GeoPhilo Emblem
GEOPHILO
PURE-PYTHON SCIENTIFIC ENGINES · PYPI DIRECTORY

8 Standalone Spatial & GeoAI SDKs

Zero C-compiler build steps, 100% pure Python, and headless high-throughput computing. Built for Jupyter Notebooks, pandas/geopandas spatial pipelines, automated backend cron jobs, and cloud microservices.

8
PyPI Engines
801
Passing Unit Tests
220
Spatial Algorithms
0
C Compile Hurdles
Quick Jump: geoai2analytics-sdk planx-sdk cad2geo NCZ2Geo multilayer-sdk myface2city osm2threejs zero2route3d_sdk
geoai2analytics-sdk

geoai2analytics-sdk

v0.12.1

Spatial Statistics, Econometrics (GWR/MGWR/SAR) & Explainable GeoAI (Spatial SHAP).

39 Tests 81 Algos Pure Python
pip install geoai2analytics-sdk
CORE CAPABILITIES & ALGORITHMS
  • Spatial Autocorrelation: Global Moran's I, Geary's C, Getis-Ord General G with permutation inference
  • Spatial Econometrics: Spatial Autoregressive (SAR), Spatial Error Model (SEM), GWR & MGWR
  • Explainable GeoAI: Spatial SHAP tree explainers, spatial cross-validation, and Moran eigenvectors
  • Performance: Pure NumPy/SciPy linear algebra, zero GDAL or C-compiler requirement
Interactive Web Manual PyPI GitLab GitHub
python_example.py 
from geoai2analytics import MoranI, SpatialRegression, SpatialSHAP

# 1. Global Moran's I spatial autocorrelation
mi = MoranI(values, weights_matrix)
print(f"Moran's I: {mi.I:.4f}, p-value: {mi.p_value:.4f}")

# 2. Train Explainable GeoAI Random Forest with Spatial SHAP
explainer = SpatialSHAP(model, coordinates)
shap_values = explainer.compute_shapley()
planx-sdk

planx-sdk

v2.27.1

Urban Resilience, Multi-Criteria Site Analysis (MCDA), EV Queues & Cellular Automata.

523 Tests 42 Algos Pure Python
pip install planx-sdk
CORE CAPABILITIES & ALGORITHMS
  • MCDA Engine: Analytic Hierarchy Process (AHP), TOPSIS, and Monte Carlo weight sensitivity simulations
  • Urban Hazards: HAZUS-based seismic vulnerability, flood exposure, and urban heat island metrics
  • Stochastic Mobility: M/M/c/K queueing simulation for metropolitan EV fast-charging demand
  • Cellular Automata: SLEUTH-style urban land-use growth and morphological transition matrices
Interactive Web Manual PyPI GitLab GitHub
python_example.py 
from planx import MCDA, CellularAutomata, EVChargingQueue

# 1. Monte Carlo Analytical Hierarchy Process (AHP)
ahp = MCDA.run_monte_carlo_ahp(criteria_matrix, n_simulations=1000)
print(f"Consistency Ratio: {ahp.cr:.3f}, Weights: {ahp.weights}")

# 2. Stochastic M/M/c/K queue for urban EV fast-charging
queue = EVChargingQueue(arrival_rate=14.2, service_rate=4.0, servers=4)
print(f"Mean Wait Time: {queue.mean_wait_time_minutes:.1f} mins")
cad2geo

cad2geo

v0.12.1

High-Speed CAD to GeoJSON converter, Bishop slope stability & CRTN traffic noise.

39 Tests 15 Algos Pure Python
pip install cad2geo
CORE CAPABILITIES & ALGORITHMS
  • Headless CAD Converter: Parses AutoCAD DXF (R12 to 2018) & binary DWG directly into GeoJSON
  • Geotechnical Stability: Bishop circular arc slice method Factor of Safety (FoS) computation
  • Environmental Acoustics: UK Calculation of Road Traffic Noise (CRTN) 18-hour L10 propagation
  • Output Formats: GeoJSON, WKT, GeoPandas DataFrame, and GeoPackage compatible dicts
Interactive Web Manual PyPI GitLab GitHub
python_example.py 
from cad2geo import CADConverter, BishopSlopeStability

# 1. Headless high-speed conversion from DXF/DWG to GeoJSON
geojson_data = CADConverter.to_geojson("site_plan.dxf")

# 2. Bishop Circular Arc Factor of Safety (FoS) calculation
bishop = BishopSlopeStability(dem_slices, cohesion=24.5, friction_angle=32)
print(f"Factor of Safety: {bishop.factor_of_safety:.3f}")
NCZ2Geo

NCZ2Geo

v0.12.1

Pure-Python Netcad NCZ/NCA parser with PlanGML zoning identity & e-Plan symbology.

37 Tests 16 Algos Pure Python
pip install NCZ2Geo
CORE CAPABILITIES & ALGORITHMS
  • Netcad NCZ/NCA Parser: High-throughput decoder for binary & ASCII Turkish cadastre files
  • PlanGML Compliance: Generates official Ministry of Environment PlanGML zoning identities
  • Symbology Engine: 100+ Turkish e-Plan cadastral line styles, hatch patterns, and color keys
  • Coordinate Systems: Native ITRF96, ED50, and UTM 3-degree/6-degree projection transforms
Interactive Web Manual PyPI GitLab GitHub
python_example.py 
from ncz2geo import NCZParser, PlanGMLBuilder

# 1. Parse Netcad NCZ/NCA binary & ASCII cadastre geometries
cadastre = NCZParser.parse_file("imar_plani.ncz")

# 2. Generate Law 3194 compliant PlanGML zoning identity
plangml = PlanGMLBuilder.from_ncz(cadastre, projection="EPSG:5256")
plangml.export_gml("plan_identity.gml")
multilayer-sdk

multilayer-sdk

v0.12.1

Synchronized Multi-Panel Map Visualization, Real-Time Crosshairs & Curtain Swipe.

