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https://github.com/ImperialCollegeLondon/pyrealm
Development of the pyrealm package, providing an integrated toolbox for modelling plant productivity, growth and demography using Python.
https://github.com/ImperialCollegeLondon/pyrealm
Last synced: about 2 months ago
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Development of the pyrealm package, providing an integrated toolbox for modelling plant productivity, growth and demography using Python.
- Host: GitHub
- URL: https://github.com/ImperialCollegeLondon/pyrealm
- Owner: ImperialCollegeLondon
- License: mit
- Created: 2020-12-01T17:31:18.000Z (about 4 years ago)
- Default Branch: develop
- Last Pushed: 2024-10-29T09:26:46.000Z (2 months ago)
- Last Synced: 2024-10-29T10:58:58.885Z (2 months ago)
- Language: Python
- Homepage: https://pyrealm.readthedocs.io/
- Size: 93.1 MB
- Stars: 22
- Watchers: 3
- Forks: 9
- Open Issues: 36
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.txt
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
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README
# The `pyrealm` package
[](https://pypi.org/project/pyrealm/)
[](https://doi.org/10.5281/zenodo.8366847)
[](https://pyrealm.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/ImperialCollegeLondon/pyrealm)
[](https://github.com/ImperialCollegeLondon/pyrealm/actions/workflows/pyrealm_ci.yaml)
[](https://results.pre-commit.ci/latest/github/ImperialCollegeLondon/pyrealm/develop)
The `pyrealm` package provides a toolbox implementing some key models for estimating
plant productivity, growth and demography in Python. The outputs of different models
can be then easily fed into other models within `pyrealm` to allow productivity
estimates to be fed forward into estimation of net primary productivity, growth and
ultimately plant community demography.
The `pyrealm` package currently includes:
* The P Model for estimating optimal rates of plant photosynthesis given the balance
between carbon capture and water loss. This includes recent extensions to incorporate
the effects of water stress, slow acclimation processes, models of C3/C4 competition
and carbon isotope fractionation.
* The T Model of the allocation of gross primary productivity to estimate net primary
productivity and hence plant growth.
* The SPLASH model for calculating soil moisture and actual evapotranspiration.
* A suite of core physics functions and other utilities used to support the modules
above.
For more details, see the package website:
[https://pyrealm.readthedocs.io/](https://pyrealm.readthedocs.io/).
## Using `pyrealm`
The `pyrealm` package requires Python 3 and the currently supported Python versions are:
3.10, 3.11 and 3.12. We make released package versions available via
[PyPi](https://pypi.org/project/pyrealm/) and also generate DOIs for each release via
[Zenodo](https://doi.org/10.5281/zenodo.8366847). You can install the most recent
release using `pip`:
```sh
pip install pyrealm
```
You can now get started using `pyrealm`. For example, to calculate the estimated gross
primary productivity of a C3 plant in a location, start a Python interpreter, using
`python`, `python3` or `ipython` depending on your installation, and run:
```python
import numpy as np
from pyrealm.pmodel import PModelEnvironment, PModel
# Calculate the photosynthetic environment given the conditions
env = PModelEnvironment(
tc=np.array([20]), vpd=np.array([1000]),
co2=np.array([400]), patm=np.array([101325.0])
)
# Calculate the predictions of the P Model for a C3 plant
pmodel_c3 = PModel(env)
# Estimate the GPP from the model given the absorbed photosynthetically active light
pmodel_c3.estimate_productivity(fapar=1, ppfd=300)
# Report the GPP in micrograms of carbon per m2 per second.
pmodel_c3.gpp
```
This should give the following output:
```python
array([76.42544948])
```
The package website provides worked examples of using `pyrealm`, for example to:
* [fit the P
Model](https://pyrealm.readthedocs.io/en/latest/users/pmodel/pmodel_details/worked_examples.html),
* [include acclimation in estimating light use
efficiency](https://pyrealm.readthedocs.io/en/latest/users/pmodel/subdaily_details/worked_example.html)
, and
* [estimate C3/C4
competition](https://pyrealm.readthedocs.io/en/latest/users/pmodel/c3c4model.html#worked-example).
These worked examples also show how `pyrealm` can be used within Python scripts or
Jupyter notebooks and how to use `pyrealm` with large datasets loaded using
[`numpy`](https://numpy.org/) or [`xarray`](https://docs.xarray.dev/en/stable/) with
`pyrealm` classes and functions.
## Citing `pyrealm`
The `pyrealm` repository can be cited following the information in the [citation
file](./CITATION.cff). If you are using `pyrealm` in research, it is better to cite the
DOI of the specific release from [Zenodo](https://doi.org/10.5281/zenodo.8366847).
## Developing `pyrealm`
If you are interested in contributing to the development of `pyrealm`, please read the
[guide for contributors](./CONTRIBUTING.md). Please do also read the [code of
conduct](./CODE_OF_CONDUCT.md) for contributing to this project.
## Support and funding
Development of the `prealm` package has been supported by the following grants and
institutions:
* The [REALM project](https://prenticeclimategroup.wordpress.com/realm-team/), funded by
an [ERC grant](https://cordis.europa.eu/project/id/787203) to Prof. Colin Prentice
(Imperial College London).
* The [LEMONTREE project](https://research.reading.ac.uk/lemontree/), funded by Schmidt
Sciences through the [VESRI
programme](https://www.schmidtfutures.com/our-work/virtual-earth-system-research-institute-vesri/)
to support an international research team lead by Prof. Sandy Harrison (University of
Reading).
* The [Virtual Rainforest project](https://pyrealm.readthedocs.io/), funded by a
Distinguished Scientist award from the [NOMIS
Foundation](https://nomisfoundation.ch/research-projects/a-virtual-rainforest-for-understanding-the-stability-resilience-and-sustainability-of-complex-ecosystems/)
to Prof. Robert Ewers (Imperial College London)
* Research software engineering support from the [Institute of Computing for Climate
Science](https://iccs.cam.ac.uk/) at the University of Cambridge, through the [Virtual
Institute for Scientific
Software](https://www.schmidtfutures.com/our-work/virtual-institute-for-scientific-software/)
program funded by Schmidt Sciences.