**Space Curves (spacecurves)** is a comprehensive Python module for space-filling curves, providing a complete implementation of Hilbert curves and other space-filling curves with advanced features. ### ✨ Features - **Multi-dimensional support**: 1-5 dimensions - **Configurable depth**: 1-16 iterations (2^p grid size) - **3 curve types**: Hilbert, Morton, Moore - **High performance**: LRU cache support, batch operations - **Neighbor search**: Find neighbors in Hilbert space - **Image compression**: Hilbert curve-based image compression - **Dimension reduction**: High-dimensional data → 2D visualization - **Hilbert ordering**: Spatial data organization - **Grid system**: Hilbert-based data structure - **Path optimization**: TSP-like route optimization - Görselleştirme araçları (HilbertVisualizer) - Yaklaşık kümeleme (HilbertClustering) ### 🚀 Quick Start ```python from spacecurves import SpaceFillingCurve, CurveType # Create a 2D Hilbert curve (16x16 grid) curve = SpaceFillingCurve(p=4, n=2) # Transform: distance → coordinates point = curve[42] # [7, 0] # Inverse transform: coordinates → distance distance = curve.inverse([7, 0]) # 42 # Batch transform distances = [0, 10, 20, 30, 40] points = curve.batch_transform(distances) # Find neighbors neighbors = curve.get_neighbors([5, 5], radius=2) 📦 Installation pip install spacecurves Or directly from source: git clone https://github.com/yourusername/spacecurves.git cd spacecurves pip install -e . 📚 Examples 1. GPS Coordinate Indexing from spacecurves import SpaceFillingCurve curve = SpaceFillingCurve(p=8, n=2) # City coordinates (latitude, longitude) cities = { 'Istanbul': [41.0082, 28.9784], 'Ankara': [39.9334, 32.8597], } # Convert to Hilbert index for name, coord in cities.items(): x = int((coord[0] - 36) / 6 * 255) y = int((coord[1] - 26) / 15 * 255) idx = curve.inverse([x, y]) print(f"{name}: {idx}") 2. Image Compression import numpy as np from spacecurves import HilbertImageCompressor # Create test image image = np.random.randint(0, 256, (256, 256), dtype=np.uint8) # Compress compressor = HilbertImageCompressor(image) compressed = compressor.compress(keep_ratio=0.2) # Keep 20% of pixels 3. Dimension Reduction from spacecurves import HilbertDimensionReducer # 10D data → 2D visualization reducer = HilbertDimensionReducer(target_dim=2, p=6) reduced = reducer.fit_transform(high_dim_data) 4. Route Optimization from spacecurves import HilbertPathOptimizer curve = SpaceFillingCurve(p=8, n=2) optimizer = HilbertPathOptimizer(curve) # Optimize delivery route optimized_route = optimizer.optimize_order(delivery_points) 5. Hilbert Grid from spacecurves import HilbertGrid grid = HilbertGrid(curve) grid[[10, 20]] = "Value A" grid[[30, 40]] = "Value B" print(grid[[10, 20]]) # Value A 🔧 API Reference SpaceFillingCurve(p, n, curve_type=CurveType.HILBERT, use_cache=True) Parameter Type Default Description p int required Iteration count (2^p grid size), 1-10 n int required Number of dimensions, 1-5 curve_type CurveType HILBERT Curve type use_cache bool True Enable caching Methods: Method Description transform(distance) Convert distance to coordinates inverse(point) Convert coordinates to distance batch_transform(distances) Batch conversion batch_inverse(points) Batch inverse conversion get_neighbors(point, radius) Find neighbors sample(n_points, method) Sample points along curve Curve Types Type Description Best For HILBERT Classic Hilbert curve General purpose MORTON Z-order curve Fast indexing MOORE Moore curve (Hilbert variant) Closed loops ALTAIR Hybrid curve Speed/locality balance 📊 Performance Transform speed: ~2.6M ops/sec (2D, p=4) Cache hit rate: >95% with repeated queries Grid access: ~0.01ms per operation 📄 License This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0-or-later). Türkçe Space Curves (spacecurves), uzay dolduran eğriler için kapsamlı bir Python modülüdür. Hilbert eğrisi ve diğer uzay dolduran eğrilerin tam bir implementasyonunu gelişmiş özelliklerle sunar. ✨ Özellikler Çok boyutlu destek: 1-5 boyut Ayarlanabilir derinlik: 1-16 iterasyon (2^p grid boyutu) 3 eğri tipi: Hilbert, Morton, Moore Yüksek performans: LRU önbellek, toplu işlemler Komşuluk arama: Hilbert uzayında komşu bulma Görüntü sıkıştırma: Hilbert eğrisi tabanlı görüntü sıkıştırma Boyut indirgeme: Yüksek boyutlu veri → 2D görselleştirme Hilbert sıralama: Mekansal veri düzenleme Grid sistemi: Hilbert tabanlı