# `swxg`
`swxg` is a Python package for modern [stochastic weather generation](https://www.ipcc-data.org/guidelines/pages/weather_generators.html). 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;
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](https://github.com/xthames/swxg)
* [Documentation](https://swxg.readthedocs.org)
If your work uses `swxg`, please cite:
* [JOSS PAPER IN PREP], specifically for software
* [WRR PAPER IN PREP], if relevant to applied (first) use case
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"description": "# `swxg`\n`swxg` is a Python package for modern [stochastic weather generation](https://www.ipcc-data.org/guidelines/pages/weather_generators.html). It quickly generates arbitrarily-long sequences of monthly or daily weather variables that match spatial and temporal correlations of input observations by: \n 1. fitting observed precipitation individually to a (Gaussian mixture model) hidden Markov model; \n 2. fitting both observed precipitation and temperature with hydroclimatic copulae;\n 3. sampling precipitation from its fit, disaggregating to finer resolution where necessary, and;\n 4. conditionally sampling temperature from the sampled precipitation and its fit, disaggregating to finer resolution where necessary\n\n## Dependencies\nThe required dependencies to use `swxg` are:\n * `Python >= 3.10`\n * `copulae >= 0.8`\n * `copulas >= 0.12`\n * `hmmlearn >= 0.3`\n * `matplotlib >= 3.10`\n * `numpy >= 2.2`\n * `pandas >= 2.3`\n * `scikit-learn >= 1.7`\n * `scipy >= 1.15`\n * `statsmodels >= 0.14`\n\n## Installation\nTo install `swxg` from PyPI with `pip`:\n\n pip install swxg\n\nAlternatively, you can install from this repository:\n\n git clone https://github.com/xthames/swxg.git\n cd swxg\n pip install .\n\n## Important Links\n * [Official Source Code](https://github.com/xthames/swxg)\n * [Documentation](https://swxg.readthedocs.org)\n\nIf your work uses `swxg`, please cite: \n * [JOSS PAPER IN PREP], specifically for software\n * [WRR PAPER IN PREP], if relevant to applied (first) use case\n\n",
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