def sleep_ranges(day):
# FIXME: if DatetimeIndex gets vectorized strftime, use it
day = util.contiguous_apply(
day,
day['awake'],
lambda v, blk: pd.DataFrame({'t': [blk.index[0].strftime(util.dtfmt)],
'awake': [v]}, index=blk.index[[0]]))
return '[%s]' % '; '.join(
map('...'.join,
zip(day.ix[~day['awake'].astype(bool),'t'],
day.ix[~day['awake'].shift().astype(bool),'t'])))
soj = util.SojournsData.from_file(soj_path, awake_ranges)
# XXX HACK
if 'steps' not in soj.raw_data.columns:
ag = util.ActiGraphDataTable.from_file(ag_path, awake_ranges)
soj.raw_data['steps'] = ag.raw_data['Steps']
soj.process()
# FIXME integrate this better
if args.soj_intermediate_path:
soj.data.iloc[:-1].to_csv(str(args.soj_intermediate_path))
util.contiguous_apply(
soj.data, soj.data['awake'], lambda val, block: pd.DataFrame(
{'counts': block['counts'].sum(),
'Dur': block['Dur'].sum(),
'METs': (block['Dur'] / block['Dur'].sum() *
block['METs']).sum(),
'ActivityLevel': ','.join(block['ActivityLevel'].dropna()
.unique())},
index=[block.index[0]]) if val and not np.isnan(val) else
pd.DataFrame(
{'counts': [], 'Dur': [], 'METs': [], 'ActivityLevel':[]}
)).to_csv(
str(args.soj_intermediate_path.with_name(
args.soj_intermediate_path.name.replace(
args.soj_intermediate_path.suffix,
'_squashed'+args.soj_intermediate_path.suffix))))
if awake_path and not awake_path.exists():
soj.awake_ranges.to_file(awake_path)
summary = ActivitySummary.from_sojourns(soj, subj)
outpath = soj_path.with_name(soj_path.stem + '_processed.csv')
summary.to_file(outpath)
