Metadata-Version: 2.4
Name: pyslfp
Version: 2.0.0
Summary: Package for computing elastic sea level fingerprints
License: BSD-3-Clause
License-File: LICENSE
Author: David Al-Attar and Dan Heathcote
Requires-Python: >=3.12
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Dist: Cartopy (>=0.23.0,<0.24.0)
Requires-Dist: cf-xarray (>=0.10.9,<0.11.0)
Requires-Dist: matplotlib (>=3.0.0)
Requires-Dist: numpy (>=1.26.0)
Requires-Dist: pre-commit (>=4.5.1,<5.0.0)
Requires-Dist: pygeoinf (>=1.7.0)
Requires-Dist: pyqt6 (>=6.0.0)
Requires-Dist: pyshtools (>=4.0.0)
Requires-Dist: regionmask (>=0.13.0,<0.14.0)
Requires-Dist: scipy (>=1.0.0)
Description-Content-Type: text/markdown

# PySLFP: Python Sea Level Fingerprints 

[![PyPI version](https://badge.fury.io/py/pyslfp.svg)](https://badge.fury.io/py/pyslfp)
[![License: BSD-3-Clause](https://img.shields.io/badge/License-BSD--3--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
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`pyslfp` is a Python package for computing elastic sea level "fingerprints". It provides a robust and user-friendly framework for solving the sea level equation, accounting for the Earth's elastic deformation, gravitational self-consistency between the ice, oceans, and solid Earth, and rotational feedback effects.

The core of the library is the `FingerPrint` class, which implements an iterative solver to determine the unique pattern of sea-level change that results from a change in a surface load, such as the melting of an ice sheet.

---

## Key Features 

* **Elastic Sea Level Equation Solver:** Implements an iterative solver for the  sea level equation and the generalised sea level equation needed within adjoint calculations.
* **Comprehensive Physics:** Accounts for Earth's elastic response (via load Love numbers), self-consistent gravity, and rotational feedbacks (polar wander).
* **Ice History Models:** Includes a data loader for the ICE-5G, ICE-6G, and ICE-7G global ice history models, allowing for easy setup of realistic background states.
* **Forward and Adjoint Modeling:** Provides a high-level interface for both forward calculations (predicting sea level change from a load) and  adjoint modeling (for use in inverse problems), powered by `pygeoinf`, and based on the theory of [Al-Attar et al.(2024)](https://academic.oup.com/gji/article/236/1/362/7338265)
* **Built-in Visualization:** Comes with high-quality map plotting utilities built on `matplotlib` and `cartopy` for easy visualization of global data grids.


---

## Installation

You can install `pyslfp` directly from PyPI using pip. The package requires Python 3.11+ and its dependencies will be installed automatically.

```bash
pip install pyslfp
```

### Installation with Poetry 

Alternatively, for development purposes, you can install pyslfp using Poetry. First, clone the repository and then run:

```bash 
poetry install 
```

To include the development dependencies (for running tests, building documentation, etc.), use the `--with dev` flag:

```bash
poetry install --with dev
```

---

## Citation 

If you use `pyslfp` in your published work, please cite the following paper:

*   Al-Attar, D., Syvret, F., Crawford, O., Mitrovica, J.X. and Lloyd, A.J., 2024. Reciprocity and sensitivity kernels for sea level fingerprints. *Geophysical Journal International*, **236**(1), 362-378.
    
Additionally, please cite the appropriate ice history model if you use the `IceNG` class from

*   [Peltier group data sets](https://www.atmosp.physics.utoronto.ca/~peltier/data.php)

---


## Tutorials

You can run the interactive tutorials directly in Google Colab to get started with the core concepts of the library.


