StatisKit/FDDPLNG18

Computational studies for the article entitled "A spatio-temporal and multiscale characterization of tree development based on patchiness"

Computational studies for the article entitled "A spatio-temporal and multiscale characterization of tree development based on patchiness"

This repository contains supplementary material for the reproducibiliy of computational studies performed in the article A spatio-temporal and multiscale characterization of tree development based on patchiness" written by:

• Pierre Fernique,
• Anaëlle Dambreville,
• Jean-Baptiste Durand,
• Christophe Pradal,
• Pierre-Éric Lauri,
• Frédéric Normand,
• Yann Guédon.

This article has been presented in the "Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA)" conference. Here is the the citation formated as the bibtex standart.

@inproceedings{
   FDDPLNG18,
   title={Characterization of mango tree patchiness using a tree-segmentation/clustering approach},
   author={Fernique, Pierre and Dambreville, Ana{\"e}ile and Durand, Jean-Baptiste and Pradal, Christophe and Lauri, Pierre-{\'E}ric and Normand, Fr{\'e}d{\'e}ric and Gu{\'e}don, Yann},
   booktitle={Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA), International Conference on},
   pages={68--74},
   year={2016},
   organization={IEEE}
 }

These studies are formatted as pre-executed Jupyter notebooks. Refers to the index.ipynb notebook which presents and references each study.

Test it !

To reproduce the studies with Docker use these images. After installing Docker, you can type the following command in a shell:

docker run -i -t -p 8888:8888 statiskit/fddplng18:v1.0.0-py3k

Then, follow the given instructions.

Note

These images correspond to the ones used for the article. Most recent images can be runned using the following command in a shell:

• For the Python 3 version

  docker run -i -t -p 8888:8888 statiskit/fddplng18:latest-py3k

Install it !

You can also install required packages on your computer to reproduce these studies. In order to ease the installation of these packages on multiple operating systems, the Conda package and environment management system is used. For more information refers to the StatisKit software suite documentation concerning prerequisites to the installation step. Then, to install the required packages, proceed as as follows:

1. Clone this repository,

   git clone --recursive https://github.com/StatisKit/FDDPLNG18

2. Create a Conda environment containing the meta-package fddplng18,

   conda create -n fddplng18 fddplng18=1.0.0 -c statiskit -c defaults --override-channels

   Note

   This meta-package corresponds to the one used for the article. Most recent meta-package can be installed by replacing fddplng18=1.0.0 by fddplng18 in previous command lines. Moreover, if you replace the statiskit channel by the statiskit/label/unstable channel, you will benefit from the latest meta-package available that has not yet been released.

3. Activate the Conda environment as advised in your terminal.

4. Enter the directory containing Jupyter notebooks,

   cd FDDPLNG18
   cd share
   cd jupyter

5. Launch the Jupyter the index.ipynb notebook,

   jupyter notebook index.ipynb

6. Execute the index.ipynb notebook to execute all examples or navigate among referenced notebooks to execute them separatly.