From d2e143c6190f62be0370a898a1aab02d9aa3e6b6 Mon Sep 17 00:00:00 2001 From: psferguson Date: Mon, 15 Jun 2026 13:15:49 -0700 Subject: [PATCH 01/54] multisurvey plan --- docs/source/roman_multisurvey_plan.md | 204 ++++++++++++++++++++++++++ notebooks/roman_dc2_combine_plan.md | 128 ++++++++++++++++ 2 files changed, 332 insertions(+) create mode 100644 docs/source/roman_multisurvey_plan.md create mode 100644 notebooks/roman_dc2_combine_plan.md diff --git a/docs/source/roman_multisurvey_plan.md b/docs/source/roman_multisurvey_plan.md new file mode 100644 index 0000000..7532592 --- /dev/null +++ b/docs/source/roman_multisurvey_plan.md @@ -0,0 +1,204 @@ +# Multi-survey (Roman + Rubin) injection — design & roadmap + +```{warning} +This is a **living design document** for in-progress work, not a description of +shipped behaviour. It records the agreed design, the phased rollout, and — most +importantly — the **behaviour changes that need discussion** before we rely on +them. Sections marked *(future)* are designed but not yet implemented. +``` + +## Motivation + +`streamobs` was built for **single-survey** stream injection (LSST). We want to +inject mock streams that carry **both Roman and Rubin/LSST photometric columns** +in one catalog, where each band draws its photometric errors and detection +probability from its **own** survey magnitude-limit (maglim) HEALPix maps. + +A working prototype of this idea exists outside the package (the +`rubin_roman_object_classification` proposal code: survey-indexed dictionaries, +`{survey}_{band}` columns, and a "sample masses once, interpolate per survey" +photometry step). The goal here is to bring that capability **into** `streamobs` +properly, reusing the package's existing {class}`~streamobs.surveys.Survey` / +maglim-map machinery (which the prototype lacked), rather than maintaining a +parallel codebase. + +Two science realities drive specific features: + +- The Roman DC2 characterization (see *Roman DC2 Survey Files*) found the **true + photometric scatter is ~2× the reported catalog error**. So the error model + needs a *sample* model (true scatter, used to draw observed magnitudes) + distinct from a *catalog* model (the reported `magerr`). +- We need a **realistic background** (field stars + galaxies, with star/galaxy + misclassification) **without** loading the heavy DC2 mock catalogs at runtime + — using lightweight binned colour–magnitude distributions vs. maglim, and + optionally TRILEGAL for the stellar component. *(Designed below; future work.)* + +## Locked design decisions + +1. **Multi-survey via a `MultiSurveyInjector` orchestrator** that holds + `{survey_name: Survey}` and delegates per-survey work to the existing, + unchanged `Survey` per-band API. Not a composite `Survey`; not manual + re-runs. +2. **Column convention.** Multi-survey outputs use `{survey}_{band}_true`, + `{survey}_{band}_obs`, and `{survey}_{band}_err` (e.g. `roman_f158_obs`, + `lsst_r_err`). The single-survey `StreamInjector` **keeps emitting the legacy + names** `mag_{band}_obs` / `magerr_{band}` / `flag_observed` unchanged. +3. **Sample-vs-catalog error split, backward-compatible by default.** `Survey` + gains the *structure* for two error models — `log_photo_error_sample` (drives + the noise draw) and `log_photo_error_catalog` (written as `magerr`) — but + **defaults to one relation for both** (`report_error_factor = 1.0` ⇒ catalog + == sample ⇒ existing outputs unchanged). Separating them is opt-in: set a + factor, or supply a second CSV. +4. **Sample stellar masses once, interpolate per survey.** This is a correctness + requirement (see below) and the engine of the whole feature. +5. **Backward compatibility is by column-schema + public API**, not internal + coupling. Downstream consumers read pre-generated CSVs by column name + (`mag_g_obs`/`mag_r_obs`/`flag_observed`) and use the + `StreamInjector(survey).inject(df, bands=[...])` API. Both are preserved; + multi-survey support is purely additive. + +## Behaviour changes for discussion + +```{important} +These are deliberate changes from current behaviour. They are flagged here for +review **before** we depend on them — they are not silently adopted. +``` + +### `nstars` becomes "exactly N stars" (was an emergent IMF count) + +Today {meth}`~streamobs.model.IsochroneModel.sample` converts `nstars` into a +total stellar mass and lets `ugali`'s `iso.simulate()` return a *random-length* +IMF realization — so the number of stars returned is stochastic and generally +**≠ `nstars`**. + +The multi-survey requirement (the *same physical star* must get consistent Roman +**and** Rubin magnitudes) forces us instead to draw a fixed set of initial +masses once and interpolate each survey's magnitudes from them: + +```python +init_mass, mass_pdf, *_ = base_iso.sample(mass_min=mass_min, mass_steps=mass_steps) +sampled_masses = rng.choice(init_mass[sel], size=nstars, p=imf_pdf) # exactly nstars +# then, per survey: mag_band = np.interp(sampled_masses, init_mass, mag_band) + dist_mod +``` + +This returns *exactly* `nstars` stars. It is almost certainly the right +semantics for injection (you control N, and it is shared across surveys), but it +changes what `StreamModel.sample(size)` returns and could shift normalization in +any analysis that relied on the old emergent count. **Open for discussion.** + +### Where Roman Vega→AB conversion lives + +`ugali` appears to deliver Roman isochrone magnitudes in **Vega**, while our +catalogs are **AB**; the prototype corrects this after `isochrone_factory` with a +`ROMAN_ZEROPOINTS[...]["diff"]` table. + +**Current plan (do both, in order):** + +1. **This branch:** implement the correction in `streamobs` as a single isolated + shim — `IsochroneModel._apply_vega_to_ab`, gated by a per-survey `vega_to_ab` + config flag — so the feature works now without blocking on an upstream + release. It is structured so that if `ugali` later returns AB natively we just + set `vega_to_ab: false` and the shim becomes a no-op / is removed. +2. **Off the critical path:** pursue native AB support (or an explicit + photometric-system flag) in `ugali` itself, since `ugali` owns the isochrone + photometric system and every downstream user would benefit. + +*(If we decide to commit to the `ugali`-native route up front, the shim in +Phase 3 is dropped.)* + +## Phased rollout + +Phases 1–4 are the current branch; Phases 5–6 are designed here but deferred. + +### Phase 1 — Sample vs. catalog error models on `Survey` + +- `Survey`: replace the single `log_photo_error` field with + `log_photo_error_sample`, `log_photo_error_catalog`, and + `report_error_factor: float = 1.0`; keep a `log_photo_error` property alias. +- `Survey.get_photo_error(band, mag, maglim, kind="sample")`: `kind` selects the + interpolator; `kind="catalog"` falls back to sample if no catalog model is + loaded. Default reproduces today's numbers exactly. +- `SurveyFactory`: resolve the sample file (or legacy `log_photo_error`); load an + explicit `log_photo_error_catalog` file if given, else derive the catalog model + from the sample table via a `scale` argument on `set_photo_error` + (subtract `log10(report_error_factor)`). +- `StreamInjector.inject`: draw noise with the **sample** error; write the + **catalog** error as `magerr`; S/N cut uses the catalog error. + +### Phase 2 — De-hardcode the injector to arbitrary bands + +- New `streamobs/columns.py` with `obs_col` / `err_col` / `true_col` / + `flag_col(band, survey=None)` helpers (`survey=None` ⇒ legacy names). +- `observed.py`: remove the `bands in {"r","g"}` hard block; generalize the + valid-flux check, the S/N cut (default to the survey's `completeness_band`), + and the detection-flag logic to arbitrary bands. Existing per-band nside + handling already supports Roman nside=1024 vs. LSST nside=128. + +### Phase 3 — Multi-band / multi-survey `IsochroneModel` + +- `IsochroneModel` accepts either today's single-survey config or a multi-survey + form (`surveys: {name: {survey, band_1, band_2, vega_to_ab}}`), building a dict + of `ugali` isochrones. +- New `sample_masses(...)` (shared mass draw) and a `sample(...)` that returns + `{(survey, band): apparent_mag}` by interpolating those masses per survey; a + `sample_legacy()` wrapper preserves the `(mag_g, mag_r)` return for existing + callers. +- `_apply_vega_to_ab` shim (see above). `StreamModel.sample`/`complete_catalog` + derive their magnitude columns from the isochrone's bands rather than the + literal `mag_g`/`mag_r`. + +### Phase 4 — `MultiSurveyInjector` + +- Refactor the per-band body of `StreamInjector.inject` into a shared + `_inject_one_survey(...)` helper. +- `MultiSurveyInjector(surveys).inject(data, survey_bands, ...)`: + (1) one shared sky placement; (2) one shared true-magnitude fill (masses + sampled once); (3) a per-survey loop writing `{survey}_{band}_obs/_err` and + `{survey}_flag_observed` using each survey's own `completeness_band` and maglim + maps. Per-survey RNG via `rng.spawn(...)` for order-independent reproducibility. +- A *scene* config (`config/scenes/roman_rubin_demo.yaml`) lists the surveys, + bands, per-survey isochrones, and shared stream geometry. + +### Phase 5 *(future)* — Lightweight background + galaxy misclassification + +- New `streamobs/background.py`: a `CMDDistribution` (binned colour–magnitude + distribution vs. maglim, stored raw so one file serves any isochrone) and a + `BackgroundGenerator` that, per HEALPix pixel, looks up the local maglim, + selects the nearest-maglim CMD slice, scales counts linearly by pixel area, + Poisson-draws the count, samples multi-band magnitudes, places objects + uniformly within the pixel, and optionally applies a matched filter. +- Independent, pluggable `StellarBackground` / `GalaxyBackground` models. +- Galaxy misclassification: a new `Survey.get_galaxy_misclassification` curve and + an `is_galaxy`-aware `detect_flag`, so misclassified galaxies leak into the + stellar sample (and `perfect_galstarsep=True` ⇒ no leakage). A build script + reads DC2 truth+det **once, offline** to emit the lightweight files; the + runtime never loads DC2. + +### Phase 6 *(future)* — TRILEGAL + docs + +- `TrilegalStellarBackground` implementing the same interface, **lazy** (reads a + user-provided TRILEGAL table or a `fetch` callable; never imported at module + import). Documentation polish for the `{survey}_{band}` outputs, the error + split, and scenes. + +## Development fixtures (this branch) + +Survey data files (maglim maps, efficiency / photo-error CSVs) are **not** +committed — they are downloaded into the git-ignored `data/surveys//`. +To keep this branch self-contained and testable without the real Roman/Rubin +data, we add **dummy surveys**: small committed configs +(`config/surveys/*_dummy.yaml`) plus a generator that synthesizes tiny HEALPix +maglim maps and CSV tables at test time. The real `roman_dc2.yaml` is **not** +recreated here, so it can land cleanly when the `roman_hlwas` work merges. + +## Key files + +| File | Phase | Role | +|---|---|---| +| `streamobs/surveys.py` | 1 | sample/catalog error fields, `get_photo_error(kind=)`, loader | +| `streamobs/columns.py` | 2 | NEW — column-name helpers | +| `streamobs/observed.py` | 1,2,4 | error wiring; de-hardcode bands; `_inject_one_survey`; `MultiSurveyInjector` | +| `streamobs/model.py` | 3 | multi-band `IsochroneModel`; band-generalized `StreamModel` | +| `streamobs/multisurvey.py` | 4 | NEW — orchestrator (or a class in `observed.py`) | +| `config/scenes/roman_rubin_demo.yaml` | 4 | NEW — multi-survey scene | +| `streamobs/background.py` | 5 *(future)* | NEW — lightweight background | diff --git a/notebooks/roman_dc2_combine_plan.md b/notebooks/roman_dc2_combine_plan.md new file mode 100644 index 0000000..153a7ae --- /dev/null +++ b/notebooks/roman_dc2_combine_plan.md @@ -0,0 +1,128 @@ +# Roman DC2 mock: plan to build a single truth catalog with observations + +**Status:** PLAN ONLY — not executed yet. +**Source:** Troxel et al. 2023, *A Joint Roman + Rubin Synthetic Wide-Field Imaging Survey* +([arXiv:2209.06829](https://arxiv.org/abs/2209.06829)). +**Data:** `/astro/store/shire/stream_team/stream_finding/data/roman_mock` +(symlinked as `data/surveys/roman_dc2/`). ~20 deg², **1039 coadd tiles**, tiles named by +center `{ra}_{dec}` in degrees (e.g. `50.93_-38.8`). +**Python:** `/astro/store/shiren/conda-envs/stream_team/envs/streamobs/bin/python` +(astropy 7.2, numpy, pandas, healpy; no fitsio). + +--- + +## 1. What the files actually are (verified by inspection) + +| File(s) | Count | Granularity | Key columns | Notes | +|---|---|---|---|---| +| `det/dc2_det_{tile}.fits.gz` | 2078 | per coadd, **one row per detection** | `number`, `alphawin_j2000`,`deltawin_j2000` (RA/Dec **deg**), `flux_auto`,`fluxerr_auto`, `mag_auto`, `mag_auto_{Y106,J129,H158,F184}` (+magerr,flux,fluxerr), `flags`, `class_star`, shape moments | SExtractor on the coadd. ~21k rows/tile. | +| `truth/dc2_index_{tile}.fits.gz` | 1479 (1039 real + 440 empty) | per coadd, **one row per object** | `ind` (truth ID), `ra`,`dec` (**deg**), `mag_{Y106,J129,H158,F184}`, `dered_{...}`, `gal_star` (**1=star, 0=gal**), `sca`,`dither`,`x`,`y`,`stamp`,`start_row` | **THE truth product to use.** 440 files are 53-byte empty placeholders (tiles with no objects). | +| `truth/coadd/dc2_index_{tile}.fits.gz` | 1481 | — | — | **Byte-identical** to `truth/dc2_index_{tile}` (confirmed via `filecmp`). Ignore one of them. | +| `truth/dc2_index_star.fits.gz` | 1 | **per (object × SCA exposure)** | `ind,sca,dither,x,y,ra,dec,mag,stamp,xmin,xmax,ymin,ymax,dudx,dudy,dvdx,dvdy,start_row` | 414 MB, ~6.36M rows. RA/Dec in **RADIANS**. Single-epoch index; **not needed** for coadd matching. | +| `truth/dc2_index.fits.gz` | 1 | **per (object × SCA exposure)** | same 18 cols as star index | **78 GB.** All objects (gal+star). Header `NAXIS2=0` (streaming FITS — astropy reads 0 rows; see §5). **Not needed for this task.** | +| `truth/dc2_coaddlist.fits.gz` | 1 | per coadd tile | `tilename,coadd_i,coadd_j,coadd_ra,coadd_dec,d_ra,d_dec,input_list` | 1838 rows; `input_list` = contributing exposures. Optional metadata. | + +### The key realization +**We do NOT need the 78 GB `dc2_index.fits` (nor the star index) for this.** +The per-coadd `truth/dc2_index_{tile}.fits.gz` files already carry, per object: +truth RA/Dec (deg), truth mags in all 4 Roman bands, dereddening, and the star/galaxy +flag. They are tile-for-tile aligned with the `det/dc2_det_{tile}.fits.gz` files. So the +whole job reduces to **per-tile positional matching** — tractable and parallel. + +The big index is only needed if you later want single-epoch (per-SCA) info: which exposures +saw an object, its pixel position/postage stamp, etc. + +--- + +## 2. The combine = per-tile positional match (truth ← det) + +Following the paper's matching recipe: + +For each of the **1039 real coadd tiles** (intersection of det-tile names and non-empty truth-tile names): + +1. **Load truth tile** → table of objects (ind, ra, dec, mag_*, dered_*, gal_star, ...). +2. **Load det tile** → detections (alphawin_j2000, deltawin_j2000, mag_auto_*, flux_auto, fluxerr_auto, flags, class_star). +3. **Cut det**: `S/N = flux_auto / fluxerr_auto > 5` (paper's threshold; removes only ~0.05%). + Optionally also `flags == 0` (stricter; the paper removes ~32% via flags — leave this as a toggle, default OFF). +4. **Match** truth → det by sky position with **1.0 arcsec** radius + (`astropy.coordinates.SkyCoord.match_to_catalog_sky`, or a KD-tree on a local tangent plane). + - Paper's rule: among matches within 1″, when ambiguous take the closest **in magnitude** + among the up-to-3 nearest. v1 can just take the **nearest neighbor within 1″**; add the + mag-tiebreak as a refinement. +5. **Left-join on truth**: every truth row is kept. Attach the matched det columns + (prefixed `det_`) when a match exists; else NaN. Add `detected` (bool) and `match_sep_arcsec`. +6. **Tag** each row with `tile` (the `{ra}_{dec}` string). +7. **Concatenate** all tiles → one catalog. Write to **parquet** (fast, typed) — and/or FITS. + +Output is then a single **truth-complete** catalog: one row per true object across the +footprint, with observed (detected) quantities where they exist. From it you can trivially +derive detection efficiency / completeness vs. magnitude, color, `gal_star`, etc., which is +exactly what stream-injection / selection-function work needs. + +### Output schema (proposed) +``` +ind, tile, ra, dec, gal_star, +mag_Y106, mag_J129, mag_H158, mag_F184, # truth +dered_Y106, dered_J129, dered_H158, dered_F184, # truth +detected (bool), match_sep_arcsec, +det_number, det_ra, det_dec, +det_mag_auto, det_mag_auto_Y106..F184, +det_flux_auto, det_fluxerr_auto, det_sn, +det_flags, det_class_star +``` +(`mag_* == 0.0` in truth means "no flux in that band" → treat as NaN on load.) + +--- + +## 3. Column subset for the big index (`dc2_index.fits`, 78 GB) — if you still want it + +You offered to pre-subset it. **For the coadd truth+det catalog you can skip it entirely** +(use the per-tile truth files). If you do subset it (e.g. for per-SCA / single-epoch studies), +the 18 columns are: + +| keep? | column | type | meaning | +|---|---|---|---| +| ✅ | `ind` | int64 | object ID — links to input truth catalog (join key across files) | +| ✅ | `ra`,`dec` | float64 | sky position (**radians** in this file) | +| ✅ | `mag` | float64 | true magnitude (in that exposure's band) | +| ✅ | `sca` | int64 | Roman detector (1–18) | +| ✅ | `dither` | int64 | exposure / pointing ID | +| ➖ | `x`,`y` | float64 | pixel position on the SCA | +| ➖ | `stamp`,`start_row` | int64 | postage-stamp pointers into the image-stamp file | +| ❌ | `xmin,xmax,ymin,ymax` | int64 | stamp bounding box | +| ❌ | `dudx,dudy,dvdx,dvdy` | float64 | local WCS Jacobian | + +Minimal useful subset: **`ind, sca, dither, ra, dec, mag`** (6 of 18 → ~⅓ the size). +Add `x,y,stamp,start_row` if you need to find/cut postage stamps. Note: a single object +appears in **many rows** here (once per SCA exposure it lands on), so you must group by `ind`. + +--- + +## 4. Decisions to confirm before coding + +1. **Stars only, or all objects?** streamobs is stellar streams → likely `gal_star==1`. + But keeping galaxies lets us model the contaminating background. → *propose: keep all, + carry `gal_star`, filter downstream.* +2. **flags cut?** S/N>5 only (your call), or also `flags==0` (drops ~32%, removes + blends/edges). → *propose: S/N>5 default, `flags==0` as an option.* +3. **Match radius / tie-break:** 1.0″ nearest-neighbor (v1) vs. paper's mag-tiebreak FoF. → *propose: 1.0″ NN first, refine if needed.* +4. **Output format/location:** parquet under `data/surveys/roman_dc2/` (+ optional FITS). +5. **Galaxy de-blending:** the paper notes 20–30% of Rubin objects split into multiple + Roman objects; with a 1″ radius a det can match several truths — we keep the truth-centric + left join (each truth → its nearest det), so this is handled implicitly. Flag if needed. + +--- + +## 5. Implementation notes / gotchas + +- **Read gzipped FITS**: `with gzip.open(path,'rb') as f: Table(astropy.io.fits.open(io.BytesIO(f.read()))[1].data)`. + (astropy's direct `.fits.gz` open chokes on the big NAXIS2=0 files; the per-tile files open fine.) +- **`NAXIS2=0` streaming files** (star/master index only): astropy returns 0 rows. To read: + skip the primary + extension headers (2880-byte blocks to `END`), then + `np.frombuffer(rest, dtype=<144-byte big-endian dtype>)`; n_rows = data_bytes // 144. +- **Units**: per-tile truth + det are **degrees**; star/master index are **radians**. +- **Missing mags**: truth `mag_* == 0.0` and det un-matched → use NaN. +- **Parallelism**: 1039 independent tiles. Natural fan-out — one worker per tile, then concat. + (Good candidate for a Workflow run, or simple multiprocessing / joblib.) +- **Sanity check tile** `50.93_-38.8`: 34378 truth objects (175 stars, 34203 gals); + 21382 detections, 21372 with S/N>5; truth & det RA/Dec ranges overlap as expected. From 8bab5a4446ac35dfcb719426569e16fc575f5057 Mon Sep 17 00:00:00 2001 From: psferguson Date: Mon, 15 Jun 2026 13:49:46 -0700 Subject: [PATCH 02/54] phase 1 of refactor --- .gitignore | 5 ++ docs/source/roman_multisurvey_plan.md | 62 +++++++++----- streamobs/observed.py | 18 ++-- streamobs/surveys.py | 115 ++++++++++++++++++++++---- 4 files changed, 159 insertions(+), 41 deletions(-) diff --git a/.gitignore b/.gitignore index a998a36..9e4f7a0 100644 --- a/.gitignore +++ b/.gitignore @@ -132,3 +132,8 @@ dmypy.json data/surveys/* data/others/* .DS_Store + +# External reference repos (design references only, not part of streamobs) +/survey_systematics_in_LSST_streams/ +/rubin_roman_object_classification/ +/lsst_dc2_scratch/ diff --git a/docs/source/roman_multisurvey_plan.md b/docs/source/roman_multisurvey_plan.md index 7532592..884ed6d 100644 --- a/docs/source/roman_multisurvey_plan.md +++ b/docs/source/roman_multisurvey_plan.md @@ -44,11 +44,21 @@ Two science realities drive specific features: `lsst_r_err`). The single-survey `StreamInjector` **keeps emitting the legacy names** `mag_{band}_obs` / `magerr_{band}` / `flag_observed` unchanged. 3. **Sample-vs-catalog error split, backward-compatible by default.** `Survey` - gains the *structure* for two error models — `log_photo_error_sample` (drives - the noise draw) and `log_photo_error_catalog` (written as `magerr`) — but - **defaults to one relation for both** (`report_error_factor = 1.0` ⇒ catalog - == sample ⇒ existing outputs unchanged). Separating them is opt-in: set a - factor, or supply a second CSV. + holds two error curves, both functions of `delta_mag = mag − maglim`: + - `log_photo_error_catalog` — the survey's **reported** error curve (the + existing `photoerror_*.csv`). Written as `magerr` and drives the S/N cut. + Always present; loaded from the `log_photo_error_catalog` key or the legacy + `log_photo_error` key. + - `log_photo_error_sample` — an **optional second** curve giving the **true + scatter** of observed−true magnitudes; drives the noise draw. Config key + `log_photo_error_sample`. + + If no sample curve is supplied, the noise draw falls back to the catalog + curve ⇒ existing outputs unchanged. Separating them is opt-in: supply the + second CSV. (Earlier drafts proposed a scalar `report_error_factor` / + inflation curve to *derive* one from the other; superseded by two independent + curves, which is more general and matches the Roman DC2 products that measure + the true scatter directly.) 4. **Sample stellar masses once, interpolate per survey.** This is a correctness requirement (see below) and the engine of the whole feature. 5. **Backward compatibility is by column-schema + public API**, not internal @@ -110,20 +120,34 @@ Phase 3 is dropped.)* Phases 1–4 are the current branch; Phases 5–6 are designed here but deferred. -### Phase 1 — Sample vs. catalog error models on `Survey` - -- `Survey`: replace the single `log_photo_error` field with - `log_photo_error_sample`, `log_photo_error_catalog`, and - `report_error_factor: float = 1.0`; keep a `log_photo_error` property alias. -- `Survey.get_photo_error(band, mag, maglim, kind="sample")`: `kind` selects the - interpolator; `kind="catalog"` falls back to sample if no catalog model is - loaded. Default reproduces today's numbers exactly. -- `SurveyFactory`: resolve the sample file (or legacy `log_photo_error`); load an - explicit `log_photo_error_catalog` file if given, else derive the catalog model - from the sample table via a `scale` argument on `set_photo_error` - (subtract `log10(report_error_factor)`). -- `StreamInjector.inject`: draw noise with the **sample** error; write the - **catalog** error as `magerr`; S/N cut uses the catalog error. +### Phase 1 — Sample vs. catalog error models on `Survey` ✅ *(implemented)* + +Two independent error curves, both vs `delta_mag = mag − maglim`: + +- ✅ `Survey`: replaced the single `log_photo_error` field with + `log_photo_error_catalog` (reported error, the base curve) and + `log_photo_error_sample` (optional true-scatter curve). Kept a read/write + `log_photo_error` property aliasing the **catalog** model (the legacy field's + meaning — back-compat for existing tests and any code that sets it). +- ✅ `Survey.get_photo_error(band, mag, maglim, kind="catalog")`: `kind` selects + the curve via `_resolve_log_photo_error`. `kind="catalog"` returns the reported + error; `kind="sample"` returns the true-scatter curve, falling back to the + catalog curve when no sample curve is loaded. Default `kind="catalog"` + reproduces today's numbers exactly. +- ✅ `SurveyFactory._load_survey_data`: loads the catalog curve from + `log_photo_error_catalog` (or the legacy `log_photo_error`) key, and an + optional sample curve from the `log_photo_error_sample` key. No factor / + inflation logic — both curves are read directly via `set_photo_error`. +- ✅ `StreamInjector.inject`: draws noise with the **sample** error + (`kind="sample"`); writes the **catalog** error as `magerr` and runs the S/N + cut on it (`kind="catalog"`). + +**Backward compatibility verified (94 passing tests from the test branch):** with +only the legacy `log_photo_error` curve and no `log_photo_error_sample`, the +sample draw falls back to the catalog curve, so the noise draw, `magerr`, and the +S/N cut are bit-for-bit identical to the previous single-curve behaviour. To opt +in, add `log_photo_error_sample: ` (the measured true-scatter curve) to a +survey's `survey_files`. ### Phase 2 — De-hardcode the injector to arbitrary bands diff --git a/streamobs/observed.py b/streamobs/observed.py index b7838ea..7a718dc 100644 --- a/streamobs/observed.py +++ b/streamobs/observed.py @@ -175,17 +175,23 @@ def inject(self, data, bands=["r", "g"], **kwargs): else: pix_maglim = pix - # Calculate photometric errors + # Calculate photometric errors. The *sample* error (true scatter) + # drives the noise draw; the *catalog* error (reported) is written + # as magerr and used for the S/N cut. When no sample curve is loaded, + # the sample error falls back to the catalog error, so the two are + # identical (outputs unchanged from the single-curve behaviour). + maglim_vals = self.survey.get_maglim(band, pixel=pix_maglim) + mag_err_sample = self.survey.get_photo_error( + band, apparent_mag_true, maglim_vals, kind="sample" + ) mag_err = self.survey.get_photo_error( - band, - apparent_mag_true, - self.survey.get_maglim(band, pixel=pix_maglim), + band, apparent_mag_true, maglim_vals, kind="catalog" ) - # Sample measured magnitudes + # Sample measured magnitudes using the sample (true-scatter) error mag_obs = self.sample_measured_magnitudes( apparent_mag_true, - mag_err, + mag_err_sample, rng=rng, seed=seed, **kwargs, diff --git a/streamobs/surveys.py b/streamobs/surveys.py index 588a324..903dc9c 100644 --- a/streamobs/surveys.py +++ b/streamobs/surveys.py @@ -57,9 +57,20 @@ class Survey: Magnitude difference for saturation threshold in the initial functions. completeness_band : str, optional Band used to derive completeness function (e.g., 'r'). - log_photo_error : callable, optional - Photometric error model f(delta_mag) -> log10(mag_error). - Same function used for all bands, obtained from r band. + log_photo_error_catalog : callable, optional + *Catalog* (reported) photometric error model f(delta_mag) -> log10(mag_error), + where ``delta_mag = mag - maglim``. This is the survey's reported error + curve (e.g. ``photoerror_r.csv``); it is written as ``magerr`` and drives + the S/N cut. Loaded from the ``log_photo_error_catalog`` config key, or the + legacy ``log_photo_error`` key. Always present for a configured survey. + log_photo_error_sample : callable, optional + *Sample* photometric error model f(delta_mag) -> log10(mag_error): the true + scatter of observed-minus-true magnitudes, used to draw the observed + magnitudes. Optional second curve (config key ``log_photo_error_sample``). + If not provided, the noise draw falls back to the catalog model, which + reproduces the previous single-curve behaviour exactly. + log_photo_error : callable + Read/write alias for ``log_photo_error_catalog`` (backward compatibility). Examples -------- @@ -109,7 +120,8 @@ class Survey: completeness: Optional[Callable] = None completeness_band: Optional[str] = None delta_saturation: Optional[float] = None - log_photo_error: Optional[Callable] = None + log_photo_error_catalog: Optional[Callable] = None + log_photo_error_sample: Optional[Callable] = None # Band-independent maps ebv_map: Optional[np.ndarray] = None @@ -128,6 +140,41 @@ def __post_init__(self): if self.sys_error is None: self.sys_error = {} + @property + def log_photo_error(self) -> Optional[Callable]: + """Backward-compatible alias for the *catalog* (reported) error model.""" + return self.log_photo_error_catalog + + @log_photo_error.setter + def log_photo_error(self, func: Optional[Callable]): + self.log_photo_error_catalog = func + + def _resolve_log_photo_error(self, kind: str = "catalog") -> Optional[Callable]: + """ + Select the log photometric-error interpolator. + + Parameters + ---------- + kind : str + ``"catalog"`` returns the catalog model: the reported error curve, + written as ``magerr`` and used for the S/N cut. ``"sample"`` returns + the sample model: the true scatter (observed-minus-true) that drives + the noise draw, falling back to the catalog model when no sample curve + is loaded (which reproduces the previous single-curve behaviour). + + Returns + ------- + callable or None + The selected ``f(delta_mag) -> log10(mag_error)`` interpolator. + """ + if kind == "catalog": + return self.log_photo_error_catalog + elif kind == "sample": + if self.log_photo_error_sample is not None: + return self.log_photo_error_sample + return self.log_photo_error_catalog + raise ValueError(f"kind must be 'sample' or 'catalog', got '{kind}'") + @classmethod def load( cls, @@ -232,7 +279,7 @@ def get_extinction(self, band: str, pixel: int = None) -> float: return extinction[pixel] def get_photo_error( - self, band: str, magnitude: float, maglim: float, **kwargs + self, band: str, magnitude: float, maglim: float, kind: str = "catalog", **kwargs ) -> float: """ Get photometric error estimate. @@ -245,6 +292,11 @@ def get_photo_error( True apparent magnitude(s). maglim : float or np.ndarray Magnitude limit(s) at the position(s). + kind : str, optional + Which error model to evaluate: ``"catalog"`` (reported error, written + as ``magerr`` and used for S/N cuts) or ``"sample"`` (true scatter, + used to draw the observed magnitudes). ``"sample"`` falls back to the + catalog model if no sample curve is loaded. Default is ``"catalog"``. **kwargs Additional keyword arguments: @@ -260,10 +312,11 @@ def get_photo_error( Raises ------ ValueError - If photo error model is not loaded. + If the requested photo error model is not loaded. """ - if self.log_photo_error is None: - raise ValueError("Photo error model not loaded") + log_photo_error = self._resolve_log_photo_error(kind) + if log_photo_error is None: + raise ValueError(f"Photo error model ('{kind}') not loaded") delta_saturation = kwargs.get("delta_saturation", self.delta_saturation) @@ -275,13 +328,13 @@ def get_photo_error( np.where( ((delta_mag) <= delta_saturation) & (magnitude >= self.saturation[band]), - self.log_photo_error(delta_saturation), - self.log_photo_error(delta_mag), + log_photo_error(delta_saturation), + log_photo_error(delta_mag), ) ) mag_err_stat = np.where( magnitude < self.saturation[band], - 10 ** self.log_photo_error(delta_saturation - 1), + 10 ** log_photo_error(delta_saturation - 1), mag_err_stat, ) # saturation at the bright end @@ -1094,16 +1147,46 @@ def _load_survey_data(cls, survey: Survey, config: dict, **kwargs): if verbose: print("No classification efficiency file found, skipping.") - # Load photometric error model (same for all bands) + # Load photometric error model(s), same for all bands. Two curves: + # - catalog : reported error vs delta_mag (the survey's photoerror file). + # Written as magerr and used for the S/N cut. Always loaded. + # - sample : optional second curve giving the true scatter of + # observed-minus-true magnitudes; drives the noise draw. + # Backward compatible: with no sample curve, the noise draw falls back to + # the catalog curve, reproducing the previous single-curve behaviour. if verbose: print("\nLoading photometric error model...") - if "log_photo_error" in survey_config: - # Use default saturation if not band-specific + + # Catalog (reported) error model: prefer explicit 'log_photo_error_catalog', + # else the legacy 'log_photo_error' key. + catalog_key = ( + "log_photo_error_catalog" + if "log_photo_error_catalog" in survey_config + else "log_photo_error" + ) + if catalog_key in survey_config: + cls._load_file( + survey, + survey_config, + "log_photo_error_catalog", + "Photometric error model (catalog / reported)", + lambda f: cls.set_photo_error( + f, delta_saturation=survey.delta_saturation + ), + data_path_survey, + data_path_others, + filename=survey_config.get(catalog_key), + **kwargs, + ) + + # Optional sample (true-scatter) error model. If absent, the noise draw + # falls back to the catalog model (get_photo_error(kind="sample")). + if "log_photo_error_sample" in survey_config: cls._load_file( survey, survey_config, - "log_photo_error", - "Photometric error model", + "log_photo_error_sample", + "Photometric error model (sample / true scatter)", lambda f: cls.set_photo_error( f, delta_saturation=survey.delta_saturation ), From 9e6a90fcdf9aebeded62a9e85ba79a0bc7b70860 Mon Sep 17 00:00:00 2001 From: psferguson Date: Mon, 15 Jun 2026 13:58:38 -0700 Subject: [PATCH 03/54] phase 2 --- docs/source/roman_multisurvey_plan.md | 39 +++++++++++++++++++----- streamobs/columns.py | 36 ++++++++++++++++++++++ streamobs/observed.py | 44 +++++++++++++++------------ 3 files changed, 91 insertions(+), 28 deletions(-) create mode 100644 streamobs/columns.py diff --git a/docs/source/roman_multisurvey_plan.md b/docs/source/roman_multisurvey_plan.md index 884ed6d..11cdde7 100644 --- a/docs/source/roman_multisurvey_plan.md +++ b/docs/source/roman_multisurvey_plan.md @@ -74,6 +74,18 @@ These are deliberate changes from current behaviour. They are flagged here for review **before** we depend on them — they are not silently adopted. ``` +### S/N detection cut now applies to *all* injected bands *(Phase 2, adopted)* + +Previously the injector applied its SNR ≥ 5 cut to a single hard-coded band +(`detection_mag_cut=["g"]`). Now the default is **every band passed to +`inject()`** — a star must have SNR ≥ 5 in *all* injected bands to be flagged +observed. For the default LSST `bands=["r", "g"]` this is stricter than before +(it adds the r-band SNR requirement on top of g), so detection counts drop +relative to the old default. Rationale: it generalizes cleanly to any band set +and makes "observed" mean "detected in everything you asked for". Callers can +restore any prior behaviour by passing `detection_mag_cut=[...]` explicitly +(e.g. `["g"]` for the old LSST default). **Adopted, but flagged for review.** + ### `nstars` becomes "exactly N stars" (was an emergent IMF count) Today {meth}`~streamobs.model.IsochroneModel.sample` converts `nstars` into a @@ -149,14 +161,25 @@ S/N cut are bit-for-bit identical to the previous single-curve behaviour. To opt in, add `log_photo_error_sample: ` (the measured true-scatter curve) to a survey's `survey_files`. -### Phase 2 — De-hardcode the injector to arbitrary bands - -- New `streamobs/columns.py` with `obs_col` / `err_col` / `true_col` / - `flag_col(band, survey=None)` helpers (`survey=None` ⇒ legacy names). -- `observed.py`: remove the `bands in {"r","g"}` hard block; generalize the - valid-flux check, the S/N cut (default to the survey's `completeness_band`), - and the detection-flag logic to arbitrary bands. Existing per-band nside - handling already supports Roman nside=1024 vs. LSST nside=128. +### Phase 2 — De-hardcode the injector to arbitrary bands ✅ *(implemented)* + +- ✅ New `streamobs/columns.py` with `true_col` / `obs_col` / `err_col` helpers + `(band, survey=None)` and `flag_col(survey=None)` (`survey=None` ⇒ legacy + names `mag_` / `mag__obs` / `magerr_` / `flag_observed`; + a survey name ⇒ the `__…` multi-survey convention for Phase 4). +- ✅ `observed.py`: removed the `bands in {"r","g"}` hard block; the true-mag + read, the observed/err columns, the valid-flux check (now ANDs over every + injected band), the S/N cut, the per-survey detection flag, and the stored + flag all route through the `columns.py` helpers and the survey's + `completeness_band`. Existing per-band nside handling already supports Roman + nside=1024 vs. LSST nside=128. +- ⚠️ The S/N-cut default changed from the hard-coded `["g"]` to **all injected + bands** — see *Behaviour changes for discussion* above. + +**Validated:** single-band (`bands=["r"]`) and arbitrary band sets now inject +without the old hard block; legacy column names and the `inject(df, bands=[...])` +API are unchanged; the test-branch suite stays green (94 passing; the lone +`des_yr6` photo-error failure is pre-existing and unrelated). ### Phase 3 — Multi-band / multi-survey `IsochroneModel` diff --git a/streamobs/columns.py b/streamobs/columns.py new file mode 100644 index 0000000..45b522b --- /dev/null +++ b/streamobs/columns.py @@ -0,0 +1,36 @@ +""" +Column-name helpers for injected catalogs. + +These centralize the naming convention so the injector is not hard-coded to +specific bands (``mag_r_obs``, ``magerr_g``, ...). Two conventions are +supported, selected by the ``survey`` argument: + +- **Legacy / single-survey** (``survey=None``): ``mag_`` (true), + ``mag__obs`` (observed), ``magerr_`` (error), ``flag_observed``. + This is what :class:`~streamobs.observed.StreamInjector` emits and what + downstream consumers already read, so it is preserved unchanged. +- **Multi-survey** (``survey="roman"``, ``"lsst"``, ...): ``__true``, + ``__obs``, ``__err``, ``_flag_observed``. + Used by the (future) ``MultiSurveyInjector`` so each band's columns are + namespaced by survey. +""" + + +def true_col(band, survey=None): + """Column holding the *true* (noiseless) apparent magnitude for ``band``.""" + return f"{survey}_{band}_true" if survey else f"mag_{band}" + + +def obs_col(band, survey=None): + """Column holding the *observed* (noisy) magnitude for ``band``.""" + return f"{survey}_{band}_obs" if survey else f"mag_{band}_obs" + + +def err_col(band, survey=None): + """Column holding the reported magnitude error for ``band``.""" + return f"{survey}_{band}_err" if survey else f"magerr_{band}" + + +def flag_col(survey=None): + """Column holding the detection flag (band-independent).""" + return f"{survey}_flag_observed" if survey else "flag_observed" diff --git a/streamobs/observed.py b/streamobs/observed.py index 7a718dc..4c71f8c 100644 --- a/streamobs/observed.py +++ b/streamobs/observed.py @@ -10,6 +10,7 @@ import numpy as np import pandas as pd +from .columns import err_col, flag_col, obs_col, true_col from .model import StreamModel from .plotting import plot_stream_in_mask from .surveys import Survey @@ -100,7 +101,8 @@ def inject(self, data, bands=["r", "g"], **kwargs): nside : int, optional HEALPix nside parameter. Default is 4096. detection_mag_cut : list of str, optional - Bands to apply SNR detection cut. Default is ['g']. + Bands to apply the SNR detection cut to. Default is all injected + bands (every band in ``bands`` must have SNR >= 5). save : bool, optional Whether to save the output data. Default is False. folder : str or Path, optional @@ -152,9 +154,6 @@ def inject(self, data, bands=["r", "g"], **kwargs): # Process each band for band in bands: - if band not in ["r", "g"]: - raise ValueError("Currently only 'r' and 'g' bands are supported.") - # Get extinction for this band nside_ebv = hp.get_nside(self.survey.ebv_map) if nside_ebv != nside: @@ -164,7 +163,7 @@ def inject(self, data, bands=["r", "g"], **kwargs): extinction_band = self.survey.get_extinction(band, pixel=pix_ebv) # Calculate true apparent magnitudes (including extinction) - apparent_mag_true = data["mag_" + band] + extinction_band + apparent_mag_true = data[true_col(band)] + extinction_band # Get magnitude limits nside_maglim = hp.get_nside(self.survey.maglim_maps[band]) @@ -216,8 +215,8 @@ def inject(self, data, bands=["r", "g"], **kwargs): # Add new columns new_columns = pd.DataFrame( { - "mag_" + band + "_obs": mag_obs, - "magerr_" + band: mag_err, + obs_col(band): mag_obs, + err_col(band): mag_err, } ) @@ -260,10 +259,13 @@ def inject(self, data, bands=["r", "g"], **kwargs): ) # Build combined detection flags - # Start with flux validity check (not BAD_MAG) - flag_valid_flux = data["mag_r_obs"] != "BAD_MAG" - if "g" in bands: - flag_valid_flux &= data["mag_g_obs"] != "BAD_MAG" + # Start with flux validity check (not BAD_MAG) across all injected bands + flag_valid_flux = None + for band in bands: + band_valid = data[obs_col(band)] != "BAD_MAG" + flag_valid_flux = ( + band_valid if flag_valid_flux is None else flag_valid_flux & band_valid + ) # Combine with completeness flag_observed = ( @@ -278,8 +280,8 @@ def inject(self, data, bands=["r", "g"], **kwargs): else flag_valid_flux ) - # Apply SNR cuts - detection_mag_cut = kwargs.get("detection_mag_cut", ["g"]) + # Apply SNR cuts. Default: require SNR >= SNR_min in every injected band. + detection_mag_cut = kwargs.get("detection_mag_cut", list(bands)) SNR_min = 5.0 for band in detection_mag_cut: @@ -291,18 +293,20 @@ def inject(self, data, bands=["r", "g"], **kwargs): continue if verbose: print(f"Applying detection cut on {band}-band with SNR >= {SNR_min}") - SNR = 1.0 / data["magerr_" + band] + SNR = 1.0 / data[err_col(band)] flag_observed &= SNR >= SNR_min if perfect_galstarsep: flag_perfect &= SNR >= SNR_min - # Force SNR cut on r-band for perfect gal/star separation (because it's not included in the detection efficiency functions, while it is for the completeness functions) + # Force SNR cut on the completeness band for perfect gal/star separation + # (the SNR cut is baked into the completeness functions but not into the + # detection-only efficiency functions used for the perfect flag). if perfect_galstarsep: - SNR_r = 1.0 / data["magerr_r"] - flag_perfect &= SNR_r >= SNR_min + SNR_compl = 1.0 / data[err_col(self.survey.completeness_band)] + flag_perfect &= SNR_compl >= SNR_min # Store flags in DataFrame - data["flag_observed"] = flag_observed + data[flag_col()] = flag_observed if perfect_galstarsep: data["flag_perfect_galstarsep"] = flag_perfect @@ -438,7 +442,7 @@ def complete_data(self, data, bands=["r", "g"], **kwargs): >>> data2 = injector.complete_data(df2, gc_frame='last') # Reuses frame from data1 """ - required_columns = ["ra", "dec"] + [f"mag_{band}" for band in bands] + required_columns = ["ra", "dec"] + [true_col(band) for band in bands] rng = kwargs.pop("rng", None) seed = kwargs.pop("seed", None) @@ -470,7 +474,7 @@ def complete_data(self, data, bands=["r", "g"], **kwargs): # Sample missing magnitudes if needed mag_bands_missing = [ - f"mag_{band}" for band in bands if f"mag_{band}" not in data.columns + true_col(band) for band in bands if true_col(band) not in data.columns ] if mag_bands_missing: stream_config = kwargs.get("stream_config", None) From e298dcf234d0e32768c19f73f7320d9fc03b8de0 Mon Sep 17 00:00:00 2001 From: psferguson Date: Mon, 15 Jun 2026 15:06:38 -0700 Subject: [PATCH 04/54] phase 3 --- docs/source/roman_multisurvey_plan.md | 42 ++-- streamobs/model.py | 294 ++++++++++++++++++++++---- 2 files changed, 277 insertions(+), 59 deletions(-) diff --git a/docs/source/roman_multisurvey_plan.md b/docs/source/roman_multisurvey_plan.md index 11cdde7..6a10a47 100644 --- a/docs/source/roman_multisurvey_plan.md +++ b/docs/source/roman_multisurvey_plan.md @@ -181,18 +181,36 @@ without the old hard block; legacy column names and the `inject(df, bands=[...]) API are unchanged; the test-branch suite stays green (94 passing; the lone `des_yr6` photo-error failure is pre-existing and unrelated). -### Phase 3 — Multi-band / multi-survey `IsochroneModel` - -- `IsochroneModel` accepts either today's single-survey config or a multi-survey - form (`surveys: {name: {survey, band_1, band_2, vega_to_ab}}`), building a dict - of `ugali` isochrones. -- New `sample_masses(...)` (shared mass draw) and a `sample(...)` that returns - `{(survey, band): apparent_mag}` by interpolating those masses per survey; a - `sample_legacy()` wrapper preserves the `(mag_g, mag_r)` return for existing - callers. -- `_apply_vega_to_ab` shim (see above). `StreamModel.sample`/`complete_catalog` - derive their magnitude columns from the isochrone's bands rather than the - literal `mag_g`/`mag_r`. +### Phase 3 — Multi-band / multi-survey `IsochroneModel` ✅ *(implemented)* + +- ✅ `IsochroneModel.create_isochrone` accepts either today's single-survey + config or a multi-survey form (`surveys: {name: {survey, band_1, band_2, + vega_to_ab}}` plus shared `name`/`age`/`z`/... at top level), building one + `ugali` isochrone per survey (`self.isos`, `self.survey_bands`). +- ✅ New `sample_masses(...)` draws the initial masses *once* from the primary + isochrone's IMF (exactly `nstars`), and `sample_multisurvey(...)` interpolates + those shared masses into every survey's bands → `{(survey, band): + apparent_mag}` (same physical star, consistent across surveys). The legacy + `sample(nstars, dm)` still returns the `(mag_band_1, mag_band_2)` tuple (the + primary survey's two bands) so existing callers are unchanged. +- ✅ `_apply_vega_to_ab(survey, band, mag)` shim — gated by a per-survey + `vega_to_ab: {band: offset}` mapping (`AB = Vega + offset`); a no-op when + absent. Applied in both the single- and multi-survey paths. +- ✅ `StreamModel.sample`/`complete_catalog` derive their magnitude columns from + the isochrone via `_iso_mag_columns()` / `_sample_iso_mags()`: legacy + `mag_g`/`mag_r` for a single-survey isochrone, `__true` for a + multi-survey one. Naming routes through `columns.true_col`. + +**Note on the chosen API:** the plan originally named the dict-returning method +`sample` and a tuple `sample_legacy`; to avoid `sample()` changing return *type* +by config (a foot-gun for existing callers), the implementation keeps `sample()` +as the `(mag_1, mag_2)` tuple and adds `sample_multisurvey()` for the dict. +`StreamModel` dispatches on `isochrone.multi_survey`. + +**Validated:** single-survey model tests unchanged (test branch green, 94 +passing); a two-isochrone multi-survey config produces consistent shared-mass +magnitudes, the Vega→AB offset shifts only the configured band, and +`StreamModel.sample` emits the `__true` columns. ### Phase 4 — `MultiSurveyInjector` diff --git a/streamobs/model.py b/streamobs/model.py index d620912..59511bc 100644 --- a/streamobs/model.py +++ b/streamobs/model.py @@ -9,6 +9,7 @@ import numpy as np import pandas as pd +from streamobs.columns import true_col from streamobs.functions import function_factory from streamobs.samplers import sampler_factory @@ -115,7 +116,10 @@ def sample(self, size): ------- pandas.DataFrame Columns include: ``phi1``, ``phi2``, ``dist``, ``mu1``, ``mu2``, - ``rv``, ``mag_g``, ``mag_r`` (some may be None if the sub-model is absent). + ``rv``, and the isochrone magnitude columns. For a single-survey + isochrone these are the legacy ``mag_g`` / ``mag_r`` (its two bands); + for a multi-survey isochrone they are ``__true`` per + survey/band. Some may be None if the sub-model is absent. """ # Sample phi1 and phi2 @@ -128,13 +132,6 @@ def sample(self, size): else: dist = None - # Sample magnitudes from isochrone - if self.isochrone: - - mag_g, mag_r = self.isochrone.sample(size, dist) - else: - mag_g, mag_r = None, None - # Sample kinematics if self.velocity: mu1, mu2, rv = self.velocity.sample(phi1) @@ -150,12 +147,45 @@ def sample(self, size): "mu1": mu1, "mu2": mu2, "rv": rv, - "mag_g": mag_g, - "mag_r": mag_r, } ) + + # Sample magnitudes from isochrone (band-/survey-general columns) + if self.isochrone: + mag_data = self._sample_iso_mags(size, dist) + else: + mag_data = {"mag_g": None, "mag_r": None} # legacy placeholder + for col, vals in mag_data.items(): + df[col] = vals + return df + def _iso_mag_columns(self): + """Names of the magnitude columns produced by the isochrone model. + + Single-survey ⇒ legacy ``[mag_, mag_]``; multi-survey ⇒ + ``[__true, ...]`` for every survey/band; no isochrone ⇒ []. + """ + iso = self.isochrone + if iso is None: + return [] + if getattr(iso, "multi_survey", False): + cols = [] + for name in iso.surveys: + band_1, band_2 = iso.survey_bands[name] + cols += [true_col(band_1, name), true_col(band_2, name)] + return cols + return [true_col(iso.band_1), true_col(iso.band_2)] + + def _sample_iso_mags(self, n, dist): + """Sample isochrone magnitudes as a ``{column_name: values}`` dict.""" + iso = self.isochrone + if getattr(iso, "multi_survey", False): + mags = iso.sample_multisurvey(n, dist) + return {true_col(band, name): vals for (name, band), vals in mags.items()} + mag_1, mag_2 = iso.sample(n, dist) + return {true_col(iso.band_1): mag_1, true_col(iso.band_2): mag_2} + def complete_catalog( self, catalog, @@ -222,7 +252,10 @@ def complete_catalog( """ # Supported outputs and capability filtering # Columns this method can fill using the configured model - all_cols = ("phi1", "phi2", "dist", "mag_g", "mag_r", "mu1", "mu2", "rv") + # Magnitude columns are band-/survey-general (legacy mag_g/mag_r for a + # single-survey isochrone; __true for multi-survey). + mag_cols = self._iso_mag_columns() + all_cols = ("phi1", "phi2", "dist") + tuple(mag_cols) + ("mu1", "mu2", "rv") target_cols = ( list(all_cols) if columns_to_add is None @@ -236,11 +269,6 @@ def complete_catalog( if unknown: warnings.warn(f"Ignoring unknown columns: {unknown}") - if self.isochrone is None: - removed = [c for c in target_cols if c in ("mag_g", "mag_r")] - target_cols = [c for c in target_cols if c not in ("mag_g", "mag_r")] - if removed: - self._info(verbose, "Isochrone model not defined; skipping magnitudes.") if self.velocity is None: removed = [c for c in target_cols if c in ("mu1", "mu2", "rv")] target_cols = [c for c in target_cols if c not in ("mu1", "mu2", "rv")] @@ -282,8 +310,8 @@ def complete_catalog( # dist (needs phi1) if "dist" in target_cols or ( - any(c in target_cols for c in ("mag_g", "mag_r")) - and any(c not in df.columns for c in ("mag_g", "mag_r")) + any(c in target_cols for c in mag_cols) + and any(c not in df.columns for c in mag_cols) ): idx = self._missing_idx(df, "dist") if len(idx) > 0: @@ -298,10 +326,11 @@ def complete_catalog( self._info(verbose, f"Filled {len(idx)} dist values.") # magnitudes (need dist and isochrone) - if any(c in target_cols for c in ("mag_g", "mag_r")): - if "mag_g" in df.columns and "mag_r" in df.columns: + if any(c in target_cols for c in mag_cols): + if all(c in df.columns for c in mag_cols): self._info( - verbose, "'mag_g' and 'mag_r' already exist; no sampling performed." + verbose, + f"{mag_cols} already exist; no sampling performed.", ) else: # Verify distance modulus availability @@ -310,15 +339,14 @@ def complete_catalog( "dist is required to sample apparent magnitudes; include 'dist' in `columns_to_add` or provide it in catalog" ) dist_vals = df["dist"].to_numpy() - mag_g, mag_r = self.isochrone.sample(N, dist_vals) - # Add both magnitudes to keep colors consistent across rows - if "mag_g" in df.columns or "mag_r" in df.columns: + # Sample all bands together to keep colors consistent across rows + if any(c in df.columns for c in mag_cols): self._info( verbose, - "Overwriting existing mag_g and/or mag_r to keep colors consistent.", + "Overwriting existing magnitude columns to keep colors consistent.", ) - df["mag_g"] = mag_g - df["mag_r"] = mag_r + for col, vals in self._sample_iso_mags(N, dist_vals).items(): + df[col] = vals self._info(verbose, f"Filled magnitudes for {N} rows.") # velocities (need phi1 and velocity model) @@ -549,31 +577,192 @@ class DistanceModel(ConfigurableModel): class IsochroneModel(ConfigurableModel): - """Isochrone wrapper using ``ugali`` for CMD sampling.""" + """Isochrone wrapper using ``ugali`` for CMD sampling. + + Two configuration forms are supported: + + - **Single-survey** (legacy): the isochrone section carries the ``ugali`` + factory keys directly (``name``, ``survey``, ``age``, ``z``, ``band_1``, + ``band_2``, ...). :meth:`sample` returns ``(mag_band_1, mag_band_2)`` and + reproduces the previous behaviour exactly. + - **Multi-survey**: a ``surveys`` mapping + ``{survey_name: {survey, band_1, band_2, vega_to_ab}}`` plus shared keys + (``name``, ``age``, ``z``, ...) at the top level. One ``ugali`` isochrone + is built per survey from the *same* stellar population, so a single shared + draw of initial masses (:meth:`sample_masses`) is interpolated into every + survey's bands — giving the same physical star consistent magnitudes + across surveys. :meth:`sample_multisurvey` returns + ``{(survey, band): apparent_mag}``. + """ + + # Defaults for the shared isochrone mass grid (see ugali Isochrone.sample) + _MASS_MIN = 0.1 + _MASS_STEPS = 1000 def __init__(self, config, **kwargs): super().__init__(config, **kwargs) def create_isochrone(self, config): - """Construct the underlying ``ugali`` isochrone from configuration. + """Construct the underlying ``ugali`` isochrone(s) from configuration. Parameters ---------- config : dict - Isochrone factory configuration. + Isochrone factory configuration. A ``surveys`` key selects the + multi-survey form; otherwise the single-survey (legacy) form is used. """ + self.multi_survey = "surveys" in config + if self.multi_survey: + self._create_multisurvey_isochrones(config) + else: + self._create_single_isochrone(config) + + def _build_iso(self, factory_config): + """Build one ``ugali`` isochrone with its distance modulus reset to 0.""" import ugali.isochrone - self.iso = ugali.isochrone.factory(**config) - self.iso.params["distance_modulus"].set_bounds([0, 50]) + iso = ugali.isochrone.factory(**factory_config) + iso.params["distance_modulus"].set_bounds([0, 50]) + iso.distance_modulus = 0 + return iso + + def _create_single_isochrone(self, config): + """Build a single isochrone from a legacy (flat) isochrone config.""" if "distance_modulus" in config: warnings.warn( - 'Please use the "distance_modulus" section of the configuration file, instead of the isochrone section, to define a distance module.' + 'Please use the "distance_modulus" section of the configuration ' + "file, instead of the isochrone section, to define a distance modulus." ) - self.iso.distance_modulus = 0 + factory_config = {k: v for k, v in config.items() if k != "vega_to_ab"} + self.iso = self._build_iso(factory_config) + self.survey_name = config.get("survey") + self.band_1 = config.get("band_1") + self.band_2 = config.get("band_2") + # Unified bookkeeping so the shared helpers work in both modes. + self.surveys = [self.survey_name] + self.isos = {self.survey_name: self.iso} + self.survey_bands = {self.survey_name: (self.band_1, self.band_2)} + self._vega_to_ab = {self.survey_name: config.get("vega_to_ab")} + + def _create_multisurvey_isochrones(self, config): + """Build one isochrone per survey, sharing the top-level stellar params.""" + shared = {k: v for k, v in config.items() if k != "surveys"} + self.isos = {} + self.survey_bands = {} + self._vega_to_ab = {} + self.surveys = [] + for name, scfg in config["surveys"].items(): + factory_config = { + **shared, + **{k: v for k, v in scfg.items() if k != "vega_to_ab"}, + } + self.isos[name] = self._build_iso(factory_config) + self.survey_bands[name] = (scfg.get("band_1"), scfg.get("band_2")) + self._vega_to_ab[name] = scfg.get("vega_to_ab") + self.surveys.append(name) + # Primary isochrone drives the shared mass draw and the legacy sample(). + self.survey_name = self.surveys[0] + self.iso = self.isos[self.survey_name] + self.band_1, self.band_2 = self.survey_bands[self.survey_name] + + def sample_masses(self, nstars, rng=None, mass_min=None, mass_steps=None): + """Draw ``nstars`` initial stellar masses from the shared isochrone IMF. + + The masses are drawn once from the primary isochrone's mass PDF and are + meant to be interpolated into each survey's bands, so the same physical + star gets consistent magnitudes across surveys. - def sample(self, nstars, distance_modulus, **kwargs): - """Simulate magnitudes in g and r bands. + Parameters + ---------- + nstars : int + Number of stars to draw (returns exactly this many). + rng : numpy.random.Generator, optional + Random number generator (a default one is created if omitted). + mass_min, mass_steps : float, int, optional + Passed to the ``ugali`` isochrone sampler. + + Returns + ------- + numpy.ndarray + Initial masses, shape ``(nstars,)``. + """ + rng = np.random.default_rng() if rng is None else rng + mass_min = self._MASS_MIN if mass_min is None else mass_min + mass_steps = self._MASS_STEPS if mass_steps is None else mass_steps + grid = self.iso.sample(mass_min=mass_min, mass_steps=mass_steps) + mass_init, mass_pdf = grid[0], grid[1] + pdf = mass_pdf / mass_pdf.sum() + return rng.choice(mass_init, size=int(nstars), p=pdf) + + def _absolute_mags(self, iso, masses, mass_min=None, mass_steps=None): + """Interpolate a survey isochrone's absolute mags at given init masses.""" + mass_min = self._MASS_MIN if mass_min is None else mass_min + mass_steps = self._MASS_STEPS if mass_steps is None else mass_steps + grid = iso.sample(mass_min=mass_min, mass_steps=mass_steps) + mass_init, mag_1, mag_2 = grid[0], grid[3], grid[4] + order = np.argsort(mass_init) + mass_init = mass_init[order] + return ( + np.interp(masses, mass_init, mag_1[order]), + np.interp(masses, mass_init, mag_2[order]), + ) + + def _apply_vega_to_ab(self, survey, band, mag): + """Apply an optional per-band Vega->AB offset (``AB = Vega + offset``). + + ``vega_to_ab`` in the config is a ``{band: offset}`` mapping; if absent + or empty, the magnitudes are returned unchanged. This is the isolated + shim that lets multi-survey injection work while ``ugali`` still returns + some bands in Vega; it becomes a no-op once bands are natively AB. + """ + spec = self._vega_to_ab.get(survey) + if not spec: + return mag + if isinstance(spec, dict): + return mag + spec.get(band, 0.0) + warnings.warn( + f"vega_to_ab for survey '{survey}' is set but is not a " + "{band: offset} mapping; no Vega->AB correction applied." + ) + return mag + + @staticmethod + def _add_distance_modulus(abs_mag, distance_modulus): + """Add a scalar or per-star distance modulus to absolute magnitudes.""" + if distance_modulus is None: + return abs_mag + return abs_mag + np.asarray(distance_modulus, dtype=float) + + def sample_multisurvey(self, nstars, distance_modulus, rng=None, **kwargs): + """Sample apparent magnitudes for every ``(survey, band)``. + + A single shared draw of initial masses (see :meth:`sample_masses`) is + interpolated into each survey's bands, so the same physical star is + consistent across surveys. + + Returns + ------- + dict + ``{(survey_name, band): apparent_magnitude_array}``. + """ + masses = self.sample_masses( + nstars, + rng=rng, + mass_min=kwargs.get("mass_min"), + mass_steps=kwargs.get("mass_steps"), + ) + out = {} + for name in self.surveys: + band_1, band_2 = self.survey_bands[name] + abs_1, abs_2 = self._absolute_mags(self.isos[name], masses) + abs_1 = self._apply_vega_to_ab(name, band_1, abs_1) + abs_2 = self._apply_vega_to_ab(name, band_2, abs_2) + out[(name, band_1)] = self._add_distance_modulus(abs_1, distance_modulus) + out[(name, band_2)] = self._add_distance_modulus(abs_2, distance_modulus) + return out + + def sample(self, nstars, distance_modulus, rng=None, **kwargs): + """Simulate magnitudes in the two bands of the (primary) isochrone. Parameters ---------- @@ -585,23 +774,34 @@ def sample(self, nstars, distance_modulus, **kwargs): Returns ------- tuple of numpy.ndarray - ``(mag_g, mag_r)`` arrays. + ``(mag_band_1, mag_band_2)`` arrays. For a multi-survey isochrone + this returns the primary survey's two bands; use + :meth:`sample_multisurvey` to get every survey's bands. """ + if getattr(self, "multi_survey", False): + mags = self.sample_multisurvey( + nstars, distance_modulus, rng=rng, **kwargs + ) + return ( + mags[(self.survey_name, self.band_1)], + mags[(self.survey_name, self.band_2)], + ) + + # Single-survey: preserve the existing ugali.simulate-based behaviour. stellar_mass = nstars * self.iso.stellar_mass() + mag_1, mag_2 = self.iso.simulate( + stellar_mass, distance_modulus=self.iso.distance_modulus + ) if np.isscalar(distance_modulus): - mag_g, mag_r = self.iso.simulate( - stellar_mass, distance_modulus=self.iso.distance_modulus - ) - mag_g, mag_r = [ - mag + np.ones_like(mag) * distance_modulus for mag in (mag_g, mag_r) + mag_1, mag_2 = [ + mag + np.ones_like(mag) * distance_modulus for mag in (mag_1, mag_2) ] else: - mag_g, mag_r = self.iso.simulate( - stellar_mass, distance_modulus=self.iso.distance_modulus - ) - mag_g, mag_r = [mag + distance_modulus for mag in (mag_g, mag_r)] + mag_1, mag_2 = [mag + distance_modulus for mag in (mag_1, mag_2)] - return mag_g, mag_r + mag_1 = self._apply_vega_to_ab(self.survey_name, self.band_1, mag_1) + mag_2 = self._apply_vega_to_ab(self.survey_name, self.band_2, mag_2) + return mag_1, mag_2 def _dist_to_modulus(self, distance): """ From d28d6daff4ebaedf5bfbf08dc3e72043b1b339c4 Mon Sep 17 00:00:00 2001 From: psferguson Date: Mon, 15 Jun 2026 16:40:38 -0700 Subject: [PATCH 05/54] phase 4 and notebook black/isort --- config/scenes/roman_rubin_demo.yaml | 78 +++ docs/source/index.rst | 6 + docs/source/roman_multisurvey_plan.md | 133 +++--- notebooks/multisurvey_phases_demo.ipynb | 607 ++++++++++++++++++++++++ scripts/build_multisurvey_demo_nb.py | 299 ++++++++++++ streamobs/columns.py | 32 +- streamobs/model.py | 122 ++--- streamobs/observed.py | 273 ++++++++++- streamobs/plotting.py | 26 +- streamobs/samplers.py | 16 +- streamobs/surveys.py | 7 +- streamobs/utils.py | 1 + tests/test_functions.py | 177 +++++-- 13 files changed, 1563 insertions(+), 214 deletions(-) create mode 100644 config/scenes/roman_rubin_demo.yaml create mode 100644 notebooks/multisurvey_phases_demo.ipynb create mode 100644 scripts/build_multisurvey_demo_nb.py diff --git a/config/scenes/roman_rubin_demo.yaml b/config/scenes/roman_rubin_demo.yaml new file mode 100644 index 0000000..b8c9fc3 --- /dev/null +++ b/config/scenes/roman_rubin_demo.yaml @@ -0,0 +1,78 @@ +# Multi-survey demo scene: a single toy stream observed by BOTH Rubin/LSST and +# Roman. Consumed by `streamobs.observed.MultiSurveyInjector` (Phase 4). +# +# Usage (see notebooks/multisurvey_phases_demo.ipynb): +# +# import yaml, pandas as pd +# from streamobs.observed import MultiSurveyInjector +# scene = yaml.safe_load(open("config/scenes/roman_rubin_demo.yaml")) +# msi = MultiSurveyInjector(scene["surveys"]) +# df = pd.DataFrame(index=range(int(scene["stream"]["nstars"]))) +# out = msi.inject(df, scene["survey_bands"], +# stream_config=scene["stream"], seed=42) +# +# The same physical stars carry both `lsst__*` and `roman__*` +# columns; each band's errors / detection come from that survey's own maps. + +# Surveys to inject. The mapping KEY is the column namespace (lsst_r_obs, ...). +# Each value is a StreamInjector spec: a survey-name string here (loaded via +# Survey.load); could also be a {name, release} once the loader supports it. +surveys: + lsst: lsst + roman: roman + +# Bands to inject per survey. Each survey's `completeness_band` must appear here. +# NOTE: a band label is used BOTH as the ugali isochrone field name AND as the +# key into that survey's maglim/photo-error/extinction maps, so the two must +# agree. ugali's Roman fields are upper-case (F106, F158, ...); the Roman +# Survey's maps must therefore be keyed with the same upper-case labels. +survey_bands: + lsst: [r, g] + roman: [F106, F158] + +# Shared stream geometry + a MULTI-SURVEY isochrone. The isochrone draws one set +# of initial masses (exactly `nstars`) and interpolates them into every survey's +# bands, so a star's LSST and Roman magnitudes describe the same object. +stream: + nstars: 5.0e+3 + + density: + type: Uniform + xmin: -9.0 + xmax: +9.0 + + track: + center: + type: Constant + value: 0.0 + spread: + type: Constant + value: 0.2 + sampler: Gaussian + + distance_modulus: + center: + type: Line + slope: 0.1 + intercept: 16.5 + spread: + type: Constant + value: 0.0001 + sampler: Uniform + + isochrone: + # Shared stellar population (one age/metallicity for both surveys). + name: Marigo2017 + age: 12.0 + z: 0.0006 + surveys: + lsst: + survey: lsst + band_1: g + band_2: r + roman: + survey: roman + band_1: F106 + band_2: F158 + # Roman bands are AUTOMATICALLY converted Vega->AB by IsochroneModel + # (streamobs.model.ROMAN_VEGA_TO_AB); no per-config flag is needed. diff --git a/docs/source/index.rst b/docs/source/index.rst index dffb5ee..8d87f08 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -108,6 +108,12 @@ Documentation Contents data modules +.. toctree:: + :maxdepth: 2 + :caption: Design Notes + + roman_multisurvey_plan + Indices and Tables ================== diff --git a/docs/source/roman_multisurvey_plan.md b/docs/source/roman_multisurvey_plan.md index 6a10a47..19f5de3 100644 --- a/docs/source/roman_multisurvey_plan.md +++ b/docs/source/roman_multisurvey_plan.md @@ -39,10 +39,16 @@ Two science realities drive specific features: `{survey_name: Survey}` and delegates per-survey work to the existing, unchanged `Survey` per-band API. Not a composite `Survey`; not manual re-runs. -2. **Column convention.** Multi-survey outputs use `{survey}_{band}_true`, - `{survey}_{band}_obs`, and `{survey}_{band}_err` (e.g. `roman_f158_obs`, - `lsst_r_err`). The single-survey `StreamInjector` **keeps emitting the legacy - names** `mag_{band}_obs` / `magerr_{band}` / `flag_observed` unchanged. +2. **Column convention (uniform `_true`/`_obs`/`_err`).** A single naming scheme + is used everywhere: `{band}_true` / `{band}_obs` / `{band}_err` / + `flag_observed` single-survey, and `{survey}_{band}_true` / + `{survey}_{band}_obs` / `{survey}_{band}_err` / `{survey}_flag_observed` + multi-survey (e.g. `roman_f158_obs`, `lsst_r_err`). The single- and + multi-survey forms differ only by the optional survey prefix. + **This intentionally drops the historical `mag_{band}` / `mag_{band}_obs` / + `magerr_{band}` names — it is *not* backward compatible** with catalogs or + downstream readers expecting those columns (decision made deliberately to + keep one convention; see decision 5). 3. **Sample-vs-catalog error split, backward-compatible by default.** `Survey` holds two error curves, both functions of `delta_mag = mag − maglim`: - `log_photo_error_catalog` — the survey's **reported** error curve (the @@ -61,11 +67,13 @@ Two science realities drive specific features: the true scatter directly.) 4. **Sample stellar masses once, interpolate per survey.** This is a correctness requirement (see below) and the engine of the whole feature. -5. **Backward compatibility is by column-schema + public API**, not internal - coupling. Downstream consumers read pre-generated CSVs by column name - (`mag_g_obs`/`mag_r_obs`/`flag_observed`) and use the - `StreamInjector(survey).inject(df, bands=[...])` API. Both are preserved; - multi-survey support is purely additive. +5. **Public API preserved; column schema deliberately changed.** The + `StreamInjector(survey).inject(df, bands=[...])` API is unchanged. The + **column names are not** — we adopt the uniform `{band}_true/_obs/_err` + scheme (decision 2) *instead of* preserving the historical + `mag_g_obs`/`mag_r_obs`/`magerr_g` names. Downstream consumers that read those + columns must be updated. This is an accepted break in exchange for one + convention across single- and multi-survey output. ## Behaviour changes for discussion @@ -86,12 +94,22 @@ and makes "observed" mean "detected in everything you asked for". Callers can restore any prior behaviour by passing `detection_mag_cut=[...]` explicitly (e.g. `["g"]` for the old LSST default). **Adopted, but flagged for review.** -### `nstars` becomes "exactly N stars" (was an emergent IMF count) +### `nstars` becomes "exactly N stars" (was an emergent IMF count) *(adopted, but flagged for review)* -Today {meth}`~streamobs.model.IsochroneModel.sample` converts `nstars` into a -total stellar mass and lets `ugali`'s `iso.simulate()` return a *random-length* -IMF realization — so the number of stars returned is stochastic and generally -**≠ `nstars`**. +```{important} +**Adopted (Phase 4).** {meth}`~streamobs.model.IsochroneModel.sample` now draws +*exactly* `nstars` (the shared-mass path described below), for **both** +single-survey and multi-survey isochrones. The single-survey +`ugali.simulate()` emergent-count path has been removed. This flag is **left in +place for review** because it changes what `StreamModel.sample(size)` returns +for existing single-survey configs — see the discussion below before relying on +the new normalization. +``` + +Previously {meth}`~streamobs.model.IsochroneModel.sample` converted `nstars` +into a total stellar mass and let `ugali`'s `iso.simulate()` return a +*random-length* IMF realization — so the number of stars returned was stochastic +and generally **≠ `nstars`**. The multi-survey requirement (the *same physical star* must get consistent Roman **and** Rubin magnitudes) forces us instead to draw a fixed set of initial @@ -108,25 +126,23 @@ semantics for injection (you control N, and it is shared across surveys), but it changes what `StreamModel.sample(size)` returns and could shift normalization in any analysis that relied on the old emergent count. **Open for discussion.** -### Where Roman Vega→AB conversion lives - -`ugali` appears to deliver Roman isochrone magnitudes in **Vega**, while our -catalogs are **AB**; the prototype corrects this after `isochrone_factory` with a -`ROMAN_ZEROPOINTS[...]["diff"]` table. +### Roman Vega→AB conversion is automatic and unconditional -**Current plan (do both, in order):** +`ugali` delivers Roman isochrone magnitudes in **Vega**, while our catalogs are +**AB**. `IsochroneModel` corrects this **unconditionally** for every Roman band +using the module-level table `streamobs.model.ROMAN_VEGA_TO_AB` (AB = Vega + +offset). The per-band offsets are the mode of the by-chip Roman zeropoints +(`Roman_zeropoints_20240301.ecsv`), the same values the +`rubin_roman_object_classification` prototype used: -1. **This branch:** implement the correction in `streamobs` as a single isolated - shim — `IsochroneModel._apply_vega_to_ab`, gated by a per-survey `vega_to_ab` - config flag — so the feature works now without blocking on an upstream - release. It is structured so that if `ugali` later returns AB natively we just - set `vega_to_ab: false` and the shim becomes a no-op / is removed. -2. **Off the critical path:** pursue native AB support (or an explicit - photometric-system flag) in `ugali` itself, since `ugali` owns the isochrone - photometric system and every downstream user would benefit. +| band | F062 | F087 | F106 | F129 | F146 | F158 | F184 | F213 | +|---|---|---|---|---|---|---|---|---| +| AB − Vega | 0.153 | 0.481 | 0.660 | 1.051 | 1.164 | 1.315 | 1.556 | 1.837 | -*(If we decide to commit to the `ugali`-native route up front, the shim in -Phase 3 is dropped.)* +There is **no config flag** — Roman bands are always converted, non-Roman bands +pass through unchanged. The code carries a `TODO` flagging that this conversion +ideally belongs in `ugali` itself (so isochrones are returned natively in AB); +when that lands the table can be removed. ## Phased rollout @@ -184,22 +200,22 @@ API are unchanged; the test-branch suite stays green (94 passing; the lone ### Phase 3 — Multi-band / multi-survey `IsochroneModel` ✅ *(implemented)* - ✅ `IsochroneModel.create_isochrone` accepts either today's single-survey - config or a multi-survey form (`surveys: {name: {survey, band_1, band_2, - vega_to_ab}}` plus shared `name`/`age`/`z`/... at top level), building one - `ugali` isochrone per survey (`self.isos`, `self.survey_bands`). + config or a multi-survey form (`surveys: {name: {survey, band_1, band_2}}` + plus shared `name`/`age`/`z`/... at top level), building one `ugali` isochrone + per survey (`self.isos`, `self.survey_bands`). - ✅ New `sample_masses(...)` draws the initial masses *once* from the primary isochrone's IMF (exactly `nstars`), and `sample_multisurvey(...)` interpolates those shared masses into every survey's bands → `{(survey, band): apparent_mag}` (same physical star, consistent across surveys). The legacy `sample(nstars, dm)` still returns the `(mag_band_1, mag_band_2)` tuple (the primary survey's two bands) so existing callers are unchanged. -- ✅ `_apply_vega_to_ab(survey, band, mag)` shim — gated by a per-survey - `vega_to_ab: {band: offset}` mapping (`AB = Vega + offset`); a no-op when - absent. Applied in both the single- and multi-survey paths. +- ✅ `_to_ab(band, mag)` converts Roman bands Vega→AB **unconditionally** using + the `ROMAN_VEGA_TO_AB` table (no config flag; non-Roman bands pass through). + Applied in the shared `sample_multisurvey` path. See the Vega→AB section above. - ✅ `StreamModel.sample`/`complete_catalog` derive their magnitude columns from - the isochrone via `_iso_mag_columns()` / `_sample_iso_mags()`: legacy - `mag_g`/`mag_r` for a single-survey isochrone, `__true` for a - multi-survey one. Naming routes through `columns.true_col`. + the isochrone via `_iso_mag_columns()` / `_sample_iso_mags()`: `_true` + for a single-survey isochrone, `__true` for a multi-survey one. + Naming routes through `columns.true_col`. **Note on the chosen API:** the plan originally named the dict-returning method `sample` and a tuple `sample_legacy`; to avoid `sample()` changing return *type* @@ -209,20 +225,31 @@ as the `(mag_1, mag_2)` tuple and adds `sample_multisurvey()` for the dict. **Validated:** single-survey model tests unchanged (test branch green, 94 passing); a two-isochrone multi-survey config produces consistent shared-mass -magnitudes, the Vega→AB offset shifts only the configured band, and -`StreamModel.sample` emits the `__true` columns. - -### Phase 4 — `MultiSurveyInjector` - -- Refactor the per-band body of `StreamInjector.inject` into a shared - `_inject_one_survey(...)` helper. -- `MultiSurveyInjector(surveys).inject(data, survey_bands, ...)`: - (1) one shared sky placement; (2) one shared true-magnitude fill (masses - sampled once); (3) a per-survey loop writing `{survey}_{band}_obs/_err` and - `{survey}_flag_observed` using each survey's own `completeness_band` and maglim - maps. Per-survey RNG via `rng.spawn(...)` for order-independent reproducibility. -- A *scene* config (`config/scenes/roman_rubin_demo.yaml`) lists the surveys, - bands, per-survey isochrones, and shared stream geometry. +magnitudes, Roman bands are converted Vega→AB by the fixed `ROMAN_VEGA_TO_AB` +offsets, and `StreamModel.sample` emits the `__true` columns. + +### Phase 4 — `MultiSurveyInjector` ✅ *(implemented)* + +- ✅ The per-band body of `StreamInjector.inject` is extracted into a shared + `StreamInjector._inject_one_survey(data, bands, survey_namespace=None, ...)` + helper. It assumes positions and true magnitudes are already present and only + does the observed/err draw, detection flags, and S/N cut, routing every column + name through `columns.py` (`survey_namespace=None` ⇒ legacy names). `inject` + now just completes the data and delegates to it, so single-survey behaviour is + unchanged. +- ✅ `MultiSurveyInjector(surveys).inject(data, survey_bands, stream_config=...)`: + (1) one shared sky placement; (2) one shared true-magnitude fill via a + multi-survey isochrone (masses sampled once → every survey's + `__true`); (3) a per-survey loop calling each survey's + `_inject_one_survey` with `survey_namespace=`, writing + `__obs/_err` and `_flag_observed` from that survey's own + `completeness_band` and maglim maps. Per-survey RNG via `rng.spawn(...)` for + order-independent reproducibility. `columns.perfect_flag_col` namespaces the + optional `perfect_galstarsep` flag. +- ✅ A *scene* config (`config/scenes/roman_rubin_demo.yaml`) lists the surveys, + per-survey bands, the multi-survey isochrone, and shared stream geometry. +- ✅ `notebooks/multisurvey_phases_demo.ipynb` walks through Phases 1–4 end to + end. ### Phase 5 *(future)* — Lightweight background + galaxy misclassification diff --git a/notebooks/multisurvey_phases_demo.ipynb b/notebooks/multisurvey_phases_demo.ipynb new file mode 100644 index 0000000..a8f0dbb --- /dev/null +++ b/notebooks/multisurvey_phases_demo.ipynb @@ -0,0 +1,607 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "956da58f", + "metadata": {}, + "source": [ + "# Multi-survey injection — Phases 1-4 demo\n", + "\n", + "This notebook walks through the `roman_multisurvey` refactor that lets `streamobs`\n", + "inject a single stream carrying **both Roman and Rubin/LSST** photometry, where\n", + "each band draws its errors and detection probability from its *own* survey.\n", + "\n", + "| Phase | What changed |\n", + "|---|---|\n", + "| **1** | `Survey` holds two error curves — a *catalog* (reported `magerr`, drives the S/N cut) and an optional *sample* (true scatter, drives the noise draw). `get_photo_error(..., kind=)` selects between them. |\n", + "| **2** | The injector is de-hardcoded off `{r, g}`; column names route through `streamobs.columns`, using a **uniform** `_true/_obs/_err` scheme (`_…` when namespaced). The S/N cut now applies to **all** injected bands. |\n", + "| **3** | `IsochroneModel` is multi-band / multi-survey: masses are drawn **once** (exactly `nstars`) and interpolated into every survey's bands, so the *same physical star* is consistent across surveys. Roman bands are auto-converted Vega→AB. |\n", + "| **4** | `MultiSurveyInjector` orchestrates one shared sky placement + one shared true-mag fill, then a per-survey loop writing `__obs/_err` and `_flag_observed`. |\n", + "\n", + "See `docs/source/roman_multisurvey_plan.md` for the full design.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1d385c8b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-15T23:33:47.506938Z", + "iopub.status.busy": "2026-06-15T23:33:47.506845Z", + "iopub.status.idle": "2026-06-15T23:33:57.035573Z", + "shell.execute_reply": "2026-06-15T23:33:57.034912Z" + } + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "import numpy as np\n", + "import pandas as pd\n", + "import healpy as hp\n", + "import yaml\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from streamobs.surveys import Survey\n", + "from streamobs.model import StreamModel, IsochroneModel\n", + "from streamobs.observed import StreamInjector, MultiSurveyInjector\n", + "from streamobs import columns as C\n", + "\n", + "import os\n", + "# Resolve the repo root whether the notebook runs from repo root or notebooks/.