Mescla

End-Member Mixing Analysis (EMMA) and water mixing-ratio methods for hydrochemistry, with a test suite that reproduces published results.

Two method families, used together:

	EMMA	Mixing ratios
Answers	How many end-members are needed, and which species behave as a mixture	In what proportions those end-members mix in each sample
Machinery	Eigen-analysis of standardised chemistry; projection into U-space	Constrained least squares, or maximum likelihood when end-members are uncertain
Cannot	give you mixing ratios	tell you whether your conceptual model is right
Key refs	Christophersen & Hooper (1992); Hooper (2003)	Carrera et al. (2004)

The whole pipeline

import mescla as am
from mescla.datasets import make_mixture

data = make_mixture(n_endmembers=3, n_species=5, n_samples=60, seed=0)

model = am.EMMA().fit(data.samples)     # we do not tell it the answer
print(model.n_endmembers)               # 3

result = am.mixing_ratios(data.endmembers, data.samples)
result.ratios_frame().head()

Getting started

• Installation
• Tutorials

Using it well

• Units
• Which method, and when
• Assigning standard deviations
• Pitfalls

Reference

• API reference
  • Containers
  • Preparing the data
  • End-member mixing analysis
  • Mixing ratios
  • Isotopes
  • Uncertainty
  • Reactions
  • Datasets
  • Plotting
• Validation
  • Carrera et al. (2004)
  • Tubau et al. (2014)
  • The solvers themselves
  • Everything else
• References
  • Methods implemented here
  • Background worth reading
  • Citing Mescla
  • Data

Acknowledgements

Mescla was developed at Amphos 21 Consulting S.L., which funded the work. The methods themselves come from different References.

License and citation

The code is MIT licensed; the bundled datasets carry their own terms, recorded in src/mescla/datasets/data/SOURCES.md.

If Mescla is useful in your work, please cite it. CITATION.cff in the repository root has the software citation in machine-readable form, and References lists the methods papers it implements — see Citing Mescla there.