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)
