# PyLAR
## Python utilities for the LAR model
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[](https://pypi.org/project/ipylar/)
[](https://pypi.org/project/ipylar/)
[](https://pypi.org/project/ipylar/)
[](https://pypi.org/project/ipylar/)
[](https://pypi.org/project/ipylar/)
[](https://doi.org/10.5281/zenodo.12167661)
[](https://doi.org/10.5194/hess-2023-172)
[](https://doi.org/10.5194/hess-28-2919-2024)
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`PyLAR` is a python package and datasets intended to test and use the LAR (Land-Atmospheric and Reservoir) model. The LAR model is intended to describe the changes in the water storage in large river basin around the world, including
atmospheric processes as a critical component of the basin
water budget.
<p align="center"><img src="https://github.com/seap-udea/pylar/blob/main/tutorials/resources/LAR-Conceptual.png?raw=true" alt="Conceptual illustration of LAR"/></p>
For the science behind the LAR model please refer to the following paper:
> Juan F. Salazar, Rubén D. Molina, Jorge I. Zuluaga, and Jesus D. Gomez-Velez (2024), **Wetting and drying trends in the land–atmosphere reservoir of large basins around the world**, [Hydrology and Earth System Sciences, HESS, 28, 2919–2947, 2024](https://hess.copernicus.org/articles/28/2919/2024/), [doi.org/10.5194/hess-28-2919-2024](https://doi.org/10.5194/hess-28-2919-2024).
All the notebooks and data required to reproduce the results of this paper, and other papers produced by our group, are available in the [`dev` directory in this repository](https://github.com/seap-udea/pylar/tree/main/dev).
## Downloading and Installing `PyLAR`
`PyLAR` is available at the `Python` package index and can be installed using:
```bash
$ sudo pip install ipylar
```
as usual this command will install all dependencies and download some useful data, scripts and constants.
> **NOTE**: If you don't have access to `sudo`, you can install `PyLAR` in your local environmen (usually at `~/.local/`). In that case you need to add to your `PATH` environmental variable the location of the local python installation. Add to `~/.bashrc` the line `export PATH=$HOME/.local/bin:$PATH`
## Quickstart
To start using `PyLAR`, you should first obtain data for a large river basin. We have provided with the package a dataset especially prepared for the Amazonas Basin we will use in this quickstart.
You must start by importing the package:
```python
import ipylar as lar
```
Create a basin:
```python
amazonas = lar.Basin(key='amazonas',name='Amazonas')
```
Once created, you should read the timeseries for the basin and load it into the pandas dataframe `amazonas.data`. The present version of `PyLAR` includes sample data. You may read the sample data using:
```python
amazonas.read_basin_data()
```
Once the data is loaded you can perform operations on the data, for instance, you can plot it:
```python
fig = amazonas.plot_basin_series()
```
<p align="center"><img src="https://github.com/seap-udea/pylar/blob/main/tutorials/resources/amazonas-lar-timeseries.png?raw=true" alt="Amazonas LAR time-series"/></p>
## Tutorials
We have prepared a set of [basic tutorials](https://github.com/seap-udea/pylar/tree/main/tutorials) for illustrating the usage of some of the tools including in `PyLAR`. The tutorials can be ran in `Google Colab`.
## What's new
For a detailed list of the newest characteristics of the code see the file [What's new](https://github.com/seap-udea/pylar/blob/master/WHATSNEW.md).
------------
This package has been designed and written by Jorge I. Zuluaga, Ruben D. Molina, Juan F. Salazar and Jesus D. Gomez-Velez (C) 2024
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"description": "# PyLAR\n## Python utilities for the LAR model\n\n<!-- This are visual tags that you may add to your package at the beginning with useful information on your package --> \n[](https://pypi.org/project/ipylar/)\n[](https://pypi.org/project/ipylar/)\n[](https://pypi.org/project/ipylar/)\n[](https://pypi.org/project/ipylar/)\n[](https://pypi.org/project/ipylar/)\n[](https://doi.org/10.5281/zenodo.12167661)\n[](https://doi.org/10.5194/hess-2023-172)\n[](https://doi.org/10.5194/hess-28-2919-2024)\n<!-- Static badge generator: https://shields.io/badges/static-badge: put the badge number and ready -->\n\n`PyLAR` is a python package and datasets intended to test and use the LAR (Land-Atmospheric and Reservoir) model. The LAR model is intended to describe the changes in the water storage in large river basin around the world, including\natmospheric processes as a critical component of the basin\nwater budget. \n\n<p align=\"center\"><img src=\"https://github.com/seap-udea/pylar/blob/main/tutorials/resources/LAR-Conceptual.png?raw=true\" alt=\"Conceptual illustration of LAR\"/></p>\n\nFor the science behind the LAR model please refer to the following paper:\n\n> Juan F. Salazar, Rub\u00e9n D. Molina, Jorge I. Zuluaga, and Jesus D. Gomez-Velez (2024), **Wetting and drying trends in the land\u2013atmosphere reservoir of large basins around the world**, [Hydrology and Earth System Sciences, HESS, 28, 2919\u20132947, 2024](https://hess.copernicus.org/articles/28/2919/2024/), [doi.org/10.5194/hess-28-2919-2024](https://doi.org/10.5194/hess-28-2919-2024).\n\nAll the notebooks and data required to reproduce the results of this paper, and other papers produced by our group, are available in the [`dev` directory in this repository](https://github.com/seap-udea/pylar/tree/main/dev).\n\n## Downloading and Installing `PyLAR` \n\n`PyLAR` is available at the `Python` package index and can be installed using:\n\n```bash\n$ sudo pip install ipylar\n```\nas usual this command will install all dependencies and download some useful data, scripts and constants.\n\n> **NOTE**: If you don't have access to `sudo`, you can install `PyLAR` in your local environmen (usually at `~/.local/`). In that case you need to add to your `PATH` environmental variable the location of the local python installation. Add to `~/.bashrc` the line `export PATH=$HOME/.local/bin:$PATH`\n\n## Quickstart\n\nTo start using `PyLAR`, you should first obtain data for a large river basin. We have provided with the package a dataset especially prepared for the Amazonas Basin we will use in this quickstart. \n\nYou must start by importing the package:\n\n```python\nimport ipylar as lar\n```\n\nCreate a basin:\n\n```python\namazonas = lar.Basin(key='amazonas',name='Amazonas')\n```\n\nOnce created, you should read the timeseries for the basin and load it into the pandas dataframe `amazonas.data`. The present version of `PyLAR` includes sample data. You may read the sample data using:\n\n```python\namazonas.read_basin_data()\n```\n\nOnce the data is loaded you can perform operations on the data, for instance, you can plot it:\n\n```python\nfig = amazonas.plot_basin_series()\n```\n\n<p align=\"center\"><img src=\"https://github.com/seap-udea/pylar/blob/main/tutorials/resources/amazonas-lar-timeseries.png?raw=true\" alt=\"Amazonas LAR time-series\"/></p>\n\n## Tutorials\n\nWe have prepared a set of [basic tutorials](https://github.com/seap-udea/pylar/tree/main/tutorials) for illustrating the usage of some of the tools including in `PyLAR`. The tutorials can be ran in `Google Colab`.\n\n## What's new\n\nFor a detailed list of the newest characteristics of the code see the file [What's new](https://github.com/seap-udea/pylar/blob/master/WHATSNEW.md).\n\n------------\n\nThis package has been designed and written by Jorge I. Zuluaga, Ruben D. Molina, Juan F. Salazar and Jesus D. Gomez-Velez (C) 2024\n",
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