Module hausdorff

Hausdorff distances.

Classes Hausdorff, HausdorffDegrees, HausdorffRadians, HausdorffCosineAndoyerLambert, HausdorffCosineForsytheAndoyerLambert, HausdorffCosineLaw, HausdorffDistanceTo, HausdorffEquirectangular, HausdorffEuclidean, HausdorffFlatLocal, HausdorffFlatPolar, HausdorffHaversine, HausdorffHubeny, HausdorffKarney, HausdorffThomas and HausdorffVincentys to compute Hausdorff distances between two sets of LatLon, NumPy, tuples or other types of points.

Only HausdorffDistanceTo -iff used with ellipsoidalKarney.LatLon points- and HausdorffKarney requires installation of Charles Karney's geographiclib.

Typical usage is as follows. First, create a Hausdorff calculator from a given set of LatLon points, called the model or template points.

h = HausdorffXyz(point1s, ...)

Get the directed or symmetric Hausdorff distance to a second set of LatLon points, named the target points, by using

t6 = h.directed(point2s)

respectively

t6 = h.symmetric(point2s).

Or, use function hausdorff_ with a proper distance function and optionally a point function passed as keyword arguments as follows

t6 = hausdorff_(point1s, point2s, ..., distance=..., point=...).

In all cases, the returned result t6 is a Hausdorff6Tuple.

For (lat, lon, ...) points in a NumPy array or plain tuples, wrap the points in a Numpy2LatLon respectively Tuple2LatLon instance, more details in the documentation thereof.

For other points, create a Hausdorff sub-class with the appropriate distance method overloading Hausdorff.distance and optionally a point method overriding Hausdorff.point as the next example.

>>> from pygeodesy import Hausdorff, hypot_
>>>
>>> class H3D(Hausdorff):
>>>     """Custom Hausdorff example.
>>>     """
>>>     def distance(self, p1, p2):
>>>         return hypot_(p1.x - p2.x, p1.y - p2.y, p1.z - p2.z)
>>>
>>> h3D = H3D(xyz1, ..., units="...")
>>> d6 = h3D.directed(xyz2)

Transcribed from the original SciPy Directed Hausdorff Code version 0.19.0, Copyright (C) Tyler Reddy, Richard Gowers, and Max Linke, 2016, distributed under the same BSD license as SciPy, including early breaking and random sampling as in Abdel Aziz Taha, Allan Hanbury "An Efficient Algorithm for Calculating the Exact Hausdorff Distance", IEEE Trans. Pattern Analysis Machine Intelligence (PAMI), vol 37, no 11, pp 2153-2163, Nov 2015.

>>> rrange = randomrangenerator('R')
>>> for r in rrange(n):
>>>    ...  # r is random in 0..n-1