Sourcery refactored master branch

src/quantifiedme/activitywatch.py

@@ -70,10 +70,9 @@ def create_fake_events(start: datetime, end: datetime) -> Iterable[Event]:
         time_passed += duration
         if start + time_passed > end:
             break
-        data = random.choices(
+        if data := random.choices(
             [d[1] for d in fakedata_weights], [d[0] for d in fakedata_weights]
-        )[0]
-        if data:
+        )[0]:
             yield Event(timestamp=timestamp, duration=duration, data=data)
 
 
@@ -183,12 +182,12 @@ def load_complete_timeline(
     # Verify that no events are older than `since`
     print(f"Query start: {since}")
     print(f"Events start: {events[0].timestamp}")
-    assert all([since <= e.timestamp for e in events])
+    assert all(since <= e.timestamp for e in events)
 
     # Verify that no events take place in the future
     # FIXME: Doesn't work with fake data, atm
     if "fake" not in datasources:
-        assert all([e.timestamp + e.duration <= now for e in events])
+        assert all(e.timestamp + e.duration <= now for e in events)
 
     # Verify that no events overlap
     verify_no_overlap(events)
@@ -200,36 +199,36 @@ def load_complete_timeline(
 
 
 def classify(events: List[Event], personal: bool) -> List[Event]:
-    # TODO: Move to example config.toml
-    classes = [
-        # (Social) Media
-        (r"Facebook|facebook.com", "Social Media", "Media"),
-        (r"Reddit|reddit.com", "Social Media", "Media"),
-        (r"Spotify|spotify.com", "Music", "Media"),
-        (r"subcategory without matching", "Video", "Media"),
-        (r"YouTube|youtube.com", "YouTube", "Video"),
-        (r"Plex|plex.tv", "Plex", "Video"),
-        (r"Fallout 4", "Games", "Media"),
-        # Work
-        (r"github.com|stackoverflow.com", "Programming", "Work"),
-        (r"[Aa]ctivity[Ww]atch|aw-.*", "ActivityWatch", "Programming"),
-        (r"[Qq]uantified[Mm]e", "QuantifiedMe", "Programming"),
-        (r"[Tt]hankful", "Thankful", "Programming"),
-        # School
-        (r"subcategory without matching", "School", "Work"),
-        (r"Duolingo|Brilliant|Khan Academy", "Self-directed", "School"),
-        (r"Analysis in One Variable", "Maths", "School"),
-        (r"Applied Machine Learning", "CS", "School"),
-        (r"Advanced Web Security", "CS", "School"),
-    ]
-
     # Now load the classes from within the notebook, or from a CSV file.
-    load_from_file = True if personal else False
+    load_from_file = personal
     if load_from_file:
         # TODO: Move categories into config.toml itself
         aw_research.classify._init_classes(filename=load_config()["data"]["categories"])
     else:
         logger.info("Using example categories")
+        # TODO: Move to example config.toml
+        classes = [
+            # (Social) Media
+            (r"Facebook|facebook.com", "Social Media", "Media"),
+            (r"Reddit|reddit.com", "Social Media", "Media"),
+            (r"Spotify|spotify.com", "Music", "Media"),
+            (r"subcategory without matching", "Video", "Media"),
+            (r"YouTube|youtube.com", "YouTube", "Video"),
+            (r"Plex|plex.tv", "Plex", "Video"),
+            (r"Fallout 4", "Games", "Media"),
+            # Work
+            (r"github.com|stackoverflow.com", "Programming", "Work"),
+            (r"[Aa]ctivity[Ww]atch|aw-.*", "ActivityWatch", "Programming"),
+            (r"[Qq]uantified[Mm]e", "QuantifiedMe", "Programming"),
+            (r"[Tt]hankful", "Thankful", "Programming"),
+            # School
+            (r"subcategory without matching", "School", "Work"),
+            (r"Duolingo|Brilliant|Khan Academy", "Self-directed", "School"),
+            (r"Analysis in One Variable", "Maths", "School"),
+            (r"Applied Machine Learning", "CS", "School"),
+            (r"Advanced Web Security", "CS", "School"),
+        ]
+
         # gives non-sensical type error on check:
         #   Argument "new_classes" to "_init_classes" has incompatible type "List[Tuple[str, str, str]]"; expected "Optional[List[Tuple[str, str, Optional[str]]]]"
         aw_research.classify._init_classes(new_classes=classes)  # type: ignore
@@ -241,7 +240,7 @@ def classify(events: List[Event], personal: bool) -> List[Event]:
 
 def load_category_df(events: List[Event]):
     tss = {}
-    all_categories = list(set(t for e in events for t in e.data["$tags"]))
+    all_categories = list({t for e in events for t in e.data["$tags"]})
     for cat in all_categories:
         try:
             tss[cat] = categorytime_per_day(events, cat)

