API

The API is under active development and should be considered unstable. Feedback is welcomed.

datasets

This utility loads 1D echelle data, inheriting from the PyTorch Dataset model. We currently only support data from the HPF spectrograph.

HPFDataset

class blase.datasets.HPFDataset(filename, device='cuda')

Read in an HPF spectrum

Parameters

• filename (list) – the Goldilocks echelle spectrum to read-in

• device (str) – On which device to house the data, “cuda” or “cpu”

get_goldilocks_dataframe(filename)

Return a pandas Dataframe given a Goldilocks FITS file name

Spectral Model

We fit an echelle spectrum with \(M\) total total orders, \(m\), with a single model. \(F(\lambda, \Theta)\) where \(\Theta\) are the set of stellar and nuisance parameters.

MultiOrder

class blase.multiorder.MultiOrder(device='cuda', wl_data=None)

A PyTorch layer that provides a parameter set and transformations to model 1D echelle spectra.

Parameters

• device (str) – Either “cuda” for GPU acceleration, or “cpu” otherwise

• wl_data (Tensor) – The array of wavelengths used for HPF science data

forward(index)

The forward pass of the spectral model

Parameters

index (int) – the index of the HPF spectral order

Returns

the 1D generative spectral model destined for backpropagation parameter tuning

Return type

(torch.tensor)

read_native_PHOENIX_model(teff, logg, PHOENIX_path='~/libraries/raw/PHOENIX/')

Return the native model wavelength and flux as a torch tensor

Parameters

• Teff (int) – The Teff label of the PHOENIX model to read in. Must exist!

• logg (float) – The logg label of the PHOENIX model to read in. Must exist!

Returns

the PHOENIX model wavelength and normalized flux at native spectral resolution

Return type

(tuple of tensors)

telluric

Telluric absorption forward modeling based on HITRAN

TelluricModel

class blase.telluric.TelluricModel(device='cuda')

Make a model of Earth’s atmospheric absorption and/or sky emission

Parameters

device (str) – On which device to run the model, “cuda” or “cpu”

S_ij_of_T(T, S_ij_296, nu_ij, g_lower, E_lower)

The Spectral Line Intensity as a function of temperature

\[S_{ij}(T) = S_{ij}(T_\mathrm{ref}) \frac{Q(T_\mathrm{ref})}{Q(T)} \frac{\exp\left( -c_2 E''/T \right)}{\exp\left( -c_2 E''/T_\mathrm{ref} \right)} \frac{[1-\exp\left( -c_2 \nu_{ij}/T \right)]}{[1-\exp\left(-c_2 \nu_{ij}/T_\mathrm{ref} \right)]}\]

Parameters

• T (float) – Temperature \(T\) in K

• S_ij_296 (float) – The spectral line intensity at \(T_{ref}=296\) K

• nu_ij (float) – Wavenumber of the spectral line transition \((\mathrm{cm^{-1}})\) in vacuum

• g_lower (float) – The lower state statistical weights \(g''\)

• E_lower (float) – The lower-state energy of the transition \(\mathrm{cm^{-1}}\)

Returns

A vector of length \(N_{\mathrm{lines}}\)

Return type

torch.Tensor

forward()

The forward pass of the neural network

gamma_of_p_and_T(p, T, p_self, n_air, gamma_air_ref, gamma_self_ref)

Compute the Lorentz half width at half maximum (HWHM) in units of \(\mathrm{cm^{-1}}\) with pressure and temperature:

\[\gamma(p, T) = \left( \frac{T_\mathrm{ref}}{T} \right)^{n_\mathrm{air}}\left( \gamma_\mathrm{air}(p_\mathrm{ref}, T_\mathrm{ref})(p-p_\mathrm{self}) + \gamma_\mathrm{self}(p_\mathrm{ref}, T_\mathrm{ref})p_\mathrm{self}\right)\]

Parameters

• p (float) – Pressure \(p\) in standard atmospheres atm

• T (float) – Temperature \(T\) in K

• p_self (float) – Partial pressure of the species in atm

• n_air (float) – The coefficient of the temperature dependence of the air-broadened half width (dimensionless)

• gamma_air_ref (float) – The air-broadened HWHM at \(T_{ref}=296\;\) K and reference pressure \(p_{ref}=1\;\) atm

