CN-Wheat

This is the Read Me file of the model CN-Wheat, a model of CN distribution for wheat.

CN-Wheat is a Functional-Structural Plant Model which simulates the distribution of carbon and nitrogen into wheat culms in relation to photosynthesis, N uptake, metabolite turnover, root exudation and tissue death.

This model was first produced as part of the project BreedWheat over the last three years, through the Investment for the Future programme managed by the French Research National Agency (ANR-10-BTBR-03). The aim of the project BreedWheat was to improve the competiveness of the French wheat breeding sector, through the definition/ identification of ideotypes, parameters of interest maximizing grain yield and quality under sustainable agricultural systems and climate scenarios.

These researches lead to the publication of project report, and two articles:

• Barillot, R., Chambon, C., & Andrieu, B. (2016). CN-Wheat, a functional–structural model of carbon and nitrogen metabolism in wheat culms after anthesis. I. Model description. Annals of Botany, 118(5), 997‑1013. https://doi.org/10.1093/aob/mcw143
• and Barillot, R., Chambon, C., & Andrieu, B. (2016). CN-Wheat, a functional–structural model of carbon and nitrogen metabolism in wheat culms after anthesis. II. Model evaluation. Annals of Botany, 118(5), 1015‑1031. https://doi.org/10.1093/aob/mcw144

1. Getting Started

These instructions will get you a copy of CN-Wheat up and running on your local machine.

1.1 Prerequisites

To install and use CN-Wheat, you need first to install the dependencies.

CN-Wheat has been tested on Windows 10 64 bit and Linux Fedora 24 64 bit.

1.1.1 Install the dependencies on Windows 10 64 bit

1. Install Python

   • go to https://www.python.org/downloads/windows/download,
   • click on "Latest Python 2 Release [...]",
   • download "Windows x86-64 MSI installer" and install it selecting the following options:
     • install for all users,
     • default destination directory,
     • install all subfeatures, including subfeature "Add python.exe to Path".

2. Install NumPy:

   • go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy,
   • download NumPy+MKL for Python 2 64 bit,
   • install it using pip installer:
     • open a command line interpreter,
     • go to the directory where you saved NumPy+MKL for Python 2 64 bit,
     • install NumPy+MKL from the downloaded wheel file.
       For example, if you downloaded file "numpy‑1.13.1+mkl‑cp27‑cp27m‑win_amd64.whl", type: pip install "numpy‑1.13.1+mkl‑cp27‑cp27m‑win_amd64.whl".

3. Install SciPy

   • go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy,
   • download SciPy for Python 2 64 bit,
   • install it using pip installer:
     • open a command line interpreter,
     • go to the directory where you saved SciPy for Python 2 64 bit,
     • install SciPy from the downloaded wheel file.
       For example, if you downloaded file "scipy‑0.19.1‑cp27‑cp27m‑win_amd64.whl", type: pip install "scipy‑0.19.1‑cp27‑cp27m‑win_amd64.whl".

4. Install Pandas

   • go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#pandas,
   • download Pandas for Python 2 64 bit,
   • install it using pip installer:
     • open a command line interpreter,
     • go to the directory where you saved Pandas for Python 2 64 bit,
     • install Pandas from the downloaded wheel file.
       For example, if you downloaded file "pandas‑0.20.3‑cp27‑cp27m‑win_amd64.whl", type: pip install "pandas‑0.20.3‑cp27‑cp27m‑win_amd64.whl".

5. Install Matplotlib

   • go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#matplotlib,
   • download Matplotlib for Python 2 64 bit,
   • install it using pip installer:
     • open a command line interpreter,
     • go to the directory where you saved Matplotlib for Python 2 64 bit,
     • install Matplotlib from the downloaded wheel file.
       For example, if you downloaded file "matplotlib‑2.0.2‑cp27‑cp27m‑win_amd64.whl", type: pip install "matplotlib‑2.0.2‑cp27‑cp27m‑win_amd64.whl".

6. Install Sphinx

   • go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#misc,
   • download Sphinx for Python 2,
   • install it using pip installer:
     • open a command line interpreter,
     • go to the directory where you saved Sphinx for Python 2,
     • install Sphinx from the downloaded wheel file.
       For example, if you downloaded file "Sphinx‑1.6.3‑py2.py3‑none‑any.whl", type: pip install "Sphinx‑1.6.3‑py2.py3‑none‑any.whl".

7. Install Nose

   • go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#misc,
   • download Nose for Python 2 64 bit,
   • install it using pip installer:
     • open a command line interpreter,
     • go to the directory where you saved Nose for Python 2 64 bit,
     • install Nose from the downloaded wheel file.
       For example, if you downloaded file "nose‑1.3.7‑py2‑none‑any.whl", type: pip install "nose‑1.3.7‑py2‑none‑any.whl".

8. Install Coverage

   • go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#coverage,
   • download Coverage for Python 2 64 bit,
   • install it using pip installer:
     • open a command line interpreter,
     • go to the directory where you saved Coverage for Python 2 64 bit,
     • install Coverage from the downloaded wheel file.
       For example, if you downloaded file "coverage‑4.4.1‑cp27‑cp27m‑win_amd64.whl", type: pip install "coverage‑4.4.1‑cp27‑cp27m‑win_amd64.whl".

9. Install Respi-Wheat

   • download the lastest public release of model Respi-Wheat from https://sourcesup.renater.fr/frs/download.php/latestzip/2087/Respi-Wheat-Stable-latest.zip and install it:
     • unzip it: you should obtain a zip file respi-wheat_*.zip,
     • unzip the zip file respi-wheat_*.zip: you should obtain a folder respi-wheat,
     • open a command line interpreter and go to folder respi-wheat,
     • run command: python setup.py install --user.

