diff --git a/.gitignore b/.gitignore index c942678b..d4d6d599 100644 --- a/.gitignore +++ b/.gitignore @@ -166,3 +166,4 @@ stixcore/data/test/idb/v2.26.38/idb.sqlite .python-version sunpy/_version.py stixcore/_version.py +.claude/* diff --git a/binder/requirements.txt b/binder/requirements.txt new file mode 100644 index 00000000..7012571d --- /dev/null +++ b/binder/requirements.txt @@ -0,0 +1,3 @@ +# Binder (mybinder.org) environment for the STIX L3 flare list showcase notebook +# the packages are those of the notebook folder; the binder/ folder keeps Binder from installing STIXCore itself +-r ../notebooks/flarelist_showcase/requirements.txt diff --git a/binder/runtime.txt b/binder/runtime.txt new file mode 100644 index 00000000..d2aca3a7 --- /dev/null +++ b/binder/runtime.txt @@ -0,0 +1 @@ +python-3.12 diff --git a/changelog/000.doc.rst b/changelog/000.doc.rst new file mode 100644 index 00000000..4bc0ffef --- /dev/null +++ b/changelog/000.doc.rst @@ -0,0 +1 @@ +Documented the SDC level-3 flare list: `~stixcore.products.level3.flarelist` and `~stixcore.io.FlareListManager` are now part of the API reference, and a new guide describes how the flare list is built from the STIX Data Center flare list and enriched (quiet-time background selection, CPD-file selection and flare-location imaging). diff --git a/docs/code_ref/io.rst b/docs/code_ref/io.rst index 5962f501..e3b8cac6 100644 --- a/docs/code_ref/io.rst +++ b/docs/code_ref/io.rst @@ -6,6 +6,9 @@ to certain directories. .. automodapi:: stixcore.io +.. automodapi:: stixcore.io.FlareListManager + :skip: SCFlareListManager + .. automodapi:: stixcore.io.product_processors.fits.processors .. automodapi:: stixcore.io.product_processors.plots.processors diff --git a/docs/code_ref/products.rst b/docs/code_ref/products.rst index e43497d0..21d69d18 100644 --- a/docs/code_ref/products.rst +++ b/docs/code_ref/products.rst @@ -2,32 +2,30 @@ STIXCore Products ***************** The ``products`` submodule contains processing classes representing high level -data products create from multiple packets with additional checks. +data products created from multiple packets with additional checks. +The products are organized by processing level (``LB`` raw binary, ``L0``-``L3``) +and by product category (``ANC`` ancillary, ``CAL`` calibration): -.. automodapi:: stixcore.products +.. toctree:: + :maxdepth: 2 -.. automodapi:: stixcore.products.common - -.. automodapi:: stixcore.products.product - :include-all-objects: - -.. automodapi:: stixcore.products.levelb - -.. automodapi:: stixcore.products.levelb.binary + products/lb + products/l0 + products/l1 + products/l2 + products/l3 + products/ll + products/anc + products/cal -.. automodapi:: stixcore.products.level0.quicklookL0 -.. automodapi:: stixcore.products.level1.quicklookL1 +Base classes +============ -.. automodapi:: stixcore.products.level0.housekeepingL0 +Shared base classes and helpers used by all product levels. -.. automodapi:: stixcore.products.level1.housekeepingL1 - -.. automodapi:: stixcore.products.level0.scienceL0 - -.. automodapi:: stixcore.products.level1.scienceL1 - -.. automodapi:: stixcore.products.level2.housekeepingL2 +.. automodapi:: stixcore.products.product + :include-all-objects: -.. automodapi:: stixcore.products.level2.quicklookL2 +.. automodapi:: stixcore.products.common diff --git a/docs/code_ref/products/anc.rst b/docs/code_ref/products/anc.rst new file mode 100644 index 00000000..ebddf3e8 --- /dev/null +++ b/docs/code_ref/products/anc.rst @@ -0,0 +1,6 @@ +Ancillary (ANC) +*************** + +Ancillary products such as the aspect / ephemeris data. + +.. automodapi:: stixcore.products.ANC.aspect diff --git a/docs/code_ref/products/cal.rst b/docs/code_ref/products/cal.rst new file mode 100644 index 00000000..357d49e0 --- /dev/null +++ b/docs/code_ref/products/cal.rst @@ -0,0 +1,6 @@ +Calibration (CAL) +***************** + +Calibration products such as the energy calibration. + +.. automodapi:: stixcore.products.CAL.energy diff --git a/docs/code_ref/products/l0.rst b/docs/code_ref/products/l0.rst new file mode 100644 index 00000000..887889da --- /dev/null +++ b/docs/code_ref/products/l0.rst @@ -0,0 +1,10 @@ +Level 0 (L0) +************ + +Decommutated, uncalibrated products in engineering/raw units. + +.. automodapi:: stixcore.products.level0.quicklookL0 + +.. automodapi:: stixcore.products.level0.housekeepingL0 + +.. automodapi:: stixcore.products.level0.scienceL0 diff --git a/docs/code_ref/products/l1.rst b/docs/code_ref/products/l1.rst new file mode 100644 index 00000000..c3f148ec --- /dev/null +++ b/docs/code_ref/products/l1.rst @@ -0,0 +1,10 @@ +Level 1 (L1) +************ + +Calibrated products in physical units with applied corrections. + +.. automodapi:: stixcore.products.level1.quicklookL1 + +.. automodapi:: stixcore.products.level1.housekeepingL1 + +.. automodapi:: stixcore.products.level1.scienceL1 diff --git a/docs/code_ref/products/l2.rst b/docs/code_ref/products/l2.rst new file mode 100644 index 00000000..3d78288c --- /dev/null +++ b/docs/code_ref/products/l2.rst @@ -0,0 +1,10 @@ +Level 2 (L2) +************ + +Higher-level calibrated products. + +.. automodapi:: stixcore.products.level2.quicklookL2 + +.. automodapi:: stixcore.products.level2.housekeepingL2 + +.. automodapi:: stixcore.products.level2.scienceL2 diff --git a/docs/code_ref/products/l3.rst b/docs/code_ref/products/l3.rst new file mode 100644 index 00000000..a01773d5 --- /dev/null +++ b/docs/code_ref/products/l3.rst @@ -0,0 +1,10 @@ +Level 3 (L3) +************ + +Level-3 products are higher-level science products derived from the lower levels. +All current L3 products belong to the flare list. + +.. toctree:: + :maxdepth: 1 + + l3_flarelist diff --git a/docs/code_ref/products/l3_flarelist.rst b/docs/code_ref/products/l3_flarelist.rst new file mode 100644 index 00000000..17a89691 --- /dev/null +++ b/docs/code_ref/products/l3_flarelist.rst @@ -0,0 +1,20 @@ +FlareList +========= + +The L3 flare-list products: the enriched SDC flare list, the per-flare peak-preview +image product, and the flare-location imaging helpers. + +.. seealso:: + + :doc:`/products/flarelist` — how the SDC flare list is built from the STIX Data Center + flare list and enriched (quiet-time background selection, CPD-file selection and + flare-location imaging). See also `~stixcore.io.FlareListManager` for the source manager. + +.. automodapi:: stixcore.products.level3.flarelist + :skip: FlarelistSC + :skip: FlarelistSCLocation + :skip: FlarelistSCLocationImage + +.. automodapi:: stixcore.products.level3.flarelistproduct + +.. automodapi:: stixcore.products.level3.processing diff --git a/docs/code_ref/products/lb.rst b/docs/code_ref/products/lb.rst new file mode 100644 index 00000000..1f9eaf9e --- /dev/null +++ b/docs/code_ref/products/lb.rst @@ -0,0 +1,6 @@ +Level B (LB) — raw binary +************************* + +Raw binary telemetry products, the entry point of the processing pipeline. + +.. automodapi:: stixcore.products.levelb.binary diff --git a/docs/code_ref/products/ll.rst b/docs/code_ref/products/ll.rst new file mode 100644 index 00000000..578c192f --- /dev/null +++ b/docs/code_ref/products/ll.rst @@ -0,0 +1,6 @@ +Low Latency (LL) +**************** + +Low-latency quicklook products. + +.. automodapi:: stixcore.products.lowlatency.quicklookLL diff --git a/docs/index.rst b/docs/index.rst index 4082f323..c09e796e 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -8,6 +8,7 @@ This is the documentation for STIXCore. whatsnew/index users + products/flarelist developers code_ref/index pipelineconfiguration diff --git a/docs/products/flarelist.rst b/docs/products/flarelist.rst new file mode 100644 index 00000000..e75185c1 --- /dev/null +++ b/docs/products/flarelist.rst @@ -0,0 +1,215 @@ +.. _flarelist_sdc: + +**************************** +SDC Flare List (Level 3) +**************************** + +The SDC flare list is an enriched, level-3 flare catalogue. It starts from the operational +flare list produced by the STIX Data Center (SDC) and adds STIX-derived quantities that the +source list does not provide reliably: quicklook lightcurve / background counts at the flare +peak, a quiet-time background spectrum, and — for the higher product levels — a flare +location and peak-preview images. + +This page describes where the raw data comes from and each enrichment step, including the +decisions taken when several inputs are available (which background period, which CPD file, +which imaging parameters). For the class and column reference see +:doc:`/code_ref/products/l3_flarelist` (`~stixcore.products.level3.flarelist`, +`~stixcore.products.level3.flarelistproduct`) and :doc:`/code_ref/io` +(`~stixcore.io.FlareListManager`). + +.. note:: + + A parallel ``FlarelistSC*`` family (a flare list detected inside STIXCore rather than + mirrored from the SDC) is planned but not yet in use; it is intentionally omitted here. + + +Product chain +============= + +The SDC products form a chain in which each level adds one enrichment step (built by +`~stixcore.processing.FlareListL3.FlareListL3` for the first level and upgraded +level-to-level by `~stixcore.processing.FLtoFL.FLtoFL`): + +.. list-table:: + :header-rows: 1 + :widths: 30 10 60 + + * - Product + - ssid + - Adds + * - `~stixcore.products.level3.flarelist.FlarelistSDC` + - 2 + - Base list: id, times, GOES class/flux, QL peak & quiet-time background, SOOP campaign. + * - `~stixcore.products.level3.flarelist.FlarelistSDCLocation` + - 3 + - Flare location from CPD imaging (+ SOLO position, imaging-quality metric, 1-AU fluxes). + * - `~stixcore.products.level3.flarelist.FlarelistSDCLocationImage` + - 4 + - Per-flare peak-preview CLEAN images (`~stixcore.products.level3.flarelistproduct.PeakPreviewImage`). + + +Raw data in — the STIX Data Center flare list +============================================= + +`~stixcore.io.FlareListManager.SDCFlareListManager` maintains a local CSV mirror of the SDC +flare list. ``read_flarelist`` fetches it with ``stixdcpy.fetch_flare_list(start, end)`` in +roughly monthly chunks from 2020-01-01 up to *now* — the API is batched by month and +throttled (~2 calls/s, so the loop sleeps between chunks), and incremental updates re-fetch +the last ~60 days to catch late revisions. The combined list is de-duplicated, sorted by +``peak_UTC`` and cached to CSV. ``get_data`` then slices out the requested month. + +The following source fields are consumed (see +`~stixcore.io.FlareListManager.SDCFlareListManager.get_data`): + +.. list-table:: + :header-rows: 1 + :widths: 45 55 + + * - Source (CSV) field + - Product column + * - ``flare_id`` + - ``flare_id`` + * - ``start_UTC``, ``duration``, ``end_UTC``, ``peak_UTC`` + - same names (UTC time columns / duration in s) + * - ``GOES_class``, ``goes_estimated_{min,max,mean}_class`` + - ``GOES_class``, ``goes_{min,max,mean}_class_est`` + * - ``GOES_flux``, ``goes_estimated_{min,max,mean}_flux`` + - ``GOES_flux``, ``goes_{min,max,mean}_flux_est`` (stored as ``10**value`` W/m²) + +.. warning:: + + The ``GOES_*`` columns describe the Earth-viewed GOES/XRS flux and are **not** derived + from STIX — they are meaningless when the flare is occulted from Earth. The source list's + own at-peak lightcurve counts and attenuator flag are **unreliable** and are deliberately + re-derived from real STIX data (next section). + + +Enrichment step — QL lightcurve & background at peak +==================================================== + +`~stixcore.io.FlareListManager.FlareListManager.add_lc_bkg_columns` builds one monthly +timeline each for the ``ql_lightcurve`` and ``ql_background`` products (via +`~stixcore.io.FlareListManager.build_month_timeline`, so the daily files are opened only +once), then for every flare picks the bin nearest the peak +(`~stixcore.io.FlareListManager.nearest_bin_index`). It records, for the five QL energy +channels, the raw counts (``lc_peak``, ``lc_bkg_peak``) and the livetime- and +area-normalized flux (``lc_peak_flux``, ``lc_bkg_peak_flux``, in +``ct s⁻¹ keV⁻¹ cm⁻²``), together with ``rcr_at_peak`` / ``rcr_max``, the derived ``att_in`` +flag and the ``energy_index`` into the energy table. This replaces the unreliable +source-CSV values with quantities taken straight from the L1 quicklook data. + + +Enrichment step — quiet-time background file +============================================ + +For the background *spectrum* a quiet-time science file has to be chosen. +`~stixcore.io.FlareListManager.find_background_file_for_time` implements the selection: + +* **Candidate ranking.** Background requests are taken from the RID look-up table + (``search_background_candidates``) and ranked **nearest-in-time first** — the request whose + start is closest to the flare, past or future — within separate look-back / look-ahead + windows (``window_past``, default 30 d; ``window_future``, default 7 d). +* **Acceptance checks.** A candidate is accepted only if its requested integration is long + enough (``min_duration``, default 1200 s), its real ``sci_xray_cpd`` L1 file can be resolved + (and the filename's request id matches), and the **attenuator is out for the whole file** + (``rcr == 0``). The first candidate that passes wins. +* **Optional stricter filters** (all default *off* except same-ELUT, because they move common + cases rather than edge cases), each backed by a ``[Processing]`` config key: + + .. list-table:: + :header-rows: 1 + :widths: 55 45 + + * - Filter + - Config key + * - Require the same ELUT as the flare time + - ``flarelist_bkg_require_same_elut`` (default on) + * - Prefer ``purpose == "Background"`` requests + - ``flarelist_bkg_purpose_penalty_days`` + * - Drop "elevated" backgrounds + - ``flarelist_bkg_exclude_elevated`` + * - Drop requests whose comment references a specific flare + - ``flarelist_bkg_exclude_flare_comment`` + +* **Validity-interval caching.** The result carries a ``[valid_from, valid_to]`` interval, so + the monthly (time-ordered) loop reuses one selection for every later flare until a different + candidate would become effectively closer (or the window edge is reached). This is why the + same background file is not re-resolved flare by flare. + +The spectrum itself (`~stixcore.io.FlareListManager.background_spectrum_from_cpd`) is the +**median over the file's time bins** — the "most recent quiet period" — summed over the 30 +imaging detectors and their pixels. It is stored as native-channel counts (``bkg_spec``) and +flux (``bkg_spec_flux``), and rebinned to the QL lightcurve bands as counts (``bkg_spec_ql``) +and flux (``bkg_spec_flux_ql``); the file's native (32-channel) binning is appended to the +energy table and referenced by ``bkg_energy_index``. + + +Enrichment step — flare location +================================ + +`~stixcore.products.level3.flarelist.FlarePositionMixin.add_flare_position` (products at +ssid ≥ 3) adds a location for each flare. Per flare it searches for the daily ancillary +``asp_ephemeris`` file (cached per day) and for ``sci_xray_cpd`` files over +``[start, end]``, recording ``anc_ephemeris_path`` and ``cpd_path``. + +CPD-file selection +------------------ + +When several CPD files cover a flare, each candidate is read as a full product +(`~stixpy.product.Product`) and scored on four criteria (in priority order):: + + cpd_res.sort(["inc_peak", "inc_flare", "_neg_min_dt", "ebins"], reverse=True) # take the top row + +* ``inc_peak`` — whether the flare **peak** time falls inside the file (preferred first), +* ``inc_flare`` — **percentage** of the flare ``start..end`` duration covered by the file (higher preferred next), +* **min time resolution** — the shortest ``timedel`` among the data-table bins overlapping the flare + (**shorter is better**; sorted via its negative ``_neg_min_dt``, in deciseconds), +* ``ebins`` — number of **energy bins** in the energy table (**more is better**). + +(A ``TODO`` notes more criteria may be added.) **The CPD file chosen here is the same file later used to +make the peak-preview images**, so the choice serves both the location and the imaging. + +Fit time & energy range +----------------------- + +The fit uses ``[peak − 20 s, peak + 20 s]`` clamped to ``[start, end]`` (falling back to the +file's own time range if there is no overlap). If ``rcr`` is not constant across that window, +the window is widened to ±40 s and the **longest constant-``rcr`` sub-sequence** +(`~stixcore.products.level3.flarelist.longest_constant_sequence`) is used instead. The energy +range is ``[4, 16] keV``, widening to ``[4, 25] keV`` when the attenuator is in +(``rcr > 0``). + +Imaging parameters +------------------ + +The location is estimated by +`~stixcore.products.level3.processing.stx_estimate_flare_location` (a port of the STIX-GSW +IDL routine of the same name): + +* meta pixels via ``create_meta_pixels(no_shadowing=True, flare_location=[0, 0]″)``, then + ``create_visibility`` and ``calibrate_visibility`` with the **Sun centre** as phase centre; +* only the **coarse sub-collimators 7–10** are used + (``isc = [3, 20, 22, 16, 14, 32, 21, 26, 4, 24, 8, 28]``); +* a back-projection map of ``512 × 512`` pixels with plate scale + ``pixel = rsun_obs · 2.6 / imsize`` (the factor 2.6 keeps the full solar disc in the field + of view, matching the IDL implementation); the flare location is the brightest pixel, + transformed to Helioprojective coordinates (a spherical screen is assumed for off-disk + positions); +* an imaging-quality metric ``sidelobes_ratio`` is computed as the strongest sidelobe outside + a 200″ radius relative to the peak — a value close to (≳ 0.9) or above the peak indicates the + location is unreliable. It is surfaced as the ``sidelobes_ratio`` column. + +.. note:: + + The peak-preview *image* product + (`~stixcore.products.level3.flarelist.FlarePeakPreviewMixin`, ssid 4) runs a fuller CLEAN + reconstruction in the ``[4, 20]`` and ``[20, 120] keV`` bands on the **same selected CPD + file**. Its output columns are not yet finalised and are out of scope for this page. + + +Outputs +======= + +The per-flare columns (units, shapes, meanings) are documented as the column descriptions in +the API reference — see `~stixcore.products.level3.flarelist` and +`~stixcore.io.FlareListManager`. A consolidated data dictionary is planned as a follow-up. diff --git a/notebooks/flarelist_sdcloc_catalog.md b/notebooks/flarelist_sdcloc_catalog.md new file mode 100644 index 00000000..05b85356 --- /dev/null +++ b/notebooks/flarelist_sdcloc_catalog.md @@ -0,0 +1,777 @@ +# SDC-Location flare-catalog column reference +Auto-generated by `notebooks/generate_flarelist_catalog_md.py` from a real product read via the stixcore `Product(path)` reader (no direct FITS access). +- **product class:** `FlarelistSDCLocation` +- **file:** `solo_L3_stix-flarelist-sdcloc_20230101T000000-20230131T231535_V04.fits` +- **rows:** 1649 (example values below are from row index 1, a fully-populated flare) +- **columns:** 53 + +**Review notes on the file itself:** +- Column descriptions **are** persisted, via astropy's serialized-column metadata (a YAML block in the header `COMMENT` cards — the standard astropy unit/`meta` framework), not in the classic per-column `TCOMMn` keyword. They round-trip through a plain `astropy` `QTable.read` as well as the stixcore `Product` reader. +- 3 columns have **no** stored description (`energy_index`, and the ICRS location columns `location_icrs` / `solo_location_icrs`); those are supplied here from a small override map. +- On read, the location is provided both as ICRS (`location_icrs`, `solo_location_icrs`, the on-disk form) and as HGS (`location_hgs`, `solo_location_hgs`). +- `source` legend: **API** = one-to-one copy from the SDC API/CSV; **stixcore** = reprocessed / newly generated by code in the stixcore package; **stixpy** = substantive computation whose code lives in the stixpy package. (In this catalog no column is **stixpy** yet: the reprocessing code all lives in stixcore, which uses stixpy/sunpy/astropy/xrayvision as libraries — noted per column where relevant. Some of these methods are intended to move into stixpy in the future, at which point those columns become **stixpy**.) + +--- + +## SDC (FlarelistSDC) + +### 1. Source CSV — [`SDCFlareListManager.get_data`](../stixcore/io/FlareListManager.py#L1361-L1499) + +Base flare definitions sliced from the local SDC flare-list CSV mirror (fetched from the STIX Data Center API). One-to-one copies unless noted. + +#### flare_id + +- **comment:** unique flare id for flarelist SDCFlareListManager +- **unit:** — +- **example:** `2301010034` +- **source:** API + +**review comment:** unique identifier for the flare maintained by stix datacenter + +#### start_UTC + +- **comment:** start time of flare +- **unit:** UTC (Time) +- **example:** `2023-01-01T00:32:16.484 (UTC)` +- **source:** API + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### duration + +- **comment:** duration of flare +- **unit:** s +- **example:** `1240 s` +- **source:** API + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### end_UTC + +- **comment:** end time of flare +- **unit:** UTC (Time) +- **example:** `2023-01-01T00:52:56.486 (UTC)` +- **source:** API + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### peak_UTC + +- **comment:** flare peak time +- **unit:** UTC (Time) +- **example:** `2023-01-01T00:34:44.484 (UTC)` +- **source:** API + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### GOES_class + +- **comment:** GOES class of the GOES XRS data at time of flare - not derived from STIX data. Do not use when flare isn't visible to Earth +- **unit:** — +- **example:** `C1.7` +- **source:** API + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### goes_min_class_est + +- **comment:** min GOES class estimate derived from STIX data +- **unit:** — +- **example:** `B6` +- **source:** API + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### goes_max_class_est + +- **comment:** max GOES class estimate derived from STIX data +- **unit:** — +- **example:** `C1` +- **source:** API + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### goes_mean_class_est + +- **comment:** mean GOES class estimate derived from STIX data +- **unit:** — +- **example:** `B8` +- **source:** API + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### GOES_flux + +- **comment:** GOES flux of the GOES XRS data at time of flare- not derived from STIX data. Do not use when the flare isn't visible to Earth +- **unit:** W / m2 +- **example:** `1.67984e-06 W / m2` +- **source:** API + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### goes_min_flux_est + +- **comment:** min GOES flux estimate derived from STIX data _(stored as 10**; otherwise a one-to-one copy from the SDC flare-list CSV.)_ +- **unit:** W / m2 +- **example:** `nan W / m2` +- **source:** API + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### goes_max_flux_est + +- **comment:** max GOES flux estimate derived from STIX data _(stored as 10**; otherwise a one-to-one copy from the SDC flare-list CSV.)_ +- **unit:** W / m2 +- **example:** `nan W / m2` +- **source:** API + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### goes_mean_flux_est + +- **comment:** mean GOES flux estimate derived from STIX data _(stored as 10**; otherwise a one-to-one copy from the SDC flare-list CSV.)_ +- **unit:** W / m2 +- **example:** `nan W / m2` +- **source:** API + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +### 2. QL peak & background — [`FlareListManager.add_lc_bkg_columns`](../stixcore/io/FlareListManager.py#L708-L826) + +At-peak lightcurve / background-detector counts and fluxes, rcr and attenuator state, derived from the real L1 QL products (the source-CSV values are unreliable). + +#### lc_peak + +- **comment:** raw counts at the L1 QL lightcurve bin nearest the flare peak (5 energy channels) +- **unit:** ct +- **example:** `[1087., 99., 75., 735., 431.] ct` +- **source:** stixcore + +**calculation summary:** Raw counts at the QL lightcurve bin nearest the flare peak (5 QL channels), from a monthly ql_lightcurve timeline built once. +Processing: [`FlareListManager.add_lc_bkg_columns`](../stixcore/io/FlareListManager.py#L708-L826), [`FlareListManager._build_ql_month_timeline`](../stixcore/io/FlareListManager.py#L618-L706), [`nearest_bin_index`](../stixcore/io/FlareListManager.py#L106-L118). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### lc_peak_flux + +- **comment:** livetime- and area-corrected flux at the peak lightcurve bin (ct/s/keV/cm2, 5 energy channels) +- **unit:** ct / (keV s cm2) +- **example:** `[1.7785, 0.1944, 0.0736, 0.2886, 0.1244] ct / (keV s cm2)` +- **source:** stixcore +- **uses:** `lc_peak` + +**calculation summary:** Livetime- and area-corrected flux at the peak bin. Flux normalization = counts / (livetime x energy-width x collecting-area) -> ct/s/keV/cm2. Livetime via stixpy `get_livetime_fraction`; collecting area from the pixel/detector masks. +Processing: [`compute_ql_count_rate`](../stixcore/io/FlareListManager.py#L57-L84), [`collecting_area_cm2`](../stixcore/io/FlareListManager.py#L164-L173). +From [`FlareListManager.py#L57-L84`](../stixcore/io/FlareListManager.py#L57-L84): + +```python +def compute_ql_count_rate(counts, timedel, triggers, energy_delta, *, n_detectors): + """Reproduce stixpy's QL count-rate normalization -> ``ct / (s * keV)``. + + Mirrors ``stixpy.timeseries.quicklook`` (lightcurve uses ``n_detectors=16``, + background uses ``n_detectors=1``). Pure, no I/O. + + Parameters + ---------- + counts : `~astropy.units.Quantity` + Raw counts, shape ``(N, 5)`` in ``ct``. + timedel : `~astropy.units.Quantity` + Bin durations, shape ``(N,)``. + triggers : array-like + Trigger counts, shape ``(N,)`` or ``(N, 1)``. + energy_delta : `~astropy.units.Quantity` + Channel widths, shape ``(5,)`` in ``keV``. + n_detectors : int + 16 for the lightcurve, 1 for the background detector. + + Returns + ------- + `~astropy.units.Quantity` + Count rate, shape ``(N, 5)`` in ``ct / (s * keV)``. + """ + timedel = timedel.to(u.s) + trig = np.asarray(triggers).reshape(-1) + live_frac, *_ = get_livetime_fraction(trig / (n_detectors * timedel)) + return counts / ((timedel * live_frac).reshape(-1, 1) * energy_delta) +``` + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### lc_bkg_peak + +- **comment:** raw counts from the STIX background detector at the L1 QL background bin nearest the flare peak (5 energy channels) — unmodulated by imaging subcollimators and not affected by the attenuator +- **unit:** ct +- **example:** `[24., 3., 5., 45., 28.] ct` +- **source:** stixcore + +**calculation summary:** Raw counts from the STIX background-monitor detector at the QL background bin nearest the peak (unmodulated by the imaging grids, unaffected by the attenuator). +Processing: [`FlareListManager.add_lc_bkg_columns`](../stixcore/io/FlareListManager.py#L708-L826), [`FlareListManager._build_ql_month_timeline`](../stixcore/io/FlareListManager.py#L618-L706). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### lc_bkg_peak_flux + +- **comment:** livetime- and area-corrected STIX background detector flux at the peak bin (ct/s/keV/cm2, 5 energy channels) +- **unit:** ct / (keV s cm2) +- **example:** `[0.5893, 0.0884, 0.0737, 0.2652, 0.1213] ct / (keV s cm2)` +- **source:** stixcore +- **uses:** `lc_bkg_peak` + +**calculation summary:** As lc_peak_flux but for the background-monitor detector (open-detector area). Flux normalization = counts / (livetime x energy-width x collecting-area) -> ct/s/keV/cm2. +Processing: [`compute_ql_count_rate`](../stixcore/io/FlareListManager.py#L57-L84), [`collecting_area_cm2`](../stixcore/io/FlareListManager.py#L164-L173). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### rcr_at_peak + +- **comment:** max rcr level at flare location estimation time range, > 0 attenuator in place +- **unit:** — +- **example:** `0` +- **source:** stixcore + +**calculation summary:** Rate-control-regime at the peak bin. NOTE: first written here, then OVERWRITTEN at the sdcloc level by add_flare_position with the max rcr over the imaging window. +Processing: [`FlareListManager.add_lc_bkg_columns`](../stixcore/io/FlareListManager.py#L708-L826), [`FlarePositionMixin.add_flare_position`](../stixcore/products/level3/flarelist.py#L213-L550). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### rcr_max + +- **comment:** max rate control regime over the flare start..end window +- **unit:** — +- **example:** `False` +- **source:** stixcore + +**calculation summary:** Maximum rate-control-regime over the flare start..end window (stored as bool when the month only has rcr in {0,1}). +Processing: [`FlareListManager.add_lc_bkg_columns`](../stixcore/io/FlareListManager.py#L708-L826), [`max_rcr_in_window`](../stixcore/io/FlareListManager.py#L121-L128). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### att_in + +- **comment:** was attenuator in during flare (rcr_max > 0) +- **unit:** — +- **example:** `False` +- **source:** stixcore +- **uses:** `rcr_max` + +**calculation summary:** Whether the attenuator was in during the flare, derived as rcr_max > 0. +Processing: [`FlareListManager.add_lc_bkg_columns`](../stixcore/io/FlareListManager.py#L708-L826). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### energy_index + +- **comment:** index into the ENERGIES table of the QL energy binning used for this flare (no description is stored on the column itself) +- **unit:** — +- **example:** `False` +- **source:** stixcore + +**calculation summary:** Index into the ENERGIES table for the QL binning used (honors a QL binning that changes over time). +Processing: [`FlareListManager.add_lc_bkg_columns`](../stixcore/io/FlareListManager.py#L708-L826). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +### 3. Quiet-time background spectrum — [`FlareListManager.add_background_file_column`](../stixcore/io/FlareListManager.py#L828-L969) + +Median quiet-period background spectrum from the selected background CPD file, native and QL-rebinned. + +#### bkg_file + +- **comment:** path to the quiet-time background CPD file +- **unit:** — +- **example:** `L1/2023/01/01/SCI/solo_L1_stix-sci-xray-cpd_20230101T110501-20230101T115341_V02_2301018231-56312.fits` +- **source:** stixcore + +**calculation summary:** Path of the selected quiet-time background CPD file (nearest-in-time candidate that passes the duration/attenuator checks). +Processing: [`FlareListManager.add_background_file_column`](../stixcore/io/FlareListManager.py#L828-L969), [`find_background_file_for_time`](../stixcore/io/FlareListManager.py#L414-L585). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### bkg_rid + +- **comment:** BSD request id of the selected background file (-1 if none) +- **unit:** — +- **example:** `2301018231` +- **source:** stixcore + +**calculation summary:** BSD request id of the selected background file (-1 if none). +Processing: [`FlareListManager.add_background_file_column`](../stixcore/io/FlareListManager.py#L828-L969), [`find_background_file_for_time`](../stixcore/io/FlareListManager.py#L414-L585). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### bkg_spec + +- **comment:** median quiet-period background counts per science energy channel (30 imaging detectors) +- **unit:** ct +- **example:** `[ 45704., 20467., 15673., 53039., 24553., 6801., 5492., 10407., 8293., 4180., 3357., 3404., + 7143., 7481., 8093., 16228., 26373., 382036., 87009., 14315., 12837., 15052., 32817., 38605., + 29864., 34574., 145977., 40422., 226197., 372446., 175127., nan] ct` +- **source:** stixcore + +**calculation summary:** Median quiet-period background counts per native science channel, summed over the 30 imaging detectors and their pixels. +Processing: [`background_spectrum_from_cpd`](../stixcore/io/FlareListManager.py#L221-L267). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### bkg_spec_ql + +- **comment:** median quiet-period background counts rebinned to the QL lightcurve energy bands +- **unit:** ct +- **example:** `[166237., 31729., 42349., 537622., 281837.] ct` +- **source:** stixcore +- **uses:** `bkg_spec`, `ENERGIES table` + +**calculation summary:** bkg_spec rebinned to the flare's QL energy bands (whole-channel sum). +Processing: [`rebin_spectrum_to_ql`](../stixcore/io/FlareListManager.py#L282-L302). +From [`FlareListManager.py#L282-L302`](../stixcore/io/FlareListManager.py#L282-L302): + +```python +def rebin_spectrum_to_ql(counts_32, energies_32, ql_block): + """Rebin a science *counts* spectrum onto the QL energy bands of ``ql_block``. + + ``ql_block`` carries the target band edges (``e_low``/``e_high``) read from the + energy table for a given flare (never hardcoded, so a QL binning that changes + over time is honored). A science channel contributes to a band when its + ``[e_low, e_high]`` lies within the band; whole channels are summed (the edges + align). Summing preserves the input unit, so the result carries ``counts_32``'s unit. + """ + e_low = _edges_kev(energies_32["e_low"]) + e_high = _edges_kev(energies_32["e_high"]) + b_low = _edges_kev(ql_block["e_low"]) + b_high = _edges_kev(ql_block["e_high"]) + unit = getattr(counts_32, "unit", None) + counts = np.asarray(getattr(counts_32, "value", counts_32), dtype=float) + out = np.full(len(b_low), np.nan) + for b in range(len(b_low)): + chans = (e_low >= b_low[b]) & (e_high <= b_high[b]) + if np.any(chans): + out[b] = np.nansum(counts[chans]) + return out * unit if unit is not None else out +``` + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### bkg_spec_flux + +- **comment:** median quiet-period background flux per science channel (ct/s/keV/cm2, 30 imaging detectors) +- **unit:** ct / (keV s cm2) +- **example:** `[0.6145, 0.2752, 0.2107, 0.7131, 0.3301, 0.0914, 0.0738, 0.1399, 0.1115, 0.0562, 0.0451, 0.0458, 0.048 , + 0.0503, 0.0544, 0.0727, 0.1182, 1.2841, 0.2925, 0.0481, 0.0345, 0.0405, 0.0735, 0.0741, 0.0574, 0.0775, + 0.2453, 0.034 , 0.1521, 0.1669, nan, nan] ct / (keV s cm2)` +- **source:** stixcore + +**calculation summary:** Livetime- and area-normalized background flux per native channel. Flux normalization = counts / (livetime x energy-width x collecting-area) -> ct/s/keV/cm2. +Processing: [`background_spectrum_from_cpd`](../stixcore/io/FlareListManager.py#L221-L267). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### bkg_spec_flux_ql + +- **comment:** median quiet-period background flux rebinned to the QL lightcurve energy bands (ct/s/keV/cm2) +- **unit:** ct / (keV s cm2) +- **example:** `[0.3725, 0.0853, 0.0569, 0.2891, 0.1114] ct / (keV s cm2)` +- **source:** stixcore +- **uses:** `bkg_spec_flux`, `ENERGIES table` + +**calculation summary:** bkg_spec_flux rebinned to the QL bands (dE-weighted mean, a density). +Processing: [`rebin_flux_to_ql`](../stixcore/io/FlareListManager.py#L305-L325). +From [`FlareListManager.py#L305-L325`](../stixcore/io/FlareListManager.py#L305-L325): + +```python +def rebin_flux_to_ql(flux_32, energies_32, ql_block): + """Rebin a science *flux* spectrum (per keV) onto the QL bands of ``ql_block``. + + Flux is a density, so bands combine as the dE-weighted mean + ``flux_band = Σ(flux_ch·dE_ch) / Σ dE_ch`` over each band's channels (edges align). + The dE weights cancel dimensionally, so the result carries ``flux_32``'s unit. + """ + e_low = _edges_kev(energies_32["e_low"]) + e_high = _edges_kev(energies_32["e_high"]) + dE = e_high - e_low + b_low = _edges_kev(ql_block["e_low"]) + b_high = _edges_kev(ql_block["e_high"]) + unit = getattr(flux_32, "unit", None) + flux = np.asarray(getattr(flux_32, "value", flux_32), dtype=float) + out = np.full(len(b_low), np.nan) + for b in range(len(b_low)): + chans = (e_low >= b_low[b]) & (e_high <= b_high[b]) + denom = np.nansum(dE[chans]) + if np.any(chans) and denom > 0: + out[b] = np.nansum(flux[chans] * dE[chans]) / denom + return out * unit if unit is not None else out +``` + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### bkg_energy_index + +- **comment:** energy table index of the native background binning (-1 if no background file) +- **unit:** — +- **example:** `1` +- **source:** stixcore + +**calculation summary:** Index into the ENERGIES table of the native (32-ch) background binning (-1 if no file). +Processing: [`FlareListManager.add_background_file_column`](../stixcore/io/FlareListManager.py#L828-L969). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### bkg_solo_sun_distance + +- **comment:** SOLO-Sun distance at the background CPD's observation time (from DSUN_OBS; NaN if none) +- **unit:** km +- **example:** `1.41632e+08 km` +- **source:** stixcore + +**calculation summary:** SOLO-Sun distance at the background CPD's observation time, from its DSUN_OBS header keyword. +Processing: [`FlareListManager.add_background_file_column`](../stixcore/io/FlareListManager.py#L828-L969). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +### 4. SOOP campaign — [`FlareSOOPMixin.add_soop`](../stixcore/products/level3/flarelist.py#L618-L649) + +Solar Orbiter observing-campaign metadata active at the flare peak. + +#### soop_encoded_type + +- **comment:** campaign ID +- **unit:** — +- **example:** `None` +- **source:** stixcore + +**calculation summary:** Encoded SOOP campaign type active at the flare peak. +Processing: [`FlareSOOPMixin.add_soop`](../stixcore/products/level3/flarelist.py#L618-L649). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### soop_id + +- **comment:** SOOP ID +- **unit:** — +- **example:** `None` +- **source:** stixcore + +**calculation summary:** SOOP campaign instance id active at the flare peak. +Processing: [`FlareSOOPMixin.add_soop`](../stixcore/products/level3/flarelist.py#L618-L649). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### soop_type + +- **comment:** name of the SOOP campaign +- **unit:** — +- **example:** `None` +- **source:** stixcore + +**calculation summary:** SOOP campaign name active at the flare peak. +Processing: [`FlareSOOPMixin.add_soop`](../stixcore/products/level3/flarelist.py#L618-L649). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +--- + +## SDCLOC additions (FlarelistSDCLocation) + +### 5. Flare position — [`FlarePositionMixin.add_flare_position`](../stixcore/products/level3/flarelist.py#L213-L550) + +Flare location by back-projection imaging (stixpy/xrayvision) plus the CPD/ephemeris bookkeeping, geometry and quality metrics. Added when the sdc product is upgraded to sdcloc. + +#### anc_ephemeris_path + +- **comment:** Path to the daily ancillary ephemeris file +- **unit:** — +- **example:** `ANC/2023/01/01/ASP/solo_ANC_stix-asp-ephemeris_20230101_V02.fits` +- **source:** stixcore + +**calculation summary:** Path of the daily ancillary aspect-ephemeris file found via FIDO for the flare peak. +Processing: [`FlarePositionMixin.add_flare_position`](../stixcore/products/level3/flarelist.py#L213-L550). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### cpd_path + +- **comment:** Path to the CPD file used for flare location estimation +- **unit:** — +- **example:** `L1/2023/01/01/SCI/solo_L1_stix-sci-xray-cpd_20230101T003107-20230101T004058_V02_2301015683-59282.fits` +- **source:** stixcore + +**calculation summary:** Path of the CPD file selected for the location (scored inc_peak/inc_flare/min-dt/ebins); the same file feeds the peak-preview imaging. +Processing: [`FlarePositionMixin.add_flare_position`](../stixcore/products/level3/flarelist.py#L213-L550). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### _position_status + +- **comment:** Status of the flare position calculation +- **unit:** — +- **example:** `True` +- **source:** stixcore + +**calculation summary:** Internal QC flag: True when a location was successfully estimated. +Processing: [`FlarePositionMixin.add_flare_position`](../stixcore/products/level3/flarelist.py#L213-L550). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### _position_message + +- **comment:** Message describing the status of the flare position calculation +- **unit:** — +- **example:** `OK` +- **source:** stixcore + +**calculation summary:** Internal QC message (OK / why the location was skipped or failed). +Processing: [`FlarePositionMixin.add_flare_position`](../stixcore/products/level3/flarelist.py#L213-L550). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### location_hgs + +- **comment:** flare location in Heliographic Stonyhurst coordinates (restored from the on-disk ICRS on read) +- **unit:** SkyCoord (heliographic_stonyhurst) +- **example:** `heliographic_stonyhurst lon/Tx≈1.9506 deg, lat/Ty≈17.2736 deg, distance=6.957e+05 km` +- **source:** stixcore + +**calculation summary:** Flare location estimated by `stx_estimate_flare_location` (stixcore): builds a back-projection image from STIX visibilities, takes the brightest pixel as the source, and transforms it to Heliographic Stonyhurst via sunpy/astropy. The visibility helpers it calls live in stixpy (`create_meta_pixels`, `create_visibility`, `calibrate_visibility`, `get_hpc_info`) and xrayvision (`vis_to_image`), but the algorithm assembly and transforms are stixcore code. +Processing: [`stx_estimate_flare_location`](../stixcore/products/level3/processing.py#L97-L195), [`FlarePositionMixin.add_flare_position`](../stixcore/products/level3/flarelist.py#L213-L550). + +**review comment:** the flare location calculation will come in from STIXPY PR: https://github.com/TCDSolar/stixpy/pull/234 + +#### location_icrs + +- **comment:** on-disk ICRS serialization of the flare location (see location_hgs) +- **unit:** SkyCoord (icrs) +- **example:** `ICRS ra=161.3131 deg, dec=18.3517 deg, distance=1.627e+06 km` +- **source:** stixcore + +**calculation summary:** On-disk ICRS serialization of location_hgs (converted on write, back to HGS on read) by the stixcore serialize hook. +Processing: [`FlarePositionMixin.on_serialize`](../stixcore/products/level3/flarelist.py#L552-L569). + +**review comment:** the flare location calculation will come in from STIXPY PR: https://github.com/TCDSolar/stixpy/pull/234 + +#### solo_location_hgs + +- **comment:** Solar Orbiter location in Heliographic Stonyhurst coordinates (restored from ICRS on read) +- **unit:** SkyCoord (heliographic_stonyhurst) +- **example:** `heliographic_stonyhurst lon/Tx≈-21.1655 deg, lat/Ty≈3.8127 deg, distance=1.415e+08 km` +- **source:** stixcore + +**calculation summary:** Solar Orbiter position assembled in `stx_estimate_flare_location` / `add_flare_position` (stixcore) and stored in HGS; the raw pointing/ephemeris comes from stixpy `get_hpc_info`. +Processing: [`stx_estimate_flare_location`](../stixcore/products/level3/processing.py#L97-L195), [`FlarePositionMixin.add_flare_position`](../stixcore/products/level3/flarelist.py#L213-L550). + +**review comment:** the flare location calculation will come in from STIXPY PR: https://github.com/TCDSolar/stixpy/pull/234 + +#### solo_location_icrs + +- **comment:** on-disk ICRS serialization of the SOLO location (see solo_location_hgs) +- **unit:** SkyCoord (icrs) +- **example:** `ICRS ra=77.3106 deg, dec=27.1101 deg, distance=1.413e+08 km` +- **source:** stixcore + +**calculation summary:** On-disk ICRS serialization of solo_location_hgs by the stixcore serialize hook. +Processing: [`FlarePositionMixin.on_serialize`](../stixcore/products/level3/flarelist.py#L552-L569). + +**review comment:** the flare location calculation will come in from STIXPY PR: https://github.com/TCDSolar/stixpy/pull/234 + +#### solo_sun_distance + +- **comment:** distance of Solar Orbiter to Sun center +- **unit:** km +- **example:** `1.41521e+08 km` +- **source:** stixcore +- **uses:** `solo_location_hgs` + +**calculation summary:** Norm of the SOLO HGS cartesian position (km) at the imaging time. +Processing: [`FlarePositionMixin.add_flare_position`](../stixcore/products/level3/flarelist.py#L213-L550). + +**review comment:** via spice + +#### sidelobes_ratio + +- **comment:** Ratio of sidelobes in the STIX image used to assess imaging quality +- **unit:** — +- **example:** `0.785695` +- **source:** stixcore + +**calculation summary:** Imaging-quality metric: strongest back-projection sidelobe outside a 200 arcsec radius relative to the peak (>~0.9 => unreliable). +Processing: [`calculate_sidelobes_ratio`](../stixcore/products/level3/processing.py#L198-L241). + +**review comment:** the flare location calculation will come in from STIXPY PR: https://github.com/TCDSolar/stixpy/pull/234 + +#### visible_from_earth + +- **comment:** Whether the flare location is visible from Earth (not occulted by the Sun) +- **unit:** — +- **example:** `True` +- **source:** stixcore + +**calculation summary:** Whether the flare location is on the Earth-facing side of the Sun. +Processing: [`is_visible`](../stixcore/products/level3/processing.py#L77-L94), [`FlarePositionMixin.add_flare_position`](../stixcore/products/level3/flarelist.py#L213-L550). + +**review comment:** is_visible also moving to STIXPY + +#### location_time_UTC + +- **comment:** time center used for flare location estimation in UTC +- **unit:** UTC (Time) +- **example:** `2023-01-01T00:34:45.133 (UTC)` +- **source:** stixcore + +**calculation summary:** Time center of the imaging window used for the location. +Processing: [`FlarePositionMixin.add_flare_position`](../stixcore/products/level3/flarelist.py#L213-L550). + +**review comment:** the actual time period (center time) on which the location is calculated is defined first in stixcore. The visibility code might (stixpy) might modify the time period to be used for the location calculation + +#### location_duration + +- **comment:** duration of the flare location estimation time range +- **unit:** s +- **example:** `63.3 s` +- **source:** stixcore + +**calculation summary:** Length of the imaging window used for the location. +Processing: [`FlarePositionMixin.add_flare_position`](../stixcore/products/level3/flarelist.py#L213-L550). + +**review comment:** duration of location_time_UTC + +#### min_exposure + +- **comment:** minimum CPD per-bin exposure ds (timedel) over the flare start..end window +- **unit:** ds +- **example:** `599 ds` +- **source:** stixcore + +**calculation summary:** Shortest CPD time-step (ds) among bins overlapping the flare start..end window. +Processing: [`cpd_timedel_range`](../stixcore/products/level3/flarelist.py#L168-L181). +From [`flarelist.py#L168-L181`](../stixcore/products/level3/flarelist.py#L168-L181): + +```python +def cpd_timedel_range(times, timedels, start, end): + """Min and max CPD time-step ``ds`` (``timedel``) for bins overlapping ``[start, end]``. + + Uses the same half-bin overlap test as the peak-window selection so partially covered + windows still contribute. Returns ``(min_exposure, max_exposure)`` as + `~astropy.units.Quantity` in deciseconds (``u.ds``, STIX's native ``timedel`` unit); + ``(NaN ds, NaN ds)`` if no bin overlaps. + """ + half = timedels / 2 + mask = (times + half >= start) & (times - half <= end) + sel = timedels[mask] + if len(sel) == 0: + return np.nan * u.ds, np.nan * u.ds + return sel.min().to(u.ds), sel.max().to(u.ds) +``` + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### max_exposure + +- **comment:** maximum CPD per-bin exposure ds (timedel) over the flare start..end window +- **unit:** ds +- **example:** `792 ds` +- **source:** stixcore + +**calculation summary:** Longest CPD time-step (ds) among bins overlapping the flare start..end window. +Processing: [`cpd_timedel_range`](../stixcore/products/level3/flarelist.py#L168-L181). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### time_shift + +- **comment:** Time(Sun to Earth) - Time(Sun to S/C) +- **unit:** s +- **example:** `18.6269 s` +- **source:** stixcore + +**calculation summary:** Time(Sun->Earth) - Time(Sun->S/C) light-travel-time difference from Spice. +Processing: [`Spice.get_earth_solo_time_shift`](../stixcore/ephemeris/manager.py#L391-L403). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### sun_disc_size + +- **comment:** Apparent photospheric solar radius +- **unit:** arcsec +- **example:** `1013.98 arcsec` +- **source:** stixcore + +**calculation summary:** Apparent photospheric solar radius (arcsec) at the flare, from Spice. +Processing: [`Spice.get_sun_disc_size`](../stixcore/ephemeris/manager.py#L374-L389). + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +### 6. 1-AU normalization — [`add_distance_normalized_flux`](../stixcore/products/level3/flarelist.py#L94-L110) + +Distance-normalized twins of the flux columns (called at the end of add_flare_position). + +**Calculation summary (shared by all columns in this step):** All columns in this step are produced by a single call to [`add_distance_normalized_flux`](../stixcore/products/level3/flarelist.py#L94-L110), each scaling its flux column by ``(r/1AU)**2`` using its mapped distance column (see **uses** per column). Flux ~ 1/r**2, so the factor is dimensionless and the unit is unchanged; the original flux columns are kept. + +From [`flarelist.py#L94-L110`](../stixcore/products/level3/flarelist.py#L94-L110): + +```python +def add_distance_normalized_flux(data, mapping=_FLUX_DISTANCE_COLS): + """Add ``_at_1au`` = `` * (r/1AU)**2`` for each present flux column, using the + per-flare distance in its mapped column (flux scales as 1/r**2). + + The existing flux columns are kept unchanged; the scale factor is dimensionless so the + ``*_at_1au`` columns keep the flux unit (ct/s/keV/cm2). NaN distance -> NaN; a flux column + (or its distance column) that is absent is skipped. + """ + for col, dist_col in mapping.items(): + if col not in data.colnames or dist_col not in data.colnames: + continue + factor = (data[dist_col] / (1 * u.AU)).decompose().value ** 2 # (N,), dimensionless + f = data[col] + data[col + "_at_1au"] = f * (factor[:, None] if f.ndim == 2 else factor) + data[col + "_at_1au"].info.description = ( + f"{col} scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from {dist_col})" + ) +``` + +#### lc_peak_flux_at_1au + +- **comment:** lc_peak_flux scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from solo_sun_distance) +- **unit:** ct / (keV s cm2) +- **example:** `[1.5917, 0.174 , 0.0659, 0.2583, 0.1114] ct / (keV s cm2)` +- **source:** stixcore +- **uses:** `lc_peak_flux`, `solo_sun_distance` + +**calculation summary:** see the shared step summary above. + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### lc_bkg_peak_flux_at_1au + +- **comment:** lc_bkg_peak_flux scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from solo_sun_distance) +- **unit:** ct / (keV s cm2) +- **example:** `[0.5274, 0.0791, 0.0659, 0.2373, 0.1086] ct / (keV s cm2)` +- **source:** stixcore +- **uses:** `lc_bkg_peak_flux`, `solo_sun_distance` + +**calculation summary:** see the shared step summary above. + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### bkg_spec_flux_at_1au + +- **comment:** bkg_spec_flux scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from bkg_solo_sun_distance) +- **unit:** ct / (keV s cm2) +- **example:** `[0.5508, 0.2466, 0.1889, 0.6392, 0.2959, 0.082 , 0.0662, 0.1254, 0.0999, 0.0504, 0.0405, 0.041 , 0.043 , + 0.0451, 0.0488, 0.0652, 0.1059, 1.151 , 0.2621, 0.0431, 0.0309, 0.0363, 0.0659, 0.0665, 0.0514, 0.0694, + 0.2199, 0.0304, 0.1363, 0.1496, nan, nan] ct / (keV s cm2)` +- **source:** stixcore +- **uses:** `bkg_spec_flux`, `bkg_solo_sun_distance` + +**calculation summary:** see the shared step summary above. + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ + +#### bkg_spec_flux_ql_at_1au + +- **comment:** bkg_spec_flux_ql scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from bkg_solo_sun_distance) +- **unit:** ct / (keV s cm2) +- **example:** `[0.3339, 0.0765, 0.051 , 0.2592, 0.0999] ct / (keV s cm2)` +- **source:** stixcore +- **uses:** `bkg_spec_flux_ql`, `bkg_solo_sun_distance` + +**calculation summary:** see the shared step summary above. + +**review comment:** _(to be filled in via `notebooks/flarelist_sdcloc_review_comments.yaml`)_ diff --git a/notebooks/flarelist_showcase/.gitignore b/notebooks/flarelist_showcase/.gitignore new file mode 100644 index 00000000..2525f3d3 --- /dev/null +++ b/notebooks/flarelist_showcase/.gitignore @@ -0,0 +1,7 @@ +# large files of the feedback page: hosted on pub099, not in git (the notebook downloads the data on demand) +*.fits +*.zip +*.zip.part +# built by build_feedback_bundle.py for the feedback page on pub099 +STIX_L3_flarelist_showcase.html +cover.png diff --git a/notebooks/flarelist_showcase/STIX_L3_flarelist_showcase.ipynb b/notebooks/flarelist_showcase/STIX_L3_flarelist_showcase.ipynb new file mode 100644 index 00000000..8e8a2bc9 --- /dev/null +++ b/notebooks/flarelist_showcase/STIX_L3_flarelist_showcase.ipynb @@ -0,0 +1,4203 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d8fd9771", + "metadata": {}, + "source": [ + "# STIX L3 flare list: a first look\n", + "\n", + "
\n", + "\n", + "**⚠ Preliminary test release, for feedback only.**\n", + "\n", + "This flare list and this notebook are shared to collect feedback from the STIX team and the community before the\n", + "first release in the Solar Orbiter Archive (SOAR). The content, the columns and their definitions, and the values\n", + "themselves **may still change**.\n", + "\n", + "**Please do not use these files for scientific analyses or publications.** Use the flare list for science only once\n", + "it is published in SOAR.\n", + "\n", + "
