API: Functions

Functions documented here.

Functions modeling reps/intensity mapping

reps_to_intensity

streprogen.reps_to_intensity(reps, slope=-4.8, constant=97.5, quadratic=True)

A function mapping from repetitions in the range 1 to 12 to intensities in the range 0 to 100.

Parameters:
  • reps – The number of repetitions to map to the intensity range.
  • slope – Slope for the linear function.
  • constant – Constant for the linear function
  • quadratic – If ‘True’, add a slight quadratic offset.
Returns:

An intensity value in the range from 0 to 100.
Return type:

intensity

Examples

>>> reps_to_intensity(5, slope = -5, constant = 100, quadratic = False)
80

>>> reps_to_intensity(8, slope = -5, constant = 100, quadratic = True)
67.45

>>> reps_to_intensity(8, slope = -5, constant = 100, quadratic = False)
65

Functions modeling progression

progression_linear

streprogen.progression_linear(week, start_weight, end_weight, start_week, end_week)

A linear progression function going through the points (‘start_week’, ‘start_weight’) and (‘end_week’, ‘end_weight’), evaluated in ‘week’.

Parameters:
  • week – The week to evaluate the linear function at.
  • start_weight – The weight at ‘start_week’.
  • end_weight – The weight at ‘end_week’.
  • start_week – The number of the first week, typically 1.
  • end_week – The number of the final week, e.g. 8.
Returns:

The weight at ‘week’.
Return type:

weight

Examples

>>> progression_linear(week = 2, start_weight = 100, end_weight = 120,
...                    start_week = 1, end_week = 3)
110.0

>>> progression_linear(3, 100, 140, 1, 5)
120.0

progression_sinusoidal

streprogen.progression_sinusoidal(week, start_weight, end_weight, start_week, end_week, periods=2, scale=0.025, offset=0)

A sinusoidal progression function going through the points (‘start_week’, ‘start_weight’) and (‘end_week’, ‘end_weight’), evaluated in ‘week’. This function calls a linear progression function and multiplies it by a sinusoid.

Parameters:
  • week – The week to evaluate the linear function at.
  • start_weight – The weight at ‘start_week’.
  • end_weight – The weight at ‘end_week’.
  • start_week – The number of the first week, typically 1.
  • end_week – The number of the final week, e.g. 8.
  • periods – Number of sinusoidal periods in the time range.
  • scale – The scale (amplitude) of the sinusoidal term.
  • offset – The offset (shift) of the sinusoid.
Returns:

The weight at ‘week’.
Return type:

weight

Examples

>>> progression_sinusoidal(1, 100, 120, 1, 8)
100.0
>>> progression_sinusoidal(8, 100, 120, 1, 8)
120.0
>>> progression_sinusoidal(4, 100, 120, 1, 8)
106.44931454758678

The RepellentGenerator

RepellentGenerator

class streprogen.RepellentGenerator(domain, probability_func=None, generated=None)

Generates objects from a domain, each time an object is drawn, the probability of it being drawn again is determined by the probability function.

__init__(domain, probability_func=None, generated=None)

Initialize a RepellentGenerator, which is a generator where when an object is generated, the probability of it begin generated changes.

Parameters:
  • domain – A list of objects to generate from, e.g. [1, 2, 3].
  • probability_func – A decreasing probability function, e.g. lambda x: 1 / 2**x.
  • generated – A user specified dictionary of the form {element1: num1, element2: num2, ...} where num1, num2, ... are the initial states descriping how many times the elements element1, element2, ... have been generated. This argument changes the initial probability distribution.
Returns:

A RepellentGenerator object.
Return type:

RepellentGenerator

Examples

>>> domain = [1, 2, 3]
>>> generator = RepellentGenerator(domain)
>>> generator.generate_one() in domain
True
generate_one()

Generate a single element.

Returns:An element from the domain.
Return type:element

Examples

>>> generator = RepellentGenerator(['a', 'b'])
>>> gen_item = generator.generate_one()
>>> gen_item in ['a', 'b']
True
yield_from_domain(num=1)

Yield ‘num’ elements from the domain.

Yields:A sequence of elements from the domain.

Examples

>>> domain = ['a', 1]
>>> generator = RepellentGenerator(domain)
>>> for element in generator.yield_from_domain(3):
...     print(element in domain)
True
True
True