athlib package

Note that all functions are available in the top level athlib package, even thought they may be defined in submodules.

Utilities

General athlib utility functions

athlib.utils.check_performance_for_discipline(discipline, textvalue)

Fix up and return what they typed in, or raise ValueError

athlib.utils.event_sort_key(event_name)

Return a tuple which will sort into programme order

Track should be ordered by distance.

athlib.utils.get_distance(discipline)

Return approx distance in metres, for sanity checking :param discipline: :return:

athlib.utils.normalize_gender(gender)

Return M, F or raise a ValueError

athlib.utils.parse_hms(t)

Parse a time duration with 0, 1 or 2 colons and return seconds.

>>> from athlib.utils import parse_hms
>>> parse_hms('10')
10
>>> parse_hms('1:10')
70
>>> parse_hms('1:1:10')
3670
>>> parse_hms('1:1:10.1')
3670.1
>>> parse_hms(3670.1)
3670.1

athlib.utils.sort_by_discipline(stuff, attr='discipline')

Sort dicts or objects into the normal athletics order

athlib.utils.text_event_sort_key(event_name)

Return a text version of the event_sort_key

Utilities for working with JSON and json-like structures - deeply nested Python dicts and lists.

This lets us iterate over child nodes and access elements with a dot-notation.

class athlib.jsondict.JSONDict

Allows dotted access

class athlib.jsondict.JSONDictSafe

Allows dotted access

Age Groups

athlib.uka.agegroups.calc_age_group(birth_date, match_date, category, vets=True, underage=False)

Return UKA age group

IAAF scoring

This file contains definitions and utility functions for determining IAAF event scores.

athlib.iaaf_score.performance(gender, event_code, score)

Function to determine performance required to achieve IAAF score, given gender and event.

In the interface, we assume performance is <seconds> for track events, and <metres> for throws and jumps. Ihe the Wikipedia-sourced factors, jumps are <centimetres>. Therefore there is a factor of 100 applied at the end.

athlib.iaaf_score.score(gender, event_code, value)

Function to determine IAAF score, based on gender, event and performance.

In the interface, we assume performance is <seconds> for track events, and <metres> for throws and jumps. Ihe the Wikipedia-sourced factors, jumps are <centimetres>. Therefore there is a factor of 100 applied at the end.

athlib.iaaf_score.scoring_key(gender, event_code)

Utility function to get the <gender>-<event> scoring key.

athlib.iaaf_score.unit_name(event_code)

Utility function to get the unit name based on event type.

Masters utilities

class athlib.wma.agegrader.AgeGrader

We implement an object to cache the data used for lookups.

end users will appear to be calling a function.

calculate_age_grade(gender, age, event, performance, verbose=False)

Return the age grade score (0 to 100ish) for this result.

>>> from athlib.wma.agegrader import AgeGrader
>>> ag=AgeGrader()
>>> "%0.4f" % ag.calculate_age_grade('m',50,'5K', '16:23')
'0.9004'
>>> "%0.4f" %  ag.calculate_age_grade('f',50,'5K', '18:00')
'0.9179'
>>>

calculate_factor(gender, age, event, distance=None)

Work out ‘slowdown factor’ for a geezer of this age taking part in this event e.g.

>>> from athlib.wma.agegrader import AgeGrader
>>> ag=AgeGrader()
>>> ag.calculate_factor('M',68,'5k')
0.7592
>>> ag.calculate_factor('M',68,'200K')
0.7561
>>> ag.calculate_factor('M',68.5,'200K')
0.7522
>>> ag.calculate_factor('f',35,'5k')
0.9935
>>> ag.calculate_factor('f',35,'200K')
0.9926
>>> ag.calculate_factor('F',35.5,'200K')
0.99095
>>> ag.calculate_factor('M',65,'10000')
0.7691
>>> ag.calculate_factor('M',69,'10000')
0.7402
>>> ag.calculate_factor('F',35,'1500')
0.9822
>>> ag.calculate_factor('f',39,'1500')
0.9547
>>> ag.calculate_factor('f',35,'SH')
0.9791
>>> ag.calculate_factor('f',39,'SH')
0.9576
>>> ag.calculate_factor('m',35,'LH')
0.9647
>>> ag.calculate_factor('m',39,'LH')
0.9254

get_data()

Defer this until the first call, so we can bubble a function up to the top of the package

world_best(gender, event)

The relevant world-record performance on the date stats were compiled

Codes

codes.JUMPS = ('HJ', 'PV', 'LJ', 'TJ')

codes.THROWS = ('DT', 'JT', 'HT', 'SP', 'WT')

codes.MULTI_EVENTS = ('PEN', 'HEP', 'DEC', 'PENI', 'PENWT')

codes.FIELD_EVENTS = ('HJ', 'PV', 'LJ', 'TJ', 'DT', 'JT', 'HT', 'SP', 'WT')

codes.FIELD_SORT_ORDER = ['HJ', 'PV', 'LJ', 'TJ', 'SP', 'DT', 'HT', 'JT']