Instance Methods

Instance Methods#

copy()#

Creates a deep copy of the GeometricGraph.

Returns#

A new GeometricGraph object with the same structure and properties.

Example#

from proximitygraphs.proximitygraphs import GG
from proximitygraphs.points import SetPoints

points = SetPoints.uniform_square(n=50, seed=1)
original = GG(points)

# Create a copy
duplicate = original.copy()
print(f"Original: {original.m} edges, Copy: {duplicate.m} edges")

entropy(variable_name, bins=10)#

Calculates the Shannon entropy of a graph property distribution.

Entropy measures the uncertainty or randomness in a distribution. Higher entropy indicates more uniform distribution.

Parameters#

  • variable_name (str): The variable to analyze. Options:

    • ‘orientation’: Entropy of edge orientations

    • ‘length’: Entropy of edge lengths

    • ‘degree’: Entropy of vertex degrees

  • bins (int, optional): Number of bins for histogram. Default 10.

Returns#

  • float: Shannon entropy in bits (using base-2 logarithm).

Raises#

  • ValueError: If variable_name is not supported.

Example#

from proximitygraphs.proximitygraphs import GG, RNG
from proximitygraphs.points import SetPoints

points = SetPoints.uniform_square(n=100, seed=1)

gabriel = GG(points)
rng = RNG(points)

# Compare entropy of different properties
print(f"Gabriel Graph:")
print(f"  Orientation entropy: {gabriel.entropy('orientation', bins=36):.3f}")
print(f"  Length entropy: {gabriel.entropy('length', bins=10):.3f}")
print(f"  Degree entropy: {gabriel.entropy('degree'):.3f}")

print(f"\nRNG:")
print(f"  Orientation entropy: {rng.entropy('orientation', bins=36):.3f}")
print(f"  Length entropy: {rng.entropy('length', bins=10):.3f}")
print(f"  Degree entropy: {rng.entropy('degree'):.3f}")