class test_CSRConnectivity(unittest.TestCase):
"""
This class tests the functionality of the connectivity patterns within *Projections*.
"""
@classmethod
def setUpClass(self):
"""
Compile the network for this test
"""
self.test_net = Network()
self.test_net.add([pop1, pop2, proj])
self.test_net.compile(silent=True, debug_build=True, clean=True)
def setUp(self):
"""
In our *setUp()* function we call *reset()* to reset the network before every test.
"""
self.test_net.reset()
def test_all_to_all(self):
"""
Tests the *all_to_all* connectivity pattern, in which every pre-synaptic neuron
is connected to every post-synaptic neuron.
We test correctness of ranks and weight values.
"""
tmp = self.test_net.get(proj)
#self.assertEqual(tmp.dendrite(3).rank, [0, 1, 2, 3, 4, 5, 6, 7, 8])
self.assertTrue(numpy.allclose(tmp.dendrite(3).w, numpy.ones((8, 1)) * 0.1))
class test_CSRConnectivity(unittest.TestCase):
"""
This class tests the functionality of the connectivity patterns within *Projections*.
"""
@classmethod
def setUpClass(self):
"""
Compile the network for this test
"""
self.test_net = Network()
self.test_net.add([pop1, pop2, proj])
self.test_net.compile(silent=True, debug_build=True, clean=True)
def setUp(self):
"""
In our *setUp()* function we call *reset()* to reset the network before every test.
"""
self.test_net.reset()
def test_all_to_all(self):
"""
Tests the *all_to_all* connectivity pattern, in which every pre-synaptic neuron
is connected to every post-synaptic neuron.
We test correctness of ranks and weight values.
"""
tmp = self.test_net.get(proj)
#self.assertEqual(tmp.dendrite(3).rank, [0, 1, 2, 3, 4, 5, 6, 7, 8])
self.assertTrue(
numpy.allclose(tmp.dendrite(3).w,
numpy.ones((8, 1)) * 0.1))
class test_CurrentInjection(unittest.TestCase):
"""
Test the implementation of the specialized projection
'CurrentInjection'. Based on the example in the documentation.
"""
@classmethod
def setUpClass(self):
"""
Compile the network for this test. Adapted the example
from documentation.
"""
SimpleSpike = Neuron(equations="mp=g_exc", spike="mp >= 1.0", reset="")
inp = Population(1, neuron=Neuron(equations="r=sin(t)"))
out = Population(1, neuron=SimpleSpike)
m = Monitor(out, "mp")
proj = CurrentInjection(inp, out, 'exc')
proj.connect_current()
self.test_net = Network()
self.test_net.add([inp, out, proj, m])
self.test_net.compile(silent=True)
self.output = self.test_net.get(out)
self.m = self.test_net.get(m)
def setUp(self):
"""
Automatically called before each test method, basically to reset the network after every test.
"""
self.test_net.reset()
def test_compile(self):
"""
Enforce compilation of the network.
"""
pass
def test_run_one_loop(self):
self.test_net.simulate(11)
rec_data = self.m.get("mp")[:, 0]
# there is 1 dt delay between the input and output
target = [0] + [sin(x) for x in range(10)]
self.assertTrue(np.allclose(rec_data, target))
class test_SynapticAccess(unittest.TestCase):
"""
ANNarchy support several global operations, there are always applied on
variables of *Population* objects.
This particular test focuses on the usage of them in synaptic learning rules
(for instance covariance).
"""
@classmethod
def setUpClass(self):
"""
Compile the network for this test
"""
neuron = Neuron(parameters="""
r=0
""")
cov = Synapse(parameters="""
tau = 5000.0
""",
equations="""
tau * dw/dt = (pre.r - mean(pre.r) ) * (post.r - mean(post.r) )
""")
pre = Population(6, neuron)
post = Population(1, neuron)
proj = Projection(pre, post, "exc",
synapse=cov).connect_all_to_all(weights=1.0)
self.test_net = Network()
self.test_net.add([pre, post, proj])
self.test_net.compile(silent=True)
self.net_pop = self.test_net.get(post)
@classmethod
def tearDownClass(cls):
del cls.test_net
def test_compile(self):
"""
Tests the result of *norm1(r)* for *pop*.
"""
pass
class test_GlobalOps_1D(unittest.TestCase):
"""
ANNarchy support several global operations, there are always applied on
variables of *Population* objects. Currently the following methods
are supported:
* mean()
* max()
* min()
* norm1()
* norm2()
They are used in the equations of our neuron definition.
