Larch and pymca compatibility

The library is using the XASObject to associate larch and pymca treatments and create as much as possible a bridge between those two.

Here is a small summary about current (August 2019) larch processes and pymca processes :

Original functions goals:

larch functions	pymca functions	description	is currently accessible from est
	processSpectrum	compute all variables for the given spectrum	YES
	fourierTransform	compute spectrum for fourier transform spectroscopy	YES
	postEdge		YES
	normalize	spectrum normalization	YES
pre_edge		Pre-edge subtraction and normalization - determine E0 - fit a line of polynomial to the region below the edge - fit a polynomial to the region above the edge - extrapolate the two curves to E0 to determine the edge jump	YES
pre_edge_baseline		needed for metal oxides to determine oxidation state and molecular configuration. To analyze the energies and relative strengths of these pre-edge peaks.	NO
mback		an alternative to pre_edge ? data are matched to the tabulated values of the imaginary part of the energy-dependent correction to the Thompson scattering factor	YES
over_absorption correction		needed for fluorescence when too much of the total X-ray absorption coefficient is from the absorbing element	NO
spectral deconvolution		to compare XAFS data from different source	NO
pre-edge peak fitting		X-ray Absorption Near Edge Structure technique	NO
Linear Combination Analysis		X-ray Absorption Near Edge Structure technique	NO
PCA		X-ray Absorption Near Edge Structure technique	NO
autobk		Post-edge Background Subtraction Background subtraction, convert mu to \(\chi(k)\) for quantitative analysis (post_edge background function)	YES
xftf		Forward XAFS Fourier transforms forward Fourier transform converts \(\chi(k)\) to \(\chi(R)\) and is of primary importance for XAFS analysis	YES
xftr		Reverse XAFS Fourier transforms	NO
ftwindow		Generating Fourier transform windows	NO
cauchy_wavelet		Continuous Cauchy Wavelet transform of Munoz et al	NO
reading and using Feff paths			NO
fitting XAFS to Feff paths			NO
computing anomalous scattering factors from XAFS data			NO

From it we can create the theoretical related functions:

goal	larch function(s)	pymca function(s)	Notes
pre edge sub and normalization	pre_edge	normalize
post edge substraction	autobk	EXAFS (postEdge (include postEdge)
fourier transform	xftf	fourier transform

Finally this is the list of variables used in larch and pymca. An important point is that larch uses a ‘group’ and can store any of this values ‘on the fly’ during the processing. Pymca uses dictionary as ‘XASObject’ to store results. So XASObject as to adapt to both behavior as much as possible.

larch variables	pymca variables	info
norm		normalized spectrum, using polynomial
norm_area		normalized mu(E), using integrated area
edge_step		edge step of spectrum
flat		flattened, normalized mu(E)
pre_edge		determined pre-edge curve
post_edge		determined post-edge, normalization curve
dmude		derivative of mu(E)
fpp		mback matched spectrum
f1		tabulated f1(E)
f2		tabulated f2(E)
delta_bkg
delta_chi
e0	_energy0	energy origin/offset
chi	EXAFSValues
chie
bkg
k	k
kmin	kmin
kmax	kmax
	knots
	KWeight
	KnotsX	x position of knots
	KnotsY	y position of knots
	PostEdgeK
	PostEdgeB
energy	Energy
mu	Mu
	Jump
	edge
	NormalizedEnergy
	NormalizedMu
	NormalizedBackground
	NormalizedSignal
	EXAFSEnergy
	EXAFSValues
	EXAFSSignal
	EXAFSNormalized
	FT WindowRange Set InterpolatedK InterpolatedSignal KWeight K FTRadius FTIntensity FTReal FTImaginary	dictionary containing fourier transform result
kwin		window function Omega(k) (length of input chi(k))
r		uniform array of R, out to rmax_out
chir		complex array of chi(R)
chir_mag		magnitude of of chi(R)
chir_re		real part of of chi(R)
chir_im		imaginary part of of chi(R)
chir_pha		phase of of chi(R)
chir		complex array of chi(R)