Installation

Requirements

Dependency	Version	Notes
Python	≥ 3.11
Julia	≥ 1.10	Install from julialang.org
KinBiont.jl	≥ 1.2	Managed automatically by pykinbiont

Install pykinbiont

git clone https://github.com/pinheiroGroup/pykinbiont
cd pykinbiont
uv sync          # or: pip install -e .

Two modes of operation

Mode 1 — Managed environment (default)

juliacall creates and manages its own isolated Julia environment and downloads KinBiont automatically. No local clone of KinBiont.jl is required.

First run

The first Python import after installation downloads Julia dependencies (~200 MB) and precompiles the package (~3–5 minutes). Subsequent imports are fast because Julia caches the compiled image.

import pykinbiont   # triggers Julia startup on first import

Mode 2 — Local KinBiont.jl development clone

If you are developing KinBiont.jl locally or need a specific unreleased version:

Step 1 — Add PythonCall to your KinBiont.jl Julia project (one-time):

julia --project=/path/to/KinBiont.jl -e 'using Pkg; Pkg.add("PythonCall")'

Step 2 — Tell pykinbiont where to find it (one-time, persisted to disk):

import pykinbiont
pykinbiont.configure("/path/to/KinBiont.jl")

From then on, import pykinbiont automatically loads your local clone.

Kernel restart required

configure() writes to juliapkg’s environment file. You must restart the Python process (or Jupyter kernel) once after calling configure() for the change to take effect.

Verify the installation

import pykinbiont
import numpy as np

# Build a tiny synthetic dataset
times  = np.linspace(0, 10, 20)
curves = np.vstack([0.01 * np.exp(0.5 * times)] * 2)
data   = pykinbiont.GrowthData(curves=curves, times=times, labels=["A", "B"])

from pykinbiont import ModelSpec, LogLinModel, FitOptions, fit
spec    = pykinbiont.ModelSpec(models=[pykinbiont.LogLinModel()], params=[[]])
results = pykinbiont.fit(data, spec)
print(results.to_dataframe()[["label", "best_model"]])

Expected output:

  label best_model
0     A    log_lin
1     B    log_lin