• gamma_self_ref (float) – The self-broadened HWHM at \(T_{ref}=296\;\) K and reference pressure \(p_{ref}=1\;\) atm

Returns

A vector of length \(N_{\mathrm{lines}}\)

Return type

torch.Tensor

get_hapi_molec_data(species)

Fetch HITRAN atomic and molecular data as torch tensors

Parameters

species (str) – Which atomic/molecular species to examine

Returns

A dictionary containing tensors of size \(N_{\mathrm{lines}}\)

for each of the 8 HITRAN columns of interest

Return type

dict

lorentz_profile(nu, p, nu_ij, gamma, dp_ref, S_ij)

Return the Lorentz line profile given vectors and parameters

\[f_\mathrm{L}(\nu; \nu_{ij}, T, p) = \frac{1}{\pi}\frac{\gamma(p,T)}{\gamma(p,T)^2 + [\nu-(\nu_{ij} + \delta(p_\mathrm{ref})p)]^2}\]

Parameters

• nu (float) – Wavenumber variable input \(\nu\) in \(\mathrm{cm^{-1}}\). For matrix output, nu should have shape \(N_{\lambda} \times 1\).

• p (float) – Pressure \(p\) in standard atmospheres atm

• nu_ij (float) – Wavenumber of the spectral line transition \((\mathrm{cm^{-1}})\) in vacuum

• gamma (float) – Lorentz half width at half maximum (HWHM), \(\gamma\) in units of \(\mathrm{cm^{-1}}\)

• dp_ref (float) – The pressure shift \(\mathrm{cm^{-1}/atm}\) at \(T_{ref}=296\) K and \(p_{ref} = 1\) atm of the line position with respect to the vacuum transition wavenumber \(\nu_{ij}\)

• S_ij (float) – The spectral line intensity \(\mathrm{cm^{-1}/(molecule·cm^{-2}})\)

Returns

Either a vector of length \(N_\lambda\) if \(\gamma\) is a scalar, or a matrix of size \(N_\lambda \times N_{lines}\) if \(\gamma\) is a vector

Return type

torch.Tensor

tips_Q_of_T(T, g_k, E_k)

Total Internal Partition Sum

\[Q(T) = \sum_k g_k \exp\left(-\frac{c_2E_k}{T}\right)\]

Parameters

• T (float) – Temperature \(T\) in K

• g_k (float) – The lower state statistical weights \(g_k\)

• E_k (float) – The lower-state energy of the transition \(\mathrm{cm^{-1}}\)

Returns

A scalar or a vector the same length as T

Return type

torch.Tensor

transmission_multilayer_atmosphere(T_vector, p_vector, nus, vol_mix_ratio, hitran)

Return the transmission spectrum \(\mathcal{T}(\nu; T, P)=\exp(-\tau_\nu)\) for a cascade of \(N_{layers}\) pathlengths with thickness 1 km.

Parameters

• T_vector (\(1 \times 1 \times N_{layers}\) tensor) – Temperature \(T\) in K

• p_vector (\(1 \times 1 \times N_{layers}\) tensor) – Pressure \(p\) in standard atmospheres atm

• nus (\(N_{\nu} \times 1 \times 1\) tensor) – Wavenumber variable input \(\nu\) in \(\mathrm{cm^{-1}}\).

• vol_mix_ratio (scalar or \(1 \times 1 \times N_{layers}\) tensor) – The volume mixing ratio of the species assuming ideal gas

• hitran (OrderedDict) – Each entry of consists of a \(N_{lines}\) vector that will be broadcasted to a \(1 \times N_{lines} \times 1\) tensor when operating with the other vectors.

Returns

A vector of length \(N_{\nu}\)

Return type

torch.Tensor

transmission_of_T_p(T, p, nus, vol_mix_ratio, hitran)

Return the transmission spectrum \(\mathcal{T}(\nu; T, P)=\exp(-\tau_\nu)\) for 1 km of pathlength

Parameters

• T (float) – Temperature \(T\) in K

• p (float) – Pressure \(p\) in standard atmospheres atm

• nus (float) – Wavenumber variable input \(\nu\) in \(\mathrm{cm^{-1}}\).

• vol_mix_ratio (float) – The volume mixing ratio of the species assuming ideal gas

Returns

A vector of length \(N_{\nu}\)

Return type

torch.Tensor