\n", + "\n", + "This notebook walks through the STIX L3 flare list (`solo_L3_stix-flarelist-sdcloc`):\n", + "- **What it is:** flares detected by STIX on Solar Orbiter, with timing, intensity, background, location and\n", + " observing geometry;\n", + "- **What each column contains:** units, description and possible use cases;\n", + "- **How to work with it:** a few simple plots and filters.\n", + "\n", + "It needs **numpy, astropy, sunpy, matplotlib, pandas and jinja2**, and optionally `ipywidgets` for choosing the data\n", + "set (see `requirements.txt`). A single optional section at the end uses the released `stixpy` to open the data files\n", + "referenced for one flare.\n", + "\n", + "### What we would like to know\n", + "\n", + "1. **First: the data in the flare list files.** Does the content support your usual use cases, e.g. the way you look\n", + " for specific flares? Is something typical missing, or is a description unclear? Look up **your favourite flares**\n", + " (put their `flare_id` into `SHOWCASE_FLARE_IDS` in section 2) and check the numbers we computed for them: detailed\n", + " feedback on the calculations is very welcome.\n", + "2. **Second: working with the flare list in `stixpy`.** All plots and filters in this notebook work directly on the\n", + " plain data, on purpose. Later, `stixpy` will provide a rich flare list class (a `Product` wrapper) that links the\n", + " file's extensions internally (e.g. the energy tables) and offers built-in plots and common filters. A preliminary\n", + " version is shown in the [stixpy flare list demo](https://stixpy--225.org.readthedocs.build/en/225/generated/gallery/flarelist_demo.html).\n", + " Some plots here may therefore look a bit more complicated than they will later; in return, they show directly how\n", + " the columns and extensions relate to each other. Keep in mind that it is a chicken-and-egg problem: first the\n", + " underlying data, then the way to work with it.\n", + "\n", + "### How to send feedback\n", + "\n", + "- **Data:** comment in [STIXCore issue #462](https://github.com/i4Ds/STIXCore/issues/462).\n", + "- **stixpy:** comment in [stixpy issue #268](https://github.com/TCDSolar/stixpy/issues/268).\n", + "- **Already raised?** Comment on it or add a 👍 reaction, so we can see what matters most to the community.\n", + "- If GitHub is not possible for you: email to [nicky.hochmuth@ateleris.ch](mailto:nicky.hochmuth@ateleris.ch?subject=STIX%20L3%20flare%20list%20feedback).\n", + "\n", + "All downloads and more details are on the [feedback page](https://pub099.cs.technik.fhnw.ch/testing/flarelist/).\n" + ] + }, + { + "cell_type": "markdown", + "id": "f282a76d", + "metadata": {}, + "source": [ + "## 1. What is the STIX flare list?\n", + "\n", + "- **Flare detection:** the flares are detected in the STIX quicklook (QL) light curves. The base list (flare id, start,\n", + " peak and end time, GOES class and flux, STIX-based GOES class estimates) is the operational flare list of the\n", + " [STIX Data Center](https://datacenter.stix.i4ds.net/view/flares/list), accessed via `stixdcpy`. This is the `sdc`\n", + " in the product name. The processing is open for more flare lists: future ones may be built around other detection\n", + " algorithms and other criteria of what a flare is, with the same set of computed values. Every column taken over from there\n", + " carries the note *\"provided by https://datacenter.stix.i4ds.net/ over stixdcpy\"* in its description.\n", + "- **Enriched by the STIX ground processing (STIXCore)** with:\n", + " - **QL peak values:** counts and fluxes in the five QL energy bands at the flare peak, and the rate control regime\n", + " (attenuator);\n", + " - **a quiet-time background** for each flare, from a quiet-period compressed pixel data (CPD) file close in time;\n", + " - **the flare location**, from back-projection imaging of a CPD file around the peak (only for flares above a count\n", + " threshold);\n", + " - **the Solar Orbiter observing campaign** (SOOP) active at the flare peak;\n", + " - **the observing geometry of Solar Orbiter at the flare peak**, for every flare: its position (`solo_loc_heeq_zxy`)\n", + " and distance to the Sun, the light-travel-time difference to Earth, the apparent solar radius, and the fluxes\n", + " scaled to 1 AU. For flares with a location, also Solar Orbiter's position at the imaging time\n", + " (`solo_location_icrs`, the observer of the location) and whether the flare is visible from Earth.\n", + "- **Files:** one file per month; these monthly files will go into the Solar Orbiter Archive (SOAR). For this\n", + " feedback round only, the monthly files are also merged into a year and a mission file (see section 2), so you can\n", + " easily look up any flare." + ] + }, + { + "cell_type": "markdown", + "id": "27e59903", + "metadata": {}, + "source": [ + "## 2. Data sets and parameters\n", + "\n", + "**The monthly file is the regular format of the flare list:** one file per month, and these monthly files will go\n", + "into the Solar Orbiter Archive (SOAR). The year and the mission file are only merged from the monthly files, for\n", + "convenience in this showcase and the feedback round. All three have the same columns and differ only in the period\n", + "they cover, and so in the time it takes to download, load and filter them: the mission file with almost 100,000\n", + "flares has every flare, but takes noticeably longer; the month file is quick; the year file is in between:\n", + "\n", + "| data set | period | file size | flares | with a location | use it for |\n", + "| --- | --- | --- | ---: | ---: | --- |\n", + "| **Month 2023-05** (regular monthly file) | 2023-05-01 .. 2023-06-01 | 4 MB | 1,695 | 650 (38%) | a quick first look with a small download |\n", + "| **Year 2023** (merged, default) | 2023-01-01 .. 2023-12-31 | 51 MB | 21,381 | 6,142 (29%) | all examples of this notebook |\n", + "| **Mission** (merged) | 2022-01-01 .. 2026-08-31 | 411 MB | 97,897 | 31,672 (32%) | your own selections over the whole mission so far |\n", + "\n", + "**Choose the data set with the radio buttons below the parameters**, then run the notebook from section 3\n", + "(*Setup and loading*) on. A file in the notebook folder is used directly; otherwise it is downloaded once into the\n", + "notebook folder and used from there next time. Without `ipywidgets`, set `DATASET` in the cell instead.\n", + "\n", + "All time windows below lie in 2023, so they exist in the year and the mission file. The month file only covers May\n", + "2023: the longer windows then simply select fewer flares. The single-flare showcases are two large flares of May 2023,\n", + "so they are in all three files.\n", + "\n", + "The plots never use the whole file. A plot over many years is not readable, so every plot works on a window of at\n", + "most 6 months, and the dedicated examples on a month, a few days or single flares.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e2421b9d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:20:44.808974Z", + "iopub.status.busy": "2026-11-12T16:20:44.808732Z", + "iopub.status.idle": "2026-11-12T16:20:44.916432Z", + "shell.execute_reply": "2026-11-12T16:20:44.914875Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "496256a5ec2b4af582628788e23987bd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(RadioButtons(description='data set:', layout=Layout(width='max-content'), options=(('Year 2023 …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pathlib import Path\n", + "\n", + "# the three sample files of the flare list: a copy in the notebook folder (DATA_DIR) is used if it exists,\n", + "# otherwise the file is downloaded from FLARELIST_URL into the notebook folder\n", + "FLARELIST_URL = \"https://pub099.cs.technik.fhnw.ch/testing/flarelist/\" # TODO: final download location on the server\n", + "DATASETS = {\n", + " \"Year 2023\": \"solo_L3_stix-flarelist-sdcloc_20230101T000000-20231231T230447_V05.fits\",\n", + " \"Mission Jan 2022 - Aug 2026\": \"solo_L3_stix-flarelist-sdcloc_20220101T000000-20260831T232151_V05.fits\",\n", + " \"Month 2023-05\": \"solo_L3_stix-flarelist-sdcloc_20230501T000000-20230601T011216_V05.fits\",\n", + "}\n", + "DATASET = \"Year 2023\" # default selection (the only one used if ipywidgets is not installed)\n", + "DATA_DIR = Path(\".\") # the notebook folder\n", + "\n", + "# primer for the data files referenced in the list (cpd_path, related_cpd_paths, bkg_file, anc_ephemeris_path)\n", + "ARCHIVE_URL = \"https://pub099.cs.technik.fhnw.ch/fits/\"\n", + "\n", + "# time windows of the showcases (all inside 2023; the single flares inside May 2023, so they are in every file)\n", + "OVERVIEW_WINDOW = (\"2023-01-01\", \"2023-07-01\") # 6 months: overview statistics and filters\n", + "SHOWCASE_MONTH = (\"2023-05-01\", \"2023-06-01\") # one month: peak fluxes, background subtraction, Carrington view\n", + "SHOWCASE_DAYS = (\"2023-05-09\", \"2023-05-14\") # a few days of an active period: disc views\n", + "SHOWCASE_FLARE_IDS = [2305161642, 2305201222] # single flares (M9.6 on 2023-05-16, M9.0 on 2023-05-20)\n", + "\n", + "# universal flare-size filter: background-subtracted peak flux in one QL band, scaled to 1 AU\n", + "NET_BAND = 0 # QL band index (0 = lowest band, 4-10 keV)\n", + "MIN_NET_FLUX_1AU = 10.0 # ct / (s keV cm2) at 1 AU\n", + "\n", + "# filters on the position of Solar Orbiter\n", + "PERIHELION_MAX_AU = 0.4 # \"near perihelion\": Solar Orbiter - Sun distance below this\n", + "STEREO_SEPARATION_DEG = (60, 120) # Solar Orbiter - Earth separation for two-viewpoint (stereoscopic) flares\n", + "\n", + "# location quality: sidelobes_ratio >= SIDELOBES_MAX suggests an unreliable location (as in STIX-GSW)\n", + "SIDELOBES_MAX = 0.9\n", + "\n", + "# radio buttons to choose the data set. The choice is kept when this cell runs again (e.g. with \"Run All\"), so\n", + "# after a change simply run the notebook again (Run All, or from section 3 on)\n", + "try:\n", + " import ipywidgets as widgets\n", + " from IPython.display import display\n", + "\n", + " _previous = globals().get(\"dataset_picker\")\n", + " if _previous is not None:\n", + " DATASET = _previous.value\n", + " dataset_picker = widgets.RadioButtons(\n", + " options=[\n", + " (f\"{key} ({'local' if (DATA_DIR / name).exists() else 'will be downloaded'})\", key)\n", + " for key, name in DATASETS.items()\n", + " ],\n", + " value=DATASET,\n", + " description=\"data set:\",\n", + " layout={\"width\": \"max-content\"},\n", + " )\n", + " _hint = widgets.Label()\n", + "\n", + " def _on_dataset_change(change):\n", + " _hint.value = f\"selected: {change['new']}. Now run the notebook again (Run All, or from section 3 on).\"\n", + "\n", + " dataset_picker.observe(_on_dataset_change, names=\"value\")\n", + " display(widgets.VBox([dataset_picker, _hint]))\n", + "except ImportError:\n", + " dataset_picker = None\n", + " print(f\"ipywidgets is not installed: using DATASET = {DATASET!r}\")" + ] + }, + { + "cell_type": "markdown", + "id": "39ca6114", + "metadata": {}, + "source": [ + "## 3. Setup and loading" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "468a327c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:20:44.919153Z", + "iopub.status.busy": "2026-11-12T16:20:44.918761Z", + "iopub.status.idle": "2026-11-12T16:20:44.924362Z", + "shell.execute_reply": "2026-11-12T16:20:44.922939Z" + } + }, + "outputs": [], + "source": [ + "# only on Google Colab: install the packages that are not preinstalled there (does nothing elsewhere)\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " %pip install -q \"sunpy>=6.0\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dbf984ac", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:20:44.926706Z", + "iopub.status.busy": "2026-11-12T16:20:44.926449Z", + "iopub.status.idle": "2026-11-12T16:20:46.694004Z", + "shell.execute_reply": "2026-11-12T16:20:46.692614Z" + } + }, + "outputs": [], + "source": [ + "import shutil\n", + "import warnings\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import astropy.units as u\n", + "from astropy.io import fits\n", + "from astropy.table import QTable\n", + "from astropy.time import Time\n", + "from astropy.utils.data import download_file\n", + "from astropy.coordinates import SkyCoord\n", + "from IPython.display import display, Markdown\n", + "from sunpy.coordinates import HeliographicStonyhurst, HeliographicCarrington, Helioprojective, get_earth\n", + "\n", + "warnings.filterwarnings(\"ignore\", module=\"erfa\")\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"astropy\")\n", + "\n", + "C = {\n", + " \"blue\": \"#2a78d6\",\n", + " \"orange\": \"#eb6834\",\n", + " \"aqua\": \"#1baf7a\",\n", + " \"yellow\": \"#eda100\",\n", + " \"grey\": \"#8a8984\",\n", + " \"violet\": \"#7c5cd6\",\n", + "}\n", + "INK, INK2, SURFACE, GRID = \"#0b0b0b\", \"#52514e\", \"#fcfcfb\", \"#e6e5e1\"\n", + "BAND_COLORS = [C[\"blue\"], C[\"aqua\"], C[\"yellow\"], C[\"orange\"], C[\"violet\"]]\n", + "GOES_COLORS = {\"A\": C[\"grey\"], \"B\": C[\"blue\"], \"C\": C[\"aqua\"], \"M\": C[\"orange\"], \"X\": \"#c0392b\"}\n", + "plt.rcParams.update(\n", + " {\n", + " \"figure.facecolor\": SURFACE,\n", + " \"axes.facecolor\": SURFACE,\n", + " \"savefig.facecolor\": SURFACE,\n", + " \"figure.dpi\": 110,\n", + " \"font.size\": 10,\n", + " \"axes.edgecolor\": GRID,\n", + " \"axes.labelcolor\": INK2,\n", + " \"axes.titlecolor\": INK,\n", + " \"axes.spines.top\": False,\n", + " \"axes.spines.right\": False,\n", + " \"axes.grid\": True,\n", + " \"grid.color\": GRID,\n", + " \"grid.linewidth\": 0.6,\n", + " \"xtick.color\": INK2,\n", + " \"ytick.color\": INK2,\n", + " \"legend.frameon\": False,\n", + " \"lines.linewidth\": 1.5,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a097d480", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:20:46.696429Z", + "iopub.status.busy": "2026-11-12T16:20:46.696141Z", + "iopub.status.idle": "2026-11-12T16:20:47.461640Z", + "shell.execute_reply": "2026-11-12T16:20:47.460300Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data set: Year 2023\n", + "file: solo_L3_stix-flarelist-sdcloc_20230101T000000-20231231T230447_V05.fits\n", + "period: 2023-01-01T00:00:00.000 .. 2023-12-31T23:04:47.333 version 05\n", + "flares: 21381 columns: 55 energy table rows: 106\n" + ] + } + ], + "source": [ + "def local_or_download(name):\n", + " # the file from the notebook folder; downloaded once into the notebook folder if it is not there\n", + " path = DATA_DIR / name\n", + " if not path.exists():\n", + " print(f\"downloading {name} into {DATA_DIR.resolve()} ...\")\n", + " shutil.move(download_file(FLARELIST_URL + name, cache=False, show_progress=True), path)\n", + " path.chmod(0o644) # the download is created as a private temporary file\n", + " return path\n", + "\n", + "\n", + "dataset = dataset_picker.value if dataset_picker is not None else DATASET\n", + "path = local_or_download(DATASETS[dataset])\n", + "header = fits.getheader(path)\n", + "data = QTable.read(path, hdu=\"DATA\", astropy_native=True) # Time, Quantity and SkyCoord columns\n", + "energies = QTable.read(path, hdu=\"ENERGIES\") # the energy binnings referenced by the data\n", + "for name in data.colnames: # FITS strings are read as bytes\n", + " if getattr(data[name], \"dtype\", None) is not None and data[name].dtype.kind == \"S\":\n", + " data[name] = data[name].astype(str)\n", + "print(f\"data set: {dataset}\\nfile: {path.name}\")\n", + "print(f\"period: {header.get('DATE-BEG')} .. {header.get('DATE-END')} version {header.get('VERSION')}\")\n", + "print(f\"flares: {len(data)} columns: {len(data.colnames)} energy table rows: {len(energies)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "57e68b15", + "metadata": {}, + "source": [ + "The flare and Solar Orbiter positions are stored in ICRS. They refer to the centre of the time range used for the\n", + "location (`location_start_UTC` .. `location_end_UTC`). Here they are converted once to Heliographic Stonyhurst (HGS),\n", + "the frame most solar analyses use. Flares without a location have NaN coordinates.\n", + "\n", + "The cell also defines `select(t0, t1)`, which every plot uses to cut the table to a time window by peak time." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "27570fd1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:20:47.464131Z", + "iopub.status.busy": "2026-11-12T16:20:47.463852Z", + "iopub.status.idle": "2026-11-12T16:20:59.175581Z", + "shell.execute_reply": "2026-11-12T16:20:59.174295Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flares with a location: 6142 of 21381 (29%), of which reliable (sidelobes_ratio < 0.9): 5633\n" + ] + } + ], + "source": [ + "loc_obstime = data[\"location_start_UTC\"] + (data[\"location_end_UTC\"] - data[\"location_start_UTC\"]) / 2\n", + "location_hgs = data[\"location_icrs\"].transform_to(HeliographicStonyhurst(obstime=loc_obstime))\n", + "solo_hgs = data[\"solo_location_icrs\"].transform_to(HeliographicStonyhurst(obstime=loc_obstime))\n", + "has_location = np.asarray(data[\"_position_status\"]).astype(bool) & np.isfinite(location_hgs.lon.deg)\n", + "# a location is considered reliable if the back-projection image has no strong sidelobe\n", + "reliable_location = has_location & (np.asarray(data[\"sidelobes_ratio\"], dtype=float) < SIDELOBES_MAX)\n", + "goes_letter = np.array(\n", + " [str(g).strip()[:1].upper() if str(g).strip() not in (\"\", \"nan\", \"None\") else \"-\" for g in data[\"GOES_class\"]]\n", + ")\n", + "peak_dt = pd.to_datetime(data[\"peak_UTC\"].datetime)\n", + "\n", + "\n", + "def select(t0, t1):\n", + " # boolean mask of the flares with peak_UTC in [t0, t1)\n", + " return np.asarray((peak_dt >= pd.Timestamp(t0)) & (peak_dt < pd.Timestamp(t1)))\n", + "\n", + "\n", + "def energy_block(index):\n", + " # rows of the ENERGIES table for one energy binning (energy_index / bkg_energy_index)\n", + " return energies[np.asarray(energies[\"index\"]) == index]\n", + "\n", + "\n", + "# single-flare showcases: the SHOWCASE_FLARE_IDS that are in the file; if none is (e.g. other ids with a file\n", + "# that does not contain them),\n", + "# the two flares with the highest GOES flux and a reliable location of the file are used instead\n", + "showcase_ids = [f for f in SHOWCASE_FLARE_IDS if f in set(np.asarray(data[\"flare_id\"]).tolist())]\n", + "if not showcase_ids:\n", + " goes_flux = np.asarray(getattr(data[\"GOES_flux\"], \"value\", data[\"GOES_flux\"]), dtype=float)\n", + " candidates = np.flatnonzero(reliable_location & np.isfinite(goes_flux))\n", + " showcase_ids = [int(f) for f in np.asarray(data[\"flare_id\"])[candidates[np.argsort(-goes_flux[candidates])][:2]]]\n", + " print(f\"SHOWCASE_FLARE_IDS are not in this file; showing the largest flares instead: {showcase_ids}\")\n", + "\n", + "print(\n", + " f\"flares with a location: {has_location.sum()} of {len(data)} ({has_location.mean():.0%}), \"\n", + " f\"of which reliable (sidelobes_ratio < {SIDELOBES_MAX}): {reliable_location.sum()}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9865fc23", + "metadata": {}, + "source": [ + "## 4. The columns\n", + "\n", + "The tables below are generated from the file itself, so they always match the file you have. For each column they give:\n", + "- the unit;\n", + "- the shape (e.g. `(5,)` for the five QL bands);\n", + "- the share of flares with a value (not NaN or empty);\n", + "- the description stored in the file.\n", + "\n", + "The columns are grouped by topic, with the main use cases of each group." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dda16bbc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:20:59.178505Z", + "iopub.status.busy": "2026-11-12T16:20:59.178236Z", + "iopub.status.idle": "2026-11-12T16:20:59.593982Z", + "shell.execute_reply": "2026-11-12T16:20:59.592796Z" + } + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "### Identification and timing\n", + "*Use cases:* Select flares by time; flare durations; join with other catalogues via the times (note `ear_tdel` for Earth-based comparisons)." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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unitshapefilleddescription
column
flare_id100%unique flare id for flarelist SDCFlareListManager (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
start_UTCUTCTime100%start time of flare (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
peak_UTCUTCTime100%flare peak time (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
end_UTCUTCTime100%end time of flare (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
durations100%duration of flare (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
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" + ], + "text/plain": [ + " unit shape filled \\\n", + "column \n", + "flare_id 100% \n", + "start_UTC UTC Time 100% \n", + "peak_UTC UTC Time 100% \n", + "end_UTC UTC Time 100% \n", + "duration s 100% \n", + "\n", + " description \n", + "column \n", + "flare_id unique flare id for flarelist SDCFlareListManager (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) \n", + "start_UTC start time of flare (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) \n", + "peak_UTC flare peak time (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) \n", + "end_UTC end time of flare (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) \n", + "duration duration of flare (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### GOES class and flux (measured, and estimated from STIX)\n", + "*Use cases:* Flare size. The measured GOES class is only meaningful for flares visible from Earth; the STIX estimates are available for all flares." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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unitshapefilleddescription
column
GOES_class96%GOES class of the GOES XRS data at time of flare - not derived from STIX data.  Do not use when flare isn't visible to Earth (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
GOES_fluxW / m2100%GOES flux of the GOES XRS data at time of flare- not derived from STIX data. Do not use when the flare isn't visible to Earth (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
GOES_min_class_est97%min GOES class estimate derived from STIX data (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
GOES_max_class_est97%max GOES class estimate derived from STIX data (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
GOES_mean_class_est97%mean GOES class estimate derived from STIX data (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
GOES_min_flux_estW / m295%min GOES flux estimate derived from STIX data (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
GOES_max_flux_estW / m295%max GOES flux estimate derived from STIX data (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
GOES_mean_flux_estW / m295%mean GOES flux estimate derived from STIX data (provided by https://datacenter.stix.i4ds.net/ over stixdcpy)
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" + ], + "text/plain": [ + " unit shape filled \\\n", + "column \n", + "GOES_class 96% \n", + "GOES_flux W / m2 100% \n", + "GOES_min_class_est 97% \n", + "GOES_max_class_est 97% \n", + "GOES_mean_class_est 97% \n", + "GOES_min_flux_est W / m2 95% \n", + "GOES_max_flux_est W / m2 95% \n", + "GOES_mean_flux_est W / m2 95% \n", + "\n", + " description \n", + "column \n", + "GOES_class GOES class of the GOES XRS data at time of flare - not derived from STIX data. Do not use when flare isn't visible to Earth (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) \n", + "GOES_flux GOES flux of the GOES XRS data at time of flare- not derived from STIX data. Do not use when the flare isn't visible to Earth (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) \n", + "GOES_min_class_est min GOES class estimate derived from STIX data (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) \n", + "GOES_max_class_est max GOES class estimate derived from STIX data (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) \n", + "GOES_mean_class_est mean GOES class estimate derived from STIX data (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) \n", + "GOES_min_flux_est min GOES flux estimate derived from STIX data (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) \n", + "GOES_max_flux_est max GOES flux estimate derived from STIX data (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) \n", + "GOES_mean_flux_est mean GOES flux estimate derived from STIX data (provided by https://datacenter.stix.i4ds.net/ over stixdcpy) " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### QL peak values and attenuator\n", + "*Use cases:* Flare intensity per energy band at the peak; compare flares (the `_flux` columns are normalised for livetime, area and energy width); identify attenuated flares (`rcr_at_peak > 0`)." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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unitshapefilleddescription
column
lc_peakct(5,)100%raw counts at the L1 QL lightcurve bin nearest the flare peak (5 energy channels); raw counts, not normalized for the detector/pixel configuration
lc_peak_fluxct / (keV s cm2)(5,)100%livetime- and area-corrected flux at the peak lightcurve bin (ct/s/keV/cm2, 5 energy channels); normalized by the enabled detectors x effective pixel area as in stixpy (grid transmission not included)
lc_bkg_peakct(5,)100%raw counts from the STIX background detector at the L1 QL background bin nearest the flare peak (5 energy channels) — unmodulated by imaging subcollimators and not affected by the attenuator; raw counts, not normalized for the detector/pixel configuration
lc_bkg_peak_fluxct / (keV s cm2)(5,)100%livetime- and area-corrected STIX background detector flux at the peak bin (ct/s/keV/cm2, 5 energy channels); normalized by the background detector's effective pixel area (all 12 pixels, as in stixpy)
rcr_at_peak100%rate control regime at the peak bin (>0 attenuator in)
rcr_max100%max rate control regime over the flare start..end window (>0 attenuator in, -1 no QL lightcurve data)
energy_index100%index into the ENERGIES table selecting the QL energy binning (5 channels) of lc_peak, lc_peak_flux, lc_bkg_peak, lc_bkg_peak_flux, bkg_spec_ql and bkg_spec_flux_ql; not a spectral index
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" + ], + "text/plain": [ + " unit shape filled \\\n", + "column \n", + "lc_peak ct (5,) 100% \n", + "lc_peak_flux ct / (keV s cm2) (5,) 100% \n", + "lc_bkg_peak ct (5,) 100% \n", + "lc_bkg_peak_flux ct / (keV s cm2) (5,) 100% \n", + "rcr_at_peak 100% \n", + "rcr_max 100% \n", + "energy_index 100% \n", + "\n", + " description \n", + "column \n", + "lc_peak raw counts at the L1 QL lightcurve bin nearest the flare peak (5 energy channels); raw counts, not normalized for the detector/pixel configuration \n", + "lc_peak_flux livetime- and area-corrected flux at the peak lightcurve bin (ct/s/keV/cm2, 5 energy channels); normalized by the enabled detectors x effective pixel area as in stixpy (grid transmission not included) \n", + "lc_bkg_peak raw counts from the STIX background detector at the L1 QL background bin nearest the flare peak (5 energy channels) — unmodulated by imaging subcollimators and not affected by the attenuator; raw counts, not normalized for the detector/pixel configuration \n", + "lc_bkg_peak_flux livetime- and area-corrected STIX background detector flux at the peak bin (ct/s/keV/cm2, 5 energy channels); normalized by the background detector's effective pixel area (all 12 pixels, as in stixpy) \n", + "rcr_at_peak rate control regime at the peak bin (>0 attenuator in) \n", + "rcr_max max rate control regime over the flare start..end window (>0 attenuator in, -1 no QL lightcurve data) \n", + "energy_index index into the ENERGIES table selecting the QL energy binning (5 channels) of lc_peak, lc_peak_flux, lc_bkg_peak, lc_bkg_peak_flux, bkg_spec_ql and bkg_spec_flux_ql; not a spectral index " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### Quiet-time background\n", + "*Use cases:* Background subtraction (`lc_peak_flux - bkg_spec_flux_ql`), peak-to-background ratio, a ready background spectrum and file for your own spectroscopy or imaging." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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unitshapefilleddescription
column
bkg_file100%path to the quiet-time background CPD file
bkg_rid100%BSD request id of the selected background file (-1 if none)
bkg_specct(32,)100%total quiet-period background counts per science energy channel (enabled imaging detectors); raw counts, not normalized for the detector/pixel configuration
bkg_spec_qlct(5,)100%total quiet-period background counts rebinned to the QL lightcurve energy bands, the same bands as lc_peak (NaN where a band is not fully covered by the native channels); sqrt(bkg_spec_ql) is the Poisson error of the background per band
bkg_spec_fluxct / (keV s cm2)(32,)100%quiet-period background flux per science channel, total counts / total livetime-corrected exposure (ct/s/keV/cm2, enabled imaging detectors); per cm2 of effective pixel area and per detector as in stixpy create_meta_pixels (no grid shadowing correction); to subtract it from stixpy meta-pixel rates (abcd_rate_kev_cm, no_shadowing=True) take the dE-weighted mean over the same science channels
bkg_spec_flux_qlct / (keV s cm2)(5,)100%quiet-period background flux (total counts / total exposure) rebinned to the QL lightcurve energy bands (ct/s/keV/cm2, NaN where a band is not fully covered by the native channels); normalized by the enabled detectors x effective pixel area as in stixpy (grid transmission not included); same bands, units and normalization as lc_peak_flux: subtract it from lc_peak_flux for a net flux, or divide for a peak-to-background ratio (not valid when the attenuator was in at the peak, rcr_at_peak > 0)
bkg_exposures100%total livetime-corrected exposure of the quiet period per enabled imaging detector (NaN if no background file); bkg_spec_flux = bkg_spec / (bkg_exposure * bkg_n_detectors * pixel area * dE)
bkg_n_detectors100%number of enabled imaging detectors summed in bkg_spec (0 if no background file)
bkg_energy_index100%index into the ENERGIES table selecting the native science energy binning of bkg_spec and bkg_spec_flux (-1 if no background file); not a spectral index
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" + ], + "text/plain": [ + " unit shape filled \\\n", + "column \n", + "bkg_file 100% \n", + "bkg_rid 100% \n", + "bkg_spec ct (32,) 100% \n", + "bkg_spec_ql ct (5,) 100% \n", + "bkg_spec_flux ct / (keV s cm2) (32,) 100% \n", + "bkg_spec_flux_ql ct / (keV s cm2) (5,) 100% \n", + "bkg_exposure s 100% \n", + "bkg_n_detectors 100% \n", + "bkg_energy_index 100% \n", + "\n", + " description \n", + "column \n", + "bkg_file path to the quiet-time background CPD file \n", + "bkg_rid BSD request id of the selected background file (-1 if none) \n", + "bkg_spec total quiet-period background counts per science energy channel (enabled imaging detectors); raw counts, not normalized for the detector/pixel configuration \n", + "bkg_spec_ql total quiet-period background counts rebinned to the QL lightcurve energy bands, the same bands as lc_peak (NaN where a band is not fully covered by the native channels); sqrt(bkg_spec_ql) is the Poisson error of the background per band \n", + "bkg_spec_flux quiet-period background flux per science channel, total counts / total livetime-corrected exposure (ct/s/keV/cm2, enabled imaging detectors); per cm2 of effective pixel area and per detector as in stixpy create_meta_pixels (no grid shadowing correction); to subtract it from stixpy meta-pixel rates (abcd_rate_kev_cm, no_shadowing=True) take the dE-weighted mean over the same science channels \n", + "bkg_spec_flux_ql quiet-period background flux (total counts / total exposure) rebinned to the QL lightcurve energy bands (ct/s/keV/cm2, NaN where a band is not fully covered by the native channels); normalized by the enabled detectors x effective pixel area as in stixpy (grid transmission not included); same bands, units and normalization as lc_peak_flux: subtract it from lc_peak_flux for a net flux, or divide for a peak-to-background ratio (not valid when the attenuator was in at the peak, rcr_at_peak > 0) \n", + "bkg_exposure total livetime-corrected exposure of the quiet period per enabled imaging detector (NaN if no background file); bkg_spec_flux = bkg_spec / (bkg_exposure * bkg_n_detectors * pixel area * dE) \n", + "bkg_n_detectors number of enabled imaging detectors summed in bkg_spec (0 if no background file) \n", + "bkg_energy_index index into the ENERGIES table selecting the native science energy binning of bkg_spec and bkg_spec_flux (-1 if no background file); not a spectral index " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### Observing campaign (SOOP)\n", + "*Use cases:* Find flares observed during specific Solar Orbiter campaigns, e.g. with remote-sensing instruments in high-cadence modes." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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unitshapefilleddescription
column
soop_encoded_type33%campaign ID
soop_id33%SOOP ID
soop_type33%name of the SOOP campaign
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" + ], + "text/plain": [ + " unit shape filled description\n", + "column \n", + "soop_encoded_type 33% campaign ID\n", + "soop_id 33% SOOP ID\n", + "soop_type 33% name of the SOOP campaign" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### Data file references\n", + "*Use cases:* Locate the STIX data for your own analysis. Paths are relative to the archive root (prefix `ARCHIVE_URL`)." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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unitshapefilleddescription
column
cpd_path29%Path to the CPD file used for flare location estimation
related_cpd_paths29%all CPD files overlapping the flare start-end window (';'-separated), the first one is the file used for flare location estimation (cpd_path)
anc_ephemeris_path29%Path to the daily ancillary ephemeris file
\n", + "
" + ], + "text/plain": [ + " unit shape filled \\\n", + "column \n", + "cpd_path 29% \n", + "related_cpd_paths 29% \n", + "anc_ephemeris_path 29% \n", + "\n", + " description \n", + "column \n", + "cpd_path Path to the CPD file used for flare location estimation \n", + "related_cpd_paths all CPD files overlapping the flare start-end window (';'-separated), the first one is the file used for flare location estimation (cpd_path) \n", + "anc_ephemeris_path Path to the daily ancillary ephemeris file " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### Location and imaging quality\n", + "*Use cases:* Where the flare is on the Sun (from STIX imaging), how reliable the location is, and whether it can be seen from Earth." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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unitshapefilleddescription
column
_position_status100%Status of the flare position calculation
_position_message100%Message describing the status of the flare position calculation
location_icrsdeg, deg, km (ICRS)SkyCoord29%Flare location in ICRS coordinates (stored form of location_hgs), at the centre of the flare location estimation time range (location_start_UTC..location_end_UTC)
location_start_UTCUTCTime100%start of the time range used for flare location estimation (imaging), UTC; the location columns refer to the centre of location_start_UTC..location_end_UTC
location_end_UTCUTCTime100%end of the time range used for flare location estimation (imaging), UTC
solo_location_icrsdeg, deg, km (ICRS)SkyCoord29%SOLO location in ICRS coordinates (stored form of solo_location_hgs), at the centre of the flare location estimation time range (location_start_UTC..location_end_UTC)
sidelobes_ratio29%ratio of the strongest sidelobe (> 200 arcsec from the peak) to the peak of the back-projection image used for the location; values >= 0.9 suggest an unreliable location (as in STIX-GSW)
location_rcr_max100%max rate control regime over the flare location estimation time range (>0 attenuator in)
visible_from_earth100%whether the flare location is visible from Earth (not occulted by the Sun), assuming the emission comes from the solar surface (photosphere) along the STIX line of sight; coronal sources (e.g. loop tops) close to the limb as seen from Earth can be visible even if False; for sources off-disk as seen from Solar Orbiter (placed on a spherical screen) the flag is not meaningful
min_exposures29%shortest time bin (timedel) between flare start and end in the CPD file used for imaging (cpd_path); other CPD files covering the flare (related_cpd_paths) may have different time bins
max_exposures29%longest time bin (timedel) between flare start and end in the CPD file used for imaging (cpd_path); other CPD files covering the flare (related_cpd_paths) may have different time bins
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" + ], + "text/plain": [ + " unit shape filled \\\n", + "column \n", + "_position_status 100% \n", + "_position_message 100% \n", + "location_icrs deg, deg, km (ICRS) SkyCoord 29% \n", + "location_start_UTC UTC Time 100% \n", + "location_end_UTC UTC Time 100% \n", + "solo_location_icrs deg, deg, km (ICRS) SkyCoord 29% \n", + "sidelobes_ratio 29% \n", + "location_rcr_max 100% \n", + "visible_from_earth 100% \n", + "min_exposure s 29% \n", + "max_exposure s 29% \n", + "\n", + " description \n", + "column \n", + "_position_status Status of the flare position calculation \n", + "_position_message Message describing the status of the flare position calculation \n", + "location_icrs Flare location in ICRS coordinates (stored form of location_hgs), at the centre of the flare location estimation time range (location_start_UTC..location_end_UTC) \n", + "location_start_UTC start of the time range used for flare location estimation (imaging), UTC; the location columns refer to the centre of location_start_UTC..location_end_UTC \n", + "location_end_UTC end of the time range used for flare location estimation (imaging), UTC \n", + "solo_location_icrs SOLO location in ICRS coordinates (stored form of solo_location_hgs), at the centre of the flare location estimation time range (location_start_UTC..location_end_UTC) \n", + "sidelobes_ratio ratio of the strongest sidelobe (> 200 arcsec from the peak) to the peak of the back-projection image used for the location; values >= 0.9 suggest an unreliable location (as in STIX-GSW) \n", + "location_rcr_max max rate control regime over the flare location estimation time range (>0 attenuator in) \n", + "visible_from_earth whether the flare location is visible from Earth (not occulted by the Sun), assuming the emission comes from the solar surface (photosphere) along the STIX line of sight; coronal sources (e.g. loop tops) close to the limb as seen from Earth can be visible even if False; for sources off-disk as seen from Solar Orbiter (placed on a spherical screen) the flag is not meaningful \n", + "min_exposure shortest time bin (timedel) between flare start and end in the CPD file used for imaging (cpd_path); other CPD files covering the flare (related_cpd_paths) may have different time bins \n", + "max_exposure longest time bin (timedel) between flare start and end in the CPD file used for imaging (cpd_path); other CPD files covering the flare (related_cpd_paths) may have different time bins " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### Observing geometry of Solar Orbiter\n", + "*Use cases:* All at the flare peak (`peak_UTC`) and for every flare. Compare flares observed at different Solar Orbiter distances (`*_at_1au`), convert times to Earth arrival (`ear_tdel`), plot on the disc as seen from Solar Orbiter (`spice_disc_size`), select flares by Solar Orbiter's position (perihelion, separation from Earth; sections 5.8 and 5.9)." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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unitshapefilleddescription
column
solo_loc_heeq_zxykm(3,)100%Solar Orbiter position in HEEQ (x, y, z) at the flare peak (peak_UTC), from SPICE; same definition as solo_loc_heeq_zxy in the ANC aspect product
solo_sun_distancekm100%distance of Solar Orbiter to Sun centre at the flare peak (peak_UTC), from SPICE
bkg_solo_sun_distancekm100%SOLO-Sun distance at the background CPD's observation time (from DSUN_OBS; NaN if none)
ear_tdels100%Time(Sun to Earth) - Time(Sun to S/C) at the flare peak (peak_UTC), from Sun centre (as EAR_TDEL)
spice_disc_sizearcsec100%apparent photospheric solar radius as seen from Solar Orbiter at the flare peak (peak_UTC), from SPICE
lc_peak_flux_at_1auct / (keV s cm2)(5,)100%lc_peak_flux scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from solo_sun_distance)
lc_bkg_peak_flux_at_1auct / (keV s cm2)(5,)100%lc_bkg_peak_flux scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from solo_sun_distance)
bkg_spec_flux_at_1auct / (keV s cm2)(32,)100%bkg_spec_flux scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from bkg_solo_sun_distance)
bkg_spec_flux_ql_at_1auct / (keV s cm2)(5,)100%bkg_spec_flux_ql scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from bkg_solo_sun_distance)
\n", + "
" + ], + "text/plain": [ + " unit shape filled \\\n", + "column \n", + "solo_loc_heeq_zxy km (3,) 100% \n", + "solo_sun_distance km 100% \n", + "bkg_solo_sun_distance km 100% \n", + "ear_tdel s 100% \n", + "spice_disc_size arcsec 100% \n", + "lc_peak_flux_at_1au ct / (keV s cm2) (5,) 100% \n", + "lc_bkg_peak_flux_at_1au ct / (keV s cm2) (5,) 100% \n", + "bkg_spec_flux_at_1au ct / (keV s cm2) (32,) 100% \n", + "bkg_spec_flux_ql_at_1au ct / (keV s cm2) (5,) 100% \n", + "\n", + " description \n", + "column \n", + "solo_loc_heeq_zxy Solar Orbiter position in HEEQ (x, y, z) at the flare peak (peak_UTC), from SPICE; same definition as solo_loc_heeq_zxy in the ANC aspect product \n", + "solo_sun_distance distance of Solar Orbiter to Sun centre at the flare peak (peak_UTC), from SPICE \n", + "bkg_solo_sun_distance SOLO-Sun distance at the background CPD's observation time (from DSUN_OBS; NaN if none) \n", + "ear_tdel Time(Sun to Earth) - Time(Sun to S/C) at the flare peak (peak_UTC), from Sun centre (as EAR_TDEL) \n", + "spice_disc_size apparent photospheric solar radius as seen from Solar Orbiter at the flare peak (peak_UTC), from SPICE \n", + "lc_peak_flux_at_1au lc_peak_flux scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from solo_sun_distance) \n", + "lc_bkg_peak_flux_at_1au lc_bkg_peak_flux scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from solo_sun_distance) \n", + "bkg_spec_flux_at_1au bkg_spec_flux scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from bkg_solo_sun_distance) \n", + "bkg_spec_flux_ql_at_1au bkg_spec_flux_ql scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from bkg_solo_sun_distance) " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "GROUPS = {\n", + " \"Identification and timing\": [\"flare_id\", \"start_UTC\", \"peak_UTC\", \"end_UTC\", \"duration\"],\n", + " \"GOES class and flux (measured, and estimated from STIX)\": [\n", + " \"GOES_class\",\n", + " \"GOES_flux\",\n", + " \"GOES_min_class_est\",\n", + " \"GOES_max_class_est\",\n", + " \"GOES_mean_class_est\",\n", + " \"GOES_min_flux_est\",\n", + " \"GOES_max_flux_est\",\n", + " \"GOES_mean_flux_est\",\n", + " ],\n", + " \"QL peak values and attenuator\": [\n", + " \"lc_peak\",\n", + " \"lc_peak_flux\",\n", + " \"lc_bkg_peak\",\n", + " \"lc_bkg_peak_flux\",\n", + " \"rcr_at_peak\",\n", + " \"rcr_max\",\n", + " \"energy_index\",\n", + " ],\n", + " \"Quiet-time background\": [\n", + " \"bkg_file\",\n", + " \"bkg_rid\",\n", + " \"bkg_spec\",\n", + " \"bkg_spec_ql\",\n", + " \"bkg_spec_flux\",\n", + " \"bkg_spec_flux_ql\",\n", + " \"bkg_exposure\",\n", + " \"bkg_n_detectors\",\n", + " \"bkg_energy_index\",\n", + " ],\n", + " \"Observing campaign (SOOP)\": [\"soop_encoded_type\", \"soop_id\", \"soop_type\"],\n", + " \"Data file references\": [\"cpd_path\", \"related_cpd_paths\", \"anc_ephemeris_path\"],\n", + " \"Location and imaging quality\": [\n", + " \"_position_status\",\n", + " \"_position_message\",\n", + " \"location_icrs\",\n", + " \"location_start_UTC\",\n", + " \"location_end_UTC\",\n", + " \"solo_location_icrs\",\n", + " \"sidelobes_ratio\",\n", + " \"location_rcr_max\",\n", + " \"visible_from_earth\",\n", + " \"min_exposure\",\n", + " \"max_exposure\",\n", + " ],\n", + " \"Observing geometry of Solar Orbiter\": [\n", + " \"solo_loc_heeq_zxy\",\n", + " \"solo_sun_distance\",\n", + " \"bkg_solo_sun_distance\",\n", + " \"ear_tdel\",\n", + " \"spice_disc_size\",\n", + " \"lc_peak_flux_at_1au\",\n", + " \"lc_bkg_peak_flux_at_1au\",\n", + " \"bkg_spec_flux_at_1au\",\n", + " \"bkg_spec_flux_ql_at_1au\",\n", + " ],\n", + "}\n", + "USE_CASES = {\n", + " \"Identification and timing\": \"Select flares by time; flare durations; join with other catalogues via the times \"\n", + " \"(note `ear_tdel` for Earth-based comparisons).\",\n", + " \"GOES class and flux (measured, and estimated from STIX)\": \"Flare size. The measured GOES class is only meaningful for \"\n", + " \"flares visible from Earth; the STIX estimates are available for all flares.\",\n", + " \"QL peak values and attenuator\": \"Flare intensity per energy band at the peak; compare flares (the `_flux` columns \"\n", + " \"are normalised for livetime, area and energy width); identify attenuated flares (`rcr_at_peak > 0`).\",\n", + " \"Quiet-time background\": \"Background subtraction (`lc_peak_flux - bkg_spec_flux_ql`), peak-to-background ratio, a \"\n", + " \"ready background spectrum and file for your own spectroscopy or imaging.\",\n", + " \"Observing campaign (SOOP)\": \"Find flares observed during specific Solar Orbiter campaigns, e.g. with remote-sensing \"\n", + " \"instruments in high-cadence modes.\",\n", + " \"Data file references\": \"Locate the STIX data for your own analysis. Paths are relative to the archive root \"\n", + " \"(prefix `ARCHIVE_URL`).\",\n", + " \"Location and imaging quality\": \"Where the flare is on the Sun (from STIX imaging), how reliable the location is, \"\n", + " \"and whether it can be seen from Earth.\",\n", + " \"Observing geometry of Solar Orbiter\": \"All at the flare peak (`peak_UTC`) and for every flare. Compare flares \"\n", + " \"observed at different Solar Orbiter distances (`*_at_1au`), \"\n", + " \"convert times to Earth arrival (`ear_tdel`), plot on the disc as seen from Solar Orbiter (`spice_disc_size`), \"\n", + " \"select flares by Solar Orbiter's position (perihelion, separation from Earth; sections 5.8 and 5.9).\",\n", + "}\n", + "EXTRA_DESCRIPTIONS = { # the descriptions of the SkyCoord columns are not kept when the file is read\n", + " \"location_icrs\": \"flare location (ICRS); see location_start_UTC/location_end_UTC for the time it refers to\",\n", + " \"solo_location_icrs\": \"Solar Orbiter location (ICRS) at the centre of the location time range\",\n", + "}\n", + "\n", + "\n", + "def column_row(name):\n", + " col = data[name]\n", + " if isinstance(col, SkyCoord):\n", + " filled = np.isfinite(col.ra.deg).mean()\n", + " unit, shape = \"deg, deg, km (ICRS)\", \"SkyCoord\"\n", + " elif isinstance(col, Time):\n", + " filled, unit, shape = 1.0, \"UTC\", \"Time\"\n", + " else:\n", + " values = np.asarray(getattr(col, \"value\", col))\n", + " if hasattr(col, \"mask\") and np.ndim(col.mask):\n", + " values = np.where(np.asarray(col.mask), np.nan if values.dtype.kind in \"fc\" else \"\", values)\n", + " if values.dtype.kind in \"fc\":\n", + " filled = np.mean(np.any(np.isfinite(values.reshape(len(values), -1)), axis=1))\n", + " else:\n", + " filled = np.mean([str(v).strip() not in (\"\", \"nan\", \"None\") for v in values])\n", + " unit = str(getattr(col, \"unit\", \"\") or \"\")\n", + " shape = str(col.shape[1:]) if col.ndim > 1 else \"\"\n", + " description = getattr(col.info, \"description\", None) or EXTRA_DESCRIPTIONS.get(name, \"\")\n", + " return dict(column=name, unit=unit, shape=shape, filled=f\"{filled:.0%}\", description=description)\n", + "\n", + "\n", + "listed = set()\n", + "for group, names in GROUPS.items():\n", + " names = [n for n in names if n in data.colnames]\n", + " listed.update(names)\n", + " display(Markdown(f\"### {group}\\n*Use cases:* {USE_CASES[group]}\"))\n", + " with pd.option_context(\"display.max_colwidth\", None):\n", + " display(pd.DataFrame([column_row(n) for n in names]).set_index(\"column\"))\n", + "rest = [n for n in data.colnames if n not in listed]\n", + "if rest:\n", + " display(Markdown(\"### Other columns\"))\n", + " display(pd.DataFrame([column_row(n) for n in rest]).set_index(\"column\"))" + ] + }, + { + "cell_type": "markdown", + "id": "beb10f05", + "metadata": {}, + "source": [ + "### Energy bands: `energy_index` and `bkg_energy_index`\n", + "\n", + "The file contains several energy binnings in its `ENERGIES` table:\n", + "- the 5-channel QL binnings, which can change over time;\n", + "- the native binnings of the background CPD files.\n", + "\n", + "Each flare says which binning its columns use:\n", + "- `energy_index` for the QL columns (`lc_peak*`, `bkg_spec_ql`, `bkg_spec_flux_ql`);\n", + "- `bkg_energy_index` for the native background spectrum (`bkg_spec`, `bkg_spec_flux`)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a256ad8a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:20:59.596974Z", + "iopub.status.busy": "2026-11-12T16:20:59.596706Z", + "iopub.status.idle": "2026-11-12T16:20:59.620377Z", + "shell.execute_reply": "2026-11-12T16:20:59.619069Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flare 2305161642: QL bands (energy_index = 0):\n", + " 4-10 keV, 10-15 keV, 15-25 keV, 25-50 keV, 50-84 keV\n", + "background spectrum (bkg_energy_index = 1): 32 native channels, 0 keV .. 150 keV and above\n", + "=== energy_index: QL energy bands of lc_peak*, bkg_spec_ql, bkg_spec_flux_ql\n", + " ENERGIES index 0: 21320 flares, 2023-01-01 .. 2023-12-31, 5 channels [keV]:\n", + " 4-10, 10-15, 15-25, 25-50, 50-84\n", + " ENERGIES index 3: 61 flares, 2023-02-01 .. 2023-02-02, 5 channels [keV]:\n", + " 4-6.95, 6.95-9.25, 9.25-13, 13-32, 32-70\n", + "\n", + "=== bkg_energy_index: native energy binning of the background CPD file (bkg_spec, bkg_spec_flux)\n", + " ENERGIES index 1: 15552 flares, 2023-01-01 .. 2023-12-31, 32 channels [keV]:\n", + " 0-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10, 10-11, 11-12, 12-13, 13-14, 14-15, 15-16, 16-18, 18-20, 20-22, 22-25, 25-28, 28-32, 32-36, 36-40, 40-45, 45-50, 50-56, 56-63, 63-70, 70-76, 76-84, 84-100, 100-120, 120-150, >150\n", + " ENERGIES index 2: 159 flares, 2023-01-30 .. 2023-02-03, 32 channels [keV]:\n", + " 0-4, 4-4.45, 4.45-4.95, 4.95-5.45, 5.45-5.95, 5.95-6.45, 6.45-6.95, 6.95-7.35, 7.35-7.75, 7.75-8.25, 8.25-8.75, 8.75-9.25, 9.25-10, 10-10.5, 10.5-11, 11-12, 12-13, 13-15, 15-18, 18-21, 21-25, 25-28, 28-32, 32-36, 36-43, 43-50, 50-59, 59-70, 70-84, 84-110, 110-150, >150\n", + " ENERGIES index 4: 5670 flares, 2023-07-31 .. 2023-11-05, 32 channels [keV]:\n", + " 0-4, 4-5.5, 5.5-6.3, 6.3-6.56, 6.56-6.8, 6.8-7.3, 7.3-8, 8-9, 9-10, 10-11, 11-13, 13-15, 15-17, 17-19, 19-21, 21-23, 23-25, 25-28, 28-32, 32-36, 36-40, 40-45, 45-50, 50-56, 56-63, 63-70, 70-76, 76-84, 84-100, 100-120, 120-150, >150\n", + "\n" + ] + } + ], + "source": [ + "i = int(np.flatnonzero(np.asarray(data[\"flare_id\"]) == showcase_ids[0])[0])\n", + "ql = energy_block(int(data[\"energy_index\"][i]))\n", + "bk = energy_block(int(data[\"bkg_energy_index\"][i]))\n", + "print(f\"flare {data['flare_id'][i]}: QL bands (energy_index = {data['energy_index'][i]}):\")\n", + "print(\" \" + \", \".join(f\"{lo.value:g}-{hi.value:g} keV\" for lo, hi in zip(ql[\"e_low\"], ql[\"e_high\"])))\n", + "print(\n", + " f\"background spectrum (bkg_energy_index = {data['bkg_energy_index'][i]}): {len(bk)} native channels, \"\n", + " f\"{bk['e_low'][0].value:g} keV .. {bk['e_low'][-1].value:g} keV and above\"\n", + ")\n", + "QL_LABELS = [f\"{lo.value:g}-{hi.value:g} keV\" for lo, hi in zip(ql[\"e_low\"], ql[\"e_high\"])]\n", + "\n", + "\n", + "SCHEMES = {\n", + " \"energy_index\": \"QL energy bands of lc_peak*, bkg_spec_ql, bkg_spec_flux_ql\",\n", + " \"bkg_energy_index\": \"native energy binning of the background CPD file (bkg_spec, bkg_spec_flux)\",\n", + "}\n", + "for column, meaning in SCHEMES.items():\n", + " print(f\"=== {column}: {meaning}\")\n", + " eidx = np.asarray(data[column])\n", + " if (eidx < 0).any():\n", + " print(f\" -1 (no background file): {(eidx < 0).sum()} flares\")\n", + " for index in np.unique(eidx[eidx >= 0]):\n", + " used = eidx == index\n", + " block = energy_block(index)\n", + " e_low = np.ma.filled(block[\"e_low\"].value, np.nan)\n", + " e_high = np.ma.filled(block[\"e_high\"].value, np.inf) # the top channel is open (masked upper edge)\n", + " bands = \", \".join(f\"{lo:g}-{hi:g}\" if np.isfinite(hi) else f\">{lo:g}\" for lo, hi in zip(e_low, e_high))\n", + " print(\n", + " f\" ENERGIES index {index}: {used.sum()} flares, \"\n", + " f\"{peak_dt[used].min():%Y-%m-%d} .. {peak_dt[used].max():%Y-%m-%d}, {len(block)} channels [keV]:\"\n", + " )\n", + " print(f\" {bands}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "id": "6ecef2b5", + "metadata": {}, + "source": [ + "## 5. Working with the list\n", + "\n", + "### 5.1 Overview of a 6-month window" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7ff97990", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:20:59.622696Z", + "iopub.status.busy": "2026-11-12T16:20:59.622441Z", + "iopub.status.idle": "2026-11-12T16:21:01.363755Z", + "shell.execute_reply": "2026-11-12T16:21:01.361999Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w = select(*OVERVIEW_WINDOW)\n", + "fig, axs = plt.subplots(1, 3, figsize=(15, 3.8))\n", + "weeks = pd.Series(1, index=peak_dt[w]).resample(\"W\").sum()\n", + "weeks_loc = pd.Series(has_location[w].astype(int), index=peak_dt[w]).resample(\"W\").sum()\n", + "axs[0].bar(weeks.index, weeks.values, width=5, color=C[\"grey\"], label=\"all flares\")\n", + "axs[0].bar(weeks_loc.index, weeks_loc.values, width=5, color=C[\"blue\"], label=\"with location\")\n", + "axs[0].set_ylabel(\"flares per week\")\n", + "axs[0].legend()\n", + "axs[0].set_title(\"flares per week\", loc=\"left\")\n", + "axs[0].xaxis.set_major_formatter(mdates.DateFormatter(\"%b\"))\n", + "\n", + "dur = data[\"duration\"][w].to_value(u.min)\n", + "axs[1].hist(dur, bins=np.arange(0, 121, 2), color=C[\"blue\"])\n", + "axs[1].set_xlabel(\"flare duration [min]\")\n", + "axs[1].set_ylabel(\"flares\")\n", + "axs[1].set_title(\"duration\", loc=\"left\")\n", + "\n", + "gm = data[\"GOES_flux\"][w].to_value(u.W / u.m**2)\n", + "ge = data[\"GOES_mean_flux_est\"][w].to_value(u.W / u.m**2)\n", + "vis = np.asarray(data[\"visible_from_earth\"][w]).astype(bool)\n", + "axs[2].scatter(gm[vis], ge[vis], s=6, color=C[\"blue\"], alpha=0.5, label=\"visible from Earth\")\n", + "axs[2].scatter(gm[~vis], ge[~vis], s=6, color=C[\"orange\"], alpha=0.5, label=\"not visible from Earth\")\n", + "lim = [1e-8, 1e-3]\n", + "axs[2].plot(lim, lim, color=INK2, lw=1)\n", + "axs[2].set_xscale(\"log\")\n", + "axs[2].set_yscale(\"log\")\n", + "axs[2].set_xlim(lim)\n", + "axs[2].set_ylim(lim)\n", + "axs[2].set_xlabel(\"GOES flux, measured [W/m²]\")\n", + "axs[2].set_ylabel(\"GOES flux, STIX estimate [W/m²]\")\n", + "axs[2].set_title(\"GOES: measured vs. STIX estimate\", loc=\"left\")\n", + "axs[2].legend(fontsize=8)\n", + "fig.suptitle(f\"{OVERVIEW_WINDOW[0]} .. {OVERVIEW_WINDOW[1]}: {w.sum()} flares\", x=0.01, ha=\"left\")\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "65287bb9", + "metadata": {}, + "source": [ + "The STIX GOES estimates exist for every flare. The measured GOES class only makes sense for flares Earth can see.\n", + "Flares behind the limb as seen from Earth (orange) show a measured flux well below the STIX estimate." + ] + }, + { + "cell_type": "markdown", + "id": "cb4e1aea", + "metadata": {}, + "source": [ + "### 5.2 Background subtraction of the peak fluxes\n", + "\n", + "`lc_peak_flux` (the QL light curve at the peak) and `bkg_spec_flux_ql` (the quiet-time background, rebinned to the\n", + "same QL bands) have the same units and the same normalisation:\n", + "- livetime-corrected;\n", + "- per keV;\n", + "- per cm² of the enabled detectors' effective pixel area.\n", + "\n", + "So the background can be subtracted directly. Two exclusions:\n", + "- **Attenuator in at the peak** (`rcr_at_peak > 0`): excluded, because the quiet-time background is unattenuated.\n", + "- **Bands not covered by the background file:** these are NaN.\n", + "\n", + "The flare list holds the values **at the peak** only, not full light curves." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3260f8b5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:01.366580Z", + "iopub.status.busy": "2026-11-12T16:21:01.366234Z", + "iopub.status.idle": "2026-11-12T16:21:03.161364Z", + "shell.execute_reply": "2026-11-12T16:21:03.160076Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "share of flares with net flux <= 0 (not shown on the log axis): 4-10 keV: 1%, 10-15 keV: 15%, 15-25 keV: 37%, 25-50 keV: 32%, 50-84 keV: 54%\n", + "median peak-to-background ratio per band in the showcase month:\n", + " 4-10 keV: 2.4, 10-15 keV: 1.5, 15-25 keV: 1.1, 25-50 keV: 1.0, 50-84 keV: 1.0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "net_flux = data[\"lc_peak_flux\"] - data[\"bkg_spec_flux_ql\"] # ct / (s keV cm2), 5 QL bands\n", + "attenuated = np.asarray(data[\"rcr_at_peak\"]) > 0\n", + "net_flux[attenuated] = np.nan\n", + "ratio = (data[\"lc_peak_flux\"] / data[\"bkg_spec_flux_ql\"]).decompose().value\n", + "ratio[attenuated] = np.nan\n", + "\n", + "w = select(*SHOWCASE_MONTH)\n", + "fig, axs = plt.subplots(1, 2, figsize=(15, 4), sharey=True)\n", + "for ax, values, title in (\n", + " (axs[0], data[\"lc_peak_flux\"], \"peak flux, raw\"),\n", + " (axs[1], net_flux, \"peak flux, background subtracted\"),\n", + "):\n", + " for b in range(5):\n", + " ax.scatter(peak_dt[w], values[w, b].value, s=7, color=BAND_COLORS[b], label=QL_LABELS[b])\n", + " if attenuated[w].any():\n", + " ax.scatter(\n", + " peak_dt[w & attenuated],\n", + " data[\"lc_peak_flux\"][w & attenuated, 0].value,\n", + " s=60,\n", + " facecolor=\"none\",\n", + " edgecolor=INK,\n", + " label=\"attenuator in (excluded)\",\n", + " )\n", + " ax.set_yscale(\"log\")\n", + " ax.set_title(title, loc=\"left\")\n", + " ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%d %b\"))\n", + "axs[0].set_ylabel(\"ct / (s keV cm²)\")\n", + "axs[1].legend(fontsize=8, ncol=2)\n", + "fig.tight_layout()\n", + "\n", + "print(\n", + " \"share of flares with net flux <= 0 (not shown on the log axis): \"\n", + " + \", \".join(f\"{QL_LABELS[b]}: {np.mean(net_flux[w & ~attenuated, b].value <= 0):.0%}\" for b in range(5))\n", + ")\n", + "print(\"median peak-to-background ratio per band in the showcase month:\")\n", + "print(\" \" + \", \".join(f\"{QL_LABELS[b]}: {np.nanmedian(ratio[w, b]):.1f}\" for b in range(5)))" + ] + }, + { + "cell_type": "markdown", + "id": "239de02c", + "metadata": {}, + "source": [ + "- **4–15 keV:** the background is small compared with most flares.\n", + "- **Above about 25 keV:** most flares are close to the background, and the net flux scatters around zero.\n", + "\n", + "The ratio shows in which bands a flare really stands out. The attenuator state of each flare is in `rcr_at_peak` (at the peak) and\n", + "`rcr_max` (over the whole flare)." + ] + }, + { + "cell_type": "markdown", + "id": "2f59a571", + "metadata": {}, + "source": [ + "### 5.3 A flare-size filter that works across the mission (normalised to 1 AU)\n", + "\n", + "Solar Orbiter's distance to the Sun changes between about 0.3 and 1 AU, so the same flare gives very different count\n", + "rates depending on when it happened. To compare flares across the mission:\n", + "- **Subtract the background first** (section 5.2).\n", + "- **Then scale the net flux with the flare's own distance:** `net_flux_1au = net_flux × (solo_sun_distance / 1 AU)²`.\n", + " `solo_sun_distance` is the distance at the flare peak and is available for every flare, with or without a location.\n", + "\n", + "The background is mostly instrumental and doesn't scale with the distance to the Sun. So don't subtract\n", + "`bkg_spec_flux_ql_at_1au` from `lc_peak_flux_at_1au`.\n", + "\n", + "The resulting criterion doesn't depend on the distance, the detector/pixel configuration or the livetime.\n", + "\n", + "**The right plot below shows what the scaling does.** Both point clouds are background-subtracted; the only\n", + "difference is the scaling to 1 AU.\n", + "- **Scaled values are lower:** closer to the Sun, Solar Orbiter measures more flux (1/r²). The scaling removes\n", + " that, so for flares observed below 1 AU the scaled values are *lower* than the measured ones (at 0.3 AU by a\n", + " factor 0.3² ≈ 0.09). Towards 1 AU both clouds meet.\n", + "- **Why the lower edge of the grey cloud is flat:** you might expect the measured fluxes to be higher close to the\n", + " Sun. But a flare only gets into the list if it is detected, and the detection works on the counts measured at the\n", + " spacecraft, with a threshold above the background that does not depend on the distance to the Sun. So at every\n", + " distance the weakest flares in the list have about the same measured flux, at that detection threshold (about\n", + " 1-2 ct/(s keV cm²) here). Closer to the Sun, STIX does not see the same flares brighter, it sees *more and\n", + " smaller* flares that would have stayed below the threshold at 1 AU.\n", + "- **Seen at 1 AU, the threshold is not flat:** scaled to 1 AU, this constant threshold becomes a rising line\n", + " (proportional to r²), the sloped lower edge of the blue cloud. Close to the Sun the list therefore reaches much\n", + " smaller flares than at 1 AU. Keep that in mind when you compare flare numbers across the mission: a cut on the\n", + " flux at 1 AU (like `MIN_NET_FLUX_1AU`) above the highest point of this edge (about 1-2 at 1 AU) gives a sample that\n", + " is complete at all distances.\n", + "- **The top of the grey cloud:** the biggest flares are missing, because flares with the attenuator in at the peak\n", + " are left out, and close to the Sun the attenuator is inserted more often.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6bb866ee", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:03.165310Z", + "iopub.status.busy": "2026-11-12T16:21:03.165020Z", + "iopub.status.idle": "2026-11-12T16:21:04.691266Z", + "shell.execute_reply": "2026-11-12T16:21:04.689878Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-01-01..2023-07-01, flares with a location and without attenuator at the peak: 2968; net flux at 1 AU > 10.0 in 4-10 keV: 636 (21%)\n", + "GOES classes of the selected flares: {'C': 485, 'M': 114, 'B': 28, 'X': 8, '-': 1}\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dist_au = data[\"solo_sun_distance\"].to_value(u.AU)\n", + "net_flux_1au = net_flux * (dist_au**2)[:, None]\n", + "strong = net_flux_1au[:, NET_BAND].value > MIN_NET_FLUX_1AU\n", + "\n", + "w = select(*OVERVIEW_WINDOW) & ~attenuated & has_location # only flares with a location\n", + "fig, axs = plt.subplots(1, 2, figsize=(14, 4))\n", + "bins = np.logspace(-2, 4, 61)\n", + "for cls in \"BCMX\":\n", + " m = w & (goes_letter == cls)\n", + " axs[0].hist(\n", + " net_flux_1au[m, NET_BAND].value,\n", + " bins=bins,\n", + " histtype=\"step\",\n", + " lw=1.8,\n", + " color=GOES_COLORS[cls],\n", + " label=f\"GOES {cls} ({m.sum()})\",\n", + " )\n", + "axs[0].axvline(MIN_NET_FLUX_1AU, color=INK, ls=\"--\", lw=1)\n", + "axs[0].set_xscale(\"log\")\n", + "axs[0].set_xlabel(f\"net peak flux at 1 AU, {QL_LABELS[NET_BAND]} [ct/(s keV cm²)]\")\n", + "axs[0].set_ylabel(\"flares\")\n", + "axs[0].legend()\n", + "axs[0].set_title(\"net peak flux at 1 AU by GOES class\", loc=\"left\")\n", + "\n", + "axs[1].scatter(\n", + " dist_au[w],\n", + " net_flux[w, NET_BAND].value,\n", + " s=6,\n", + " color=C[\"grey\"],\n", + " alpha=0.5,\n", + " label=\"net peak flux at Solar Orbiter (measured)\",\n", + ")\n", + "axs[1].scatter(\n", + " dist_au[w], net_flux_1au[w, NET_BAND].value, s=6, color=C[\"blue\"], alpha=0.5, label=\"net peak flux, scaled to 1 AU\"\n", + ")\n", + "axs[1].set_yscale(\"log\")\n", + "axs[1].set_xlabel(\"Solar Orbiter - Sun distance [AU]\")\n", + "axs[1].set_ylabel(\"ct / (s keV cm²)\")\n", + "axs[1].legend(fontsize=8)\n", + "axs[1].set_title(\"why the 1 AU scaling matters\", loc=\"left\")\n", + "fig.tight_layout()\n", + "\n", + "sel = w & strong\n", + "print(\n", + " f\"{OVERVIEW_WINDOW[0]}..{OVERVIEW_WINDOW[1]}, flares with a location and without attenuator at the peak: {w.sum()}; \"\n", + " f\"net flux at 1 AU > {MIN_NET_FLUX_1AU} in {QL_LABELS[NET_BAND]}: {sel.sum()} ({sel.sum() / w.sum():.0%})\"\n", + ")\n", + "print(\"GOES classes of the selected flares:\", pd.Series(goes_letter[sel]).value_counts().to_dict())" + ] + }, + { + "cell_type": "markdown", + "id": "74eac88d", + "metadata": {}, + "source": [ + "### 5.4 The quiet-time background spectrum of a flare\n", + "\n", + "For each flare, the list stores the background from a quiet-period CPD file close in time (`bkg_file`):\n", + "- `bkg_spec`: the total counts over the quiet period;\n", + "- `bkg_exposure`: the livetime-corrected exposure per detector;\n", + "- `bkg_n_detectors`: the number of detectors used;\n", + "- `bkg_spec_flux`: the flux, as total counts / total exposure.\n", + "\n", + "With the counts, the Poisson error of the background follows directly.\n", + "A quiet period of about 48 min collects thousands of counts per channel, so the statistical error is\n", + "below 2%. On the log axis the error bars are therefore hidden behind the markers." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d0b0c53f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:04.695319Z", + "iopub.status.busy": "2026-11-12T16:21:04.695019Z", + "iopub.status.idle": "2026-11-12T16:21:05.130689Z", + "shell.execute_reply": "2026-11-12T16:21:05.129261Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "exposure per detector: 2818 s, detectors: 30, total counts: 1684280\n", + "relative Poisson error per channel: 0.2% .. 1.8%\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = int(np.flatnonzero(np.asarray(data[\"flare_id\"]) == showcase_ids[0])[0])\n", + "bk = energy_block(int(data[\"bkg_energy_index\"][i]))\n", + "n = len(bk)\n", + "flux = data[\"bkg_spec_flux\"][i, :n].value\n", + "counts = data[\"bkg_spec\"][i, :n].value\n", + "err = np.where(counts > 0, flux / np.sqrt(counts), np.nan) # Poisson error of the total counts\n", + "e_lo, e_hi = bk[\"e_low\"].value, bk[\"e_high\"].value\n", + "finite = np.isfinite(e_hi) & (e_hi < 200) & np.isfinite(flux) # drops e.g. the NaN 0-4 keV channel\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 4))\n", + "ax.errorbar(\n", + " np.sqrt(e_lo * e_hi)[finite],\n", + " flux[finite],\n", + " xerr=None,\n", + " yerr=err[finite],\n", + " fmt=\"o\",\n", + " ms=3,\n", + " color=C[\"blue\"],\n", + " ecolor=C[\"grey\"],\n", + ")\n", + "# repeat the last value at its upper edge, so the step also draws the last channel\n", + "ax.step(\n", + " np.append(e_lo[finite], e_hi[finite][-1]),\n", + " np.append(flux[finite], flux[finite][-1]),\n", + " where=\"post\",\n", + " color=C[\"blue\"],\n", + " lw=0.8,\n", + ")\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "ax.set_xlabel(\"energy [keV]\")\n", + "ax.set_ylabel(\"ct / (s keV cm²)\")\n", + "ax.set_title(\n", + " f\"background of flare {data['flare_id'][i]}: {Path(str(data['bkg_file'][i])).name}\", loc=\"left\", fontsize=9\n", + ")\n", + "print(\n", + " f\"exposure per detector: {data['bkg_exposure'][i]:.0f}, detectors: {data['bkg_n_detectors'][i]}, \"\n", + " f\"total counts: {np.nansum(counts):.0f}\"\n", + ")\n", + "print(\n", + " f\"relative Poisson error per channel: {np.nanmin((err / flux)[finite]):.1%} .. {np.nanmax((err / flux)[finite]):.1%}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "add5b6b6", + "metadata": {}, + "source": [ + "### 5.5 Where are the flares?\n", + "\n", + "Locations come from STIX back-projection imaging and are only available for flares above a count threshold.\n", + "- **Disc views** use a few days (`SHOWCASE_DAYS`), because solar rotation moves an active region by about 13° per\n", + " day.\n", + "- **The Carrington view** co-rotates with the Sun, so it can show a whole month.\n", + "\n", + "Points are coloured by GOES class. Only reliable locations are shown (`sidelobes_ratio < SIDELOBES_MAX`, see below)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bd52ef90", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:05.133097Z", + "iopub.status.busy": "2026-11-12T16:21:05.132789Z", + "iopub.status.idle": "2026-11-12T16:21:06.900186Z", + "shell.execute_reply": "2026-11-12T16:21:06.898758Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w = select(*SHOWCASE_DAYS) & reliable_location\n", + "cols = [GOES_COLORS.get(g, C[\"grey\"]) for g in goes_letter[w]]\n", + "t_w = loc_obstime[w]\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(16, 5))\n", + "axs[0].scatter(location_hgs.lon.deg[w], location_hgs.lat.deg[w], c=cols, s=18)\n", + "axs[0].set_xlim(-180, 180)\n", + "axs[0].set_ylim(-90, 90)\n", + "axs[0].set_xlabel(\"HGS longitude [deg]\")\n", + "axs[0].set_ylabel(\"HGS latitude [deg]\")\n", + "axs[0].set_title(\"Heliographic Stonyhurst\", loc=\"left\")\n", + "\n", + "earth = get_earth(t_w)\n", + "hpc_e = location_hgs[w].transform_to(Helioprojective(observer=earth, obstime=t_w))\n", + "r_earth = np.median(np.arcsin((1 * u.R_sun / earth.radius).decompose()).to_value(u.arcsec))\n", + "axs[1].add_patch(plt.Circle((0, 0), r_earth, fill=False, color=INK2))\n", + "front = np.asarray(data[\"visible_from_earth\"][w]).astype(bool)\n", + "cols = np.array(cols)\n", + "axs[1].scatter(hpc_e.Tx.arcsec[front], hpc_e.Ty.arcsec[front], c=cols[front], s=18)\n", + "axs[1].scatter(hpc_e.Tx.arcsec[~front], hpc_e.Ty.arcsec[~front], facecolor=\"none\", edgecolor=cols[~front], s=18)\n", + "axs[1].set_aspect(\"equal\")\n", + "axs[1].set_xlim(-1.4 * r_earth, 1.4 * r_earth)\n", + "axs[1].set_ylim(-1.4 * r_earth, 1.4 * r_earth)\n", + "axs[1].set_xlabel(\"Tx [arcsec]\")\n", + "axs[1].set_ylabel(\"Ty [arcsec]\")\n", + "axs[1].set_title(\"as seen from Earth (open: not visible from Earth)\", loc=\"left\")\n", + "\n", + "hpc_s = location_hgs[w].transform_to(Helioprojective(observer=solo_hgs[w], obstime=t_w))\n", + "r_solo = np.median(data[\"spice_disc_size\"][w].to_value(u.arcsec))\n", + "axs[2].add_patch(plt.Circle((0, 0), r_solo, fill=False, color=INK2))\n", + "axs[2].scatter(hpc_s.Tx.arcsec, hpc_s.Ty.arcsec, c=cols, s=18)\n", + "axs[2].set_aspect(\"equal\")\n", + "axs[2].set_xlim(-1.4 * r_solo, 1.4 * r_solo)\n", + "axs[2].set_ylim(-1.4 * r_solo, 1.4 * r_solo)\n", + "axs[2].set_xlabel(\"Tx [arcsec]\")\n", + "axs[2].set_ylabel(\"Ty [arcsec]\")\n", + "axs[2].set_title(\"as seen from Solar Orbiter (disc radius: spice_disc_size)\", loc=\"left\")\n", + "for cls, colr in GOES_COLORS.items():\n", + " axs[0].scatter([], [], color=colr, label=cls)\n", + "axs[0].legend(title=\"GOES\", fontsize=8)\n", + "fig.suptitle(f\"{SHOWCASE_DAYS[0]} .. {SHOWCASE_DAYS[1]}: {w.sum()} flares with a reliable location\", x=0.01, ha=\"left\")\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "68e860d9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:06.903205Z", + "iopub.status.busy": "2026-11-12T16:21:06.902919Z", + "iopub.status.idle": "2026-11-12T16:21:07.686336Z", + "shell.execute_reply": "2026-11-12T16:21:07.685070Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w = select(*SHOWCASE_MONTH) & reliable_location\n", + "hgc = location_hgs[w].transform_to(HeliographicCarrington(observer=solo_hgs[w], obstime=loc_obstime[w]))\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "ax.scatter(hgc.lon.deg, hgc.lat.deg, c=[GOES_COLORS.get(g, C[\"grey\"]) for g in goes_letter[w]], s=12)\n", + "ax.set_xlim(0, 360)\n", + "ax.set_ylim(-60, 60)\n", + "ax.set_xlabel(\"Carrington longitude [deg]\")\n", + "ax.set_ylabel(\"latitude [deg]\")\n", + "ax.set_title(\n", + " f\"Carrington view, {SHOWCASE_MONTH[0]} .. {SHOWCASE_MONTH[1]}: active regions stay at a fixed longitude\", loc=\"left\"\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "e893793a", + "metadata": {}, + "source": [ + "**How reliable is a location?**\n", + "- `_position_status` tells whether a location could be computed; `_position_message` gives the reason if not.\n", + "- `sidelobes_ratio` compares the strongest sidelobe (more than 200″ away from the peak) with the main peak of the\n", + " back-projection image. Around 0.75 the main peak clearly stands out. Values of 0.9 or more mean a second spot is\n", + " almost as bright, so the location may be wrong. This 0.9 limit is the one used in the STIX ground software\n", + " (STIX-GSW) and is set as `SIDELOBES_MAX` in the parameter cell. Weak flares (fewer counts) exceed it more often.\n", + "- `location_rcr_max` gives the attenuator state during the imaging time range." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ccd14de6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:07.688907Z", + "iopub.status.busy": "2026-11-12T16:21:07.688629Z", + "iopub.status.idle": "2026-11-12T16:21:08.016006Z", + "shell.execute_reply": "2026-11-12T16:21:08.014571Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "why some flares have no location:\n" + ] + }, + { + "data": { + "text/html": [ + "
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flares
flare did not pass the filter function7444
OK2974
no CPD data found12
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" + ], + "text/plain": [ + " flares\n", + "flare did not pass the filter function 7444\n", + "OK 2974\n", + "no CPD data found 12" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flares with a location: 2974, reliable (sidelobes_ratio < 0.9): 2738 (92%)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w = select(*OVERVIEW_WINDOW)\n", + "print(\"why some flares have no location:\")\n", + "display(pd.Series([str(m) for m in data[\"_position_message\"][w]]).value_counts().head(6).rename(\"flares\").to_frame())\n", + "fig, ax = plt.subplots(figsize=(8, 3.2))\n", + "ax.hist(data[\"sidelobes_ratio\"][w & has_location], bins=np.linspace(0.65, 1.0, 24), color=C[\"blue\"])\n", + "ax.axvline(SIDELOBES_MAX, color=INK, ls=\"--\", lw=1, label=f\"SIDELOBES_MAX = {SIDELOBES_MAX}\")\n", + "ax.set_xlabel(\"sidelobes_ratio\")\n", + "ax.set_ylabel(\"flares\")\n", + "ax.set_title(\"imaging quality metric\", loc=\"left\")\n", + "ax.legend(fontsize=8)\n", + "loc_w = w & has_location\n", + "print(\n", + " f\"flares with a location: {loc_w.sum()}, reliable (sidelobes_ratio < {SIDELOBES_MAX}): \"\n", + " f\"{(w & reliable_location).sum()} ({(w & reliable_location).sum() / loc_w.sum():.0%})\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f5dadcdd", + "metadata": {}, + "source": [ + "### 5.6 Filtering examples" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1b739c80", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:08.018463Z", + "iopub.status.busy": "2026-11-12T16:21:08.018175Z", + "iopub.status.idle": "2026-11-12T16:21:08.051232Z", + "shell.execute_reply": "2026-11-12T16:21:08.050060Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M/X flares with a reliable location near the Earth-facing limb (|HGS lon| > 70 deg): 64\n" + ] + }, + { + "data": { + "text/html": [ + "
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flare_idpeak_UTCGOESHGS lonHGS lat
023010600562023-01-06 00:56:39.935X1.2-79.5-22.4
123010808512023-01-08 08:51:24.185M1.4-94.8-17.1
223010815062023-01-08 15:06:16.226M1.4-91.3-17.7
323010818552023-01-08 18:55:40.249M1.1-87.2-15.8
423010900562023-01-09 00:56:48.286M1.1-83.5-15.8
523010900592023-01-09 00:59:20.286M1.1-83.5-15.8
623010918502023-01-09 18:50:12.298X2.0-71.0-15.0
723010918522023-01-09 18:52:44.298X2.0-71.6-15.3
823011000152023-01-10 00:15:16.332M5.2-74.622.2
923011002202023-01-10 02:20:48.345M2.7-112.7-17.2
\n", + "
" + ], + "text/plain": [ + " flare_id peak_UTC GOES HGS lon HGS lat\n", + "0 2301060056 2023-01-06 00:56:39.935 X1.2 -79.5 -22.4\n", + "1 2301080851 2023-01-08 08:51:24.185 M1.4 -94.8 -17.1\n", + "2 2301081506 2023-01-08 15:06:16.226 M1.4 -91.3 -17.7\n", + "3 2301081855 2023-01-08 18:55:40.249 M1.1 -87.2 -15.8\n", + "4 2301090056 2023-01-09 00:56:48.286 M1.1 -83.5 -15.8\n", + "5 2301090059 2023-01-09 00:59:20.286 M1.1 -83.5 -15.8\n", + "6 2301091850 2023-01-09 18:50:12.298 X2.0 -71.0 -15.0\n", + "7 2301091852 2023-01-09 18:52:44.298 X2.0 -71.6 -15.3\n", + "8 2301100015 2023-01-10 00:15:16.332 M5.2 -74.6 22.2\n", + "9 2301100220 2023-01-10 02:20:48.345 M2.7 -112.7 -17.2" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flares with a location visible from Earth: 1622 of 2974 (assumes emission from the solar surface along the STIX line of sight)\n", + "\n", + "1880 of 10430 flares during a Solar Orbiter observing campaign (SOOP), top 5:\n" + ] + }, + { + "data": { + "text/html": [ + "
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flares
L_SMALL_HRES_HCAD_Slow-Wind-Connection565
L_FULL_HRES_HCAD_Eruption-Watch266
L_FULL_HRES_HCAD_Coronal-Dynamics232
R_FULL_HRES_HCAD_Density-Fluctuations174
L_SMALL_MRES_MCAD_Connection-Mosaic158
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" + ], + "text/plain": [ + " flares\n", + "L_SMALL_HRES_HCAD_Slow-Wind-Connection 565\n", + "L_FULL_HRES_HCAD_Eruption-Watch 266\n", + "L_FULL_HRES_HCAD_Coronal-Dynamics 232\n", + "R_FULL_HRES_HCAD_Density-Fluctuations 174\n", + "L_SMALL_MRES_MCAD_Connection-Mosaic 158" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w = select(*OVERVIEW_WINDOW)\n", + "lon = location_hgs.lon.deg\n", + "mx = np.isin(goes_letter, [\"M\", \"X\"])\n", + "limb = w & mx & reliable_location & (np.abs(lon) > 70)\n", + "print(f\"M/X flares with a reliable location near the Earth-facing limb (|HGS lon| > 70 deg): {limb.sum()}\")\n", + "display(\n", + " pd.DataFrame(\n", + " {\n", + " \"flare_id\": data[\"flare_id\"][limb],\n", + " \"peak_UTC\": data[\"peak_UTC\"][limb].iso,\n", + " \"GOES\": data[\"GOES_class\"][limb],\n", + " \"HGS lon\": np.round(lon[limb], 1),\n", + " \"HGS lat\": np.round(location_hgs.lat.deg[limb], 1),\n", + " }\n", + " ).head(10)\n", + ")\n", + "\n", + "vis = np.asarray(data[\"visible_from_earth\"]).astype(bool)\n", + "print(\n", + " f\"flares with a location visible from Earth: {(w & has_location & vis).sum()} of {(w & has_location).sum()} \"\n", + " \"(assumes emission from the solar surface along the STIX line of sight)\"\n", + ")\n", + "\n", + "soop = pd.Series(np.asarray(data[\"soop_type\"][w]))\n", + "print(f\"\\n{(soop != 'None').sum()} of {len(soop)} flares during a Solar Orbiter observing campaign (SOOP), top 5:\")\n", + "display(soop[soop != \"None\"].value_counts().head(5).rename(\"flares\").to_frame())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "dd6359ec", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:08.054019Z", + "iopub.status.busy": "2026-11-12T16:21:08.053757Z", + "iopub.status.idle": "2026-11-12T16:21:08.063127Z", + "shell.execute_reply": "2026-11-12T16:21:08.061675Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data files for flare 2305161642 (M9.6, peak 2023-05-16 16:42:51.314):\n", + " CPD used for the location: https://pub099.cs.technik.fhnw.ch/fits/L1/2023/05/16/SCI/solo_L1_stix-sci-xray-cpd_20230516T161647-20230516T170440_V02_2305160609-57001.fits\n", + " overlapping CPD: https://pub099.cs.technik.fhnw.ch/fits/L1/2023/05/16/SCI/solo_L1_stix-sci-xray-cpd_20230516T161647-20230516T170440_V02_2305160609-57001.fits\n", + " overlapping CPD: https://pub099.cs.technik.fhnw.ch/fits/L1/2023/05/16/SCI/solo_L1_stix-sci-xray-cpd_20230516T170226-20230516T191603_V02_2305167899-52419.fits\n", + " quiet-time background CPD: https://pub099.cs.technik.fhnw.ch/fits/L1/2023/05/17/SCI/solo_L1_stix-sci-xray-cpd_20230517T061006-20230517T065706_V02_2305176702-52442.fits\n", + " ancillary ephemeris: https://pub099.cs.technik.fhnw.ch/fits/ANC/2023/05/16/ASP/solo_ANC_stix-asp-ephemeris_20230516_V02.fits\n" + ] + } + ], + "source": [ + "i = int(np.flatnonzero(np.asarray(data[\"flare_id\"]) == showcase_ids[0])[0])\n", + "print(f\"data files for flare {data['flare_id'][i]} ({data['GOES_class'][i]}, peak {data['peak_UTC'][i].iso}):\")\n", + "print(\" CPD used for the location:\", ARCHIVE_URL + str(data[\"cpd_path\"][i]))\n", + "for p in [x for x in str(data[\"related_cpd_paths\"][i]).split(\";\") if x.strip()]:\n", + " print(\" overlapping CPD: \", ARCHIVE_URL + p)\n", + "print(\" quiet-time background CPD:\", ARCHIVE_URL + str(data[\"bkg_file\"][i]))\n", + "print(\" ancillary ephemeris: \", ARCHIVE_URL + str(data[\"anc_ephemeris_path\"][i]))" + ] + }, + { + "cell_type": "markdown", + "id": "e04b7332", + "metadata": {}, + "source": [ + "### 5.7 Times as seen from Earth\n", + "\n", + "All times in the list are as observed at Solar Orbiter. Light from the flare reaches Earth `ear_tdel` later, or\n", + "earlier if the value is negative. `ear_tdel` is computed at the flare peak (`peak_UTC`) for every flare. Add it before comparing with Earth-based instruments (GOES, ground-based\n", + "observatories)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f20a6da3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:08.065417Z", + "iopub.status.busy": "2026-11-12T16:21:08.065152Z", + "iopub.status.idle": "2026-11-12T16:21:08.078813Z", + "shell.execute_reply": "2026-11-12T16:21:08.077497Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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flare_idGOESpeak at Solar Orbiter (UTC)ear_tdel [s]peak as seen from Earth (UTC)Solar Orbiter distance at the peak [AU]
02305161642M9.62023-05-16 16:42:51.314158.92023-05-16 16:45:30.1660.693
12305201222M9.02023-05-20 12:22:07.394140.72023-05-20 12:24:28.0500.730
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" + ], + "text/plain": [ + " flare_id GOES peak at Solar Orbiter (UTC) ear_tdel [s] \\\n", + "0 2305161642 M9.6 2023-05-16 16:42:51.314 158.9 \n", + "1 2305201222 M9.0 2023-05-20 12:22:07.394 140.7 \n", + "\n", + " peak as seen from Earth (UTC) Solar Orbiter distance at the peak [AU] \n", + "0 2023-05-16 16:45:30.166 0.693 \n", + "1 2023-05-20 12:24:28.050 0.730 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ids = np.isin(np.asarray(data[\"flare_id\"]), showcase_ids)\n", + "display(\n", + " pd.DataFrame(\n", + " {\n", + " \"flare_id\": data[\"flare_id\"][ids],\n", + " \"GOES\": data[\"GOES_class\"][ids],\n", + " \"peak at Solar Orbiter (UTC)\": data[\"peak_UTC\"][ids].iso,\n", + " \"ear_tdel [s]\": np.round(data[\"ear_tdel\"][ids].to_value(u.s), 1),\n", + " \"peak as seen from Earth (UTC)\": (data[\"peak_UTC\"][ids] + data[\"ear_tdel\"][ids]).iso,\n", + " \"Solar Orbiter distance at the peak [AU]\": np.round(dist_au[ids], 3),\n", + " }\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c47d8374", + "metadata": {}, + "source": [ + "### 5.8 Flares near perihelion\n", + "\n", + "Solar Orbiter flies on an elliptic orbit. The closest point to the Sun, the **perihelion**, is at about 0.3 AU (1 AU\n", + "is the Sun–Earth distance). Flares close to perihelion are of special interest:\n", + "- **More photons:** the X-ray flux grows as 1/distance², so at 0.3 AU STIX receives about 11× more counts than at\n", + " 1 AU, which means better statistics for spectroscopy and imaging.\n", + "- **Finer detail:** one arcsecond covers a smaller patch of the Sun.\n", + "- **Coordinated observations:** the main remote-sensing campaigns of Solar Orbiter (see `soop_type`) run around\n", + " perihelion.\n", + "\n", + "`solo_sun_distance` is available for every flare (at the flare peak), so the filter is a simple threshold\n", + "(`PERIHELION_MAX_AU`)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "87e18869", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:08.081110Z", + "iopub.status.busy": "2026-11-12T16:21:08.080843Z", + "iopub.status.idle": "2026-11-12T16:21:08.679476Z", + "shell.execute_reply": "2026-11-12T16:21:08.678148Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flares with Solar Orbiter closer than 0.4 AU: 1573 (2023-03-29 .. 2023-04-23)\n", + "GOES classes: {'C': 918, 'B': 585, '-': 56, 'M': 11, 'X': 2, 'A': 1}\n" + ] + }, + { + "data": { + "text/html": [ + "
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flare_idpeak_UTCGOESdistance [AU]SOOP
023032902272023-03-29 02:27:44.147X1.30.399L_SMALL_MRES_MCAD_Composition-Mosaic
123032902322023-03-29 02:32:00.147X1.30.399L_SMALL_MRES_MCAD_Composition-Mosaic
223032913592023-03-29 13:59:56.227M1.30.393R_SMALL_HRES_HCAD_RS-burst
323032923402023-03-29 23:40:40.190M1.20.387R_SMALL_HRES_MCAD_Polar-Observations
423033007302023-03-30 07:30:44.240M5.40.383L_SMALL_HRES_HCAD_Slow-Wind-Connection
523040605362023-04-06 05:36:24.020M2.70.309None
623040801242023-04-08 01:24:36.006M2.90.298L_FULL_HRES_HCAD_Coronal-Dynamics
723041005212023-04-10 05:21:00.246M2.80.293R_BOTH_HRES_HCAD_Nanoflares
823041110102023-04-11 10:10:20.434M1.40.293None
923041416132023-04-14 16:13:16.745M1.10.308R_FULL_HRES_HCAD_Density-Fluctuations
1023041423172023-04-14 23:17:16.691M1.50.310R_FULL_HRES_HCAD_Density-Fluctuations
1123042117492023-04-21 17:49:36.841M1.80.382L_SMALL_HRES_HCAD_Slow-Wind-Connection
1223042118572023-04-21 18:57:48.848M1.40.383L_SMALL_HRES_HCAD_Slow-Wind-Connection
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" + ], + "text/plain": [ + " flare_id peak_UTC GOES distance [AU] \\\n", + "0 2303290227 2023-03-29 02:27:44.147 X1.3 0.399 \n", + "1 2303290232 2023-03-29 02:32:00.147 X1.3 0.399 \n", + "2 2303291359 2023-03-29 13:59:56.227 M1.3 0.393 \n", + "3 2303292340 2023-03-29 23:40:40.190 M1.2 0.387 \n", + "4 2303300730 2023-03-30 07:30:44.240 M5.4 0.383 \n", + "5 2304060536 2023-04-06 05:36:24.020 M2.7 0.309 \n", + "6 2304080124 2023-04-08 01:24:36.006 M2.9 0.298 \n", + "7 2304100521 2023-04-10 05:21:00.246 M2.8 0.293 \n", + "8 2304111010 2023-04-11 10:10:20.434 M1.4 0.293 \n", + "9 2304141613 2023-04-14 16:13:16.745 M1.1 0.308 \n", + "10 2304142317 2023-04-14 23:17:16.691 M1.5 0.310 \n", + "11 2304211749 2023-04-21 17:49:36.841 M1.8 0.382 \n", + "12 2304211857 2023-04-21 18:57:48.848 M1.4 0.383 \n", + "\n", + " SOOP \n", + "0 L_SMALL_MRES_MCAD_Composition-Mosaic \n", + "1 L_SMALL_MRES_MCAD_Composition-Mosaic \n", + "2 R_SMALL_HRES_HCAD_RS-burst \n", + "3 R_SMALL_HRES_MCAD_Polar-Observations \n", + "4 L_SMALL_HRES_HCAD_Slow-Wind-Connection \n", + "5 None \n", + "6 L_FULL_HRES_HCAD_Coronal-Dynamics \n", + "7 R_BOTH_HRES_HCAD_Nanoflares \n", + "8 None \n", + "9 R_FULL_HRES_HCAD_Density-Fluctuations \n", + "10 R_FULL_HRES_HCAD_Density-Fluctuations \n", + "11 L_SMALL_HRES_HCAD_Slow-Wind-Connection \n", + "12 L_SMALL_HRES_HCAD_Slow-Wind-Connection " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solar Orbiter campaigns (SOOP) during these flares, top 5:\n" + ] + }, + { + "data": { + "text/html": [ + "
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flares
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R_FULL_HRES_HCAD_Density-Fluctuations174
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w = select(*OVERVIEW_WINDOW)\n", + "dist_au = data[\"solo_sun_distance\"].to_value(u.AU)\n", + "near = w & (dist_au < PERIHELION_MAX_AU)\n", + "\n", + "fig, ax = plt.subplots(figsize=(13, 3.8))\n", + "for cls in \"BCMX\":\n", + " m = w & (goes_letter == cls)\n", + " ax.scatter(\n", + " peak_dt[m],\n", + " dist_au[m],\n", + " s=10 if cls in \"MX\" else 3,\n", + " color=GOES_COLORS[cls],\n", + " label=f\"GOES {cls}\",\n", + " zorder=3 if cls in \"MX\" else 2,\n", + " )\n", + "ax.axhline(PERIHELION_MAX_AU, color=INK, ls=\"--\", lw=1)\n", + "if near.any():\n", + " ax.axvspan(peak_dt[near].min(), peak_dt[near].max(), color=C[\"yellow\"], alpha=0.15, label=\"near perihelion\")\n", + "ax.set_ylabel(\"Solar Orbiter - Sun distance at the peak [AU]\")\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%b\"))\n", + "ax.legend(fontsize=8, ncol=5)\n", + "ax.set_title(\"flares and the distance of Solar Orbiter\", loc=\"left\")\n", + "fig.tight_layout()\n", + "\n", + "period = f\" ({peak_dt[near].min():%Y-%m-%d} .. {peak_dt[near].max():%Y-%m-%d})\" if near.any() else \"\"\n", + "print(f\"flares with Solar Orbiter closer than {PERIHELION_MAX_AU} AU: {near.sum()}{period}\")\n", + "print(\"GOES classes:\", pd.Series(goes_letter[near]).value_counts().to_dict())\n", + "mx_near = near & np.isin(goes_letter, [\"M\", \"X\"])\n", + "display(\n", + " pd.DataFrame(\n", + " {\n", + " \"flare_id\": data[\"flare_id\"][mx_near],\n", + " \"peak_UTC\": data[\"peak_UTC\"][mx_near].iso,\n", + " \"GOES\": data[\"GOES_class\"][mx_near],\n", + " \"distance [AU]\": np.round(dist_au[mx_near], 3),\n", + " \"SOOP\": data[\"soop_type\"][mx_near],\n", + " }\n", + " )\n", + ")\n", + "soop = pd.Series(np.asarray(data[\"soop_type\"][near]))\n", + "print(\"Solar Orbiter campaigns (SOOP) during these flares, top 5:\")\n", + "display(soop[soop != \"None\"].value_counts().head(5).rename(\"flares\").to_frame())" + ] + }, + { + "cell_type": "markdown", + "id": "5337a597", + "metadata": {}, + "source": [ + "### 5.9 Stereoscopic flares: two viewpoints\n", + "\n", + "Earth and Solar Orbiter look at the Sun from different directions, like two cameras around an object. When the\n", + "angle between them (the **separation**) is large, e.g. 60–120°, the same flare is seen from two sides: by STIX and by\n", + "Earth-based instruments (GOES, SDO, ground-based observatories). Such flares are used to study the 3D shape and\n", + "height of the X-ray sources and whether the X-ray emission is equally bright in all directions (directivity).\n", + "\n", + "`solo_loc_heeq_zxy` gives Solar Orbiter's position at the flare peak in Heliocentric Earth Equatorial (HEEQ)\n", + "coordinates, whose x-axis points towards Earth. So the separation is simply the longitude `atan2(y, x)`, and\n", + "Solar Orbiter's latitude above the solar equator is `asin(z / r)`. Both are available for every flare." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "73a51791", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:08.681931Z", + "iopub.status.busy": "2026-11-12T16:21:08.681649Z", + "iopub.status.idle": "2026-11-12T16:21:09.222858Z", + "shell.execute_reply": "2026-11-12T16:21:09.221497Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stereoscopic flares (60-120 deg): 738 (2023-04-09 .. 2023-04-20)\n", + "GOES classes: {'C': 623, 'B': 99, '-': 11, 'M': 4, 'A': 1}\n" + ] + }, + { + "data": { + "text/html": [ + "
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flare_idpeak_UTCGOESseparation [deg]Solar Orbiter lat [deg]locatedreliable locationvisible_from_earth
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" + ], + "text/plain": [ + " flare_id peak_UTC GOES separation [deg] \\\n", + "0 2304100521 2023-04-10 05:21:00.246 M2.8 63.1 \n", + "1 2304111010 2023-04-11 10:10:20.434 M1.4 71.1 \n", + "2 2304141613 2023-04-14 16:13:16.745 M1.1 91.7 \n", + "3 2304142317 2023-04-14 23:17:16.691 M1.5 93.5 \n", + "\n", + " Solar Orbiter lat [deg] located reliable location visible_from_earth \n", + "0 -3.3 False False - \n", + "1 -2.0 True True True \n", + "2 1.3 True True True \n", + "3 1.6 False False - " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "heeq = data[\"solo_loc_heeq_zxy\"].to_value(u.km)\n", + "separation = np.degrees(np.arctan2(heeq[:, 1], heeq[:, 0])) # Solar Orbiter - Earth, + = west of Earth\n", + "solo_lat = np.degrees(np.arcsin(heeq[:, 2] / np.linalg.norm(heeq, axis=1)))\n", + "\n", + "w = select(*OVERVIEW_WINDOW)\n", + "lo, hi = STEREO_SEPARATION_DEG\n", + "stereo = w & (np.abs(separation) >= lo) & (np.abs(separation) <= hi)\n", + "\n", + "fig, ax = plt.subplots(figsize=(13, 3.8))\n", + "ax.axhspan(lo, hi, color=C[\"aqua\"], alpha=0.15, label=f\"stereoscopic band ({lo}-{hi} deg)\")\n", + "ax.axhspan(-hi, -lo, color=C[\"aqua\"], alpha=0.15)\n", + "for cls in \"BCMX\":\n", + " m = w & (goes_letter == cls)\n", + " ax.scatter(\n", + " peak_dt[m],\n", + " separation[m],\n", + " s=10 if cls in \"MX\" else 3,\n", + " color=GOES_COLORS[cls],\n", + " label=f\"GOES {cls}\",\n", + " zorder=3 if cls in \"MX\" else 2,\n", + " )\n", + "ax.set_ylim(-180, 180)\n", + "ax.set_yticks(range(-180, 181, 60))\n", + "ax.set_ylabel(\"Solar Orbiter - Earth separation [deg]\")\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%b\"))\n", + "ax.legend(fontsize=8, ncol=5)\n", + "ax.set_title(\"separation of Solar Orbiter from Earth at each flare peak\", loc=\"left\")\n", + "fig.tight_layout()\n", + "\n", + "print(\n", + " f\"stereoscopic flares ({lo}-{hi} deg): {stereo.sum()}\"\n", + " + (f\" ({peak_dt[stereo].min():%Y-%m-%d} .. {peak_dt[stereo].max():%Y-%m-%d})\" if stereo.any() else \"\")\n", + ")\n", + "print(\"GOES classes:\", pd.Series(goes_letter[stereo]).value_counts().to_dict())\n", + "mx_stereo = stereo & np.isin(goes_letter, [\"M\", \"X\"])\n", + "display(\n", + " pd.DataFrame(\n", + " {\n", + " \"flare_id\": data[\"flare_id\"][mx_stereo],\n", + " \"peak_UTC\": data[\"peak_UTC\"][mx_stereo].iso,\n", + " \"GOES\": data[\"GOES_class\"][mx_stereo],\n", + " \"separation [deg]\": np.round(separation[mx_stereo], 1),\n", + " \"Solar Orbiter lat [deg]\": np.round(solo_lat[mx_stereo], 1),\n", + " \"located\": has_location[mx_stereo],\n", + " \"reliable location\": reliable_location[mx_stereo],\n", + " # visible_from_earth needs a location; \"-\" where there is none\n", + " \"visible_from_earth\": np.where(\n", + " has_location[mx_stereo], np.asarray(data[\"visible_from_earth\"][mx_stereo]).astype(bool), \"-\"\n", + " ),\n", + " }\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d85cec6e", + "metadata": {}, + "source": [ + "- At a large separation, Earth may see the flare only partly (behind the limb) or not at all. Then the measured\n", + " GOES class is low or missing, while the STIX-based GOES estimate is not affected (compare the orange points in\n", + " section 5.1).\n", + "- Above about 150°, Solar Orbiter looks at the far side of the Sun as seen from Earth (in this window: May–June\n", + " 2023). STIX then observes flares that no Earth-based X-ray instrument can see." + ] + }, + { + "cell_type": "markdown", + "id": "2b5854c0", + "metadata": {}, + "source": [ + "## 6. One flare in detail: what to do next (optional, uses `stixpy`)\n", + "\n", + "This section opens the data files referenced for one flare with the released\n", + "[`stixpy`](https://stixpy.readthedocs.io). It's the only part that needs `stixpy`; if it isn't installed, the section is\n", + "skipped. It doesn't plot anything, it only opens and prints the files. The CPD file of a large flare can be\n", + "more than 100 MB.\n", + "\n", + "- **Preferred:** a standard Fido search, keeping the file with exactly the referenced name.\n", + "- **Fallback:** if the search doesn't return that file, e.g. because the public archive holds a different version, the\n", + " link is built from `ARCHIVE_URL` plus the stored path." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "26c68b0e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:09.225177Z", + "iopub.status.busy": "2026-11-12T16:21:09.224897Z", + "iopub.status.idle": "2026-11-12T16:21:11.431951Z", + "shell.execute_reply": "2026-11-12T16:21:11.430714Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "cpd_path: solo_L1_stix-sci-xray-cpd_20230516T161647-20230516T170440_V02_2305160609-57001.fits\n", + " found via Fido search: https://pub099.cs.technik.fhnw.ch/data/fits/L1/2023/05/16/SCI/solo_L1_stix-sci-xray-cpd_20230516T161647-20230516T170440_V02_2305160609-57001.fits\n", + "CompressedPixelData \n", + " Start: 2023-05-16 16:16:47\n", + " End: 2023-05-16 17:04:40\n", + " Center:2023-05-16 16:40:44\n", + " Duration:0.03325462962962955 days or\n", + " 0.7981111111111092 hours or\n", + " 47.88666666666656 minutes or\n", + " 2873.199999999993 seconds\n", + " DetectorMasks\n", + " [0...335]: [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]\n", + "\n", + " PixelMasks\n", + " [0...335]: [['1' '1' '1' '1' '1' '1' '1' '1' '0' '0' '0' '0']]\n", + "\n", + " EnergyEdgeMasks\n", + " [0]: [_,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,_,_,_,_,_,_,_,_,_,_,_,_,_,_]\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "bkg_file: solo_L1_stix-sci-xray-cpd_20230517T061006-20230517T065706_V02_2305176702-52442.fits\n", + " found via Fido search: https://pub099.cs.technik.fhnw.ch/data/fits/L1/2023/05/17/SCI/solo_L1_stix-sci-xray-cpd_20230517T061006-20230517T065706_V02_2305176702-52442.fits\n", + "CompressedPixelData \n", + " Start: 2023-05-17 06:10:06\n", + " End: 2023-05-17 06:57:06\n", + " Center:2023-05-17 06:33:36\n", + " Duration:0.032638888888888884 days or\n", + " 0.7833333333333332 hours or\n", + " 46.99999999999999 minutes or\n", + " 2819.9999999999995 seconds\n", + " DetectorMasks\n", + " [0]: [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]\n", + "\n", + " PixelMasks\n", + " [0]: [['1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1']]\n", + "\n", + " EnergyEdgeMasks\n", + " [0]: [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]\n", + "\n" + ] + } + ], + "source": [ + "try:\n", + " from sunpy.net import Fido, attrs as a\n", + " import stixpy.net.client # noqa: F401 registers the STIX Fido client\n", + " from stixpy.product import Product\n", + "\n", + " HAVE_STIXPY = True\n", + "except ImportError:\n", + " HAVE_STIXPY = False\n", + " print(\"stixpy is not installed: this section is skipped (pip install stixpy)\")\n", + "\n", + "i = int(np.flatnonzero(np.asarray(data[\"flare_id\"]) == showcase_ids[0])[0])\n", + "references = {\"cpd_path\": str(data[\"cpd_path\"][i]), \"bkg_file\": str(data[\"bkg_file\"][i])}\n", + "\n", + "\n", + "def find_file(rel_path, t0, t1):\n", + " # Fido search for the referenced file; fall back to ARCHIVE_URL + rel_path\n", + " name = Path(rel_path).name\n", + " try:\n", + " res = Fido.search(a.Time(t0, t1), a.Instrument.stix, a.stix.DataProduct.sci_xray_cpd)\n", + " for table in res:\n", + " for row in table:\n", + " if Path(str(row[\"url\"])).name == name:\n", + " return str(row[\"url\"]), \"Fido search\"\n", + " except Exception as e:\n", + " print(f\" Fido search failed: {type(e).__name__}: {e}\")\n", + " return ARCHIVE_URL + rel_path, \"ARCHIVE_URL + stored path\"\n", + "\n", + "\n", + "def fetch(url):\n", + " # download once into the sunpy download directory, keeping the file name (stixpy needs the .fits extension)\n", + " import sunpy\n", + " from urllib.request import urlretrieve\n", + "\n", + " local = Path(sunpy.config.get(\"downloads\", \"download_dir\")) / Path(url).name\n", + " if not local.exists():\n", + " local.parent.mkdir(parents=True, exist_ok=True)\n", + " urlretrieve(url, local)\n", + " return local\n", + "\n", + "\n", + "if HAVE_STIXPY:\n", + " windows = {\n", + " \"cpd_path\": (data[\"start_UTC\"][i], data[\"end_UTC\"][i]),\n", + " \"bkg_file\": (data[\"peak_UTC\"][i] - 3 * u.day, data[\"peak_UTC\"][i] + 3 * u.day),\n", + " }\n", + " for key, rel in references.items():\n", + " url, how = find_file(rel, *(t.iso for t in windows[key]))\n", + " print(f\"\\n{key}: {Path(rel).name}\\n found via {how}: {url}\")\n", + " try:\n", + " print(Product(fetch(url)))\n", + " except Exception as e:\n", + " print(f\" could not open: {type(e).__name__}: {e}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3729dd17", + "metadata": {}, + "source": [ + "**What to do next with a found flare:** the file references in the list lead to the data for your own imaging or\n", + "spectroscopy.\n", + "- `cpd_path`: the CPD file used for the location.\n", + "- `related_cpd_paths`: all CPD files overlapping the flare (with `cpd_path` first).\n", + "- `bkg_file`: the quiet-time background CPD file.\n", + "\n", + "For imaging with these files, see the stixpy imaging example:\n", + "https://stixpy.readthedocs.io/en/latest/generated/gallery/imaging_demo.html" + ] + }, + { + "cell_type": "markdown", + "id": "4924666c", + "metadata": {}, + "source": [ + "## 7. The FITS header and the search in SOAR\n", + "\n", + "Besides the tables, each file has a primary header (extension 0) with keywords that describe the file as a whole.\n", + "The table below lists all of them for the monthly file of May 2023 (independent of the data set chosen above), with\n", + "the description from the FITS comment and an example value.\n", + "\n", + "**Only a few standard keywords are searchable in the Solar Orbiter Archive (SOAR).** SOAR does not take over all\n", + "header keywords into its search database, only a small set of standard ones. For the flare list we reuse some of\n", + "these searchable standard keywords for dedicated search options:\n", + "- `DATAMIN` / `DATAMAX` (unit in `BUNIT`): the smallest / largest flare of the file, as background-subtracted peak\n", + " flux in QL light curve channel 0 (`energy_index`, usually 4-10 keV) scaled to 1 AU (as in section 5.3; flares\n", + " with the attenuator in at the peak are left out). In other STIX products they hold the smallest / largest data\n", + " value.\n", + "- `XPOSURE` / `XPOMAX`: the shortest / longest flare `duration`. In other STIX products they hold the exposure time.\n", + "\n", + "Some keywords are **only informative**: they are in the file, but SOAR does not search them:\n", + "- `MAXGOES`: the largest measured GOES class of a flare in the file (GOES XRS, as seen from Earth; for flares\n", + " not visible from Earth this is not the class of the STIX flare);\n", + "- `MAXGOESE`: the largest GOES class of a flare in the file as estimated from STIX data\n", + " (`GOES_max_class_est`), also for flares not visible from Earth.\n", + "\n", + "The other keywords are the same as in the other STIX products: identification (file name, level, versions), the\n", + "period of the file (`DATE-BEG` .. `DATE-END`, the month; `DATE-AVG` its middle), the position of Solar Orbiter at\n", + "`DATE-AVG` (once for the whole month) and the observing campaign (SOOP).\n", + "\n", + "
\n", + "\n", + "**Feedback wanted:** we can't add new searchable keywords, so the question is whether **the right values are in the\n", + "searchable standard keywords**. Would you search monthly flare list files in SOAR by the smallest / largest flare\n", + "(net peak flux at 1 AU) and by the shortest / longest flare duration, or would other values in `DATAMIN` /\n", + "`DATAMAX` / `XPOSURE` / `XPOMAX` serve you better?\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c7c82df5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-11-12T16:21:11.435090Z", + "iopub.status.busy": "2026-11-12T16:21:11.434629Z", + "iopub.status.idle": "2026-11-12T16:21:11.524220Z", + "shell.execute_reply": "2026-11-12T16:21:11.522926Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
keyworddescription (FITS comment)example value (May 2023)
SIMPLEconforms to FITS standardTrue
BITPIXarray data type8
NAXISnumber of array dimensions0
EXTENDTrue
FILENAMEFITS filenamesolo_L3_stix-flarelist-sdcloc_20230501T000000-20230601T011216_V05.fits
RAW_FILERaw filename(s)
PARENTsolo_L3_stix-flarelist-sdc_20230501T000000-20230601T011216_V05.fits
DATEFITS file creation date in UTC2026-11-12T14:03:15.891
BLANKValue marking undefined pixels (before the appl4294967295
LEVELProcessing level of the dataL3
ORIGINFHNWSTIX Team, FHNW
CREATORFITS creation softwarestixcore
VERS_SWVersion of SW that provided FITS1.6.3.dev18+g10d188db3.d20260922
VERS_CFGVersion of the common instrument configurationv0.1.9
VERSIONVersion of data product05
OBSRVTRYSatellite nameSolar Orbiter
TELESCOPTelescope/Sensor nameSOLO/STIX
INSTRUMEInstrument nameSTIX
STYPEService Type0
SSTYPESub-service Type0
SSIDScience Structure ID3
SCI_RIDScience data request ID
SCI_TCCScience data request TC packet seq. control
DATAMINsmallest flare: net QL lc ch. 0 peak flux 1 AU0.002958
DATAMAXlargest flare: net QL lc ch. 0 peak flux 1 AU2119.283592
BUNITUnit of DATAMIN and DATAMAXct s-1 keV-1 cm-2
XPOSURE[s] shortest flare duration in the file8.000000
XPOMAX[s] longest flare duration in the file6200.000000
TARGETType of target from planningnone
SOOPTYPECampaign ID(s) that the data belong toCC6
SOOPNAMEName of the SOOP Campaign that the data belongCC_OFFPOI_STAR
OBS_IDUnique ID of the individual observationSSTX_110A_000_000_5Md2_112;SSTX_110A_000_000_vFLg_13J;SSTX_110A_000_000_vFLg_13K;SSTX_110A_000_000_vFLg_13L;SSTX_110A_000_000_vFLg_13M;SSTX_110A_000_000_vFLg_13N;SSTX_110A_000_000_vFLg_13V;SSTX_110A_000_000_vFLg_13W;SSTX_110A_000_000_vFLg_13X;SSTX_110A_000_000_vFLg_13Y;SSTX_110A_000_000_vFLg_13Z;SSTX_110A_000_000_vFLg_13h;SSTX_110A_000_000_vFLg_13i;SSTX_110A_000_000_vFLg_13j;SSTX_110A_000_000_vFLg_13k
OBS_TYPEEncoded version of OBS_MODE5Md2;vFLg
OBS_MODEObservation modeSTIX_ANALYSIS;STIX_BASIC
OBT_BEGStart of acquisition time in OBT736214227.437964
OBT_ENDEnd of acquisition time in OBT738896958.799908
TIMESYSSystem used for time keywordsUTC
DATE-OBSStart of acquisition time in UTC2023-05-01T00:00:00.000
DATE-BEGStart of acquisition time in UTC2023-05-01T00:00:00.000
DATE-AVGCenter of acquisition time in UTC2023-05-16T12:36:08.022
DATE-ENDEnd of acquisition time in UTC2023-06-01T01:12:16.045
SPICE_MKSPICE meta kernel filesolo_ANC_soc-pred-mk_V112_20261104_002.tm, solo_ANC_soc-flown-mk_V112_20261111_001.tm
RSUN_ARC[arcsec] Apparent photospheric solar radius1388.237956
HGLT_OBS[deg] s/c heliographic latitude (B0 angle)7.508564
HGLN_OBS[deg] s/c heliographic longitude161.945604
CRLT_OBS[deg] s/c Carrington latitude (B0 angle)7.508564
CRLN_OBS[deg] s/c Carrington longitude (L0 angle)180.015167
DSUN_OBS[m] s/c distance from Sun103368092226.256378
HEEX_OBS[m] s/c Heliocentric Earth Ecliptic X-97940729889.368179
HEEY_OBS[m] s/c Heliocentric Earth Ecliptic Y32620601364.919426
HEEZ_OBS[m] s/c Heliocentric Earth Ecliptic Z5335942824.345186
HCIX_OBS[m] s/c Heliocentric Inertial X80106994933.163742
HCIY_OBS[m] s/c Heliocentric Inertial Y-63916958957.075760
HCIZ_OBS[m] s/c Heliocentric Inertial Z13507561251.144291
HCIX_VOB[m/s] s/c Heliocentric Inertial X Velocity31810.242944
HCIY_VOB[m/s] s/c Heliocentric Inertial Y Velocity11479.624614
HCIZ_VOB[m/s] s/c Heliocentric Inertial Z Velocity937.074705
HAEX_OBS[m] s/c Heliocentric Aries Ecliptic X82808927800.477707
HAEY_OBS[m] s/c Heliocentric Aries Ecliptic Y61637818456.129753
HAEZ_OBS[m] s/c Heliocentric Aries Ecliptic Z82808927800.477707
HEQX_OBS[m] s/c Heliocentric Earth Equatorial X-97435822895.162537
HEQY_OBS[m] s/c Heliocentric Earth Equatorial Y31761119254.285561
HEQZ_OBS[m] s/c Heliocentric Earth Equatorial Z13507561251.144291
GSEX_OBS[m] s/c Geocentric Solar Ecliptic X249187095576.787140
GSEY_OBS[m] s/c Geocentric Solar Ecliptic Y-32620742284.198997
GSEZ_OBS[m] s/c Geocentric Solar Ecliptic Y5334919421.684801
OBS_VR[m/s] Radial velocity of spacecraft relative to19868.893790
EAR_TDEL[s] Time(Sun to Earth) - Time(Sun to S/C)159.704718
SUN_TIME[s] Time(Sun to s/c)344.798842
DATE_EARStart time of observation, corrected to Ea2023-05-01T00:02:39.705
DATE_SUNStart time of observation, corrected to Su2023-04-30T23:54:15.201
MAXGOESlargest GOES class of a flare (GOES XRS)M9.6
MAXGOESElargest GOES class of a flare, STIX estimateX2
CHECKSUMHDU checksum updated 2026-11-12T14:03:16fcPAhZO6fbOAfZO3
DATASUMdata unit checksum updated 2026-11-12T14:03:160
HISTORYProcessed by STIXCore L2
HISTORYProcessed by STIXCore ANC
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "month_header = fits.getheader(local_or_download(DATASETS[\"Month 2023-05\"]), ext=0)\n", + "display(\n", + " pd.DataFrame(\n", + " [(card.keyword, card.comment, card.value) for card in month_header.cards],\n", + " columns=[\"keyword\", \"description (FITS comment)\", \"example value (May 2023)\"],\n", + " )\n", + " .style.hide(axis=\"index\")\n", + " .set_properties(**{\"text-align\": \"left\"})\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7b2bd913", + "metadata": {}, + "source": [ + "## 8. Known limitations of this version\n", + "\n", + "- **Locations:** only for flares above a count threshold (about a third of the flares).\n", + "- **`visible_from_earth`:** assumes the emission comes from the solar surface along the STIX line of sight. Coronal sources (e.g. loop\n", + " tops) close to the limb as seen from Earth can be visible even if it says no.\n", + "- **Background:**\n", + " - it comes from one quiet CPD file close in time, not from the flare's own pre-flare period;\n", + " - the QL peak flux and the CPD background use slightly different livetime conventions, which matters only at very\n", + " high count rates;\n", + " - `bkg_spec_flux_at_1au` / `bkg_spec_flux_ql_at_1au` scale the mostly instrumental background as if it were solar.\n", + " For a net flux at 1 AU, subtract first, then scale (section 5.3).\n", + "- **GOES:** the STIX-based GOES estimates are estimates. The measured GOES class only applies to flares visible from\n", + " Earth.\n", + "- **Still subject to change:** column names, definitions and values may change after this feedback round." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv (3.12.7)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "181b15b4927c466cb5e38a6e1ef951db": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": 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a/notebooks/flarelist_showcase/build_feedback_bundle.py b/notebooks/flarelist_showcase/build_feedback_bundle.py new file mode 100644 index 00000000..cd460af8 --- /dev/null +++ b/notebooks/flarelist_showcase/build_feedback_bundle.py @@ -0,0 +1,96 @@ +"""Build the downloads of the flare list feedback page (index.html) in this folder. + +- ``STIX_L3_flarelist_showcase.html``: the executed notebook as a static page (not re-executed), + marked as not to be indexed by search engines; +- ``STIX_L3_flarelist_showcase_with_data.zip``: notebook, requirements.txt and the three data files; +- ``cover.png``: the cover picture of index.html, taken from the notebook output (disc views, section 5.5). + +Also checks that every package of requirements.txt is listed on index.html, and prints the file +sizes for index.html. + +Run from anywhere: python build_feedback_bundle.py +""" + +import re +import sys +import json +import base64 +import zipfile +import subprocess +from pathlib import Path + +HERE = Path(__file__).resolve().parent +NOTEBOOK = HERE / "STIX_L3_flarelist_showcase.ipynb" +HTML = HERE / "STIX_L3_flarelist_showcase.html" +ZIP = HERE / "STIX_L3_flarelist_showcase_with_data.zip" +REQUIREMENTS = HERE / "requirements.txt" +INDEX = HERE / "index.html" +COVER = HERE / "cover.png" +COVER_CELL_ID = "bd52ef90" # notebook cell with the disc views (section 5.5) +DATA_FILES = [ + "solo_L3_stix-flarelist-sdcloc_20230501T000000-20230601T011216_V05.fits", # month 2023-05 + "solo_L3_stix-flarelist-sdcloc_20230101T000000-20231231T230447_V05.fits", # year 2023 + "solo_L3_stix-flarelist-sdcloc_20220101T000000-20260831T232151_V05.fits", # mission +] +ROBOTS = '' +ZIP_FOLDER = "STIX_L3_flarelist_showcase" # top folder inside the zip + + +def size(path): + return ( + f"{path.stat().st_size / 1e6:.0f} MB" if path.stat().st_size >= 1e6 else f"{path.stat().st_size / 1e3:.0f} kB" + ) + + +def export_html(): + # the saved notebook already has its outputs: export only, no execution + subprocess.run( + [sys.executable, "-m", "jupyter", "nbconvert", "--to", "html", str(NOTEBOOK), "--output", HTML.stem], + check=True, + cwd=HERE, + ) + page = HTML.read_text(encoding="utf-8") + if ROBOTS not in page: + page = page.replace("", f"\n{ROBOTS}", 1) + HTML.write_text(page, encoding="utf-8") + + +def export_cover(): + # the figure of the disc view cell, as saved in the executed notebook + cell = next(c for c in json.loads(NOTEBOOK.read_text())["cells"] if c.get("id") == COVER_CELL_ID) + png = next(o["data"]["image/png"] for o in cell["outputs"] if "image/png" in o.get("data", {})) + COVER.write_bytes(base64.b64decode(png)) + + +def build_zip(): + missing = [name for name in DATA_FILES if not (HERE / name).exists()] + if missing: + sys.exit(f"data files missing in {HERE}: {missing}") + tmp = ZIP.with_suffix(".zip.part") + with zipfile.ZipFile(tmp, "w", compression=zipfile.ZIP_DEFLATED, compresslevel=6) as z: + for path in [NOTEBOOK, REQUIREMENTS] + [HERE / name for name in DATA_FILES]: + z.write(path, f"{ZIP_FOLDER}/{path.name}") + tmp.replace(ZIP) + + +def check_requirements_listed(): + # every package of requirements.txt must appear on index.html (the "how to get started" list) + packages = [ + re.split(r"[<>=~!\[ ]", line.strip(), maxsplit=1)[0] + for line in REQUIREMENTS.read_text().splitlines() + if line.strip() and not line.strip().startswith("#") + ] + page = INDEX.read_text(encoding="utf-8") + missing = [p for p in packages if p not in page] + if missing: + sys.exit(f"packages of requirements.txt not listed on index.html: {missing}") + print(f"requirements listed on index.html: {', '.join(packages)}") + + +if __name__ == "__main__": + check_requirements_listed() + export_html() + export_cover() + build_zip() + for path in [HTML, COVER, NOTEBOOK, ZIP, REQUIREMENTS] + [HERE / name for name in DATA_FILES]: + print(f"{size(path):>8} {path.name}") diff --git a/notebooks/flarelist_showcase/index.html b/notebooks/flarelist_showcase/index.html new file mode 100644 index 00000000..5c515967 --- /dev/null +++ b/notebooks/flarelist_showcase/index.html @@ -0,0 +1,283 @@ + + + + + + +STIX flare list feedback + + + +
+ +

