API Reference

Global States

The following functions are used to set and get global states in the Pyharp package.

species_names() → list[str]: Retrieves the list of species names

species_weights() → list[float]: Retrieves the list of species molecular weights [kg/mol]

shared() → Iterator[tuple[str, torch.Tensor]]

Pyharp module deposits data – tensors – to a shared dictionary, which can be accessed by other modules. This function returns an iterator over the shared data.

After running the forward method of the RadiationBand, the shared data with the following keys are available:

Key

Description

“radiation/<band_name>/total_flux”

total flux in a band

Yields:: torch.Tensor – shared data of the pyharp module

Examples

>>> import pyharp
>>> import torch

# ... after calling the forward method

# loop over the shared data
>>> for data in pyharp.shared():
>>>     print(type(data), data.size())  # prints the shared data

set_search_paths(path: str) → list[str]

Set the search paths for resource files.

Parameters:: path (str) – The search paths
Returns:: The search paths
Return type:: str

Example

>>> import pyharp

# set the search paths
>>> pyharp.set_search_paths("/path/to/resource/files")

get_search_paths() → list[str]

Get the search paths for resource files.

Returns:: The search paths
Return type:: str

Example

>>> import pyharp

# get the search paths
>>> pyharp.get_search_paths()

find_resource(filename: str) → str

Find a resource file from the search paths.

Parameters:: filename (str) – The name of the resource file.
Returns:: The full path to the resource file.
Return type:: str

Example

>>> import pyharp

# find a resource file
>>> path = pyharp.find_resource("example.txt")
>>> print(path)  # /path/to/resource/files/example.txt

Radiation Classes

The following classes are used to configure and compute radiative transfer.

class RadiationOptions

bands(*args, **kwargs)

Overloaded function.

bands(self: pyharp.RadiationOptions) -> dict[str, pyharp.RadiationBandOptions]

Set radiation band options

Parameters:: bands (dict[str,RadiationBandOptions]) – radiation band options
Returns:: class object if argument is not empty, otherwise the radiation band options
Return type:: RadiationOptions | dict[str,RadiationBandOptions]

Examples

>>> import torch
>>> from pyharp import RadiationOptions
>>> op = RadiationOptions().bands({'band1': 'outdir1', 'band2': 'outdir2'})
>>> print(op)

bands(self: pyharp.RadiationOptions, arg0: dict[str, pyharp.RadiationBandOptions]) -> pyharp.RadiationOptions

Set radiation band options

Parameters:: bands (dict[str,RadiationBandOptions]) – radiation band options
Returns:: class object if argument is not empty, otherwise the radiation band options
Return type:: RadiationOptions | dict[str,RadiationBandOptions]

Examples

>>> import torch
>>> from pyharp import RadiationOptions
>>> op = RadiationOptions().bands({'band1': 'outdir1', 'band2': 'outdir2'})
>>> print(op)

static from_yaml(filename: str) → pyharp.RadiationOptions

Create a pyharp.RadiationOptions object from a YAML file

Parameters:: filename (str) – YAML file name
Returns:: class object
Return type:: RadiationOptions

Examples

>>> import torch
>>> from pyharp import RadiationOptions
>>> op = RadiationOptions.from_yaml('radiation.yaml')

outdirs(*args, **kwargs)

Overloaded function.

outdirs(self: pyharp.RadiationOptions) -> str

Set outgoing ray directions

Parameters:: outdirs (str) – outgoing ray directions
Returns:: class object if argument is not empty, otherwise the outgoing ray directions
Return type:: RadiationOptions | str

Examples

>>> import torch
>>> from pyharp import RadiationOptions
>>> op = RadiationOptions().outdirs('(0, 10), (0, 20)')
>>> print(op)

outdirs(self: pyharp.RadiationOptions, arg0: str) -> pyharp.RadiationOptions

Set outgoing ray directions

Parameters:: outdirs (str) – outgoing ray directions
Returns:: class object if argument is not empty, otherwise the outgoing ray directions
Return type:: RadiationOptions | str

Examples

>>> import torch
>>> from pyharp import RadiationOptions
>>> op = RadiationOptions().outdirs('(0, 10), (0, 20)')
>>> print(op)

class RadiationBandOptions

disort(*args, **kwargs)

