emfit: propagate errors properly, expose dataframe

my/core/types.py

@@ -0,0 +1,9 @@
+import typing
+
+if typing.TYPE_CHECKING:
+    from typing import Any
+    # todo would be nice to use some real stubs..
+    DataFrameT = Any
+else:
+    import pandas # type: ignore
+    DataFrameT = pandas.DataFrame

my/emfit/__init__.py

@@ -6,17 +6,20 @@ Consumes data exported by https://github.com/karlicoss/emfitexport
 """
 from datetime import date
 from pathlib import Path
-from typing import Dict, List, Iterator
+from typing import Dict, List, Iterable
 
 from ..core import get_files
 from ..core.common import mcachew
 from ..core.cachew import cache_dir
 from ..core.error import Res
+from ..core.types import DataFrameT
 
 from my.config import emfit as config
 
 
 import emfitexport.dal as dal
+# todo ugh. need to make up my mind on log vs logger naming... I guessl ogger makes more sense
+logger = dal.log
 Emfit  = dal.Emfit
 
 
@@ -28,42 +31,87 @@ def dir_hash(path: Path):
 
 # TODO take __file__ into account somehow?
 @mcachew(cache_path=cache_dir() / 'emfit.cache', hashf=dir_hash, logger=dal.log)
-def iter_datas(path: Path=config.export_path) -> Iterator[Res[Emfit]]:
-    # TODO FIMXE excluded_sids
+def datas(path: Path=config.export_path) -> Iterable[Res[Emfit]]:
+    # TODO FIXME excluded_sids
     yield from dal.sleeps(config.export_path)
 
 
-def get_datas() -> List[Emfit]:
-    return list(sorted(iter_datas(), key=lambda e: e.start))
-# TODO move away old entries if there is a diff??
-
-
-# TODO merge with jawbone data first
-def by_night() -> Dict[date, Emfit]:
-    res: Dict[date, Emfit] = {}
+# TODO should be used for jawbone data as well?
+def pre_dataframe() -> Iterable[Res[Emfit]]:
     # TODO shit. I need some sort of interrupted sleep detection?
-    from more_itertools import bucket
-    grouped = bucket(get_datas(), key=lambda s: s.date)
-    for dd in grouped:
-        sleeps = list(grouped[dd])
-        if len(sleeps) > 1:
-            dal.log.warning("multiple sleeps per night, not handled yet: %s", sleeps)
+    g: List[Emfit] = []
+
+    def flush() -> Iterable[Res[Emfit]]:
+        if len(g) == 0:
+            return
+        elif len(g) == 1:
+            r = g[0]
+            g.clear()
+            yield r
+        else:
+            err = RuntimeError('Multiple sleeps per night, not supprted yet: {g}')
+            g.clear()
+            yield err
+
+    for x in datas():
+        if isinstance(x, Exception):
+            yield x
             continue
-        [s] = sleeps
-        res[s.date] = s
-    return res
+        # otherwise, Emfit
+        if len(g) != 0 and g[-1].date != x.date:
+            yield from flush()
+        g.append(x)
+    yield from flush()
 
 
-def stats():
-    return {
-        'nights': len(by_night()),
+def dataframe() -> DataFrameT:
+    from datetime import timedelta
+    dicts: List[Dict] = []
+    last = None
+    for s in pre_dataframe():
+        if isinstance(s, Exception):
+            # todo date would be nice too?
+            d = {'error': str(s)}
+        else:
+            dd = s.date
+            pday = dd - timedelta(days=1)
+            if last is None or last.date != pday:
+                hrv_change = None
+            else:
+                # todo it's change during the day?? dunno if reasonable metric
+                hrv_change = s.hrv_evening - last.hrv_morning
+
+            # TODO use 'workdays' provider....
+            d = {
+                'date'       : dd,
+
+                'sleep_start': s.sleep_start,
+                'sleep_end'  : s.sleep_end,
+                'bed_time'   : s.time_in_bed, # eh, this is derived frop sleep start / end. should we compute it on spot??
+
+                # these are emfit specific
+                'coverage'   : s.sleep_hr_coverage,
+                'avg_hr'     : s.measured_hr_avg,
+                'hrv_evening': s.hrv_evening,
+                'hrv_morning': s.hrv_morning,
+                'recovery'   : s.recovery,
+                'hrv_change' : hrv_change,
             }
+            last = s # meh
+        dicts.append(d)
 
 
-def main():
-    for k, v in by_night().items():
-        print(k, v.start, v.end)
+    import pandas # type: ignore
+    return pandas.DataFrame(dicts)
+
+# TODO add dataframe support to stat()
+def stats():
+    from ..core import stat
+    return stat(pre_dataframe)
 
 
-if __name__ == '__main__':
-    main()
+# TODO remove/deprecate it? I think used by timeline
+def get_datas() -> List[Emfit]:
+    # todo ugh. run lint properly
+    return list(sorted(datas(), key=lambda e: e.start))
+# TODO move away old entries if there is a diff??

tests/emfit.py

@@ -1,23 +1,24 @@
-from my.emfit import get_datas
+from my.emfit import datas
 
 
-def test():
-    datas = get_datas()
-    for d in datas:
+def test() -> None:
+    ds = [x for x in datas() if not isinstance(x, Exception)]
+    for d in ds:
         assert d.start.tzinfo is not None
         assert d.end.tzinfo is not None
         assert d.sleep_start.tzinfo is not None
         assert d.sleep_end.tzinfo is not None
 
 
-def test_tz():
-    datas = get_datas()
+def test_tz() -> None:
+    # TODO check errors too?
+    ds = [x for x in datas() if not isinstance(x, Exception)]
 
     # this was winter time, so GMT, UTC+0
-    [s0109] = [s for s in datas if s.date.strftime('%Y%m%d') == '20190109']
+    [s0109] = [s for s in ds if s.date.strftime('%Y%m%d') == '20190109']
     assert s0109.end.strftime('%H:%M') == '06:42'
 
     # TODO FIXME ugh, it's broken?...
     # summer time, so UTC+1
-    [s0411] = [s for s in datas if s.date.strftime('%Y%m%d') == '20190411']
+    [s0411] = [s for s in ds if s.date.strftime('%Y%m%d') == '20190411']
     assert s0411.end.strftime('%H:%M') == '09:30'