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Testing Guide for Astro Planner

Overview

This document describes how to test the Astro Planner system to ensure it's functioning correctly.

Quick Health Check

1. Check Services are Running

docker-compose ps

You should see:

  • ✅ astronomus - Main API (port 9247)
  • ✅ astronomus-celery - Background worker
  • ✅ astronomus-redis - Task queue

All should show status Up and be healthy.

2. API Health Check

curl http://localhost:9247/api/health | python3 -m json.tool

Expected output:

{
  "status": "healthy",
  "service": "astronomus-api",
  "version": "1.0.0",
  "telescope_connected": false
}

3. Check Docker-in-Docker

Verify both containers have Docker CLI:

# Main API container
docker exec astronomus docker --version

# Celery worker container
docker exec astronomus-celery docker --version

Both should return: Docker version 28.5.2, build ecc6942 (or similar)

Processing Pipeline Test

Automated Test Script

The easiest way to test the complete processing pipeline:

# Install requirements
pip install astropy requests numpy

# Run the test
python test_processing.py

This script will:

  1. ✅ Check API health
  2. 📸 Create synthetic star field FITS file
  3. 📁 Create processing session
  4. 📤 Upload FITS file
  5. ⚙️ Start processing job
  6. ⏳ Monitor progress
  7. 📥 Download result
  8. ✅ Verify success

Expected output:

============================================================
🧪 Astro Planner Processing Pipeline Test
============================================================

🏥 Checking API health...
✓ API is healthy: {'status': 'healthy', ...}

📸 Creating synthetic star field...
✓ Created test FITS: /tmp/tmpXXXX.fits
  Size: 1024x1024 pixels
  Stars: 100
  File size: 4.19 MB

📁 Creating processing session...
✓ Created session: test_session_1699401234 (ID: 1)

📤 Uploading FITS file...
✓ Uploaded: tmpXXXX.fits (4.19 MB)

✅ Finalizing session...
✓ Session finalized and ready for processing

⚙️  Starting processing with preset 'quick_dso'...
✓ Processing job started (ID: 1)

⏳ Monitoring job progress (timeout: 120s)...
  ⏸️ PENDING: 0.0% -
  🔄 RUNNING: 25.0% - Loading FITS file
  🔄 RUNNING: 50.0% - Applying histogram stretch
  🔄 RUNNING: 75.0% - Exporting to JPEG
  🔄 RUNNING: 100.0% - Complete

✅ Processing completed successfully!
   Output: /fits/sessions/1/result.jpg

📥 Downloading result...
✓ Downloaded: result.jpg (245.3 KB)

============================================================
✅ ALL TESTS PASSED!
============================================================

📄 Result saved to: result.jpg

Manual Testing via UI

1. Open Web Interface

open http://localhost:9247
# or visit in browser

2. Test Processing Tab

  1. Click "Process" tab
  2. Create a new session:
    • Enter session name: manual_test_001
    • Click "Create Session"
  3. Upload a FITS file:
    • Drag and drop or click to browse
    • Watch upload progress bar
    • Should see ✓ with file name when complete
  4. Click a processing preset button:
    • "Quick DSO" for fast auto-stretch
    • "PixInsight Export" for 16-bit TIFF
  5. Monitor job progress:
    • Progress bar updates every 2 seconds
    • Shows current step
    • GPU badge if GPU used
  6. Download result when complete:
    • Click "📥 Download Result" button
    • Image opens or downloads

Unit Tests

Run Backend Tests

cd backend
pytest tests/ -v

Run Specific Test Suites

# Processing tests only
pytest tests/test_processing_integration.py -v

# With coverage
pytest tests/ --cov=app --cov-report=html

# Verbose output with print statements
pytest tests/ -v -s

Test Coverage

Expected coverage:

  • Processing pipeline: >80%
  • Direct processor: >90%
  • API endpoints: >70%
  • Astronomy services: >85%

Integration Tests

1. Test Planner

curl -X POST http://localhost:9247/api/planner/plan \
  -H "Content-Type: application/json" \
  -d '{
    "date": "2025-11-15",
    "location": {
      "latitude": 45.9183,
      "longitude": -111.5433,
      "elevation": 1234
    },
    "target_preferences": {
      "target_type": "dso",
      "min_altitude": 30
    }
  }' | python3 -m json.tool

Should return scheduled targets with rise/set times.

2. Test Weather API

curl http://localhost:9247/api/weather/current | python3 -m json.tool

Should return current weather conditions (if API key configured).

3. Test Target Search

curl "http://localhost:9247/api/targets/search?query=M31&limit=5" | python3 -m json.tool

Should return Andromeda Galaxy and similar targets.

