >= 5'])
assert_equal(G.indices[5:], G.indices['i >= ext_var'])
assert_equal(G.indices['v >= 5'], np.nonzero(G.v >= 5)[0])
def test_linked_variable_repeat():
'''
Test a "repeat"-like connection between two groups of different size
'''
G1 = NeuronGroup(5, 'w : 1')
G2 = NeuronGroup(10, 'v : 1 (linked)')
G2.v = linked_var(G1.w, index=np.arange(5).repeat(2))
G1.w = np.arange(5) * 0.1
assert_equal(G2.v[:], np.arange(5).repeat(2) * 0.1)
def test_linked_variable_correct():
'''
Test correct uses of linked variables.
'''
tau = 10*ms
G1 = NeuronGroup(10, 'dv/dt = -v / tau : volt')
G1.v = linspace(0*mV, 20*mV, 10)
G2 = NeuronGroup(10, 'v : volt (linked)')
G2.v = linked_var(G1.v)
mon1 = StateMonitor(G1, 'v', record=True)
mon2 = StateMonitor(G2, 'v', record=True)
run(10*ms)
assert_equal(mon1.v[:, :], mon2.v[:, :])
# Make sure that printing the variable values works
assert len(str(G2.v)) > 0
assert len(repr(G2.v)) > 0
assert len(str(G2.v[:])) > 0
assert len(repr(G2.v[:])) > 0
def test_linked_variable_correct():
'''
Test correct uses of linked variables.
'''
tau = 10*ms
G1 = NeuronGroup(10, 'dv/dt = -v / tau : volt')
G1.v = np.linspace(0*mV, 20*mV, 10)
G2 = NeuronGroup(10, 'v : volt (linked)')
G2.v = linked_var(G1.v)
mon1 = StateMonitor(G1, 'v', record=True)
mon2 = StateMonitor(G2, 'v', record=True)
run(10*ms)
assert_equal(mon1.v[:, :], mon2.v[:, :])
# Make sure that printing the variable values works
assert len(str(G2.v)) > 0
assert len(repr(G2.v)) > 0
assert len(str(G2.v[:])) > 0
assert len(repr(G2.v[:])) > 0
def test_threshold_reset():
'''
Test that threshold and reset work in the expected way.
'''
# Membrane potential does not change by itself
G = NeuronGroup(3, 'dv/dt = 0 / second : 1',
threshold='v > 1', reset='v=0.5')
G.v = np.array([0, 1, 2])
run(defaultclock.dt)
assert_equal(G.v[:], np.array([0, 1, 0.5]))
def test_threshold_reset():
'''
Test that threshold and reset work in the expected way.
'''
# Membrane potential does not change by itself
G = NeuronGroup(3, 'dv/dt = 0 / second : 1',
threshold='v > 1', reset='v=0.5')
G.v = np.array([0, 1, 2])
run(defaultclock.dt)
assert_equal(G.v[:], np.array([0, 1, 0.5]))
def test_linked_subexpression_2():
'''
Test a linked variable referring to a subexpression without indices
'''
G = NeuronGroup(2, '''dv/dt = 100*Hz : 1
I = clip(v, 0, inf) : 1''',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(2, '''I_l : 1 (linked) ''')
G2.I_l = linked_var(G.I)
mon1 = StateMonitor(G, 'I', record=True)
mon = StateMonitor(G2, 'I_l', record=True)
run(5*ms)
assert all(mon[0].I_l == mon1[0].I)
assert all(mon[1].I_l == mon1[1].I)
def test_linked_subexpression_2():
'''
Test a linked variable referring to a subexpression without indices
'''
G = NeuronGroup(2, '''dv/dt = 100*Hz : 1
I = clip(v, 0, inf) : 1''',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(2, '''I_l : 1 (linked) ''')
G2.I_l = linked_var(G.I)
mon1 = StateMonitor(G, 'I', record=True)
mon = StateMonitor(G2, 'I_l', record=True)
run(5*ms)
assert all(mon[0].I_l == mon1[0].I)
assert all(mon[1].I_l == mon1[1].I)
def test_linked_subexpression():
'''
Test a subexpression referring to a linked variable.
