API

This is the full API documentation for Exo_Skryer.

Reference/API

exo_skryer.aux_functions Module

aux_functions.py

Functions

pchip_1d(x, x_nodes, y_nodes)	Piecewise Cubic Hermite Interpolating Polynomial (PCHIP).
latin_hypercube(key, n_samples, n_dim, *, ...)	Generate Latin hypercube samples in the unit hypercube [0, 1)^n_dim.
simpson(y, *[, x, dx, axis, even])	JAX-compatible composite Simpson integrator, similar to scipy.integrate.simpson.
simpson_padded(y, x, n_valid, *[, axis, even])	Composite Simpson integration for padded arrays with non-uniform spacing.

exo_skryer.build_chem Module

build_chem.py

Functions

infer_trace_species(cfg, ...)
infer_log10_vmr_keys(trace_species)
infer_clr_keys(trace_species)	Return CLR parameter keys for each trace species.
validate_log10_vmr_params(cfg, trace_species)
validate_clr_params(cfg, trace_species)	Validate that abundance parameters are present for CLR mode.
clr_samples_to_vmr(samples_dict, species)	Convert CLR posterior samples to physical VMR (and log10 VMR) columns.
prepare_chemistry_kernel(cfg, ...)	Prepare and validate chemistry kernel with inferred species.
load_nasa9_if_needed(cfg, exp_dir)	Load NASA-9 thermo coefficients if RateJAX chemistry requires it.
init_fastchem_grid_if_needed(cfg, exp_dir)	Initialise and cache the FastChem 5D grid chemistry backend if required.
init_element_potentials_if_needed(cfg, exp_dir)	Initialise and cache the element-potentials chemistry backend if required.
init_atmodeller_if_needed(cfg, exp_dir)	Initialise the global atmodeller EquilibriumModel if required.
init_quench_approx_if_needed(cfg, exp_dir)	Initialise the FastChem 5D grid and quench species list for quench_approx.

exo_skryer.build_model Module

build_model.py

Functions

build_forward_model(cfg, obs[, ...])

Build a JIT-compiled forward model for atmospheric retrieval.

exo_skryer.build_opacities Module

build_opacities.py

Functions

build_opacities(cfg, obs[, exp_dir])
read_master_wl(cfg, obs[, exp_dir])
master_wavelength()
master_wavelength_cut()
has_line_data()
has_ck_data()
has_cia_data()
has_ray_data()
line_master_wavelength()
line_pressure_grid()
line_log10_pressure_grid()
line_temperature_grid()
line_temperature_grids()
line_log10_temperature_grids()
line_sigma_cube()
line_species_names()
line_runtime_species_order()
load_line_registry(cfg, obs[, lam_master, ...])
ck_master_wavelength()
ck_pressure_grid()
ck_log10_pressure_grid()
ck_temperature_grid()
ck_temperature_grids()
ck_log10_temperature_grids()
ck_sigma_cube()
ck_species_names()
ck_g_points()
ck_g_weights()
ck_runtime_species_order()
ck_g_points_1d()
ck_g_weights_1d()
load_ck_registry(cfg, obs[, lam_master, ...])
load_cia_registry(cfg, obs[, lam_master, ...])
load_ray_registry(cfg, obs[, lam_master])
cia_master_wavelength()
cia_temperature_grid()
cia_temperature_grids()
cia_log10_temperature_grids()
cia_sigma_cube()
cia_species_names()
cia_runtime_species_order()
cia_kept_pair_indices()
cia_pair_species_i()
cia_pair_species_j()
cia_retained_sigma_cube()
cia_retained_temperature_grids()
cia_retained_log10_temperature_grids()
ray_master_wavelength()
ray_sigma_table()
ray_nm1_table()
ray_nd_ref()
ray_species_names()
ray_runtime_species_order()
ray_sigma_linear_table()
ray_refractivity_coeff_table()
ray_pick_arrays()
has_special_data()
special_master_wavelength()
hminus_temperature_grid()
hminus_log10_temperature_grid()
hminus_bf_log10_sigma_table()
hminus_ff_log10_sigma_table()
has_cloud_nk_data()	Return True if cloud n,k arrays have been cached.
cloud_nk_wavelength()
cloud_nk_n()
cloud_nk_k()

Classes

LineRegistryEntry(name, idx, pressures, ...)
CKRegistryEntry(name, idx, pressures, ...)
CiaRegistryEntry(name, idx, temperatures, ...)
RayRegistryEntry(name, idx, wavelengths, ...)

exo_skryer.data_constants Module

data_constants.py

exo_skryer.ck_mix_PRAS Module

ck_mix_PRAS.py

Functions

mix_k_tables_pras(sigma_values_log, ...)

exo_skryer.ck_mix_RORR Module

ck_mix_RORR.py

Functions

mix_k_tables_rorr(sigma_values, ...)