Overloaded function.

disort(self: pyharp.RadiationBandOptions) -> disort::DisortOptions

Set or get disort options

Parameters:: disort (pydisort.DisortOptions) – disort options
Returns:: class object if argument is not empty, otherwise the disort options
Return type:: RadiationBandOptions | pydisort.DisortOptions

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> from pydisort import DisortOptions
>>> op = RadiationBandOptions().disort(DisortOptions().nwave(10))
>>> print(op)

disort(self: pyharp.RadiationBandOptions, arg0: disort::DisortOptions) -> pyharp.RadiationBandOptions

Set or get disort options

Parameters:: disort (pydisort.DisortOptions) – disort options
Returns:: class object if argument is not empty, otherwise the disort options
Return type:: RadiationBandOptions | pydisort.DisortOptions

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> from pydisort import DisortOptions
>>> op = RadiationBandOptions().disort(DisortOptions().nwave(10))
>>> print(op)

integration(*args, **kwargs)

Overloaded function.

integration(self: pyharp.RadiationBandOptions) -> str

Set or get integration method

Parameters:: integration (str) – integration method
Returns:: class object if argument is not empty, otherwise the integration method
Return type:: RadiationBandOptions | str

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().integration('simpson')
>>> print(op)

integration(self: pyharp.RadiationBandOptions, arg0: str) -> pyharp.RadiationBandOptions

Set or get integration method

Parameters:: integration (str) – integration method
Returns:: class object if argument is not empty, otherwise the integration method
Return type:: RadiationBandOptions | str

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().integration('simpson')
>>> print(op)

name(*args, **kwargs)

Overloaded function.

name(self: pyharp.RadiationBandOptions) -> str

Set or get radiation band name

Parameters:: name (str) – radiation band name
Returns:: class object if argument is not empty, otherwise the band name
Return type:: RadiationBandOptions | str

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().name('band1')
>>> print(op)

name(self: pyharp.RadiationBandOptions, arg0: str) -> pyharp.RadiationBandOptions

Set or get radiation band name

Parameters:: name (str) – radiation band name
Returns:: class object if argument is not empty, otherwise the band name
Return type:: RadiationBandOptions | str

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().name('band1')
>>> print(op)

opacities(*args, **kwargs)

Overloaded function.

opacities(self: pyharp.RadiationBandOptions) -> dict[str, pyharp.opacity.AttenuatorOptions]

Set or get opacities

Parameters:: opacities (dict[str,AttenuatorOptions]) – opacities
Returns:: class object if argument is not empty, otherwise the attenuator options
Return type:: RadiationBandOptions | dict[str,AttenuatorOptions]

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().opacities({'band1': 'opacity1', 'band2': 'opacity2'})
>>> print(op)

opacities(self: pyharp.RadiationBandOptions, arg0: dict[str, pyharp.opacity.AttenuatorOptions]) -> pyharp.RadiationBandOptions

Set or get opacities

Parameters:: opacities (dict[str,AttenuatorOptions]) – opacities
Returns:: class object if argument is not empty, otherwise the attenuator options
Return type:: RadiationBandOptions | dict[str,AttenuatorOptions]

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().opacities({'band1': 'opacity1', 'band2': 'opacity2'})
>>> print(op)

outdirs(*args, **kwargs)

Overloaded function.

outdirs(self: pyharp.RadiationBandOptions) -> str

Set or get outgoing ray directions

Parameters:: outdirs (str) – outgoing ray directions
Returns:: class object if argument is not empty, otherwise the outgoing ray directions
Return type:: RadiationBandOptions | str

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().outdirs('(0, 10), (0, 20)')
>>> print(op)

outdirs(self: pyharp.RadiationBandOptions, arg0: str) -> pyharp.RadiationBandOptions

Set or get outgoing ray directions

Parameters:: outdirs (str) – outgoing ray directions
Returns:: class object if argument is not empty, otherwise the outgoing ray directions
Return type:: RadiationBandOptions | str

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().outdirs('(0, 10), (0, 20)')
>>> print(op)

query_waves(self: pyharp.RadiationBandOptions, op_name: str = '') → list[float]