'''
G = NeuronGroup(2, 'dv/dt = 100*Hz : 1',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(10, '''I = clip(x, 0, inf) : 1
x : 1 (linked) ''')
G2.x = linked_var(G.v, index=np.array([0, 1]).repeat(5))
mon = StateMonitor(G2, 'I', record=True)
run(5*ms)
# Due to the linking, the first 5 and the second 5 recorded I vectors should
# be identical
assert all((all(mon[i].I == mon[0].I) for i in xrange(5)))
assert all((all(mon[i+5].I == mon[5].I) for i in xrange(5)))
def test_integer_variables_and_mod():
'''
Test that integer operations and variable definitions work.
'''
n = 10
eqs = '''
dv/dt = (a+b+j+k)/second : 1
j = i%n : integer
k = i/n : integer
a = v%(i+1) : 1
b = v%(2*v) : 1
'''
G = NeuronGroup(100, eqs)
G.v = np.random.rand(len(G))
run(1*ms)
assert_equal(G.j[:], G.i[:]%n)
assert_equal(G.k[:], G.i[:]/n)
assert_equal(G.a[:], G.v[:]%(G.i[:]+1))
def test_integer_variables_and_mod():
'''
Test that integer operations and variable definitions work.
'''
n = 10
eqs = '''
dv/dt = (a+b+j+k)/second : 1
j = i%n : integer
k = i/n : integer
a = v%(i+1) : 1
b = v%(2*v) : 1
'''
G = NeuronGroup(100, eqs)
G.v = np.random.rand(len(G))
run(1*ms)
assert_equal(G.j[:], G.i[:]%n)
assert_equal(G.k[:], G.i[:]/n)
assert_equal(G.a[:], G.v[:]%(G.i[:]+1))
def test_linked_subexpression():
'''
Test a subexpression referring to a linked variable.
'''
G = NeuronGroup(2, 'dv/dt = 100*Hz : 1',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(10, '''I = clip(x, 0, inf) : 1
x : 1 (linked) ''')
G2.x = linked_var(G.v, index=np.array([0, 1]).repeat(5))
mon = StateMonitor(G2, 'I', record=True)
run(5*ms)
# Due to the linking, the first 5 and the second 5 recorded I vectors should
# be identical
assert all((all(mon[i].I == mon[0].I) for i in xrange(5)))
assert all((all(mon[i+5].I == mon[5].I) for i in xrange(5)))
def test_linked_subexpression_3():
'''
Test a linked variable referring to a subexpression with indices
'''
G = NeuronGroup(2, '''dv/dt = 100*Hz : 1
I = clip(v, 0, inf) : 1''',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(10, '''I_l : 1 (linked) ''')
G2.I_l = linked_var(G.I, index=np.array([0, 1]).repeat(5))
mon1 = StateMonitor(G, 'I', record=True)
mon = StateMonitor(G2, 'I_l', record=True)
run(5*ms)
# Due to the linking, the first 5 and the second 5 recorded I vectors should
# refer to the
assert all((all(mon[i].I_l == mon1[0].I) for i in xrange(5)))
assert all((all(mon[i+5].I_l == mon1[1].I) for i in xrange(5)))
def test_linked_subexpression_3():
'''
Test a linked variable referring to a subexpression with indices
'''
G = NeuronGroup(2, '''dv/dt = 100*Hz : 1
I = clip(v, 0, inf) : 1''',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(10, '''I_l : 1 (linked) ''')
G2.I_l = linked_var(G.I, index=np.array([0, 1]).repeat(5))
mon1 = StateMonitor(G, 'I', record=True)
mon = StateMonitor(G2, 'I_l', record=True)
run(5*ms)
# Due to the linking, the first 5 and the second 5 recorded I vectors should
# refer to the
assert all((all(mon[i].I_l == mon1[0].I) for i in xrange(5)))