Mix correlated-k tables across species using RORR.

exo_skryer.help_io Module

help_io.py

Functions

to_inferencedata(samples_dict, cfg[, ...])	Convert {param: array} mapping into ArviZ InferenceData.
save_inferencedata(idata, outdir[, stem, ...])	Save an InferenceData object to NetCDF (and optionally summary CSV + evidence JSON).
save_observed_data_csv(outdir, lam, dlam, y, dy)	Save observed data as CSV with columns: lam,dlam,y,dy,response_mode,offset_group Values are written with full float precision; NaNs are allowed.

exo_skryer.help_print Module

help_print.py

Functions

format_duration(seconds)	Format a duration in seconds into a human-readable string.
print_cfg(cfg)	Pretty-print the entire configuration (SimpleNamespace tree) in a compact, table-like format.

exo_skryer.help_runtime Module

help_runtime.py

Functions

apply_runtime_env(rc)

Apply optional rc.runtime settings before importing JAX / NumPyro.

exo_skryer.instru_convolve Module

instru_convolve.py

Functions

apply_response_functions(spectrum)	Apply instrument response functions to convolve spectrum onto observational bins.
get_bandpass_cache()	Materialize bandpass registry arrays into a single PyTree cache (outside jit).
apply_response_functions_cached(spectrum, cache)	Convolve spectrum using a provided bandpass cache (jit-friendly).

exo_skryer.kernel_registry Module

kernel_registry.py

Central registry mapping YAML physics-scheme names to their Python kernel functions.

Adding a new module is a two-step process:

1. Implement the function in the appropriate vert_*.py or opacity_*.py file and make sure it is exported in that module’s __all__.

2. Import it below and add one entry to the relevant registry dict.

3. Use the new key string in your YAML config — done!

Aliases (multiple keys that map to the same function) are marked with # alias.

Registry layout

VERT_TP : temperature-pressure profile kernels VERT_ALT : altitude / hydrostatic-structure kernels VERT_CHEM : chemistry / VMR-profile kernels VERT_MU : mean-molecular-weight kernels VERT_CLOUD : cloud vertical-profile kernels OPAC_CLOUD : cloud opacity kernels (None entry = disabled)

Functions

resolve(name, registry, cfg_key)

Return the kernel for name from registry, raising a clear error on failure.

exo_skryer.kk_schemes Module

kk_schemes.py

Kramers-Kronig transform functions for computing real refractive index from imaginary part using causality relations.

Functions

kk_n_from_k_wavenumber_cached(nu, k_nu, ...)	Compute n(ν) from k(ν) via a singly-subtracted Kramers–Kronig relation.
kk_n_from_k_wavenumber_fast(nu, k_nu, ...[, ...])	Optimized KK relation using precomputed grid quantities.
kk_n_from_k_wavenumber(nu, k_nu, nu_ref, n_ref)	Compute n(ν) from k(ν) via a singly-subtracted KK relation.
kk_n_from_k_wavelength_um(wl_um, k_wl, ...)	Compute n(λ) from k(λ) via KK, using wavelength inputs in microns.

exo_skryer.lxmie_mod Module

lxmie_mod.py

LX-MIE Mie code refactored into JAX (Kitzmann et al. 2018).

Functions

lxmie_jax(ri, x, *[, nmax, cf_max_terms, cf_eps])	JIT-safe LX-MIE Mie solver.
lxmie_jax_vmap(ri, x, *[, nmax, ...])	Batched wrapper around lxmie_jax with static args bound.

exo_skryer.mie_schemes Module

mie_schemes.py

Modular implementations of Mie scattering approximations and exact solutions. All schemes take the same inputs: real refractive index n, imaginary refractive index k, and size parameter x.

