def test_resample(num_samps, resample_num_samps, window):
cpu_time = np.linspace(0, 10, num_samps, endpoint=False)
cpu_sig = np.cos(-cpu_time**2 / 6.0)
gpu_sig = cp.asarray(cpu_sig)
cpu_resample = signal.resample(cpu_sig, resample_num_samps, window=window)
gpu_resample = cp.asnumpy(
cusignal.resample(cpu_sig, resample_num_samps, window=window))
assert array_equal(cpu_resample, gpu_resample)
def test_resample(self, linspace_data_gen, num_samps, resample_num_samps,
window):
cpu_sig, gpu_sig = linspace_data_gen(0, 10, num_samps, endpoint=False)
cpu_resample = signal.resample(cpu_sig,
resample_num_samps,
window=window)
gpu_resample = cp.asnumpy(
cusignal.resample(gpu_sig, resample_num_samps, window=window))
assert array_equal(cpu_resample, gpu_resample)
def __call__(self, signal: Signals, device):
signal.to(device)
if self.old is None:
self.old = signal.sps
if self.new is None:
self.new = signal.sps_in_fiber
if device == 'cuda':
from cusignal import resample_poly as resample
signal.samples_in_fiber = resample(signal.samples,
int(self.new / self.old),
1,
axis=1)
else:
from resampy import resample
signal.samples_in_fiber = resample(signal.samples, self.old,
self.new)
if self.is_quantize:
assert self.clipping_ratio is not None and self.resoultion_bits is not None
self.quantize(signal, device)
return signal
def gpu_version(self, sig, resample_num_samps, window):
with cp.cuda.Stream.null:
out = cusignal.resample(sig, resample_num_samps, window=window)
cp.cuda.Stream.null.synchronize()
return out