py-simba-pop
py-simba-pop simulates a perennial banana plantation's population,
flowering and harvest week by week with SIMBA-POP, the banana cohort model
published by Tixier, Malézieux & Dorel (2004), Ecological Modelling
180:407–417. One SimbaPopEngine runs everything from daily temperatures.
Real ribbon-tag bagging and sucker-selection records can drive it directly,
and whenever a record isn't available the engine falls back to the model's
own prediction, always with an explicit notice. The engine is distributed as
compiled wheels.
Installation
pip install py-simba-pop
Python 3.11–3.13. Wheels are published for Linux x86-64 (manylinux_2_28 and
musllinux), macOS (universal2) and Windows x86-64. To read .xlsx input
files, also install openpyxl; for the example notebooks, install
py-simba-pop[notebooks].
Quick start
from py_simba_pop import ParameterSet, SimbaPopEngine, WeatherProvider
params = ParameterSet.from_tixier_defaults()
weather = WeatherProvider.from_file_and_params("weather.csv", params) # date, tmean columns
engine = SimbaPopEngine.from_seed_population(params, weather, seed_population=1000.0)
output = engine.run(n_weeks=200) # a pandas DataFrame, one row per week
Every parameter defaults to the published Tixier et al. (2004) value; load
your own calibration with ParameterSet.from_yaml("my_farm_params.yaml").
Documentation and examples
- User guide: https://py-simba-pop.readthedocs.io
- Example notebooks with synthetic farm data: https://github.com/cpohagwu/py-simba-pop-docs/tree/main/docs/examples
- Questions and bug reports: https://github.com/cpohagwu/py-simba-pop-docs/issues
Citation
If you use py-simba-pop in your research, please cite it using the metadata in CITATION.cff ("Cite this repository" on the GitHub page).
License
Apache License 2.0. See LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for py-simba-pop 0.4.1
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Built distributions (wheels)
Total release size: 8.2 MB
Release files / py_simba_pop-0.4.1-cp313-cp313-win_amd64.whl
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