Helper Functions

The following functions are auxiliary (helper) functions frequently used in radiative transfer problems.

bbflux_wavenumber(*args, **kwargs)

Overloaded function.

bbflux_wavenumber(wave: torch.Tensor, temp: float, ncol: int = 1) -> torch.Tensor

Calculate blackbody flux using wavenumber

Parameters:

wave (torch.Tensor) – wavenumber [cm^-1]
temp (float) – temperature [K]
ncol (int, optional) – number of columns, default to 1

Returns:

blackbody flux [w/(m^2 cm^-1)]

Return type:

torch.Tensor

Examples

>>> import torch
>>> from pyharp import bbflux_wavenumber

>>> wave = torch.tensor([1.0, 2.0, 3.0])
>>> temp = 300.0
>>> flux = bbflux_wavenumber(wave, temp)

bbflux_wavenumber(wn1: float, wn2: float, temp: torch.Tensor = 1) -> torch.Tensor

Calculate blackbody flux using wavenumber

Parameters:

wn1 (float) – wavenumber [cm^-1]
wn2 (float) – temperature [K]
temp (torch.Tensor) – number of columns, default to 1

Returns:

blackbody flux [w/(m^2 cm^-1)]

Return type:

torch.Tensor

Examples

bbflux_wavelength(wave: torch.Tensor, temp: float, ncol: int = 1) → torch.Tensor

Calculate blackbody flux using wavelength

Parameters:

wave (torch.Tensor) – wavelength [um]
temp (float) – temperature [K]
ncol (int, optional) – number of columns, default to 1

Returns:

blackbody flux [w/(m^2 um^-1)]

Return type:

torch.Tensor

Examples

>>> from pyharp import bbflux_wavelength
>>> wave = torch.tensor([1.0, 2.0, 3.0])
>>> temp = 300.0
>>> flux = bbflux_wavelength(wave, temp)

calc_dz_hypsometric(pres: torch.Tensor, temp: torch.Tensor, g_ov_R: torch.Tensor) → torch.Tensor

Calculate the height between pressure levels using the hypsometric equation

\[dz = \frac{RT}{g} \cdot d\ln p\]

where \(R\) is the specific gas constant, \(g\) is the gravity, \(T\) is the temperature, \(p_1\) and \(p_2\) are the pressure levels.

Parameters:

pres (torch.Tensor) – pressure [pa] at layers
temp (torch.Tensor) – temperature [K] at layers
g_ov_R (torch.Tensor) – gravity over specific gas constant [K/m] at layers

Returns:

height between pressure levels [m]

Return type:

torch.Tensor

Examples

>>> from pyharp import calc_dz_hypsometric
>>> pres = torch.tensor([1.0, 2.0, 3.0])
>>> temp = torch.tensor([300.0, 310.0, 320.0])
>>> g_ov_R = torch.tensor([1.0, 2.0, 3.0])
>>> dz = calc_dz_hypsometric(pres, temp, g_ov_R)

interpn(query: list[torch.Tensor], coords: list[torch.Tensor], lookup: torch.Tensor, extrapolate: bool = False) → torch.Tensor

Multidimensional linear interpolation

Parameters:

query_coords (list[torch.Tensor]) – Query coordinates
coords (list[torch.Tensor]) – Coordinate arrays, len = ndim, each tensor has shape (nx1,), (nx2,) …
lookup (torch.Tensor) – Lookup tensor (nx1, nx2, …, nval)

Returns:

Interpolated values

Return type:

torch.Tensor

Examples

>>> import torch
>>> from pyharp import interpn
>>> query = [torch.tensor([0.5]), torch.tensor([0.5])]
>>> coords = [torch.tensor([0.0, 1.0]), torch.tensor([0.0, 1.0])]
>>> lookup = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
>>> interpn(query, coords, lookup)
tensor(2.5000)