This particular test focuses on a one-dimensional *Population*.
"""
@classmethod
def setUpClass(self):
"""
Compile the network for this test
"""
neuron = Neuron(parameters="""
r=0
""",
equations="""
mean_r = mean(r)
max_r = max(r)
min_r = min(r)
l1 = norm1(r)
l2 = norm2(r)
""")
pop = Population(6, neuron)
self.test_net = Network()
self.test_net.add([pop])
self.test_net.compile(silent=True)
self.net_pop = self.test_net.get(pop)
@classmethod
def tearDownClass(cls):
del cls.test_net
def setUp(self):
"""
In our *setUp()* function we set the variable *r*.
We also call *simulate()* to calculate mean/max/min.
"""
# reset() set all variables to init value (default 0), which is
# unfortunately meaningless for mean/max/min. So we set here some
# better values
self.net_pop.r = [2.0, 1.0, 0.0, -5.0, -3.0, -1.0]
# 1st step: calculate mean/max/min and store in intermediate
# variables
# 2nd step: write intermediate variables to accessible variables.
self.test_net.simulate(2)
def tearDown(self):
"""
After each test we call *reset()* to reset the network.
"""
self.test_net.reset()
def test_get_mean_r(self):
"""
Tests the result of *mean(r)* for *pop*.
"""
self.assertTrue(numpy.allclose(self.net_pop.mean_r, -1.0))
def test_get_max_r(self):
"""
Tests the result of *max(r)* for *pop*.
"""
self.assertTrue(numpy.allclose(self.net_pop.max_r, 2.0))
def test_get_min_r(self):
"""
Tests the result of *min(r)* for *pop*.
"""
self.assertTrue(numpy.allclose(self.net_pop.min_r, -5.0))
def test_get_l1_norm(self):
"""
Tests the result of *norm1(r)* (L1 norm) for *pop*.
"""
self.assertTrue(numpy.allclose(self.net_pop.l1, 12.0))
def test_get_l2_norm(self):
"""
Tests the result of *norm2(r)* (L2 norm) for *pop*.
"""
# compute control value
l2norm = numpy.linalg.norm(self.net_pop.r, ord=2)
# test
self.assertTrue(numpy.allclose(self.net_pop.l2, l2norm))
class test_GlobalOps_1D_Large(unittest.TestCase):
@classmethod
def setUpClass(self):
"""
Compile the network for this test
"""
neuron = Neuron(parameters="""
r=0
""",
equations="""
mean_r = mean(r)
max_r = max(r)
min_r = min(r)
l1 = norm1(r)
l2 = norm2(r)
""")
pop = Population(500, neuron)
self.test_net = Network()
self.test_net.add([pop])
self.test_net.compile(silent=True)
self.net_pop = self.test_net.get(pop)
@classmethod
def tearDownClass(cls):
del cls.test_net
def tearDown(self):
"""
After each test we call *reset()* to reset the network.
"""
self.test_net.reset()
def test_mean_r(self):
"""
"""
rand_val = numpy.random.random(500)
self.net_pop.r = rand_val
self.test_net.simulate(2)
self.assertTrue(
numpy.allclose(self.net_pop.mean_r, numpy.mean(rand_val)))
def test_min_r(self):
"""
"""
rand_val = numpy.random.random(500)
self.net_pop.r = rand_val
self.test_net.simulate(2)
self.assertTrue(
numpy.allclose(self.net_pop.min_r, numpy.amin(rand_val)))
def test_max_r(self):
"""
"""
rand_val = numpy.random.random(500)
self.net_pop.r = rand_val
self.test_net.simulate(2)
self.assertTrue(
numpy.allclose(self.net_pop.max_r, numpy.amax(rand_val)))
#inp_i = Projection(pre=I_i, post=I, target='exc')
#inp_e.connect_one_to_one(1.0)
#inp_i.connect_one_to_one(1.0)
E.i_offset = 5.0
I.i_offset = 2.0
# monitoring
obs_e = Monitor(E, variables=['spike', 'v'], start=True)
obs_i = Monitor(I, variables=['spike', 'v'], start=True)
# simulation
############
# annarchy simulation
net = Network(everything=True)
net.compile()
net.simulate(duration=T)
# conversion to pyrates
rate_e = pyrates_from_annarchy(monitors=[net.get(obs_e)],
vars=['spike'],
pop_average=True)
rate_i = pyrates_from_annarchy(monitors=[net.get(obs_i)],
vars=['spike'],
pop_average=True)
v_e = pyrates_from_annarchy(monitors=[net.get(obs_e)],
vars=['v'],
pop_average=False)
v_i = pyrates_from_annarchy(monitors=[net.get(obs_i)],
vars=['v'],
pop_average=False)
def grid_search_annarchy(param_grid: dict, param_map: dict, dt: float, simulation_time: float,
inputs: dict, outputs: dict, sampling_step_size: Optional[float] = None,
permute_grid: bool = False, circuit=None, **kwargs) -> DataFrame:
"""Function that runs multiple parametrizations of the same circuit in parallel and returns a combined output.
