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my.body.exercise: more robuse handling + handle mismatching timezones

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Dima Gerasimov 2020-09-14 22:13:49 +01:00 • committed by karlicoss
parent afce09d1d4
commit eb14d5988d
1 changed files with 16 additions and 3 deletions
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19
my/body/exercise.py
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@ -50,12 +50,14 @@ def cross_trainer_data():
mappers = {
'duration': lambda s: parse_mm_ss(s),
'date' : lambda s: tzify(parse_org_datetime(s)),
'comment' : str,
}
for row in cross_table.lines:
# todo make more defensive, fallback on nan for individual fields??
try:
d = {}
for k, v in row.items():
# todo have something smarter... e.g. allow pandas to infer the type??
mapper = mappers.get(k, maybe(float))
d[k] = mapper(v)
yield d
@ -76,7 +78,10 @@ def cross_trainer_manual_dataframe():
df = pd.DataFrame(cross_trainer_data())
return df
# this should be enough?..
_DELTA = timedelta(hours=10)
# todo check error handling by introducing typos (e.g. especially dates) in org-mode
@cdf
def cross_trainer_dataframe():
'''
@ -89,16 +94,24 @@ def cross_trainer_dataframe():
edf = edf[edf['sport'].str.contains('Cross training')]
mdf = cross_trainer_manual_dataframe()
# TODO shit. need to always remember to split errors???
# on the other hand, dfs are always untyped. so it's not too bad??
# now for each manual entry, find a 'close enough' endomondo entry
# ideally it's a 1-1 (or 0-1) relationship, but there might be errors
rows = []
idxs = []
NO_ENDOMONDO = 'no endomondo matches'
for i, row in mdf.iterrows():
mdate = row['date']
close = edf[edf['start_time'].apply(lambda t: pd_date_diff(t, mdate)).abs() < timedelta(hours=3)]
idx: Optional[int]
rd = row.to_dict()
mdate = row['date']
if pd.isna(mdate):
# todo error handling got to be easier. seriously, mypy friendly dataframes would be amazing
idxs.append(None)
rows.append(rd) # presumably has an error set
continue
idx: Optional[int]
close = edf[edf['start_time'].apply(lambda t: pd_date_diff(t, mdate)).abs() < _DELTA]
if len(close) == 0:
idx = None
d = {