def __init__(self, size, buffer_length, dtype=np.double, init=None):
self.size = size
if not isinstance(dtype, np.dtype): dtype = np.dtype(dtype)
self.dtype = dtype
self.buffer_length = buffer_length
if init:
try:
init = dtype(init)
self.parr = parray.ones(
(buffer_length, size), dtype) * init
except:
self.parr = parray.zeros(
(buffer_length, size), dtype)
else:
self.parr = parray.zeros(
(buffer_length, size), dtype)
self.current = 0
self.gpudata = self.parr.gpudata
self.ld = self.parr.ld
def __init__(self, num_gpot_neurons, gpot_delay_steps,
rest, num_spike_neurons, spike_delay_steps):
self.num_gpot_neurons = num_gpot_neurons
if num_gpot_neurons > 0:
self.dtype = np.double
self.gpot_delay_steps = gpot_delay_steps
self.gpot_buffer = parray.empty((gpot_delay_steps, num_gpot_neurons),np.double)
self.gpot_current = 0
for i in range(gpot_delay_steps):
cuda.memcpy_dtod(int(self.gpot_buffer.gpudata) + \
self.gpot_buffer.ld * i * self.gpot_buffer.dtype.itemsize,\
rest.gpudata, rest.nbytes)
self.num_spike_neurons = num_spike_neurons
if num_spike_neurons > 0:
self.spike_delay_steps = spike_delay_steps
self.spike_buffer = parray.zeros((spike_delay_steps,num_spike_neurons),np.int32)
self.spike_current = 0
def __init__(self, num_gpot_neurons, gpot_delay_steps,
rest, num_spike_neurons, spike_delay_steps):
self.num_gpot_neurons = num_gpot_neurons
if num_gpot_neurons > 0:
self.dtype = np.double
self.gpot_delay_steps = gpot_delay_steps
self.gpot_buffer = parray.empty(
(gpot_delay_steps, num_gpot_neurons), np.double)
self.gpot_current = 0
for i in range(gpot_delay_steps):
cuda.memcpy_dtod(
int(self.gpot_buffer.gpudata) +
self.gpot_buffer.ld * i * self.gpot_buffer.dtype.itemsize,
rest.gpudata, rest.nbytes)
self.num_spike_neurons = num_spike_neurons
if num_spike_neurons > 0:
self.spike_delay_steps = spike_delay_steps
self.spike_buffer = parray.zeros(
(spike_delay_steps, num_spike_neurons), np.int32)
self.spike_current = 0