STIX L3 flare list: request for feedback

+

Solar Orbiter / STIX · preliminary flare list, version V05 · November 2026

+ +
+ + Flare locations of 9 to 14 May 2023 from the STIX flare list, coloured by GOES class: on a heliographic map, as seen from Earth and as seen from Solar Orbiter + +
From the notebook: flares of 9–14 May 2023 with a STIX location, as seen from Earth and from + Solar Orbiter. Open circles are flares not visible from Earth.
+
+ +

Dear colleagues,

+

+ the STIX team is preparing the first release of the STIX L3 flare list for the Solar Orbiter Archive (SOAR). + Before we publish it, we would like to hear from you: from the STIX team and from everyone who works, or plans to + work, with STIX flares. This page has a notebook that walks through the flare list, the data files, and how to send + us your feedback. +

+ +
+

⚠ Preliminary test release, for feedback only.

+

The content, the columns and their definitions, and the values themselves may still change.

+

+ Please do not use these files for scientific analyses or publications. Use the flare list for + science only once it is published in SOAR. +

+
+ +

One flare list of possibly several

+

+ A flare list is defined by the way its flares are detected: what counts as a flare, and when it starts, peaks and + ends. This version takes the operational flare list of the + STIX Data Center as input (detected in the STIX quicklook light + curves) and adds the values computed in the STIX ground processing: peak values, background, location, observing + geometry and the Solar Orbiter campaign. This is what the sdc in the product name + stix-flarelist-sdcloc stands for. +

+

+ The processing is open for more flare lists: future ones may be built around other detection algorithms and other + criteria of what a flare is, with the same set of computed values. So feedback on the computed values helps every + future flare list, too. +

+ +

What we would like to know

+ +

First: the data in the flare list files

+

Our main interest is the content of the flare list files:

+
    +
  • Does it support your usual use cases, for example the way you look for specific flares?
    • +
  • Is something typical missing: a column, a value, a description that is unclear?
    • +
  • + Only a few standard header keywords of a file are searchable in SOAR. For the monthly flare list files we reuse + them for dedicated search options: the smallest / largest flare (net peak flux at 1 AU) and the shortest / + longest flare duration (section 7 of the notebook). Are these the right values to search by, or would others in + these keywords serve you better? +
    • +
+

+ The flare list will be published as monthly files in SOAR, one file per month. For this feedback + round only, we also merged the monthly files into a year file and a mission file (January 2022 to August 2026), + so that you can easily look up your favourite flares, the ones you know well from your own + work. Check the numbers we computed for them: peak counts and fluxes, background, location, observing geometry. + Detailed feedback on these calculations is very welcome, e.g. a value that looks wrong, a different result from + your own analysis, or a definition you would compute differently. To look at a flare in the notebook, put its + flare_id into SHOWCASE_FLARE_IDS in section 2 (choose the mission data set for flares + outside 2023). +

+ +

Second: working with the flare list in stixpy

+

+ All plots and filters in the notebook work directly on the plain data, on purpose. In stixpy we plan a + rich wrapper class (a Product) that gives you convenient access to the flare list and built-in plotting + routines, so you don't have to write them yourself. A preliminary version is shown in this demo: + stixpy flare list demo. +

+

+ You can give feedback on this next step in a separate GitHub issue: + stixpy issue #268. + Keep in mind that it is a chicken-and-egg problem: first we need to get the underlying data right, then the way to + work with it. +

+ +

Get the notebook

+

The most useful feedback comes from working with the data yourself, so the options are listed from the recommended one to the quick look.

+
+
+

Notebook + data (40 MB)

+

Recommended. Notebook, requirements.txt and all three data files, to work offline.

+ Download .zip +
+
+

Run in your browser

+

Run and change the notebook online, nothing to install. Your changes stay in your own session. + Colab needs a Google account; Binder needs none, but starts slower and has too little memory for the mission + file.

+
+ Open in Colab + Open in Binder +
+
+
+

Notebook only (1 MB)

+

The Jupyter notebook. It downloads the data file you choose once, into its own folder.

+ Download .ipynb +
+
+

View only

+

The notebook with all plots as a web page. Nothing to install: just read it.

+ Open as web page +
+
+ +

The data files

+

All three files have the same columns; they differ only in the period they cover, and so in the time it takes to download, load and filter them. The mission file with almost 100,000 flares is the one to look up any flare, but it takes noticeably longer; the month file is quick; the year file is in between and is the notebook default.

+
+ + + + + + + + + + + + + + + + + + +
data setperiodflaressize
Month 2023-05 + (regular monthly file)2023-05-01 .. 2023-06-011,6954 MB
Year 2023 + (merged, notebook default)2023-01-01 .. 2023-12-3121,38151 MB
Mission + (merged)2022-01-01 .. 2026-08-3197,897411 MB
+
+ +

How to get started

+

The notebook needs Python 3.12 or newer (as stixpy) and these packages + (requirements.txt):

+
+ + + + + + + + + + + + + +
packageversionused for
numpy≥ 2.0arrays
astropy≥ 6.0reading the FITS file, units, times, coordinates
sunpy≥ 6.0solar coordinate frames
matplotlib≥ 3.8plots
pandas≥ 2.0tables and time handling
jinja2≥ 3.0table styling
jupyterlab≥ 4.0running the notebook
ipywidgets≥ 8.0optional: radio buttons to choose the data set
stixpyoptional: only section 6, opening the data files of one flare
+
+
    +
  1. Unpack the zip, or put the .ipynb (and requirements.txt) into an empty folder.
    2. +
  2. Create a virtual environment and install the packages: +
    python -m venv venv
+source venv/bin/activate        # Windows: venv\Scripts\activate
+pip install -r requirements.txt
    +
    3. +
  3. Start Jupyter, open STIX_L3_flarelist_showcase.ipynb, choose the data set in section 2 and + run all cells: +
    jupyter lab
    +
    4. +
+

+ If the chosen data file is not in the notebook folder, the notebook downloads it once into that folder and uses it + from there next time. +

+ +

How to send feedback

+
    +
  • + Preferably as a comment in the GitHub issue: + STIXCore issue #462. +
    • +
  • + Already raised? If someone has already made your point in the issue, please comment on it or + at least add a 👍 (+1) reaction. That shows us how important a point is to the community and helps us + decide what to do first. +
    • +
  • + If GitHub is not possible for you: send an email to + nicky.hochmuth@ateleris.ch. +
    • +
+ +

What happens with your feedback

+
    +
  • Obvious errors (wrong values, broken columns, misleading descriptions) will be fixed before + the first release in SOAR.
    • +
  • New requests (additional columns, values or header keywords) will be collected and + prioritised, and may come in a later version of the flare list. Your comments and 👍 reactions in the + issues help us set the priorities.
    • +
+ +

Thank you for your time and your feedback!