Query the spectral grids

Parameters:: op_name (str) – opacity name
Returns:: spectral grids
Return type:: list[float]

Examples

>>> import torch
>>> from pyharp import RadiationOptions
>>> op = RadiationOptions().query_waves()

query_weights(self: pyharp.RadiationBandOptions, op_name: str = '') → list[float]

Query the weights

Parameters:: op_name (str) – opacity name
Returns:: weights
Return type:: list[float]

Examples

>>> import torch
>>> from pyharp import RadiationOptions
>>> op = RadiationOptions().query_weights()

solver_name(*args, **kwargs)

Overloaded function.

solver_name(self: pyharp.RadiationBandOptions) -> str

Set or get solver name

Parameters:: solver_name (str) – solver name
Returns:: class object if argument is not empty, otherwise the solver name
Return type:: RadiationBandOptions | str

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().solver_name('disort')
>>> print(op)

solver_name(self: pyharp.RadiationBandOptions, arg0: str) -> pyharp.RadiationBandOptions

Set or get solver name

Parameters:: solver_name (str) – solver name
Returns:: class object if argument is not empty, otherwise the solver name
Return type:: RadiationBandOptions | str

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().solver_name('disort')
>>> print(op)

ww(*args, **kwargs)

Overloaded function.

ww(self: pyharp.RadiationBandOptions) -> list[float]

Set or get wavelength, wavenumber or weights for a wave grid

Parameters:: ww (list[float]) – wavenumbers/wavelengths/weights
Returns:: class object if argument is not empty, otherwise the wavenumbers/wavelengths/weights
Return type:: pyharp.RadiationBandOptions | list[float]

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().ww([1.0, 2.0, 3.0])
>>> print(op)

ww(self: pyharp.RadiationBandOptions, arg0: list[float]) -> pyharp.RadiationBandOptions

Set or get wavelength, wavenumber or weights for a wave grid

Parameters:: ww (list[float]) – wavenumbers/wavelengths/weights
Returns:: class object if argument is not empty, otherwise the wavenumbers/wavelengths/weights
Return type:: pyharp.RadiationBandOptions | list[float]

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().ww([1.0, 2.0, 3.0])
>>> print(op)

class RadiationBand

__init__(*args, **kwargs)

Overloaded function.

__init__(self: pyharp.cpp.RadiationBand) -> None

Construct a new default module.

__init__(self: pyharp.cpp.RadiationBand, options: pyharp.RadiationBandOptions) -> None

Construct a RadiationBand module

forward(*args, **kwargs)

Overloaded function.

forward(self: pyharp.cpp.RadiationBand, arg0: torch.Tensor, arg1: torch.Tensor, arg2: dict[str, torch.Tensor], arg3: dict[str, torch.Tensor]) -> torch.Tensor
forward(self: pyharp.cpp.RadiationBand, conc: torch.Tensor, dz: torch.Tensor, bc: dict[str, torch.Tensor], kwargs: dict[str, torch.Tensor]) -> torch.Tensor

Calculate the net radiation flux for a band

Parameters:

conc (torch.Tensor) – concentration [mol/m^3]
dz (torch.Tensor) – height [m]
bc (dict[str, torch.Tensor]) – boundary conditions
kwargs (dict[str, torch.Tensor]) – additional arguments

Returns:

[W/m^2] (ncol, nlyr+1)

Return type:

torch.Tensor

Examples

>>> import torch
>>> from pyharp import RadiationBandOptions
>>> op = RadiationBandOptions().band_options(['band1', 'band2'])

class Radiation

__init__(*args, **kwargs)

Overloaded function.

__init__(self: pyharp.cpp.Radiation) -> None

Construct a new default module.