Parameters
----------
param_grid
Key-value pairs for each circuit parameter that should be altered over different circuit parametrizations.
param_map
Key-value pairs that map the keys of param_grid to concrete circuit variables.
dt
Simulation step-size in s.
simulation_time
Simulation time in s.
inputs
Inputs as provided to the `run` method of `:class:ComputeGraph`.
outputs
Outputs as provided to the `run` method of `:class:ComputeGraph`.
sampling_step_size
Sampling step-size as provided to the `run` method of `:class:ComputeGraph`.
permute_grid
If true, all combinations of the provided param_grid values will be realized. If false, the param_grid values
will be traversed pairwise.
circuit
Instance of ANNarchy network.
kwargs
Additional keyword arguments passed to the `:class:ComputeGraph` initialization.
Returns
-------
DataFrame
Simulation results stored in a multi-index data frame where each index lvl refers to one of the parameters of
param_grid.
"""
from ANNarchy import Population, Projection, Network, TimedArray, Monitor, ANNarchyException
# linearize parameter grid if necessary
if type(param_grid) is dict:
param_grid = linearize_grid(param_grid, permute_grid)
# create annarchy net if necessary
if circuit is None:
circuit = Network(everything=True)
# assign parameter updates to each circuit and combine them to unconnected network
circuit_names = []
param_info = []
param_split = "__"
val_split = "--"
comb = "_"
populations, projections = {}, {}
for n in range(param_grid.shape[0]):
# copy and re-parametrize populations
try:
for p in circuit.get_populations():
name = f'net{n}/{p.name}'
p_new = Population(geometry=p.geometry, neuron=p.neuron_type, name=name,
stop_condition=p.stop_condition, storage_order=p._storage_order,
copied=False)
p_new = adapt_pop(p_new, param_grid.iloc[n, :], param_map)
populations[name] = p_new
# add input to population
for node, inp in inputs.items():
if node in name:
inp_name = f'{name}_inp'
inp = TimedArray(rates=inp, name=inp_name)
proj = Projection(pre=inp, post=p_new, target='exc')
proj.connect_one_to_one(1.0)
populations[inp_name] = inp
projections[inp_name] = proj
except ANNarchyException:
pass
# copy and re-parametrize projections
try:
for c in circuit.get_projections():
source = c.pre if type(c.pre) is str else c.pre.name
target = c.post if type(c.post) is str else c.post.name
source = f'net{n}/{source}'
target = f'net{n}/{target}'
name = f'{source}/{target}/{c.name}'
c_new = Projection(pre=source, post=target, target=c.target, synapse=c.synapse_type, name=name,
copied=False)
c_new._store_connectivity(c._connection_method, c._connection_args, c._connection_delay, c._storage_format)
c_new = adapt_proj(c_new, param_grid.iloc[n, :], param_map)
projections[name] = c_new
except ANNarchyException:
pass
# collect parameter and circuit name infos
circuit_names.append(f'net{n}')
param_names = list(param_grid.columns.values)
param_info_tmp = [f"{param_names[i]}{val_split}{val}" for i, val in enumerate(param_grid.iloc[n, :])]
param_info.append(param_split.join(param_info_tmp))
net = Network()
for p in populations.values():
net.add(p)
for c in projections.values():
net.add(c)
# adjust output of simulation to combined network
nodes = [p.name for p in circuit.get_populations()]
out_names, var_names, out_lens, monitors, monitor_names = [], [], [], [], []
for out_key, out in outputs.copy().items():
out_names_tmp, out_lens_tmp = [], []
if out[0] in nodes:
for i, name in enumerate(param_info):
out_tmp = list(out)
out_tmp[0] = f'{circuit_names[i]}/{out_tmp[0]}'
p = net.get_population(out_tmp[0])
monitors.append(Monitor(p, variables=out_tmp[-1], period=sampling_step_size, start=True,
net_id=net.id))
monitor_names.append(f'{name}{param_split}out_var{val_split}{out_key}{comb}{out[0]}')
var_names.append(out_tmp[-1])
