def test_coherence_complex(num_samps, fs, nperseg):
cpu_x = np.random.rand(num_samps) + 1j * np.random.rand(num_samps)
cpu_y = np.random.rand(num_samps) + 1j * np.random.rand(num_samps)
gpu_x = cp.asarray(cpu_x)
gpu_y = cp.asarray(cpu_y)
cf, cpu_coherence = signal.coherence(cpu_x, cpu_y, fs, nperseg=nperseg)
gf, gpu_coherence = cusignal.coherence(gpu_x, gpu_y, fs, nperseg=nperseg)
gpu_coherence = cp.asnumpy(gpu_coherence)
assert array_equal(cpu_coherence, gpu_coherence)
def test_coherence(self, rand_data_gen, num_samps, fs, nperseg):
cpu_x, gpu_x = rand_data_gen(num_samps)
cpu_y, gpu_y = rand_data_gen(num_samps)
_, cpu_coherence = signal.coherence(cpu_x, cpu_y, fs, nperseg=nperseg)
_, gpu_coherence = cusignal.coherence(gpu_x,
gpu_y,
fs,
nperseg=nperseg)
gpu_coherence = cp.asnumpy(gpu_coherence)
assert array_equal(cpu_coherence, gpu_coherence)
def gpu_version(self, x, y, fs, nperseg):
with cp.cuda.Stream.null:
out = cusignal.coherence(x, y, fs, nperseg=nperseg)
cp.cuda.Stream.null.synchronize()
return out