AgriFoodPy

AgriFoodPy is a collection of methods for manipulating and modelling agrifood data. It provides modelling for a variety of aspects of the food system, including food consumption paterns, environmental impact and emissions data, population and land use. It also provides an interface to run external models by using xarray as the data container.

AgriFoodPy also provides a pipeline manager to build end-to-end simulations and analysis toolchains. Modules can also be executed in standalone mode, which does not require a pipeline to be defined.

In addition to this package, we have also pre-packaged some datasets for use with agrifood. These can be found on the agrifoodpy_data repository https://github.com/FixOurFood/agrifoodpy-data

Installation:

AgriFoodPy can be installed using pip, by running

pip install agrifoodpy

UK data to test the package is available from the agrifoodpy_data repository which currently can be installed using

pip install git+https://github.com/FixOurFood/agrifoodpy-data.git@importable

Usage:

AgriFoodPy modules can be used to manipulate food system data in standalone mode or by constructing a pipeline of modules which can be executed partially or completely.

To build a pipeline

from agrifoodpy.pipeline import Pipeline
from agrifoodpy.utils.load_dataset import load_dataset
from agrifoodpy.food.model import 
import matplotlib.pyplot as plt

# Create pipeline object
fs = Pipeline()

# Add node to load food balance sheet data from external module.
fs.add_node(load_dataset,
            {
                "datablock_path": "food",
                "module": "agrifoodpy_data.food",
                "data_attr": "FAOSTAT",
                "coords": {"Year":np.arange(1990, 2010), "Region":229}
            })


# Add node convert scale Food Balance Sheet by a constant
fs.add_node(fbs_convert,
            {
                "fbs":"food",
                "convertion_arr":1e-6 # From 1000 Tonnes to kg
            })

fs.run()

results = fs.datablock

Examples and documentation

Examples demonstrating the functionality of AgriFoodPy can be the found in the package documentation. These include the use of accessors to manipulate data and access to basic models.

Contributing

AgriFoodPy is an open-source project which aims at improving the transparency of evidence base food system interventions and policy making. As such, we are happy to hear the input and ideas from the community.

If you want to contribute, have a look at the discussions page or open a new issue

For a comprehensive guide, please refer to the contributing guidelines to open a pull request to contribute new functionality