def generate_batch_spectrum(self, param_vector, noise=True):
line_vec = (6302.5, 2.5, 1)
line_arg = 1000 * (np.linspace(6302.0692255, 6303.2544205, 56) -
line_vec[0])
spectrum = me_model(param_vector,
line_arg,
line_vec,
with_ff=True,
with_noise=noise)
cont = param_vector[:, 6] + line_vec[2] * param_vector[:, 7]
cont = cont * np.amax(spectrum.reshape(-1, 224),
axis=1) / self.hps.cont_scale
cont = torch.from_numpy(cont.reshape(-1, 1)).float().to(self.device)
y = normalize_output(param_vector,
mode=self.hps.mode,
logB=self.hps.logB)
y = torch.from_numpy(y).float().to(self.device)
if 'rescale' in self.hps.transform_type:
rescaled = (np.swapaxes(spectrum, 0, 2) *
np.array(self.hps.factors).reshape(4, 1, 1)).swapaxes(
0, 2)
if 'mlp' in self.hps.transform_type:
rescaled = rescaled.reshape(-1, 224, order='F')
rescaled = torch.from_numpy(rescaled).float().to(self.device)
else:
NotImplementedError('Only rescale transform')
data = {'X': [rescaled, cont], 'Y': y}
return data
def test_normalize_output_array_angle(self):
project_path = get_project_root()
filename = os.path.join(project_path, 'data/small_parameters_base.fits')
params = fits.open(filename)[0].data[5:11]
sample = normalize_output(params, mode='range', logB=True)
assert sample.min() >= 0
assert sample.max() <= 1
def test_normalize_output_array_angle(self):
project_path = get_project_root()
filename = os.path.join(project_path, 'data/small_parameters_base.fits')
params = fits.open(filename)[0].data[5:11]
sample = normalize_output(params, mode='range', logB=True, angle_transformation=True)
assert sample.min() >= 0
assert sample.max() < 1
assert sample[0, 1] == pytest.approx(np.sin(params[0, 1]*np.pi/180), 0.01)
assert True
def test_normalize_output_range_angle(self, sample_from_database):
y = sample_from_database['Y']
sample = normalize_output(y, mode='range', logB=True, angle_transformation=True)
assert sample[0][1] == pytest.approx(1, 0.1)
assert min(sample) > 0
assert max(sample) < 1
def test_normalize_output_range_no_angle(self, sample_from_database):
y = sample_from_database['Y']
sample = normalize_output(y, mode='range', logB=True)
assert min(sample) > 0
assert max(sample) < 1