---
license: mit
tags:
- heliophysics
- solar_active_regions
- AR_emergence
- space_weather
- machine_learning
size_categories:
- 1GB<n<100GB
---

# AR Emergence Dataset  

The **AR Emergence Dataset** is designed to support research on the early detection of solar Active Regions (ARs) and the development of predictive models for space weather. By characterizing the evolution of ARs before, during, and after their emergence, the dataset enables studies of pre-emergence signatures and early warning methods.  

This dataset is derived from NASA’s **Solar Dynamics Observatory (SDO)** using measurements from the **Helioseismic and Magnetic Imager (HMI)**. It includes timeline data of:  

- **Acoustic power** (from Doppler velocity maps)  
- **Photospheric magnetic field**  
- **Continuum intensity**  

for **56 large ARs** that emerged on the visible solar disk between **2010 and 2023**. Each AR is tracked within a **30° × 30° patch** over multiple days.  

These data products have already been applied successfully in machine learning models for AR emergence forecasting in [1].  

[1] Kasapis, S., Kitiashvili, I. N., Kosovichev, A. G. & Stefan, J. T. Prediction of intensity variations associated with emerging active regions using helioseismic power maps and machine learning. The Astrophys. J. Suppl. Ser. 10.3847/1538-4365/adfbe2 (2025)

---

## File Structure  

The repository contains the following files and folders:  

- `data.zip` — compressed folder containing all Active Region (AR) subfolders:  
  - `AR11130/`  
    - `mean_int11130_flat.npz` → continuum intensity timeline  
    - `mean_mag11130_flat.npz` → magnetic field timeline  
    - `mean_pmdop11130_flat.npz` → acoustic power timeline  
  - `AR11149/`  
  - …  
  - `AR13183/`  

- `train.csv` — training split (36 ARs)  
- `valid.csv` — validation split (8 ARs)  
- `test.csv` — test split (12 ARs)  
- `README.md` — dataset description  

Each AR folder contains three `.npz` files:  

- `mean_int{AR}_flat.npz` → continuum intensity timeline  
- `mean_mag{AR}_flat.npz` → magnetic field timeline  
- `mean_pmdop{AR}_flat.npz` → 4 acoustic power timelines  

The split CSV files (`train.csv`, `valid.csv`, `test.csv`) include:  

| Column         | Description                                   |
|----------------|-----------------------------------------------|
| `AR`           | NOAA Active Region number                     |
| `t_start`      | Start time of tracked patch                   |
| `t_end`        | End time of tracked patch                     |
| `dataset_type` | train/valid/test split assignment             |
| `mean_int_path`| Path to continuum intensity `.npz` file       |
| `mean_mag_path`| Path to magnetic field `.npz` file            |
| `mean_pmdop_path` | Path to acoustic power `.npz` file         |

---

## Example Usage  

```python
import numpy as np
import pandas as pd

# Load CSV metadata
df = pd.read_csv("train.csv")
print(df.head())

# Load one AR’s continuum intensity data
sample_path = df.iloc[0]["mean_int_path"]

# Update to local path after unzipping data.zip
sample_path = sample_path.replace("/Users/sk6617/Desktop/data", "data")  

data = np.load(sample_path)
print("Keys in npz file:", data.files)
print("Data shape:", data[data.files[0]].shape)