out_names_tmp.append(f'{out_key}{comb}{out[0]}')
out_lens_tmp.append(p.geometry[0])
elif out[0] == 'all':
for node in nodes:
for i, name in enumerate(param_info):
out_tmp = list(out)
out_tmp[0] = f'{circuit_names[i]}/{node}'
p = net.get_population(out_tmp[0])
monitors.append(Monitor(p, variables=out_tmp[-1], period=sampling_step_size, start=True,
net_id=net.id))
monitor_names.append(f'{name}{param_split}out_var{val_split}{out_key}{comb}{node}')
var_names.append(out_tmp[-1])
out_names_tmp.append(f'{out_key}{comb}{node}')
out_lens_tmp.append(p.geometry[0])
else:
node_found = False
for node in nodes:
if out[0] in node:
node_found = True
for i, name in enumerate(param_info):
out_tmp = list(out)
out_tmp[0] = f'{circuit_names[i]}/{node}'
p = net.get_population(out_tmp[0])
monitors.append(Monitor(p, variables=out_tmp[-1], period=sampling_step_size, start=True,
net_id=net.id))
monitor_names.append(f'{name}{param_split}out_var{val_split}{out_key}{comb}{node}')
var_names.append(out_tmp[-1])
out_names_tmp.append(f'{out_key}{comb}{node}')
out_lens_tmp.append(p.geometry[0])
if not node_found:
raise ValueError(f'Invalid output identifier in output: {out_key}. '
f'Node {out[0]} is not part of this network')
out_names += list(set(out_names_tmp))
out_lens += list(set(out_lens_tmp))
#net.add(monitors)
# simulate the circuits behavior
net.compile()
net.simulate(duration=simulation_time)
# transform output into pyrates-compatible data format
results = pyrates_from_annarchy(monitors, vars=list(set(var_names)),
monitor_names=monitor_names, **kwargs)
# transform results into long-form dataframe with changed parameters as columns
multi_idx = [param_grid[key].values for key in param_grid.keys()]
n_iters = len(multi_idx[0])
outs = []
for out_name, out_len in zip(out_names, out_lens):
outs += [f'{out_name}_n{i}' for i in range(out_len)] * n_iters
multi_idx_final = []
for idx in multi_idx:
for val in idx:
for out_len in out_lens:
multi_idx_final += [val]*len(out_names)*out_len
index = MultiIndex.from_arrays([multi_idx_final, outs], names=list(param_grid.keys()) + ["out_var"])
index = MultiIndex.from_tuples(list(set(index)), names=list(param_grid.keys()) + ["out_var"])
results_final = DataFrame(columns=index, data=np.zeros_like(results.values), index=results.index)
for col in results.keys():
params = col.split(param_split)
indices = [None] * len(results_final.columns.names)
for param in params:
var, val = param.split(val_split)[:2]
idx = list(results_final.columns.names).index(var)
try:
indices[idx] = float(val)
except ValueError:
indices[idx] = val
results_final.loc[:, tuple(indices)] = results[col].values
return results_final
class test_CustomFunc(unittest.TestCase):
"""
This class tests the definition of custom functions, they
can defined on three levels:
* globally
* within neurons
* within synapses
"""
@classmethod
def setUpClass(self):
"""
Compile the network for this test
"""
neuron = Neuron(
equations = "r = transfer_function(sum(exc), 0.0)",
functions = "transfer_function(x, t) = if x > t: if x > 2*t : (x - 2*t)^2 else: x - t else: 0."
)
neuron2 = Neuron(
equations = "r = glob_pos(sum(exc))"
)
synapse = Synapse(
equations="w += hebb(pre.r, post.r)",
functions="hebb(x, y) = x * y"
)
pop = Population(10, neuron)
pop2 = Population(10, neuron2)
proj = Projection(pop, pop, 'exc', synapse).connect_all_to_all(1.0)
self.test_net = Network()
self.test_net.add([pop, pop2, proj])
self.test_net.compile(silent=True)
self.net_pop = self.test_net.get(pop)
self.net_proj = self.test_net.get(proj)
@classmethod
def tearDownClass(cls):
del cls.test_net
def setUp(self):
"""
In our *setUp()* function we call *reset()* to reset the network.
"""
self.test_net.reset()
def test_neuron(self):
"""
Custom func defined within a neuron object, providing numpy.array data.