veri yapısı Rota optimizasyonu: TSP benzeri rota optimizasyonu Görselleştirme araçları (HilbertVisualizer) Yaklaşık kümeleme (HilbertClustering) 🚀 Hızlı Başlangıç from spacecurves import SpaceFillingCurve, CurveType # 2D Hilbert eğrisi oluştur (16x16 grid) curve = SpaceFillingCurve(p=4, n=2) # Dönüşüm: mesafe → koordinat point = curve[42] # [7, 0] # Ters dönüşüm: koordinat → mesafe distance = curve.inverse([7, 0]) # 42 # Toplu dönüşüm distances = [0, 10, 20, 30, 40] points = curve.batch_transform(distances) # Komşu bulma neighbors = curve.get_neighbors([5, 5], radius=2) 📦 Kurulum pip install spacecurves Veya doğrudan kaynaktan: git clone https://github.com/WhiteSymmetry/spacecurves.git cd spacecurves pip install -e . 📚 Örnekler 1. GPS Koordinat İndeksleme from spacecurves import SpaceFillingCurve curve = SpaceFillingCurve(p=8, n=2) # Şehir koordinatları (enlem, boylam) sehirler = { 'İstanbul': [41.0082, 28.9784], 'Ankara': [39.9334, 32.8597], } # Hilbert indeksine dönüştür for ad, koord in sehirler.items(): x = int((koord[0] - 36) / 6 * 255) y = int((koord[1] - 26) / 15 * 255) idx = curve.inverse([x, y]) print(f"{ad}: {idx}") 2. Görüntü Sıkıştırma import numpy as np from spacecurves import HilbertImageCompressor # Test görüntüsü oluştur gorsel = np.random.randint(0, 256, (256, 256), dtype=np.uint8) # Sıkıştır sikistirici = HilbertImageCompressor(gorsel) sikistirilmis = sikistirici.compress(keep_ratio=0.2) # Piksellerin %20'sini tut 3. Boyut İndirgeme from spacecurves import HilbertDimensionReducer # 10D veri → 2D görselleştirme indirgeyici = HilbertDimensionReducer(target_dim=2, p=6) indirgenmis = indirgeyici.fit_transform(yuksek_boyutlu_veri) 4. Rota Optimizasyonu from spacecurves import HilbertPathOptimizer curve = SpaceFillingCurve(p=8, n=2) optimizer = HilbertPathOptimizer(curve) # Teslimat rotasını optimize et optimize_rota = optimizer.optimize_order(teslimat_noktalari) 5. Hilbert Grid from spacecurves import HilbertGrid grid = HilbertGrid(curve) grid[[10, 20]] = "Değer A" grid[[30, 40]] = "Değer B" print(grid[[10, 20]]) # Değer A 🔧 API Referansı SpaceFillingCurve(p, n, curve_type=CurveType.HILBERT, use_cache=True) Parametre Tip Varsayılan Açıklama p int gerekli İterasyon sayısı (2^p grid boyutu), 1-10 n int gerekli Boyut sayısı, 1-5 curve_type CurveType HILBERT Eğri tipi use_cache bool True Önbellek kullanımı Metotlar: Metot Açıklama transform(distance) Mesafeyi koordinata dönüştürür inverse(point) Koordinatı mesafeye dönüştürür batch_transform(distances) Toplu dönüşüm batch_inverse(points) Toplu ters dönüşüm get_neighbors(point, radius) Komşuları bulur sample(n_points, method) Eğri boyunca örnek noktalar Eğri Tipleri Tip Açıklama En İyi Kullanım Alanı HILBERT Klasik Hilbert eğrisi Genel amaçlı MORTON Z-order eğrisi Hızlı indeksleme MOORE Moore eğrisi (Hilbert varyantı) Kapalı döngüler ALTAIR Hibrid eğri Hız/lokalite dengesi 📊 Performans Dönüşüm hızı: ~2.6M işlem/saniye (2D, p=4) Önbellek isabet oranı: Tekrarlı sorgularda >%95 Grid erişimi: ~0.01ms işlem başına 📄 Lisans Bu proje GNU Affero General Public License v3.0 (AGPL-3.0-or-later) ile lisanslanmıştır. 👤 Yazar Mehmet Keçeci Acknowledgments / Teşekkürler Hilbert curve algorithm based on the work of David Hilbert (1891) Morton order (Z-order curve) by Guy Macdonald Morton (1966) Moore curve by E. H. Moore (1900) --- # Pixi: [](https://prefix.dev/channels/bilgi) pixi init spacecurves cd spacecurves pixi workspace channel add [https://prefix.dev/channels/bilgi](https://prefix.dev/channels/bilgi) --prepend ✔ Added https://prefix.dev/channels/bilgi pixi add spacecurves ✔ Added spacecurves >=...,<1 pixi install pixi shell pixi run python -c "import spacecurves; print(spacecurves.__version__)" ### Çıktı: pixi remove spacecurves conda install -c https://prefix.dev/channels/bilgi spacecurves pixi run python -c "import spacecurves; print(spacecurves.__version__)" ### Çıktı: pixi run pip list | grep spacecurves ### spacecurves pixi run pip show spacecurves Name: spacecurves Version: Summary: Keçeci Numbers: Keçeci Sayıları (Keçeci Conjecture) Home-page: https://github.com/WhiteSymmetry/spacecurves Author: Mehmet Keçeci Author-email: Mehmet Keçeci <...> License: GNU AFFERO GENERAL PUBLIC LICENSE Copyright (c) 2025-2026 Mehmet Keçeci ---- 1. https://pypi.org/project/spacecurves/ 2. https://anaconda.org/bilgi/spacecurves 3. https://prefix.dev/channels/bilgi/packages/spacecurves