On Windows 10 64 bit, CN-Wheat has been tested with the following versions of the dependencies:

• Python 2.7.13 64 bit,
• NumPy+MKL 1.13.1 64 bit,
• SciPy 0.19.1 64 bit,
• Pandas 0.20.3 64 bit,
• Matplotlib 2.0.2 64 bit,
• Sphinx 1.6.3,
• Nose 1.3.7,
• Coverage 4.4.1 64 bit.

1.1.2 Install the dependencies on Linux Fedora 24 64 bit

To install the dependencies on Linux Fedora 24 64 bit:

• open a terminal,
• run this command with superuser privileges: dnf -y install python2 python2-numpy python2-scipy python2-pandas python2-matplotlib python2-sphinx python2-nose python2-coverage
• download the lastest public release of model Respi-Wheat from https://sourcesup.renater.fr/frs/download.php/latestzip/2087/Respi-Wheat-Stable-latest.zip and install it:
  • unzip file Respi-Wheat-Stable-latest.zip: you should obtain a zip file respi-wheat_*.zip,
  • unzip file respi-wheat_*.zip: you should obtain a folder respi-wheat,
  • go to folder respi-wheat,
  • run command: python setup.py install --user.

On Linux Fedora 24 64 bit, CN-Wheat has been tested with the following versions of the dependencies:

• Python 2.7.13 64 bit,
• NumPy 1.11.0 64 bit,
• SciPy 0.16.1 64 bit,
• Pandas 0.18.0 64 bit,
• Matplotlib 1.5.2rc2 64 bit,
• Sphinx 1.4.8,
• Nose 1.3.7,
• Coverage 4.4.1 64 bit.

1.2 Installing

Note: We suppose you already installed the dependencies for your operating system. Otherwise follow these instructions.

You can install CN-Wheat either in "install" or "develop" mode.

1.2.1 Install CN-Wheat in "install" mode

Install CN-Wheat in "install" mode if you're not going to develop, edit or debug it, i.e. you just want to used it as third party package.

To install CN-Wheat in "end-user" mode:

• open a command line interpreter,
• go to your local copy of project CN-Wheat,
• run command: python setup.py install --user.

1.2.2 Install CN-Wheat in "develop" mode

Install CN-Wheat in "develop" mode if you want to get CN-Wheat installed and then be able to frequently edit the code and not have to re-install CN-Wheat to have the changes to take effect immediately.

To install CN-Wheat in "develop" mode:

• open a command line interpreter,
• go to your local copy of project CN-Wheat,
• run command: python setup.py develop --user.

1.3 Running

Note: We suppose you already installed the model. Otherwise follow these instructions.

To run a simulation example, compute post-processing and generate graphs for validation:

• open a command line interpreter,
• go to the directory example/ of your local copy of project CN-Wheat,
• run command: python main.py.

See the user guide for a step by step explanation of how to set and run model CN-Wheat.

2. Reading the docs

To build the user and reference guides:

• install the model (see Installation of the model),
• open a command line interpreter,
• go to the top directory of your local copy of the project,
• run this command: python setup.py build_sphinx,
• and direct your browser to file doc/_build/html/index.html.

3. Testing

The automated test permits to verify that the model implementation accurately represents the developer’s conceptual description of the model and its solution.

The automated test:

• initializes the model from input data in CSV files,
• runs the model on 2 steps, forcing the photosynthesis and senescence parameters before each run of the model,
• concatenate the outputs of the model in dataframes, with one dataframe per topological scale,
• write the outputs dataframes to CSV files,
• compare actual to expected outputs,
• raise an error if actual and expected outputs are not equal up to a given tolerance.

To run the automated test with coverage report:

• install the model (see Installation of the model),
• open a command line interpreter,
• go to the directory test of your local copy of the project,
• and run this command: nosetests --with-coverage --cover-package=cnwheat test_cnwheat.py.

The automated test does not verify the validity of the model, i.e. it doesn't permit to determine the degree to which the model is an accurate representation of the real world from the perspective of the intended uses of the model.
To help verifying the validity of the model, use the plotting tools implemented in module cnwheat.tools.

Deployment

CN-Wheat can be coupled with other ecophysiological models, to simulate the interaction between CN distribution and (for example) leaves elongation, photosynthesis, growth, senescence, light interception and topology of wheat crops.
Please contact cn-wheat-request@groupes.renater.fr for more information about the possibility of coupling and integrate CN-Wheat with other ecophysiological models.

Built With

• Python, NumPy, SciPy, Pandas, Respi-Wheat: implementation and deployment of the model,
• Matplotlib: generation of graphs to help validating the model,
• Sphinx: building of the documentation,
• Nose: run of the automated tests,
• Coverage: coverage of code testing.

Contributing

First, send an email to cn-wheat-request@groupes.renater.fr to be added to the project.

Then,

• check for open issues or open a fresh issue to start a discussion around a feature idea or a bug: https://sourcesup.renater.fr/tracker/?group_id=1515.
• If you feel uncomfortable or uncertain about an issue or your changes, feel free to email cn-wheat@groupes.renater.fr.

Contact

For any question, send an email to cn-wheat-request@groupes.renater.fr.

Versioning

We use an SVN repository on SourceSup for versioning: https://sourcesup.renater.fr/projects/cn-wheat/.
If you need an access to the current in development version of the model, please send an email to cn-wheat-request@groupes.renater.fr.

Authors

See file AUTHORS for details

License

This project is licensed under the CeCILL-C License - see file LICENSE for details

Acknowledgments

The research leading these results has received funding through the Investment for the Future programme managed by the Research National Agency (BreedWheat project ANR-10-BTBR-03).