class TestModel: def setup_class(self): self.DataSpectrum = DataSpectrum.open("../data/WASP14/WASP-14_2009-06-15_04h13m57s_cb.spec.flux", orders=np.array([22])) self.Instrument = TRES() self.HDF5Interface = HDF5Interface("../libraries/PHOENIX_submaster.hdf5") stellar_Starting = {"temp":6000, "logg":4.05, "Z":-0.4, "vsini":10.5, "vz":15.5, "logOmega":-19.665} stellar_tuple = C.dictkeys_to_tuple(stellar_Starting) cheb_tuple = ("c1", "c2", "c3") cov_tuple = ("sigAmp", "logAmp", "l") region_tuple = ("h", "loga", "mu", "sigma") self.Model = Model(self.DataSpectrum, self.Instrument, self.HDF5Interface, stellar_tuple=stellar_tuple, cheb_tuple=cheb_tuple, cov_tuple=cov_tuple, region_tuple=region_tuple, outdir="") def test_update(self): self.Model.OrderModels[0].update_Cheb({"c1": -0.017, "c2": -0.017, "c3": -0.003}) cov_Starting = {"sigAmp":1, "logAmp":-14.0, "l":0.15} self.Model.OrderModels[0].update_Cov(cov_Starting) params = {"temp":6005, "logg":4.05, "Z":-0.4, "vsini":10.5, "vz":15.5, "logOmega":-19.665} self.Model.update_Model(params) #This also updates downsampled_fls #For order in myModel, do evaluate, and sum the results. def test_evaluate(self): self.Model.evaluate() def test_to_json(self): self.Model.to_json() def test_from_json(self): newModel = Model.from_json("final_model.json", self.DataSpectrum, self.Instrument, self.HDF5Interface)
class TestModel: def setup_class(self): self.DataSpectrum = DataSpectrum.open( "../data/WASP14/WASP-14_2009-06-15_04h13m57s_cb.spec.flux", orders=np.array([22])) self.Instrument = TRES() self.HDF5Interface = HDF5Interface( "../libraries/PHOENIX_submaster.hdf5") stellar_Starting = { "temp": 6000, "logg": 4.05, "Z": -0.4, "vsini": 10.5, "vz": 15.5, "logOmega": -19.665 } stellar_tuple = C.dictkeys_to_tuple(stellar_Starting) cheb_tuple = ("c1", "c2", "c3") cov_tuple = ("sigAmp", "logAmp", "l") region_tuple = ("h", "loga", "mu", "sigma") self.Model = Model(self.DataSpectrum, self.Instrument, self.HDF5Interface, stellar_tuple=stellar_tuple, cheb_tuple=cheb_tuple, cov_tuple=cov_tuple, region_tuple=region_tuple, outdir="") def test_update(self): self.Model.OrderModels[0].update_Cheb({ "c1": -0.017, "c2": -0.017, "c3": -0.003 }) cov_Starting = {"sigAmp": 1, "logAmp": -14.0, "l": 0.15} self.Model.OrderModels[0].update_Cov(cov_Starting) params = { "temp": 6005, "logg": 4.05, "Z": -0.4, "vsini": 10.5, "vz": 15.5, "logOmega": -19.665 } self.Model.update_Model(params) #This also updates downsampled_fls #For order in myModel, do evaluate, and sum the results. def test_evaluate(self): self.Model.evaluate() def test_to_json(self): self.Model.to_json() def test_from_json(self): newModel = Model.from_json("final_model.json", self.DataSpectrum, self.Instrument, self.HDF5Interface)