https://github.com/agroscope-ch/plasmopy

Infection prediction modeling of Plasmopara viticola life cycle stages in vineyards using weather data. This project represents a revisited and customizable Python version of the VitiMeteo-Plasmopara model in use at Agroscope (Switzerland) and the Weinbauinstitut (Germany) for downy mildew infection forecasting.

https://github.com/agroscope-ch/plasmopy

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Infection prediction modeling of Plasmopara viticola life cycle stages in vineyards using weather data. This project represents a revisited and customizable Python version of the VitiMeteo-Plasmopara model in use at Agroscope (Switzerland) and the Weinbauinstitut (Germany) for downy mildew infection forecasting.

README.html

Plasmopy v1.0
Infection prediction modeling of Plasmopara viticola life
cycle stages in vineyards using weather data. This project represents a
revisited and customizable Python version of the VitiMeteo-Plasmopara
model in use at Agroscope (Switzerland) for downy mildew infection
forecasting.


Quick setup & run

Setup with Bash console

Make sure to have Python3 in your Unix system. Install Poetry, a Python
packaging and dependency manager:

curl -sSL https://install.python-poetry.org | python3 - --version 2.1.1

Make sure to have Git installed on your computer (https://github.com/git-guides/install-git), then clone
Plasmopy with the following command in your terminal:

git clone https://github.com/agroscope-ch/plasmopy.git

Move into the plasmopy folder where the Makefile is located
(replace user_path_to_plasmopy with your folder path):

cd user_path_to_plasmopy/plasmopy

Install all dependencies and activate the virtual environment for
this project:

make setup
N.B. To separately install the required packages and activate the
virtual environment, the respective specific commands are:
make install
and
make activate


Data processing, model configuration and run from terminal

Load your raw timeseries input data in the folder
data/input/
Choose your data processing and model parameters configurations
by customizing the main.yaml config file in the
config/ folder.
Run the infection prediction model with the command:

make run


Data processing, model configuration and run from the streamlit
web-app
Launch the web-app from your browser with the command:
make app




Developer Tools

Tools used in this project

Poetry:
Dependency management - article
hydra: Manage configuration files -
article
pre-commit plugins: Automate
code reviewing formatting
DVC: Data version control - article
pdoc: Automatically
create an API documentation for your project



Project Structure
.
├── config                      
│   └── main.yaml                   # Main configuration file
├── data            
│   ├── input                       # input data
│   ├── output                      # output from model run, including logs and graphs
│   ├── tmp                         # temporary processing data needed for running
│   ├── raw                         # raw data
│   └── raw.dvc                     # DVC file of data/raw
├── docs                            # documentation for your project
├── .gitignore                      # ignore files that cannot commit to Git
├── Makefile                        # store useful commands to set up the environment and run the model
├── models                          # store models
├── notebooks                       # store notebooks
├── .pre-commit-config.yaml         # configurations for pre-commit
├── pyproject.toml                  # dependencies for poetry
├── README.md                       # describe your project
├── src                             # store source code
│   ├── __init__.py                 # make src a Python module 
│   ├── main.py                     # orchestrates the execution of the scripts
│   ├── load_data.py                # functions for loading raw data
│   ├── process_data.py             # functions for formatting and processing weather raw data
│   ├── infection_model.py          # launches the specific modeling algorithm at each infection stage
│   └── infection_functions         # store the algorithms for each infection stage
└── tests                           # store tests
    ├── __init__.py                 # make tests a Python module 
    ├── test_process.py             # test functions for process_data.py
    └── test_model.py               # test functions for infection_model.py


Recreate the project folder template
This project development guide is inspired from Khuyen
Tran’s blog.

Install cookiecutter (allows for a structured / organized creation
of a new python project https://github.com/cookiecutter/cookiecutter) via
pip:

pip install cookiecutter

Add local installation binaries to $PATH temporarily on the Bash
console (replace #### with your user folder):

export PATH="/home/###/.local/bin:$PATH"
Or permanently by adding the same command line at the end of the
.bashrc file in the users’s home folder.
If the change was made in a permanent fashion, make sure to apply the
changes by sourcing again the .bashrc file, by first moving to your
personal home folder:
cd ~
And reloading the .bashrc profile:
source .bashrc

We can now call the cookiecutter software and download a default
project template to start our project specifications:

cookiecutter https://github.com/khuyentran1401/data-science-template --checkout dvc-poetry
This command will start a prompt asking for the project folder name,
the author’s name, and the compatible python version. At the time of
writing, the Python version installed in RStudio Server “Agsad” is
Python 3.10.12.


Virtual environment plasmopy
Creating a Python project environment for the Plasmopy model
implementation in Python (vitimeteo-plasmopara-py).

Install poetry for optimized package version management:

curl -sSL https://install.python-poetry.org | python3 - --version 2.1.1

Download dependencies and create the project’s specific virtual
environment with the following command line code launched from within
the project’s home folder:

poetry install
To add a new library, run:
poetry add <library-name>
To remove an existing library, run:
poetry remove <library-name>


Initialize a git repository
git init



Check code before committing
This template uses the following hooks, called in the hidden file
.pre-commit-config.yaml:

Ruff: An extremely fast Python linter, written in Rust. It supports
500 lint rules, many of which are inspired by popular tools like Flake8,
isort, pyupgrade, and others.
black is a code formatter in Python.
interrogate: Checks your code base for missing docstrings.

To add pre-commit to git hooks, type:
pre-commit install
Now, whenever you run git commit, your code will be automatically
checked and reformatted before being committed.

Version your data
To track changes to the data directory, type:
dvc add data
This command will create the data.dvc file, which
contains a unique identifier and the location of the data
directory in the file system.
To keep track of the data associated with a particular version,
commit the data.dvc file to Git:
git add data.dvc
git commit -m "add data"
To push the data to remote storage, type:
dvc push 


Auto-generate API documentation
To auto-generate API document for your project, run:
make docs

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