__init__(self: pyharp.cpp.Radiation, options: pyharp.RadiationOptions) -> None

Construct a Radiation module

forward(*args, **kwargs)

Overloaded function.

forward(self: pyharp.cpp.Radiation, arg0: torch.Tensor, arg1: torch.Tensor, arg2: dict[str, torch.Tensor], arg3: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]
forward(self: pyharp.cpp.Radiation, conc: torch.Tensor, dz: torch.Tensor, bc: dict[str, torch.Tensor], kwargs: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]

Calculate the net radiation flux

Parameters:

conc (torch.Tensor) – concentration [mol/m^3]
dz (torch.Tensor) – height [m]
bc (dict[str, torch.Tensor]) – boundary conditions
kwargs (dict[str, torch.Tensor]) – additional arguments

Returns:

net flux [w/m^2]

Return type:

torch.Tensor

Examples

>>> import torch
>>> from pyharp import RadiationOptions
>>> op = RadiationOptions().band_options(['band1', 'band2'])

Opacity Classes

class AttenuatorOptions

bname(*args, **kwargs)

Overloaded function.

bname(self: pyharp.opacity.AttenuatorOptions) -> str

Set or get the name of the band that the opacity is associated with

Parameters:: bname (str) – name of the band that the opacity is associated with
Returns:: class object if argument is not empty, otherwise the band name
Return type:: AttenuatorOptions | str

Examples

>>> import torch
>>> from pyharp.opacity import AttenuatorOptions
>>> op = AttenuatorOptions().bname('band1')

bname(self: pyharp.opacity.AttenuatorOptions, arg0: str) -> pyharp.opacity.AttenuatorOptions

Set or get the name of the band that the opacity is associated with

Parameters:: bname (str) – name of the band that the opacity is associated with
Returns:: class object if argument is not empty, otherwise the band name
Return type:: AttenuatorOptions | str

Examples

>>> import torch
>>> from pyharp.opacity import AttenuatorOptions
>>> op = AttenuatorOptions().bname('band1')

fractions(*args, **kwargs)

Overloaded function.

fractions(self: pyharp.opacity.AttenuatorOptions) -> list[float]

Set or get fractions of species in cia calculatioin

Parameters:: fractions (list[float]) – list of species fractions
Returns:: class object if argument is not empty, otherwise the list of species fractions
Return type:: AttenuatorOptions | list[float]

Examples

>>> import torch
>>> from pyharp.opacity import AttenuatorOptions
>>> op = AttenuatorOptions().fractions([0.9, 0.1])

fractions(self: pyharp.opacity.AttenuatorOptions, arg0: list[float]) -> pyharp.opacity.AttenuatorOptions

Set or get fractions of species in cia calculatioin

Parameters:: fractions (list[float]) – list of species fractions
Returns:: class object if argument is not empty, otherwise the list of species fractions
Return type:: AttenuatorOptions | list[float]

Examples

>>> import torch
>>> from pyharp.opacity import AttenuatorOptions
>>> op = AttenuatorOptions().fractions([0.9, 0.1])

opacity_files(*args, **kwargs)

Overloaded function.

opacity_files(self: pyharp.opacity.AttenuatorOptions) -> list[str]

Set or get the list of opacity data files

Parameters:: opacity_files (list) – list of opacity data files
Returns:: class object if argument is not empty, otherwise the list of opacity data files
Return type:: AttenuatorOptions | list[str]

Examples

>>> import torch
>>> from pyharp.opacity import AttenuatorOptions
>>> op = AttenuatorOptions().opacity_files(['file1', 'file2'])

opacity_files(self: pyharp.opacity.AttenuatorOptions, arg0: list[str]) -> pyharp.opacity.AttenuatorOptions

Set or get the list of opacity data files

Parameters:: opacity_files (list) – list of opacity data files
Returns:: class object if argument is not empty, otherwise the list of opacity data files
Return type:: AttenuatorOptions | list[str]

Examples

>>> import torch
>>> from pyharp.opacity import AttenuatorOptions
>>> op = AttenuatorOptions().opacity_files(['file1', 'file2'])

species_ids(*args, **kwargs)

Overloaded function.

species_ids(self: pyharp.opacity.AttenuatorOptions) -> list[int]

Set or get the list of dependent species indices

Parameters:: species_ids (list[int]) – list of dependent species indices
Returns:: class object if argument is not empty, otherwise the list of dependent species indices
Return type:: AttenuatorOptions | list[int]

Examples

>>> import torch
>>> from pyharp.opacity import AttenuatorOptions
>>> op = AttenuatorOptions().species_ids([1, 2])

species_ids(self: pyharp.opacity.AttenuatorOptions, arg0: list[int]) -> pyharp.opacity.AttenuatorOptions

