Working with results

fit() returns a GrowthFitResults which contains:

• data — the post-preprocessing GrowthData (smoothed, blank-subtracted, etc.)

• results — a list of CurveFitResult, one per curve

GrowthFitResults

to_dataframe()

Returns a pandas DataFrame with one row per curve:

df = results.to_dataframe()
print(df.columns.tolist())
# ['label', 'cluster', 'best_model', 'aic', 'loss', 'param_1', 'param_2', ...]

Column	Description
label	Curve identifier
cluster	Cluster ID (integer, or None if no clustering was run)
best_model	Name of the winning model
aic	AICc of the best model
loss	Final loss value
param_1, param_2, …	Best-fit parameters in positional order

Iteration

GrowthFitResults is iterable and indexable:

# Iterate
for r in results:
    print(r.label, r.best_model)

# Index
first = results[0]

# Length
print(len(results))

CurveFitResult

Each CurveFitResult has:

Attribute	Type	Description
label	str	Curve identifier
best_model	str	Name of the selected model
best_params	list[float]	Best-fit parameter values
param_names	list[str]	Parameter names (matches best_params)
best_aic	float	AICc of the best model
fitted_curve	ndarray	Fitted OD values (same time axis as times)
times	ndarray	Time axis for the fitted curve
loss	float	Final loss value
all_results	list[dict]	All candidate models with their params and AICc

Accessing parameters by name

for r in results:
    params = dict(zip(r.param_names, r.best_params))
    print(f"{r.label}: {params}")

Plotting fitted vs observed

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, len(results), figsize=(4 * len(results), 3))

for ax, r in zip(axes, results):
    idx = results.data.labels.index(r.label)
    obs = results.data.curves[idx]

    ax.plot(results.data.times, obs, ".", alpha=0.4, label="data")
    ax.plot(r.times, r.fitted_curve, "-", label=r.best_model)
    ax.set_title(r.label)
    ax.set_xlabel("Time")
    ax.set_ylabel("OD")
    ax.legend(fontsize=7)

plt.tight_layout()

Comparing candidate models

all_results contains every model that was tried:

r = results[0]
for candidate in sorted(r.all_results, key=lambda x: x["aic"]):
    print(f"  {candidate['model_name']:20s}  AIC={candidate['aic']:.1f}  loss={candidate['loss']:.4f}")