Model.hm_diffencodings

Model.hm_diffencodings(encodings1, encodings2, encoding_algorithm=0, sphhar_bandwidth=16, sphhar_falloff=2.828427, sphhar_radii=32, sphhar_resolution=64)

Calculates the distance between two encoded lists representing shapes of geometries. The geometries can be CAD, FE or a mixture. Typically this is the Euclidean distance normalized by the number of entries in the encoding. The encodings are generated by Model.hm_encodeshape().

Parameters:

• encodings1 (hwDoubleList) – The first list of encodings generated by Model.hm_encodeshape().

• encodings2 (hwDoubleList) – The second list of encodings generated by Model.hm_encodeshape().

• encoding_algorithm (int) –

  The optional encoding algorithm to use. Valid values are:

  0 - Spherical harmonics (default)

• sphhar_bandwidth (int) – Optional, valid for encoding_algorithm 0. (default=16)

• sphhar_falloff (double) – Optional, valid for encoding_algorithm 0. (default=2.828427)

• sphhar_radii (int) – Optional, valid for encoding_algorithm 0. (default=32)

• sphhar_resolution (int) – Optional, valid for encoding_algorithm 0. (default=64)

Returns:

• hwReturnStatus - Status object

• HmQueryResult - Result object containing the output values:

  • distance (numpy.ndarray)

Example

Calculate and diff the encodings from all surfaces against all elements

import hm
import hm.entities as ent

model = hm.Model()

_, result = model.hm_encodeshape(entity_collection=hm.Collection(model, ent.Surface))
x = result.encoding

_, result = model.hm_encodeshape(entity_collection=hm.Collection(model, ent.Element))
y = result.encoding

_, result = model.hm_diffencodings(encodings1=x, encodings2=y)
print("distance", result.distance)