| Tutorial Name                 | Link to Colab                                                                                                                                                                                                                                    |
| :---------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Tutorial 1 -  Calculating a Basic Sea Level Fingerprint | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/da380/pyslfp/blob/main/docs/source/tutorials/tutorial1.ipynb)                                                                                  |
| Tutorial 2 - A Deeper Dive into the Sea Level Equation   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/da380/pyslfp/blob/main/docs/source/tutorials/tutorial2.ipynb)                                                                                  |
| Tutorial 3 - Reciprocity and Generalised Sea Level Forcing  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/da380/pyslfp/blob/main/docs/source/tutorials/tutorial3.ipynb)                                                                                  |
| Tutorial 4 -  Adjoints and Sensitivity Kernels with `pygeoinf` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/da380/pyslfp/blob/main/docs/source/tutorials/tutorial4.ipynb)                                                                                  |
| Tutorial 5 - A Bayesian Inverse Problem - Inferring Ice Melt from Tide Gauges | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/da380/pyslfp/blob/main/docs/source/tutorials/tutorial5.ipynb)                                                                                  |



## Quick Start

Here's a simple example of how to compute and plot the sea level fingerprint for the melting of 10% of the Northern Hemisphere's ice sheets.

```python
import matplotlib.pyplot as plt
from pyslfp import FingerPrint, plot, IceModel

# 1. Initialise the fingerprint model
# lmax sets the spherical harmonic resolution.
fp = FingerPrint(lmax=256)

# 2. Set the background state (ice and sea level) to the present day
# This uses the built-in ICE-7G model loader.
fp.set_state_from_ice_ng(version=IceModel.ICE7G, date=0.0)

# 3. Define a surface mass load
# This function calculates the load corresponding to melting 10% of
# the Northern Hemisphere's ice mass.
direct_load = fp.northern_hemisphere_load(fraction=0.1)

# 4. Solve the sea level equation for the given load
# This returns the sea level change, surface displacement, gravity change,
# and angular velocity change. In this instance, only the first of the
# returned fields is used. 
sea_level_change, _, _, _ = fp(direct_load=direct_load)

# 5. Plot the resulting sea level fingerprint,
# showing the result only over the oceans.
fig, ax, im = plot(
    sea_level_change * fp.ocean_projection(),
)

# Customize the plot
ax.set_title("Sea Level Fingerprint of Northern Hemisphere Ice Melt", y=1.1)
cbar = fig.colorbar(im, ax=ax, orientation="horizontal", pad=0.05, shrink=0.7)
cbar.set_label("Sea Level Change (meters)")

plt.show()
```

The output of the above script will look similar to the following figure:

![Example of Bayesian Inference on a Circle](docs/figures/sl.png)

---
## Core Components

* The library is organized into a few key modules:

* finger_print.py: Contains the main FingerPrint class, which orchestrates the calculations.

* ice_ng.py: Provides the IceNG class for loading and interpolating global ice history models.

* plotting.py: Includes the plot function for visualizing pyshtools.SHGrid objects.

* physical_parameters.py: Defines the EarthModelParameters class, which manages physical constants and non-dimensionalization schemes.

---

## Dependencies

`pyslfp` is built on top of a robust stack of scientific Python packages:

* **numpy & scipy**: For numerical operations.

* **pyshtools**: For spherical harmonic transforms and grid representations.

* **pygeoinf**: For formulating and solving associated inverse problems

* **Cartopy & matplotlib**: For creating high-quality map projections and plots.

* **regionmask & cf-xarray**: For working with geospatial masks.
 
* **pyqt6**: As a backend for interactive plotting.
---

## License

This project is licensed under the BSD-3-Clause License.

--- 

## Citations

If you use `pyslfp` in your published work, please cite the following paper:

*    Al-Attar, D., Syvret, F., Crawford, O., Mitrovica, J.X. and Lloyd, A.J., 2024. *Reciprocity and sensitivity kernels for sea level fingerprints*. Geophysical Journal International, **236(1)**, pp.362-378.
  
Furthermore, if you use the ice models contained in the `IceNG` class, please cite the appropriate ice history model:

[Peltier Group Data Sets](https://www.atmosp.physics.utoronto.ca/~peltier/data.php)

## Contributing

Contributions are welcome! If you have a suggestion or find a bug, please open an issue. Pull requests are also encouraged.
