transitkit 2.0.2

Professional Exoplanet Transit Light Curve Analysis Toolkit

TransitKit v2.0

Professional Exoplanet Transit Light Curve Analysis Toolkit

TransitKit is a comprehensive Python package for analyzing exoplanet transit light curves. It provides publication-quality tools for transit detection, parameter estimation, validation, and visualization.

🌐 Live Demo

Try the interactive web app: https://transitkit.streamlit.app

No installation required - explore TransitKit directly in your browser!

Features

Core Analysis

• Transit Signal Generation: Mandel & Agol (2002) limb-darkened transit models via batman
• Period Detection: Multiple methods (BLS, GLS, PDM) with consensus weighting
• Parameter Estimation: MCMC-based fitting with full uncertainty quantification
• Transit Timing Variations: Automatic TTV detection and analysis

Data Handling

• TESS/Kepler Support: Native lightkurve integration for space-based data
• Ground-based Data: Flexible I/O for various formats (CSV, FITS, HDF5)
• NASA Exoplanet Archive: Direct TAP queries for known planet parameters

Validation & Quality

• Detection Significance: Bootstrap FAP estimation
• Odd-Even Tests: Eclipse depth consistency checks
• Injection-Recovery: Detection efficiency assessment
• Secondary Eclipse: False positive screening

Visualization

• Publication-Quality Plots: AAS/Nature journal styles
• Interactive GUI: Full-featured Tkinter application
• MCMC Diagnostics: Corner plots and chain visualization

Installation

Basic Installation

pip install transitkit

With All Features

pip install "transitkit[full]"

Development Installation

git clone https://github.com/arifsolmaz/transitkit.git
cd transitkit
pip install -e ".[dev,full]"

Optional Dependencies

Extra	Packages	Use Case
mcmc	emcee, corner	MCMC parameter estimation
cli	click, rich	Command-line interface
full	All above + batman	Full functionality
dev	pytest, black, etc.	Development
docs	sphinx, etc.	Documentation building

Interactive Streamlit App

TransitKit includes a Streamlit web app for interactive exploration:

🌐 Online (No Installation)

Visit https://transitkit.streamlit.app

💻 Run Locally

pip install streamlit plotly scipy
streamlit run app.py

App Features

Tab	Description
🌟 Synthetic Transit	Generate custom transits with adjustable parameters
🔬 Multi-Method Detection	Compare BLS, GLS, PDM detection algorithms
⏱️ TTV Analysis	Explore transit timing variations
📊 Injection-Recovery	Run detection efficiency tests
📖 Documentation	Quick start guide and API reference

Streamlit App Requirements

streamlit>=1.28.0
numpy>=1.24.0
pandas>=2.0.0
plotly>=5.18.0
scipy>=1.11.0

Quick Start

Basic Transit Detection

import numpy as np
from transitkit.core import (
    generate_transit_signal_mandel_agol,
    find_transits_bls_advanced,
    add_noise
)

# Generate synthetic data
time = np.linspace(0, 30, 2000)
flux = generate_transit_signal_mandel_agol(
    time, 
    period=5.0, 
    t0=2.5, 
    depth=0.01  # 1% transit depth
)
flux_noisy = add_noise(flux, noise_level=0.001)

# Detect transit
result = find_transits_bls_advanced(time, flux_noisy)
print(f"Detected period: {result['period']:.4f} days")
print(f"Transit depth: {result['depth']:.5f}")
print(f"SNR: {result['snr']:.1f}")

Load TESS Data

from transitkit.io import load_tess_data_advanced

# Load TESS data for a known planet
lc_collection = load_tess_data_advanced("TIC 25155310", sectors=[1, 2])

# Process each sector
for lc in lc_collection:
    time = lc.time.value
    flux = lc.flux.value
    # Analyze...

Multi-Method Analysis

from transitkit.core import find_transits_multiple_methods

# Use BLS, GLS, and PDM together
results = find_transits_multiple_methods(
    time, flux,
    min_period=1.0,
    max_period=20.0,
    methods=["bls", "gls", "pdm"]
)

print(f"BLS period: {results['bls']['period']:.4f} d")
print(f"GLS period: {results['gls']['period']:.4f} d")
print(f"PDM period: {results['pdm']['period']:.4f} d")
print(f"Consensus: {results['consensus']['period']:.4f} d")

TTV Analysis

from transitkit.analysis import measure_transit_timing_variations

ttv_result = measure_transit_timing_variations(
    time, flux,
    period=5.0,
    t0=2.5,
    duration=0.15
)

print(f"TTVs detected: {ttv_result['ttvs_detected']}")
print(f"RMS TTV: {ttv_result['rms_ttv']*24*60:.2f} minutes")

Publication-Quality Plots

from transitkit.visualization import create_transit_report_figure, setup_publication_style
from transitkit.core import TransitParameters

params = TransitParameters(
    period=5.0, period_err=0.001,
    t0=2.5, t0_err=0.01,
    duration=0.15, duration_err=0.01,
    depth=0.01, depth_err=0.001,
    snr=50.0
)

setup_publication_style(style='aas', dpi=300)
fig = create_transit_report_figure(time, flux, params)
fig.savefig('transit_report.pdf', bbox_inches='tight')

Command-Line Interface

Note: CLI requires the cli extra: pip install "transitkit[cli]"

# Get version info
transitkit version

# Run BLS detection
transitkit analyze detect lightcurve.csv --method bls --min-period 1 --max-period 20

GUI Application

# Launch the GUI (requires: pip install "transitkit[full]")
python -m transitkit.gui_app

Or from Python:

from transitkit.gui_app import main
main()

API Reference

Core Module (transitkit.core)

Function	Description
generate_transit_signal_mandel_agol()	Generate limb-darkened transit model
find_transits_bls_advanced()	Box Least Squares with SNR/FAP
find_transits_multiple_methods()	Multi-method consensus detection
find_period_gls()	Generalized Lomb-Scargle
find_period_pdm()	Phase Dispersion Minimization
estimate_parameters_mcmc()	MCMC parameter estimation
add_noise()	Add Gaussian noise to flux

Analysis Module (transitkit.analysis)

Function	Description
detrend_light_curve_gp()	Gaussian Process detrending
remove_systematics_pca()	PCA systematics removal
measure_transit_timing_variations()	TTV measurement
fit_transit_time()	Fit individual transit times

Validation Module (transitkit.validation)

Function	Description
validate_transit_parameters()	Physical parameter validation
perform_injection_recovery_test()	Detection efficiency test
calculate_detection_significance()	Bootstrap significance
validate_against_secondary_eclipse()	Secondary eclipse check

I/O Module (transitkit.io)

Function	Description
load_tess_data_advanced()	Load TESS light curves
load_kepler_data()	Load Kepler/K2 data
load_ground_based_data()	Load ground-based data
export_transit_results()	Export results (JSON/CSV/HDF5)

Citation

If you use TransitKit in your research, please cite:

@software{transitkit,
  author = {Solmaz, Arif},
  title = {TransitKit: Professional Exoplanet Transit Analysis Toolkit},
  year = {2025},
  url = {https://github.com/arifsolmaz/transitkit},
  version = {2.0.0}
}

Contributing

Contributions are welcome! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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

Acknowledgments

• batman for transit models
• lightkurve for TESS/Kepler data access
• astropy for astronomical utilities
• emcee for MCMC sampling
• Streamlit for the interactive web application

Changelog

See CHANGELOG.md for version history.