---
pretty_name: LightModulation
license: mit
task_categories:
- other
tags:
- polymer-electronics
- simulation
- light-modulation
- organic-photovoltaics
- current-density
- fft-analysis
size_categories:
- 1K<n<10K
---

# Light Modulation Analysis Dataset
[![Code](https://img.shields.io/badge/GitHub-Code-black?logo=github)](https://github.com/baskargroup/LightModulation)

## Dataset Description

This dataset contains current-time curve data, extracted features, and analysis outputs from light modulation simulations of organic photovoltaic devices. The dataset includes both raw JT curves and processed features from FFT (Fast Fourier Transform) and JT cycle analysis.

### Dataset Summary

- **Total Files**: ~5,000 files
- **Dataset Size**: ~436 MB (compressed: ~XXX MB)
- **Modulation Types**: Sinusoidal and Square waveforms
- **Data Types**: 
  - Raw JT curve time-series data (.txt files)
  - Extracted JT cycle features (CSV)
  - FFT spectral analysis features (CSV)
  - GRASPI morphological features (CSV)
  - Analysis plots and visualizations (PNG)

## Dataset Structure

```
data/
├── modulation_profile/          # Light modulation waveform definitions
│   ├── LM_sinusoidal.txt
│   └── LM_square.txt
├── all_jt_curves/               # Raw JT curve time-series data
│   ├── sinusoidal/             # Sinusoidal modulation JT curves
│   └── square/                 # Square modulation JT curves
├── sample_jt_curves/           # Sample subset of JT curves
├── morph_images/               # Morphology images
├── jt_features_out/             # Extracted JT cycle features
│   ├── jt_features_sinusoidal.csv
│   ├── jt_features_square.csv
│   └── figs_*/                 # Analysis plots
├── fft_features_out/           # FFT spectral analysis features
│   ├── fft_features_sinusoidal.csv
│   ├── fft_features_square.csv
│   └── */                      # FFT spectrum plots
├── features_data.csv           # Merged feature dataset
├── graspi_features.csv         # GRASPI morphological features
└── figures/                    # Publication-quality figures
```

## Data Fields

### JT Curve Files (.txt)
Each JT curve file contains time-series data with the following columns:
- `time(sec)`: Time in seconds
- `J`: Current density in mA/cm²

### JT Features 
Extracted features from JT cycle analysis:
- **Peak Features**: `peak_value`, `peak_time`, `left_min_value`, `right_min_value`
- **Amplitude Features**: `left_amplitude`, `right_amplitude`, `avg_amplitude`
- **Temporal Features**: `rise_time_ps`, `fall_time_ps`, `cycle_time_ps`, `time_avg_value`
- **Phase Features**: `phase_shift_ps`
- **Shape Features**: `skewness`, `skewness_2`, `asymmetry`
- **Metadata**: `filename`, `modulation_type`

### FFT Features
Spectral analysis features:
- `f1_Hz`: Fundamental frequency (Hz)
- `H1_amp`: Fundamental harmonic amplitude
- `THD_ratio`: Total Harmonic Distortion
- `OER_odd_over_even`: Odd-to-even harmonic ratio
- `harmonic_decay_slope_per_k`: Harmonic amplitude decay slope
- `sampling_jitter`: Temporal sampling jitter
- `filename`, `modulation_type`


## Usage

### Download the Dataset

#### Option 1: Using the provided script (Recommended)

1. Clone the Light Modulation Analysis repository:
   ```bash
   git clone https://github.com/baskargroup/LightModulation.git
   cd LightModulation
   ```

2. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

3. Download the data:
   ```bash
   python download_data_from_hf.py --repo-id BGlab/light-modulation-data
   ```

The script will automatically:
- Download the compressed archive
- Extract to `data/` folder in the git root
- Verify the extraction

#### Option 2: Using Hugging Face Hub

```python
from huggingface_hub import hf_hub_download
import tarfile

# Download the archive
archive_path = hf_hub_download(
    repo_id="BGlab/light-modulation-data",
    filename="data.tar.gz",
    repo_type="dataset"
)

# Extract to current directory
with tarfile.open(archive_path, 'r:gz') as tar:
    tar.extractall()
```

### Using the Data

Once downloaded, you can load the feature datasets:

```python
import pandas as pd

# Load merged features
features = pd.read_csv('data/features_data.csv')

# Load GRASPI features
graspi = pd.read_csv('data/graspi_features.csv', delimiter=' ')
```

## Dataset Creation

### Source Data
The dataset was generated from light modulation simulations of organic photovoltaic devices. JT curves were collected from simulation outputs and processed using the Light Modulation Analysis pipeline.

### Preprocessing
1. **JT Cycle Analysis**: Extracted temporal and amplitude features from current-time cycles
2. **FFT Analysis**: Performed spectral decomposition to extract harmonic content
3. **Feature Merging**: Combined JT, FFT, and GRASPI features into unified dataset

### Analysis Windows
- **Sinusoidal modulation**: Analysis window [0.89e-9, 0.96e-9] seconds
- **Square modulation**: Analysis window [0.8e-9, 0.9e-9] seconds

## Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{light_modulation_2024,
  title={Light Modulation Analysis Dataset},
  author={BGlab},
  year={2024},
  url={https://huggingface.co/datasets/BGlab/light-modulation-data}
}
```

## License

This dataset is released under the MIT License.

## Contact

For questions or issues, please contact the maintainers.

## Acknowledgments

This dataset was created as part of research on light modulation effects in organic photovoltaic devices.