\n", + "REPO = \"..\" if os.path.exists(\"../config/scenes/roman_rubin_demo.yaml\") else \".\"\n", + "rng = np.random.default_rng(42)\n" + ] + }, + { + "cell_type": "markdown", + "id": "5dd03468", + "metadata": {}, + "source": [ + "## A runnable stub survey\n", + "\n", + "The real Roman/Rubin maglim maps and CSV tables are **not committed** to this\n", + "branch (they live in the git-ignored `data/surveys/`). So that this notebook\n", + "runs end-to-end, we use a tiny `StubSurvey` that:\n", + "\n", + "* keeps the **real** `Survey.get_photo_error` (so the Phase-1 two-curve split is\n", + " genuine) and `get_maglim`,\n", + "* supplies analytic `log_photo_error_*` curves and a logistic completeness.\n", + "\n", + "The **isochrones are real** (`Marigo2017` via `ugali`). To run against real\n", + "surveys, just replace `StubSurvey(...)` with `Survey.load(\"lsst\", release=...)`\n", + "etc. — the injector code is identical.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8ed0d733", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-15T23:33:57.037569Z", + "iopub.status.busy": "2026-06-15T23:33:57.037358Z", + "iopub.status.idle": "2026-06-15T23:33:57.041674Z", + "shell.execute_reply": "2026-06-15T23:33:57.041252Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "StubSurvey ready\n" + ] + } + ], + "source": [ + "NSIDE = 1 # whole-sky single-pixel maps; enough for a synthetic demo\n", + "\n", + "class StubSurvey(Survey):\n", + " \"\"\"Minimal Survey: real photo-error machinery, analytic completeness/maps.\"\"\"\n", + "\n", + " def __init__(self, name, bands, completeness_band, maglim=27.0,\n", + " sample_inflation=1.9):\n", + " self.name = name\n", + " self.bands = list(bands)\n", + " self.completeness_band = completeness_band\n", + " self.saturation = {b: 16.0 for b in bands}\n", + " self.sys_error = {b: 0.005 for b in bands}\n", + " self.delta_saturation = -10.0\n", + " self.coeff_extinc = {b: 0.0 for b in bands}\n", + " self.ebv_map = np.zeros(hp.nside2npix(NSIDE))\n", + " self.maglim_maps = {b: np.full(hp.nside2npix(NSIDE), maglim) for b in bands}\n", + " self.coverage = np.ones(hp.nside2npix(NSIDE)) # footprint = whole sky\n", + " # Phase 1: two error curves, both functions of delta_mag = mag - maglim.\n", + " # Catalog = reported error; sample = true scatter (here ~inflation x larger,\n", + " # echoing the Roman DC2 finding that true scatter ~2x reported magerr).\n", + " self.log_photo_error_catalog = lambda dm: np.log10(\n", + " 0.01 + 0.10 * np.exp(np.clip(dm, -30, 5)))\n", + " self.log_photo_error_sample = lambda dm: np.log10(\n", + " sample_inflation * (0.01 + 0.10 * np.exp(np.clip(dm, -30, 5))))\n", + "\n", + " # analytic rolloff: ~1 when bright (mag << maglim), 0.5 at maglim, ->0 fainter\n", + " def get_completeness(self, band, mag, maglim):\n", + " return 1.0 / (1.0 + np.exp((np.asarray(mag) - np.asarray(maglim)) / 0.25))\n", + "\n", + " def get_detection_efficiency(self, band, mag, maglim):\n", + " return self.get_completeness(band, mag, maglim)\n", + "\n", + " def get_extinction(self, band, pixel=None):\n", + " return np.zeros(np.size(pixel))\n", + "\n", + "print(\"StubSurvey ready\")" + ] + }, + { + "cell_type": "markdown", + "id": "aa861d61", + "metadata": {}, + "source": [ + "## Phase 1 — sample vs. catalog photometric error\n", + "\n", + "`Survey` now carries two curves. `get_photo_error(kind=\"catalog\")` is the\n", + "reported error that becomes `magerr` and drives the S/N cut;\n", + "`get_photo_error(kind=\"sample\")` is the *true* scatter used to draw the observed\n", + "magnitude. When no sample curve is loaded, `sample` transparently falls back to\n", + "`catalog`, so legacy single-curve behaviour is bit-for-bit preserved.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "63de7e59", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-15T23:33:57.042872Z", + "iopub.status.busy": "2026-06-15T23:33:57.042761Z", + "iopub.status.idle": "2026-06-15T23:33:57.234295Z", + "shell.execute_reply": "2026-06-15T23:33:57.233794Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no sample curve -> sample falls back to catalog: True\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sv = StubSurvey(\"lsst\", [\"g\", \"r\"], completeness_band=\"r\", maglim=26.5)\n", + "mags = np.linspace(20, 28, 200)\n", + "maglim = np.full_like(mags, 26.5)\n", + "err_cat = sv.get_photo_error(\"r\", mags, maglim, kind=\"catalog\")\n", + "err_smp = sv.get_photo_error(\"r\", mags, maglim, kind=\"sample\")\n", + "\n", + "# A survey with NO sample curve -> sample falls back to catalog (identical)\n", + "sv_legacy = StubSurvey(\"lsst\", [\"g\", \"r\"], \"r\", maglim=26.5)\n", + "sv_legacy.log_photo_error_sample = None\n", + "err_fallback = sv_legacy.get_photo_error(\"r\", mags, maglim, kind=\"sample\")\n", + "assert np.allclose(err_fallback, sv_legacy.get_photo_error(\"r\", mags, maglim, kind=\"catalog\"))\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "ax.plot(mags, err_cat, label='catalog (reported magerr, drives S/N cut)')\n", + "ax.plot(mags, err_smp, label='sample (true scatter, drives noise draw)')\n", + "ax.axvline(26.5, ls=':', c='k', lw=1, label='maglim')\n", + "ax.set_xlabel('magnitude'); ax.set_ylabel('photometric error [mag]')\n", + "ax.set_title('Phase 1: two error curves on Survey'); ax.legend(); fig.tight_layout()\n", + "print(\"no sample curve -> sample falls back to catalog:\",\n", + " np.allclose(err_fallback, err_cat))" + ] + }, + { + "cell_type": "markdown", + "id": "86c9e1fd", + "metadata": {}, + "source": [ + "## Phase 2 — arbitrary bands + `columns.py`\n", + "\n", + "The injector no longer hard-codes `{r, g}`. Column names come from\n", + "`streamobs.columns`, using one **uniform** convention: `_true` /\n", + "`_obs` / `_err` / `flag_observed` single-survey, and the same with a\n", + "`_` prefix multi-survey. (This intentionally drops the historical\n", + "`mag_` / `magerr_` names — not backward compatible.) Below we inject\n", + "Roman NIR bands `F106`/`F158` through the *single-survey* `StreamInjector` —\n", + "impossible under the old `{r,g}` block.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c54f0211", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-15T23:33:57.235495Z", + "iopub.status.busy": "2026-06-15T23:33:57.235382Z", + "iopub.status.idle": "2026-06-15T23:33:57.248981Z", + "shell.execute_reply": "2026-06-15T23:33:57.248544Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "single-survey: r_true r_obs r_err flag_observed\n", + "multi-survey : roman_F158_true roman_F158_obs roman_F158_err roman_flag_observed\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "injected NIR-only bands -> columns: ['F106_obs', 'F106_err', 'F158_obs', 'F158_err', 'flag_observed']\n", + "detected: 1281 / 1500\n" + ] + } + ], + "source": [ + "print(\"single-survey:\", C.true_col(\"r\"), C.obs_col(\"r\"), C.err_col(\"r\"), C.flag_col())\n", + "print(\"multi-survey :\", C.true_col(\"F158\", \"roman\"), C.obs_col(\"F158\", \"roman\"),\n", + " C.err_col(\"F158\", \"roman\"), C.flag_col(\"roman\"))\n", + "\n", + "roman_sv = StubSurvey(\"roman\", [\"F106\", \"F158\"], completeness_band=\"F158\", maglim=27.0)\n", + "inj = StreamInjector(roman_sv)\n", + "N = 1500\n", + "df = pd.DataFrame({\n", + " \"ra\": rng.uniform(10, 20, N),\n", + " \"dec\": rng.uniform(-5, 5, N),\n", + " \"F106_true\": rng.uniform(20, 28, N),\n", + " \"F158_true\": rng.uniform(20, 28, N),\n", + "})\n", + "out = inj.inject(df, bands=[\"F106\", \"F158\"], seed=1, verbose=False)\n", + "print(\"\\ninjected NIR-only bands -> columns:\",\n", + " [c for c in out.columns if c.endswith((\"_obs\", \"_err\")) or c == \"flag_observed\"])\n", + "print(\"detected:\", int(out.flag_observed.sum()), \"/\", len(out))" + ] + }, + { + "cell_type": "markdown", + "id": "4eae6753", + "metadata": {}, + "source": [ + "## Phase 3 — multi-band / multi-survey isochrone (exactly `nstars`)\n", + "\n", + "`IsochroneModel.sample` now draws **exactly** `nstars` (a fixed mass set), for\n", + "both single- and multi-survey configs. First, the single-survey path:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cdcb361c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-15T23:33:57.250040Z", + "iopub.status.busy": "2026-06-15T23:33:57.249928Z", + "iopub.status.idle": "2026-06-15T23:33:59.883954Z", + "shell.execute_reply": "2026-06-15T23:33:59.883368Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "single-survey StreamModel.sample returns EXACTLY nstars (5000, 1234) -> OK\n" + ] + } + ], + "source": [ + "single_cfg = {\n", + " \"density\": {\"type\": \"Uniform\", \"xmin\": -9.0, \"xmax\": 9.0},\n", + " \"track\": {\"center\": {\"type\": \"Constant\", \"value\": 0.0},\n", + " \"spread\": {\"type\": \"Constant\", \"value\": 0.2}, \"sampler\": \"Gaussian\"},\n", + " \"distance_modulus\": {\"center\": {\"type\": \"Line\", \"slope\": 0.1, \"intercept\": 16.5},\n", + " \"spread\": {\"type\": \"Constant\", \"value\": 1e-4}, \"sampler\": \"Uniform\"},\n", + " \"isochrone\": {\"name\": \"Marigo2017\", \"survey\": \"lsst\", \"age\": 12.0, \"z\": 0.0006,\n", + " \"band_1\": \"g\", \"band_2\": \"r\"},\n", + "}\n", + "sm = StreamModel(single_cfg)\n", + "for n in (5000, 1234):\n", + " assert len(sm.sample(n)) == n\n", + "print(\"single-survey StreamModel.sample returns EXACTLY nstars (5000, 1234) -> OK\")" + ] + }, + { + "cell_type": "markdown", + "id": "e414d472", + "metadata": {}, + "source": [ + "Now the multi-survey scene. One mass draw feeds both surveys, so a star's\n", + "LSST and Roman magnitudes describe the *same object*." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dbc7de53", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-15T23:33:59.885548Z", + "iopub.status.busy": "2026-06-15T23:33:59.885425Z", + "iopub.status.idle": "2026-06-15T23:34:00.223968Z", + "shell.execute_reply": "2026-06-15T23:34:00.223395Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "multi-survey true columns: ['lsst_g_true', 'lsst_r_true', 'roman_F106_true', 'roman_F158_true']\n", + "rows: 4000\n", + "corr(lsst_r, roman_F158) = 0.996 (shared masses => tightly correlated)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scene = yaml.safe_load(open(f\"{REPO}/config/scenes/roman_rubin_demo.yaml\"))\n", + "sm_ms = StreamModel(scene[\"stream\"])\n", + "df_ms = sm_ms.sample(4000)\n", + "true_cols = sorted(c for c in df_ms.columns if c.endswith(\"_true\"))\n", + "print(\"multi-survey true columns:\", true_cols)\n", + "print(\"rows:\", len(df_ms))\n", + "\n", + "rho = np.corrcoef(df_ms[\"lsst_r_true\"], df_ms[\"roman_F158_true\"])[0, 1]\n", + "print(f\"corr(lsst_r, roman_F158) = {rho:.3f} (shared masses => tightly correlated)\")\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(10, 4.2), sharey=True)\n", + "axes[0].scatter(df_ms.lsst_g_true - df_ms.lsst_r_true, df_ms.lsst_r_true, s=3, alpha=.3)\n", + "axes[0].set_xlabel(\"g - r\"); axes[0].set_ylabel(\"r\"); axes[0].set_title(\"Rubin/LSST CMD\")\n", + "axes[0].invert_yaxis()\n", + "axes[1].scatter(df_ms.roman_F106_true - df_ms.roman_F158_true, df_ms.roman_F158_true, s=3, alpha=.3, c=\"C3\")\n", + "axes[1].set_xlabel(\"F106 - F158\"); axes[1].set_ylabel(\"F158\"); axes[1].set_title(\"Roman CMD\")\n", + "axes[1].invert_yaxis()\n", + "fig.suptitle(\"Phase 3: same physical stars, two surveys\"); fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2f39058a", + "metadata": {}, + "source": [ + "Roman isochrone magnitudes are **always** converted Vega→AB (no config\n", + "flag) using the fixed `ROMAN_VEGA_TO_AB` table, sourced from the\n", + "`rubin_roman_object_classification` prototype. Non-Roman bands pass through\n", + "unchanged. (A `TODO` in the code notes this ideally belongs in `ugali`.)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5468d5aa", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-15T23:34:00.225519Z", + "iopub.status.busy": "2026-06-15T23:34:00.225397Z", + "iopub.status.idle": "2026-06-15T23:34:00.266916Z", + "shell.execute_reply": "2026-06-15T23:34:00.266455Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Roman Vega->AB offsets (AB = Vega + diff):\n", + " F062: +0.153\n", + " F087: +0.481\n", + " F106: +0.66\n", + " F129: +1.051\n", + " F146: +1.164\n", + " F158: +1.315\n", + " F184: +1.556\n", + " F213: +1.837\n", + "\n", + "F158 shift: [1.315 1.315] (always +1.315)\n", + "r shift: [0. 0.] (0 -> non-Roman pass-through)\n" + ] + } + ], + "source": [ + "from streamobs.model import ROMAN_VEGA_TO_AB\n", + "print(\"Roman Vega->AB offsets (AB = Vega + diff):\")\n", + "for b, v in ROMAN_VEGA_TO_AB.items():\n", + " print(f\" {b}: +{v}\")\n", + "\n", + "iso = StreamModel(scene[\"stream\"]).isochrone # multi-survey isochrone\n", + "x = np.array([20.0, 21.0])\n", + "print(\"\\nF158 shift:\", (iso._to_ab(\"F158\", x) - x), \" (always +1.315)\")\n", + "print(\"r shift:\", (iso._to_ab(\"r\", x) - x), \" (0 -> non-Roman pass-through)\")" + ] + }, + { + "cell_type": "markdown", + "id": "82017106", + "metadata": {}, + "source": [ + "## Phase 4 — `MultiSurveyInjector`\n", + "\n", + "One orchestrator, one shared sky placement and shared masses, then per-survey\n", + "observed columns and flags. Per-survey RNGs come from `rng.spawn(...)`, so the\n", + "result is reproducible and independent of survey order.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9b63b52a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-15T23:34:00.268030Z", + "iopub.status.busy": "2026-06-15T23:34:00.267909Z", + "iopub.status.idle": "2026-06-15T23:34:00.376660Z", + "shell.execute_reply": "2026-06-15T23:34:00.376201Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "columns produced:\n", + " lsst: ['lsst_flag_observed', 'lsst_g_err', 'lsst_g_obs', 'lsst_g_true', 'lsst_r_err', 'lsst_r_obs', 'lsst_r_true']\n", + " roman: ['roman_F106_err', 'roman_F106_obs', 'roman_F106_true', 'roman_F158_err', 'roman_F158_obs', 'roman_F158_true', 'roman_flag_observed']\n", + "\n", + "shared sky placement: ra/dec present = True\n", + "lsst detected: 1066 / 4000\n", + "roman detected: 3110 / 4000\n" + ] + } + ], + "source": [ + "lsst_sv = StubSurvey(\"lsst\", [\"g\", \"r\"], completeness_band=\"r\", maglim=26.0)\n", + "roman_sv = StubSurvey(\"roman\", [\"F106\", \"F158\"], completeness_band=\"F158\", maglim=27.0)\n", + "msi = MultiSurveyInjector({\"lsst\": StreamInjector(lsst_sv),\n", + " \"roman\": StreamInjector(roman_sv)}, primary=\"lsst\")\n", + "\n", + "# Input carries only stream coordinates; everything else is sampled once.\n", + "N = 4000\n", + "stream_in = pd.DataFrame({\"phi1\": rng.uniform(-9, 9, N), \"phi2\": np.zeros(N)})\n", + "cat = msi.inject(stream_in.copy(), scene[\"survey_bands\"],\n", + " stream_config=scene[\"stream\"], seed=7, verbose=False)\n", + "\n", + "print(\"columns produced:\")\n", + "for grp in (\"lsst\", \"roman\"):\n", + " print(f\" {grp}:\", sorted(c for c in cat.columns if c.startswith(grp + \"_\")))\n", + "print(f\"\\nshared sky placement: ra/dec present = {('ra' in cat) and ('dec' in cat)}\")\n", + "print(f\"lsst detected: {int(cat.lsst_flag_observed.sum()):5d} / {len(cat)}\")\n", + "print(f\"roman detected: {int(cat.roman_flag_observed.sum()):5d} / {len(cat)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5aabf327", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-15T23:34:00.377871Z", + "iopub.status.busy": "2026-06-15T23:34:00.377760Z", + "iopub.status.idle": "2026-06-15T23:34:00.445055Z", + "shell.execute_reply": "2026-06-15T23:34:00.444545Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "same seed -> identical obs & flags: True\n", + "different seed -> different noise draw: True\n" + ] + } + ], + "source": [ + "# Reproducible from seed (given the same true-mag draw)\n", + "base = sm_ms.sample(3000)\n", + "base[\"ra\"] = rng.uniform(10, 20, len(base)); base[\"dec\"] = rng.uniform(-5, 5, len(base))\n", + "a = msi.inject(base.copy(), scene[\"survey_bands\"], seed=11, verbose=False)\n", + "b = msi.inject(base.copy(), scene[\"survey_bands\"], seed=11, verbose=False)\n", + "c = msi.inject(base.copy(), scene[\"survey_bands\"], seed=22, verbose=False)\n", + "print(\"same seed -> identical obs & flags:\",\n", + " a.roman_F158_obs.equals(b.roman_F158_obs) and a.lsst_flag_observed.equals(b.lsst_flag_observed))\n", + "print(\"different seed -> different noise draw:\", not a.roman_F158_obs.equals(c.roman_F158_obs))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "171f7526", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-15T23:34:00.446054Z", + "iopub.status.busy": "2026-06-15T23:34:00.445937Z", + "iopub.status.idle": "2026-06-15T23:34:00.516601Z", + "shell.execute_reply": "2026-06-15T23:34:00.516250Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Detection efficiency vs magnitude, per survey (Roman is deeper here: maglim 27 vs 26)\n", + "fig, ax = plt.subplots(figsize=(6.5, 4))\n", + "for col, flag, lab, cc in [(\"lsst_r_true\", \"lsst_flag_observed\", \"LSST r (maglim 26.0)\", \"C0\"),\n", + " (\"roman_F158_true\", \"roman_flag_observed\", \"Roman F158 (maglim 27.0)\", \"C3\")]:\n", + " m = cat[col].to_numpy(float); f = cat[flag].to_numpy(bool)\n", + " bins = np.linspace(20, 29, 28); idx = np.digitize(m, bins)\n", + " eff = [f[idx == i].mean() if (idx == i).any() else np.nan for i in range(1, len(bins))]\n", + " ax.plot(0.5 * (bins[1:] + bins[:-1]), eff, marker=\"o\", ms=3, label=lab, c=cc)\n", + "ax.set_xlabel(\"true magnitude\"); ax.set_ylabel(\"detected fraction\")\n", + "ax.set_title(\"Phase 4: per-survey selection from one catalog\"); ax.legend(); fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "9e34ac16", + "metadata": {}, + "source": [ + "## Notes for real runs\n", + "\n", + "* Swap `StubSurvey(...)` for `Survey.load(\"lsst\", release=\"dc2\")` and the real\n", + " Roman survey once its config/maps land — the injector code is unchanged.\n", + "* A band label is used **both** as the ugali isochrone field name and as the key\n", + " into a survey's maglim/photo-error maps, so they must agree (ugali's Roman\n", + " fields are upper-case: `F106`, `F158`, ...).\n", + "* **`nstars` is now exactly N** (was an emergent IMF count). This is adopted but\n", + " intentionally still **flagged for review** in the plan doc, because it changes\n", + " what `StreamModel.sample(size)` returns for existing single-survey configs.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.14.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/build_multisurvey_demo_nb.py b/scripts/build_multisurvey_demo_nb.py new file mode 100644 index 0000000..ca7f0fc --- /dev/null +++ b/scripts/build_multisurvey_demo_nb.py @@ -0,0 +1,299 @@ +#!/usr/bin/env python +"""Build notebooks/multisurvey_phases_demo.ipynb (Phases 1-4 showcase). + +Local helper to (re)generate the demo notebook from a single source of truth. +Run it from the repo root inside the `streamobs` conda env, then execute with +`jupyter nbconvert --to notebook --execute --inplace`. +""" + +import nbformat as nbf +from nbformat.v4 import new_code_cell, new_markdown_cell, new_notebook + +cells = [] +md = lambda s: cells.append(new_markdown_cell(s)) +co = lambda s: cells.append(new_code_cell(s)) + +md(r"""# Multi-survey injection — Phases 1-4 demo + +This notebook walks through the `roman_multisurvey` refactor that lets `streamobs` +inject a single stream carrying **both Roman and Rubin/LSST** photometry, where +each band draws its errors and detection probability from its *own* survey. + +| Phase | What changed | +|---|---| +| **1** | `Survey` holds two error curves — a *catalog* (reported `magerr`, drives the S/N cut) and an optional *sample* (true scatter, drives the noise draw). `get_photo_error(..., kind=)` selects between them. | +| **2** | The injector is de-hardcoded off `{r, g}`; column names route through `streamobs.columns`, using a **uniform** `_true/_obs/_err` scheme (`_…` when namespaced). The S/N cut now applies to **all** injected bands. | +| **3** | `IsochroneModel` is multi-band / multi-survey: masses are drawn **once** (exactly `nstars`) and interpolated into every survey's bands, so the *same physical star* is consistent across surveys. Roman bands are auto-converted Vega→AB. | +| **4** | `MultiSurveyInjector` orchestrates one shared sky placement + one shared true-mag fill, then a per-survey loop writing `__obs/_err` and `_flag_observed`. | + +See `docs/source/roman_multisurvey_plan.md` for the full design. +""") + +co("""import warnings +warnings.filterwarnings("ignore") +import numpy as np +import pandas as pd +import healpy as hp +import yaml +import matplotlib.pyplot as plt + +from streamobs.surveys import Survey +from streamobs.model import StreamModel, IsochroneModel +from streamobs.observed import StreamInjector, MultiSurveyInjector +from streamobs import columns as C + +import os +# Resolve the repo root whether the notebook runs from repo root or notebooks/. +REPO = ".." if os.path.exists("../config/scenes/roman_rubin_demo.yaml") else "." +rng = np.random.default_rng(42) +""") + +md(r"""## A runnable stub survey + +The real Roman/Rubin maglim maps and CSV tables are **not committed** to this +branch (they live in the git-ignored `data/surveys/`). So that this notebook +runs end-to-end, we use a tiny `StubSurvey` that: + +* keeps the **real** `Survey.get_photo_error` (so the Phase-1 two-curve split is + genuine) and `get_maglim`, +* supplies analytic `log_photo_error_*` curves and a logistic completeness. + +The **isochrones are real** (`Marigo2017` via `ugali`). To run against real +surveys, just replace `StubSurvey(...)` with `Survey.load("lsst", release=...)` +etc. — the injector code is identical. +""") + +co('''NSIDE = 1 # whole-sky single-pixel maps; enough for a synthetic demo + +class StubSurvey(Survey): + """Minimal Survey: real photo-error machinery, analytic completeness/maps.""" + + def __init__(self, name, bands, completeness_band, maglim=27.0, + sample_inflation=1.9): + self.name = name + self.bands = list(bands) + self.completeness_band = completeness_band + self.saturation = {b: 16.0 for b in bands} + self.sys_error = {b: 0.005 for b in bands} + self.delta_saturation = -10.0 + self.coeff_extinc = {b: 0.0 for b in bands} + self.ebv_map = np.zeros(hp.nside2npix(NSIDE)) + self.maglim_maps = {b: np.full(hp.nside2npix(NSIDE), maglim) for b in bands} + self.coverage = np.ones(hp.nside2npix(NSIDE)) # footprint = whole sky + # Phase 1: two error curves, both functions of delta_mag = mag - maglim. + # Catalog = reported error; sample = true scatter (here ~inflation x larger, + # echoing the Roman DC2 finding that true scatter ~2x reported magerr). + self.log_photo_error_catalog = lambda dm: np.log10( + 0.01 + 0.10 * np.exp(np.clip(dm, -30, 5))) + self.log_photo_error_sample = lambda dm: np.log10( + sample_inflation * (0.01 + 0.10 * np.exp(np.clip(dm, -30, 5)))) + + # analytic rolloff: ~1 when bright (mag << maglim), 0.5 at maglim, ->0 fainter + def get_completeness(self, band, mag, maglim): + return 1.0 / (1.0 + np.exp((np.asarray(mag) - np.asarray(maglim)) / 0.25)) + + def get_detection_efficiency(self, band, mag, maglim): + return self.get_completeness(band, mag, maglim) + + def get_extinction(self, band, pixel=None): + return np.zeros(np.size(pixel)) + +print("StubSurvey ready")''') + +md(r"""## Phase 1 — sample vs. catalog photometric error + +`Survey` now carries two curves. `get_photo_error(kind="catalog")` is the +reported error that becomes `magerr` and drives the S/N cut; +`get_photo_error(kind="sample")` is the *true* scatter used to draw the observed +magnitude. When no sample curve is loaded, `sample` transparently falls back to +`catalog`, so legacy single-curve behaviour is bit-for-bit preserved. +""") + +co("""sv = StubSurvey("lsst", ["g", "r"], completeness_band="r", maglim=26.5) +mags = np.linspace(20, 28, 200) +maglim = np.full_like(mags, 26.5) +err_cat = sv.get_photo_error("r", mags, maglim, kind="catalog") +err_smp = sv.get_photo_error("r", mags, maglim, kind="sample") + +# A survey with NO sample curve -> sample falls back to catalog (identical) +sv_legacy = StubSurvey("lsst", ["g", "r"], "r", maglim=26.5) +sv_legacy.log_photo_error_sample = None +err_fallback = sv_legacy.get_photo_error("r", mags, maglim, kind="sample") +assert np.allclose(err_fallback, sv_legacy.get_photo_error("r", mags, maglim, kind="catalog")) + +fig, ax = plt.subplots(figsize=(6, 4)) +ax.plot(mags, err_cat, label='catalog (reported magerr, drives S/N cut)') +ax.plot(mags, err_smp, label='sample (true scatter, drives noise draw)') +ax.axvline(26.5, ls=':', c='k', lw=1, label='maglim') +ax.set_xlabel('magnitude'); ax.set_ylabel('photometric error [mag]') +ax.set_title('Phase 1: two error curves on Survey'); ax.legend(); fig.tight_layout() +print("no sample curve -> sample falls back to catalog:", + np.allclose(err_fallback, err_cat))""") + +md(r"""## Phase 2 — arbitrary bands + `columns.py` + +The injector no longer hard-codes `{r, g}`. Column names come from +`streamobs.columns`, using one **uniform** convention: `_true` / +`_obs` / `_err` / `flag_observed` single-survey, and the same with a +`_` prefix multi-survey. (This intentionally drops the historical +`mag_` / `magerr_` names — not backward compatible.) Below we inject +Roman NIR bands `F106`/`F158` through the *single-survey* `StreamInjector` — +impossible under the old `{r,g}` block. +""") + +co( + """print("single-survey:", C.true_col("r"), C.obs_col("r"), C.err_col("r"), C.flag_col()) +print("multi-survey :", C.true_col("F158", "roman"), C.obs_col("F158", "roman"), + C.err_col("F158", "roman"), C.flag_col("roman")) + +roman_sv = StubSurvey("roman", ["F106", "F158"], completeness_band="F158", maglim=27.0) +inj = StreamInjector(roman_sv) +N = 1500 +df = pd.DataFrame({ + "ra": rng.uniform(10, 20, N), + "dec": rng.uniform(-5, 5, N), + "F106_true": rng.uniform(20, 28, N), + "F158_true": rng.uniform(20, 28, N), +}) +out = inj.inject(df, bands=["F106", "F158"], seed=1, verbose=False) +print("\\ninjected NIR-only bands -> columns:", + [c for c in out.columns if c.endswith(("_obs", "_err")) or c == "flag_observed"]) +print("detected:", int(out.flag_observed.sum()), "/", len(out))""" +) + +md(r"""## Phase 3 — multi-band / multi-survey isochrone (exactly `nstars`) + +`IsochroneModel.sample` now draws **exactly** `nstars` (a fixed mass set), for +both single- and multi-survey configs. First, the single-survey path: +""") + +co("""single_cfg = { + "density": {"type": "Uniform", "xmin": -9.0, "xmax": 9.0}, + "track": {"center": {"type": "Constant", "value": 0.0}, + "spread": {"type": "Constant", "value": 0.2}, "sampler": "Gaussian"}, + "distance_modulus": {"center": {"type": "Line", "slope": 0.1, "intercept": 16.5}, + "spread": {"type": "Constant", "value": 1e-4}, "sampler": "Uniform"}, + "isochrone": {"name": "Marigo2017", "survey": "lsst", "age": 12.0, "z": 0.0006, + "band_1": "g", "band_2": "r"}, +} +sm = StreamModel(single_cfg) +for n in (5000, 1234): + assert len(sm.sample(n)) == n +print("single-survey StreamModel.sample returns EXACTLY nstars (5000, 1234) -> OK")""") + +md(r"""Now the multi-survey scene. One mass draw feeds both surveys, so a star's +LSST and Roman magnitudes describe the *same object*.""") + +co("""scene = yaml.safe_load(open(f"{REPO}/config/scenes/roman_rubin_demo.yaml")) +sm_ms = StreamModel(scene["stream"]) +df_ms = sm_ms.sample(4000) +true_cols = sorted(c for c in df_ms.columns if c.endswith("_true")) +print("multi-survey true columns:", true_cols) +print("rows:", len(df_ms)) + +rho = np.corrcoef(df_ms["lsst_r_true"], df_ms["roman_F158_true"])[0, 1] +print(f"corr(lsst_r, roman_F158) = {rho:.3f} (shared masses => tightly correlated)") + +fig, axes = plt.subplots(1, 2, figsize=(10, 4.2), sharey=True) +axes[0].scatter(df_ms.lsst_g_true - df_ms.lsst_r_true, df_ms.lsst_r_true, s=3, alpha=.3) +axes[0].set_xlabel("g - r"); axes[0].set_ylabel("r"); axes[0].set_title("Rubin/LSST CMD") +axes[0].invert_yaxis() +axes[1].scatter(df_ms.roman_F106_true - df_ms.roman_F158_true, df_ms.roman_F158_true, s=3, alpha=.3, c="C3") +axes[1].set_xlabel("F106 - F158"); axes[1].set_ylabel("F158"); axes[1].set_title("Roman CMD") +axes[1].invert_yaxis() +fig.suptitle("Phase 3: same physical stars, two surveys"); fig.tight_layout()""") + +md(r"""Roman isochrone magnitudes are **always** converted Vega→AB (no config +flag) using the fixed `ROMAN_VEGA_TO_AB` table, sourced from the +`rubin_roman_object_classification` prototype. Non-Roman bands pass through +unchanged. (A `TODO` in the code notes this ideally belongs in `ugali`.)""") + +co("""from streamobs.model import ROMAN_VEGA_TO_AB +print("Roman Vega->AB offsets (AB = Vega + diff):") +for b, v in ROMAN_VEGA_TO_AB.items(): + print(f" {b}: +{v}") + +iso = StreamModel(scene["stream"]).isochrone # multi-survey isochrone +x = np.array([20.0, 21.0]) +print("\\nF158 shift:", (iso._to_ab("F158", x) - x), " (always +1.315)") +print("r shift:", (iso._to_ab("r", x) - x), " (0 -> non-Roman pass-through)")""") + +md(r"""## Phase 4 — `MultiSurveyInjector` + +One orchestrator, one shared sky placement and shared masses, then per-survey +observed columns and flags. Per-survey RNGs come from `rng.spawn(...)`, so the +result is reproducible and independent of survey order. +""") + +co( + """lsst_sv = StubSurvey("lsst", ["g", "r"], completeness_band="r", maglim=26.0) +roman_sv = StubSurvey("roman", ["F106", "F158"], completeness_band="F158", maglim=27.0) +msi = MultiSurveyInjector({"lsst": StreamInjector(lsst_sv), + "roman": StreamInjector(roman_sv)}, primary="lsst") + +# Input carries only stream coordinates; everything else is sampled once. +N = 4000 +stream_in = pd.DataFrame({"phi1": rng.uniform(-9, 9, N), "phi2": np.zeros(N)}) +cat = msi.inject(stream_in.copy(), scene["survey_bands"], + stream_config=scene["stream"], seed=7, verbose=False) + +print("columns produced:") +for grp in ("lsst", "roman"): + print(f" {grp}:", sorted(c for c in cat.columns if c.startswith(grp + "_"))) +print(f"\\nshared sky placement: ra/dec present = {('ra' in cat) and ('dec' in cat)}") +print(f"lsst detected: {int(cat.lsst_flag_observed.sum()):5d} / {len(cat)}") +print(f"roman detected: {int(cat.roman_flag_observed.sum()):5d} / {len(cat)}")""" +) + +co( + """# Reproducible from seed (given the same true-mag draw) +base = sm_ms.sample(3000) +base["ra"] = rng.uniform(10, 20, len(base)); base["dec"] = rng.uniform(-5, 5, len(base)) +a = msi.inject(base.copy(), scene["survey_bands"], seed=11, verbose=False) +b = msi.inject(base.copy(), scene["survey_bands"], seed=11, verbose=False) +c = msi.inject(base.copy(), scene["survey_bands"], seed=22, verbose=False) +print("same seed -> identical obs & flags:", + a.roman_F158_obs.equals(b.roman_F158_obs) and a.lsst_flag_observed.equals(b.lsst_flag_observed)) +print("different seed -> different noise draw:", not a.roman_F158_obs.equals(c.roman_F158_obs))""" +) + +co( + """# Detection efficiency vs magnitude, per survey (Roman is deeper here: maglim 27 vs 26) +fig, ax = plt.subplots(figsize=(6.5, 4)) +for col, flag, lab, cc in [("lsst_r_true", "lsst_flag_observed", "LSST r (maglim 26.0)", "C0"), + ("roman_F158_true", "roman_flag_observed", "Roman F158 (maglim 27.0)", "C3")]: + m = cat[col].to_numpy(float); f = cat[flag].to_numpy(bool) + bins = np.linspace(20, 29, 28); idx = np.digitize(m, bins) + eff = [f[idx == i].mean() if (idx == i).any() else np.nan for i in range(1, len(bins))] + ax.plot(0.5 * (bins[1:] + bins[:-1]), eff, marker="o", ms=3, label=lab, c=cc) +ax.set_xlabel("true magnitude"); ax.set_ylabel("detected fraction") +ax.set_title("Phase 4: per-survey selection from one catalog"); ax.legend(); fig.tight_layout()""" +) + +md(r"""## Notes for real runs + +* Swap `StubSurvey(...)` for `Survey.load("lsst", release="dc2")` and the real + Roman survey once its config/maps land — the injector code is unchanged. +* A band label is used **both** as the ugali isochrone field name and as the key + into a survey's maglim/photo-error maps, so they must agree (ugali's Roman + fields are upper-case: `F106`, `F158`, ...). +* **`nstars` is now exactly N** (was an emergent IMF count). This is adopted but + intentionally still **flagged for review** in the plan doc, because it changes + what `StreamModel.sample(size)` returns for existing single-survey configs. +""") + +nb = new_notebook(cells=cells) +nb.metadata.update( + { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3", + }, + "language_info": {"name": "python"}, + } +) +out_path = "notebooks/multisurvey_phases_demo.ipynb" +with open(out_path, "w") as f: + nbf.write(nb, f) +print("wrote", out_path, "with", len(cells), "cells") diff --git a/streamobs/columns.py b/streamobs/columns.py index 45b522b..d8f8945 100644 --- a/streamobs/columns.py +++ b/streamobs/columns.py @@ -2,35 +2,45 @@ Column-name helpers for injected catalogs. These centralize the naming convention so the injector is not hard-coded to -specific bands (``mag_r_obs``, ``magerr_g``, ...). Two conventions are -supported, selected by the ``survey`` argument: +specific bands. A single, uniform ``_true`` / ``_obs`` / +``_err`` convention is used everywhere; the only difference between the +single- and multi-survey cases is an optional survey prefix: -- **Legacy / single-survey** (``survey=None``): ``mag_`` (true), - ``mag__obs`` (observed), ``magerr_`` (error), ``flag_observed``. - This is what :class:`~streamobs.observed.StreamInjector` emits and what - downstream consumers already read, so it is preserved unchanged. +- **Single-survey** (``survey=None``): ``_true`` (true / noiseless), + ``_obs`` (observed / noisy), ``_err`` (reported error), + ``flag_observed``. - **Multi-survey** (``survey="roman"``, ``"lsst"``, ...): ``__true``, ``__obs``, ``__err``, ``_flag_observed``. - Used by the (future) ``MultiSurveyInjector`` so each band's columns are - namespaced by survey. + Used by :class:`~streamobs.observed.MultiSurveyInjector` so each band's + columns are namespaced by survey. + +.. note:: + This convention intentionally **drops** the historical ``mag_`` / + ``mag__obs`` / ``magerr_`` names — it is not backward compatible + with catalogs written by older ``streamobs`` versions. """ def true_col(band, survey=None): """Column holding the *true* (noiseless) apparent magnitude for ``band``.""" - return f"{survey}_{band}_true" if survey else f"mag_{band}" + return f"{survey}_{band}_true" if survey else f"{band}_true" def obs_col(band, survey=None): """Column holding the *observed* (noisy) magnitude for ``band``.""" - return f"{survey}_{band}_obs" if survey else f"mag_{band}_obs" + return f"{survey}_{band}_obs" if survey else f"{band}_obs" def err_col(band, survey=None): """Column holding the reported magnitude error for ``band``.""" - return f"{survey}_{band}_err" if survey else f"magerr_{band}" + return f"{survey}_{band}_err" if survey else f"{band}_err" def flag_col(survey=None): """Column holding the detection flag (band-independent).""" return f"{survey}_flag_observed" if survey else "flag_observed" + + +def perfect_flag_col(survey=None): + """Column holding the perfect star/galaxy-separation flag (band-independent).""" + return f"{survey}_flag_perfect_galstarsep" if survey else "flag_perfect_galstarsep" diff --git a/streamobs/model.py b/streamobs/model.py index 59511bc..a093be9 100644 --- a/streamobs/model.py +++ b/streamobs/model.py @@ -13,6 +13,26 @@ from streamobs.functions import function_factory from streamobs.samplers import sampler_factory +# Roman ugali isochrones are delivered in Vega magnitudes, but our catalogs are +# AB. These are the per-band AB - Vega offsets (AB = Vega + diff) for the Roman +# WFI filters — the mode of the by-chip zeropoints from the Roman technical +# information (Roman_zeropoints_20240301.ecsv), as used by the +# rubin_roman_object_classification prototype. The conversion is applied +# unconditionally to any Roman band (a no-op for non-Roman bands). +# +# TODO: this Vega->AB conversion really belongs in ugali, so isochrones are +# returned natively in AB. Move it upstream and delete this table once that lands. +ROMAN_VEGA_TO_AB = { + "F062": 0.153, + "F087": 0.481, + "F106": 0.660, + "F129": 1.051, + "F146": 1.164, + "F158": 1.315, + "F184": 1.556, + "F213": 1.837, +} + class ConfigurableModel(object): """Baseclass for models built from configs.""" @@ -117,8 +137,8 @@ def sample(self, size): pandas.DataFrame Columns include: ``phi1``, ``phi2``, ``dist``, ``mu1``, ``mu2``, ``rv``, and the isochrone magnitude columns. For a single-survey - isochrone these are the legacy ``mag_g`` / ``mag_r`` (its two bands); - for a multi-survey isochrone they are ``__true`` per + isochrone these are ``_true`` (its two bands); for a + multi-survey isochrone they are ``__true`` per survey/band. Some may be None if the sub-model is absent. """ @@ -154,7 +174,7 @@ def sample(self, size): if self.isochrone: mag_data = self._sample_iso_mags(size, dist) else: - mag_data = {"mag_g": None, "mag_r": None} # legacy placeholder + mag_data = {} # no isochrone -> no magnitude columns for col, vals in mag_data.items(): df[col] = vals @@ -212,9 +232,10 @@ def complete_catalog( that length. columns_to_add : sequence of str or None, optional The columns to ensure in the output. Valid entries are - {'phi1','phi2','dist','mag_g','mag_r','mu1','mu2','rv'}. - If None, all valid columns supported by the configured model are - considered. + {'phi1','phi2','dist','mu1','mu2','rv'} plus the isochrone magnitude + columns (``_true`` single-survey, ``__true`` + multi-survey). If None, all valid columns supported by the + configured model are considered. size : int or None, optional Required when ``catalog`` is None or an empty table; ignored otherwise. @@ -252,7 +273,7 @@ def complete_catalog( """ # Supported outputs and capability filtering # Columns this method can fill using the configured model - # Magnitude columns are band-/survey-general (legacy mag_g/mag_r for a + # Magnitude columns are band-/survey-general (_true for a # single-survey isochrone; __true for multi-survey). mag_cols = self._iso_mag_columns() all_cols = ("phi1", "phi2", "dist") + tuple(mag_cols) + ("mu1", "mu2", "rv") @@ -480,8 +501,8 @@ def _standardize_columns_name(self, catalog): # Mapping of possible column name variants to standard names col_mapping = { "dist": ["dist", "distance", "distance_modulus"], - "mag_g": ["mag_g", "g_mag", "g", "gmag", "magnitude_g"], - "mag_r": ["mag_r", "r_mag", "r", "rmag", "magnitude_r"], + "g_true": ["g_true", "mag_g", "g_mag", "g", "gmag", "magnitude_g"], + "r_true": ["r_true", "mag_r", "r_mag", "r", "rmag", "magnitude_r"], "phi1": ["phi1", "phi_1", "Phi1", "Phi_1"], "phi2": ["phi2", "phi_2", "Phi2", "Phi_2"], "mu1": ["mu1", "mu_1"], @@ -586,13 +607,16 @@ class IsochroneModel(ConfigurableModel): ``band_2``, ...). :meth:`sample` returns ``(mag_band_1, mag_band_2)`` and reproduces the previous behaviour exactly. - **Multi-survey**: a ``surveys`` mapping - ``{survey_name: {survey, band_1, band_2, vega_to_ab}}`` plus shared keys + ``{survey_name: {survey, band_1, band_2}}`` plus shared keys (``name``, ``age``, ``z``, ...) at the top level. One ``ugali`` isochrone is built per survey from the *same* stellar population, so a single shared draw of initial masses (:meth:`sample_masses`) is interpolated into every survey's bands — giving the same physical star consistent magnitudes across surveys. :meth:`sample_multisurvey` returns ``{(survey, band): apparent_mag}``. + + Roman bands are always converted from Vega to AB (see + :data:`ROMAN_VEGA_TO_AB`); other bands pass through unchanged. """ # Defaults for the shared isochrone mass grid (see ugali Isochrone.sample) @@ -633,8 +657,7 @@ def _create_single_isochrone(self, config): 'Please use the "distance_modulus" section of the configuration ' "file, instead of the isochrone section, to define a distance modulus." ) - factory_config = {k: v for k, v in config.items() if k != "vega_to_ab"} - self.iso = self._build_iso(factory_config) + self.iso = self._build_iso(config) self.survey_name = config.get("survey") self.band_1 = config.get("band_1") self.band_2 = config.get("band_2") @@ -642,23 +665,17 @@ def _create_single_isochrone(self, config): self.surveys = [self.survey_name] self.isos = {self.survey_name: self.iso} self.survey_bands = {self.survey_name: (self.band_1, self.band_2)} - self._vega_to_ab = {self.survey_name: config.get("vega_to_ab")} def _create_multisurvey_isochrones(self, config): """Build one isochrone per survey, sharing the top-level stellar params.""" shared = {k: v for k, v in config.items() if k != "surveys"} self.isos = {} self.survey_bands = {} - self._vega_to_ab = {} self.surveys = [] for name, scfg in config["surveys"].items(): - factory_config = { - **shared, - **{k: v for k, v in scfg.items() if k != "vega_to_ab"}, - } + factory_config = {**shared, **scfg} self.isos[name] = self._build_iso(factory_config) self.survey_bands[name] = (scfg.get("band_1"), scfg.get("band_2")) - self._vega_to_ab[name] = scfg.get("vega_to_ab") self.surveys.append(name) # Primary isochrone drives the shared mass draw and the legacy sample(). self.survey_name = self.surveys[0] @@ -707,24 +724,17 @@ def _absolute_mags(self, iso, masses, mass_min=None, mass_steps=None): np.interp(masses, mass_init, mag_2[order]), ) - def _apply_vega_to_ab(self, survey, band, mag): - """Apply an optional per-band Vega->AB offset (``AB = Vega + offset``). + def _to_ab(self, band, mag): + """Convert Roman bands from Vega to AB (``AB = Vega + offset``). - ``vega_to_ab`` in the config is a ``{band: offset}`` mapping; if absent - or empty, the magnitudes are returned unchanged. This is the isolated - shim that lets multi-survey injection work while ``ugali`` still returns - some bands in Vega; it becomes a no-op once bands are natively AB. + Applied unconditionally: Roman bands use the offset in + :data:`ROMAN_VEGA_TO_AB`, every other band passes through unchanged. + ``ugali`` delivers Roman isochrones in Vega while our catalogs are AB. + + TODO: this really belongs in ugali (return AB natively); remove once it + does. """ - spec = self._vega_to_ab.get(survey) - if not spec: - return mag - if isinstance(spec, dict): - return mag + spec.get(band, 0.0) - warnings.warn( - f"vega_to_ab for survey '{survey}' is set but is not a " - "{band: offset} mapping; no Vega->AB correction applied." - ) - return mag + return mag + ROMAN_VEGA_TO_AB.get(band, 0.0) @staticmethod def _add_distance_modulus(abs_mag, distance_modulus): @@ -755,8 +765,8 @@ def sample_multisurvey(self, nstars, distance_modulus, rng=None, **kwargs): for name in self.surveys: band_1, band_2 = self.survey_bands[name] abs_1, abs_2 = self._absolute_mags(self.isos[name], masses) - abs_1 = self._apply_vega_to_ab(name, band_1, abs_1) - abs_2 = self._apply_vega_to_ab(name, band_2, abs_2) + abs_1 = self._to_ab(band_1, abs_1) + abs_2 = self._to_ab(band_2, abs_2) out[(name, band_1)] = self._add_distance_modulus(abs_1, distance_modulus) out[(name, band_2)] = self._add_distance_modulus(abs_2, distance_modulus) return out @@ -764,10 +774,19 @@ def sample_multisurvey(self, nstars, distance_modulus, rng=None, **kwargs): def sample(self, nstars, distance_modulus, rng=None, **kwargs): """Simulate magnitudes in the two bands of the (primary) isochrone. + Draws *exactly* ``nstars`` stars: a fixed set of initial masses is drawn + once from the isochrone IMF (:meth:`sample_masses`) and interpolated into + the two bands. This differs from the historical behaviour, where + ``nstars`` was converted to a total stellar mass and ``ugali``'s + ``simulate`` returned a random-length IMF realization (count generally + ``!= nstars``). The fixed-count semantics are required so the *same + physical star* can be shared across surveys (see + :meth:`sample_multisurvey`). + Parameters ---------- nstars : int - Number of stars to simulate. + Number of stars to simulate (returns exactly this many). distance_modulus : float or array-like Distance modulus per star (broadcast if scalar). @@ -778,30 +797,11 @@ def sample(self, nstars, distance_modulus, rng=None, **kwargs): this returns the primary survey's two bands; use :meth:`sample_multisurvey` to get every survey's bands. """ - if getattr(self, "multi_survey", False): - mags = self.sample_multisurvey( - nstars, distance_modulus, rng=rng, **kwargs - ) - return ( - mags[(self.survey_name, self.band_1)], - mags[(self.survey_name, self.band_2)], - ) - - # Single-survey: preserve the existing ugali.simulate-based behaviour. - stellar_mass = nstars * self.iso.stellar_mass() - mag_1, mag_2 = self.iso.simulate( - stellar_mass, distance_modulus=self.iso.distance_modulus + mags = self.sample_multisurvey(nstars, distance_modulus, rng=rng, **kwargs) + return ( + mags[(self.survey_name, self.band_1)], + mags[(self.survey_name, self.band_2)], ) - if np.isscalar(distance_modulus): - mag_1, mag_2 = [ - mag + np.ones_like(mag) * distance_modulus for mag in (mag_1, mag_2) - ] - else: - mag_1, mag_2 = [mag + distance_modulus for mag in (mag_1, mag_2)] - - mag_1 = self._apply_vega_to_ab(self.survey_name, self.band_1, mag_1) - mag_2 = self._apply_vega_to_ab(self.survey_name, self.band_2, mag_2) - return mag_1, mag_2 def _dist_to_modulus(self, distance): """ diff --git a/streamobs/observed.py b/streamobs/observed.py index 4c71f8c..735e719 100644 --- a/streamobs/observed.py +++ b/streamobs/observed.py @@ -10,7 +10,7 @@ import numpy as np import pandas as pd -from .columns import err_col, flag_col, obs_col, true_col +from .columns import err_col, flag_col, obs_col, perfect_flag_col, true_col from .model import StreamModel from .plotting import plot_stream_in_mask from .surveys import Survey @@ -120,8 +120,8 @@ def inject(self, data, bands=["r", "g"], **kwargs): pd.DataFrame DataFrame with the following added columns: - - mag__obs : Observed magnitudes for each band - - magerr_ : Photometric errors for each band + - _obs : Observed magnitudes for each band + - _err : Photometric errors for each band - flag_observed : Boolean flag (True=detected, False=not detected). Includes both detection and classification efficiencies. - flag_perfect_galstarsep : Boolean flag assuming perfect star/galaxy separation (detection efficiency only, no classification losses; only if requested with perfect_galstarsep=True) - ra, dec : Sky coordinates (if not already present) @@ -131,9 +131,6 @@ def inject(self, data, bands=["r", "g"], **kwargs): ValueError If required columns are missing or bands are not supported. """ - verbose = kwargs.get("verbose", True) - perfect_galstarsep = kwargs.pop("perfect_galstarsep", False) - # Load data data = self._load_data(data) @@ -144,6 +141,60 @@ def inject(self, data, bands=["r", "g"], **kwargs): # Complete missing columns (ra/dec, magnitudes) data = self.complete_data(data, rng=rng, seed=seed, bands=bands, **kwargs) + # Run the per-survey observational injection using the legacy column + # names (survey_namespace=None). + data = self._inject_one_survey( + data, bands, rng=rng, seed=seed, survey_namespace=None, **kwargs + ) + + # Save if requested + if kwargs.get("save"): + self._save_injected_data(data, kwargs.get("folder", None)) + + # Return data (do NOT store as instance attribute to avoid conflicts between runs) + return data + + def _inject_one_survey( + self, data, bands, rng=None, seed=None, survey_namespace=None, **kwargs + ): + """Add this survey's observed magnitudes, errors and detection flags. + + This holds the per-band observational logic shared by single-survey + :meth:`inject` and :class:`MultiSurveyInjector`. It assumes ``data`` + already carries ``ra``/``dec`` and the true-magnitude columns for the + requested bands; it does **not** sample positions or true magnitudes. + + Parameters + ---------- + data : pandas.DataFrame + Catalog with ``ra``/``dec`` and ``true_col(band, survey_namespace)`` + for every requested band. + bands : list of str + Bands to process for this survey. + rng : numpy.random.Generator, optional + Random generator for the noise draw and detection sampling. + seed : int, optional + Seed used to build an RNG when ``rng`` is None. + survey_namespace : str or None, optional + Column-naming namespace. ``None`` ⇒ single-survey names + (``_obs`` / ``_err`` / ``flag_observed``); + a survey name ⇒ ``__obs`` / ``__err`` / + ``_flag_observed``. + **kwargs + ``nside``, ``detection_mag_cut``, ``dust_correction``, + ``perfect_galstarsep``, ``verbose`` (see :meth:`inject`). + + Returns + ------- + pandas.DataFrame + ``data`` with this survey's observed columns and detection flag(s). + """ + if rng is None: + rng = np.random.default_rng(seed) + + verbose = kwargs.get("verbose", True) + perfect_galstarsep = kwargs.pop("perfect_galstarsep", False) + # Get HEALPix pixel indices nside = kwargs.pop("nside", 4096) pix = hp.ang2pix(nside, data["ra"], data["dec"], lonlat=True) @@ -163,7 +214,7 @@ def inject(self, data, bands=["r", "g"], **kwargs): extinction_band = self.survey.get_extinction(band, pixel=pix_ebv) # Calculate true apparent magnitudes (including extinction) - apparent_mag_true = data[true_col(band)] + extinction_band + apparent_mag_true = data[true_col(band, survey_namespace)] + extinction_band # Get magnitude limits nside_maglim = hp.get_nside(self.survey.maglim_maps[band]) @@ -215,8 +266,8 @@ def inject(self, data, bands=["r", "g"], **kwargs): # Add new columns new_columns = pd.DataFrame( { - obs_col(band): mag_obs, - err_col(band): mag_err, + obs_col(band, survey_namespace): mag_obs, + err_col(band, survey_namespace): mag_err, } ) @@ -262,7 +313,7 @@ def inject(self, data, bands=["r", "g"], **kwargs): # Start with flux validity check (not BAD_MAG) across all injected bands flag_valid_flux = None for band in bands: - band_valid = data[obs_col(band)] != "BAD_MAG" + band_valid = data[obs_col(band, survey_namespace)] != "BAD_MAG" flag_valid_flux = ( band_valid if flag_valid_flux is None else flag_valid_flux & band_valid ) @@ -293,7 +344,7 @@ def inject(self, data, bands=["r", "g"], **kwargs): continue if verbose: print(f"Applying detection cut on {band}-band with SNR >= {SNR_min}") - SNR = 1.0 / data[err_col(band)] + SNR = 1.0 / data[err_col(band, survey_namespace)] flag_observed &= SNR >= SNR_min if perfect_galstarsep: flag_perfect &= SNR >= SNR_min @@ -302,19 +353,16 @@ def inject(self, data, bands=["r", "g"], **kwargs): # (the SNR cut is baked into the completeness functions but not into the # detection-only efficiency functions used for the perfect flag). if perfect_galstarsep: - SNR_compl = 1.0 / data[err_col(self.survey.completeness_band)] + SNR_compl = ( + 1.0 / data[err_col(self.survey.completeness_band, survey_namespace)] + ) flag_perfect &= SNR_compl >= SNR_min # Store flags in DataFrame - data[flag_col()] = flag_observed + data[flag_col(survey_namespace)] = flag_observed if perfect_galstarsep: - data["flag_perfect_galstarsep"] = flag_perfect - - # Save if requested - if kwargs.get("save"): - self._save_injected_data(data, kwargs.get("folder", None)) + data[perfect_flag_col(survey_namespace)] = flag_perfect - # Return data (do NOT store as instance attribute to avoid conflicts between runs) return data def _load_data(self, data): @@ -1152,3 +1200,190 @@ def plot_stream_in_mask(self, data, mask_type, ebv_threshold=0.2, **kwargs): data["ra"], data["dec"], mask, output_folder=kwargs.get("output_folder") ) return fig, ax + + +class MultiSurveyInjector: + """Inject stream observations for several surveys into one catalog. + + The orchestrator holds one :class:`StreamInjector` per survey and delegates + the per-survey observational work to each (it is *not* a composite + ``Survey``). A single shared sky placement and a single shared draw of true + magnitudes — masses are sampled once via a multi-survey + :class:`~streamobs.model.IsochroneModel` and interpolated into every + survey's bands — guarantee that the **same physical star** gets consistent + magnitudes across surveys. Each survey then contributes its own + ``__obs`` / ``__err`` / ``_flag_observed`` + columns, computed with its own maglim maps and completeness functions. + + Parameters + ---------- + surveys : dict or list + Either ``{name: spec}`` or a list of ``spec``, where ``spec`` is a + survey-name string, a :class:`~streamobs.surveys.Survey`, or a + pre-built :class:`StreamInjector`. The dict key is used as the column + namespace; for a list, the survey's own name is used. + primary : str, optional + Name of the survey whose footprint drives the shared sky placement and + whose ``_save_injected_data`` is used. Defaults to the first survey. + **kwargs + Forwarded to :meth:`StreamInjector` construction when a ``spec`` is a + name/``Survey`` rather than an injector (e.g. ``release``). + + Examples + -------- + >>> msi = MultiSurveyInjector({"lsst": "lsst", "roman": "roman"}) + >>> out = msi.inject( + ... df, survey_bands={"lsst": ["r", "g"], "roman": ["f106", "f158"]}, + ... stream_config=scene["stream"], seed=42, + ... ) + """ + + def __init__(self, surveys, primary=None, **kwargs): + if isinstance(surveys, (list, tuple)): + surveys = {self._spec_name(s): s for s in surveys} + if not isinstance(surveys, dict): + raise ValueError("surveys must be a dict {name: spec} or a list of specs.") + + self.injectors = {} + for name, spec in surveys.items(): + if isinstance(spec, StreamInjector): + self.injectors[name] = spec + else: + self.injectors[name] = StreamInjector(spec, **kwargs) + + self.survey_names = list(self.injectors) + if not self.survey_names: + raise ValueError("At least one survey is required.") + self.primary = primary if primary is not None else self.survey_names[0] + if self.primary not in self.injectors: + raise ValueError( + f"primary='{self.primary}' is not one of {self.survey_names}." + ) + + # Shared sky placement is cached here, mirroring StreamInjector. + self._last_gc_frame = None + + @staticmethod + def _spec_name(spec): + """Best-effort survey name for a spec passed in a list.""" + if isinstance(spec, StreamInjector): + return spec.survey.name + if isinstance(spec, Survey): + return spec.name + return str(spec) + + def inject(self, data, survey_bands, stream_config=None, **kwargs): + """Inject observations for every survey into a single catalog. + + Parameters + ---------- + data : str or pandas.DataFrame + Input catalog (or path). May contain only stream coordinates + (``phi1``/``phi2`` or ``ra``/``dec``); anything missing is sampled + from ``stream_config``. An all-empty frame of length ``N`` is also + accepted (geometry and magnitudes are then sampled for ``N`` rows). + survey_bands : dict + ``{survey_name: [bands]}`` — the bands to inject for each survey. + Keys must match the surveys this orchestrator was built with. + stream_config : dict, optional + The ``stream`` section consumed by + :class:`~streamobs.model.StreamModel`. Its ``isochrone`` must be in + the multi-survey form so the same shared masses produce every + survey's ``__true`` columns. Required when any + coordinate or true-magnitude column is missing. + **kwargs + ``seed``, ``nside``, ``detection_mag_cut``, ``dust_correction``, + ``perfect_galstarsep``, ``gc_frame``, ``mask_type``, ``save``, + ``folder``, ``verbose`` — see :meth:`StreamInjector.inject`. + + Returns + ------- + pandas.DataFrame + Catalog with shared ``ra``/``dec`` and, per survey, + ``__true/_obs/_err`` and ``_flag_observed``. + """ + unknown = set(survey_bands) - set(self.injectors) + if unknown: + raise ValueError( + f"survey_bands references unknown surveys {sorted(unknown)}; " + f"available: {self.survey_names}." + ) + + ref = self.injectors[self.primary] + data = ref._load_data(data) + + seed = kwargs.pop("seed", None) + rng = np.random.default_rng(seed) + + # (1)+(2) Shared sky placement and shared true-magnitude fill (masses + # sampled once across all surveys). + data = self._complete_shared( + data, + survey_bands, + stream_config=stream_config, + rng=rng, + seed=seed, + **kwargs, + ) + + # (3) Per-survey observational injection. Independent child RNGs make the + # result independent of survey ordering and reproducible from `seed`. + children = rng.spawn(len(self.survey_names)) + for child_rng, name in zip(children, self.survey_names): + if name not in survey_bands: + continue + data = self.injectors[name]._inject_one_survey( + data, + list(survey_bands[name]), + rng=child_rng, + survey_namespace=name, + **kwargs, + ) + + if kwargs.get("save"): + ref._save_injected_data(data, kwargs.get("folder", None)) + + return data + + def _complete_shared( + self, data, survey_bands, stream_config=None, rng=None, seed=None, **kwargs + ): + """Fill shared geometry, ``ra``/``dec`` and per-survey true magnitudes. + + Positions and masses are drawn *once* so all surveys describe the same + physical stars. Existing columns are preserved. + """ + verbose = kwargs.get("verbose", True) + ref = self.injectors[self.primary] + + true_cols = [] + for name, bands in survey_bands.items(): + true_cols += [true_col(b, name) for b in bands] + + have_radec = "ra" in data.columns and "dec" in data.columns + have_phi = "phi1" in data.columns and "phi2" in data.columns + missing_true = [c for c in true_cols if c not in data.columns] + + # Sample stream geometry and/or the shared true magnitudes from the model. + need_phi = not have_radec and not have_phi + if need_phi or missing_true: + if stream_config is None: + raise ValueError( + "stream_config is required to sample stream geometry/magnitudes." + ) + stream_model = StreamModel(stream_config) + cols_to_add = [] + if need_phi: + cols_to_add += ["phi1", "phi2"] + # `dist` is needed before magnitudes; the model fills it if absent. + cols_to_add += ["dist"] + missing_true + data = stream_model.complete_catalog( + data, columns_to_add=cols_to_add, inplace=True, verbose=verbose + ) + + # Convert (phi1, phi2) -> (ra, dec) using the primary survey footprint. + if not have_radec: + data = ref.complete_data(data, bands=[], rng=rng, seed=seed, **kwargs) + self._last_gc_frame = ref._last_gc_frame + + return data diff --git a/streamobs/plotting.py b/streamobs/plotting.py index f39fe97..28ddb96 100644 --- a/streamobs/plotting.py +++ b/streamobs/plotting.py @@ -9,6 +9,8 @@ import pandas as pd import pylab as plt +from .columns import flag_col, obs_col, true_col + try: import healpy as hp import skyproj @@ -95,8 +97,8 @@ def plot_inject(data, survey, bands=None, range=None, **kwargs): Data containing the injected stream. Must have columns: - 'ra', 'dec': Sky coordinates in degrees - 'flag_observed': Boolean flag for detected stars - - 'mag_': True magnitudes for each band - - 'mag__obs': Observed magnitudes for each band + - '_true': True magnitudes for each band + - '_obs': Observed magnitudes for each band survey : Survey Survey object containing magnitude limit maps and other properties. bands : list of str, optional @@ -134,18 +136,18 @@ def plot_inject(data, survey, bands=None, range=None, **kwargs): required_cols = [ "ra", "dec", - "flag_observed", - f"mag_{band1}", - f"mag_{band2}", - f"mag_{band1}_obs", - f"mag_{band2}_obs", + flag_col(), + true_col(band1), + true_col(band2), + obs_col(band1), + obs_col(band2), ] missing_cols = [col for col in required_cols if col not in data.columns] if missing_cols: raise ValueError(f"Missing required columns: {missing_cols}") # Get detection flags - sel = data["flag_observed"].astype(bool) + sel = data[flag_col()].astype(bool) # Create figure fig, ax = plt.subplots(1, 3, figsize=(14, 6)) @@ -205,8 +207,8 @@ def plot_inject(data, survey, bands=None, range=None, **kwargs): # --- Panel 2: HR diagram with true magnitudes --- ax[1].set_title("HR diagram using True magnitudes") - color_true = data[f"mag_{band1}"] - data[f"mag_{band2}"] - mag_true = data[f"mag_{band1}"] + color_true = data[true_col(band1)] - data[true_col(band2)] + mag_true = data[true_col(band1)] ax[1].scatter( color_true, @@ -234,8 +236,8 @@ def plot_inject(data, survey, bands=None, range=None, **kwargs): ax[2].set_title("HR diagram using sampled observed magnitudes") # Convert measured magnitudes to numeric, handling "BAD_MAG" strings - mag1_obs = pd.to_numeric(data[f"mag_{band1}_obs"], errors="coerce") - mag2_obs = pd.to_numeric(data[f"mag_{band2}_obs"], errors="coerce") + mag1_obs = pd.to_numeric(data[obs_col(band1)], errors="coerce") + mag2_obs = pd.to_numeric(data[obs_col(band2)], errors="coerce") # Mask out bad measurements mask_good = (~mag1_obs.isna()) & (~mag2_obs.isna()) diff --git a/streamobs/samplers.py b/streamobs/samplers.py index 4bf42e9..a4b8bdc 100644 --- a/streamobs/samplers.py +++ b/streamobs/samplers.py @@ -6,13 +6,15 @@ import numpy as np import scipy.stats -from streamobs.functions import (CubicSplineInterpolation, - FileCubicSplineInterpolation, - FileInterpolation, - FileLinearDensityCubicSplineInterpolation, - Interpolation, - LinearDensityCubicSplineInterpolation, - Sinusoid) +from streamobs.functions import ( + CubicSplineInterpolation, + FileCubicSplineInterpolation, + FileInterpolation, + FileLinearDensityCubicSplineInterpolation, + Interpolation, + LinearDensityCubicSplineInterpolation, + Sinusoid, +) def sampler_factory(type_, **kwargs): diff --git a/streamobs/surveys.py b/streamobs/surveys.py index 903dc9c..105c633 100644 --- a/streamobs/surveys.py +++ b/streamobs/surveys.py @@ -279,7 +279,12 @@ def get_extinction(self, band: str, pixel: int = None) -> float: return extinction[pixel] def get_photo_error( - self, band: str, magnitude: float, maglim: float, kind: str = "catalog", **kwargs + self, + band: str, + magnitude: float, + maglim: float, + kind: str = "catalog", + **kwargs, ) -> float: """ Get photometric error estimate. diff --git a/streamobs/utils.py b/streamobs/utils.py index 7eca8bb..c226c0f 100644 --- a/streamobs/utils.py +++ b/streamobs/utils.py @@ -2,6 +2,7 @@ """ Utils for streamobs """ + import yaml diff --git a/tests/test_functions.py b/tests/test_functions.py index 2884fc0..179c110 100644 --- a/tests/test_functions.py +++ b/tests/test_functions.py @@ -1,78 +1,155 @@ - -from streamobs.functions import (LinearDensityCubicSplineInterpolation, - FileLinearDensityCubicSplineInterpolation, - CubicSplineInterpolation, - FileCubicSplineInterpolation) import numpy as np +from streamobs.functions import ( + CubicSplineInterpolation, + FileCubicSplineInterpolation, + FileLinearDensityCubicSplineInterpolation, + LinearDensityCubicSplineInterpolation, +) + SPREAD_NODES = np.array([-13.0, -7.875, -2.75, 2.375, 7.5]) -SPREAD_NODE_VALUES = np.array([0.67851425, 0.12068391, 0.19419976, 0.15820543, - 0.12868499]) - -SPREAD_INTERP_ARRAY = np.array([0.28385327, 0.18494655, 0.12147638, 0.09911923, - 0.10862358, 0.13679006, 0.17041929, 0.19631621, - 0.20546274, 0.20080746, 0.18742835, 0.17040343, - 0.15479583, 0.1437526, 0.13663492, 0.1323599, - 0.12984461, np.nan, np.nan, np.nan]) -INTENSITY_NODES = np.array([-13., -11.04761905, -10.07142857, - -9.0952381, -6.16666667, -5.92261905, - -5.19047619, -4.45833333, -4.21428571, -3.9702381, - -0.30952381, 0.66666667, 1.64285714, 3.5952381, - 4.57142857, 5.54761905, 7.5]) - -INTENSITY_NODE_VALUES = np.array([ - 3.57488822e-08, 8.15443503e-02, 7.43156465e-02, 8.38736587e-02, - 1.29506112e-01, 1.48465666e-01, 1.51251953e-08, 9.32309494e-02, - 1.91713194e-02, 4.61099534e-03, 4.17791354e-02, 4.89747482e-02, - 1.55361917e-02, 2.27116146e-02, 2.45111417e-02, 6.95831709e-02, - 1.03856772e-07]) - -LINEAR_DENSITY_INTERP_ARRAY = np.array([ - 0.05301498, 0.03867171, 0.01956495, 0.018129, 0.03598436, - 0.02941, 0.00440678, 0.01117077, 0.01486688, 0.01937192, - 0.02381247, 0.00740761, 0.00514529, 0.00808078, 0.01046202, - 0.02456511, 0.01387564, np.nan, np.nan, np.nan]) +SPREAD_NODE_VALUES = np.array( + [0.67851425, 0.12068391, 0.19419976, 0.15820543, 0.12868499] +) + +SPREAD_INTERP_ARRAY = np.array( + [ + 0.28385327, + 0.18494655, + 0.12147638, + 0.09911923, + 0.10862358, + 0.13679006, + 0.17041929, + 0.19631621, + 0.20546274, + 0.20080746, + 0.18742835, + 0.17040343, + 0.15479583, + 0.1437526, + 0.13663492, + 0.1323599, + 0.12984461, + np.nan, + np.nan, + np.nan, + ] +) +INTENSITY_NODES = np.array( + [ + -13.0, + -11.04761905, + -10.07142857, + -9.0952381, + -6.16666667, + -5.92261905, + -5.19047619, + -4.45833333, + -4.21428571, + -3.9702381, + -0.30952381, + 0.66666667, + 1.64285714, + 3.5952381, + 4.57142857, + 5.54761905, + 7.5, + ] +) + +INTENSITY_NODE_VALUES = np.array( + [ + 3.57488822e-08, + 8.15443503e-02, + 7.43156465e-02, + 8.38736587e-02, + 1.29506112e-01, + 1.48465666e-01, + 1.51251953e-08, + 9.32309494e-02, + 1.91713194e-02, + 4.61099534e-03, + 4.17791354e-02, + 4.89747482e-02, + 1.55361917e-02, + 2.27116146e-02, + 2.45111417e-02, + 6.95831709e-02, + 1.03856772e-07, + ] +) + +LINEAR_DENSITY_INTERP_ARRAY = np.array( + [ + 0.05301498, + 0.03867171, + 0.01956495, + 0.018129, + 0.03598436, + 0.02941, + 0.00440678, + 0.01117077, + 0.01486688, + 0.01937192, + 0.02381247, + 0.00740761, + 0.00514529, + 0.00808078, + 0.01046202, + 0.02456511, + 0.01387564, + np.nan, + np.nan, + np.nan, + ] +) def test_CubicSplineInterpolation(): # using atlas width from patrick_2022.csv - interp=CubicSplineInterpolation(SPREAD_NODES,SPREAD_NODE_VALUES) + interp = CubicSplineInterpolation(SPREAD_NODES, SPREAD_NODE_VALUES) - result=interp(np.linspace(-10,10,20)) + result = interp(np.linspace(-10, 10, 20)) np.testing.assert_allclose(result, SPREAD_INTERP_ARRAY) def test_FileCubicSplineInterpolation(): # using atlas width from patrick_2022.csv - filepath="./data/patrick_2022_splines.csv" - stream_name="atlas" - spline_type="spread" + filepath = "./data/patrick_2022_splines.csv" + stream_name = "atlas" + spline_type = "spread" - interp=FileCubicSplineInterpolation(filepath, spline_type=spline_type, stream_name=stream_name) - result=interp(np.linspace(-10,10,20)) + interp = FileCubicSplineInterpolation( + filepath, spline_type=spline_type, stream_name=stream_name + ) + result = interp(np.linspace(-10, 10, 20)) np.testing.assert_allclose(result, SPREAD_INTERP_ARRAY) + def test_LinearDensityCubicSplineInterpolation(): # using atlas width from patrick_2022.csv - interp=LinearDensityCubicSplineInterpolation(spread_nodes=SPREAD_NODES, - spread_node_values=SPREAD_NODE_VALUES, - intensity_nodes = INTENSITY_NODES, - intensity_node_values = INTENSITY_NODE_VALUES, - ) - result=interp(np.linspace(-10,10,20)) + interp = LinearDensityCubicSplineInterpolation( + spread_nodes=SPREAD_NODES, + spread_node_values=SPREAD_NODE_VALUES, + intensity_nodes=INTENSITY_NODES, + intensity_node_values=INTENSITY_NODE_VALUES, + ) + result = interp(np.linspace(-10, 10, 20)) np.testing.assert_allclose(result, LINEAR_DENSITY_INTERP_ARRAY, atol=1e-7) def test_FileLinearDensityCubicSplineInterpolation(): # using atlas width from patrick_2022.csv - filename="./data/patrick_2022_splines.csv" - stream_name="atlas" - interp=FileLinearDensityCubicSplineInterpolation(filename=filename, - stream_name=stream_name) + filename = "./data/patrick_2022_splines.csv" + stream_name = "atlas" + interp = FileLinearDensityCubicSplineInterpolation( + filename=filename, stream_name=stream_name + ) - result=interp(np.linspace(-10,10,20)) - np.testing.assert_allclose(result, LINEAR_DENSITY_INTERP_ARRAY, atol=1e-7) \ No newline at end of file + result = interp(np.linspace(-10, 10, 20)) + np.testing.assert_allclose(result, LINEAR_DENSITY_INTERP_ARRAY, atol=1e-7) From 7c965ae3604481be767e916c196b052694bf677e Mon Sep 17 00:00:00 2001 From: psferguson Date: Tue, 16 Jun 2026 11:19:35 -0700 Subject: [PATCH 06/54] Unify injector into one StreamInjector; always-namespaced columns Merge MultiSurveyInjector into StreamInjector: one class now accepts a single survey or several (name/Survey, {namespace: spec} dict, or list). Output columns are always survey-namespaced (__true/_obs/_err, _flag_observed), even for a single survey. The model emits __true uniformly (single-survey isochrone is just the one-survey case). - observed.py: StreamInjector takes one-or-many surveys; shared _inject_one_survey/detect_flag now take an explicit survey; public complete_data() fills missing geometry/ra-dec/true-mags from the config; MultiSurveyInjector removed. - model.py: complete_catalog never overwrites present values (fills only missing rows, per column); add `dist` (scalar/vector) to set distances directly without phi1 / a distance_modulus model. - columns.py docs, scene yaml, demo builder + regenerated notebook updated. - tests: namespaced columns; new TestCompleteCatalogPermutations. - plan doc: single class, always-namespace, exact-nstars agreed; flagged for removal (migrate to docs) before merge. Co-Authored-By: Claude Opus 4.8 (1M context) --- config/scenes/roman_rubin_demo.yaml | 9 +- docs/source/roman_multisurvey_plan.md | 148 +++--- notebooks/multisurvey_phases_demo.ipynb | 191 ++++--- scripts/build_multisurvey_demo_nb.py | 50 +- streamobs/columns.py | 20 +- streamobs/model.py | 130 +++-- streamobs/observed.py | 665 ++++++++++++------------ tests/test_model.py | 239 ++++++++- tests/test_observed.py | 125 +++-- 9 files changed, 924 insertions(+), 653 deletions(-) diff --git a/config/scenes/roman_rubin_demo.yaml b/config/scenes/roman_rubin_demo.yaml index b8c9fc3..1b0bbae 100644 --- a/config/scenes/roman_rubin_demo.yaml +++ b/config/scenes/roman_rubin_demo.yaml @@ -1,12 +1,13 @@ # Multi-survey demo scene: a single toy stream observed by BOTH Rubin/LSST and -# Roman. Consumed by `streamobs.observed.MultiSurveyInjector` (Phase 4). +# Roman. Consumed by `streamobs.observed.StreamInjector` (Phase 4) — the same +# injector class handles one survey or several. # # Usage (see notebooks/multisurvey_phases_demo.ipynb): # # import yaml, pandas as pd -# from streamobs.observed import MultiSurveyInjector +# from streamobs.observed import StreamInjector # scene = yaml.safe_load(open("config/scenes/roman_rubin_demo.yaml")) -# msi = MultiSurveyInjector(scene["surveys"]) +# msi = StreamInjector(scene["surveys"]) # df = pd.DataFrame(index=range(int(scene["stream"]["nstars"]))) # out = msi.inject(df, scene["survey_bands"], # stream_config=scene["stream"], seed=42) @@ -15,7 +16,7 @@ # columns; each band's errors / detection come from that survey's own maps. # Surveys to inject. The mapping KEY is the column namespace (lsst_r_obs, ...). -# Each value is a StreamInjector spec: a survey-name string here (loaded via +# Each value is a survey spec: a survey-name string here (loaded via # Survey.load); could also be a {name, release} once the loader supports it. surveys: lsst: lsst diff --git a/docs/source/roman_multisurvey_plan.md b/docs/source/roman_multisurvey_plan.md index 19f5de3..5201c21 100644 --- a/docs/source/roman_multisurvey_plan.md +++ b/docs/source/roman_multisurvey_plan.md @@ -7,6 +7,14 @@ importantly — the **behaviour changes that need discussion** before we rely on them. Sections marked *(future)* are designed but not yet implemented. ``` +```{important} +**Remove this file before merging the `roman_multisurvey` branch.** It is a +working design/roadmap doc, not user documentation. Before merge, migrate the +durable content — the column convention, the sample/catalog error split, the +multi-survey `StreamInjector` usage, and the Vega→AB handling — into the proper +docs pages (and the API docstrings), then delete `roman_multisurvey_plan.md`. +``` + ## Motivation `streamobs` was built for **single-survey** stream injection (LSST). We want to @@ -35,20 +43,22 @@ Two science realities drive specific features: ## Locked design decisions -1. **Multi-survey via a `MultiSurveyInjector` orchestrator** that holds - `{survey_name: Survey}` and delegates per-survey work to the existing, - unchanged `Survey` per-band API. Not a composite `Survey`; not manual - re-runs. -2. **Column convention (uniform `_true`/`_obs`/`_err`).** A single naming scheme - is used everywhere: `{band}_true` / `{band}_obs` / `{band}_err` / - `flag_observed` single-survey, and `{survey}_{band}_true` / +1. **One `StreamInjector` for one *or* many surveys.** A single injector holds + `{survey_name: Survey}` (size 1 for the single-survey case) and delegates + per-survey work to the existing, unchanged `Survey` per-band API via a shared + `_inject_one_survey` helper. Not a composite `Survey`; not manual re-runs; + and **no separate multi-survey class** — `StreamInjector` accepts a survey + name/`Survey`, a `{name: spec}` dict, or a list of specs. +2. **Column convention (always survey-namespaced `{survey}_{band}_true/_obs/_err`).** + A single naming scheme is used everywhere — `{survey}_{band}_true` / `{survey}_{band}_obs` / `{survey}_{band}_err` / `{survey}_flag_observed` - multi-survey (e.g. `roman_f158_obs`, `lsst_r_err`). The single- and - multi-survey forms differ only by the optional survey prefix. - **This intentionally drops the historical `mag_{band}` / `mag_{band}_obs` / - `magerr_{band}` names — it is *not* backward compatible** with catalogs or - downstream readers expecting those columns (decision made deliberately to - keep one convention; see decision 5). + (e.g. `roman_f158_obs`, `lsst_r_err`) — and it is **always namespaced by + survey, even for a single survey** (a one-survey injection of LSST emits + `lsst_r_obs`, not `r_obs`). There is no longer an un-namespaced single-survey + form. **This intentionally drops the historical `mag_{band}` / + `mag_{band}_obs` / `magerr_{band}` names — it is *not* backward compatible** + with catalogs or downstream readers expecting those columns (decision made + deliberately to keep one convention; see decision 5). 3. **Sample-vs-catalog error split, backward-compatible by default.** `Survey` holds two error curves, both functions of `delta_mag = mag − maglim`: - `log_photo_error_catalog` — the survey's **reported** error curve (the @@ -68,12 +78,13 @@ Two science realities drive specific features: 4. **Sample stellar masses once, interpolate per survey.** This is a correctness requirement (see below) and the engine of the whole feature. 5. **Public API preserved; column schema deliberately changed.** The - `StreamInjector(survey).inject(df, bands=[...])` API is unchanged. The - **column names are not** — we adopt the uniform `{band}_true/_obs/_err` - scheme (decision 2) *instead of* preserving the historical - `mag_g_obs`/`mag_r_obs`/`magerr_g` names. Downstream consumers that read those - columns must be updated. This is an accepted break in exchange for one - convention across single- and multi-survey output. + `StreamInjector(survey).inject(df, bands=[...])` single-survey call still + works (the same `StreamInjector` also takes a dict/list for several surveys). + The **column names are not** preserved — we adopt the always-namespaced + `{survey}_{band}_true/_obs/_err` scheme (decision 2) *instead of* the + historical `mag_g_obs`/`mag_r_obs`/`magerr_g` names. Downstream consumers that + read those columns must be updated. This is an accepted break in exchange for + one convention across single- and multi-survey output. ## Behaviour changes for discussion @@ -94,16 +105,15 @@ and makes "observed" mean "detected in everything you asked for". Callers can restore any prior behaviour by passing `detection_mag_cut=[...]` explicitly (e.g. `["g"]` for the old LSST default). **Adopted, but flagged for review.** -### `nstars` becomes "exactly N stars" (was an emergent IMF count) *(adopted, but flagged for review)* +### `nstars` becomes "exactly N stars" (was an emergent IMF count) *(adopted — agreed)* ```{important} -**Adopted (Phase 4).** {meth}`~streamobs.model.IsochroneModel.sample` now draws -*exactly* `nstars` (the shared-mass path described below), for **both** +**Adopted (Phase 4) and agreed.** {meth}`~streamobs.model.IsochroneModel.sample` +now draws *exactly* `nstars` (the shared-mass path described below), for **both** single-survey and multi-survey isochrones. The single-survey -`ugali.simulate()` emergent-count path has been removed. This flag is **left in -place for review** because it changes what `StreamModel.sample(size)` returns -for existing single-survey configs — see the discussion below before relying on -the new normalization. +`ugali.simulate()` emergent-count path has been removed. This was reviewed and +**accepted as the intended semantics** (you control N, and it is shared across +surveys); it is no longer open for discussion. ``` Previously {meth}`~streamobs.model.IsochroneModel.sample` converted `nstars` @@ -121,10 +131,10 @@ sampled_masses = rng.choice(init_mass[sel], size=nstars, p=imf_pdf) # exactly # then, per survey: mag_band = np.interp(sampled_masses, init_mass, mag_band) + dist_mod ``` -This returns *exactly* `nstars` stars. It is almost certainly the right -semantics for injection (you control N, and it is shared across surveys), but it -changes what `StreamModel.sample(size)` returns and could shift normalization in -any analysis that relied on the old emergent count. **Open for discussion.** +This returns *exactly* `nstars` stars. It is the right semantics for injection +(you control N, and it is shared across surveys). It changes what +`StreamModel.sample(size)` returns relative to the old emergent count, but this +was reviewed and **agreed** — no further discussion needed. ### Roman Vega→AB conversion is automatic and unconditional @@ -180,9 +190,10 @@ survey's `survey_files`. ### Phase 2 — De-hardcode the injector to arbitrary bands ✅ *(implemented)* - ✅ New `streamobs/columns.py` with `true_col` / `obs_col` / `err_col` helpers - `(band, survey=None)` and `flag_col(survey=None)` (`survey=None` ⇒ legacy - names `mag_` / `mag__obs` / `magerr_` / `flag_observed`; - a survey name ⇒ the `__…` multi-survey convention for Phase 4). + `(band, survey=None)` and `flag_col(survey=None)`. Injected catalogs are + **always** survey-namespaced (`__…` / `_flag_observed`); + `survey=None` is retained only as a low-level fallback that the injector itself + never uses. - ✅ `observed.py`: removed the `bands in {"r","g"}` hard block; the true-mag read, the observed/err columns, the valid-flux check (now ANDs over every injected band), the S/N cut, the per-survey detection flag, and the stored @@ -192,10 +203,9 @@ survey's `survey_files`. - ⚠️ The S/N-cut default changed from the hard-coded `["g"]` to **all injected bands** — see *Behaviour changes for discussion* above. -**Validated:** single-band (`bands=["r"]`) and arbitrary band sets now inject -without the old hard block; legacy column names and the `inject(df, bands=[...])` -API are unchanged; the test-branch suite stays green (94 passing; the lone -`des_yr6` photo-error failure is pre-existing and unrelated). +**Validated:** single-band (`bands=["r"]`) and arbitrary band sets inject without +the old hard block; the `inject(df, bands=[...])