"""
self.assertTrue(numpy.allclose(self.net_pop.transfer_function(numpy.array([0., 1., 2., 3.]), numpy.array([2., 2., 2., 2.])), [0, 0, 0, 1]))
def test_neuron2(self):
"""
Custom func defined within a neuron object, providing simple lists.
"""
self.assertTrue(numpy.allclose(self.net_pop.transfer_function([0., 1., 2., 3.], [2., 2., 2., 2.]), [0, 0, 0, 1]))
def test_synapse(self):
"""
Custom func defined within a synapse object, providing simple lists.
"""
self.assertTrue(numpy.allclose(self.net_proj.hebb(numpy.array([0., 1., 2., 3.]), numpy.array([0., 1., 2., 3.])), [0, 1, 4, 9]))
def test_synapse2(self):
"""
Custom func defined within a synapse object, providing simple lists.
"""
self.assertTrue(numpy.allclose(self.net_proj.hebb([0., 1., 2., 3.], [0., 1., 2., 3.]), [0, 1, 4, 9]))
class test_BuiltinFunctions(unittest.TestCase):
"""
Test the correct evaluation of builtin functions
"""
@classmethod
def setUpClass(self):
"""
Compile the network for this test
"""
BuiltinFuncs = Neuron(parameters="""
base = 2.0
""",
equations="""
r = modulo(t,3)
pr = power(base,3)
clip_below = clip(-2, -1, 1)
clip_within = clip(0, -1, 1)
clip_above = clip(2, -1, 1)
""")
pop1 = Population(1, BuiltinFuncs)
mon = Monitor(pop1,
['r', 'pr', 'clip_below', 'clip_within', 'clip_above'])
self.test_net = Network()
self.test_net.add([pop1, mon])
self.test_net.compile(silent=True)
self.test_mon = self.test_net.get(mon)
@classmethod
def tearDownClass(cls):
"""
All tests of this class are done. We can destroy the network.
"""
del cls.test_net
def setUp(self):
"""
Automatically called before each test method, basically to reset the network after every test.
"""
self.test_net.reset()
def tearDown(self):
"""
Since all tests are independent, after every test we use the *get()* method for every monotor to clear all recordings.
"""
self.test_mon.get()
def test_modulo(self):
"""
Test modulo function.
"""
self.test_net.simulate(10)
data_m = self.test_mon.get('r')
self.assertTrue(
np.allclose(data_m, [[0.0], [1.0], [2.0], [0.0], [1.0], [2.0],
[0.0], [1.0], [2.0], [0.0]]))
def test_integer_power(self):
"""
Test integer power function.
"""
self.test_net.simulate(1)
data_m = self.test_mon.get('pr')
self.assertTrue(np.allclose(data_m, [[8.0]]))
def test_clip_below(self):
"""
The clip(x, a, b) method ensures that x is within range [a,b]. This tests validates that x = -2 is clipped to -1
"""
data_clip_below = self.test_mon.get('clip_below')
self.assertTrue(np.allclose(data_clip_below, [[-1.0]]))
def test_clip_within(self):
"""
The clip(x, a, b) method ensures that x is within range [a,b]. This tests validates that x = 0 retains.
"""
data_clip_within = self.test_mon.get('clip_within')
self.assertTrue(np.allclose(data_clip_within, [[0.0]]))
def test_clip_above(self):
"""
The clip(x, a, b) method ensures that x is within range [a,b]. This tests validates that x = 2 is clipped to 1.
"""
data_clip_above = self.test_mon.get('clip_above')
self.assertTrue(np.allclose(data_clip_above, [[1.0]]))
class test_Dendrite(unittest.TestCase):
"""
This class tests the *Dendrite* object, which gathers all synapses
belonging to a post-synaptic neuron in a *Projection*:
* access to parameters
* the *rank* method
* the *size* method
"""
@classmethod
def setUpClass(self):
"""
Compile the network for this test
"""
neuron = Neuron(parameters="tau = 10", equations="r += 1/tau * t")
neuron2 = Neuron(parameters="tau = 10: population",
equations="r += 1/tau * t: init = 1.0")
Oja = Synapse(parameters="""
tau = 5000.0 : postsynaptic
alpha = 8.0
""",
equations="""
tau * dw/dt = pre.r * post.r - alpha * post.r^2 * w
""")
pop1 = Population(5, neuron)
pop2 = Population(8, neuron2)
proj = Projection(pre=pop1, post=pop2, target="exc", synapse=Oja)
proj.connect_all_to_all(weights=1.0)
self.test_net = Network()
self.test_net.add([pop1, pop2, proj])
self.test_net.compile(silent=True)
self.net_proj = self.test_net.get(proj)
@classmethod
def tearDownClass(cls):
del cls.test_net
def setUp(self):
"""
In our *setUp()* function we call *reset()* to reset the network.