assert all((all(mon[i+5].I_l == mon1[1].I) for i in xrange(5)))
def test_linked_subexpression_synapse():
'''
Test a complicated setup (not unlikely when using brian hears)
'''
G = NeuronGroup(2, 'dv/dt = 100*Hz : 1',
threshold='v>1', reset='v=0')
G.v = [0, .5]
G2 = NeuronGroup(10, '''I = clip(x, 0, inf) : 1
x : 1 (linked) ''')
# This will not be able to include references to `I` as `I_pre` etc., since
# the indirect indexing would have to change depending on the synapses
G2.x = linked_var(G.v, index=np.array([0, 1]).repeat(5))
S = Synapses(G2, G2, '')
S.connect('i==j')
assert 'I' not in S.variables
assert 'I_pre' not in S.variables
assert 'I_post' not in S.variables
assert 'x' not in S.variables
assert 'x_pre' not in S.variables
assert 'x_post' not in S.variables
def test_state_variable_access_strings():
for codeobj_class in codeobj_classes:
G = NeuronGroup(10, 'v:volt', codeobj_class=codeobj_class)
G.v = np.arange(10) * volt
# Indexing with strings
assert G.v['i==2'] == G.v[2]
assert G.v_['i==2'] == G.v_[2]
assert_equal(G.v['v >= 3*volt'], G.v[3:])
assert_equal(G.v_['v >= 3*volt'], G.v_[3:])
# Should also check for units
assert_raises(DimensionMismatchError, lambda: G.v['v >= 3'])
assert_raises(DimensionMismatchError, lambda: G.v['v >= 3*second'])
# Setting with strings
# --------------------
# String value referring to i
G.v = '2*i*volt'
assert_equal(G.v[:], 2*np.arange(10)*volt)
# String value referring to i
G.v[:5] = '3*i*volt'
assert_equal(G.v[:],
np.array([0, 3, 6, 9, 12, 10, 12, 14, 16, 18])*volt)
G.v = np.arange(10) * volt
# String value referring to a state variable
G.v = '2*v'
assert_equal(G.v[:], 2*np.arange(10)*volt)
G.v[:5] = '2*v'
assert_equal(G.v[:],
np.array([0, 4, 8, 12, 16, 10, 12, 14, 16, 18])*volt)
G.v = np.arange(10) * volt
# String value referring to state variables, i, and an external variable
ext = 5*volt
G.v = 'v + ext + (N + i)*volt'
assert_equal(G.v[:], 2*np.arange(10)*volt + 15*volt)
G.v = np.arange(10) * volt
G.v[:5] = 'v + ext + (N + i)*volt'
assert_equal(G.v[:],
np.array([15, 17, 19, 21, 23, 5, 6, 7, 8, 9])*volt)
G.v = 'v + randn()*volt' # only check that it doesn't raise an error
G.v[:5] = 'v + randn()*volt' # only check that it doesn't raise an error
G.v = np.arange(10) * volt
# String index using a random number
G.v['rand() <= 1'] = 0*mV
assert_equal(G.v[:], np.zeros(10)*volt)
G.v = np.arange(10) * volt
# String index referring to i and setting to a scalar value
G.v['i>=5'] = 0*mV
assert_equal(G.v[:], np.array([0, 1, 2, 3, 4, 0, 0, 0, 0, 0])*volt)
# String index referring to a state variable
G.v['v<3*volt'] = 0*mV
assert_equal(G.v[:], np.array([0, 0, 0, 3, 4, 0, 0, 0, 0, 0])*volt)
# String index referring to state variables, i, and an external variable
ext = 2*volt
G.v['v>=ext and i==(N-6)'] = 0*mV
assert_equal(G.v[:], np.array([0, 0, 0, 3, 0, 0, 0, 0, 0, 0])*volt)