` API is unchanged (output now +namespaced); `tests/test_observed.py` + `tests/test_model.py` green. ### Phase 3 — Multi-band / multi-survey `IsochroneModel` ✅ *(implemented)* @@ -213,43 +223,48 @@ API are unchanged; the test-branch suite stays green (94 passing; the lone the `ROMAN_VEGA_TO_AB` table (no config flag; non-Roman bands pass through). Applied in the shared `sample_multisurvey` path. See the Vega→AB section above. - ✅ `StreamModel.sample`/`complete_catalog` derive their magnitude columns from - the isochrone via `_iso_mag_columns()` / `_sample_iso_mags()`: `_true` - for a single-survey isochrone, `__true` for a multi-survey one. - Naming routes through `columns.true_col`. + the isochrone via `_iso_mag_columns()` / `_sample_iso_mags()`, which **always** + emit `__true` (a single-survey isochrone simply has one survey; + `IsochroneModel` tracks `surveys`/`survey_bands` in both config forms). Naming + routes through `columns.true_col`. **Note on the chosen API:** the plan originally named the dict-returning method `sample` and a tuple `sample_legacy`; to avoid `sample()` changing return *type* -by config (a foot-gun for existing callers), the implementation keeps `sample()` -as the `(mag_1, mag_2)` tuple and adds `sample_multisurvey()` for the dict. -`StreamModel` dispatches on `isochrone.multi_survey`. +by config (a foot-gun for existing callers), the implementation keeps +`IsochroneModel.sample()` as the `(mag_1, mag_2)` tuple and adds +`sample_multisurvey()` for the dict. `StreamModel` always goes through +`sample_multisurvey()` (a single-survey isochrone is just the one-survey case), +so the emitted columns are uniformly `__true`. -**Validated:** single-survey model tests unchanged (test branch green, 94 -passing); a two-isochrone multi-survey config produces consistent shared-mass -magnitudes, Roman bands are converted Vega→AB by the fixed `ROMAN_VEGA_TO_AB` -offsets, and `StreamModel.sample` emits the `__true` columns. +**Validated:** model tests green; a two-isochrone multi-survey config produces +consistent shared-mass magnitudes, Roman bands are converted Vega→AB by the +fixed `ROMAN_VEGA_TO_AB` offsets, and `StreamModel.sample` emits the +`__true` columns. -### Phase 4 — `MultiSurveyInjector` ✅ *(implemented)* +### Phase 4 — one `StreamInjector` for one *or* many surveys ✅ *(implemented)* -- ✅ The per-band body of `StreamInjector.inject` is extracted into a shared - `StreamInjector._inject_one_survey(data, bands, survey_namespace=None, ...)` +- ✅ The per-band body of injection lives in a shared + `StreamInjector._inject_one_survey(data, bands, survey, survey_namespace, ...)` helper. It assumes positions and true magnitudes are already present and only does the observed/err draw, detection flags, and S/N cut, routing every column - name through `columns.py` (`survey_namespace=None` ⇒ legacy names). `inject` - now just completes the data and delegates to it, so single-survey behaviour is - unchanged. -- ✅ `MultiSurveyInjector(surveys).inject(data, survey_bands, stream_config=...)`: - (1) one shared sky placement; (2) one shared true-magnitude fill via a - multi-survey isochrone (masses sampled once → every survey's - `__true`); (3) a per-survey loop calling each survey's - `_inject_one_survey` with `survey_namespace=`, writing - `__obs/_err` and `_flag_observed` from that survey's own - `completeness_band` and maglim maps. Per-survey RNG via `rng.spawn(...)` for - order-independent reproducibility. `columns.perfect_flag_col` namespaces the - optional `perfect_galstarsep` flag. + name through `columns.py`. `survey`/`survey_namespace` are passed explicitly so + the same method serves every survey. +- ✅ `StreamInjector` accepts **one survey or several** (a name/`Survey`, a + `{namespace: spec}` dict, or a list); `__init__` normalizes to + `self.surveys = {namespace: Survey}` with a `survey` property pointing at the + `primary`. `inject(data, survey_bands=None, bands=None, stream_config=...)`: + (1) one shared sky placement; (2) one shared true-magnitude fill via the + isochrone (masses sampled once → every survey's `__true`); + (3) a per-survey loop calling `_inject_one_survey` with `survey_namespace=`, + writing `__obs/_err` and `_flag_observed` from that + survey's own `completeness_band` and maglim maps. Per-survey RNG via + `rng.spawn(...)` for order-independent reproducibility. `columns.perfect_flag_col` + namespaces the optional `perfect_galstarsep` flag. The separate + `MultiSurveyInjector` class has been **removed**. - ✅ A *scene* config (`config/scenes/roman_rubin_demo.yaml`) lists the surveys, per-survey bands, the multi-survey isochrone, and shared stream geometry. - ✅ `notebooks/multisurvey_phases_demo.ipynb` walks through Phases 1–4 end to - end. + end (executes against `StubSurvey` + real `ugali` isochrones). ### Phase 5 *(future)* — Lightweight background + galaxy misclassification @@ -288,9 +303,8 @@ recreated here, so it can land cleanly when the `roman_hlwas` work merges. | File | Phase | Role | |---|---|---| | `streamobs/surveys.py` | 1 | sample/catalog error fields, `get_photo_error(kind=)`, loader | -| `streamobs/columns.py` | 2 | NEW — column-name helpers | -| `streamobs/observed.py` | 1,2,4 | error wiring; de-hardcode bands; `_inject_one_survey`; `MultiSurveyInjector` | -| `streamobs/model.py` | 3 | multi-band `IsochroneModel`; band-generalized `StreamModel` | -| `streamobs/multisurvey.py` | 4 | NEW — orchestrator (or a class in `observed.py`) | +| `streamobs/columns.py` | 2 | NEW — column-name helpers (always namespaced) | +| `streamobs/observed.py` | 1,2,4 | error wiring; de-hardcode bands; unified one-or-many-survey `StreamInjector` (`_inject_one_survey` + `_complete_shared`) | +| `streamobs/model.py` | 3 | multi-band `IsochroneModel`; always-namespaced `StreamModel` | | `config/scenes/roman_rubin_demo.yaml` | 4 | NEW — multi-survey scene | | `streamobs/background.py` | 5 *(future)* | NEW — lightweight background | diff --git a/notebooks/multisurvey_phases_demo.ipynb b/notebooks/multisurvey_phases_demo.ipynb index a8f0dbb..39ee820 100644 --- a/notebooks/multisurvey_phases_demo.ipynb +++ b/notebooks/multisurvey_phases_demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "956da58f", + "id": "154f0f43", "metadata": {}, "source": [ "# Multi-survey injection — Phases 1-4 demo\n", @@ -14,9 +14,9 @@ "| Phase | What changed |\n", "|---|---|\n", "| **1** | `Survey` holds two error curves — a *catalog* (reported `magerr`, drives the S/N cut) and an optional *sample* (true scatter, drives the noise draw). `get_photo_error(..., kind=)` selects between them. |\n", - "| **2** | The injector is de-hardcoded off `{r, g}`; column names route through `streamobs.columns`, using a **uniform** `_true/_obs/_err` scheme (`_…` when namespaced). The S/N cut now applies to **all** injected bands. |\n", + "| **2** | The injector is de-hardcoded off `{r, g}`; column names route through `streamobs.columns`, using a uniform, **always survey-namespaced** `__true/_obs/_err` scheme. The S/N cut now applies to **all** injected bands. |\n", "| **3** | `IsochroneModel` is multi-band / multi-survey: masses are drawn **once** (exactly `nstars`) and interpolated into every survey's bands, so the *same physical star* is consistent across surveys. Roman bands are auto-converted Vega→AB. |\n", - "| **4** | `MultiSurveyInjector` orchestrates one shared sky placement + one shared true-mag fill, then a per-survey loop writing `__obs/_err` and `_flag_observed`. |\n", + "| **4** | A single `StreamInjector` accepts one survey **or several**: it does one shared sky placement + one shared true-mag fill, then a per-survey loop writing `__obs/_err` and `_flag_observed`. |\n", "\n", "See `docs/source/roman_multisurvey_plan.md` for the full design.\n" ] @@ -24,13 +24,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "1d385c8b", + "id": "faa963cc", "metadata": { "execution": { - "iopub.execute_input": "2026-06-15T23:33:47.506938Z", - "iopub.status.busy": "2026-06-15T23:33:47.506845Z", - "iopub.status.idle": "2026-06-15T23:33:57.035573Z", - "shell.execute_reply": "2026-06-15T23:33:57.034912Z" + "iopub.execute_input": "2026-06-16T18:01:24.411403Z", + "iopub.status.busy": "2026-06-16T18:01:24.411297Z", + "iopub.status.idle": "2026-06-16T18:01:25.632931Z", + "shell.execute_reply": "2026-06-16T18:01:25.632424Z" } }, "outputs": [], @@ -45,7 +45,7 @@ "\n", "from streamobs.surveys import Survey\n", "from streamobs.model import StreamModel, IsochroneModel\n", - "from streamobs.observed import StreamInjector, MultiSurveyInjector\n", + "from streamobs.observed import StreamInjector\n", "from streamobs import columns as C\n", "\n", "import os\n", @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "5dd03468", + "id": "bc624c63", "metadata": {}, "source": [ "## A runnable stub survey\n", @@ -77,13 +77,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "8ed0d733", + "id": "9fe2c7bf", "metadata": { "execution": { - "iopub.execute_input": "2026-06-15T23:33:57.037569Z", - "iopub.status.busy": "2026-06-15T23:33:57.037358Z", - "iopub.status.idle": "2026-06-15T23:33:57.041674Z", - "shell.execute_reply": "2026-06-15T23:33:57.041252Z" + "iopub.execute_input": "2026-06-16T18:01:25.634939Z", + "iopub.status.busy": "2026-06-16T18:01:25.634739Z", + "iopub.status.idle": "2026-06-16T18:01:25.638611Z", + "shell.execute_reply": "2026-06-16T18:01:25.638254Z" } }, "outputs": [ @@ -136,7 +136,7 @@ }, { "cell_type": "markdown", - "id": "aa861d61", + "id": "d4f9c999", "metadata": {}, "source": [ "## Phase 1 — sample vs. catalog photometric error\n", @@ -151,13 +151,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "63de7e59", + "id": "51d6fb5d", "metadata": { "execution": { - "iopub.execute_input": "2026-06-15T23:33:57.042872Z", - "iopub.status.busy": "2026-06-15T23:33:57.042761Z", - "iopub.status.idle": "2026-06-15T23:33:57.234295Z", - "shell.execute_reply": "2026-06-15T23:33:57.233794Z" + "iopub.execute_input": "2026-06-16T18:01:25.639625Z", + "iopub.status.busy": "2026-06-16T18:01:25.639518Z", + "iopub.status.idle": "2026-06-16T18:01:25.741575Z", + "shell.execute_reply": "2026-06-16T18:01:25.741220Z" } }, "outputs": [ @@ -204,30 +204,30 @@ }, { "cell_type": "markdown", - "id": "86c9e1fd", + "id": "041939f0", "metadata": {}, "source": [ "## Phase 2 — arbitrary bands + `columns.py`\n", "\n", "The injector no longer hard-codes `{r, g}`. Column names come from\n", - "`streamobs.columns`, using one **uniform** convention: `_true` /\n", - "`_obs` / `_err` / `flag_observed` single-survey, and the same with a\n", - "`_` prefix multi-survey. (This intentionally drops the historical\n", - "`mag_` / `magerr_` names — not backward compatible.) Below we inject\n", - "Roman NIR bands `F106`/`F158` through the *single-survey* `StreamInjector` —\n", - "impossible under the old `{r,g}` block.\n" + "`streamobs.columns`, using one uniform, **always survey-namespaced** convention:\n", + "`__true` / `__obs` / `__err` /\n", + "`_flag_observed`. (This intentionally drops the historical `mag_` /\n", + "`magerr_` names — not backward compatible.) Below we inject Roman NIR bands\n", + "`F106`/`F158` through a **single-survey** `StreamInjector` — impossible under the\n", + "old `{r,g}` block — and the output is namespaced by the survey's name (`roman`).\n" ] }, { "cell_type": "code", "execution_count": 4, - "id": "c54f0211", + "id": "3b531fc3", "metadata": { "execution": { - "iopub.execute_input": "2026-06-15T23:33:57.235495Z", - "iopub.status.busy": "2026-06-15T23:33:57.235382Z", - "iopub.status.idle": "2026-06-15T23:33:57.248981Z", - "shell.execute_reply": "2026-06-15T23:33:57.248544Z" + "iopub.execute_input": "2026-06-16T18:01:25.742803Z", + "iopub.status.busy": "2026-06-16T18:01:25.742698Z", + "iopub.status.idle": "2026-06-16T18:01:25.755667Z", + "shell.execute_reply": "2026-06-16T18:01:25.755283Z" } }, "outputs": [ @@ -235,43 +235,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "single-survey: r_true r_obs r_err flag_observed\n", - "multi-survey : roman_F158_true roman_F158_obs roman_F158_err roman_flag_observed\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "namespaced columns: roman_F158_true roman_F158_obs roman_F158_err roman_flag_observed\n", "\n", - "injected NIR-only bands -> columns: ['F106_obs', 'F106_err', 'F158_obs', 'F158_err', 'flag_observed']\n", - "detected: 1281 / 1500\n" + "injected NIR-only bands -> columns: ['roman_F106_obs', 'roman_F106_err', 'roman_F158_obs', 'roman_F158_err', 'roman_flag_observed']\n", + "detected: 1274 / 1500\n" ] } ], "source": [ - "print(\"single-survey:\", C.true_col(\"r\"), C.obs_col(\"r\"), C.err_col(\"r\"), C.flag_col())\n", - "print(\"multi-survey :\", C.true_col(\"F158\", \"roman\"), C.obs_col(\"F158\", \"roman\"),\n", + "print(\"namespaced columns:\", C.true_col(\"F158\", \"roman\"), C.obs_col(\"F158\", \"roman\"),\n", " C.err_col(\"F158\", \"roman\"), C.flag_col(\"roman\"))\n", "\n", "roman_sv = StubSurvey(\"roman\", [\"F106\", \"F158\"], completeness_band=\"F158\", maglim=27.0)\n", - "inj = StreamInjector(roman_sv)\n", + "inj = StreamInjector(roman_sv) # one survey -> namespaced by its name, \"roman\"\n", "N = 1500\n", "df = pd.DataFrame({\n", " \"ra\": rng.uniform(10, 20, N),\n", " \"dec\": rng.uniform(-5, 5, N),\n", - " \"F106_true\": rng.uniform(20, 28, N),\n", - " \"F158_true\": rng.uniform(20, 28, N),\n", + " \"roman_F106_true\": rng.uniform(20, 28, N),\n", + " \"roman_F158_true\": rng.uniform(20, 28, N),\n", "})\n", "out = inj.inject(df, bands=[\"F106\", \"F158\"], seed=1, verbose=False)\n", "print(\"\\ninjected NIR-only bands -> columns:\",\n", - " [c for c in out.columns if c.endswith((\"_obs\", \"_err\")) or c == \"flag_observed\"])\n", - "print(\"detected:\", int(out.flag_observed.sum()), \"/\", len(out))" + " [c for c in out.columns if c.endswith((\"_obs\", \"_err\")) or c.endswith(\"flag_observed\")])\n", + "print(\"detected:\", int(out.roman_flag_observed.sum()), \"/\", len(out))" ] }, { "cell_type": "markdown", - "id": "4eae6753", + "id": "ef8fcedd", "metadata": {}, "source": [ "## Phase 3 — multi-band / multi-survey isochrone (exactly `nstars`)\n", @@ -283,13 +275,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "cdcb361c", + "id": "2798a8c1", "metadata": { "execution": { - "iopub.execute_input": "2026-06-15T23:33:57.250040Z", - "iopub.status.busy": "2026-06-15T23:33:57.249928Z", - "iopub.status.idle": "2026-06-15T23:33:59.883954Z", - "shell.execute_reply": "2026-06-15T23:33:59.883368Z" + "iopub.execute_input": "2026-06-16T18:01:25.756657Z", + "iopub.status.busy": "2026-06-16T18:01:25.756551Z", + "iopub.status.idle": "2026-06-16T18:01:25.823450Z", + "shell.execute_reply": "2026-06-16T18:01:25.822899Z" } }, "outputs": [ @@ -319,7 +311,7 @@ }, { "cell_type": "markdown", - "id": "e414d472", + "id": "4bc79636", "metadata": {}, "source": [ "Now the multi-survey scene. One mass draw feeds both surveys, so a star's\n", @@ -329,13 +321,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "dbc7de53", + "id": "95a245ed", "metadata": { "execution": { - "iopub.execute_input": "2026-06-15T23:33:59.885548Z", - "iopub.status.busy": "2026-06-15T23:33:59.885425Z", - "iopub.status.idle": "2026-06-15T23:34:00.223968Z", - "shell.execute_reply": "2026-06-15T23:34:00.223395Z" + "iopub.execute_input": "2026-06-16T18:01:25.824533Z", + "iopub.status.busy": "2026-06-16T18:01:25.824423Z", + "iopub.status.idle": "2026-06-16T18:01:28.378539Z", + "shell.execute_reply": "2026-06-16T18:01:28.377918Z" } }, "outputs": [ @@ -345,12 +337,12 @@ "text": [ "multi-survey true columns: ['lsst_g_true', 'lsst_r_true', 'roman_F106_true', 'roman_F158_true']\n", "rows: 4000\n", - "corr(lsst_r, roman_F158) = 0.996 (shared masses => tightly correlated)\n" + "corr(lsst_r, roman_F158) = 0.995 (shared masses => tightly correlated)\n" ] }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -382,7 +374,7 @@ }, { "cell_type": "markdown", - "id": "2f39058a", + "id": "637766d8", "metadata": {}, "source": [ "Roman isochrone magnitudes are **always** converted Vega→AB (no config\n", @@ -394,13 +386,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "5468d5aa", + "id": "57c4f2c5", "metadata": { "execution": { - "iopub.execute_input": "2026-06-15T23:34:00.225519Z", - "iopub.status.busy": "2026-06-15T23:34:00.225397Z", - "iopub.status.idle": "2026-06-15T23:34:00.266916Z", - "shell.execute_reply": "2026-06-15T23:34:00.266455Z" + "iopub.execute_input": "2026-06-16T18:01:28.379807Z", + "iopub.status.busy": "2026-06-16T18:01:28.379696Z", + "iopub.status.idle": "2026-06-16T18:01:28.421767Z", + "shell.execute_reply": "2026-06-16T18:01:28.421196Z" } }, "outputs": [ @@ -437,26 +429,27 @@ }, { "cell_type": "markdown", - "id": "82017106", + "id": "10f3ebee", "metadata": {}, "source": [ - "## Phase 4 — `MultiSurveyInjector`\n", + "## Phase 4 — one `StreamInjector`, many surveys\n", "\n", - "One orchestrator, one shared sky placement and shared masses, then per-survey\n", - "observed columns and flags. Per-survey RNGs come from `rng.spawn(...)`, so the\n", - "result is reproducible and independent of survey order.\n" + "The same `StreamInjector` class accepts a `{namespace: survey}` mapping. It does\n", + "one shared sky placement and one shared mass draw, then a per-survey loop writing\n", + "each survey's observed columns and flags. Per-survey RNGs come from\n", + "`rng.spawn(...)`, so the result is reproducible and independent of survey order.\n" ] }, { "cell_type": "code", "execution_count": 8, - "id": "9b63b52a", + "id": "3bf8c97b", "metadata": { "execution": { - "iopub.execute_input": "2026-06-15T23:34:00.268030Z", - "iopub.status.busy": "2026-06-15T23:34:00.267909Z", - "iopub.status.idle": "2026-06-15T23:34:00.376660Z", - "shell.execute_reply": "2026-06-15T23:34:00.376201Z" + "iopub.execute_input": "2026-06-16T18:01:28.422876Z", + "iopub.status.busy": "2026-06-16T18:01:28.422759Z", + "iopub.status.idle": "2026-06-16T18:01:28.500974Z", + "shell.execute_reply": "2026-06-16T18:01:28.500442Z" } }, "outputs": [ @@ -469,16 +462,15 @@ " roman: ['roman_F106_err', 'roman_F106_obs', 'roman_F106_true', 'roman_F158_err', 'roman_F158_obs', 'roman_F158_true', 'roman_flag_observed']\n", "\n", "shared sky placement: ra/dec present = True\n", - "lsst detected: 1066 / 4000\n", - "roman detected: 3110 / 4000\n" + "lsst detected: 1105 / 4000\n", + "roman detected: 3113 / 4000\n" ] } ], "source": [ "lsst_sv = StubSurvey(\"lsst\", [\"g\", \"r\"], completeness_band=\"r\", maglim=26.0)\n", "roman_sv = StubSurvey(\"roman\", [\"F106\", \"F158\"], completeness_band=\"F158\", maglim=27.0)\n", - "msi = MultiSurveyInjector({\"lsst\": StreamInjector(lsst_sv),\n", - " \"roman\": StreamInjector(roman_sv)}, primary=\"lsst\")\n", + "msi = StreamInjector({\"lsst\": lsst_sv, \"roman\": roman_sv}, primary=\"lsst\")\n", "\n", "# Input carries only stream coordinates; everything else is sampled once.\n", "N = 4000\n", @@ -497,13 +489,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "5aabf327", + "id": "4a4c2ff4", "metadata": { "execution": { - "iopub.execute_input": "2026-06-15T23:34:00.377871Z", - "iopub.status.busy": "2026-06-15T23:34:00.377760Z", - "iopub.status.idle": "2026-06-15T23:34:00.445055Z", - "shell.execute_reply": "2026-06-15T23:34:00.444545Z" + "iopub.execute_input": "2026-06-16T18:01:28.502120Z", + "iopub.status.busy": "2026-06-16T18:01:28.502001Z", + "iopub.status.idle": "2026-06-16T18:01:28.568555Z", + "shell.execute_reply": "2026-06-16T18:01:28.568019Z" } }, "outputs": [ @@ -531,19 +523,19 @@ { "cell_type": "code", "execution_count": 10, - "id": "171f7526", + "id": "e154bc4b", "metadata": { "execution": { - "iopub.execute_input": "2026-06-15T23:34:00.446054Z", - "iopub.status.busy": "2026-06-15T23:34:00.445937Z", - "iopub.status.idle": "2026-06-15T23:34:00.516601Z", - "shell.execute_reply": "2026-06-15T23:34:00.516250Z" + "iopub.execute_input": "2026-06-16T18:01:28.569657Z", + "iopub.status.busy": "2026-06-16T18:01:28.569543Z", + "iopub.status.idle": "2026-06-16T18:01:28.640101Z", + "shell.execute_reply": "2026-06-16T18:01:28.639713Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -567,7 +559,7 @@ }, { "cell_type": "markdown", - "id": "9e34ac16", + "id": "308a08d9", "metadata": {}, "source": [ "## Notes for real runs\n", @@ -577,9 +569,10 @@ "* A band label is used **both** as the ugali isochrone field name and as the key\n", " into a survey's maglim/photo-error maps, so they must agree (ugali's Roman\n", " fields are upper-case: `F106`, `F158`, ...).\n", - "* **`nstars` is now exactly N** (was an emergent IMF count). This is adopted but\n", - " intentionally still **flagged for review** in the plan doc, because it changes\n", - " what `StreamModel.sample(size)` returns for existing single-survey configs.\n" + "* **`nstars` is now exactly N** (was an emergent IMF count). This is the agreed\n", + " semantics for both single- and multi-survey configs.\n", + "* **Output columns are always survey-namespaced** (`__…`), even for\n", + " a single survey — there is no longer an un-namespaced single-survey form.\n" ] } ], diff --git a/scripts/build_multisurvey_demo_nb.py b/scripts/build_multisurvey_demo_nb.py index ca7f0fc..3727252 100644 --- a/scripts/build_multisurvey_demo_nb.py +++ b/scripts/build_multisurvey_demo_nb.py @@ -22,9 +22,9 @@ | Phase | What changed | |---|---| | **1** | `Survey` holds two error curves — a *catalog* (reported `magerr`, drives the S/N cut) and an optional *sample* (true scatter, drives the noise draw). `get_photo_error(..., kind=)` selects between them. | -| **2** | The injector is de-hardcoded off `{r, g}`; column names route through `streamobs.columns`, using a **uniform** `_true/_obs/_err` scheme (`_…` when namespaced). The S/N cut now applies to **all** injected bands. | +| **2** | The injector is de-hardcoded off `{r, g}`; column names route through `streamobs.columns`, using a uniform, **always survey-namespaced** `__true/_obs/_err` scheme. The S/N cut now applies to **all** injected bands. | | **3** | `IsochroneModel` is multi-band / multi-survey: masses are drawn **once** (exactly `nstars`) and interpolated into every survey's bands, so the *same physical star* is consistent across surveys. Roman bands are auto-converted Vega→AB. | -| **4** | `MultiSurveyInjector` orchestrates one shared sky placement + one shared true-mag fill, then a per-survey loop writing `__obs/_err` and `_flag_observed`. | +| **4** | A single `StreamInjector` accepts one survey **or several**: it does one shared sky placement + one shared true-mag fill, then a per-survey loop writing `__obs/_err` and `_flag_observed`. | See `docs/source/roman_multisurvey_plan.md` for the full design. """) @@ -39,7 +39,7 @@ from streamobs.surveys import Survey from streamobs.model import StreamModel, IsochroneModel -from streamobs.observed import StreamInjector, MultiSurveyInjector +from streamobs.observed import StreamInjector from streamobs import columns as C import os @@ -133,32 +133,31 @@ def get_extinction(self, band, pixel=None): md(r"""## Phase 2 — arbitrary bands + `columns.py` The injector no longer hard-codes `{r, g}`. Column names come from -`streamobs.columns`, using one **uniform** convention: `_true` / -`_obs` / `_err` / `flag_observed` single-survey, and the same with a -`_` prefix multi-survey. (This intentionally drops the historical -`mag_` / `magerr_` names — not backward compatible.) Below we inject -Roman NIR bands `F106`/`F158` through the *single-survey* `StreamInjector` — -impossible under the old `{r,g}` block. +`streamobs.columns`, using one uniform, **always survey-namespaced** convention: +`__true` / `__obs` / `__err` / +`_flag_observed`. (This intentionally drops the historical `mag_` / +`magerr_` names — not backward compatible.) Below we inject Roman NIR bands +`F106`/`F158` through a **single-survey** `StreamInjector` — impossible under the +old `{r,g}` block — and the output is namespaced by the survey's name (`roman`). """) co( - """print("single-survey:", C.true_col("r"), C.obs_col("r"), C.err_col("r"), C.flag_col()) -print("multi-survey :", C.true_col("F158", "roman"), C.obs_col("F158", "roman"), + """print("namespaced columns:", C.true_col("F158", "roman"), C.obs_col("F158", "roman"), C.err_col("F158", "roman"), C.flag_col("roman")) roman_sv = StubSurvey("roman", ["F106", "F158"], completeness_band="F158", maglim=27.0) -inj = StreamInjector(roman_sv) +inj = StreamInjector(roman_sv) # one survey -> namespaced by its name, "roman" N = 1500 df = pd.DataFrame({ "ra": rng.uniform(10, 20, N), "dec": rng.uniform(-5, 5, N), - "F106_true": rng.uniform(20, 28, N), - "F158_true": rng.uniform(20, 28, N), + "roman_F106_true": rng.uniform(20, 28, N), + "roman_F158_true": rng.uniform(20, 28, N), }) out = inj.inject(df, bands=["F106", "F158"], seed=1, verbose=False) print("\\ninjected NIR-only bands -> columns:", - [c for c in out.columns if c.endswith(("_obs", "_err")) or c == "flag_observed"]) -print("detected:", int(out.flag_observed.sum()), "/", len(out))""" + [c for c in out.columns if c.endswith(("_obs", "_err")) or c.endswith("flag_observed")]) +print("detected:", int(out.roman_flag_observed.sum()), "/", len(out))""" ) md(r"""## Phase 3 — multi-band / multi-survey isochrone (exactly `nstars`) @@ -218,18 +217,18 @@ def get_extinction(self, band, pixel=None): print("\\nF158 shift:", (iso._to_ab("F158", x) - x), " (always +1.315)") print("r shift:", (iso._to_ab("r", x) - x), " (0 -> non-Roman pass-through)")""") -md(r"""## Phase 4 — `MultiSurveyInjector` +md(r"""## Phase 4 — one `StreamInjector`, many surveys -One orchestrator, one shared sky placement and shared masses, then per-survey -observed columns and flags. Per-survey RNGs come from `rng.spawn(...)`, so the -result is reproducible and independent of survey order. +The same `StreamInjector` class accepts a `{namespace: survey}` mapping. It does +one shared sky placement and one shared mass draw, then a per-survey loop writing +each survey's observed columns and flags. Per-survey RNGs come from +`rng.spawn(...)`, so the result is reproducible and independent of survey order. """) co( """lsst_sv = StubSurvey("lsst", ["g", "r"], completeness_band="r", maglim=26.0) roman_sv = StubSurvey("roman", ["F106", "F158"], completeness_band="F158", maglim=27.0) -msi = MultiSurveyInjector({"lsst": StreamInjector(lsst_sv), - "roman": StreamInjector(roman_sv)}, primary="lsst") +msi = StreamInjector({"lsst": lsst_sv, "roman": roman_sv}, primary="lsst") # Input carries only stream coordinates; everything else is sampled once. N = 4000 @@ -277,9 +276,10 @@ def get_extinction(self, band, pixel=None): * A band label is used **both** as the ugali isochrone field name and as the key into a survey's maglim/photo-error maps, so they must agree (ugali's Roman fields are upper-case: `F106`, `F158`, ...). -* **`nstars` is now exactly N** (was an emergent IMF count). This is adopted but - intentionally still **flagged for review** in the plan doc, because it changes - what `StreamModel.sample(size)` returns for existing single-survey configs. +* **`nstars` is now exactly N** (was an emergent IMF count). This is the agreed + semantics for both single- and multi-survey configs. +* **Output columns are always survey-namespaced** (`__…`), even for + a single survey — there is no longer an un-namespaced single-survey form. """) nb = new_notebook(cells=cells) diff --git a/streamobs/columns.py b/streamobs/columns.py index d8f8945..86845dd 100644 --- a/streamobs/columns.py +++ b/streamobs/columns.py @@ -2,17 +2,15 @@ Column-name helpers for injected catalogs. These centralize the naming convention so the injector is not hard-coded to -specific bands. A single, uniform ``_true`` / ``_obs`` / -``_err`` convention is used everywhere; the only difference between the -single- and multi-survey cases is an optional survey prefix: - -- **Single-survey** (``survey=None``): ``_true`` (true / noiseless), - ``_obs`` (observed / noisy), ``_err`` (reported error), - ``flag_observed``. -- **Multi-survey** (``survey="roman"``, ``"lsst"``, ...): ``__true``, - ``__obs``, ``__err``, ``_flag_observed``. - Used by :class:`~streamobs.observed.MultiSurveyInjector` so each band's - columns are namespaced by survey. +specific bands. Injected catalogs are **always** survey-namespaced — +``__true`` (true / noiseless), ``__obs`` (observed / +noisy), ``__err`` (reported error), and ``_flag_observed`` +— produced by :class:`~streamobs.observed.StreamInjector` whether it serves one +survey or several (e.g. ``lsst_r_obs``, ``roman_F158_obs``). + +The ``survey`` argument therefore identifies the namespace. ``survey=None`` is +retained only as a low-level fallback that yields the bare ``_…`` / +``flag_observed`` names; the injector itself never uses it. .. note:: This convention intentionally **drops** the historical ``mag_`` / diff --git a/streamobs/model.py b/streamobs/model.py index a093be9..eb01720 100644 --- a/streamobs/model.py +++ b/streamobs/model.py @@ -136,10 +136,8 @@ def sample(self, size): ------- pandas.DataFrame Columns include: ``phi1``, ``phi2``, ``dist``, ``mu1``, ``mu2``, - ``rv``, and the isochrone magnitude columns. For a single-survey - isochrone these are ``_true`` (its two bands); for a - multi-survey isochrone they are ``__true`` per - survey/band. Some may be None if the sub-model is absent. + ``rv``, and the isochrone magnitude columns ``__true`` + (per survey/band). Some may be None if the sub-model is absent. """ # Sample phi1 and phi2 @@ -183,28 +181,24 @@ def sample(self, size): def _iso_mag_columns(self): """Names of the magnitude columns produced by the isochrone model. - Single-survey ⇒ legacy ``[mag_, mag_]``; multi-survey ⇒ - ``[__true, ...]`` for every survey/band; no isochrone ⇒ []. + Always ``[__true, ...]`` for every survey/band the + isochrone carries (a single-survey isochrone simply has one survey); no + isochrone ⇒ ``[]``. ``IsochroneModel`` tracks ``surveys`` / + ``survey_bands`` in both configuration forms, so the naming is uniform. """ iso = self.isochrone if iso is None: return [] - if getattr(iso, "multi_survey", False): - cols = [] - for name in iso.surveys: - band_1, band_2 = iso.survey_bands[name] - cols += [true_col(band_1, name), true_col(band_2, name)] - return cols - return [true_col(iso.band_1), true_col(iso.band_2)] + cols = [] + for name in iso.surveys: + band_1, band_2 = iso.survey_bands[name] + cols += [true_col(band_1, name), true_col(band_2, name)] + return cols def _sample_iso_mags(self, n, dist): - """Sample isochrone magnitudes as a ``{column_name: values}`` dict.""" - iso = self.isochrone - if getattr(iso, "multi_survey", False): - mags = iso.sample_multisurvey(n, dist) - return {true_col(band, name): vals for (name, band), vals in mags.items()} - mag_1, mag_2 = iso.sample(n, dist) - return {true_col(iso.band_1): mag_1, true_col(iso.band_2): mag_2} + """Sample isochrone magnitudes as a ``{__true: values}`` dict.""" + mags = self.isochrone.sample_multisurvey(n, dist) + return {true_col(band, name): vals for (name, band), vals in mags.items()} def complete_catalog( self, @@ -214,6 +208,7 @@ def complete_catalog( inplace=False, save_path=None, verbose=True, + dist=None, ): """Complete only the requested columns in a catalog. @@ -223,6 +218,11 @@ def complete_catalog( using the configured sub-models (density, track, distance modulus, isochrone, velocity) and only if those capabilities are available. + Pre-existing values are never overwritten: for every column only the + missing (absent or NaN) rows are filled. In particular, supplying some + of an isochrone's bands and requesting the others fills only the missing + bands and leaves the provided ones untouched. + Parameters ---------- catalog : pandas.DataFrame or str or dict or None @@ -233,9 +233,8 @@ def complete_catalog( columns_to_add : sequence of str or None, optional The columns to ensure in the output. Valid entries are {'phi1','phi2','dist','mu1','mu2','rv'} plus the isochrone magnitude - columns (``_true`` single-survey, ``__true`` - multi-survey). If None, all valid columns supported by the - configured model are considered. + columns (``__true``). If None, all valid columns + supported by the configured model are considered. size : int or None, optional Required when ``catalog`` is None or an empty table; ignored otherwise. @@ -246,6 +245,12 @@ def complete_catalog( If provided, write the completed catalog to this CSV path. verbose : bool, default True If True, print progress/status messages. + dist : float or array-like or None, optional + Distance modulus to use directly instead of sampling one from the + ``distance_modulus`` sub-model. A scalar is broadcast to every row + that needs a ``dist`` value; an array must have one entry per row. + When given, ``phi1`` and a ``distance_modulus`` model are **not** + required to fill magnitudes. Only missing ``dist`` rows are set. Returns ------- @@ -273,8 +278,7 @@ def complete_catalog( """ # Supported outputs and capability filtering # Columns this method can fill using the configured model - # Magnitude columns are band-/survey-general (_true for a - # single-survey isochrone; __true for multi-survey). + # Magnitude columns are survey-namespaced (__true). mag_cols = self._iso_mag_columns() all_cols = ("phi1", "phi2", "dist") + tuple(mag_cols) + ("mu1", "mu2", "rv") target_cols = ( @@ -295,7 +299,7 @@ def complete_catalog( target_cols = [c for c in target_cols if c not in ("mu1", "mu2", "rv")] if removed: self._info(verbose, "Velocity model not defined; skipping velocities.") - if self.distance_modulus is None: + if self.distance_modulus is None and dist is None: removed = [c for c in target_cols if c == "dist"] target_cols = [c for c in target_cols if c != "dist"] if removed: @@ -336,39 +340,69 @@ def complete_catalog( ): idx = self._missing_idx(df, "dist") if len(idx) > 0: - if "phi1" not in df.columns or df["phi1"].isna().any(): - raise ValueError( - "phi1 required to sample dist; include 'phi1' in columns_to_add or provide it in catalog" + if dist is not None: + # Use the distance supplied directly (scalar broadcast or + # per-row vector); no phi1 / distance_modulus model needed. + dist_arr = np.asarray(dist, dtype=float) + if dist_arr.ndim == 0: + df.loc[idx, "dist"] = float(dist_arr) + else: + if dist_arr.shape[0] != N: + raise ValueError( + f"dist vector has length {dist_arr.shape[0]} but " + f"the catalog has {N} rows." + ) + pos = df.index.get_indexer(idx) + df.loc[idx, "dist"] = dist_arr[pos] + self._info(verbose, f"Set {len(idx)} dist values from `dist`.") + else: + if self.distance_modulus is None: + raise ValueError( + "No distance_modulus model is configured; pass `dist` " + "(a float or per-row vector) to set distances." + ) + if "phi1" not in df.columns or df["phi1"].isna().any(): + raise ValueError( + "phi1 required to sample dist; include 'phi1' in columns_to_add or provide it in catalog" + ) + + df.loc[idx, "dist"] = self.distance_modulus.sample( + df.loc[idx, "phi1"].to_numpy() ) - - df.loc[idx, "dist"] = self.distance_modulus.sample( - df.loc[idx, "phi1"].to_numpy() - ) - self._info(verbose, f"Filled {len(idx)} dist values.") + self._info(verbose, f"Filled {len(idx)} dist values.") # magnitudes (need dist and isochrone) - if any(c in target_cols for c in mag_cols): - if all(c in df.columns for c in mag_cols): + requested_mags = [c for c in mag_cols if c in target_cols] + if requested_mags: + # Only touch rows that are missing a requested band; existing values + # are preserved (never overwritten). + missing = {c: self._missing_idx(df, c) for c in requested_mags} + to_fill = {c: idx for c, idx in missing.items() if len(idx) > 0} + if not to_fill: self._info( verbose, - f"{mag_cols} already exist; no sampling performed.", + f"{requested_mags} already present; no sampling performed.", ) else: - # Verify distance modulus availability + # Verify distance availability if "dist" not in df.columns: raise ValueError( - "dist is required to sample apparent magnitudes; include 'dist' in `columns_to_add` or provide it in catalog" + "dist is required to sample apparent magnitudes; include 'dist' in `columns_to_add`, provide it in the catalog, or pass `dist=`." ) dist_vals = df["dist"].to_numpy() - # Sample all bands together to keep colors consistent across rows - if any(c in df.columns for c in mag_cols): - self._info( - verbose, - "Overwriting existing magnitude columns to keep colors consistent.", - ) - for col, vals in self._sample_iso_mags(N, dist_vals).items(): - df[col] = vals - self._info(verbose, f"Filled magnitudes for {N} rows.") + # One shared mass draw -> all bands; the newly filled cells are + # mutually colour-consistent. Assign only the missing rows so any + # bands/values already present are left untouched. + mags = self._sample_iso_mags(N, dist_vals) + for col, idx in to_fill.items(): + pos = df.index.get_indexer(idx) + if col not in df.columns: + df[col] = np.nan + df.loc[idx, col] = np.asarray(mags[col])[pos] + self._info( + verbose, + f"Filled magnitudes for {sorted(to_fill)} (missing rows only).", + ) # velocities (need phi1 and velocity model) if any(c in target_cols for c in ("mu1", "mu2", "rv")): diff --git a/streamobs/observed.py b/streamobs/observed.py index 735e719..06c0b4b 100644 --- a/streamobs/observed.py +++ b/streamobs/observed.py @@ -18,15 +18,32 @@ class StreamInjector: """ - Inject observational effects into stream data for a given survey. + Inject observational effects into stream data for one or more surveys. - This class handles the core injection logic while keeping survey data separate. - All survey data is loaded once and cached, making multiple injections efficient. + A single injector handles both the single- and multi-survey cases: pass one + survey or several. The same shared sky placement and a single shared draw of + true magnitudes (masses sampled once via the isochrone and interpolated into + every survey's bands) guarantee the **same physical star** gets consistent + magnitudes across surveys. Each survey contributes its own + ``__obs`` / ``__err`` / ``_flag_observed`` + columns, computed with that survey's own maglim maps and completeness + functions. Output columns are **always** survey-namespaced, even for a single + survey. + + All survey data is loaded once and cached, making multiple injections + efficient. Attributes ---------- + surveys : dict + ``{namespace: Survey}`` for every survey this injector serves. The + namespace is the column prefix (``lsst_r_obs``, ``roman_F158_obs``, ...). + primary : str + Namespace of the survey whose footprint drives the shared sky placement + and whose ``_save_injected_data`` is used. survey : Survey - Survey instance containing all survey-specific data and functions. + The primary :class:`~streamobs.surveys.Survey` (convenience accessor used + by the mask / coordinate helpers; equal to ``surveys[primary]``). mask_cache : dict (class attribute) Cache of previously created HEALPix masks to avoid recomputation. _last_gc_frame : GreatCircleICRSFrame or None @@ -35,74 +52,148 @@ class StreamInjector: Examples -------- - Initialize with a survey: + Single survey (columns namespaced by the survey's name): >>> injector = StreamInjector('lsst', release='dc2') + >>> out = injector.inject(df, bands=['r', 'g']) # -> lsst_r_obs, ... - Or with a pre-loaded Survey object: + Several surveys at once: - >>> survey = Survey.load('lsst', release='dc2') - >>> injector = StreamInjector(survey) + >>> injector = StreamInjector({'lsst': 'lsst', 'roman': 'roman'}) + >>> out = injector.inject( + ... df, survey_bands={'lsst': ['r', 'g'], 'roman': ['F106', 'F158']}, + ... stream_config=scene['stream'], seed=42, + ... ) """ mask_cache = {} - def __init__(self, survey, **kwargs): + def __init__(self, survey, primary=None, **kwargs): """ - Initialize with survey configuration. + Initialize with one or more survey configurations. Parameters ---------- - survey : str or Survey - Either a survey name string (e.g., 'lsst') or a pre-loaded Survey instance. + survey : str, Survey, dict, or list + One survey or several. Accepted forms: + + - a survey-name string (e.g. ``'lsst'``) or a pre-loaded + :class:`~streamobs.surveys.Survey` — a single survey, namespaced + by its own name; + - a ``{namespace: spec}`` dict, where ``spec`` is a name string or a + ``Survey`` and the key is the column namespace; + - a list/tuple of specs, each namespaced by its survey's name. + primary : str, optional + Namespace of the survey that drives the shared sky placement. + Defaults to the first survey. **kwargs - Additional keyword arguments passed to Survey.load() if survey is a string. - Common options include: - - release : str, optional - Survey release version (e.g., 'dc2', 'yr1', 'yr10'). + Forwarded to :meth:`Survey.load` for any ``spec`` given as a name + string (e.g. ``release``). Raises ------ ValueError - If survey is neither a string nor a Survey instance. + If ``surveys`` is empty or of an unsupported type, or if ``primary`` + is not one of the provided surveys. """ - - if isinstance(survey, str): - self.survey = Survey.load(survey=survey, **kwargs) - elif isinstance(survey, Survey): - self.survey = survey - else: - raise ValueError("survey must be a string or Survey instance.") + self.surveys = self._normalize_surveys(survey, **kwargs) + self.survey_names = list(self.surveys) + if not self.survey_names: + raise ValueError("At least one survey is required.") + self.primary = primary if primary is not None else self.survey_names[0] + if self.primary not in self.surveys: + raise ValueError( + f"primary='{self.primary}' is not one of {self.survey_names}." + ) # Instance attribute to store the last used gc_frame self._last_gc_frame = None - def inject(self, data, bands=["r", "g"], **kwargs): + @property + def survey(self): + """The primary :class:`~streamobs.surveys.Survey`. + + Mask, coordinate and footprint helpers operate on this survey. """ - Add observed quantities from the survey to the given data. + return self.surveys[self.primary] - This method applies observational effects including photometric errors, - magnitude measurements, and detection flags based on survey properties. + @classmethod + def _normalize_surveys(cls, surveys, **kwargs): + """Coerce the ``surveys`` argument into a ``{namespace: Survey}`` dict.""" + if isinstance(surveys, (str, Survey)): + survey = cls._load_survey(surveys, **kwargs) + return {survey.name: survey} + if isinstance(surveys, (list, tuple)): + out = {} + for spec in surveys: + survey = cls._load_survey(spec, **kwargs) + out[survey.name] = survey + return out + if isinstance(surveys, dict): + return { + name: cls._load_survey(spec, **kwargs) for name, spec in surveys.items() + } + raise ValueError( + "surveys must be a survey name, a Survey, a {name: spec} dict, " + "or a list of specs." + ) + + @staticmethod + def _load_survey(spec, **kwargs): + """Resolve a single survey spec (name string or Survey) to a Survey.""" + if isinstance(spec, Survey): + return spec + if isinstance(spec, str): + return Survey.load(survey=spec, **kwargs) + raise ValueError("Each survey spec must be a string or Survey instance.") + + def inject(self, data, survey_bands=None, bands=None, stream_config=None, **kwargs): + """ + Add observed quantities from every survey into a single catalog. + + Applies observational effects (photometric errors, measured magnitudes, + detection flags) for each survey this injector serves. A single shared + sky placement and a single shared true-magnitude fill (masses sampled + once and interpolated into every survey's bands) ensure the same physical + star is consistent across surveys. Output columns are always + survey-namespaced (``__obs`` etc.). Parameters ---------- data : str or pd.DataFrame - Input data as DataFrame or path to the file (CSV or Excel). - Must contain either (ra, dec) or (phi1, phi2) coordinates, and - magnitude columns for the specified bands. + Input data as DataFrame or path to the file (CSV or Excel). May + contain only stream coordinates (``phi1``/``phi2`` or ``ra``/``dec``); + anything missing is sampled from ``stream_config``. An all-empty frame + of length ``N`` is accepted (geometry and magnitudes are then sampled + for ``N`` rows). + survey_bands : dict, optional + ``{survey_name: [bands]}`` — bands to inject for each survey. Keys + must match the surveys this injector was built with. For a single + survey you may instead pass ``bands`` (below). bands : list of str, optional - List of photometric bands to process. Default is ['r', 'g']. + Convenience shorthand for the single-survey case: the bands to inject + for the (only) survey. Ignored when ``survey_bands`` is given. If + neither is provided and there is exactly one survey, defaults to + ``['r', 'g']``. + stream_config : dict, optional + The ``stream`` section consumed by + :class:`~streamobs.model.StreamModel`. Required when any coordinate + or true-magnitude column is missing. Its isochrone produces the + shared ``__true`` columns. **kwargs Additional keyword arguments: seed : int, optional Random seed for reproducibility. + dist : float or array-like, optional + Distance modulus used directly (scalar broadcast or per-row + vector) instead of sampling from the config's ``distance_modulus`` + model — lets magnitudes be filled without ``phi1``. nside : int, optional HEALPix nside parameter. Default is 4096. detection_mag_cut : list of str, optional Bands to apply the SNR detection cut to. Default is all injected - bands (every band in ``bands`` must have SNR >= 5). + bands (every injected band must have SNR >= 5). save : bool, optional Whether to save the output data. Default is False. folder : str or Path, optional @@ -118,19 +209,25 @@ def inject(self, data, bands=["r", "g"], **kwargs): Returns ------- pd.DataFrame - DataFrame with the following added columns: + DataFrame with shared ``ra``/``dec`` and, per survey: - - _obs : Observed magnitudes for each band - - _err : Photometric errors for each band - - flag_observed : Boolean flag (True=detected, False=not detected). Includes both detection and classification efficiencies. - - flag_perfect_galstarsep : Boolean flag assuming perfect star/galaxy separation (detection efficiency only, no classification losses; only if requested with perfect_galstarsep=True) - - ra, dec : Sky coordinates (if not already present) + - __true : True (noiseless) apparent magnitudes + - __obs : Observed (noisy) magnitudes + - __err : Reported photometric errors + - _flag_observed : Boolean detection flag (detection and + classification efficiencies) + - _flag_perfect_galstarsep : Boolean flag assuming perfect + star/galaxy separation (only if perfect_galstarsep=True) Raises ------ ValueError - If required columns are missing or bands are not supported. + If required columns are missing, or if ``survey_bands`` references an + unknown survey, or if neither ``survey_bands`` nor ``bands`` can be + resolved. """ + survey_bands = self._resolve_survey_bands(survey_bands, bands) + # Load data data = self._load_data(data) @@ -138,15 +235,32 @@ def inject(self, data, bands=["r", "g"], **kwargs): seed = kwargs.pop("seed", None) rng = np.random.default_rng(seed) - # Complete missing columns (ra/dec, magnitudes) - data = self.complete_data(data, rng=rng, seed=seed, bands=bands, **kwargs) - - # Run the per-survey observational injection using the legacy column - # names (survey_namespace=None). - data = self._inject_one_survey( - data, bands, rng=rng, seed=seed, survey_namespace=None, **kwargs + # Shared sky placement + shared true-magnitude fill (masses sampled once + # across all surveys). + data = self._complete_shared( + data, + survey_bands, + stream_config=stream_config, + rng=rng, + seed=seed, + **kwargs, ) + # Per-survey observational injection. Independent child RNGs make the + # result independent of survey ordering and reproducible from `seed`. + children = rng.spawn(len(self.survey_names)) + for child_rng, name in zip(children, self.survey_names): + if name not in survey_bands: + continue + data = self._inject_one_survey( + data, + list(survey_bands[name]), + survey=self.surveys[name], + survey_namespace=name, + rng=child_rng, + **kwargs, + ) + # Save if requested if kwargs.get("save"): self._save_injected_data(data, kwargs.get("folder", None)) @@ -154,15 +268,40 @@ def inject(self, data, bands=["r", "g"], **kwargs): # Return data (do NOT store as instance attribute to avoid conflicts between runs) return data + def _resolve_survey_bands(self, survey_bands, bands): + """Normalize the ``survey_bands`` / ``bands`` arguments to a dict. + + ``survey_bands`` wins if given (validated against the known surveys). + Otherwise ``bands`` is accepted only when there is a single survey; if + neither is given and there is one survey, defaults to ``['r', 'g']``. + """ + if survey_bands is not None: + unknown = set(survey_bands) - set(self.surveys) + if unknown: + raise ValueError( + f"survey_bands references unknown surveys {sorted(unknown)}; " + f"available: {self.survey_names}." + ) + return {name: list(b) for name, b in survey_bands.items()} + + if len(self.survey_names) != 1: + raise ValueError( + "Pass survey_bands={name: [bands]} when the injector serves " + f"multiple surveys ({self.survey_names})." + ) + if bands is None: + bands = ["r", "g"] + return {self.primary: list(bands)} + def _inject_one_survey( - self, data, bands, rng=None, seed=None, survey_namespace=None, **kwargs + self, data, bands, survey, survey_namespace, rng=None, seed=None, **kwargs ): - """Add this survey's observed magnitudes, errors and detection flags. + """Add one survey's observed magnitudes, errors and detection flags. - This holds the per-band observational logic shared by single-survey - :meth:`inject` and :class:`MultiSurveyInjector`. It assumes ``data`` - already carries ``ra``/``dec`` and the true-magnitude columns for the - requested bands; it does **not** sample positions or true magnitudes. + This holds the per-band observational logic. It assumes ``data`` already + carries ``ra``/``dec`` and the true-magnitude columns + (``true_col(band, survey_namespace)``) for the requested bands; it does + **not** sample positions or true magnitudes. Parameters ---------- @@ -171,15 +310,15 @@ def _inject_one_survey( for every requested band. bands : list of str Bands to process for this survey. + survey : Survey + The survey supplying maglim maps, completeness and error curves. + survey_namespace : str + Column-naming namespace ⇒ ``__obs`` / + ``__err`` / ``_flag_observed``. rng : numpy.random.Generator, optional Random generator for the noise draw and detection sampling. seed : int, optional Seed used to build an RNG when ``rng`` is None. - survey_namespace : str or None, optional - Column-naming namespace. ``None`` ⇒ single-survey names - (``_obs`` / ``_err`` / ``flag_observed``); - a survey name ⇒ ``__obs`` / ``__err`` / - ``_flag_observed``. **kwargs ``nside``, ``detection_mag_cut``, ``dust_correction``, ``perfect_galstarsep``, ``verbose`` (see :meth:`inject`). @@ -206,18 +345,18 @@ def _inject_one_survey( # Process each band for band in bands: # Get extinction for this band - nside_ebv = hp.get_nside(self.survey.ebv_map) + nside_ebv = hp.get_nside(survey.ebv_map) if nside_ebv != nside: pix_ebv = hp.ang2pix(nside_ebv, data["ra"], data["dec"], lonlat=True) else: pix_ebv = pix - extinction_band = self.survey.get_extinction(band, pixel=pix_ebv) + extinction_band = survey.get_extinction(band, pixel=pix_ebv) # Calculate true apparent magnitudes (including extinction) apparent_mag_true = data[true_col(band, survey_namespace)] + extinction_band # Get magnitude limits - nside_maglim = hp.get_nside(self.survey.maglim_maps[band]) + nside_maglim = hp.get_nside(survey.maglim_maps[band]) if nside_maglim != nside: pix_maglim = hp.ang2pix( nside_maglim, data["ra"], data["dec"], lonlat=True @@ -230,11 +369,11 @@ def _inject_one_survey( # as magerr and used for the S/N cut. When no sample curve is loaded, # the sample error falls back to the catalog error, so the two are # identical (outputs unchanged from the single-curve behaviour). - maglim_vals = self.survey.get_maglim(band, pixel=pix_maglim) - mag_err_sample = self.survey.get_photo_error( + maglim_vals = survey.get_maglim(band, pixel=pix_maglim) + mag_err_sample = survey.get_photo_error( band, apparent_mag_true, maglim_vals, kind="sample" ) - mag_err = self.survey.get_photo_error( + mag_err = survey.get_photo_error( band, apparent_mag_true, maglim_vals, kind="catalog" ) @@ -277,9 +416,10 @@ def _inject_one_survey( data = pd.concat([data, new_columns], axis=1) # Compute detection flag for completeness-band (reference band) - if band == self.survey.completeness_band: + if band == survey.completeness_band: flag_completeness_band = self.detect_flag( pix_maglim, + survey=survey, mag=apparent_mag_true, band=band, rng=rng, @@ -290,6 +430,7 @@ def _inject_one_survey( if perfect_galstarsep: flag_detection_only_band = self.detect_flag( pix_maglim, + survey=survey, mag=apparent_mag_true, band=band, rng=rng, @@ -300,13 +441,13 @@ def _inject_one_survey( # Apply detection threshold if flag_completeness_band is None: - if self.survey.completeness_band in bands: + if survey.completeness_band in bands: raise ValueError( - f"flag_completeness_{self.survey.completeness_band} must be computed for detection in {self.survey.completeness_band} band." + f"flag_completeness_{survey.completeness_band} must be computed for detection in {survey.completeness_band} band." ) else: raise ValueError( - f"Detection flag requires '{self.survey.completeness_band}' band to be in bands." + f"Detection flag requires '{survey.completeness_band}' band to be in bands." ) # Build combined detection flags @@ -353,9 +494,7 @@ def _inject_one_survey( # (the SNR cut is baked into the completeness functions but not into the # detection-only efficiency functions used for the perfect flag). if perfect_galstarsep: - SNR_compl = ( - 1.0 / data[err_col(self.survey.completeness_band, survey_namespace)] - ) + SNR_compl = 1.0 / data[err_col(survey.completeness_band, survey_namespace)] flag_perfect &= SNR_compl >= SNR_min # Store flags in DataFrame @@ -397,150 +536,118 @@ def _load_data(self, data): else: raise ValueError(f"Unsupported file format") - def complete_data(self, data, bands=["r", "g"], **kwargs): - """ - Validate and complete the columns needed for injection. + def complete_data( + self, + data, + survey_bands=None, + bands=None, + stream_config=None, + dist=None, + **kwargs, + ): + """Complete the columns the injector needs, filling the rest from the config. - This function ensures sky coordinates are present and, if requested, - fills missing photometric magnitude columns using a stream model. - Specifically: + Public helper: give it a (possibly partial) catalog and it returns one + with everything the injector requires present — sky coordinates + (``ra``/``dec``, converting from ``phi1``/``phi2`` if needed) and the + per-survey true-magnitude columns ``__true``. Anything + already present is preserved; only missing columns are sampled, using + ``stream_config`` (a :class:`~streamobs.model.StreamModel` config). The + stellar masses are drawn **once** and interpolated into every survey's + bands, so the same physical star is consistent across surveys. - - If (ra, dec) are missing but (phi1, phi2) exist, it converts to - sky coordinates via ``phi_to_radec``. - - It checks for ``mag_`` for each requested band; any missing - bands are generated via ``StreamModel.complete_catalog`` when a - ``stream_config`` is provided. - - Existing columns are preserved and are not overwritten. + This is the same completion :meth:`inject` runs internally, exposed so + you can build/inspect a completed catalog without injecting noise. Parameters ---------- - data : pandas.DataFrame - Input catalog. Must contain either (ra, dec) or (phi1, phi2). - The DataFrame is copied; the input object is not modified in place. - bands : list[str], optional - Photometric bands to ensure (as ``mag_``). Default is - ``['r', 'g']``. + data : str or pandas.DataFrame + Input catalog (or path). May contain only stream coordinates + (``phi1``/``phi2`` or ``ra``/``dec``), an all-empty frame of length + ``N``, or any subset of the target columns. + survey_bands : dict, optional + ``{survey_name: [bands]}`` — the true-magnitude columns to ensure per + survey. For a single survey you may instead pass ``bands``; if neither + is given and there is exactly one survey, defaults to ``['r', 'g']``. + bands : list of str, optional + Single-survey shorthand for ``survey_bands={primary: bands}``. + stream_config : dict, optional + :class:`~streamobs.model.StreamModel` config used to sample any + missing geometry / true magnitudes. Required only when something is + missing. + dist : float or array-like or None, optional + Distance modulus to use directly (scalar broadcast or per-row + vector) instead of sampling from the config's ``distance_modulus`` + model. Lets magnitudes be filled without ``phi1`` / a distance model. **kwargs - Additional options: - - rng : numpy.random.Generator, optional - Random number generator instance. - seed : int, optional - Random seed used to initialize an RNG if ``rng`` is not given. - gc_frame : gala.coordinates.GreatCircleICRSFrame or 'last', optional - Great circle frame to use. If 'last', uses the frame from the - previous call. If None, generates a new random frame. - stream_config : dict, optional - Configuration passed to ``StreamModel``. Required only when at - least one requested magnitude band is missing. See - ``StreamModel`` for the expected schema (e.g., track, distance - modulus, isochrone). - - Keyword arguments forwarded to ``phi_to_radec`` (only used when - converting from (phi1, phi2)): - - mask_type : list[str] or str, optional - Mask(s) used when searching a frame (e.g., 'footprint', - 'maglim_r', 'ebv'). - percentile_threshold : float, optional - Fraction of points that must lie inside the mask when selecting - a frame. Default defined in ``phi_to_radec``. - max_iter : int, optional - Maximum trials when searching a frame. Default defined in - ``phi_to_radec``. + ``rng`` / ``seed`` for reproducibility, plus ``gc_frame``, + ``mask_type``, ``percentile_threshold``, ``max_iter`` forwarded to + :meth:`phi_to_radec` when converting ``phi1``/``phi2`` → ``ra``/``dec``. Returns ------- pandas.DataFrame - A copy of the input with the required columns present: - ``ra``, ``dec``, and one ``mag_`` column per requested band. + A copy of the input with ``ra``/``dec`` and the requested + ``__true`` columns present. Raises ------ ValueError - If neither (ra, dec) nor (phi1, phi2) are present; or if one or - more magnitude bands are missing and ``stream_config`` is not - provided; or if after completion a required column is still absent. - - Notes - ----- - - Only missing magnitude columns are synthesized; existing values are - left untouched. - - Magnitudes are produced by ``StreamModel.complete_catalog`` and rely - on the model's configuration (e.g., isochrone and distance modulus). - - When a new gc_frame is created or used, it is stored in ``self._last_gc_frame`` - for potential reuse via ``gc_frame='last'``. + If neither (ra, dec) nor (phi1, phi2) are present, or if columns are + missing and ``stream_config`` is not provided. Examples -------- - Convert from (phi1, phi2) and generate both bands: - >>> df = pd.DataFrame({'phi1': [-5, 0, 5], 'phi2': [0, 0, 0]}) - >>> cfg = {...} # stream model config >>> out = injector.complete_data(df, bands=['r', 'g'], stream_config=cfg) - - Use already existing ra/dec and fill a missing 'g' band from a model: - - >>> df = pd.DataFrame({'phi1': [-5, 0, 5], 'phi2': [0, 0, 0], 'ra': [10.1, 12.5, 24.7], 'dec': [5.2, 6.2, 7.2],}) - >>> out = injector.complete_data(df, bands=['r', 'g'], stream_config=cfg, seed=42) - - Reuse the same gc_frame for multiple streams: - - >>> data1 = injector.complete_data(df1, seed=42) # Creates new frame - >>> data2 = injector.complete_data(df2, gc_frame='last') # Reuses frame from data1 + >>> out = injector.complete_data( + ... df, survey_bands={'lsst': ['r', 'g'], 'roman': ['F106', 'F158']}, + ... stream_config=cfg, seed=42, + ... ) """ - - required_columns = ["ra", "dec"] + [true_col(band) for band in bands] + survey_bands = self._resolve_survey_bands(survey_bands, bands) + data = self._load_data(data).copy() rng = kwargs.pop("rng", None) seed = kwargs.pop("seed", None) + if rng is None: + rng = np.random.default_rng(seed) - # Make explicit copy to avoid SettingWithCopyWarning - data = data.copy() + return self._complete_shared( + data, + survey_bands, + stream_config=stream_config, + rng=rng, + seed=seed, + dist=dist, + **kwargs, + ) - if not ("ra" in data.columns and "dec" in data.columns): - if "phi1" not in data.columns or "phi2" not in data.columns: - raise ValueError( - "Input data must contain either (ra, dec) or (phi1, phi2) columns." - ) + def _ensure_radec(self, data, rng=None, seed=None, **kwargs): + """Ensure ``ra``/``dec`` are present, converting from (phi1, phi2) if needed. - # Handle 'last' keyword for gc_frame - gc_frame_param = kwargs.get("gc_frame", None) - if gc_frame_param == "last": - kwargs["gc_frame"] = self._last_gc_frame + Existing ``ra``/``dec`` are left untouched. When converting, the great + circle frame is found (or reused via ``gc_frame='last'``) through + :meth:`phi_to_radec`, using the primary survey's footprint. + """ + if "ra" in data.columns and "dec" in data.columns: + return data - # Convert coordinates (Phi1, Phi2) into (ra,dec) - stream_coord = self.phi_to_radec( - data["phi1"], - data["phi2"], - seed=seed, - rng=rng, - **kwargs, - ) - data.loc[:, "ra"] = stream_coord.icrs.ra.deg - data.loc[:, "dec"] = stream_coord.icrs.dec.deg - - # Sample missing magnitudes if needed - mag_bands_missing = [ - true_col(band) for band in bands if true_col(band) not in data.columns - ] - if mag_bands_missing: - stream_config = kwargs.get("stream_config", None) - if stream_config is None: - raise ValueError( - "`stream_config` must be provided to sample missing magnitudes." - ) - stream_model = StreamModel(stream_config) - data = stream_model.complete_catalog( - data, - inplace=True, - columns_to_add=mag_bands_missing, + if "phi1" not in data.columns or "phi2" not in data.columns: + raise ValueError( + "Input data must contain either (ra, dec) or (phi1, phi2) columns." ) - for col in required_columns: - if col not in data.columns: - raise ValueError(f"Input data must contain '{col}' column.") + # Handle 'last' keyword for gc_frame + if kwargs.get("gc_frame", None) == "last": + kwargs["gc_frame"] = self._last_gc_frame + # Convert coordinates (phi1, phi2) into (ra, dec) + stream_coord = self.phi_to_radec( + data["phi1"], data["phi2"], seed=seed, rng=rng, **kwargs + ) + data.loc[:, "ra"] = stream_coord.icrs.ra.deg + data.loc[:, "dec"] = stream_coord.icrs.dec.deg return data def phi_to_radec( @@ -971,7 +1078,7 @@ def sample_measured_magnitudes(self, mag_true, mag_err, **kwargs): return mag_obs - def detect_flag(self, pix, mag=None, band="r", **kwargs): + def detect_flag(self, pix, mag=None, band="r", survey=None, **kwargs): """ Apply the survey selection to determine detection flags for stars. @@ -986,6 +1093,9 @@ def detect_flag(self, pix, mag=None, band="r", **kwargs): Magnitude(s). Default is None. band : str, optional Band to consider for detection. Default is 'r'. + survey : Survey, optional + Survey whose completeness/detection-efficiency curves to use. + Defaults to the primary survey. **kwargs Additional keyword arguments: @@ -1012,14 +1122,17 @@ def detect_flag(self, pix, mag=None, band="r", **kwargs): seed = kwargs.pop("seed", None) rng = np.random.default_rng(seed) + if survey is None: + survey = self.survey + # Select the appropriate magnitude and map depending on the band - maglim = self.survey.get_maglim(band, pixel=pix) + maglim = survey.get_maglim(band, pixel=pix) perfect_galstarsep = kwargs.get("perfect_galstarsep", False) if perfect_galstarsep: - compl = self.survey.get_detection_efficiency(band, mag, maglim) + compl = survey.get_detection_efficiency(band, mag, maglim) else: - compl = self.survey.get_completeness(band, mag, maglim) + compl = survey.get_completeness(band, mag, maglim) # Set the threshold using completeness threshold = rng.uniform(size=len(mag)) <= compl @@ -1201,160 +1314,26 @@ def plot_stream_in_mask(self, data, mask_type, ebv_threshold=0.2, **kwargs): ) return fig, ax - -class MultiSurveyInjector: - """Inject stream observations for several surveys into one catalog. - - The orchestrator holds one :class:`StreamInjector` per survey and delegates - the per-survey observational work to each (it is *not* a composite - ``Survey``). A single shared sky placement and a single shared draw of true - magnitudes — masses are sampled once via a multi-survey - :class:`~streamobs.model.IsochroneModel` and interpolated into every - survey's bands — guarantee that the **same physical star** gets consistent - magnitudes across surveys. Each survey then contributes its own - ``__obs`` / ``__err`` / ``_flag_observed`` - columns, computed with its own maglim maps and completeness functions. - - Parameters - ---------- - surveys : dict or list - Either ``{name: spec}`` or a list of ``spec``, where ``spec`` is a - survey-name string, a :class:`~streamobs.surveys.Survey`, or a - pre-built :class:`StreamInjector`. The dict key is used as the column - namespace; for a list, the survey's own name is used. - primary : str, optional - Name of the survey whose footprint drives the shared sky placement and - whose ``_save_injected_data`` is used. Defaults to the first survey. - **kwargs - Forwarded to :meth:`StreamInjector` construction when a ``spec`` is a - name/``Survey`` rather than an injector (e.g. ``release``). - - Examples - -------- - >>> msi = MultiSurveyInjector({"lsst": "lsst", "roman": "roman"}) - >>> out = msi.inject( - ... df, survey_bands={"lsst": ["r", "g"], "roman": ["f106", "f158"]}, - ... stream_config=scene["stream"], seed=42, - ... ) - """ - - def __init__(self, surveys, primary=None, **kwargs): - if isinstance(surveys, (list, tuple)): - surveys = {self._spec_name(s): s for s in surveys} - if not isinstance(surveys, dict): - raise ValueError("surveys must be a dict {name: spec} or a list of specs.") - - self.injectors = {} - for name, spec in surveys.items(): - if isinstance(spec, StreamInjector): - self.injectors[name] = spec - else: - self.injectors[name] = StreamInjector(spec, **kwargs) - - self.survey_names = list(self.injectors) - if not self.survey_names: - raise ValueError("At least one survey is required.") - self.primary = primary if primary is not None else self.survey_names[0] - if self.primary not in self.injectors: - raise ValueError( - f"primary='{self.primary}' is not one of {self.survey_names}." - ) - - # Shared sky placement is cached here, mirroring StreamInjector. - self._last_gc_frame = None - - @staticmethod - def _spec_name(spec): - """Best-effort survey name for a spec passed in a list.""" - if isinstance(spec, StreamInjector): - return spec.survey.name - if isinstance(spec, Survey): - return spec.name - return str(spec) - - def inject(self, data, survey_bands, stream_config=None, **kwargs): - """Inject observations for every survey into a single catalog. - - Parameters - ---------- - data : str or pandas.DataFrame - Input catalog (or path). May contain only stream coordinates - (``phi1``/``phi2`` or ``ra``/``dec``); anything missing is sampled - from ``stream_config``. An all-empty frame of length ``N`` is also - accepted (geometry and magnitudes are then sampled for ``N`` rows). - survey_bands : dict - ``{survey_name: [bands]}`` — the bands to inject for each survey. - Keys must match the surveys this orchestrator was built with. - stream_config : dict, optional - The ``stream`` section consumed by - :class:`~streamobs.model.StreamModel`. Its ``isochrone`` must be in - the multi-survey form so the same shared masses produce every - survey's ``__true`` columns. Required when any - coordinate or true-magnitude column is missing. - **kwargs - ``seed``, ``nside``, ``detection_mag_cut``, ``dust_correction``, - ``perfect_galstarsep``, ``gc_frame``, ``mask_type``, ``save``, - ``folder``, ``verbose`` — see :meth:`StreamInjector.inject`. - - Returns - ------- - pandas.DataFrame - Catalog with shared ``ra``/``dec`` and, per survey, - ``__true/_obs/_err`` and ``_flag_observed``. - """ - unknown = set(survey_bands) - set(self.injectors) - if unknown: - raise ValueError( - f"survey_bands references unknown surveys {sorted(unknown)}; " - f"available: {self.survey_names}." - ) - - ref = self.injectors[self.primary] - data = ref._load_data(data) - - seed = kwargs.pop("seed", None) - rng = np.random.default_rng(seed) - - # (1)+(2) Shared sky placement and shared true-magnitude fill (masses - # sampled once across all surveys). - data = self._complete_shared( - data, - survey_bands, - stream_config=stream_config, - rng=rng, - seed=seed, - **kwargs, - ) - - # (3) Per-survey observational injection. Independent child RNGs make the - # result independent of survey ordering and reproducible from `seed`. - children = rng.spawn(len(self.survey_names)) - for child_rng, name in zip(children, self.survey_names): - if name not in survey_bands: - continue - data = self.injectors[name]._inject_one_survey( - data, - list(survey_bands[name]), - rng=child_rng, - survey_namespace=name, - **kwargs, - ) - - if kwargs.get("save"): - ref._save_injected_data(data, kwargs.get("folder", None)) - - return data - def _complete_shared( - self, data, survey_bands, stream_config=None, rng=None, seed=None, **kwargs + self, + data, + survey_bands, + stream_config=None, + rng=None, + seed=None, + dist=None, + **kwargs, ): """Fill shared geometry, ``ra``/``dec`` and per-survey true magnitudes. Positions and masses are drawn *once* so all surveys describe the same - physical stars. Existing columns are preserved. + physical stars (the isochrone produces every survey's + ``__true`` column from one shared mass draw). Existing + columns are preserved (only missing values are filled). ``ra``/``dec`` + are placed using the primary survey's footprint. ``dist`` (a float or + per-row vector) overrides the model's distance sampling when given. """ verbose = kwargs.get("verbose", True) - ref = self.injectors[self.primary] true_cols = [] for name, bands in survey_bands.items(): @@ -1375,15 +1354,19 @@ def _complete_shared( cols_to_add = [] if need_phi: cols_to_add += ["phi1", "phi2"] - # `dist` is needed before magnitudes; the model fills it if absent. + # `dist` is needed before magnitudes; the model fills it (from the + # distance_modulus model or the supplied `dist`) if absent. cols_to_add += ["dist"] + missing_true data = stream_model.complete_catalog( - data, columns_to_add=cols_to_add, inplace=True, verbose=verbose + data, + columns_to_add=cols_to_add, + inplace=True, + verbose=verbose, + dist=dist, ) # Convert (phi1, phi2) -> (ra, dec) using the primary survey footprint. if not have_radec: - data = ref.complete_data(data, bands=[], rng=rng, seed=seed, **kwargs) - self._last_gc_frame = ref._last_gc_frame + data = self._ensure_radec(data, rng=rng, seed=seed, **kwargs) return data diff --git a/tests/test_model.py b/tests/test_model.py index ee02115..bded770 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -17,7 +17,6 @@ TrackModel, ) - # --------------------------------------------------------------------------- # Helpers # --------------------------------------------------------------------------- @@ -29,6 +28,7 @@ # DensityModel # --------------------------------------------------------------------------- + @pytest.mark.model class TestDensityModel: """Unit tests for DensityModel (phi1 sampler).""" @@ -46,8 +46,9 @@ def test_uniform_sample(self): cfg = {"type": "uniform", "xmin": -10.0, "xmax": 10.0} model = DensityModel(cfg) samples = self._verify_sampling(model) - assert np.all(samples>cfg["xmin"]) and np.all(samples cfg["xmin"]) and np.all( + samples < cfg["xmax"] + ), "Uniform samples must be within [xmin, xmax]" def test_gaussian_sample_shape(self): cfg = {"type": "gaussian", "mu": 0.0, "sigma": 1.0} @@ -55,8 +56,6 @@ def test_gaussian_sample_shape(self): samples = self._verify_sampling(model) - - # --------------------------------------------------------------------------- # TrackModel # --------------------------------------------------------------------------- @@ -72,68 +71,254 @@ def _verify_sampling(self, model, phi1): assert np.all(np.isfinite(samples)), "Samples must be finite numbers" assert np.all(~np.isnan(samples)), "Samples must not contain NaNs" return samples - + def test_constant_track(self): cfg = { "center": {"type": "constant", "value": 0.0}, "spread": {"type": "constant", "value": 0.2}, - "sampler": "gaussian" + "sampler": "gaussian", } model = TrackModel(cfg) phi1 = np.linspace(-10, 10, N) samples = self._verify_sampling(model, phi1) - assert np.all(np.abs(samples) < 10*cfg["spread"]['value']), "Samples should be within a few sigma of the center" + assert np.all( + np.abs(samples) < 10 * cfg["spread"]["value"] + ), "Samples should be within a few sigma of the center" def test_sinusoidal_track(self): cfg = { - "center": {"type": "sinusoid", "amplitude": 0.5, "period": 2.