+ +
+ STIX team · STIX L3 flare list, preliminary version V05 · page of November 2026 +
+ +
+ + diff --git a/notebooks/flarelist_showcase/requirements.txt b/notebooks/flarelist_showcase/requirements.txt new file mode 100644 index 00000000..980bf71b --- /dev/null +++ b/notebooks/flarelist_showcase/requirements.txt @@ -0,0 +1,17 @@ +# Requirements of the STIX L3 flare list showcase notebook (STIX_L3_flarelist_showcase.ipynb) +# Python 3.12 or newer (as stixpy). Install with: pip install -r requirements.txt +# Tested with python 3.12, numpy 2.3, astropy 8.0, sunpy 8.0, matplotlib 3.11, pandas 3.0, ipywidgets 8.1 + +numpy>=2.0 +astropy>=6.0 +sunpy>=6.0 +matplotlib>=3.8 +pandas>=2.0 +jinja2>=3.0 # table styling (pandas Styler) +jupyterlab>=4.0 + +# optional: radio buttons to choose the data set (without it, set DATASET in the parameters cell) +ipywidgets>=8.0 + +# optional, only for section 6 (open the data files of one flare): stixpy +# pip install stixpy diff --git a/stixcore/data/stixcore.ini b/stixcore/data/stixcore.ini index cdc8b347..3d7afdd7 100644 --- a/stixcore/data/stixcore.ini +++ b/stixcore/data/stixcore.ini @@ -36,3 +36,15 @@ soop_files_download = ./stixcore/data/soop ecc_path = /opt/stix_det_cal/bin/ [Processing] flarelist_sdc_min_count = 1000 +# background-data-file lookup (find_background_file_for_time) +flarelist_bkg_window_past_days = 21 +flarelist_bkg_window_future_days = 21 +flarelist_bkg_min_duration_s = 500 +# require the background file to share the flare's on-board ELUT configuration (ELUTManager lookup) +flarelist_bkg_require_same_elut = True +# optional stricter selection (default OFF - all three move common cases, not just edge cases): +# purpose preference: prefer purpose=="Background" unless a subject-only match is this many days closer +# (0.0 disables); exclude "elevated" backgrounds; drop requests whose comment references a flare id +flarelist_bkg_purpose_penalty_days = 0.0 +flarelist_bkg_exclude_elevated = False +flarelist_bkg_exclude_flare_comment = True diff --git a/stixcore/data/test/publish/rid_lut.csv b/stixcore/data/test/publish/rid_lut.csv index a6f03173..1eae888e 100644 --- a/stixcore/data/test/publish/rid_lut.csv +++ b/stixcore/data/test/publish/rid_lut.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:2ba33aa45a7d045dcbc08d1ddddf8bdf8d01dde93e01fdf4db0c12e1dfb0b4bf -size 1005 +oid sha256:577485ea766335f15fb1ac4e40f1803d96e82919fd77164c429029a11f7afcac +size 1383 diff --git a/stixcore/ephemeris/manager.py b/stixcore/ephemeris/manager.py index 787e9490..adee19ff 100644 --- a/stixcore/ephemeris/manager.py +++ b/stixcore/ephemeris/manager.py @@ -388,6 +388,20 @@ def get_sun_disc_size(self, *, date): return rsun_arc + def get_earth_solo_time_shift(self, *, date): + """gets Time(Sun to Earth) - Time(Sun to S/C) + + Returns + ------- + `astropy.units.Quantity` + Time difference between Sun to Earth and Sun to S/C in seconds + """ + et = spiceypy.scs2e(SOLAR_ORBITER_ID, str(date)) + solo_sun_hg, sun_solo_lt = spiceypy.spkezr("SOLO", et, "SUN_EARTH_CEQU", "None", "Sun") + sun_earth_hee, sun_earth_lt = spiceypy.spkezr("Earth", et, "SOLO_HEE", "None", "Sun") + + return (sun_earth_lt - sun_solo_lt) * u.s + def get_position(self, *, date, frame): """ Get the position of SolarOrbiter at the given date in the given coordinate frame. diff --git a/stixcore/io/FlareListManager.py b/stixcore/io/FlareListManager.py index 38359be6..b854b2f9 100644 --- a/stixcore/io/FlareListManager.py +++ b/stixcore/io/FlareListManager.py @@ -1,29 +1,604 @@ import sys import time +from pathlib import Path from datetime import datetime, timedelta +from collections import namedtuple import numpy as np import pandas as pd from stixdcpy.net import Request as stixdcpy_req +from stixpy.calibration.livetime import get_livetime_fraction +from stixpy.product import Product as STIXPYProduct from sunpy.net import attrs as a import astropy.units as u from astropy.table import Column, QTable, vstack from astropy.time import Time +from stixcore.calibration.elut_manager import ELUTManager from stixcore.config.config import CONFIG +from stixcore.io.product_processors.fits.processors import CreateUtcColumn +from stixcore.io.RidLutManager import ( + DEFAULT_BKG_EXCLUDE_KEYWORDS, + DEFAULT_BKG_KEYWORDS, + RidLutManager, + search_background_candidates, +) from stixcore.products.level3.flarelist import FlarelistSC, FlarelistSDC from stixcore.products.product import Product from stixcore.util.logging import get_logger from stixcore.util.singleton import Singleton from stixcore.util.util import url_to_path -__all__ = ["FlareListManager", "SDCFlareListManager", "SCFlareListManager"] +__all__ = [ + "FlareListManager", + "SDCFlareListManager", + "SCFlareListManager", + "compute_ql_count_rate", + "build_month_timeline", + "nearest_bin_index", + "max_rcr_in_window", + "find_background_file_for_time", + "BackgroundSelection", + "background_spectrum_from_cpd", + "rebin_spectrum_to_ql", + "rebin_flux_to_ql", + "collecting_area_cm2", +] logger = get_logger(__name__) +#: Expected unit of the livetime- and area-corrected flux columns. Computations carry +#: astropy units through and validate against this with ``.to(FLUX_UNIT)`` rather than +#: labelling bare floats. +FLUX_UNIT = u.ct / (u.s * u.keV * u.cm**2) + + +def compute_ql_count_rate(counts, timedel, triggers, energy_delta, *, n_detectors): + """Reproduce stixpy's QL count-rate normalization -> ``ct / (s * keV)``. + + Mirrors ``stixpy.timeseries.quicklook`` (lightcurve uses ``n_detectors=16``, + background uses ``n_detectors=1``). Pure, no I/O. + + Parameters + ---------- + counts : `~astropy.units.Quantity` + Raw counts, shape ``(N, 5)`` in ``ct``. + timedel : `~astropy.units.Quantity` + Bin durations, shape ``(N,)``. + triggers : array-like + Trigger counts, shape ``(N,)`` or ``(N, 1)``. + energy_delta : `~astropy.units.Quantity` + Channel widths, shape ``(5,)`` in ``keV``. + n_detectors : int + 16 for the lightcurve, 1 for the background detector. + + Returns + ------- + `~astropy.units.Quantity` + Count rate, shape ``(N, 5)`` in ``ct / (s * keV)``. + """ + timedel = timedel.to(u.s) + trig = np.asarray(triggers).reshape(-1) + live_frac, *_ = get_livetime_fraction(trig / (n_detectors * timedel)) + return counts / ((timedel * live_frac).reshape(-1, 1) * energy_delta) + + +def build_month_timeline(daily_data_tables): + """Stack per-day QTables into one time-sorted timeline with unique timestamps. + + ``None``/empty inputs are ignored; an empty list yields an empty ``QTable``. + After sorting by ``time`` duplicate timestamps (day-boundary overlaps) are + dropped so the timeline is strictly increasing. + """ + tables = [t for t in daily_data_tables if t is not None and len(t) > 0] + if not tables: + return QTable() + timeline = vstack(tables, metadata_conflicts="silent") + timeline.sort("time") + if len(timeline) > 1: + keep = np.ones(len(timeline), dtype=bool) + keep[1:] = np.diff(timeline["time"].jd) > 0 + timeline = timeline[keep] + return timeline + + +def nearest_bin_index(times, target, tol): + """Index of the bin in ``times`` nearest ``target``. + + Returns ``None`` if ``times`` is empty or the nearest gap exceeds ``tol`` + (both ``target`` and ``times`` are `~astropy.time.Time`, ``tol`` a duration). + """ + if times is None or len(times) == 0: + return None + dt = np.abs((times - target).to_value(u.s)) + j = int(np.argmin(dt)) + if dt[j] > tol.to_value(u.s): + return None + return j + + +def max_rcr_in_window(times, rcr, start, end, *, fallback): + """Highest ``rcr`` for bins with ``start <= time <= end``; ``fallback`` if none.""" + if times is None or len(times) == 0: + return fallback + mask = (times >= start) & (times <= end) + if not np.any(mask): + return fallback + return int(np.asarray(rcr)[mask].max()) + + +#: 0-based detector indices used for a background spectrum: all 32 science +#: sub-collimators except the Coarse Flare Locator (idx 8) and the Background +#: monitor (idx 9), i.e. the 30 imaging detectors. +_IMAGING_DETECTORS = [d for d in range(32) if d not in (8, 9)] + + +def _edges_kev(col): + """Return an energy-edge column as a plain float ndarray in keV.""" + return np.asarray(col.to_value(u.keV) if hasattr(col, "to_value") else col, dtype=float) + + +_PIXEL_AREA_CACHE = None + + +def _pixel_area_cm2(): + """12-pixel active-area vector (cm^2) from stixcore's ``stx_subc_params`` config. + + STIX Caliste layout is 8 large pixels then 4 small; pixel sizes are identical + across detectors so one 12-vector applies to all. Cached after first read. + """ + global _PIXEL_AREA_CACHE + if _PIXEL_AREA_CACHE is None: + import stixcore.config + from stixcore.config.reader import read_subc_params + + path = Path(stixcore.config.__file__).parent / "data" / "common" / "detector" / "stx_subc_params.csv" + t = read_subc_params(path) + large = float(t["L Pixel Xsize"][0]) * float(t["L Pixel Ysize"][0]) # mm^2 + small = float(t["S Pixel Xsize"][0]) * float(t["S Pixel Ysize"][0]) # mm^2 + _PIXEL_AREA_CACHE = np.array([large] * 8 + [small] * 4, dtype=float) / 100.0 # mm^2 -> cm^2 + return _PIXEL_AREA_CACHE + + +def collecting_area_cm2(product): + """Geometric collecting area (cm^2) of a detector+pixel-summed product = + ``detector_mask.sum() * sum(pixel_area[pixel_mask])``. Returns ``None`` when the + product's control has no detector/pixel mask (e.g. the QL background monitor).""" + ctrl = getattr(product, "control", None) + if ctrl is None or "detector_mask" not in ctrl.colnames or "pixel_mask" not in ctrl.colnames: + return None + det = np.asarray(ctrl["detector_mask"][0], dtype=bool) + pix = np.asarray(ctrl["pixel_mask"][0], dtype=bool) + return float(det.sum()) * float(_pixel_area_cm2()[pix].sum()) + + +def _bkg_monitor_area_cm2(): + """Fixed area (cm^2) of the QL background monitor: one open (grid-less) detector + over all 12 pixels.""" + return float(_pixel_area_cm2().sum()) + + +def _cpd_present_channel_mask(cpd): + """Boolean mask (length 32) of which science channels are present in a CPD's + ``counts`` when only a telemetered subset is stored, or ``None`` if unknown.""" + ctrl = getattr(cpd, "control", None) + if ctrl is None: + return None + if "energy_bin_mask" in ctrl.colnames: + m = np.asarray(ctrl["energy_bin_mask"][0], dtype=bool).ravel() + return m if m.size == 32 else None + if "energy_bin_edge_mask" in ctrl.colnames: + edges = np.asarray(ctrl["energy_bin_edge_mask"][0], dtype=bool).ravel() + if edges.size == 33: # 33 edges -> a channel is present when both its edges are set + return edges[:-1] & edges[1:] + return None + + +def _align_to_energies(spec, nE, energies, cpd): + """Pad/align a per-channel ``spec`` (length ``nE``) to the energies-table length. + + Preserves an astropy unit on ``spec`` (the NaN padding is created in the same unit, + so assignment stays unit-checked).""" + ne_en = len(energies) + if nE == ne_en: + return spec + unit = getattr(spec, "unit", None) + aligned = np.full(ne_en, np.nan) + if unit is not None: + aligned = aligned * unit + mask = _cpd_present_channel_mask(cpd) + if mask is not None and mask.size == ne_en and int(mask.sum()) == nE: + aligned[mask] = spec + elif nE < ne_en: + aligned[:nE] = spec + else: + logger.warning(f"CPD counts have {nE} channels but energies has {ne_en}; truncating") + aligned[:] = spec[:ne_en] + return aligned + + +def background_spectrum_from_cpd(cpd): + """Median quiet-period background spectrum (counts and flux) from a CPD product. + + Sums ``counts`` over the 30 imaging detectors (excl. CFL idx 8, BKG monitor idx 9) + and all pixels, then takes the median over the file's time bins (the whole quiet + period). ``flux`` is the livetime- and area-normalized rate density + ``ct s^-1 keV^-1 cm^-2`` (per-bin then median), mirroring stixpy's + ``create_meta_pixels`` normalization: + ``flux[t,E] = Σ_dp counts / ((Σ_d livefrac[t,d]·timedel[t]) · (Σ_p area[p]) · dE[E])``. + Both are aligned to the product's own ``energies`` table (length = telemetered + channels). Reads ``cpd.data["counts"]`` directly (``get_data`` is not robust on all + files). Any failure computing the flux yields NaN flux (counts still returned). + + Returns + ------- + (counts, flux, energies) + """ + counts = cpd.data["counts"] + counts_q = counts if hasattr(counts, "unit") else np.asarray(counts, dtype=float) * u.ct # (nt,32,npix,nE) ct + nE = counts_q.shape[-1] + energies = cpd.energies + csum = counts_q[:, _IMAGING_DETECTORS, :, :].sum(axis=(1, 2)) # (nt, nE) ct over imaging det+pix + counts_spec = _align_to_energies(np.nanmedian(csum, axis=0), nE, energies, cpd) # Quantity ct + + flux_spec = np.full(len(energies), np.nan) * FLUX_UNIT + try: + timedel = cpd.data["timedel"].to(u.s) # (nt,) — units carried through the livetime rate + trig = np.asarray(getattr(cpd.data["triggers"], "value", cpd.data["triggers"]), dtype=float) # (nt,16) + from stixpy.calibration.visibility import STIX_INSTRUMENT + + subcol = np.asarray(STIX_INSTRUMENT.subcol_adc_mapping) # (32,) + livefrac = get_livetime_fraction(trig[:, subcol] / timedel[:, None])[0] # (nt, 32), dimensionless + livefrac = np.asarray(getattr(livefrac, "value", livefrac), dtype=float) + exp_t = (livefrac[:, _IMAGING_DETECTORS] * timedel[:, None]).sum(axis=1) # (nt,) s (Σ over dets) + pmask = _cpd_pixel_mask(cpd) # which of 12 pixels are telemetered + pa = _pixel_area_cm2() + area = (pa[pmask].sum() if pmask is not None else pa[: counts_q.shape[2]].sum()) * u.cm**2 # cm^2 + dE = (energies["e_high"] - energies["e_low"]).to(u.keV) # (ne_en,) keV + dE_c = dE if nE == len(energies) else dE[:nE] + with np.errstate(divide="ignore", invalid="ignore"): + # astropy derives the unit; .to(FLUX_UNIT) both converts and asserts it is correct + flux_t = (csum / (exp_t[:, None] * area * dE_c[None, :])).to(FLUX_UNIT) # (nt, nE) + flux_spec = _align_to_energies(np.nanmedian(flux_t, axis=0), nE, energies, cpd) + except Exception as e: + logger.warning(f"could not compute background flux: {e}") + + return counts_spec, flux_spec, energies + + +def _cpd_pixel_mask(cpd): + """Boolean (length 12) of telemetered pixels from the CPD ``pixel_masks`` (row 0), + or ``None`` when unavailable.""" + data = getattr(cpd, "data", None) + try: + if data is not None and "pixel_masks" in data.colnames: + return np.asarray(data["pixel_masks"][0], dtype=bool).ravel() + except Exception: + pass + return None + + +def rebin_spectrum_to_ql(counts_32, energies_32, ql_block): + """Rebin a science *counts* spectrum onto the QL energy bands of ``ql_block``. + + ``ql_block`` carries the target band edges (``e_low``/``e_high``) read from the + energy table for a given flare (never hardcoded, so a QL binning that changes + over time is honored). A science channel contributes to a band when its + ``[e_low, e_high]`` lies within the band; whole channels are summed (the edges + align). Summing preserves the input unit, so the result carries ``counts_32``'s unit. + """ + e_low = _edges_kev(energies_32["e_low"]) + e_high = _edges_kev(energies_32["e_high"]) + b_low = _edges_kev(ql_block["e_low"]) + b_high = _edges_kev(ql_block["e_high"]) + unit = getattr(counts_32, "unit", None) + counts = np.asarray(getattr(counts_32, "value", counts_32), dtype=float) + out = np.full(len(b_low), np.nan) + for b in range(len(b_low)): + chans = (e_low >= b_low[b]) & (e_high <= b_high[b]) + if np.any(chans): + out[b] = np.nansum(counts[chans]) + return out * unit if unit is not None else out + + +def rebin_flux_to_ql(flux_32, energies_32, ql_block): + """Rebin a science *flux* spectrum (per keV) onto the QL bands of ``ql_block``. + + Flux is a density, so bands combine as the dE-weighted mean + ``flux_band = Σ(flux_ch·dE_ch) / Σ dE_ch`` over each band's channels (edges align). + The dE weights cancel dimensionally, so the result carries ``flux_32``'s unit. + """ + e_low = _edges_kev(energies_32["e_low"]) + e_high = _edges_kev(energies_32["e_high"]) + dE = e_high - e_low + b_low = _edges_kev(ql_block["e_low"]) + b_high = _edges_kev(ql_block["e_high"]) + unit = getattr(flux_32, "unit", None) + flux = np.asarray(getattr(flux_32, "value", flux_32), dtype=float) + out = np.full(len(b_low), np.nan) + for b in range(len(b_low)): + chans = (e_low >= b_low[b]) & (e_high <= b_high[b]) + denom = np.nansum(dE[chans]) + if np.any(chans) and denom > 0: + out[b] = np.nansum(flux[chans] * dE[chans]) / denom + return out * unit if unit is not None else out + + +def _intern_energy_block(energy, energy_look_up, energies_src): + """Append ``energies_src`` (channel/e_low/e_high) to the ``energy`` table as a new + block if its binning is unseen; return ``(energy, index)``. Blocks are hash-deduped + and get an ``index`` one past the current maximum so they never collide with the + QL blocks already present.""" + e_sub = QTable() + e_sub["channel"] = energies_src["channel"] + e_sub["e_low"] = energies_src["e_low"] + e_sub["e_high"] = energies_src["e_high"] + e_hash = frozenset(pd.core.util.hashing.hash_array(e_sub.as_array())) + if e_hash in energy_look_up: + return energy, energy_look_up[e_hash] + idx = (int(np.max(energy["index"])) + 1) if (len(energy) > 0 and "index" in energy.colnames) else 0 + energy_look_up[e_hash] = idx + e_sub["index"] = Column(idx, description="energy edge table index", dtype=np.int8) + return vstack([energy, e_sub]), idx + + +def _ql_block_for(energy, eidx): + """The rows of the ``energy`` table belonging to block ``eidx`` (a flare's QL + binning), or ``None`` when the table is empty / has no such block.""" + if energy is None or len(energy) == 0 or "index" not in energy.colnames: + return None + sel = np.asarray(energy["index"]) == int(eidx) + return energy[sel] if np.any(sel) else None + + +#: Result of :func:`find_background_file_for_time`. ``path`` is a `~pathlib.Path` +#: (or ``None`` when nothing qualifies), ``rid`` the selected BSD request id +#: (``-1`` when none), and ``valid_from``/``valid_to`` the `~astropy.time.Time` +#: interval over which this selection stays valid for a time-ordered caller. +BackgroundSelection = namedtuple("BackgroundSelection", ["path", "rid", "valid_from", "valid_to"]) + + +def _rid_from_filename(path): + """Parse the BSD request id embedded in a science FITS filename. + + Mirrors ``stixcore.processing.publish`` (the 6th ``_``-separated segment is + ``-``). Returns ``None`` when the name doesn't carry one. + """ + parts = Path(path).name.split("_") + if len(parts) <= 5: + return None + try: + return int(parts[5].replace(".fits", "").split("-")[0]) + except ValueError: + return None + + +def _elut_id(time): + """Identifier of the ELUT active at ``time`` (the resolved ELUT filename), or + ``None`` when the ELUT index has no unambiguous entry for it. Metadata-only + lookup via `~stixcore.calibration.elut_manager.ELUTManager` — reads no FITS.""" + try: + return ELUTManager.instance._find_elut_file(time.to_datetime()) + except Exception as e: + logger.debug(f"no ELUT resolved for {getattr(time, 'isot', time)}: {e}") + return None + + +def _effective_crossover(t0, sa, pa, sb, pb): + """Earliest ``t >= t0`` (all in JD days) at which candidate ``b``'s effective + distance ``|t - sb| + pb`` drops to/below ``a``'s ``|t - sa| + pa``; ``None`` if + it never does. Bounds the nearest-in-time validity interval under the purpose + penalty (piecewise-linear, breakpoints at the two starts).""" + + def val(x): + return abs(x - sb) - abs(x - sa) + (pb - pa) + + if val(t0) < 0: + return t0 + lefts = [t0] + sorted(x for x in (sa, sb) if x > t0) + for i, left in enumerate(lefts): + right = lefts[i + 1] if i + 1 < len(lefts) else None + v = val(left) + if v < 0: + return left + probe = (left + right) / 2 if right is not None else left + 1.0 + slope = ((abs(probe - sb) - abs(probe - sa)) - (abs(left - sb) - abs(left - sa))) / (probe - left) + if slope < 0: + tc = left + v / (-slope) + if right is None or tc <= right: + return tc + return None + + +def find_background_file_for_time( + time, + *, + fido_client, + rid_lut=None, + window_past=None, + window_future=None, + min_duration=None, + require_same_elut=None, + purpose_penalty=None, + keywords=DEFAULT_BKG_KEYWORDS, + exclude_keywords=None, + exclude_flare_comment=None, +): + """Find the best quiet-time background CPD file applicable at ``time``. + + Strategy (see :func:`stixcore.io.RidLutManager.search_background_candidates`): + rank background requests from the RID LUT **nearest-in-time first** (closest + request start, past or future, within the separate ``window_past`` / + ``window_future`` bounds). For each candidate resolve the real ``sci_xray_cpd`` + L1 file(s) via FIDO, keep those whose filename carries the candidate rid, and + accept the first one that passes the "good background" checks (requested long + enough, attenuator out over the whole interval). + + Three optional stricter filters exist but default **off** (they move common + cases, not just edge cases): a ``purpose == "Background"`` preference + (``purpose_penalty`` > 0), dropping "elevated" backgrounds (``exclude_keywords``), + and dropping requests whose comment references a specific flare id + (``exclude_flare_comment``). See the ``[Processing]`` config keys. + + Alongside the winning file a validity interval ``[valid_from, valid_to]`` is + returned so a time-ordered caller (monthly flare processing) can reuse the + result for every later time inside the interval. ``valid_to`` is the earliest + time at which another candidate's effective distance would overtake the chosen + one, or the chosen start plus ``window_past`` when there is none — capped at + ``time + window_future`` so newly-reachable candidates are re-scanned. A + "no file found" result is cached until the next candidate could appear. + + Parameters + ---------- + time : `~astropy.time.Time` or str + The query time (e.g. a flare peak). + fido_client : `~stixpy.net.client.STIXClient` + Client used for the ``sci_xray_cpd`` L1 search. + rid_lut : `~astropy.table.Table`, optional + The RID LUT; defaults to ``RidLutManager.instance.rid_lut``. + window_past, window_future : `~astropy.units.Quantity`, optional + Search windows; default to the ``[Processing]`` config keys + ``flarelist_bkg_window_past_days`` / ``flarelist_bkg_window_future_days``. + min_duration : `~astropy.units.Quantity`, optional + Minimum requested integration time; defaults to + ``flarelist_bkg_min_duration_s``. + require_same_elut : bool, optional + If True, a candidate is only accepted when the ELUT active at its request + start (per `~stixcore.calibration.elut_manager.ELUTManager`) matches the + one active at ``time`` — i.e. the background was taken under the same + on-board ELUT configuration as the flare. Defaults to the ``[Processing]`` + config key ``flarelist_bkg_require_same_elut``. + purpose_penalty : `~astropy.units.Quantity`, optional + Distance penalty for non-``Background``-purpose candidates; defaults to the + ``[Processing]`` config key ``flarelist_bkg_purpose_penalty_days``. + keywords, exclude_keywords : tuple of str, optional + Positive / negative background keywords for the candidate search. + exclude_flare_comment : bool, optional + Drop candidates whose comment references a specific flare id. + + Returns + ------- + BackgroundSelection + """ + if window_past is None: + window_past = CONFIG.getfloat("Processing", "flarelist_bkg_window_past_days", fallback=30.0) * u.day + if window_future is None: + window_future = CONFIG.getfloat("Processing", "flarelist_bkg_window_future_days", fallback=7.0) * u.day + if min_duration is None: + min_duration = CONFIG.getfloat("Processing", "flarelist_bkg_min_duration_s", fallback=1200.0) * u.s + if require_same_elut is None: + require_same_elut = CONFIG.getboolean("Processing", "flarelist_bkg_require_same_elut", fallback=True) + if purpose_penalty is None: + purpose_penalty = CONFIG.getfloat("Processing", "flarelist_bkg_purpose_penalty_days", fallback=0.0) * u.day + if exclude_keywords is None: + exclude_keywords = ( + DEFAULT_BKG_EXCLUDE_KEYWORDS + if CONFIG.getboolean("Processing", "flarelist_bkg_exclude_elevated", fallback=False) + else () + ) + if exclude_flare_comment is None: + exclude_flare_comment = CONFIG.getboolean("Processing", "flarelist_bkg_exclude_flare_comment", fallback=False) + + t = time if isinstance(time, Time) else Time(time) + if rid_lut is None: + rid_lut = RidLutManager.instance.rid_lut + + # ELUT active at the flare time; only enforced when it can be resolved + flare_elut = _elut_id(t) if require_same_elut else None + + candidates = search_background_candidates( + rid_lut, + t, + window_past=window_past, + window_future=window_future, + keywords=keywords, + exclude_keywords=exclude_keywords, + exclude_flare_comment=exclude_flare_comment, + purpose_penalty=purpose_penalty, + ) + + t_jd = t.to_value("jd") + penalty_days = purpose_penalty.to_value(u.day) + + def _valid_to(chosen): + # the selection holds until another candidate's *effective* distance overtakes + # the chosen one's (accounts for the purpose penalty), or the chosen leaves the + # past window if none does. Capped at time + window_future so newly-reachable + # candidates get re-scanned. + pa = 0.0 if chosen.is_background else penalty_days + sa = chosen.start.to_value("jd") + edge = sa + window_past.to_value(u.day) + for j in candidates: + if j.rid == chosen.rid: + continue + pb = 0.0 if j.is_background else penalty_days + tc = _effective_crossover(t_jd, sa, pa, j.start.to_value("jd"), pb) + if tc is not None: + edge = min(edge, tc) + return min(Time(edge, format="jd", scale="utc"), t + window_future) + + for cand in candidates: + if (cand.end - cand.start) < min_duration: + logger.debug(f"bkg candidate rid {cand.rid}: requested duration below {min_duration}; skipping") + continue + if flare_elut is not None and _elut_id(cand.start) != flare_elut: + logger.debug(f"bkg candidate rid {cand.rid}: different ELUT configuration; skipping") + continue + try: + res = fido_client.search( + a.Time(cand.start, cand.end), + a.Instrument.stix, + a.stix.DataProduct.sci_xray_cpd, + a.Level("L1"), + ) + except Exception as e: + logger.warning(f"bkg candidate rid {cand.rid}: CPD search failed: {e}") + continue + if len(res) == 0: + continue + res.filter_for_latest_version() + url_to_path(res) + if "path" not in res.columns: + continue + for path in res["path"]: + if path is None: + continue + if _rid_from_filename(path) != cand.rid: + continue + try: + p = STIXPYProduct(path) + except Exception as e: + logger.warning(f"bkg candidate rid {cand.rid}: could not load {path}: {e}") + continue + rcr = p.data["rcr"] if "rcr" in p.data.colnames else None + if rcr is None or np.any(np.asarray(rcr) != 0): + logger.debug(f"bkg candidate rid {cand.rid}: attenuator in (rcr != 0); skipping {path}") + continue + valid_to = _valid_to(cand) + logger.info(f"selected background file {path} (rid {cand.rid}, {cand.side}) for {t.isot}") + return BackgroundSelection(path=Path(str(path)), rid=cand.rid, valid_from=t, valid_to=valid_to) + + later = [c.start for c in candidates if c.start > t] + valid_to = min(later) if later else t + window_future + logger.info(f"no usable background file found for {t.isot}") + return BackgroundSelection(path=None, rid=-1, valid_from=t, valid_to=valid_to) + class FlareListManager: + """Base class for the flare-list source managers. + + Holds the source flare list and the product class it feeds, and provides the shared + enrichment machinery that turns raw flare definitions into an enriched flare list: + `~stixcore.io.FlareListManager.FlareListManager._build_ql_month_timeline`, + `~stixcore.io.FlareListManager.FlareListManager.add_lc_bkg_columns` (quicklook peak / + background counts and fluxes) and + `~stixcore.io.FlareListManager.FlareListManager.add_background_file_column` (quiet-time + background spectrum). The active subclass is + `~stixcore.io.FlareListManager.SDCFlareListManager`, which mirrors the operational STIX + Data Center flare list. See :doc:`/products/flarelist`. + """ + @property def flarelist(self): return self._flarelist @@ -40,6 +615,359 @@ def flarelistname(self): def productCls(self): return self._product_cls + def _build_ql_month_timeline(self, *, start, end, fido_client, data_product, n_detectors, track_energy): + """Search + load all L1 QL files of ``data_product`` for ``[start, end)`` and + stack them into one time-sorted timeline with a per-bin count-rate column. + + Each daily file is opened exactly once (used for both the energy-table + lookup and the counts), so the whole month costs one open per day. + + Returns + ------- + (timeline, energy, date_to_eidx) + ``timeline`` : QTable with columns ``time``, ``counts`` (raw ``ct``, + ``(N, 5)``), ``counts_rate`` (``(N, 5)``) and, for the lightcurve, + ``rcr``. ``energy`` and ``date_to_eidx`` are only populated when + ``track_energy`` is True. + """ + energy = QTable() + energy_look_up = {} + date_to_eidx = {} + daily_tables = [] + + try: + res = fido_client.search(a.Time(start, end), a.Instrument.stix, data_product, a.Level("L1")) + if len(res) > 0: + res.filter_for_latest_version() + url_to_path(res) + except Exception as e: + logger.error(f"error searching L1 QL {data_product} files for month {start}: {e}") + return QTable(), energy, date_to_eidx + + if len(res) == 0 or "path" not in res.columns: + return QTable(), energy, date_to_eidx + + for path in res["path"]: + if path is None: + continue + try: + p = STIXPYProduct(path) + except Exception as e: + logger.warning(f"could not load QL product {path}: {e}") + continue + + energies = getattr(p, "_energies", None) + if energies is None: + logger.warning(f"QL product {path} has no energies table; skipping") + continue + + counts = p.data["counts"] + if counts.shape[1] != 5: + logger.warning(f"QL product {path} has {counts.shape[1]} channels (expected 5); skipping") + continue + + energy_delta = energies["e_high"] - energies["e_low"] + rate = compute_ql_count_rate( + counts, p.data["timedel"], p.data["triggers"], energy_delta, n_detectors=n_detectors + ) + # area-normalize to a flux (ct/s/keV/cm^2): use the product's detector/pixel + # masks, falling back to the fixed open-detector area for the QL background monitor + area = collecting_area_cm2(p) + if area is None: + area = _bkg_monitor_area_cm2() + rate = rate / (area * u.cm**2) + + daily = QTable() + daily["time"] = p.data["time"] + daily["counts"] = counts + daily["counts_rate"] = rate + if "rcr" in p.data.colnames: + daily["rcr"] = np.asarray(p.data["rcr"]).astype(np.int16) + daily_tables.append(daily) + + if track_energy: + e_sub = QTable() + e_sub["channel"] = energies["channel"] + e_sub["e_low"] = energies["e_low"] + e_sub["e_high"] = energies["e_high"] + e_hash = frozenset(pd.core.util.hashing.hash_array(e_sub.as_array())) + if e_hash not in energy_look_up: + e_idx = len(energy_look_up) + energy_look_up[e_hash] = e_idx + e_sub["index"] = Column(e_idx, description="energy edge table index", dtype=np.int8) + energy = vstack([energy, e_sub]) + if e_idx > 0: + logger.warning(f"multiple energy ql-lc tables found for month {start}") + eidx = energy_look_up[e_hash] + for d in {t.to_datetime().date() for t in p.data["time"]}: + date_to_eidx.setdefault(d, eidx) + logger.info(f"loaded QL product {path} with {len(p.data)} bins and {len(energies)} energy channels") + + return build_month_timeline(daily_tables), energy, date_to_eidx + + def add_lc_bkg_columns(self, data, *, start, end, fido_client): + """Populate LC/BKG peak counts + flux, RCR and ``att_in`` on ``data`` from the + real L1 QL lightcurve + background products, and return the energy QTable. + + ``data`` must already carry ``flare_id`` and the astropy ``Time`` columns + ``start_UTC`` / ``end_UTC`` / ``peak_UTC``. Columns are added in place: + ``lc_peak``, ``lc_peak_flux``, ``lc_bkg_peak``, ``lc_bkg_peak_flux``, + ``rcr_at_peak``, ``rcr_max``, ``att_in``, ``energy_index``. ``*_flux`` are + livetime- and area-normalized (ct/s/keV/cm2). + """ + n = len(data) + tol = CONFIG.getfloat("Processing", "flarelist_peak_max_dist_s", fallback=60.0) * u.s + + lc_timeline, energy, date_to_eidx = self._build_ql_month_timeline( + start=start, + end=end, + fido_client=fido_client, + data_product=a.stix.DataProduct.ql_lightcurve, + n_detectors=16, + track_energy=True, + ) + bkg_timeline, _, _ = self._build_ql_month_timeline( + start=start, + end=end, + fido_client=fido_client, + data_product=a.stix.DataProduct.ql_background, + n_detectors=1, + track_energy=False, + ) + + # unit-carrying accumulators: assigning a Quantity slice below is unit-checked, so + # a wrong-unit timeline value would raise rather than be silently stored + # raw counts kept as unsigned int32 (u.Quantity floats by default; explicit dtype keeps + # it uint32 -> FITS 'J' + unsigned TZERO, 4 B/elem exact). No-match rows stay 0, never NaN. + lc_peak = u.Quantity(np.zeros((n, 5), dtype=np.uint32), u.ct, dtype=np.uint32) + lc_peak_flux = np.zeros((n, 5), dtype=np.float32) * FLUX_UNIT + lc_bkg_peak = u.Quantity(np.zeros((n, 5), dtype=np.uint32), u.ct, dtype=np.uint32) + lc_bkg_peak_flux = np.zeros((n, 5), dtype=np.float32) * FLUX_UNIT + rcr_at_peak = np.full(n, -1, dtype=np.int8) + rcr_max = np.full(n, -1, dtype=np.int8) + energy_index = np.zeros(n, dtype=np.int8) + + lc_has = len(lc_timeline) > 0 + bkg_has = len(bkg_timeline) > 0 + if not lc_has: + logger.warning(f"No L1 QL lightcurve data found for month {start}") + if not bkg_has: + logger.warning(f"No L1 QL background data found for month {start}") + + for i, row in enumerate(data): + peak = row["peak_UTC"] + fid = row["flare_id"] + if lc_has: + j = nearest_bin_index(lc_timeline["time"], peak, tol) + if j is not None: + lc_peak[i] = lc_timeline["counts"][j] # Quantity ct -> unit-checked assignment + lc_peak_flux[i] = lc_timeline["counts_rate"][j].to(FLUX_UNIT) # assert flux unit + rcr_at_peak[i] = int(lc_timeline["rcr"][j]) + rcr_max[i] = max_rcr_in_window( + lc_timeline["time"], + lc_timeline["rcr"], + row["start_UTC"], + row["end_UTC"], + fallback=int(rcr_at_peak[i]), + ) + energy_index[i] = date_to_eidx.get(peak.to_datetime().date(), 0) + else: + logger.warning(f"flare {fid}: no LC bin within {tol} of peak {peak.isot}") + if bkg_has: + k = nearest_bin_index(bkg_timeline["time"], peak, tol) + if k is not None: + lc_bkg_peak[i] = bkg_timeline["counts"][k] # Quantity ct -> unit-checked assignment + lc_bkg_peak_flux[i] = bkg_timeline["counts_rate"][k].to(FLUX_UNIT) # assert flux unit + else: + logger.warning(f"flare {fid}: no BKG bin within {tol} of peak {peak.isot}") + + # Drop orphan energy-binning blocks: a QL product with a different binning can add + # rows to `energy` that no flare references (date_to_eidx keeps the first binning + # seen per date). Keep only referenced blocks and renumber energy_index contiguously. + if len(energy) > 0 and "index" in energy.colnames: + used = sorted({int(x) for x in energy_index}) + remap = {old: new for new, old in enumerate(used)} + energy = energy[[k for k, e in enumerate(energy["index"]) if int(e) in remap]] + energy["index"] = np.array([remap[int(x)] for x in energy["index"]], dtype=energy["index"].dtype) + energy_index = np.array([remap[int(x)] for x in energy_index], dtype=energy_index.dtype) + + data["lc_peak"] = Column( + lc_peak, # already a Quantity in ct + description="raw counts at the L1 QL lightcurve bin nearest the flare peak (5 energy channels)", + dtype=np.uint32, + ) + data["lc_peak_flux"] = Column( + lc_peak_flux, # already a Quantity in ct/s/keV/cm2 + description="livetime- and area-corrected flux at the peak lightcurve bin (ct/s/keV/cm2, 5 energy channels)", + ) + data["lc_bkg_peak"] = Column( + lc_bkg_peak, # already a Quantity in ct + description="raw counts from the STIX background detector at the L1 QL background bin nearest the flare peak (5 energy channels) — unmodulated by imaging subcollimators and not affected by the attenuator", + dtype=np.uint32, + ) + data["lc_bkg_peak_flux"] = Column( + lc_bkg_peak_flux, # already a Quantity in ct/s/keV/cm2 + description="livetime- and area-corrected STIX background detector flux at the peak bin (ct/s/keV/cm2, 5 energy channels)", + ) + data["rcr_at_peak"] = Column( + rcr_at_peak, description="rate control regime at the peak bin (>0 attenuator in)", dtype=np.int8 + ) + data["rcr_max"] = Column( + rcr_max, description="max rate control regime over the flare start..end window", dtype=np.int8 + ) + data["att_in"] = Column(rcr_max > 0, description="was attenuator in during flare (rcr_max > 0)") + data["energy_index"] = Column(energy_index, description="energy band index", dtype=np.int8) + + # these CSV-derived placeholders (added in get_data) are superseded here; drop if present + for _col in ("bkg_baseline", "bkg_quiet_period"): + if _col in data.colnames: + del data[_col] + + return energy + + def add_background_file_column(self, data, *, energy, fido_client): + """Add background-file and quiet-period background-spectrum columns per flare. + + Writes ``bkg_file`` / ``bkg_rid`` (the best quiet-time background CPD file for + each flare peak) and, extracted from that file, the median quiet-period + background spectrum: raw counts in the native science channels (``bkg_spec``) and + rebinned to the flare's QL bands (``bkg_spec_ql``), plus the livetime- and + area-normalized flux (ct/s/keV/cm2) in native channels (``bkg_spec_flux``) and + rebinned to the QL bands (``bkg_spec_flux_ql``); ``bkg_energy_index`` points at the + native binning appended to the ``energy`` table. + + ``data`` rows are assumed peak-time ascending, so the per-time background + search (:func:`find_background_file_for_time`) is cached with its validity + interval and only re-run when a flare peak leaves that period. The extracted + spectrum is likewise cached per background file id, so each CPD is opened only + once no matter how many flares share it. Requires the ``energy`` table (from + :meth:`add_lc_bkg_columns`) so the QL rebin follows the actual QL binning; + returns the (possibly extended, orphan-pruned) ``energy`` table. + """ + n = len(data) + bkg_files = [""] * n + bkg_rids = np.full(n, -1, dtype=np.int64) + spec_rows = [None] * n # native-binning counts spectrum per flare (Quantity ct, length varies) + flux_rows = [None] * n # native-binning flux spectrum per flare (Quantity, ct/s/keV/cm2) + bkg_spec_ql = np.full((n, 5), np.nan) * u.ct # native counts rebinned to QL bands + bkg_spec_flux_ql = np.full((n, 5), np.nan) * FLUX_UNIT # unit-checked on assignment below + bkg_energy_index = np.full(n, -1, dtype=np.int16) + bkg_solo_sun_distance = np.full(n, np.nan) * u.km # SOLO-Sun distance at the bkg CPD's time + + primer = "" + baseurl = getattr(fido_client, "baseurl", None) + datapath = getattr(fido_client, "datapath", None) + if baseurl is not None and datapath is not None: + primer = baseurl.replace(datapath, "") + primer = primer[7:] if primer.startswith("file://") else primer + + energy_look_up_32 = {} # native binning hash -> index in `energy` + spectrum_cache = {} # rid -> (counts, flux, energies, eidx, dsun_km) + rebin_cache = {} # (rid, energy_index) -> flux_ql (5,) + selection = None + searches = 0 + opens = 0 + for i, row in enumerate(data): + peak = row["peak_UTC"] + if selection is None or not (selection.valid_from <= peak <= selection.valid_to): + selection = find_background_file_for_time(peak, fido_client=fido_client) + searches += 1 + if selection.path is None: + continue + bkg_files[i] = str(selection.path).replace(primer, "") + bkg_rids[i] = selection.rid + + if selection.rid not in spectrum_cache: + try: + cpd = STIXPYProduct(selection.path) + c32, f32, e32 = background_spectrum_from_cpd(cpd) + energy, eidx32 = _intern_energy_block(energy, energy_look_up_32, e32) + meta = getattr(cpd, "meta", None) or {} + dsun = float(meta["DSUN_OBS"]) * u.m if "DSUN_OBS" in meta else np.nan * u.m + spectrum_cache[selection.rid] = (c32, f32, e32, eidx32, dsun.to(u.km)) + opens += 1 + except Exception as e: + logger.warning(f"could not extract background spectrum from {selection.path}: {e}") + spectrum_cache[selection.rid] = None + cached = spectrum_cache[selection.rid] + if cached is None: + continue + c32, f32, e32, eidx32, dsun = cached + spec_rows[i] = c32 + flux_rows[i] = f32 + bkg_energy_index[i] = eidx32 + bkg_solo_sun_distance[i] = dsun + + eidx_ql = int(data["energy_index"][i]) if "energy_index" in data.colnames else -1 + ql_block = _ql_block_for(energy, eidx_ql) + if ql_block is not None: + key = (selection.rid, eidx_ql) + if key not in rebin_cache: + rebin_cache[key] = ( + rebin_spectrum_to_ql(c32, e32, ql_block), + rebin_flux_to_ql(f32, e32, ql_block), + ) + bkg_spec_ql[i], bkg_spec_flux_ql[i] = rebin_cache[key] + + # Drop orphan energy blocks (keep those referenced by either the QL energy_index + # or the background bkg_energy_index) and renumber both index columns contiguously. + if len(energy) > 0 and "index" in energy.colnames: + ref_ql = {int(x) for x in data["energy_index"]} if "energy_index" in data.colnames else set() + ref_bkg = {int(x) for x in bkg_energy_index if x >= 0} + used = sorted(ref_ql | ref_bkg) + remap = {old: new for new, old in enumerate(used)} + energy = energy[[k for k, e in enumerate(energy["index"]) if int(e) in remap]] + energy["index"] = np.array([remap[int(x)] for x in energy["index"]], dtype=energy["index"].dtype) + if "energy_index" in data.colnames: + data["energy_index"] = np.array( + [remap.get(int(x), 0) for x in data["energy_index"]], dtype=data["energy_index"].dtype + ) + bkg_energy_index = np.array([remap[int(x)] if x >= 0 else -1 for x in bkg_energy_index], dtype=np.int16) + + # native-binning spectra can differ in length across files (telemetered channel + # count varies); build rectangular Quantity columns at the widest, padding with NaN. + # Assigning the Quantity rows into the Quantity arrays is unit-checked. + width = max((len(r) for r in spec_rows if r is not None), default=32) + bkg_spec = np.full((n, width), np.nan) * u.ct + bkg_spec_flux = np.full((n, width), np.nan) * FLUX_UNIT + for i in range(n): + if spec_rows[i] is not None: + bkg_spec[i, : len(spec_rows[i])] = spec_rows[i] + if flux_rows[i] is not None: + bkg_spec_flux[i, : len(flux_rows[i])] = flux_rows[i] + + data["bkg_file"] = Column(bkg_files, description="path to the quiet-time background CPD file") + data["bkg_rid"] = Column( + bkg_rids, description="BSD request id of the selected background file (-1 if none)", dtype=np.int64 + ) + data["bkg_spec"] = Column( + bkg_spec, # already a Quantity in ct + description="median quiet-period background counts per science energy channel (30 imaging detectors)", + ) + data["bkg_spec_ql"] = Column( + bkg_spec_ql, # already a Quantity in ct + description="median quiet-period background counts rebinned to the QL lightcurve energy bands", + ) + data["bkg_spec_flux"] = Column( + bkg_spec_flux, # already a Quantity in ct/s/keV/cm2 + description="median quiet-period background flux per science channel (ct/s/keV/cm2, 30 imaging detectors)", + ) + data["bkg_spec_flux_ql"] = Column( + bkg_spec_flux_ql, # already a Quantity in ct/s/keV/cm2 + description="median quiet-period background flux rebinned to the QL lightcurve energy bands (ct/s/keV/cm2)", + ) + data["bkg_energy_index"] = Column( + bkg_energy_index, + description="energy table index of the native background binning (-1 if no background file)", + dtype=np.int16, + ) + data["bkg_solo_sun_distance"] = Column( + bkg_solo_sun_distance, # already a Quantity in km + description="SOLO-Sun distance at the background CPD's observation time (from DSUN_OBS; NaN if none)", + ) + logger.info(f"background file search ran {searches}x, opened {opens} CPD files for {n} flares") + return energy + class SCFlareListManager(FlareListManager, metaclass=Singleton): """Manages a local copy of the flarelist provided by STIXCore or runs the flare detection @@ -188,15 +1116,19 @@ def get_data(self, *, start, end, fido_client): data["flare_id"] = Column( mt["flare_id"].astype(int), description=f"unique flare id for flarelist {self.flarelistname}" ) - data["start_UTC"] = Column(0, description="start time of flare") - data["start_UTC"] = [Time(d, format="isot", scale="utc") for d in mt["start_UTC"]] + CreateUtcColumn( + data, + [Time(d, format="isot", scale="utc") for d in mt["start_UTC"]], + "start_UTC", + description="start time of flare", + ) + data["duration"] = Column(mt["duration"].astype(float) * u.s, description="duration of flare") - data["end_UTC"] = Column(0, description="end time of flare") + data["end_UTC"] = CreateUtcColumn(description="end time of flare") data["end_UTC"] = [Time(d, format="isot", scale="utc") for d in mt["end_UTC"]] - data["peak_UTC"] = Column(0, description="flare peak time") + data["peak_UTC"] = CreateUtcColumn(description="flare peak time") data["peak_UTC"] = [Time(d, format="isot", scale="utc") for d in