Skip to main content

Collection of tasks for initializing ABM simulations.

Project description

Build status Lint status Documentation Coverage Code style Version License

Collection of tasks for initializing ABM simulations. Designed to be used both in Prefect workflows and as modular, useful pieces of code.

Installation

The collection can be installed using:

pip install abm-initialization-collection

We recommend using Poetry to manage and install dependencies. To install into your Poetry project, use:

poetry add abm-initialization-collection

Usage

Prefect workflows

All tasks in this collection are wrapped in a Prefect @task decorator, and can be used directly in a Prefect @flow. Running tasks within a Prefect flow enables you to take advantage of features such as automatically retrying failed tasks, monitoring workflow states, running tasks concurrently, deploying and scheduling flows, and more.

from prefect import flow
from abm_initialization_collection.<module_name> import <task_name>

@flow
def run_flow():
    <task_name>()

if __name__ == "__main__":
    run_flow()

See cell-abm-pipeline for examples of using tasks from different collections to build a pipeline for simulating and analyzing agent-based model data.

Individual tasks

Not all use cases require a full workflow. Tasks in this collection can be used without the Prefect @task decorator by simply importing directly from the module:

from abm_initialization_collection.<module_name>.<task_name> import <task_name>

def main():
    <task_name>()

if __name__ == "__main__":
    main()

or using the .fn() method:

from abm_initialization_collection.<module_name> import <task_name>

def main():
    <task_name>.fn()

if __name__ == "__main__":
    main()

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

abm_initialization_collection-0.7.0.tar.gz (12.9 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file abm_initialization_collection-0.7.0.tar.gz.

File metadata

File hashes

Hashes for abm_initialization_collection-0.7.0.tar.gz
Algorithm Hash digest
SHA256 b6983347f2c0e1cb91c3a6107ad16e65c1ce7e4011405c0a32566af352daa735
MD5 55960418a138833abe76a104ba283de1
BLAKE2b-256 7a01f0e2e7d6da9812dc050b5d9077706f2d168ef2850ffbfafd7ebf0f9f01b3

See more details on using hashes here.

File details

Details for the file abm_initialization_collection-0.7.0-py3-none-any.whl.

File metadata

File hashes

Hashes for abm_initialization_collection-0.7.0-py3-none-any.whl
Algorithm Hash digest
SHA256 cc6e50d5f5e586e939f446223d0c75e8159ca46e0f4bfba97a47ae32342e2c00
MD5 236a58bc995d29604d07567651f16bd2
BLAKE2b-256 68ceaf6927d79c91375c80ab397ec77ad216c6dc0d5096e9690f3b6191a6afbf

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page