Rewrite `slice_from_time()` using `numba`

Gives approx a 3-4x speedup using plain old iterate-with-for-loop style
though still not really happy with this .5 to 1 ms latency..

Move the core `@njit` part to a `_slice_from_time()` with a pure python
func with orig name around it. Also, drop the output `mask` array since
we can generally just use the slices in the caller to accomplish the
same input array slicing, duh..

piker/data/_pathops.py

@@ -33,6 +33,7 @@ from ._m4 import ds_m4
 from .._profile import (
     Profiler,
     pg_profile_enabled,
+    ms_slower_then,
 )
 
 
@@ -269,6 +270,87 @@ def ohlc_flatten(
     return x, flat
 
 
+@njit
+def _slice_from_time(
+    arr: np.ndarray,
+    start_t: float,
+    stop_t: float,
+
+) -> tuple[
+    tuple[int, int],
+    tuple[int, int],
+    np.ndarray | None,
+]:
+    '''
+    Slice an input struct array to a time range and return the absolute
+    and "readable" slices for that array as well as the indexing mask
+    for the caller to use to slice the input array if needed.
+
+    '''
+    times = arr['time']
+    index = arr['index']
+
+    if (
+        start_t < 0
+        or start_t >= stop_t
+    ):
+        return (
+            (
+                index[0],
+                index[-1],
+            ),
+            (
+                0,
+                len(arr),
+            ),
+        )
+
+    # TODO: if we can ensure each time field has a uniform
+    # step we can instead do some arithmetic to determine
+    # the equivalent index like we used to?
+    # return array[
+    #     lbar - ifirst:
+    #     (rbar - ifirst) + 1
+    # ]
+
+    read_i_0: int = 0
+    read_i_last: int = 0
+
+    for i in range(times.shape[0]):
+        time = times[i]
+        if time >= start_t:
+            read_i_0 = i
+            break
+
+    for i in range(read_i_0, times.shape[0]):
+        time = times[i]
+        if time > stop_t:
+            read_i_last = time
+            break
+
+    abs_i_0 = int(index[0]) + read_i_0
+    abs_i_last = int(index[0]) + read_i_last
+
+    if read_i_last == 0:
+        read_i_last = times.shape[0]
+
+    abs_slc = (
+        int(abs_i_0),
+        int(abs_i_last),
+    )
+
+    read_slc = (
+        int(read_i_0),
+        int(read_i_last),
+    )
+
+    # also return the readable data from the timerange
+    return (
+        abs_slc,
+        read_slc,
+    )
+
+
 def slice_from_time(
     arr: np.ndarray,
     start_t: float,
@@ -279,93 +361,29 @@ def slice_from_time(
     slice,
     np.ndarray | None,
 ]:
-    '''
-    Slice an input struct array to a time range and return the absolute
-    and "readable" slices for that array as well as the indexing mask
-    for the caller to use to slice the input array if needed.
-
-    '''
     profiler = Profiler(
         msg='slice_from_time()',
         disabled=not pg_profile_enabled(),
-        ms_threshold=4,
-        # ms_threshold=ms_slower_then,
+        ms_threshold=ms_slower_then,
     )
 
-    times = arr['time']
-    index = arr['index']
-
-    if (
-        start_t < 0
-        or start_t >= stop_t
-    ):
-        return (
-            slice(
-                index[0],
-                index[-1],
-            ),
-            slice(
-                0,
-                len(arr),
-            ),
-            None,
-        )
-
-    # use advanced indexing to map the
-    # time range to the index range.
-    mask: np.ndarray = np.where(
-        (times >= start_t)
-        &
-        (times < stop_t)
-    )
-    profiler('advanced indexing slice')
-    # TODO: if we can ensure each time field has a uniform
-    # step we can instead do some arithmetic to determine
-    # the equivalent index like we used to?
-    # return array[
-    #     lbar - ifirst:
-    #     (rbar - ifirst) + 1
-    # ]
-
-    i_by_t = index[mask]
-    try:
-        i_0 = i_by_t[0]
-        i_last = i_by_t[-1]
-        i_first_read = index[0]
-    except IndexError:
-        if (
-            start_t < times[0]
-            or stop_t >= times[-1]
-        ):
-            return (
-                slice(
-                    index[0],
-                    index[-1],
-                ),
-                slice(
-                    0,
-                    len(arr),
-                ),
-                None,
-            )
-
-    abs_slc = slice(i_0, i_last)
-
-    # slice data by offset from the first index
-    # available in the passed datum set.
-    read_slc = slice(
-        i_0 - i_first_read,
-        i_last - i_first_read + 1,
+    (
+        abs_slc_tuple,
+        read_slc_tuple,
+    ) = _slice_from_time(
+        arr,
+        start_t,
+        stop_t,
     )
 
+    abs_slc = slice(*abs_slc_tuple)
+    read_slc = slice(*read_slc_tuple)
     profiler(
         'slicing complete'
         f'{start_t} -> {abs_slc.start} | {read_slc.start}\n'
         f'{stop_t} -> {abs_slc.stop} | {read_slc.stop}\n'
     )
-    # also return the readable data from the timerange
     return (
         abs_slc,
         read_slc,
-        mask,
     )