def mock_trained_emulator(mock_emulator):
filename = os.path.join(test_base, "data", "emu.hdf5")
if os.path.exists(filename):
yield Emulator.load(filename)
else:
mock_emulator.train()
mock_emulator.save(filename)
yield mock_emulator
def test_save_load(self, mock_emulator, tmpdir):
init = mock_emulator.get_param_dict()
filename = tmpdir.join("emu.hdf5")
mock_emulator.save(filename)
emulator = Emulator.load(filename)
final = emulator.get_param_dict()
assert init == final
assert emulator._trained == mock_emulator._trained
def __init__(
self,
emulator: Union[str, Emulator],
data: Union[str, Spectrum],
grid_params: Sequence[float],
max_deque_len: int = 100,
name: str = "SpectrumModel",
**params,
):
if isinstance(emulator, str):
emulator = Emulator.load(emulator)
if isinstance(data, str):
data = Spectrum.load(data)
if len(data) > 1:
raise ValueError(
"Multiple orders detected in data, please use EchelleModel")
self.emulator: Emulator = emulator
self.data_name = data.name
self.data = data[0]
dv = calculate_dv(self.data.wave)
self.min_dv_wave = create_log_lam_grid(dv, self.emulator.wl.min(),
self.emulator.wl.max())["wl"]
self.bulk_fluxes = resample(self.emulator.wl,
self.emulator.bulk_fluxes,
self.min_dv_wave)
self.residuals = deque(maxlen=max_deque_len)
# manually handle cheb coeffs to offset index by 1
chebs = params.pop("cheb", [])
cheb_idxs = [str(i) for i in range(1, len(chebs) + 1)]
params["cheb"] = dict(zip(cheb_idxs, chebs))
# load rest of params into FlatterDict
self.params = FlatterDict(params)
self.frozen = []
self.name = name
# Unpack the grid parameters
self.n_grid_params = len(grid_params)
self.grid_params = grid_params
# None means "yet to be calculated", do not use NaN
self._lnprob = None
self._glob_cov = None
self._loc_cov = None
self.log = logging.getLogger(self.__class__.__name__)
self.flux_scalar_func = flux_scalar = LinearNDInterpolator(
self.emulator.grid_points, self.emulator.flux_scalar)
