Decay functions

A suite of decay functions to simulate demand dropoff as distance increases.

All decay functions operate on one-dimensional numpy arrays.

aceso.decay.gaussian_decay(distance_array, sigma)

Transform a measurement array using a normal (Gaussian) distribution.

Some sample values. Measurements are in multiple of sigma; decay value are in fractions of the maximum value:

measurement	decay value
0.0	1.0
0.7582	0.75
1.0	0.60647
1.17	0.5
2.0	0.13531

aceso.decay.get_decay_function(name)

Return the decay function with the given name.

Parameters:name (str) –

The name of the requested decay function.

Available names:

• 'uniform'
• 'raised_cosine'
• 'gaussian'
• 'parabolic'

aceso.decay.parabolic_decay(distance_array, scale)

Transform a measurement array using the Epanechnikov (parabolic) kernel.

Some sample values. Measurements are in multiple of scale; decay value are in fractions of the maximum value:

measurement	decay value
0.0	1.0
0.25	0.9375
0.5	0.75
0.75	0.4375
1.0	0.0

aceso.decay.raised_cosine_decay(distance_array, scale)

Transform a measurement array using a raised cosine distribution.

Some sample values. Measurements are in multiple of scale; decay value are in fractions of the maximum value:

measurement	decay value
0.0	1.0
0.25	0.853553
0.5	0.5
0.75	0.146447
1.0	0.0

aceso.decay.uniform_decay(distance_array, scale)

Transform a measurement array using a uniform distribution.

The output is 1 below the scale parameter and 0 above it.

Some sample values. Measurements are in multiple of scale; decay value are in fractions of the maximum value:

measurement	decay value
0.0	1.0
0.25	1.0
0.5	1.0
0.75	1.0
1.0	1.0