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)
class AccuracyComparison:
'''
Gather the data products necessary to make a test about accuracy of the reduced grid sizes.
'''
def __init__(self, DataSpectrum, Instrument, LibraryHA, LibraryLA,
parameters, deltaParameters):
'''Initialize the comparison object.
:param DataSpectrum: the spectrum that provides a wl grid + natural resolution
:type DataSpectrum: :obj:`grid_tools.DataSpectrum`
:param Instrument: the instrument object on which the DataSpectrum was acquired (ie, TRES, SPEX...)
:type Instrument: :obj:`grid_tools.Instrument`
:param LibraryHA: the path to the native resolution spectral library
:type LibraryHA: string
:param LibraryLA: the path to the approximate spectral library
:type LibraryLA: string
'''
self.DataSpectrum = DataSpectrum
self.Instrument = Instrument
self.HDF5InterfaceHA = HDF5Interface(LibraryHA)
self.HDF5InterfaceLA = HDF5Interface(LibraryLA)
print("Bounds of the grids are")
print("HA", self.HDF5InterfaceHA.bounds)
print("LA", self.HDF5InterfaceLA.bounds)
#If the DataSpectrum contains more than one order, we only take the first one. To get behavior with a
# different order, you should only load that via the DataSpectrum(orders=[22]) flag.
self.wl = self.DataSpectrum.wls[0]
self.fullModelLA = Model(self.DataSpectrum,
self.Instrument,
self.HDF5InterfaceLA,
stellar_tuple=("temp", "logg", "Z", "vsini",
"vz", "logOmega"),
cheb_tuple=("c1", "c2", "c3"),
cov_tuple=("sigAmp", "logAmp", "l"),
region_tuple=("loga", "mu", "sigma"))
self.modelLA = self.fullModelLA.OrderModels[0]
self.fullModelHA = ModelHA(self.DataSpectrum,
self.Instrument,
self.HDF5InterfaceHA,
stellar_tuple=("temp", "logg", "Z", "vsini",
"vz", "logOmega"),
cheb_tuple=("c1", "c2", "c3"),
cov_tuple=("sigAmp", "logAmp", "l"),
region_tuple=("loga", "mu", "sigma"))
self.modelHA = self.fullModelHA.OrderModels[0]
self.parameters = parameters
self.deltaParameters = deltaParameters
self.base = self.get_specHA(self.parameters)
self.baseLA = self.get_specLA(self.parameters)
self.approxResid = get_resid_spec(
self.base, self.baseLA) #modelHA - modelLA @ parameters
def get_specHA(self, parameters):
'''
Update the model and then query the spectrum
:param parameters: Dictionary of fundamental stellar parameters
:type parameters: dict
:returns: flux spectrum
'''
params = parameters.copy()
params.update({"vsini": 0., "vz": 0, "logOmega": 0.})
self.fullModelHA.update_Model(params)
return self.modelHA.get_spectrum()
def get_specLA(self, parameters):
'''
Update the model and then query the spectrum
:param parameters: Dictionary of fundamental stellar parameters
:type parameters: dict
:returns: flux spectrum
'''
params = parameters.copy()
params.update({"vsini": 0., "vz": 0, "logOmega": 0.})
self.fullModelLA.update_Model(params)
return self.modelLA.get_spectrum()
def createEnvelopeSpectrum(self, direction='both'):
'''
The parameters should always be specified at a grid point of the HDF5 file.
For this, do the deltaParameters interpolation.
Direction specifies whether to do interpolation up (+ 10 K, etc.), down (- 10 K), or
do both and then find the minimum envelope between the two.
For now, only up is implemented.
'''
#For each key, add the delta parameters
temp_params = self.parameters.copy()
temp_params["temp"] += self.deltaParameters["temp"]
temp_spec = get_resid_spec(self.base, self.get_specHA(temp_params))
logg_params = self.parameters.copy()
logg_params["logg"] += self.deltaParameters["logg"]
logg_spec = get_resid_spec(self.base, self.get_specHA(logg_params))
Z_params = self.parameters.copy()
Z_params["Z"] += self.deltaParameters["Z"]
Z_spec = get_resid_spec(self.base, self.get_specHA(Z_params))
self.envelope = get_min_spec([temp_spec, logg_spec, Z_spec])
def plot_quality(self):
'''
Visualize the quality of the interpolation.
Two-panel plot.
Top: HA and LA spectrum
Bottom: Residual between HA + LA spectrum and the HA spectrum error bounds for deltaParameters
'''
self.createEnvelopeSpectrum()
fig, ax = plt.subplots(nrows=2, figsize=(8, 6), sharex=True)
ax[0].plot(self.wl, self.base, "b", label="HA")
ax[0].plot(self.wl, self.baseLA, "r", label="LA")
ax[0].legend()
ax[0].set_ylabel(r"$\propto f_\lambda$")
ax[0].set_title(
"Temp={temp:} logg={logg:} Z={Z:}".format(**self.parameters))
ax[1].semilogy(self.wl, self.approxResid, "k", label="(HA - LA)/HA")
ax[1].semilogy(self.wl, self.envelope, "b", label="Interp Envelope")
ax[1].legend()
ax[1].set_xlabel(r"$\lambda$\AA")
ax[1].set_ylabel("fractional error")
return fig
class AccuracyComparison:
"""
Gather the data products necessary to make a test about accuracy of the reduced grid sizes.