Functions

rayleigh(n, k, x)	Compute extinction and scattering efficiencies using Rayleigh approximation.
madt(n, k, x)	Compute extinction and scattering efficiencies using Modified Anomalous Diffraction Theory (MADT.
lxmie(n, k, x[, nmax, cf_max_terms, cf_eps])	Compute extinction and scattering efficiencies using exact Mie theory.

exo_skryer.opacity_cia Module

opacity_cia.py

Functions

compute_cia_opacity(state, opac, params)	Compute collision-induced absorption (CIA) mass opacity for all molecular pairs.
zero_cia_opacity(state, params)	Return a zero CIA opacity array.

exo_skryer.opacity_ck Module

opacity_ck.py

Functions

zero_ck_opacity(state, opac, params)	Return a zero correlated-k opacity array.
compute_ck_opacity(state, opac, params)	Compute correlated-k opacity with multi-species mixing.
compute_ck_opacity_perspecies(state, opac, ...)	Compute per-species correlated-k opacities WITHOUT mixing.

exo_skryer.opacity_cloud Module

opacity_cloud.py

Functions

compute_cloud_efficiencies(wl, r_cm, params, ...)	Second-stage landing function for (Q_ext, Q_sca, g).
compute_cloud_opacity(state, params[, ...])	Main landing function for cloud opacity calculation.
zero_cloud_opacity(state, params)	Return zero-valued cloud optical properties.
grey_const_cloud(state, params)	Compute a globally constant grey cloud opacity.
grey_profile_cloud(state, params)	Compute grey cloud opacity masked by the vertical cloud profile.
deck_and_powerlaw(state, params)
F18_cloud(wl, r_cm, params)	Fisher & Heng (2018) extinction-efficiency model (optics only).
direct_nk(state, params)	Compute cloud optical properties from retrieved refractive-index nodes.
nk_f18_blend(state, params)	Additive combination of direct_nk and F18 cloud opacity.
f18_skew_cloud(state, params)	F18 continuum plus a skew-normal spectral feature with a Rayleigh size window.

exo_skryer.opacity_line Module

opacity_line.py

Functions

zero_line_opacity(state, params)	Return a zero opacity-sampling opacity array.
compute_line_opacity(state, opac, params)	Compute opacity-sampling mass opacity for all molecular/atomic absorbers.

exo_skryer.opacity_ray Module

opacity_ray.py

Functions

zero_ray_opacity(state, params)	Return a zero Rayleigh scattering opacity array.
compute_ray_opacity(state, opac, params)	Compute Rayleigh scattering mass opacity for the configured scatterers.

exo_skryer.opacity_special Module

opacity_special.py

Functions

zero_special_opacity(state, params)	Return a zero special-opacity array.
compute_hminus_bf_opacity(state, opac, params)	Compute H⁻ bound-free continuum mass opacity from the special registry.
compute_hminus_ff_opacity(state, opac, params)	Compute H⁻ free-free continuum mass opacity from the special registry.
compute_special_opacity(state, opac, params)	Compute the summed special-opacity contribution.

exo_skryer.rate_jax Module

rate_jax.py

Functions

load_nasa9_cache(nasa9_dir)	Load NASA-9 polynomial coefficient files into the global NASA-9 cache.
get_nasa9_cache()	Return the cached NASA-9 thermo table.
clear_nasa9_cache()	Clear the cached NASA-9 thermo table.
is_nasa9_cache_loaded()	Return True if the NASA-9 cache is loaded.

Classes

NASA9ThermoJAX(data)	JAX-friendly NASA-9 thermo evaluator.
RateJAX(thermo[, C, N, O, fHe])	RATE-style thermochemical equilibrium solver implemented in JAX.

exo_skryer.read_obs Module

read_obs.py

Functions

resolve_obs_path(cfg)	Resolve the observational data path from configuration.
read_obs_data(path[, base_dir])	Input: path to observational data file Output: Dictionary containing observational data

exo_skryer.read_stellar Module

read_stellar.py

Functions

read_stellar_spectrum(cfg, lam_master, ck_mode)

Read and interpolate the stellar spectrum onto the master grid.

exo_skryer.read_yaml Module

read_yaml.py

Functions

read_yaml(path)

Input: path to YAML configuration file.