src/quantifiedme/influxdb.py

@@ -33,8 +33,7 @@ def copy_bucket_to_influxdb(aw: ActivityWatchClient, bucket_id: str):
 def init_influxdb():
     print("Creating bucket")
     bucket_api = client.buckets_api()
-    existing_bucket = bucket_api.find_bucket_by_name(bucket_name_influx)
-    if existing_bucket:
+    if existing_bucket := bucket_api.find_bucket_by_name(bucket_name_influx):
         print("Bucket already existed, replacing")
         bucket_api.delete_bucket(existing_bucket)
     bucket_api.create_bucket(bucket_name=bucket_name_influx)
@@ -67,5 +66,5 @@ def send_events_to_influxdb(events: list[Event], bucket_id_aw: str):
 if __name__ == "__main__":
     init_influxdb()
     aw = ActivityWatchClient(testing=True)
-    copy_bucket_to_influxdb(aw, "aw-watcher-afk_" + hostname)
-    copy_bucket_to_influxdb(aw, "aw-watcher-window_" + hostname)
+    copy_bucket_to_influxdb(aw, f"aw-watcher-afk_{hostname}")
+    copy_bucket_to_influxdb(aw, f"aw-watcher-window_{hostname}")

src/quantifiedme/load_toggl.py

@@ -13,12 +13,10 @@
 
 
 def load_toggl(start: datetime, stop: datetime) -> List[Event]:
-    # The Toggl API has a query limit of 1 year
     if stop - start < timedelta(days=360):
         return _load_toggl(start, stop)
-    else:
-        split = stop - timedelta(days=360)
-        return load_toggl(start, split) + _load_toggl(split, stop)
+    split = stop - timedelta(days=360)
+    return load_toggl(start, split) + _load_toggl(split, stop)
 
 
 def _load_toggl(start: datetime, stop: datetime) -> List[Event]:
@@ -31,7 +29,7 @@ def entries_from_all_workspaces() -> List[dict]:
         workspaces = list(api.Workspace.objects.all())
         print(workspaces)
         print([w.id for w in workspaces])
-        print(f"Found {len(workspaces)} workspaces: {list(w.name for w in workspaces)}")
+        print(f"Found {len(workspaces)} workspaces: {[w.name for w in workspaces]}")
         entries: List[dict] = [
             e.to_dict()
             for workspace in workspaces

src/quantifiedme/qslang.py

@@ -48,7 +48,7 @@ def __exit__(self, exc_type, exc_val, exc_tb):
 def load_df(events: List[Event] = None) -> pd.DataFrame:
     if events is None:
         events = load_events()
-    events = [e for e in events]
+    events = list(events)
 
     with DuplicateFilter(logger):
         for e in events:
@@ -75,13 +75,13 @@ def load_df(events: List[Event] = None) -> pd.DataFrame:
                 "date": (e.timestamp - date_offset).date(),
                 "substance": e.data.get("substance"),
                 "dose": e.data.get("amount_pint") or 0,
-                # FIXME: Only supports one tag
-                "tag": list(sorted(e.data.get("tags") or set(["none"])))[0],
+                "tag": list(sorted(e.data.get("tags") or {"none"}))[0],
             }
             for e in events
         ]
     )
 
+
     # Replace NaN with mean of non-zero doses
     for substance in set(df["substance"]):
         mean_dose = df[df["substance"] == substance].mean()
@@ -99,7 +99,7 @@ def to_series(df: pd.DataFrame, tag: str = None, substance: str = None) -> pd.Se
     """
     assert tag or substance
     key = "tag" if tag else "substance"
-    val = tag if tag else substance
+    val = tag or substance
 
     # Filter for the tag/substance we want
     df = df[df[key] == val]
@@ -133,7 +133,7 @@ def to_df_daily(events: List[Event]):
     for tag in tags:
         df[f"tag:{tag}"] = to_series(df_src, tag=tag)
 
-    substances = set(s for s in df_src["substance"] if s)
+    substances = {s for s in df_src["substance"] if s}
     for subst in substances:
         colname = subst.lower().replace("-", "").replace(" ", "")
         df[colname] = to_series(df_src, substance=subst)
@@ -143,8 +143,9 @@ def _missing_dates():
     # Useful helper function to find dates without any entries
     df = load_df()
     dates_with_entries = sorted(
-        set((d + timedelta(hours=-4)).date() for d in df["timestamp"])
+        {(d + timedelta(hours=-4)).date() for d in df["timestamp"]}
     )
+
     all_dates = sorted(
         d.date()
         for d in pd.date_range(min(dates_with_entries), max(dates_with_entries))

tests/test_load.py

@@ -27,7 +27,7 @@ def test_load_qslang():
         series_nonzero = series[series != 0]
 
         # More than 10mg
-        assert (10e-6 <= series_nonzero).all()
+        assert (series_nonzero >= 10e-6).all()
 
         # Less than 500mg
         assert (series_nonzero <= 500e-6).all()
@@ -40,7 +40,7 @@ def test_load_qslang():
         series_nonzero = series[series != 0]
 
         # More than 100mg
-        assert (100e-6 <= series_nonzero).all()
+        assert (series_nonzero >= 100e-6).all()
 
         # Less than 5000mg
         # FIXME: Seems some entries have >5000mg?