swxg 0.2.0

Fast, region-agnostic stochastic weather generation

swxg

swxg is a Python package for modern stochastic weather generation. It quickly generates arbitrarily-long sequences of monthly or daily weather variables that match spatial and temporal correlations of input observations by:

1. fitting observed precipitation individually to a (Gaussian mixture model) hidden Markov model with 1 or more hidden states;
2. fitting both observed precipitation and temperature with hydroclimatic copulae;
3. sampling precipitation from its fit, disaggregating to finer resolution where necessary, and;
4. conditionally sampling temperature from the sampled precipitation and its fit, disaggregating to finer resolution where necessary

Dependencies

The required dependencies to use swxg are:

• Python >= 3.10
• copulae >= 0.8
• copulas >= 0.12
• hmmlearn >= 0.3
• matplotlib >= 3.10
• numpy >= 2.2
• pandas >= 2.3
• scikit-learn >= 1.7
• scipy >= 1.15
• statsmodels >= 0.14

Installation

To install swxg from PyPI with pip:

pip install swxg

Alternatively, you can install from this repository:

git clone https://github.com/xthames/swxg.git
cd swxg
pip install .

Important Links

• Official Source Code
• Documentation

If your work uses swxg, please cite:

• [JOSS PAPER IN PREP], specifically for the software
• [WRR PAPER IN PREP], if relevant to applied (first) use case