pyrealm

Development of the pyrealm package, providing an integrated toolbox for modelling plant productivity, growth and demography using Python.

https://github.com/imperialcollegelondon/pyrealm

Science Score: 77.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 7 DOI reference(s) in README
✓
Academic publication links
Links to: zenodo.org
✓
Committers with academic emails
1 of 14 committers (7.1%) from academic institutions
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (17.7%) to scientific vocabulary

Keywords from Contributors

yolov5s pipeline-testing datacleaner data-profilers battery animal parallel meshing interpretability fluxes

Last synced: 6 months ago · JSON representation ·

Repository

Development of the pyrealm package, providing an integrated toolbox for modelling plant productivity, growth and demography using Python.

Basic Info

Host: GitHub
Owner: ImperialCollegeLondon
License: mit
Language: Python
Default Branch: develop
Homepage: https://pyrealm.readthedocs.io/
Size: 144 MB

Statistics

Stars: 33
Watchers: 3
Forks: 9
Open Issues: 57
Releases: 10

Created about 5 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation

README.md

The pyrealm logo: a green leaf over the shining sun.

PyPI - Python Version

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 SPLASH model for calculating soil moisture and actual evapotranspiration.
Initial components of a demography module for modelling plant functional types, cohorts, communities and canopies, including allocation calculations using the T Model.
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/.

Version 2.0.0 development

New functionality being implemented after version 1.0.0 has lead to some immediate breaking changes in the API, for example in the handling of quantum yield settings in the class signatures for the PModel and SubdailyPModel. As the package uses semantic versioning, these changes to the API require that new releases be made under a new major version.

We will be publishing a series of "release candidates" of the 2.0.0 package. These will be used to identify issues with the current API and try to stabilise a new API. The content of version 2.0.0 is not yet finalised, so these release candidates may also add new functionality.

We recommend that you update to the most recent release candidate of version 2.0.0. The documentation now includes a migration guide to help update existing code.

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 and also generate DOIs for each release via Zenodo. 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], fapar=1, ppfd=300) )

Calculate the predictions of the P Model for a C3 plant

pmodel_c3 = PModel(env)

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:

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 or xarray with pyrealm classes and functions.

Citing `pyrealm`

The pyrealm repository can be cited following the information in the citation file. If you are using pyrealm in research, it is better to cite the DOI of the specific release from Zenodo.

Developing `pyrealm`

If you are interested in contributing to the development of pyrealm, please read the guide for contributors. Please do also read the code of conduct for contributing to this project.

Support and funding

Development of the pyrealm package has been supported by the following grants and institutions:

The REALM project, funded by an ERC grant to Prof. Colin Prentice (Imperial College London).
The LEMONTREE project, funded by Schmidt Sciences through the VESRI programme to support an international research team lead by Prof. Sandy Harrison (University of Reading).
The Virtual Rainforest project, funded by a Distinguished Scientist award from the NOMIS Foundation to Prof. Robert Ewers (Imperial College London)
Research software engineering support from the Institute of Computing for Climate Science at the University of Cambridge, through the Virtual Institute for Scientific Software program funded by Schmidt Sciences.

Owner

Name: Imperial College London
Login: ImperialCollegeLondon
Kind: organization
Email: icgithub-support@imperial.ac.uk
Location: Imperial College London

Repositories: 311
Profile: https://github.com/ImperialCollegeLondon

Imperial College main code repository

Citation (CITATION.cff)

cff-version: 1.2.0
title: pyrealm
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: C. David L.
    family-names: Orme
    email: d.orme@imperial.ac.uk
    affiliation: Department of Life Sciences, Imperial College London
    orcid: 'https://orcid.org/0000-0002-7005-1394'
  - given-names: Alienor
    family-names: Lavergne
    email: alienor.lavergne@springernature.com
    affiliation: Department of Life Sciences, Imperial College London
  - given-names: Vivienne
    family-names: Groner
    email: v.groner@imperial.ac.uk
    affiliation: Department of Life Sciences, Imperial College London
  - given-names: Marion
    family-names: Weinzierl
    email: mw925@cam.ac.uk
    affiliation: Institute of Computing for Climate Science, University of Cambridge
  - given-names: Tianzhang
    family-names: Cai
    email: tc684@cam.ac.uk
    affiliation: Institute of Computing for Climate Science, University of Cambridge
  - given-names: Surbhi
    family-names: Goel
    email: sg2147@cam.ac.uk
    affiliation: Institute of Computing for Climate Science, University of Cambridge
  - given-names: Alexander
    family-names: Smith
    email: as3402@cam.ac.uk
    affiliation: Institute of Computing for Climate Science, University of Cambridge
  - given-names: James
    family-names: Emberton
    email: je484@cam.ac.uk
    affiliation: Institute of Computing for Climate Science, University of Cambridge
  - given-names: Amy
    family-names: Pike
    email: ap766@cam.ac.uk
    affiliation: Institute of Computing for Climate Science, University of Cambridge

repository-code: 'https://github.com/ImperialCollegeLondon/pyrealm'
url: 'https://pyrealm.readthedocs.io'
repository: 'https://pypi.org/project/pyrealm/'
abstract: >-
  The pyrealm package provides an integrated set of Python 3
  modules for modelling plant productivity, growth and
  demography and the estimation of growing conditions.
license: MIT

GitHub Events

Total

Create event: 82
Release event: 5
Issues event: 152
Watch event: 12
Delete event: 76
Member event: 4
Issue comment event: 260
Push event: 530
Pull request review event: 259
Pull request review comment event: 187
Pull request event: 180
Fork event: 2

