CIA (taurex.cia)

Base Class

Base collisionally induced absorption class.

class CIA(name: str, pair_name: str)[source]

Bases: Loggable

Abstract class

This is the base class for collisionally induced absorption opacities. To function in Taurex3, it requires concrete implementations of:

Parameters:

  • name (str) – Name to use for logging

  • pair_name (str) – pair of molecules this class represents. e.g. ‘H2-H2’ or ‘H2-He’

cia(temperature: float, wngrid: ndarray[tuple[int, ...], dtype[float64]] | None = None)[source]

For a given temperature, computes the appropriate cross section. If wavenumber grid ( wngrid ) is provided then the cross-section is interpolated to it.

Parameters:

  • temperature (float) – Temeprature in Kelvin

  • wngrid (array , optional) – Wavenumber grid to interpolate to

Returns:

CIA cross section at desired temeprature on either its native grid or interpolated on wngrid if supplied

Return type:

array

compute_cia(temperature: float) → ndarray[tuple[int, ...], dtype[float64]][source]

Computes the collisionaly induced cross-section for a given temeprature.

Unimplemented, this must be implemented in any derived class to be considered compatible in Taurex3

The rules are:

  1. It must accept temperature in Kelvin (K)

  2. If the temperature falls outside of temperatureGrid()

    it must be set to zero

  3. The returned array must be of equal size to wavenumberGrid()

Parameters:

temperature (float) – Temeprature in Kelvin

Returns:

CIA cross section at desired temeprature on its native grid

Return type:

array

Raises:

NotImplementedError – Only if derived class does not implement this

property pairName: str

The assigned pair of molecules of this CIA

Returns:

The pair of molecules of this object in the form: Molecule1-Molecule2

Return type:

str

property pairOne: str

The name of the first molecule in the pair

Returns:

First molecule in the pair

Return type:

str

property pairTwo: str

The name of the second molecule in the pair

Returns:

Second molecule in the pair

Return type:

str

property temperatureGrid: ndarray[tuple[int, ...], dtype[float64]]

The native temperature grid of the CIA cross-sections.

Returns:

Native temeprature grid in Kelvin

Return type:

array

Raises:

NotImplementedError – Only if derived class does not implement this

property wavenumberGrid: ndarray[tuple[int, ...], dtype[float64]]

The native wavenumber grid (cm-1) of the CIA.

Must be implemented in derived classes

Returns:

Native wavenumber grid

Return type:

array

Raises:

NotImplementedError – Only if derived class does not implement this

HITRAN CIA (.cia)

Module contains classes that handle loading of HITRAN cia files

exception EndOfHitranCIAError[source]

Bases: Exception

An exception that occurs atr the end of a HITRAN file

class HitranCIA(filename: str)[source]

Bases: CIA

A class that directly deals with HITRAN

Takes HITRAN cia and turns them into generic CIA objects that nicely produces cross sections for us. This will handle CIAs that have wavenumber grids split across temperatures by unifying them into single grids.

To use it simply do:

>>> h2h2 = HitranCIA('path/to/H2-He.cia')

And now you can painlessly compute cross-sections like this:

>>> h2h2.cia(400)

Or if you have a wavenumber grid, we can also interpolate it:

>>> h2h2.cia(400,mywngrid)

And all it cost was buying me a beer!

Parameters:

filename (str) – Path to HITRAN cia file

compute_cia(temperature: float) → ndarray[tuple[int, ...], dtype[float64]][source]

Computes the collisionally induced absorption cross-section using our final native temperature and cross-section grids

Parameters:

temperature (float) – Temperature in Kelvin

Returns:

out – Temperature interpolated cross-section

Return type:

array

compute_final_grid() → None[source]

Build the final wavenumber grid.

Collects all HitranCiaGrid objects. We’ve created and unifies them into a single temperature, cross-section and wavenumber grid for us to FINALLY interpolate and produce collisionaly induced cross-sections

fill_gaps(temperature: float) → None[source]

Fills gaps in temperature grid for all wavenumber grid objects we’ve created

Parameters:

temperature (array_like) – Master temperature grid

find_closest_temperature_index(temperature: float) → Tuple[int, int][source]

Finds the nearest indices for a particular temperature.

Parameters:

temperature (float) – Temeprature in Kelvin

Returns:

  • t_min (int) – index on temprature grid to the left of temperature

  • t_max (int) – index on temprature grid to the right of temperature

interp_linear_grid(temperature: float, t_idx_min: int, t_idx_max: int) → ndarray[tuple[int, ...], dtype[float64]][source]

For a given temperature and indicies. Interpolate the cross-sections linearly from temperature grid to temperature T

Parameters:

  • temperature (float) – Temeprature in Kelvin

  • t_min (int) – index on temprature grid to the left of temperature

  • t_max (int) – index on temprature grid to the right of temperature

Returns:

out – Interpolated cross-section

Return type:

array

load_hitran_file(filename: str) → None[source]

Handles loading of the HITRAN file by reading and figuring out the wavenumber and temperature grids and matching them up

Parameters:

filename (str) – Path to HITRAN cia file

read_header(f: TextIO) → Tuple[float, float, int, float, float][source]

Reads single header in the file.

