Kinalite - Simple VTNA

Kinalite is a Python package that provides a simple API for running Variable Time Normalization Analysis on chemical data.

Requirements

Python 3.6+

Installation

Kinalite can be installed with pip:

$ pip install kinalite

Usage

To use kinalite you will need to provide two DataFrames with experiment data to compare. Kinalite will then use VTNA to find a best order for the first experiment.

Reading data

The first step is using the Pandas package to convert your experiment data into DataFrames:

import pandas as pd

# supply an absolute or relative path to CSV files
experiment_a_data = pd.read_csv('./data/experiment_a.csv')
experiment_b_data = pd.read_csv('./data/experiment_b.csv')

These CSV files need to have a single header row and time in the first columns. All values, including Time, are numbers. For example:

Running VTNA

Next you can use your converted data to create an Experiment and run VTNA. You must also provide the column indexes for the substrate and product, starting at 0 (Time is column index 0):

import pandas as pd
from kinalite.experiment import Experiment

# supply an absolute or relative path to CSV files
experiment_a_data = pd.read_csv('./data/experiment_a.csv')
experiment_b_data = pd.read_csv('./data/experiment_b.csv')

# create an experiment and supply the column indexes for the substrate and product
experiment = Experiment('A', [experiment_a_data, experiment_b_data], substrate_index=2, product_index=4)
# run VTNA and print out the best order
result = experiment.calculate_best_result()
print('Order in A: ', result.order)

Plotting data

Kinalite provides some plotting methods to help visualize the results of running VTNA:

from kinalite.plots import plot_experiment_results

plot_experiment_results(experiment)

Example Script

There is also an example script with a comparison of multiple sets of data: kinalite_example/main.py