Drop `_slice_from_time()`
parent
5c417fe815
commit
688d7d7f2f
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@ -272,55 +272,6 @@ def ohlc_flatten(
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return x, flat
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@njit
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def _slice_from_time(
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arr: np.ndarray,
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start_t: float,
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stop_t: float,
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) -> tuple[int, int]:
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'''
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Slice an input struct array to a time range and return the absolute
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and "readable" slices for that array as well as the indexing mask
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for the caller to use to slice the input array if needed.
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'''
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times = arr['time']
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index = arr['index']
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if (
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start_t < 0
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or start_t >= stop_t
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):
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return (
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(
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index[0],
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index[-1],
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),
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(
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0,
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len(arr),
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),
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)
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read_i_0: int = 0
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read_i_last: int = 0
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for i in range(times.shape[0]):
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time = times[i]
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if time >= start_t:
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read_i_0 = i
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break
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for i in range(read_i_0, times.shape[0]):
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time = times[i]
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if time > stop_t:
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read_i_last = time
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break
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return read_i_0, read_i_last
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def slice_from_time(
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arr: np.ndarray,
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start_t: float,
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@ -437,20 +388,6 @@ def slice_from_time(
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# )
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read_i_start = new_read_i_start
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# old much slower non-bin-search ``numba`` approach..
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# (
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# read_i_start,
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# read_i_stop,
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# ) = _slice_from_time(
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# arr,
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# start_t,
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# stop_t,
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# )
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# abs_i_start = int(index[0]) + read_i_0
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# abs_i_stop = int(index[0]) + read_i_last
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# if read_i_stop == 0:
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# read_i_stop = times.shape[0]
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# read-relative indexes: gives a slice where `shm.array[read_slc]`
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# will be the data spanning the input time range `start_t` ->
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# `stop_t`
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