Implement `stop_t` gap adjustments; the good lord said it is the problem
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			@ -305,6 +305,7 @@ def slice_from_time(
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    index = arr['index']
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    i_first = index[0]
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    i_last = index[-1]
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    read_i_max = arr.shape[0]
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    if (
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			@ -392,18 +393,30 @@ def slice_from_time(
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            # )
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            read_i_start = new_read_i_start - 1
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        t_iv_stop = times[read_i_stop - 1]
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    i_iv_stop = index[read_i_stop - 1]
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    t_iv_stop = times[read_i_stop - 1]
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    if (
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        i_iv_stop < i_last
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        # i_stop_t <= t_last
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        and t_iv_stop > i_stop_t
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    ):
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        # t_diff = stop_t - t_iv_stop
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        # print(
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        #     f"WE'RE CUTTING OUT TIME - STEP:{step}\n"
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        #     f'calced iv stop:{t_iv_stop} -> stop_t:{stop_t}\n'
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        #     f'diff: {t_diff}\n'
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        #     # f'SHOULD REMAP STOP: {read_i_start} -> {new_read_i_start}\n'
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        # )
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        new_read_i_stop = np.searchsorted(
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            times[read_i_start:],
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            i_stop_t,
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            side='right',
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        )
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        if (
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            i_stop_t <= t_last
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            and t_iv_stop < i_stop_t
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            new_read_i_stop < read_i_stop
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        ):
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            t_diff = stop_t - t_iv_stop
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            print(
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                f"WE'RE CUTTING OUT TIME - STEP:{step}\n"
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                f'calced iv stop:{t_iv_stop} -> stop_t:{stop_t}\n'
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                f'diff: {t_diff}\n'
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                # f'SHOULD REMAP STOP: {read_i_start} -> {new_read_i_start}\n'
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            )
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            read_i_stop = read_i_start + new_read_i_stop
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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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