Metadata-Version: 2.4
Name: DAPyr
Version: 1.0.0
Summary: A Python package for running idealized model data assimilation experiments
Project-URL: Homepage, https://github.com/nikolaitM/DApyr
Project-URL: Documentation, https://dapyr.readthedocs.io/en/latest/
Project-URL: Issues, https://github.com/nikolaitM/DApyr/issues
Author-email: Maria Nikolaitchik <mnikolai@umd.edu>
License: MIT
Keywords: DA,Lorenz,data assimilation
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.10
Requires-Dist: matplotlib>=3.10.0
Requires-Dist: numba>=0.60.0
Requires-Dist: numbalsoda>=0.3.4
Requires-Dist: numpy>=1.26.1
Requires-Dist: scipy>=1.15.2
Requires-Dist: xarray>=2024.11.0
Description-Content-Type: text/markdown

# DAPyr: (D)ata (A)ssimilation (Py)ton (R)elease

DAPyr (pronounced "dapper") is a Python package for running, testing, and experimenting with data assimilation methods using Lorenz models. DAPyr supports quick configuration and running of experiments, allowing one to setup and run data assimilation cycles in as little as two lines. In addition, DAPyr utilizes [numbalsoda](https://github.com/Nicholaswogan/numbalsoda/tree/main) and [numba](https://numba.pydata.org/) for model integration, allowing for faster runtimes.

For a in-depth tutorials and explanations, see the [documentation](https://dapyr.readthedocs.io/en/latest/).

DAPyr currently supports the following:

secret readme edit

### Models:
1. Lorenz 1963
2. Lorenz 1996
3. Lorenz 2005

### Data Assimilation Methods:
1. Ensemble Square Root Filter [(Whitaker and Hamill, 2002)](https://journals.ametsoc.org/view/journals/mwre/130/7/1520-0493_2002_130_1913_edawpo_2.0.co_2.xml)
2. Localized Particle Filter [(Poterjoy, 2022)](https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.4328)

### Measurement Operators:
1. Linear
2. Quadratic
3. Logarithmic

## Installation

Pip:
```
python -m pip install DAPyr
```

## Basic Usage

```python
import DAPyr as dap
import numpy as np

# Use the Expt class to initialize an new experiment and calculate all necessary initial states
# Experiment will use Lorenz 63 (model_flag: 0), run for 300 time steps, and use the EnSRF method (expt_flag: 0)
expt = dap.Expt('Basic_L63', {'model_flag': 0, 'expt_flag': 0, 'T': 300})

# Run the experiment using the runDA function
dap.runDA(expt)

# Access RMSE and spread using the rmse and spread attributes
print('Avg. RMSE: {:.3f}'.format(np.mean(expt.rmse)))
print('Avg. Prior Spread: {:.3f}'.format(np.mean(expt.spread[:, 0])))
print('Avg. Posteriod Spread: {:.3f}'.format(np.mean(expt.spread[:, 1])))

#Save the experiment to filesystem using saveExpt, the file will be called it's experiment name
dap.saveExpt('./', expt)

#Load in previous experiments using loadExpt
expt2 = dap.loadExpt('./Basic_L63.expt')

```

For more examples, see `tutorial.ipynb` in `src`.

## Citation 
Please cite this repository [![DOI](https://zenodo.org/badge/967644537.svg)](https://doi.org/10.5281/zenodo.15792171) if you use DAPyr in your research publications or presentations.

## Contributions

Many thanks to the following for their contributions to DAPyr:
- Henry Santer (LPF Python implementation and many, many bug discoveries)
- Raelynn Wonacott (For advice on speeding up model integration using numba)

## Acknowledgement

The development of this software is supported by the following funding sources:
- NSF CAREER Award #1848363