"""
def __init__(self, DataSpectrum, Instrument, LibraryHA, LibraryLA, parameters, deltaParameters):
"""Initialize the comparison object.
:param DataSpectrum: the spectrum that provides a wl grid + natural resolution
:type DataSpectrum: :obj:`grid_tools.DataSpectrum`
:param Instrument: the instrument object on which the DataSpectrum was acquired (ie, TRES, SPEX...)
:type Instrument: :obj:`grid_tools.Instrument`
:param LibraryHA: the path to the native resolution spectral library
:type LibraryHA: string
:param LibraryLA: the path to the approximate spectral library
:type LibraryLA: string
"""
self.DataSpectrum = DataSpectrum
self.Instrument = Instrument
self.HDF5InterfaceHA = HDF5Interface(LibraryHA)
self.HDF5InterfaceLA = HDF5Interface(LibraryLA)
print("Bounds of the grids are")
print("HA", self.HDF5InterfaceHA.bounds)
print("LA", self.HDF5InterfaceLA.bounds)
# If the DataSpectrum contains more than one order, we only take the first one. To get behavior with a
# different order, you should only load that via the DataSpectrum(orders=[22]) flag.
self.wl = self.DataSpectrum.wls[0]
self.fullModelLA = Model(
self.DataSpectrum,
self.Instrument,
self.HDF5InterfaceLA,
stellar_tuple=("temp", "logg", "Z", "vsini", "vz", "logOmega"),
cheb_tuple=("c1", "c2", "c3"),
cov_tuple=("sigAmp", "logAmp", "l"),
region_tuple=("loga", "mu", "sigma"),
)
self.modelLA = self.fullModelLA.OrderModels[0]
self.fullModelHA = ModelHA(
self.DataSpectrum,
self.Instrument,
self.HDF5InterfaceHA,
stellar_tuple=("temp", "logg", "Z", "vsini", "vz", "logOmega"),
cheb_tuple=("c1", "c2", "c3"),
cov_tuple=("sigAmp", "logAmp", "l"),
region_tuple=("loga", "mu", "sigma"),
)
self.modelHA = self.fullModelHA.OrderModels[0]
self.parameters = parameters
self.deltaParameters = deltaParameters
self.base = self.get_specHA(self.parameters)
self.baseLA = self.get_specLA(self.parameters)
self.approxResid = get_resid_spec(self.base, self.baseLA) # modelHA - modelLA @ parameters
def get_specHA(self, parameters):
"""
Update the model and then query the spectrum
:param parameters: Dictionary of fundamental stellar parameters
:type parameters: dict
:returns: flux spectrum
"""
params = parameters.copy()
params.update({"vsini": 0.0, "vz": 0, "logOmega": 0.0})
self.fullModelHA.update_Model(params)
return self.modelHA.get_spectrum()
def get_specLA(self, parameters):
"""
Update the model and then query the spectrum
:param parameters: Dictionary of fundamental stellar parameters
:type parameters: dict
:returns: flux spectrum
"""
params = parameters.copy()
params.update({"vsini": 0.0, "vz": 0, "logOmega": 0.0})
self.fullModelLA.update_Model(params)
return self.modelLA.get_spectrum()
def createEnvelopeSpectrum(self, direction="both"):
"""
The parameters should always be specified at a grid point of the HDF5 file.
For this, do the deltaParameters interpolation.
Direction specifies whether to do interpolation up (+ 10 K, etc.), down (- 10 K), or
do both and then find the minimum envelope between the two.
For now, only up is implemented.
"""
# For each key, add the delta parameters
temp_params = self.parameters.copy()
temp_params["temp"] += self.deltaParameters["temp"]
temp_spec = get_resid_spec(self.base, self.get_specHA(temp_params))
logg_params = self.parameters.copy()
logg_params["logg"] += self.deltaParameters["logg"]
logg_spec = get_resid_spec(self.base, self.get_specHA(logg_params))
Z_params = self.parameters.copy()
Z_params["Z"] += self.deltaParameters["Z"]
Z_spec = get_resid_spec(self.base, self.get_specHA(Z_params))
self.envelope = get_min_spec([temp_spec, logg_spec, Z_spec])
def plot_quality(self):
"""
Visualize the quality of the interpolation.
Two-panel plot.
Top: HA and LA spectrum
Bottom: Residual between HA + LA spectrum and the HA spectrum error bounds for deltaParameters
"""
self.createEnvelopeSpectrum()
fig, ax = plt.subplots(nrows=2, figsize=(8, 6), sharex=True)
ax[0].plot(self.wl, self.base, "b", label="HA")
ax[0].plot(self.wl, self.baseLA, "r", label="LA")
ax[0].legend()
ax[0].set_ylabel(r"$\propto f_\lambda$")
ax[0].set_title("Temp={temp:} logg={logg:} Z={Z:}".format(**self.parameters))
ax[1].semilogy(self.wl, self.approxResid, "k", label="(HA - LA)/HA")
ax[1].semilogy(self.wl, self.envelope, "b", label="Interp Envelope")
ax[1].legend()
ax[1].set_xlabel(r"$\lambda$\AA")
ax[1].set_ylabel("fractional error")
return fig