Timezone handling cleanup.

datacube_ows/data.py

@@ -859,15 +859,14 @@ def feature_info(args):
             pt_native = None
             for d in all_time_datasets.coords["time"].values:
                 dt_datasets = all_time_datasets.sel(time=d)
-                dt = datetime.utcfromtimestamp(d.astype(int) * 1e-9)
-                if params.product.time_resolution.is_solar():
-                    dt = solar_date(dt, tz)
                 for ds in dt_datasets.values.item():
                     if pt_native is None:
                         pt_native = geo_point.to_crs(ds.crs)
                     elif pt_native.crs != ds.crs:
                         pt_native = geo_point.to_crs(ds.crs)
                     if ds.extent and ds.extent.contains(pt_native):
+                        # tolist() converts a numpy datetime64 to a python datatime
+                        dt = stacker.group_by.group_by_func(ds).tolist()
                         if params.product.time_resolution.is_subday():
                             feature_json["data_available_for_dates"].append(dt.isoformat())
                         else:

datacube_ows/utils.py

@@ -10,6 +10,7 @@
 from typing import Any, Callable, List, Optional, TypeVar
 
 import pytz
+from numpy import datetime64 as npdt64
 
 F = TypeVar('F', bound=Callable[..., Any])
 
@@ -65,20 +66,20 @@ def group_by_begin_datetime(pnames: Optional[List[str]] = None,
     else:
         sort_key = base_sort_key
     if truncate_dates:
-        grp_by = lambda ds: datetime.datetime(
+        grp_by = lambda ds: npdt64(datetime.datetime(
             ds.time.begin.year,
             ds.time.begin.month,
             ds.time.begin.day,
-            tzinfo=pytz.utc)
+            tzinfo=pytz.utc))
     else:
-        grp_by = lambda ds: datetime.datetime(
+        grp_by = lambda ds: npdt64(datetime.datetime(
             ds.time.begin.year,
             ds.time.begin.month,
             ds.time.begin.day,
             ds.time.begin.hour,
             ds.time.begin.minute,
             ds.time.begin.second,
-            tzinfo=ds.time.begin.tzinfo)
+            tzinfo=ds.time.begin.tzinfo))
     return GroupBy(
         dimension='time',
         group_by_func=grp_by,
@@ -87,13 +88,6 @@ def group_by_begin_datetime(pnames: Optional[List[str]] = None,
     )
 
 
-def group_by_subday() -> "datacube.api.query.GroupBy":
-    """
-    Returns an ODC GroupBy object, suitable for sub-day level data
-
-    :return:
-    """
-
 def group_by_solar(pnames: Optional[List[str]] = None) -> "datacube.api.query.GroupBy":
     from datacube.api.query import GroupBy, solar_day
     base_sort_key = lambda ds: ds.time.begin
@@ -126,7 +120,7 @@ def group_by_mosaic(pnames: Optional[List[str]] = None) -> "datacube.api.query.G
         sort_key = lambda ds: (solar_day(ds), base_sort_key(ds))
     return GroupBy(
         dimension='time',
-        group_by_func=lambda n: datetime.datetime(1970, 1, 1, tzinfo=datetime.timezone.utc),
+        group_by_func=lambda n: npdt64(datetime.datetime(1970, 1, 1, tzinfo=datetime.timezone.utc)),
         units='seconds since 1970-01-01 00:00:00',
         sort_key=sort_key
     )