Last Year

Create event: 82
Release event: 5
Issues event: 152
Watch event: 12
Delete event: 76
Member event: 4
Issue comment event: 260
Push event: 530
Pull request review event: 259
Pull request review comment event: 187
Pull request event: 180
Fork event: 2

Committers

Last synced: 7 months ago

All Time

Total Commits: 2,172
Total Committers: 14
Avg Commits per committer: 155.143
Development Distribution Score (DDS): 0.304

Past Year

Commits: 923
Committers: 8
Avg Commits per committer: 115.375
Development Distribution Score (DDS): 0.411

Top Committers

Name	Email	Commits
David Orme	d****e	1,511
MarionBWeinzierl	m**5@c**k	178
James Emberton	j**n@g**m	146
tztsai	1**4@q**m	88
pre-commit-ci[bot]	6****]	77
TZCai	1**9@1**m	39
Sam Avis	a**6@g**m	38
Surbhi Goel	s**7@g**m	38
Sally	s**n@g**m	17
Alexander Smith	a**x@g**m	15
arne	s**e@g**m	13
Amy Pike	a**e@h**k	6
vgro	v****o	5
github-actions[bot]	4****]	1

Committer Domains (Top 20 + Academic)

hotmail.co.uk: 1 github.com: 1 163.com: 1 qq.com: 1 cam.ac.uk: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 220
Total pull requests: 397
Average time to close issues: about 2 months
Average time to close pull requests: 14 days
Total issue authors: 13
Total pull request authors: 12
Average comments per issue: 0.71
Average comments per pull request: 2.05
Merged pull requests: 323
Bot issues: 0
Bot pull requests: 90

Past Year

Issues: 130
Pull requests: 243
Average time to close issues: 10 days
Average time to close pull requests: 5 days
Issue authors: 7
Pull request authors: 7
Average comments per issue: 0.42
Average comments per pull request: 1.74
Merged pull requests: 203
Bot issues: 0
Bot pull requests: 57

View more stats

Top Authors

Issue Authors

davidorme (150)
MarionBWeinzierl (29)
j-emberton (17)
tztsai (5)
surbhigoel77 (4)
sjavis (3)
AmyOctoCat (3)
arne-exe (2)
sallymatson (2)
a-smith-github (2)
Syuizen (1)
mondus (1)
VallesMarinerisExplorer (1)

Pull Request Authors

davidorme (217)
pre-commit-ci[bot] (76)
MarionBWeinzierl (26)
tztsai (25)
github-actions[bot] (14)
j-emberton (12)
surbhigoel77 (8)
AmyOctoCat (7)
sjavis (7)
arne-exe (2)
sallymatson (2)
a-smith-github (1)

Top Labels

Issue Labels

bug (31) enhancement (25) rse (17) roadmap (11) meta (8) core devs (7) documentation (5) code review (2) icebox (2) duplicate (1) testing (1)

Pull Request Labels

enhancement (21) rse (7) documentation (4) roadmap (3) code review (2) testing (2) core devs (1)

Packages

Total packages: 3
Total downloads:
- pypi 1,267 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 1
(may contain duplicates)
Total versions: 38
Total maintainers: 1

proxy.golang.org: github.com/ImperialCollegeLondon/pyrealm

Documentation: https://pkg.go.dev/github.com/ImperialCollegeLondon/pyrealm#section-documentation
License: mit
Latest release: v1.0.0
published over 1 year ago

Versions: 6
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 6 months ago

proxy.golang.org: github.com/imperialcollegelondon/pyrealm

Documentation: https://pkg.go.dev/github.com/imperialcollegelondon/pyrealm#section-documentation
License: mit
Latest release: v1.0.0
published over 1 year ago

Versions: 6
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 6 months ago

pypi.org: pyrealm

Python tools for modelling plant productivity and demography.

Homepage: https://pyrealm.readthedocs.io/
Documentation: https://pyrealm.readthedocs.io/
License: MIT
Latest release: 1.0.0
published over 1 year ago

Versions: 26
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 1,267 Last month

Rankings

Dependent packages count: 10.1%

Forks count: 15.3%

Average: 17.1%

Stargazers count: 18.5%

Downloads: 20.3%

Dependent repos count: 21.6%

Maintainers (1)

david_orme

Last synced: 6 months ago

Dependencies

requirements.txt pypi

netCDF4 *
pre-commit *
pytest *
pyyaml *

.github/workflows/pre-commit_autoupdate.yaml actions

actions/checkout v2 composite
actions/setup-python v2 composite
browniebroke/pre-commit-autoupdate-action main composite
peter-evans/create-pull-request v3 composite

.github/workflows/pyrealm_ci.yaml actions

abatilo/actions-poetry v2.1.6 composite
actions/checkout v3 composite
actions/setup-python v4 composite
pre-commit/action v3.0.0 composite

poetry.lock pypi

131 dependencies

pyproject.toml pypi

Bottleneck ^1.3.5
dacite ^1.6.0
numpy ^1.16.5
python >=3.9,<3.11
scipy ^1.7.3
tabulate ^0.8.10

pyrealm

Science Score: 77.0%

Keywords from Contributors

Repository

Basic Info

Statistics

Metadata Files

README.md

Version 2.0.0 development

Using pyrealm

Calculate the photosynthetic environment given the conditions

Calculate the predictions of the P Model for a C3 plant

Report the GPP in micrograms of carbon per m2 per second.

Citing pyrealm

Developing pyrealm

Support and funding

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

proxy.golang.org: github.com/ImperialCollegeLondon/pyrealm

Rankings

proxy.golang.org: github.com/imperialcollegelondon/pyrealm

Rankings

pypi.org: pyrealm

Rankings

Maintainers (1)

Dependencies

Using `pyrealm`

Citing `pyrealm`

Developing `pyrealm`