exo_skryer.registry_bandpass Module

instru_bandpass.py

Functions

reset_bandpass_registry()	Reset all bandpass-related registries and caches.
has_bandpass_data()	Returns True if the bandpass registry has been initialised.
load_bandpass_registry(obs, full_grid, cut_grid)	Build the bandpass registry and JAX-ready padded arrays for each observational bin.
bandpass_num_bins()	Number of observational bins in the bandpass registry.
bandpass_bin_edges()	Bin edges as an array of shape (n_bins, 2): [λ_low, λ_high] for each bin.
bandpass_wavelengths_padded()	Padded wavelength grid for each bin, shape (n_bins, max_len).
bandpass_weights_padded()	Padded weights for each bin, shape (n_bins, max_len).
bandpass_indices_padded()	Padded index array into the high-res spectrum grid, shape (n_bins, max_len).
bandpass_coefficients_padded()	Padded linear convolution coefficients, shape (n_bins, max_len).
bandpass_norms()	Normalisation constants for each bin, shape (n_bins,).
bandpass_valid_lengths()	Valid (non-padded) length for each bin, shape (n_bins,).
bandpass_is_boxcar()	Boxcar detection flags for each bin, shape (n_bins,).

Classes

BinConvolutionEntry(method, wavelengths, ...)

Holds information needed to convolve a single observational bin at runtime.

exo_skryer.registry_cia Module

registry_cia.py

Functions

reset_registry()
has_cia_data()
load_cia_registry(cfg, obs[, lam_master, ...])
cia_species_names()
cia_master_wavelength()
cia_sigma_cube()
cia_temperature_grid()
cia_temperature_grids()
cia_log10_temperature_grids()
cia_runtime_species_order()
cia_kept_pair_indices()
cia_pair_species_i()
cia_pair_species_j()
cia_retained_sigma_cube()
cia_retained_temperature_grids()
cia_retained_log10_temperature_grids()

Classes

CiaRegistryEntry(name, idx, temperatures, ...)

exo_skryer.registry_ck Module

registry_ck.py

Functions

reset_registry()
has_ck_data()
load_ck_registry(cfg, obs[, lam_master, ...])
ck_species_names()
ck_master_wavelength()
ck_pressure_grid()
ck_temperature_grid()
ck_temperature_grids()
ck_sigma_cube()
ck_g_points()
ck_g_weights()
ck_log10_pressure_grid()
ck_log10_temperature_grids()
ck_runtime_species_order()
ck_g_points_1d()
ck_g_weights_1d()

Classes

CKRegistryEntry(name, idx, pressures, ...)

exo_skryer.registry_cloud Module

registry_cloud.py

Functions

compute_kk_grid_cache(nu)	Precompute grid-dependent quantities for Kramers-Kronig relation.
get_or_create_kk_cache(nu)	Retrieve cached KK grid data or compute if not exists.
clear_kk_cache()	Clear all cached KK grid data.
get_kk_cache_stats()	Get statistics about the current KK cache state.
set_cloud_nk_data(wl, n, k)	Store cloud refractive index data in the global registry.
get_cloud_nk_data()	Retrieve cached cloud refractive index data.
clear_cloud_nk_data()	Clear cached cloud refractive index data.
has_cloud_nk_data()	Return True if cloud n,k arrays have been cached.
cloud_nk_wavelength()
cloud_nk_n()
cloud_nk_k()
load_cloud_nk_data(path, wl_master)	Load a refractive index table from disk, interpolate to wl_master, and cache.

exo_skryer.registry_line Module

registry_line.py

Functions

reset_registry()
has_line_data()
load_line_registry(cfg, obs[, lam_master, ...])
line_species_names()
line_master_wavelength()
line_pressure_grid()
line_temperature_grid()
line_temperature_grids()
line_sigma_cube()
line_log10_pressure_grid()
line_log10_temperature_grids()
line_runtime_species_order()

Classes

LineRegistryEntry(name, idx, pressures, ...)

exo_skryer.registry_ray Module

registry_ray.py

Functions

reset_registry()
has_ray_data()
load_ray_registry(cfg, obs[, lam_master])
ray_species_names()
ray_master_wavelength()
ray_sigma_table()
ray_nm1_table()
ray_nd_ref()
ray_runtime_species_order()
ray_sigma_linear_table()
ray_refractivity_coeff_table()
ray_pick_arrays()

Classes

RayRegistryEntry(name, idx, wavelengths, ...)

exo_skryer.RT_alb_1D Module

RT_alb_1D.py

Functions

compute_albedo_spectrum_1d(state, params, ...)