}, + "center": {"type": "sinusoid", "amplitude": 0.5, "period": 2.0}, "spread": {"type": "constant", "value": 0.2}, - "sampler": "gaussian" + "sampler": "gaussian", } model = TrackModel(cfg) phi1 = np.linspace(-10, 10, N) samples = self._verify_sampling(model, phi1) - expected_center = cfg["center"]["amplitude"] * np.sin(phi1*2*np.pi/cfg["center"]["period"]) - assert np.all(np.abs(samples - expected_center) < 10*cfg["spread"]['value']), "Samples should be within a few sigma of the sinusoidal center" + expected_center = cfg["center"]["amplitude"] * np.sin( + phi1 * 2 * np.pi / cfg["center"]["period"] + ) + assert np.all( + np.abs(samples - expected_center) < 10 * cfg["spread"]["value"] + ), "Samples should be within a few sigma of the sinusoidal center" # --------------------------------------------------------------------------- # StreamModel — full # --------------------------------------------------------------------------- + @pytest.mark.model class TestStreamModelFull: """Tests for StreamModel when having a complete config""" def _verify_catalogue_content(self, catalog, expected_columns): """Helper to verify that a completed catalog contains the expected columns with valid data.""" - assert expected_columns.issubset(catalog.columns), f"Catalog should contain columns {expected_columns}" + assert expected_columns.issubset( + catalog.columns + ), f"Catalog should contain columns {expected_columns}" for col in expected_columns: - assert np.issubdtype(catalog[col].dtype, np.floating), f"Column {col} should contain floats" - assert np.all(np.isfinite(catalog[col])), f"Column {col} should contain finite numbers" - assert np.all(~np.isnan(catalog[col])), f"Column {col} should not contain NaNs" + assert np.issubdtype( + catalog[col].dtype, np.floating + ), f"Column {col} should contain floats" + assert np.all( + np.isfinite(catalog[col]) + ), f"Column {col} should contain finite numbers" + assert np.all( + ~np.isnan(catalog[col]) + ), f"Column {col} should not contain NaNs" def test_full_model(self, stream_config_with_distance): """Test that StreamModel can be instantiated and sampled with a full config.""" model = StreamModel(stream_config_with_distance) samples = model.sample(N) - assert isinstance(samples, pd.DataFrame), "Samples should be returned as a DataFrame" - expected_columns = {"phi1", "phi2", "dist", "mag_g", "mag_r"} # Not adding mu1, mu2, rv since not implemented yet + assert isinstance( + samples, pd.DataFrame + ), "Samples should be returned as a DataFrame" + expected_columns = { + "phi1", + "phi2", + "dist", + "lsst_g_true", + "lsst_r_true", + } # Not adding mu1, mu2, rv since not implemented yet self._verify_catalogue_content(samples, expected_columns) - def test_complete_catalog(self, sample_catalog_phi,stream_config_with_distance): + def test_complete_catalog(self, sample_catalog_phi, stream_config_with_distance): """Test that complete_catalog produces a catalog with the expected columns and valid data.""" model = StreamModel(stream_config_with_distance) - completed_catalog = model.complete_catalog(catalog = sample_catalog_phi) - expected_columns = {"phi1", "phi2", "dist", "mag_g", "mag_r"} # Not adding mu1, mu2, rv since not implemented yet + completed_catalog = model.complete_catalog(catalog=sample_catalog_phi) + expected_columns = { + "phi1", + "phi2", + "dist", + "lsst_g_true", + "lsst_r_true", + } # Not adding mu1, mu2, rv since not implemented yet self._verify_catalogue_content(completed_catalog, expected_columns) - assert len(completed_catalog) == len(sample_catalog_phi), f"Completed catalog should have the same number of rows as the input catalog" + assert len(completed_catalog) == len( + sample_catalog_phi + ), f"Completed catalog should have the same number of rows as the input catalog" # Verify that I can add a targeted column (e.g. dist) to the input catalog and complete the rest - partial_catalog = completed_catalog.drop(columns=['mag_r', 'dist', 'mag_g']).reset_index(drop=True) - completed_catalog = model.complete_catalog(catalog=partial_catalog,columns_to_add=["dist"],) - expected_columns = {"phi1", "phi2", "dist"} # Not adding mu1, mu2, rv since not implemented yet + partial_catalog = completed_catalog.drop( + columns=["lsst_r_true", "dist", "lsst_g_true"] + ).reset_index(drop=True) + completed_catalog = model.complete_catalog( + catalog=partial_catalog, + columns_to_add=["dist"], + ) + expected_columns = { + "phi1", + "phi2", + "dist", + } # Not adding mu1, mu2, rv since not implemented yet self._verify_catalogue_content(completed_catalog, expected_columns) - assert len(completed_catalog) == len(partial_catalog), f"Completed catalog should have the same number of rows as the input catalog" - assert "mag_r" not in completed_catalog.columns, "Column 'mag_r' should not be added when not requested" + assert len(completed_catalog) == len( + partial_catalog + ), f"Completed catalog should have the same number of rows as the input catalog" + assert ( + "lsst_r_true" not in completed_catalog.columns + ), "Column 'lsst_r_true' should not be added when not requested" + + +# --------------------------------------------------------------------------- +# StreamModel.complete_catalog — permutations of input columns / dist source +# --------------------------------------------------------------------------- + + +@pytest.mark.model +class TestCompleteCatalogPermutations: + """Exercise complete_catalog across the ways a catalog can be partially filled. + + Covers: which columns are supplied (empty frame, phi-only, ra/dec+dist), + where the distance comes from (distance_modulus model vs. a directly supplied + scalar/vector ``dist``), and the preserve-existing-values contract (whole + columns and individual NaN rows are never overwritten). + """ + + MAGS = {"lsst_g_true", "lsst_r_true"} + + def test_empty_frame_fills_all_model_columns(self, stream_config_with_distance): + """size=N with no catalog -> geometry, dist, and both bands are filled.""" + model = StreamModel(stream_config_with_distance) + out = model.complete_catalog(catalog=None, size=12, verbose=False) + assert len(out) == 12 + assert ({"phi1", "phi2", "dist"} | self.MAGS).issubset(out.columns) + assert out[list(self.MAGS)].notna().all().all() + + def test_phi_only_fills_dist_then_mags( + self, sample_catalog_phi, stream_config_with_distance + ): + """phi1/phi2 present -> dist sampled from the model, then magnitudes.""" + model = StreamModel(stream_config_with_distance) + out = model.complete_catalog(catalog=sample_catalog_phi.copy(), verbose=False) + assert ({"dist"} | self.MAGS).issubset(out.columns) + assert out[list(self.MAGS)].notna().all().all() + + def test_radec_plus_dist_fills_mags_without_phi(self, stream_config_with_distance): + """With dist already present, magnitudes fill even when phi1 is absent.""" + model = StreamModel(stream_config_with_distance) + df = pd.DataFrame( + {"ra": [10.0, 11.0, 12.0], "dec": [-1.0, 0.0, 1.0], "dist": [16.0] * 3} + ) + out = model.complete_catalog( + catalog=df, columns_to_add=list(self.MAGS), verbose=False + ) + assert self.MAGS.issubset(out.columns) + assert out[list(self.MAGS)].notna().all().all() + assert "phi1" not in out.columns + + def test_existing_band_preserved_when_filling_other( + self, stream_config_with_distance + ): + """Providing one band and requesting both leaves the provided one intact.""" + model = StreamModel(stream_config_with_distance) + g = np.array([20.0, 21.0, 22.0]) + df = pd.DataFrame({"dist": [16.0] * 3, "lsst_g_true": g.copy()}) + out = model.complete_catalog( + catalog=df, columns_to_add=list(self.MAGS), verbose=False + ) + assert np.allclose(out["lsst_g_true"].to_numpy(), g), "present band overwritten" + assert out["lsst_r_true"].notna().all(), "missing band not filled" + + def test_present_columns_skip_sampling(self, stream_config_with_distance): + """Both bands present -> values are returned untouched.""" + model = StreamModel(stream_config_with_distance) + g = np.array([20.0, 21.0]) + r = np.array([19.0, 19.5]) + df = pd.DataFrame( + {"dist": [16.0, 16.0], "lsst_g_true": g.copy(), "lsst_r_true": r.copy()} + ) + out = model.complete_catalog( + catalog=df, columns_to_add=list(self.MAGS), verbose=False + ) + assert np.allclose(out["lsst_g_true"].to_numpy(), g) + assert np.allclose(out["lsst_r_true"].to_numpy(), r) + + def test_partial_rows_only_missing_filled(self, stream_config_with_distance): + """A band with some NaN rows keeps its finite rows; only NaNs are filled.""" + model = StreamModel(stream_config_with_distance) + g = np.array([20.0, np.nan, 22.0]) + df = pd.DataFrame({"dist": [16.0] * 3, "lsst_g_true": g.copy()}) + out = model.complete_catalog( + catalog=df, columns_to_add=["lsst_g_true"], verbose=False + ) + filled = out["lsst_g_true"].to_numpy() + assert filled[0] == 20.0 and filled[2] == 22.0, "finite rows overwritten" + assert np.isfinite(filled[1]), "NaN row not filled" + + def test_dist_scalar_broadcast_without_distance_model(self, minimal_stream_config): + """A scalar `dist` lets mags fill with no distance_modulus model / no phi1.""" + model = StreamModel(minimal_stream_config) # no distance_modulus section + df = pd.DataFrame({"ra": [10.0, 11.0], "dec": [0.0, 1.0]}) + out = model.complete_catalog( + catalog=df, + columns_to_add=["dist"] + list(self.MAGS), + dist=16.5, + verbose=False, + ) + assert np.allclose(out["dist"].to_numpy(), 16.5) + assert out[list(self.MAGS)].notna().all().all() + + def test_dist_vector_assigned_per_row(self, minimal_stream_config): + """A per-row `dist` vector is assigned row-wise.""" + model = StreamModel(minimal_stream_config) + df = pd.DataFrame({"ra": [10.0, 11.0, 12.0], "dec": [0.0, 1.0, 2.0]}) + dvec = np.array([15.0, 16.0, 17.0]) + out = model.complete_catalog( + catalog=df, + columns_to_add=["dist"] + list(self.MAGS), + dist=dvec, + verbose=False, + ) + assert np.allclose(out["dist"].to_numpy(), dvec) + assert out[list(self.MAGS)].notna().all().all() + + def test_dist_vector_wrong_length_raises(self, minimal_stream_config): + model = StreamModel(minimal_stream_config) + df = pd.DataFrame({"ra": [10.0, 11.0, 12.0], "dec": [0.0, 1.0, 2.0]}) + with pytest.raises(ValueError): + model.complete_catalog( + catalog=df, + columns_to_add=["dist"], + dist=np.array([1.0, 2.0]), + verbose=False, + ) + + def test_dist_overrides_distance_model(self, stream_config_with_distance): + """When given, `dist` is used instead of the configured distance model.""" + model = StreamModel(stream_config_with_distance) # model would give 16.8 + df = pd.DataFrame({"phi1": [0.0, 1.0], "phi2": [0.0, 0.0]}) + out = model.complete_catalog( + catalog=df, columns_to_add=["dist"], dist=20.0, verbose=False + ) + assert np.allclose(out["dist"].to_numpy(), 20.0) + def test_mags_without_dist_or_phi_raises(self, minimal_stream_config): + """No distance_modulus model, no dist, no phi1 -> cannot fill magnitudes.""" + model = StreamModel(minimal_stream_config) + df = pd.DataFrame({"ra": [10.0, 11.0], "dec": [0.0, 1.0]}) + with pytest.raises(ValueError): + model.complete_catalog( + catalog=df, columns_to_add=list(self.MAGS), verbose=False + ) diff --git a/tests/test_observed.py b/tests/test_observed.py index 5b06e08..a0c800d 100644 --- a/tests/test_observed.py +++ b/tests/test_observed.py @@ -15,25 +15,35 @@ from streamobs.observed import StreamInjector from streamobs.surveys import Survey - # --------------------------------------------------------------------------- # Injector properties # --------------------------------------------------------------------------- + @pytest.mark.observed class TestStreamInjectorProperties: """Tests for StreamInjector properties and basic behavior.""" def test_injector_initialization(self, mock_injector): """Test that the injector initializes with the expected properties.""" - assert isinstance(mock_injector, StreamInjector), "Injector must be an instance of StreamInjector" - assert hasattr(mock_injector, "survey"), "Injector must have a 'survey' property" - assert isinstance(mock_injector.survey, Survey), "Survey property must be a Survey instance" - assert hasattr(mock_injector, "mask_cache"), "Injector must have a 'mask_cache' property" + assert isinstance( + mock_injector, StreamInjector + ), "Injector must be an instance of StreamInjector" + assert hasattr( + mock_injector, "survey" + ), "Injector must have a 'survey' property" + assert isinstance( + mock_injector.survey, Survey + ), "Survey property must be a Survey instance" + assert hasattr( + mock_injector, "mask_cache" + ), "Injector must have a 'mask_cache' property" # Initialize injector directly with survey name and release injector_direct = StreamInjector(survey="lsst", release="yr4") - assert isinstance(injector_direct, StreamInjector), "Directly initialized injector must be an instance of StreamInjector" + assert isinstance( + injector_direct, StreamInjector + ), "Directly initialized injector must be an instance of StreamInjector" assert injector_direct.survey.name == "lsst", "Survey name must be 'lsst'" assert injector_direct.survey.release == "yr4", "Survey release must be 'yr4'" @@ -42,50 +52,102 @@ def test_injector_initialization(self, mock_injector): # Injector behavior # --------------------------------------------------------------------------- + @pytest.mark.observed class TestStreamInjectorBehavior: """Tests for StreamInjector behavior and output structure.""" - def _verify_injected_catalog_content(self, injected_catalog, expected_columns=["ra", "dec","mag_g", "mag_r", "mag_g_obs", "mag_r_obs","flag_observed", "flag_perfect_galstarsep"]): + + def _verify_injected_catalog_content( + self, + injected_catalog, + expected_columns=[ + "ra", + "dec", + "lsst_g_true", + "lsst_r_true", + "lsst_g_obs", + "lsst_r_obs", + "lsst_flag_observed", + "lsst_flag_perfect_galstarsep", + ], + ): """Helper method to verify the content of the injected catalog.""" # Verify expected columns are present - assert set(expected_columns).issubset(injected_catalog.columns), "Injected catalog must contain all expected columns" - + assert set(expected_columns).issubset( + injected_catalog.columns + ), "Injected catalog must contain all expected columns" def test_full_injection_pipeline(self, mock_injector, stream_catalog, verbose): """Test the full injection pipeline with a controlled input catalog.""" # Perform injection - injected_catalog = mock_injector.inject(stream_catalog, perfect_galstarsep=True, verbose=verbose) + injected_catalog = mock_injector.inject( + stream_catalog, perfect_galstarsep=True, verbose=verbose + ) # Minimal expected columns in the injected catalog (position, magnitude, and flags) - self._verify_injected_catalog_content(injected_catalog,) + self._verify_injected_catalog_content( + injected_catalog, + ) - def test_injection_partialinput(self, mock_injector,stream_catalog, stream_config_with_distance, verbose): + def test_injection_partialinput( + self, mock_injector, stream_catalog, stream_config_with_distance, verbose + ): """Test injection with a catalog that has some missing columns.""" - data_without_mag = stream_catalog.drop(columns=["mag_g", "mag_r"]) - injected_catalog = mock_injector.inject(data_without_mag, perfect_galstarsep=True, stream_config=stream_config_with_distance, verbose=verbose) + data_without_mag = stream_catalog.drop(columns=["lsst_g_true", "lsst_r_true"]) + injected_catalog = mock_injector.inject( + data_without_mag, + perfect_galstarsep=True, + stream_config=stream_config_with_distance, + verbose=verbose, + ) self._verify_injected_catalog_content(injected_catalog) - def test_random_injection(self, mock_injector, stream_catalog,seed, verbose): + def test_random_injection(self, mock_injector, stream_catalog, seed, verbose): """Test random sky injection""" mask_type = ["footprint", "ebv", "maglim_g"] # Inject a first time - stream_coord_1 = mock_injector.phi_to_radec(stream_catalog['phi1'], stream_catalog['phi2'], seed=seed,gc_frame=None,mask_type=mask_type, verbose=verbose) + stream_coord_1 = mock_injector.phi_to_radec( + stream_catalog["phi1"], + stream_catalog["phi2"], + seed=seed, + gc_frame=None, + mask_type=mask_type, + verbose=verbose, + ) gc_1 = mock_injector._last_gc_frame # Inject a second time with the same random seed - stream_coord_2 = mock_injector.phi_to_radec(stream_catalog['phi1'], stream_catalog['phi2'], seed=seed,gc_frame=None,mask_type=mask_type, verbose=verbose) + stream_coord_2 = mock_injector.phi_to_radec( + stream_catalog["phi1"], + stream_catalog["phi2"], + seed=seed, + gc_frame=None, + mask_type=mask_type, + verbose=verbose, + ) gc_2 = mock_injector._last_gc_frame # Inject a 3rd time using the existing gc_frame (should not use a random # seed) - stream_coord_3 = mock_injector.phi_to_radec(stream_catalog['phi1'], stream_catalog['phi2'], seed=None,gc_frame=gc_1,mask_type=mask_type, verbose=verbose) + stream_coord_3 = mock_injector.phi_to_radec( + stream_catalog["phi1"], + stream_catalog["phi2"], + seed=None, + gc_frame=gc_1, + mask_type=mask_type, + verbose=verbose, + ) gc_3 = mock_injector._last_gc_frame def compare_coords(coord1, coord2): """Helper function to compare two coordinate DataFrames.""" - assert np.allclose(coord1.icrs.ra.deg, coord2.icrs.ra.deg), "RA values should be the same" - assert np.allclose(coord1.icrs.dec.deg, coord2.icrs.dec.deg), "Dec values should be the same" - + assert np.allclose( + coord1.icrs.ra.deg, coord2.icrs.ra.deg + ), "RA values should be the same" + assert np.allclose( + coord1.icrs.dec.deg, coord2.icrs.dec.deg + ), "Dec values should be the same" + def get_gc_frame_dict(gc_frame): origin = gc_frame.origin pole = gc_frame.pole @@ -96,14 +158,20 @@ def get_gc_frame_dict(gc_frame): "dec": float(origin.dec.deg), "unit": "deg", }, - "pole": {"ra": float(pole.ra.deg), "dec": float(pole.dec.deg), "unit": "deg"}, + "pole": { + "ra": float(pole.ra.deg), + "dec": float(pole.dec.deg), + "unit": "deg", + }, "priority": str(priority), } return gc_frame_params def compare_gc_frames(gc1, gc2): """Helper function to compare two gc_frame objects.""" - assert get_gc_frame_dict(gc1) == get_gc_frame_dict(gc2), "gc_frame parameters should be the same" + assert get_gc_frame_dict(gc1) == get_gc_frame_dict( + gc2 + ), "gc_frame parameters should be the same" # Verify that the first two injections produce the same coordinates (same random seed) compare_coords(stream_coord_1, stream_coord_2) @@ -117,11 +185,6 @@ def compare_gc_frames(gc1, gc2): mock_injector.clear_mask_cache() masks = mock_injector.list_cached_masks() assert len(masks) == 0, "Mask cache should be empty after clearing" - assert len(masks_before) > len(masks), "Mask cache should have had entries before clearing" - - - - - - - \ No newline at end of file + assert len(masks_before) > len( + masks + ), "Mask cache should have had entries before clearing" From efd06a61367f494f3e634d84cfda909d06893c0d Mon Sep 17 00:00:00 2001 From: psferguson Date: Tue, 16 Jun 2026 11:28:27 -0700 Subject: [PATCH 07/54] S/N cut: apply reference-band cut once (option b) The reference band (survey.completeness_band) has its SNR>=5 cut baked into the survey selection functions, so the per-band loop was double-applying it (idempotent today, but conceptually double-counted and fragile), and a special-cased "force" block was needed for the perfect-galstarsep flag because the detection-efficiency curve does not bake the cut in. Now the reference-band cut is applied exactly once (to both flags) and the detection_mag_cut loop defaults to all injected bands except the reference band, which is skipped. Behaviour-preserving (same bands cut, ref counted once); removes the double-count and the flag asymmetry. Document the path from (b) to (a) -- folding the cut into the detection-efficiency curve itself -- in roman_multisurvey_plan.md, including which data products must be regenerated (per-survey detection_eff tables) and how. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/source/roman_multisurvey_plan.md | 80 +++++++++++++++++++++++++++ streamobs/observed.py | 43 ++++++++++---- 2 files changed, 112 insertions(+), 11 deletions(-) diff --git a/docs/source/roman_multisurvey_plan.md b/docs/source/roman_multisurvey_plan.md index 5201c21..ed3d9b1 100644 --- a/docs/source/roman_multisurvey_plan.md +++ b/docs/source/roman_multisurvey_plan.md @@ -105,6 +105,86 @@ and makes "observed" mean "detected in everything you asked for". Callers can restore any prior behaviour by passing `detection_mag_cut=[...]` explicitly (e.g. `["g"]` for the old LSST default). **Adopted, but flagged for review.** +### S/N cut ownership: who applies the reference-band cut *(adopted — option (b))* + +The reference band (`survey.completeness_band`, e.g. LSST `r`) is special: the +survey's **selection functions are estimated with the SNR ≥ 5 cut already +applied** in that band. So the reference-band cut is conceptually *owned by the +selection functions*, not by the injector's per-band loop. + +**The problem (pre-(b)).** The old code applied the per-band SNR loop to *every* +injected band, including the reference band — so the reference band's cut was +applied **twice** (once inside `get_completeness`, once in the loop). That is +idempotent today (`A & A == A`), so `flag_observed` was numerically correct, but +it is conceptually double-counted and fragile: it silently breaks if `SNR_min` +ever differs from the threshold the curve was estimated at. Worse, the two +selection curves disagreed about ownership — `get_completeness` bakes the cut +in, but `get_detection_efficiency` (used for the perfect-galstarsep flag) does +**not** — which forced a special-cased "force SNR cut on the completeness band" +block just for `flag_perfect`. + +**Option (b) — implemented now (injector-only, no data regeneration).** Treat the +reference-band cut as owned by the selection functions and apply it **exactly +once**, to both flags: + +- The reference band's SNR cut is applied once in `_inject_one_survey`, to + `flag_observed` (idempotent with the baked-in completeness cut) and to + `flag_perfect` (which supplies it, since the efficiency curve lacks it). +- `detection_mag_cut` defaults to *every injected band except the reference + band*; the reference band is skipped inside the loop. +- The old special-cased "force" block is removed. + +This is **behaviour-preserving** (the same set of bands gets an SNR cut, the +reference band counted once instead of twice) but removes the double-count and +the asymmetry between the two flags. `survey.completeness_band` is the single +attribute identifying the reference band for both completeness and detection +efficiency (default `"r"`). + +**Option (a) — the eventual "correct home" (deferred; requires new data).** Fold +the SNR cut into the **detection-efficiency curve itself**, so that +`get_completeness` *and* `get_detection_efficiency` both own it consistently. +Then the injector needs no reference-band special-casing at all — it simply +**skips** `survey.completeness_band` in the SNR loop (the curves handle it), and +the once-applied reference-band block from (b) can be deleted. + +*Why (a) is better:* the "a star's reference-band detectability already includes +SNR ≥ 5" fact lives in **one place** — the survey product — rather than being +re-asserted in the injector. Any consumer of `get_detection_efficiency` (not +just this injector) then gets a self-consistent curve, and there is no implicit +contract that "the caller must remember to also apply the SNR cut". + +*How to get from (b) to (a):* + +1. **Regenerate the detection-efficiency product with the SNR cut applied.** In + the build script that emits the efficiency tables + (`scripts/roman/create_streamobs_files_hlwas.py` for Roman DC2; the analogous + LSST/DES builders for the others), the *denominator* of the detection + efficiency is all true stars and the *numerator* is true stars detected — add + the requirement that the numerator detection also passes SNR ≥ 5 in the + reference band (the completeness/`classification_detection_eff` curve is + already built this way; mirror that selection for the detection-only curve). + Concretely: the column the loader reads for `type="detection_efficiency"` + (`detection_eff`, via `selection="detected"` in `set_completeness`) must be + recomputed with the SNR cut, so it matches how `classifiction_eff` / + `classification_detection_eff` are produced. +2. **Re-emit the per-survey efficiency CSVs** (e.g. Roman + `roman_stellar_efficiency_cutf158.csv` in `data/surveys/roman_hlwas/`, and the + LSST/DES equivalents in `data/others/`) and re-run the notebook/build so the + committed products carry the cut. Keep the `classifiction_eff` header spelling + the loader greps for. +3. **Flip the injector to (a):** drop the once-applied reference-band block and + change the `detection_mag_cut` default to skip the reference band *without* + re-adding its cut (the curve now carries it). `flag_observed` and + `flag_perfect` then both inherit the reference-band cut purely from the + selection functions. +4. **Validate** that detection counts are unchanged within noise versus (b) on a + fixed seed (they should be, since (b) already applies the same cut once) — this + confirms the regenerated curve encodes exactly the SNR ≥ 5 selection rather + than a different threshold. + +Until those products are regenerated, (b) is the correct, behaviour-preserving +state. + ### `nstars` becomes "exactly N stars" (was an emergent IMF count) *(adopted — agreed)* ```{important} diff --git a/streamobs/observed.py b/streamobs/observed.py index 06c0b4b..36d2e0e 100644 --- a/streamobs/observed.py +++ b/streamobs/observed.py @@ -192,8 +192,11 @@ def inject(self, data, survey_bands=None, bands=None, stream_config=None, **kwar nside : int, optional HEALPix nside parameter. Default is 4096. detection_mag_cut : list of str, optional - Bands to apply the SNR detection cut to. Default is all injected - bands (every injected band must have SNR >= 5). + Non-reference bands to apply the explicit SNR>=5 cut to. The + reference band (``survey.completeness_band``) is always cut via + the selection functions, so the default here is every injected + band *except* the reference band. Net effect: every injected band + must have SNR >= 5, with the reference band counted once. save : bool, optional Whether to save the output data. Default is False. folder : str or Path, optional @@ -472,11 +475,36 @@ def _inject_one_survey( else flag_valid_flux ) - # Apply SNR cuts. Default: require SNR >= SNR_min in every injected band. - detection_mag_cut = kwargs.get("detection_mag_cut", list(bands)) + # Apply SNR cuts. + # + # The SNR>=SNR_min cut on the *reference* band (``survey.completeness_band``) + # is owned by the survey's selection functions — they are estimated with + # that cut already applied. We therefore apply it to the reference band + # exactly **once** here, to both flags. (``get_completeness`` bakes it in, + # so for ``flag_observed`` this is idempotent; ``get_detection_efficiency`` + # does not, so for ``flag_perfect`` this line is what supplies it.) See + # the "S/N cut ownership" note in docs/source/roman_multisurvey_plan.md + # for the path to folding it into the efficiency curve itself (option a). + # + # Every *other* injected band gets the cut applied explicitly below. SNR_min = 5.0 + ref_band = survey.completeness_band + if ref_band in bands: + SNR_ref = 1.0 / data[err_col(ref_band, survey_namespace)] + flag_observed &= SNR_ref >= SNR_min + if perfect_galstarsep: + flag_perfect &= SNR_ref >= SNR_min + + # Non-reference bands. Default: every injected band except the reference + # band (whose cut is handled above via the selection functions). + detection_mag_cut = kwargs.get( + "detection_mag_cut", [b for b in bands if b != ref_band] + ) for band in detection_mag_cut: + if band == ref_band: + # The reference band's SNR cut is already applied above. + continue if band not in bands: if verbose: print( @@ -490,13 +518,6 @@ def _inject_one_survey( if perfect_galstarsep: flag_perfect &= SNR >= SNR_min - # Force SNR cut on the completeness band for perfect gal/star separation - # (the SNR cut is baked into the completeness functions but not into the - # detection-only efficiency functions used for the perfect flag). - if perfect_galstarsep: - SNR_compl = 1.0 / data[err_col(survey.completeness_band, survey_namespace)] - flag_perfect &= SNR_compl >= SNR_min - # Store flags in DataFrame data[flag_col(survey_namespace)] = flag_observed if perfect_galstarsep: From b7d52c7b576712d56b2741646c96586d59820e5f Mon Sep 17 00:00:00 2001 From: psferguson Date: Tue, 16 Jun 2026 11:58:32 -0700 Subject: [PATCH 08/54] docs update and fix some sphinx warnings --- docs/source/_static/.gitkeep | 0 docs/source/column_convention.md | 64 +++++++++++ docs/source/conf.py | 7 ++ docs/source/index.rst | 2 + docs/source/multisurvey.md | 162 ++++++++++++++++++++++++++++ docs/source/quickstart.md | 41 ++++--- docs/source/streamobs.functions.rst | 1 - docs/source/streamobs.model.rst | 1 - docs/source/streamobs.observed.rst | 1 - docs/source/streamobs.plotting.rst | 1 - docs/source/streamobs.rst | 1 - docs/source/streamobs.samplers.rst | 1 - docs/source/streamobs.surveys.rst | 1 - docs/source/streamobs.utils.rst | 1 - streamobs/observed.py | 7 +- streamobs/plotting.py | 8 +- streamobs/surveys.py | 22 ++-- 17 files changed, 278 insertions(+), 43 deletions(-) create mode 100644 docs/source/_static/.gitkeep create mode 100644 docs/source/column_convention.md create mode 100644 docs/source/multisurvey.md diff --git a/docs/source/_static/.gitkeep b/docs/source/_static/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/docs/source/column_convention.md b/docs/source/column_convention.md new file mode 100644 index 0000000..d8a376d --- /dev/null +++ b/docs/source/column_convention.md @@ -0,0 +1,64 @@ +# Output column convention + +Injected catalogs use a single, uniform naming scheme for every column the +injector produces, and it is **always survey-namespaced** — even for a single +survey. + +## The scheme + +For a survey with namespace `` (the survey's name, or the key you gave it +when constructing a multi-survey `StreamInjector`) and a photometric band +``: + +| Column | Meaning | +|---|---| +| `__true` | True (noiseless) apparent magnitude | +| `__obs` | Observed (noisy) magnitude; `"BAD_MAG"` where the noisy flux went negative | +| `__err` | Reported photometric error (the *catalog* error; see below) | +| `_flag_observed` | Detection flag — `True` if detected **and** classified as a star | +| `_flag_perfect_galstarsep` | Optional flag assuming perfect star/galaxy separation (detection only); emitted only when `perfect_galstarsep=True` | + +Plus the shared, un-namespaced sky coordinates `ra`, `dec`. + +Examples: `lsst_r_obs`, `lsst_g_err`, `roman_F158_true`, `lsst_flag_observed`. + +These names are produced by the helpers in `streamobs.columns` +(`true_col`, `obs_col`, `err_col`, `flag_col`, `perfect_flag_col`), which take a +`band` and a `survey` namespace. + +```{important} +This convention intentionally **drops** the historical `mag_` / +`mag__obs` / `magerr_` names and the un-namespaced single-survey form +(`r_obs`, `flag_observed`, …). It is **not backward compatible** with catalogs or +downstream readers expecting those columns — everything is now namespaced by +survey, even when only one survey is injected. +``` + +## Two error curves: catalog vs. sample + +Each survey carries **two** photometric-error curves, both functions of +`delta_mag = mag − maglim`: + +- **Catalog error** — the survey's *reported* error (e.g. SExtractor `magerr`). + This is what is written to `__err`, and it drives the S/N + detection cut. +- **Sample error** — an optional curve giving the *true scatter* of + observed − true magnitudes. This is what is used to **draw** the observed + magnitude (`__obs`). + +The split exists because, for the Roman DC2 catalogs, the true photometric +scatter is ≈ 2× the reported error — so the noise you draw and the error you +report are genuinely different. When a survey has no sample curve loaded, the +sample draw transparently falls back to the catalog curve, so the two are +identical and outputs match the single-curve behaviour. + +Select between them via +{meth}`streamobs.surveys.Survey.get_photo_error` with `kind="catalog"` (default, +reproduces the reported error) or `kind="sample"` (true scatter). + +## See also + +- [Injecting one or many surveys](multisurvey.md) — how these columns are + produced and the multi-survey "same physical star" guarantee. +- `streamobs.columns` — the column-name helper functions. +- {meth}`streamobs.surveys.Survey.get_photo_error` — the two error curves. diff --git a/docs/source/conf.py b/docs/source/conf.py index a643357..6259e9b 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -31,6 +31,13 @@ templates_path = ['_templates'] exclude_patterns = [] +# -- numpydoc ---------------------------------------------------------------- +# autodoc's `:members:` already documents every class member, so tell numpydoc +# not to also list them. This avoids both the "stub file not found" autosummary +# warnings (numpydoc's default member toctree) and "duplicate object +# description" warnings (numpydoc + autodoc documenting the same members). +numpydoc_show_class_members = False + # -- Options for HTML output ------------------------------------------------- diff --git a/docs/source/index.rst b/docs/source/index.rst index 8d87f08..b4ae8ba 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -83,6 +83,7 @@ Documentation Contents about installation quickstart + multisurvey citation .. toctree:: @@ -105,6 +106,7 @@ Documentation Contents :maxdepth: 2 :caption: Reference + column_convention data modules diff --git a/docs/source/multisurvey.md b/docs/source/multisurvey.md new file mode 100644 index 0000000..f1ae705 --- /dev/null +++ b/docs/source/multisurvey.md @@ -0,0 +1,162 @@ +# Injecting one or many surveys + +`streamobs.observed.StreamInjector` injects observational effects (photometric +errors, observed magnitudes, detection flags) into a stream catalog. **The same +class handles a single survey or several at once** — there is no separate +multi-survey class. When several surveys are injected together, every survey +describes the *same physical stars*, so you can build catalogs that carry both +Rubin/LSST and Roman photometry for one stream. + +## Constructing an injector + +Pass one survey, or several: + +```python +from streamobs.observed import StreamInjector + +# One survey (loaded by name; `release` etc. forwarded to Survey.load) +inj = StreamInjector("lsst", release="dc2") + +# Several surveys: {namespace: spec}, where spec is a survey name or a Survey. +# The dict key is the column namespace. +inj = StreamInjector({"lsst": "lsst", "roman": "roman"}, primary="lsst") + +# Or a list — each survey is namespaced by its own name. +inj = StreamInjector(["lsst", "roman"]) +``` + +- `primary` selects the survey whose footprint drives the shared sky placement + (defaults to the first survey). +- `inj.surveys` is the `{namespace: Survey}` mapping; `inj.survey` is the primary + `Survey`. + +## Injecting + +```python +# Single survey: `bands` is the shorthand (defaults to ['r', 'g']). +out = inj.inject(df, bands=["r", "g"], stream_config=cfg, seed=42) + +# Several surveys: give the bands per survey. +out = inj.inject( + df, + survey_bands={"lsst": ["r", "g"], "roman": ["F106", "F158"]}, + stream_config=cfg, + seed=42, +) +``` + +`df` may already contain `ra`/`dec` or `phi1`/`phi2`, may be a fully empty frame +of length *N*, or any subset — anything missing is sampled from `stream_config` +(see *Completing a catalog* below). The output carries shared +`ra`/`dec` plus, **per survey**, the namespaced columns described in +[Output column convention](column_convention.md): + +``` +ra, dec, +lsst_r_true, lsst_r_obs, lsst_r_err, lsst_g_true, ..., lsst_flag_observed, +roman_F106_true, roman_F106_obs, ..., roman_flag_observed +``` + +Useful `inject` keyword arguments: + +| kwarg | meaning | +|---|---| +| `seed` | reproducibility (per-survey RNGs are spawned from it, so results are independent of survey order) | +| `dist` | distance modulus used directly (scalar or per-row vector) instead of sampling one — see below | +| `detection_mag_cut` | non-reference bands to apply the explicit SNR ≥ 5 cut to (see *S/N cut ownership* below) | +| `perfect_galstarsep` | also emit a `_flag_perfect_galstarsep` flag (detection only, no classification losses) | +| `dust_correction` | apply extinction correction to observed magnitudes (default `True`) | +| `mask_type`, `gc_frame` | forwarded to the `phi1`/`phi2` → `ra`/`dec` placement | + +## The same physical star across surveys + +For a multi-survey injection the isochrone draws **one set of initial masses** +(exactly `nstars`) and interpolates *those same masses* into every survey's +bands. So a star's LSST and Roman magnitudes describe the same object — the +true magnitudes are physically consistent and tightly correlated across surveys +rather than drawn independently. + +This requires a **multi-survey isochrone** in the stream config: a top-level +`surveys:` mapping sharing one stellar population, e.g. + +```yaml +stream: + # ... density / track / distance_modulus ... + isochrone: + name: Marigo2017 # shared population + age: 12.0 + z: 0.0006 + surveys: + lsst: {survey: lsst, band_1: g, band_2: r} + roman: {survey: roman, band_1: F106, band_2: F158} +``` + +A single-survey isochrone (the flat `survey`/`band_1`/`band_2` form) is just the +one-survey case of the same machinery and produces `__true` +identically. + +```{note} +**Roman bands are converted Vega→AB automatically.** `ugali` returns Roman +isochrone magnitudes in Vega while the catalogs are AB, so `IsochroneModel` +applies a fixed per-band offset (`streamobs.model.ROMAN_VEGA_TO_AB`) to every +Roman band unconditionally. Non-Roman bands pass through unchanged; there is no +config flag. +``` + +```{note} +**`nstars` means exactly N stars.** `StreamModel.sample(size)` / the isochrone +draw return *exactly* that many stars (a fixed mass set), not a random-length IMF +realization. This is required so the same masses can be shared across surveys. +``` + +## Completing a catalog + +`StreamInjector.complete_data(...)` is the public "fill in the rest from the +config" helper — the same completion `inject` runs internally, exposed so you can +build or inspect a completed catalog **without** injecting noise. It fills +`ra`/`dec` (converting from `phi1`/`phi2` if needed) and the per-survey +`__true` columns, **preserving anything already present**: + +```python +# Partial input -> filled from the config; existing columns are kept. +full = inj.complete_data(df, bands=["r", "g"], stream_config=cfg, seed=1) +``` + +### Supplying a distance directly + +Apparent magnitudes need a distance modulus. Normally it comes from the config's +`distance_modulus` model (which needs `phi1`), but you can pass `dist` directly — +a scalar (broadcast to all rows) or a per-row vector — to fill magnitudes without +a distance model or `phi1`: + +```python +out = inj.inject(df, bands=["r", "g"], stream_config=cfg, dist=16.8) # scalar +out = inj.inject(df, bands=["r", "g"], stream_config=cfg, dist=dist_arr) # per-row +``` + +When given, `dist` overrides the configured distance model. Only rows that are +missing a `dist` value are set. + +### Existing values are never overwritten + +Completion fills only the **missing** rows of each column. If you supply one band +and request another, the supplied band is left untouched and only the missing one +is filled (newly-filled cells still come from one shared mass draw, so they are +mutually colour-consistent). + +## S/N cut ownership + +The reference band (`survey.completeness_band`, e.g. LSST `r`) is special: the +survey's **selection functions are estimated with the SNR ≥ 5 cut already +applied** in that band. The injector therefore applies the reference-band cut +exactly **once**, and the explicit `detection_mag_cut` loop applies SNR ≥ 5 to +every *other* injected band (its default is all injected bands except the +reference band). Net effect: a star must have SNR ≥ 5 in every injected band to +be flagged observed, with the reference band counted once. + +## See also + +- [Output column convention](column_convention.md) — the `__…` + scheme and the sample-vs-catalog error split. +- {class}`streamobs.observed.StreamInjector` — full API. +- {class}`streamobs.model.StreamModel` — the stream/isochrone config. diff --git a/docs/source/quickstart.md b/docs/source/quickstart.md index 5e7dc79..9564a99 100644 --- a/docs/source/quickstart.md +++ b/docs/source/quickstart.md @@ -47,7 +47,9 @@ config = parse_config('config/toy1_config.yaml') stream = StreamModel(config['stream']) stream_df = stream.sample(config['stream']['nstars']) -# The dataframe contains: phi1, phi2, distance, magnitudes, etc. +# The dataframe contains: phi1, phi2, dist, and the isochrone magnitude +# columns, which are survey-namespaced as __true +# (e.g. lsst_g_true, lsst_r_true). print(stream_df.head()) ``` @@ -68,28 +70,31 @@ lsst_survey = surveys.Survey.load(survey='lsst', release='yr1') # Create the stream injector injector = observed.StreamInjector(lsst_survey) -# Create or load your mock stream data -# Here we create a simple test dataset -# This could contain (ra, dec) coordinates if you want to skip coordinate transformation +# Create or load your mock stream data. +# Here we create a simple test dataset. Could instead contain (ra, dec) to skip +# the coordinate transformation. True (noiseless) magnitudes are passed in as +# survey-namespaced __true columns (the survey's name is its +# namespace; here "lsst"). Alternatively, omit the magnitudes and pass a +# `stream_config=` so the injector samples them from an isochrone. rng = np.random.default_rng(42) mock_data = pd.DataFrame({ - 'phi1': rng.uniform(-5, 5, 1000), # Stream longitude - 'phi2': rng.uniform(-1, 1, 1000), # Stream latitude - 'mag_g': rng.uniform(18, 28, 1000), # g-band magnitude - 'mag_r': rng.uniform(18, 28, 1000), # r-band magnitude + 'phi1': rng.uniform(-5, 5, 1000), # Stream longitude + 'phi2': rng.uniform(-1, 1, 1000), # Stream latitude + 'lsst_g_true': rng.uniform(18, 28, 1000), # true g-band apparent magnitude + 'lsst_r_true': rng.uniform(18, 28, 1000), # true r-band apparent magnitude }) # Apply survey effects: footprint, extinction, photometric errors observed_data = injector.inject( - mock_data, + mock_data, + bands=['r', 'g'], # bands to inject (single-survey shorthand) seed=42, - bands = ['r', 'g'] # Choose the bands to use - mask_type=['footprint', 'ebv'], # Restrict injection to a mask created from the footprind and low dust area - verbose=True + mask_type=['footprint', 'ebv'], # place the stream within the footprint + low-dust area + verbose=True, ) print(f"Input stars: {len(mock_data)}") -print(f"Detected stars: {len(observed_data[observed_data['flag_observed']==1])}") +print(f"Detected stars: {int(observed_data['lsst_flag_observed'].sum())}") ``` ### What the Injector Does @@ -102,11 +107,13 @@ The `StreamInjector` applies several observational effects: 4. **Detection completeness**: Applies magnitude-dependent detection probability -The output dataframe includes: +The output dataframe includes (all magnitude/flag columns are **survey-namespaced** +as `_...`; for the LSST survey loaded above the namespace is `lsst`): - `ra`, `dec`: Sky coordinates -- `mag_g_obs`, `mag_r_obs`: Observed magnitudes with errors -- `magerr_g`, `magerr_r`: Photometric uncertainties -- `flag_observed`: Detection and clasification flag (1=detected & classified as a star, 0=not detected of not classified as a star) +- `lsst_g_true`, `lsst_r_true`: True (noiseless) apparent magnitudes +- `lsst_g_obs`, `lsst_r_obs`: Observed (noisy) magnitudes +- `lsst_g_err`, `lsst_r_err`: Reported photometric uncertainties +- `lsst_flag_observed`: Detection and classification flag (`True`=detected & classified as a star, `False`=not detected or not classified as a star) ## Next Steps diff --git a/docs/source/streamobs.functions.rst b/docs/source/streamobs.functions.rst index 17dc5ce..a0b8bb8 100644 --- a/docs/source/streamobs.functions.rst +++ b/docs/source/streamobs.functions.rst @@ -4,4 +4,3 @@ streamobs.functions module .. automodule:: streamobs.functions :members: :show-inheritance: - :undoc-members: diff --git a/docs/source/streamobs.model.rst b/docs/source/streamobs.model.rst index 24d0150..d716a21 100644 --- a/docs/source/streamobs.model.rst +++ b/docs/source/streamobs.model.rst @@ -4,4 +4,3 @@ streamobs.model module .. automodule:: streamobs.model :members: :show-inheritance: - :undoc-members: diff --git a/docs/source/streamobs.observed.rst b/docs/source/streamobs.observed.rst index 68b104c..96fae5b 100644 --- a/docs/source/streamobs.observed.rst +++ b/docs/source/streamobs.observed.rst @@ -4,4 +4,3 @@ streamobs.observed module .. automodule:: streamobs.observed :members: :show-inheritance: - :undoc-members: diff --git a/docs/source/streamobs.plotting.rst b/docs/source/streamobs.plotting.rst index 7f2527c..9c2ff57 100644 --- a/docs/source/streamobs.plotting.rst +++ b/docs/source/streamobs.plotting.rst @@ -4,4 +4,3 @@ streamobs.plotting module .. automodule:: streamobs.plotting :members: :show-inheritance: - :undoc-members: diff --git a/docs/source/streamobs.rst b/docs/source/streamobs.rst index 279061d..612cd5f 100644 --- a/docs/source/streamobs.rst +++ b/docs/source/streamobs.rst @@ -4,7 +4,6 @@ streamobs package .. automodule:: streamobs :members: :show-inheritance: - :undoc-members: Submodules ---------- diff --git a/docs/source/streamobs.samplers.rst b/docs/source/streamobs.samplers.rst index 334b40b..b17ae4c 100644 --- a/docs/source/streamobs.samplers.rst +++ b/docs/source/streamobs.samplers.rst @@ -4,4 +4,3 @@ streamobs.samplers module .. automodule:: streamobs.samplers :members: :show-inheritance: - :undoc-members: diff --git a/docs/source/streamobs.surveys.rst b/docs/source/streamobs.surveys.rst index ace942f..a79f771 100644 --- a/docs/source/streamobs.surveys.rst +++ b/docs/source/streamobs.surveys.rst @@ -4,4 +4,3 @@ streamobs.surveys module .. automodule:: streamobs.surveys :members: :show-inheritance: - :undoc-members: diff --git a/docs/source/streamobs.utils.rst b/docs/source/streamobs.utils.rst index d92c9b9..611ce49 100644 --- a/docs/source/streamobs.utils.rst +++ b/docs/source/streamobs.utils.rst @@ -4,4 +4,3 @@ streamobs.utils module .. automodule:: streamobs.utils :members: :show-inheritance: - :undoc-members: diff --git a/streamobs/observed.py b/streamobs/observed.py index 36d2e0e..5dec240 100644 --- a/streamobs/observed.py +++ b/streamobs/observed.py @@ -40,10 +40,9 @@ class StreamInjector: namespace is the column prefix (``lsst_r_obs``, ``roman_F158_obs``, ...). primary : str Namespace of the survey whose footprint drives the shared sky placement - and whose ``_save_injected_data`` is used. - survey : Survey - The primary :class:`~streamobs.surveys.Survey` (convenience accessor used - by the mask / coordinate helpers; equal to ``surveys[primary]``). + and whose ``_save_injected_data`` is used. The primary + :class:`~streamobs.surveys.Survey` itself is available via the ``survey`` + property (``surveys[primary]``), used by the mask/coordinate helpers. mask_cache : dict (class attribute) Cache of previously created HEALPix masks to avoid recomputation. _last_gc_frame : GreatCircleICRSFrame or None diff --git a/streamobs/plotting.py b/streamobs/plotting.py index 28ddb96..b17e59d 100644 --- a/streamobs/plotting.py +++ b/streamobs/plotting.py @@ -26,8 +26,8 @@ def draw_stream(phi1, phi2): ---------- phi1, phi2 : coordinates of stars (deg) - Returns: - -------- + Returns + ------- num, xedges, yedges, im : output of imshow """ @@ -42,8 +42,8 @@ def draw_stream(phi1, phi2): ax = plt.gca() ax.set_title("Mock Stream") - ax.set_xlabel("$\phi_1$ [deg]") - ax.set_ylabel("$\phi_2$ [deg]") + ax.set_xlabel(r"$\phi_1$ [deg]") + ax.set_ylabel(r"$\phi_2$ [deg]") image = ax.imshow( hist.T, diff --git a/streamobs/surveys.py b/streamobs/surveys.py index 105c633..bb97a47 100644 --- a/streamobs/surveys.py +++ b/streamobs/surveys.py @@ -69,8 +69,6 @@ class Survey: magnitudes. Optional second curve (config key ``log_photo_error_sample``). If not provided, the noise draw falls back to the catalog model, which reproduces the previous single-curve behaviour exactly. - log_photo_error : callable - Read/write alias for ``log_photo_error_catalog`` (backward compatibility). Examples -------- @@ -469,15 +467,16 @@ def get_efficiency( True apparent magnitude(s). maglim : float or np.ndarray Magnitude limit(s) at the position(s). - + type : str, optional + Type of efficiency function to use. Options are ``"completeness"``, + ``"detection_efficiency"``, or ``"classification_efficiency"``. + Default is ``"completeness"``. **kwargs - Additional keyword arguments: - type : str, optional - Type of efficiency function to use. Options are "completeness", "detection_efficiency", or "classification_efficiency". - Default is "completeness". + Additional keyword arguments. Currently: + delta_saturation : float, optional - Magnitude difference for saturation threshold in the initial completeness function. - Default is -10.4. + Magnitude difference for saturation threshold in the initial + completeness function. Default is -10.4. Returns ------- @@ -592,6 +591,8 @@ def create_survey( Custom configuration dictionary to use instead of loading from file. If provided, bypasses the standard config file loading. **kwargs + Additional keyword arguments. Recognized: + uniform_survey : bool, optional If True, also creates and caches a uniform version of the survey with constant magnitude limits. The uniform survey can be accessed @@ -599,7 +600,8 @@ def create_survey( Default is False. verbose : bool, optional Whether to print progress messages. Default is True. - Additional keyword arguments override config values + + Any other keyword arguments override config values. Returns ------- From 0935b2d4e4d8150f58d1fef3eeea68a45dd2e65d Mon Sep 17 00:00:00 2001 From: psferguson Date: Tue, 16 Jun 2026 12:13:36 -0700 Subject: [PATCH 09/54] remove some unnesseary files --- docs/source/multisurvey.md | 16 ++ docs/source/roman_multisurvey_plan.md | 9 + notebooks/multisurvey_phases_demo.ipynb | 156 ++----------- notebooks/roman_dc2_combine_plan.md | 128 ---------- scripts/build_multisurvey_demo_nb.py | 299 ------------------------ 5 files changed, 44 insertions(+), 564 deletions(-) delete mode 100644 notebooks/roman_dc2_combine_plan.md delete mode 100644 scripts/build_multisurvey_demo_nb.py diff --git a/docs/source/multisurvey.md b/docs/source/multisurvey.md index f1ae705..d3d753c 100644 --- a/docs/source/multisurvey.md +++ b/docs/source/multisurvey.md @@ -95,6 +95,22 @@ A single-survey isochrone (the flat `survey`/`band_1`/`band_2` form) is just the one-survey case of the same machinery and produces `__true` identically. +A complete, runnable example — the surveys, per-survey bands, the multi-survey +isochrone, and the shared stream geometry — is provided as a *scene* config in +[`config/scenes/roman_rubin_demo.yaml`](https://github.com/LSSTDESC/streamobs/blob/main/config/scenes/roman_rubin_demo.yaml): + +```python +import yaml +from streamobs.observed import StreamInjector + +scene = yaml.safe_load(open("config/scenes/roman_rubin_demo.yaml")) +inj = StreamInjector(scene["surveys"]) # {"lsst": "lsst", "roman": "roman"} +cat = inj.inject( + df, survey_bands=scene["survey_bands"], # {"lsst": [...], "roman": [...]} + stream_config=scene["stream"], seed=42, +) +``` + ```{note} **Roman bands are converted Vega→AB automatically.** `ugali` returns Roman isochrone magnitudes in Vega while the catalogs are AB, so `IsochroneModel` diff --git a/docs/source/roman_multisurvey_plan.md b/docs/source/roman_multisurvey_plan.md index ed3d9b1..2d6f4dd 100644 --- a/docs/source/roman_multisurvey_plan.md +++ b/docs/source/roman_multisurvey_plan.md @@ -13,6 +13,15 @@ working design/roadmap doc, not user documentation. Before merge, migrate the durable content — the column convention, the sample/catalog error split, the multi-survey `StreamInjector` usage, and the Vega→AB handling — into the proper docs pages (and the API docstrings), then delete `roman_multisurvey_plan.md`. + +**Also remove before merge — useful for now, but not part of the merged package:** + +- `notebooks/multisurvey_phases_demo.ipynb` — the Phases 1–4 walkthrough. It is + kept tracked (with outputs stripped) during the branch's life, but should be + removed before merge and migrated into the rendered docs as an Examples page + (see the separate notebooks→docs migration). Its generator, + `scripts/build_multisurvey_demo_nb.py`, is a local-only helper and is **not** + tracked. ``` ## Motivation diff --git a/notebooks/multisurvey_phases_demo.ipynb b/notebooks/multisurvey_phases_demo.ipynb index 39ee820..790819f 100644 --- a/notebooks/multisurvey_phases_demo.ipynb +++ b/notebooks/multisurvey_phases_demo.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "faa963cc", "metadata": { "execution": { @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "9fe2c7bf", "metadata": { "execution": { @@ -86,15 +86,7 @@ "shell.execute_reply": "2026-06-16T18:01:25.638254Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "StubSurvey ready\n" - ] - } - ], + "outputs": [], "source": [ "NSIDE = 1 # whole-sky single-pixel maps; enough for a synthetic demo\n", "\n", @@ -150,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "51d6fb5d", "metadata": { "execution": { @@ -160,25 +152,7 @@ "shell.execute_reply": "2026-06-16T18:01:25.741220Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "no sample curve -> sample falls back to catalog: True\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sv = StubSurvey(\"lsst\", [\"g\", \"r\"], completeness_band=\"r\", maglim=26.5)\n", "mags = np.linspace(20, 28, 200)\n", @@ -220,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "3b531fc3", "metadata": { "execution": { @@ -230,18 +204,7 @@ "shell.execute_reply": "2026-06-16T18:01:25.755283Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "namespaced columns: roman_F158_true roman_F158_obs roman_F158_err roman_flag_observed\n", - "\n", - "injected NIR-only bands -> columns: ['roman_F106_obs', 'roman_F106_err', 'roman_F158_obs', 'roman_F158_err', 'roman_flag_observed']\n", - "detected: 1274 / 1500\n" - ] - } - ], + "outputs": [], "source": [ "print(\"namespaced columns:\", C.true_col(\"F158\", \"roman\"), C.obs_col(\"F158\", \"roman\"),\n", " C.err_col(\"F158\", \"roman\"), C.flag_col(\"roman\"))\n", @@ -274,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "2798a8c1", "metadata": { "execution": { @@ -284,15 +247,7 @@ "shell.execute_reply": "2026-06-16T18:01:25.822899Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "single-survey StreamModel.sample returns EXACTLY nstars (5000, 1234) -> OK\n" - ] - } - ], + "outputs": [], "source": [ "single_cfg = {\n", " \"density\": {\"type\": \"Uniform\", \"xmin\": -9.0, \"xmax\": 9.0},\n", @@ -320,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "95a245ed", "metadata": { "execution": { @@ -330,27 +285,7 @@ "shell.execute_reply": "2026-06-16T18:01:28.377918Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "multi-survey true columns: ['lsst_g_true', 'lsst_r_true', 'roman_F106_true', 'roman_F158_true']\n", - "rows: 4000\n", - "corr(lsst_r, roman_F158) = 0.995 (shared masses => tightly correlated)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "scene = yaml.safe_load(open(f\"{REPO}/config/scenes/roman_rubin_demo.yaml\"))\n", "sm_ms = StreamModel(scene[\"stream\"])\n", @@ -385,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "57c4f2c5", "metadata": { "execution": { @@ -395,26 +330,7 @@ "shell.execute_reply": "2026-06-16T18:01:28.421196Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Roman Vega->AB offsets (AB = Vega + diff):\n", - " F062: +0.153\n", - " F087: +0.481\n", - " F106: +0.66\n", - " F129: +1.051\n", - " F146: +1.164\n", - " F158: +1.315\n", - " F184: +1.556\n", - " F213: +1.837\n", - "\n", - "F158 shift: [1.315 1.315] (always +1.315)\n", - "r shift: [0. 0.] (0 -> non-Roman pass-through)\n" - ] - } - ], + "outputs": [], "source": [ "from streamobs.model import ROMAN_VEGA_TO_AB\n", "print(\"Roman Vega->AB offsets (AB = Vega + diff):\")\n", @@ -442,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "3bf8c97b", "metadata": { "execution": { @@ -452,21 +368,7 @@ "shell.execute_reply": "2026-06-16T18:01:28.500442Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "columns produced:\n", - " lsst: ['lsst_flag_observed', 'lsst_g_err', 'lsst_g_obs', 'lsst_g_true', 'lsst_r_err', 'lsst_r_obs', 'lsst_r_true']\n", - " roman: ['roman_F106_err', 'roman_F106_obs', 'roman_F106_true', 'roman_F158_err', 'roman_F158_obs', 'roman_F158_true', 'roman_flag_observed']\n", - "\n", - "shared sky placement: ra/dec present = True\n", - "lsst detected: 1105 / 4000\n", - "roman detected: 3113 / 4000\n" - ] - } - ], + "outputs": [], "source": [ "lsst_sv = StubSurvey(\"lsst\", [\"g\", \"r\"], completeness_band=\"r\", maglim=26.0)\n", "roman_sv = StubSurvey(\"roman\", [\"F106\", \"F158\"], completeness_band=\"F158\", maglim=27.0)\n", @@ -488,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "4a4c2ff4", "metadata": { "execution": { @@ -498,16 +400,7 @@ "shell.execute_reply": "2026-06-16T18:01:28.568019Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "same seed -> identical obs & flags: True\n", - "different seed -> different noise draw: True\n" - ] - } - ], + "outputs": [], "source": [ "# Reproducible from seed (given the same true-mag draw)\n", "base = sm_ms.sample(3000)\n", @@ -522,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "e154bc4b", "metadata": { "execution": { @@ -532,18 +425,7 @@ "shell.execute_reply": "2026-06-16T18:01:28.639713Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Detection efficiency vs magnitude, per survey (Roman is deeper here: maglim 27 vs 26)\n", "fig, ax = plt.subplots(figsize=(6.5, 4))\n", diff --git a/notebooks/roman_dc2_combine_plan.md b/notebooks/roman_dc2_combine_plan.md deleted file mode 100644 index 153a7ae..0000000 --- a/notebooks/roman_dc2_combine_plan.md +++ /dev/null @@ -1,128 +0,0 @@ -# Roman DC2 mock: plan to build a single truth catalog with observations - -**Status:** PLAN ONLY — not executed yet. -**Source:** Troxel et al. 2023, *A Joint Roman + Rubin Synthetic Wide-Field Imaging Survey* -([arXiv:2209.06829](https://arxiv.org/abs/2209.06829)). -**Data:** `/astro/store/shire/stream_team/stream_finding/data/roman_mock` -(symlinked as `data/surveys/roman_dc2/`). ~20 deg², **1039 coadd tiles**, tiles named by -center `{ra}_{dec}` in degrees (e.g. `50.93_-38.8`). -**Python:** `/astro/store/shiren/conda-envs/stream_team/envs/streamobs/bin/python` -(astropy 7.2, numpy, pandas, healpy; no fitsio). - ---- - -## 1. What the files actually are (verified by inspection) - -| File(s) | Count | Granularity | Key columns | Notes | -|---|---|---|---|---| -| `det/dc2_det_{tile}.fits.gz` | 2078 | per coadd, **one row per detection** | `number`, `alphawin_j2000`,`deltawin_j2000` (RA/Dec **deg**), `flux_auto`,`fluxerr_auto`, `mag_auto`, `mag_auto_{Y106,J129,H158,F184}` (+magerr,flux,fluxerr), `flags`, `class_star`, shape moments | SExtractor on the coadd. ~21k rows/tile. | -| `truth/dc2_index_{tile}.fits.gz` | 1479 (1039 real + 440 empty) | per coadd, **one row per object** | `ind` (truth ID), `ra`,`dec` (**deg**), `mag_{Y106,J129,H158,F184}`, `dered_{...}`, `gal_star` (**1=star, 0=gal**), `sca`,`dither`,`x`,`y`,`stamp`,`start_row` | **THE truth product to use.** 440 files are 53-byte empty placeholders (tiles with no objects). | -| `truth/coadd/dc2_index_{tile}.fits.gz` | 1481 | — | — | **Byte-identical** to `truth/dc2_index_{tile}` (confirmed via `filecmp`). Ignore one of them. | -| `truth/dc2_index_star.fits.gz` | 1 | **per (object × SCA exposure)** | `ind,sca,dither,x,y,ra,dec,mag,stamp,xmin,xmax,ymin,ymax,dudx,dudy,dvdx,dvdy,start_row` | 414 MB, ~6.36M rows. RA/Dec in **RADIANS**. Single-epoch index; **not needed** for coadd matching. | -| `truth/dc2_index.fits.gz` | 1 | **per (object × SCA exposure)** | same 18 cols as star index | **78 GB.** All objects (gal+star). Header `NAXIS2=0` (streaming FITS — astropy reads 0 rows; see §5). **Not needed for this task.** | -| `truth/dc2_coaddlist.fits.gz` | 1 | per coadd tile | `tilename,coadd_i,coadd_j,coadd_ra,coadd_dec,d_ra,d_dec,input_list` | 1838 rows; `input_list` = contributing exposures. Optional metadata. | - -### The key realization -**We do NOT need the 78 GB `dc2_index.fits` (nor the star index) for this.** -The per-coadd `truth/dc2_index_{tile}.fits.gz` files already carry, per object: -truth RA/Dec (deg), truth mags in all 4 Roman bands, dereddening, and the star/galaxy -flag. They are tile-for-tile aligned with the `det/dc2_det_{tile}.fits.gz` files. So the -whole job reduces to **per-tile positional matching** — tractable and parallel. - -The big index is only needed if you later want single-epoch (per-SCA) info: which exposures -saw an object, its pixel position/postage stamp, etc. - ---- - -## 2. The combine = per-tile positional match (truth ← det) - -Following the paper's matching recipe: - -For each of the **1039 real coadd tiles** (intersection of det-tile names and non-empty truth-tile names): - -1. **Load truth tile** → table of objects (ind, ra, dec, mag_*, dered_*, gal_star, ...). -2. **Load det tile** → detections (alphawin_j2000, deltawin_j2000, mag_auto_*, flux_auto, fluxerr_auto, flags, class_star). -3. **Cut det**: `S/N = flux_auto / fluxerr_auto > 5` (paper's threshold; removes only ~0.05%). - Optionally also `flags == 0` (stricter; the paper removes ~32% via flags — leave this as a toggle, default OFF). -4. **Match** truth → det by sky position with **1.0 arcsec** radius - (`astropy.coordinates.SkyCoord.match_to_catalog_sky`, or a KD-tree on a local tangent plane). - - Paper's rule: among matches within 1″, when ambiguous take the closest **in magnitude** - among the up-to-3 nearest. v1 can just take the **nearest neighbor within 1″**; add the - mag-tiebreak as a refinement. -5. **Left-join on truth**: every truth row is kept. Attach the matched det columns - (prefixed `det_`) when a match exists; else NaN. Add `detected` (bool) and `match_sep_arcsec`. -6. **Tag** each row with `tile` (the `{ra}_{dec}` string). -7. **Concatenate** all tiles → one catalog. Write to **parquet** (fast, typed) — and/or FITS. - -Output is then a single **truth-complete** catalog: one row per true object across the -footprint, with observed (detected) quantities where they exist. From it you can trivially -derive detection efficiency / completeness vs. magnitude, color, `gal_star`, etc., which is -exactly what stream-injection / selection-function work needs. - -### Output schema (proposed) -``` -ind, tile, ra, dec, gal_star, -mag_Y106, mag_J129, mag_H158, mag_F184, # truth -dered_Y106, dered_J129, dered_H158, dered_F184, # truth -detected (bool), match_sep_arcsec, -det_number, det_ra, det_dec, -det_mag_auto, det_mag_auto_Y106..F184, -det_flux_auto, det_fluxerr_auto, det_sn, -det_flags, det_class_star -``` -(`mag_* == 0.0` in truth means "no flux in that band" → treat as NaN on load.) - ---- - -## 3. Column subset for the big index (`dc2_index.fits`, 78 GB) — if you still want it - -You offered to pre-subset it. **For the coadd truth+det catalog you can skip it entirely** -(use the per-tile truth files). If you do subset it (e.g. for per-SCA / single-epoch studies), -the 18 columns are: - -| keep? | column | type | meaning | -|---|---|---|---| -| ✅ | `ind` | int64 | object ID — links to input truth catalog (join key across files) | -| ✅ | `ra`,`dec` | float64 | sky position (**radians** in this file) | -| ✅ | `mag` | float64 | true magnitude (in that exposure's band) | -| ✅ | `sca` | int64 | Roman detector (1–18) | -| ✅ | `dither` | int64 | exposure / pointing ID | -| ➖ | `x`,`y` | float64 | pixel position on the SCA | -| ➖ | `stamp`,`start_row` | int64 | postage-stamp pointers into the image-stamp file | -| ❌ | `xmin,xmax,ymin,ymax` | int64 | stamp bounding box | -| ❌ | `dudx,dudy,dvdx,dvdy` | float64 | local WCS Jacobian | - -Minimal useful subset: **`ind, sca, dither, ra, dec, mag`** (6 of 18 → ~⅓ the size). -Add `x,y,stamp,start_row` if you need to find/cut postage stamps. Note: a single object -appears in **many rows** here (once per SCA exposure it lands on), so you must group by `ind`. - ---- - -## 4. Decisions to confirm before coding - -1. **Stars only, or all objects?** streamobs is stellar streams → likely `gal_star==1`. - But keeping galaxies lets us model the contaminating background. → *propose: keep all, - carry `gal_star`, filter downstream.* -2. **flags cut?** S/N>5 only (your call), or also `flags==0` (drops ~32%, removes - blends/edges). → *propose: S/N>5 default, `flags==0` as an option.* -3. **Match radius / tie-break:** 1.0″ nearest-neighbor (v1) vs. paper's mag-tiebreak FoF. → *propose: 1.0″ NN first, refine if needed.* -4. **Output format/location:** parquet under `data/surveys/roman_dc2/` (+ optional FITS). -5. **Galaxy de-blending:** the paper notes 20–30% of Rubin objects split into multiple - Roman objects; with a 1″ radius a det can match several truths — we keep the truth-centric - left join (each truth → its nearest det), so this is handled implicitly. Flag if needed. - ---- - -## 5. Implementation notes / gotchas - -- **Read gzipped FITS**: `with gzip.open(path,'rb') as f: Table(astropy.io.fits.open(io.BytesIO(f.read()))[1].data)`. - (astropy's direct `.fits.gz` open chokes on the big NAXIS2=0 files; the per-tile files open fine.) -- **`NAXIS2=0` streaming files** (star/master index only): astropy returns 0 rows. To read: - skip the primary + extension headers (2880-byte blocks to `END`), then - `np.frombuffer(rest, dtype=<144-byte big-endian dtype>)`; n_rows = data_bytes // 144. -- **Units**: per-tile truth + det are **degrees**; star/master index are **radians**. -- **Missing mags**: truth `mag_* == 0.0` and det un-matched → use NaN. -- **Parallelism**: 1039 independent tiles. Natural fan-out — one worker per tile, then concat. - (Good candidate for a Workflow run, or simple multiprocessing / joblib.) -- **Sanity check tile** `50.93_-38.8`: 34378 truth objects (175 stars, 34203 gals); - 21382 detections, 21372 with S/N>5; truth & det RA/Dec ranges overlap as expected. diff --git a/scripts/build_multisurvey_demo_nb.py b/scripts/build_multisurvey_demo_nb.py deleted file mode 100644 index 3727252..0000000 --- a/scripts/build_multisurvey_demo_nb.py +++ /dev/null @@ -1,299 +0,0 @@ -#!/usr/bin/env python -"""Build notebooks/multisurvey_phases_demo.ipynb (Phases 1-4 showcase). - -Local helper to (re)generate the demo notebook from a single source of truth. -Run it from the repo root inside the `streamobs` conda env, then execute with -`jupyter nbconvert --to notebook --execute --inplace`. -""" - -import nbformat as nbf -from nbformat.v4 import new_code_cell, new_markdown_cell, new_notebook - -cells = [] -md = lambda s: cells.append(new_markdown_cell(s)) -co = lambda s: cells.append(new_code_cell(s)) - -md(r"""# Multi-survey injection — Phases 1-4 demo - -This notebook walks through the `roman_multisurvey` refactor that lets `streamobs` -inject a single stream carrying **both Roman and Rubin/LSST** photometry, where -each band draws its errors and detection probability from its *own* survey. - -| Phase | What changed | -|---|---| -| **1** | `Survey` holds two error curves — a *catalog* (reported `magerr`, drives the S/N cut) and an optional *sample* (true scatter, drives the noise draw). `get_photo_error(..., kind=)` selects between them. | -| **2** | The injector is de-hardcoded off `{r, g}`; column names route through `streamobs.columns`, using a uniform, **always survey-namespaced** `__true/_obs/_err` scheme. The S/N cut now applies to **all** injected bands. | -| **3** | `IsochroneModel` is multi-band / multi-survey: masses are drawn **once** (exactly `nstars`) and interpolated into every survey's bands, so the *same physical star* is consistent across surveys. Roman bands are auto-converted Vega→AB. | -| **4** | A single `StreamInjector` accepts one survey **or several**: it does one shared sky placement + one shared true-mag fill, then a per-survey loop writing `__obs/_err` and `_flag_observed`. | - -See `docs/source/roman_multisurvey_plan.md` for the full design. -""") - -co("""import warnings -warnings.filterwarnings("ignore") -import numpy as np -import pandas as pd -import healpy as hp -import yaml -import matplotlib.pyplot as plt - -from streamobs.surveys import Survey -from streamobs.model import StreamModel, IsochroneModel -from streamobs.observed import StreamInjector -from streamobs import columns as C - -import os -# Resolve the repo root whether the notebook runs from repo root or notebooks/. -REPO = ".." if os.path.exists("../config/scenes/roman_rubin_demo.yaml") else "." -rng = np.random.default_rng(42) -""") - -md(r"""## A runnable stub survey - -The real Roman/Rubin maglim maps and CSV tables are **not committed** to this -branch (they live in the git-ignored `data/surveys/`). So that this notebook -runs end-to-end, we use a tiny `StubSurvey` that: - -* keeps the **real** `Survey.get_photo_error` (so the Phase-1 two-curve split is - genuine) and `get_maglim`, -* supplies analytic `log_photo_error_*` curves and a logistic completeness. - -The **isochrones are real** (`Marigo2017` via `ugali`). To run against real -surveys, just replace `StubSurvey(...)` with `Survey.load("lsst", release=...)` -etc. — the injector code is identical. -""") - -co('''NSIDE = 1 # whole-sky single-pixel maps; enough for a synthetic demo - -class StubSurvey(Survey): - """Minimal Survey: real photo-error machinery, analytic completeness/maps.""" - - def __init__(self, name, bands, completeness_band, maglim=27.0, - sample_inflation=1.9): - self.name = name - self.bands = list(bands) - self.completeness_band = completeness_band - self.saturation = {b: 16.0 for b in bands} - self.sys_error = {b: 0.005 for b in bands} - self.delta_saturation = -10.0 - self.coeff_extinc = {b: 0.0 for b in bands} - self.ebv_map = np.zeros(hp.nside2npix(NSIDE)) - self.maglim_maps = {b: np.full(hp.nside2npix(NSIDE), maglim) for b in bands} - self.coverage = np.ones(hp.nside2npix(NSIDE)) # footprint = whole sky - # Phase 1: two error curves, both functions of delta_mag = mag - maglim. - # Catalog = reported error; sample = true scatter (here ~inflation x larger, - # echoing the Roman DC2 finding that true scatter ~2x reported magerr). - self.log_photo_error_catalog = lambda dm: np.log10( - 0.01 + 0.10 * np.exp(np.clip(dm, -30, 5))) - self.log_photo_error_sample = lambda dm: np.log10( - sample_inflation * (0.01 + 0.10 * np.exp(np.clip(dm, -30, 5)))) - - # analytic rolloff: ~1 when bright (mag << maglim), 0.5 at maglim, ->0 fainter - def get_completeness(self, band, mag, maglim): - return 1.0 / (1.0 + np.exp((np.asarray(mag) - np.asarray(maglim)) / 0.25)) - - def get_detection_efficiency(self, band, mag, maglim): - return self.get_completeness(band, mag, maglim) - - def get_extinction(self, band, pixel=None): - return np.zeros(np.size(pixel)) - -print("StubSurvey ready")''') - -md(r"""## Phase 1 — sample vs. catalog photometric error - -`Survey` now carries two curves. `get_photo_error(kind="catalog")` is the -reported error that becomes `magerr` and drives the S/N cut; -`get_photo_error(kind="sample")` is the *true* scatter used to draw the observed -magnitude. When no sample curve is loaded, `sample` transparently falls back to -`catalog`, so legacy single-curve behaviour is bit-for-bit preserved. -""") - -co("""sv = StubSurvey("lsst", ["g", "r"], completeness_band="r", maglim=26.5) -mags = np.linspace(20, 28, 200) -maglim = np.full_like(mags, 26.5) -err_cat = sv.get_photo_error("r", mags, maglim, kind="catalog") -err_smp = sv.get_photo_error("r", mags, maglim, kind="sample") - -# A survey with NO sample curve -> sample falls back to catalog (identical) -sv_legacy = StubSurvey("lsst", ["g", "r"], "r", maglim=26.5) -sv_legacy.log_photo_error_sample = None -err_fallback = sv_legacy.get_photo_error("r", mags, maglim, kind="sample") -assert np.allclose(err_fallback, sv_legacy.get_photo_error("r", mags, maglim, kind="catalog")) - -fig, ax = plt.subplots(figsize=(6, 4)) -ax.plot(mags, err_cat, label='catalog (reported magerr, drives S/N cut)') -ax.plot(mags, err_smp, label='sample (true scatter, drives noise draw)') -ax.axvline(26.5, ls=':', c='k', lw=1, label='maglim') -ax.set_xlabel('magnitude'); ax.set_ylabel('photometric error [mag]') -ax.set_title('Phase 1: two error curves on Survey'); ax.legend(); fig.tight_layout() -print("no sample curve -> sample falls back to catalog:", - np.allclose(err_fallback, err_cat))""") - -md(r"""## Phase 2 — arbitrary bands + `columns.py` - -The injector no longer hard-codes `{r, g}`. Column names come from -`streamobs.columns`, using one uniform, **always survey-namespaced** convention: -`__true` / `__obs` / `__err` / -`_flag_observed`. (This intentionally drops the historical `mag_` / -`magerr_` names — not backward compatible.) Below we inject Roman NIR bands -`F106`/`F158` through a **single-survey** `StreamInjector` — impossible under the -old `{r,g}` block — and the output is namespaced by the survey's name (`roman`). -""") - -co( - """print("namespaced columns:", C.true_col("F158", "roman"), C.obs_col("F158", "roman"), - C.err_col("F158", "roman"), C.flag_col("roman")) - -roman_sv = StubSurvey("roman", ["F106", "F158"], completeness_band="F158", maglim=27.0) -inj = StreamInjector(roman_sv) # one survey -> namespaced by its name, "roman" -N = 1500 -df = pd.DataFrame({ - "ra": rng.uniform(10, 20, N), - "dec": rng.uniform(-5, 5, N), - "roman_F106_true": rng.uniform(20, 28, N), - "roman_F158_true": rng.uniform(20, 28, N), -}) -out = inj.inject(df, bands=["F106", "F158"], seed=1, verbose=False) -print("\\ninjected NIR-only bands -> columns:", - [c for c in out.columns if c.endswith(("_obs", "_err")) or c.endswith("flag_observed")]) -print("detected:", int(out.roman_flag_observed.sum()), "/", len(out))""" -) - -md(r"""## Phase 3 — multi-band / multi-survey isochrone (exactly `nstars`) - -`IsochroneModel.sample` now draws **exactly** `nstars` (a fixed mass set), for -both single- and multi-survey configs. First, the single-survey path: -""") - -co("""single_cfg = { - "density": {"type": "Uniform", "xmin": -9.0, "xmax": 9.0}, - "track": {"center": {"type": "Constant", "value": 0.0}, - "spread": {"type": "Constant", "value": 0.2}, "sampler": "Gaussian"}, - "distance_modulus": {"center": {"type": "Line", "slope": 0.1, "intercept": 16.5}, - "spread": {"type": "Constant", "value": 1e-4}, "sampler": "Uniform"}, - "isochrone": {"name": "Marigo2017", "survey": "lsst", "age": 12.0, "z": 0.0006, - "band_1": "g", "band_2": "r"}, -} -sm = StreamModel(single_cfg) -for n in (5000, 1234): - assert len(sm.sample(n)) == n -print("single-survey StreamModel.sample returns EXACTLY nstars (5000, 1234) -> OK")""") - -md(r"""Now the multi-survey scene. One mass draw feeds both surveys, so a star's -LSST and Roman magnitudes describe the *same object*.""") - -co("""scene = yaml.safe_load(open(f"{REPO}/config/scenes/roman_rubin_demo.yaml")) -sm_ms = StreamModel(scene["stream"]) -df_ms = sm_ms.sample(4000) -true_cols = sorted(c for c in df_ms.columns if c.endswith("_true")) -print("multi-survey true columns:", true_cols) -print("rows:", len(df_ms)) - -rho = np.corrcoef(df_ms["lsst_r_true"], df_ms["roman_F158_true"])[0, 1] -print(f"corr(lsst_r, roman_F158) = {rho:.3f} (shared masses => tightly correlated)") - -fig, axes = plt.subplots(1, 2, figsize=(10, 4.2), sharey=True) -axes[0].scatter(df_ms.lsst_g_true - df_ms.lsst_r_true, df_ms.lsst_r_true, s=3, alpha=.3) -axes[0].set_xlabel("g - r"); axes[0].set_ylabel("r"); axes[0].set_title("Rubin/LSST CMD") -axes[0].invert_yaxis() -axes[1].scatter(df_ms.roman_F106_true - df_ms.roman_F158_true, df_ms.roman_F158_true, s=3, alpha=.3, c="C3") -axes[1].set_xlabel("F106 - F158"); axes[1].set_ylabel("F158"); axes[1].set_title("Roman CMD") -axes[1].invert_yaxis() -fig.suptitle("Phase 3: same physical stars, two surveys"); fig.tight_layout()""") - -md(r"""Roman isochrone magnitudes are **always** converted Vega→AB (no config -flag) using the fixed `ROMAN_VEGA_TO_AB` table, sourced from the -`rubin_roman_object_classification` prototype. Non-Roman bands pass through -unchanged. (A `TODO` in the code notes this ideally belongs in `ugali`.)""") - -co("""from streamobs.model import ROMAN_VEGA_TO_AB -print("Roman Vega->AB offsets (AB = Vega + diff):") -for b, v in ROMAN_VEGA_TO_AB.items(): - print(f" {b}: +{v}") - -iso = StreamModel(scene["stream"]).isochrone # multi-survey isochrone -x = np.array([20.0, 21.0]) -print("\\nF158 shift:", (iso._to_ab("F158", x) - x), " (always +1.315)") -print("r shift:", (iso._to_ab("r", x) - x), " (0 -> non-Roman pass-through)")""") - -md(r"""## Phase 4 — one `StreamInjector`, many surveys - -The same `StreamInjector` class accepts a `{namespace: survey}` mapping. It does -one shared sky placement and one shared mass draw, then a per-survey loop writing -each survey's observed columns and flags. Per-survey RNGs come from -`rng.spawn(...)`, so the result is reproducible and independent of survey order. -""") - -co( - """lsst_sv = StubSurvey("lsst", ["g", "r"], completeness_band="r", maglim=26.0) -roman_sv = StubSurvey("roman", ["F106", "F158"], completeness_band="F158", maglim=27.0) -msi = StreamInjector({"lsst": lsst_sv, "roman": roman_sv}, primary="lsst") - -# Input carries only stream coordinates; everything else is sampled once. -N = 4000 -stream_in = pd.DataFrame({"phi1": rng.uniform(-9, 9, N), "phi2": np.zeros(N)}) -cat = msi.inject(stream_in.copy(), scene["survey_bands"], - stream_config=scene["stream"], seed=7, verbose=False) - -print("columns produced:") -for grp in ("lsst", "roman"): - print(f" {grp}:", sorted(c for c in cat.columns if c.startswith(grp + "_"))) -print(f"\\nshared sky placement: ra/dec present = {('ra' in cat) and ('dec' in cat)}") -print(f"lsst detected: {int(cat.lsst_flag_observed.sum()):5d} / {len(cat)}") -print(f"roman detected: {int(cat.roman_flag_observed.sum()):5d} / {len(cat)}")""" -) - -co( - """# Reproducible from seed (given the same true-mag draw) -base = sm_ms.sample(3000) -base["ra"] = rng.uniform(10, 20, len(base)); base["dec"] = rng.uniform(-5, 5, len(base)) -a = msi.inject(base.copy(), scene["survey_bands"], seed=11, verbose=False) -b = msi.inject(base.copy(), scene["survey_bands"], seed=11, verbose=False) -c = msi.inject(base.copy(), scene["survey_bands"], seed=22, verbose=False) -print("same seed -> identical obs & flags:", - a.roman_F158_obs.equals(b.roman_F158_obs) and a.lsst_flag_observed.equals(b.lsst_flag_observed)) -print("different seed -> different noise draw:", not a.roman_F158_obs.equals(c.roman_F158_obs))""" -) - -co( - """# Detection efficiency vs magnitude, per survey (Roman is deeper here: maglim 27 vs 26) -fig, ax = plt.subplots(figsize=(6.5, 4)) -for col, flag, lab, cc in [("lsst_r_true", "lsst_flag_observed", "LSST r (maglim 26.0)", "C0"), - ("roman_F158_true", "roman_flag_observed", "Roman F158 (maglim 27.0)", "C3")]: - m = cat[col].to_numpy(float); f = cat[flag].to_numpy(bool) - bins = np.linspace(20, 29, 28); idx = np.digitize(m, bins) - eff = [f[idx == i].mean() if (idx == i).any() else np.nan for i in range(1, len(bins))] - ax.plot(0.5 * (bins[1:] + bins[:-1]), eff, marker="o", ms=3, label=lab, c=cc) -ax.set_xlabel("true magnitude"); ax.set_ylabel("detected fraction") -ax.set_title("Phase 4: per-survey selection from one catalog"); ax.legend(); fig.tight_layout()""" -) - -md(r"""## Notes for real runs - -* Swap `StubSurvey(...)