Saving results

save_results() writes a GrowthFitResults to three CSV files in a directory.

from pykinbiont import save_results

paths = save_results(results, "output/", prefix="my_experiment")

Output files

Three files are created (or overwritten):

<prefix>_summary.csv

One row per curve with the best model, AICc, loss, and all parameter values.

Column	Description
label	Curve identifier
cluster	Cluster assignment (empty if no clustering)
best_model	Winning model name
n_params	Number of fitted parameters
param_names	Semicolon-separated list of parameter names
aic	AICc of the best fit
loss	Final loss
param_1, param_2, …	Parameter values in positional order

<prefix>_fitted_curves.csv

Long-format table with one row per (curve, time point).

Column	Description
label	Curve identifier
time	Time point
observed	Observed OD value
fitted	Fitted OD value from the best model

<prefix>_all_models.csv

All candidate models per curve (useful for comparing AICc values across models).

Column	Description
label	Curve identifier
model_name	Candidate model name
aic	AICc for this model/curve combination
loss	Loss for this model/curve combination
is_best	True if this was the selected model
param_1, …	Parameter values

Return value

save_results returns a dict with the three file paths:

paths = save_results(results, "output/")
print(paths["summary"])        # output/kinbiont_summary.csv
print(paths["fitted_curves"])  # output/kinbiont_fitted_curves.csv
print(paths["all_models"])     # output/kinbiont_all_models.csv

Reading results back

import pandas as pd

summary = pd.read_csv(paths["summary"])
fitted  = pd.read_csv(paths["fitted_curves"])

# Plot fitted vs observed for the first well
well = summary.iloc[0]["label"]
well_df = fitted[fitted["label"] == well]
well_df.plot(x="time", y=["observed", "fitted"])