mt["peak_UTC"]] data["att_in"] = Column(mt["att_in"].astype(bool), description="was attenuator in during flare") - data["bkg_baseline"] = Column(mt["LC0_BKG"] * u.ct, description="background baseline at 4-10 keV") data["GOES_class"] = Column( mt["GOES_class"].astype(str), description="GOES class of the GOES XRS data at time of flare" @@ -220,15 +1152,15 @@ def get_data(self, *, start, end, fido_client): "flare isn't visible to Earth", ) data["goes_min_flux_est"] = Column( - mt["goes_estimated_min_flux"].astype(float) * u.W / u.m**2, + (10 ** mt["goes_estimated_min_flux"].astype(float)) * u.W / u.m**2, description="min GOES flux estimate derived from STIX data", ) data["goes_max_flux_est"] = Column( - mt["goes_estimated_max_flux"].astype(float) * u.W / u.m**2, + (10 ** mt["goes_estimated_max_flux"].astype(float)) * u.W / u.m**2, description="max GOES flux estimate derived from STIX data", ) data["goes_mean_flux_est"] = Column( - mt["goes_estimated_mean_flux"].astype(float) * u.W / u.m**2, + (10 ** mt["goes_estimated_mean_flux"].astype(float)) * u.W / u.m**2, description="mean GOES flux estimate derived from STIX data", ) @@ -256,7 +1188,7 @@ def get_data(self, *, start, end, fido_client): dtype=np.int64, ) - data["lc_bgk_peak"] = Column( + data["lc_bkg_peak"] = Column( ( np.vstack( ( @@ -277,7 +1209,7 @@ def get_data(self, *, start, end, fido_client): data.add_index("flare_id") - # add energy axis for the lightcurve peek time data for each flare + # add energy axis for the lightcurve peak time data for each flare # the energy bins are taken from the daily ql-lightcurve products # as the definition of the lc energy chanel's are will change only very seldom # the ql-lightcurve products assume a constant definition for an entire day. @@ -356,22 +1288,25 @@ def update_list(self): @classmethod def read_flarelist(cls, file, update=False): - """Reads or creates the LUT of all BSD RIDs and the request reason comment. + """Read the local flare-list CSV mirror, optionally refreshing it from the STIX Data Center. - On creation or update an api endpoint from the STIX data center is used - to get the information and persists as a LUT locally. + When ``update`` is set (or the file does not yet exist) the operational flare list is + fetched from the STIX Data Center via ``stixdcpy.fetch_flare_list`` in ~monthly chunks + (the API is batched by month and throttled, so the loop sleeps between chunks) from + 2020-01-01 to now; an incremental update re-fetches the last ~60 days. The chunks are + concatenated, de-duplicated, sorted by ``peak_UTC`` and cached back to ``file``. Parameters ---------- file : Path - path the to LUT file. + Path to the local flare-list CSV mirror. update : bool, optional - should the LUT be updated at start up?, by default False + Refresh from the STIX Data Center before reading, by default False. Returns ------- - Table - the LUT od RIDs and request reasons. + `~pandas.DataFrame` + The full flare list. """ if update or not file.exists(): # the api is limited to batch sizes of a month. in order to get the full table we have @@ -424,6 +1359,30 @@ def filter_flare_function(col): return col["lc_peak"][0].value > CONFIG.getint("Processing", "flarelist_sdc_min_count", fallback=1000) def get_data(self, *, start, end, fido_client): + """Build the enriched SDC flare list for the ``[start, end)`` month. + + Slices the requested month from the local CSV mirror and builds the base columns + (``flare_id``, the UTC time columns, GOES class/flux and the source quiet-period + background ``bkg_baseline`` / ``bkg_quiet_period``). It then enriches each flare with + the quicklook peak/background counts and fluxes + (`~stixcore.io.FlareListManager.FlareListManager.add_lc_bkg_columns`) and the quiet-time + background spectrum + (`~stixcore.io.FlareListManager.FlareListManager.add_background_file_column`). The + source CSV's own at-peak lightcurve counts and attenuator flag are unreliable and are + replaced by these STIX-derived values. See :doc:`/products/flarelist`. + + Parameters + ---------- + start, end : `~datetime.datetime` + Half-open month boundaries; flares with ``start <= start_UTC < end`` are kept. + fido_client : `~stixpy.net.client.STIXClient` + Client used to resolve the quicklook and CPD files during enrichment. + + Returns + ------- + tuple + ``(data, control, energy)`` QTables, or ``(None, None, None)`` if the month is empty. + """ month_data = self.flarelist[ (self.flarelist["start_UTC"] >= start.isoformat()) & (self.flarelist["start_UTC"] < end.isoformat()) ] @@ -439,15 +1398,28 @@ def get_data(self, *, start, end, fido_client): data["flare_id"] = Column( mt["flare_id"].astype(int), description=f"unique flare id for flarelist {self.flarelistname}" ) - data["start_UTC"] = Column(0, description="start time of flare") - data["start_UTC"] = [Time(d, format="isot", scale="utc") for d in mt["start_UTC"]] + + CreateUtcColumn( + data, + [Time(d, format="isot", scale="utc") for d in mt["start_UTC"]], + "start_UTC", + description="start time of flare", + ) data["duration"] = Column(mt["duration"].astype(float) * u.s, description="duration of flare") - data["end_UTC"] = Column(0, description="end time of flare") - data["end_UTC"] = [Time(d, format="isot", scale="utc") for d in mt["end_UTC"]] - data["peak_UTC"] = Column(0, description="flare peak time") - data["peak_UTC"] = [Time(d, format="isot", scale="utc") for d in mt["peak_UTC"]] - data["att_in"] = Column(mt["att_in"].astype(bool), description="was attenuator in during flare") - data["bkg_baseline"] = Column(mt["LC0_BKG"] * u.ct, description="background baseline at 4-10 keV") + + CreateUtcColumn( + data, + [Time(d, format="isot", scale="utc") for d in mt["end_UTC"]], + "end_UTC", + description="end time of flare", + ) + CreateUtcColumn( + data, + [Time(d, format="isot", scale="utc") for d in mt["peak_UTC"]], + "peak_UTC", + description="flare peak time", + ) + data["GOES_class"] = Column( mt["GOES_class"].astype(str), description="GOES class of the GOES XRS data at time of flare" @@ -471,15 +1443,15 @@ def get_data(self, *, start, end, fido_client): "flare isn't visible to Earth", ) data["goes_min_flux_est"] = Column( - mt["goes_estimated_min_flux"].astype(float) * u.W / u.m**2, + (10 ** mt["goes_estimated_min_flux"].astype(float)) * u.W / u.m**2, description="min GOES flux estimate derived from STIX data", ) data["goes_max_flux_est"] = Column( - mt["goes_estimated_max_flux"].astype(float) * u.W / u.m**2, + (10 ** mt["goes_estimated_max_flux"].astype(float)) * u.W / u.m**2, description="max GOES flux estimate derived from STIX data", ) data["goes_mean_flux_est"] = Column( - mt["goes_estimated_mean_flux"].astype(float) * u.W / u.m**2, + (10 ** mt["goes_estimated_mean_flux"].astype(float)) * u.W / u.m**2, description="mean GOES flux estimate derived from STIX data", ) @@ -490,24 +1462,13 @@ def get_data(self, *, start, end, fido_client): # description="coarse flare location in y direction provided by" # "onboard algorithm. (0,0) represents disk center") - data["lc_peak"] = Column( - ( - np.vstack( - ( - mt["LC0_PEAK_COUNTS_4S"].value, - mt["LC1_PEAK_COUNTS_4S"].value, - mt["LC2_PEAK_COUNTS_4S"].value, - mt["LC3_PEAK_COUNTS_4S"].value, - mt["LC4_PEAK_COUNTS_4S"].value, - ) - ).T - * u.ct - ).astype(int), - description="counts in 4s peak window from quicklook lightcurve", - dtype=np.int64, + # background estimates carried over from the source flare list (distinct from the + # QL-derived at-peak background added below) + data["bkg_baseline"] = Column( + mt["LC0_BKG"].astype(float) * u.ct, + description="median value of the fitted baseline", ) - - data["lc_bgk_peak"] = Column( + data["bkg_quiet_period"] = Column( ( np.vstack( ( @@ -519,55 +1480,21 @@ def get_data(self, *, start, end, fido_client): ) ).T * u.ct - ).astype(int), - description="background counts in 4s peak windowfrom quicklook lightcurve", + ).astype(np.int64), + description="background counts per QL energy channel, median value for the most recent quiet period", dtype=np.int64, ) - data["energy_index"] = Column(0, description="energy band index", dtype=np.int8) + # LC/BKG peak counts+rates, RCR, att_in and energy_index are derived from the + # real L1 QL lightcurve + background products (the source CSV values are + # unreliable) using one monthly timeline per product built once. + energy = self.add_lc_bkg_columns(data, start=start, end=end, fido_client=fido_client) - data.add_index("flare_id") + # select the best quiet-time background data file for each flare peak and extract + # its median quiet-period background spectrum (adds the 32-ch binning to `energy`) + energy = self.add_background_file_column(data, energy=energy, fido_client=fido_client) - # add energy axis for the lightcurve peek time data for each flare - # the energy bins are taken from the daily ql-lightcurve products - # as the definition of the lc energy chanel's are will change only very seldom - # the ql-lightcurve products assume a constant definition for an entire day. - # So we do the lookup also just grouped by peak day in order to save file lookups - - energy_look_up = {} - data["peak_day"] = [d.datetime.day for d in data["peak_UTC"]] - data_by_day = data.group_by("peak_day") - - for day, flares in zip(data_by_day.groups.keys, data_by_day.groups): - time = flares["peak_UTC"][0] - lc_data = fido_client.search(a.Time(time, time), a.Instrument.stix, a.stix.DataProduct.ql_lightcurve) - lc_data.filter_for_latest_version() - url_to_path(lc_data) - - if len(lc_data) == 0: - logger.warning(f"No lightcurve data found for flare at time {time}") - continue - lc = Product(lc_data["path"][0]) - - energy_table_hash = frozenset(pd.core.util.hashing.hash_array(lc.energies.as_array())) - - # add the energy table to the energy table list if not already - # present and define a new index number - if energy_table_hash not in energy_look_up: - e_idx = len(energy_look_up.keys()) - energy_look_up[energy_table_hash] = e_idx - lc.energies["index"] = Column(e_idx, description="energy edge table index", dtype=np.int8) - energy = vstack([energy, lc.energies]) - if e_idx > 0: - logger.warning(f"multiple energy ql-lc tables found for month {start}") - - # add the energy index to the flare data to all flares of the same day - # https://docs.astropy.org/en/latest/table/modify_table.html#caveats - replace = data.loc[flares["flare_id"]] - replace["energy_index"] = energy_look_up[energy_table_hash] - data.loc[flares["flare_id"]] = replace - - del data["peak_day"] + data.add_index("flare_id") return data, control, energy diff --git a/stixcore/io/RidLutManager.py b/stixcore/io/RidLutManager.py index bd1f9d26..154956b8 100644 --- a/stixcore/io/RidLutManager.py +++ b/stixcore/io/RidLutManager.py @@ -1,23 +1,163 @@ +import re import sys import time import tempfile import urllib.request from datetime import date, datetime, timedelta +from collections import namedtuple import numpy as np +import astropy.units as u from astropy.io import ascii from astropy.table import Table from astropy.table.operations import unique, vstack +from astropy.time import Time from stixcore.config.config import CONFIG from stixcore.util.logging import get_logger from stixcore.util.singleton import Singleton -__all__ = ["RidLutManager"] +__all__ = ["RidLutManager", "BackgroundCandidate", "search_background_candidates"] logger = get_logger(__name__) +#: Keywords (case-insensitive) that mark a BSD request as a background/quiet +#: observation in the descriptive columns of the RID LUT. +DEFAULT_BKG_KEYWORDS = ("bkg", "quiet", "background", "non-flaring") + +#: Negative keywords: a candidate whose descriptive text contains any of these is +#: rejected (e.g. "elevated" background is not a clean quiet baseline). +DEFAULT_BKG_EXCLUDE_KEYWORDS = ("elevated",) + +#: Matches a specific flare id referenced in a comment, e.g. "for Flare 2309081508". +#: Such rows are flare data requests, not dedicated backgrounds. +_FLARE_REF_RE = re.compile(r"flare\s*\d{3,}") + +#: A single background-request candidate from the RID LUT. ``start``/``end``/``mid`` +#: are `~astropy.time.Time`, ``side`` is ``"past"``/``"future"`` relative to the query +#: time, and ``is_background`` is True when ``purpose == "Background"`` (a clean +#: background request, preferred over subject-only keyword matches). +BackgroundCandidate = namedtuple("BackgroundCandidate", ["rid", "start", "end", "mid", "side", "is_background"]) + + +def _col_as_lower_str(tbl, name): + """Return column ``name`` of ``tbl`` as a lower-cased ``str`` numpy array.""" + col = tbl[name] + try: + col = col.filled("") + except (AttributeError, TypeError): + pass + return np.char.lower(np.asarray(col, dtype=str)) + + +def search_background_candidates( + rid_lut, + time, + *, + window_past, + window_future, + keywords=DEFAULT_BKG_KEYWORDS, + exclude_keywords=DEFAULT_BKG_EXCLUDE_KEYWORDS, + exclude_flare_comment=True, + purpose_penalty=1.0 * u.day, +): + """Find background-request candidates in a RID LUT near ``time``. + + Rows are recognised as background requests by a case-insensitive keyword + match (``keywords``) over the ``subject``/``purpose``/``comment`` columns, + then filtered and ranked: + + * **exclude keywords** — a row whose text contains any of ``exclude_keywords`` + (default ``"elevated"``) is dropped. + * **flare-id comment** — if ``exclude_flare_comment`` a row whose comment + references a specific flare id (e.g. "for Flare 2309081508") is dropped. + * **ranking** — **nearest-in-time first** by ``|time - start|`` (past preferred + on a tie), but a request with ``purpose == "Background"`` is preferred over a + subject-only keyword match unless the latter is more than ``purpose_penalty`` + closer. This is implemented as an *effective* distance + ``|time - start| + purpose_penalty`` for non-Background rows. + + ``window_past`` / ``window_future`` remain separate bounds on how far a + candidate's start may lie in each direction. + + Parameters + ---------- + rid_lut : `~astropy.table.Table` + The RID LUT (as produced by `RidLutManager.read_rid_lut`). + time : `~astropy.time.Time` or str + The query time (e.g. a flare peak). + window_past, window_future : `~astropy.units.Quantity` + How far back / forward from ``time`` a candidate's start may lie. + keywords, exclude_keywords : tuple of str, optional + Positive / negative case-insensitive keywords. + exclude_flare_comment : bool, optional + Drop rows whose comment references a specific flare id. + purpose_penalty : `~astropy.units.Quantity`, optional + Distance penalty added to non-``Background``-purpose candidates so a clean + Background request wins unless a subject-only match is clearly closer. + + Returns + ------- + list of BackgroundCandidate + Ranked by ascending effective distance (past preferred on a tie). Empty + if the LUT has no matching rows in range. + """ + if rid_lut is None or len(rid_lut) == 0: + return [] + t = time if isinstance(time, Time) else Time(time) + + subj = _col_as_lower_str(rid_lut, "subject") + purp = _col_as_lower_str(rid_lut, "purpose") + comm = _col_as_lower_str(rid_lut, "comment") + haystack = np.char.add(np.char.add(subj, " "), np.char.add(purp, np.char.add(" ", comm))) + + mask = np.zeros(len(rid_lut), dtype=bool) + for kw in keywords: + mask |= np.char.find(haystack, kw.lower()) >= 0 + for kw in exclude_keywords: # negative keywords drop the row + mask &= np.char.find(haystack, kw.lower()) < 0 + if not np.any(mask): + return [] + + sub = rid_lut[mask] + purp_sub = purp[mask] + comm_sub = comm[mask] + starts = Time(np.asarray(sub["start_utc"], dtype=str), format="isot", scale="utc") + durations = np.asarray(sub["duration"], dtype=float) * u.s + ends = starts + durations + mids = starts + durations / 2 + rids = np.asarray(sub["unique_id"]).astype(np.int64) + + wp = window_past.to_value(u.day) + wf = window_future.to_value(u.day) + penalty = purpose_penalty.to_value(u.day) + kept = [] + for i in range(len(sub)): + if exclude_flare_comment and _FLARE_REF_RE.search(comm_sub[i]): + continue + d = (t - starts[i]).to_value(u.day) # > 0 starts before ``time`` (past), < 0 after (future) + if d >= 0: + if d > wp: + continue + side = "past" + else: + if -d > wf: + continue + side = "future" + is_bg = purp_sub[i].strip() == "background" + effective = abs(d) + (0.0 if is_bg else penalty) # prefer Background at equal-ish distance + kept.append( + ( + effective, + 0 if side == "past" else 1, + BackgroundCandidate(int(rids[i]), starts[i], ends[i], mids[i], side, is_bg), + ) + ) + + kept.sort(key=lambda x: (x[0], x[1])) + return [c for _, _, c in kept] + class RidLutManager(metaclass=Singleton): """Manages metadata for BSD requests @@ -78,6 +218,13 @@ def get_reason(self, rid): logger.warning("can't get request purpose: no request founds for rid: {rid}") return "" + def find_background_candidates(self, time, *, window_past, window_future, keywords=DEFAULT_BKG_KEYWORDS): + """Find background-request candidates near ``time`` (see + :func:`search_background_candidates`).""" + return search_background_candidates( + self.rid_lut, time, window_past=window_past, window_future=window_future, keywords=keywords + ) + def get_scaling_factor(self, rid): """Gets the trigger descaling factor connected to the BSD request. diff --git a/stixcore/io/product_processors/fits/processors.py b/stixcore/io/product_processors/fits/processors.py index 90630eea..b843f369 100644 --- a/stixcore/io/product_processors/fits/processors.py +++ b/stixcore/io/product_processors/fits/processors.py @@ -58,6 +58,24 @@ def set_bscale_unsigned(table_hdu): return table_hdu +def CreateUtcColumn(table, data, colname, description="UTC Time"): + """ + Create UTC time column for FITS tables. + + Parameters + ---------- + description : `str` + Description for the column + + Returns + ------- + `astropy.table.Column` + Column representing UTC time + """ + table[colname] = data + table[colname].info.description = description + + def add_default_tuint(table_hdu): """ Add a default empty string tunit if not already defined @@ -805,10 +823,11 @@ def generate_primary_header(self, filename, product, *, version=0): if default[0] not in soop_key_names: soop_headers += tuple([default]) + scet_range = product.scet_timerange time_headers = ( # Name, Value, Comment - ("OBT_BEG", product.scet_timerange.start.as_float().value, "Start of acquisition time in OBT"), - ("OBT_END", product.scet_timerange.end.as_float().value, "End of acquisition time in OBT"), + ("OBT_BEG", scet_range.start.as_float().value, "Start of acquisition time in OBT"), + ("OBT_END", scet_range.end.as_float().value, "End of acquisition time in OBT"), ("TIMESYS", "UTC", "System used for time keywords"), ("LEVEL", "L1", "Processing level of the data"), ("DATE-OBS", product.utc_timerange.start.fits, "Start of acquisition time in UTC"), @@ -1049,7 +1068,8 @@ def write_fits(self, prod, *, version=0): elif fitspath_complete.exists(): logger.warning("Complete Fits file %s exists will be overridden", fitspath.name) - data = prod.data + data = prod.data.copy() + prod.on_serialize(data) primary_header, header_override = self.generate_primary_header(filename, prod, version=version) primary_hdu = fits.PrimaryHDU() @@ -1126,7 +1146,8 @@ def write_fits(self, prod, *, version=0): elif fitspath_complete.exists(): logger.warning("Complete Fits file %s exists will be overridden", fitspath.name) - data = prod.data + data = prod.data.copy() + prod.on_serialize(data) primary_header, header_override = self.generate_primary_header(filename, prod, version=version) @@ -1139,7 +1160,7 @@ def write_fits(self, prod, *, version=0): # Add comment and history [primary_hdu.header.add_comment(com) for com in prod.comment] [primary_hdu.header.add_history(com) for com in prod.history] - primary_hdu.header.update({"HISTORY": "Processed by STIXCore ANC"}) + primary_hdu.header.update({"HISTORY": "Processed by STIXCore L3"}) if hasattr(prod, "maps") and len(prod.maps) > 0: # fig = plt.figure(figsize=(12, 6)) diff --git a/stixcore/io/tests/test_flarelistmanager.py b/stixcore/io/tests/test_flarelistmanager.py new file mode 100644 index 00000000..a6e90d99 --- /dev/null +++ b/stixcore/io/tests/test_flarelistmanager.py @@ -0,0 +1,928 @@ +from pathlib import Path + +import numpy as np +import pytest + +import astropy.units as u +from astropy.table import QTable, Table +from astropy.time import Time + +import stixcore.io.FlareListManager as flm_mod +from stixcore.io.FlareListManager import ( + BackgroundSelection, + FlareListManager, + background_spectrum_from_cpd, + build_month_timeline, + collecting_area_cm2, + compute_ql_count_rate, + find_background_file_for_time, + max_rcr_in_window, + nearest_bin_index, + rebin_flux_to_ql, + rebin_spectrum_to_ql, +) +from stixcore.io.RidLutManager import RidLutManager, search_background_candidates + +RATE_UNIT = u.ct / (u.s * u.keV) +FLUX_UNIT = u.ct / (u.s * u.keV * u.cm**2) + + +# --- helpers to build synthetic QL data --------------------------------------- + + +def _energies(): + e = QTable() + e["channel"] = np.arange(5, dtype=np.uint8) + e["e_low"] = [4, 10, 15, 25, 50] * u.keV + e["e_high"] = [10, 15, 25, 50, 84] * u.keV + return e + + +def _ql_data(t0, n, *, rcr=None, counts_value=100, with_rcr=True): + """Build a synthetic QL product ``.data`` QTable on a 4 s grid.""" + times = Time(t0) + np.arange(n) * 4 * u.s + data = QTable() + data["time"] = times + data["timedel"] = np.full(n, 4.0) * u.s + data["triggers"] = np.zeros(n) # zero triggers -> live_frac == 1 (deterministic) + data["counts"] = (np.full((n, 5), counts_value)).astype(int) * u.ct + if with_rcr: + data["rcr"] = np.zeros(n, dtype=np.ubyte) if rcr is None else np.asarray(rcr, dtype=np.ubyte) + return data + + +class FakeProduct: + def __init__(self, data, energies): + self.data = data + self._energies = energies + + +class FakeResponse: + """Minimal stand-in for a StixQueryResponse carrying a ``path`` column.""" + + def __init__(self, paths): + self._paths = list(paths) + + def __len__(self): + return len(self._paths) + + @property + def columns(self): + return ["path"] if self._paths else [] + + def filter_for_latest_version(self): + pass + + def __getitem__(self, key): + assert key == "path" + return self._paths + + +class FakeFido: + def __init__(self, lc_paths, bkg_paths): + self.lc_paths = lc_paths + self.bkg_paths = bkg_paths + + def search(self, time, instrument, data_product, level): + name = getattr(data_product, "value", str(data_product)) + if "background" in name: + return FakeResponse(self.bkg_paths) + return FakeResponse(self.lc_paths) + + +# --- compute_ql_count_rate ---------------------------------------------------- + + +def test_count_rate_units_and_shape(): + data = _ql_data("2024-06-15T12:00:00", 3) + ed = _energies()["e_high"] - _energies()["e_low"] + rate = compute_ql_count_rate(data["counts"], data["timedel"], data["triggers"], ed, n_detectors=16) + assert rate.shape == (3, 5) + assert rate.unit.is_equivalent(RATE_UNIT) + + +def test_count_rate_zero_triggers_is_deterministic(): + # zero triggers => live_frac == 1 => rate == counts / (timedel * energy_delta), + # independent of n_detectors, so LC and BKG agree exactly. + data = _ql_data("2024-06-15T12:00:00", 2, counts_value=200) + ed = _energies()["e_high"] - _energies()["e_low"] + lc = compute_ql_count_rate(data["counts"], data["timedel"], data["triggers"], ed, n_detectors=16) + bkg = compute_ql_count_rate(data["counts"], data["timedel"], data["triggers"], ed, n_detectors=1) + expected = data["counts"] / ((data["timedel"]).reshape(-1, 1) * ed) + assert u.allclose(lc, expected.to(RATE_UNIT)) + assert u.allclose(lc, bkg) + + +def test_count_rate_more_detectors_gives_smaller_rate_when_triggers_nonzero(): + # nonzero triggers: larger n_detectors -> lower trigger rate -> higher live_frac + # -> larger denominator -> smaller count rate. + data = _ql_data("2024-06-15T12:00:00", 1) + data["triggers"] = np.array([5000.0]) + ed = _energies()["e_high"] - _energies()["e_low"] + lc = compute_ql_count_rate(data["counts"], data["timedel"], data["triggers"], ed, n_detectors=16) + bkg = compute_ql_count_rate(data["counts"], data["timedel"], data["triggers"], ed, n_detectors=1) + assert np.all(lc.to_value(RATE_UNIT) < bkg.to_value(RATE_UNIT)) + + +# --- build_month_timeline ----------------------------------------------------- + + +def test_build_timeline_sorts_and_dedups_overlap(): + d1 = _ql_data("2024-06-15T12:00:00", 3) # 12:00:00, :04, :08 + d2 = _ql_data("2024-06-15T12:00:08", 3) # :08 (dup), :12, :16 + timeline = build_month_timeline([d2, d1]) # pass out of order + t = timeline["time"] + assert len(timeline) == 5 # 6 bins minus 1 duplicate at :08 + assert np.all(np.diff(t.jd) > 0) # strictly increasing + + +def test_build_timeline_empty(): + assert len(build_month_timeline([])) == 0 + assert len(build_month_timeline([None, QTable()])) == 0 + + +# --- nearest_bin_index -------------------------------------------------------- + + +def test_nearest_bin_exact(): + times = Time("2024-06-15T12:00:00") + np.arange(5) * 4 * u.s + assert nearest_bin_index(times, times[2], 2 * u.s) == 2 + + +def test_nearest_bin_within_tol(): + times = Time("2024-06-15T12:00:00") + np.arange(5) * 4 * u.s + target = times[3] + 1 * u.s + assert nearest_bin_index(times, target, 2 * u.s) == 3 + + +def test_nearest_bin_beyond_tol_returns_none(): + times = Time("2024-06-15T12:00:00") + np.arange(5) * 4 * u.s + target = times[-1] + 1 * u.h + assert nearest_bin_index(times, target, 60 * u.s) is None + + +def test_nearest_bin_empty_returns_none(): + assert nearest_bin_index(Time([], format="isot"), Time("2024-06-15T12:00:00"), 60 * u.s) is None + + +# --- max_rcr_in_window -------------------------------------------------------- + + +def test_max_rcr_over_window(): + times = Time("2024-06-15T12:00:00") + np.arange(6) * 4 * u.s + rcr = np.array([0, 0, 1, 2, 1, 0]) + assert max_rcr_in_window(times, rcr, times[1], times[4], fallback=-1) == 2 + + +def test_max_rcr_peak_only_window(): + times = Time("2024-06-15T12:00:00") + np.arange(6) * 4 * u.s + rcr = np.array([0, 0, 1, 2, 1, 0]) + assert max_rcr_in_window(times, rcr, times[0], times[0], fallback=-1) == 0 + + +def test_max_rcr_empty_window_returns_fallback(): + times = Time("2024-06-15T12:00:00") + np.arange(6) * 4 * u.s + rcr = np.array([0, 0, 1, 2, 1, 0]) + before = Time("2024-06-15T11:00:00") + assert max_rcr_in_window(times, rcr, before, before, fallback=7) == 7 + + +# --- add_lc_bkg_columns (integration, monkeypatched) -------------------------- + + +@pytest.fixture +def flare_data(): + # bin grid starts 2024-06-15T12:00:00, 4 s cadence; bins 5-7 attenuated (rcr=1) + peaks = Time( + [ + "2024-06-15T12:00:20", # bin 5 (attenuated), window covers attenuated bins + "2024-06-15T12:00:00", # bin 0, no attenuation + "2024-06-15T13:00:00", # far away -> beyond tolerance + ] + ) + data = QTable() + data["flare_id"] = [1, 2, 3] + data["start_UTC"] = peaks - 8 * u.s + data["end_UTC"] = peaks + 8 * u.s + data["peak_UTC"] = peaks + return data + + +def _patch_fido(monkeypatch): + n = 20 + rcr = np.zeros(n, dtype=np.ubyte) + rcr[5:8] = 1 + lc = FakeProduct(_ql_data("2024-06-15T12:00:00", n, rcr=rcr), _energies()) + bkg = FakeProduct(_ql_data("2024-06-15T12:00:00", n, with_rcr=False), _energies()) + + products = {"lc": lc, "bkg": bkg} + monkeypatch.setattr("stixcore.io.FlareListManager.STIXPYProduct", lambda path: products[path]) + return FakeFido(lc_paths=["lc"], bkg_paths=["bkg"]) + + +def test_add_lc_bkg_columns(flare_data, monkeypatch): + fido = _patch_fido(monkeypatch) + from datetime import date + + energy = FlareListManager().add_lc_bkg_columns( + flare_data, start=date(2024, 6, 1), end=date(2024, 7, 1), fido_client=fido + ) + + # shapes / units (QTable stores unit-bearing columns as float64 Quantity, + # same as the pre-existing lc_peak column; values stay integral counts) + assert flare_data["lc_peak"].shape == (3, 5) + assert flare_data["lc_peak"].unit == u.ct + assert np.all(flare_data["lc_peak"].value == np.round(flare_data["lc_peak"].value)) + assert flare_data["lc_peak_flux"].unit.is_equivalent(FLUX_UNIT) + assert flare_data["lc_bkg_peak_flux"].unit.is_equivalent(FLUX_UNIT) + assert flare_data["lc_bkg_peak"].shape == (3, 5) + assert flare_data["lc_bkg_peak"].unit == u.ct + assert flare_data["att_in"].dtype == bool + assert flare_data["energy_index"].dtype == np.int8 + + # rcr semantics + assert np.all(flare_data["rcr_max"] >= flare_data["rcr_at_peak"]) + assert list(flare_data["att_in"]) == [True, False, False] + assert flare_data["rcr_max"][0] == 1 # window covers attenuated bins + assert flare_data["rcr_at_peak"][0] == 1 + assert flare_data["rcr_at_peak"][1] == 0 + + # far flare -> beyond tolerance -> zero filled, rcr -1 + assert np.all(flare_data["lc_peak"][2].to_value(u.ct) == 0) + assert flare_data["rcr_at_peak"][2] == -1 + assert flare_data["rcr_max"][2] == -1 + + # counts pulled from the real timeline for in-range flares + assert np.all(flare_data["lc_peak"][0].to_value(u.ct) == 100) + assert np.all(flare_data["lc_bkg_peak"][0].to_value(u.ct) == 100) + + # returned energy table schema + assert set(energy.colnames) == {"channel", "e_low", "e_high", "index"} + assert len(energy) == 5 + + +def test_add_lc_bkg_columns_no_files(flare_data, monkeypatch): + from datetime import date + + fido = FakeFido(lc_paths=[], bkg_paths=[]) + energy = FlareListManager().add_lc_bkg_columns( + flare_data, start=date(2024, 6, 1), end=date(2024, 7, 1), fido_client=fido + ) + + assert np.all(flare_data["lc_peak"].to_value(u.ct) == 0) + assert np.all(flare_data["lc_bkg_peak"].to_value(u.ct) == 0) + assert np.all(flare_data["rcr_at_peak"] == -1) + assert np.all(flare_data["rcr_max"] == -1) + assert not np.any(flare_data["att_in"]) + assert len(energy) == 0 + + +def _energies_alt(): + """A second, different QL energy binning (distinct edges -> distinct hash).""" + e = QTable() + e["channel"] = np.arange(5, dtype=np.uint8) + e["e_low"] = [4, 11, 16, 26, 51] * u.keV + e["e_high"] = [11, 16, 26, 51, 85] * u.keV + return e + + +def test_energy_table_prunes_orphan_binning(flare_data, monkeypatch): + from datetime import date + + # two LC products for the month with DIFFERENT binnings, both on the same date; + # date_to_eidx keeps the first (primary) binning, so the alternate block is an orphan. + n = 20 + rcr = np.zeros(n, dtype=np.ubyte) + rcr[5:8] = 1 + lc = FakeProduct(_ql_data("2024-06-15T12:00:00", n, rcr=rcr), _energies()) + lc_alt = FakeProduct(_ql_data("2024-06-15T12:00:00", n, rcr=rcr), _energies_alt()) + bkg = FakeProduct(_ql_data("2024-06-15T12:00:00", n, with_rcr=False), _energies()) + products = {"lc": lc, "lc_alt": lc_alt, "bkg": bkg} + monkeypatch.setattr("stixcore.io.FlareListManager.STIXPYProduct", lambda path: products[path]) + fido = FakeFido(lc_paths=["lc", "lc_alt"], bkg_paths=["bkg"]) + + energy = FlareListManager().add_lc_bkg_columns( + flare_data, start=date(2024, 6, 1), end=date(2024, 7, 1), fido_client=fido + ) + + # the orphan alternate block is dropped: only the single referenced 5-row block remains + assert len(energy) == 5 + assert {int(x) for x in energy["index"]} == {0} + assert {int(x) for x in flare_data["energy_index"]} == {0} + + +# --- background candidate search (RID LUT) ------------------------------------ + + +def _bkg_lut(rows): + """Build a minimal RID LUT ``Table`` from ``(rid, start_iso, duration_s)`` rows.""" + t = Table() + t["unique_id"] = [r[0] for r in rows] + t["start_utc"] = [r[1] for r in rows] + t["duration"] = [r[2] for r in rows] + t["subject"] = ["BKG quiet"] * len(rows) + t["purpose"] = ["Background"] * len(rows) + t["comment"] = [""] * len(rows) + return t + + +def test_candidates_nearest_in_time_regardless_of_side(): + lut = _bkg_lut( + [ + (3001, "2023-06-13T00:00:00", 3600), # past 2 d + (3003, "2023-06-18T00:00:00", 3600), # future 3 d + (3002, "2023-06-05T00:00:00", 3600), # past 10 d + (3004, "2023-04-15T00:00:00", 3600), # past 61 d -> outside 30 d window + ] + ) + t = Time("2023-06-15T00:00:00") + cands = search_background_candidates(lut, t, window_past=30 * u.day, window_future=7 * u.day) + # nearest start first regardless of side: 2 d past, 3 d future, 10 d past + assert [c.rid for c in cands] == [3001, 3003, 3002] + assert [c.side for c in cands] == ["past", "future", "past"] + + # widening the past window pulls in the legacy request, ranked by its (large) distance + cands90 = search_background_candidates(lut, t, window_past=90 * u.day, window_future=7 * u.day) + assert [c.rid for c in cands90] == [3001, 3003, 3002, 3004] + + +def test_candidates_tie_prefers_past(): + # equal distance past vs future -> past wins the tie + lut = _bkg_lut([(3001, "2023-06-10T00:00:00", 3600), (3003, "2023-06-20T00:00:00", 3600)]) + t = Time("2023-06-15T00:00:00") + cands = search_background_candidates(lut, t, window_past=30 * u.day, window_future=30 * u.day) + assert [c.rid for c in cands] == [3001, 3003] + assert cands[0].side == "past" + + +def _bkg_lut_full(rows): + """RID LUT from ``(rid, start_iso, duration_s, subject, purpose, comment)`` rows.""" + t = Table() + t["unique_id"] = [r[0] for r in rows] + t["start_utc"] = [r[1] for r in rows] + t["duration"] = [r[2] for r in rows] + t["subject"] = [r[3] for r in rows] + t["purpose"] = [r[4] for r in rows] + t["comment"] = [r[5] for r in rows] + return t + + +def test_candidates_exclude_elevated(): + lut = _bkg_lut_full( + [ + (1, "2023-06-14T00:00:00", 3600, "BKG elevated", "Background", ""), # closer but excluded + (2, "2023-06-10T00:00:00", 3600, "BKG quiet", "Background", ""), + ] + ) + cands = search_background_candidates( + lut, Time("2023-06-15T00:00:00"), window_past=30 * u.day, window_future=7 * u.day + ) + assert [c.rid for c in cands] == [2] + + +def test_candidates_exclude_flare_comment(): + lut = _bkg_lut_full( + [ + ( + 1, + "2023-06-14T00:00:00", + 3600, + "non-flaring AR?", + "Solar Flare", + "CL1 data request for Flare 2309081508", + ), # closer but flare-referenced + (2, "2023-06-10T00:00:00", 3600, "BKG quiet", "Background", ""), + ] + ) + cands = search_background_candidates( + lut, Time("2023-06-15T00:00:00"), window_past=30 * u.day, window_future=7 * u.day + ) + assert [c.rid for c in cands] == [2] + # keeping flare-referenced rows brings the closer one back to the front + keep = search_background_candidates( + lut, Time("2023-06-15T00:00:00"), window_past=30 * u.day, window_future=7 * u.day, exclude_flare_comment=False + ) + assert [c.rid for c in keep] == [1, 2] + + +def test_candidates_prefer_background_purpose(): + P = 1.0 * u.day + t = Time("2023-06-15T00:00:00") + # subject-only match 0.5 d closer than the Background request -> Background still wins (within penalty) + lut1 = _bkg_lut_full( + [ + (1, "2023-06-13T00:00:00", 3600, "BKG quiet", "Background", ""), # 2.0 d -> eff 2.0 + (2, "2023-06-13T12:00:00", 3600, "quiet region", "obs", ""), # 1.5 d -> eff 2.5 + ] + ) + c1 = search_background_candidates(lut1, t, window_past=30 * u.day, window_future=7 * u.day, purpose_penalty=P) + assert [c.rid for c in c1] == [1, 2] + assert c1[0].is_background + + # subject-only match clearly closer (>penalty) -> it wins + lut2 = _bkg_lut_full( + [ + (1, "2023-06-13T00:00:00", 3600, "BKG quiet", "Background", ""), # 2.0 d -> eff 2.0 + (2, "2023-06-14T12:00:00", 3600, "quiet region", "obs", ""), # 0.5 d -> eff 1.5 + ] + ) + c2 = search_background_candidates(lut2, t, window_past=30 * u.day, window_future=7 * u.day, purpose_penalty=P) + assert [c.rid for c in c2] == [2, 1] + assert not c2[0].is_background + + +def test_candidates_future_window_excludes_far_future(): + lut = _bkg_lut([(4001, "2023-06-25T00:00:00", 3600)]) # 10 d in the future + t = Time("2023-06-15T00:00:00") + assert search_background_candidates(lut, t, window_past=30 * u.day, window_future=7 * u.day) == [] + got = search_background_candidates(lut, t, window_past=30 * u.day, window_future=14 * u.day) + assert [c.rid for c in got] == [4001] + + +def test_candidates_keyword_match_only(): + lut = Table() + lut["unique_id"] = [1, 2] + lut["start_utc"] = ["2023-06-10T00:00:00", "2023-06-11T00:00:00"] + lut["duration"] = [3600, 3600] + lut["subject"] = ["Solar Flare", "some quiet interval"] # only row 2 matches + lut["purpose"] = ["flare", "obs"] + lut["comment"] = ["", ""] + cands = search_background_candidates( + lut, Time("2023-06-15T00:00:00"), window_past=30 * u.day, window_future=7 * u.day + ) + assert [c.rid for c in cands] == [2] + + +def test_candidates_empty_lut(): + assert ( + search_background_candidates(Table(), Time("2023-06-15"), window_past=30 * u.day, window_future=7 * u.day) == [] + ) + + +def test_ridlutmanager_singleton_find_background_candidates(): + # exercises the method against the shipped test LUT (rows 3001-3004) + cands = RidLutManager.instance.find_background_candidates( + Time("2023-06-15T00:00:00"), window_past=30 * u.day, window_future=7 * u.day + ) + # 3001 (5 d past) and 3003 (5 d future) are equidistant -> past wins tie, then 3002 (14 d past) + assert [c.rid for c in cands] == [3001, 3003, 3002] + + +# --- find_background_file_for_time -------------------------------------------- + + +def _cpd_name(rid): + return f"solo_L1_stix-sci-xray-cpd_20230101T000000-20230101T010000_V01_{rid:010d}-00001.fits" + + +class FakeCpdFido: + """Returns the same CPD file list for every search; the rid-in-filename filter + inside ``find_background_file_for_time`` selects the per-candidate file.""" + + def __init__(self, paths): + self._paths = list(paths) + self.searches = 0 + + def search(self, time, instrument, data_product, level): + self.searches += 1 + return FakeResponse(self._paths) + + +def _patch_cpd_products(monkeypatch, spec): + """``spec``: {rid: rcr_array_or_None}. Builds CPD filenames + FakeProducts and + monkeypatches STIXPYProduct to resolve them.""" + products = {} + paths = [] + for rid, rcr in spec.items(): + name = _cpd_name(rid) + paths.append(name) + with_rcr = rcr is not None + products[name] = FakeProduct(_ql_data("2023-06-10T00:00:00", 5, rcr=rcr, with_rcr=with_rcr), _energies()) + monkeypatch.setattr("stixcore.io.FlareListManager.STIXPYProduct", lambda path: products[path]) + return FakeCpdFido(paths) + + +def test_find_bkg_picks_closest_usable(monkeypatch): + # 3001 starts 5 d before, 3002 starts 7 d after -> 3001 is nearest in time + lut = _bkg_lut([(3001, "2023-06-10T00:00:00", 3600), (3002, "2023-06-22T00:00:00", 3600)]) + fido = _patch_cpd_products(monkeypatch, {3001: np.zeros(5), 3002: np.zeros(5)}) + t = Time("2023-06-15T00:00:00") + sel = find_background_file_for_time( + t, fido_client=fido, rid_lut=lut, min_duration=600 * u.s, require_same_elut=False + ) + assert sel.rid == 3001 # nearest-in-time wins + assert sel.path == Path(_cpd_name(3001)) + assert sel.valid_from == t + # valid until the midpoint to the next later start: (2023-06-10 + 2023-06-22) / 2 = 2023-06-16 + assert sel.valid_to == Time("2023-06-16T00:00:00") + + +def test_find_bkg_skips_attenuator_in(monkeypatch): + # 3001 (closer past) has attenuator in (rcr>0) -> fall through to 3002 + lut = _bkg_lut([(3001, "2023-06-10T00:00:00", 3600), (3002, "2023-06-05T00:00:00", 3600)]) + fido = _patch_cpd_products(monkeypatch, {3001: np.ones(5), 3002: np.zeros(5)}) + sel = find_background_file_for_time( + Time("2023-06-15T00:00:00"), + fido_client=fido, + rid_lut=lut, + min_duration=600 * u.s, + require_same_elut=False, + ) + assert sel.rid == 3002 + + +def test_find_bkg_skips_too_short(monkeypatch): + # 3001 requested only 300 s (< min_duration) -> skipped without even a search + lut = _bkg_lut([(3001, "2023-06-10T00:00:00", 300), (3002, "2023-06-05T00:00:00", 3600)]) + fido = _patch_cpd_products(monkeypatch, {3001: np.zeros(5), 3002: np.zeros(5)}) + sel = find_background_file_for_time( + Time("2023-06-15T00:00:00"), + fido_client=fido, + rid_lut=lut, + min_duration=600 * u.s, + require_same_elut=False, + ) + assert sel.rid == 3002 + + +def test_find_bkg_future_fallback(monkeypatch): + # no past candidate -> the future one is used + lut = _bkg_lut([(3003, "2023-06-18T00:00:00", 3600)]) + fido = _patch_cpd_products(monkeypatch, {3003: np.zeros(5)}) + sel = find_background_file_for_time( + Time("2023-06-15T00:00:00"), + fido_client=fido, + rid_lut=lut, + min_duration=600 * u.s, + require_same_elut=False, + ) + assert sel.rid == 3003 + + +def test_find_bkg_none_when_nothing_qualifies(monkeypatch): + # only candidate has attenuator in -> no file, but a valid period is still returned + lut = _bkg_lut([(3001, "2023-06-10T00:00:00", 3600)]) + fido = _patch_cpd_products(monkeypatch, {3001: np.ones(5)}) + t = Time("2023-06-15T00:00:00") + sel = find_background_file_for_time( + t, + fido_client=fido, + rid_lut=lut, + window_future=7 * u.day, + min_duration=600 * u.s, + require_same_elut=False, + ) + assert sel.path is None + assert sel.rid == -1 + assert sel.valid_from == t + assert sel.valid_to == t + 7 * u.day # no later candidate -> t + window_future + + +# --- same-ELUT criterion ------------------------------------------------------ + + +class _FakeELUTInstance: + def __init__(self, fn): + self._fn = fn + + def _find_elut_file(self, dt): + return self._fn(dt) + + +def _patch_elut(monkeypatch, fn): + import types + + monkeypatch.setattr(flm_mod, "ELUTManager", types.SimpleNamespace(instance=_FakeELUTInstance(fn))) + + +def test_find_bkg_requires_same_elut(monkeypatch): + # 3001 (closer past) is under a DIFFERENT ELUT than the flare; 3002 matches it + lut = _bkg_lut([(3001, "2023-06-10T00:00:00", 3600), (3002, "2023-06-05T00:00:00", 3600)]) + fido = _patch_cpd_products(monkeypatch, {3001: np.zeros(5), 3002: np.zeros(5)}) + # flare(15)->'A', 3001(10)->'B' (different), 3002(5)->'A' (same) + _patch_elut(monkeypatch, lambda dt: "B" if dt.day == 10 else "A") + t = Time("2023-06-15T00:00:00") + + sel = find_background_file_for_time( + t, fido_client=fido, rid_lut=lut, min_duration=600 * u.s, require_same_elut=True + ) + assert sel.rid == 3002 # 3001 skipped: different ELUT configuration + + sel_off = find_background_file_for_time( + t, fido_client=fido, rid_lut=lut, min_duration=600 * u.s, require_same_elut=False + ) + assert sel_off.rid == 3001 # criterion off -> closest past wins + + +def test_find_bkg_same_elut_skipped_when_flare_elut_unknown(monkeypatch): + # ELUT can't be resolved for the flare -> criterion cannot be enforced -> not restrictive + lut = _bkg_lut([(3001, "2023-06-10T00:00:00", 3600)]) + fido = _patch_cpd_products(monkeypatch, {3001: np.zeros(5)}) + _patch_elut(monkeypatch, lambda dt: None) + sel = find_background_file_for_time( + Time("2023-06-15T00:00:00"), fido_client=fido, rid_lut=lut, min_duration=600 * u.s, require_same_elut=True + ) + assert sel.rid == 3001 + + +# --- add_background_file_column (valid-period cache) -------------------------- + + +def test_add_background_file_column_uses_valid_period_cache(monkeypatch): + calls = [] + + def fake_find(time, *, fido_client, **kwargs): + calls.append(time) + # each selection stays valid for 2 days from the queried time + return BackgroundSelection(path=Path("bkg.fits"), rid=42, valid_from=time, valid_to=time + 2 * u.day) + + monkeypatch.setattr(flm_mod, "find_background_file_for_time", fake_find) + + data = QTable() + data["peak_UTC"] = Time( + ["2023-06-15T00:00:00", "2023-06-16T00:00:00", "2023-06-18T00:00:00"] # 3rd is beyond the 1st period + ) + # STIXPYProduct isn't patched here; the missing "bkg.fits" is swallowed, leaving NaN spectra. + FlareListManager().add_background_file_column(data, energy=QTable(), fido_client=object()) + + assert len(calls) == 2 # 1st + 3rd flare trigger a search; 2nd reuses the cache + assert list(data["bkg_rid"]) == [42, 42, 42] + assert list(data["bkg_file"]) == ["bkg.fits"] * 3 + + +# --- background-spectrum extraction ------------------------------------------ + + +def _sci_energies_32(): + """The 32-channel science energy binning (channel/e_low/e_high in keV).""" + lows = [ + 0, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 18, + 20, + 22, + 25, + 28, + 32, + 36, + 40, + 45, + 50, + 56, + 63, + 70, + 76, + 84, + 100, + 120, + 150, + ] + highs = [ + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 20, + 22, + 25, + 28, + 32, + 36, + 40, + 45, + 50, + 56, + 63, + 70, + 76, + 84, + 100, + 120, + 150, + 1e6, + ] + e = QTable() + e["channel"] = np.arange(32, dtype=np.uint8) + e["e_low"] = lows * u.keV + e["e_high"] = highs * u.keV + return e + + +class FakeCPD: + """CPD stand-in: builds a data table with counts + timedel + zero triggers + (livefrac=1) + pixel_masks (first npix pixels present) so the flux path runs.""" + + def __init__(self, counts, energies, control=None, timedel_s=1.0, dsun_m=1.496e11): + nt, ndet, npix, nE = counts.shape + d = QTable() + d["counts"] = counts + d["timedel"] = np.full(nt, timedel_s) * u.s + d["triggers"] = np.zeros((nt, 16)) + pm = np.zeros((nt, 12), dtype=np.ubyte) + pm[:, :npix] = 1 + d["pixel_masks"] = pm + self.data = d + self.energies = energies + self.control = control + self.meta = {"DSUN_OBS": dsun_m} # SOLO-Sun distance (m), as in a CPD primary header + + +class FakeCtrlProduct: + """Minimal product exposing a control table with detector/pixel masks.""" + + def __init__(self, detector_mask, pixel_mask): + c = QTable() + c["detector_mask"] = [np.asarray(detector_mask, dtype=np.ubyte)] + c["pixel_mask"] = [np.asarray(pixel_mask, dtype=np.ubyte)] + self.control = c + + +def test_collecting_area_cm2(): + # 30 imaging detectors, all 12 pixels -> 30 * sum(12 pixel areas) + det = np.ones(32, dtype=np.ubyte) + det[[8, 9]] = 0 + from stixcore.io.FlareListManager import _pixel_area_cm2 + + a_full = collecting_area_cm2(FakeCtrlProduct(det, np.ones(12))) + assert np.isclose(a_full, 30 * _pixel_area_cm2().sum()) + # 8 large pixels only -> smaller + pm8 = np.zeros(12, dtype=np.ubyte) + pm8[:8] = 1 + a8 = collecting_area_cm2(FakeCtrlProduct(det, pm8)) + assert np.isclose(a8, 30 * _pixel_area_cm2()[:8].sum()) + assert a8 < a_full + # no masks -> None + assert collecting_area_cm2(FakeCPD(np.ones((1, 32, 12, 5)) * u.ct, _energies())) is None + + +def test_background_spectrum_from_cpd(): + from stixcore.io.FlareListManager import _pixel_area_cm2 + + nt = 5 + counts = np.ones((nt, 32, 12, 32), dtype=float) + for t in range(nt): + counts[t] *= t + 1 # time-varying so the median is exercised + counts[:, 8] = 9999 # CFL detector - must be excluded + counts[:, 9] = 9999 # BKG monitor - must be excluded + spec, flux, e32 = background_spectrum_from_cpd(FakeCPD(counts * u.ct, _sci_energies_32(), timedel_s=1.0)) + # counts: 30 imaging dets * 12 pixels * (t+1) -> [360..1800]; median = 1080 + assert spec.shape == (32,) + assert spec.unit == u.ct + assert np.allclose(spec.to_value(u.ct), 1080.0) + assert len(e32) == 32 + # flux = counts / (exp * area * dE); triggers=0 -> livefrac=1, timedel=1s + # exp = 30 dets * 1s = 30; area = sum(12 pixel areas) + dE = e32["e_high"].to_value(u.keV) - e32["e_low"].to_value(u.keV) + area = _pixel_area_cm2().sum() + expected = 1080.0 / (30.0 * area * dE) + assert flux.shape == (32,) + assert flux.unit.is_equivalent(FLUX_UNIT) + assert np.allclose(flux.to_value(FLUX_UNIT), expected, rtol=1e-6) + + +def test_background_spectrum_from_cpd_ragged_energies(): + # real archive CPDs telemeter a subset of channels (e.g. 20) with fewer pixels (8); + # counts nE must stay aligned to the energies table (no forced 32-channel expansion) + energies = _sci_energies_32()[:20] # 20-channel binning + counts = np.ones((4, 32, 8, 20), dtype=float) + counts[:, 8] = 5.0 # CFL - excluded + counts[:, 9] = 5.0 # BKG monitor - excluded + spec, flux, e = background_spectrum_from_cpd(FakeCPD(counts * u.ct, energies)) + assert spec.shape == (20,) # matches energies, not forced to 32 + assert len(e) == 20 + assert np.allclose(spec.to_value(u.ct), 30 * 8) # 30 imaging dets * 8 pixels * 1 count + assert flux.shape == (20,) + assert np.all(np.isfinite(flux.to_value(FLUX_UNIT))) + # rebin against a QL block works (lengths consistent) and preserves units + c5 = rebin_spectrum_to_ql(spec, e, _energies()) + assert c5.shape == (5,) + assert c5.unit == u.ct + assert c5.to_value(u.ct)[0] == 6 * (30 * 8) # 4-10 keV -> 6 sci channels + f5 = rebin_flux_to_ql(flux, e, _energies()) + assert f5.shape == (5,) + assert f5.unit.is_equivalent(FLUX_UNIT) + assert np.isfinite(f5.to_value(FLUX_UNIT)[0]) + + +def test_rebin_flux_to_ql(): + e32 = _sci_energies_32() + # constant flux density -> dE-weighted mean is the same constant in every band + flux = np.full(32, 3.0) + f5 = rebin_flux_to_ql(flux, e32, _energies()) + assert np.allclose(f5, 3.0) + # a shifted top band changes the channel set but a constant stays constant; + # use a ramp to show band membership matters + flux2 = np.arange(32, dtype=float) + a = rebin_flux_to_ql(flux2, e32, _energies()) + ql2 = _energies() + ql2["e_high"] = [10, 15, 25, 50, 100] * u.keV + b = rebin_flux_to_ql(flux2, e32, ql2) + assert a[4] != b[4] # 50-84 vs 50-100 include different channels + + +def test_rebin_spectrum_to_ql(): + e32 = _sci_energies_32() + counts32 = np.arange(32, dtype=float) # channel c contributes c counts + ql = _energies() # QL bands read from the (energy) table, not hardcoded + c5 = rebin_spectrum_to_ql(counts32, e32, ql) + assert c5[0] == sum(range(1, 7)) # 4-10 keV -> ch 1..6 + assert c5[1] == sum(range(7, 12)) # 10-15 -> ch 7..11 + assert c5[2] == sum(range(12, 17)) # 15-25 -> ch 12..16 + assert c5[3] == sum(range(17, 23)) # 25-50 -> ch 17..22 + assert c5[4] == sum(range(23, 28)) # 50-84 -> ch 23..27 + + # a shifted top band (50-100) must pull in channel 28 (84-100): proves it follows the table + ql2 = _energies() + ql2["e_high"] = [10, 15, 25, 50, 100] * u.keV + c5b = rebin_spectrum_to_ql(counts32, e32, ql2) + assert c5b[4] == sum(range(23, 29)) + assert c5b[4] != c5[4] + + +def test_add_background_file_column_extracts_and_caches(monkeypatch): + t0 = "2024-10-01T00:00:00" + T0 = Time(t0) + data = QTable() + data["flare_id"] = [1, 2, 3, 4] + data["peak_UTC"] = Time([t0, "2024-10-01T01:00:00", "2024-10-01T02:00:00", "2024-10-01T05:00:00"]) + data["energy_index"] = np.zeros(4, dtype=np.int8) # all reference the QL block (index 0) + + energy = _energies() + energy["index"] = np.zeros(5, dtype=np.int8) # QL 5-ch block, index 0 + + def fake_find(time, *, fido_client, **kwargs): + if time <= T0 + 1.5 * u.h: + return BackgroundSelection(path="cpdA", rid=100, valid_from=T0, valid_to=T0 + 1.5 * u.h) + if time <= T0 + 3.5 * u.h: + return BackgroundSelection(path="cpdB", rid=200, valid_from=T0 + 1.5 * u.h, valid_to=T0 + 3.5 * u.h) + return BackgroundSelection(path=None, rid=-1, valid_from=time, valid_to=time + 1 * u.h) + + monkeypatch.setattr(flm_mod, "find_background_file_for_time", fake_find) + + opens = [] + + dsun_by_path = {"cpdA": 0.5 * 1.495978707e11, "cpdB": 0.8 * 1.495978707e11} # m + + def fake_product(path): + opens.append(path) + return FakeCPD(np.ones((3, 32, 12, 32)) * u.ct, _sci_energies_32(), dsun_m=dsun_by_path[path]) + + monkeypatch.setattr(flm_mod, "STIXPYProduct", fake_product) + + energy = FlareListManager().add_background_file_column(data, energy=energy, fido_client=object()) + + # each CPD opened exactly once (cpdA shared by flares 0 & 1; cpdB flare 2; flare 3 has no file) + assert opens == ["cpdA", "cpdB"] + assert list(data["bkg_rid"]) == [100, 100, 200, -1] + # both CPDs share the same 32-ch binning -> one new block (index 1); QL block stays index 0 + assert {int(x) for x in energy["index"]} == {0, 1} + assert int(np.sum(np.asarray(energy["index"]) == 1)) == 32 + assert list(data["bkg_energy_index"]) == [1, 1, 1, -1] + # counts spectra filled for in-file flares, NaN for the no-file flare + assert data["bkg_spec"].shape == (4, 32) + assert not np.isnan(data["bkg_spec"][0].to_value(u.ct)).any() + assert np.isnan(data["bkg_spec"][3].to_value(u.ct)).all() + assert np.allclose(data["bkg_spec"][0].to_value(u.ct), 360.0) # 30 dets * 12 pix * 1 + # counts rebinned to the QL bands + assert data["bkg_spec_ql"].shape == (4, 5) + assert data["bkg_spec_ql"].unit == u.ct + assert not np.isnan(data["bkg_spec_ql"][0].to_value(u.ct)).any() + assert np.isnan(data["bkg_spec_ql"][3].to_value(u.ct)).all() + # each QL band sums whole native channels (360 ct each), so every band is a multiple of 360 + assert np.allclose(data["bkg_spec_ql"][0].to_value(u.ct) % 360.0, 0.0) + # flux columns (ct/s/keV/cm2), native + QL-rebinned + assert data["bkg_spec_flux"].shape == (4, 32) + assert data["bkg_spec_flux"].unit.is_equivalent(FLUX_UNIT) + assert not np.isnan(data["bkg_spec_flux"][0].to_value(FLUX_UNIT)).any() + assert np.isnan(data["bkg_spec_flux"][3].to_value(FLUX_UNIT)).all() + assert data["bkg_spec_flux_ql"].shape == (4, 5) + assert not np.isnan(data["bkg_spec_flux_ql"][0].to_value(FLUX_UNIT)).any() + assert np.isnan(data["bkg_spec_flux_ql"][3].to_value(FLUX_UNIT)).all() + # bkg_solo_sun_distance read from each CPD's DSUN_OBS header (km); NaN for the no-file flare + d = data["bkg_solo_sun_distance"].to_value(u.AU) + assert np.isclose(d[0], 0.5) + assert np.isclose(d[1], 0.5) + assert np.isclose(d[2], 0.8) + assert np.isnan(d[3]) diff --git a/stixcore/processing/FLtoFL.py b/stixcore/processing/FLtoFL.py index 087f0d36..7f08acf1 100644 --- a/stixcore/processing/FLtoFL.py +++ b/stixcore/processing/FLtoFL.py @@ -15,7 +15,7 @@ ) from stixcore.products.level3.flarelist import ( FlareList, - FlarePeekPreviewMixin, + FlarePeakPreviewMixin, FlarePositionMixin, FlareSOOPMixin, ) @@ -107,7 +107,7 @@ def test_for_processing( """ try: c_header = fits.getheader(candidate) - f_data_end = datetime.fromisoformat(c_header["DATE-END"]) + # f_data_end = datetime.fromisoformat(c_header["DATE-END"]) f_create_date = datetime.fromisoformat(c_header["DATE"]) cfn = get_complete_file_name_and_path(candidate) @@ -128,8 +128,9 @@ def test_for_processing( # safety margin of 1day until we process higher products with position and pointing # only use flown spice kernels not predicted once as pointing information # can be "very off" - if f_data_end > (Spice.instance.get_mk_date(meta_kernel_type="flown") - timedelta(hours=24)): - return TestForProcessingResult.NotSuitable + # TODO redo + # if f_data_end > (Spice.instance.get_mk_date(meta_kernel_type="flown") - timedelta(hours=24)): + # return TestForProcessingResult.NotSuitable # safety margin of x until we start with processing the list files if f_create_date >= (datetime.now() - self.cadence): @@ -187,9 +188,9 @@ def process_fits_files( if issubclass(out_product, FlareSOOPMixin) and not issubclass(in_product, FlareSOOPMixin): out_product.add_soop(data) - # add peek preview images if not already present - if issubclass(out_product, FlarePeekPreviewMixin) and not issubclass(in_product, FlarePeekPreviewMixin): - out_product.add_peek_preview(data, energy, file_path.name, fido_client, img_processor, month=month) + # add peak preview images if not already present + if issubclass(out_product, FlarePeakPreviewMixin) and not issubclass(in_product, FlarePeakPreviewMixin): + out_product.add_peak_preview(data, energy, file_path.name, fido_client, img_processor, month=month) out_prod = out_product(control=control, data=data, month=month, energy=energy) out_prod.parent = file_path.name diff --git a/stixcore/processing/FlareListL3.py b/stixcore/processing/FlareListL3.py index 89e6d5c8..20a5f3ec 100644 --- a/stixcore/processing/FlareListL3.py +++ b/stixcore/processing/FlareListL3.py @@ -24,7 +24,7 @@ class FlareListL3(SingleProductProcessingStepMixin): """Processing step from a FlareListManager to monthly solo_L3_stix-flarelist-*.fits file.""" - STARTDATE = date(2024, 1, 1) + STARTDATE = date(2023, 1, 1) def __init__(self, flm: FlareListManager, output_dir: Path): """Crates a new Processor. @@ -54,19 +54,19 @@ def find_processing_months(self, phs: ProcessingHistoryStorage) -> list[date]: return fl_months def test_for_processing(self, month: date, phm: ProcessingHistoryStorage) -> TestForProcessingResult: - """_summary_ + """Decide whether the flare list for ``month`` needs (re)processing. Parameters ---------- - candidate : Path - a fits file candidate + month : date + the month to test phm : ProcessingHistoryStorage the processing history persistent handler Returns ------- TestForProcessingResult - what should happen with the candidate in the next processing step + what should happen with the month in the next processing step """ try: wp = phm.has_processed_fits_products( diff --git a/stixcore/processing/pipeline_daily.py b/stixcore/processing/pipeline_daily.py index 09b19181..680e9ec3 100644 --- a/stixcore/processing/pipeline_daily.py +++ b/stixcore/processing/pipeline_daily.py @@ -9,34 +9,27 @@ from stixcore.config.config import CONFIG from stixcore.ephemeris.manager import Spice, SpiceKernelManager +from stixcore.io.FlareListManager import SDCFlareListManager from stixcore.io.ProcessingHistoryStorage import ProcessingHistoryStorage from stixcore.io.product_processors.fits.processors import ( # FitsANCProcessor,; FitsL3Processor, + FitsANCProcessor, FitsL2Processor, + FitsL3Processor, ) from stixcore.io.product_processors.plots.processors import PlotProcessor from stixcore.io.RidLutManager import RidLutManager from stixcore.processing.AspectANC import AspectANC +from stixcore.processing.FlareListL3 import FlareListL3 +from stixcore.processing.FLtoFL import FLtoFL from stixcore.processing.LL import LL03QL from stixcore.processing.pipeline import PipelineStatus from stixcore.processing.SingleStep import SingleProcessingStepResult from stixcore.products.level1.quicklookL1 import LightCurve +from stixcore.products.level3.flarelist import FlarelistSDC, FlarelistSDCLocation from stixcore.products.lowlatency.quicklookLL import LightCurveL3 from stixcore.soop.manager import SOOPManager from stixcore.util.logging import STX_LOGGER_DATE_FORMAT, STX_LOGGER_FORMAT, get_logger -# from stixpy.net.client import STIXClient -# from stixcore.io.FlareListManager import SCFlareListManager, SDCFlareListManager -# from stixcore.processing.FlareListL3 import FlareListL3 -# from stixcore.processing.FLtoFL import FLtoFL -# from stixcore.products.level3.flarelist import ( -# FlarelistSC, -# FlarelistSCLoc, -# FlarelistSCLocImg, -# FlarelistSDC, -# FlarelistSDCLoc, -# FlarelistSDCLocImg, -# ) - logger = get_logger(__name__) @@ -215,8 +208,8 @@ def run_daily_pipeline(args): # SCFlareListManager.instance = SCFlareListManager(flare_lut_file, fido_client, update=True) # TODO reactivate once flarelist processing is finalized - # flare_lut_file = Path(CONFIG.get("Pipeline", "flareid_sdc_lut_file")) - # SDCFlareListManager.instance = SDCFlareListManager(flare_lut_file, update=False) + flare_lut_file = Path(CONFIG.get("Pipeline", "flareid_sdc_lut_file")) + SDCFlareListManager.instance = SDCFlareListManager(flare_lut_file, update=False) RidLutManager.instance = RidLutManager(Path(CONFIG.get("Publish", "rid_lut_file")), update=False) @@ -249,19 +242,19 @@ def run_daily_pipeline(args): aspect_anc_processor = AspectANC(fits_in_dir, fits_out_dir) # TODO reactivate once flarelist processing is finalized - # flarelist_sdc = FlareListL3(SDCFlareListManager.instance, fits_out_dir) + flarelist_sdc = FlareListL3(SDCFlareListManager.instance, fits_out_dir) # flarelist_sc = FlareListL3(SCFlareListManager.instance, fits_out_dir) - # fl_to_fl = FLtoFL( - # fits_in_dir, - # fits_out_dir, - # products_in_out=[ - # (FlarelistSDC, FlarelistSDCLoc), - # (FlarelistSDCLoc, FlarelistSDCLocImg), - # (FlarelistSC, FlarelistSCLoc), - # (FlarelistSCLoc, FlarelistSCLocImg), - # ], - # cadence=timedelta(seconds=1), - # ) + fl_to_fl = FLtoFL( + fits_in_dir, + fits_out_dir, + products_in_out=[ + (FlarelistSDC, FlarelistSDCLocation), + # (FlarelistSDCLocation, FlarelistSDCLocationImage), + # (FlarelistSC, FlarelistSCLocation), + # (FlarelistSCLocation, FlarelistSCLocationImage), + ], + cadence=timedelta(seconds=1), + ) ll03ql = LL03QL( fits_in_dir, fits_out_dir, in_product=LightCurve, out_product=LightCurveL3, cadence=timedelta(seconds=1) @@ -270,25 +263,29 @@ def run_daily_pipeline(args): plot_writer = PlotProcessor(fits_out_dir) l2_fits_writer = FitsL2Processor(fits_out_dir) # TODO reactivate once flarelist processing is finalized - # l3_fits_writer = FitsL3Processor(fits_out_dir) - # anc_fits_writer = FitsANCProcessor(fits_out_dir) + l3_fits_writer = FitsL3Processor(fits_out_dir) + anc_fits_writer = FitsANCProcessor(fits_out_dir) - hk_in_files = aspect_anc_processor.get_processing_files(phs) + # hk_in_files = aspect_anc_processor.get_processing_files(phs) + hk_in_files = [] - ll_candidates = ll03ql.get_processing_files(phs) - # ll_candidates = [] + # ll_candidates = ll03ql.get_processing_files(phs) + ll_candidates = [] # TODO reactivate once flarelist processing is finalized - # fl_sdc_months = flarelist_sdc.find_processing_months(phs) - # fl_sdc_months = [] + fl_sdc_months = flarelist_sdc.find_processing_months(phs) + fl_sdc_months = [] + # fl_sdc_months = [f for f in fl_sdc_months if f.year == 2024 and f.month == 10] # TODO reactivate once flarelist processing is finalized # fl_sc_months = flarelist_sc.find_processing_months(phs) # fl_sc_months = [] # TODO reactivate once flarelist processing is finalized - # fl_to_fl_files = fl_to_fl.get_processing_files(phs) + fl_to_fl_files = fl_to_fl.get_processing_files(phs) + # fl_to_fl_files = fl_to_fl_files[2:-1] # fl_to_fl_files = [] + fl_to_fl_files = [f for f in fl_to_fl_files if "/2024/10/" in str(f[2]) and "V04" in str(f[2])] # all processing files should be terminated before the next step as the different # processing steeps might create new candidates @@ -306,16 +303,16 @@ def run_daily_pipeline(args): ) ) # TODO reactivate once flarelist processing is finalized - # jobs.append( - # executor.submit( - # flarelist_sdc.process_fits_files, - # fl_sdc_months, - # soopmanager=SOOPManager.instance, - # spice_kernel_path=Spice.instance.meta_kernel_path, - # processor=l3_fits_writer, - # config=CONFIG, - # ) - # ) + jobs.append( + executor.submit( + flarelist_sdc.process_fits_files, + fl_sdc_months, + soopmanager=SOOPManager.instance, + spice_kernel_path=Spice.instance.meta_kernel_path, + processor=l3_fits_writer, + config=CONFIG, + ) + ) # jobs.append( # executor.submit( @@ -330,17 +327,17 @@ def run_daily_pipeline(args): # # TODO a owen processing step for each flarelist file? # # for fl_to_fl_file in fl_to_fl_files: - # jobs.append( - # executor.submit( - # fl_to_fl.process_fits_files, - # fl_to_fl_files, - # soopmanager=SOOPManager.instance, - # spice_kernel_path=Spice.instance.meta_kernel_path, - # fl_processor=anc_fits_writer, - # img_processor=l3_fits_writer, - # config=CONFIG, - # ) - # ) + jobs.append( + executor.submit( + fl_to_fl.process_fits_files, + fl_to_fl_files, + soopmanager=SOOPManager.instance, + spice_kernel_path=Spice.instance.meta_kernel_path, + fl_processor=anc_fits_writer, + img_processor=l3_fits_writer, + config=CONFIG, + ) + ) jobs.append( executor.submit( diff --git a/stixcore/products/__init__.py b/stixcore/products/__init__.py index 7863b1bf..974838ed 100644 --- a/stixcore/products/__init__.py +++ b/stixcore/products/__init__.py @@ -10,5 +10,6 @@ from stixcore.products.level2.housekeepingL2 import * from stixcore.products.level2.quicklookL2 import * from stixcore.products.level2.scienceL2 import * +from stixcore.products.level3.flarelist import * from stixcore.products.levelb.binary import LevelB from stixcore.products.lowlatency.quicklookLL import * diff --git a/stixcore/products/level3/flarelist.py b/stixcore/products/level3/flarelist.py index 188f6193..21418ccc 100644 --- a/stixcore/products/level3/flarelist.py +++ b/stixcore/products/level3/flarelist.py @@ -1,5 +1,7 @@ from pathlib import Path from datetime import datetime +from itertools import groupby +from collections import namedtuple import numpy as np from stixpy.calibration.visibility import ( @@ -10,7 +12,7 @@ from stixpy.coordinates.transforms import get_hpc_info from stixpy.net.client import STIXClient from stixpy.product import Product as STIXPYProduct -from sunpy.coordinates import HeliographicStonyhurst, Helioprojective +from sunpy.coordinates import HeliographicStonyhurst, Helioprojective, SphericalScreen from sunpy.map import make_fitswcs_header from sunpy.net import attrs as a from sunpy.time import TimeRange @@ -18,14 +20,16 @@ import astropy.units as u from astropy.coordinates import SkyCoord +from astropy.coordinates.representation import CartesianRepresentation from astropy.io import fits from astropy.table import Column, QTable from astropy.time import Time from stixcore.config.config import CONFIG from stixcore.ephemeris.manager import Spice -from stixcore.products.level3.flarelistproduct import PeekPreviewImage -from stixcore.products.product import CountDataMixin, GenericProduct, L2Mixin, read_qtable +from stixcore.products.level3.flarelistproduct import PeakPreviewImage +from stixcore.products.level3.processing import stx_estimate_flare_location +from stixcore.products.product import CountDataMixin, GenericProduct, L2Mixin from stixcore.soop.manager import SOOPManager from stixcore.time import SCETime, SCETimeRange from stixcore.util.logging import get_logger @@ -38,11 +42,13 @@ "FlarePositionMixin", "FlareSOOPMixin", "FlareList", - "FlarelistSDCLocImg", - "FlarePeekPreviewMixin", + "FlarelistSDCLocation", + "FlarelistSDCLocationImage", + "FlarePeakPreviewMixin", "FlarelistSC", - "FlarelistSCLoc", - "FlarelistSCLocImg", + "FlarelistSCLocation", + "FlarelistSCLocationImage", + "add_distance_normalized_flux", ] logger = get_logger(__name__) @@ -75,8 +81,134 @@ def make_stix_fitswcs_header(data, flare_position, *, scale, exposure, rotation_ return header -class FlarePositionMixin: - """_summary_""" +#: Which per-flare distance column each flux column is normalized by. LC peak fluxes use the +#: flare-time SOLO distance; the quiet-period background fluxes use the background CPD's own time. +_FLUX_DISTANCE_COLS = { + "lc_peak_flux": "solo_sun_distance", + "lc_bkg_peak_flux": "solo_sun_distance", + "bkg_spec_flux": "bkg_solo_sun_distance", + "bkg_spec_flux_ql": "bkg_solo_sun_distance", +} + + +def add_distance_normalized_flux(data, mapping=_FLUX_DISTANCE_COLS): + """Add ``_at_1au`` = `` * (r/1AU)**2`` for each present flux column, using the + per-flare distance in its mapped column (flux scales as 1/r**2). + + The existing flux columns are kept unchanged; the scale factor is dimensionless so the + ``*_at_1au`` columns keep the flux unit (ct/s/keV/cm2). NaN distance -> NaN; a flux column + (or its distance column) that is absent is skipped. + """ + for col, dist_col in mapping.items(): + if col not in data.colnames or dist_col not in data.colnames: + continue + factor = (data[dist_col] / (1 * u.AU)).decompose().value ** 2 # (N,), dimensionless + f = data[col] + data[col + "_at_1au"] = f * (factor[:, None] if f.ndim == 2 else factor) + data[ + col + "_at_1au" + ].info.description = f"{col} scaled to what would be seen at 1 AU (x (r_solo/1AU)**2, r_solo from {dist_col})" + + +#: Per-flare result of :meth:`FlarePositionMixin.add_flare_position`. One record per flare row; +#: skipped/failed flares use :func:`_empty_flare_position` (NaN geometry, ``peak_time`` placeholder). +FlarePositionResult = namedtuple( + "FlarePositionResult", + [ + "anc_path", + "cpd_path", + "status", + "message", + "flare_x", # HGS cartesian, km + "flare_y", + "flare_z", + "solo_time", # location time center + "duration", # location window length + "solo_x", # SOLO HGS cartesian, km + "solo_y", + "solo_z", + "rcr_at_peak", + "sidelobe", + "min_exposure", # CPD per-bin exposure ds over the flare window (u.ds) + "max_exposure", + ], +) + + +def _empty_flare_position(solo_time, **overrides): + """A :class:`FlarePositionResult` for a skipped/failed flare. + + NaN geometry and zero window, with ``solo_time`` kept as the per-row time placeholder (it + must stay a valid `~astropy.time.Time` for the downstream coordinate/Spice handling). + ``overrides`` set the few fields a given skip site knows, e.g. ``message`` / ``anc_path`` / + ``cpd_path``. + """ + base = dict( + anc_path="", + cpd_path="", + status=False, + message="", + flare_x=np.nan * u.km, + flare_y=np.nan * u.km, + flare_z=np.nan * u.km, + solo_time=solo_time, + duration=0 * u.s, + solo_x=np.nan * u.km, + solo_y=np.nan * u.km, + solo_z=np.nan * u.km, + rcr_at_peak=0, + sidelobe=np.nan, + min_exposure=np.nan * u.ds, + max_exposure=np.nan * u.ds, + ) + base.update(overrides) + return FlarePositionResult(**base) + + +def cpd_timedel_range(times, timedels, start, end): + """Min and max CPD time-step ``ds`` (``timedel``) for bins overlapping ``[start, end]``. + + Uses the same half-bin overlap test as the peak-window selection so partially covered + windows still contribute. Returns ``(min_exposure, max_exposure)`` as + `~astropy.units.Quantity` in deciseconds (``u.ds``, STIX's native ``timedel`` unit); + ``(NaN ds, NaN ds)`` if no bin overlaps. + """ + half = timedels / 2 + mask = (times + half >= start) & (times - half <= end) + sel = timedels[mask] + if len(sel) == 0: + return np.nan * u.ds, np.nan * u.ds + return sel.min().to(u.ds), sel.max().to(u.ds) + + +class _SerializeMixin: + """No-op chain terminator for on_serialize/on_deserialize. + + Functional mixins inherit from this so super() calls always land safely + instead of hitting object and raising AttributeError. + """ + + def on_serialize(self, data): + pass + + def on_deserialize(self, data, **kwargs): + pass + + +class FlarePositionMixin(_SerializeMixin): + """Mixin adding a STIX-derived flare location to a flare-list product. + + For every flare it selects a compressed-pixel-data (CPD) file, estimates the source + position by back-projection imaging (`~stixcore.products.level3.processing.stx_estimate_flare_location`), + and stores the location (Heliographic Stonyhurst / Helioprojective), the Solar Orbiter + position and distance, an imaging-quality metric and related timing columns. On + serialization the location columns are converted to ICRS (and back on deserialization) so + they survive the FITS round-trip. + + See :doc:`/products/flarelist` for the CPD-selection and imaging details. Used by + `~stixcore.products.level3.flarelist.FlarelistSDCLocation` (and the image product built on + it). + """ @classmethod def add_flare_position( @@ -85,18 +217,38 @@ def add_flare_position( fido_client: STIXClient, *, filter_function=lambda x: True, - peek_time_colname="peak_UTC", + peak_time_colname="peak_UTC", start_time_colname="start_UTC", end_time_colname="end_UTC", + location_time_colname="location_time_UTC", keep_all_flares=True, month=None, ): - data["flare_position"] = [SkyCoord(0, 0, frame="icrs", unit="deg") for i in range(0, len(data))] + """Estimate and add the flare location columns for every flare in ``data``. + + For each flare passing ``filter_function`` the daily ancillary ephemeris and the + science CPD file(s) covering ``[start, end]`` are looked up; the best CPD is scored and + selected, a constant-``rcr`` time window around the peak is chosen, and the location is + estimated by back-projection imaging. See :doc:`/products/flarelist` for the selection + and imaging parameters. + + Parameters + ---------- + data : `~astropy.table.QTable` + The flare list; location columns are added in place. + fido_client : `~stixpy.net.client.STIXClient` + Client used to search for the ephemeris and CPD files. + filter_function : callable, optional + ``row -> bool`` deciding which flares to process (default: all). + peak_time_colname, start_time_colname, end_time_colname, location_time_colname : str + Names of the time columns to read / write. + keep_all_flares : bool, optional + If False, flares that did not pass ``filter_function`` are removed from ``data``. + month : optional + Month being processed, used for logging only. + """ + position_results = [] - data["anc_ephemeris_path"] = Column(" " * 500, dtype=str, description="TDB") - data["cpd_path"] = Column(" " * 500, dtype=str, description="TDB") - data["_position_status"] = Column(False, dtype=bool, description="TDB") - data["_position_message"] = Column(" " * 500, dtype=str, description="TDB") to_remove = [] pass_filter = 0 no_ephemeris = 0 @@ -108,12 +260,12 @@ def add_flare_position( day_asp_ephemeris_cache = dict() for i, row in enumerate(data): - if filter_function(row): + peak_time = row[peak_time_colname] + start_time = row[start_time_colname] + end_time = row[end_time_colname] + logger.info(f"Processing flare {i}/{len(data)} at time {start_time} : {end_time} (peak at {peak_time})") + if filter_function(row): # and i < 60: pass_filter += 1 - peak_time = row[peek_time_colname] - start_time = row[start_time_colname] - end_time = row[end_time_colname] - day = peak_time.to_datetime().date() if day in day_asp_ephemeris_cache: @@ -129,72 +281,266 @@ def add_flare_position( if len(anc_res) < 1: logger.warning(f"No ephemeris data found for flare at time {start_time} : {end_time}") - data[i]["_position_message"] = "no ephemeris data found" no_ephemeris += 1 + position_results.append(_empty_flare_position(peak_time, message="no ephemeris data found")) continue - data[i]["anc_ephemeris_path"] = anc_res["path"][0] + _anc_path = str(anc_res["path"][0]) - if start_time.datetime.hour < 2: - start_time = start_time - 2 * u.hour + # widen only the FIDO search window near midnight; keep the true flare start_time + search_start = start_time - 2 * u.hour if start_time.datetime.hour < 2 else start_time cpd_res = fido_client.search( - a.Time(start_time, end_time), a.Instrument.stix, a.stix.DataProduct.sci_xray_cpd + a.Time(search_start, end_time), a.Instrument.stix, a.stix.DataProduct.sci_xray_cpd ) if cpd_res: cpd_res.filter_for_latest_version() url_to_path(cpd_res) if len(cpd_res) < 1: - logger.warning(f"No CPD data found for flare at time {start_time} : {end_time}") - data[i]["_position_message"] = "no CPD data found" + logger.warning(f"No CPD data found for flare at time {search_start} : {end_time}") no_cpd += 1 + position_results.append( + _empty_flare_position(peak_time, anc_path=_anc_path, message="no CPD data found") + ) continue if len(cpd_res) > 1: - logger.debug(f"Many CPD data found for flare at time {start_time} : {end_time}") - # select the best available CPD data file - cpd_res["inc_peak"] = np.logical_and( - peak_time >= cpd_res["Start Time"], peak_time <= cpd_res["End Time"] - ) - cpd_res["file_size"] = [path.stat().st_size for path in cpd_res["path"]] - cpd_res["exposure"] = 0.0 - cpd_res["duration"] = 0.0 - cpd_res["tbins"] = 0 - cpd_res["ebins"] = 0 - cpd_res["estart"] = 0.0 - cpd_res["eend"] = 0.0 - cpd_res["maxcount"] = 0 + logger.debug(f"Many CPD data found for flare at time {search_start} : {end_time}") + # select the best available CPD data file (full product read per candidate) + cpd_res["inc_peak"] = False # flare peak time inside the file + cpd_res["inc_flare"] = 0.0 # % of the flare duration covered by the file + cpd_res["min_dt"] = np.inf # min time resolution (ds) during the flare; inf if no overlap + cpd_res["ebins"] = 0 # number of energy bins (energy table) many_cpd += 1 - for i, path in enumerate(cpd_res["path"]): - header = fits.getheader(path) - header_data = fits.getheader(path, "DATA") - energies = read_qtable(path, "ENERGIES") - cpd_res["maxcount"][i] = header["DATAMAX"] - cpd_res["exposure"][i] = header["XPOSURE"] - cpd_res["tbins"][i] = header_data["NAXIS2"] - cpd_res["ebins"][i] = len(energies) - cpd_res["estart"][i] = energies["e_low"][0].value - cpd_res["eend"][i] = ( - energies["e_high"][-1].value if len(energies) < 31 else energies["e_high"][-2].value - ) - cpd_res["duration"][i] = header["OBT_END"] - header["OBT_BEG"] - + flare_dur_s = (end_time - start_time).sec # flare duration in seconds + for ci, path in enumerate(cpd_res["path"]): + cpd = STIXPYProduct(Path(path)) + # FIDO reports Start/End Time as strings; parse to Time for arithmetic + file_start = Time(cpd_res["Start Time"][ci]) + file_end = Time(cpd_res["End Time"][ci]) + cpd_res["inc_peak"][ci] = file_start <= peak_time <= file_end + # percentage of the flare [start_time, end_time] duration covered by the file + overlap_s = (min(file_end, end_time) - max(file_start, start_time)).sec + cpd_res["inc_flare"][ci] = 100.0 * max(0.0, overlap_s) / flare_dur_s if flare_dur_s > 0 else 0.0 + # shortest time resolution among bins overlapping the flare (shorter is better) + dt_min, _ = cpd_timedel_range(cpd.data["time"], cpd.data["timedel"], start_time, end_time) + cpd_res["min_dt"][ci] = dt_min.to_value(u.ds) if np.isfinite(dt_min.value) else np.inf + cpd_res["ebins"][ci] = len(cpd.energies) + + # best = peak inside, then most flare coverage, then shortest time resolution, + # then most energy bins. shorter min_dt is better, so sort on its negative. # TODO: add more criteria to select the best CPD file - cpd_res.sort(["tbins", "duration"]) + cpd_res["_neg_min_dt"] = -cpd_res["min_dt"] + cpd_res.sort(["inc_peak", "inc_flare", "_neg_min_dt", "ebins"], reverse=True) # cpd_res.pprint() best_cpd_idx = 0 else: one_cpd += 1 best_cpd_idx = 0 - data[i]["cpd_path"] = cpd_res["path"][best_cpd_idx] + _cpd_path = str(cpd_res["path"][best_cpd_idx]) + + try: + stixpy_cpd = STIXPYProduct(Path(_cpd_path)) - # do the calculations with stixpy + # CPD per-bin exposure (ds) over the full flare start..end window (partial overlap ok) + min_exposure, max_exposure = cpd_timedel_range( + stixpy_cpd.data["time"], stixpy_cpd.data["timedel"], start_time, end_time + ) + + time_range = TimeRange(max(peak_time - 20 * u.s, start_time), min(peak_time + 20 * u.s, end_time)) + overlaps = calculate_overlap(stixpy_cpd.time_range, time_range) + if overlaps is None: + logger.warning( + f"CPD data does not cover time range around peak time {time_range.start} to {time_range.end}" + ) + time_range = stixpy_cpd.time_range + contains_peak_time = False + else: + contains_peak_time = True + time_range = overlaps + + _times = stixpy_cpd.data["time"] + _half_bin = stixpy_cpd.data["timedel"] / 2 + mask = (_times + _half_bin >= time_range.start) & (_times - _half_bin <= time_range.end) + data_at_peak = stixpy_cpd.data[mask] + energy_range = [4, 16] * u.keV + + if len(np.unique(data_at_peak["rcr"])) > 1: + logger.warning( + f"Multiple rcr values found for flare at time {time_range.start} : {time_range.end}" + ) + # allow a larger time range for finding a constant rcr sequence + if contains_peak_time: + time_range = TimeRange( + max(peak_time - 40 * u.s, start_time), min(peak_time + 40 * u.s, end_time) + ) + mask = (_times + _half_bin >= time_range.start) & (_times - _half_bin <= time_range.end) + data_at_peak = stixpy_cpd.data[mask] + length, start_idx, rcr = longest_constant_sequence(data_at_peak["rcr"].value) + time_range = TimeRange( + data_at_peak["time"][start_idx], data_at_peak["time"][start_idx + length - 1] + ) + logger.info( + f"Using time range {time_range.start} to {time_range.end} for flare at around {peak_time} with constant rcr={rcr}" + ) + + rcr_at_peak = data_at_peak["rcr"].max() + if rcr_at_peak > 0: + energy_range = [4, 25] * u.keV - data[i]["flare_position"] = SkyCoord(1, 1, frame="icrs", unit="deg") - data[i]["_position_status"] = True - data[i]["_position_message"] = "OK" + _, flare_loc, sidelobe, solo, img_time_range = stx_estimate_flare_location( + stixpy_cpd, time_range, energy_range + ) + + with SphericalScreen(solo, only_off_disk=True): + center_hgs = flare_loc.transform_to( + HeliographicStonyhurst(obstime=img_time_range.center) + ).cartesian + position_results.append( + FlarePositionResult( + anc_path=_anc_path, + cpd_path=_cpd_path, + status=True, + message="OK", + flare_x=center_hgs.x, + flare_y=center_hgs.y, + flare_z=center_hgs.z, + solo_time=img_time_range.center, + duration=img_time_range.seconds, + solo_x=solo.x, + solo_y=solo.y, + solo_z=solo.z, + rcr_at_peak=rcr_at_peak, + sidelobe=sidelobe, + min_exposure=min_exposure, + max_exposure=max_exposure, + ) + ) + except Exception as e: + logger.warning(f"Error calculating flare position for flare at time {start_time} : {end_time}: {e}") + position_results.append( + _empty_flare_position( + peak_time, anc_path=_anc_path, cpd_path=_cpd_path, message=f"Error: {type(e)}" + ) + ) else: to_remove.append(i) + position_results.append( + _empty_flare_position(peak_time, message="flare did not pass the filter function") + ) + + # transpose the per-flare records in one pass (a namedtuple is a tuple), field order must + # match FlarePositionResult + ( + anc_ephemeris_paths, + cpd_paths, + position_statuses, + position_messages, + flare_x, + flare_y, + flare_z, + solo_times, + duration, + solo_x, + solo_y, + solo_z, + rcr_at_peak, + sidelobe, + min_exposure, + max_exposure, + ) = zip(*position_results) + solo_times = Time(solo_times) + + primer = fido_client.baseurl.replace(fido_client.datapath, "") + primer = primer[7:] if primer.startswith("file://") else primer + + data["anc_ephemeris_path"] = [v.replace(primer, "") for v in anc_ephemeris_paths] + data["anc_ephemeris_path"].info.description = "Path to the daily ancillary ephemeris file" + + data["cpd_path"] = [v.replace(primer, "") for v in cpd_paths] + data["cpd_path"].info.description = "Path to the CPD file used for flare location estimation" + + data["_position_status"] = position_statuses + data["_position_status"].info.description = "Status of the flare position calculation" + + data["_position_message"] = position_messages + data["_position_message"].info.description = "Message describing the status of the flare position calculation" + + hgs_coords = SkyCoord( + u.Quantity(flare_x), + u.Quantity(flare_y), + u.Quantity(flare_z), + frame=HeliographicStonyhurst(obstime=solo_times), + representation_type="cartesian", + ) + + solo_coords = SkyCoord( + u.Quantity(solo_x), + u.Quantity(solo_y), + u.Quantity(solo_z), + frame=HeliographicStonyhurst(obstime=solo_times), + representation_type="cartesian", + ) + + # hgc_coords = hgs_coords.transform_to(HeliographicCarrington(obstime=solo_times, observer="Earth")) + hp_coords = hgs_coords.transform_to(Helioprojective(obstime=solo_times, observer="Earth")) + + data["location_hgs"] = hgs_coords + data["location_hgs"].info.description = "Flare location in Heliographic Stonyhurst coordinates" + + data["solo_location_hgs"] = solo_coords + data["solo_location_hgs"].info.description = "SOLO location in Heliographic Stonyhurst coordinates" + + data["solo_sun_distance"] = solo_coords.cartesian.norm().to(u.km) + data["solo_sun_distance"].info.description = "distance of Solar Orbiter to Sun center" + + # add 1-AU-normalized twins of the flux columns (flux ~ 1/r^2); keeps the originals + add_distance_normalized_flux(data) + + data["sidelobes_ratio"] = sidelobe + data["sidelobes_ratio"].info.description = "Ratio of sidelobes in the STIX image used to assess imaging quality" + + data["rcr_at_peak"] = rcr_at_peak + data[ + "rcr_at_peak" + ].info.description = "max rcr level at flare location estimation time range, > 0 attenuator in place" + + data["visible_from_earth"] = FlarePositionMixin.is_visible(hp_coords) + data[ + "visible_from_earth" + ].info.description = "Whether the flare location is visible from Earth (not occulted by the Sun)" + + data[location_time_colname] = solo_times + data[location_time_colname].info.description = "time center used for flare location estimation in UTC" + + data["location_duration"] = duration + data["location_duration"].info.description = "duration of the flare location estimation time range" + + data["min_exposure"] = u.Quantity(min_exposure) # unit u.ds (deciseconds) + data[ + "min_exposure" + ].info.description = "minimum CPD per-bin exposure ds (timedel) over the flare start..end window" + data["max_exposure"] = u.Quantity(max_exposure) + data[ + "max_exposure" + ].info.description = "maximum CPD per-bin exposure ds (timedel) over the flare start..end window" + + ( + time_shift, + disc_size, + ) = zip( + *[ + (Spice.instance.get_earth_solo_time_shift(date=scet), Spice.instance.get_sun_disc_size(date=scet)) + for t in solo_times + for scet in (Spice.instance.datetime_to_scet(t),) + ] + ) + + data["time_shift"] = time_shift + data["time_shift"].info.description = "Time(Sun to Earth) - Time(Sun to S/C)" + + data["sun_disc_size"] = disc_size + data["sun_disc_size"].info.description = "Apparent photospheric solar radius" if not keep_all_flares: data.remove_rows(to_remove) @@ -203,23 +549,95 @@ def add_flare_position( f"Flare position calculated for month {month} with {total_flares} flares, " f"passed filter: {pass_filter} no ephemeris data found for {no_ephemeris} " f"flares, no CPD data found for {no_cpd} flares, many CPD data found for " - f"{many_cpd} flares, one CPD data found for {one_cpd} flares" + f"{many_cpd} flares, one CPD data found for {one_cpd} flares." + f"finally {len(data) - len(to_remove)} flare locations found" + ) + + def on_serialize(self, data): + logger.warning( + "FlarePositionMixin on_serialize called, transforming location columns to ICRS for serialization" + ) + + if "location_hgs" in data.colnames: + icrs = data["location_hgs"].icrs + icrs_coord = SkyCoord(icrs.ra, icrs.dec, icrs.distance, frame="icrs") + col_idx = data.colnames.index("location_hgs") + data.remove_column("location_hgs") + data.add_column(icrs_coord, name="location_icrs", index=col_idx) + if "solo_location_hgs" in data.colnames: + icrs = data["solo_location_hgs"].icrs + icrs_coord = SkyCoord(icrs.ra, icrs.dec, icrs.distance, frame="icrs") + col_idx = data.colnames.index("solo_location_hgs") + data.remove_column("solo_location_hgs") + data.add_column(icrs_coord, name="solo_location_icrs", index=col_idx) + super().on_serialize(data) + + def on_deserialize(self, data, *, location_time_colname=None, **kwargs): + logger.warning( + "FlarePositionMixin on_deserialize called, transforming location columns back to heliographic coordinates" ) + time_col = location_time_colname or self.location_time_colname + if time_col not in data.colnames: + logger.warning(f"on_deserialize: column '{time_col}' not found, skipping location transform") + else: + obstime = Time(data[time_col]) + if "location_icrs" in data.colnames: + data["location_hgs"] = data["location_icrs"].transform_to(HeliographicStonyhurst(obstime=obstime)) + if "solo_location_icrs" in data.colnames: + data["solo_location_hgs"] = data["solo_location_icrs"].transform_to( + HeliographicStonyhurst(obstime=obstime) + ) + + super().on_deserialize(data, **kwargs) + + @classmethod + def is_visible(cls, coord): + """ + Returns whether the coordinate is on the visible side of the Sun. + This function is a modified version of PR#7118 + """ + + coord = coord.make_3d() + data = coord.cartesian + data_to_sun = coord.observer.radius * CartesianRepresentation(1, 0, 0) - data + + is_behind = data.x < 0 + # print(data.x.to(u.AU)) + is_beyond_limb = np.sqrt(1 - (data.x / data.norm()) ** 2) > coord.rsun / coord.observer.radius + # is_above_surface = data_to_sun.norm() >= coord.rsun + + is_on_near_side = data.dot(data_to_sun) >= 0 + + return is_behind | is_beyond_limb | (is_on_near_side) + +class FlareSOOPMixin(_SerializeMixin): + """Mixin adding the Solar Orbiter observing-campaign (SOOP) columns to a flare list. -class FlareSOOPMixin: - """_summary_""" + For each flare it queries `~stixcore.soop.manager.SOOPManager` for the campaign active at + the flare peak and stores its encoded type, instance id and name (``soop_encoded_type``, + ``soop_id``, ``soop_type``). Used by `~stixcore.products.level3.flarelist.FlarelistSDC`. + """ @classmethod def add_soop( - self, data, *, peek_time_colname="peak_UTC", start_time_colname="start_UTC", end_time_colname="end_UTC" + self, data, *, peak_time_colname="peak_UTC", start_time_colname="start_UTC", end_time_colname="end_UTC" ): + """Add the SOOP campaign columns for every flare in ``data`` (in place). + + Parameters + ---------- + data : `~astropy.table.QTable` + The flare list. + peak_time_colname, start_time_colname, end_time_colname : str + Names of the time columns; the campaign is looked up at ``peak_time_colname``. + """ soop_encoded_type = list() soop_id = list() soop_type = list() for row in data: - soops = SOOPManager.instance.find_soops(start=row[peek_time_colname]) + soops = SOOPManager.instance.find_soops(start=row[peak_time_colname]) if soops: soop = soops[0] soop_encoded_type.append(soop.encodedSoopType) @@ -234,14 +652,22 @@ def add_soop( data["soop_id"] = Column(soop_id, dtype=str, description="SOOP ID") data["soop_type"] = Column(soop_type, dtype=str, description="name of the SOOP campaign") + # def on_serialize(self, data): + # logger.info("FlareSOOPMixin on_serialize called, but no special handling implemented for SOOP data") + # super().on_serialize(data) + + # def on_deserialize(self, data, **kwargs): + # logger.info("FlareSOOPMixin on_deserialize called, but no special handling implemented for SOOP data") + # super().on_deserialize(data, **kwargs) + -class FlarePeekPreviewMixin: - """Mixin class to add peek preview images to flare list products. - This class provides a method to generate and add peek preview images +class FlarePeakPreviewMixin: + """Mixin class to add peak preview images to flare list products. + This class provides a method to generate and add peak preview images to the flare list data. The images are generated based on the flare's peak time, start time, and end time, using the STIXPy library for visibility calculations and image reconstruction. - The generated images are stored in the 'peek_preview_path' column of the data. + The generated images are stored in the 'peak_preview_path' column of the data. The method also updates the status and message columns to indicate the success or failure of the image generation process. @@ -249,7 +675,7 @@ class FlarePeekPreviewMixin: """ @classmethod - def add_peek_preview( + def add_peak_preview( cls, data, energies, @@ -257,7 +683,7 @@ def add_peek_preview( fido_client: STIXClient, img_processor, *, - peek_time_colname="peak_UTC", + peak_time_colname="peak_UTC", start_time_colname="start_UTC", end_time_colname="end_UTC", anc_ephemeris_path_colname="anc_ephemeris_path", @@ -266,17 +692,20 @@ def add_peek_preview( keep_all_flares=True, month=None, ): - data["peek_preview_path"] = Column(" " * 500, dtype=str, description="TDB") - data["preview_start_UTC"] = [Time(d, format="isot", scale="utc") for d in data[peek_time_colname]] - data["preview_end_UTC"] = [Time(d, format="isot", scale="utc") for d in data[peek_time_colname]] - data["_peek_preview_status"] = Column(False, dtype=bool, description="TDB") - data["_peek_preview_message"] = Column(" " * 500, dtype=str, description="TDB") + """Reconstruct and attach per-flare peak-preview CLEAN images (4-20 and 20-120 keV) for + each flare in ``data``, using its selected CPD file, writing one + `~stixcore.products.level3.flarelistproduct.PeakPreviewImage` product per flare.""" + data["peak_preview_path"] = Column(" " * 500, dtype=str, description="TDB") + data["preview_start_UTC"] = [Time(d, format="isot", scale="utc") for d in data[peak_time_colname]] + data["preview_end_UTC"] = [Time(d, format="isot", scale="utc") for d in data[peak_time_colname]] + data["_peak_preview_status"] = Column(False, dtype=bool, description="TDB") + data["_peak_preview_message"] = Column(" " * 500, dtype=str, description="TDB") to_remove = [] products = [] images = 0 for i, row in enumerate(data): - peak_time = row[peek_time_colname] + peak_time = row[peak_time_colname] row[start_time_colname] row[end_time_colname] @@ -286,8 +715,8 @@ def add_peek_preview( status = False message = "" - peek_preview_start = row[peek_time_colname] - peek_preview_end = row[peek_time_colname] + peak_preview_start = row[peak_time_colname] + peak_preview_end = row[peak_time_colname] if anc_ephemeris_path.exists() and cpd_path.exists(): try: @@ -295,18 +724,18 @@ def add_peek_preview( # do the imaging with stixpy preview_data = data[i : i + 1] - del preview_data["peek_preview_path"] - del preview_data["_peek_preview_status"] - del preview_data["_peek_preview_message"] + del preview_data["peak_preview_path"] + del preview_data["_peak_preview_status"] + del preview_data["_peak_preview_message"] - peek_preview_start = row[peek_time_colname] - 10 * u.s - peek_preview_end = row[peek_time_colname] + 10 * u.s + peak_preview_start = row[peak_time_colname] - 10 * u.s + peak_preview_end = row[peak_time_colname] + 10 * u.s - preview_data["preview_start_UTC"] = peek_preview_start - preview_data["preview_end_UTC"] = peek_preview_end + preview_data["preview_start_UTC"] = peak_preview_start + preview_data["preview_end_UTC"] = peak_preview_end cpd_sci = STIXPYProduct(cpd_path) - time_range_sci = [peek_preview_start, peek_preview_end] + time_range_sci = [peak_preview_start, peak_preview_end] maps = [] for energy_range in [[4, 20], [20, 120]] * u.keV: # flare_position = preview_data['flare_position'][0] @@ -388,7 +817,7 @@ def add_peek_preview( maps.append((map_with_erange, header)) - ppi = PeekPreviewImage( + ppi = PeakPreviewImage( control=QTable(), data=preview_data, month=month, @@ -407,18 +836,18 @@ def add_peek_preview( status = False message = str(e) - data[i]["preview_start_UTC"] = peek_preview_start - data[i]["preview_end_UTC"] = peek_preview_end - data[i]["peek_preview_path"] = "test" - data[i]["_peek_preview_status"] = status - data[i]["_peek_preview_message"] = message + data[i]["preview_start_UTC"] = peak_preview_start + data[i]["preview_end_UTC"] = peak_preview_end + data[i]["peak_preview_path"] = "test" + data[i]["_peak_preview_status"] = status + data[i]["_peak_preview_message"] = message if not keep_all_flares: data.remove_rows(to_remove) logger.info( f"Flare images created for month {month} with {len(data)} flares, " - f"{len(products)} peek previews created, with total {images} images" + f"{len(products)} peak previews created, with total {images} images" ) return products @@ -475,12 +904,19 @@ def enhance_from_product(self, in_prod: GenericProduct): class FlarelistSDC(FlareList, FlareSOOPMixin): - """Flarelist product class for StixDataCenter flares. - - In L3 product format. + """Base SDC flare-list product (``NAME="sdc"``, ssid 2, level L3). + + Mirrors the operational STIX Data Center flare list and enriches every flare with the + quicklook peak / quiet-time background counts and fluxes (added by + `~stixcore.io.FlareListManager.SDCFlareListManager`) and the SOOP campaign (via + `~stixcore.products.level3.flarelist.FlareSOOPMixin`). It is the first level of the SDC + chain: `~stixcore.products.level3.flarelist.FlarelistSDC` -> + `~stixcore.products.level3.flarelist.FlarelistSDCLocation` -> + `~stixcore.products.level3.flarelist.FlarelistSDCLocationImage`. See + :doc:`/products/flarelist`. """ - PRODUCT_PROCESSING_VERSION = 2 + PRODUCT_PROCESSING_VERSION = 4 NAME = "sdc" def __init__(self, *, service_type=0, service_subtype=0, ssid=2, data, month, **kwargs): @@ -530,20 +966,25 @@ def is_datasource_for(cls, *, service_type, service_subtype, ssid, **kwargs): return kwargs["level"] == "L3" and service_type == 0 and service_subtype == 0 and ssid == 2 -class FlarelistSDCLoc(FlarelistSDC, FlarePositionMixin): - """Flarelist product class for StixDataCenter flares. +class FlarelistSDCLocation(FlarelistSDC, FlarePositionMixin): + """SDC flare list with a STIX-derived flare location (``NAME="sdcloc"``, ssid 3, level L3). - In ANC product format. + Extends `~stixcore.products.level3.flarelist.FlarelistSDC` with the flare position + (and SOLO position/distance, imaging-quality metric and 1-AU-normalized fluxes) via + `~stixcore.products.level3.flarelist.FlarePositionMixin`. Only flares above + ``[Processing] flarelist_sdc_min_count`` peak counts are located. See + :doc:`/products/flarelist`. """ - PRODUCT_PROCESSING_VERSION = 2 + PRODUCT_PROCESSING_VERSION = 4 NAME = "sdcloc" def __init__(self, *, service_type=0, service_subtype=0, ssid=3, data, month, **kwargs): super().