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import ctypes
import numpy
import torch
spires_lib = ctypes.CDLL("../spires/build/.libspires.so")
spires_lib.spires_reservoir_step.argtypes = [ctypes.POINTER(ctypes.c_float)]
spires_lib.spires_reservoir_step.restype = None
#change currents from tensor to a flat C pointer array for spires to read
def send_currents_to_spires(currents):
# ----- extract from pytorhc graph -----
# .detach() removes it from auto gradient tracking
# .cpu() make sure data is in RAM, not VRAM
# .numpy() maps it to numpy array
numpy_array = torch_tensor.detach().cpu().numpy()
# ----- makes suren layout matches 32 bit float C-array -----
# .astype(npfloat32) forces standard CC float precistion
# .flatten() makes sure the memory is a 1D block
contiguous_array = numpy_array.astype(np.float32).flatten()
# ----- Ensure raw memory pointer -----
c_float_ptr = contiguous_array.ctypes.data_as(ctypes.POINTER(ctypes.c_float))
array_size = contiguous_array.size
# ----- Call the C library -----
spires_lib.spires_reservoir_step(c_float_ptr, array_size)
return contiguous_array
# NEED TO MODIFY SPIRES FOR THIS?? :((
def read_spikes_from_spires():
placeholder = 0
return 0
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