py-simba-pop-tools
Calibration, validation and backtesting tools for py-simba-pop, the SIMBA-POP banana cohort model.
pip install py-simba-pop-tools
Releases are compiled wheels for CPython 3.11-3.13 on Windows, macOS and
Linux, with type stubs (py.typed) for editors and type checkers. There is no
source distribution. Documentation:
simba-ecosystem-docs.
Contents
| Module | What it does |
|---|---|
py_simba_pop_tools.calibration.harvest |
Harvest timing from ribbon-tagged production records: a nonparametric life table, and a maximum-likelihood fit in the engine's own weekly model with field losses as a competing risk, with a cohort-block bootstrap; the field-loss (reproductive death) rate as an exposure hazard, by period, with a drift test |
py_simba_pop_tools.calibration.vegetative |
Flowering and follower-selection timing (threshold, peak, spread) fitted to recorded bagging and parous/nulliparous selections, with the engine driven by the recorded selections; and flowering from bagging alone, through a temporal split with follower selection fixed, with a block bootstrap. Keeps the deviance profile over the threshold |
py_simba_pop_tools.calibration.bootstrap |
Moving-block bootstrap weights for records ordered in time |
py_simba_pop_tools.calibration.literature |
Parameter values reproduced from published calibrations, each with its source table |
py_simba_pop_tools.evaluation.backtest |
Rolling-origin backtests with as-of data (each table filtered by its contract's lag; forecasters start from the last ledger week known, so a longer lag is forecast through): persistence, seasonal naive and Mode B baselines, and a calibrated Mode B that refits the field-loss rate and harvest timing at every origin; MAE, RMSE and bias by horizon and subgroup; Diebold-Mariano (Harvey-Leybourne-Newbold) with Holm across horizons |
py_simba_pop_tools.evaluation.curves |
Lag-scanned agreement between a simulated and an observed weekly series (validation only, not a fit) |
py_simba_pop_tools.data.contracts |
The canonical tables calibration and evaluation read: ribbon ledger, daily weather, plant monitoring |
py_simba_pop_tools.data.snapshots |
Immutable, hashed as-of snapshots of those tables, for backtests that use only data known at each origin |
py_simba_pop_tools.data.reconstruction |
An initial population reconstructed from production records, or spun up from a number of mats |
py_simba_pop_tools.synthetic |
Records made by py-simba-pop's own model, with switches that break the calibration's assumptions (bagging lag, harvest shift, incomplete or noisy desuckering records, recording error) -- tests and robustness studies only |
The package reads canonical tables only. Converting a farm's own exports into them is the job of a separate, farm-specific adapter.
Building from source (development)
A release build compiles every module (Cython, setup.py) and needs a C
compiler. Without one, a development install uses the plain sources:
PY_SIMBA_POP_TOOLS_PURE=1 pip install -e "packages/py-simba-pop-tools[test]" # PowerShell: $env:PY_SIMBA_POP_TOOLS_PURE = "1"
pytest packages/py-simba-pop-tools
release/check_wheel.py audits a built wheel: compiled modules, __init__
files, type stubs and py.typed only.
Licence and attribution
Apache-2.0 (see LICENSE).
Calibration, validation and backtesting tools for py-simba-pop. Values in calibration.literature are reproduced from the publications cited there (currently Guarín & Ochoa 2012, Table 2). Not affiliated with or endorsed by CIRAD or the original authors.
1 maintainer
Metadata
1 maintainer
Release files for py-simba-pop-tools 0.1.0
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Total release size: 39.7 MB
Release files / py_simba_pop_tools-0.1.0-cp313-cp313-win_amd64.whl
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