Set or get the list of dependent species indices

Parameters:: species_ids (list[int]) – list of dependent species indices
Returns:: class object if argument is not empty, otherwise the list of dependent species indices
Return type:: AttenuatorOptions | list[int]

Examples

>>> import torch
>>> from pyharp.opacity import AttenuatorOptions
>>> op = AttenuatorOptions().species_ids([1, 2])

type(*args, **kwargs)

Overloaded function.

type(self: pyharp.opacity.AttenuatorOptions) -> str

Set or get the type of the opacity source format

Valid options are:

Key	Description
‘rfm-lbl’	Line-by-line absorption data computed by RFM
‘rfm-ck’	Correlated-k absorption computed from line-by-line data
‘multiband-ck’	Multi-band opacity data from saved torch “.pt” file
‘fourcolumn’	Four-column opacity data (wavelength [um]/wavenumber [cm^{-1}], cross-section [m^2/kg], ssa, g)

Parameters:: type (str) – type of the opacity source
Returns:: class object if argument is not empty, otherwise the type
Return type:: AttenuatorOptions | str

Examples

>>> import torch
>>> from pyharp.opacity import AttenuatorOptions
>>> op = AttenuatorOptions().type('rfm-lbl')
>>> print(op)

type(self: pyharp.opacity.AttenuatorOptions, arg0: str) -> pyharp.opacity.AttenuatorOptions

Set or get the type of the opacity source format

Valid options are:

Key	Description
‘rfm-lbl’	Line-by-line absorption data computed by RFM
‘rfm-ck’	Correlated-k absorption computed from line-by-line data
‘multiband-ck’	Multi-band opacity data from saved torch “.pt” file
‘fourcolumn’	Four-column opacity data (wavelength [um]/wavenumber [cm^{-1}], cross-section [m^2/kg], ssa, g)

Parameters:: type (str) – type of the opacity source
Returns:: class object if argument is not empty, otherwise the type
Return type:: AttenuatorOptions | str

Examples

>>> import torch
>>> from pyharp.opacity import AttenuatorOptions
>>> op = AttenuatorOptions().type('rfm-lbl')
>>> print(op)

class FourColumn

__init__(*args, **kwargs)

Overloaded function.

__init__(self: pyharp.opacity.cpp.FourColumn) -> None

Construct a new default module.

__init__(self: pyharp.opacity.cpp.FourColumn, options: pyharp.opacity.AttenuatorOptions) -> None

Construct a FourColumn module

forward(*args, **kwargs)

Overloaded function.

forward(self: pyharp.opacity.cpp.FourColumn, arg0: torch.Tensor, arg1: dict[str, torch.Tensor]) -> torch.Tensor
forward(self: pyharp.opacity.cpp.FourColumn, conc: torch.Tensor, kwargs: dict[str, torch.Tensor]) -> torch.Tensor

Four-column opacity data

Parameters:

conc (torch.Tensor) – concentration of the species in mol/cm^3
kwargs (dict[str, torch.Tensor]) – keyword arguments. Either ‘wavelength’ or ‘wavenumber’ must be provided if ‘wavelength’ is provided, the unit is um. if ‘wavenumber’ is provided, the unit is cm^-1.

Returns:

attenuation [1/m], single scattering albedo and scattering phase function The shape of the output tensor is (nwave, ncol, nlyr, 2 + nmom) where nwave is the number of wavelengths, ncol is the number of columns, nlyr is the number of layers, 2 is for attenuation and scattering coefficients, and nmom is the number of scattering moments.

Return type:

torch.Tensor

Examples

>>> import torch
>>> from pyharp.opacity import FourColumn, AttenuatorOptions
>>> op = FourColumn(AttenuatorOptions())

class MultiBand

__init__(*args, **kwargs)

Overloaded function.

__init__(self: pyharp.opacity.cpp.MultiBand) -> None

Construct a new default module.