Placeholder shortwave albedo RT kernel.

exo_skryer.RT_em_1D_ck Module

RT_em_1D_ck.py

Functions

compute_emission_spectrum_1d_ck(state, ...)

exo_skryer.RT_em_1D_os Module

RT_em_1D_os.py

Functions

compute_emission_spectrum_1d_os(state, ...)

exo_skryer.RT_em_schemes Module

RT_em_schemes.py

Emission radiative transfer solvers for thermal emission calculations.

This module provides multiple methods for solving the radiative transfer equation in thermal emission mode:

1. Alpha-EAA (solve_alpha_eaa): - Single-angle approximation with alpha-EAA scaling - Fast and accurate for most cases - Supports contribution function calculation

2. Toon89 (solve_toon89_picaso): - Full multi-stream Toon et al. (1989) method - 8-stream Gaussian quadrature integration - More accurate for optically thick, scattering atmospheres - Higher computational cost than EAA

All solvers use the same interface and can be selected via configuration using the em_scheme parameter in the physics section.

Functions

solve_alpha_eaa(be_levels, dtau_layers, ssa, ...)
solve_toon89_picaso(be_levels, dtau_layers, ...)	Toon et al. (1989) thermal emission solver with multi-stream quadrature.
get_emission_solver(name)	Get emission RT solver function by name.

exo_skryer.RT_trans_1D_ck Module

RT_trans_1D_ck.py

Functions

compute_transit_depth_1d_ck(state, params, ...)

exo_skryer.RT_trans_1D_ck_trans Module

RT_trans_1D_ck_trans.py

Transit transmission spectrum calculation using the transmission multiplication random overlap method for correlated-k species mixing.

This module differs from RT_trans_1D_ck.py in that species are combined by multiplying their mean transmissions under the random-overlap assumption:

T_total = exp(-tau_cont) * Π_s [ Σ_g w_g exp(-tau_s(g)) ]

This avoids ROM sorting / k-distribution mixing entirely and is intended as a fast transmission-only approximation.

Functions

compute_transit_depth_1d_ck_trans(state, ...)

Compute 1D transit depth using transmission multiplication random overlap.

exo_skryer.RT_trans_1D_os Module

RT_trans_1D_os.py

Functions

compute_transit_depth_1d_os(state, params, ...)

exo_skryer.refraction Module

refraction.py

Approximate refraction support for transmission spectroscopy.

Current implementation: “cutoff” mode (option A) that applies a refractive boundary (fully opaque below a wavelength-dependent impact parameter) without curved-ray optical-depth integration.

Functions

refraction_cutoff_mask(state, params, opac)	Return a boolean mask for impact parameters blocked by refraction.
maybe_refraction_cutoff_mask(state, params, opac)	Return a JAX-safe refraction mask or an all-false mask.

exo_skryer.registry_special Module

registry_special.py

Special (non-line, non-Rayleigh, non-CIA) opacity registries.

Currently supported: - H- bound-free (bf) continuum cross-sections σ_bf(λ, T) - H- free-free (ff) continuum cross-sections σ_ff(λ, T)

Tables are precomputed on the forward-model master wavelength grid and a fixed temperature grid, then cached as device arrays for JAX kernels.

Functions

reset_registry()
has_special_data()
load_special_registry(cfg, obs[, ...])	Load/build special opacity tables and cache them on device.
special_master_wavelength()
hminus_temperature_grid()
hminus_log10_temperature_grid()
hminus_bf_log10_sigma_table()
hminus_ff_log10_sigma_table()

exo_skryer.run_retrieval Module

run_retrieval.py

Functions

main()	Run a retrieval defined by a YAML configuration.
format_duration(seconds)	Format a duration in seconds into a human-readable string.

exo_skryer.vert_alt Module

vert_alt.py

Functions

hypsometric(p_lev, T_lay, mu_lay, params)	Compute an altitude profile using the hypsometric equation (constant gravity).
g_at_z(R0, z, g_ref)	Compute gravity as a function of altitude assuming spherical geometry.
hypsometric_variable_g(p_lev, T_lay, mu_lay, ...)	Compute an altitude profile with altitude-dependent gravity.
hypsometric_variable_g_pref(p_lev, T_lay, ...)	Compute an altitude profile with altitude-dependent gravity anchored at p_ref.

