eppaurora.brems.berger1974()

Complexity

Conditions

4

Size

Total Lines	84
Code Lines	31

Duplication

Lines	84
Ratio	100 %

Importance

Changes

0

# coding: utf-8
def berger1974(

This code seems to be duplicated in your project.

	energy, flux,
	scale_height, rho,
	ens=E_BR, zm_p_en=Z_BR, coeffs=A_BR,
	fillna=None, log3=True,
):
	"""Bremsstrahlung ionization by secondary electrons

	Formulae and parameters as described in [2]_.

	.. [2] Berger, M.J., Seltzer, S.M., Maeda, K.,
		Some new results on electron transport in the atmosphere,
		Journal of Atmospheric and Terrestrial Physics, v36, i4, pp. 591--617,
		April 1974,
		doi: 10.1016/0021-9169(74)90085-3

	Parameters
	----------
	energy: array_like (M, ...)
		Energy E_0 of the mono-energetic electron beam [keV].
	flux: array_like (M,...)
		Energy flux Q_0 of the mono-energetic electron beam [keV / cm² / s¹].
		Has currently no effect, kept for compatibility with the other methods.
	scale_height: array_like (N, ...)
		The atmospheric scale heights [cm].
	rho: array_like (N, ...)
		The atmospheric mass density [g / cm³].
	ens: array_like (I,), optional
		The energies (one axis) of the coefficient array,
		used to interpolate the coefficients to `energy`.
		Defaults to the Berger [2]_ coefficients.
	zm_p_en: array_like (J,), optional
		The atmospheric depth (the other axis) of the coefficient array,
		used to interpolate the coefficients to `z` = `scale_height` * `rhos`.
		Defaults to the Berger [2]_ coefficients.
	coeffs: array_like, (J, I), optional
		The bremsstrahlung energy dissipation coefficients.
		Defaults to the Berger [2]_ coefficients.
	fillna: float or None, optional (default `None`)
		Value to use for `nan` values in `coeffs`, `None` skips them.
	log3: bool, optional (default `True`)
		Interpolate the coefficients as log(ens)-log(zm)-log(coeff)
		instead of a linear variant.

	Returns
	-------
	a_br: array_like (M, N)
		[1. / (g cm⁻²)]
		Multiply by density [g cm⁻³] and energy flux [keV cm⁻² s⁻¹]
		to obtain energy dissipation flux.
	"""
	ens = np.asarray(ens)
	zm_p_en = np.asarray(zm_p_en)

	coeffs = np.array(coeffs, copy=fillna is not None)
	nans = np.isnan(coeffs)
	if fillna is not None:
		coeffs[nans] = fillna
		# update nan positions (should be all False)
		nans = np.isnan(coeffs)

	z = scale_height * rho / energy
	if log3:
		# use log of everything
		energy = np.log(energy)
		z = np.log(z)
		ens = np.log(ens)
		zm_p_en = np.log(zm_p_en)
		coeffs = np.log(coeffs)
	pts = [
		(_e, _z)
		for _i, _e in enumerate(ens)
		for _j, _z in enumerate(zm_p_en)
		if not nans[_i, _j]
	]
	intp = interpolate.LinearNDInterpolator(
		pts,
		coeffs[~nans].ravel(),
	)
	abr_zm = intp((energy, z))

	if log3:
		abr_zm = np.exp(abr_zm)
	return abr_zm