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and Vegetation","monthly_downloads":2085,"total_dependent_repos":1,"total_dependent_packages":0,"readme":"\n\u003c!-- markdownlint-disable-next-line MD041 MD033 --\u003e\n\u003cimg\n  alt=\"The pyrealm logo: a green leaf over the shining sun.\"\n  src=\"/docs/source/_static/images/pyrealm_logo.png\" width=50%\u003e\n\n---\n\n[![PyPI - Version](https://img.shields.io/pypi/v/pyrealm)](https://pypi.org/project/pyrealm/)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyrealm)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8366847.svg)](https://doi.org/10.5281/zenodo.8366847)\n[![Documentation\nStatus](https://readthedocs.org/projects/pyrealm/badge/?version=latest)](https://pyrealm.readthedocs.io/en/latest/?badge=latest)\n[![codecov](https://codecov.io/gh/ImperialCollegeLondon/pyrealm/branch/develop/graph/badge.svg)](https://codecov.io/gh/ImperialCollegeLondon/pyrealm)\n[![Test and\nbuild](https://github.com/ImperialCollegeLondon/pyrealm/actions/workflows/pyrealm_ci.yaml/badge.svg?branch=develop)](https://github.com/ImperialCollegeLondon/pyrealm/actions/workflows/pyrealm_ci.yaml)\n[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/ImperialCollegeLondon/pyrealm/develop.svg)](https://results.pre-commit.ci/latest/github/ImperialCollegeLondon/pyrealm/develop)\n[![SPEC 0 — Minimum Supported Dependencies](https://img.shields.io/badge/SPEC-0-green?labelColor=%23004811\u0026color=%235CA038)](https://scientific-python.org/specs/spec-0000/)\n\nThe `pyrealm` package provides a toolbox implementing some key models for estimating\nplant productivity, growth and demography in Python. The outputs of different models\ncan be then easily fed into other models within `pyrealm` to allow productivity\nestimates to be fed forward into estimation of net primary productivity, growth and\nultimately plant community demography.\n\nThe `pyrealm` package currently includes:\n\n* The P Model for estimating optimal rates of plant photosynthesis given the balance\n  between carbon capture and water loss. This includes recent extensions to incorporate\n  the effects of water stress, slow acclimation processes, models of C3/C4 competition\n  and carbon isotope fractionation.\n* The SPLASH model for calculating soil moisture and actual evapotranspiration.\n* Initial components of a demography module for modelling plant functional types,\n  cohorts, communities and canopies, including allocation calculations using the T\n  Model.\n* A suite of core physics functions and other utilities used to support the modules\n  above.\n\nFor more details, see the package website:\n[https://pyrealm.readthedocs.io/](https://pyrealm.readthedocs.io/).\n\n## Version overview\n\nThe `pyrealm` package uses [semantic versioning](https://semver.org/) and the list below\nsummarise the major changes:\n\n* Version `1.0.0`: initial implementation of the P Model and associated functionality\n* Version `2.0.0`: wide refactor of P Model code to a new API, extension of the package\n  functionality to include the SPLASH v1 model and experimental modules developing\n  functionality for plant demography and phenology.\n\nWe **strongly** recommend that you update to version `2.0.0`. The documentation\nincludes a [migration\nguide](https://pyrealm.readthedocs.io/en/develop/users/versions.html) to help update\nexisting code using version `1.0.0`.\n\n## Using `pyrealm`\n\nThe `pyrealm` package requires Python 3.11 or greater and we currently test all\n`pyrealm` code using Python 3.11, 3.12 and 3.13. We make released package versions\navailable via [PyPi](https://pypi.org/project/pyrealm/) and also generate DOIs for each\nrelease via [Zenodo](https://doi.org/10.5281/zenodo.8366847). You can install the most\nrecent release using `pip`:\n\n```sh\npip install pyrealm\n```\n\nYou can now get started using `pyrealm`. For example, to calculate the estimated gross\nprimary productivity of a C3 plant in a location, start a Python interpreter, using\n`python`, `python3` or `ipython` depending on your installation, and run:\n\n```python\nimport numpy as np\nfrom pyrealm.pmodel import PModelEnvironment, PModel\n\n# Calculate the photosynthetic environment given the conditions\nenv = PModelEnvironment(\n    tc=np.array([20]), vpd=np.array([1000]),\n    co2=np.array([400]), patm=np.array([101325.0],\n    fapar=1, ppfd=300)\n)\n\n# Calculate the predictions of the P Model for a C3 plant\npmodel_c3 = PModel(env)\n\n# Report the GPP in micrograms of carbon per m2 per second.\npmodel_c3.gpp\n```\n\nThis should give the following output:\n\n```python\narray([76.42544948])\n```\n\nThe package website provides worked examples of using `pyrealm`, for example to:\n\n* [fit the P\n  Model](https://pyrealm.readthedocs.io/en/latest/users/pmodel/pmodel_details/worked_examples.html),\n* [include acclimation in estimating light use\n  efficiency](https://pyrealm.readthedocs.io/en/latest/users/pmodel/subdaily_details/worked_example.html)\n  , and\n* [estimate C3/C4\n  competition](https://pyrealm.readthedocs.io/en/latest/users/pmodel/c3c4model.html#worked-example).\n\nThese worked examples also show how `pyrealm` can be used within Python scripts or\nJupyter notebooks and how to use `pyrealm` with large datasets loaded using\n[`numpy`](https://numpy.org/) or [`xarray`](https://docs.xarray.dev/en/stable/) with\n`pyrealm` classes and functions.\n\n## Citing `pyrealm`\n\nThe `pyrealm` repository can be cited following the information in the [citation\nfile](./CITATION.cff). If you are using `pyrealm` in research, it is better to cite the\nDOI of the specific release from [Zenodo](https://doi.org/10.5281/zenodo.8366847).\n\n## Developing `pyrealm`\n\nIf you are interested in contributing to the development of `pyrealm`, please read the\n[guide for contributors](./CONTRIBUTING.md). Please do also read the [code of\nconduct](./CODE_OF_CONDUCT.md) for contributing to this project.\n\n## Support and funding\n\nDevelopment of the `pyrealm` package has been supported by the following grants and\ninstitutions:\n\n* The [REALM project](https://prenticeclimategroup.wordpress.com/realm-team/), funded by\n  an [ERC grant](https://cordis.europa.eu/project/id/787203) to Prof. Colin Prentice\n  (Imperial College London).\n* The [LEMONTREE project](https://research.reading.ac.uk/lemontree/), funded by Schmidt\n  Sciences through the [VESRI\n  programme](https://www.schmidtfutures.com/our-work/virtual-earth-system-research-institute-vesri/)\n  to support an international research team lead by Prof. Sandy Harrison (University of\n  Reading).\n* The [Virtual Rainforest project](https://pyrealm.readthedocs.io/), funded by a\n  Distinguished Scientist award from the [NOMIS\n  Foundation](https://nomisfoundation.ch/research-projects/a-virtual-rainforest-for-understanding-the-stability-resilience-and-sustainability-of-complex-ecosystems/)\n  to Prof. Robert Ewers (Imperial College London)\n* Research software engineering support from the [Institute of Computing for Climate\n  Science](https://iccs.cam.ac.uk/) at the University of Cambridge, through the [Virtual\n  Institute for Scientific\n  Software](https://www.schmidtfutures.com/our-work/virtual-institute-for-scientific-software/)\n  program funded by Schmidt Sciences.\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.5281/zenodo.8366847"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["energy-system-model","crop","images","distribution","accessor","categories","root","mesh","pypi","routing"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/26885","html_url":"https://ost.ecosyste.ms/projects/26885"}