exo_skryer.vert_chem Module

vert_chem.py

Functions

constant_vmr(species_order)	Build a JIT-optimized function for constant VMR profiles.
constant_vmr_clr(species_order[, use_log10_vmr])	Build a JIT-optimized function for constant VMR profiles using centered-log-ratio (CLR) parameterization.
build_constant_vmr_kernel(species_order)	Build a constant-VMR chemistry kernel for an explicit species ordering.
CE_fastchem_jax(p_lay, T_lay, params, nlay)	Compatibility alias for FastChem-grid interpolation backend.
CE_fastchem_grid_jax(p_lay, T_lay, params, nlay)	Interpolate FastChem 5D grid over (T, P, M/H, C/O).
CE_rate_jax(p_lay, T_lay, params, nlay)	Compute chemical equilibrium profiles using the RateJAX solver.
CE_easychem_jax(p_lay, T_lay, params, nlay)	Compute equilibrium profiles using the production CE JAX backend.
quench_approx(p_lay, T_lay, params, nlay)	Compute quenched chemical abundance profiles using the FastChem 5D grid.
CE_atmodeller(p_lay, T_lay, params, nlay)	Compute chemical equilibrium profiles using the atmodeller backend.
load_element_potentials_cache(species_list, ...)	Build and cache the production CE model for retrieval use.
is_element_potentials_cache_loaded()	Return True if the global element-potentials cache is initialised.
load_atmodeller_cache(species_list, nlay[, ...])	Build an atmodeller.EquilibriumModel from species_list and cache it globally.
is_atmodeller_cache_loaded()	Return True if the global atmodeller cache has been initialised.
load_fastchem_grid_cache(grid_path, ...[, ...])	Load and cache a FastChem 5D interpolation grid.
is_fastchem_grid_cache_loaded()	Return True if FastChem grid interpolation cache is initialised.
get_fastchem_grid_cache_info()	Return lightweight diagnostics for the cached FastChem grid backend.
load_quench_approx_cache(quench_species)	Store the list of species to quench for the quench_approx kernel.
is_quench_approx_cache_loaded()	Return True if the quench species list has been configured.

exo_skryer.vert_cloud Module

Vertical cloud profile kernels.

This module contains functions that compute the vertical distribution of cloud mass mixing ratio (q_c_lay) as a function of pressure and atmospheric conditions.

Functions

no_cloud(p_lay, T_lay, mu_lay, rho_lay, ...)	Return zero cloud mass mixing ratio (no clouds).
exponential_decay_profile(p_lay, T_lay, ...)	Exponential decay cloud profile with hard base cutoff.
slab_profile(p_lay, T_lay, mu_lay, rho_lay, ...)	Uniform slab cloud profile with hard pressure cutoffs.
const_profile(p_lay, T_lay, mu_lay, rho_lay, ...)	Constant cloud mass mixing ratio throughout the entire atmosphere.

exo_skryer.vert_mu Module

vert_mu.py

Functions

constant_mu(params, nlay)	Return a constant mean molecular weight (μ) profile.
compute_mu(vmr_lay)	Compute mean molecular weight from volume mixing ratios.
build_compute_mu(species_order)	Build a mean-molecular-weight kernel with a fixed species ordering.

exo_skryer.vert_Tp Module

vert_Tp.py

Functions

hopf_function(tau)	Compute the Hopf function for radiative transfer.
isothermal(p_lev, params)	Generate an isothermal temperature profile.
Barstow(p_lev, params)	Generate a Barstow et al. (2020) temperature profile.
Milne(p_lev, params)	Generate a Milne temperature profile for an internally heated atmosphere.
Modified_Milne(p_lev, params)	Generate a modified Milne temperature profile with stretched exponential transition.
Guillot(p_lev, params)	Generate a Guillot (2010) analytical temperature profile.
Modified_Guillot(p_lev, params)	Generate a modified Guillot profile with a flexible irradiated Hopf term.
MandS(p_lev, params)	Generate a Madhusudhan & Seager (2009) three-region T-P profile.
picket_fence(p_lev, params)	Generate a Parmentier & Guillot (2014,2015) picket fence T-P profile.
dry_convective_adjustment(T_lay, p_lay, ...)	Apply dry convective adjustment to a temperature profile.