QUICK_START.md

Quick Start: SSZ StarMaps mit echten GAIA-Daten

Ready in 3 Minuten! 🚀

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Installation

cd E:\clone\Segmented-Spacetime-StarMaps

# Install dependencies
pip install -e .

# Or with interactive tools
pip install -e .[interactive]

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Quick Demo (100 Stars)

python demo_quick_start.py

Output:

outputs_quick_start/
├── sky_comparison.png        # Minkowski vs SSZ side-by-side
├── distance_histogram.png    # Distance distributions
└── stars_ssz.csv             # Transformed catalog

Runtime: ~30 seconds (with GAIA fetch)

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Usage Examples

1. Fetch Nearby Stars

from ssz_starmaps.catalogs import CatalogManager

manager = CatalogManager()

# 100 nearest stars within 100 parsecs
stars = manager.fetch_nearby(distance_pc=100, max_stars=100)

print(f"Found {len(stars)} stars")

2. Apply SSZ Transformation

from ssz_starmaps.transform import transform_catalog

stars_ssz = transform_catalog(stars)

# Check results
mean_stretch = stars_ssz['stretch_factor'].mean()
print(f"Average radial stretch: {mean_stretch:.4f}")

3. Visualize

from ssz_starmaps.viz import plot_sky_comparison

plot_sky_comparison(stars_ssz, output='comparison.png')

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Pre-Defined Regions

# Orion Nebula
stars = manager.fetch_interesting('orion', max_stars=500)

# Pleiades
stars = manager.fetch_interesting('pleiades', max_stars=300)

# Available regions:
regions = manager.list_regions()
# ['orion', 'pleiades', 'andromeda', 'cygnus', 'galactic_center']

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Offline Mode (No Internet)

manager = CatalogManager(offline=True)

# Uses mock catalog
stars = manager.fetch_nearby(distance_pc=50, max_stars=100)

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Full Pipeline Example

from ssz_starmaps.catalogs import CatalogManager
from ssz_starmaps.transform import transform_catalog, print_statistics
from ssz_starmaps.viz import plot_sky_comparison, plot_distance_histogram

# Fetch
manager = CatalogManager()
stars = manager.fetch_nearby(distance_pc=50, max_stars=200)

# Transform
stars_ssz = transform_catalog(stars)

# Statistics
print_statistics(stars_ssz)

# Plots
plot_sky_comparison(stars_ssz, output='sky.png')
plot_distance_histogram(stars_ssz, output='histogram.png')

# Save
stars_ssz.to_csv('stars_ssz.csv', index=False)

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Validated Physics

This implementation is validated against 161 tests from the Mass-Projection repository:

Test	Status
r*/r_s = 1.387	✅ 0.001% error
PPN β = γ = 1	✅ Perfect match
Singularity-free	✅ D(r_s) finite
Dual velocity	✅ < 10^-16 error

See MASS_PROJECTION_REPO_ANALYSIS.md for details.

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Troubleshooting

GAIA Query Fails

# Use cached data
stars = manager.fetch_nearby(distance_pc=100, use_cache=True)

# Or offline mode
manager = CatalogManager(offline=True)
stars = manager.fetch_nearby(distance_pc=100)

Import Errors

# Install missing dependencies
pip install astropy astroquery pandas matplotlib scipy tqdm

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Next Steps

• See EXAMPLES_REAL_DATA.md for advanced examples
• See API_REFERENCE.md for full API documentation
• See ROADMAP_REAL_STARMAPS.md for development roadmap

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© 2025 Carmen Wrede, Lino Casu
Licensed under the Anti-Capitalist Software License v1.4