# gwac_image_analysis

**Repository Path**: mseeworld/gwac_image_analysis

## Basic Information

- **Project Name**: gwac_image_analysis
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-11-22
- **Last Updated**: 2025-11-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# GWAC Image Analysis

This project provides tools for analyzing astronomical image data from the GWAC (Ground-based Wide Angle Camera) system.

## Features

- Analysis of H_wcs_template directory statistics
- Calculation of ratios between different catalogs and image data
- Analysis of A_cat and G_diff_cat .cat files line count distributions

## Requirements

- Python 3.7+
- numpy

## Installation

1. Install the required packages:
   ```
   pip install -r requirements.txt
   ```

   Or if you're using a conda environment:
   ```
   python -m pip install -r requirements.txt
   ```

## Usage

There are several ways to run the analysis:

### Method 1: Direct script execution
```bash
python run_analysis.py
```

### Method 2: Module execution
```bash
python -m src.main
```

### Method 3: Import in your own scripts
```python
from src.data_analyzer import GWACDataAnalyzer

analyzer = GWACDataAnalyzer()
h_wcs_data = analyzer.analyze_h_wcs_template()
# ... additional analysis
```

### Method 4: Using Docker (Recommended)

The application can be run using Docker with pre-built images:

```bash
# Build the Docker image
docker build -t gwac_data_analyzer .

# Run the analysis with default directories
docker run --rm \
  -v /path/to/your/data:/data \
  -v /path/to/output/results:/results \
  gwac_data_analyzer

# Run with custom directories
docker run --rm \
  -v /path/to/your/data:/data \
  -v /path/to/output/results:/results \
  gwac_data_analyzer --data-dir /data --output-dir /results
```

The Docker image uses the base image `registry.cn-beijing.aliyuncs.com/astronomy/python:3.12-slim` and:
- Mounts the input data directory to `/data` inside the container
- Mounts the output results directory to `/results` inside the container
- By default, looks for data in `/data` and writes results to `/results`

### Method 5: Using Docker Compose

For easier management, you can also use Docker Compose:

```bash
# Build and run with docker-compose
docker-compose up --build

# Run in detached mode
docker-compose up -d

# Stop the service
docker-compose down
```

With Docker Compose, the data and results directories are mapped to the local `./data` and `./results` directories by default.

A `docker-compose.override.yml` file is also provided for development purposes. This override maps the current directory to `/app` in the container, allowing you to modify the code and see changes without rebuilding the image.

## Program Output Explanation

The program analyzes astronomical image data and produces several key statistics:

1. **H_wcs_template Directory Statistics**:
   - 3-second image target counts (from template.cat files)
   - 30-second image target counts (from template_4detection.cat files)
   - UCAC4 star catalog counts (from bright_star_align_filtered_correct.cat files)
   - Gaia bright star counts (magnitude ≤ 13, from bright_star_deep_align_filtered_correct.cat files)

2. **Calculated Ratios**:
   - 3-second image targets to UCAC4 stars ratio
   - 30-second image targets to UCAC4 stars ratio
   - UCAC4 stars to Gaia bright stars ratio

3. **A_cat Directory Statistics**:
   - Distribution of .cat file line counts
   - Number of files, range, and average line count

4. **G_diff_cat Directory Statistics**:
   - Distribution of .cat file line counts
   - Number of files, range, and average line count

5. **JSON Output Files**:
   - Statistics organized by data subdirectory and saved as JSON files in the `results/` directory
   - Each JSON file contains all statistics for a specific data subdirectory

## Data Structure

The analyzer expects the following data structure:
```
data/
├── YYYYMMDD/
│   └── 011_XXXXXXXX_XXXXXX/
│       ├── A_cat/
│       ├── G_diff_cat/
│       └── H_wcs_template/
│           └── [Observation directories]
│               ├── template.cat
│               ├── template_4detection.cat
│               ├── bright_star_align_filtered_correct.cat
│               └── bright_star_deep_align_filtered_correct.cat
└── ...
```

The directory identification logic:
- Date directories are identified by the pattern `YYYYMMDD` (starting with "20")
- Data subdirectories are identified by the naming structure `011_XXXXXXXX_XXXXXX` (three parts separated by underscores, starting with "011")

## JSON Output Format

The program saves all statistics as a single JSON array in `results/all_subdirectories_statistics.json`. Each element in the array represents one data subdirectory and contains:

- `subdirectory`: Path to the data subdirectory
- `h_wcs_template_data`: Statistics from H_wcs_template directory
- `a_cat_data`: Statistics from A_cat directory including file counts and line counts
- `g_diff_cat_data`: Statistics from G_diff_cat directory including file counts and line counts
- `ratios`: Calculated ratios between different datasets

## Customization

You can customize the data root directory by passing a parameter to the GWACDataAnalyzer constructor:
```python
analyzer = GWACDataAnalyzer("/path/to/your/data")
```

docker build -t gwac_image_analysis:v1.0 .
docker tag gwac_image_analysis:v1.0 10.36.1.155:5000/astronomy/gwac_image_analysis:v1.0
docker push 10.36.1.155:5000/astronomy/gwac_image_analysis:v1.0

docker pull 10.36.1.155:5000/astronomy/gwac_image_analysis:v1.0
docker tag 10.36.1.155:5000/astronomy/gwac_image_analysis:v1.0 sealos.hub:5000/astronomy/gwac_image_analysis:v1.0
docker push sealos.hub:5000/astronomy/gwac_image_analysis:v1.0