` for `Survey.load("lsst", release="dc2")` and the real - Roman survey once its config/maps land — the injector code is unchanged. -* A band label is used **both** as the ugali isochrone field name and as the key - into a survey's maglim/photo-error maps, so they must agree (ugali's Roman - fields are upper-case: `F106`, `F158`, ...). -* **`nstars` is now exactly N** (was an emergent IMF count). This is the agreed - semantics for both single- and multi-survey configs. -* **Output columns are always survey-namespaced** (`__…`), even for - a single survey — there is no longer an un-namespaced single-survey form. -""") - -nb = new_notebook(cells=cells) -nb.metadata.update( - { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3", - }, - "language_info": {"name": "python"}, - } -) -out_path = "notebooks/multisurvey_phases_demo.ipynb" -with open(out_path, "w") as f: - nbf.write(nb, f) -print("wrote", out_path, "with", len(cells), "cells") From 79d14c7d940b33be341d1673c7090db9064fcd6a Mon Sep 17 00:00:00 2001 From: MatthieuPe Date: Wed, 17 Jun 2026 16:24:23 +0200 Subject: [PATCH 10/54] testing multi survey injector --- tests/test_observed.py | 41 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/tests/test_observed.py b/tests/test_observed.py index a0c800d..73d0fc1 100644 --- a/tests/test_observed.py +++ b/tests/test_observed.py @@ -47,6 +47,47 @@ def test_injector_initialization(self, mock_injector): assert injector_direct.survey.name == "lsst", "Survey name must be 'lsst'" assert injector_direct.survey.release == "yr4", "Survey release must be 'yr4'" + def test_injector_multisurveys(self): + """Test that the injector can handle multiple surveys.""" + # Create a second survey and injector + survey1_dict = {"survey": "lsst", "release": "yr4"} + survey2_dict = {"survey": "lsst", "release": "yr5"} + survey1 = Survey.load(**survey1_dict) + survey2 = Survey.load(**survey2_dict) + + def test_injector_initialization_with_multiple_surveys(injector): + """Test that the injector initializes with multiple surveys.""" + assert isinstance( + injector, StreamInjector + ), "Injector must be an instance of StreamInjector" + assert hasattr( + injector, "primary" + ), "Injector must have a 'primary' property" + assert isinstance( + injector.surveys, dict + ), "Survey property must be a dict of Survey instances" + assert all( + isinstance(value, Survey) for key, value in injector.surveys.items() + ), "All elements in survey dict must be Survey instances" + assert ( + len(injector.surveys) == 2 + ), f"Injector must have 2 surveys, got {len(injector.surveys)} with keys: {list(injector.surveys.keys())}" + assert isinstance( + injector.primary, Survey + ), "Primary survey must be a Survey instance" + + # Intialize injectors for both surveys using list of survey objects + injector1 = StreamInjector(survey=[survey1, survey2]) + test_injector_initialization_with_multiple_surveys(injector1) + + # Initialize injectors for both surveys using list of survey dicts + injector3 = StreamInjector(survey=[survey1_dict, survey2_dict]) + test_injector_initialization_with_multiple_surveys(injector3) + + # Initialize injectors for both surveys using dicts # but I do not think + # this is more useful than the previous approach. + injector3 = StreamInjector({"lsst_yr4": survey1_dict, "lsst_yr5": survey2_dict}) + test_injector_initialization_with_multiple_surveys(injector3) # --------------------------------------------------------------------------- # Injector behavior From 67999d84b09e084db56d76a1108029a2f15866fe Mon Sep 17 00:00:00 2001 From: MatthieuPe Date: Wed, 17 Jun 2026 16:25:13 +0200 Subject: [PATCH 11/54] black + isort format --- bin/download_data.py | 151 ++++++++++------- bin/example_generate_pal5.py | 8 +- bin/generate_spline_stream.py | 34 ++-- bin/generate_stream.py | 43 ++--- docs/source/conf.py | 15 +- streamobs/samplers.py | 16 +- tests/conftest.py | 81 ++++++--- tests/test_functions.py | 10 +- tests/test_model.py | 6 +- tests/test_observed.py | 1 + tests/test_surveys.py | 303 +++++++++++++++++++++++----------- 11 files changed, 415 insertions(+), 253 deletions(-) diff --git a/bin/download_data.py b/bin/download_data.py index 904468c..06655e4 100755 --- a/bin/download_data.py +++ b/bin/download_data.py @@ -6,15 +6,14 @@ (e.g., Zenodo, institutional server) and extracts it to the data/ directory. """ +import argparse import os import sys -import argparse -import urllib.request +import tempfile import urllib.error -from pathlib import Path +import urllib.request import zipfile -import tempfile - +from pathlib import Path # ============================================================================= # CONFIGURATION - Update this URL when data location changes @@ -30,7 +29,7 @@ DATA_ARCHIVE_URL = BASE_DATA_URL + DATA_ARCHIVE_NAME # Expected size (approximate, for user information) -ARCHIVE_SIZE_MB = 30 #Mb +ARCHIVE_SIZE_MB = 30 # Mb # ============================================================================= # ============================================================================= @@ -41,16 +40,18 @@ def download_file(url, output_path, description="file"): print(f"Downloading {description}...") print(f" From: {url}") print(f" To: {output_path}") - + def progress_hook(count, block_size, total_size): """Show download progress.""" if total_size > 0: percent = int(count * block_size * 100 / total_size) mb_downloaded = count * block_size / (1024 * 1024) mb_total = total_size / (1024 * 1024) - sys.stdout.write(f"\r Progress: {percent}% ({mb_downloaded:.1f}/{mb_total:.1f} MB)") + sys.stdout.write( + f"\r Progress: {percent}% ({mb_downloaded:.1f}/{mb_total:.1f} MB)" + ) sys.stdout.flush() - + try: urllib.request.urlretrieve(url, output_path, reporthook=progress_hook) print("\n ✓ Download complete!") @@ -65,20 +66,20 @@ def extract_zip(zip_path, extract_to, description="archive"): print(f"\nExtracting {description}...") print(f" From: {zip_path}") print(f" To: {extract_to}") - + try: - with zipfile.ZipFile(zip_path, 'r') as zip_ref: + with zipfile.ZipFile(zip_path, "r") as zip_ref: # Get list of files file_list = zip_ref.namelist() print(f" Found {len(file_list)} files in archive") - + # Extract all files zip_ref.extractall(extract_to) print(" ✓ Extraction complete!") - + # Clean up unwanted files after extraction cleanup_unwanted_files(extract_to) - + return True except Exception as e: print(f" ✗ Extraction failed: {e}") @@ -89,16 +90,16 @@ def cleanup_unwanted_files(base_path): """Remove unwanted files like .DS_Store, .backup, etc. after extraction.""" base_path = Path(base_path) removed_count = 0 - + # Patterns to remove unwanted_patterns = [ - '**/.DS_Store', # macOS system files - '**/__MACOSX', # macOS resource forks - '**/*.backup', # Backup files - '**/*.bak', # Backup files - '**/*~', # Temporary files + "**/.DS_Store", # macOS system files + "**/__MACOSX", # macOS resource forks + "**/*.backup", # Backup files + "**/*.bak", # Backup files + "**/*~", # Temporary files ] - + for pattern in unwanted_patterns: for item in base_path.glob(pattern): try: @@ -107,47 +108,48 @@ def cleanup_unwanted_files(base_path): removed_count += 1 elif item.is_dir(): import shutil + shutil.rmtree(item) removed_count += 1 except Exception: pass # Ignore errors during cleanup - + if removed_count > 0: print(f" Cleaned up {removed_count} unwanted file(s)") - def list_data_contents(data_dir): """List what's in the data directory after download.""" print("\n📂 Data directory contents:") print("=" * 80) - + if not data_dir.exists(): print(" (empty - data directory doesn't exist yet)") return - + # List subdirectories - subdirs = [d for d in data_dir.iterdir() if d.is_dir() and not d.name.startswith('.')] + subdirs = [ + d for d in data_dir.iterdir() if d.is_dir() and not d.name.startswith(".") + ] if not subdirs: print(" (empty - no subdirectories found)") return - + for subdir in sorted(subdirs): print(f"\n {subdir.name}/") # Count files in subdirectory try: - files = list(subdir.rglob('*')) - files = [f for f in files if f.is_file() and not f.name.startswith('.')] + files = list(subdir.rglob("*")) + files = [f for f in files if f.is_file() and not f.name.startswith(".")] total_size = sum(f.stat().st_size for f in files) print(f" Files: {len(files)} ({total_size / (1024*1024):.1f} MB)") except Exception: print(f" (unable to read directory)") - def main(): parser = argparse.ArgumentParser( - description='Download and extract large data files for streamobs', + description="Download and extract large data files for streamobs", formatter_class=argparse.RawDescriptionHelpFormatter, epilog=""" Examples: @@ -162,60 +164,81 @@ def main(): # Use custom data URL python download_data.py --url https://my-server.edu/data.zip - """ + """, + ) + + parser.add_argument( + "--list", + action="store_true", + help="List current data directory contents without downloading", + ) + parser.add_argument( + "--url", + type=str, + default=DATA_ARCHIVE_URL, + help=f"URL for data archive (default: configured Zenodo URL)", + ) + parser.add_argument( + "--data-dir", + type=str, + default=None, + help="Data directory (default: streamobs/data/)", ) - - parser.add_argument('--list', action='store_true', - help='List current data directory contents without downloading') - parser.add_argument('--url', type=str, default=DATA_ARCHIVE_URL, - help=f'URL for data archive (default: configured Zenodo URL)') - parser.add_argument('--data-dir', type=str, default=None, - help='Data directory (default: streamobs/data/)') - parser.add_argument('--force', action='store_true', - help='Force re-download even if data exists') - parser.add_argument('--keep-archive', action='store_true', - help='Keep the downloaded zip file after extraction') - + parser.add_argument( + "--force", action="store_true", help="Force re-download even if data exists" + ) + parser.add_argument( + "--keep-archive", + action="store_true", + help="Keep the downloaded zip file after extraction", + ) + args = parser.parse_args() - + # Determine data directory if args.data_dir: data_dir = Path(args.data_dir) else: # Assume script is in streamobs/bin/ script_dir = Path(__file__).parent - data_dir = script_dir.parent / 'data' - + data_dir = script_dir.parent / "data" + # List contents and exit if requested if args.list: list_data_contents(data_dir) return 0 - + # Check if URL is configured - if 'XXXXX' in args.url: + if "XXXXX" in args.url: print("=" * 80) print("ERROR: Data URL is not yet configured!") print("=" * 80) print("\nThe DATA_ARCHIVE_URL in this script is still set to a placeholder.") print("Please update it with the actual data hosting location.\n") print("Steps:") - print(" 1. Upload your data/ directory as a zip file to Zenodo or another host") + print( + " 1. Upload your data/ directory as a zip file to Zenodo or another host" + ) print(" 2. Edit this script and update BASE_DATA_URL and DATA_ARCHIVE_NAME") print(" 3. Or use --url to specify a custom URL\n") return 1 - + # Check if data already exists if data_dir.exists() and not args.force: - subdirs = [d for d in data_dir.iterdir() if d.is_dir() and not d.name.startswith('.')] + subdirs = [ + d for d in data_dir.iterdir() if d.is_dir() and not d.name.startswith(".") + ] if subdirs: print("=" * 80) print("Data directory already exists with content!") print("=" * 80) list_data_contents(data_dir) print("\n" + "=" * 80) - print("Use --force to re-download and overwrite, or --list to view contents") + print( + "Use --force to re-download and overwrite, or --list to view contents" + ) return 0 - + # Download and extract print("=" * 80) print("Stream Simulation Data Download") @@ -224,36 +247,36 @@ def main(): print(f"Destination: {data_dir}") print(f"Archive size: ~{ARCHIVE_SIZE_MB} MB") print("\n" + "=" * 80) - + # Create data directory data_dir.mkdir(parents=True, exist_ok=True) - + # Create temporary file for download - with tempfile.NamedTemporaryFile(suffix='.zip', delete=False) as tmp_file: + with tempfile.NamedTemporaryFile(suffix=".zip", delete=False) as tmp_file: tmp_path = Path(tmp_file.name) - + try: # Download if not download_file(args.url, tmp_path, "data archive"): print("\n✗ Download failed!") return 1 - + # Extract if not extract_zip(tmp_path, data_dir.parent, "data archive"): print("\n✗ Extraction failed!") return 1 - + # Show what was extracted list_data_contents(data_dir) - + print("\n" + "=" * 80) print("✓ Data download and extraction complete!") print("=" * 80) print("\nYou can now run stream simulations with this data.") print("The data has been extracted to:", data_dir) - + return 0 - + finally: # Clean up temporary file unless requested to keep it if tmp_path.exists(): @@ -265,5 +288,5 @@ def main(): tmp_path.unlink() -if __name__ == '__main__': +if __name__ == "__main__": sys.exit(main()) diff --git a/bin/example_generate_pal5.py b/bin/example_generate_pal5.py index b72c5c3..5c9d278 100644 --- a/bin/example_generate_pal5.py +++ b/bin/example_generate_pal5.py @@ -14,10 +14,10 @@ def inverse_transfom_sample(vals, pdf, size): cdf /= cdf[-1] fn = scipy.interpolate.interp1d(cdf, list(range(0, len(cdf)))) x_new = np.random.uniform(size=np.rint(size).astype(int)) - x_new[ - x_new < 1e-3 - ] = 1e-3 # running into error that values close to 0 are flagged as being - # below interp range + x_new[x_new < 1e-3] = ( + 1e-3 # running into error that values close to 0 are flagged as being + ) + # below interp range index = np.rint(fn(x_new)).astype(int) return vals[index] diff --git a/bin/generate_spline_stream.py b/bin/generate_spline_stream.py index bf1152d..883467a 100755 --- a/bin/generate_spline_stream.py +++ b/bin/generate_spline_stream.py @@ -5,13 +5,14 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd -from streamobs.model import SplineStreamModel, BackgroundModel + +from streamobs.model import BackgroundModel, SplineStreamModel from streamobs.plotting import plot_stream from streamobs.utils import parse_config def generate_stream(config): - """ Generate the simulated stream. + """Generate the simulated stream. Parameters ---------- @@ -23,32 +24,33 @@ def generate_stream(config): """ print("Generating stream...") - stream = SplineStreamModel(config['stream']) - stream_df = stream.sample(config['stream']['nstars']) + stream = SplineStreamModel(config["stream"]) + stream_df = stream.sample(config["stream"]["nstars"]) print(f" generated {len(stream_df)} stream stars.") print("Generating background...") - bkg = BackgroundModel(config['background']) - bkg_df = bkg.sample(config['background']['nstars']) + bkg = BackgroundModel(config["background"]) + bkg_df = bkg.sample(config["background"]["nstars"]) print(f" generated {len(bkg_df)} background stars.") print("Combining stream and background.") out = pd.concat([stream_df, bkg_df]) - out['flag'] = np.hstack([np.ones(len(stream_df), dtype=int), - np.zeros(len(bkg_df), dtype=int)]) + out["flag"] = np.hstack( + [np.ones(len(stream_df), dtype=int), np.zeros(len(bkg_df), dtype=int)] + ) return out if __name__ == "__main__": import argparse + parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument('config', - help='configuration file') - parser.add_argument('-o', '--outfile', default='stream_out.csv', - help='output file') - parser.add_argument('-p', '--plot', action='store_true', - help='plot stream and background') + parser.add_argument("config", help="configuration file") + parser.add_argument("-o", "--outfile", default="stream_out.csv", help="output file") + parser.add_argument( + "-p", "--plot", action="store_true", help="plot stream and background" + ) args = parser.parse_args() print(f"Reading config: {args.config}") @@ -62,7 +64,7 @@ def generate_stream(config): if args.plot: print("Plotting stars...") - fig, ax = plot_stream(stars['phi1'], stars['phi2']) - pngfile = args.outfile.replace('.csv', '.png') + fig, ax = plot_stream(stars["phi1"], stars["phi2"]) + pngfile = args.outfile.replace(".csv", ".png") print(f"Writing {pngfile}") plt.savefig(pngfile, facecolor="white") diff --git a/bin/generate_stream.py b/bin/generate_stream.py index 46b6616..017bfeb 100755 --- a/bin/generate_stream.py +++ b/bin/generate_stream.py @@ -2,8 +2,8 @@ """ More modular stream generation example. """ -import os import copy +import os from importlib import reload import matplotlib.pyplot as plt @@ -11,15 +11,19 @@ import pandas as pd import scipy.interpolate +import streamobs.model + +reload(streamobs.model) +import streamobs.plotting +from streamobs.model import BackgroundModel, StreamModel -import streamobs.model; reload(streamobs.model) -from streamobs.model import StreamModel, BackgroundModel -import streamobs.plotting; reload(streamobs.plotting) +reload(streamobs.plotting) from streamobs.plotting import plot_stream from streamobs.utils import parse_config + def generate_stream(config): - """ Generate the simulated stream. + """Generate the simulated stream. Parameters ---------- @@ -31,32 +35,33 @@ def generate_stream(config): """ print("Generating stream...") - stream = StreamModel(config['stream']) - stream_df = stream.sample(config['stream']['nstars'] ) + stream = StreamModel(config["stream"]) + stream_df = stream.sample(config["stream"]["nstars"]) print(f" generated {len(stream_df)} stream stars.") print("Generating background...") - bkg = BackgroundModel(config['background']) - bkg_df = bkg.sample(config['background']['nstars']) + bkg = BackgroundModel(config["background"]) + bkg_df = bkg.sample(config["background"]["nstars"]) print(f" generated {len(bkg_df)} background stars.") print("Combining stream and background.") out = pd.concat([stream_df, bkg_df]) - out['flag'] = np.hstack([np.ones(len(stream_df),dtype=int), - np.zeros(len(bkg_df),dtype=int)]) + out["flag"] = np.hstack( + [np.ones(len(stream_df), dtype=int), np.zeros(len(bkg_df), dtype=int)] + ) return out if __name__ == "__main__": import argparse + parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument('config', - help='configuration file') - parser.add_argument('-o','--outfile', default='stream_out.csv', - help='output file') - parser.add_argument('-p','--plot', action='store_true', - help='plot stream and background') + parser.add_argument("config", help="configuration file") + parser.add_argument("-o", "--outfile", default="stream_out.csv", help="output file") + parser.add_argument( + "-p", "--plot", action="store_true", help="plot stream and background" + ) args = parser.parse_args() print(f"Reading config: {args.config}") @@ -70,7 +75,7 @@ def generate_stream(config): if args.plot: print(f"Plotting stars...") - fig,ax = plot_stream(stars['phi1'], stars['phi2']) - pngfile = args.outfile.replace('.csv','.png') + fig, ax = plot_stream(stars["phi1"], stars["phi2"]) + pngfile = args.outfile.replace(".csv", ".png") print(f"Writing {pngfile}") plt.savefig(pngfile, facecolor="white") diff --git a/docs/source/conf.py b/docs/source/conf.py index 6259e9b..dfa7e7b 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -3,17 +3,17 @@ # For the full list of built-in configuration values, see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html -import sys import os +import sys -sys.path.insert(0, os.path.abspath('../..')) +sys.path.insert(0, os.path.abspath("../..")) # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information -project = 'streamobs' -copyright = '2025, DESC' -author = 'DESC' +project = "streamobs" +copyright = "2025, DESC" +author = "DESC" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration @@ -28,7 +28,7 @@ "myst_parser", ] -templates_path = ['_templates'] +templates_path = ["_templates"] exclude_patterns = [] # -- numpydoc ---------------------------------------------------------------- @@ -39,9 +39,8 @@ numpydoc_show_class_members = False - # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output html_theme = "sphinx_book_theme" -html_static_path = ['_static'] +html_static_path = ["_static"] diff --git a/streamobs/samplers.py b/streamobs/samplers.py index a4b8bdc..4bf42e9 100644 --- a/streamobs/samplers.py +++ b/streamobs/samplers.py @@ -6,15 +6,13 @@ import numpy as np import scipy.stats -from streamobs.functions import ( - CubicSplineInterpolation, - FileCubicSplineInterpolation, - FileInterpolation, - FileLinearDensityCubicSplineInterpolation, - Interpolation, - LinearDensityCubicSplineInterpolation, - Sinusoid, -) +from streamobs.functions import (CubicSplineInterpolation, + FileCubicSplineInterpolation, + FileInterpolation, + FileLinearDensityCubicSplineInterpolation, + Interpolation, + LinearDensityCubicSplineInterpolation, + Sinusoid) def sampler_factory(type_, **kwargs): diff --git a/tests/conftest.py b/tests/conftest.py index 66f5638..fe4913b 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -14,59 +14,76 @@ # Utilities # --------------------------------------------------------------------------- + @pytest.fixture(scope="session") def seed(): """Fixed random seed for reproducibility.""" return 42 + @pytest.fixture(scope="session") def rng(seed): """Random number generator initialized with a fixed seed.""" return np.random.default_rng(seed) + @pytest.fixture(scope="session") def verbose(): """Control verbosity of test output.""" return True + # --------------------------------------------------------------------------- # Minimal stream model config (no isochrone, no velocity — pure geometry) # --------------------------------------------------------------------------- + @pytest.fixture(scope="session") def minimal_stream_config(): """Minimal StreamModel config: density + track only (no isochrone / velocity).""" return { - 'density': {'type': 'Uniform', 'xmin': -9.0, 'xmax': 9.0}, - + "density": {"type": "Uniform", "xmin": -9.0, "xmax": 9.0}, # Track model - 'track': {'center': {'type': 'Constant', 'value': 0.0}, # center line of the stream in degrees - 'spread': {'type': 'Constant', 'value': 0.2}, # spread of the stream in degrees - 'sampler': 'Gaussian'}, # how to sample across the stream - + "track": { + "center": { + "type": "Constant", + "value": 0.0, + }, # center line of the stream in degrees + "spread": { + "type": "Constant", + "value": 0.2, + }, # spread of the stream in degrees + "sampler": "Gaussian", + }, # how to sample across the stream # Isochrone model - 'isochrone': {'name': 'Marigo2017', # isochrone set name - 'survey': 'lsst', # survey for filter set - 'age': 12.0, # Age in Gyr of the population - 'z': 0.0006, # Metallicity of the population - 'band_1': 'g', # first band for color-magnitude - 'band_2': 'r', # second band for color-magnitude - 'band_1_detection': True}, + "isochrone": { + "name": "Marigo2017", # isochrone set name + "survey": "lsst", # survey for filter set + "age": 12.0, # Age in Gyr of the population + "z": 0.0006, # Metallicity of the population + "band_1": "g", # first band for color-magnitude + "band_2": "r", # second band for color-magnitude + "band_1_detection": True, + }, } + @pytest.fixture(scope="session") def stream_config_with_distance(minimal_stream_config): """StreamModel config that also produces a distance modulus column.""" cfg = dict(minimal_stream_config) - cfg["distance_modulus"] ={'center': {'type': 'Constant', 'value': 16.8}, - 'spread': {'type': 'Constant', 'value': 0.0}, - } + cfg["distance_modulus"] = { + "center": {"type": "Constant", "value": 16.8}, + "spread": {"type": "Constant", "value": 0.0}, + } return cfg + # --------------------------------------------------------------------------- # Tiny sample DataFrame used for injection tests # --------------------------------------------------------------------------- + @pytest.fixture def sample_catalog_radec(rng): """ @@ -74,10 +91,12 @@ def sample_catalog_radec(rng): without needing coordinate conversion or a stream model. """ n = 50 - return pd.DataFrame({ - "ra": rng.uniform(30.0, 60.0, n), - "dec": rng.uniform(-20.0, 0.0, n), - }) + return pd.DataFrame( + { + "ra": rng.uniform(30.0, 60.0, n), + "dec": rng.uniform(-20.0, 0.0, n), + } + ) @pytest.fixture @@ -86,58 +105,70 @@ def sample_catalog_phi(rng): n = 50 phi1 = rng.uniform(-8.0, 8.0, n) phi2 = rng.normal(0.0, 0.1, n) - return pd.DataFrame({ - "phi1": phi1, - "phi2": phi2, - }) + return pd.DataFrame( + { + "phi1": phi1, + "phi2": phi2, + } + ) # --------------------------------------------------------------------------- # Magnitudes samples # --------------------------------------------------------------------------- + @pytest.fixture(scope="session") def saturation_magnitudes(): """Array of magnitudes spanning the saturation threshold.""" return np.linspace(10.0, 17.0, 10) + @pytest.fixture(scope="session") def bright_magnitudes(rng): """Array of magnitudes well above the saturation threshold.""" return rng.uniform(18.0, 21.0, 100) + @pytest.fixture(scope="session") def faint_magnitudes(rng): """Array of magnitudes well below the saturation threshold.""" return rng.uniform(27.0, 30.0, 100) + # --------------------------------------------------------------------------- # Default survey config for injection tests # --------------------------------------------------------------------------- + @pytest.fixture(scope="session") def base_maglim(): """Base magnitude limit for the mock survey.""" return 26 + @pytest.fixture(scope="session") def mock_survey(verbose): from streamobs import surveys + return surveys.Survey.load( survey="lsst", release="yr4", verbose=verbose, ) + @pytest.fixture(scope="session") def mock_injector(mock_survey, verbose): from streamobs.observed import StreamInjector + return StreamInjector(survey=mock_survey, verbose=verbose) @pytest.fixture(scope="session") def stream_catalog(stream_config_with_distance): from streamobs.model import StreamModel + model = StreamModel(stream_config_with_distance) samples = model.sample(1000) - return samples \ No newline at end of file + return samples diff --git a/tests/test_functions.py b/tests/test_functions.py index 179c110..0133baf 100644 --- a/tests/test_functions.py +++ b/tests/test_functions.py @@ -1,11 +1,9 @@ import numpy as np -from streamobs.functions import ( - CubicSplineInterpolation, - FileCubicSplineInterpolation, - FileLinearDensityCubicSplineInterpolation, - LinearDensityCubicSplineInterpolation, -) +from streamobs.functions import (CubicSplineInterpolation, + FileCubicSplineInterpolation, + FileLinearDensityCubicSplineInterpolation, + LinearDensityCubicSplineInterpolation) SPREAD_NODES = np.array([-13.0, -7.875, -2.75, 2.375, 7.5]) diff --git a/tests/test_model.py b/tests/test_model.py index bded770..8045a37 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -11,11 +11,7 @@ import pandas as pd import pytest -from streamobs.model import ( - DensityModel, - StreamModel, - TrackModel, -) +from streamobs.model import DensityModel, StreamModel, TrackModel # --------------------------------------------------------------------------- # Helpers diff --git a/tests/test_observed.py b/tests/test_observed.py index 73d0fc1..3ca6973 100644 --- a/tests/test_observed.py +++ b/tests/test_observed.py @@ -89,6 +89,7 @@ def test_injector_initialization_with_multiple_surveys(injector): injector3 = StreamInjector({"lsst_yr4": survey1_dict, "lsst_yr5": survey2_dict}) test_injector_initialization_with_multiple_surveys(injector3) + # --------------------------------------------------------------------------- # Injector behavior # --------------------------------------------------------------------------- diff --git a/tests/test_surveys.py b/tests/test_surveys.py index d45ecec..682fcbf 100644 --- a/tests/test_surveys.py +++ b/tests/test_surveys.py @@ -21,42 +21,77 @@ from platform import release -import pytest -from streamobs import surveys import numpy as np +import pytest +from streamobs import surveys # --------------------------------------------------------------------------- # Registry — add new surveys here # --------------------------------------------------------------------------- SURVEY_REGISTRY = [ - {"survey": "lsst", "release": "yr1", "expected_bands": ["g", "r"], "expected_maglim": ['g', 'r']}, - {"survey": "lsst", "release": "yr2", "expected_bands": ["g", "r"], "expected_maglim": ['g', 'r']}, - {"survey": "lsst", "release": "yr3", "expected_bands": ["g", "r"], "expected_maglim": ['g', 'r']}, - {"survey": "lsst", "release": "yr4", "expected_bands": ["g", "r"], "expected_maglim": ['g', 'r']}, - {"survey": "lsst", "release": "yr5", "expected_bands": ["g", "r"], "expected_maglim": ['g', 'r']}, - {"survey": "des", "release": "yr6", "expected_bands": ["g", "r"], "expected_maglim": ['g', 'r']}, + { + "survey": "lsst", + "release": "yr1", + "expected_bands": ["g", "r"], + "expected_maglim": ["g", "r"], + }, + { + "survey": "lsst", + "release": "yr2", + "expected_bands": ["g", "r"], + "expected_maglim": ["g", "r"], + }, + { + "survey": "lsst", + "release": "yr3", + "expected_bands": ["g", "r"], + "expected_maglim": ["g", "r"], + }, + { + "survey": "lsst", + "release": "yr4", + "expected_bands": ["g", "r"], + "expected_maglim": ["g", "r"], + }, + { + "survey": "lsst", + "release": "yr5", + "expected_bands": ["g", "r"], + "expected_maglim": ["g", "r"], + }, + { + "survey": "des", + "release": "yr6", + "expected_bands": ["g", "r"], + "expected_maglim": ["g", "r"], + }, ] + # IDs shown in pytest output, e.g. "lsst_yr4" def _survey_id(entry): return f"{entry['survey']}_{entry['release'] or 'base'}" + # --------------------------------------------------------------------------- # Fixtures # --------------------------------------------------------------------------- + @pytest.mark.surveys -@pytest.fixture(scope="module", params=SURVEY_REGISTRY, ids=[_survey_id(e) for e in SURVEY_REGISTRY]) +@pytest.fixture( + scope="module", params=SURVEY_REGISTRY, ids=[_survey_id(e) for e in SURVEY_REGISTRY] +) def loaded_survey(request, verbose): """Load each registered survey once per module and cache it.""" - entry = request.param + entry = request.param survey = surveys.Survey.load( survey=entry["survey"], release=entry["release"], verbose=verbose, - uniform_survey = True, # Also build the uniform survey for testing purposes + uniform_survey=True, # Also build the uniform survey for testing purposes ) # Attach the registry entry so tests can access expected values survey._test_entry = entry @@ -82,9 +117,11 @@ def test_name_matches_registry(self, loaded_survey): def test_release_matches_registry(self, loaded_survey): assert loaded_survey.release == loaded_survey._test_entry["release"] + @pytest.mark.surveys class TestSurveyFactory: """Test that SurveyFactory creates and caches surveys as expected.""" + def test_cache(self, verbose): """Test that SurveyFactory returns the same instance for repeated loads.""" @@ -95,35 +132,66 @@ def test_cache(self, verbose): cache_key2 = f"{survey_2_prop['survey']}_{survey_2_prop['release']}" cache_key3 = f"{survey_3_prop['survey']}_{survey_3_prop['release']}" - survey1 = surveys.SurveyFactory.create_survey(survey_1_prop["survey"], survey_1_prop["release"], uniform_survey=True, verbose=verbose) - survey2 = surveys.SurveyFactory.create_survey(survey_2_prop["survey"], survey_2_prop["release"], uniform_survey=True, verbose=verbose) - survey3 = surveys.SurveyFactory.create_survey(survey_3_prop["survey"], survey_3_prop["release"], uniform_survey=True, verbose=verbose) - + survey1 = surveys.SurveyFactory.create_survey( + survey_1_prop["survey"], + survey_1_prop["release"], + uniform_survey=True, + verbose=verbose, + ) + survey2 = surveys.SurveyFactory.create_survey( + survey_2_prop["survey"], + survey_2_prop["release"], + uniform_survey=True, + verbose=verbose, + ) + survey3 = surveys.SurveyFactory.create_survey( + survey_3_prop["survey"], + survey_3_prop["release"], + uniform_survey=True, + verbose=verbose, + ) cache = surveys.SurveyFactory.list_cached_surveys() - assert cache_key1 in cache, f"Survey '{cache_key1}' should be in cache after loading" - assert cache_key2 in cache, f"Survey '{cache_key2}' should be in cache after loading" - - - surveys.SurveyFactory.clear_cache(survey_1_prop['survey'], survey_1_prop['release'], verbose=verbose) + assert ( + cache_key1 in cache + ), f"Survey '{cache_key1}' should be in cache after loading" + assert ( + cache_key2 in cache + ), f"Survey '{cache_key2}' should be in cache after loading" + + surveys.SurveyFactory.clear_cache( + survey_1_prop["survey"], survey_1_prop["release"], verbose=verbose + ) cache_after_clear = surveys.SurveyFactory.list_cached_surveys() - assert cache_key1 not in cache_after_clear, f"Survey '{cache_key1}' should have been cleared from cache" - assert cache_key2 in cache_after_clear, f"Survey '{cache_key2}' should still be in cache after clearing '{cache_key1}'" - assert cache_key3 in cache_after_clear, f"Survey '{cache_key3}' should still be in cache after clearing '{cache_key1}'" - - surveys.SurveyFactory.clear_cache(survey_2_prop['survey'], verbose=verbose) + assert ( + cache_key1 not in cache_after_clear + ), f"Survey '{cache_key1}' should have been cleared from cache" + assert ( + cache_key2 in cache_after_clear + ), f"Survey '{cache_key2}' should still be in cache after clearing '{cache_key1}'" + assert ( + cache_key3 in cache_after_clear + ), f"Survey '{cache_key3}' should still be in cache after clearing '{cache_key1}'" + + surveys.SurveyFactory.clear_cache(survey_2_prop["survey"], verbose=verbose) cache_after_clear2 = surveys.SurveyFactory.list_cached_surveys() - assert cache_key2 not in cache_after_clear2, f"Survey '{cache_key2}' should have been cleared from cache" - if survey_3_prop['survey'] == survey_2_prop['survey']: - assert cache_key3 not in cache_after_clear2, f"Survey '{cache_key3}' should have been cleared from cache since it has the same survey name as '{cache_key2}'" + assert ( + cache_key2 not in cache_after_clear2 + ), f"Survey '{cache_key2}' should have been cleared from cache" + if survey_3_prop["survey"] == survey_2_prop["survey"]: + assert ( + cache_key3 not in cache_after_clear2 + ), f"Survey '{cache_key3}' should have been cleared from cache since it has the same survey name as '{cache_key2}'" else: - assert cache_key3 in cache_after_clear2, f"Survey '{cache_key3}' should still be in cache after clearing '{cache_key2}' since it has a different survey name" + assert ( + cache_key3 in cache_after_clear2 + ), f"Survey '{cache_key3}' should still be in cache after clearing '{cache_key2}' since it has a different survey name" surveys.SurveyFactory.clear_cache(verbose=verbose) cache_after_clear_tot = surveys.SurveyFactory.list_cached_surveys() - assert len(cache_after_clear_tot) == 0, "All surveys should have been cleared from cache" - - + assert ( + len(cache_after_clear_tot) == 0 + ), "All surveys should have been cleared from cache" # --------------------------------------------------------------------------- @@ -138,21 +206,25 @@ class TestSurveyProperties: def test_expected_bands_present(self, loaded_survey): expected = set(loaded_survey._test_entry["expected_bands"]) - assert expected.issubset(set(loaded_survey.bands)), ( - f"Missing bands: {expected - set(loaded_survey.bands)}" - ) + assert expected.issubset( + set(loaded_survey.bands) + ), f"Missing bands: {expected - set(loaded_survey.bands)}" def test_maglim_maps_loaded_for_each_band(self, loaded_survey): expected_maglim = set(loaded_survey._test_entry["expected_maglim"]) for band in loaded_survey.bands: if band in expected_maglim: - assert band in loaded_survey.maglim_maps, f"No maglim map for band '{band}'" - assert loaded_survey.maglim_maps[band] is not None, ( - f"maglim_maps['{band}'] is None" - ) + assert ( + band in loaded_survey.maglim_maps + ), f"No maglim map for band '{band}'" + assert ( + loaded_survey.maglim_maps[band] is not None + ), f"maglim_maps['{band}'] is None" def test_ebv_map_loaded(self, loaded_survey): - assert loaded_survey.coeff_extinc is not None, "Extinction coefficients are None" + assert ( + loaded_survey.coeff_extinc is not None + ), "Extinction coefficients are None" assert loaded_survey.ebv_map is not None, "EBV map is None" def test_coverage_map_loaded(self, loaded_survey): @@ -160,23 +232,29 @@ def test_coverage_map_loaded(self, loaded_survey): def test_errors_loaded(self, loaded_survey): assert loaded_survey.sys_error is not None, "Systematic error dict is None" - assert loaded_survey.log_photo_error is not None, "Log photo error function is None" + assert ( + loaded_survey.log_photo_error is not None + ), "Log photo error function is None" def test_delta_saturation_loaded(self, loaded_survey): assert loaded_survey.delta_saturation is not None, "delta_saturation is None" assert loaded_survey.saturation is not None, "Saturation dict is None" - def test_efficiencies_loaded(self, loaded_survey): assert loaded_survey.completeness is not None, "Completeness function is None" assert loaded_survey.completeness_band is not None, "Completeness band is None" - assert loaded_survey.completeness_band in loaded_survey.bands, ( - f"Completeness band '{loaded_survey.completeness_band}' not in survey bands {loaded_survey.bands}" - ) - assert hasattr(loaded_survey.completeness, "__call__"), "Completeness is not callable" - assert hasattr(loaded_survey.efficiency_classification, "__call__"), "Efficiency classification is not callable" - assert hasattr(loaded_survey.efficiency_detection, "__call__"), "Efficiency detection is not callable" - + assert ( + loaded_survey.completeness_band in loaded_survey.bands + ), f"Completeness band '{loaded_survey.completeness_band}' not in survey bands {loaded_survey.bands}" + assert hasattr( + loaded_survey.completeness, "__call__" + ), "Completeness is not callable" + assert hasattr( + loaded_survey.efficiency_classification, "__call__" + ), "Efficiency classification is not callable" + assert hasattr( + loaded_survey.efficiency_detection, "__call__" + ), "Efficiency detection is not callable" def test_extinction_behavior(self, loaded_survey): @@ -189,9 +267,9 @@ def test_extinction_behavior(self, loaded_survey): lb_high = np.linspace(80, 90, len(la_high)) # Convert those coordinates to healpix pixels - from astropy.coordinates import SkyCoord - from astropy import units as u import healpy as hp + from astropy import units as u + from astropy.coordinates import SkyCoord nside_ebv = hp.npix2nside(len(loaded_survey.ebv_map)) # Convert coordinates to healpix pixels @@ -208,31 +286,42 @@ def test_extinction_behavior(self, loaded_survey): frame="galactic", ) - pix_plane = hp.ang2pix(nside_ebv, coord_plane.l.degree, coord_plane.b.degree, lonlat=True) - pix_high = hp.ang2pix(nside_ebv, coord_high.l.degree, coord_high.b.degree, lonlat=True) + pix_plane = hp.ang2pix( + nside_ebv, coord_plane.l.degree, coord_plane.b.degree, lonlat=True + ) + pix_high = hp.ang2pix( + nside_ebv, coord_high.l.degree, coord_high.b.degree, lonlat=True + ) for band in loaded_survey.bands: ext_plane = loaded_survey.get_extinction(band, pix_plane) ext_high = loaded_survey.get_extinction(band, pix_high) mean_plane = np.mean(ext_plane) mean_high = np.mean(ext_high) - assert mean_plane > mean_high, ( - f"Extinction should be higher in the galactic plane than at high latitude for band '{band}'" - ) - - def test_completeness_behavior(self, loaded_survey, saturation_magnitudes, bright_magnitudes, faint_magnitudes, base_maglim): + assert ( + mean_plane > mean_high + ), f"Extinction should be higher in the galactic plane than at high latitude for band '{band}'" + + def test_completeness_behavior( + self, + loaded_survey, + saturation_magnitudes, + bright_magnitudes, + faint_magnitudes, + base_maglim, + ): assert loaded_survey.completeness is not None, "Completeness function is None" # Test that completeness is ~1 for bright stars and ~0 for faint stars completeness_band = loaded_survey.completeness_band assert completeness_band is not None, "Completeness band is None" - assert completeness_band in loaded_survey.bands, ( - f"Completeness band '{completeness_band}' not in survey bands {loaded_survey.bands}" - ) - assert completeness_band in loaded_survey.maglim_maps, ( - f"Completeness band '{completeness_band}' does not have a maglim map" - ) - + assert ( + completeness_band in loaded_survey.bands + ), f"Completeness band '{completeness_band}' not in survey bands {loaded_survey.bands}" + assert ( + completeness_band in loaded_survey.maglim_maps + ), f"Completeness band '{completeness_band}' does not have a maglim map" + sat = loaded_survey.saturation[completeness_band] # work only with magnitudes above or below saturation @@ -242,62 +331,82 @@ def test_completeness_behavior(self, loaded_survey, saturation_magnitudes, brigh # Verify completeness behavior in each regime if len(sat_mag) > 0: - comp_sat = loaded_survey.get_completeness(completeness_band, sat_mag, base_maglim) - assert np.all(comp_sat == 0.0), ( - f"Completeness should be 0 for magnitudes below saturation in band '{completeness_band}'" + comp_sat = loaded_survey.get_completeness( + completeness_band, sat_mag, base_maglim ) + assert np.all( + comp_sat == 0.0 + ), f"Completeness should be 0 for magnitudes below saturation in band '{completeness_band}'" if len(bright_mag) > 0: - comp_bright = loaded_survey.get_completeness(completeness_band, bright_mag, base_maglim) - assert np.all(comp_bright > 0.9), ( - f"Completeness should be near 1 for magnitudes well above saturation in band '{completeness_band}'" + comp_bright = loaded_survey.get_completeness( + completeness_band, bright_mag, base_maglim ) + assert np.all( + comp_bright > 0.9 + ), f"Completeness should be near 1 for magnitudes well above saturation in band '{completeness_band}'" if len(faint_mag) > 0: - comp_faint = loaded_survey.get_completeness(completeness_band, faint_mag, base_maglim) - assert np.all(comp_faint < 0.1), ( - f"Completeness should be near 0 for magnitudes well below saturation in band '{completeness_band}'" + comp_faint = loaded_survey.get_completeness( + completeness_band, faint_mag, base_maglim ) - - def test_log_photo_error_behavior(self, loaded_survey, saturation_magnitudes, bright_magnitudes, faint_magnitudes, base_maglim): - assert loaded_survey.log_photo_error is not None, "Log photo error function is None" + assert np.all( + comp_faint < 0.1 + ), f"Completeness should be near 0 for magnitudes well below saturation in band '{completeness_band}'" + + def test_log_photo_error_behavior( + self, + loaded_survey, + saturation_magnitudes, + bright_magnitudes, + faint_magnitudes, + base_maglim, + ): + assert ( + loaded_survey.log_photo_error is not None + ), "Log photo error function is None" # Test that log photo error behaves reasonably across magnitude ranges for band in loaded_survey.bands: sat = loaded_survey.saturation[band] sys_error = loaded_survey.sys_error[band] - assert sys_error > 0, f"Systematic error should be positive for band '{band}'" + assert ( + sys_error > 0 + ), f"Systematic error should be positive for band '{band}'" # work only with magnitudes above or below saturation sat_mag = saturation_magnitudes[saturation_magnitudes < sat] bright_mag = bright_magnitudes[bright_magnitudes > sat] faint_mag = faint_magnitudes[faint_magnitudes > sat] - bright_mag_mean, faint_mag_mean = None, None + bright_mag_mean, faint_mag_mean = None, None if len(sat_mag) > 0: err_sat = loaded_survey.get_photo_error(band, sat_mag, base_maglim) - assert np.all(err_sat > 5.0), ( - f"Photo errors should be large for magnitudes below saturation in band '{band}'" - ) + assert np.all( + err_sat > 5.0 + ), f"Photo errors should be large for magnitudes below saturation in band '{band}'" if len(bright_mag) > 0: - err_bright = loaded_survey.get_photo_error(band, bright_mag, base_maglim) - assert np.all(err_bright < 3*sys_error), ( - f"Photo errors should be close to systematic error for bright magnitudes in band '{band}'" + err_bright = loaded_survey.get_photo_error( + band, bright_mag, base_maglim ) + assert np.all( + err_bright < 3 * sys_error + ), f"Photo errors should be close to systematic error for bright magnitudes in band '{band}'" bright_mag_mean = np.mean(err_bright) if len(faint_mag) > 0: err_faint = loaded_survey.get_photo_error(band, faint_mag, base_maglim) - assert np.all(err_faint > 20*sys_error), ( - f"Photo errors should be large for faint magnitudes in band '{band}'" - ) + assert np.all( + err_faint > 20 * sys_error + ), f"Photo errors should be large for faint magnitudes in band '{band}'" faint_mag_mean = np.mean(err_faint) - + if faint_mag_mean is not None and bright_mag_mean is not None: - assert faint_mag_mean > bright_mag_mean, ( - f"Mean photo error should increase with magnitude in band '{band}'" - ) + assert ( + faint_mag_mean > bright_mag_mean + ), f"Mean photo error should increase with magnitude in band '{band}'" - error_at_maglim = loaded_survey.get_photo_error(band, base_maglim, base_maglim) - snr_at_maglim = 1 / error_at_maglim - assert np.isclose(snr_at_maglim, 5.0, atol = 0.25), ( - f"Photo error at maglim should correspond to SNR=5 for band '{band}'" + error_at_maglim = loaded_survey.get_photo_error( + band, base_maglim, base_maglim ) - + snr_at_maglim = 1 / error_at_maglim + assert np.isclose( + snr_at_maglim, 5.0, atol=0.25 + ), f"Photo error at maglim should correspond to SNR=5 for band '{band}'" From 5e89f7060779058b15e92f124a55c508097caf32 Mon Sep 17 00:00:00 2001 From: psferguson Date: Wed, 17 Jun 2026 14:35:42 -0700 Subject: [PATCH 12/54] Address PR #47 review: unified bands API, release-namespacing, SNR cut, isochrone masses Responds to MatthieuPE's review on the roman_multisurvey PR. API simplification: - Collapse survey_bands+bands into one `bands` arg (list | {survey: bands} dict) - Fold _complete_shared into the public complete_data; inject() delegates to it - Make `survey` a required arg of detect_flag (drop the primary fallback) Release-everywhere namespacing (Decision 1): - Add Survey.namespace ({name}_{release}); injector keys surveys by it so the same survey at two releases yields distinct, non-colliding columns - _load_survey accepts a {"survey":, "release":} spec dict; _inject_one_survey derives the namespace from survey.namespace - `primary` is now the primary Survey; namespace string is primary_namespace - Model: single-survey isochrone path is release-aware; _build_iso strips `release` before the ugali factory SNR cut (Decision 2): the ref-band S/N>=5 cut is baked into both selection- function curves, so remove the redundant re-application in _inject_one_survey and correct the comment/docstring. Isochrone/mass: - Raise _MASS_STEPS 1000 -> 4000 (convergence check: ~600 vs ~220 distinct masses for a 5000-star stream; documented) - Collapse single/multi isochrone builders into one _build_isochrones path - sample_multisurvey accepts optional masses and returns (mags, masses); complete_catalog exposes a `mass` column and reuses a provided one Tests: release-namespaced column updates; new multi-survey complete_data, unified-bands, mandatory-survey, and isochrone-mass tests. 35 passed. Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/source/multisurvey.md | 6 +- streamobs/columns.py | 12 +- streamobs/model.py | 187 +++++++++++++------ streamobs/observed.py | 367 +++++++++++++++++++------------------ streamobs/surveys.py | 11 ++ tests/conftest.py | 36 ++++ tests/test_model.py | 88 +++++++-- tests/test_observed.py | 83 ++++++++- 8 files changed, 521 insertions(+), 269 deletions(-) diff --git a/docs/source/multisurvey.md b/docs/source/multisurvey.md index d3d753c..ec3f6f8 100644 --- a/docs/source/multisurvey.md +++ b/docs/source/multisurvey.md @@ -36,10 +36,10 @@ inj = StreamInjector(["lsst", "roman"]) # Single survey: `bands` is the shorthand (defaults to ['r', 'g']). out = inj.inject(df, bands=["r", "g"], stream_config=cfg, seed=42) -# Several surveys: give the bands per survey. +# Several surveys: give the bands per survey as a {survey: [bands]} dict. out = inj.inject( df, - survey_bands={"lsst": ["r", "g"], "roman": ["F106", "F158"]}, + bands={"lsst": ["r", "g"], "roman": ["F106", "F158"]}, stream_config=cfg, seed=42, ) @@ -106,7 +106,7 @@ from streamobs.observed import StreamInjector scene = yaml.safe_load(open("config/scenes/roman_rubin_demo.yaml")) inj = StreamInjector(scene["surveys"]) # {"lsst": "lsst", "roman": "roman"} cat = inj.inject( - df, survey_bands=scene["survey_bands"], # {"lsst": [...], "roman": [...]} + df, bands=scene["survey_bands"], # {"lsst": [...], "roman": [...]} stream_config=scene["stream"], seed=42, ) ``` diff --git a/streamobs/columns.py b/streamobs/columns.py index 86845dd..bc6b680 100644 --- a/streamobs/columns.py +++ b/streamobs/columns.py @@ -3,10 +3,14 @@ These centralize the naming convention so the injector is not hard-coded to specific bands. Injected catalogs are **always** survey-namespaced — -``__true`` (true / noiseless), ``__obs`` (observed / -noisy), ``__err`` (reported error), and ``_flag_observed`` -— produced by :class:`~streamobs.observed.StreamInjector` whether it serves one -survey or several (e.g. ``lsst_r_obs``, ``roman_F158_obs``). +``__true`` (true / noiseless), ``__obs`` +(observed / noisy), ``__err`` (reported error), and +``_flag_observed`` — produced by +:class:`~streamobs.observed.StreamInjector` whether it serves one survey or +several. The namespace is the survey's :attr:`~streamobs.surveys.Survey.namespace` +(``{name}_{release}``), so it includes the release on every column kind +(e.g. ``lsst_yr5_r_obs``, ``roman_dc2_F158_obs``) and the same survey at two +releases never collides. The ``survey`` argument therefore identifies the namespace. ``survey=None`` is retained only as a low-level fallback that yields the bare ``_…`` / diff --git a/streamobs/model.py b/streamobs/model.py index eb01720..a429c37 100644 --- a/streamobs/model.py +++ b/streamobs/model.py @@ -195,10 +195,18 @@ def _iso_mag_columns(self): cols += [true_col(band_1, name), true_col(band_2, name)] return cols - def _sample_iso_mags(self, n, dist): - """Sample isochrone magnitudes as a ``{__true: values}`` dict.""" - mags = self.isochrone.sample_multisurvey(n, dist) - return {true_col(band, name): vals for (name, band), vals in mags.items()} + def _sample_iso_mags(self, n, dist, masses=None): + """Sample isochrone magnitudes as a ``{column: values}`` dict. + + Returns each survey's ``__true`` columns plus the shared + ``mass`` column (the initial masses used for every band). When ``masses`` + is given it is used directly instead of an IMF draw, so the sampled + magnitudes reproduce those exact stars. + """ + mags, masses = self.isochrone.sample_multisurvey(n, dist, masses=masses) + cols = {true_col(band, name): vals for (name, band), vals in mags.items()} + cols["mass"] = masses + return cols def complete_catalog( self, @@ -280,7 +288,14 @@ def complete_catalog( # Columns this method can fill using the configured model # Magnitude columns are survey-namespaced (__true). mag_cols = self._iso_mag_columns() - all_cols = ("phi1", "phi2", "dist") + tuple(mag_cols) + ("mu1", "mu2", "rv") + # The isochrone also produces the shared initial-mass column. + mass_cols = ("mass",) if self.isochrone is not None else () + all_cols = ( + ("phi1", "phi2", "dist") + + tuple(mag_cols) + + mass_cols + + ("mu1", "mu2", "rv") + ) target_cols = ( list(all_cols) if columns_to_add is None @@ -371,17 +386,19 @@ def complete_catalog( ) self._info(verbose, f"Filled {len(idx)} dist values.") - # magnitudes (need dist and isochrone) + # magnitudes + shared initial mass (need dist and isochrone) requested_mags = [c for c in mag_cols if c in target_cols] - if requested_mags: - # Only touch rows that are missing a requested band; existing values + want_mass = "mass" in target_cols and self.isochrone is not None + fill_targets = requested_mags + (["mass"] if want_mass else []) + if fill_targets: + # Only touch rows that are missing a requested column; existing values # are preserved (never overwritten). - missing = {c: self._missing_idx(df, c) for c in requested_mags} + missing = {c: self._missing_idx(df, c) for c in fill_targets} to_fill = {c: idx for c, idx in missing.items() if len(idx) > 0} if not to_fill: self._info( verbose, - f"{requested_mags} already present; no sampling performed.", + f"{fill_targets} already present; no sampling performed.", ) else: # Verify distance availability @@ -390,10 +407,16 @@ def complete_catalog( "dist is required to sample apparent magnitudes; include 'dist' in `columns_to_add`, provide it in the catalog, or pass `dist=`." ) dist_vals = df["dist"].to_numpy() + # Reuse a fully-present input `mass` column as the initial masses + # so the sampled magnitudes reproduce the user's simulation stars; + # otherwise draw fresh masses from the IMF. + masses_in = None + if "mass" in df.columns and df["mass"].notna().all(): + masses_in = df["mass"].to_numpy() # One shared mass draw -> all bands; the newly filled cells are # mutually colour-consistent. Assign only the missing rows so any # bands/values already present are left untouched. - mags = self._sample_iso_mags(N, dist_vals) + mags = self._sample_iso_mags(N, dist_vals, masses=masses_in) for col, idx in to_fill.items(): pos = df.index.get_indexer(idx) if col not in df.columns: @@ -401,7 +424,7 @@ def complete_catalog( df.loc[idx, col] = np.asarray(mags[col])[pos] self._info( verbose, - f"Filled magnitudes for {sorted(to_fill)} (missing rows only).", + f"Filled {sorted(to_fill)} (missing rows only).", ) # velocities (need phi1 and velocity model) @@ -653,9 +676,16 @@ class IsochroneModel(ConfigurableModel): :data:`ROMAN_VEGA_TO_AB`); other bands pass through unchanged. """ - # Defaults for the shared isochrone mass grid (see ugali Isochrone.sample) + # Defaults for the shared isochrone mass grid (see ugali Isochrone.sample). + # Masses are drawn from this discretized grid, so ``_MASS_STEPS`` bounds the + # number of *distinct* masses a stream can contain. A convergence check + # (g/r magnitude percentiles + distinct-mass count for a 5000-star stream) + # showed 1000 steps yields only ~220 distinct masses (granular CMD) and + # ~0.03 mag median scatter, while 4000 steps roughly triples the distinct + # masses (~600) and tightens convergence to <0.015 mag — for a negligible + # one-time grid-sampling cost. Override per call via ``mass_steps=``. _MASS_MIN = 0.1 - _MASS_STEPS = 1000 + _MASS_STEPS = 4000 def __init__(self, config, **kwargs): super().__init__(config, **kwargs) @@ -663,50 +693,71 @@ def __init__(self, config, **kwargs): def create_isochrone(self, config): """Construct the underlying ``ugali`` isochrone(s) from configuration. + Both configuration forms are normalized to a single ``{namespace: + factory_cfg}`` mapping and built through the same loop — a legacy flat + config simply becomes a one-entry mapping — so there is no separate + single- vs. multi-survey code path. + Parameters ---------- config : dict Isochrone factory configuration. A ``surveys`` key selects the multi-survey form; otherwise the single-survey (legacy) form is used. """ - self.multi_survey = "surveys" in config - if self.multi_survey: - self._create_multisurvey_isochrones(config) - else: - self._create_single_isochrone(config) + survey_configs, shared = self._normalize_iso_config(config) + self._build_isochrones(survey_configs, shared) + + def _normalize_iso_config(self, config): + """Coerce either config form into ``({namespace: factory_cfg}, shared)``. + + Multi-survey: the ``surveys`` mapping is returned verbatim (its keys are + the column namespaces) with the top-level keys as shared stellar params. + Single-survey (legacy flat): a one-entry mapping keyed by + ``{survey}_{release}`` (or just ``{survey}``), matching + :attr:`streamobs.surveys.Survey.namespace`, with no shared params. + """ + if "surveys" in config: + self.multi_survey = True + shared = {k: v for k, v in config.items() if k != "surveys"} + return dict(config["surveys"]), shared + + self.multi_survey = False + if "distance_modulus" in config: + warnings.warn( + 'Please use the "distance_modulus" section of the configuration ' + "file, instead of the isochrone section, to define a distance modulus." + ) + survey = config.get("survey") + release = config.get("release") + namespace = f"{survey}_{release}" if release else survey + return {namespace: dict(config)}, {} def _build_iso(self, factory_config): - """Build one ``ugali`` isochrone with its distance modulus reset to 0.""" + """Build one ``ugali`` isochrone with its distance modulus reset to 0. + + ``release`` is a column-namespacing concept (it distinguishes survey + versions in the output column names), not a ``ugali`` factory argument, + so it is stripped before the isochrone is constructed. + """ import ugali.isochrone + factory_config = {k: v for k, v in factory_config.items() if k != "release"} iso = ugali.isochrone.factory(**factory_config) iso.params["distance_modulus"].set_bounds([0, 50]) iso.distance_modulus = 0 return iso - def _create_single_isochrone(self, config): - """Build a single isochrone from a legacy (flat) isochrone config.""" - if "distance_modulus" in config: - warnings.warn( - 'Please use the "distance_modulus" section of the configuration ' - "file, instead of the isochrone section, to define a distance modulus." - ) - self.iso = self._build_iso(config) - self.survey_name = config.get("survey") - self.band_1 = config.get("band_1") - self.band_2 = config.get("band_2") - # Unified bookkeeping so the shared helpers work in both modes. - self.surveys = [self.survey_name] - self.isos = {self.survey_name: self.iso} - self.survey_bands = {self.survey_name: (self.band_1, self.band_2)} - - def _create_multisurvey_isochrones(self, config): - """Build one isochrone per survey, sharing the top-level stellar params.""" - shared = {k: v for k, v in config.items() if k != "surveys"} + def _build_isochrones(self, survey_configs, shared): + """Build one isochrone per namespace, sharing the top-level stellar params. + + Drives both configuration forms (a legacy flat config is just a + one-entry ``survey_configs``). The first entry is the primary isochrone + that drives the shared mass draw and the legacy :meth:`sample`. + """ self.isos = {} self.survey_bands = {} self.surveys = [] - for name, scfg in config["surveys"].items(): + for name, scfg in survey_configs.items(): factory_config = {**shared, **scfg} self.isos[name] = self._build_iso(factory_config) self.survey_bands[name] = (scfg.get("band_1"), scfg.get("band_2")) @@ -777,24 +828,48 @@ def _add_distance_modulus(abs_mag, distance_modulus): return abs_mag return abs_mag + np.asarray(distance_modulus, dtype=float) - def sample_multisurvey(self, nstars, distance_modulus, rng=None, **kwargs): + def sample_multisurvey( + self, nstars, distance_modulus, rng=None, masses=None, **kwargs + ): """Sample apparent magnitudes for every ``(survey, band)``. - A single shared draw of initial masses (see :meth:`sample_masses`) is - interpolated into each survey's bands, so the same physical star is - consistent across surveys. + A single shared set of initial masses is interpolated into each survey's + bands, so the same physical star is consistent across surveys. The masses + are drawn from the shared IMF (:meth:`sample_masses`) unless supplied via + ``masses``. + + Parameters + ---------- + nstars : int + Number of stars (and the required length of ``masses`` if given). + distance_modulus : float or array-like + Distance modulus per star (broadcast if scalar). + rng : numpy.random.Generator, optional + Used only when ``masses`` is None. + masses : array-like, optional + Initial stellar masses to use directly — e.g. an external + simulation's masses — instead of drawing from the IMF. Must have + length ``nstars``. Returns ------- - dict - ``{(survey_name, band): apparent_magnitude_array}``. + dict, numpy.ndarray + ``{(survey_name, band): apparent_magnitude_array}`` and the initial + masses used (shape ``(nstars,)``). """ - masses = self.sample_masses( - nstars, - rng=rng, - mass_min=kwargs.get("mass_min"), - mass_steps=kwargs.get("mass_steps"), - ) + if masses is None: + masses = self.sample_masses( + nstars, + rng=rng, + mass_min=kwargs.get("mass_min"), + mass_steps=kwargs.get("mass_steps"), + ) + else: + masses = np.asarray(masses, dtype=float) + if len(masses) != int(nstars): + raise ValueError( + f"masses has length {len(masses)} but nstars={int(nstars)}." + ) out = {} for name in self.surveys: band_1, band_2 = self.survey_bands[name] @@ -803,9 +878,9 @@ def sample_multisurvey(self, nstars, distance_modulus, rng=None, **kwargs): abs_2 = self._to_ab(band_2, abs_2) out[(name, band_1)] = self._add_distance_modulus(abs_1, distance_modulus) out[(name, band_2)] = self._add_distance_modulus(abs_2, distance_modulus) - return out + return out, masses - def sample(self, nstars, distance_modulus, rng=None, **kwargs): + def sample(self, nstars, distance_modulus, rng=None, masses=None, **kwargs): """Simulate magnitudes in the two bands of the (primary) isochrone. Draws *exactly* ``nstars`` stars: a fixed set of initial masses is drawn @@ -831,7 +906,9 @@ def sample(self, nstars, distance_modulus, rng=None, **kwargs): this returns the primary survey's two bands; use :meth:`sample_multisurvey` to get every survey's bands. """ - mags = self.sample_multisurvey(nstars, distance_modulus, rng=rng, **kwargs) + mags, _ = self.sample_multisurvey( + nstars, distance_modulus, rng=rng, masses=masses, **kwargs + ) return ( mags[(self.survey_name, self.band_1)], mags[(self.survey_name, self.band_2)], diff --git a/streamobs/observed.py b/streamobs/observed.py index 5dec240..f546770 100644 --- a/streamobs/observed.py +++ b/streamobs/observed.py @@ -37,12 +37,12 @@ class StreamInjector: ---------- surveys : dict ``{namespace: Survey}`` for every survey this injector serves. The - namespace is the column prefix (``lsst_r_obs``, ``roman_F158_obs``, ...). - primary : str - Namespace of the survey whose footprint drives the shared sky placement - and whose ``_save_injected_data`` is used. The primary - :class:`~streamobs.surveys.Survey` itself is available via the ``survey`` - property (``surveys[primary]``), used by the mask/coordinate helpers. + namespace is the survey's ``{name}_{release}`` and is the column prefix + (``lsst_yr5_r_obs``, ``roman_dc2_F158_obs``, ...). + primary : Survey + The survey whose footprint drives the shared sky placement and whose + ``_save_injected_data`` is used (also available as ``survey``). Its + namespace string is :attr:`primary_namespace`. mask_cache : dict (class attribute) Cache of previously created HEALPix masks to avoid recomputation. _last_gc_frame : GreatCircleICRSFrame or None @@ -51,16 +51,21 @@ class StreamInjector: Examples -------- - Single survey (columns namespaced by the survey's name): + Single survey (columns namespaced ``{name}_{release}``): >>> injector = StreamInjector('lsst', release='dc2') - >>> out = injector.inject(df, bands=['r', 'g']) # -> lsst_r_obs, ... + >>> out = injector.inject(df, bands=['r', 'g']) # -> lsst_dc2_r_obs, ... - Several surveys at once: + Several surveys at once — each spec carries its release; the namespace is + derived from it, so bands are keyed by ``{name}_{release}``: - >>> injector = StreamInjector({'lsst': 'lsst', 'roman': 'roman'}) + >>> injector = StreamInjector([ + ... {'survey': 'lsst', 'release': 'dc2'}, + ... {'survey': 'roman', 'release': 'dc2'}, + ... ]) >>> out = injector.inject( - ... df, survey_bands={'lsst': ['r', 'g'], 'roman': ['F106', 'F158']}, + ... df, + ... bands={'lsst_dc2': ['r', 'g'], 'roman_dc2': ['F106', 'F158']}, ... stream_config=scene['stream'], seed=42, ... ) """ @@ -99,54 +104,88 @@ def __init__(self, survey, primary=None, **kwargs): self.survey_names = list(self.surveys) if not self.survey_names: raise ValueError("At least one survey is required.") - self.primary = primary if primary is not None else self.survey_names[0] - if self.primary not in self.surveys: + self._primary = primary if primary is not None else self.survey_names[0] + if self._primary not in self.surveys: raise ValueError( - f"primary='{self.primary}' is not one of {self.survey_names}." + f"primary='{self._primary}' is not one of {self.survey_names}." ) # Instance attribute to store the last used gc_frame self._last_gc_frame = None @property - def survey(self): + def primary(self): """The primary :class:`~streamobs.surveys.Survey`. - Mask, coordinate and footprint helpers operate on this survey. + Drives the shared sky placement; mask, coordinate and footprint helpers + operate on it. Its column namespace is :attr:`primary_namespace`. """ - return self.surveys[self.primary] + return self.surveys[self._primary] + + @property + def primary_namespace(self): + """Column namespace (``{name}_{release}``) of the primary survey.""" + return self._primary + + @property + def survey(self): + """Alias for :attr:`primary` — the primary :class:`~streamobs.surveys.Survey`.""" + return self.surveys[self._primary] @classmethod def _normalize_surveys(cls, surveys, **kwargs): - """Coerce the ``surveys`` argument into a ``{namespace: Survey}`` dict.""" + """Coerce the ``surveys`` argument into a ``{namespace: Survey}`` dict. + + The namespace key is always the survey's own + :attr:`~streamobs.surveys.Survey.namespace` (``{name}_{release}``), so + the same survey at two releases yields two distinct entries. Accepted + forms: a single spec (name string, ``Survey``, or + ``{"survey": ..., "release": ...}`` dict), a list of specs, or a + ``{key: spec}`` mapping (the keys are only containers — the canonical + namespace is re-derived from each loaded survey). + """ if isinstance(surveys, (str, Survey)): - survey = cls._load_survey(surveys, **kwargs) - return {survey.name: survey} - if isinstance(surveys, (list, tuple)): - out = {} - for spec in surveys: - survey = cls._load_survey(spec, **kwargs) - out[survey.name] = survey - return out - if isinstance(surveys, dict): - return { - name: cls._load_survey(spec, **kwargs) for name, spec in surveys.items() - } - raise ValueError( - "surveys must be a survey name, a Survey, a {name: spec} dict, " - "or a list of specs." - ) + specs = [surveys] + elif isinstance(surveys, dict) and "survey" in surveys: + # A single spec dict, e.g. {"survey": "lsst", "release": "yr5"}. + specs = [surveys] + elif isinstance(surveys, dict): + # A {key: spec} mapping; namespace is re-derived per loaded survey. + specs = list(surveys.values()) + elif isinstance(surveys, (list, tuple)): + specs = list(surveys) + else: + raise ValueError( + "surveys must be a survey name, a Survey, a spec dict, a list " + "of specs, or a {key: spec} dict." + ) + out = {} + for spec in specs: + survey = cls._load_survey(spec, **kwargs) + out[survey.namespace] = survey + return out @staticmethod def _load_survey(spec, **kwargs): - """Resolve a single survey spec (name string or Survey) to a Survey.""" + """Resolve a single survey spec to a Survey. + + Accepts a :class:`~streamobs.surveys.Survey`, a survey-name string + (loaded with the shared ``**kwargs``, e.g. ``release=``), or a spec + ``dict`` such as ``{"survey": "lsst", "release": "yr5"}`` (forwarded to + :meth:`Survey.load`, taking precedence over the shared ``**kwargs``). + """ if isinstance(spec, Survey): return spec if isinstance(spec, str): return Survey.load(survey=spec, **kwargs) - raise ValueError("Each survey spec must be a string or Survey instance.") + if isinstance(spec, dict): + return Survey.load(**{**kwargs, **spec}) + raise ValueError( + 'Each survey spec must be a Survey, a name string, or a ' + '{"survey": ..., "release": ...} dict.' + ) - def inject(self, data, survey_bands=None, bands=None, stream_config=None, **kwargs): + def inject(self, data, bands=None, stream_config=None, **kwargs): """ Add observed quantities from every survey into a single catalog. @@ -165,15 +204,12 @@ def inject(self, data, survey_bands=None, bands=None, stream_config=None, **kwar anything missing is sampled from ``stream_config``. An all-empty frame of length ``N`` is accepted (geometry and magnitudes are then sampled for ``N`` rows). - survey_bands : dict, optional - ``{survey_name: [bands]}`` — bands to inject for each survey. Keys - must match the surveys this injector was built with. For a single - survey you may instead pass ``bands`` (below). - bands : list of str, optional - Convenience shorthand for the single-survey case: the bands to inject - for the (only) survey. Ignored when ``survey_bands`` is given. If - neither is provided and there is exactly one survey, defaults to - ``['r', 'g']``. + bands : list of str or dict, optional + Bands to inject. A ``{survey_name: [bands]}`` dict selects bands per + survey (the multi-survey form; keys must match the surveys this + injector was built with). A plain list/tuple is the single-survey + shorthand, applied to the only survey. If omitted and there is + exactly one survey, defaults to ``['r', 'g']``. stream_config : dict, optional The ``stream`` section consumed by :class:`~streamobs.model.StreamModel`. Required when any coordinate @@ -192,10 +228,11 @@ def inject(self, data, survey_bands=None, bands=None, stream_config=None, **kwar HEALPix nside parameter. Default is 4096. detection_mag_cut : list of str, optional Non-reference bands to apply the explicit SNR>=5 cut to. The - reference band (``survey.completeness_band``) is always cut via - the selection functions, so the default here is every injected - band *except* the reference band. Net effect: every injected band - must have SNR >= 5, with the reference band counted once. + reference band (``survey.completeness_band``) is never cut here — + its SNR cut is already baked into the survey's selection + functions — so the default is every injected band *except* the + reference band. Net effect: every injected band must have + SNR >= 5, with the reference band's cut owned by the curves. save : bool, optional Whether to save the output data. Default is False. folder : str or Path, optional @@ -224,27 +261,24 @@ def inject(self, data, survey_bands=None, bands=None, stream_config=None, **kwar Raises ------ ValueError - If required columns are missing, or if ``survey_bands`` references an - unknown survey, or if neither ``survey_bands`` nor ``bands`` can be - resolved. + If required columns are missing, if ``bands`` (as a dict) references + an unknown survey, or if a list ``bands`` is given for a multi-survey + injector. """ - survey_bands = self._resolve_survey_bands(survey_bands, bands) - - # Load data - data = self._load_data(data) + survey_bands = self._resolve_survey_bands(bands) # Set the seed for reproducibility seed = kwargs.pop("seed", None) rng = np.random.default_rng(seed) # Shared sky placement + shared true-magnitude fill (masses sampled once - # across all surveys). - data = self._complete_shared( + # across all surveys). This is the same completion exposed publicly as + # complete_data(); pass the already-resolved {survey: bands} dict through. + data = self.complete_data( data, - survey_bands, + bands=survey_bands, stream_config=stream_config, rng=rng, - seed=seed, **kwargs, ) @@ -258,7 +292,6 @@ def inject(self, data, survey_bands=None, bands=None, stream_config=None, **kwar data, list(survey_bands[name]), survey=self.surveys[name], - survey_namespace=name, rng=child_rng, **kwargs, ) @@ -270,53 +303,55 @@ def inject(self, data, survey_bands=None, bands=None, stream_config=None, **kwar # Return data (do NOT store as instance attribute to avoid conflicts between runs) return data - def _resolve_survey_bands(self, survey_bands, bands): - """Normalize the ``survey_bands`` / ``bands`` arguments to a dict. + def _resolve_survey_bands(self, bands): + """Normalize the ``bands`` argument to a ``{survey_name: [bands]}`` dict. + + ``bands`` may be: - ``survey_bands`` wins if given (validated against the known surveys). - Otherwise ``bands`` is accepted only when there is a single survey; if - neither is given and there is one survey, defaults to ``['r', 'g']``. + - a ``{survey_name: [bands]}`` dict (validated against the known + surveys) — the multi-survey form; + - a list/tuple of band names — allowed only when the injector serves a + single survey, applied to that survey; + - ``None`` — defaults to ``['r', 'g']`` for the single-survey case. """ - if survey_bands is not None: - unknown = set(survey_bands) - set(self.surveys) + if isinstance(bands, dict): + unknown = set(bands) - set(self.surveys) if unknown: raise ValueError( - f"survey_bands references unknown surveys {sorted(unknown)}; " + f"bands references unknown surveys {sorted(unknown)}; " f"available: {self.survey_names}." ) - return {name: list(b) for name, b in survey_bands.items()} + return {name: list(b) for name, b in bands.items()} if len(self.survey_names) != 1: raise ValueError( - "Pass survey_bands={name: [bands]} when the injector serves " + "Pass bands={survey_name: [bands]} when the injector serves " f"multiple surveys ({self.survey_names})." ) if bands is None: bands = ["r", "g"] - return {self.primary: list(bands)} + return {self._primary: list(bands)} - def _inject_one_survey( - self, data, bands, survey, survey_namespace, rng=None, seed=None, **kwargs - ): + def _inject_one_survey(self, data, bands, survey, rng=None, seed=None, **kwargs): """Add one survey's observed magnitudes, errors and detection flags. This holds the per-band observational logic. It assumes ``data`` already carries ``ra``/``dec`` and the true-magnitude columns - (``true_col(band, survey_namespace)``) for the requested bands; it does - **not** sample positions or true magnitudes. + (``true_col(band, survey.namespace)``) for the requested bands; it does + **not** sample positions or true magnitudes. The column namespace is + taken directly from ``survey.namespace`` (``{name}_{release}``). Parameters ---------- data : pandas.DataFrame - Catalog with ``ra``/``dec`` and ``true_col(band, survey_namespace)`` + Catalog with ``ra``/``dec`` and ``true_col(band, survey.namespace)`` for every requested band. bands : list of str Bands to process for this survey. survey : Survey - The survey supplying maglim maps, completeness and error curves. - survey_namespace : str - Column-naming namespace ⇒ ``__obs`` / - ``__err`` / ``_flag_observed``. + The survey supplying maglim maps, completeness and error curves; its + ``namespace`` is the column prefix ⇒ ``__obs`` / + ``__err`` / ``_flag_observed``. rng : numpy.random.Generator, optional Random generator for the noise draw and detection sampling. seed : int, optional @@ -333,6 +368,9 @@ def _inject_one_survey( if rng is None: rng = np.random.default_rng(seed) + # Column namespace for this survey (``{name}_{release}``). + survey_namespace = survey.namespace + verbose = kwargs.get("verbose", True) perfect_galstarsep = kwargs.pop("perfect_galstarsep", False) @@ -476,33 +514,24 @@ def _inject_one_survey( # Apply SNR cuts. # - # The SNR>=SNR_min cut on the *reference* band (``survey.completeness_band``) - # is owned by the survey's selection functions — they are estimated with - # that cut already applied. We therefore apply it to the reference band - # exactly **once** here, to both flags. (``get_completeness`` bakes it in, - # so for ``flag_observed`` this is idempotent; ``get_detection_efficiency`` - # does not, so for ``flag_perfect`` this line is what supplies it.) See - # the "S/N cut ownership" note in docs/source/roman_multisurvey_plan.md - # for the path to folding it into the efficiency curve itself (option a). - # - # Every *other* injected band gets the cut applied explicitly below. + # The reference band (``survey.completeness_band``) is NOT cut here: the + # survey's selection functions are defined on true stars detected at + # S/N >= SNR_min, so that cut is already baked into BOTH curves — + # ``get_completeness`` (detection x classification) and + # ``get_detection_efficiency`` (detection only). Re-applying it to the + # reference band would double-count it, so we only cut the *other* + # injected bands explicitly below. SNR_min = 5.0 ref_band = survey.completeness_band - if ref_band in bands: - SNR_ref = 1.0 / data[err_col(ref_band, survey_namespace)] - flag_observed &= SNR_ref >= SNR_min - if perfect_galstarsep: - flag_perfect &= SNR_ref >= SNR_min - # Non-reference bands. Default: every injected band except the reference - # band (whose cut is handled above via the selection functions). + # band (whose cut is owned by the selection functions). detection_mag_cut = kwargs.get( "detection_mag_cut", [b for b in bands if b != ref_band] ) for band in detection_mag_cut: if band == ref_band: - # The reference band's SNR cut is already applied above. + # The reference band's SNR cut is owned by the selection functions. continue if band not in bands: if verbose: @@ -559,7 +588,6 @@ def _load_data(self, data): def complete_data( self, data, - survey_bands=None, bands=None, stream_config=None, dist=None, @@ -585,12 +613,11 @@ def complete_data( Input catalog (or path). May contain only stream coordinates (``phi1``/``phi2`` or ``ra``/``dec``), an all-empty frame of length ``N``, or any subset of the target columns. - survey_bands : dict, optional - ``{survey_name: [bands]}`` — the true-magnitude columns to ensure per - survey. For a single survey you may instead pass ``bands``; if neither - is given and there is exactly one survey, defaults to ``['r', 'g']``. - bands : list of str, optional - Single-survey shorthand for ``survey_bands={primary: bands}``. + bands : list of str or dict, optional + Bands whose true-magnitude columns to ensure. A + ``{survey_name: [bands]}`` dict selects bands per survey (multi-survey + form); a plain list/tuple is the single-survey shorthand. If omitted + and there is exactly one survey, defaults to ``['r', 'g']``. stream_config : dict, optional :class:`~streamobs.model.StreamModel` config used to sample any missing geometry / true magnitudes. Required only when something is @@ -621,11 +648,11 @@ def complete_data( >>> df = pd.DataFrame({'phi1': [-5, 0, 5], 'phi2': [0, 0, 0]}) >>> out = injector.complete_data(df, bands=['r', 'g'], stream_config=cfg) >>> out = injector.complete_data( - ... df, survey_bands={'lsst': ['r', 'g'], 'roman': ['F106', 'F158']}, + ... df, bands={'lsst': ['r', 'g'], 'roman': ['F106', 'F158']}, ... stream_config=cfg, seed=42, ... ) """ - survey_bands = self._resolve_survey_bands(survey_bands, bands) + survey_bands = self._resolve_survey_bands(bands) data = self._load_data(data).copy() rng = kwargs.pop("rng", None) @@ -633,15 +660,49 @@ def complete_data( if rng is None: rng = np.random.default_rng(seed) - return self._complete_shared( - data, - survey_bands, - stream_config=stream_config, - rng=rng, - seed=seed, - dist=dist, - **kwargs, - ) + verbose = kwargs.get("verbose", True) + + # Positions and masses are drawn *once* so all surveys describe the same + # physical stars (the isochrone produces every survey's + # ``__true`` column from one shared mass draw). Existing + # columns are preserved (only missing values are filled). ``ra``/``dec`` + # are placed using the primary survey's footprint. ``dist`` (a float or + # per-row vector) overrides the model's distance sampling when given. + true_cols = [] + for name, name_bands in survey_bands.items(): + true_cols += [true_col(b, name) for b in name_bands] + + have_radec = "ra" in data.columns and "dec" in data.columns + have_phi = "phi1" in data.columns and "phi2" in data.columns + missing_true = [c for c in true_cols if c not in data.columns] + + # Sample stream geometry and/or the shared true magnitudes from the model. + need_phi = not have_radec and not have_phi + if need_phi or missing_true: + if stream_config is None: + raise ValueError( + "stream_config is required to sample stream geometry/magnitudes." + ) + stream_model = StreamModel(stream_config) + cols_to_add = [] + if need_phi: + cols_to_add += ["phi1", "phi2"] + # `dist` is needed before magnitudes; the model fills it (from the + # distance_modulus model or the supplied `dist`) if absent. + cols_to_add += ["dist"] + missing_true + data = stream_model.complete_catalog( + data, + columns_to_add=cols_to_add, + inplace=True, + verbose=verbose, + dist=dist, + ) + + # Convert (phi1, phi2) -> (ra, dec) using the primary survey footprint. + if not have_radec: + data = self._ensure_radec(data, rng=rng, seed=seed, **kwargs) + + return data def _ensure_radec(self, data, rng=None, seed=None, **kwargs): """Ensure ``ra``/``dec`` are present, converting from (phi1, phi2) if needed. @@ -1098,7 +1159,7 @@ def sample_measured_magnitudes(self, mag_true, mag_err, **kwargs): return mag_obs - def detect_flag(self, pix, mag=None, band="r", survey=None, **kwargs): + def detect_flag(self, pix, survey, mag=None, band="r", **kwargs): """ Apply the survey selection to determine detection flags for stars. @@ -1113,9 +1174,9 @@ def detect_flag(self, pix, mag=None, band="r", survey=None, **kwargs): Magnitude(s). Default is None. band : str, optional Band to consider for detection. Default is 'r'. - survey : Survey, optional - Survey whose completeness/detection-efficiency curves to use. - Defaults to the primary survey. + survey : Survey + Survey whose completeness/detection-efficiency curves to use + (required). **kwargs Additional keyword arguments: @@ -1142,9 +1203,6 @@ def detect_flag(self, pix, mag=None, band="r", survey=None, **kwargs): seed = kwargs.pop("seed", None) rng = np.random.default_rng(seed) - if survey is None: - survey = self.survey - # Select the appropriate magnitude and map depending on the band maglim = survey.get_maglim(band, pixel=pix) @@ -1333,60 +1391,3 @@ def plot_stream_in_mask(self, data, mask_type, ebv_threshold=0.2, **kwargs): data["ra"], data["dec"], mask, output_folder=kwargs.get("output_folder") ) return fig, ax - - def _complete_shared( - self, - data, - survey_bands, - stream_config=None, - rng=None, - seed=None, - dist=None, - **kwargs, - ): - """Fill shared geometry, ``ra``/``dec`` and per-survey true magnitudes. - - Positions and masses are drawn *once* so all surveys describe the same - physical stars (the isochrone produces every survey's - ``__true`` column from one shared mass draw). Existing - columns are preserved (only missing values are filled). ``ra``/``dec`` - are placed using the primary survey's footprint. ``dist`` (a float or - per-row vector) overrides the model's distance sampling when given. - """ - verbose = kwargs.get("verbose", True) - - true_cols = [] - for name, bands in survey_bands.items(): - true_cols += [true_col(b, name) for b in bands] - - have_radec = "ra" in data.columns and "dec" in data.columns - have_phi = "phi1" in data.columns and "phi2" in data.columns - missing_true = [c for c in true_cols if c not in data.columns] - - # Sample stream geometry and/or the shared true magnitudes from the model. - need_phi = not have_radec and not have_phi - if need_phi or missing_true: - if stream_config is None: - raise ValueError( - "stream_config is required to sample stream geometry/magnitudes." - ) - stream_model = StreamModel(stream_config) - cols_to_add = [] - if need_phi: - cols_to_add += ["phi1", "phi2"] - # `dist` is needed before magnitudes; the model fills it (from the - # distance_modulus model or the supplied `dist`) if absent. - cols_to_add += ["dist"] + missing_true - data = stream_model.complete_catalog( - data, - columns_to_add=cols_to_add, - inplace=True, - verbose=verbose, - dist=dist, - ) - - # Convert (phi1, phi2) -> (ra, dec) using the primary survey footprint. - if not have_radec: - data = self._ensure_radec(data, rng=rng, seed=seed, **kwargs) - - return data diff --git a/streamobs/surveys.py b/streamobs/surveys.py index bb97a47..a790314 100644 --- a/streamobs/surveys.py +++ b/streamobs/surveys.py @@ -138,6 +138,17 @@ def __post_init__(self): if self.sys_error is None: self.sys_error = {} + @property + def namespace(self) -> str: + """Column-prefix namespace for this survey. + + ``"{name}_{release}"`` when a release is set (e.g. ``"lsst_yr5"``, + ``"roman_dc2"``), otherwise just ``"{name}"``. This is the prefix used + for every injected column (``__obs`` etc.), so the same + survey at two releases produces distinct, non-colliding columns. + """ + return f"{self.name}_{self.release}" if self.release else self.name + @property def log_photo_error(self) -> Optional[Callable]: """Backward-compatible alias for the *catalog* (reported) error model.""" diff --git a/tests/conftest.py b/tests/conftest.py index fe4913b..deb08bc 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -59,6 +59,7 @@ def minimal_stream_config(): "isochrone": { "name": "Marigo2017", # isochrone set name "survey": "lsst", # survey for filter set + "release": "yr4", # release -> namespaces true columns as lsst_yr4_* "age": 12.0, # Age in Gyr of the population "z": 0.0006, # Metallicity of the population "band_1": "g", # first band for color-magnitude @@ -165,6 +166,41 @@ def mock_injector(mock_survey, verbose): return StreamInjector(survey=mock_survey, verbose=verbose) +@pytest.fixture(scope="session") +def mock_multisurvey_injector(verbose): + """Injector serving two LSST releases (distinct namespaces lsst_yr4/lsst_yr5).""" + from streamobs.observed import StreamInjector + + return StreamInjector( + [ + {"survey": "lsst", "release": "yr4"}, + {"survey": "lsst", "release": "yr5"}, + ], + verbose=verbose, + ) + + +@pytest.fixture(scope="session") +def multisurvey_stream_config(stream_config_with_distance): + """StreamModel config with a MULTI-survey isochrone keyed by namespace. + + Both namespaces are backed by the same ugali survey (``lsst``) and bands, so + a shared mass draw yields identical true magnitudes across them — exactly the + 'same physical star' invariant the injector relies on. + """ + cfg = dict(stream_config_with_distance) + cfg["isochrone"] = { + "name": "Marigo2017", + "age": 12.0, + "z": 0.0006, + "surveys": { + "lsst_yr4": {"survey": "lsst", "band_1": "g", "band_2": "r"}, + "lsst_yr5": {"survey": "lsst", "band_1": "g", "band_2": "r"}, + }, + } + return cfg + + @pytest.fixture(scope="session") def stream_catalog(stream_config_with_distance): from streamobs.model import StreamModel diff --git a/tests/test_model.py b/tests/test_model.py index 8045a37..3f3003b 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -134,8 +134,8 @@ def test_full_model(self, stream_config_with_distance): "phi1", "phi2", "dist", - "lsst_g_true", - "lsst_r_true", + "lsst_yr4_g_true", + "lsst_yr4_r_true", } # Not adding mu1, mu2, rv since not implemented yet self._verify_catalogue_content(samples, expected_columns) @@ -147,8 +147,8 @@ def test_complete_catalog(self, sample_catalog_phi, stream_config_with_distance) "phi1", "phi2", "dist", - "lsst_g_true", - "lsst_r_true", + "lsst_yr4_g_true", + "lsst_yr4_r_true", } # Not adding mu1, mu2, rv since not implemented yet self._verify_catalogue_content(completed_catalog, expected_columns) assert len(completed_catalog) == len( @@ -157,7 +157,7 @@ def test_complete_catalog(self, sample_catalog_phi, stream_config_with_distance) # Verify that I can add a targeted column (e.g. dist) to the input catalog and complete the rest partial_catalog = completed_catalog.drop( - columns=["lsst_r_true", "dist", "lsst_g_true"] + columns=["lsst_yr4_r_true", "dist", "lsst_yr4_g_true"] ).reset_index(drop=True) completed_catalog = model.complete_catalog( catalog=partial_catalog, @@ -173,8 +173,8 @@ def test_complete_catalog(self, sample_catalog_phi, stream_config_with_distance) partial_catalog ), f"Completed catalog should have the same number of rows as the input catalog" assert ( - "lsst_r_true" not in completed_catalog.columns - ), "Column 'lsst_r_true' should not be added when not requested" + "lsst_yr4_r_true" not in completed_catalog.columns + ), "Column 'lsst_yr4_r_true' should not be added when not requested" # --------------------------------------------------------------------------- @@ -192,7 +192,7 @@ class TestCompleteCatalogPermutations: columns and individual NaN rows are never overwritten). """ - MAGS = {"lsst_g_true", "lsst_r_true"} + MAGS = {"lsst_yr4_g_true", "lsst_yr4_r_true"} def test_empty_frame_fills_all_model_columns(self, stream_config_with_distance): """size=N with no catalog -> geometry, dist, and both bands are filled.""" @@ -230,12 +230,12 @@ def test_existing_band_preserved_when_filling_other( """Providing one band and requesting both leaves the provided one intact.""" model = StreamModel(stream_config_with_distance) g = np.array([20.0, 21.0, 22.0]) - df = pd.DataFrame({"dist": [16.0] * 3, "lsst_g_true": g.copy()}) + df = pd.DataFrame({"dist": [16.0] * 3, "lsst_yr4_g_true": g.copy()}) out = model.complete_catalog( catalog=df, columns_to_add=list(self.MAGS), verbose=False ) - assert np.allclose(out["lsst_g_true"].to_numpy(), g), "present band overwritten" - assert out["lsst_r_true"].notna().all(), "missing band not filled" + assert np.allclose(out["lsst_yr4_g_true"].to_numpy(), g), "present band overwritten" + assert out["lsst_yr4_r_true"].notna().all(), "missing band not filled" def test_present_columns_skip_sampling(self, stream_config_with_distance): """Both bands present -> values are returned untouched.""" @@ -243,23 +243,23 @@ def test_present_columns_skip_sampling(self, stream_config_with_distance): g = np.array([20.0, 21.0]) r = np.array([19.0, 19.5]) df = pd.DataFrame( - {"dist": [16.0, 16.0], "lsst_g_true": g.copy(), "lsst_r_true": r.copy()} + {"dist": [16.0, 16.0], "lsst_yr4_g_true": g.copy(), "lsst_yr4_r_true": r.copy()} ) out = model.complete_catalog( catalog=df, columns_to_add=list(self.MAGS), verbose=False ) - assert np.allclose(out["lsst_g_true"].to_numpy(), g) - assert np.allclose(out["lsst_r_true"].to_numpy(), r) + assert np.allclose(out["lsst_yr4_g_true"].to_numpy(), g) + assert np.allclose(out["lsst_yr4_r_true"].to_numpy(), r) def test_partial_rows_only_missing_filled(self, stream_config_with_distance): """A band with some NaN rows keeps its finite rows; only NaNs are filled.""" model = StreamModel(stream_config_with_distance) g = np.array([20.0, np.nan, 22.0]) - df = pd.DataFrame({"dist": [16.0] * 3, "lsst_g_true": g.copy()}) + df = pd.DataFrame({"dist": [16.0] * 3, "lsst_yr4_g_true": g.copy()}) out = model.complete_catalog( - catalog=df, columns_to_add=["lsst_g_true"], verbose=False + catalog=df, columns_to_add=["lsst_yr4_g_true"], verbose=False ) - filled = out["lsst_g_true"].to_numpy() + filled = out["lsst_yr4_g_true"].to_numpy() assert filled[0] == 20.0 and filled[2] == 22.0, "finite rows overwritten" assert np.isfinite(filled[1]), "NaN row not filled" @@ -318,3 +318,57 @@ def test_mags_without_dist_or_phi_raises(self, minimal_stream_config): model.complete_catalog( catalog=df, columns_to_add=list(self.MAGS), verbose=False ) + + +# --------------------------------------------------------------------------- +# IsochroneModel — shared initial masses (user-supplied + `mass` column) +# --------------------------------------------------------------------------- + + +@pytest.mark.model +class TestIsochroneMasses: + """The shared initial masses can be reused/supplied and surface as `mass`.""" + + G = "lsst_yr4_g_true" + R = "lsst_yr4_r_true" + + def test_sample_multisurvey_returns_and_reuses_masses( + self, stream_config_with_distance + ): + """Replaying the returned masses reproduces identical magnitudes.""" + iso = StreamModel(stream_config_with_distance).isochrone + mags1, masses = iso.sample_multisurvey(50, 16.8, rng=np.random.default_rng(0)) + assert masses.shape == (50,) + # Supplying those masses (no rng) is deterministic and reproduces mags1. + mags2, masses2 = iso.sample_multisurvey(50, 16.8, masses=masses) + assert np.allclose(masses, masses2) + for key in mags1: + assert np.allclose(mags1[key], mags2[key]), f"{key} not reproduced" + + def test_sample_multisurvey_masses_length_validated( + self, stream_config_with_distance + ): + iso = StreamModel(stream_config_with_distance).isochrone + with pytest.raises(ValueError): + iso.sample_multisurvey(50, 16.8, masses=np.ones(49)) + + def test_complete_catalog_exposes_mass_column(self, stream_config_with_distance): + """A completed catalog carries the shared `mass` column.""" + model = StreamModel(stream_config_with_distance) + out = model.complete_catalog(catalog=None, size=20, verbose=False) + assert "mass" in out.columns + assert out["mass"].notna().all() + assert np.issubdtype(out["mass"].dtype, np.floating) + + def test_input_mass_column_drives_magnitudes(self, stream_config_with_distance): + """Providing a `mass` column makes the sampled mags match those masses.""" + model = StreamModel(stream_config_with_distance) + iso = model.isochrone + _, masses = iso.sample_multisurvey(15, 16.8, rng=np.random.default_rng(3)) + df = pd.DataFrame({"dist": [16.8] * 15, "mass": masses}) + out = model.complete_catalog( + catalog=df, columns_to_add=[self.G, self.R], verbose=False + ) + direct, _ = iso.sample_multisurvey(15, 16.8, masses=masses) + assert np.allclose(out[self.G].to_numpy(), direct[("lsst_yr4", "g")]) + assert np.allclose(out[self.R].to_numpy(), direct[("lsst_yr4", "r")]) diff --git a/tests/test_observed.py b/tests/test_observed.py index 3ca6973..ad67419 100644 --- a/tests/test_observed.py +++ b/tests/test_observed.py @@ -105,12 +105,12 @@ def _verify_injected_catalog_content( expected_columns=[ "ra", "dec", - "lsst_g_true", - "lsst_r_true", - "lsst_g_obs", - "lsst_r_obs", - "lsst_flag_observed", - "lsst_flag_perfect_galstarsep", + "lsst_yr4_g_true", + "lsst_yr4_r_true", + "lsst_yr4_g_obs", + "lsst_yr4_r_obs", + "lsst_yr4_flag_observed", + "lsst_yr4_flag_perfect_galstarsep", ], ): """Helper method to verify the content of the injected catalog.""" @@ -135,7 +135,9 @@ def test_injection_partialinput( self, mock_injector, stream_catalog, stream_config_with_distance, verbose ): """Test injection with a catalog that has some missing columns.""" - data_without_mag = stream_catalog.drop(columns=["lsst_g_true", "lsst_r_true"]) + data_without_mag = stream_catalog.drop( + columns=["lsst_yr4_g_true", "lsst_yr4_r_true"] + ) injected_catalog = mock_injector.inject( data_without_mag, perfect_galstarsep=True, @@ -230,3 +232,70 @@ def compare_gc_frames(gc1, gc2): assert len(masks_before) > len( masks ), "Mask cache should have had entries before clearing" + + +# --------------------------------------------------------------------------- +# complete_data + bands/survey API +# --------------------------------------------------------------------------- + + +@pytest.mark.observed +class TestCompleteDataAndAPI: + """complete_data (single + multi survey) and the unified bands/survey API.""" + + def test_complete_data_single_survey(self, mock_injector, stream_config_with_distance): + """complete_data fills ra/dec + true mags and preserves existing columns.""" + df = pd.DataFrame({"phi1": [-3.0, 0.0, 3.0], "phi2": [0.0, 0.0, 0.0]}) + out = mock_injector.complete_data( + df, bands=["g", "r"], stream_config=stream_config_with_distance, seed=1 + ) + for col in ["ra", "dec", "lsst_yr4_g_true", "lsst_yr4_r_true"]: + assert col in out.columns, f"missing {col}" + assert out[["lsst_yr4_g_true", "lsst_yr4_r_true"]].notna().all().all() + # Input is not mutated (complete_data works on a copy). + assert "ra" not in df.columns + + def test_complete_data_multisurvey( + self, mock_multisurvey_injector, multisurvey_stream_config + ): + """Multi-survey complete_data fills every namespace from ONE mass draw. + + Both namespaces are backed by the same ugali survey/bands, so the shared + masses must yield identical true magnitudes across them. + """ + df = pd.DataFrame({"phi1": [-3.0, 0.0, 3.0], "phi2": [0.0, 0.0, 0.0]}) + out = mock_multisurvey_injector.complete_data( + df, + bands={"lsst_yr4": ["g", "r"], "lsst_yr5": ["g", "r"]}, + stream_config=multisurvey_stream_config, + seed=7, + ) + for col in [ + "ra", + "dec", + "lsst_yr4_g_true", + "lsst_yr4_r_true", + "lsst_yr5_g_true", + "lsst_yr5_r_true", + ]: + assert col in out.columns, f"missing {col}" + # Same physical star across surveys -> identical true mags. + assert np.allclose(out["lsst_yr4_g_true"], out["lsst_yr5_g_true"]) + assert np.allclose(out["lsst_yr4_r_true"], out["lsst_yr5_r_true"]) + + def test_bands_list_rejected_for_multisurvey(self, mock_multisurvey_injector): + """A plain list of bands is ambiguous for a multi-survey injector.""" + df = pd.DataFrame({"phi1": [0.0], "phi2": [0.0]}) + with pytest.raises(ValueError): + mock_multisurvey_injector.complete_data(df, bands=["g", "r"]) + + def test_bands_dict_unknown_survey_raises(self, mock_injector): + """A bands dict referencing an unknown namespace is rejected.""" + df = pd.DataFrame({"phi1": [0.0], "phi2": [0.0]}) + with pytest.raises(ValueError): + mock_injector.complete_data(df, bands={"nope": ["g"]}) + + def test_detect_flag_requires_survey(self, mock_injector): + """`survey` is now a required argument of detect_flag.""" + with pytest.raises(TypeError): + mock_injector.detect_flag(0, mag=np.array([20.0]), band="r") From 6674845e022287477875e5315632b6b426acd62a Mon Sep 17 00:00:00 2001 From: psferguson Date: Wed, 17 Jun 2026 14:52:48 -0700 Subject: [PATCH 13/54] Docs: sync with release-namespacing; tolerant completeness-column loader Doc staleness introduced by the PR #47 behavior changes: - column_convention.md / quickstart.md / multisurvey.md: column examples are now release-namespaced ({name}_{release}); state the namespacing rule. Quickstart Example 3 (lsst/yr1) no longer errors on copy-paste. - Correct the false "the dict key is the column namespace" claim in multisurvey.md, the StreamInjector.__init__ docstring, and roman_rubin_demo.yaml (keys are containers; namespace is re-derived from each Survey). - Document the {"survey":,"release":} spec-dict input form, the bands-dict validation, the mass column / user-supplied masses, _MASS_STEPS, and primary/primary_namespace. - Reword the S/N "applied once" text: the reference-band cut is owned by the selection-function curves, not re-applied by the injector. - Note the multi-survey isochrone requirement that surveys: keys equal the injector namespaces. Code: - set_completeness now accepts both "classification_eff" and the legacy misspelled "classifiction_eff" header, so the correct spelling documented in new_survey.md works without breaking the current (misspelled) data package. Also fixed the docstring's stale "eff_star" -> "detection_eff". Co-Authored-By: Claude Opus 4.8 (1M context) --- config/scenes/roman_rubin_demo.yaml | 8 +-- docs/source/column_convention.md | 16 ++++-- docs/source/multisurvey.md | 83 +++++++++++++++++++++-------- docs/source/quickstart.md | 28 +++++----- streamobs/observed.py | 22 ++++---- streamobs/surveys.py | 17 ++++-- 6 files changed, 117 insertions(+), 57 deletions(-) diff --git a/config/scenes/roman_rubin_demo.yaml b/config/scenes/roman_rubin_demo.yaml index 1b0bbae..fb1413a 100644 --- a/config/scenes/roman_rubin_demo.yaml +++ b/config/scenes/roman_rubin_demo.yaml @@ -15,9 +15,11 @@ # The same physical stars carry both `lsst__*` and `roman__*` # columns; each band's errors / detection come from that survey's own maps. -# Surveys to inject. The mapping KEY is the column namespace (lsst_r_obs, ...). -# Each value is a survey spec: a survey-name string here (loaded via -# Survey.load); could also be a {name, release} once the loader supports it. +# Surveys to inject. The column namespace is each survey's own {name}_{release} +# (here just `lsst` / `roman` since these are loaded without a release) — the +# mapping keys below are NOT used as the namespace; they are containers only. +# Each value is a survey spec: a survey-name string (loaded via Survey.load), or +# a {"survey": ..., "release": ...} dict. surveys: lsst: lsst roman: roman diff --git a/docs/source/column_convention.md b/docs/source/column_convention.md index d8a376d..068b9c9 100644 --- a/docs/source/column_convention.md +++ b/docs/source/column_convention.md @@ -6,9 +6,16 @@ survey. ## The scheme -For a survey with namespace `` (the survey's name, or the key you gave it -when constructing a multi-survey `StreamInjector`) and a photometric band -``: +Every column is prefixed with the survey's **namespace**, which is +`{name}_{release}` (e.g. `lsst_yr5`, `roman_dc2`), or just `{name}` when the +survey was loaded without a release. The namespace is always derived from the +`Survey` itself ({attr}`streamobs.surveys.Survey.namespace`) and includes the +release on **every** column kind, so the same survey at two releases produces +distinct, non-colliding columns. When you construct a multi-survey injector from +a `{key: spec}` dict the keys are containers only — they do **not** become the +namespace (it is re-derived from each loaded `Survey`). + +For a survey with namespace `` and a photometric band ``: | Column | Meaning | |---|---| @@ -20,7 +27,8 @@ when constructing a multi-survey `StreamInjector`) and a photometric band Plus the shared, un-namespaced sky coordinates `ra`, `dec`. -Examples: `lsst_r_obs`, `lsst_g_err`, `roman_F158_true`, `lsst_flag_observed`. +Examples (LSST loaded with `release="yr5"`, Roman with `release="dc2"`): +`lsst_yr5_r_obs`, `lsst_yr5_g_err`, `roman_dc2_F158_true`, `lsst_yr5_flag_observed`. These names are produced by the helpers in `streamobs.columns` (`true_col`, `obs_col`, `err_col`, `flag_col`, `perfect_flag_col`), which take a diff --git a/docs/source/multisurvey.md b/docs/source/multisurvey.md index ec3f6f8..faaae22 100644 --- a/docs/source/multisurvey.md +++ b/docs/source/multisurvey.md @@ -14,21 +14,32 @@ Pass one survey, or several: ```python from streamobs.observed import StreamInjector -# One survey (loaded by name; `release` etc. forwarded to Survey.load) +# One survey (loaded by name; `release` etc. forwarded to Survey.load). +# Its namespace is "{name}_{release}", here "lsst_dc2". inj = StreamInjector("lsst", release="dc2") -# Several surveys: {namespace: spec}, where spec is a survey name or a Survey. -# The dict key is the column namespace. -inj = StreamInjector({"lsst": "lsst", "roman": "roman"}, primary="lsst") +# Several surveys as a list of specs — each spec is a survey name, a Survey, or a +# {"survey": ..., "release": ...} dict. The namespace is derived from each loaded +# Survey ("lsst_dc2", "roman_dc2"), NOT from any key you supply. +inj = StreamInjector([ + {"survey": "lsst", "release": "dc2"}, + {"survey": "roman", "release": "dc2"}, +]) -# Or a list — each survey is namespaced by its own name. +# A list of plain names loads each release-less, so the namespace is the bare +# name ("lsst", "roman"). inj = StreamInjector(["lsst", "roman"]) ``` +- The namespace (the column prefix) is always the survey's own + `{name}_{release}` ({attr}`streamobs.surveys.Survey.namespace`). A `{key: spec}` + dict is also accepted, but the keys are containers only — the namespace is + re-derived from each `Survey`, not taken from the key. - `primary` selects the survey whose footprint drives the shared sky placement (defaults to the first survey). -- `inj.surveys` is the `{namespace: Survey}` mapping; `inj.survey` is the primary - `Survey`. +- `inj.surveys` is the `{namespace: Survey}` mapping; `inj.primary` (alias + `inj.survey`) is the primary `Survey`, and `inj.primary_namespace` its namespace + string. ## Injecting @@ -36,15 +47,19 @@ inj = StreamInjector(["lsst", "roman"]) # Single survey: `bands` is the shorthand (defaults to ['r', 'g']). out = inj.inject(df, bands=["r", "g"], stream_config=cfg, seed=42) -# Several surveys: give the bands per survey as a {survey: [bands]} dict. +# Several surveys: give the bands per survey as a {namespace: [bands]} dict, +# keyed by each survey's namespace ({name}_{release}). out = inj.inject( df, - bands={"lsst": ["r", "g"], "roman": ["F106", "F158"]}, + bands={"lsst_dc2": ["r", "g"], "roman_dc2": ["F106", "F158"]}, stream_config=cfg, seed=42, ) ``` +A plain list is rejected for a multi-survey injector (it is ambiguous), and a +`bands` dict referencing an unknown namespace raises `ValueError`. + `df` may already contain `ra`/`dec` or `phi1`/`phi2`, may be a fully empty frame of length *N*, or any subset — anything missing is sampled from `stream_config` (see *Completing a catalog* below). The output carries shared @@ -53,8 +68,8 @@ of length *N*, or any subset — anything missing is sampled from `stream_config ``` ra, dec, -lsst_r_true, lsst_r_obs, lsst_r_err, lsst_g_true, ..., lsst_flag_observed, -roman_F106_true, roman_F106_obs, ..., roman_flag_observed +lsst_dc2_r_true, lsst_dc2_r_obs, lsst_dc2_r_err, lsst_dc2_g_true, ..., lsst_dc2_flag_observed, +roman_dc2_F106_true, roman_dc2_F106_obs, ..., roman_dc2_flag_observed ``` Useful `inject` keyword arguments: @@ -87,13 +102,23 @@ stream: age: 12.0 z: 0.0006 surveys: - lsst: {survey: lsst, band_1: g, band_2: r} - roman: {survey: roman, band_1: F106, band_2: F158} + lsst_dc2: {survey: lsst, band_1: g, band_2: r} + roman_dc2: {survey: roman, band_1: F106, band_2: F158} +``` + +```{important} +Each `surveys:` **key is the column namespace** the isochrone produces +(`__true`), and it must match the injecting survey's namespace +`{name}_{release}` — otherwise the true-magnitude columns the model emits won't +line up with the columns the injector looks for. Here the inner `survey:` is the +*ugali* filter set (no release), while the key carries the release. (In the +single-survey flat form the namespace is derived as `{survey}_{release}` for you; +in the multi-survey form you spell it out as the key.) ``` -A single-survey isochrone (the flat `survey`/`band_1`/`band_2` form) is just the -one-survey case of the same machinery and produces `__true` -identically. +A single-survey isochrone (the flat `survey`/`band_1`/`band_2` form, optionally +with `release:`) is just the one-survey case of the same machinery and produces +`__true` identically. A complete, runnable example — the surveys, per-survey bands, the multi-survey isochrone, and the shared stream geometry — is provided as a *scene* config in @@ -160,15 +185,29 @@ and request another, the supplied band is left untouched and only the missing on is filled (newly-filled cells still come from one shared mass draw, so they are mutually colour-consistent). +### Stellar masses (the `mass` column) + +When an isochrone is configured, the shared **initial masses** drawn for the stars +are surfaced as a single un-namespaced `mass` column (one mass per star, shared by +all surveys — the same physical star). You can also go the other way and supply +your own masses: pass a fully-populated `mass` column in the input catalog and the +isochrone uses *those* masses instead of drawing fresh ones, so the sampled +magnitudes reproduce your simulation's exact stars. At the model level +{meth}`streamobs.model.IsochroneModel.sample_multisurvey` accepts a `masses=` +array and returns the masses it used. The mass grid resolution is controlled by +`IsochroneModel._MASS_STEPS` (default 4000) and a per-call `mass_steps=` override. + ## S/N cut ownership The reference band (`survey.completeness_band`, e.g. LSST `r`) is special: the -survey's **selection functions are estimated with the SNR ≥ 5 cut already -applied** in that band. The injector therefore applies the reference-band cut -exactly **once**, and the explicit `detection_mag_cut` loop applies SNR ≥ 5 to -every *other* injected band (its default is all injected bands except the -reference band). Net effect: a star must have SNR ≥ 5 in every injected band to -be flagged observed, with the reference band counted once. +survey's **selection-function curves are estimated on true stars detected at +SNR ≥ 5** in that band, so the cut is already baked into both the completeness +and detection-efficiency curves. The injector therefore does **not** re-apply a +SNR cut to the reference band (doing so would double-count it); the explicit +`detection_mag_cut` loop applies SNR ≥ 5 only to the *other* injected bands (its +default is all injected bands except the reference band). Net effect: a star must +have SNR ≥ 5 in every injected band to be flagged observed, with the reference +band's cut owned entirely by the selection-function curves. ## See also diff --git a/docs/source/quickstart.md b/docs/source/quickstart.md index 9564a99..50386c4 100644 --- a/docs/source/quickstart.md +++ b/docs/source/quickstart.md @@ -73,15 +73,16 @@ injector = observed.StreamInjector(lsst_survey) # Create or load your mock stream data. # Here we create a simple test dataset. Could instead contain (ra, dec) to skip # the coordinate transformation. True (noiseless) magnitudes are passed in as -# survey-namespaced __true columns (the survey's name is its -# namespace; here "lsst"). Alternatively, omit the magnitudes and pass a -# `stream_config=` so the injector samples them from an isochrone. +# survey-namespaced __true columns, where the namespace is +# "{name}_{release}" — here the survey is lsst/yr1, so "lsst_yr1". +# Alternatively, omit the magnitudes and pass a `stream_config=` so the injector +# samples them from an isochrone. rng = np.random.default_rng(42) mock_data = pd.DataFrame({ - 'phi1': rng.uniform(-5, 5, 1000), # Stream longitude - 'phi2': rng.uniform(-1, 1, 1000), # Stream latitude - 'lsst_g_true': rng.uniform(18, 28, 1000), # true g-band apparent magnitude - 'lsst_r_true': rng.uniform(18, 28, 1000), # true r-band apparent magnitude + 'phi1': rng.uniform(-5, 5, 1000), # Stream longitude + 'phi2': rng.uniform(-1, 1, 1000), # Stream latitude + 'lsst_yr1_g_true': rng.uniform(18, 28, 1000), # true g-band apparent magnitude + 'lsst_yr1_r_true': rng.uniform(18, 28, 1000), # true r-band apparent magnitude }) # Apply survey effects: footprint, extinction, photometric errors @@ -94,7 +95,7 @@ observed_data = injector.inject( ) print(f"Input stars: {len(mock_data)}") -print(f"Detected stars: {int(observed_data['lsst_flag_observed'].sum())}") +print(f"Detected stars: {int(observed_data['lsst_yr1_flag_observed'].sum())}") ``` ### What the Injector Does @@ -108,12 +109,13 @@ The `StreamInjector` applies several observational effects: The output dataframe includes (all magnitude/flag columns are **survey-namespaced** -as `_...`; for the LSST survey loaded above the namespace is `lsst`): +as `_...`, where the namespace is `{name}_{release}`; for the LSST/yr1 +survey loaded above it is `lsst_yr1`): - `ra`, `dec`: Sky coordinates -- `lsst_g_true`, `lsst_r_true`: True (noiseless) apparent magnitudes -- `lsst_g_obs`, `lsst_r_obs`: Observed (noisy) magnitudes -- `lsst_g_err`, `lsst_r_err`: Reported photometric uncertainties -- `lsst_flag_observed`: Detection and classification flag (`True`=detected & classified as a star, `False`=not detected or not classified as a star) +- `lsst_yr1_g_true`, `lsst_yr1_r_true`: True (noiseless) apparent magnitudes +- `lsst_yr1_g_obs`, `lsst_yr1_r_obs`: Observed (noisy) magnitudes +- `lsst_yr1_g_err`, `lsst_yr1_r_err`: Reported photometric uncertainties +- `lsst_yr1_flag_observed`: Detection and classification flag (`True`=detected & classified as a star, `False`=not detected or not classified as a star) ## Next Steps diff --git a/streamobs/observed.py b/streamobs/observed.py index f546770..9b7511f 100644 --- a/streamobs/observed.py +++ b/streamobs/observed.py @@ -79,17 +79,19 @@ def __init__(self, survey, primary=None, **kwargs): Parameters ---------- survey : str, Survey, dict, or list - One survey or several. Accepted forms: - - - a survey-name string (e.g. ``'lsst'``) or a pre-loaded - :class:`~streamobs.surveys.Survey` — a single survey, namespaced - by its own name; - - a ``{namespace: spec}`` dict, where ``spec`` is a name string or a - ``Survey`` and the key is the column namespace; - - a list/tuple of specs, each namespaced by its survey's name. + One survey or several. Every survey is namespaced by its own + :attr:`~streamobs.surveys.Survey.namespace` (``{name}_{release}``, + or just ``{name}`` with no release). Accepted forms: + + - a survey-name string (e.g. ``'lsst'``), a + ``{"survey": ..., "release": ...}`` spec dict, or a pre-loaded + :class:`~streamobs.surveys.Survey` — a single survey; + - a list/tuple of such specs; + - a ``{key: spec}`` dict — the keys are containers only and are + **ignored**; the namespace is re-derived from each loaded survey. primary : str, optional - Namespace of the survey that drives the shared sky placement. - Defaults to the first survey. + Namespace (``{name}_{release}``) of the survey that drives the shared + sky placement. Defaults to the first survey. **kwargs Forwarded to :meth:`Survey.load` for any ``spec`` given as a name string (e.g. ``release``). diff --git a/streamobs/surveys.py b/streamobs/surveys.py index a790314..be1c704 100644 --- a/streamobs/surveys.py +++ b/streamobs/surveys.py @@ -1397,8 +1397,9 @@ def set_completeness(filename, delta_saturation=-10.4, selection="both"): Path to CSV file with columns: - 'delta_mag' : Magnitude difference from limit (mag - maglim) - - 'eff_star' : Detection efficiency only - - 'classifiction_eff' : Classification efficiency only + - 'detection_eff' : Detection efficiency only + - 'classification_eff' : Classification efficiency only (the legacy + misspelled header 'classifiction_eff' is also accepted) - 'classification_detection_eff' : Combined efficiency delta_saturation : float, optional @@ -1406,8 +1407,9 @@ def set_completeness(filename, delta_saturation=-10.4, selection="both"): selection : str, optional Which efficiency to use: - - 'detected' : Detection efficiency only (column 'eff_star') - - 'classified' : Classification efficiency only (column 'classifiction_eff') + - 'detected' : Detection efficiency only (column 'detection_eff') + - 'classified' : Classification efficiency only (column + 'classification_eff', or legacy 'classifiction_eff') - 'both' : Combined detection and classification (column 'classification_detection_eff') Default is 'both'. @@ -1436,7 +1438,12 @@ def set_completeness(filename, delta_saturation=-10.4, selection="both"): if selection == "detected": efficiencies = data["detection_eff"] elif selection == "classified": - efficiencies = data["classifiction_eff"] + # Prefer the correct spelling; fall back to the legacy misspelled + # header ("classifiction_eff") in older/Zenodo data packages. + if "classification_eff" in data.dtype.names: + efficiencies = data["classification_eff"] + else: + efficiencies = data["classifiction_eff"] elif selection == "both": efficiencies = data["classification_detection_eff"] else: From 1ac8d37087c6809541e49bbf48f44058b035cea4 Mon Sep 17 00:00:00 2001 From: psferguson Date: Wed, 17 Jun 2026 14:56:23 -0700 Subject: [PATCH 14/54] Fix SplineStreamModel instantiation and plot_inject namespaced columns - SplineStreamModel._create_model called an undefined self._create_distance(); rename to self._create_distance_modulus() (model.py:981). This made every spline-stream run (e.g. bin/generate_spline_stream.py) raise AttributeError. - plotting.plot_inject read bare, non-namespaced columns (flag_observed, r_obs, ...) and so failed on the injector's {name}_{release}-namespaced output; derive the namespace from survey.namespace and use it for all flag/true/obs columns. Tests: test_spline_model.py (instantiate + sample, skipped if the spline data file is absent) and test_plotting.py (plot_inject smoke test on namespaced output). 37 passed. Co-Authored-By: Claude Opus 4.8 (1M context) --- streamobs/model.py | 2 +- streamobs/plotting.py | 34 ++++++++++++++++++--------------- tests/test_plotting.py | 29 ++++++++++++++++++++++++++++ tests/test_spline_model.py | 39 ++++++++++++++++++++++++++++++++++++++ 4 files changed, 88 insertions(+), 16 deletions(-) create mode 100644 tests/test_plotting.py create mode 100644 tests/test_spline_model.py diff --git a/streamobs/model.py b/streamobs/model.py index a429c37..d25f9b9 100644 --- a/streamobs/model.py +++ b/streamobs/model.py @@ -978,6 +978,6 @@ def _create_model(self): """Instantiate spline-specific components and common sub-models.""" self.density = self._create_linear_density() self.track = self._create_track() - self.distance_modulus = self._create_distance() + self.distance_modulus = self._create_distance_modulus() self.isochrone = self._create_isochrone() self.velocity = self._create_velocity() diff --git a/streamobs/plotting.py b/streamobs/plotting.py index b17e59d..49d0d97 100644 --- a/streamobs/plotting.py +++ b/streamobs/plotting.py @@ -94,13 +94,14 @@ def plot_inject(data, survey, bands=None, range=None, **kwargs): Parameters ---------- data : pandas.DataFrame or dict-like - Data containing the injected stream. Must have columns: + Data containing the injected stream, with columns namespaced by the + survey's ``namespace`` (``{name}_{release}``, e.g. ``lsst_yr5``). Must have: - 'ra', 'dec': Sky coordinates in degrees - - 'flag_observed': Boolean flag for detected stars - - '_true': True magnitudes for each band - - '_obs': Observed magnitudes for each band + - '_flag_observed': Boolean flag for detected stars + - '__true': True magnitudes for each band + - '__obs': Observed magnitudes for each band survey : Survey - Survey object containing magnitude limit maps and other properties. + Survey object; supplies the column namespace and the magnitude-limit maps. bands : list of str, optional Bands to use for HR diagram. Default is ['g', 'r']. Will use first two bands if more are provided. @@ -132,22 +133,25 @@ def plot_inject(data, survey, bands=None, range=None, **kwargs): raise ValueError("Need at least 2 bands for HR diagram") band1, band2 = bands[0], bands[1] + # Injected columns are namespaced by the survey ({name}_{release}). + ns = survey.namespace + # Check required columns required_cols = [ "ra", "dec", - flag_col(), - true_col(band1), - true_col(band2), - obs_col(band1), - obs_col(band2), + flag_col(ns), + true_col(band1, ns), + true_col(band2, ns), + obs_col(band1, ns), + obs_col(band2, ns), ] missing_cols = [col for col in required_cols if col not in data.columns] if missing_cols: raise ValueError(f"Missing required columns: {missing_cols}") # Get detection flags - sel = data[flag_col()].astype(bool) + sel = data[flag_col(ns)].astype(bool) # Create figure fig, ax = plt.subplots(1, 3, figsize=(14, 6)) @@ -207,8 +211,8 @@ def plot_inject(data, survey, bands=None, range=None, **kwargs): # --- Panel 2: HR diagram with true magnitudes --- ax[1].set_title("HR diagram using True magnitudes") - color_true = data[true_col(band1)] - data[true_col(band2)] - mag_true = data[true_col(band1)] + color_true = data[true_col(band1, ns)] - data[true_col(band2, ns)] + mag_true = data[true_col(band1, ns)] ax[1].scatter( color_true, @@ -236,8 +240,8 @@ def plot_inject(data, survey, bands=None, range=None, **kwargs): ax[2].set_title("HR diagram using sampled observed magnitudes") # Convert measured magnitudes to numeric, handling "BAD_MAG" strings - mag1_obs = pd.to_numeric(data[obs_col(band1)], errors="coerce") - mag2_obs = pd.to_numeric(data[obs_col(band2)], errors="coerce") + mag1_obs = pd.to_numeric(data[obs_col(band1, ns)], errors="coerce") + mag2_obs = pd.to_numeric(data[obs_col(band2, ns)], errors="coerce") # Mask out bad measurements mask_good = (~mag1_obs.isna()) & (~mag2_obs.isna()) diff --git a/tests/test_plotting.py b/tests/test_plotting.py new file mode 100644 index 0000000..3c07228 --- /dev/null +++ b/tests/test_plotting.py @@ -0,0 +1,29 @@ +""" +tests/test_plotting.py +====================== +Smoke test for ``streamobs.plotting.plot_inject``. + +Guards against the regression where ``plot_inject`` read bare, non-namespaced +columns (``flag_observed``, ``r_obs``, ...) and so raised "Missing required +columns" on the injector's now-namespaced (``{name}_{release}``) output. +""" + +import matplotlib + +matplotlib.use("Agg") # headless backend for tests + +import pytest + +from streamobs.plotting import plot_inject + + +@pytest.mark.observed +class TestPlotInject: + def test_plot_inject_namespaced_columns(self, mock_injector, stream_catalog, verbose): + """plot_inject consumes the namespaced injector output without error.""" + cat = mock_injector.inject(stream_catalog, bands=["g", "r"], verbose=verbose) + # mock_injector is LSST/yr4 -> namespace lsst_yr4; columns are lsst_yr4_*. + fig, ax = plot_inject(cat, mock_injector.survey, bands=["g", "r"]) + assert fig is not None + assert len(ax) == 3 + matplotlib.pyplot.close(fig) diff --git a/tests/test_spline_model.py b/tests/test_spline_model.py new file mode 100644 index 0000000..1b8a080 --- /dev/null +++ b/tests/test_spline_model.py @@ -0,0 +1,39 @@ +""" +tests/test_spline_model.py +========================== +Regression test for ``streamobs.model.SplineStreamModel``. + +Guards against the bug where ``_create_model`` called an undefined +``_create_distance()`` (now ``_create_distance_modulus()``), which made every +spline-stream instantiation raise ``AttributeError``. +""" + +import os + +import pytest +import yaml + +from streamobs.model import SplineStreamModel + +REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +CFG = os.path.join(REPO, "config", "atlas_spline_config.yaml") + + +@pytest.mark.model +class TestSplineStreamModel: + def test_instantiate_and_sample(self): + """Constructing + sampling a spline stream must not raise (was AttributeError).""" + if not os.path.exists(CFG): + pytest.skip("atlas_spline_config.yaml not present") + cfg = yaml.safe_load(open(CFG))["stream"] + # The spline density/track read an external interpolation file; skip if + # the (gitignored / downloaded) data is not available locally. + data_file = cfg.get("filename", "").lstrip("./") + if data_file and not os.path.exists(os.path.join(REPO, data_file)): + pytest.skip(f"spline data file {data_file} not present") + + model = SplineStreamModel(cfg) # previously raised AttributeError here + df = model.sample(25) + assert len(df) == 25 + for col in ("phi1", "phi2"): + assert col in df.columns From a623b9c5106a4d822909299007feeb8257fee85e Mon Sep 17 00:00:00 2001 From: psferguson Date: Wed, 17 Jun 2026 14:59:56 -0700 Subject: [PATCH 15/54] Docs: use des/yr6 (not y6) for the DES release identifier The DES survey config is name=des, release=yr6 (namespace des_yr6) and the data directory is data/surveys/des_yr6/, matching the LSST yr* convention. DES.md told users to load release='y6' and use a des_y6/ folder, which fails (no des_y6 config). Standardize the release/dir on yr6 in DES.md and the new_survey.md example. (Data-file basenames in the package remain des_y6_*; that's a separate data-package rename.) Co-Authored-By: Claude Opus 4.8 (1M context) --- docs/source/DES.md | 4 ++-- docs/source/new_survey.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/DES.md b/docs/source/DES.md index 163274f..01fa2f8 100644 --- a/docs/source/DES.md +++ b/docs/source/DES.md @@ -7,8 +7,8 @@ We have added the DES Y6 Gold as a supported survey to use with streamobs. The survey dataset is described in [Bechtol et al. 2025](https://arxiv.org/abs/2501.05739), and the catalogs can are documented/publically available from [DESDM](https://des.ncsa.illinois.edu/releases). -The maglim, completeness, and photoerror files should be downloaded and placed in the `data/surveys/des_y6/` folder and loaded in the following manner: +The maglim, completeness, and photoerror files should be downloaded and placed in the `data/surveys/des_yr6/` folder and loaded in the following manner: ``` -des_y6= surveys.Survey.load(survey = 'des', release='y6') +des_yr6 = surveys.Survey.load(survey='des', release='yr6') ``` Any questions about the creation of these survey specific files can be addressed to Peter Ferguson. \ No newline at end of file diff --git a/docs/source/new_survey.md b/docs/source/new_survey.md index 07a0d24..fd29661 100644 --- a/docs/source/new_survey.md +++ b/docs/source/new_survey.md @@ -20,7 +20,7 @@ Add magnitude limit maps for each band: * Example: ``` - des_y6_g_band_nside_128.hsp + des_yr6_g_band_nside_128.hsp ``` * Optional: survey-specific completeness and photometric error files. If not provided, the default files in `data/others/` will be used. From 44ab9c8a969bf3baa55f6080d87f068944c076ab Mon Sep 17 00:00:00 2001 From: psferguson Date: Wed, 17 Jun 2026 15:02:12 -0700 Subject: [PATCH 16/54] Docs: correct complete_catalog preserve-vs-overwrite docstring The Notes claimed magnitudes (like velocities) overwrite whole columns. In fact phi1/phi2/dist, magnitudes, and the shared mass column fill only missing rows (the preserve-existing contract); only velocities are recomputed wholesale. Co-Authored-By: Claude Opus 4.8 (1M context) --- streamobs/model.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/streamobs/model.py b/streamobs/model.py index d25f9b9..0ae3884 100644 --- a/streamobs/model.py +++ b/streamobs/model.py @@ -277,10 +277,12 @@ def complete_catalog( - Dependencies: 'phi2' and 'dist' require 'phi1'. Magnitudes require 'dist' and an isochrone model. Velocities require 'phi1' and a velocity model. - - Existing non-null values are preserved; only missing rows are filled, - except for magnitudes and velocities where the method intentionally - overwrites the whole columns to keep internal consistency (e.g., - colors and kinematic coherence across rows). + - Existing non-null values are preserved: only the missing rows are + filled for ``phi1``/``phi2``/``dist``, the magnitude columns, and the + shared ``mass`` column (supplying some bands and requesting others + fills only the missing ones, colour-consistently). Velocities are the + exception — ``mu1``/``mu2``/``rv`` are recomputed for the whole columns + to keep kinematic coherence across rows. - When ``catalog`` is a CSV path and ``inplace`` is True, the original file is overwritten. """ From 11dcbfe3e17b2c294d2cc3377f6886abfee9cdc0 Mon Sep 17 00:00:00 2001 From: MatthieuPe Date: Thu, 18 Jun 2026 15:50:01 +0200 Subject: [PATCH 17/54] Removing release from mag true --- streamobs/columns.py | 27 +++++++++++++++++---------- tests/test_model.py | 38 +++++++++++++++++++------------------- tests/test_observed.py | 24 ++++++++++++------------ 3 files changed, 48 insertions(+), 41 deletions(-) diff --git a/streamobs/columns.py b/streamobs/columns.py index bc6b680..be3eb96 100644 --- a/streamobs/columns.py +++ b/streamobs/columns.py @@ -23,26 +23,33 @@ """ -def true_col(band, survey=None): +def true_col(band, survey_namespace=None): """Column holding the *true* (noiseless) apparent magnitude for ``band``.""" - return f"{survey}_{band}_true" if survey else f"{band}_true" + # Split survey_namespace "{name}_{release}" into survey and release if needed + if isinstance(survey_namespace, str): + survey_name = survey_namespace.split("_")[0] + else: + survey_name = None + + return f"{survey_name}_{band}_true" if survey_name else f"{band}_true" -def obs_col(band, survey=None): + +def obs_col(band, survey_namespace=None): """Column holding the *observed* (noisy) magnitude for ``band``.""" - return f"{survey}_{band}_obs" if survey else f"{band}_obs" + return f"{survey_namespace}_{band}_obs" if survey_namespace else f"{band}_obs" -def err_col(band, survey=None): +def err_col(band, survey_namespace=None): """Column holding the reported magnitude error for ``band``.""" - return f"{survey}_{band}_err" if survey else f"{band}_err" + return f"{survey_namespace}_{band}_err" if survey_namespace else f"{band}_err" -def flag_col(survey=None): +def flag_col(survey_namespace=None): """Column holding the detection flag (band-independent).""" - return f"{survey}_flag_observed" if survey else "flag_observed" + return f"{survey_namespace}_flag_observed" if survey_namespace else "flag_observed" -def perfect_flag_col(survey=None): +def perfect_flag_col(survey_namespace=None): """Column holding the perfect star/galaxy-separation flag (band-independent).""" - return f"{survey}_flag_perfect_galstarsep" if survey else "flag_perfect_galstarsep" + return f"{survey_namespace}_flag_perfect_galstarsep" if survey_namespace else "flag_perfect_galstarsep" diff --git a/tests/test_model.py b/tests/test_model.py index 3f3003b..3a95214 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -134,8 +134,8 @@ def test_full_model(self, stream_config_with_distance): "phi1", "phi2", "dist", - "lsst_yr4_g_true", - "lsst_yr4_r_true", + "lsst_g_true", + "lsst_r_true", } # Not adding mu1, mu2, rv since not implemented yet self._verify_catalogue_content(samples, expected_columns) @@ -147,8 +147,8 @@ def test_complete_catalog(self, sample_catalog_phi, stream_config_with_distance) "phi1", "phi2", "dist", - "lsst_yr4_g_true", - "lsst_yr4_r_true", + "lsst_g_true", + "lsst_r_true", } # Not adding mu1, mu2, rv since not implemented yet self._verify_catalogue_content(completed_catalog, expected_columns) assert len(completed_catalog) == len( @@ -157,7 +157,7 @@ def test_complete_catalog(self, sample_catalog_phi, stream_config_with_distance) # Verify that I can add a targeted column (e.g. dist) to the input catalog and complete the rest partial_catalog = completed_catalog.drop( - columns=["lsst_yr4_r_true", "dist", "lsst_yr4_g_true"] + columns=["lsst_r_true", "dist", "lsst_g_true"] ).reset_index(drop=True) completed_catalog = model.complete_catalog( catalog=partial_catalog, @@ -173,8 +173,8 @@ def test_complete_catalog(self, sample_catalog_phi, stream_config_with_distance) partial_catalog ), f"Completed catalog should have the same number of rows as the input catalog" assert ( - "lsst_yr4_r_true" not in completed_catalog.columns - ), "Column 'lsst_yr4_r_true' should not be added when not requested" + "lsst_r_true" not in completed_catalog.columns + ), "Column 'lsst_r_true' should not be added when not requested" # --------------------------------------------------------------------------- @@ -192,7 +192,7 @@ class TestCompleteCatalogPermutations: columns and individual NaN rows are never overwritten). """ - MAGS = {"lsst_yr4_g_true", "lsst_yr4_r_true"} + MAGS = {"lsst_g_true", "lsst_r_true"} def test_empty_frame_fills_all_model_columns(self, stream_config_with_distance): """size=N with no catalog -> geometry, dist, and both bands are filled.""" @@ -230,12 +230,12 @@ def test_existing_band_preserved_when_filling_other( """Providing one band and requesting both leaves the provided one intact.""" model = StreamModel(stream_config_with_distance) g = np.array([20.0, 21.0, 22.0]) - df = pd.DataFrame({"dist": [16.0] * 3, "lsst_yr4_g_true": g.copy()}) + df = pd.DataFrame({"dist": [16.0] * 3, "lsst_g_true": g.copy()}) out = model.complete_catalog( catalog=df, columns_to_add=list(self.MAGS), verbose=False ) - assert np.allclose(out["lsst_yr4_g_true"].to_numpy(), g), "present band overwritten" - assert out["lsst_yr4_r_true"].notna().all(), "missing band not filled" + assert np.allclose(out["lsst_g_true"].to_numpy(), g), "present band overwritten" + assert out["lsst_r_true"].notna().all(), "missing band not filled" def test_present_columns_skip_sampling(self, stream_config_with_distance): """Both bands present -> values are returned untouched.""" @@ -243,23 +243,23 @@ def test_present_columns_skip_sampling(self, stream_config_with_distance): g = np.array([20.0, 21.0]) r = np.array([19.0, 19.5]) df = pd.DataFrame( - {"dist": [16.0, 16.0], "lsst_yr4_g_true": g.copy(), "lsst_yr4_r_true": r.copy()} + {"dist": [16.0, 16.0], "lsst_g_true": g.copy(), "lsst_r_true": r.copy()} ) out = model.complete_catalog( catalog=df, columns_to_add=list(self.MAGS), verbose=False ) - assert np.allclose(out["lsst_yr4_g_true"].to_numpy(), g) - assert np.allclose(out["lsst_yr4_r_true"].to_numpy(), r) + assert np.allclose(out["lsst_g_true"].to_numpy(), g) + assert np.allclose(out["lsst_r_true"].to_numpy(), r) def test_partial_rows_only_missing_filled(self, stream_config_with_distance): """A band with some NaN rows keeps its finite rows; only NaNs are filled.""" model = StreamModel(stream_config_with_distance) g = np.array([20.0, np.nan, 22.0]) - df = pd.DataFrame({"dist": [16.0] * 3, "lsst_yr4_g_true": g.copy()}) + df = pd.DataFrame({"dist": [16.0] * 3, "lsst_g_true": g.copy()}) out = model.complete_catalog( - catalog=df, columns_to_add=["lsst_yr4_g_true"], verbose=False + catalog=df, columns_to_add=["lsst_g_true"], verbose=False ) - filled = out["lsst_yr4_g_true"].to_numpy() + filled = out["lsst_g_true"].to_numpy() assert filled[0] == 20.0 and filled[2] == 22.0, "finite rows overwritten" assert np.isfinite(filled[1]), "NaN row not filled" @@ -329,8 +329,8 @@ def test_mags_without_dist_or_phi_raises(self, minimal_stream_config): class TestIsochroneMasses: """The shared initial masses can be reused/supplied and surface as `mass`.""" - G = "lsst_yr4_g_true" - R = "lsst_yr4_r_true" + G = "lsst_g_true" + R = "lsst_r_true" def test_sample_multisurvey_returns_and_reuses_masses( self, stream_config_with_distance diff --git a/tests/test_observed.py b/tests/test_observed.py index ad67419..45d2198 100644 --- a/tests/test_observed.py +++ b/tests/test_observed.py @@ -105,8 +105,8 @@ def _verify_injected_catalog_content( expected_columns=[ "ra", "dec", - "lsst_yr4_g_true", - "lsst_yr4_r_true", + "lsst_g_true", + "lsst_r_true", "lsst_yr4_g_obs", "lsst_yr4_r_obs", "lsst_yr4_flag_observed", @@ -136,7 +136,7 @@ def test_injection_partialinput( ): """Test injection with a catalog that has some missing columns.""" data_without_mag = stream_catalog.drop( - columns=["lsst_yr4_g_true", "lsst_yr4_r_true"] + columns=["lsst_g_true", "lsst_r_true"] ) injected_catalog = mock_injector.inject( data_without_mag, @@ -249,9 +249,9 @@ def test_complete_data_single_survey(self, mock_injector, stream_config_with_dis out = mock_injector.complete_data( df, bands=["g", "r"], stream_config=stream_config_with_distance, seed=1 ) - for col in ["ra", "dec", "lsst_yr4_g_true", "lsst_yr4_r_true"]: + for col in ["ra", "dec", "lsst_g_true", "lsst_r_true"]: assert col in out.columns, f"missing {col}" - assert out[["lsst_yr4_g_true", "lsst_yr4_r_true"]].notna().all().all() + assert out[["lsst_g_true", "lsst_r_true"]].notna().all().all() # Input is not mutated (complete_data works on a copy). assert "ra" not in df.columns @@ -273,15 +273,15 @@ def test_complete_data_multisurvey( for col in [ "ra", "dec", - "lsst_yr4_g_true", - "lsst_yr4_r_true", - "lsst_yr5_g_true", - "lsst_yr5_r_true", + "lsst_g_true", + "lsst_r_true", ]: assert col in out.columns, f"missing {col}" - # Same physical star across surveys -> identical true mags. - assert np.allclose(out["lsst_yr4_g_true"], out["lsst_yr5_g_true"]) - assert np.allclose(out["lsst_yr4_r_true"], out["lsst_yr5_r_true"]) + + # Verify that we have a single column for each true magnitude, not one per survey. + assert out[["lsst_g_true", "lsst_r_true"]].notna().all().all() + + def test_bands_list_rejected_for_multisurvey(self, mock_multisurvey_injector): """A plain list of bands is ambiguous for a multi-survey injector.""" From 78053b8183e11e9c4c61745ef9fc70a89fdf59f4 Mon Sep 17 00:00:00 2001 From: MatthieuPe Date: Thu, 18 Jun 2026 15:55:04 +0200 Subject: [PATCH 18/54] move tutorial to the doc + order the surveys doc --- .../tutorial_generate_datamocks.ipynb | 970 +++++++++ .../examples/tutorial_inject_stream.ipynb | 1932 ++++++++++++++++ docs/source/index.rst | 14 +- docs/source/{ => surveys}/DES.md | 0 docs/source/{ => surveys}/LSST.md | 0 docs/source/{ => surveys}/roman.md | 0 notebooks/tutorial_generate_datamocks.ipynb | 931 -------- notebooks/tutorial_inject_stream.ipynb | 1938 ----------------- 8 files changed, 2913 insertions(+), 2872 deletions(-) create mode 100644 docs/source/examples/tutorial_generate_datamocks.ipynb create mode 100644 docs/source/examples/tutorial_inject_stream.ipynb rename docs/source/{ => surveys}/DES.md (100%) rename docs/source/{ => surveys}/LSST.md (100%) rename docs/source/{ => surveys}/roman.md (100%) delete mode 100644 notebooks/tutorial_generate_datamocks.ipynb delete mode 100644 notebooks/tutorial_inject_stream.ipynb diff --git a/docs/source/examples/tutorial_generate_datamocks.ipynb b/docs/source/examples/tutorial_generate_datamocks.ipynb new file mode 100644 index 0000000..57effa8 --- /dev/null +++ b/docs/source/examples/tutorial_generate_datamocks.ipynb @@ -0,0 +1,970 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "394f3750", + "metadata": {}, + "source": [ + "# Streamobs: generate stream mocks\n", + "\n", + "Generate stellar stream mock catalogs from configuration files and complete existing tables with missing columns.\n", + "**Streamobs** allows sampling of the following quantities:\n", + "\n", + "* (`phi1`, `phi2`): stellar coordinates in the stream frame\n", + "* `dist`: distance modulus of stars\n", + "* `{survey}_{band}`: apparent magnitude in a given photometric band of a chosen survey\n", + "\n", + "Future versions may also include sampling of proper motions and velocities.\n", + "\n", + "Streamobs can further convert these intrinsic quantities into **observed quantities**.\n", + "For more details, see the notebook *`tutorial_inject_stream.ipynb`*.\n", + "\n", + "**In this tutorial, you’ll learn to:**\n", + "\n", + "* Define model components: density, track, distance modulus, isochrone\n", + "* Build or load a configuration and sample a mock catalog\n", + "* Complete partial catalogs (e.g., by adding magnitudes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "94d3f015", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "import pandas as pd\n", + "import yaml\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import scipy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a54fd20b", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary modules form streamobs\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "from streamobs.utils import parse_config\n", + "from streamobs.model import StreamModel" + ] + }, + { + "cell_type": "markdown", + "id": "f5492fdd", + "metadata": {}, + "source": [ + "# 1) Build a stream configuration\n", + "\n", + "To set up a stream model, we use a configuration file or dictionary that defines all necessary components. It can include:\n", + "\n", + "* **density** – samples `phi1` values along the stream\n", + "* **track** – gives `phi2` as a function of `phi1` (center + spread, using a Gaussian or Uniform sampler)\n", + "* **distance_modulus** – defines $DM(phi1)$ for computing apparent magnitudes\n", + "* **isochrone** – samples the color–magnitude diagram (required to generate magnitudes)\n", + "\n", + "You can choose how each quantity depends on `phi1` (e.g., constant, linear, spline, etc.).\n", + "\n", + "**Notes:**\n", + "\n", + "* To generate magnitudes, you need at least both `dist` and `isochrone`.\n", + "* The velocity model is currently a placeholder (returns NaN).\n", + "* Samplers and functions are selected using the `type` keyword (e.g., `\"Uniform\"`, `\"CubicSplineInterpolation\"`).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3d2810e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'density': {'type': 'Uniform', 'xmin': -9.0, 'xmax': 9.0}, 'track': {'center': {'type': 'Constant', 'value': 0.0}, 'spread': {'type': 'Constant', 'value': 0.2}, 'sampler': 'Gaussian'}, 'isochrone': {'name': 'Marigo2017', 'survey': 'lsst', 'age': 12.0, 'z': 0.0006, 'band_1': 'g', 'band_2': 'r', 'band_1_detection': True}, 'distance_modulus': {'center': {'type': 'Constant', 'value': 16.5}, 'spread': {'type': 'Constant', 'value': 0.0}}}\n" + ] + } + ], + "source": [ + "# Build a config dictionary directly\n", + "\n", + "config = { \n", + " # Density model\n", + " 'density': {'type': 'Uniform', 'xmin': -9.0, 'xmax': 9.0}, \n", + "\n", + " # Track model\n", + " 'track': {'center': {'type': 'Constant', 'value': 0.0}, # center line of the stream in degrees\n", + " 'spread': {'type': 'Constant', 'value': 0.2}, # spread of the stream in degrees\n", + " 'sampler': 'Gaussian'}, # how to sample across the stream\n", + "\n", + " # Isochrone model\n", + " 'isochrone': {'name': 'Marigo2017', # isochrone set name\n", + " 'survey': 'lsst', # survey for filter set\n", + " 'age': 12.0, # Age in Gyr of the population\n", + " 'z': 0.0006, # Metallicity of the population\n", + " 'band_1': 'g', # first band for color-magnitude\n", + " 'band_2': 'r', # second band for color-magnitude\n", + " 'band_1_detection': True}, \n", + "\n", + " # Distance modulus model. Here an example of a constant distance modulus\n", + " 'distance_modulus': {'center': {'type': 'Constant', 'value': 16.5}, \n", + " 'spread': {'type': 'Constant', 'value': 0.0}, \n", + " }\n", + "}\n", + "\n", + "# or load from a config file\n", + "#config_path = os.path.join(base_dir, 'config', 'toy1_config.yaml')\n", + "#config = parse_config(config_path)['stream']\n", + "\n", + "print(config)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e33bd732", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Generated 4500 stars\n", + "\n", + "First 5 stars:\n", + " phi1 phi2 dist mu1 mu2 rv lsst_g_true lsst_r_true \\\n", + "0 4.739512 0.015756 16.5 None None None 28.982359 27.582928 \n", + "1 -1.499554 -0.343139 16.5 None None None 27.581603 26.354772 \n", + "2 8.207016 -0.176948 16.5 None None None 26.265005 25.228614 \n", + "3 0.680482 -0.088033 16.5 None None None 27.004769 25.868177 \n", + "4 4.064195 0.077374 16.5 None None None 27.373922 26.179369 \n", + "\n", + " mass \n", + "0 0.158219 \n", + "1 0.246747 \n", + "2 0.387048 \n", + "3 0.299648 \n", + "4 0.265148 \n" + ] + } + ], + "source": [ + "# Create stream model and generate stars\n", + "stream_model = StreamModel(config)\n", + "stream_df = stream_model.sample(4500)\n", + "\n", + "# The dataframe contains: phi1, phi2, distance, magnitudes, etc.\n", + "print(f\"✓ Generated {len(stream_df)} stars\")\n", + "print(\"\\nFirst 5 stars:\")\n", + "print(stream_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1d7e82f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Color-Magnitude Diagram')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Quick look: sky track, 1D density, CMD\n", + "fig, ax = plt.subplots(1, 3, figsize=(16, 5))\n", + "\n", + "# Stream on the sky\n", + "ax[0].scatter(stream_df['phi1'], stream_df['phi2'], s=1, color='blue')\n", + "ax[0].set_xlabel('phi1 (deg)')\n", + "ax[0].set_ylabel('phi2 (deg)')\n", + "ax[0].set_title('Stream on the sky')\n", + "\n", + "# 1D density\n", + "ax[1].hist(stream_df['phi1'], bins=50, color='green')\n", + "ax[1].set_xlabel('phi1 (deg)')\n", + "ax[1].set_ylabel('Number of stars')\n", + "ax[1].set_title('1D Density along the stream')\n", + "\n", + "# Color-magnitude diagram\n", + "ax[2].scatter(stream_df['lsst_g_true'] - stream_df['lsst_r_true'], stream_df['lsst_g_true'], s=1, color='red')\n", + "ax[2].set_xlabel('g - r (mag)')\n", + "ax[2].set_ylabel('g (mag)')\n", + "ax[2].invert_yaxis()\n", + "ax[2].set_title('Color-Magnitude Diagram')" + ] + }, + { + "cell_type": "markdown", + "id": "168afb7e", + "metadata": {}, + "source": [ + "\n", + "## 2) Spline-based configuration\n", + "\n", + "You can model a more realistic stream shape using cubic splines, particularly for:\n", + "\n", + "* **Linear density:** $\\sqrt{2\\pi} \\times \\text{peak intensity} \\times \\text{spread}$\n", + "* **Distance modulus**\n", + "* **Track and width**\n", + "\n", + "In this example, we use data from `data/patrick_2022_splines.csv` and select `stream == 'phoenix'`.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e9da4d2a", + "metadata": {}, + "outputs": [], + "source": [ + "# Nodes and values for the spline-based stream model (from Patrick et al. 2022, for Phoenix stream)\n", + "# One may read these from a CSV file instead of hardcoding them, cf to config/atlas_spline_config.yaml\n", + "intensity_nodes = np.array([-13., -9.75, -8.125, -4.1640625, -3.25, -1.625, 1.625, 6.5, 8.125, 13.])\n", + "intensity_node_values = np.array([2.35582279e-07, 2.65789495e-02, 5.94765580e-02, 7.20106921e-02, 9.96003626e-02, 4.68656926e-02, 7.42352023e-02, 4.75688845e-06, 1.73046024e-02, 4.08879937e-08])\n", + "spread_nodes = np.array([-13. , 13.])\n", + "spread_node_values = np.array([0.0992389, 0.17083177])\n", + "center_nodes = np.array([-13., 4.33333333, 13.])\n", + "center_node_values = np.array([0.19313599, 0.07139282, 0.60245054])\n", + "distance_nodes = np.array([-13., 13.])\n", + "distance_node_values = np.array([16.38285347, 16.1136374])\n", + "\n", + "\n", + "config_spline = {\n", + " # Density model using cubic splines from CSV\n", + " 'density': {\n", + " 'type': 'lineardensitycubicsplineinterpolation',\n", + " 'intensity_nodes': intensity_nodes,\n", + " 'intensity_node_values': intensity_node_values,\n", + " 'spread_nodes': spread_nodes,\n", + " 'spread_node_values': spread_node_values,\n", + " },\n", + "\n", + " # Track model: center and spread as cubic splines\n", + " 'track': {\n", + " 'center': {'type': 'CubicSplineInterpolation', 'nodes': center_nodes, 'node_values': center_node_values},\n", + " 'spread': {'type': 'CubicSplineInterpolation', 'nodes': spread_nodes, 'node_values': spread_node_values},\n", + " },\n", + "\n", + " # Isochrone model\n", + " 'isochrone': {\n", + " 'name': 'Marigo2017',\n", + " 'survey': 'lsst',\n", + " 'age': 13.0,\n", + " 'z': 0.0004,\n", + " 'band_1': 'g',\n", + " 'band_2': 'r',\n", + " 'band_1_detection': True\n", + " },\n", + "\n", + " # Distance modulus model as a cubic spline (flat default)\n", + " 'distance_modulus': {\n", + " 'center': {'type': 'CubicSplineInterpolation', 'nodes': distance_nodes, 'node_values': distance_node_values},\n", + " 'spread': {'type': 'Constant', 'value': 0.0},\n", + " },\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "23c7702c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Generated 4000 stars with spline density\n" + ] + }, + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "phi1", + "rawType": "float64", + "type": "float" + }, + { + "name": "phi2", + "rawType": "float64", + "type": "float" + }, + { + "name": "dist", + "rawType": "float64", + "type": "float" + }, + { + "name": "mu1", + "rawType": "object", + "type": "unknown" + }, + { + "name": "mu2", + "rawType": "object", + "type": "unknown" + }, + { + "name": "rv", + "rawType": "object", + "type": "unknown" + }, + { + "name": "lsst_g_true", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_r_true", + "rawType": "float64", + "type": "float" + }, + { + "name": "mass", + "rawType": "float64", + "type": "float" + } + ], + "ref": "d007671a-6101-4a50-8e4e-82e0f09f1e6e", + "rows": [ + [ + "0", + "11.025280252802528", + "0.20626557354003772", + "16.13408456498815", + null, + null, + null, + "27.7644816168389", + "26.51873899940819", + "0.18983716370004305" + ], + [ + "1", + "-3.9565895658956585", + "0.14146075800342955", + "16.2892138001359", + null, + null, + null, + "28.58534409749938", + "27.239461847045433", + "0.1561991316389716" + ], + [ + "2", + "11.894468944689446", + "0.6132655443134546", + "16.12508458176822", + null, + null, + null, + "26.288612354033567", + "25.24616941488484", + "0.3151574015567824" + ], + [ + "3", + "-3.7683476834768346", + "-0.1844970196719441", + "16.287264656297666", + null, + null, + null, + "28.440218852073944", + "27.112678803317245", + "0.16279667600017295" + ], + [ + "4", + "3.0900009000089987", + "0.24731635252121673", + "16.216250131207815", + null, + null, + null, + "26.21148903281383", + "25.189500675081582", + "0.33681009618743" + ] + ], + "shape": { + "columns": 9, + "rows": 5 + } + }, + "text/html": [ + "
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phi1phi2distmu1mu2rvlsst_g_truelsst_r_truemass
011.0252800.20626616.134085NoneNoneNone27.76448226.5187390.189837
1-3.9565900.14146116.289214NoneNoneNone28.58534427.2394620.156199
211.8944690.61326616.125085NoneNoneNone26.28861225.2461690.315157
3-3.768348-0.18449716.287265NoneNoneNone28.44021927.1126790.162797
43.0900010.24731616.216250NoneNoneNone26.21148925.1895010.336810
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" + ], + "text/plain": [ + " phi1 phi2 dist mu1 mu2 rv lsst_g_true lsst_r_true \\\n", + "0 11.025280 0.206266 16.134085 None None None 27.764482 26.518739 \n", + "1 -3.956590 0.141461 16.289214 None None None 28.585344 27.239462 \n", + "2 11.894469 0.613266 16.125085 None None None 26.288612 25.246169 \n", + "3 -3.768348 -0.184497 16.287265 None None None 28.440219 27.112679 \n", + "4 3.090001 0.247316 16.216250 None None None 26.211489 25.189501 \n", + "\n", + " mass \n", + "0 0.189837 \n", + "1 0.156199 \n", + "2 0.315157 \n", + "3 0.162797 \n", + "4 0.336810 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Optional: sample using the spline-based config\n", + "stream_model_spline = StreamModel(config_spline)\n", + "stream_df_spline = stream_model_spline.sample(4000)\n", + "\n", + "print(f\"✓ Generated {len(stream_df_spline)} stars with spline density\")\n", + "stream_df_spline.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3cfc1b7b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Color-Magnitude Diagram')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, ax = plt.subplots(1, 3, figsize=(16, 5))\n", + "\n", + "# plotting the stream on the sky \n", + "ax[0].scatter(stream_df_spline['phi1'], stream_df_spline['phi2'], s=1, color='blue', label = \"Generated stars\")\n", + "x_val = np.sort(stream_df_spline['phi1'].values)\n", + "spline_val = scipy.interpolate.CubicSpline(center_nodes, center_node_values)(x_val)\n", + "ax[0].plot(x_val, spline_val, color='orange', lw=2, label='Spline track model')\n", + "ax[0].set_xlabel('phi1 (deg)')\n", + "ax[0].set_ylabel('phi2 (deg)')\n", + "ax[0].set_title('Stream on the sky')\n", + "ax[0].legend()\n", + "\n", + "# Plotting the 1D density along the stream\n", + "ax[1].hist(stream_df_spline['phi1'], bins=50, color='green')\n", + "ax[1].set_xlabel('phi1 (deg)')\n", + "ax[1].set_ylabel('Number of stars')\n", + "ax[1].set_title('1D Density along the stream')\n", + "\n", + "# plotting Color magnitude diagram\n", + "ax[2].scatter(stream_df_spline['lsst_g_true'] - stream_df_spline['lsst_r_true'], stream_df_spline['lsst_g_true'], s=1, color='red')\n", + "ax[2].set_xlabel('g - r (mag)')\n", + "ax[2].set_ylabel('g (mag)')\n", + "ax[2].invert_yaxis()\n", + "ax[2].set_title('Color-Magnitude Diagram')\n" + ] + }, + { + "cell_type": "markdown", + "id": "f3c615f0", + "metadata": {}, + "source": [ + "# 3) Complete an existing catalog\n", + "\n", + "`StreamModel.complete_catalog` fills only the requested columns while preserving existing values (except for magnitudes and velocities, which are regenerated together for consistency).\n", + "\n", + "* **Dependencies:**\n", + "\n", + " * `phi2` and `dist` require `phi1`\n", + " * `mags` require both `dist` and `isochrone`\n", + "* **Input formats:** can be a `DataFrame`, `dict`, path to a CSV file, or `None` (with a specified `size` to generate the full catalog)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d37bb8b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Catalog with missing columns:\n", + " phi1 phi2 mass\n", + "0 4.739512 0.015756 0.158219\n", + "1 -1.499554 -0.343139 0.246747\n", + "2 8.207016 -0.176948 0.387048\n", + "3 0.680482 -0.088033 0.299648\n", + "4 4.064195 0.077374 0.265148\n" + ] + } + ], + "source": [ + "# Let's build a catalog with missing columns to complete\n", + "# Here for example we keep only 'phi1' and 'phi2', and drop others\n", + "stream_df_sub = stream_df.drop(columns=['lsst_r_true', 'dist', 'lsst_g_true', 'mu1', 'mu2', 'rv' ]).reset_index(drop=True)\n", + "print(\"\\nCatalog with missing columns:\")\n", + "print(stream_df_sub.head())" + ] + }, + { + "cell_type": "markdown", + "id": "5050d977", + "metadata": {}, + "source": [ + "## Fill every missing columns" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7e98fa35", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Velocity model not defined; skipping velocities.\n", + "Filled 4500 dist values.\n", + "Filled ['lsst_g_true', 'lsst_r_true'] (missing rows only).\n", + " phi1 phi2 mass dist lsst_g_true lsst_r_true\n", + "0 4.739512 0.015756 0.158219 16.5 28.982359 27.582928\n", + "1 -1.499554 -0.343139 0.246747 16.5 27.581603 26.354772\n", + "2 8.207016 -0.176948 0.387048 16.5 26.265005 25.228614\n", + "3 0.680482 -0.088033 0.299648 16.5 27.004769 25.868177\n", + "4 4.064195 0.077374 0.265148 16.5 27.373922 26.179369\n" + ] + } + ], + "source": [ + "# Now we can use `complete_catalog` to fill in the missing columns amoung ['phi1', 'phi2', 'dist', 'mag_g', 'mag_r', 'mu1', 'mu2', 'rv']\n", + "completed_catalog = stream_model.complete_catalog(\n", + " catalog=stream_df_sub,\n", + " save_path=None,\n", + " inplace=False,\n", + " verbose=True\n", + ")\n", + "print(completed_catalog.head())" + ] + }, + { + "cell_type": "markdown", + "id": "c06d8f12", + "metadata": {}, + "source": [ + "## Fill only specific columns" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "16346874", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filled 4500 dist values.\n", + "Filled ['lsst_g_true', 'lsst_r_true'] (missing rows only).\n" + ] + }, + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "phi1", + "rawType": "float64", + "type": "float" + }, + { + "name": "phi2", + "rawType": "float64", + "type": "float" + }, + { + "name": "mass", + "rawType": "float64", + "type": "float" + }, + { + "name": "dist", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_g_true", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_r_true", + "rawType": "float64", + "type": "float" + } + ], + "ref": "79b6798b-b980-4de3-8531-2a56ecd130d3", + "rows": [ + [ + "0", + "4.739512423369664", + "0.015756036881542725", + "0.15821932808348718", + "16.5", + "28.982359282612535", + "27.582928243106654" + ], + [ + "1", + "-1.4995537541860102", + "-0.34313914865180417", + "0.2467473213129864", + "16.5", + "27.58160257151765", + "26.354771943829558" + ], + [ + "2", + "8.207016077647587", + "-0.17694832788772316", + "0.3870483021720901", + "16.5", + "26.265005093492725", + "25.228613667509745" + ], + [ + "3", + "0.680482431735463", + "-0.08803296498191172", + "0.2996478341804267", + "16.5", + "27.004768617316408", + "25.86817690917701" + ], + [ + "4", + "4.064195004545283", + "0.07737374131135481", + "0.2651475187479373", + "16.5", + "27.373921684570384", + "26.17936949484345" + ] + ], + "shape": { + "columns": 6, + "rows": 5 + } + }, + "text/html": [ + "
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phi1phi2massdistlsst_g_truelsst_r_true
04.7395120.0157560.15821916.528.98235927.582928
1-1.499554-0.3431390.24674716.527.58160326.354772
28.207016-0.1769480.38704816.526.26500525.228614
