# PowerSpectra can be used instead
#===============================================================================
f = PowerSpectra(
time_data=t1,
window='Hanning',
overlap='50%',
block_size=128, #FFT-parameters
ind_low=8,
ind_high=16) #to save computational effort, only
# frequencies with index 1-30 are used
#===============================================================================
# the environment, i.e. medium characteristics
# (in this case, the speed of sound is set)
#===============================================================================
env = Environment(c=346.04)
# =============================================================================
# a steering vector instance. SteeringVector provides the standard freefield
# sound propagation model in the steering vectors.
# =============================================================================
st = SteeringVector(grid=g, mics=m, env=env)
#===============================================================================
# beamformers in frequency domain
#===============================================================================
bb = BeamformerBase(freq_data=f, steer=st, r_diag=True)
bd = BeamformerDamas(beamformer=bb, n_iter=100)
be = BeamformerEig(freq_data=f, steer=st, r_diag=True, n=54)
bo = BeamformerOrth(beamformer=be, eva_list=list(range(38, 54)))
bs = BeamformerCleansc(freq_data=f, steer=st, r_diag=True)
x_max=0.2,
y_min=-0.2,
y_max=0.2,
z_min=0.5,
z_max=0.9,
increment=0.2)
gc = g.gpos
flows = [
SlotJet(v0=70.0, origin=(-0.7, 0, 0.7)),
OpenJet(v0=70.0, origin=(-0.7, 0, 0.7)),
RotatingFlow(v0=70.0, rpm=1000.0)
]
envs = [
Environment(),
UniformFlowEnvironment(ma=0.3),
GeneralFlowEnvironment(ff=OpenJet(v0=70.0, origin=(-0.7, 0, 0.7)))
]
class acoular_env_test(unittest.TestCase):
def test_flow_results(self):
for fl in flows:
with self.subTest(fl.__class__.__name__):
name = join('reference_data', f'{fl.__class__.__name__}.npy')
# stack all results
actual_data = np.array([np.vstack(fl.v(x)) for x in gc.T])
if WRITE_NEW_REFERENCE_DATA:
np.save(name, actual_data)
ref_data = np.load(name)