Parameters:

f (file object)

Returns:

  • start_wn (float) – Start wavenumber for temperature

  • end_wn (float) – End wavenumber for temperature

  • total_points (int) – total number of points in temperature

  • T (float) – Temperature in Kelvin

  • max_cia (float) – Maximum CIA value in temperature

property temperatureGrid: ndarray[tuple[int, ...], dtype[float64]]

Unified temperature grid

Returns:

Native temperature grid in Kelvin

Return type:

array

property wavenumberGrid: ndarray[tuple[int, ...], dtype[float64]]

Unified wavenumber grid

Returns:

Native wavenumber grid

Return type:

array

class HitranCiaGrid(wn_min: float, wn_max: float)[source]

Bases: Loggable

Class that handles a particular HITRAN cia wavenumber grid.

Since temperatures for CIA sometimes have different wavenumber grids this class helps to simplify managing them by only dealing with one at a time. These will help us unify into a single grid eventually

Parameters:

  • wn_min (float) – The minimum wavenumber for this grid

  • wn_max (float) – The maximum wavenumber for this grid

add_temperature(temperature: float, sigma: ndarray[tuple[int, ...], dtype[float64]]) → None[source]

Adds a temeprature and crossection to this wavenumber grid

Parameters:

  • T (float) – Temeprature in Kelvin

  • sigma (array) – cross-sections for this grid

fill_temperature(temperatures: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]) → None[source]

Here the ‘master’ temperature grid is passed into here and gaps filled.

Any gaps in our grid is filled with zero cross-sections to produce our final temperature-crosssection grid that matches with every other wavenumber grid. Temperatures that don’t exist in the current grid but are withing the minimum and maximum for us are produced by linear interpolation.

Parameters:

temperatures (array_like) – Master temperature grid

find_closest_temperature_index(temperature: float) → Tuple[int, int][source]

Finds the nearest indices for a particular temperature.

Parameters:

temperature (float) – Temeprature in Kelvin

Returns:

  • t_min (int) – index on temprature grid to the left of temperature

  • t_max (int) – index on temprature grid to the right of temperature

interp_linear_grid(temperature: float, t_idx_min: int, t_idx_max: int) → ndarray[tuple[int, ...], dtype[float64]][source]

For a given temperature and indicies. Interpolate the cross-sections.

Interpolates linearly from temperature grid to temperature temperature

Parameters:

  • temperature (float) – Temeprature in Kelvin

  • t_min (int) – index on temprature grid to the left of temperature

  • t_max (int) – index on temprature grid to the right of temperature

Returns:

out – Interpolated cross-section

Return type:

array

property sigma: ndarray[tuple[int, ...], dtype[float64]]

Gets the currently loaded crossections for this wavenumber grid.

Returns:

Cross-section grid

Return type:

array

sortTempSigma()[source]

Sorts the temperature-sigma list.

property temperature: float

Gets the current temeprature grid for this wavenumber grid.

Returns:

Temeprature grid in Kelvin

Return type:

array

hashwn(start_wn: float, end_wn: float) → str[source]

Simple wavenumber hash function.

Pickle CIA (.db)

Handling of .pickle/.db CIA files.

class PickleCIA(filename: str, pair_name: float64 | None = None)[source]

Bases: CIA

Class for using pickled (.db) collisionally induced absorptions Very simple since the format is simple

Parameters:

  • filename (str) – Path to pickle

  • pair_name (str , optional) – Whilst the name of the pair is determined by the pickle filename since these can be different you can optionally force the name through this parameter

compute_cia(temperature: float) → ndarray[tuple[int, ...], dtype[float64]][source]

Computes the collisionally induced absorption cross-section using our native temperature and cross-section grids

Parameters:

temperature (float) – Temperature in Kelvin

Returns:

out – Temperature interpolated cross-section

Return type:

array

find_closest_temperature_index(temperature: float) → Tuple[int, int][source]

Finds the nearest indices for a particular temperature

Parameters:

temperature (float) – Temeprature in Kelvin

Returns:

  • t_min (int) – index on temprature grid to the left of temperature

  • t_max (int) – index on temprature grid to the right of temperature

interp_linear_grid(temperature: float, t_idx_min: int, t_idx_max: int) → float[source]

For a given temperature and indicies. Interpolate the cross-sections linearly from temperature grid to temperature T

Parameters:

  • temperature (float) – Temeprature in Kelvin

  • t_min (int) – index on temprature grid to the left of temperature

  • t_max (int) – index on temprature grid to the right of temperature

Returns:

out – Interpolated cross-section

Return type:

array

property temperatureGrid: ndarray[tuple[int, ...], dtype[float64]]

returns: Native temperature grid in Kelvin :rtype: array

property wavenumberGrid: ndarray[tuple[int, ...], dtype[float64]]

returns: Native wavenumber grid :rtype: array