"""
self.test_net.reset()
def test_none(self):
"""
If a non-existent *Dendrite* is accessed, an error should be thrown.
This is tested here.
"""
from ANNarchy.core.Global import ANNarchyException
with self.assertRaises(ANNarchyException) as cm:
d = self.net_proj.dendrite(14)
# self.assertEqual(cm.exception.code, 1)
def test_pre_ranks(self):
"""
Tests the *pre_ranks* method, which returns the ranks of the
pre-synaptic neurons belonging to the accessed *Dendrite*.
"""
self.assertEqual(self.net_proj.dendrite(5).pre_ranks, [0, 1, 2, 3, 4])
def test_dendrite_size(self):
"""
Tests the *size* method, which returns the number of pre-synaptic
neurons belonging to the accessed *Dendrite*.
"""
self.assertEqual(self.net_proj.dendrite(3).size, 5)
def test_get_dendrite_tau(self):
"""
Tests the direct access of the parameter *tau* of a *Dendrite*.
"""
self.assertTrue(numpy.allclose(self.net_proj.dendrite(1).tau, 5000.0))
def test_get_dendrite_alpha(self):
"""
Tests the direct access of the variable *alpha* of a *Dendrite*.
"""
self.assertTrue(
numpy.allclose(
self.net_proj.dendrite(0).alpha, [8.0, 8.0, 8.0, 8.0, 8.0]))
def test_get_dendrite_weights(self):
"""
Tests the direct access of the parameter *w* (weights) of a *Dendrite*.
"""
self.assertTrue(
numpy.allclose(
self.net_proj.dendrite(7).w, [1.0, 1.0, 1.0, 1.0, 1.0]))
def test_set_tau(self):
"""
Tests the setting of the parameter *tau* for the whole *Projection* through a single value.
"""
self.net_proj.tau = 6000.0
self.assertTrue(numpy.allclose(self.net_proj.dendrite(0).tau, 6000.0))
def test_set_tau_2(self):
"""
Tests the setting of the parameter *tau* for a single dendrite with a single value.
"""
old_value = self.net_proj.tau
old_value[1] = 7000.0
self.net_proj.dendrite(1).tau = 7000.0
self.assertTrue(numpy.allclose(self.net_proj.dendrite(1).tau, 7000.0))
self.assertTrue(numpy.allclose(self.net_proj.tau, old_value))
def test_set_alpha(self):
"""
Tests the setting of the parameter *alpha* of a *Dendrite*.
"""
self.net_proj.dendrite(4).alpha = 9.0
self.assertTrue(
numpy.allclose(
self.net_proj.dendrite(4).alpha, [9.0, 9.0, 9.0, 9.0, 9.0]))
def test_set_alpha_2(self):
"""
Tests the setting of the parameter *alpha* of a specific synapse in a *Dendrite*.
"""
self.net_proj.dendrite(4)[1].alpha = 10.0
self.assertTrue(
numpy.allclose(
self.net_proj.dendrite(4).alpha, [9.0, 10.0, 9.0, 9.0, 9.0]))
def test_set_weights(self):
"""
Tests the setting of the parameter *w* (weights) of a *Dendrite*.
"""
self.net_proj.dendrite(6).w = 2.0
self.assertTrue(
numpy.allclose(
self.net_proj.dendrite(6).w, [2.0, 2.0, 2.0, 2.0, 2.0]))
def test_set_weights_2(self):
"""
Tests the setting of the parameter *w* (weights) of a specific synapse in a *Dendrite*.
"""
self.net_proj.dendrite(6)[2].w = 3.0
self.assertTrue(
numpy.allclose(
self.net_proj.dendrite(6).w, [2.0, 2.0, 3.0, 2.0, 2.0]))
def test_set_with_dict(self):
"""
Test the setting of attributes using a dictionary.
"""
new_value = self.net_proj.tau
new_value[1] = 7000.0
update = dict({'tau': 7000})
self.net_proj.dendrite(1).set(update)
self.assertTrue(numpy.allclose(self.net_proj.tau, new_value))
def test_get_by_name(self):
"""
Test the retrieval of an attribute by the name.
"""
val = self.net_proj.dendrite(1).get('tau')
self.assertTrue(numpy.allclose(val, 5000.0))