30 Tests 14 Algos Pure Python
pip install multilayer-sdk
CORE CAPABILITIES & ALGORITHMS
  • Multi-Panel Synchronization: Synchronizes up to 8 interactive map canvas extents & zoom levels
  • Laser Pointer Crosshairs: Broadcasts real-time coordinate cursor across split-screen viewports
  • Curtain Swipe: Hardware-accelerated split-screen before/after change detection slider
  • Proximity Radar: Multi-tier concentric isochronic buffers (100m, 300m, 500m) for accessibility
Interactive Web Manual PyPI GitLab GitHub
python_example.py 
from multilayer import MultiCanvasSync, POIProximityRadar

# 1. Synchronize multiple map canvases with shared laser crosshairs
viewer = MultiCanvasSync(layers=[satellite_layer, zoning_layer])

# 2. Multi-Scale POI Proximity Radar (100m, 300m, 500m)
radar = POIProximityRadar(study_area, pois=["hospital", "school", "park"])
radar_scores = radar.compute_isochronic_radii()
myface2city

myface2city

v0.12.1

Optical urban portrait engine, vector halftone stippling & Japanese Suminagashi marbling.

29 Tests 12 Algos Pure Python
pip install myface2city
CORE CAPABILITIES & ALGORITHMS
  • Optical Urban Art Engine: Transforms portrait photographs into living street networks
  • Halftone & Dithering: Vector circular halftone, Floyd-Steinberg dithering, and Voronoi stippling
  • Suminagashi Marbling: Fluid dynamic Japanese paper marbling simulation over road networks
  • Export Formats: High-resolution SVG, PDF art posters, and QGIS-ready GeoJSON polylines
Interactive Web Manual PyPI GitLab GitHub
python_example.py 
from myface2city import FaceToCityEngine, HalftoneStipple

# 1. Render portrait photography into living street network geometry
network = FaceToCityEngine.render_street_network("portrait.jpg", density=0.8)

# 2. Export Voronoi stippling & Japanese Suminagashi vector poster
network.export_svg("urban_face_poster.svg")
osm2threejs

osm2threejs

v0.12.1

Headless 3D procedural city generation from OpenStreetMap into Three.js WebGL & glTF.

29 Tests 18 Algos Pure Python
pip install osm2threejs
CORE CAPABILITIES & ALGORITHMS
  • OSM 3D Generation: Headless OpenStreetMap building footprint ingestion and procedural extrusion
  • Solar Shadow Simulation: Solar azimuth/elevation calculation with cumulative shadow heatmaps
  • WebGL & glTF Export: Compiles optimized 3D glTF/GLB digital twin meshes with PBR materials
  • Browser Orbit Controls: Embedded Three.js viewer with fly-through camera trajectory recording
Interactive Web Manual PyPI GitLab GitHub
python_example.py 
from osm2threejs import OSMCityBuilder, ShadowEngine

# 1. Generate 3D procedural buildings directly from bounding box
scene = OSMCityBuilder.from_bbox([38.41, 27.12, 38.43, 27.15])

# 2. Cumulative solar shadow exposure and glTF export
shadows = ShadowEngine.simulate_annual_shadows(scene, latitude=38.42)
scene.export_gltf("izmir_digital_twin.glb")
zero2route3d_sdk

zero2route3d_sdk

v0.12.1

3D DEM slope kinematics, Minetti metabolic caloric routing & HMM Viterbi map-matching.

75 Tests 22 Algos Pure Python
pip install zero2route3d-sdk
CORE CAPABILITIES & ALGORITHMS
  • 3D Kinematic Routing: Elevation-aware A* pathfinding over Digital Elevation Model (DEM) rasters
  • Metabolic Physiology: Minetti energetic expenditure modeling (Joules/kg) along terrain gradients
  • Terrain Resistance: Surface friction matrices combining land cover, slope, and surface roughness
  • Map-Matching: Hidden Markov Model (HMM) Viterbi algorithm for snapping GPS tracks to 3D networks
Interactive Web Manual PyPI GitLab GitHub
python_example.py 
from zero2route3d import KinematicRouter, MinettiEnergy

# 1. 3D elevation-aware metabolic pathfinding over DEM raster
router = KinematicRouter(dem_raster="izmir_elevation.tif")
path = router.find_optimal_path(origin, destination, profile="pedestrian")

# 2. Minetti metabolic caloric consumption along slope gradients
calories = MinettiEnergy.calculate_energy_joules(path.gradients, mass_kg=75)
FORENSIC TELEMETRY & LIVE SANDBOX

Explore In-Browser Simulation & Growth Models

Test interactive simulation modes directly on live Leaflet maps in our Unified Playground, or view the complete Bass diffusion econometric growth models and PyPI download history in our SDK Governance Studio.

Open Telemetry Hub Interactive Playground