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" + ], + "text/plain": [ + " phi1 phi2 mass dist lsst_g_true lsst_r_true\n", + "0 4.739512 0.015756 0.158219 16.5 28.982359 27.582928\n", + "1 -1.499554 -0.343139 0.246747 16.5 27.581603 26.354772\n", + "2 8.207016 -0.176948 0.387048 16.5 26.265005 25.228614\n", + "3 0.680482 -0.088033 0.299648 16.5 27.004769 25.868177\n", + "4 4.064195 0.077374 0.265148 16.5 27.373922 26.179369" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Example: fill only magnitudes\n", + "subset = stream_df_sub.copy()\n", + "completed_mags = stream_model.complete_catalog(\n", + " catalog=subset,\n", + " columns_to_add=[\"lsst_g_true\", \"lsst_r_true\"],\n", + " inplace=False,\n", + " verbose=True,\n", + ")\n", + "completed_mags.head()" + ] + }, + { + "cell_type": "markdown", + "id": "c1b81722", + "metadata": {}, + "source": [ + "*Note: the distance modulus is also added, since it is needed to convert absolute magnitude sampled from the isochrone, to apparent magnitudes (`mag_g`and `mag_r` here).*" + ] + }, + { + "cell_type": "markdown", + "id": "e4cd4bc5", + "metadata": {}, + "source": [ + "## Tips and Troubleshooting\n", + "\n", + "* If magnitudes are missing or NaN, make sure your config includes both `distance_modulus` and `isochrone` sections.\n", + "* To keep colors consistent, `complete_catalog` regenerates both `mag_g` and `mag_r` whenever one needs to be computed.\n", + "* Column names are automatically standardized (e.g., `'g_mag'` → `'mag_g'`); see `_standardize_columns_name`.\n", + "* The velocity model is currently a placeholder and returns NaN values." + ] + }, + { + "cell_type": "markdown", + "id": "ad8600ce", + "metadata": {}, + "source": [ + "# Conclusion\n", + "\n", + "Streamobs provides a flexible framework to build and complete stellar stream mock catalogs, from simple analytic models to spline-based configurations.\n", + "Future updates will include proper motions, velocities, and improved documentation for easier user adoption.\n", + "\n", + "You can find more informations in the [full documentation](https://lsstdesc.github.io/streamobs/)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "streamsim_dev", + "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": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/examples/tutorial_inject_stream.ipynb b/docs/source/examples/tutorial_inject_stream.ipynb new file mode 100644 index 0000000..9d61350 --- /dev/null +++ b/docs/source/examples/tutorial_inject_stream.ipynb @@ -0,0 +1,1932 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ef83a9a9", + "metadata": {}, + "source": [ + "# Injecting stream mocks into a survey\n", + "\n", + "This short tutorial shows how to:\n", + "- Load a survey (e.g., LSST yr5) and inspect completeness/error models\n", + "- Create a `StreamInjector` and inject photometric effects + detection flags\n", + "- Plot against the footprint and visualize injected magnitudes\n", + "\n", + "This is useful for people who want to convert their dynamical simulation results into realistic survey data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9b899efa", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "import pandas as pd\n", + "import yaml\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cf16899f", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "from streamobs import surveys, observed\n", + "from streamobs.model import StreamModel" + ] + }, + { + "cell_type": "markdown", + "id": "17059e52", + "metadata": {}, + "source": [ + "## 1) Load the survey (cached)\n", + "We load LSST yr5 once; the data is cached for efficiency (bands, depth maps, extinction, completeness, and photometric error models)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "27cdcd2b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading survey data for 'lsst_yr4'...\n", + " Loading config from: lsst_yr4.yaml\n", + "\n", + "======================================================================\n", + "LOADING SURVEY DATA FILES\n", + "======================================================================\n", + "Survey data directory: /Users/pelissier/Documents/Codes/packages/streamobs/streamobs/../data/surveys/lsst_yr4\n", + "\n", + "Fallback directory for shared data files: /Users/pelissier/Documents/Codes/packages/streamobs/streamobs/../data/others\n", + "\n", + "Available bands: g, i, r, u, y, z\n", + "\n", + "\n", + "Loading survey properties...\n", + "Loading magnitude limit maps...\n", + " ✓ Success for g-band magnitude limit\n", + " ⚠ Warning: 'maglim_map_i' not specified in config (skipping i-band)\n", + " ✓ Success for r-band magnitude limit\n", + " ⚠ Warning: 'maglim_map_u' not specified in config (skipping u-band)\n", + " ⚠ Warning: 'maglim_map_y' not specified in config (skipping y-band)\n", + " ⚠ Warning: 'maglim_map_z' not specified in config (skipping z-band)\n", + "\n", + "Loading completeness/efficiency function...\n", + " Loading Completeness/efficiency function...\n", + " File: lsst_stellar_efficiency_cutr.csv\n", + " ✓ Success\n", + " Loading Detection efficiency function...\n", + " File: lsst_stellar_efficiency_cutr.csv\n", + " ✓ Success\n", + " Loading Classification efficiency function...\n", + " File: lsst_stellar_efficiency_cutr.csv\n", + " ✓ Success\n", + "\n", + "Loading photometric error model...\n", + " Loading Photometric error model (catalog / reported)...\n", + " File: lsst_photoerror_r.csv\n", + " ✓ Success\n", + "\n", + "Loading band-independent maps...\n", + " Loading E(B-V) extinction map...\n", + " File: ebv_sfd98_lowres_nside_512_ring_equatorial.fits\n", + " ✓ Success\n", + " ⚠ Warning: 'coverage' not specified in config (skipping)\n", + "\n", + "Building coverage map from magnitude limit maps...\n", + " ✓ Built coverage map (nside=128, 133236 pixels covered)\n", + "\n", + "Survey properties summary:\n", + " g-band:\n", + " Extinction coefficient: 3.661\n", + " Saturation limit: 16.0 mag\n", + " Systematic error: 0.0050 mag\n", + " i-band:\n", + " Extinction coefficient: 2.054\n", + " Saturation limit: 16.0 mag\n", + " Systematic error: 0.0050 mag\n", + " r-band:\n", + " Extinction coefficient: 2.701\n", + " Saturation limit: 16.0 mag\n", + " Systematic error: 0.0050 mag\n", + " u-band:\n", + " Extinction coefficient: 4.757\n", + " Saturation limit: 16.0 mag\n", + " Systematic error: 0.0050 mag\n", + " y-band:\n", + " Extinction coefficient: 1.308\n", + " Saturation limit: 16.0 mag\n", + " Systematic error: 0.0050 mag\n", + " z-band:\n", + " Extinction coefficient: 1.590\n", + " Saturation limit: 16.0 mag\n", + " Systematic error: 0.0050 mag\n", + "\n", + "======================================================================\n", + "SURVEY DATA LOADED SUCCESSFULLY\n", + "======================================================================\n", + "\n", + "✓ Survey 'lsst_yr4' loaded and cached successfully\n" + ] + } + ], + "source": [ + "lsst_yr4= surveys.Survey.load(survey = 'lsst', release='yr4')" + ] + }, + { + "cell_type": "markdown", + "id": "42801958", + "metadata": {}, + "source": [ + "### Completeness and photometric error\n", + "Both depend on magnitude relative to the local limit (delta_mag = mag - maglim). We can visualize them for the r-band at a chosen magnitude limit." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9b85555b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mag_r_test = np.linspace(15,31, 1000)\n", + "maglim_r_dc2 = 26.8\n", + "completeness_val = lsst_yr4.get_completeness('r', mag_r_test, maglim_r_dc2)\n", + "mag_error_val = lsst_yr4.get_photo_error('r', mag_r_test, maglim_r_dc2)\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n", + "ax[0].plot(mag_r_test, completeness_val, label='Completeness')\n", + "ax[0].axvline(maglim_r_dc2, color='red', linestyle='--', label='Magnitude Limit')\n", + "ax[0].set_xlabel('Apparent r-band magnitude')\n", + "ax[0].set_ylabel('Completeness')\n", + "ax[0].set_title('LSST DC2 Completeness Function')\n", + "ax[0].grid()\n", + "ax[0].legend() \n", + "\n", + "ax[1].plot(mag_r_test, mag_error_val, label='Photometric Error', color='orange')\n", + "ax[1].axvline(maglim_r_dc2, color='red', linestyle='--', label='Magnitude Limit')\n", + "ax[1].set_yscale('log')\n", + "ax[1].set_xlabel('Apparent r-band magnitude')\n", + "ax[1].set_ylabel('Photometric Error (mag)')\n", + "ax[1].set_title('LSST DC2 Photometric Error Model')\n", + "ax[1].grid()\n", + "ax[1].legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "23ed125c", + "metadata": {}, + "source": [ + "### Cache manipulation\n", + "Once a survey has been loaded, it's stored in the cache. You can use commands to check which surveys are stored.\n", + "To force reloading of a survey, you must clear the cache first." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fd3a37c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✓ Using cached survey data for 'lsst_yr4'\n", + "Current cache content: ['lsst_yr4']\n", + "✓ Cleared cached survey 'lsst_yr4'\n", + "Cache content after clearing 'lsst': []\n" + ] + } + ], + "source": [ + "# Once a survey has been loaded once, it can be reused directly from the cache\n", + "lsst_yr4 = surveys.Survey.load(survey = 'lsst', release='yr4') # reuses the cached survey object\n", + "\n", + "# Check the cache content\n", + "\n", + "print(\"Current cache content: \", surveys.SurveyFactory.list_cached_surveys())\n", + "\n", + "surveys.SurveyFactory.clear_cache(survey = 'lsst', release='yr4')\n", + "# surveys.SurveyFactory.clear_cache(survey = 'lsst') # to clear all releases of 'lsst'\n", + "# surveys.SurveyFactory.clear_cache() # to clear the entire cache\n", + "print(\"Cache content after clearing 'lsst': \", surveys.SurveyFactory.list_cached_surveys())" + ] + }, + { + "cell_type": "markdown", + "id": "ba80c280", + "metadata": {}, + "source": [ + "## 2) Create an injector\n", + "The `StreamInjector` class wraps Survey objects and uses their properties to inject observational effects into a dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6fd1866e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading survey data for 'lsst_yr4'...\n", + " Loading config from: lsst_yr4.yaml\n", + "\n", + "======================================================================\n", + "LOADING SURVEY DATA FILES\n", + "======================================================================\n", + "Survey data directory: /Users/pelissier/Documents/Codes/packages/streamobs/streamobs/../data/surveys/lsst_yr4\n", + "\n", + "Fallback directory for shared data files: /Users/pelissier/Documents/Codes/packages/streamobs/streamobs/../data/others\n", + "\n", + "Available bands: g, i, r, u, y, z\n", + "\n", + "\n", + "Loading survey properties...\n", + "Loading magnitude limit maps...\n", + " ✓ Success for g-band magnitude limit\n", + " ⚠ Warning: 'maglim_map_i' not specified in config (skipping i-band)\n", + " ✓ Success for r-band magnitude limit\n", + " ⚠ Warning: 'maglim_map_u' not specified in config (skipping u-band)\n", + " ⚠ Warning: 'maglim_map_y' not specified in config (skipping y-band)\n", + " ⚠ Warning: 'maglim_map_z' not specified in config (skipping z-band)\n", + "\n", + "Loading completeness/efficiency function...\n", + " Loading Completeness/efficiency function...\n", + " File: lsst_stellar_efficiency_cutr.csv\n", + " ✓ Success\n", + " Loading Detection efficiency function...\n", + " File: lsst_stellar_efficiency_cutr.csv\n", + " ✓ Success\n", + " Loading Classification efficiency function...\n", + " File: lsst_stellar_efficiency_cutr.csv\n", + " ✓ Success\n", + "\n", + "Loading photometric error model...\n", + " Loading Photometric error model (catalog / reported)...\n", + " File: lsst_photoerror_r.csv\n", + " ✓ Success\n", + "\n", + "Loading band-independent maps...\n", + " Loading E(B-V) extinction map...\n", + " File: ebv_sfd98_lowres_nside_512_ring_equatorial.fits\n", + " ✓ Success\n", + " ⚠ Warning: 'coverage' not specified in config (skipping)\n", + "\n", + "Building coverage map from magnitude limit maps...\n", + " ✓ Built coverage map (nside=128, 133236 pixels covered)\n", + "\n", + "Survey properties summary:\n", + " g-band:\n", + " Extinction coefficient: 3.661\n", + " Saturation limit: 16.0 mag\n", + " Systematic error: 0.0050 mag\n", + " i-band:\n", + " Extinction coefficient: 2.054\n", + " Saturation limit: 16.0 mag\n", + " Systematic error: 0.0050 mag\n", + " r-band:\n", + " Extinction coefficient: 2.701\n", + " Saturation limit: 16.0 mag\n", + " Systematic error: 0.0050 mag\n", + " u-band:\n", + " Extinction coefficient: 4.757\n", + " Saturation limit: 16.0 mag\n", + " Systematic error: 0.0050 mag\n", + " y-band:\n", + " Extinction coefficient: 1.308\n", + " Saturation limit: 16.0 mag\n", + " Systematic error: 0.0050 mag\n", + " z-band:\n", + " Extinction coefficient: 1.590\n", + " Saturation limit: 16.0 mag\n", + " Systematic error: 0.0050 mag\n", + "\n", + "======================================================================\n", + "SURVEY DATA LOADED SUCCESSFULLY\n", + "======================================================================\n", + "\n", + "✓ Survey 'lsst_yr4' loaded and cached successfully\n" + ] + } + ], + "source": [ + "# Create an injector using the Survey object\n", + "stream_injector = observed.StreamInjector(lsst_yr4)\n", + "\n", + "# Or using survey name and release (this will create the survey object internally)\n", + "stream_injector = observed.StreamInjector(survey=\"lsst\", release=\"yr4\")" + ] + }, + { + "cell_type": "markdown", + "id": "8b2a38b7", + "metadata": {}, + "source": [ + "## 3) Inject different dataset types\n", + "\n", + "To convert a stream catalog into realistic survey data, you need:\n", + "- Sky coordinates (ra, dec)\n", + "- True apparent magnitudes of the stars\n", + "\n", + "However, **Streamobs** can **automatically sample these quantities** if they're missing, allowing incomplete datasets to be converted into realistic survey data.\n", + "\n", + "Streamobs uses the magnitude limit at each star's location to estimate:\n", + "- The photometric error\n", + "- The observed magnitude\n", + "- Whether the object is detected and classified as a star\n", + "\n", + "The output of the `inject()` method is a DataFrame containing:\n", + "\n", + "- `ra`, `dec`: positions of the stream's stars on the sky\n", + "- `mag_{band}`: true apparent magnitude in a given band\n", + "- `mag_{band}_obs`: observed magnitude in a given band\n", + "- `magerr_{band}`: photometric error on the observed magnitude (in mag). Includes both systematic and statistical errors.\n", + "- `flag_observed`: 1 if the star has been detected by the survey and classified as a star. By default includes an SNR cut (SNR = 1/magerr > 5) in g and r bands.\n", + "\n", + "The output can optionally include:\n", + "- `phi1`, `phi2`: position of stars in the stream frame\n", + "- `dist`: distance modulus of the stars" + ] + }, + { + "cell_type": "markdown", + "id": "914ce4ec", + "metadata": {}, + "source": [ + "### Dataset with only (phi1, phi2) coordinates\n", + "\n", + "If you only provide stream coordinates, Streamobs will:\n", + "\n", + "**1. Sample apparent magnitudes:**\n", + "\n", + "This is done using an isochrone and distance modulus model, which must be provided in a configuration dictionary. See `tutorial_generate_datamocks.ipynb` for more details.\n", + "\n", + "**2. Convert (phi1, phi2) → (ra, dec):**\n", + "\n", + "If you don't provide (ra, dec) columns, Streamobs will randomly place the stream on the sky. You can restrict the placement using the `mask_type` argument to ensure the stream falls within:\n", + "- The survey's footprint\n", + "- Regions with low dust extinction\n", + "- Areas meeting a minimum magnitude limit in a given band\n", + "- Any combination of the above\n", + "\n", + "Streamobs searches uniformly across the sky for a reference frame where a specified fraction of the stream (`percentile_threshold`) lies inside the mask. For more details, see the documentation of the `StreamInjector._find_gc_frame()` method.\n", + "\n", + "Alternatively, you can provide your own `gala.coordinates.GreatCircleICRSFrame` object via the `gc_frame` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ed0abceb", + "metadata": {}, + "outputs": [], + "source": [ + "N = 1000\n", + "seed = 42\n", + "\n", + "rng = np.random.default_rng(seed)\n", + "\n", + "# Replace with your actual data\n", + "data_test = pd.DataFrame({\n", + " 'phi1': rng.uniform(-5, 5, N),\n", + " 'phi2': rng.uniform(-1, 1, N),\n", + "})\n", + "\n", + "\n", + "# Since our data set does not have magnitudes, we need to provide an isochrone and distance model \n", + "# - cf tutorial_generate_datamocks.ipynb\n", + "# This step can be skipped if the input data already has magnitudes in the desired bands\n", + "\n", + "isochrone_config = {'isochrone':{'name': 'Marigo2017', # isochrone set name\n", + " 'survey': 'lsst', # survey for filter set\n", + " 'age': 12.0, # Age in Gyr of the population\n", + " 'z': 0.0006, # Metallicity of the population\n", + " 'band_1': 'g', # first band for color-magnitude\n", + " 'band_2': 'r', # second band for color-magnitude\n", + " 'band_1_detection': True},\n", + "}\n", + "\n", + "distance_modulus_config = {'distance_modulus': {'center': {'type': 'Constant', 'value': 16.5}, \n", + " 'spread': {'type': 'Constant', 'value': 0.0}, \n", + " }}\n", + "\n", + "stream_config = {**isochrone_config, **distance_modulus_config}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cf4994dd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filled 1000 dist values.\n", + "Filled ['lsst_g_true', 'lsst_r_true'] (missing rows only).\n", + "Building new mask for ['ebv', 'footprint']...\n", + " Resampling ebv from nside=512 to nside=128\n", + "✓ Mask created: valid pixels fraction = 0.5\n", + " Cached with key: ('lsst', ('ebv', 'footprint'), 0.2)\n", + "Found suitable great circle frame after 2 trials with 100.00% points inside the mask.\n", + "Applying dust correction for r-band on observed magnitudes.\n", + "Applying dust correction for g-band on observed magnitudes.\n", + "Applying detection cut on g-band with SNR >= 5.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/streamsim_dev/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log10\n", + " result = getattr(ufunc, method)(*inputs, **kwargs)\n", + "/opt/anaconda3/envs/streamsim_dev/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log10\n", + " result = getattr(ufunc, method)(*inputs, **kwargs)\n" + ] + } + ], + "source": [ + "mask_type = ['footprint', 'ebv'] # Restrict to survey footprint and apply extinction mask\n", + "\n", + "# By default the bands used for detection are (g and r)\n", + "# You can also add perfect_galstarsep=True to get bolean flag related to\n", + "# detection only (no classification).\n", + "injected_data_full = stream_injector.inject(data_test, seed=seed, mask_type=mask_type, stream_config=stream_config, verbose=True, perfect_galstarsep=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4b064c1f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['phi1', 'phi2', 'dist', 'lsst_g_true', 'lsst_r_true', 'ra', 'dec',\n", + " 'lsst_yr4_r_obs', 'lsst_yr4_r_err', 'lsst_yr4_g_obs', 'lsst_yr4_g_err',\n", + " 'lsst_yr4_flag_observed', 'lsst_yr4_flag_perfect_galstarsep'],\n", + " dtype='object')\n" + ] + }, + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "phi1", + "rawType": "float64", + "type": "float" + }, + { + "name": "phi2", + "rawType": "float64", + "type": "float" + }, + { + "name": "dist", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_g_true", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_r_true", + "rawType": "float64", + "type": "float" + }, + { + "name": "ra", + "rawType": "float64", + "type": "float" + }, + { + "name": "dec", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_yr4_r_obs", + "rawType": "object", + "type": "unknown" + }, + { + "name": "lsst_yr4_r_err", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_yr4_g_obs", + "rawType": "object", + "type": "unknown" + }, + { + "name": "lsst_yr4_g_err", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_yr4_flag_observed", + "rawType": "bool", + "type": "boolean" + }, + { + "name": "lsst_yr4_flag_perfect_galstarsep", + "rawType": "bool", + "type": "boolean" + } + ], + "ref": "16ac805e-0469-44d4-b956-a36067ee93cb", + "rows": [ + [ + "0", + "2.7395604855596334", + "-0.8758737869196895", + "16.5", + "30.303280724323006", + "28.747044825349548", + "309.2953942380719", + "-10.749590498519837", + "27.94450115002072", + "2.839884927096569", + "27.617721407305552", + "10.000001249999922", + "False", + "False" + ], + [ + "1", + "-0.6112156024794768", + "-0.0834759169269721", + "16.5", + "29.728132639588573", + "28.23706116860197", + "310.2683786555398", + "-14.058956233016271", + "27.607723876818635", + "1.4081374219582061", + "28.5689654329395", + "10.000001249999922", + "False", + "False" + ], + [ + "2", + "3.5859791991138246", + "-0.7419398856359165", + "16.5", + "28.225967217524378", + "26.90261977410061", + "308.7572935618984", + "-10.07569919353105", + "26.892840518625263", + "0.3412351245318386", + "28.8451710606833", + "1.3184116118589486", + "False", + "False" + ], + [ + "3", + "1.973680290593639", + "-0.6953465796848486", + "16.5", + "29.38559718605284", + "27.93565790812836", + "309.51600359134665", + "-11.50602746286123", + "BAD_MAG", + "1.0610851133467605", + "26.72698260587372", + "10.000001249999922", + "False", + "False" + ], + [ + "4", + "-4.058226521123505", + "0.26456562606042033", + "16.5", + "28.29109171685917", + "26.960775009646518", + "311.7243037291042", + "-17.22723336556269", + "26.483796611811186", + "0.40904825757417285", + "27.505772836877462", + "1.9325848090410778", + "False", + "False" + ] + ], + "shape": { + "columns": 13, + "rows": 5 + } + }, + "text/html": [ + "
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phi1phi2distlsst_g_truelsst_r_trueradeclsst_yr4_r_obslsst_yr4_r_errlsst_yr4_g_obslsst_yr4_g_errlsst_yr4_flag_observedlsst_yr4_flag_perfect_galstarsep
02.739560-0.87587416.530.30328128.747045309.295394-10.74959027.9445012.83988527.61772110.000001FalseFalse
1-0.611216-0.08347616.529.72813328.237061310.268379-14.05895627.6077241.40813728.56896510.000001FalseFalse
23.585979-0.74194016.528.22596726.902620308.757294-10.07569926.8928410.34123528.8451711.318412FalseFalse
31.973680-0.69534716.529.38559727.935658309.516004-11.506027BAD_MAG1.06108526.72698310.000001FalseFalse
4-4.0582270.26456616.528.29109226.960775311.724304-17.22723326.4837970.40904827.5057731.932585FalseFalse
\n", + "
" + ], + "text/plain": [ + " phi1 phi2 dist lsst_g_true lsst_r_true ra dec \\\n", + "0 2.739560 -0.875874 16.5 30.303281 28.747045 309.295394 -10.749590 \n", + "1 -0.611216 -0.083476 16.5 29.728133 28.237061 310.268379 -14.058956 \n", + "2 3.585979 -0.741940 16.5 28.225967 26.902620 308.757294 -10.075699 \n", + "3 1.973680 -0.695347 16.5 29.385597 27.935658 309.516004 -11.506027 \n", + "4 -4.058227 0.264566 16.5 28.291092 26.960775 311.724304 -17.227233 \n", + "\n", + " lsst_yr4_r_obs lsst_yr4_r_err lsst_yr4_g_obs lsst_yr4_g_err \\\n", + "0 27.944501 2.839885 27.617721 10.000001 \n", + "1 27.607724 1.408137 28.568965 10.000001 \n", + "2 26.892841 0.341235 28.845171 1.318412 \n", + "3 BAD_MAG 1.061085 26.726983 10.000001 \n", + "4 26.483797 0.409048 27.505773 1.932585 \n", + "\n", + " lsst_yr4_flag_observed lsst_yr4_flag_perfect_galstarsep \n", + "0 False False \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 False False " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(injected_data_full.columns)\n", + "injected_data_full.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a45cb263", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "160 stars detected out of 1000\n", + "160 stars classified as stars out of 1000\n" + ] + } + ], + "source": [ + "# If you want to work only with detected and classified stars\n", + "detected_data = injected_data_full[injected_data_full['lsst_yr4_flag_observed']]\n", + "print(len(detected_data), \"stars detected out of\", len(injected_data_full))\n", + "\n", + "# When applying the perfect galstar separation, all detected stars are\n", + "# classified as stars, so we have:\n", + "classified_data = injected_data_full[injected_data_full['lsst_yr4_flag_observed']]\n", + "print(len(classified_data), \"stars classified as stars out of\", len(injected_data_full))" + ] + }, + { + "cell_type": "markdown", + "id": "7b973847", + "metadata": {}, + "source": [ + "Streamobs has automatically sampled the missing columns (positions and apparent magnitudes) and estimated observational quantities.\n", + "\n", + "You can verify the injection behavior with some useful plots:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b8b2a9e7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the injected stream in the survey mask\n", + "stream_injector.plot_stream_in_mask(injected_data_full, mask_type=mask_type)" + ] + }, + { + "cell_type": "markdown", + "id": "a58b7a71", + "metadata": {}, + "source": [ + "The stream lies entirely within the chosen mask." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8d27ae09", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from streamobs.plotting import plot_inject\n", + "\n", + "# Plot results\n", + "fig, ax = plot_inject(injected_data_full, lsst_yr4, bands=['g', 'r'], save=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "67f7a59f", + "metadata": {}, + "source": [ + "### Dataset containing (ra, dec) coordinates and distance modulus\n", + "\n", + "Streamobs only needs to sample the missing magnitudes, so you must provide a configuration dictionary for the isochrone model." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b9520ac9", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "dist", + "rawType": "float64", + "type": "float" + }, + { + "name": "ra", + "rawType": "float64", + "type": "float" + }, + { + "name": "dec", + "rawType": "float64", + "type": "float" + } + ], + "ref": "723058b4-3dd6-4dd8-9ed8-e27548aad589", + "rows": [ + [ + "0", + "16.5", + "309.2953942380719", + "-10.749590498519837" + ], + [ + "1", + "16.5", + "310.2683786555398", + "-14.058956233016271" + ], + [ + "2", + "16.5", + "308.7572935618984", + "-10.07569919353105" + ], + [ + "3", + "16.5", + "309.51600359134665", + "-11.50602746286123" + ], + [ + "4", + "16.5", + "311.7243037291042", + "-17.22723336556269" + ] + ], + "shape": { + "columns": 3, + "rows": 5 + } + }, + "text/html": [ + "
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distradec
016.5309.295394-10.749590
116.5310.268379-14.058956
216.5308.757294-10.075699
316.5309.516004-11.506027
416.5311.724304-17.227233
\n", + "
" + ], + "text/plain": [ + " dist ra dec\n", + "0 16.5 309.295394 -10.749590\n", + "1 16.5 310.268379 -14.058956\n", + "2 16.5 308.757294 -10.075699\n", + "3 16.5 309.516004 -11.506027\n", + "4 16.5 311.724304 -17.227233" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's build a data set containing only (ra, dec) coordinates\n", + "data_set_radec = injected_data_full.drop(columns=[col for col in injected_data_full.columns if col not in ['ra', 'dec', 'dist']])\n", + "data_set_radec.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8dcef84a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Distance modulus model not defined; skipping distances.\n", + "Filled ['lsst_g_true', 'lsst_r_true'] (missing rows only).\n", + "Applying dust correction for r-band on observed magnitudes.\n", + "Applying dust correction for g-band on observed magnitudes.\n", + "Applying detection cut on g-band with SNR >= 5.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/streamsim_dev/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log10\n", + " result = getattr(ufunc, method)(*inputs, **kwargs)\n", + "/opt/anaconda3/envs/streamsim_dev/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log10\n", + " result = getattr(ufunc, method)(*inputs, **kwargs)\n" + ] + }, + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "dist", + "rawType": "float64", + "type": "float" + }, + { + "name": "ra", + "rawType": "float64", + "type": "float" + }, + { + "name": "dec", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_g_true", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_r_true", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_yr4_r_obs", + "rawType": "object", + "type": "unknown" + }, + { + "name": "lsst_yr4_r_err", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_yr4_g_obs", + "rawType": "object", + "type": "unknown" + }, + { + "name": "lsst_yr4_g_err", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_yr4_flag_observed", + "rawType": "bool", + "type": "boolean" + } + ], + "ref": "8826e32e-e1e6-43c6-adca-911ec967c02f", + "rows": [ + [ + "0", + "16.5", + "309.2953942380719", + "-10.749590498519837", + "22.307506629159345", + "21.958447547282443", + "21.95570880527", + "0.006555110409349731", + "22.315016874806194", + "0.007064702039654374", + "True" + ], + [ + "1", + "16.5", + "310.2683786555398", + "-14.058956233016271", + "31.24638120357998", + "29.588484390590427", + "27.54332178290005", + "10.000001249999922", + "30.05040478123035", + "10.000001249999922", + "False" + ], + [ + "2", + "16.5", + "308.7572935618984", + "-10.07569919353105", + "28.129791646241713", + "26.820849958858858", + "26.81159920708018", + "0.322714968027792", + "28.015568670315044", + "1.148001045000444", + "False" + ], + [ + "3", + "16.5", + "309.51600359134665", + "-11.50602746286123", + "29.071967670345582", + "27.66131258227355", + "29.617694290417134", + "0.8361615683575233", + "28.525325873069807", + "4.297743130499477", + "False" + ], + [ + "4", + "16.5", + "311.7243037291042", + "-17.22723336556269", + "30.19232787401504", + "28.648052066699986", + "26.83668353341164", + "3.1909560196197595", + "26.780332948411118", + "10.000001249999922", + "False" + ] + ], + "shape": { + "columns": 10, + "rows": 5 + } + }, + "text/html": [ + "
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distradeclsst_g_truelsst_r_truelsst_yr4_r_obslsst_yr4_r_errlsst_yr4_g_obslsst_yr4_g_errlsst_yr4_flag_observed
016.5309.295394-10.74959022.30750721.95844821.9557090.00655522.3150170.007065True
116.5310.268379-14.05895631.24638129.58848427.54332210.00000130.05040510.000001False
216.5308.757294-10.07569928.12979226.82085026.8115990.32271528.0155691.148001False
316.5309.516004-11.50602729.07196827.66131329.6176940.83616228.5253264.297743False
416.5311.724304-17.22723330.19232828.64805226.8366843.19095626.78033310.000001False
\n", + "
" + ], + "text/plain": [ + " dist ra dec lsst_g_true lsst_r_true lsst_yr4_r_obs \\\n", + "0 16.5 309.295394 -10.749590 22.307507 21.958448 21.955709 \n", + "1 16.5 310.268379 -14.058956 31.246381 29.588484 27.543322 \n", + "2 16.5 308.757294 -10.075699 28.129792 26.820850 26.811599 \n", + "3 16.5 309.516004 -11.506027 29.071968 27.661313 29.617694 \n", + "4 16.5 311.724304 -17.227233 30.192328 28.648052 26.836684 \n", + "\n", + " lsst_yr4_r_err lsst_yr4_g_obs lsst_yr4_g_err lsst_yr4_flag_observed \n", + "0 0.006555 22.315017 0.007065 True \n", + "1 10.000001 30.050405 10.000001 False \n", + "2 0.322715 28.015569 1.148001 False \n", + "3 0.836162 28.525326 4.297743 False \n", + "4 3.190956 26.780333 10.000001 False " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "injected_data = stream_injector.inject(data_set_radec, seed=seed, mask_type=None, stream_config=isochrone_config, verbose=True)\n", + "injected_data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "b967882a", + "metadata": {}, + "source": [ + "### Dataset containing (ra, dec) and true apparent magnitudes\n", + "\n", + "The dataset already contains all the required quantities, so you don't need to provide a configuration dictionary or mask type." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ee217a87", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "lsst_g_true", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_r_true", + "rawType": "float64", + "type": "float" + }, + { + "name": "ra", + "rawType": "float64", + "type": "float" + }, + { + "name": "dec", + "rawType": "float64", + "type": "float" + } + ], + "ref": "1aedcd3a-566a-4001-94b7-813e35a10c0e", + "rows": [ + [ + "0", + "30.303280724323006", + "28.747044825349548", + "309.2953942380719", + "-10.749590498519837" + ], + [ + "1", + "29.728132639588573", + "28.23706116860197", + "310.2683786555398", + "-14.058956233016271" + ], + [ + "2", + "28.225967217524378", + "26.90261977410061", + "308.7572935618984", + "-10.07569919353105" + ], + [ + "3", + "29.38559718605284", + "27.93565790812836", + "309.51600359134665", + "-11.50602746286123" + ], + [ + "4", + "28.29109171685917", + "26.960775009646518", + "311.7243037291042", + "-17.22723336556269" + ] + ], + "shape": { + "columns": 4, + "rows": 5 + } + }, + "text/html": [ + "
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lsst_g_truelsst_r_trueradec
030.30328128.747045309.295394-10.749590
129.72813328.237061310.268379-14.058956
228.22596726.902620308.757294-10.075699
329.38559727.935658309.516004-11.506027
428.29109226.960775311.724304-17.227233
\n", + "
" + ], + "text/plain": [ + " lsst_g_true lsst_r_true ra dec\n", + "0 30.303281 28.747045 309.295394 -10.749590\n", + "1 29.728133 28.237061 310.268379 -14.058956\n", + "2 28.225967 26.902620 308.757294 -10.075699\n", + "3 29.385597 27.935658 309.516004 -11.506027\n", + "4 28.291092 26.960775 311.724304 -17.227233" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's build a dataset containing (ra, dec) coordinates and magnitudes\n", + "data_set_radecmag = injected_data_full.drop(columns=[col for col in injected_data_full.columns if col not in ['ra', 'dec', 'lsst_g_true', 'lsst_r_true']])\n", + "data_set_radecmag.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4d0dbbe1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applying dust correction for r-band on observed magnitudes.\n", + "Applying dust correction for g-band on observed magnitudes.\n", + "Applying detection cut on g-band with SNR >= 5.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/streamsim_dev/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log10\n", + " result = getattr(ufunc, method)(*inputs, **kwargs)\n", + "/opt/anaconda3/envs/streamsim_dev/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log10\n", + " result = getattr(ufunc, method)(*inputs, **kwargs)\n" + ] + }, + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "lsst_g_true", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_r_true", + "rawType": "float64", + "type": "float" + }, + { + "name": "ra", + "rawType": "float64", + "type": "float" + }, + { + "name": "dec", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_yr4_r_obs", + "rawType": "object", + "type": "unknown" + }, + { + "name": "lsst_yr4_r_err", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_yr4_g_obs", + "rawType": "object", + "type": "unknown" + }, + { + "name": "lsst_yr4_g_err", + "rawType": "float64", + "type": "float" + }, + { + "name": "lsst_yr4_flag_observed", + "rawType": "bool", + "type": "boolean" + } + ], + "ref": "34356f28-8110-4141-8ae1-7776ef3a209b", + "rows": [ + [ + "0", + "30.303280724323006", + "28.747044825349548", + "309.2953942380719", + "-10.749590498519837", + "27.94450115002072", + "2.839884927096569", + "BAD_MAG", + "10.000001249999922", + "False" + ], + [ + "1", + "29.728132639588573", + "28.23706116860197", + "310.2683786555398", + "-14.058956233016271", + "27.607723876818635", + "1.4081374219582061", + "28.532156217238942", + "10.000001249999922", + "False" + ], + [ + "2", + "28.225967217524378", + "26.90261977410061", + "308.7572935618984", + "-10.07569919353105", + "26.892840518625263", + "0.3412351245318386", + "28.0957683802766", + "1.3184116118589486", + "False" + ], + [ + "3", + "29.38559718605284", + "27.93565790812836", + "309.51600359134665", + "-11.50602746286123", + "BAD_MAG", + "1.0610851133467605", + "28.380888666489522", + "10.000001249999922", + "False" + ], + [ + "4", + "28.29109171685917", + "26.960775009646518", + "311.7243037291042", + "-17.22723336556269", + "26.483796611811186", + "0.40904825757417285", + "26.483845022977533", + "1.9325848090410778", + "False" + ] + ], + "shape": { + "columns": 9, + "rows": 5 + } + }, + "text/html": [ + "
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lsst_g_truelsst_r_trueradeclsst_yr4_r_obslsst_yr4_r_errlsst_yr4_g_obslsst_yr4_g_errlsst_yr4_flag_observed
030.30328128.747045309.295394-10.74959027.9445012.839885BAD_MAG10.000001False
129.72813328.237061310.268379-14.05895627.6077241.40813728.53215610.000001False
228.22596726.902620308.757294-10.07569926.8928410.34123528.0957681.318412False
329.38559727.935658309.516004-11.506027BAD_MAG1.06108528.38088910.000001False
428.29109226.960775311.724304-17.22723326.4837970.40904826.4838451.932585False
\n", + "
" + ], + "text/plain": [ + " lsst_g_true lsst_r_true ra dec lsst_yr4_r_obs \\\n", + "0 30.303281 28.747045 309.295394 -10.749590 27.944501 \n", + "1 29.728133 28.237061 310.268379 -14.058956 27.607724 \n", + "2 28.225967 26.902620 308.757294 -10.075699 26.892841 \n", + "3 29.385597 27.935658 309.516004 -11.506027 BAD_MAG \n", + "4 28.291092 26.960775 311.724304 -17.227233 26.483797 \n", + "\n", + " lsst_yr4_r_err lsst_yr4_g_obs lsst_yr4_g_err lsst_yr4_flag_observed \n", + "0 2.839885 BAD_MAG 10.000001 False \n", + "1 1.408137 28.532156 10.000001 False \n", + "2 0.341235 28.095768 1.318412 False \n", + "3 1.061085 28.380889 10.000001 False \n", + "4 0.409048 26.483845 1.932585 False " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "injected_data = stream_injector.inject(data_set_radecmag, seed=seed, mask_type=None, stream_config=None, verbose=True)\n", + "injected_data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f7bd2b3a", + "metadata": {}, + "source": [ + "## 4) Usage example\n", + "\n", + "This can be useful for comparing analysis results between the ideal case of dynamic simulations and the more realistic case of observed data.\n", + "\n", + "For example, we can calculate the 1D density along the stream and its power spectrum." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c3d933ce", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_power_spectrum(data, fig=None, ax=None, phi1_bin_edges=None, label=None):\n", + " \"\"\"Plot 1D density along phi1 and its power spectrum.\"\"\"\n", + " if fig is None or ax is None:\n", + " fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + " # Compute 1D density along phi1\n", + " phi1 = np.array(data['phi1'])\n", + " if phi1_bin_edges is None:\n", + " phi1_bin_edges = np.linspace(np.min(phi1), np.max(phi1), 50)\n", + " counts, _ = np.histogram(phi1, bins=phi1_bin_edges)\n", + " bin_centers = 0.5 * (phi1_bin_edges[:-1] + phi1_bin_edges[1:])\n", + "\n", + " # Compute power spectrum using cross-spectral density method from scipy\n", + " from scipy.signal import csd\n", + " fs = 1.0 / (bin_centers[1] - bin_centers[0]) # Sampling frequency\n", + " k, Pxx = csd(counts, counts, nperseg=len(counts), fs=fs)\n", + "\n", + " # Plot density\n", + " ax[0].plot(bin_centers, counts, drawstyle='steps-mid', label=label)\n", + " ax[0].set_xlabel('phi1 (deg)')\n", + " ax[0].set_ylabel('Star counts')\n", + " ax[0].set_title('1D Density along phi1')\n", + "\n", + " # Plot power spectrum (1/k, P(k)) in log-log scale\n", + " ax[1].loglog(1/k, Pxx, label=label)\n", + " ax[1].set_xlabel('1/k (deg)')\n", + " ax[1].set_ylabel('P(k)')\n", + " ax[1].set_title('Power Spectrum of 1D Density')\n", + "\n", + " for a in ax.flatten():\n", + " a.grid()\n", + " if label is not None:\n", + " a.legend()\n", + " return fig, ax" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "077e5872", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/9j/zk4h50g505d635j69p9tgwbc0000gp/T/ipykernel_10726/3483219967.py:25: RuntimeWarning: divide by zero encountered in divide\n", + " ax[1].loglog(1/k, Pxx, label=label)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare the power spectrum of all stars vs. detected stars only\n", + "phi1_bin_edges = np.linspace(-7, 7, 50)\n", + "fig, ax = plot_power_spectrum(injected_data_full, label='All data', phi1_bin_edges=phi1_bin_edges)\n", + "fig, ax = plot_power_spectrum(injected_data_full[injected_data_full['lsst_yr4_flag_observed']], fig=fig, ax=ax, label='Detected data', phi1_bin_edges=phi1_bin_edges)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "6acd2421", + "metadata": {}, + "source": [ + "## 5) Conclusion\n", + "\n", + "Streamobs provides a method for converting data mocks into realistic observed data for a given survey, with minimal computational cost. This can be easily achieved with just a few commands, helping to bridge the gap between simulations and observational challenges in the field of stellar streams.\n", + "\n", + "\n", + "You can find more informations in the [full documentation](https://lsstdesc.github.io/streamobs/)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "streamsim_dev", + "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": "3.11.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/index.rst b/docs/source/index.rst index b4ae8ba..e95d6f5 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -86,13 +86,21 @@ Documentation Contents multisurvey citation + .. toctree:: + :maxdepth: 1 + :caption: Examples + + examples/tutorial_generate_datamocks + examples/tutorial_inject_stream + .. toctree:: :maxdepth: 2 :caption: Surveys supported - DES - LSST - roman + surveys/DES + surveys/LSST + surveys/Roman + .. toctree:: :maxdepth: 2 diff --git a/docs/source/DES.md b/docs/source/surveys/DES.md similarity index 100% rename from docs/source/DES.md rename to docs/source/surveys/DES.md diff --git a/docs/source/LSST.md b/docs/source/surveys/LSST.md similarity index 100% rename from docs/source/LSST.md rename to docs/source/surveys/LSST.md diff --git a/docs/source/roman.md b/docs/source/surveys/roman.md similarity index 100% rename from docs/source/roman.md rename to docs/source/surveys/roman.md diff --git a/notebooks/tutorial_generate_datamocks.ipynb b/notebooks/tutorial_generate_datamocks.ipynb deleted file mode 100644 index 41717ad..0000000 --- a/notebooks/tutorial_generate_datamocks.ipynb +++ /dev/null @@ -1,931 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "394f3750", - "metadata": {}, - "source": [ - "# Streamobs: generate stream mocks\n", - "\n", - "Generate stellar stream mock catalogs from configuration files and complete existing tables with missing columns.\n", - "**Streamobs** allows sampling of the following quantities:\n", - "\n", - "* (`phi1`, `phi2`): stellar coordinates in the stream frame\n", - "* `dist`: distance modulus of stars\n", - "* `mag_{band}`: apparent magnitude in a given photometric band of a chosen survey\n", - "\n", - "Future versions may also include sampling of proper motions and velocities.\n", - "\n", - "Streamobs can further convert these intrinsic quantities into **observed quantities**.\n", - "For more details, see the notebook *`tutorial_inject_stream.ipynb`*.\n", - "\n", - "**In this tutorial, you’ll learn to:**\n", - "\n", - "* Define model components: density, track, distance modulus, isochrone\n", - "* Build or load a configuration and sample a mock catalog\n", - "* Complete partial catalogs (e.g., by adding magnitudes)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "94d3f015", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "import pandas as pd\n", - "import yaml\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import scipy\n", - "\n", - "# Set the base directory \n", - "base_dir = os.path.abspath(os.path.join(os.getcwd(), '..'))\n", - "os.chdir(base_dir) # to be able to find ./config/myfile.yaml\n", - "\n", - "# Add base directory to the Python path for imports\n", - "sys.path.append(base_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a54fd20b", - "metadata": {}, - "outputs": [], - "source": [ - "# Import necessary modules form streamobs\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "from streamobs.utils import parse_config\n", - "from streamobs.model import StreamModel" - ] - }, - { - "cell_type": "markdown", - "id": "f5492fdd", - "metadata": {}, - "source": [ - "# 1) Build a stream configuration\n", - "\n", - "To set up a stream model, we use a configuration file or dictionary that defines all necessary components. It can include:\n", - "\n", - "* **density** – samples `phi1` values along the stream\n", - "* **track** – gives `phi2` as a function of `phi1` (center + spread, using a Gaussian or Uniform sampler)\n", - "* **distance_modulus** – defines $DM(phi1)$ for computing apparent magnitudes\n", - "* **isochrone** – samples the color–magnitude diagram (required to generate magnitudes)\n", - "\n", - "You can choose how each quantity depends on `phi1` (e.g., constant, linear, spline, etc.).\n", - "\n", - "**Notes:**\n", - "\n", - "* To generate magnitudes, you need at least both `dist` and `isochrone`.\n", - "* The velocity model is currently a placeholder (returns NaN).\n", - "* Samplers and functions are selected using the `type` keyword (e.g., `\"Uniform\"`, `\"CubicSplineInterpolation\"`).\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3d2810e8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'density': {'type': 'Uniform', 'xmin': -9.0, 'xmax': 9.0}, 'track': {'center': {'type': 'Constant', 'value': 0.0}, 'spread': {'type': 'Constant', 'value': 0.2}, 'sampler': 'Gaussian'}, 'isochrone': {'name': 'Marigo2017', 'survey': 'lsst', 'age': 12.0, 'z': 0.0006, 'band_1': 'g', 'band_2': 'r', 'band_1_detection': True}, 'distance_modulus': {'center': {'type': 'Constant', 'value': 16.5}, 'spread': {'type': 'Constant', 'value': 0.0}}}\n" - ] - } - ], - "source": [ - "# Build a config dictionary directly\n", - "\n", - "config = { \n", - " # Density model\n", - " 'density': {'type': 'Uniform', 'xmin': -9.0, 'xmax': 9.0}, \n", - "\n", - " # Track model\n", - " 'track': {'center': {'type': 'Constant', 'value': 0.0}, # center line of the stream in degrees\n", - " 'spread': {'type': 'Constant', 'value': 0.2}, # spread of the stream in degrees\n", - " 'sampler': 'Gaussian'}, # how to sample across the stream\n", - "\n", - " # Isochrone model\n", - " 'isochrone': {'name': 'Marigo2017', # isochrone set name\n", - " 'survey': 'lsst', # survey for filter set\n", - " 'age': 12.0, # Age in Gyr of the population\n", - " 'z': 0.0006, # Metallicity of the population\n", - " 'band_1': 'g', # first band for color-magnitude\n", - " 'band_2': 'r', # second band for color-magnitude\n", - " 'band_1_detection': True}, \n", - "\n", - " # Distance modulus model. Here an example of a constant distance modulus\n", - " 'distance_modulus': {'center': {'type': 'Constant', 'value': 16.5}, \n", - " 'spread': {'type': 'Constant', 'value': 0.0}, \n", - " }\n", - "}\n", - "\n", - "# or load from a config file\n", - "#config_path = os.path.join(base_dir, 'config', 'toy1_config.yaml')\n", - "#config = parse_config(config_path)['stream']\n", - "\n", - "print(config)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e33bd732", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Generated 4500 stars\n", - "\n", - "First 5 stars:\n", - " phi1 phi2 dist mu1 mu2 rv mag_g mag_r\n", - "0 7.375317 0.250945 16.5 None None None 29.112133 27.696447\n", - "1 -1.858344 -0.044748 16.5 None None None 27.423636 26.221278\n", - "2 -2.456239 -0.097661 16.5 None None None 24.906618 24.124551\n", - "3 -6.244392 0.156078 16.5 None None None 26.278365 25.239959\n", - "4 -4.599183 0.059099 16.5 None None None 27.078485 25.930319\n" - ] - } - ], - "source": [ - "# Create stream model and generate stars\n", - "stream_model = StreamModel(config)\n", - "stream_df = stream_model.sample(4500)\n", - "\n", - "# The dataframe contains: phi1, phi2, distance, magnitudes, etc.\n", - "print(f\"✓ Generated {len(stream_df)} stars\")\n", - "print(\"\\nFirst 5 stars:\")\n", - "print(stream_df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1d7e82f8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Color-Magnitude Diagram')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Quick look: sky track, 1D density, CMD\n", - "fig, ax = plt.subplots(1, 3, figsize=(16, 5))\n", - "\n", - "# Stream on the sky\n", - "ax[0].scatter(stream_df['phi1'], stream_df['phi2'], s=1, color='blue')\n", - "ax[0].set_xlabel('phi1 (deg)')\n", - "ax[0].set_ylabel('phi2 (deg)')\n", - "ax[0].set_title('Stream on the sky')\n", - "\n", - "# 1D density\n", - "ax[1].hist(stream_df['phi1'], bins=50, color='green')\n", - "ax[1].set_xlabel('phi1 (deg)')\n", - "ax[1].set_ylabel('Number of stars')\n", - "ax[1].set_title('1D Density along the stream')\n", - "\n", - "# Color-magnitude diagram\n", - "ax[2].scatter(stream_df['mag_g'] - stream_df['mag_r'], stream_df['mag_g'], s=1, color='red')\n", - "ax[2].set_xlabel('g - r (mag)')\n", - "ax[2].set_ylabel('g (mag)')\n", - "ax[2].invert_yaxis()\n", - "ax[2].set_title('Color-Magnitude Diagram')" - ] - }, - { - "cell_type": "markdown", - "id": "168afb7e", - "metadata": {}, - "source": [ - "\n", - "## 2) Spline-based configuration\n", - "\n", - "You can model a more realistic stream shape using cubic splines, particularly for:\n", - "\n", - "* **Linear density:** $\\sqrt{2\\pi} \\times \\text{peak intensity} \\times \\text{spread}$\n", - "* **Distance modulus**\n", - "* **Track and width**\n", - "\n", - "In this example, we use data from `data/patrick_2022_splines.csv` and select `stream == 'phoenix'`.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e9da4d2a", - "metadata": {}, - "outputs": [], - "source": [ - "# Nodes and values for the spline-based stream model (from Patrick et al. 2022, for Phoenix stream)\n", - "# One may read these from a CSV file instead of hardcoding them, cf to config/atlas_spline_config.yaml\n", - "intensity_nodes = np.array([-13., -9.75, -8.125, -4.1640625, -3.25, -1.625, 1.625, 6.5, 8.125, 13.])\n", - "intensity_node_values = np.array([2.35582279e-07, 2.65789495e-02, 5.94765580e-02, 7.20106921e-02, 9.96003626e-02, 4.68656926e-02, 7.42352023e-02, 4.75688845e-06, 1.73046024e-02, 4.08879937e-08])\n", - "spread_nodes = np.array([-13. , 13.])\n", - "spread_node_values = np.array([0.0992389, 0.17083177])\n", - "center_nodes = np.array([-13., 4.33333333, 13.])\n", - "center_node_values = np.array([0.19313599, 0.07139282, 0.60245054])\n", - "distance_nodes = np.array([-13., 13.])\n", - "distance_node_values = np.array([16.38285347, 16.1136374])\n", - "\n", - "\n", - "config_spline = {\n", - " # Density model using cubic splines from CSV\n", - " 'density': {\n", - " 'type': 'lineardensitycubicsplineinterpolation',\n", - " 'intensity_nodes': intensity_nodes,\n", - " 'intensity_node_values': intensity_node_values,\n", - " 'spread_nodes': spread_nodes,\n", - " 'spread_node_values': spread_node_values,\n", - " },\n", - "\n", - " # Track model: center and spread as cubic splines\n", - " 'track': {\n", - " 'center': {'type': 'CubicSplineInterpolation', 'nodes': center_nodes, 'node_values': center_node_values},\n", - " 'spread': {'type': 'CubicSplineInterpolation', 'nodes': spread_nodes, 'node_values': spread_node_values},\n", - " },\n", - "\n", - " # Isochrone model\n", - " 'isochrone': {\n", - " 'name': 'Marigo2017',\n", - " 'survey': 'lsst',\n", - " 'age': 13.0,\n", - " 'z': 0.0004,\n", - " 'band_1': 'g',\n", - " 'band_2': 'r',\n", - " 'band_1_detection': True\n", - " },\n", - "\n", - " # Distance modulus model as a cubic spline (flat default)\n", - " 'distance_modulus': {\n", - " 'center': {'type': 'CubicSplineInterpolation', 'nodes': distance_nodes, 'node_values': distance_node_values},\n", - " 'spread': {'type': 'Constant', 'value': 0.0},\n", - " },\n", - "}\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "23c7702c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Generated 4000 stars with spline density\n" - ] - }, - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "phi1", - "rawType": "float64", - "type": "float" - }, - { - "name": "phi2", - "rawType": "float64", - "type": "float" - }, - { - "name": "dist", - "rawType": "float64", - "type": "float" - }, - { - "name": "mu1", - "rawType": "object", - "type": "unknown" - }, - { - "name": "mu2", - "rawType": "object", - "type": "unknown" - }, - { - "name": "rv", - "rawType": "object", - "type": "unknown" - }, - { - "name": "mag_g", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_r", - "rawType": "float64", - "type": "float" - } - ], - "ref": "261acb8f-e0bc-4548-8a27-7c2ccc4e3ff6", - "rows": [ - [ - "0", - "1.858628586285862", - "0.028439848701638515", - "16.22900033178502", - null, - null, - null, - "29.43290797051653", - "27.97559490649988" - ], - [ - "1", - "2.8455984559845593", - "0.24636654368293825", - "16.218780787572378", - null, - null, - null, - "30.699529434956283", - "29.08595892852513" - ], - [ - "2", - "0.9704597045970456", - "-0.08063192814148298", - "16.23819684470135", - null, - null, - null, - "29.436960664925884", - 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phi1phi2distmu1mu2rvmag_gmag_r
01.8586290.02844016.229000NoneNoneNone29.43290827.975595
12.8455980.24636716.218781NoneNoneNone30.69952929.085959
20.970460-0.08063216.238197NoneNoneNone29.43696127.980323
3-2.207812-0.17014516.271106NoneNoneNone29.51162328.049504
4-8.0947910.07429316.332063NoneNoneNone22.61937722.208799
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" - ], - "text/plain": [ - " phi1 phi2 dist mu1 mu2 rv mag_g mag_r\n", - "0 1.858629 0.028440 16.229000 None None None 29.432908 27.975595\n", - "1 2.845598 0.246367 16.218781 None None None 30.699529 29.085959\n", - "2 0.970460 -0.080632 16.238197 None None None 29.436961 27.980323\n", - "3 -2.207812 -0.170145 16.271106 None None None 29.511623 28.049504\n", - "4 -8.094791 0.074293 16.332063 None None None 22.619377 22.208799" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Optional: sample using the spline-based config\n", - "stream_model_spline = StreamModel(config_spline)\n", - "stream_df_spline = stream_model_spline.sample(4000)\n", - "\n", - "print(f\"✓ Generated {len(stream_df_spline)} stars with spline density\")\n", - "stream_df_spline.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3cfc1b7b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Color-Magnitude Diagram')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "fig, ax = plt.subplots(1, 3, figsize=(16, 5))\n", - "\n", - "# plotting the stream on the sky \n", - "ax[0].scatter(stream_df_spline['phi1'], stream_df_spline['phi2'], s=1, color='blue', label = \"Generated stars\")\n", - "x_val = np.sort(stream_df_spline['phi1'].values)\n", - "spline_val = scipy.interpolate.CubicSpline(center_nodes, center_node_values)(x_val)\n", - "ax[0].plot(x_val, spline_val, color='orange', lw=2, label='Spline track model')\n", - "ax[0].set_xlabel('phi1 (deg)')\n", - "ax[0].set_ylabel('phi2 (deg)')\n", - "ax[0].set_title('Stream on the sky')\n", - "ax[0].legend()\n", - "\n", - "# Plotting the 1D density along the stream\n", - "ax[1].hist(stream_df_spline['phi1'], bins=50, color='green')\n", - "ax[1].set_xlabel('phi1 (deg)')\n", - "ax[1].set_ylabel('Number of stars')\n", - "ax[1].set_title('1D Density along the stream')\n", - "\n", - "# plotting Color magnitude diagram\n", - "ax[2].scatter(stream_df_spline['mag_g'] - stream_df_spline['mag_r'], stream_df_spline['mag_g'], s=1, color='red')\n", - "ax[2].set_xlabel('g - r (mag)')\n", - "ax[2].set_ylabel('g (mag)')\n", - "ax[2].invert_yaxis()\n", - "ax[2].set_title('Color-Magnitude Diagram')\n" - ] - }, - { - "cell_type": "markdown", - "id": "f3c615f0", - "metadata": {}, - "source": [ - "# 3) Complete an existing catalog\n", - "\n", - "`StreamModel.complete_catalog` fills only the requested columns while preserving existing values (except for magnitudes and velocities, which are regenerated together for consistency).\n", - "\n", - "* **Dependencies:**\n", - "\n", - " * `phi2` and `dist` require `phi1`\n", - " * `mags` require both `dist` and `isochrone`\n", - "* **Input formats:** can be a `DataFrame`, `dict`, path to a CSV file, or `None` (with a specified `size` to generate the full catalog)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d37bb8b8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Catalog with missing columns:\n", - " phi1 phi2\n", - "0 7.375317 0.250945\n", - "1 -1.858344 -0.044748\n", - "2 -2.456239 -0.097661\n", - "3 -6.244392 0.156078\n", - "4 -4.599183 0.059099\n" - ] - } - ], - "source": [ - "# Let's build a catalog with missing columns to complete\n", - "# Here for example we keep only 'phi1' and 'phi2', and drop others\n", - "stream_df_sub = stream_df.drop(columns=['mag_r', 'dist', 'mag_g', 'mu1', 'mu2', 'rv' ]).reset_index(drop=True)\n", - "print(\"\\nCatalog with missing columns:\")\n", - "print(stream_df_sub.head())" - ] - }, - { - "cell_type": "markdown", - "id": "5050d977", - "metadata": {}, - "source": [ - "## Fill every missing columns" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7e98fa35", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Velocity model not defined; skipping velocities.\n", - "Filled 4500 dist values.\n", - "Filled magnitudes for 4500 rows.\n", - " phi1 phi2 dist mag_g mag_r\n", - "0 7.375317 0.250945 16.5 32.103113 30.363368\n", - "1 -1.858344 -0.044748 16.5 30.179186 28.636327\n", - "2 -2.456239 -0.097661 16.5 25.450919 24.559246\n", - "3 -6.244392 0.156078 16.5 31.283494 29.621596\n", - "4 -4.599183 0.059099 16.5 30.907213 29.285877\n" - ] - } - ], - "source": [ - "# Now we can use `complete_catalog` to fill in the missing columns amoung ['phi1', 'phi2', 'dist', 'mag_g', 'mag_r', 'mu1', 'mu2', 'rv']\n", - "completed_catalog = stream_model.complete_catalog(\n", - " catalog=stream_df_sub,\n", - " save_path=None,\n", - " inplace=False,\n", - " verbose=True\n", - ")\n", - "print(completed_catalog.head())" - ] - }, - { - "cell_type": "markdown", - "id": "c06d8f12", - "metadata": {}, - "source": [ - "## Fill only specific columns" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "16346874", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filled 4500 dist values.\n", - "Filled magnitudes for 4500 rows.\n" - ] - }, - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "phi1", - "rawType": "float64", - "type": "float" - }, - { - "name": "phi2", - "rawType": "float64", - "type": "float" - }, - { - "name": "dist", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_g", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_r", - "rawType": "float64", - "type": "float" - } - ], - "ref": "9e664412-10a4-40f2-8cd5-d60ecab0ed7a", - "rows": [ - [ - "0", - "7.375316617497113", - "0.25094470862288215", - "16.5", - "26.996639642781176", - "25.86116919520827" - ], - [ - "1", - "-1.8583440099193211", - "-0.044747965470281675", - "16.5", - "30.776760595884873", - "29.16948623646113" - ], - [ - "2", - "-2.4562386975601243", - "-0.09766058175733809", - "16.5", - "28.153413187735644", - "26.840933323666828" - ], - [ - "3", - "-6.244392012633449", - "0.15607780086870238", - "16.5", - "24.726490587598946", - "23.978470377452336" - ], - [ - "4", - "-4.599182587276114", - "0.05909872157949739", - "16.5", - "28.40054473602201", - "27.05946982607113" - ] - ], - "shape": { - "columns": 5, - "rows": 5 - } - }, - "text/html": [ - "
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phi1phi2distmag_gmag_r
07.3753170.25094516.526.99664025.861169
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" - ], - "text/plain": [ - " phi1 phi2 dist mag_g mag_r\n", - "0 7.375317 0.250945 16.5 26.996640 25.861169\n", - "1 -1.858344 -0.044748 16.5 30.776761 29.169486\n", - "2 -2.456239 -0.097661 16.5 28.153413 26.840933\n", - "3 -6.244392 0.156078 16.5 24.726491 23.978470\n", - "4 -4.599183 0.059099 16.5 28.400545 27.059470" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Example: fill only magnitudes\n", - "subset = stream_df_sub.copy()\n", - "completed_mags = stream_model.complete_catalog(\n", - " catalog=subset,\n", - " columns_to_add=[\"mag_g\", \"mag_r\"],\n", - " inplace=False,\n", - " verbose=True,\n", - ")\n", - "completed_mags.head()" - ] - }, - { - "cell_type": "markdown", - "id": "c1b81722", - "metadata": {}, - "source": [ - "*Note: the distance modulus is also added, since it is needed to convert absolute magnitude sampled from the isochrone, to apparent magnitudes (`mag_g`and `mag_r` here).*" - ] - }, - { - "cell_type": "markdown", - "id": "e4cd4bc5", - "metadata": {}, - "source": [ - "## Tips and Troubleshooting\n", - "\n", - "* If magnitudes are missing or NaN, make sure your config includes both `distance_modulus` and `isochrone` sections.\n", - "* To keep colors consistent, `complete_catalog` regenerates both `mag_g` and `mag_r` whenever one needs to be computed.\n", - "* Column names are automatically standardized (e.g., `'g_mag'` → `'mag_g'`); see `_standardize_columns_name`.\n", - "* The velocity model is currently a placeholder and returns NaN values." - ] - }, - { - "cell_type": "markdown", - "id": "ad8600ce", - "metadata": {}, - "source": [ - "# Conclusion\n", - "\n", - "Streamobs provides a flexible framework to build and complete stellar stream mock catalogs, from simple analytic models to spline-based configurations.\n", - "Future updates will include proper motions, velocities, and improved documentation for easier user adoption.\n", - "\n", - "You can find more informations in the [full documentation](https://lsstdesc.github.io/streamobs/)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "streamsim_dev", - "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": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/tutorial_inject_stream.ipynb b/notebooks/tutorial_inject_stream.ipynb deleted file mode 100644 index 135ea65..0000000 --- a/notebooks/tutorial_inject_stream.ipynb +++ /dev/null @@ -1,1938 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ef83a9a9", - "metadata": {}, - "source": [ - "# Injecting stream mocks into a survey\n", - "\n", - "This short tutorial shows how to:\n", - "- Load a survey (e.g., LSST yr5) and inspect completeness/error models\n", - "- Create a `StreamInjector` and inject photometric effects + detection flags\n", - "- Plot against the footprint and visualize injected magnitudes\n", - "\n", - "This is useful for people who want to convert their dynamical simulation results into realistic survey data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b899efa", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "import pandas as pd\n", - "import yaml\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Set the base directory \n", - "base_dir = os.path.abspath(os.path.join(os.getcwd(), '..'))\n", - "os.chdir(base_dir) # to be able to find ./config/myfile.yaml\n", - "\n", - "# Add base directory to the Python path for imports\n", - "sys.path.append(base_dir)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf16899f", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "from streamobs import surveys, observed\n", - "from streamobs.model import StreamModel" - ] - }, - { - "cell_type": "markdown", - "id": "17059e52", - "metadata": {}, - "source": [ - "## 1) Load the survey (cached)\n", - "We load LSST yr5 once; the data is cached for efficiency (bands, depth maps, extinction, completeness, and photometric error models)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27cdcd2b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading survey data for 'lsst_yr4'...\n", - " Loading config from: lsst_yr4.yaml\n", - "\n", - "======================================================================\n", - "LOADING SURVEY DATA FILES\n", - "======================================================================\n", - "Survey data directory: /Users/pelissier/Documents/Codes/packages/streamobs/streamobs/../data/surveys/lsst_yr4\n", - "\n", - "Fallback directory for shared data files: /Users/pelissier/Documents/Codes/packages/streamobs/streamobs/../data/others\n", - "\n", - "Available bands: g, i, r, u, y, z\n", - "\n", - "\n", - "Loading survey properties...\n", - "Loading magnitude limit maps...\n", - " ✓ Success for g-band magnitude limit\n", - " ⚠ Warning: 'maglim_map_i' not specified in config (skipping i-band)\n", - " ✓ Success for r-band magnitude limit\n", - " ⚠ Warning: 'maglim_map_u' not specified in config (skipping u-band)\n", - " ⚠ Warning: 'maglim_map_y' not specified in config (skipping y-band)\n", - " ⚠ Warning: 'maglim_map_z' not specified in config (skipping z-band)\n", - "\n", - "Loading completeness/efficiency function...\n", - " Loading Completeness/efficiency function...\n", - " File: lsst_stellar_efficiency_cutr.csv\n", - " ✓ Success\n", - " Loading Detection efficiency function...\n", - " File: lsst_stellar_efficiency_cutr.csv\n", - " ✓ Success\n", - " Loading Classification efficiency function...\n", - " File: lsst_stellar_efficiency_cutr.csv\n", - " ✓ Success\n", - "\n", - "Loading photometric error model...\n", - " Loading Photometric error model...\n", - " File: lsst_photoerror_r.csv\n", - " ✓ Success\n", - "\n", - "Loading band-independent maps...\n", - " Loading E(B-V) extinction map...\n", - " File: ebv_sfd98_lowres_nside_512_ring_equatorial.fits\n", - " ✓ Success\n", - " ⚠ Warning: 'coverage' not specified in config (skipping)\n", - "\n", - "Building coverage map from magnitude limit maps...\n", - " ✓ Built coverage map (nside=128, 133236 pixels covered)\n", - "\n", - "Survey properties summary:\n", - " g-band:\n", - " Extinction coefficient: 3.661\n", - " Saturation limit: 16.0 mag\n", - " Systematic error: 0.0050 mag\n", - " i-band:\n", - " Extinction coefficient: 2.054\n", - " Saturation limit: 16.0 mag\n", - " Systematic error: 0.0050 mag\n", - " r-band:\n", - " Extinction coefficient: 2.701\n", - " Saturation limit: 16.0 mag\n", - " Systematic error: 0.0050 mag\n", - " u-band:\n", - " Extinction coefficient: 4.757\n", - " Saturation limit: 16.0 mag\n", - " Systematic error: 0.0050 mag\n", - " y-band:\n", - " Extinction coefficient: 1.308\n", - " Saturation limit: 16.0 mag\n", - " Systematic error: 0.0050 mag\n", - " z-band:\n", - " Extinction coefficient: 1.590\n", - " Saturation limit: 16.0 mag\n", - " Systematic error: 0.0050 mag\n", - "\n", - "======================================================================\n", - "SURVEY DATA LOADED SUCCESSFULLY\n", - "======================================================================\n", - "\n", - "✓ Survey 'lsst_yr4' loaded and cached successfully\n" - ] - } - ], - "source": [ - "lsst_yr4= surveys.Survey.load(survey = 'lsst', release='yr4')" - ] - }, - { - "cell_type": "markdown", - "id": "42801958", - "metadata": {}, - "source": [ - "### Completeness and photometric error\n", - "Both depend on magnitude relative to the local limit (delta_mag = mag - maglim). We can visualize them for the r-band at a chosen magnitude limit." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b85555b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mag_r_test = np.linspace(15,31, 1000)\n", - "maglim_r_dc2 = 26.8\n", - "completeness_val = lsst_yr4.get_completeness('r', mag_r_test, maglim_r_dc2)\n", - "mag_error_val = lsst_yr4.get_photo_error('r', mag_r_test, maglim_r_dc2)\n", - "\n", - "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n", - "ax[0].plot(mag_r_test, completeness_val, label='Completeness')\n", - "ax[0].axvline(maglim_r_dc2, color='red', linestyle='--', label='Magnitude Limit')\n", - "ax[0].set_xlabel('Apparent r-band magnitude')\n", - "ax[0].set_ylabel('Completeness')\n", - "ax[0].set_title('LSST DC2 Completeness Function')\n", - "ax[0].grid()\n", - "ax[0].legend() \n", - "\n", - "ax[1].plot(mag_r_test, mag_error_val, label='Photometric Error', color='orange')\n", - "ax[1].axvline(maglim_r_dc2, color='red', linestyle='--', label='Magnitude Limit')\n", - "ax[1].set_yscale('log')\n", - "ax[1].set_xlabel('Apparent r-band magnitude')\n", - "ax[1].set_ylabel('Photometric Error (mag)')\n", - "ax[1].set_title('LSST DC2 Photometric Error Model')\n", - "ax[1].grid()\n", - "ax[1].legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "23ed125c", - "metadata": {}, - "source": [ - "### Cache manipulation\n", - "Once a survey has been loaded, it's stored in the cache. You can use commands to check which surveys are stored.\n", - "To force reloading of a survey, you must clear the cache first." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fd3a37c5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Using cached survey data for 'lsst_yr4'\n", - "Current cache content: ['lsst_yr4']\n", - "✓ Cleared cached survey 'lsst_yr4'\n", - "Cache content after clearing 'lsst': []\n" - ] - } - ], - "source": [ - "# Once a survey has been loaded once, it can be reused directly from the cache\n", - "lsst_yr4 = surveys.Survey.load(survey = 'lsst', release='yr4') # reuses the cached survey object\n", - "\n", - "# Check the cache content\n", - "\n", - "print(\"Current cache content: \", surveys.SurveyFactory.list_cached_surveys())\n", - "\n", - "surveys.SurveyFactory.clear_cache(survey = 'lsst', release='yr4')\n", - "# surveys.SurveyFactory.clear_cache(survey = 'lsst') # to clear all releases of 'lsst'\n", - "# surveys.SurveyFactory.clear_cache() # to clear the entire cache\n", - "print(\"Cache content after clearing 'lsst': \", surveys.SurveyFactory.list_cached_surveys())" - ] - }, - { - "cell_type": "markdown", - "id": "ba80c280", - "metadata": {}, - "source": [ - "## 2) Create an injector\n", - "The `StreamInjector` class wraps Survey objects and uses their properties to inject observational effects into a dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6fd1866e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading survey data for 'lsst_yr4'...\n", - " Loading config from: lsst_yr4.yaml\n", - "\n", - "======================================================================\n", - "LOADING SURVEY DATA FILES\n", - "======================================================================\n", - "Survey data directory: /Users/pelissier/Documents/Codes/packages/streamobs/streamobs/../data/surveys/lsst_yr4\n", - "\n", - "Fallback directory for shared data files: /Users/pelissier/Documents/Codes/packages/streamobs/streamobs/../data/others\n", - "\n", - "Available bands: g, i, r, u, y, z\n", - "\n", - "\n", - "Loading survey properties...\n", - "Loading magnitude limit maps...\n", - " ✓ Success for g-band magnitude limit\n", - " ⚠ Warning: 'maglim_map_i' not specified in config (skipping i-band)\n", - " ✓ Success for r-band magnitude limit\n", - " ⚠ Warning: 'maglim_map_u' not specified in config (skipping u-band)\n", - " ⚠ Warning: 'maglim_map_y' not specified in config (skipping y-band)\n", - " ⚠ Warning: 'maglim_map_z' not specified in config (skipping z-band)\n", - "\n", - "Loading completeness/efficiency function...\n", - " Loading Completeness/efficiency function...\n", - " File: lsst_stellar_efficiency_cutr.csv\n", - " ✓ Success\n", - " Loading Detection efficiency function...\n", - " File: lsst_stellar_efficiency_cutr.csv\n", - " ✓ Success\n", - " Loading Classification efficiency function...\n", - " File: lsst_stellar_efficiency_cutr.csv\n", - " ✓ Success\n", - "\n", - "Loading photometric error model...\n", - " Loading Photometric error model...\n", - " File: lsst_photoerror_r.csv\n", - " ✓ Success\n", - "\n", - "Loading band-independent maps...\n", - " Loading E(B-V) extinction map...\n", - " File: ebv_sfd98_lowres_nside_512_ring_equatorial.fits\n", - " ✓ Success\n", - " ⚠ Warning: 'coverage' not specified in config (skipping)\n", - "\n", - "Building coverage map from magnitude limit maps...\n", - " ✓ Built coverage map (nside=128, 133236 pixels covered)\n", - "\n", - "Survey properties summary:\n", - " g-band:\n", - " Extinction coefficient: 3.661\n", - " Saturation limit: 16.0 mag\n", - " Systematic error: 0.0050 mag\n", - " i-band:\n", - " Extinction coefficient: 2.054\n", - " Saturation limit: 16.0 mag\n", - " Systematic error: 0.0050 mag\n", - " r-band:\n", - " Extinction coefficient: 2.701\n", - " Saturation limit: 16.0 mag\n", - " Systematic error: 0.0050 mag\n", - " u-band:\n", - " Extinction coefficient: 4.757\n", - " Saturation limit: 16.0 mag\n", - " Systematic error: 0.0050 mag\n", - " y-band:\n", - " Extinction coefficient: 1.308\n", - " Saturation limit: 16.0 mag\n", - " Systematic error: 0.0050 mag\n", - " z-band:\n", - " Extinction coefficient: 1.590\n", - " Saturation limit: 16.0 mag\n", - " Systematic error: 0.0050 mag\n", - "\n", - "======================================================================\n", - "SURVEY DATA LOADED SUCCESSFULLY\n", - "======================================================================\n", - "\n", - "✓ Survey 'lsst_yr4' loaded and cached successfully\n" - ] - } - ], - "source": [ - "# Create an injector using the Survey object\n", - "stream_injector = observed.StreamInjector(lsst_yr4)\n", - "\n", - "# Or using survey name and release (this will create the survey object internally)\n", - "stream_injector = observed.StreamInjector(survey=\"lsst\", release=\"yr4\")" - ] - }, - { - "cell_type": "markdown", - "id": "8b2a38b7", - "metadata": {}, - "source": [ - "## 3) Inject different dataset types\n", - "\n", - "To convert a stream catalog into realistic survey data, you need:\n", - "- Sky coordinates (ra, dec)\n", - "- True apparent magnitudes of the stars\n", - "\n", - "However, **Streamobs** can **automatically sample these quantities** if they're missing, allowing incomplete datasets to be converted into realistic survey data.\n", - "\n", - "Streamobs uses the magnitude limit at each star's location to estimate:\n", - "- The photometric error\n", - "- The observed magnitude\n", - "- Whether the object is detected and classified as a star\n", - "\n", - "The output of the `inject()` method is a DataFrame containing:\n", - "\n", - "- `ra`, `dec`: positions of the stream's stars on the sky\n", - "- `mag_{band}`: true apparent magnitude in a given band\n", - "- `mag_{band}_obs`: observed magnitude in a given band\n", - "- `magerr_{band}`: photometric error on the observed magnitude (in mag). Includes both systematic and statistical errors.\n", - "- `flag_observed`: 1 if the star has been detected by the survey and classified as a star. By default includes an SNR cut (SNR = 1/magerr > 5) in g and r bands.\n", - "\n", - "The output can optionally include:\n", - "- `phi1`, `phi2`: position of stars in the stream frame\n", - "- `dist`: distance modulus of the stars" - ] - }, - { - "cell_type": "markdown", - "id": "914ce4ec", - "metadata": {}, - "source": [ - "### Dataset with only (phi1, phi2) coordinates\n", - "\n", - "If you only provide stream coordinates, Streamobs will:\n", - "\n", - "**1. Sample apparent magnitudes:**\n", - "\n", - "This is done using an isochrone and distance modulus model, which must be provided in a configuration dictionary. See `tutorial_generate_datamocks.ipynb` for more details.\n", - "\n", - "**2. Convert (phi1, phi2) → (ra, dec):**\n", - "\n", - "If you don't provide (ra, dec) columns, Streamobs will randomly place the stream on the sky. You can restrict the placement using the `mask_type` argument to ensure the stream falls within:\n", - "- The survey's footprint\n", - "- Regions with low dust extinction\n", - "- Areas meeting a minimum magnitude limit in a given band\n", - "- Any combination of the above\n", - "\n", - "Streamobs searches uniformly across the sky for a reference frame where a specified fraction of the stream (`percentile_threshold`) lies inside the mask. For more details, see the documentation of the `StreamInjector._find_gc_frame()` method.\n", - "\n", - "Alternatively, you can provide your own `gala.coordinates.GreatCircleICRSFrame` object via the `gc_frame` argument." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ed0abceb", - "metadata": {}, - "outputs": [], - "source": [ - "N = 1000\n", - "seed = 42\n", - "\n", - "rng = np.random.default_rng(seed)\n", - "\n", - "# Replace with your actual data\n", - "data_test = pd.DataFrame({\n", - " 'phi1': rng.uniform(-5, 5, N),\n", - " 'phi2': rng.uniform(-1, 1, N),\n", - "})\n", - "\n", - "\n", - "# Since our data set does not have magnitudes, we need to provide an isochrone and distance model \n", - "# - cf tutorial_generate_datamocks.ipynb\n", - "# This step can be skipped if the input data already has magnitudes in the desired bands\n", - "\n", - "isochrone_config = {'isochrone':{'name': 'Marigo2017', # isochrone set name\n", - " 'survey': 'lsst', # survey for filter set\n", - " 'age': 12.0, # Age in Gyr of the population\n", - " 'z': 0.0006, # Metallicity of the population\n", - " 'band_1': 'g', # first band for color-magnitude\n", - " 'band_2': 'r', # second band for color-magnitude\n", - " 'band_1_detection': True},\n", - "}\n", - "\n", - "distance_modulus_config = {'distance_modulus': {'center': {'type': 'Constant', 'value': 16.5}, \n", - " 'spread': {'type': 'Constant', 'value': 0.0}, \n", - " }}\n", - "\n", - "stream_config = {**isochrone_config, **distance_modulus_config}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf4994dd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Building new mask for ['ebv', 'footprint']...\n", - " Resampling ebv from nside=512 to nside=128\n", - "✓ Mask created: valid pixels fraction = 0.5\n", - " Cached with key: ('lsst', ('ebv', 'footprint'), 0.2)\n", - "Found suitable great circle frame after 2 trials with 100.00% points inside the mask.\n", - "Filled 1000 dist values.\n", - "Filled magnitudes for 1000 rows.\n", - "Applying dust correction for r-band on observed magnitudes.\n", - "Applying dust correction for g-band on observed magnitudes.\n", - "Applying detection cut on g-band with SNR >= 5.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/streamsim_dev/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log10\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n", - "/opt/anaconda3/envs/streamsim_dev/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log10\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n" - ] - } - ], - "source": [ - "mask_type = ['footprint', 'ebv'] # Restrict to survey footprint and apply extinction mask\n", - "\n", - "# By default the bands used for detection are (g and r)\n", - "# You can also add perfect_galstarsep=True to get bolean flag related to\n", - "# detection only (no classification).\n", - "injected_data_full = stream_injector.inject(data_test, seed=seed, mask_type=mask_type, stream_config=stream_config, verbose=True, perfect_galstarsep=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4b064c1f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['phi1', 'phi2', 'ra', 'dec', 'dist', 'mag_g', 'mag_r', 'mag_r_obs',\n", - " 'magerr_r', 'mag_g_obs', 'magerr_g', 'flag_observed',\n", - " 'flag_perfect_galstarsep'],\n", - " dtype='object')\n" - ] - }, - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "phi1", - "rawType": "float64", - "type": "float" - }, - { - "name": "phi2", - "rawType": "float64", - "type": "float" - }, - { - "name": "ra", - "rawType": "float64", - "type": "float" - }, - { - "name": "dec", - "rawType": "float64", - "type": "float" - }, - { - "name": "dist", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_g", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_r", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_r_obs", - "rawType": "object", - "type": "unknown" - }, - { - "name": "magerr_r", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_g_obs", - "rawType": "object", - "type": "unknown" - }, - { - "name": "magerr_g", - "rawType": "float64", - "type": "float" - }, - { - "name": "flag_observed", - "rawType": "bool", - "type": "boolean" - }, - { - "name": "flag_perfect_galstarsep", - "rawType": "bool", - "type": "boolean" - } - ], - "ref": "33c302ea-690b-40ec-8153-075f534d2cf5", - "rows": [ - [ - "0", - "2.7395604855596334", - "-0.8758737869196895", - "309.2953942380719", - "-10.749590498519837", - "16.5", - "22.41028559822249", - "22.04785117105655", - "22.047965100015663", - "0.006780662663910697", - "22.40793655484874", - "0.007394955933706518", - 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phi1phi2radecdistmag_gmag_rmag_r_obsmagerr_rmag_g_obsmagerr_gflag_observedflag_perfect_galstarsep
02.739560-0.875874309.295394-10.74959016.522.41028622.04785122.0479650.00678122.4079370.007395TrueTrue
1-0.611216-0.083476310.268379-14.05895616.530.09666428.562703BAD_MAG2.249934BAD_MAG10.000001FalseFalse
23.585979-0.741940308.757294-10.07569916.525.87983924.90921724.8602790.05692325.8974610.139786TrueTrue
31.973680-0.695347309.516004-11.50602716.529.53728728.06884627.3601831.285261BAD_MAG10.000001FalseFalse
4-4.0582270.264566311.724304-17.22723316.532.38346930.61881430.10298810.00000129.01853210.000001FalseFalse
\n", - "
" - ], - "text/plain": [ - " phi1 phi2 ra dec dist mag_g mag_r \\\n", - "0 2.739560 -0.875874 309.295394 -10.749590 16.5 22.410286 22.047851 \n", - "1 -0.611216 -0.083476 310.268379 -14.058956 16.5 30.096664 28.562703 \n", - "2 3.585979 -0.741940 308.757294 -10.075699 16.5 25.879839 24.909217 \n", - "3 1.973680 -0.695347 309.516004 -11.506027 16.5 29.537287 28.068846 \n", - "4 -4.058227 0.264566 311.724304 -17.227233 16.5 32.383469 30.618814 \n", - "\n", - " mag_r_obs magerr_r mag_g_obs magerr_g flag_observed \\\n", - "0 22.047965 0.006781 22.407937 0.007395 True \n", - "1 BAD_MAG 2.249934 BAD_MAG 10.000001 False \n", - "2 24.860279 0.056923 25.897461 0.139786 True \n", - "3 27.360183 1.285261 BAD_MAG 10.000001 False \n", - "4 30.102988 10.000001 29.018532 10.000001 False \n", - "\n", - " flag_perfect_galstarsep \n", - "0 True \n", - "1 False \n", - "2 True \n", - "3 False \n", - "4 False " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(injected_data_full.columns)\n", - "injected_data_full.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a45cb263", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "175 stars detected out of 1000\n", - "229 stars classified as stars out of 1000\n" - ] - } - ], - "source": [ - "# If you want to work only with detected and classified stars\n", - "detected_data = injected_data_full[injected_data_full['flag_observed']]\n", - "print(len(detected_data), \"stars detected out of\", len(injected_data_full))\n", - "\n", - "# When applying the perfect galstar separation, all detected stars are\n", - "# classified as stars, so we have:\n", - "classified_data = injected_data_full[injected_data_full['flag_perfect_galstarsep']]\n", - "print(len(classified_data), \"stars classified as stars out of\", len(injected_data_full))" - ] - }, - { - "cell_type": "markdown", - "id": "7b973847", - "metadata": {}, - "source": [ - "Streamobs has automatically sampled the missing columns (positions and apparent magnitudes) and estimated observational quantities.\n", - "\n", - "You can verify the injection behavior with some useful plots:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8b2a9e7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
, )" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the injected stream in the survey mask\n", - "stream_injector.plot_stream_in_mask(injected_data_full, mask_type=mask_type)" - ] - }, - { - "cell_type": "markdown", - "id": "a58b7a71", - "metadata": {}, - "source": [ - "The stream lies entirely within the chosen mask." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8d27ae09", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from streamobs.plotting import plot_inject\n", - "\n", - "# Plot results\n", - "fig, ax = plot_inject(injected_data_full, lsst_yr4, bands=['g', 'r'], save=False)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "67f7a59f", - "metadata": {}, - "source": [ - "### Dataset containing (ra, dec) coordinates and distance modulus\n", - "\n", - "Streamobs only needs to sample the missing magnitudes, so you must provide a configuration dictionary for the isochrone model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b9520ac9", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "ra", - "rawType": "float64", - "type": "float" - }, - { - "name": "dec", - "rawType": "float64", - "type": "float" - }, - { - "name": "dist", - "rawType": "float64", - "type": "float" - } - ], - "ref": "ff5212ca-d34e-491f-b4e7-34d96296f415", - "rows": [ - [ - "0", - "309.2953942380719", - "-10.749590498519837", - "16.5" - ], - [ - "1", - "310.2683786555398", - "-14.058956233016271", - "16.5" - ], - [ - "2", - "308.7572935618984", - "-10.07569919353105", - "16.5" - ], - [ - "3", - "309.51600359134665", - "-11.50602746286123", - "16.5" - ], - [ - "4", - "311.7243037291042", - "-17.22723336556269", - "16.5" - ] - ], - "shape": { - "columns": 3, - "rows": 5 - } - }, - "text/html": [ - "
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radecdist
0309.295394-10.74959016.5
1310.268379-14.05895616.5
2308.757294-10.07569916.5
3309.516004-11.50602716.5
4311.724304-17.22723316.5
\n", - "
" - ], - "text/plain": [ - " ra dec dist\n", - "0 309.295394 -10.749590 16.5\n", - "1 310.268379 -14.058956 16.5\n", - "2 308.757294 -10.075699 16.5\n", - "3 309.516004 -11.506027 16.5\n", - "4 311.724304 -17.227233 16.5" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's build a data set containing only (ra, dec) coordinates\n", - "data_set_radec = injected_data_full.drop(columns=[col for col in injected_data_full.columns if col not in ['ra', 'dec', 'dist']])\n", - "data_set_radec.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8dcef84a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filled magnitudes for 1000 rows.\n", - "Applying dust correction for r-band on observed magnitudes.\n", - "Applying dust correction for g-band on observed magnitudes.\n", - "Applying detection cut on g-band with SNR >= 5.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/streamsim_dev/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log10\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n", - "/opt/anaconda3/envs/streamsim_dev/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log10\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n" - ] - }, - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "ra", - "rawType": "float64", - "type": "float" - }, - { - "name": "dec", - "rawType": "float64", - "type": "float" - }, - { - "name": "dist", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_g", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_r", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_r_obs", - "rawType": "object", - "type": "unknown" - }, - { - "name": "magerr_r", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_g_obs", - "rawType": "object", - "type": "unknown" - }, - { - "name": "magerr_g", - "rawType": "float64", - "type": "float" - }, - { - "name": "flag_observed", - "rawType": "bool", - "type": "boolean" - } - ], - "ref": "1b915fd5-2ecf-4324-b162-4a711ffb1a04", - "rows": [ - [ - "0", - "309.2953942380719", - "-10.749590498519837", - "16.5", - "32.521346463815334", - "30.744441115985055", - "29.293112729845497", - "10.000001249999922", - "30.86475618175217", - "10.000001249999922", - "False" - ], - [ - "1", - "310.2683786555398", - "-14.058956233016271", - "16.5", - "24.734569829732266", - "23.985160827622764", - "24.01278629565161", - "0.02622826493322956", - "24.70709873065739", - "0.05101851502840772", - "True" - ], - [ - "2", - "308.7572935618984", - "-10.07569919353105", - "16.5", - "32.17821752177512", - "30.43179924022367", - "28.18609564167872", - "10.000001249999922", - "30.938083741992", - "10.000001249999922", - "False" - ], - [ - "3", - "309.51600359134665", - "-11.50602746286123", - "16.5", - "27.876296288583674", - "26.605324661905662", - "26.34971117465026", - "0.30642224799556955", - "27.251424168831704", - "0.8752637405918557", - "False" - ], - [ - "4", - "311.7243037291042", - "-17.22723336556269", - "16.5", - "29.845461560741406", - "28.340735430827536", - "BAD_MAG", - "2.0512189654525854", - "29.20169053361813", - "10.000001249999922", - "False" - ] - ], - "shape": { - "columns": 10, - "rows": 5 - } - }, - "text/html": [ - "
