# Gallery¶

Here we provide a gallery containing selected example results of several applications (a non-exhaustive list) using SPUX framework for Bayesian inference and uncertainty quantification.

## Randomwalk¶

A simple one-dimensional randomwalk with uncertain origin, drift, and the observation error.

## Linear bucket¶

`rpy2`

bindings to Python.Work in progress.

## Stochastic inputs¶

`numba`

compiled C code for computationally expensive parts.Publication: Del Giudice, D. et al., (2016) “Describing the catchment-averaged precipitation as a stochastic process improves parameter and input estimation; Water Resources Research. John Wiley & Sons, Ltd, 52(4), pp. 3162–3186. doi: 10.1002/2015WR017871.

Work in progress.

## Stochastic parameters¶

`ctypes`

bindings of the compiled Fortran model library to Python.Work in progress.

## Prey-Predator¶

`JPype`

bindings to Python.Publication (preprint available at http://arxiv.org/abs/1711.01410):

```
Šukys, J. and Kattwinkel, M.
"SPUX: Scalable Particle Markov Chain Monte Carlo
for uncertainty quantification in stochastic ecological models".
Advances in Parallel Computing - Parallel Computing is Everywhere,
IOS Press, (32), pp. 159–168, 2018.
```

Work in progress.

## River invertebrates¶

`JPype`

bindings to Python.Work in progress.

## DATALAKES¶

`ctypes`

bindings of the compiled Fortran model library to Python.Work in progress.

## In-stream herbicides¶

`ctypes`

bindings of the compiled Fortran model library to Python.Work in progress.

## Urban hydrology¶

`Swig`

wrapper for Python.Work in progress.

## Solar dynamo¶

BISTOM - calibration of the solar dynamo simulations.

Work in progress.