G.v = np.arange(10) * volt
# Strings for both condition and values
G.v['i>=5'] = 'v*2'
assert_equal(G.v[:], np.array([0, 1, 2, 3, 4, 10, 12, 14, 16, 18])*volt)
G.v['v>=5*volt'] = 'i*volt'
assert_equal(G.v[:], np.arange(10)*volt)
def test_state_variable_access_strings():
G = NeuronGroup(10, '''v : volt
dv_ref/dt = -v_ref/(10*ms) : 1 (unless refractory)''',
threshold='v_ref>1', reset='v_ref=1', refractory=1*ms)
G.v = np.arange(10) * volt
# Indexing with strings
assert G.v['i==2'] == G.v[2]
assert G.v_['i==2'] == G.v_[2]
assert_equal(G.v['v >= 3*volt'], G.v[3:])
assert_equal(G.v_['v >= 3*volt'], G.v_[3:])
# Should also check for units
assert_raises(DimensionMismatchError, lambda: G.v['v >= 3'])
assert_raises(DimensionMismatchError, lambda: G.v['v >= 3*second'])
# Setting with strings
# --------------------
# String value referring to i
G.v = '2*i*volt'
assert_equal(G.v[:], 2*np.arange(10)*volt)
# String value referring to i
G.v[:5] = '3*i*volt'
assert_equal(G.v[:],
np.array([0, 3, 6, 9, 12, 10, 12, 14, 16, 18])*volt)
G.v = np.arange(10) * volt
# Conditional write variable
G.v_ref = '2*i'
assert_equal(G.v_ref[:], 2*np.arange(10))
# String value referring to a state variable
G.v = '2*v'
assert_equal(G.v[:], 2*np.arange(10)*volt)
G.v[:5] = '2*v'
assert_equal(G.v[:],
np.array([0, 4, 8, 12, 16, 10, 12, 14, 16, 18])*volt)
G.v = np.arange(10) * volt
# String value referring to state variables, i, and an external variable
ext = 5*volt
G.v = 'v + ext + (N + i)*volt'
assert_equal(G.v[:], 2*np.arange(10)*volt + 15*volt)
G.v = np.arange(10) * volt
G.v[:5] = 'v + ext + (N + i)*volt'
assert_equal(G.v[:],
np.array([15, 17, 19, 21, 23, 5, 6, 7, 8, 9])*volt)
G.v = 'v + randn()*volt' # only check that it doesn't raise an error
G.v[:5] = 'v + randn()*volt' # only check that it doesn't raise an error
G.v = np.arange(10) * volt
# String index using a random number
G.v['rand() <= 1'] = 0*mV
assert_equal(G.v[:], np.zeros(10)*volt)
G.v = np.arange(10) * volt
# String index referring to i and setting to a scalar value
G.v['i>=5'] = 0*mV
assert_equal(G.v[:], np.array([0, 1, 2, 3, 4, 0, 0, 0, 0, 0])*volt)
# String index referring to a state variable
G.v['v<3*volt'] = 0*mV
assert_equal(G.v[:], np.array([0, 0, 0, 3, 4, 0, 0, 0, 0, 0])*volt)
# String index referring to state variables, i, and an external variable
ext = 2*volt
G.v['v>=ext and i==(N-6)'] = 0*mV
assert_equal(G.v[:], np.array([0, 0, 0, 3, 0, 0, 0, 0, 0, 0])*volt)
G.v = np.arange(10) * volt
# Strings for both condition and values
G.v['i>=5'] = 'v*2'
assert_equal(G.v[:], np.array([0, 1, 2, 3, 4, 10, 12, 14, 16, 18])*volt)
G.v['v>=5*volt'] = 'i*volt'
assert_equal(G.v[:], np.arange(10)*volt)
G.v['i<=5'] = '(100 + rand())*volt'
assert_equal(G.v[6:], np.arange(4)*volt + 6*volt) # unchanged
assert all(G.v[:6] >= 100*volt)
assert all(G.v[:6] <= 101*volt)
assert np.var(G.v_[:6]) > 0
def test_state_variables():
'''
Test the setting and accessing of state variables.
