def test_iteration_items(self):
ps = ParameterSpace({'a': [2, 3, 5, 8, 13], 'b': 7, 'c': lambda i: 3*i+2}, shape=(5,))
ps.evaluate(mask=[1, 3, 4])
expected = {'a': np.array([3, 8, 13]),
'c': np.array([5, 11, 14]),
'b': np.array([7, 7, 7])}
for key, value in ps.items():
assert_array_equal(expected[key], value)
def test_iteration_items(self):
ps = ParameterSpace({'a': [2, 3, 5, 8, 13], 'b': 7, 'c': lambda i: 3 * i + 2}, shape=(5,))
ps.evaluate(mask=[1, 3, 4])
expected = {'a': np.array([3, 8, 13]),
'c': np.array([5, 11, 14]),
'b': np.array([7, 7, 7])}
for key, value in ps.items():
assert_array_equal(expected[key], value)
class NestCurrentSource(BaseCurrentSource):
"""Base class for a nest source of current to be injected into a neuron."""
def __init__(self, **parameters):
self._device = nest.Create(self.nest_name)
self.cell_list = []
self.parameter_space = ParameterSpace(self.default_parameters,
self.get_schema(),
shape=(1, ))
if parameters:
self.parameter_space.update(**parameters)
self.min_delay = state.min_delay
self.timestep = state.dt # NoisyCurrentSource has a parameter called "dt", so use "timestep" here
state.current_sources.append(self)
def inject_into(self, cells):
for id in cells:
if id.local and not id.celltype.injectable:
raise TypeError("Can't inject current into a spike source.")
if isinstance(cells, (Population, PopulationView, Assembly)):
self.cell_list = [cell for cell in cells]
else:
self.cell_list = cells
nest.Connect(self._device,
self.cell_list,
syn_spec={"delay": state.min_delay})
def _reset(self):
# after a reset, need to adjust parameters for time offset
updated_params = {}
for name, value in self.parameter_space.items():
if name in ("start", "stop", "amplitude_times"):
updated_params[name] = value
if updated_params:
state.set_status(self._device, updated_params)
def _delay_correction(self, value):
"""
A change in a device requires a min_delay to take effect at the target
"""
corrected = value - self.min_delay
# set negative times to zero
if isinstance(value, np.ndarray):
corrected = np.where(corrected > 0, corrected, 0.0)
else:
corrected = max(corrected, 0.0)
return corrected
def record(self):
self.i_multimeter = nest.Create('multimeter',
params={
'record_from': ['I'],
'interval': state.dt
})
nest.Connect(self.i_multimeter, self._device)
def _get_data(self):
events = nest.GetStatus(self.i_multimeter)[0]['events']
# Similar to recording.py: NEST does not record values at
# the zeroth time step, so we add them here.
t_arr = np.insert(np.array(events['times']), 0, 0.0)
i_arr = np.insert(np.array(events['I'] / 1000.0), 0, 0.0)
# NEST and pyNN have different concepts of current initiation times
# To keep this consistent across simulators, we will have current
# initiating at the electrode at t_start and effect on cell at next dt
# This requires padding min_delay equivalent period with 0's
pad_length = int(self.min_delay / self.timestep)
i_arr = np.insert(i_arr[:-pad_length], 0, [0] * pad_length)
return t_arr, i_arr