__init__(service_type=0, service_subtype=0, ssid=3, data=data, month=month, **kwargs) - self.name = FlarelistSDCLoc.NAME + self.name = FlarelistSDCLocation.NAME self.ssid = 3 + self.location_time_colname = "location_time_UTC" def enhance_from_product(self, in_prod: GenericProduct): pass @@ -558,10 +999,10 @@ def add_flare_position(cls, data, fido_client: STIXClient, *, month=None): data, fido_client, filter_function=cls.filter_flare_function, - peek_time_colname="peak_UTC", + peak_time_colname="peak_UTC", start_time_colname="start_UTC", end_time_colname="end_UTC", - keep_all_flares=False, + keep_all_flares=True, month=month, ) @@ -570,33 +1011,36 @@ def is_datasource_for(cls, *, service_type, service_subtype, ssid, **kwargs): return kwargs["level"] == "L3" and service_type == 0 and service_subtype == 0 and ssid == 3 -class FlarelistSDCLocImg(FlarelistSDCLoc, FlarePeekPreviewMixin): - """Flarelist product class for StixDataCenter flares. +class FlarelistSDCLocationImage(FlarelistSDCLocation, FlarePeakPreviewMixin): + """Located SDC flare list plus peak-preview images (``NAME="sdclocimg"``, ssid 4, level L3). - In ANC product format. + Extends `~stixcore.products.level3.flarelist.FlarelistSDCLocation` with per-flare + peak-preview CLEAN images (`~stixcore.products.level3.flarelistproduct.PeakPreviewImage`), + reconstructed on the same CPD file used for the flare location, via + `~stixcore.products.level3.flarelist.FlarePeakPreviewMixin`. See :doc:`/products/flarelist`. """ - PRODUCT_PROCESSING_VERSION = 2 + PRODUCT_PROCESSING_VERSION = 4 NAME = "sdclocimg" def __init__(self, *, service_type=0, service_subtype=0, ssid=4, data, month, **kwargs): super().__init__(service_type=0, service_subtype=0, ssid=4, data=data, month=month, **kwargs) - self.name = FlarelistSDCLocImg.NAME + self.name = FlarelistSDCLocationImage.NAME self.ssid = 4 def enhance_from_product(self, in_prod: GenericProduct): pass @classmethod - def add_peek_preview(cls, data, energies, parent, fido_client: STIXClient, img_processor, *, month=None): - super().add_peek_preview( + def add_peak_preview(cls, data, energies, parent, fido_client: STIXClient, img_processor, *, month=None): + super().add_peak_preview( data, energies, parent, fido_client, img_processor, - peek_time_colname="peak_UTC", + peak_time_colname="peak_UTC", start_time_colname="start_UTC", end_time_colname="end_UTC", anc_ephemeris_path_colname="anc_ephemeris_path", @@ -667,7 +1111,7 @@ def is_datasource_for(cls, *, service_type, service_subtype, ssid, **kwargs): return kwargs["level"] == "L3" and service_type == 0 and service_subtype == 0 and ssid == 6 -class FlarelistSCLoc(FlarelistSC, FlarePositionMixin): +class FlarelistSCLocation(FlarelistSC, FlarePositionMixin): """Flarelist product class for STIXCore flares. In L3 product format. @@ -679,8 +1123,9 @@ class FlarelistSCLoc(FlarelistSC, FlarePositionMixin): def __init__(self, *, service_type=0, service_subtype=0, ssid=7, data, month, **kwargs): super().__init__(service_type=0, service_subtype=0, ssid=7, data=data, month=month, **kwargs) - self.name = FlarelistSCLoc.NAME + self.name = FlarelistSCLocation.NAME self.ssid = 7 + self.peak_time_colname = "peak_UTC" def enhance_from_product(self, in_prod: GenericProduct): pass @@ -695,7 +1140,7 @@ def add_flare_position(cls, data, fido_client: STIXClient, *, month=None): data, fido_client, filter_function=cls.filter_flare_function, - peek_time_colname="peak_UTC", + peak_time_colname="peak_UTC", start_time_colname="start_UTC", end_time_colname="end_UTC", keep_all_flares=False, @@ -707,7 +1152,7 @@ def is_datasource_for(cls, *, service_type, service_subtype, ssid, **kwargs): return kwargs["level"] == "L3" and service_type == 0 and service_subtype == 0 and ssid == 7 -class FlarelistSCLocImg(FlarelistSCLoc, FlarePeekPreviewMixin): +class FlarelistSCLocationImage(FlarelistSCLocation, FlarePeakPreviewMixin): """Flarelist product class for StixCore flares. In ANC product format. @@ -719,21 +1164,21 @@ class FlarelistSCLocImg(FlarelistSCLoc, FlarePeekPreviewMixin): def __init__(self, *, service_type=0, service_subtype=0, ssid=8, data, month, **kwargs): super().__init__(service_type=0, service_subtype=0, ssid=8, data=data, month=month, **kwargs) - self.name = FlarelistSCLocImg.NAME + self.name = FlarelistSCLocationImage.NAME self.ssid = 8 def enhance_from_product(self, in_prod: GenericProduct): pass @classmethod - def add_peek_preview(cls, data, energies, parent, fido_client: STIXClient, img_processor, *, month=None): - super().add_peek_preview( + def add_peak_preview(cls, data, energies, parent, fido_client: STIXClient, img_processor, *, month=None): + super().add_peak_preview( data, energies, parent, fido_client, img_processor, - peek_time_colname="peak_UTC", + peak_time_colname="peak_UTC", start_time_colname="start_UTC", end_time_colname="end_UTC", anc_ephemeris_path_colname="anc_ephemeris_path", @@ -746,3 +1191,44 @@ def add_peek_preview(cls, data, energies, parent, fido_client: STIXClient, img_p @classmethod def is_datasource_for(cls, *, service_type, service_subtype, ssid, **kwargs): return kwargs["level"] == "L3" and service_type == 0 and service_subtype == 0 and ssid == 8 + + +def longest_constant_sequence(state_array): + """Find the longest sequence where state is constant. + In case of equal length, prefer the one with the lower state value.""" + if len(state_array) == 0: + return 0, None, None + + max_length = 0 + max_state = None + max_start_idx = None + current_idx = 0 + + for state, group in groupby(state_array): + length = len(list(group)) + # Update if longer, OR if equal length but lower state value + if length > max_length or (length == max_length and (max_state is None or state < max_state)): + max_length = length + max_state = state + max_start_idx = current_idx + current_idx += length + + return max_length, max_start_idx, max_state + + +def calculate_overlap(range1, range2): + """Calculate the overlap between two TimeRanges. + Returns the overlap duration and the overlapping TimeRange, or None if no overlap.""" + + # Check if they intersect first + if not range1.intersects(range2): + return None + + # Calculate intersection boundaries + overlap_start = max(range1.start, range2.start) + overlap_end = min(range1.end, range2.end) + + # Create the overlapping TimeRange + overlap_range = TimeRange(overlap_start, overlap_end) + + return overlap_range diff --git a/stixcore/products/level3/flarelistproduct.py b/stixcore/products/level3/flarelistproduct.py index ae7cb0d4..0729ae50 100644 --- a/stixcore/products/level3/flarelistproduct.py +++ b/stixcore/products/level3/flarelistproduct.py @@ -6,7 +6,7 @@ from stixcore.time.datetime import SCETime, SCETimeRange from stixcore.util.logging import get_logger -__all__ = ["FlareListProduct", "PeekPreviewImage"] +__all__ = ["FlareListProduct", "PeakPreviewImage"] logger = get_logger(__name__) @@ -19,20 +19,41 @@ class FlareListProduct(GenericProduct, L3Mixin): @classmethod def from_timerange(cls, timerange: SCETimeRange, *, flarelistparent: str = ""): + """Create a product for the given time range and parent flare list. + + Parameters + ---------- + timerange : `~stixcore.time.datetime.SCETimeRange` + The time range the product covers. + flarelistparent : str, optional + Identifier of the parent flare list. + + Notes + ----- + Not yet implemented (stub). + """ pass -class PeekPreviewImage(FlareListProduct): +class PeakPreviewImage(FlareListProduct): + """Per-flare peak-preview image product (``Name="peakpreviewimg"``, ssid 5, level L3). + + Holds the CLEAN back-projection `~sunpy.map.Map` images reconstructed around a flare peak + (see `~stixcore.products.level3.flarelist.FlarePeakPreviewMixin`), together with the parent + flare-list product(s) they were derived from. One instance is written per flare. + """ + PRODUCT_PROCESSING_VERSION = 1 Level = "L3" Type = "sci" - Name = "peekpreviewimg" + Name = "peakpreviewimg" def __init__(self, control, data, energy, maps, parents, *, product_name_suffix="", **kwargs): + """Build the product from its tables, reconstructed maps and parent product(s).""" super().__init__(service_type=0, service_subtype=0, ssid=5, control=control, data=data, energy=energy, **kwargs) - self.name = f"{PeekPreviewImage.Name}-{product_name_suffix}" - self.level = PeekPreviewImage.Level - self.type = PeekPreviewImage.Type + self.name = f"{PeakPreviewImage.Name}-{product_name_suffix}" + self.level = PeakPreviewImage.Level + self.type = PeakPreviewImage.Type self.energy = energy self.maps = maps self.parents = parents @@ -41,14 +62,17 @@ def __init__(self, control, data, energy, maps, parents, *, product_name_suffix= @property def parent(self): + """The parent flare-list product(s) this image was derived from, as a 1-d array.""" return np.atleast_1d(self.parents) @property def utc_timerange(self): + """The preview time range in UTC (`~sunpy.time.TimeRange`).""" return TimeRange(self.data["preview_start_UTC"][0], self.data["preview_end_UTC"][0]) @property def scet_timerange(self): + """The preview time range in spacecraft elapsed time (approximated via Spice).""" tr = self.utc_timerange logger.warning( "scet_timerange will be approximated using Spice. Better to work with utc_timerange property to avoid automatic time conversion" @@ -58,8 +82,10 @@ def scet_timerange(self): return SCETimeRange(start=start, end=end) def split_to_files(self): + """Yield the product(s) to write; a peak-preview image is always a single file.""" return [self] @classmethod def is_datasource_for(cls, *, service_type, service_subtype, ssid, **kwargs): - return kwargs["level"] == PeekPreviewImage.Level and service_type == 0 and service_subtype == 0 and ssid == 5 + """Whether this class handles the given L3 / ssid-5 product identifiers.""" + return kwargs["level"] == PeakPreviewImage.Level and service_type == 0 and service_subtype == 0 and ssid == 5 diff --git a/stixcore/products/level3/processing.py b/stixcore/products/level3/processing.py new file mode 100644 index 00000000..722d8282 --- /dev/null +++ b/stixcore/products/level3/processing.py @@ -0,0 +1,428 @@ +######################################################### +### Temporary code should be come from stixpy finally ### +######################################################### + +import numpy as np +import stixpy.calibration.visibility +import stixpy.coordinates.transforms +import sunpy.map +import sunpy.sun.constants as sun_const +import sunpy.time +import xrayvision.imaging +from stixpy.calibration.visibility import ( + calibrate_visibility, + create_meta_pixels, + create_visibility, +) +from stixpy.coordinates.frames import STIXImaging +from stixpy.coordinates.transforms import get_hpc_info +from sunpy.coordinates import HeliographicStonyhurst, SphericalScreen, frames +from sunpy.time import TimeRange +from xrayvision.imaging import vis_to_image + +from astropy import units as u +from astropy.coordinates import SkyCoord +from astropy.coordinates.representation import CartesianRepresentation +from astropy.time import Time + +__all__ = [ + "get_rsun_obs", + "get_distance_off_limb", + "generate_blank_map", + "is_visible", + "stx_estimate_flare_location", + "calculate_sidelobes_ratio", +] + + +def get_rsun_obs(observer): + """ + Get the observed radius of the Sun from an observer location. + """ + + rsun_obs = ((sun_const.radius / (observer.spherical.distance - sun_const.radius)).decompose() * u.radian).to( + u.arcsec + ) + return rsun_obs + + +def get_distance_off_limb(coord): + theta_x = coord.Tx + theta_y = coord.Ty + rsun_obs = get_rsun_obs(coord.observer) + distance_off_limb = np.sqrt(theta_x**2 + theta_y**2) - rsun_obs + distance_r_sun = np.sqrt(theta_x**2 + theta_y**2) / rsun_obs + + return distance_off_limb, distance_r_sun + + +def generate_blank_map(date_obs, observer): + """ + Given a date and an observer create a blank map + + """ + data = np.full((12, 12), np.nan) + + # Define a reference coordinate and create a header using sunpy.map.make_fitswcs_header + skycoord = SkyCoord(0 * u.arcsec, 0 * u.arcsec, frame=frames.Helioprojective(observer=observer, obstime=date_obs)) + + # Scale set to the following for solar limb to be in the field of view + header = sunpy.map.make_fitswcs_header(data, skycoord, scale=[600, 600] * u.arcsec / u.pixel) + + # Use sunpy.map.Map to create the blank map + blank_map = sunpy.map.Map(data, header) + return blank_map + + +def is_visible(coord): + """ + Returns whether the coordinate is on the visible side of the Sun. + This function is a modified version of PR#7118 + """ + + coord = coord.make_3d() + data = coord.cartesian + data_to_sun = coord.observer.radius * CartesianRepresentation(1, 0, 0) - data + + is_behind = data.x < 0 + # print(data.x.to(u.AU)) + is_beyond_limb = np.sqrt(1 - (data.x / data.norm()) ** 2) > coord.rsun / coord.observer.radius + # is_above_surface = data_to_sun.norm() >= coord.rsun + + is_on_near_side = data.dot(data_to_sun) >= 0 + + return is_behind | is_beyond_limb | (is_on_near_side) + + +def stx_estimate_flare_location(cpd_sci, time_range, energy_range): + """ + Estimate the flare location using STIX imaging data. + + This function processes the imaging data from the STIX instrument on Solar Orbiter to estimate the location of a solar flare. + It is based on the IDL software `stx_estimate_flare_location`. + + It creates back-projected images in both STIX imaging coordinates and Helioprojective coordinates, and finds the maximum location of the pixel. + + Optionally, it plots the results showing the maximum pixel locations in both coordinate systems. + + Parameters + ---------- + cpd_sci : CPDProduct + the STIX pixel data product. + time_range : `sunpy.time.TimeRange` + The time range over which to estimate the flare location. + energy_range : `astropy.units.Quantity` + The energy range (e.g., in keV) for the analysis. + + Returns + ------- + max_stix : `astropy.coordinates.SkyCoord` + The estimated flare location in STIX imaging coordinates. + max_hpc : `astropy.coordinates.SkyCoord` + The estimated flare location in Helioprojective Cartesian coordinates. + + Notes + ----- + The function involves the following steps: + - Reading STIX pixel data and generating meta pixels for a given time and energy range. + - Creating visibility data from the meta pixels. + - Obtaining solar observer coordinates and converting them to the Heliographic Stonyhurst frame. + - Creating a back-projected image from the visibility data. + - Transforming the coordinates of the maximum pixel in the image to Helioprojective coordinates. + + """ + + meta_pixels_sci = create_meta_pixels( + cpd_sci, + time_range=[time_range.start, time_range.end], + energy_range=energy_range, + flare_location=[0, 0] * u.arcsec, + no_shadowing=True, + ) + + # create visibilities + vis = create_visibility(meta_pixels_sci) + vis_tr = TimeRange(vis.meta["time_range"]) + + roll, solo_xyz, pointing = get_hpc_info(vis_tr.start, vis_tr.end) + solo = frames.HeliographicStonyhurst(*solo_xyz, obstime=vis_tr.center, representation_type="cartesian") + + center_map = SkyCoord(0 * u.arcsec, 0 * u.arcsec, frame=frames.Helioprojective(observer=solo, obstime=solo.obstime)) + center_coord = center_map.transform_to(STIXImaging(obstime=vis_tr.start, obstime_end=vis_tr.end, observer=solo)) + + # get calibrated visibilities - use center of Sun as phase center + cal_vis = calibrate_visibility(vis, flare_location=center_coord) + + # order by sub-collimator e.g. 10a, 10b, 10c, 9a, 9b, 9c .... + isc_10_7 = [3, 20, 22, 16, 14, 32, 21, 26, 4, 24, 8, 28] + idx = np.argwhere(np.isin(cal_vis.meta["isc"], isc_10_7)).ravel() + + # only use subcolimators 7 - 10 + vis10_7 = cal_vis[idx] + + # set up image size + imsize = [512, 512] * u.pixel + + # to make sure the full Sun is within FOV - the 2.6 is taken to be the same as the IDL software + pixel = get_rsun_obs(solo) * 2.6 / imsize + + # get back projection image + bp_image = vis_to_image(vis10_7, imsize, pixel_size=pixel) + + # Make a sunpy map from the bp_image, in STIX imaging frame + header = sunpy.map.make_fitswcs_header( + bp_image, center_coord, telescope="STIX", observatory="Solar Orbiter", scale=pixel + ) + fd_bp_map = sunpy.map.Map((bp_image, header)) + + sidelobes_ratio = calculate_sidelobes_ratio(fd_bp_map) + + # Make a sunpy map from the bp_image, in HPC from STIX observer + hpc_ref = center_coord.transform_to(frames.Helioprojective(observer=solo, obstime=vis_tr.center)) + header_hp = sunpy.map.make_fitswcs_header(bp_image, hpc_ref, scale=pixel, rotation_angle=90 * u.deg + roll) + hp_map = sunpy.map.Map((bp_image, header_hp)) + + # get the position of the max pixel + max_pixel = np.argwhere(fd_bp_map.data == fd_bp_map.data.max()).ravel() * u.pixel + # get the world coord of the max pixel - (note WCS axes and array are reversed) + max_stix = fd_bp_map.pixel_to_world(max_pixel[1], max_pixel[0]) + + # get the coordinate of the max pixel in HPC - if coordinate is off limb, assume spherical screen for transform + with SphericalScreen(hp_map.observer_coordinate, only_off_disk=True): + max_hpc = max_stix.transform_to(hp_map.coordinate_frame) + + vis_time_range = TimeRange(vis.meta["time_range"][0], vis.meta["time_range"][1]) + return max_stix, max_hpc, sidelobes_ratio, solo, vis_time_range + + +def calculate_sidelobes_ratio(bp_nat_map, threshold=200 * u.arcsec): + """ + + Calculate the sidelobes ratio for a back-projected image map. + + The sidelobes ratio is a measure of the relative strength of the sidelobes compared to the main peak of the image. + This ratio helps determine the reliability of the flare location. A sidelobes ratio close to or above 0.9 suggests + that the flare location may not be reliable due to significant sidelobe interference. + + Parameters + ---------- + bp_nat_map : `sunpy.map.Map` + The back-projected image map (in natural units) to analyze. This map is typically generated from visibility data + and contains the image of the flare. + threshold : `astropy.units.Quantity`, optional + The angular separation threshold (in arcseconds) around the peak within which sidelobes are excluded from the calculation. + Default is 200 arcseconds. + + Returns + ------- + sidelobes_ratio : float + The ratio of the maximum sidelobe intensity to the peak intensity in the back-projected image. + A value close to 1 indicates significant sidelobes, potentially making the flare location unreliable. + + Notes + ----- + - This is based upon the methodology in the STIX-GSW IDL software. + """ + max_bp = np.max(bp_nat_map.data) + ind_max = np.unravel_index(np.argmax(bp_nat_map.data, axis=None), bp_nat_map.data.shape) + max_bp_coord = bp_nat_map.pixel_to_world(ind_max[1] * u.pix, ind_max[0] * u.pix) + + yy, xx = np.indices(bp_nat_map.data.shape) + world_coords = bp_nat_map.pixel_to_world(xx * u.pix, yy * u.pix) + + distance_wrt_peak = world_coords.separation(max_bp_coord) + + bp_image_masked = np.copy(bp_nat_map.data) + mask = distance_wrt_peak <= threshold + bp_image_masked[mask] = 0 + + sidelobes_ratio = np.max(bp_image_masked) / max_bp + + return sidelobes_ratio + + +def _construct_stix_calibrated_visibilities( + cpd_sci, + flare_location, + time_range=None, + energy_range=None, + subcollimators=None, + cpd_bkg=None, + time_range_bkg=None, + **kwargs, +): + """ + Constructs calibrated STIX visibilities from STIX compressed pixel data. + + Extra kwargs are passed to `stixpy.calibration.visibility.create_meta_pixels` + + Parameters + ---------- + cpd_sci: `stixpy.product.Product` + The STIX pixel data. Assumed to be already background subtracted. + flare_location: `astropy.coordinates.SkyCoord` + The flare location. Frame must be convertible to `stixpy.coordinates.transdforms.STIXImaging`. + time_range: `sunpy.time.TimeRange` (optional) + The time range over which to estimate the flare location. + Default is all times in cpd_sci. + energy_range: `astropy.units.Quantity` in spectral units (optional) + Length-2 quantity giving the lower and upper bounds of the energy range to use for imaging. + Default is all finite energies in cpd_sci. + subcollimators: `iterable` of `str` + The labels of the subcollimators to use in estimating the flare locations, e.g. + ``['10a', '10b', '10c',...]`` + Default is all subcollimators in cpd_sci. + cpd_bkg: stixpy.product.Product` (optional) + The background to subtract from the pixel data before determining the flare location. + If None and time_range_bkg is also None, no background is subtracted. + time_range_bkg: `sunpy.time.TimeRange` + The time range within cpd_bkg to use for the background subtraction. + If None, entire time range of cpd_bkg is used to determine background. + If not None, and cpd_bkg is None, the background is determined from this time range + applied to cpd_sci. + + Returns + ------- + vis: `xrayvision.visibility.Visibilities` + The calibrated STIX visibilities. + """ + # Sanitze inputs. + if time_range is None: + time_range = cpd_sci.time_range + times = Time([time_range.start, time_range.end]) + if energy_range is None: + energy_range = u.Quantity( + [ + cpd_sci.energies["e_low"][np.isfinite(cpd_sci.energies["e_low"])][0], + cpd_sci.energies["e_high"][np.isfinite(cpd_sci.energies["e_high"])][-1], + ] + ) + no_shadowing = kwargs.pop("no_shadowing", True) + # Generate meta_pixels from pixel_data. + meta_pixels = stixpy.calibration.visibility.create_meta_pixels( + cpd_sci, + time_range=times, + energy_range=energy_range, + flare_location=flare_location, + no_shadowing=no_shadowing, + **kwargs, + ) + # Subtract background if background pixel data provided. + if cpd_bkg is None and time_range_bkg is not None: + cpd_bkg = cpd_sci + if cpd_bkg is not None: + if time_range_bkg is None: + time_range_bkg = cpd_bkg.time_range + times_bkg = Time([time_range_bkg.start, time_range_bkg.end]) + meta_pixels_bkg = stixpy.calibration.visibility.create_meta_pixels( + cpd_bkg, + time_range=times_bkg, + energy_range=energy_range, + flare_location=[0, 0] * u.arcsec, + no_shadowing=no_shadowing, + **kwargs, + ) + meta_pixels = _subtract_background_from_stix_meta_pixels(meta_pixels, meta_pixels_bkg) + # Generate and calibrate visibilities. + vis = stixpy.calibration.visibility.create_visibility(meta_pixels) + vis = stixpy.calibration.visibility.calibrate_visibility(vis, flare_location=SkyCoord(flare_location)) + if subcollimators is not None: + idx_subcol = np.argwhere(np.isin(vis.meta["vis_labels"], subcollimators)).ravel() + vis = vis[idx_subcol] + return vis + + +def _estimate_stix_flare_location( + cpd_sci, time_range=None, energy_range=None, subcollimators=None, cpd_bkg=None, time_range_bkg=None +): + """ + Estimates flare location from STIX compressed pixel data. + + FLare location is assumed to be the location of the brightest pixel in the backprojection + map calculated from the input STIX pixel data. + + Parameters + ---------- + cpd_sci: `stixpy.product.Product` + The STIX pixel data. Assumed to be already background subtracted. + time_range: `sunpy.time.TimeRange` (optional) + The time range over which to estimate the flare location. + Default is all times in cpd_sci. + energy_range: `astropy.units.Quantity` in spectral units (optional) + Length-2 quantity giving the lower and upper bounds of the energy range to use for imaging. + Default is all finite energies in cpd_sci. + subcollimators: `iterable` of `str` (optional) + The labels of the subcollimators to include in the output Visibilities object, e.g. + ``['10a', '10b', '10c',...]`` + Default is all subcollimators in cpd_sci + cpd_bkg: stixpy.product.Product` (optional) + The background to subtract from the pixel data before determining the flare location. + Passed to construct_stix_calibrated_visibilities(). + time_range_bkg: `sunpy.time.TimeRange` + The time range within cpd_bkg to use for the background subtraction. + Passed to construct_stix_calibrated_visibilities(). + + Returns + ------- + flare_loc: `astropy.coordinates.SkyCoord` + The estimated flare location. + map_bp: `sunpy.map.Map` + The backprojection map from whose brightest pixel the flare location was estimated. + """ + if time_range is None: + time_range = cpd_sci.time_range + if subcollimators is None: + subcollimators = ["10a", "10b", "10c", "9a", "9b", "9c", "8a", "8b", "8c", "7a", "7b", "7c"] + # Construct STIX location and centre of FOV. + roll, solo_xyz, pointing = stixpy.coordinates.transforms.get_hpc_info(time_range.start, time_range.end) + solo = HeliographicStonyhurst(*solo_xyz, obstime=time_range.center, representation_type="cartesian") + fov_centre = STIXImaging( + 0 * u.arcsec, 0 * u.arcsec, obstime=time_range.start, obstime_end=time_range.end, observer=solo + ) + # Generate calibrated visibilities using coarser subcollimators. + vis = _construct_stix_calibrated_visibilities( + cpd_sci, fov_centre, time_range=time_range, energy_range=energy_range, subcollimators=subcollimators + ) + # Produce backprojected image and find brightest pixel. Use this for flare location. + imsize = [512, 512] * u.pixel # number of pixels of the map to reconstruct + plate_scale = [10, 10] * u.arcsec / u.pixel # pixel size in arcsec + bp_image = xrayvision.imaging.vis_to_image(vis, imsize, pixel_size=plate_scale) + max_idx = np.argwhere(bp_image == bp_image.max()).ravel() + # Calculate WCS for backprojected image in STIXImaging and HPC frames. + # Recalculate STIX HPC info as slightly different times will have been used + # than input times due to onboard STIX time binning. + vis_tr = sunpy.time.TimeRange(vis.meta["time_range"]) + roll, solo_xyz, pointing = stixpy.coordinates.transforms.get_hpc_info(vis_tr.start, vis_tr.end) + solo = HeliographicStonyhurst(*solo_xyz, obstime=vis_tr.center, representation_type="cartesian") + coord = STIXImaging(0 * u.arcsec, 0 * u.arcsec, obstime=vis_tr.start, obstime_end=vis_tr.end, observer=solo) + header_bp = sunpy.map.make_fitswcs_header( + bp_image, coord, telescope="STIX", observatory="Solar Orbiter", scale=plate_scale + ) + map_bp = sunpy.map.Map(bp_image, header_bp) + wcs_bp = map_bp.wcs + # Estimate flare location from brightest pixel in backprojection image + flare_loc = wcs_bp.array_index_to_world(*max_idx) + return flare_loc, map_bp, solo + + +def _subtract_background_from_stix_meta_pixels(meta_pixels_sci, meta_pixels_bkg): + """ + Estimates flare location from STIX pixel data. + + Parameters + ---------- + meta_pixels_sci: `dict` + The STIX meta pixel representing the observations. Format must be + same as output from `stixpy.calibration.visibility.create_meta_pixels`. + meta_pixels_bkg: `dict` + The STIX meta pixels representing the background. Format must be + same as output from `stixpy.calibration.visibility.create_meta_pixels`. + """ + meta_pixels_bkg_sub = { + **meta_pixels_sci, + "abcd_rate_kev_cm": meta_pixels_sci["abcd_rate_kev_cm"] - meta_pixels_bkg["abcd_rate_kev_cm"], + "abcd_rate_error_kev_cm": np.sqrt( + meta_pixels_sci["abcd_rate_error_kev_cm"] ** 2 + meta_pixels_bkg["abcd_rate_error_kev_cm"] ** 2 + ), + } + return meta_pixels_bkg_sub diff --git a/stixcore/products/product.py b/stixcore/products/product.py index 02712266..4d9dbf7d 100644 --- a/stixcore/products/product.py +++ b/stixcore/products/product.py @@ -74,7 +74,10 @@ def read_qtable(file, hdu, hdul=None): `astropy.table.QTable` The corrected QTable with correct data types """ - qtable = QTable.read(file, hdu) + astropy_native = True + if (hdu.upper() == "DATA") and (file.name.startswith("solo_L0_stix-sci-aspect-burst")): + astropy_native = False + qtable = QTable.read(file, hdu, astropy_native=astropy_native) if hdul is None: hdul = fits.open(file) @@ -91,7 +94,7 @@ def read_qtable(file, hdu, hdul=None): if hasattr(dtype, "subdtype"): dtype = dtype.base - + # qtable[col.name] = qtable[col.name].astype(dtype) if col.coord_type != "UTC": qtable[col.name] = qtable[col.name].astype(dtype) else: @@ -336,6 +339,9 @@ def __call__(self, *args, **kwargs): month=month, ) + if hasattr(p, "on_deserialize") and callable(getattr(p, "on_deserialize")): + p.on_deserialize(p.data) + if hasattr(p, "get_additional_extensions") and data is not None: for _, name in p.get_additional_extensions(): # read the additional extension data @@ -609,6 +615,23 @@ def max_exposure(self): # default for FITS HEADER return 0.0 + def on_serialize(self, data): + """Hook called before writing data to FITS. Mixins override and chain via super(). + + Uses getattr so plain products without functional mixins are safe — + the functional mixins (FlarePositionMixin etc.) appear after GenericProduct + in the MRO, so pass would stop the chain before reaching them. + """ + serialize = getattr(super(), "on_serialize", None) + if serialize is not None: + serialize(data) + + def on_deserialize(self, data, **kwargs): + """Hook called after reading data from FITS. Mixins override and chain via super().""" + deserialize = getattr(super(), "on_deserialize", None) + if deserialize is not None: + deserialize(data, **kwargs) + def find_parent_products(self, root): """ Convenient way to get access to the parent products. diff --git a/stixcore/products/tests/test_flarelist.py b/stixcore/products/tests/test_flarelist.py new file mode 100644 index 00000000..b64066fd --- /dev/null +++ b/stixcore/products/tests/test_flarelist.py @@ -0,0 +1,290 @@ +from datetime import date + +import numpy as np +import pytest +from sunpy.coordinates import HeliographicStonyhurst +from sunpy.time import TimeRange + +import astropy.units as u +from astropy.coordinates import SkyCoord +from astropy.io import fits +from astropy.table import QTable +from astropy.tests.helper import assert_quantity_allclose +from astropy.time import Time + +from stixcore.io.product_processors.fits.processors import FitsL3Processor +from stixcore.products.level3.flarelist import ( + FlarelistSDCLocation, + FlarePositionResult, + _empty_flare_position, + add_distance_normalized_flux, + calculate_overlap, + cpd_timedel_range, + longest_constant_sequence, +) +from stixcore.products.product import Product + +N = 10 + + +@pytest.fixture +def flare_data(): + peak_times = Time("2022-01-01T12:00:00") + np.arange(N) * 600 * u.s + + lon = np.linspace(0, 30, N) + lat = np.linspace(-5, 5, N) + # mark same rows fully NaN so the entire SkyCoord row is invalid + lon[2] = lat[2] = np.nan + lon[7] = lat[7] = np.nan + + hgs_coords = SkyCoord( + lon=lon * u.deg, + lat=lat * u.deg, + radius=np.ones(N) * 1.0 * u.AU, + frame=HeliographicStonyhurst(obstime=peak_times), + ) + + data = QTable() + data["peak_UTC"] = peak_times + data["location_time_UTC"] = peak_times + data["start_UTC"] = peak_times - 60 * u.s + data["end_UTC"] = peak_times + 60 * u.s + data["duration"] = np.ones(N) * 120 * u.s + data["lc_peak"] = np.ones((N, 5)) * u.ct / u.s + data["location_hgs"] = hgs_coords + + return data + + +@pytest.fixture +def written_fits(flare_data, tmp_path): + prod = FlarelistSDCLocation( + data=flare_data, + month=date(2022, 1, 1), + control=QTable(), + ) + + # minimal header bypasses the Spice-dependent header generation chain + header = fits.Header() + header["LEVEL"] = "L3" + header["STYPE"] = 0 + header["SSTYPE"] = 0 + header["SSID"] = 3 + header["DATE-BEG"] = "2022-01-01T00:00:00" + prod.fits_header = header + prod.energy = None + prod._additional_header_keywords = [] + + writer = FitsL3Processor(tmp_path) + written = writer.write_fits(prod) + assert len(written) == 1 + + return prod, written[0] + + +def test_flarelist_sdcloc_location_roundtrip(written_fits): + prod, fits_path = written_fits + orig_hgs_lon = prod.data["location_hgs"].lon.copy() + orig_hgs_lat = prod.data["location_hgs"].lat.copy() + + # read back via Product factory — calls on_deserialize internally + recovered = Product(fits_path) + + assert isinstance(recovered, FlarelistSDCLocation) + assert_quantity_allclose(recovered.data["location_hgs"].lon, orig_hgs_lon, atol=1e-6 * u.deg, equal_nan=True) + assert_quantity_allclose(recovered.data["location_hgs"].lat, orig_hgs_lat, atol=1e-6 * u.deg, equal_nan=True) + + +def test_flarelist_sdcloc_fits_stores_icrs(written_fits): + prod, fits_path = written_fits + orig_hgs_lon = prod.data["location_hgs"].lon.copy() + orig_hgs_lat = prod.data["location_hgs"].lat.copy() + + # read the DATA extension directly — no on_deserialize, raw FITS content + raw = QTable.read(fits_path, hdu="DATA", astropy_native=True) + + assert "location_hgs" not in raw.colnames, "HGS column should not be stored in FITS" + assert "location_icrs" in raw.colnames, "ICRS column should be present in FITS" + + # manually transform ICRS back to HGS and compare with original + obstime = Time(raw["location_time_UTC"]) + hgs = raw["location_icrs"].transform_to(HeliographicStonyhurst(obstime=obstime)) + assert_quantity_allclose(hgs.lon, orig_hgs_lon, atol=1e-6 * u.deg, equal_nan=True) + assert_quantity_allclose(hgs.lat, orig_hgs_lat, atol=1e-6 * u.deg, equal_nan=True) + + +# --- longest_constant_sequence --- + + +def test_lcs_empty(): + length, start, state = longest_constant_sequence([]) + assert length == 0 + assert start is None + assert state is None + + +def test_lcs_single_element(): + length, start, state = longest_constant_sequence([5]) + assert length == 1 + assert start == 0 + assert state == 5 + + +def test_lcs_all_same(): + length, start, state = longest_constant_sequence([3, 3, 3, 3]) + assert length == 4 + assert start == 0 + assert state == 3 + + +def test_lcs_clear_winner(): + length, start, state = longest_constant_sequence([1, 2, 2, 2, 3, 3]) + assert length == 3 + assert start == 1 + assert state == 2 + + +def test_lcs_tie_prefers_lower_state(): + # two runs of length 2: state=1 at index 0, state=3 at index 2 + length, start, state = longest_constant_sequence([1, 1, 3, 3]) + assert length == 2 + assert start == 0 + assert state == 1 + + +def test_lcs_numpy_array(): + arr = np.array([0, 0, 1, 1, 1, 0]) + length, start, state = longest_constant_sequence(arr) + assert length == 3 + assert start == 2 + assert state == 1 + + +# --- calculate_overlap --- + + +def test_overlap_no_intersection(): + r1 = TimeRange("2024-01-01T00:00:00", "2024-01-01T01:00:00") + r2 = TimeRange("2024-01-01T02:00:00", "2024-01-01T03:00:00") + assert calculate_overlap(r1, r2) is None + + +def test_overlap_partial(): + r1 = TimeRange("2024-01-01T00:00:00", "2024-01-01T02:00:00") + r2 = TimeRange("2024-01-01T01:00:00", "2024-01-01T03:00:00") + result = calculate_overlap(r1, r2) + assert result is not None + assert result.start == Time("2024-01-01T01:00:00") + assert result.end == Time("2024-01-01T02:00:00") + + +def test_overlap_contained(): + r1 = TimeRange("2024-01-01T00:00:00", "2024-01-01T04:00:00") + r2 = TimeRange("2024-01-01T01:00:00", "2024-01-01T03:00:00") + result = calculate_overlap(r1, r2) + assert result is not None + assert result.start == Time("2024-01-01T01:00:00") + assert result.end == Time("2024-01-01T03:00:00") + + +def test_overlap_identical(): + r1 = TimeRange("2024-01-01T00:00:00", "2024-01-01T01:00:00") + result = calculate_overlap(r1, r1) + assert result is not None + assert result.start == r1.start + assert result.end == r1.end + + +# --- add_distance_normalized_flux --- + +FLUX_UNIT = u.ct / (u.s * u.keV * u.cm**2) + + +def test_add_distance_normalized_flux(): + data = QTable() + # flare-time distance (for LC) and background-time distance (for bkg) differ per row + data["solo_sun_distance"] = [0.5, 1.0, np.nan] * u.AU + data["bkg_solo_sun_distance"] = [0.8, 1.0, 0.5] * u.AU + data["lc_peak_flux"] = np.array([[100.0, 10, 1, 5, 2]] * 3) * FLUX_UNIT + data["bkg_spec_flux"] = np.array([[4.0, 3, 2, 1, 0.5]] * 3) * FLUX_UNIT + # lc_bkg_peak_flux / bkg_spec_flux_ql intentionally absent -> must be skipped, no error + + add_distance_normalized_flux(data) + + # new columns added, originals unchanged + assert "lc_peak_flux_at_1au" in data.colnames + assert "bkg_spec_flux_at_1au" in data.colnames + assert "lc_bkg_peak_flux_at_1au" not in data.colnames # source column was absent + assert np.allclose(data["lc_peak_flux"].to_value(FLUX_UNIT), [[100, 10, 1, 5, 2]] * 3) + + # LC uses solo_sun_distance: 0.5 AU -> x0.25, 1 AU -> x1, NaN -> NaN + lc = data["lc_peak_flux_at_1au"].to_value(FLUX_UNIT) + assert np.allclose(lc[0], np.array([100, 10, 1, 5, 2]) * 0.25) + assert np.allclose(lc[1], [100, 10, 1, 5, 2]) + assert np.all(np.isnan(lc[2])) + # bkg uses bkg_solo_sun_distance: 0.8 AU -> x0.64, 0.5 AU (row 2) -> x0.25 + bk = data["bkg_spec_flux_at_1au"].to_value(FLUX_UNIT) + assert np.allclose(bk[0], np.array([4, 3, 2, 1, 0.5]) * 0.64) + assert np.allclose(bk[2], np.array([4, 3, 2, 1, 0.5]) * 0.25) + # unit preserved + assert data["lc_peak_flux_at_1au"].unit.is_equivalent(FLUX_UNIT) + + +# --- cpd_timedel_range --- + + +def test_cpd_timedel_range(): + # four bins at t = 0,10,20,30 s with widths 4,4,10,20 ds (0.4,0.4,1.0,2.0 s) + times = np.array([0, 10, 20, 30]) * u.s + timedels = np.array([4, 4, 10, 20]) * u.ds + + # window fully inside the file -> bins 1 and 2 overlap [5, 25] s + ds_min, ds_max = cpd_timedel_range(times, timedels, 5 * u.s, 25 * u.s) + assert ds_min.unit == u.ds + assert ds_max.unit == u.ds + assert ds_min == 4 * u.ds + assert ds_max == 10 * u.ds + + # window extends beyond the file (not fully covered) -> all bins overlap + ds_min, ds_max = cpd_timedel_range(times, timedels, -100 * u.s, 100 * u.s) + assert ds_min == 4 * u.ds + assert ds_max == 20 * u.ds + + # no overlap -> (NaN ds, NaN ds) + ds_min, ds_max = cpd_timedel_range(times, timedels, 100 * u.s, 200 * u.s) + assert ds_min.unit == u.ds + assert ds_max.unit == u.ds + assert np.isnan(ds_min.value) + assert np.isnan(ds_max.value) + + +# --- _empty_flare_position --- + + +def test_empty_flare_position_defaults(): + peak = Time("2022-01-01T12:00:00") + r = _empty_flare_position(peak) + + assert isinstance(r, FlarePositionResult) + assert r.status is False + assert r.message == "" + assert r.anc_path == "" + assert r.cpd_path == "" + assert r.rcr_at_peak == 0 + assert r.solo_time == peak # per-row placeholder passed through + # NaN geometry + for f in (r.flare_x, r.flare_y, r.flare_z, r.solo_x, r.solo_y, r.solo_z): + assert f.unit == u.km + assert np.isnan(f.value) + assert np.isnan(r.sidelobe) + # new ds columns default to NaN deciseconds + assert r.min_exposure.unit == u.ds + assert np.isnan(r.min_exposure.value) + assert r.max_exposure.unit == u.ds + assert np.isnan(r.max_exposure.value) + + # overrides applied + r2 = _empty_flare_position(peak, message="no CPD data found", anc_path="/some/anc.fits") + assert r2.message == "no CPD data found" + assert r2.anc_path == "/some/anc.fits" + assert r2.status is False # untouched