'''
for codeobj_class in codeobj_classes:
G = NeuronGroup(10, 'v : volt', codeobj_class=codeobj_class)
# The variable N should be always present
assert G.N == 10
# But it should be read-only
assert_raises(TypeError, lambda: G.__setattr__('N', 20))
assert_raises(TypeError, lambda: G.__setattr__('N_', 20))
G.v = -70*mV
assert_raises(DimensionMismatchError, lambda: G.__setattr__('v', -70))
G.v_ = float(-70*mV)
assert_allclose(G.v[:], -70*mV)
G.v = -70*mV + np.arange(10)*mV
assert_allclose(G.v[:], -70*mV + np.arange(10)*mV)
G.v = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] * volt
assert_allclose(G.v[:], np.arange(10) * volt)
# incorrect size
assert_raises(ValueError, lambda: G.__setattr__('v', [0, 1]*volt))
assert_raises(ValueError, lambda: G.__setattr__('v', np.arange(11)*volt))
G.v = -70*mV
# Numpy methods should be able to deal with state variables
# (discarding units)
assert_allclose(np.mean(G.v), float(-70*mV))
# Getting the content should return a Quantity object which then natively
# supports numpy functions that access a method
assert_allclose(np.mean(G.v[:]), -70*mV)
# You should also be able to set variables with a string
G.v = '-70*mV + i*mV'
assert_allclose(G.v[0], -70*mV)
assert_allclose(G.v[9], -61*mV)
assert_allclose(G.v[:], -70*mV + np.arange(10)*mV)
# And it should raise an unit error if the units are incorrect
assert_raises(DimensionMismatchError,
lambda: G.__setattr__('v', '70 + i'))
assert_raises(DimensionMismatchError,
lambda: G.__setattr__('v', '70 + i*mV'))
# Calculating with state variables should work too
# With units
assert all(G.v - G.v == 0)
assert all(G.v - G.v[:] == 0*mV)
assert all(G.v[:] - G.v == 0*mV)
assert all(G.v + 70*mV == G.v[:] + 70*mV)
assert all(70*mV + G.v == G.v[:] + 70*mV)
assert all(G.v + G.v == 2*G.v)
assert all(G.v / 2.0 == 0.5*G.v)
assert all(1.0 / G.v == 1.0 / G.v[:])
assert_equal((-G.v)[:], -G.v[:])
assert_equal((+G.v)[:], G.v[:])
#Without units
assert all(G.v_ - G.v_ == 0)
assert all(G.v_ - G.v_[:] == 0)
assert all(G.v_[:] - G.v_ == 0)
assert all(G.v_ + float(70*mV) == G.v_[:] + float(70*mV))
assert all(float(70*mV) + G.v_ == G.v_[:] + float(70*mV))
assert all(G.v_ + G.v_ == 2*G.v_)
assert all(G.v_ / 2.0 == 0.5*G.v_)
assert all(1.0 / G.v_ == 1.0 / G.v_[:])
assert_equal((-G.v)[:], -G.v[:])
assert_equal((+G.v)[:], G.v[:])
# And in-place modification should work as well
G.v += 10*mV
G.v -= 10*mV
G.v *= 2
G.v /= 2.0
# with unit checking
assert_raises(DimensionMismatchError, lambda: G.v.__iadd__(3*second))
assert_raises(DimensionMismatchError, lambda: G.v.__iadd__(3))
assert_raises(DimensionMismatchError, lambda: G.v.__imul__(3*second))
# in-place modification with strings should not work
assert_raises(TypeError, lambda: G.v.__iadd__('string'))
assert_raises(TypeError, lambda: G.v.__imul__('string'))
assert_raises(TypeError, lambda: G.v.__idiv__('string'))
assert_raises(TypeError, lambda: G.v.__isub__('string'))
