def detect(self, x_in):
# Compute the instantaneous power for the current buffer
x_envelope = cp.abs(x_in)
# Filter and decimate the envelope to a lower data rate
self._envelope[:] = cusignal.upfirdn(self._win,
x_envelope)[self._filter_roi]
# Update threshold
# Add summation of current envelope to the threshold fifo array
self._bkg_sum_arr[self._fifo_index] = cp.sum(self._envelope)
# Update fifo index for next detection window
self._fifo_index = (self._fifo_index + 1) % self._fifo_len
# Calculate avg background power level for the previous buffers in fifo
bkg_avg = np.sum(self._bkg_sum_arr) / (self._fifo_len * self._buff_len)
# Calculate new threshold value
self._thresh = bkg_avg * self._thresh_offset
# Calc index vector where power is above the threshold
envelope_det_idx = self._envelope > self._thresh
n_detections = cp.sum(envelope_det_idx)
# Make sure at least samp_above_thresh are higher than the threshold
if n_detections > self._samp_above_thresh:
# Copy to cupy array as workaround to issue cuSignal #178
x_out = cp.array(x_in)
x_out[~envelope_det_idx] = 0 # Zero out samples below threshold
else:
x_out = None
return x_out
def test_upfirdn2d(self, rand_2d_data_gen, num_samps, up, down, use_numba):
cpu_sig, gpu_sig = rand_2d_data_gen(num_samps)
h = [1, 1, 1]
cpu_resample = signal.upfirdn(h, cpu_sig, up, down)
gpu_resample = cp.asnumpy(
cusignal.upfirdn(h, gpu_sig, up, down, use_numba=use_numba))
assert array_equal(cpu_resample, gpu_resample)
def gpu_version(self, sig, up, down, axis):
with cp.cuda.Stream.null:
out = cusignal.upfirdn([1, 1, 1], sig, up, down, axis)
cp.cuda.Stream.null.synchronize()
return out