(numericaldepr)¶

This module provides a way for the user to specify an arbitrary phase space distribution function \(f(r, p, \xi)\).

Warning

This module uses an old distribution function format and is deprecated since December 2019. Users are instead urged to use the new (numerical) distribution function module .

Summary of options¶

Option	Description
`numericaldepr name`	Name of file containing numerical distribution function.
`numericaldepr interptype`	Interpolation method to use.
`numericaldepr logarithmize`	Whether or not to interpolate in the logarithm of the distribution function.

Example configuration¶

The following example configures a SOFT numerical distribution function:

@DistributionFunction ourDistributionFunction (numericaldepr) {
    name = "/path/to/distribution.mat";
}

File layout¶

The following variables must/may be present in the distribution function file.

Variable	Required	Description
`f`	Yes	Distribution function \(f(r, p, \xi)\) of size \(n_p n_\xi n_r\).
`fp0`	No	Verification vector; \(f(r_0, p, \xi_0)\).
`fr0`	No	Verification vector; \(f(r, p_0, \xi_0)\).
`fxi0`	No	Verification vector; \(f(r_0, p_0, \xi)\).
`p`	Yes	Momentum grid vector with \(n_p\) elements.
`punits`	Yes	Unit of the given momentum.
`r`	Yes	Radial grid vector with \(n_r\) elements.
`xi`	Yes	Pitch (cosine of pitch angle) grid vector with \(n_\xi\) elements.

All options¶

name¶

Default value:	Nothing
Allowed values:	Any valid file name

Name of the file containing the distribution function.

interptype¶

Default value:	`cubic`
Allowed values:	`cubic` or `linear`

SOFT interpolates in the given distribution function to evaluate it at arbitrary points on the phase space grid. A linear interpolation scheme is always used to interpolate in the radial coordinate, but interpolation in the momentum coordinates (\(p\) and \(\xi\)) can either be done using bi-linear or bi-cubic splines.

logarithmize¶

Default value:	`no`
Allowed values:	`yes` or `no`

If yes, interpolates in the logarithm of the distribution function instead of in the distribution function directly. This can aid in fitting sharply decaying ditsribution functions.