Metadata-Version: 2.1
Name: uqtils
Version: 0.4.0
Summary: Assorted utilities for uncertainty quantification and scientific computing.
Keywords: Uncertainty quantification,Bayesian inference
Author-Email: Joshua Eckels <eckelsjd@umich.edu>
License: GPL-3.0
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Intended Audience :: Science/Research
Project-URL: Repository, https://github.com/eckelsjd/uqtils.git
Project-URL: Documentation, https://eckelsjd.github.io/uqtils/
Requires-Python: >=3.11
Requires-Dist: numpy>=2.0
Requires-Dist: scipy>=1.14
Requires-Dist: matplotlib>=3.9
Requires-Dist: tqdm>=4.66
Requires-Dist: h5py>=3.10
Description-Content-Type: text/markdown

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Assorted utilities for uncertainty quantification and scientific computing.

## ⚙️ Installation
```shell
pip install uqtils
```
If you are using [pdm](https://github.com/pdm-project/pdm) in your own project, then you can use:
```shell
pdm add uqtils

# Or in editable mode from a local clone...
pdm add -e ./uqtils --dev
```

## 📍 Quickstart
```python
import numpy as np
import uqtils as uq

ndim, nsamples = 3, 1000

mu = np.random.rand(ndim)
cov = np.eye(ndim)

samples = uq.normal_sample(mu, cov, nsamples)
fig, ax = uq.ndscatter(samples)
```

## 🏗️ Contributing
See the [contribution](https://github.com/eckelsjd/uqtils/blob/main/CONTRIBUTING.md) guidelines.

<sup><sub>Made with the [copier-numpy](https://github.com/eckelsjd/copier-numpy.git) template.</sub></sup>