Performance Tests

Processing Performance

Test with various file sizes:

# Small file (512x512)
python test_processing.py  # ~5-10 seconds

# Medium file (1024x1024)
# Modify script to create larger image

# Large file (2048x2048)
# Should complete in <60 seconds

Concurrent Jobs

Test multiple jobs running simultaneously:

# Create multiple sessions and start jobs
for i in range(5):
    # Create session
    # Upload file
    # Start processing

# All should complete successfully

Troubleshooting Tests

Processing Job Fails

Check celery worker logs:

docker-compose logs -f celery-worker

Common issues:

  1. "Docker is not available"

    • Solution: Rebuild celery-worker
    docker-compose build celery-worker
docker-compose up -d celery-worker

  2. "File not found"

    • Check FITS_DIR volume mount
    • Verify file was uploaded successfully

  3. "Memory error"

    • Increase Docker memory limit
    • Process smaller files first

API Not Responding

Check main API logs:

docker-compose logs -f astronomus

Restart services:

docker-compose restart

Database Issues

Reset database:

docker-compose down
rm -rf data/*.db
docker-compose up -d

Port Already in Use

Find what's using port 9247:

lsof -i :9247

Kill native processes:

./scripts/docker-clean.sh

Continuous Testing

Set Up Pre-commit Hook

Create .git/hooks/pre-commit:

#!/bin/bash
# Run tests before commit

echo "Running tests..."
cd backend
pytest tests/ -v

if [ $? -ne 0 ]; then
    echo "Tests failed! Commit aborted."
    exit 1
fi

echo "All tests passed!"
exit 0

Make executable:

chmod +x .git/hooks/pre-commit

GitHub Actions (Future)

Create .github/workflows/test.yml:

name: Tests

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: 3.11

    - name: Install dependencies
      run: |
        pip install -r backend/requirements.txt
        pip install -r backend/requirements-test.txt

    - name: Run tests
      run: |
        cd backend
        pytest tests/ -v --cov=app

    - name: Upload coverage
      uses: codecov/codecov-action@v2

Test Data

Synthetic FITS Files

The test_processing.py script creates synthetic star fields. You can also create custom test data:

from astropy.io import fits
import numpy as np

# Create test image
data = np.random.poisson(lam=100, size=(1024, 1024)).astype(np.float32)

# Add gradient (to test gradient removal)
y, x = np.ogrid[0:1024, 0:1024]
gradient = (y / 1024) * 500
data += gradient

# Add stars
# ... (see test_processing.py for star generation code)

# Save
hdu = fits.PrimaryHDU(data=data)
hdu.header['OBJECT'] = 'Test with Gradient'
hdu.writeto('test_gradient.fits', overwrite=True)

Real Data

For testing with real Seestar S50 data:

  1. Capture images with Seestar S50 app
  2. Export FITS files
  3. Copy to fits/ directory
  4. Upload via web UI or test script

Regression Testing

Baseline Images

Keep a set of baseline processed images to detect regressions:

# Process baseline image
python test_processing.py

# Save result as baseline
cp result.jpg baselines/test_001_baseline.jpg

# On future changes, compare
python test_processing.py
compare result.jpg baselines/test_001_baseline.jpg -metric RMSE diff.png

Expected Metrics

Track key metrics:

  • Processing time: <30s for 1024x1024 image
  • Output file size: 200-300 KB for JPEG
  • Memory usage: <2GB per job
  • Success rate: >95%

Periodic Health Checks

Daily Checks

# Check API health
curl http://localhost:9247/api/health

# Check disk space
df -h data/
df -h fits/

# Check logs for errors
docker-compose logs --tail=100 | grep ERROR

Weekly Checks

# Run full test suite
pytest backend/tests/ -v

# Check database size
ls -lh data/*.db

# Clean old processed files
find fits/sessions/ -type f -mtime +30 -delete

Monthly Checks

# Update dependencies
pip list --outdated

# Check Docker image sizes
docker images | grep astronomus

# Review and archive old sessions

Test Checklist

Before releasing changes:

  • All unit tests pass
  • Integration tests pass
  • Manual UI testing complete
  • Processing pipeline tested with real data
  • Memory usage acceptable
  • No error logs during testing
  • Documentation updated
  • Changelog updated

Getting Help

If tests fail:

  1. Check logs: docker-compose logs
  2. Verify Docker setup: docker-compose ps
  3. Review error messages carefully
  4. Check TESTING_GUIDE.md troubleshooting section
  5. Open issue on GitHub with:
    • Test output
    • Logs
    • System info (docker version, python --version)
    • Steps to reproduce

Summary

Quick daily test:

python test_processing.py

Full test suite:

pytest backend/tests/ -v

System health:

docker-compose ps
curl http://localhost:9247/api/health

Keep testing regularly to catch issues early!

Last Updated: 2025-11-07