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edeymc 2025-11-06 09:25:12 -08:00
parent 513964d898
commit c041e77ad7
4 changed files with 81 additions and 67 deletions
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69
ParaUtils/__init__.py
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@ -1,67 +1,4 @@
#Bits and bobs used for many different projects. Junk drawer module
import pandas
import numpy
from .utils import *
#Useful functions for parsing race-like durations, and normalizing their string reps
def t2f(S):
if not(S or False):
return None
m,s = ('0:%s' % S).split(':')[-2:]
try:
return numpy.round(60*int(m) + float(s),2)
except:
return -1
def f2t(S):
if not(S or False) or (int(''.join([c for c in '0'+str(S) if c.isdigit()])) == 0) or (S <= 0):
return ''
S = float(S)
m = int(S/60)
s = S-60*m
hs = s-int(s)
s = int(s)
return f'{m:02}:{s:02}.{round(hs*100):02}'
#Canada Summer Games eligible events
CSG = pandas.DataFrame([
{'Stroke':'Butterfly','Distance':50,'SportClass':[1,2,3,4,5,6,7]},
{'Stroke':'Butterfly','Distance':100,'SportClass':[8,9,10,11,12,13,14]},
{'Stroke':'Backstroke','Distance':50,'SportClass':[1,2,3,4,5]},
{'Stroke':'Backstroke','Distance':100,'SportClass':[1,2,6,7,8,9,10,11,12,13,14]},
{'Stroke':'Breaststroke','Distance':50,'SportClass':[1,2,3]},
{'Stroke':'Breaststroke','Distance':100,'SportClass':[4,5,6,7,8,9,11,12,13,14]},
{'Stroke':'Medley','Distance':150,'SportClass':[1,2,3,4]},
{'Stroke':'Medley','Distance':200,'SportClass':[5,6,7,8,9,10,11,12,13,14]},
{'Stroke':'Freestyle','Distance':50,'SportClass':[1,2,3,4,5,6,7,8,9,10,11,12,13,14]},
{'Stroke':'Freestyle','Distance':100,'SportClass':[1,2,3,4,5,6,7,8,9,10,11,12,13,14]},
{'Stroke':'Freestyle','Distance':200,'SportClass':[1,2,3,4,5,14]},
{'Stroke':'Freestyle','Distance':400,'SportClass':[6,7,8,9,10,11,12,13]},
]).explode('SportClass').reset_index(drop=True)
def parse_WPS_rankings(DF):
'''Takes a pandas dataframe, usually loaded from an excel file, and cleans it up for use'''
return (
DF
#Makes .dot notation easier
.pipe(lambda df: df.rename(columns={c:c.replace(' ','_') for c in df.columns}))
#Drop VACANT events and relay results
.pipe(lambda df: df[df.SDMS_ID.notnull()])
#Parse out event information into useful columns
.pipe(lambda df: pd.concat([df,df.Event.str.extract(r"'s (?P<Distance>\d+) m (Individual )*(?P<Stroke>[A-z]+) [SBM]+(?P<SportClass>\d+)")],axis=1))
#Cleanup column dtypes
.assign(Date=lambda df: pd.to_datetime(df.Date))
.assign(Year=lambda df: df.Date.dt.year)
.assign(Seconds=lambda df: df.Result.apply(t2f))
.astype({'SportClass':int,'Distance':int,'SDMS_ID':int})
#Drop excess columns
.pipe(lambda df: df['Gender SportClass Distance Stroke Result Seconds SDMS_ID NPC Given_Name Family_Name Date'.split()])
)
__version__ = "0.1.1"
__version__ = "0.1.1"
__all__ = 't2f ft2 CSG parse_WPS_rankings.split()

67
ParaUtils/utils.py Normal file
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#Bits and bobs used for many different projects. Junk drawer module
import pandas
import numpy
#Useful functions for parsing race-like durations, and normalizing their string reps
def t2f(S):
if not(S or False):
return None
m,s = ('0:%s' % S).split(':')[-2:]
try:
return numpy.round(60*int(m) + float(s),2)
except:
return -1
def f2t(S):
if not(S or False) or (int(''.join([c for c in '0'+str(S) if c.isdigit()])) == 0) or (S <= 0):
return ''
S = float(S)
m = int(S/60)
s = S-60*m
hs = s-int(s)
s = int(s)
return f'{m:02}:{s:02}.{round(hs*100):02}'
#Canada Summer Games eligible events
CSG = pandas.DataFrame([
{'Stroke':'Butterfly','Distance':50,'SportClass':[1,2,3,4,5,6,7]},
{'Stroke':'Butterfly','Distance':100,'SportClass':[8,9,10,11,12,13,14]},
{'Stroke':'Backstroke','Distance':50,'SportClass':[1,2,3,4,5]},
{'Stroke':'Backstroke','Distance':100,'SportClass':[1,2,6,7,8,9,10,11,12,13,14]},
{'Stroke':'Breaststroke','Distance':50,'SportClass':[1,2,3]},
{'Stroke':'Breaststroke','Distance':100,'SportClass':[4,5,6,7,8,9,11,12,13,14]},
{'Stroke':'Medley','Distance':150,'SportClass':[1,2,3,4]},
{'Stroke':'Medley','Distance':200,'SportClass':[5,6,7,8,9,10,11,12,13,14]},
{'Stroke':'Freestyle','Distance':50,'SportClass':[1,2,3,4,5,6,7,8,9,10,11,12,13,14]},
{'Stroke':'Freestyle','Distance':100,'SportClass':[1,2,3,4,5,6,7,8,9,10,11,12,13,14]},
{'Stroke':'Freestyle','Distance':200,'SportClass':[1,2,3,4,5,14]},
{'Stroke':'Freestyle','Distance':400,'SportClass':[6,7,8,9,10,11,12,13]},
]).explode('SportClass').reset_index(drop=True)
def parse_WPS_rankings(DF):
'''Takes a pandas dataframe, usually loaded from an excel file, and cleans it up for use'''
return (
DF
#Makes .dot notation easier
.pipe(lambda df: df.rename(columns={c:c.replace(' ','_') for c in df.columns}))
#Drop VACANT events and relay results
.pipe(lambda df: df[df.SDMS_ID.notnull()])
#Parse out event information into useful columns
.pipe(lambda df: pd.concat([df,df.Event.str.extract(r"'s (?P<Distance>\d+) m (Individual )*(?P<Stroke>[A-z]+) [SBM]+(?P<SportClass>\d+)")],axis=1))
#Cleanup column dtypes
.assign(Date=lambda df: pd.to_datetime(df.Date))
.assign(Year=lambda df: df.Date.dt.year)
.assign(Seconds=lambda df: df.Result.apply(t2f))
.astype({'SportClass':int,'Distance':int,'SDMS_ID':int})
#Drop excess columns
.pipe(lambda df: df['Gender SportClass Distance Stroke Result Seconds SDMS_ID NPC Given_Name Family_Name Date'.split()])
)
__version__ = "0.1.1"

6
pyproject.toml
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@ -1,5 +1,9 @@
[build-system]
requires = ["setuptools>=45", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "ParaUtil"
name = "ParaUtils"
description = "Bits and bobs useful for many para swimming data manipulation projects"
readme = "README.md"
license = "MIT"

6
setup.py Normal file
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from setuptools import setup, find_packages
setup(
name="ParaUtils",
packages=find_packages(),
)