Reconvolution Curve Fit

This repository contains Python scripts for performing reconvolution curve fitting using both custom NNLS-based and lmfit-based methods. These scripts are designed to analyze time-resolved fluorescence decay data by fitting exponential decay models convolved with an instrument response function (IRF).

Features

• Custom NNLS-based fitting: Uses non-negative least squares (NNLS) for fitting decay curves.
• lmfit-based fitting: Utilizes the lmfit library for flexible model fitting with parameter constraints.
• Grid fitting: Splits the parameter space into equal intervals and performs fitting for each combination.
• Data smoothing: Optional data smoothing using Savitzky-Golay filter.

Installation

Clone the repository and install the required packages:

git clone https://github.com/mkizilov/TCSCP-Reconvolution
cd reconvolution-curve-fit
pip install -r requirements.txt

Usage

Preparing Data

You can prepare and smooth your data using the prepare_data function:

from utils import prepare_data

# Load your data into pandas DataFrames
df_signal = pd.read_csv('signal.csv')
df_irf = pd.read_csv('irf.csv')

# Prepare and smooth the data
signal, irf = prepare_data(df_signal, df_irf, window_length=5, polyorder=3)

NNLS-based Reconvolution Fit

Use the reconvolution_curve_fit function for NNLS-based reconvolution fitting:

from fit_functions import reconvolution_curve_fit

# Perform reconvolution curve fitting
popt, pcov = reconvolution_curve_fit(df_signal, df_irf, exp_num=3, tau_bounds=(0, 0.8), num_splits=10, plot_title='Exponential Decay Reconvolution', filename='fit_result')

lmfit-based Reconvolution Fit

Use the reconvolution_lmfit function for lmfit-based reconvolution fitting:

from fit_functions import reconvolution_lmfit

# Perform lmfit-based reconvolution fitting
best_result, best_values = reconvolution_lmfit(df_signal, df_irf, model_type='double', num_splits=10, plot_title='Exponential Decay Reconvolution')

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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