__init__(self: pyharp.opacity.cpp.MultiBand, options: pyharp.opacity.AttenuatorOptions) -> None

Construct a MultiBand module

forward(*args, **kwargs)

Overloaded function.

forward(self: pyharp.opacity.cpp.MultiBand, arg0: torch.Tensor, arg1: dict[str, torch.Tensor]) -> torch.Tensor
forward(self: pyharp.opacity.cpp.MultiBand, conc: torch.Tensor, kwargs: dict[str, torch.Tensor]) -> torch.Tensor

Multi-band opacity data

Parameters:

conc (torch.Tensor) – concentration of the species in mol/cm^3
kwargs (dict[str, torch.Tensor]) – keyword arguments. Both ‘temp’ [k] and ‘pres’ [pa] must be provided

Returns:

attenuation [1/m], single scattering albedo and scattering phase function The shape of the output tensor is (nwave, ncol, nlyr, 1) where nwave is the number of wavelengths, ncol is the number of columns, nlyr is the number of layers, 1 is for attenuation coefficients, and nmom is the number of scattering moments.

Return type:

torch.Tensor

Examples

>>> import torch
>>> from pyharp.opacity import MultiBand, AttenuatorOptions
>>> op = MultiBand(AttenuatorOptions())

class WaveTemp

__init__(*args, **kwargs)

Overloaded function.

__init__(self: pyharp.opacity.cpp.WaveTemp) -> None

Construct a new default module.

__init__(self: pyharp.opacity.cpp.WaveTemp, options: pyharp.opacity.AttenuatorOptions) -> None

Construct a WaveTemp module

forward(*args, **kwargs)

Overloaded function.

forward(self: pyharp.opacity.cpp.WaveTemp, arg0: torch.Tensor, arg1: dict[str, torch.Tensor]) -> torch.Tensor
forward(self: pyharp.opacity.cpp.WaveTemp, conc: torch.Tensor, kwargs: dict[str, torch.Tensor]) -> torch.Tensor

Wave-Temp opacity data

Parameters:

conc (torch.Tensor) – concentration of the species in mol/cm^3
kwargs (dict[str, torch.Tensor]) – keyword arguments. Both ‘temp’ [k] and (‘wavenumber’ [cm^{-1}] or ‘wavelength’ [num]) must be provided

Returns:

attenuation [1/m], single scattering albedo and scattering phase function The shape of the output tensor is (nwave, ncol, nlyr, 1) where nwave is the number of wavelengths, ncol is the number of columns, nlyr is the number of layers, 1 is for attenuation coefficients, and nmom is the number of scattering moments.

Return type:

torch.Tensor

Examples

>>> import torch
>>> from pyharp.opacity import WaveTemp, AttenuatorOptions
>>> op = MultiBand(AttenuatorOptions())

class RFM

__init__(*args, **kwargs)

Overloaded function.

__init__(self: pyharp.opacity.cpp.RFM) -> None

Construct a new default module.

__init__(self: pyharp.opacity.cpp.RFM, options: pyharp.opacity.AttenuatorOptions) -> None

Construct a RFM module

forward(*args, **kwargs)

Overloaded function.

forward(self: pyharp.opacity.cpp.RFM, arg0: torch.Tensor, arg1: dict[str, torch.Tensor]) -> torch.Tensor
forward(self: pyharp.opacity.cpp.RFM, conc: torch.Tensor, kwargs: dict[str, torch.Tensor]) -> torch.Tensor

Line-by-line absorption data computed by RFM

Parameters:

conc (torch.Tensor) – concentration of the species in mol/cm^3
kwargs (dict[str, torch.Tensor]) – keyword arguments Either ‘wavelength’ or ‘wavenumber’ must be provided if ‘wavelength’ is provided, the unit is nm. if ‘wavenumber’ is provided, the unit is cm^-1.

Returns:

attenuation [1/m], single scattering albedo and scattering phase function: The shape of the output tensor is (nwave, ncol, nlyr, 2 + nmom) where nwave is the number of wavelengths, ncol is the number of columns, nlyr is the number of layers, 2 is for attenuation and scattering coefficients, and nmom is the number of moments.

Return type:

torch.Tensor

Examples

>>> import torch
>>> from pyharp.opacity import RFM, AttenuatorOptions
>>> op = RFM(AttenuatorOptions())

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)

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

\[dz = \frac{R}{g} \cdot T \cdot \ln\left(\frac{p_1}{p_2}\right)\]

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)

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)