pykinbiont#

Python interface for KinBiont.jl

pykinbiont lets you run the full KinBiont microbial kinetics analysis pipeline from Python — loading plate-reader data, fitting growth models, clustering curves, and exporting results — while keeping all numerical computation inside Julia via juliacall.

Julia required

pykinbiont calls Julia under the hood. You need Julia ≥ 1.10 installed separately. On first run Julia downloads and installs KinBiont and its dependencies automatically (~5 min).

Highlights#

  • One-call fitting — pass a GrowthData, a ModelSpec, and FitOptions; get back a GrowthFitResults with the best model per curve selected by AICc.

  • All KinBiont models — log-linear, logistic, Gompertz, Richards, ODEs, and more, accessible via MODEL_REGISTRY.

  • Custom models in Python — wrap any f(p, t) function as an NLModel or write an in-place ODE as an ODEModel; the Julia bridge handles the callback.

  • Pure-Python preprocessing — smoothing, blank subtraction, negative-value correction, stationary-phase trimming, and k-means clustering via preprocess().

  • Tidy output — to_dataframe() and save_results() give you pandas/CSV-ready results.

Quick example#

import pykinbiont
from pykinbiont import GrowthData, FitOptions, ModelSpec, MODEL_REGISTRY, fit, save_results

# Point at a local KinBiont.jl clone (run once; persists to disk)
pykinbiont.configure("/path/to/KinBiont.jl")   # omit to use registry version

# Load a plate-reader CSV
data = GrowthData.from_csv("experiment.csv")   # time column first, then wells

# Choose a model from the registry
logistic = MODEL_REGISTRY["NL_logistic"]
spec = ModelSpec(
    models=[logistic],
    params=[[1.2, 0.01, 0.5]],    # [N_max, N0, mu]
    lower=[[0.3, 1e-3, 0.05]],
    upper=[[3.0, 0.05, 3.0]],
)

# Fit with smoothing
opts = FitOptions(smooth=True, smooth_method="rolling_avg", multistart=True, n_restart=20)
results = fit(data, spec, opts)

# Inspect
print(results.to_dataframe()[["label", "best_model", "aic"]])

# Save to CSV files
save_results(results, "output/", prefix="my_experiment")

Getting started

User guide

Examples

API reference