Package: geodistpy
Geodistpy: Fast and Accurate Geospatial Distance ComputationsGeodistpy is a powerful Python library designed for lightning-fast geospatial distance computations. In this README, we'll compare Geodistpy with two other popular libraries, Geopy and Geographiclib, to highlight the significant performance advantages of Geodistpy.Documentation:https://pawangeek.github.io/geodistpy/Github Repo:https://github.com/pawangeek/geodistpyPyPI:https://pypi.org/project/geodistpy/Speed Comparison# Import librariesfromgeopy.distanceimportgeodesicasgeodesic_geopyfromgeographiclib.geodesicimportGeodesicasgeodesic_gglibfromgeokernels.geodesicsimportgeodesic_vincenty# Define two coordinatescoord1=(52.5200,13.4050)# Berlincoord2=(48.8566,2.3522)# Paris# Calculate distance with Geopy (based on Geographiclib)distance_geopy=geodesic_geopy(coord1,coord2).meters# Calculate distance with Geographiclibdistance_gglib=geodesic_gglib.WGS84.Inverse(coord1[0],coord1[1],coord2[0],coord2[1])['s12']# Calculate distance with Geokernelsdistance_geokernels=geodesic_vincenty(coord1,coord2)# Print the resultsprint(f"Distance between Berlin and Paris:")print(f"Geopy:{distance_geopy}meters")print(f"Geographiclib:{distance_gglib}meters")print(f"Geokernels:{distance_geokernels}meters")We conducted a speed comparison between Geodistpy, Geopy, and Geographiclib using 1000 random samples of coordinates (latitude and longitude). The goal was to calculate all pairwise distances between these coordinates.Geopy (Geodesic from Geographiclib)Computation Time: Approximately 53.356 secondsAccuracy: Comparable to GeographiclibGeopy is widely known but relatively slow for distance calculations.GeographiclibComputation Time: Approximately 36.824 secondsAccuracy: HighGeographiclib is established but still lags in terms of speed.Geodistpy (Accelerated Vincenty's Inverse)Computation Time: Approximately 0.701 seconds (initial run, including Numba compilation) and 0.393 seconds (subsequent runs)Accuracy: High, comparable to GeographiclibGeodistpy uses an optimized Vincenty's Inverse method for blazingly fast distance calculations.Performance ComparisonGeodistpy is 78 to 142 times faster than Geopy.Geodistpy is 53 to 94 times faster than Geographiclib.Context and BackgroundThe Python packagegeodistpyis a versatile library designed for geospatial calculations involving distances between geographical coordinates. It is built on the principles of geodesy and uses the WGS 84 coordinate system, which is commonly used in GPS and mapping applications.Why it was CreatedThe package was created to simplify and standardize geospatial distance calculations. Geographical distance calculations can be complex due to the curvature of the Earth's surface, and this library abstracts away those complexities, allowing users to focus on their specific geospatial tasks.Examples and ApproachesLet's explore multiple examples and approaches to working with thegeodistpylibrary:Example 1: Calculating Distance Between Two Coordinatesfromgeodistpyimportgeodist# Define two coordinates in (latitude, longitude) formatcoord1=(52.5200,13.4050)# Berlin, Germanycoord2=(48.8566,2.3522)# Paris, France# Calculate the distance between the two coordinates in kilometersdistance_km=geodist(coord1,coord2,metric='km')print(f"Distance between Berlin and Paris:{distance_km}kilometers")Example 2: Calculating Distance Between Multiple Coordinatesfromgeodistpyimportgreatcircle_matrix# Define a list of coordinatescoords=[(52.5200,13.4050),(48.8566,2.3522),(37.7749,-122.4194)]# Calculate the distance matrix between all pairs of coordinates in milesdistance_matrix_miles=greatcircle_matrix(coords,metric='mile')print("Distance matrix in miles:")print(distance_matrix_miles)Example 3: Working with Different MetricsThegeodistpylibrary allows you to work with various distance metrics, such as meters, kilometers, miles, and nautical miles. You can easily switch between them by specifying themetricparameter.fromgeodistpyimportgeodistcoord1=(52.5200,13.4050)# Berlin, Germanycoord2=(48.8566,2.3522)# Paris, France# Calculate the distance in metersdistance_meters=geodist(coord1,coord2,metric='meter')# Calculate the distance in nautical milesdistance_nautical_miles=geodist(coord1,coord2,metric='nmi')print(f"Distance in meters:{distance_meters}")print(f"Distance in nautical miles:{distance_nautical_miles}")ConclusionFor applications that demand rapid and precise geospatial distance computations, Geodistpy is the clear choice. It offers exceptional speed improvements over both Geopy and Geographiclib, making it ideal for tasks involving large datasets or real-time geospatial applications. Despite its speed, Geodistpy maintains accuracy on par with Geographiclib, ensuring that fast calculations do not compromise precision.By adopting Geodistpy, you can significantly enhance the efficiency and performance of your geospatial projects. It is a valuable tool for geospatial professionals and developers seeking both speed and accuracy in their distance computations.To get started with Geodistpy, visit theGeodistpyand explore the documentation for comprehensive usage instructions.