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This turns the DE rates into a histogram product, summing the annotated direct events into histogram bins for comparison with the hist-rates and monitor-rates products.

Opening in draft mode for now because I haven't added any tests and have quite a few TODOs in here. This is to get a product out and have the code associated with it up.

This turns the DE rates into a histogram product, summing the
annotated direct events into histogram bins for comparison with
the hist-rates and monitor-rates products.
@greglucas greglucas added this to the January 2026 milestone Jan 8, 2026
@greglucas greglucas self-assigned this Jan 8, 2026
@greglucas greglucas added Ins: Lo Related to the IMAP-Lo instrument Level: L1 Level 1 processing labels Jan 8, 2026
Comment on lines +1733 to +1740
# TODO: Does ESA mode change over time?
# We can't use set_esa_mode here since that sets esa_mode as a function
# of time. We also need the sweep-df LUT as an ancillary file.
ds["esa_mode"] = xr.ones_like(ds["esa_step"], dtype=int)

# TODO: Each DE has a "mode" associated with it, do we average, take first?
# mode (N)
ds["mode"] = xr.zeros_like(ds["epoch"], dtype=int)
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Merge these two modes together. The dimension should be (N, 7) indicating a mode as a function of time and esa_step.

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