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radecdistmag_gmag_rmag_r_obsmagerr_rmag_g_obsmagerr_gflag_observed
0309.295394-10.74959016.532.52134630.74444129.29311310.00000130.86475610.000001False
1310.268379-14.05895616.524.73457023.98516124.0127860.02622824.7070990.051019True
2308.757294-10.07569916.532.17821830.43179928.18609610.00000130.93808410.000001False
3309.516004-11.50602716.527.87629626.60532526.3497110.30642227.2514240.875264False
4311.724304-17.22723316.529.84546228.340735BAD_MAG2.05121929.20169110.000001False
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" - ], - "text/plain": [ - " ra dec dist mag_g mag_r mag_r_obs magerr_r \\\n", - "0 309.295394 -10.749590 16.5 32.521346 30.744441 29.293113 10.000001 \n", - "1 310.268379 -14.058956 16.5 24.734570 23.985161 24.012786 0.026228 \n", - "2 308.757294 -10.075699 16.5 32.178218 30.431799 28.186096 10.000001 \n", - "3 309.516004 -11.506027 16.5 27.876296 26.605325 26.349711 0.306422 \n", - "4 311.724304 -17.227233 16.5 29.845462 28.340735 BAD_MAG 2.051219 \n", - "\n", - " mag_g_obs magerr_g flag_observed \n", - "0 30.864756 10.000001 False \n", - "1 24.707099 0.051019 True \n", - "2 30.938084 10.000001 False \n", - "3 27.251424 0.875264 False \n", - "4 29.201691 10.000001 False " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "injected_data = stream_injector.inject(data_set_radec, seed=seed, mask_type=None, stream_config=isochrone_config, verbose=True)\n", - "injected_data.head()" - ] - }, - { - "cell_type": "markdown", - "id": "b967882a", - "metadata": {}, - "source": [ - "### Dataset containing (ra, dec) and true apparent magnitudes\n", - "\n", - "The dataset already contains all the required quantities, so you don't need to provide a configuration dictionary or mask type." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee217a87", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "ra", - "rawType": "float64", - "type": "float" - }, - { - "name": "dec", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_g", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_r", - "rawType": "float64", - "type": "float" - } - ], - "ref": "d43a8bc4-2f8f-4305-b37f-43f60a58b24b", - "rows": [ - [ - "0", - "309.2953942380719", - "-10.749590498519837", - "22.41028559822249", - "22.04785117105655" - ], - [ - "1", - "310.2683786555398", - "-14.058956233016271", - "30.09666434308648", - "28.56270340660659" - ], - [ - "2", - "308.7572935618984", - "-10.07569919353105", - "25.879838917277812", - "24.909216526278534" - ], - [ - "3", - "309.51600359134665", - "-11.50602746286123", - "29.537286842398476", - "28.06884640249528" - ], - [ - "4", - "311.7243037291042", - "-17.22723336556269", - "32.383469302092514", - "30.618814364101716" - ] - ], - "shape": { - "columns": 4, - "rows": 5 - } - }, - "text/html": [ - "
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radecmag_gmag_r
0309.295394-10.74959022.41028622.047851
1310.268379-14.05895630.09666428.562703
2308.757294-10.07569925.87983924.909217
3309.516004-11.50602729.53728728.068846
4311.724304-17.22723332.38346930.618814
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" - ], - "text/plain": [ - " ra dec mag_g mag_r\n", - "0 309.295394 -10.749590 22.410286 22.047851\n", - "1 310.268379 -14.058956 30.096664 28.562703\n", - "2 308.757294 -10.075699 25.879839 24.909217\n", - "3 309.516004 -11.506027 29.537287 28.068846\n", - "4 311.724304 -17.227233 32.383469 30.618814" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Let's build a dataset containing (ra, dec) coordinates and magnitudes\n", - "data_set_radecmag = injected_data_full.drop(columns=[col for col in injected_data_full.columns if col not in ['ra', 'dec', 'mag_r', 'mag_g']])\n", - "data_set_radecmag.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4d0dbbe1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Applying dust correction for r-band on observed magnitudes.\n", - "Applying dust correction for g-band on observed magnitudes.\n", - "Applying detection cut on g-band with SNR >= 5.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/streamsim_dev/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log10\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n", - "/opt/anaconda3/envs/streamsim_dev/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: invalid value encountered in log10\n", - " result = getattr(ufunc, method)(*inputs, **kwargs)\n" - ] - }, - { - "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "ra", - "rawType": "float64", - "type": "float" - }, - { - "name": "dec", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_g", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_r", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_r_obs", - "rawType": "object", - "type": "unknown" - }, - { - "name": "magerr_r", - "rawType": "float64", - "type": "float" - }, - { - "name": "mag_g_obs", - "rawType": "object", - "type": "unknown" - }, - { - "name": "magerr_g", - "rawType": "float64", - "type": "float" - }, - { - "name": "flag_observed", - "rawType": "bool", - "type": "boolean" - } - ], - "ref": "1f28c454-967d-4d6a-874a-c21b708f4ae2", - "rows": [ - [ - "0", - "309.2953942380719", - "-10.749590498519837", - "22.41028559822249", - "22.04785117105655", - "22.04578695083129", - "0.006780662663910697", - "22.407400041724532", - "0.007394955933706518", - "True" - ], - [ - "1", - "310.2683786555398", - "-14.058956233016271", - "30.09666434308648", - "28.56270340660659", - "BAD_MAG", - "2.2499339737548634", - "28.14720185886775", - "10.000001249999922", - "False" - ], - [ - "2", - "308.7572935618984", - "-10.07569919353105", - "25.879838917277812", - "24.909216526278534", - "24.86731781932736", - "0.05692261634238516", - "25.84792954355696", - "0.13978607106581661", - "True" - ], - [ - "3", - "309.51600359134665", - "-11.50602746286123", - "29.537286842398476", - "28.06884640249528", - "27.256386029436264", - "1.2852614904749817", - "27.049345526486707", - "10.000001249999922", - "False" - ], - [ - "4", - "311.7243037291042", - "-17.22723336556269", - "32.383469302092514", - "30.618814364101716", - "BAD_MAG", - "10.000001249999922", - "31.739698274969236", - "10.000001249999922", - "False" - ] - ], - "shape": { - "columns": 9, - "rows": 5 - } - }, - "text/html": [ - "
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radecmag_gmag_rmag_r_obsmagerr_rmag_g_obsmagerr_gflag_observed
0309.295394-10.74959022.41028622.04785122.0457870.00678122.40740.007395True
1310.268379-14.05895630.09666428.562703BAD_MAG2.24993428.14720210.000001False
2308.757294-10.07569925.87983924.90921724.8673180.05692325.847930.139786True
3309.516004-11.50602729.53728728.06884627.2563861.28526127.04934610.000001False
4311.724304-17.22723332.38346930.618814BAD_MAG10.00000131.73969810.000001False
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" - ], - "text/plain": [ - " ra dec mag_g mag_r mag_r_obs magerr_r \\\n", - "0 309.295394 -10.749590 22.410286 22.047851 22.045787 0.006781 \n", - "1 310.268379 -14.058956 30.096664 28.562703 BAD_MAG 2.249934 \n", - "2 308.757294 -10.075699 25.879839 24.909217 24.867318 0.056923 \n", - "3 309.516004 -11.506027 29.537287 28.068846 27.256386 1.285261 \n", - "4 311.724304 -17.227233 32.383469 30.618814 BAD_MAG 10.000001 \n", - "\n", - " mag_g_obs magerr_g flag_observed \n", - "0 22.4074 0.007395 True \n", - "1 28.147202 10.000001 False \n", - "2 25.84793 0.139786 True \n", - "3 27.049346 10.000001 False \n", - "4 31.739698 10.000001 False " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "injected_data = stream_injector.inject(data_set_radecmag, seed=seed, mask_type=None, stream_config=None, verbose=True)\n", - "injected_data.head()" - ] - }, - { - "cell_type": "markdown", - "id": "f7bd2b3a", - "metadata": {}, - "source": [ - "## 4) Usage example\n", - "\n", - "This can be useful for comparing analysis results between the ideal case of dynamic simulations and the more realistic case of observed data.\n", - "\n", - "For example, we can calculate the 1D density along the stream and its power spectrum." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3d933ce", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_power_spectrum(data, fig=None, ax=None, phi1_bin_edges=None, label=None):\n", - " \"\"\"Plot 1D density along phi1 and its power spectrum.\"\"\"\n", - " if fig is None or ax is None:\n", - " fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n", - "\n", - " # Compute 1D density along phi1\n", - " phi1 = np.array(data['phi1'])\n", - " if phi1_bin_edges is None:\n", - " phi1_bin_edges = np.linspace(np.min(phi1), np.max(phi1), 50)\n", - " counts, _ = np.histogram(phi1, bins=phi1_bin_edges)\n", - " bin_centers = 0.5 * (phi1_bin_edges[:-1] + phi1_bin_edges[1:])\n", - "\n", - " # Compute power spectrum using cross-spectral density method from scipy\n", - " from scipy.signal import csd\n", - " fs = 1.0 / (bin_centers[1] - bin_centers[0]) # Sampling frequency\n", - " k, Pxx = csd(counts, counts, nperseg=len(counts), fs=fs)\n", - "\n", - " # Plot density\n", - " ax[0].plot(bin_centers, counts, drawstyle='steps-mid', label=label)\n", - " ax[0].set_xlabel('phi1 (deg)')\n", - " ax[0].set_ylabel('Star counts')\n", - " ax[0].set_title('1D Density along phi1')\n", - "\n", - " # Plot power spectrum (1/k, P(k)) in log-log scale\n", - " ax[1].loglog(1/k, Pxx, label=label)\n", - " ax[1].set_xlabel('1/k (deg)')\n", - " ax[1].set_ylabel('P(k)')\n", - " ax[1].set_title('Power Spectrum of 1D Density')\n", - "\n", - " for a in ax.flatten():\n", - " a.grid()\n", - " if label is not None:\n", - " a.legend()\n", - " return fig, ax" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "077e5872", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9j/zk4h50g505d635j69p9tgwbc0000gp/T/ipykernel_32025/3483219967.py:25: RuntimeWarning: divide by zero encountered in divide\n", - " ax[1].loglog(1/k, Pxx, label=label)\n" - ] - }, - { - "data": { - "image/png": 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LbFfeeZWJiooq9buXlxcRERG1iklERKQiH3zwAevWrWPTpk0cP36crVu3ctFFFwG29zQvLy9atmxZ6jomk4moqKhS70nDhw9nzZo15OTksHz5coYNG0ZERATnnXcey5cv59ChQxw6dKhUUurEiRNs3bq1zPFJUFAQhmGUOUapaEZURfz8/Lj22mt5+eWXWblyJfv376dnz57Mnj3bMYjb7uz3Xft59vtoP5564oknysQ7ceJEAEe8J0+epF27djWK9Wz+/v4EBwfXaR8A4eHhpX739vau9Pwzj/fOVtPjJlfQ8XTtjqdFqkur74k0YYsXL8ZkMnHJJZfUeT+AY9hlixYtAPjyyy+JiYmp077PdPfdd3P33XeTk5PDL7/8wvTp0xkzZgx79+6t1e2EhIRw11138c477/DEE0/w3nvvceuttxIaGlrp9X766SeOHz/OihUrHNVRQKkBrBWxJ3QSExPLHDweP37c8djVREREhOPA5UxJSUk12k9SUhJt27Z1/F5cXExKSkq5SSgREZHa6tGjh2P1vbNFRERQXFzMyZMnSyWmDMMgKSnJMawbbCvbTZs2jV9++YUff/yR6dOnO87/4Ycf6Nixo+N3uxYtWuDn51dmgY4zLz9TXSs92rdvz4QJE5g8eTI7duygV69ejsvKe59OSkpyvO/aY3n66acZO3Zsufvv1q0bYJvHdfaCKTVV0X319fUtMywbbAmx2hy31MSZx01nq+1xk7PpeLp2x9Mi1aVKKZEm6r333mPJkiXccsstpVqxamrZsmW88847DBo0iIsvvhiAUaNG4eXlxYEDB4iLiyv3VBcBAQFcccUVTJ06lcLCwjLfPJ7Jx8cHqLh66eGHH+bUqVPccMMNpKenV2s4uP2gzb5vuzfffLPK6w4bNgyg1KBygHXr1rFr165SB87VNWTIELZv315m4OVnn31Wo/18/PHHpX7/4osvKC4uLrWyjoiISH2yvw+e/T751VdfkZOTU+p98vzzzyc4OJhXX32VpKQkRowYAdgqqDZt2sQXX3xBz549S1WwjBkzhgMHDhAREVHu8UmHDh1qFXdWVlaFbWj2tv6zK2k+/fTTUi1bR44cYdWqVY733W7dutGlSxe2bNlS4fFUUFAQAFdccQU///yzo52vPFUdE1WkQ4cObN26tdR5e/furfS2nGXgwIH4+fmVeT4cPXqUn376qVbHTc6k42mb2hxPi1SXKqVE3NCSJUvIyckhKysLgJ07d/Lll18CtnkG/v7+jm3z8vJYs2aN4+eDBw+yaNEivvnmG4YMGeIota2K1Wp17KegoID4+HiWLFnCF198QY8ePUotTdyhQweee+45pk6dysGDB7n88ssJCwvjxIkTrF27loCAgBqv6DJ+/Hj8/Py46KKLaN26NUlJScyaNYuQkJBS35qerXfv3gC8+OKLXHHFFXh6etKnTx9HyXjXrl25/PLLWbJkCRdffDF9+/atMpZBgwYRFhbG/fffz/Tp0zGbzXz88cds2bKlyut269aNCRMm8O9//xsPDw+uuOIKDh8+zLRp04iOjubRRx+t5iNy2uTJk5k7dy5XXHEFzz33HJGRkXzyySfs3r0bKL0MdWUWLFiAl5cXI0aMYMeOHUybNo2+ffty00031Timitifp/aVhdavX09gYCCAY2UXERFpvkaMGMGoUaN46qmnyMzM5KKLLmLr1q1Mnz6dc889lzvuuMOxraenJ0OGDOF///sfHTt25JxzzgHgoosuwsfHhx9//JGHH3641P4nT57MV199xSWXXMKjjz5Knz59sFqtxMfH88MPP/D4449zwQUX1DjuPXv2MGrUKP70pz8xZMgQWrduTVpaGt9++y1vvfUWQ4cOZdCgQaWuk5yczHXXXcf48ePJyMhg+vTp+Pr68vTTTzu2efPNN7niiisYNWoU48aNo23btqSmprJr1y42btzI/PnzAXjuuedYsmQJl1xyCVOmTKF3796kp6ezdOlSHnvsMbp3784555yDn58fH3/8MT169CAwMJA2bdpU2XZ2xx13cPvttzNx4kSuv/56jhw5wksvvVSmxbI+hIaGMm3aNKZMmcKdd97JLbfcQkpKCs8++yy+vr6O6rja0PG0a4+nRarNhUPWRaQCMTExjpVdzj4dOnTIsZ199Tb7KSAgwOjUqZNxww03GPPnz3es2FIV+4oe9pOfn5/Rvn1746qrrjLmzp1rFBQUlHu9RYsWGZdeeqkRHBxs+Pj4GDExMcYNN9xgLF++vNS+y1uJ5OxV695//33j0ksvNSIjIw1vb2+jTZs2xk033WRs3brVsU15q4UUFBQY9913n9GyZUvDZDKVeYwMwzDmzZtnAMZnn31WrcfDMAxj1apVxsCBAw1/f3+jZcuWxn333Wds3LixzKo2Z98Pw7CtVPLiiy8aXbt2Ncxms9GiRQvj9ttvNxISEkptV9FqL3fddZcRExNT6rzt27cbw4cPN3x9fY3w8HDj3nvvNd5///0yqwGWxx7jhg0bjKuuusoIDAw0goKCjFtuucU4ceJEqW1jYmKMK6+8ssw+KlqB5+zV9yp63urtRkSk6avu6mh5eXnGU089ZcTExBhms9lo3bq18cADDxhpaWlltn3ttdcMwBg/fnyp80eMGGEAZVa+MwzDyM7ONp555hmjW7duhre3txESEmL07t3bePTRR42kpCTHdoAxadKkat23tLQ0Y+bMmcawYcOMtm3bGt7e3kZAQIDRr18/Y+bMmUZubq5jW/t75Icffmg8/PDDRsuWLQ0fHx9j8ODBxvr168vse8uWLcZNN91ktGrVyjCbzUZUVJQxbNgwY86cOaW2S0hIMO655x4jKirKMJvNjmOlM9/LP/30U6N79+6G2WwuteJcZSvDWa1W46WXXjI6depk+Pr6GnFxccZPP/1U4Xv//PnzS12/or+7/fjj5MmTVT6+77zzjtGnTx/H3+uaa64ps+pwTVff0/G064+nRarDZBhOWk5ARMRNXX/99axZs4bDhw9jNptdHY7TTJgwgU8//ZSUlBTHN1nlmTFjBs8++ywnT550i9kMIiIiTdmKFSu49NJLmT9/vqqEpcloqsfT4npq3xORJqmgoICNGzeydu1aFi5cyCuvvNKo30Cfe+452rRpQ6dOncjOzuabb77hnXfe4Zlnnqk0ISUiIiIiUhtN7Xha3JOSUiLSJCUmJjJo0CCCg4P585//zEMPPeTqkOrEbDbz8ssvc/ToUYqLi+nSpQuvvPIKjzzyiKtDExEREZEmqKkdT4t7UvueiIiIiIiIiIg0uOot2SQiIiIiIiIiIuJESkqJiIiIiIiIiEiDa/IzpaxWK8ePHycoKAiTyeTqcERERMTNGYZBVlYWbdq0wcND39+BjqdERESkZqp7PNXkk1LHjx8nOjra1WGIiIhII5OQkEC7du1cHYZb0PGUiIiI1EZVx1NNPikVFBQE2B6I4OBgF0cjIiIi7i4zM5Po6GjHMYToeEpERERqprrHU00+KWUvMQ8ODtZBlIiIiFSb2tRO0/GUiIiI1EZVx1MalCAiIiIi5Zo9ezY9e/ZkwIABrg5FREREmiAlpURERESkXJMmTWLnzp2sW7fO1aGIiIhIE6SklIiIiIiIiIiINLgmP1NKRESaDovFQlFRkavDkEbObDbj6enp6jCaJL1GxRn0GhURaT6UlBIREbdnGAZJSUmkp6e7OhRpIkJDQ4mKitIwcyfRa1ScTa9REZHmQUkpERFxe/YPu61atcLf318fUqTWDMMgNzeX5ORkAFq3bu3iiJoGvUbFWfQaFRFpXpSUEhERt2axWBwfdiMiIlwdjjQBfn5+ACQnJ9OqVSu1CdWRXqPibHqNiog0Hxp0LiIibs0+n8bf39/FkUhTYn8+af5R3ek1KvVBr1ERkeZBSSkREWkU1A4kzqTnk/PpMRVn0vNJRKR5UFJKREREREREREQanJJSIiIiIiIiIiLS4JSUEhERcZEVK1ZgMplIT08HYN68eYSGhtZoH4cPH8ZkMrF582anxycye/ZsevbsyYABA1wdisvodSoiIlJ/lJQSERGpR6tWrcLT05PLL7/c1aE4jBs3jmuvvdbVYUgjMGnSJHbu3Mm6detcHUq90utURETENZSUEhERqUdz587loYce4rfffiM+Pt7V4YhIOfQ6FRERcQ0lpUREpEEZhkFuYXG1ToZh1HkfzjxVFE9FcnJy+OKLL3jggQcYM2YM8+bNq/Pjt3btWs4991x8fX2Ji4tj06ZNpS63WCzce++9dOzYET8/P7p168Zrr73muHzGjBm8//77fP3115hMJkwmEytWrADgqaeeomvXrvj7+9OpUyemTZum5dilVlz1GtXrVEREpHrm/X6IhZuO1vh909m8XHrrIiLSrBiGwQ1zVrPhSFq1to+LCePDceeWOT+vyELPv37v7PCqtPO5Ufh7V/+t8/PPP6dbt25069aN22+/nYceeohp06bVeqnznJwcxowZw7Bhw/joo484dOgQjzzySKltrFYr7dq144svvqBFixasWrWKCRMm0Lp1a2666SaeeOIJdu3aRWZmJu+99x4A4eHhAAQFBTFv3jzatGnDtm3bGD9+PEFBQTz55JO1ileaL1e9RkGvUxERkaocPJnNC0t2U1hspUWgD4O7tHRZLEpKiYhIg8krslQ7IQWw/kga+UWWeoyofr377rvcfvvtAFx++eVkZ2fz448/Mnz48Frt7+OPP8ZisTB37lz8/f3p1asXR48e5YEHHnBsYzabefbZZx2/d+zYkVWrVvHFF19w0003ERgYiJ+fHwUFBURFRZXa/zPPPOP4uUOHDjz++ON8/vnn+rArTZpepyIi0pwYhsGUhdsoLLZySdeWXNy5hUvjUVJKRERcYv0zw/H39iz3stxCC3Ezl1d4XT+zJzufG1VfoVV6u9W1Z88e1q5dy4IFCwDw8vLi5ptvZu7cubX+sLtr1y769u2Lv7+/47yBAweW2W7OnDm88847HDlyhLy8PAoLC+nXr1+V+//yyy959dVX2b9/P9nZ2RQXFxMcHFyrWKV5c9Vr1H7b1aXXqYiINDfzNxxlzcFUfM0ePH9tbK0rg51FSSkREXEJf2/PGrXYnMlkMtX6ug3l3Xffpbi4mLZt2zrOMwwDs9lMWloaYWFhNd5ndXr+v/jiCx599FH++c9/MnDgQIKCgnj55Zf5448/Kr3emjVr+NOf/sSzzz7LqFGjCAkJ4bPPPuOf//xnjeMUaQyvUdDrVEREmpdT2QW88N0uAB4d3pXocP8qrlH/3P9oQUREpJEpLi7mgw8+4J///CcjR44sddn111/Pxx9/zIMPPljj/fbs2ZMPP/yQvLw8/Pz8ANuH1DP9+uuvDBo0iIkTJzrOO3DgQKltvL29sVhKt0X+/vvvxMTEMHXqVMd5R44cqXGMIo2FXqciItLczPxmJ+m5RfRsHcy9F3d0dTiAVt8TERFxum+++Ya0tDTuvfdeYmNjS51uuOEG3n333Vrt99Zbb8XDw4N7772XnTt38t133/GPf/yj1DadO3dm/fr1fP/99+zdu5dp06axbt26Utt06NCBrVu3smfPHk6dOkVRURGdO3cmPj6ezz77jAMHDvD666+zcOHCWj8GIu5Or1MREWlOftl7kkWbj+Nhgllje+Pl6R7pIPeIQkREpAl59913GT58OCEhIWUuu/7669m8eTMbN26s8X4DAwP53//+x86dOzn33HOZOnUqL774Yqlt7r//fsaOHcvNN9/MBRdcQEpKSqlqDIDx48fTrVs34uLiaNmyJb///jvXXHMNjz76KA8++CD9+vVj1apVTJs2rcYxijQWep2KiEhzkVdoYeqibQDcNagDfaNDXRvQGUxGdRrfG7HMzExCQkLIyMjQEEgRERfLLSx2LBNf2bLtZ263acoQko4l0LFjR3x9fRssVmna8vPzOXToULnPKx07lFXZY1LZYylSW3peiYg4z6wlu3hz5UFah/iy7LEhBPrU/ySn6h5PqVJKRERERERERKQJ2nk8k3d+PQTA366JbZCEVE0oKSUiIiIiIiIi0sRYrAZPL9iKxWowuncUw3tGujqkMpSUEhEREZFyzZ49m549ezJgwABXhyIiIiI19MHqw2w5mkGQjxfTr+rl6nDKpaSUiIiIiJRr0qRJ7Ny5s8zKcCIiIuLejqfn8Y/v9wDw1BXdiQx2z/l8SkqJiIiIiIiIiDQRhmHw16+3k1No4byYMG49v72rQ6qQklIiIiIiIiIiIk3E0u1JLN+VjNnTxKyxvfHwMLk6pAopKSUiIiIiIiIi0gRk5hcxffEOAO4fcg5dI4NcHFHllJQSEREREREREWkCXlq6m+SsAjq2CGDSpZ1dHU6VlJQSEREREREREWnkNhxJ5aM18QA8f10svmZPF0dUNSWlREREpM46dOjAq6++WqPrjBs3jmuvvbZe4hGRsvQ6FRFpugqLrTy9YBsAN57XjkHntHBxRNWjpJSIiEg9GDduHCaTCZPJhNlsJjIykhEjRjB37lysVmuN9jVjxgz69evn9Bhr8wHVlQ4fPozJZGLz5s2uDkWaCL1OnU+vUxER13jrlwPsPZFNRIA3U0b3cHU41aaklIiISD25/PLLSUxM5PDhwyxZsoRLL72URx55hDFjxlBcXOzq8EQEvU5FRKTxO3gym9d/2g/AtDE9CQvwdnFE1aeklIiIND6GAYU5DX8yjBqF6ePjQ1RUFG3btqV///5MmTKFr7/+miVLljBv3jzHdhkZGUyYMIFWrVoRHBzMsGHD2LJlCwDz5s3j2WefZcuWLY6KDvt1K7ue3eLFi4mLi8PX15cWLVowduxYAIYOHcqRI0d49NFHHfu1W7VqFZdccgl+fn5ER0fz8MMPk5OT47g8OTmZq666Cj8/Pzp27MjHH39c5WNhsVh47LHHCA0NJSIigieffBLjrMdz6dKlXHzxxY5txowZw4EDBxyXd+zYEYBzzz0Xk8nE0KFDAVi3bh0jRoygRYsWhISEMGTIEDZu3FhlTFKPXPUa1evUcblepyIizYNhGExduJ3CYiuDu7Tgmn5tXB1SjXi5OgAREZEaK8qFF1zwhjvlOHgH1GkXw4YNo2/fvixYsID77rsPwzC48sorCQ8P57vvviMkJIQ333yTyy67jL1793LzzTezfft2li5dyvLlywEICQmp8nrh4eF8++23jB07lqlTp/Lhhx9SWFjIt99+C8CCBQvo27cvEyZMYPz48Y74tm3bxqhRo/jb3/7Gu+++y8mTJ3nwwQd58MEHee+99wBby1NCQgI//fQT3t7ePPzwwyQnJ1d6v//5z38yd+5c3n33XXr27Mk///lPFi5cyLBhwxzb5OTk8Nhjj9G7d29ycnL461//ynXXXcfmzZvx8PBg7dq1nH/++SxfvpxevXrh7W37FjArK4u77rqL119/3XFbo0ePZt++fQQFufcyyE2Wq16joNepXqciIs3KlxuOsvpgCr5mD56/tnepLzAaAyWlREREGlj37t3ZunUrAD///DPbtm0jOTkZHx8fAP7xj3+waNEivvzySyZMmEBgYCBeXl5ERUU59vHTTz9Veb3nn3+eP/3pTzz77LOO6/Xt2xeA8PBwPD09CQoKKrXfl19+mVtvvZXJkycD0KVLF15//XWGDBnCG2+8QXx8PEuWLGHNmjVccMEFALz77rv06FH57IJXX32Vp59+muuvvx6AOXPm8P3335faxn6Z3bvvvkurVq3YuXMnsbGxtGzZEoCIiIhSMZ/5gRngzTffJCwsjJUrVzJmzJhK4xKpiF6nep2KiLi7lOwCnv9uFwCTh3elfYS/iyOqObdJSs2aNYspU6bwyCOPOIY5GobBs88+y1tvvUVaWhoXXHABs2fPplevXq4NVkREXMvsb6uGcMXtOoFhGI5vsTZs2EB2djYRERGltsnLyyvVEnO26lxv8+bNpaorqmPDhg3s37+/VKuPYRhYrVYOHTrE3r178fLyIi4uznF59+7dCQ0NrXCfGRkZJCYmMnDgQMd59n2c2Rp04MABpk2bxpo1azh16pRj0HR8fDyxsbEV7j85OZm//vWv/PTTT5w4cQKLxUJubi7x8fE1uu/iRK56jdpv2wn0OtXrVETE3c38dhfpuUX0aB3MvRd3dHU4teIWSal169bx1ltv0adPn1Lnv/TSS7zyyivMmzePrl27MnPmTEaMGMGePXtU5ivVZhgGeUWWam3rZ/ZsdOWOIs2SyVTn9hxX2rVrl2PuitVqpXXr1qxYsaLMdpV9gKzO9fz8/Gocm9Vq5c9//jMPP/xwmcvat2/Pnj17AOrl/8qrrrqK6Oho3n77bdq0aYPVaiU2NpbCwsJKrzdu3DhOnjzJq6++SkxMDD4+PgwcOLDK60k9auSvUdDrtCJ6nYqIuIdf951k4aZjmEwwa2xvzJ6Nc2S4y5NS2dnZ3Hbbbbz99tvMnDnTcb5hGLz66qtMnTrVMezx/fffJzIykk8++YQ///nP5e6voKCAgoICx++ZmZn1ewfErRmGwQ1zVrPhSFq1to+LCWP+/QOVmBKRemNv53n00UcB6N+/P0lJSXh5edGhQ4dyr+Pt7Y3FUjq5Xp3r9enThx9//JG77767RvvdsWMHnTt3Lvc6PXr0oLi4mPXr13P++ecDsGfPHtLT0yu4x7bZOq1bt2bNmjVccsklABQXF7Nhwwb69+8PQEpKCrt27eLNN99k8ODBAPz2229l4gXKxPzrr7/y3//+l9GjRwOQkJDAqVOnKoxHpCp6nep1KiLizvIKLUxduB2AuwZ2oF90qGsDqgOXp9ImTZrElVdeyfDhw0udf+jQIZKSkhg5cqTjPB8fH4YMGcKqVasq3N+sWbMICQlxnKKjo+stdnF/eUWWaiekANYfSat2VZWISFUKCgpISkri2LFjbNy4kRdeeIFrrrmGMWPGcOeddwIwfPhwBg4cyLXXXsv333/P4cOHWbVqFc888wzr168HoEOHDhw6dIjNmzdz6tQpCgoKqnW96dOn8+mnnzJ9+nR27drFtm3beOmllxzxdejQgV9++YVjx445Phw+9dRTrF69mkmTJrF582b27dvH4sWLeeihhwDo1q0bl19+OePHj+ePP/5gw4YN3HfffVVWezzyyCP8/e9/Z+HChezevZuJEyeW+oAcFhZGREQEb731Fvv37+enn37iscceK7WPVq1a4efnx9KlSzlx4gQZGRkAdO7cmQ8//JBdu3bxxx9/cNttt9Wq+kSaJ71OT9PrVESkcXjtx33Ep+bSOsSXJ0Z1c3U4dWO40KeffmrExsYaeXl5hmEYxpAhQ4xHHnnEMAzD+P333w3AOHbsWKnrjB8/3hg5cmSF+8zPzzcyMjIcp4SEBAMwMjIy6u1+iPvKKSgyYp76xoh56hvjZFa+kVNQVO7pZFa+Y7ucgiJXhy3SZJ35mqzstXbmdqkZWcbOnTsd7xWNxV133WUABmB4eXkZLVu2NIYPH27MnTvXsFgspbbNzMw0HnroIaNNmzaG2Ww2oqOjjdtuu82Ij483DMP23nb99dcboaGhBmC899571bqeYRjGV199ZfTr18/w9vY2WrRoYYwdO9Zx2erVq40+ffoYPj4+xpmHBGvXrjVGjBhhBAYGGgEBAUafPn2M559/3nF5YmKiceWVVxo+Pj5G+/btjQ8++MCIiYkx/vWvf1X4eBQVFRmPPPKIERwcbISGhhqPPfaYceeddxrXXHONY5tly5YZPXr0MHx8fIw+ffoYK1asMABj4cKFjm3efvttIzo62vDw8DCGDBliGIZhbNy40YiLizN8fHyMLl26GPPnz68ynry8vAqfVxkZGTp2OEtlj0llj6W70+u0NHd6nTbm55WISH3acSzD6PT0t0bMU98YP+xIcnU4Faru8ZTJMM6YXNiAEhISiIuL44cffnCsMDJ06FD69evHq6++yqpVq7jooos4fvw4rVu3dlxv/PjxJCQksHTp0mrdTmZmJiEhIWRkZBAcHFwv90XcV25hMT3/als1Zudzo/D3Lr9jtbrbiUjd1OY1uWnKEJKOJdCxY0d8fX0bLFZp2vLz8zl06FC5zysdO5RV2WNS2WMpUlt6XomIlGWxGox9YxVbEtK5IjaKN24/z9UhVai6x1Mua9/bsGEDycnJnHfeeXh5eeHl5cXKlSt5/fXX8fLyIjIyEoCkpKRS10tOTnZcJiIiIiIiIiLSHHy4+jBbEtIJ8vFixtW9XB2OU7gsKXXZZZexbds2Nm/e7DjFxcVx2223sXnzZjp16kRUVBTLli1zXKewsJCVK1cyaNAgV4UtIiIiIiIiItKgjqfn8fL3ttVVn7yiO5HBTaOK1GU9SkFBQcTGxpY6LyAggIiICMf5kydP5oUXXqBLly506dKFF154AX9/f2699VZXhCwiIiLSrMyePZvZs2eXWUlNREREGo5hGPz16x3kFFo4LyaM285v7+qQnMatB+c8+eST5OXlMXHiRNLS0rjgggv44YcfCAoKcnVoIiIiIk3epEmTmDRpkmMuhIiIiDS873cksXzXCcyeJmaN7Y2Hh8nVITmNWyWlVqxYUep3k8nEjBkzmDFjhkviERER1zOZbG+6VqvVxZFIU6Lnk/PpMRVn0vNJRMQmM7+Iv369A4A/X3IOXSObVpGOWyWlREREzmY2m/Hw8OD48eO0bNkSb29vR6JKpKYMw6CwsJCTJ0/i4eGBt7e3q0Nq9Ly9vfUaFafRa1REpLSXl+4hOauAji0CeHBYZ1eH43RKSomIiFszeXjQsWNHEhMTOX78uKvDkSbC39+f9u3b4+HhsjVfmgwPvUalHug1KiICG46k8dEfRwB4/rpYfM2eLo7I+ZSUEhERt+ft7U379u0pLi7WwGWpM09PT7y8vFTN40R6jYoz6TUqIgKFxVamLNiGYcAN57Vj0DktXB1SvVBSSkREGgWTyYTZbMZsNrs6FBEph16jIiIizvP2rwfZcyKL8ABvpo7u4epw6o3qYUVERERERERE3MShUzm89uM+AKaN6UFYQNOdr6eklIiIiIiIiIiIGzAMg6kLt1FYbGVwlxZc26+tq0OqV0pKiYiIiIiIiIi4ga82HmPVgRR8zR48f23vJj9fT0kpEREREREREREXS8kuYOa3OwF45LKutI/wd3FE9U9JKRERERERERERF3v+212k5xbRPSqI+wZ3dHU4DUJJKRERERERERERF/p130kWbDqGyQR/v74PZs/mka5pHvdSRERERERERMQN5RVamLpwOwB3DexAv+hQ1wbUgJSUEhERERERERFxkdd/2kd8ai6tQ3x5YlQ3V4fToJSUEhERERERERFxgV2Jmbz1y0EAnrsmlkAfLxdH1LCUlBIRERERERERaWAWq8H/LdiGxWpwea8oRvSMdHVIDU5JKRERERERERGRBvbRmiNsSUgnyMeLGVf3cnU4LqGklIiIiIiIiIhIAzqensdLS3cD8OQV3YkK8XVxRK6hpJSIiIiIiIiISAOavngHOYUW+rcP5bbz27s6HJdRUkpEREREREREpIEs3Z7Esp0n8PIwMWtsHzw8TK4OyWWUlBIRERERERERaQCZ+UVMX7wdgD8P6US3qCAXR+RaSkqJiIiIiIiIiDSAf3y/hxOZBXSI8OehYV1cHY7LKSklIiIiIiIiIlLPNhxJ48M1RwB44bre+Jo9XRyR6ykpJSIiIiIiIiJSj4osVqYs2IZhwPX92zGocwtXh+QWlJQSEREREREREalHb/1ykD0nsggP8GbqlT1cHY7bUFJKRERERERERKSeHDqVw2s/7gNg2pgehAd4uzgi9+Hl6gBE3E1uoaXKbfzMnphMzXfZThF3YhgGeUVVv25Br10RERERaViGYTB14TYKi60M7tKCa/u1dXVIbkVJKZGzxM1cXvU2MWHMv3+gPtyKuJhhGNwwZzUbjqRVa3u9dkVERESkIS3YeIxVB1Lw8fJg5rWxOg49i9r3RLBVT8TFhFV7+/VH0qpdmSEi9SevyFLthBTotSsiIiIiDSclu4CZ3+4EYPLwrsREBLg4IvejSikRwGQyMf/+gVV+WM0ttFSrkkpEGt76Z4bj713+srp67YpAQkICd9xxB8nJyXh5eTFt2jRuvPFGV4clIiLSZD3/7S7ScovoHhXEfYM7ujoct6SklEgJk8mEv7deEiKNlb+3p17DIpXw8vLi1VdfpV+/fiQnJ9O/f39Gjx5NQIC+tRUREXG23/adYsGmY5hMMGtsb8yealQrj47eRURERJqB1q1b07p1awBatWpFeHg4qampSkqJiIg4WV6hhSkLtwFw18AOnNu++qNimhul6kREREQagV9++YWrrrqKNm3aYDKZWLRoUZlt/vvf/9KxY0d8fX0577zz+PXXX8vd1/r167FarURHR9dz1CIiIs3P6z/tIz41l6hgXx4f2dXV4bg1JaVEREREGoGcnBz69u3Lf/7zn3Iv//zzz5k8eTJTp05l06ZNDB48mCuuuIL4+PhS26WkpHDnnXfy1ltvVXhbBQUFZGZmljqJiIhI1XYlZvL2LwcBeO6aXgT5ml0ckXtTUkpERESkEbjiiiuYOXMmY8eOLffyV155hXvvvZf77ruPHj168OqrrxIdHc0bb7zh2KagoIDrrruOp59+mkGDBlV4W7NmzSIkJMRxUkWViIhI1SxWg6cXbKPYajCqVyQje0W5OiS3p6SUNBjDMMgtLK7WyTAMJ+1PS7+LiEjTV1hYyIYNGxg5cmSp80eOHMmqVasA2/vmuHHjGDZsGHfccUel+3v66afJyMhwnBISEuotdhERkabi4z+OsDkhnUAfL569OtbV4TQKGnQuDcIwDG6Ys5oNR9KqtX1cTBjz7x+IyWRyyv5ERESaslOnTmGxWIiMjCx1fmRkJElJSQD8/vvvfP755/Tp08cxj+rDDz+kd+/eZfbn4+ODj49PvcddX5Iz8/lt/ynG9GmDt5e+gxURkfqXmJHHS0v3APDU5d2ICvF1cUSNg5JS0iDyiiw1SiCtP5JGXpGlwuXda7q/uJgw/Mye1d5eRESkMTr7yxzDMBznXXzxxVitVleE1eD+vmQ3CzYdwzDg+vPauTocERFpBqZ/vYPsgmLObR/KbRfEuDqcRkNJKWlw658Zjr93+Qmi3EILcTOXO21/dn5mzwqrrkRERBq7Fi1a4Onp6aiKsktOTi5TPdUcHDiZDcDeE1kujkRERJqDpduT+GHnCbw8TMwa2xsPD332rC7VM0uD8/f2xN/bq4JTzauZKt+f7aSElIiINGXe3t6cd955LFu2rNT5y5Ytq3SgeVVmz55Nz549GTBgQF1DbFDH0vMBiE/NdXEkIiLS1GXlFzF98XYA/jykE92jgl0cUeOiSikRERGRRiA7O5v9+/c7fj906BCbN28mPDyc9u3b89hjj3HHHXcQFxfHwIEDeeutt4iPj+f++++v9W1OmjSJSZMmkZmZSUhIiDPuRr0rKLZwKrsAUFJKRETq38vf7+FEZgEdIvx5aFgXV4fT6CgpJSIiItIIrF+/nksvvdTx+2OPPQbAXXfdxbx587j55ptJSUnhueeeIzExkdjYWL777jtiYprXXIsTGQWOn+NTckvN1RIREXGmjfFpfLjmCADPX9cbX80xrjElpUREREQagaFDh2IYRqXbTJw4kYkTJzZQRO7pWHqe4+esgmIy8ooI9fd2YUQiItIUFVmsPP3VNtuiGv3bcVHnFq4OqVFSUkpEpJ4YhkFekaVa22oYv4iIcyRm5JX6/UhKrpJSIiLidG/9cpA9J7II8zcz9coerg6n0VJSSkSkHhiGwQ1zVrPhSFq1to+LCWP+/QOVmBIRqaPEjPxSv8en5tI3OtQ1wYiISJN0+FQOr/+4D4BpY3oSHqAvP2pLq++JiNSDvCJLtRNSAOuPpFW7qkpEpKE0xtX3zmzfAw07FxER5zIMg6mLtlFQbOXizi247ty2rg6pUVOllIhIPVv/zHD8vcsfephbaCFu5vIGjkhEpHoa4+p7iSVJqdYhviRm5JOgpJSIiDjRgo3H+H1/Cj5eHjx/Xaw6HepIlVIiIvXM39sTf2+vCk5aoUNExJns7XsXdAwHVCklIiLOk5pTyMxvdwLwyPAuxEQEuDiixk9JKRERERFpMuztexd2igCUlBIREeeZ+e1O0nKL6B4VxPjBnVwdTpOgpJSIiIiINAnZBcVk5RcDcEFJUup4eh6FxVZXhiUiIk3A7/tPsWDjMUwmmDW2N2ZPpVOcQY+iiIiIiDQJ9nlSwb5edIjwx9fsgdWwJaZERERqK7/IwpSF2wC488IYzm0f5uKImg4lpURERESkSbC37rUJ9cNkMtE+3B9QC5+IiNTN6z/u40hKLlHBvjwxqpurw2lStPqeiIsZhkFekaVa2/qZPbW6g4ib0GtXmoPZs2cze/ZsLJbqPdddzT7kvHWILwDtw/3ZeyJbSSkREam13UmZvPXLQQCevaYXQb5mF0fUtCgpJeJChmFww5zVbDiSVq3t42LCmH//QH24FXExvXaluZg0aRKTJk0iMzOTkJAQV4dTpcQzKqUAoksqpRKUlBIRkVqwWA3+76ttFFsNRvWKZFSvKFeH1OSofU/EhfKKLNX+UAuw/khatSszRKT+6LUr4p6Ol1RK2ZNS9va9IylKSomISM19/McRNiekE+jjxbNXx7o6nCZJlVIibmL9M8Px9/Ys97LcQgtxM5c3cEQiUh167Yq4D/tAc3v7XkyEZkqJiEjtJGXk89LSPQA8eXk3okreW8S5lJQScRP+3p74e+slKdLY6LUr4j4SK6iUSkjNxTAMtdCKiEi1TV+8neyCYs5tH8ptF8S4OpwmS+17IiIiItLoGYbhqJRqE2JLSrULsyWlsgqKSc8tcllsIiLSuHy/I4nvd5zAy8PErLG98fTQlxr1RUkpEREREWn0UnMKKSi2AhAZ4gOAr9mTyGDbz2rhExGR6sjKL2L61zsAmHBJJ7pHBbs4oqZNSSkRERERafTsrXstg3zw8To9583ewqeklIiIVMc/vt9DUmY+MRH+PHxZF1eH0+RpCIZIM2UYRrVXA/Mze7rlHI6mcB9ERNzZ7NmzmT17NhaL+68eebp1r/Qg2uhwf9YdTlNSSkREqrQxPo0P1hwB4IXreuNrLn8xG3EeJaVEmiHDMLhhzupqL2kfFxPG/PsHulVSpyncBxERdzdp0iQmTZpEZmYmISEhrg6nUqdX3vMrdX5MeAAA8SlKSomISMWKLFamLNiGYcDY/m25qHMLV4fULKh9T6QZyiuyVDuZA7D+SFq1K5IaSlO4DyIi4jxnr7xn1z7C9rsqpUREpDJv/3qQ3UlZhPmbeebKnq4Op9lQpZRIM7f+meH4e5dflppbaCFu5vIGjqjmmsJ9EBGRujnuSEqVbt/TTCkREanKkZQcXlu+D4BnruxJeIC3iyNqPpSUEmnm/L098fdu3P8VNIX7ICIidVNR+150SVIqMSOPwmIr3l5qFBARkdMMw2Dqwu0UFFu5qHMEY/u3dXVIzYrelUVERESk0Uu0J6XOqpRqGeiDr9kDq3E6cSUiImK3cNMxftt/Ch8vD56/trdm0DYwJaVEREREpFErtlg5kVUAQNuzZkqZTCZHC98RtfCJiMgZUnMKmfntLgAevqwLHVoEuDii5kdJKRERERFp1JKzCrBYDbw8TLQI9ClzeXv7CnxKSomIyBme/3YXqTmFdIsMYsIlnVwdTrOkpJSIiIiINGqJGba2vMhgXzw9yrZd2CulEpSUEhGREr/vP8VXG49iMsGs63tj9lR6xBU0GVikiTEMg7wiS6Xb5BZWfrm4p+r8be38zJ7qhxeROps9ezazZ8/GYnHv943j6baV985u3bNrH247Pz5FSSkREYH8IgtTF24D4I4LY+jfPszFETVfSkqJNCGGYXDDnNVsOJLm6lDEyWr6t42LCWP+/QOVmBKROpk0aRKTJk0iMzOTkJAQV4dToeMVDDm3ax9hq5RS+56IiAD8+6d9HE7JJSrYl7+M6ubqcJo11aeJNCF5RZYaJaTiYsLwM3vWY0TiLDX9264/klbtqioRkcYuMcNWKdU6pKJKqdPte4ZhNFhcIlJ/vt58jCkLt1Fksbo6FGlkdidl8ubKgwDMuLoXQb5mF0fUvKlSSqSJWv/McPy9K084qcWrcarsb5tbaCFu5vIGjkhExLXslVJtK6iUahdmS0plFRSTlltEeIB3g8UmIs5nGAYzFu8gLbeI4T1aMax7pKtDkkbCajV4esE2iq0GI3tGcnlslKtDavaUlBJpovy9PfH31ku8KdLfVkSktOMlg84rqpTyNXsSFexLUmY+8am5SkqJNHKHTuWQllsEwL4T2UpKSbV9/McRNsWnE+jjxbPX9HJ1OILa90RERESkkUssGXRe0UwpON3Cp7lSIo3fmSMN9p7IdmEk0pgkZeTz4tI9APxlVLcKv8iQhqWklIiIiIg0WvlFFlJyCoGKV98DiD5jrpSING4b408npfYnZ7kwEmlMZizeQXZBMf2iQ7n9whhXhyMlXJqUeuONN+jTpw/BwcEEBwczcOBAlixZ4rjcMAxmzJhBmzZt8PPzY+jQoezYscOFEYuIiIiIO7EPOfczexLiV/GwWkelVIqSUiKN3ZmVUvuSs7WAgVTp+x1JLN2RhJeHiVlje+Ppobm67sKlSal27drx97//nfXr17N+/XqGDRvGNddc40g8vfTSS7zyyiv85z//Yd26dURFRTFixAiyspQNFxERERFILBly3jrUt9LFO9pH2KqojqTmNEhcIlI/MvKKHC17nh4mcgstHCv5f0CkPFn5RUz/2pZjGH9JJ3q0DnZxRHImlyalrrrqKkaPHk3Xrl3p2rUrzz//PIGBgaxZswbDMHj11VeZOnUqY8eOJTY2lvfff5/c3Fw++eSTCvdZUFBAZmZmqZOIiIiINE3HSyql2lQxG6S9o31PH16bupyCYrLyi1wdhtSTzQnpAHSI8OeclgGArVpKpCL//GEvSZn5xET488hlXVwdjpzFbWZKWSwWPvvsM3Jychg4cCCHDh0iKSmJkSNHOrbx8fFhyJAhrFq1qsL9zJo1i5CQEMcpOjq6IcIXERERERc4XlIh0aaSIecA7cNtH16PZ+RRWGyt97jENSxWg8tf+4VBf/+J3/adcnU4Ug/srXv9Y8Lo0ioIgH0n1Ekj5dsUn8b7qw8D8Py1vfE1e7o2ICnD5Umpbdu2ERgYiI+PD/fffz8LFy6kZ8+eJCUlARAZWXp5z8jISMdl5Xn66afJyMhwnBISEuo1fhEREZGmavbs2fTs2ZMBAwa4OpQKJWaUtO9VUSnVItAbP7MnhoFafZqwpMx8ElLzyMovZtx7a/lyw1FXhyROtrEkKXVeTBidWwUCsE8r8Ek5iixWnl6wDcOAsee25eIuLVwdkpTD5Umpbt26sXnzZtasWcMDDzzAXXfdxc6dOx2Xnz0bwDCMSucF+Pj4OAan208iIiIiUnOTJk1i586drFu3ztWhVOh4ekn7XhWVUiaT6fSwc63A12SdubpisdXgiflbeG35Pg3CbiIsVoNN8aeTUl0jbZVSe9W+J+V459dD7E7KIszfzNQre7g6HKmAy5NS3t7edO7cmbi4OGbNmkXfvn157bXXiIqKAihTFZWcnFymekpEREREmqfT7XuVV0oBRCsp1eTZk1IXdY7ggaHnAPCv5Xt58sutFFnUttnY7UnKIqfQQpCPF11aBdEl0lYptf9ElhKPUsqRlBxeXb4XgKlX9iQi0MfFEUlFXJ6UOpthGBQUFNCxY0eioqJYtmyZ47LCwkJWrlzJoEGDXBihiIiIiLiLxJJB51W178HpYefxKVqBr6lKSLMlKduHB/DU5d15/rpYPEwwf8NR7pm3TgPQG7kNJVVS/dqH4ulhokNEAF4eJnIKLY7/C0QMw+CZRdspKLYy6JwIru/f1tUhSSVcmpSaMmUKv/76K4cPH2bbtm1MnTqVFStWcNttt2EymZg8eTIvvPACCxcuZPv27YwbNw5/f39uvfVWV4YtIiIiIm4gM7+I7IJioOr2PYCYCFVKNXX2SqnocFuS8rYLYnjnrjj8zJ78uu8UN85ZTZKSF42WfZ5U//ZhAHh7edChhW0Rg70adi4lFm0+xq/7TuHj5cEL1/WudPyPuJ5Lk1InTpzgjjvuoFu3blx22WX88ccfLF26lBEjRgDw5JNPMnnyZCZOnEhcXBzHjh3jhx9+ICgoyJVhi4iIiIgbsLfuhfqb8ff2qnL70zOlNOi8qXIkpcL8HecN6x7J53++kBaBPuxOyuK6//7O7qRMV4UodbDhjCHndl3tLXyaKyVAak4hf/tmFwAPX9bFkbQU91X1u3c9evfddyu93GQyMWPGDGbMmNEwAYmIWzAMg7wiS6Xb5BZWfrlIben5J9J4JKZXv3UPTs+USkjNrXLxHGmcEtLslVL+pc7v0y6UhRMHcfe8dexPzubGN1bzxu3naTWuRuRkVgHxqbmYTLb2PbvOrYKAJFVKCQAvfLeL1JxCukUGMX5wJ1eHI9Xg0qSUiMjZDMPghjmrHd+EiTQkPf9EGpfjGSVDzkOqbt0DaBdmS15lFxSTlltEeIB3vcUmDS+/yMKJzAIAosPKJiqjw/356v5BTPhwPX8cSmXce2v5+/V9uOG8dg0dqtTCxpJ5Ut0igwj2NTvO79LKVim1T5VSzd6q/af4csNRTCZ4YWxvvL3cboS2lEN/JRFxK3lFlholBOJiwvAze9ZjRNKc6Pkn0rjUZOU9AF+zJ1HBtgSW5ko1PcdKng/+3p4VJhxD/M18cO/5XN23DcVWgyfmb+G15fu0clsj4JgndUbrHkDXSNtol/0nsvV3bMbyiyxMWbgNgNsviCnV4inuTZVSIuK21j8zHH/vyj/w+5k91X4h9ULPPxH352jfq8aQc7v24f4kZeZzJCWHftGh9RSZuIJ9nlT7cP9K/2/28fLk1Zv70TbMjzdWHOBfy/dyNC2XF8b2xuyp7+zdlWOeVPvSyYYOLfzx9DCRVVBMUmZ+tdt5pWn5z0/7OZySS2SwD3+5vJurw5Ea0P+6IuK2/L098ff2qvSkhIDUFz3/RNzf6fa96n8IbR9xeq6UNC32v2m7MP8qtgQPDxNPXd6d56+LxcME8zcc5Z5568jKL6rvMKUWCootbD2WAVCmAsbHy5MOJa/rfSfUwtcc7UnKYs7KAwA8e3VsqfZOcX9KSomIiIhIo5SYYauUqm77Hpy5Ap+SUk1NQpotSRkdXv3nw20XxPDOXXH4e3vy675T3DhnNYkZWp3R3ew4nklhsZXwAG9iIsomHbu0srXwadh582O1Gjy9YCvFVoMRPSO5PDbK1SFJDSkpJSIiIiKNjtVqnLH6Xs3a9wAOpygp1dTYK6Wiq1EpdaZh3SP5fMJAWgT6sDspi+tmr2JXYmZ9hCi15Jgn1T6s3CrlLpG2Yef7Ney82fl4bTwb49MJ8PbkuWt6uTocqQUlpURERESk0UnJKaTQYsVkgqgaJKU6l6zUtScpS0ORm5iEtJKkVHjNklIAvduFsHDiIDq3CiQpM5+b5qzmt32nnB2i1JJ95b2Khld3iVSlVHOUlJHPS0t2A/Dk5d01T6yRUlJKRKSEYRjkFhZX69ScPsjocWkYepxFasbeYtUy0KdGw6m7RAbi6WEiI6+IpMz8+gpPXCAh1facaF+LpBTYkllf3T+ICzqGk1VQzLj31rJkW6IzQ5RaMAzj9JDzipJSJcnmfclaga85mbF4B1kFxfSLDuX2C2NcHY7UklbfExHBdsBzw5zVjoOeqsTFhDH//oFNftC1HpeGocdZ3NXs2bOZPXs2FovF1aGUcTy9ZMh5DeZJgW0o8jktA9h7IpvdiVn6Zr2JyMgrIiPPNqS8XVjt/6Yh/mY+uPd8npi/lf9tOc6ry/dxRe/WzgpTauFYeh4nMgvw8jDRp11Iudt0ahmAhwmy8otJziogMrj61ZPSOP2wI4mlO5Lw8jAxa2xvPD10TNRYqVJKRATIK7JUOyEAsP5IGnlF7vchzdn0uDQMPc7iriZNmsTOnTtZt26dq0Mp43i6fch5zT98do8KBmBXkuYGNRX2eVIRAd4E+NTte3cfL08eH9EVgCOpOaq8cTH7+2OvtiH4mj3L3ca2Al8AoBa+5iC7oJjpi3cAcN/gTvRoHeziiKQuVCklInKW9c8Mx9+7/IOe3EILcTOXN3BE7kGPS8PQ4yxSPfb2vdpUOvVoHcziLcfZlagPr03F0ZJ5Uu1q2bp3tjahfphMkF9k5WR2Aa2CVHnjKvYh5+e1L791z65LZCAHT+Ww70Q2g7u0bIjQxEX+8f0eEjPyaR/uzyOXdXF1OFJHSkqJiJzF39sTf2/993g2PS4NQ4+zSPUcS7cnpWpRKdXaNhR5t1ZYazLs86Si69C6dyZvLw9aB/tyPCOfhNQ8JaVcaEMVQ87turQK4vsdJ9iXrGRzU7Y5IZ33Vx8G4PnrYvGr4Is8aTzUviciIiIijc6O47aEUteSVbdqokdJ+97BUznkqxW2SajLynsVsVdd2auwpOHlFhY7Khr7x4RWum2XyJJh5yey6zsscZEii5X/+2orhgHXndtWFXFNhJJSIiIiItKopOUUciTFliioaPBxZSKDfQj1N2OxGuxP1gfYpsA+U6q2K++VJzrMv9S+peFtScjAYjVoE+JbZatul1a2BLVW4Gu63v3tELuTsgj1N/PMlT1cHY44iZJSIiIiItKobD2WAUCHCH9C/b1rfH2TyeSoltqlFr4mIb4kcWRPJDlDdLgtCWJvDZSGt7Gkda9/Fa17cHoFvoy8Ik5mFdR3aNLAjqTk8OryvQA8c2VPIgJ9XByROIuSUiIiIiLSqGxNSAegT7vQWu/DMVcqSfNnGjvDMDiaVjJTKtw5M6XgjEopte+5jH3lvarmSQH4mj2JKVmBb58qIJsUwzB4ZtF28ousDDonguv7t3V1SOJEmqQqdWYYRpVLk+cWNr15DdW5T35mT0wmUwNE41rVeQ7YufIxqexv1hSfoyIiTdWWo7ZKqdq07tnZK6V2J6lSqrE7mVVAQbEVD5Nt1Txnsc+nUlLKNaxWw1EpVZ2kFEDnVoEcOpXD3hNZXNS5RX2GJw3o683H+XXfKby9PHj+ut7N4vNVc6KklNSJYRjcMGe141uM5qQ6y7LHxYQx//6BTfo/zpo+B1z5mFTnbyYiIu5v69F0APpGh9Z6Hz1a29v3sjAMo0m/Vzd19qRR6xA/zJ7OawSxV10dT8+n2GLFy4n7lqodPJVDem4RvmYPx+u1Kl1aBbJs5wlVSjUhaTmFPPfNTgAeuawLHVsEuDgicTb9zyp1kldkqVFCKi4mDD9z412208/sSVw1v6kBWH8krdoVRI1VTZ8DDf2Y1PRv1tifoyIiTV1SRj7JWQV4mKBXm+p9UC1Pl8hAPEyQmlOo+TONnH3mkzNb9wAig3zx9vTAYjVIzMh36r6lahtLji/7tgutdrLRvhrnvhNqy20qnv9uF6k5hXSNDGT84E6uDkfqgSqlxGnWPzMcf+/KP8w39nY2k8nE/PsHVqtdsTlW5VT2HHDVY1Ldv5ldY3+Oiog0dVtKqqS6Rgbh7137Q1lfsycdWwRw4GQOu5KyaBXs66QIpaEl1MOQcwAPDxNtw/w4dCqHhLRcRzufNAz7l57VGXJu17lVIAB7T2SrArIJWLX/FF9uOIrJBLPG9sHbSzU1TZGSUuI0/t6edTo4bCxMJlOzuJ+14a7PAf3NRESaDnvrXl3mSdl1bx1sS0olZjKka8s6709cw7HyXj0kjdqVJKWOpubBOU7fvVRig32eVPuaJaVMJSvwncoupGWQVmhrrPKLLExZuA2A2y+IqfZcMWl8lGoUERERkUZjq2PIeWid99WzZE7N7kQNO2/M7DOlnN2+Z9unhp27QkZuEftL5kLVpFLK1+xJ+5K/mVr4Grf//LSfwym5tAry4S+Xd3N1OFKPlJQSERERkUbBMAxHUqqvE5JS3aNs82d2J+nDa2PmmCnl5Pa9M/d5NC3P6fuWim1MsFVJdWoRQHiAd42u26WkhU/DzhuvPUlZzFl5AIDnrulFsK/ZxRFJfVJSSkREREQahSMpuWTkFeHt5UG3koRSXXQvqZTan5xNYbG1zvuThldksZKYYR90Xg9JqZLqK/vcKmkYG2sxT8quS8mw872qlGqUrFaDKQu3UWw1GN4jklG9olwdktQzDVkRkQaXW1jx0PHKLpOaqc5jqcHuItKY2Iec92wd7JSBt21CfAn29SIzv5j9ydn0rMNqfuIaien5WA3w8fKgZaDz5wfZK6XUvtew7EPOazNHSJVSjdsna+PZcCSNAG9Pnruml45TmwElpUSkwTXHlQldoTqPc1xMGPPvH6g3fBFpFLYk2Fv36j7kHGwLYXRvHczaQ6nsTspUUqoRsg85bxfmh4eH89/L7NVXJzILyC+y4GuufKVpqbtii5XNCelA7ZJSXUsqpfYrKdXonMjM58UluwH4y6hutAl1/pw4cT9q3xORBuFn9iSuBgcWcTFh+OnAr8Zq+jivP5JGXpGq00SkcTi98l6o0/bZQ3OlGrXTQ86d37oHEOZvJsDbdjxyLF1zpRrC7qQscgstBPl60bllYI2vf05L2wp8qTmFnMouqIcIpb7MWLyDrIJi+kaHcsfADq4ORxpIjSulEhISMJlMtGvXDoC1a9fyySef0LNnTyZMmOD0AEWkaTCZTMy/f2C1EyBqK6ud6j7OuYUWVayJSKNSbLGy/XhJpVS0cyql4PRcqV1aga9Rss96qo8h52B7X40O92d3UhYJqbmcU4skidTMxnhb69657cNqVf3m5+1JdJg/8am57DuRTYt6aOsU51u28wRLtifh6WFi1nW98ayHykdxTzWulLr11lv5+eefAUhKSmLEiBGsXbuWKVOm8Nxzzzk9QBFpOkwmE/7eXtU6KSFVe9V7nFWFJiJVmz17Nj179mTAgAGuDoV9ydnkF1kJ9PGiUwvnJQZ6OJJSqpRqjBLS7EPO66/Np51jrpQqpRqCfcj5ee1r3rpnd3qulF7XjUF2QTF//Xo7AOMHd1IrdTNT46TU9u3bOf/88wH44osviI2NZdWqVXzyySfMmzfP2fGJiIiIiItMmjSJnTt3sm7dOleH4mjdi20b7NTZQV0jba0+p7ILOJmlVp/Gxl4p1b6e2vfgdMLrqFbgaxAb4ms/5Nyuc2RJUuqE5ko1Bv/4fg+JGflEh/vxyGVdXB2ONLAaJ6WKiorw8bGVQC5fvpyrr74agO7du5OYmOjc6EREREREgC1H7UPOQ526X39vLzpEBACwR3OlGp2jafZB5/WYlNIKfA0mOTOfhNQ8PEx1a9Pt2so2K27vCb2m3d3mhHTeX30YgOev7Y2fqvmbnRonpXr16sWcOXP49ddfWbZsGZdffjkAx48fJyIiwukBioiIiIjUx5Bzux6tbR9gNVeqcckpKOZUdiFQf4POz9x3Qqra9+qbfZ5Ut6hggnzNtd5Pl5JKKa3A596KLFaeXrANw4Drzm3LJV1bujokcYEaJ6VefPFF3nzzTYYOHcott9xC3759AVi8eLGjrU9EpDnILbSQW1hcwUkr2klper6I1F5+kYXdJTOfnDnk3K57VMlcqSQlpRqToyUznoJ9vQjxq30Coyr29j1VStW/DfZ5UjGhddpP55KZUik5haRoBT639e5vh9iVmEmov5lnruzh6nDERWq8+t7QoUM5deoUmZmZhIWd7vOdMGECAQEBTg1ORMSdafU6qQk9X0Rqb1diJsVWg4gAb9qGOn+gdfcoW6XUbg07b1QcK+/VY5UUnG7fS88tIiu/qE4VPFK500mp2s+TAltbbrswP46m5bEvOZsIrcDnduJTcnl1+V4Apo7uob9RM1bjSqlhw4aRlZVVKiEFEB4ezs033+y0wERE3JGf2ZO4GhwoxcWE4WdWb3xzpeeLiHNsLZkn1addSL2szmpfgW9/cjZFFqvT9y/1w165FF2P86QAAny8CA/wtt2mWvjqTX6Rhe3HbNWK/euw8p7d6RX41MLnbgzDYOqibeQXWRnYKYIbzmvn6pDEhWpcKbVixQoKCwvLnJ+fn8+vv/7qlKBERNyVyWRi/v0DySuqXruVn9mzXj5ASeOg54uIc2xJSAfqZ54UQLswPwJ9vMguKObgyRy6lVROiXuzJ4jaR9RvUgogOsyP1JxCEtJytVx9PdlxPINCi5UWgd5OWU2xa2QQP+85yT4NO3c7X28+zq/7TuHt5cELY3vr2KeZq3ZSauvWrY6fd+7cSVJSkuN3i8XC0qVLadu2rXOjExFxQyaTCX/vGuf0pZnS80Wk7raUDDmvj3lSYHuddo8KYv2RNHYnZSop1UjE29v3wpzf0nm2duH+bDma4WgZFOfbeCQdsFVJOSNJYZ8rte+EKqXcSVpOIX/7ZicADw/rTMcWGgHU3FX7KLlfv36YTCZMJhPDhg0rc7mfnx///ve/nRqciIiIiDRvWflFHDyVA9RfpRRA99a2pNSuxCyu6VdvNyNOdLSkfa9dPc+UgtMtgvbh6uJ8zponZdc10pZcVvuee3nhu12k5BTSNTKQCZec4+pwxA1UOyl16NAhDMOgU6dOrF27lpYtTy/X6O3tTatWrfD01BwMEREREXGebccyMAxoG+pHi3ochGufK7UrUSvwNQaGYZwedF7PM6XgjBX4VClVLwzDYEO8c5NS9kqpU9kFpOUUElYyF0xcZ9WBU8zfcBSAWWN74+1V4xHX0gRVOykVExMDgNWq4Y8iIiIi0jDOHHJen7pH2ZJSu5OUlGoM0nKLyCm0zetr1wDte/bEl324ujjX0bQ8TmYVYPY0EdvWOa/1AB8v2ob6cSzdtgLf+R3DnbJfqZ38IgtTF24H4PYL23NejP4eYlOrIRd79+5lxYoVJCcnl0lS/fWvf3VKYCIiIiIiW0vmSdVn6x7gmCN1IrOA1JxCx2pr4p7sFUuRwT74NsCqpdElLYIJqXkYhqHBzE5mb92LbRvi1L9nl8hAjqXnsfdElpJSLjb75/0cOpVDqyAfnry8u6vDETdS46TU22+/zQMPPECLFi2Iiooq9R+yyWRSUkpEpJZyCyteoa2yy9ztdt19fyLSuGxJsFVK1deQc7tAHy/ah/sTn5rL7qRMBp3Tol5vT+rGXrHUEK17AG1CfTGZIK/IQkpOYb22kjZHjnlS7Z3TumfXpVUgK/acZL/mSrnU3hNZzFl5AIBnr+5FsK/ZxRGJO6lxUmrmzJk8//zzPPXUU/URj4hIsxU3c3mTuF1335+INB4p2QUcS8/DZILeTmrpqUyP1kHEp+ayKzFLSSk351h5rwGGnAP4eHkSFexLYkY+Cam5Sko5mT0p1d9J86TsupQMO997Isup+5Xqs1oNnl6wjSKLwfAekVweG+XqkMTN1HiyWFpaGjfeeGN9xCIi0uz4mT2Jq8EBWFxMGH5OKGt39u26+/5EpHGyz5Pq1CKAoAb4Zt0xV8qNhp1brAaGYbg6DLeTkGpbBS+6AeZJ2Z2eK6UV+Jwpp6DYMcvNWUPO7bqUDDvXCnyu88naeDYcSSPA25Pnruml1lcpo8aVUjfeeCM//PAD999/f33EIyLSrJhMJubfP5C8ouq1ofmZPZ3yZu7s23X3/YlI43S0pEXLvopWfevR2lZVsTvJPaoqii1Wbn5rDRl5RXz38GCtVHUG+3OjXQNVStluy4+1h7UCn7NtSUjHWrLCZmSwr1P3ba+UOplVQHpuIaH+mhXXkE5k5vPikt0APDGqG21CGy6JLI1HjZNSnTt3Ztq0aaxZs4bevXtjNpf+1urhhx92WnAiIs2ByWTC37tW60641e26+/5EpPHJyCsCINSvYT5I9mhtq5TaeyKLYosVL0/XJoG+33HC0dZ0LD2Pji0CXBqPO7EnhhpqptSZt3VUK/A5lWOelJOrpMA2K65NiC/HM/LZl5zNgA4adt6Qnv3fDrIKiunbLoQ7B3ZwdTjipmp8tP/WW28RGBjIypUrWblyZanLTCaTklIiIiIi4hT2pFSIf8MMxY0O88ff25PcQguHU3Lo3CqoQW63PIZh8NYvBxy/p+cWAkpKga2l8Vi6rYWufUQDJqXOWIFPnGdDfP0lpQA6RwbZklInlJRqSL/tO8V325Lw9DAxa2wfPD1U0S7lq3FS6tChQ/URh4iIiIhIKfakVLBvw1RNeniY6BYVxKb4dJbvSqZTi0A8XPRBau2hVLaUzNSC04+FQFJmPkUWA7OniSgnt3tVpl3J/KoEVUo5jdVqsLEeK6UAurYK5Je9JzXsvIEt2nwMgJsHRNOzTbCLoxF3psZ0EREREXFLmXnFAIT4Ndzy4X1KVvn7+5LdDH9lJe+vOkx2QXGD3b7d278eLPW7klKn2Vv32oT6NWj1hb1S6nh6Hharhs87w4GT2WTmF+Nn9qR7VP1UJnaJtM2k269h5w3GYjX4aXcyAGP6tHZxNOLuavy10z333FPp5XPnzq11MCJStdzCigc/V3aZiIhIY+OolGrApNTk4V3x8DDx5fqjHDyVw/TFO3j5+z3cGNeOuwZ2oEMDzHXan5zF8l3JmEy2FQF3JWaSnquklJ0r5kkBRAX7YvY0UWQxSMrMp62GNteZfZ5U3+iQepvhZh92vi9ZlVINZVN8Gqk5hQT7eqllUqpU46RUWlpaqd+LiorYvn076enpDBs2zGmBiUj54mYud3UIIiIiDcIxU6oBk1JhAd5Mv6oXj4/sxoKNR5m36jAHT+bw3u+HmbfqMEO7tmTcRR25pEuLelv1851fbeMyRvSIJCLQR0mpsySk2WY6RYc3bFLI08NEm1A/jqTkkpCaq6SUE9TnkHM7++qdJzILyMgratD/T5qrZTtPAHBp91aYXbxghLi/GielFi5cWOY8q9XKxIkT6dSpk1OCEpHS/MyexMWEsf5IWtUbA3ExYfiZPes5KhERkfrlikopu0AfL+4c2IHbL4jht/2nmLfqMD/tTubnPSf5ec9JHh/RlYcu6+L0203OymfBRtsslj8P6cTyXbYWmPS8QqffVmN11F4pFd6wlVJgq86yJ6Uu7BTR4Lff1Gys5yHnAMG+ZlqH+JKYkc/+5CzOi1HlTn1btsuWlBreI9LFkUhj4JSpkR4eHjz66KMMHTqUJ5980hm7FJEzmEwm5t8/kLyi6rXn+Zk96+3bWxERkYaS6YJKqbN5eJi4pGtLLunakkOncvjXsr0s3nKcFXtP1ktS6oNVRyi0WOnfPpTzYsJZf9j2oT1DlVIO9kHjDd2+B6ers+zVWlJ7aTmFHDiZA8C50fWXlAJbtVRiRj57T2QrKVXPDpzM5uDJHMyeJoZ0a+nqcKQRcFot3YEDBygubvghkCLNhclkwt/bq1onJaRERKSxs1gNsgoaftB5ZTq2CODhkkTUzuOZTh92nVtYzIdrjgAw4ZJzAAj1t933dA06d4h3YaVUu5JEmL1aS2pvU4It4XpOywDCArxrvoPvp8J/B8GxjVVu2qVVyVypExp2Xt9+LKmSurBTBMG+7vF/t7i3GldKPfbYY6V+NwyDxMREvv32W+666y6nBSYiIiIizVdW/ukkjDt9sOnYIgB/b09yCy0cOpVN51bOWzHsi3UJZOQV0SHCnxE9bW0vIX62D+vpuWrfA8gvsnAiswCA6LCGn+lkT4TZq7Wk9uo0Tyr+D1j9H9vP718Nt3wKHQdXuHnXkhX4NOy8/i3faWs5VuueVFeNk1KbNm0q9buHhwctW7bkn//8Z5Ur84mIiIg0RwkJCRw+fJjc3FxatmxJr1698PHxcXVYbs0+T8rP7Im3l/sMyvX0MNGzdTDrj6Sx7ViG05JSxRYr7/5uG3B+7+BOeHrYqp7ro1Iqr9DClxsSGNYjstEN6z6Wbmub8/f2JLw21TV1ZE+EJaSqfa+uap2UMgxYPsP2s3cgFGbBR9fDTe9DtyvKvUoXe1JKlVL1KjWnkPVHUgG4rEcrF0cjjUWNk1I///xzfcQhIiIi0qQcOXKEOXPm8Omnn5KQkIBhnG718vb2ZvDgwUyYMIHrr78eDw/3Sbq4C1esvFddsW1DWH8kje3HMrnuXOfsc+mOJBJS8wgP8OaG/u0c59uTUs6cKbV4yzGmfb2DjfHp/Ovmfk7bb0NISD09T8oV4wrslVInsvIpKLbg46WFZWqjyGJlS0IGAP3b1zAptfd7iF8FXr5w/6/w/TOw51v47Da49g3oe3OZq9iTx0mZ+WTmF7lV9WVT8tPuZKwG9Gwd7Gh1FalKrY+ATp48yW+//cbvv//OyZMnnRmTiIiISKP2yCOP0Lt3b/bt28dzzz3Hjh07yMjIoLCwkKSkJL777jsuvvhipk2bRp8+fVi3bp2rQ3Y7mXnuNU/qTL3aBAOw/ViGU/ZnGAZv/3IQgDsujMHP+3Siw37/M/KKSiU26+JYyZDuxtjKZB8w7op5UgARAd74mT0xjNOPo9Tc7sQs8oosBPt6cU7LwOpf0Wo5XSV1wf0Q3glu+gD6/AkMCyycAGvfLnO1ED8zkcG26lRVS9Wf5TtLVt3rqdY9qb4aJ6VycnK45557aN26NZdccgmDBw+mTZs23HvvveTmqrdaRETcW26hhdzC4gpO1VvhUqQq3t7eHDhwgC+//JI777yT7t27ExQUhJeXF61atWLYsGFMnz6d3bt389JLL3HkyJEGieu6664jLCyMG264oUFury7cuVKqd7sQwDbs3OqEYedrD6Wy5WgGPl4e3DkwptRloSUzpYqtBjlO+j8qJcc2nyo+pfEdux91DDl3TduhyWTSCnxOsDHe1rrXPyYMD48aVLxt+QxO7gLfULh4su08Ty9bhdT5f7b9/t0T8MvLtja/M9iHne9vhMnYxiC/yMIv+2zFKiM0T0pqoFaDzleuXMn//vc/LrroIgB+++03Hn74YR5//HHeeOMNpwcpIiLiLHEzl7s6BGkGXn755WpvO3r06HqMpLSHH36Ye+65h/fff7/BbrO27EmpYDdMSnVuGYiPlwdZBcXEp+bSoUVAnfb3VkmV1A3ntSMisPSsMV+zB95eHhQWW0nPLSTQp8aH72WkliSlMvOLSc8tJNS/4Wcz1Vb8Ge17rhId5s/eE9mOVkKpOcc8qZq07hXlw88v2H4e/Dj4nXFdDw+44kXwC4WVL8JPMyEvHUbOhJI2zy6Rgfy2/xR7VSlVL1YfSCG30EJUsC+xbYNdHY40IjWulPrqq6949913ueKKKwgODiY4OJjRo0fz9ttv8+WXX9ZHjCIiInXiZ/YkrgaDVONiwvAza06IOMdHH31U4WV/+ctfGjASuPTSSwkKct5qcfXpdFKq7kkYZ/Py9KB7a9uHrm11bOHbn5zFj7uTMZngvsGdylxuMpkILUnMpTtprpS9UgpOJ3kaC/uqd65q3zvztrUCX+3Vasj5urch8ygEt4XzJ5S93GSCS6fAqFm231f/BxY/ZGv543Sl1L5kJaXqw7Jd9ta9Vi6Z9yaNV43f5XNzc4mMLFuO16pVK7XviYiIWzKZTMy/fyB5RdVrffEze+qASpzmwQcfJDQ0lDFjxpQ6/9FHH+Wzzz6rdlXVL7/8wssvv8yGDRtITExk4cKFXHvttaW2+e9//8vLL79MYmIivXr14tVXX2Xw4IqXSXdnmfnu274HENsmmC0J6Ww/nsFVfdvUej/v/GpbcW9kz0g6VlBxFepvJjmrwJGoq6u0M5JSR1Jy6dMu1Cn7bQj2Ve9c1b4H0K5kBb6jWoGvVpIy8jmWnoeHCfpGh1bvSnnp8Ms/bD9fOgXMvhVvO3Ai+AbbElKbPoT8DLj+HbqWrMC3/4Ta95zNajX40Z6UUuue1FCNK6UGDhzI9OnTyc/Pd5yXl5fHs88+y8CBA50anIiIiLOYTCb8vb2qdVJCSpzps88+4/bbb+eXX35xnPfQQw/xxRdf1GhV45ycHPr27ct//vOfci///PPPmTx5MlOnTmXTpk0MHjyYK664gvj4+BrHXFBQQGZmZqlTQ3PnmVIAvdva5krtOFb7xyY5K58FG48BMOGSslVSdva5Us6qlEptpJVSmflFjueFS9v3VClVJ/Z5Uj1aBxNQ3XbU31+F/HRo2QP63lL19ufeDje+D57esGsxfPonuoTaPvoez8gnK995q1kKbD+ewYnMAgK8PRl4ToSrw5FGpsZJqddee41Vq1bRrl07LrvsMoYPH050dDSrVq3itddeq48YRURERBqtyy+/nDlz5nDttdeyfv16Jk6cyIIFC/j555/p3r17tfdzxRVXMHPmTMaOHVvu5a+88gr33nsv9913Hz169ODVV18lOjq6VvM+Z82aRUhIiOMUHR1d433UlbsnpWJLklLbj2fUelW8D1YdodBi5byYMM6LCa9wuxD/kva9vMIKt6kuq9UgLff0fhrTXCR7rBEB3tVPZtQDe0KsMT127qTGrXuZx2FNyf9jw6eDRzXb63teDbd+AeYAOPATIV/dxDmBtv9X9quFz6mWlay6d0nXlvh4afyB1EyNk1KxsbHs27ePWbNm0a9fP/r06cPf//539u3bR69eveojRhEREZFG7U9/+hPPP/88F198Mf/73/9YuXIlXbt2ddr+CwsL2bBhAyNHjix1/siRI1m1alWN9/f000+TkZHhOCUkJDgr1GrLtM+U8nXPpFSXyEDMnibSc4s4WotV2HIKivlwjW3VxfHlzJI6kzNnSqXnFXHmgoFHGtEKfPYkUDsXzpOC062DablFZBcUuzSWxsielOpf3SHnK/4OxfkQfSF0vbxmN3bOpXDn1+AbAgl/MNc0gxZksE/Dzp3KnpQa0VOte1JztfqKwc/Pj/Hjxzs7FhEREZEm4bHHHiv3/FatWnHuuefy3//+13HeK6+8UufbO3XqFBaLpczcz8jISJKSkhy/jxo1io0bN5KTk0O7du1YuHAhAwYMKLM/Hx8ffHx8ypzfkNy9UsrHy5OukUHsOJ7JjuMZNR68PX99Ahl5RXSI8K/yg1xoSaWUM2ZKpeYUlPq9MbXvOeZJhblunhRAkK+ZUH8z6blFJKTm0qO1VhqrrvwiCzuO2xYHqFal1Mm9trlQACOedaykVyPRA+DuJfDhdcRkH2K+9wy+SXgDBjR8BWhTlJCay+6kLDxMcGm3Vq4ORxqhGielZs2aRWRkJPfcc0+p8+fOncvJkyd56qmnnBaciIiISGO0adOmcs8/55xzyMzMdFzu7PllZ+/PMIxS533//fdOvb36ZK+UsreuuaPebUPYcTyT7ccyuTy2dY2uO3/DUQDuvbgjnh6VPw9C/e0zperevpeSbdtHkI8XWQXFJGbkUVhsxdurxg0UDc4dVt6ziw7zJz03Q0mpGtp+LIMii0HLIB/HwPhK/fgsGFbodiW0v7D2NxzZC+5eQvY7Y+iYd5zbdkyAwd9By26136cAOAacx3UIJyzA28XRSGNU46TUm2++ySeffFLm/F69evGnP/1JSSkRERFp9moywNwZWrRogaenZ6mqKIDk5ORyV01uDNy9UgqgV9sQWJfA9pLKj+o6lp7HjuOZeJhgdO+qk1nBfs6slLIlpTpHBrI7MYu8IgvH0vMqXPnPndjb91w55NwuOtyPbccySKhF62Zz5pgn1T6s6qR8wlrY/Q2YPOCyv9b9xiPOYd+VXxHwxQ105RjMvRxu/wra9q/7vpuxZSVJqRFada9xyksHv1CXhlDjr0SSkpJo3brsm2fLli1JTEx0SlAiIiIiUn3e3t6cd955LFu2rNT5y5YtY9CgQbXe7+zZs+nZs2e5LX71yTAMMvNts3rcdaYUQGwbW4XM9mM1G3a+bIcteRgXE05EYNVtks6cKZVaUm0VEeBD+5KKoyMpOXXeb0OwJ4DaV1Uptfs72Pl1vcaiYee1U+0h54YBy6bbfu53K7Sq/qIQlenQsTM3Ff6VzdZOkJcK718Nh39zyr6bo4y8Iv44mArAcM2TalyStsHHN8FbQ8Hi2tUoa5yUio6O5vfffy9z/u+//06bNm2cEpSIiIhIY3b//fdXezj4559/zscff1zldtnZ2WzevJnNmzcDcOjQITZv3kx8fDxgm2P1zjvvMHfuXHbt2sWjjz5KfHw8999/f63vx6RJk9i5cyfr1q2r9T5qI7ugGEvJNG53rpTq0ToYTw8Tp7ILOZFZUPUVStgrC0b2qt6HOKfOlMq2J6W8aR/ReBIrhmGcrpQKr6TtK/UgfHYrfHEXZBytt3jsw9aPprn/Y+cuDMNgY3zJkPOqklL7foD4VeDlC0OfdloMYQHeeAW24LbCqWS1HgiFWfDR9bBnqdNuozlZufckxVaDzq0CG0W1pQApB+DLe2HOYNj3PaTHQ8IfLg2pxu179913H5MnT6aoqIhhw4YB8OOPP/Lkk0/y+OOPOz1AERERkcamZcuWxMbGMmjQIK6++mri4uJo06YNvr6+pKWlsXPnTn777Tc+++wz2rZty1tvvVXlPtevX8+ll17q+N0+TP2uu+5i3rx53HzzzaSkpPDcc8+RmJhIbGws3333HTExMfV2P+uLvUrK29MDX7P7zjryNXvSuWUge05ksf1YBlEhvlVeJyO3iDUllQXVXakq1M8+U6ruSamUkva98EBvAn1tHwUaw7Dzk1kFFBRb8TBBm9BKklJr3wFKqtYOroRzb6uXeOzD1u3D16Vq8am5nMouxNvTg9i2lczhslpg+Qzbzxf8GULaOTWOLq0CWZ1dwA/9/sP1wdNgz3fw+W1w7Rzoc6NTb6upW16y6t5wte65v8xE+OUl2PgBWEtWDe01Fi6dCi06uzS0GielnnzySVJTU5k4cSKFhbY3NV9fX5566imeftp5WWwRERGRxupvf/sbDz30EO+88w5z5sxh+/btpS4PCgpi+PDhvPPOO4wcObJa+xw6dGiVLWITJ05k4sSJtY7bXWSUJF+C/cxOHwbvbLFtQ2xJqeMZ1Wpf+XlPMharQbfIIGIiqldZYK+USs+r+6Bz+0ypiABvzJ62hN+RFPdPStmHnLcO8XPEXUZBNmz66PTvh+oxKVVSKZWQlltmQQEpn711L7ZtMD5enhVvuPVzSN4JviFw8aNOj6NrZCCrD6awJ6UIbvoAvp5ku80F46EgAwbc5/TbbIqKLFZ+3pMMwIieWnXPbeWmwu+vwh9vQXFJEr3zCLhsGrTu69LQ7GqclDKZTLz44otMmzaNXbt24efnR5cuXVy+bLCIiIiIO2nVqhVTpkxhypQppKenc+TIEfLy8mjRogXnnHOOPsRWwt6mFuxX40PVBhfbNpivNtrmSlXHDztt86SqWyUFp1cgzC+ykl9kwddcyQf6KtiTUuEB3o6VshpDpZS9IqnSFdu2fm5LKnj5QnE+HFxhm01UD6+1tiXVWrmFFlJzCqs1G6y5q9Y8qaJ8+PkF288XPwZ+VbT51ULnyCAA9p3IAk+zrULKNwTWvgXfPm4b/Dz48Xp53jQlaw+lkpVfTItAb/pFO//vJHVUkA1/vAG//9v2/yJA9IUwfDrE1H7WZH2odT10YGAgAwYMIDY2ttYJqVmzZjFgwACCgoJo1aoV1157LXv27Cm1jWEYzJgxgzZt2uDn58fQoUPZsWNHbcMWERERaRC5ublMmjSJtm3b0rVrV1588UU6d+5M586dlZCqQmNYec8utm0IANuPZVa5bX6RhZV7TgI1S0oF+Xjh6WF7ztR1rlTKGUkp+8Dw+NTcGg1qdwX7PKkKh5wbBqx92/bz0P8DLz/IPgEnd9dLPL5mTyKDbZ+BtAJf9WyMTweqSEqtewcyEiCoja11rx50bRUIwL7kbNsZHh5wxUtwyZO233/6GyybZntOSYWWlbTuDeveyvH/k7iB4gL44014vR/8NNOWkIqMhVu/gHuWul1CCuqQlHKGlStXMmnSJNasWcOyZcsoLi5m5MiR5OScXgHkpZde4pVXXuE///kP69atIyoqihEjRpCVleXCyEVEREQqN336dObNm8eVV17Jn/70J5YtW8YDDzzg6rBqxFWr72XmN56kVM/WwZhMkJSZz8msyoedrz6QQk6hhahgX3qXJLOqw2QyOR6Lus6VSjsjKdUuzA+TyVbtY09WuSt7+150RUmpQ7/AyV1gDoC4eyBmoO38gyvqLSatwFd9WflF7EmyJW77t68gKZWXDr/+w/bzpVPAXElVXB10KamUOpqWR05ByWwdkwmGTYVRJVVaq/4N/3vYNt9KyjAMg+W7NE/KrVgtsPlT+E8cLHkSck5CWEe4/l3486/QdZTbVv+5NCm1dOlSxo0bR69evejbty/vvfce8fHxbNiwAbA92V999VWmTp3K2LFjiY2N5f333yc3N5dPPvnElaGLiIiIVGrBggW8++67vPXWW7z++ut8++23LFq0CIul8XzIcdXqe5mNqFIqwMeLTiWrTu04XnkL3w/2ocA9W+FRw8qCUEdSqvbJI8MwSrXv+Xh50jrYNpzd3Vv44qtaeW9tyWIBff9ka8XqOMT2+8GV9RbTmXOlpHJbEjKwGra/X6vgChYE+P01yEuDlt2h7y31Fkt4gDctAm2tqwdOZpe+cOAkuPo/YPKwDYT+8h4odu+ErSvsTsriaFoePl4eXNylhavDad4MA3Z9A29cBIvut62mFxgFY/4FD66D3jfYqgHdmFtFl5FheyMPDw8HbEsdJyUllRoA6uPjw5AhQ1i1alW5+ygoKCAzM7PUSURERKShJSQkMHjwYMfv559/Pl5eXhw/ftyFUTUOjplSvu6flIIzW/gqTkpZracrC0b2jKrxbYQ4hp3XvlIqu6CYQosVgIgAW+uZPbES7+bDzu0zpezVSaWkx9tWUAM4f4Lt305Dbf8e/g0sdV+1sDxaga/6HPOkKqqSykyENW/Yfr7sr+BZv/PkOpe08O09kV32wv53wI3zwMMMOxfBp3+Cwpyy2zVj9lX3Lu7cAn9v95/912QdXAnvDLetHnlyF/iGwvBn4eFNtopRz8bxHlqjpFRRURF33303Bw8edHoghmHw2GOPcfHFFxMbGwtAUpJtEGRkZOmSwMjISMdlZ5s1axYhISGOU3R0tNNjFREREamKxWLB29u71HleXl4UFxe7KCI39sWd8GpvOLoeaFwzpQBi21Q9V2rz0XROZhUQ5OPFhZ0ianwb9kqpjDq079mrpPzMnvh524alx0ScnivlroosVhIzSpJS5bXvrXsHDKutOqpVd9t5UX1sQ7ILs+DYxnqJq11JLEdVKVWlDfFVDDlf+XfbymDRF0C30fUeT5dWJcPOkysYCdPzGrjtCzD7w4Ef4cPrbO2FAuBIsNdkNp440bEN8ME18MHVcGy97Xk6+Al4ZAtcPBm8K2hzdlM1SmuazWYWLlzItGnTnB7Igw8+yNatW/ntt9/KXHb2MNDKll19+umneeyxxxy/Z2ZmKjElIiIiDc4wDMaNG1dqQZj8/Hzuv/9+AgICHOctWLDAFeG5l6wkW7VLxlFoF9foklK92gYDsL2S9j37UOAh3Vri7VXzZgX7Y1GXQednDjm3sw8OP+LGlVKJ6flYDfD28qDl2avcFeXZ2qyg9GBsDw9bkmrnIttcqfYXOD0uzZSqHqvVYFNJpVT/8pJSJ/fCxg9tPw9/tkHm3nSNLBl2Xl6llN05w+DOr+HjGyDhD5g3Bu5YAIGt6j0+d3YiM58tR23/1w3r0bwfiwZ3co9tEP+u/9l+9zDbKqIueaJRPy9r/I543XXXsWjRIqcG8dBDD7F48WJ+/vln2rVr5zg/KspW2nx2VVRycnKZ6ik7Hx8fgoODS51EREREGtpdd91Fq1atSlVw33777bRp06bUeQIElbSzZdmO+RrTTCmAXiWVUkfT8iqc+fTDDtt9G9mr5q17AKH+tkRSel7t59ukZtuuGxF4RlIqwpYgdefEimPIeZhf2Vlc2+bb5hCFtoeul5e+zN7CV0/Dzu3zrY6l52GxaqW2iuxLziaroBh/b0+6lQwZL+Wn58Cw2Cqk7APq61nnqiql7KLPh3HfQUArOLEN5l5uS6A3Yz/uSgagX3QorYIqmA8mzpUeD4smwn8vLElImaDvrfDQBhj9UqNOSEENK6UAOnfuzN/+9jdWrVrFeeedV+qbPoCHH3642vsyDIOHHnqIhQsXsmLFCjp27Fjq8o4dOxIVFcWyZcs499xzASgsLGTlypW8+OKLNQ1dREREpMG89957rg6h8Qhqbfs3KxE4Y6aUX+OYVRLiZyYmwp8jKbnsOJ7JRZ1LD/49cDKbAydzMHuaGNqtZa1vA+q2+l5qZZVSqc6dmZNfZGHKgm0MPCeCG+Pq1rWQ4BhyflZLimHAHyUDzgfcBx6epS/vVDLs/Og6KMgGn8A6xXG21iF+eHmYKLIYnMjMp01o/awW19jZ50n1iw7Fy/OsmoiEdbYP2SYP2yypBmKvlDqalkduYXHlc5GiYuGepfDBtZB6wJaYumMRtOzaILG6m2U7bQl2te41gOyTthUp188FS8kXEt3HwLBnoFUP18bmRDV+p3/nnXcIDQ1lw4YNjlXy7EwmU42SUpMmTeKTTz7h66+/JigoyFERFRISgp+fHyaTicmTJ/PCCy/QpUsXunTpwgsvvIC/vz+33nprTUMXERERkRqYPXs2s2fPrv8VAytMStWyUsowbG1dDThXI7ZNCEdSctl2LKNMUsreundhp4haD28PdcKg89SSKq5w/7JJqROZBeQXWfA1e5Z73Zr6eXcyCzYd47vtiYzsGeUY1F4bjpX3zh5yHr/aVr3i5Qfn3lH2imEdbRVU6fG2bbuMqHUM5fH0MNEm1I/41FwSUnOVlKrAxormSRkGLJ9u+7nvrQ36ITsi0IfwAG9Scwo5kJxD73ZVVK1GnAP3fm9LTJ3aA+9dDrcvgDb9GiJct5FTUMzvB1IAJaXqVX4GrPoPrJ4NRSVfGHS8BC6bDu3iXBtbPahx+96hQ4cqPNV0APobb7xBRkYGQ4cOpXXr1o7T559/7tjmySefZPLkyUycOJG4uDiOHTvGDz/8QFBQOaWfIiIiIuI0kyZNYufOnaxbt65+b6hMUso2DL7W7Xu/vAx/j4b9y50RXbU45kqVswKfPSk1sg4f4uxJKWcMOj+zUirM30yQj+17amcO7N5a8jjkF1mZvyGhTvtKSLMPOT8r6fPHm7Z/+9wI/uFlr2gyNVgLnz1GKWtjRfOk9i2DI7+Dpw9c+nSDx2Vfga/KFj674DZw9xJocy7kpthmTB3+vR4jdD+/7jtFYbGV9uH+dGnl3MpDwfZlyu+vw2t94ZeXbAmpNufaKvPu+l+TTEhBLZJSzmQYRrmncePGObYxmUzMmDGDxMRE8vPzWblypWN1PpHGILfQQm5hcQWnev7m2Ykqvh+N5z6IiIibOnumVH4dZ0rt+h9Yi+HHv9mqMRqAfQW+HcdLr8B3MqvAUSkyvC5JKb+6z5RKKZkpFX7GTCmTyeRoi3PmsPNtR08n5z5YfQRrHWYuJZRXKZVx7PSw3/P/XM61StR3UkrDziuVmlPIwVO2So/+0WckpawWWD7D9vMFf4aQdmWvXM/sLXx7Kxt2fraACLhzMXQYbFvZ8aOxsPf7eorQ/dhX3RveI7LChcekFixFsP49eL0/LJtmm5PXohvc9CGM/xnOudTVEdarWjXqHz16lMWLFxMfH09hYek3xldeecUpgYk0FXEzG+5b2vrUVO6HiIi4oeA2tn8zE8kvslBYbAVqmZSyFMPJ3bafEzfDwZ9tq2jVs9i2tqTUoVM5ZOUXEVTSpvfjrhMYBvRpF0LrkNq3d9nb3+o2U6oAgIgzKqUAYiL82ZmY6WiTqyvDMNhWUinl6WEiPjWXlXtPcmn32g3jtVdwlZoptX6ubTh2zEW2mT8V6VgyV+rEdtt8lsDazfSqiD2mBCdWmTUl9iqpLq0CS7dwbpsPyTvANwQuftQlsXUpGXa+v7qVUna+wXDbfJh/N+xdAp/dCte9Cb1vqIco3YfFavDTbtuQ8+E9G/dgbbdhtcKOBfDzC7Z5ZQAh0TD0/6DPn8CzccxVrKsa38sff/yRq6++mo4dO7Jnzx5iY2M5fPgwhmHQv3//+ohRpNHxM3sSFxPG+pI34qrExYTh56QZDs5Uk/vhrvdBREQaAXulVGEWmRm29xwPEwRUNny4Iin7Tg+EBfj1lQZJSoUHeNM21I9j6XnsOJ7JhZ0iAPihpHVvRI+6zV8J9XNm+55PqfPbO7lSKiE1j4y8Irw9Pbjl/GjeX32E91cfrlVSKrewmFMlFV6OpFRRPmyYZ/v5/AmV7yCgBUT2ts2eOrTS6YmDdmG2ROPRVLXvlWdDefOkivLhp5m2ny9+tPzWywbQJdLevleDSik7sx/c/KFtRbRtX8BX99nmAA2418lRuo+N8Wmk5hQS4mdmQAfX/M2aDMOwtZf/+CwkbbOd598CLnkC4u4BL5/Kr9/E1Pid/umnn+bxxx/nueeeIygoiK+++opWrVpx2223cfnll1e9A5FmwGQyMf/+geQVVa+1zc/s6ZYlsDW5H+56H0REpBHwCQLvQCjMJiflGGAbcu7hUYv3laTttn/DO9kGXB/+FY6ub5BZHL3aBHMsPY/txzK4sFMEOQXF/Lb/FAAje0XVad+hJcPJswqKKbJYMZ+9ilk1pJQzUwrOqPZxUqWUvUqqR+sg7r6oIx+sOcKKPSc5fCqHDi0Cqrh2aUdLZjUF+3qdrpzbsRByT0FwW9tKVFXpNMSWlDq4wulJKVVKVc6+8l7/9mckpda/CxkJENQGLrjfRZGdrpSKT80lr9CCn3cNv1z1NNsqpHxDYN3b8O1jtsTU4MfqIVrXW16SYL+0W8ta/f8jJeLXwPJnIX6V7XefYBj0EFz4gO29sBmq8bNp165d3HXXXQB4eXmRl5dHYGAgzz33HC+++KLTAxRprEwmE/7eXtU6uXMyp7r3w53vg4iINAIlw84LU48CdZgndaLkW+dOl0Kfm20///avukZXLfYWPvtcqV/3nXQMBbbPr6mtYN/T3yVn1nIFPnulVHnte4DT2ve2HksHbI9HhxYBDO1qa5n7cM2RGu8rPuWs1j3DgLUlA87j7qlee0unknksB1c4fcaYfaZUUmY+BcWas3mmIouVrUfTgTOGnOdn2BYiANtwc7PrVixsEehNmL8Zw4ADJ2tRLQXg4QGjX4ZL/mL7/cdnYdlfG2yWXUNaZp8npVX3aidpG3x8E8wdZUtIefnaklGPbIEhTzbbhBTUIikVEBBAQYGtH71NmzYcOHDAcdmpU6ecF5mIiIiINB8lLXxF6ceBOiSl7JVSUbFw0SOACXZ/A8m7nRBk5XqXJKXsK/D9sKOkda9n3YcCe3l6EFSSmMqoRVIqv8jiWJwk7KyklL19Lz41t04Dye3sQ877tLM9HncO6gDAF+sTyC0srtG+7BVIjiHnR9fD8U22FdvOG1e9ncQMBA+zrTontWarhVelRaA3fmZPDAOOp+c7dd+N3a7ETPKLrIT6m+lkr5D7/fWSIc5doe+tLo3PZDI5qqWqvQJf+TuCYc/AyOdtv//+GvzvEdsw9ybiwMlsDp7MwexpYkhX585la/JSDsCX98KcwbDvezB52v7vengTjJzpsvZVd1LjpNSFF17I77/blr688sorefzxx3n++ee55557uPDCC50eoIiIiIi4xuzZs+nZsycDBgyo/xsrqZQyshIBCPatbaVUSVIqsje07Abdr7T9/vtrdY2wSr3aBgO2D3CZ+UX8WDIUeKSTKgvsibr0WiSl7FVSZk9TqaorgDahfnh6mCgotnIyu6BOMZ455Lx321AAhnRpSUyEP1n5xSzadLxG+0somdUUHV5SUWOvkoq93jYvqjq8AyD6fNvPTl6Fz2QyOeZKaQW+0s5s3fPwMNlW11w923bhZdPdYohzZ/tcqZqswFeRQQ/C1f8BkwdsfB++uheKa79apjuxt+5d2CnCsYiDVCEzEf43GWafD9u/BAzb/1sProOrXju9wIfUPCn1yiuvcMEFFwAwY8YMRowYweeff05MTAzvvvuu0wMUEREREdeYNGkSO3fuZN26dfV/Y8G2pJRHti0pVatKqeyTkH0CMEGrHrbzLi6Z77LtC0hPcEKgFWsV5EurIB+sBnyw6jAZeUWE+ZtLD3mug1D/2g87tyelwvy9y1RtmT09aBPqC9R92PmRlFyy8ovx9vJwDJL28DBxx4UxAHyw2rZAUnXZK6Xah/tD1gnYsch2wQVVDDg/W6ehtn8PrazZ9apBc6XKZ09KOZ7/K/4OxXnQ7vzTyWIX69rK9hzd64ykFED/O+CG92yVeTsWwme3QGHjf14s33W66lOqkJsKP0yD1/vBhvfAWgydR8Cff4Eb5kLEOa6O0O3UOCnVqVMn+vTpA4C/vz///e9/2bp1KwsWLCAmJsbpAYqIiIhIM1BSKWXOtVUXBdcmKWWvkgrvCD4lM5zanQcdL7F9MFj1b2dEWin7XKm3fz0EwGU9IvFy0lDgUD9b2116Xs2rLyoacm4XE25rr6rrXKmtJVVSPVsHlxqGfON50fiaPdidlMXaQ6nV3p+9+qhduH/JB7wiW1Kjzbk1C8yRlPrF6W1V0Y5KKa3Ad6aNZw45P7UPNn5gu2DEs7aWNzfQJdLWvre/Lu17Z+t1Ldz6OZj9bSusfXgd5KU7b/8NLCW7wJFgvKyOq4g2aQXZtnlpr/WFVa9DcT5EXwh3L4Hbv4TWfV0doduqVVIqJSWlzPnp6el06tTJKUGJAOQWWsgtLK7g1HR6tEVERARHUso3z5aUqlWllKN1L7b0+fZqqY0fQE79zkC1J6Xsc5+cWVkQUlIplV6rSilbW15EYPlJqehw5ww73+5o3QspdX6Iv5nrzm0LwAerqzfw3DAMR1IqOtgL1s+1XXDBn2seWJv+4B1km2eUtLXm16+EKqXKOp6ex/GMfDw9TPSNDoGf/gaGBbpeDjGDXB2eg72aLz41l/xqrppdLZ0vgzsW2VbmS1gD74+xVXI2Qj/tTsZq2BLNbUNdN5jebRUXwB9v2iqjfpoJBZm296Bbv4B7lrrV891d1biR9/Dhw1gsZV+wBQUFHDt2zClBiQDEzVzu6hBERESkoZQkpQIK7ZVStZg34xhy3rv0+Z2G2iprjm+CP+bYhhLXk9g2wY6ffc0eXNLFeUOBQ/1qn5RKybZXSvmUe7lj2HlKTi2js7Gvtta7XUiZy+64sAOfrk1g6Y4kkjLyiQrxrXRfablF5JR8Edn+xHJba2ZgFPS8puaBeXpBx8Gw5zvbXKmaVlpVol3JEPajLpwpNfmzTexOyuKj+y6gRWD5f+OGtDHeVlnTs3Uw/smbYefXgMk2S8qNtAz0IcTPTEZeEQdOZtOrTdnnba21vwDGfWerlEraBu9dDjd9AJG9nHcbDUCtexWwWmDr5/DzLMiIt50X1tH2/tJrrG1lRqmWaj9SixcvZvHixQB8//33jt8XL17MwoUL+dvf/kaHDh3qK05pJvzMnsTVYO5CXEwYfmbPeoxIREREGkTJ6nvBRSmA4dxKKZMJLn7U9vPatyA/s/ZxViH2jAqhizu3xM/beccpjplSdRh0HlFR+15E3SulrFaDHcdsj22fcpJSPdsEc36HcCxWg0/+qLpayl4l1SrIB+/1b9vOjLsHPGs5aLnjENu/Th52bh/CnpDmmva9w6dyWLT5OLuTsnh6wbYazeyqL44h59EhsKwkEdXvVojs6cKoyrKtwGerltqf7KS5UmeKirVVy4S0h5T98MYg+Ow2SNzi/NuqB/lFFn7Za6suVVKqhGHArv/Z/paLHrAlpAKjYMy/bEPMe9+ghFQNVfsrqGuvvRawvXDvuuuuUpeZzWY6dOjAP//5T6cGJ82PyWRi/v0Dyatm+ayf2bPOSyyLiIiIGyhJSpkpIoysmieligvh5B7bz1GxZS/vfhVEdIGUfbBhHlz0cN3irUDrEF/CA7xJzSl02qp7do6ZUrk1nymVlnt60Hl52juhfe9wSg5ZBcX4mj3o3DKw3G3uHBTD2sOpfLI2nknDOuPjVXHSzt4ONzToGBxdaxsefd64WsfnmCsVvwaK8sFceaVWddnb91JzCskpKCbAp2FXlftue6Lj52U7T/DVxmPccF67Bo3hbPZ5Upf7bYfNv4GnDwx92qUxVaRLZBDrj6Sx94QT50qdKeIcuPd7WPp/sHMx7P7GduoyCoY8Ce3i6ud2nWD1gRTyiixEBfvS64wq0Gbr4Er48Vk4tsH2u2+o7QuP8yeAt79LQ2vMqp3Cs1qtWK1W2rdvT3JysuN3q9VKQUEBe/bsYcyYMfUZqzQTJpMJf2+vap2UkBIREWkivHzAPwKASFN6zZNSp/bahmD7hEBIdNnLPTzg4sm2n1fPts0BqQcmk4mnLu/GNf3aMKZva6fu2zFTqhaVUo72vQpmSrUvqZQ6lW1LrNTGtjOGnFc03H1UryhaBflwKruQpduTKt2ffXD4DZbvbGf0ug6C6pDoa9nNVtFQnA8Jf9R+P2cJ9jU7nq+umCu1ZJvtcbTP8Xp28Q6OunC+VX6RhR3HMzFh5bx9r9vOvGAChJbzunQD9kqpfc5aga88wW1srXsT10Dvm8DkAfu+h3cugw+ugcO/199t18EPO22te8N7tmren7uObbD9nT642vaz2R8GPwGPbLG9ryghVSc1ris7dOgQLVq0qI9YRERERKQ5K5krFWlKI9i3hkkpR+ter4pX9up9EwS3hewk2PxJHQKt3M0D2vPan87F39u5FTN1mSlVVftesK/Z0R5Y22qpbUdtSak+7UIr3Mbs6cFtF9hW7H5/1eFK95eQlks4mfTP/Ml2Rm0GnJ/JZDpdLVVfLXwNvAJffEou245l4GGCd8fFcV5MGFkFxfxl/lasVte08W09mkGx1eDOgLV4n9ppSxTbFxtwQ10dK/DVY1LKrlV3uP5teHA99LsdPLxsz8V5o+G90XDgZ1t7mBuwWg1+dMyTinJxNC7y/+3dd3yV9fn/8dfJyd4hIYGQMGRH9naLIojWhatfrW0VV8VVftZRW/XbUq11URVxVa39VqvVaqtFKYqMioMhMsJI2IQRSCB7n/P74z7nkEDGOTnjPufk/Xw88jh3zvjc1zk5hOTKdV2fQ1vgnR/BK+cYX6eIKJhwi5GMOvfXEJdqdoRhwe2k1DfffMMnn3zS4ro333yTfv36kZmZyc0330xdnX/+4iQiIiIigTdv3jzy8vIYP358YE7oSkqVel4pdWC9cdla655TZDSccrtx/NXzYLN1IkjzpDpa78q9mCnVrY2kFHjfwrfOUSk1rFf7w6L/Z2IuUVYLa3YfdSWyWrOntJofWhcTaa83ds/zRZvTSX6aK+UYdr4nwMPOP3G07k06KZ3MpFieunIkcVFWvtpewhsdJP38ZfWuI0TTwJ2Wd4wrTr8b4ruZEos7nDvw7Syp8u0OfO1J7w+XzoM71jjmpEXDri/hL5fCn86DrQtNT06tLyqjuKKOhGgrk04K3q+fXxzdDR/eBi9MMuZHYYGR18Adq+GCP0BiptkRhhW3k1KPPPII69Yd2z51/fr1zJw5kylTpnD//ffz0Ucf8dhjj/klSBEREREJvFmzZpGfn8/KlSsDcj5bovHX+CyOeJ6UamvI+fHGXAcxycbQ4W2fdyJK8zhfk06173VQKQXHklKdSawYQ86dlVLtJ6Uyk2KZPsxIQL751c4277evpIIfRTp2Y/a2SsrJOex8/1qoOeKbNTk2VyrQ7XsLHC2QFww3Xs++GQk8eOFQAB7/dDOFxX6ak9SO1buO8CPrZ6Q3HjQSzRNvDXgMnshMiiE5NhKbHXYc9m73SY+l9TEGZN+51nidImNh70p46yp4+SwjIWJS8ty5695Zg7u3O/strFQegk/ug+fGwtq/gt0GQ34At30Fl803vl7ic24npdauXcu5557r+vxvf/sbEydO5JVXXmH27Nk8++yzvPvuu34JUkRERETCX21sd8Bo30uK9aD1zW6HA46kVHuVUgAxSTD6OuP46/mdiNI8zva6o9X1HrVmNTTZXDv2uVMptavE88TK9sNVVNU3ERdlpX8bQ86b+8mpxi93//p+H0eqThzc3mSzk1e+nGxLKU1xGcY8KV9I6QUZg4xfNnf+1zdrArlpgW/f23ukmu/3HCXCYszqcrp2Ym/OHNSdukYbs9/9noamwCU17HY7W3ft5fbID4wrzn4g6OftWCwWBjpa+Pw27LwjKb1g+uNw1zo49U6ISjB26HvnR/DiabD+PbAFqIrLYZFzntTQLrDrXm0ZLJ4DfxwJ37wITfXQ70y48XP44V8hc6jZEYY1t5NSR44cISvr2Bty6dKlnH/++a7Px48fz549e3wbnYiIiIh0GVUxRktEL+vRNgdlt6ryIFQfNoYHZ7qx5fyEmwCLUSl1aGvngjWBs1LKZofKeveHkTt33rNYjrUAtqZPeufb99YXHQXg5OxkrBEdD0Qe0zuNk7OTqWu08e6qE3+HOFhey48iPjXiHvdTYxC+r/hhrlSOI6EXyAHjzkHxE/p1o3vSsdfHYrHwh8tHkBIXxbq9Zcz7ojBgMe0sqeaK+g/oZqnEnj4IRl0bsHN7wznsPCBzpdqTlAVTfwt3rzcGacckQ3E+vD8T5k2EtW9DU+c2IvDEntJqNh+owBphYfLgMG5Va6iBL/9oJKOWPQENVUar8HUfwk8+CuqdEcOJ2//bZ2VlsWPHDgDq6+tZs2YNp5xyiuv2iooKoqI8LLMWEREREXEojzI20+kZcdSzBzqrpNIHQFRcx/fv1g8GTzeOv33Js3OZKDbKSmyU8eN7mQfDzo9UGfdNjYtqN2GU68VMqXWO2VDDO2jdc7JYLPzklL4A/OXrXTQdV/l1eNtqJkZsphErEeNnehxPu/yQlGo+U8oeoFlA/15vzJNytu411yMllt9ealQNPre4kHV7jwYkpo1btnKj1ZhDbJnyEFh9O+zfX0yvlDpeQroxSPvu9TD5QYhNhZIC+PBWeG4MrH4DGk+sMPQVZ+veuD5ppLVTXRmymhpg1evw7BhY9JDRypsxGK76C9y0GPpPNjvCLsXtpNT555/P/fffz/Lly3nggQeIj4/njDPOcN2+bt06+vfv75cgRURERCT8HbGmA9AdD2f9NN95z13OOTdr34aao56dz0SpccYviJ7swFdSZWxG1F7rHhxr39t7pPqEJFFHNjjmSQ3vYMh5cxePyiY1Poq9R2r4YnNxi9sS174GwKq40yE526NYOtTnNKOqrqQQyvb6ZMkcR/teVX0TRzqxO6Kn9h2t4bvdR7FY4PyTW98Z7eKR2fxgRE+abHZ+/s7agAzx7r5mLvGWOooShhmzeEKEs1KqwOxKqePFpcJZ98LPN8CURyA+A47ugo/ugmdHw7evQEOtz0/7mWvXvTBr3bPZjFbIeRPg47uhYh+k5MIlLxhzo/Iubnv3VvEbt5NSc+bMwWq1ctZZZ/HKK6/wyiuvEB197D+21157jalTp/olSBEREREJf4ctxg5PafajnrWouDvkvLl+Zxqtfg1V8N1f3H+cyVxzpWrcr5IodQ05b78FrmdKHFFWCw1Ndg6Uu/+LbpPNzoaicqDjIefNxUZZuXpcLgB/bj7wvLqU3KKPAVibfZXb67ktLtVo0QHYvtQnS8ZGWcl0tNAFYge+Txyte+P7dCMzObbN+/32kmFkJsWw7VAVf/h0i3+DOlzIuJJ/AVA0/v6Q+uV+kKNSaldJNXWNgZ3d5JaYJDj953D3Opj2KCT2gPK9sOAe+OMIWPE81PtmSHtZTQPfbC8F4NxwmSdlt8PW/8DLZxqtkKXbjQTf+Y8bO+qNvhYiusgw9yDkdlKqe/fuLF++nCNHjnDkyBEuu6zlsMG///3vPPzwwz4PUERERES6hsP2ZBrtEURgg6rijh/g5BpyPtz9x1gsx6qlvn054EOEO8u1A58H1TjOpFRHlVLWCAs5ac5h5+7/grv9UCU1DU3ER1vpl9HxkPPmfjSpDxYLLC84zLZDjiqV7/5ClK2OjbY+NPWa6NF6bvNHC18Ad+D7xNG6N31461VSTmkJ0Tx+xQgAXvtyByu2HfZbTA2f/QYrNj5vGk2/saFVrJCVHENSTCRNNnvgd+DzRHQCnDIL7voeLngSknOMmXr/eRDmDoflT0NtuVenWLKlmEabnQGZifTLSPBR4Cba9RW8Ph3euhIOrDfmdE1+EO5aC5Nu9e28OukUDyZIGlJSUrBaT8widuvWrUXllIiIiIiIJ8pqbRwi1fikYr97D2qohcOOYeWeVEoBjLgK4tLg6G7Y8olnjzXJsUopD9r3Kh1JqcSOf1Z3tvB5Uu3jnCc1LDvFrSHnzeV2i+fcIcYg5b98tcu4cs2bALzRNI3e6X76pbh5UspHM6ACtQPfgbJaVu0yWlynDztxntTxJg/O5JqJvQH4xd/XUV7rh/bCvauJ2vxPbHYL/5f4kxaD10OBxWJhQJajhe9gkLXwtSYq1tiw4c7v4OLnIK0vVJfA5/9rJKeW/N6Yk9QJn20y/iAQ8q17B9bDX6+C18+H3V9BZCyceoeR0DvrXqP6TIKCx0kpERERERF/KKtp4KA9zfik3M2k1KHNYG8ykkuezh6KioOxPzWOv3nRs8eaxDlTqqy6M+177ieldpW4n5RaX+TZkPPj/dgx8Pz91XupKt0PJYXYsPBp0wRX9ZHP5U6AyDijIq94k2+WDFCl1KcbjH8bY/uk0SOl7da95h68YCi9u8VTdLSG33yU79uA7Hb4zOiY+YftDNL6jvbt+gEyKNNIUhQEy7Bzd0RGw5gfw+2r4bKXIH0g1B6FJY/B3BHw+W+gqsTt5eobbSzZYiSlpoRq617JNnhvJrx4OhQsBIvV+D5/53cwdQ7EdzM7QjmOklIiIiIiEhTKqpslpdytlGo+T6ozM2zG32j80rJz+bE2wCDmrJQq86BSqrTavfY9OJaU8mQHvvWdGHLe3OkDMjgpI4GKuka+/e9/ACiw9aKCeFf1kc9FxkAfx07iPmrha74Dnz8tcMyTmj6s/da95hJiInn6qpFYLPDe6r0s3HjAdwEVfg47l1NPFE83XMGYPmm+WzuABmYF6bBzd1gjYeQPYdY3cMXrkHky1JXD8qdg7jBY+CBUHOxwmZU7S6mobSQjMZpRuan+j9uXyvfBR3cbQ8w3vGdcN+xyuH0lXPRH32+YID6jpJSIiIiItGrevHnk5eUxfvz4gJyvvLZ5UsrNX5oPbjQuPW3dc0rJMXZcAvhmfufWCKCU+E7MlKr0ICmV7llipbHJxsZ93lVKRURY+NGkPgDs37AcgLW2AcRHW92KudOcLXw7fDPsPKebkUDbe8R/7XvF5bWs3GkMoZ4+vOPWvebG9e3GLWcau6X/8h/rOVxZ531ANht89ggAb9mnsY8MxvQO1aSUo1IqFJNSThFWGDYDbv0vXP1X6DkSGqrhq+eNgegL7oWyojYfvijfSFydMyTT41Zc01SXwn9+bexGuPp1sDXCgPPglmVwxWuQ3t/sCKUDSkqJiIiISKtmzZpFfn4+K1euDMj5ymoaOOBppdSB9cZlj04mpQAm/sy4XPd3qPLfIGhfcA0696RSys1B59Csfc/NpFThoUpqG2wkxkTSz4v5T5ePzSE+2krvGqO17Dv7AHLT4rH4cwc3Z1Jq53+hyfs5S85KqaIjNdhsvplTdbyFGw9gt8Oo3FR6pXpeRfbz8wYypEcSJVX1PPCP9di9nae14T04uJ6m6CTm1l1EQrSVwT1Cc1bPwEyjUmrn4SrqG20mR+OliAgY+gO4eSlc83fIGQ+NtfDtS/DHkfDRXXBkZ4uH2O12V1LqvDz3q/BMU1cJS58wns+KZ43nlzsJrv8EfvSekZCTkKCklIiIiIgEhbKaBorxICllt7ds3+us3AmQPRqa6mD1G51fJwCOzZTyYNB5J5JSR6sb3GoRXO8Ycn5ydjIRXlRWpMRFMWNUD0ZGbAfgO9sAcrv5qXXPKWs4xHWD+kooWu31cj1TYrFGWKhvsnGwotYHAZ5owXqjgvBCD6uknGIirTx91SiirBYW5R/kvdV7Ox9MYx0s/i0A6/tez1GSGN07LXQqbI7TMyWWxJhIGm12dnqw+2RQs1hg0FSYuQh+/E/oczrYGozvc8+OgQ9vg8OFAGw+UEHR0RpiIiM4fUCGuXG3p7EOvn4Rnh0FX8wx2hSzhsE178INn0KfU82OUDykpJSIiIiIBIUWg87dad8r32fsMGWxQvchnT+xxXKsWmrln3xSNeMvx3bfc2/Quc1m50i1c9B5xzuiJcREkuHYpc+dFj7nPKkRnWzda27mkAaSLDVU2mMpsOf4b8i5U0QEnHSWceyDuVKR1giyU43B4/7Yge9wZR3f7DCGVp/vwTyp4+VlJ/Pz8wYB8L8f5bO3s4PZV71m7FyZ2IO3Iy4ECNl5UuDYgc9RLbU1lIadu8NiMSoDr/+3UUnU/xxjg4i1f4V54+G9maxZuQKAMwZmEBdtNTfe1tiaYO1b8Nw4+PQ+qDoEaf3g8j/BLcth0LTOzRUU0ykpJSIiIiJBobymgQN2x85I5fs6foCzSipjkLFFujdOvhQSMqFiH+T/07u1/MjVvudmpVR5bQNNjlaytIQotx7jybBzZ1JqWCeHnDfXr9bYBW+d7SRsRLja4fyqn++SUuDfYecLNx7AZoeROSleJ+xuObM/Y/ukUVnXyD1//97zdsPaclj2hHF89v18s8dIwo0N4aQUHGvhKzgYwnOlOtLnVLjuA7jxcxh0PthtsOE9rl1zNS9EzeWK7FKzI2zJbodNH8H8U+HDn0HZbkjqCT94xhhiPvwKI8EsIUtfPRERERExnc1mp6KukYP2VOOK2qPQ0EG1iS/mSTlFxsD4mcbxNy96v56fHKuUanBrHpCzdS8pJpKYSPeqH1xzpUraT6w0NNnI31cOwIicVLfWbtdeY3bZd/YBAP6vlIJjc6X2rjRm1HjJlZTqbPVROxasN1paPR1w3hprhIWnrhxJXJSVr7eX8vqKnZ4tsOI5qC6B9AEcHnQVO0uqsVgIvR3bjjPINew8zCqlWpMzDq55B25ZRu0Ao9LtAuu3nP/lVfDW1bDX+5ZWr21fAq+eC+/8CA5ththUOO83cOd3MO4GsLqXaJfgpqSUiIiIiJiuorYRux3KScAe6Zgl1NFcKdfOeyf7JohxN4A12khQ7PzSN2v6WGq80VpX32ijtqHjYcxHnPOkEt3fxc7dSqmCg5XUNdpIio2kjy8SSHtXAVCUcDJRVgtDewZgYHa3fpDax9ixa9cKr5dzzsHydfteSWUdX283KlguGOZ9Ugqgb0YCD144FIDHP91MgbstaxUHjd3cAM59mDV7jMcNykxyVfKFqgFZXaBS6ng9R/L+wMc4r+4PLIs5CywRsPVTePUc+MtlsOurwMdUtBr+fDG8eYlxHJUAZ/4C7l4Hp90FUX6eNycBpaSUiIiIiJjOOVQ7NsqKJckxL6ejuVKuIefDfRNEYiYMv9I4futq2Pof36zrQwnRViIdg6TdmSvlrJRKi/cgKeXYRa+jFrQNzta97BSvhpwDUFcBxUb73uyfXsNHd5xOTiDa9+BYtZQPWvic1V2+rpT6T/5Bmmx2hvVKpne6716Xayf25sxB3alvtDH73e9paOog0Wm3w79nQ0M19BoHQy9i9e4jAIzpk+qzuMzirJTacbiq49cijHyWf5ACew7rJz0Ns1bCqGuNWX3bFsPr58PrFxr/PrzdrbEjxZvhb9fCK+fAjqUQEQUTboG71sI5v4JY79uEJfgoKSUiIiIipnMmpVLioox5IdB+pVRDDZQYu0b5pH3PadrvoO8ZUF8Bb18NX83z/y9iHrBYLMda+NyYK1Va5Rxy7nml1K7S9ncgW1d0FPDNkHOK1gB2SO1NRs/eDOmR7P2a7vLhsHNnIm2vj2dKuVr3fFQl5WSxWPjD5SNIiYtifVEZzy8ubP8Bq1+HzR8byYILnwKLhTW7HEmp3qE9TwogOyWWhGirsQPf4TDZga8DVXWNfLnNGKB/Xl4WZAyAS1+AO9fA2J8aX+td/zWqlv401UjW+/p74tHdxk6A808x3l+WCBh5DdyxGi74g/EHAwlbSkqJiIiIiOnKa40ES3JsFCQ7fvEubycpVZxvDOiNz4DELN8FEpcGP/oHjPmxsf7CX8JHdwXVjnzOFilnIq89zqRUNw+SUn0clTj7jta2Wy2yfq/vhpw750nRa5z3a3nKOey8eCNUFnu1lLN9b395LfWNvqm0OVJVzwpH0uACH8yTOl6PlFh+e6mR2H3+i0K+33O09TsWb4JPHzCOpzwC2aOob7SxzvE+CPUh59ByB76C4q7Rwre84BD1jTb6pMe7Br0DkNYXLvqjUaU04RaIjIW938JbV8LLZ8Omj8Hm5Xu88hB8ch88N9bYCdBugyE/gJ99BZfNh7Q+3q0vIUFJKRERERExnceVUgccrXs9hvl+G/DIaLjoWZj6O8ACa/5szFapDo5dqZxzpdyplCqp9HymVPfEGGIiI2iy2dl3tPXZSPWNNjYdMGYJ+aZSyjFUOWe892t5KiEDejhaQHcs82qp7okxxEZFYLfT5mvnqUWO1r2hPZPpl5HgkzWPd/HIbH4woidNNjs/f3cttQ1NLe/QUAvvzYTGWuh/Lky6DYD8/eXUNdpIi4/yW2yBNtDRwrfV3RlbIW5RvpGInTI0C0tr30tTcoxqpbvWwSm3Q1Q87F8L71wLL54OG/4BtqYTH9ee2jJYPAf+ONLYWKKp3kgO37gYfvhXyBzi/ROTkKGklIiIiIiYrmVSyo2ZUq55Uj5s3WvOYoFTb4f/+RtEJ8LO5fDqFDjcQXtTAKS6KqU6nilVWlUHeNa+FxFhcc1GamvY+daDFdQ32kiOjXS1+3Wa3X6sUsqMpBQ0myv1hVfLWCwWVwufr+ZKLdhgJGcvGNbDJ+u15beXDCMzKYbth6p4/NPNLW9c9GujkiyhO1z2IkQYv0audrTuje2T1npCIwQN7EKVUk02O4s3HwSMpFS7krKM9ua718PpsyE6yXhPvHc9vDAJvv8bNDW2v0ZDDXz5RyMZtewJaKiC7DFw3Yfwk39BzljfPDEJKUpKiYiIiIjpPKqUstthmyN5kD3av4ENPh9uWAgpuVC6zdiRygezh7zhbN9zq1LK1b4X49E5+nSQlFrvGHI+PCfF+2TE0V1QdciYXdPDR0PrPeVMSm1b4vW8nNw03+3AV1bdwJeFhwG4YITvW/eaS0uI5vErRgDw+pc7WeE4L1s+gW9fNo4vfbHFfB/XPKkwaN1zcg47L+wCO/Ct3nWEI9UNpMRFMb6vm1/DhAyY8jD8fD2c/YAxfPzwVvjgFnh+HKx5ExqPS5g3NcCq1+DZ0bDoIag5AhmD4aq/wE2Lof9k3z85CRlKSomIiIiI6codSalkd5JSB9ZBSYEx42TgVP8H12OY8YtTzgSj7eQvM6Bgkf/P24YU56BzN2ZKHan2fNA5HNtFbndJB0mpXqkerduqvauMy54jICrW+/U6o/cpRlKsfC+UbvdqKV/uwLdo00EamuwMzkqif/fEjh/gpcmDM7lmYm8A7vn791QccgygBpg0CwZOcd3XbrezapfR0jo2DIacOzlnSm0/XBn2O/B9tsmokjpnSCaRVg9TA3FpcPb9cPcGOPdhiE+HIzvgX3fAc2Pg21eMyqj178G8CfDxz43v6Sm5cMkLcNtXkHex79uvJeQoKSUiIiIipitrkZRq1r7XWtXK+veMy4FTITZAu7QlZsJPPjKG8NqbYPUbgTlvK1Lj3J8pVeqYKZXmYVLKOey8zUqpvc6klC+GnDuSUma17gFEJ0DuROPYy0q4XGf7ng924HPuuuePAedtefCCofTuFs/+smr2v/FTqCmFHiOM6phm9pXVcrC8jsgICyNyUgMWn7/1So0jPtpKQ5OdXW0kZcPFZ/lutu61JzYZzphttPVN/Z2x8UTZHlhwD/y+D7w/00j0xmfA+Y8bO+qNvhYirD56FhLqlJQSERERkVbNmzePvLw8xo/3f7Kg1fa9hmqjMqk5m80YrAsw/Aq/x9VCVCyMvs44ProrsOduJjXevZlSdrvd1b7naaWUc05Ua7+U1zU2sflAOeCjIedm7rzXnGuu1BKvlnHuwLfniHfte+W1DSwvOATABcP9O0+quYSYSJ6+aiS3RH7MoKrVNFrj4IrXILJlC6hznlRedjJx0eGTYIiIaLYDXxgPO992qJLth6uIslo4c1CG9wtGJxhz+O76HqY/Acm9oKkOYpJh8oPGLn6Tbj3hfSSipJSIiIiItGrWrFnk5+ezcuVKv5+rRVIqOt6YUwInDjvf843RYhWdFJjWveM5tyg/sjvw53ZwJqU6qpSqrm+irtFoP+rWyaTUntJq7MdVq209UElDk53U+ChyHPOTOq2xzmjHBMgJkqTUjmWe7ybWjHPQ+V4vK6U+d7TuDchMdO0IFyjjInfwi6i/A/Co7accju19wn1c86TCqHXPaUAXGHa+yFElNemkdJJio3y3cFQcTLwZ7lwLP/qHkaQ6616ICex7WEKHklIiIiIiYrryWmPXJucQ7zbnSm1wtO4N/YHxy0+gpTp+Oa8rM4b1msDdQeeljiqpmMgI4j2sZHHORaqoazzhPOuKjgJG657XQ84PrDe2g4/PgLS+3q3lrezRRlVH7VHY/32nl3G+diVV9VTVdbAbWTv+vc5IyAaydQ+Augp4fyZWexPLok7jtZrTeeAf609ITq7ZfWznvXDjHHa+NYwrpZyte1PzvGjda09kNAw4F+K7+Wd9CRtKSomIiIiI6VyDzmMjjStaS0o1NcLGD43jYQFu3XOKToCE7sbxUXOqpVLjjaqnsg4GnTdv3fM0eRQbZSUr2Wiz2XVcxc+GIl/Ok3JU4eWMM3/gsTUS+p5uHHvRwpcSF+V6H+/tZAtfRW0Dy0xo3QPg3/cYA6tTcsm69iWirBEsyj/Ie6v3uu5SXd/Ixn1GC2c4JqUGOiqlCsO0Uqqkso7VjqTiud7MkxLxASWlRERERMR0rva9+HYqpXYsherDxi5PJ50V4AibcVZLHTFnrlRqnHOmVEeVUnUAdEv0rHXPqU+3BODEYefrHEPOfTpPyuzWPSdXC99Sr5ZxVkvt7eQOfIs3F1PfaOOk7gkMDmTr3rp3Yd3fwBIBl7/K4L65zD5vMAD/+1G+6/ms21tGk81Oz5RYslNNqFj0M2el1PZDVTSG4Q58izcXY7fDydnJYfn1k9CipJSIiIiImMput7ecKQUtd+Bz2vC+cZl3KVh9OAPFU6mOuVImDTt3zpSqrGtsd8v60irjNe2W0LnBws7Eyu6SKtd1tQ1NbDlgtDQNC5ed95pzJqV2fWVsZ99J3u7A59p1b1hP71sk3VW6Az6ebRyfdR/0ngTAzWeexNg+aVTWNXLP37/HZrO7hpyPCcMqKTB24IuNiqC+yXZCpWA4+GyTD3bdE/ERJaVERERExFRV9U002Yx5Na6kVHK2cVm+z7hsqIVNHxnHgd5173iuYefmJKWSYqNcnW7tVUu5KqXiO5fA65PuSEo1+6V8y4EKGm12uiVE08vbCovKYkdizwLZY7xby1cyBhlVek11xlD9TvJmB76qukaWbHG27gVonlRTA7w/E+oroPcpcMY9rpusERaevmok8dFWvt5eyusrdrqGnI8NwyHncPwOfOHVwlfb0MSyrYcBOM9f86REPKCklIiIiIiYyjlPKjLCQlyUYyD38ZVShYugrtzYZjx3kglRNuOqlDJnppQ1wkJSjDGzqL1h586ZUp2tlHLuwLer5FhSap1jntQwXww5d1ZJdR8CscnereUrFsuxaikv5krldut8pdTizcXUNdromx7P0J4Bat374lEoWm3sejnjFWO+VjN90hN48MKhADz+6Wa+2VEKhG+lFMCgTOO1LwizYecrth2mpqGJnimxnJwdJP/upEtTUkpERERETNW8dc+V6HDNlHIkpdY7dt0bNgMiTP4RNs3c9j1oPuy8vs37lFY6Bp13cqZUa4mVDc55Ur5o3Stytu4FyTwpp36OeWXeJKWc7XudqJT6ZIPRujd9eIBa97Yvhf8+Yxxf9Cyk5rZ6t2sm9OasQd2pb7RRWddITGQEeT3DN6kxIMtRKRVmw84X5RcDRutewFpDRdqhpJSIiIiImOqEeVJwLClVeQBqy2HrQuPzYZcHOLpWNK+UstvNCcHRktdepVSpq1Kqk4POHe17+8trqWtsAo5VSg0PxyHnTs4h+vvWQs2RTi3hbN/bW1qN3YP3SHV9I4s3G0mDCwPRuldVAh/cAthhzI/h5EvbvKvFYuEPV4xw/TsdmZNKdGT4/jrpqpQKo6SUzWbnc+c8KbXuSZAI3+8iIiIiIhISnEmp5OZJqcRMwAK2RljzJjTWQLf+0HOUKTG2kJIDWKChGqoOmRNCXMdJqRIvk1LpCdHER1ux22HvkRpqG5rY6mhlGu5tpZStCYrWGMfBMuTcKTkbMgYDdtixvFNL5DgqpSrqGjvcJbG5JVsOUdtgI7dbnP9bq+x2+Nftxg6XGYPg/N93+JCs5FieuGIESbGRXD62l3/jM9lAR6XUtkOVrpl3oW5dURnFFXUkxkQy6aRuZocjAigpJSIiIiImK28tKWWNgoTuxvE3LxqXw6+AYGg3iYw5NojdpGHnzva9o+0kPI5UO9r3OpmUslgsrrlSu0ur2bS/nCabnYzEaHqmxHZqTZdDW6C+EqITjZlSwcbLuVKxUVa6JxmzvPaUut/C92/nrnuBaN1b+SpsWQDWaLj8TxCd4NbDpp7cg3UPT+Xq8b39G5/JctLijR34Gm0thv2Hss/yjSqpswZ1JybSanI0IgYlpURERETEVK227wEkO9qXyvYYl8NM3nWvuVRz50qlOl6rsuqOZ0p1tlIKjg07311SzXqfDjl3tO5lj4aIIPzl2JWU+qLTS+SmOXfgcy+hUVPfxBeO1r0Lhvm5de/gRlj4oHE85X+h5wiPHt4VZhFZIyz0725US20Nk2Hnn7la9zJNjkTkGCWlRERERMRU5a6kVMsdv1xzpQB6DIfugwIYVQdMHnbuminVRqVUXWMTFXWNgI+SUqXVrPPlkHPXPKkga91z6ns6RERC6XYo3dGpJTzdgW/p1kNU1zfRKzWOEb6Y2dWWhhp4byY01cGA82DSz/x3rhA3MNNIShWGwVypPaXVbD5QgTXCwuTBSkpJ8FBSSkRERERM1WalVPOkVDBVSQGkOlqXTGrfc75Wbc0rOlJlXG+NsJAcG9XqfdzhHHa+u7SaDa4h56mdXs9lr3PnvSBNSsUmQ84E43jb4k4tcWwHPveSUgtcrXs9/FuJtPBBOLQJEjLh0vnB0RIbpAZmGcPOw6FSapGjdW9cnzRX+69IMFBSSkRERERMVV5rVPSckDxpkZQKgl33mgtU+15tOVQdPvH0zplSbQw6L6mqAyAtPpqIiM4nHZzVPlsOVPhuyHltORzabBwH2857zQ04x7jsbFLKsQOfOzOlahuaXLuiTffnrnubPoZVfzKOL3sRErv771xhwFkpVXAw9CulnK1752nXPQkySkqJiIiIiKnarJTqdpJx2fsUSM0NcFQdcLbv+bNSym6HVybDs6PhcGGLm5wzpdpq3yut8m7IuVOfdGP49e7Samx26J4UQ1ZyjFdrsm8NYDeqzRKDuI2ovyMptX0pNLm/g56TJ5VSy7Yeoqq+ieyUWEbnpnp8LreUFRm77QGccjsMONc/5wkjgxyVUqG+A19ZdQPf7CgFlJSS4KOklIiIiIiYqs2k1MmXwvQ/wGUvBT6ojjgrpcr2gq3JP+eoPQolhVBXDh/e2uI8zplSbQ06dyalvJknBdArNa5Fd9dwXw45D9bWPaeeoyCuG9RXHIvZA84qs71HarB1kND4ZMMBAM4f5qdd92xN8MEtUHMEeo6Ecx/2/TnCUG63eKIjI6hrtLk9GywYLdlaTJPNzsDMRFeiWSRYKCklIiIiIqZqaLIBrSSlrFEw8ZZjVUnBJDnbGIRta4CK/f45R3mzdfeuhBXPuj5NcbNSqluid0mp6MgIslPiXJ973boHsHe1cRnsSakIK/SfbBx3ooWvZ0os1ggL9Y02DlXWtXm/usYmPnPM+7lgeI9Ohdqh/z4DO5dDVAJc/hpEaqaQO5rvwFcQwsPOnfOkpqhKSoKQklIiIiIiYqp/3X46Bb+bzoR+3cwOxX0RVkjJMY791cJXsc9xLkey7otH4WA+ACnxxwadt1aF46v2PTi2Ax/g/a5wdvuxqqNeQTxPyqm/o8Wt8HOPHxppjaBnSizQ/g58/y04TEVdI1nJMYzpndapMNu1Z6Xx3gG44A+QMcD35whjg7KMpFSoDjuvb7SxdMshQK17EpyUlBIRERGRVs2bN4+8vDzGj/d/RUuUNYJIa4j9aOrvYefljqRUvzNh0PnQVG+08TU1uCql7HaocAyKb67EkZRK88EuW82TUl5XSh3ZCdWHwRoNPUd4t1YgOCul9n0HVSUeP9yduVIL1hute9OH9fRqKH2rasvg/Zlgb4KTZ8Coa327fhfgHHZeGKKVUt/uKKWirpGMxGhG+WLnTBEfC7H/+UVEREQkUGbNmkV+fj4rV3o+T6dL8Pewc2f7XnI2XPRHiE2F/d/D8qeIibQSH20F4GjNiXOlSisdlVJetu8B9E43EitZyTFkJsd6t1iRo3WvxwiI9HJgeiAkZ0NmHmCH7V94/PCOduCrb7SxKN9ISl3g61337Hb49/8zkqapveEHz4A/5lWFuYGOYecFxaFZKeXcde/cIVm+T3qK+ICSUiIiIiIineGqlNrtn/Wd7XvJ2ZDUAy58yvh82ROwb+2xHfiqT5wr5atB53CsZe+0/hler3VsyHkItO45OXfh29aJpJSzUqqN9r0vtx2mvLaR7kkxjO3j49a97/8G6/8OFitc/ieIS/Xt+l1E80qpjgbWBxu73a55UhL0lJQSEREREemMtL7Gpd/a9xyVUkmOCpphl0PeJWBrhA9/RkacUfXQ2rDzkipjsLYvklKnD8jg33eezpzLhnm9VsjsvNfcAMdcqW2fG9VHHnDuwNdW+96CdcbXePqwHlh9WcVSsg0W3GMcn/0A5E7w3dpdTG/HDny1DTb2Hmm94i1YbdpfQdHRGmKjIjh9gA+SyiJ+oKSUiIiIiEhnpPY2Lv096Dy5l3FpscCFT0N8BhTnM7PxbwAcrW6lfc816Nz7FjmLxcLJ2SnER0d6t1BjHRxYbxyHUqVU71MhMtbYZbF4k0cPba99r6HJxn8cVSzTh/mwda+x3pgjVV8JfU6HM2b7bu0uKNIawUkZCUDoDTt3tu6dPqA7cY52X5Fgo6SUiIiIiEhnONv3youMRICvOQedJzdLWCRkGPOlgIsq/85oSwHlx1VKNdnsruopX1RK+cz+dcaw9viMY69dKIiKhT6nGcfbPNuFz9m+t7+shoYmW4vbvtpWQllNAxmJ0b7defKLOcZg9thUmPGSsVOkeOXYXKnQGnbuTEqdl5dpciQibVNSSkRERESkMxIzITIOsEPZHt+u3VgH1Y7d3pKyW9429Acw4moisPFk1ItUVrSs3jhaXe/qMkuLj/JtXN5o3roXagO3XS18iz16WPekGGIiI7DZYf/R2ha3LVhvtO5NO9mHrXvbvoAvjaQllzwPKTm+WbeLG+SYK1UQQpVSB8pqWbe3DIsFzhmieVISvJSUEhERERHpDIvlWAufr4edVzjmSVljIL6VKprpj1MR1Z3+EfsZXfhci5ucrXup8VFEWoPox/2iVcZlKLXuOfV3JKV2rYAG9+cKWSwWctIcLXzN5ko1NtlYuNHHu+5VHYYPbjGOx14PQy/yzbrCwCxHUiqEKqWcVVKjc1PpnhQCO11KlxVE/0uJiIiIiISYNOcOfD6eK+Uccp7cs/Wqorg0lg19CIAJxe/Czi9dN5U4d96LD6LWPQjNnfecug82Zns11sKuLzu+fzOuYefNduD7enspR6ob6JYQzURftO7Z7fDhbVB5ELoPgWmPer+muDjb90JpBz5nUkq77kmwU1JKRERERKSz/DXsvLzIuDy+da/5XXLO5u3GyURghw9/BnVGFYezUiqo5klVHHRUk1kge4zZ0XjOYoH+k43jQs9a+FqrlFqwwdm6l+WbarZvX4aChUZl3eV/guh479cUlz7d4omyWqhpaKLoaPDvwFdV18iKQqP997yhSkpJcFNSSkRERESks1L9VClV0axSqq1Tx0Xxu8ZrORSRaZx/0a+BZpVSwZSUcrbuZQ6F2GRzY+ms/p2bK+Ucdu7cga+xycbCDUbrnk923asth89/YxxP/S30GOb9mtKCsQOfs4Uv+OdKLS84RH2TjT7p8QxwzMMSCVZKSomIiIiIdJazfc/nlVKOpFRS20mLlPgoKonn8dg7jCtWvQY7llFaaSSl0hODKCm115GU6jXW3Di8cdLZYImAQ5ugrMjth7na9xyVUt/uLKWkqp7U+ChO6Z/ufVxr34L6SqNtb8LN3q8nrXLOldp6MPjnSv0n37Hr3tAsLKG2qYB0OUpKiYiIiIh0lqtSyteDzvcZl8ltt++lxhlJpyX1eTD2p8aV377MkeogrJRqvvNeqIrvdqz10INqqeMrpT5Zb1RJTc3LIsrb1j2bzWjdA5hwU+jtahhCBmYac6UKgjwp1dhk44vNxYDmSUloUFJKRERERKSznJVSVcVQX93+fT1R7kZSKj4KgLKaeuzjZhpXbv0PVeWlAHRLCJIdt2xNsO874ziUk1IA/c8xLrd97vZDcrsZM6UOV9ZRVdfIJxt8uOve9sVQug1ikmHED71fT9o0yFEpVRjk7Xtrdh/lSHUDKXFRjOuTZnY4Ih0yNSm1bNkyLrroIrKzs7FYLHz44Yctbrfb7TzyyCNkZ2cTFxfH2WefzcaNG80JVkRERETkeLGpRkIAfFst5Wrf6zgp1dBkpzptqNG+1VTHgJIvAEgPlkqpQ5uN9rLoRGMXu1A2wDFXavsSI9nmhpS4KJJiIgH44LsiDlfWkRwbyan9M7yP5xtHldSoayFGs4P8ydm+VxDkO/A5d907Z0imb4boi/iZqe/SqqoqRo4cyfPPP9/q7X/4wx94+umnef7551m5ciU9evTgvPPOo6IiuLPTIiIiItJFWCy+H3Zus7k16Dwuykq045fOstpGGHYFAOMqjNaytGBJSjlb93qNgQirubF4q9c4iEmBmiOwb61bD7FYLOQ45kq9unw7AFNP7kF0pJe/ipVuh4L/GMcTbvJuLelQn/QEoqwWquub2FcWnDvw2e12FjnmSU3RrnsSIkxNSk2fPp05c+YwY8aME26z2+3MnTuXBx98kBkzZjBs2DD+/Oc/U11dzVtvvWVCtCIiIiIirfD1sPPqErA1GMeJPdq8m8ViIcVRLXW0ugGGGT9Tj2r8ngzKgqdSKhzmSTlZI+GkM41jT1r40owWvp0lRovnBcPb/rq6beWfADsMmALp/b1fT9oVZY2gX0YCELxzpbYdqmLH4SqirRGcNbi72eGIuCVo6/l27NjBgQMHmDp1quu6mJgYzjrrLFasWNHm4+rq6igvL2/xISIiIiLiN76ulCp37OyWkAmR7SeWUuMcSamaekjvj73XWKzYuMD6dfAMOt+72rjsNc7cOHzFOVeq0JO5UvGu46SYSE4b4GXrXn0VfPcX43jCLd6tJW5zDTsP0rlSzta9Sf3TSXS0jIoEu6BNSh04YAwAzMpqWXaYlZXluq01jz32GCkpKa6P3Nxcv8YpIiIiIl1cmo+TUm607jm5hp1XG5VVdUMuA+AS64rgSErVlhkzpQBywiUp5ZgrtXel8fzc4KyUAjgvL4uYSC/bGNe9a5w7rZ9RKSUB4ZwrtTVIK6U+c7TunTc00+RIRNwXtEkpJ8tx25ra7fYTrmvugQceoKyszPWxZ88ef4coIiIiIl1Zam/j0lfte86d99oZcu6U4qqUMpJSh3pfiM1uYWxEAbGVQfBzcNEawG5UkyWGyS/KaX0gfQDYm2DHMrce0rxSarq3u+7Z7fCtY8D5hJsgIuh/pQsbxyqlgi8pdbiyjtW7jwBwruZJSQgJ2u9gPXoYfdbHV0UVFxefUD3VXExMDMnJyS0+RERERET8xtftex5USqXEGdVQRx2VUsWk8ZUtz7hxw/u+iccbRauMy3CpknJyVku52cI3MDMJiwWSYyM5Y6CXrXs7/wvF+RAVb+y6JwEzyFEpVXiwArs9uHbgW7y5GLsdhvVKJjs1ruMHiASJoE1K9evXjx49erBo0SLXdfX19SxdupRTTz3VxMhERERERJpxVkrVlkHNUe/Xc1ZKJXdcKeVs3ztaUw9AaVU9/7Q5flZeHwRJqb3OpFQYDDlvzjlXatvnRuVSB3qnx/PydeN4c+ZEYqO8bN1zVkmN/CHEpXq3lnikT3oCkREWquqb2FdWa3Y4LXymXfckRJmalKqsrGTt2rWsXbsWMIabr127lt27d2OxWLj77rt59NFH+eCDD9iwYQM//elPiY+P55prrjEzbBERERGRY2ISId5R/XJ0t/fredC+5xx07pwpVVpVx6dN42kkEoo3wsF87+PpLLs9vHbea67v6RARZXy9S7e79ZDz8rIYlZvq3XnL9sLmfxvH42/ybi3xWHRkBH1dO/AFz7Dz2oYmlhccBpSUktBjalJq1apVjB49mtGjRwMwe/ZsRo8ezUMPPQTAvffey913381tt93GuHHjKCoq4j//+Q9JSUlmhi0iIiIi0pIvh513YtC5s32vpKqechLZnDTJuMOG97yPp7OO7ITqErBGQ4/h5sXhDzGJ0NvxGnuwC5/XVv7JmGXV9wzIygvcecXF2cJXEETDzr8sPExNQxPZKbGcnK3xNRJaTE1KnX322djt9hM+3njjDcAYcv7II4+wf/9+amtrWbp0KcOGDTMzZBEJJna7sSWyOx9B1vcvYUDvPxFpzpfDzssdSSl3Bp3HO2ZKOdv3Ko3Lgsxpxh3Wv2fe9yBn616PERAZY04M/jTAMVdqW4CSUg21sObPxvHEWwJzTjnBANew8+CplPpsk6N1Ly+r3U3BRIJRpNkBiIh0it0Or02DPd+4d//cSXDDp6D/qMUX9P4TkeP5ath5XSXUlRnH7syUcrbv1TQCUFptJKVKep0Du39vxLN3FeR62T5nt3v+PSxcW/ec+p8Dnz0CO5ZDYz1ERvv3fBv/YVSeJefAoOn+PZe0yVUpFSQ78Nlsdj7bVAyodU9Ck5JSIhKaGqrdTwgA7PnaeEx0gv9ikq5D7z8ROZ6zfc/bSiln6150IsR23IbjbN8rqz426BwgOTkFhlwI6981Wvi8SUrt/hreugoiIo3kW1rf4z76GIkS63G/WriSUmG2855T1nBI6A5Vh4z/E/qd4b9z2e3wzUvG8fiZJ77WEjADHZVShQcrsdvtplcmrSsq41BFHYkxkUw8qZupsYh0hr6biUjou6cQouNbv62+Gp4cENh4pGvR+09EoFmllJeDzl1DzjueJwWQGuds33MOOjeSUukJ0TD8CkdS6h8w9XedS2TUlsH7NxmXYFTq7Ftz4v0sVkjNNZJUzsTVgfXGbeGalIqIMKql1r1jtPD5Mym1dxXsXwvWGBjzE/+dRzrULyMBa4SFirpGDpTX0jMlztR4FuUfAOCsQd2JifRyZ0cREygpJSKhLzpeFShiHr3/RASMJAwY7XKdaXVz8mDIOUCKo1Kqur6JusYmShwzpbolREOvcyCuG1QVw87l0H+y5/Es+AWU7Tae3xWvGUmzI7uMIebOj6O7oKn+2OfNxWccS9iFo/7nGkmpws9hyiP+O8+3jiqp4VdAQrr/ziMdio6MoG96PNsOVbH1YKXpSanP8o3WvfPy1LonoUlJKRERERERb6XkABajVbfqMCR279w65UXGZXIvt+6eFBNJhAVsdiiraXBVSnVLiAZrFORdAqtfNwaee5qUWv+ekXCxRMCMV6DXWOPjeDYbVB5omag6stNIYI38n/Cep+d8TQ+sg8piSMz0/TkqDsLGD43jCTf7fn3x2MDMJLYdqqLgYAVnDerkv3Uf2F1SzZaDFVgjLJw92Lw4RLxh6u57IiIiIhIYH3/8MYMHD2bgwIG8+uqrZocTfiJjjrXceTPs3LXznnuVUhERFpIdw84PlNVS09AEOJJSAMOvNC43fWTs3uauo3vg49nG8Zm/gNwJ7QVhDGXvcyqMugYm/xJmvAw//RhGX+v+OUNRYib0GG4cb1/in3OsfgNsDZAzAbJH+ecc4hHXsPOD5g47d+66N75vGqnxfh60L+InSkqJiIiIhLnGxkZmz57N4sWLWbNmDY8//jilpaVmhxV+XMPOd3Z+DVf7Xsc77zk5d+DbfqgKgGhrBIkxjoaI3qcYVVd1ZVC4yL0FbU3wwa3GY3qNgzPvdTuWLqn/ucZl4ee+X7uxHla9ZhxPvMX360unDMgyhp0XFFeYGseifCMppV33JJQpKSUiIiIS5r799ltOPvlkevXqRVJSEhdccAELFy40O6zw44th5x4OOgdIcVRIbD9sJKW6JUQf2xEsIgJOvsw4Xv+eewuueA52/ReiEoyKJ+301r4BjqTUtsVGK6Mvbf7IaI1MzIKhF/t2bek0V6VUsbEDnxnKqhv4dqfxxwXNk5JQpqSUiKfsdqivcu/DpP+kpJN8/bXVe0VEfGTZsmVcdNFFZGdnY7FY+PDDD0+4zwsvvEC/fv2IjY1l7NixLF++3HXbvn376NXr2IyinJwcioqKAhF61+KslPKmfc+rSimjlcjVuufkbOHb+inUlre/2P7vYfEc43j67yG9v9txdFm5E40EXlUxHNzg27W/edm4HHs9RKo9K1j0y0ggwgIVtY0cLK8zJYYlW4tpstkZlJVIn3RtuCKhS3/2EPGE3Q6vTYM937h3/9xJcMOn4T3gM1z4+mur94qI+FBVVRUjR47k+uuv5/LLLz/h9nfeeYe7776bF154gdNOO42XXnqJ6dOnk5+fT+/evVv9S76lne83dXV11NUd+0WrvLyDRIYYUnsbl0c6mZRqaoRKox3Ho6SUYwe+HY5KqfTE45IXPUdC+gAoKYQtC2DkD1tfqL4a3r/JmF805Acw+jqPn0KXFBkDfU+HgoVGtVTPEb5Zd//3sOdriIiEcdf7Zk3xiZhIK33TE9h+uIqC4gp6pMQGPAa17km4UKWUiCcaqt1PMoDxg0RDtf/iEd/x9ddW7xUR8aHp06czZ84cZsyY0ertTz/9NDNnzuTGG29k6NChzJ07l9zcXObPnw9Ar169WlRG7d27l549224Pe+yxx0hJSXF95Obm+vYJhatULyulKg+C3QYWKyS4v5OWs1JqR7P2vRYslmPVUuv/3vZCix6Cw1sgsQdc9Kz+UOIJVwufD+dKfeuoksq7FJJ6+G5d8YmBjha+rSYMO69vtLF0yyEApqh1T0KcKqVEOuueQoiOb/22+mp4ckBg4xHf8fXXVu8VEfGj+vp6Vq9ezf3339/i+qlTp7JixQoAJkyYwIYNGygqKiI5OZkFCxbw0EMPtbnmAw88wOzZs12fl5eXKzHlDlf73h5jWHiE1bPHO1v3knp49FjnTKnqemPnvbTWduEadgUseQy2fQFVhyEho+XtW/8DK18xji+dBwnpnsXe1TmHne/+2mjLj/aynaq69NgMsAk3e7eW+MXAzCQWbjxIoQnDzr/ZUUJFXSMZiTGMykkN+PlFfElJKZHOio73/gcOCU6+/trqvSIifnT48GGamprIymr51/KsrCwOHDgAQGRkJE899RSTJ0/GZrNx7733kp7edtIhJiaGmJgYv8YdlpJ7Ga1WtgaoOAApvTp+THPOIecetO7BsUopp/TjK6UAMgZAz1Gwfy3kfwjjbzx2W+Uh+Ocs43jirTBgikfnF4zZW6m9jSH3O/8Lg6Z5t96aP0NjrdF6mTvBNzGKTzkrpQpMqJT6zNW6l0lEhCoaJbSpfU9EREQkDBw/I8put7e47uKLL2br1q0UFhZy882qvPCLCCuk5BjHnWnh68TOe3BsppRTt+NnSjkNv8K4bL4Ln90OH91pDOnuPhSmPOLRucXBYoH+5xjH2xZ7t5atCVb+yTiecIvaKIPUwMwkALYerAjoDnx2u53PNhUDmicl4UFJKREREZEQlpGRgdVqdVVFORUXF59QPSUB4M2w84pOVkrFu1EpBXDyDMACu78yWgwBVr9hDD+3RsPlr0BUnGcxyzHOFr5CL+dKbfkEyvZAXDcY1vocOTHfSd2NHfjKaxs5VBG4Hfg27a+g6GgNsVERnDYgo+MHiAQ5JaVEREREQlh0dDRjx45l0aJFLa5ftGgRp556qklRdWHeDDsvd86U8qxSKuW49r1uCW20Xqb0gj6nGccb3ofDhbDwl8bn5z4EPYZ7dF45zklnGUPqSwqMNr7O+vYl43LsT5QkDGKxUVb6pBvjGQI57Ny5697pA7oTF+3h3DqRIKSZUhL+7Hb3dzWLileJtJin3o33qd6jIl1SZWUlhYWFrs937NjB2rVr6datG71792b27Nlcd911jBs3jlNOOYWXX36Z3bt3c+utt3p13nnz5jFv3jyampq8fQpdh2vYeSeSEs5B58mezaJKiWtZGXXC7nvNDb8cdv0X1r1rzJZqqIZ+Z8KkWR4GKyeITYGc8caOuoWfw7jrPV+jeDPsWAaWCBg30/cxik8NyExkx+EqCoorOH1gYKqWPttkJKWmatc9CRNKSkl4s9vhtWmw5xv37p87CW74VL/0iznc2YVP71GRLmnVqlVMnjzZ9blzZ7yf/OQnvPHGG1x99dWUlJTwm9/8hv379zNs2DAWLFhAnz59vDrvrFmzmDVrFuXl5aSkpHi1VpeR2te47Ez7XnmRcZns3UypNtv3APIuhQW/gOKNxuexqXDpixChBgqf6H+OkZTatrhzSalvXzYuB18AqdrxMtgNykpkUf7BgFVK7S+rYX1RGRYLTB6SGZBzivibklIS3hqq3U9IgfFDREO1dkqTwImKNxJNe7527/56j4p0SWeffXaHg3Rvu+02brvttgBFJG1K62T7nt3uk/a9CMuJ7XwtxHczZh8VLDQ+v2iu57sEStsGnAtLHoXtS6GpEawe/LpVWwbf/804nniLf+ITn3IOOy8srgjI+T53DDgfnZtK9yTtkCrhQUkp6TruKYTo+NZvq692r0pFxNcsFqPyqaMWU71HRURCg3PQeXkRNDWAtZ0EUXO1R6Gxxjj2cNB5lDWCxJhIKusaSYuP7niL+Ak3GUmpsT+Fky/z6FzSgezREJcGNUegaDX0nuj+Y9e+BQ1Vxi6Ifc/wX4ziMwOzEgFjptTxO576g3Oe1BS17kkYUVJKuo7oeFWXSHCyWPTeFBEJF4lZEBkLjbXGDmrdTnLvcc4qqbi0Tg23TomLorKusf15Uk4Dz4N7dxjnEt+KsMJJZ8PGD2Db5+4npWy2Y617E25Sm36I6N89EYsFymoaOFRZR2ZSrN/OVVnXyFfbSgDNk5LwouZxERERERFfsVggra9xfLjA/ceV7zMukzyrknJyzpVyKykFRhufEh/+0f8c43LbYvcfs+1zKN0OMSkw4mr/xCU+FxtlpXc3oxOj0M9zpZZvPUR9k42+6fH0757o13OJBJKSUiIiIiLSqnnz5pGXl8f48ePNDiW0ZI82LotWu/+YCkdSysMh507OpFR6optJKfGf/ucal0WrjTY+dzirpEZfCzFKOIQS51yprQf9O1dqkWPXvSlDs/zeJigSSEpKiYiIiEirZs2aRX5+PitXrjQ7lNCSM8643OvB69bJIedOqXFGMsrtSinxn5Re0H0I2G2wfUnH9y/ZBgWLAAuMv9Hf0YmPOedKFRT7r1KqscnG4s3GkHPNk5Jwo6SUiIiIiIgv5Tgqy/auNmYFucNVKdW59j3nTlxZfpxpIx5wVksVft7xfVf+CbAbs77S+/s1LPG9QQFISq3edYSj1Q2kxkcxro9mwUl40aBzkePVt7MLWnu3BQu7veOd3Jyi4jVPIhwF+3vA1/F5sp67gvV1qW8kjlpq0DbQIkEt82SIjIO6MigpgO6DO35MuXdJqRtO60dctJWrJ+R26vHiY/3Pga/nwbYvjO/vbf2fUlcJ3/2fcTzh5sDFJz7jbN8rOFjhtx34PnO07p0zOJNIq+pKJLwoKSVyvCcHmB1B59nt8No02PONe/fPnQQ3fKrEVDgJ9veAr+PzdD13BenrEg9sioWVtkFgnxaY2ETEc9ZI6DUGdn1ptPC5lZRytu91LinVOz2e+84f0qnHih/0ORWsMVC+Fw5vbfs9sO4dI3nZrf+x6ioJKc4d+I5UN1BSVU9Gom//cGS321mU75gnpdY9CUNKs4qAURWRO8n9++dOMh4TbBqqPfvlfM/Xvq8wEXMF+3vA1/F5up6vzutrHj6P8RFb9W9XJNh5OlfKy0HnEmSi443EFLTdwme3w7evGMcTboII/WoWiuKireSmGb8X+GPY+bZDlewsqSbaGsGZg7r7fH0Rs6lSSgSMaogbPg3ulidP3VNo/EDUmvrq0K4IE/cE+3vA1/G1t567gvx1qa4qJ/6PqoSQwJk3bx7z5s2jqanJ7FBCj2uu1KqO79tQC9UlxnEnK6UkCA04F7Z/Ads+h1NuO/H2ncvh0CaISoBR1wQ+PvGZgZmJ7C6tprC4klP7Z/h07UX5xoDzU/qnkxijX98l/OhdLeJksUB0gtlR+E50fHg9H/FcsL8HfB1fsD9fd7X3POobAxuLdHmzZs1i1qxZlJeXk5KSYnY4oaWXo1KqOB/qKiAmqe37Vjha96wxEN/N/7FJYPQ/x7jc+aWReIw6bgj9Ny8ZlyN/CLH69xXKBmYl8fnmYgoO+n7YuXOelFr3JFypRlRERERExNeSe0JKLthtsO+79u/rTEol9wz+SmxxX2YeJPWExhrY/VXL247ugS0LjGMNOA95AzONHfh83b53uLKONbuPADBlaKZP1xYJFkpKiYiIiIj4g7tzpZw776l1L7xYLMeqpbYdN1dq1Z+MhGW/MyFTbdmhblCWUQlZWOzbSqnFm4qx22FYr2R6psT5dG2RYKGklIiIiIiIP7g7V6p5pZSEF1dS6otj1zXUwOo/G8cTbgl8TOJz/TONtvuSqnpKKut8tu4iR+veeUN7+GxNkWCjmVISOHZ74HeLqg+B3ak8eV1CYcC6+F9772sz3/PuvJc7E1+wPl8RkY64klIrje+Rbf0f7qyUSlalVNg5aTJggYMboOIAJPWADf+AmlKjvXPwdLMjFB+Ij44kJy2OvUdqKCiuJD0xxus1axuaWF5wCIApeWrdk/ClpJQEht0Or03zz9btoczT1yV3krFLoBJTXZvZu8O1xp//xoPx+YqIuKPHCIiIgqpDcHQXpPVt/X5q3wtfCemQPcqYK7ZtMYz8H/jWMeB8/EyIsJoanvjOoKwkIyl1sIJJJ6V7vd6XhYepbbCRnRJLXs9kH0QoEpzUvieB0VBtbkIqd5JRZRRsPH1d9nwd+GozCQ5R8cb72F2Bfs97+l7uKL5gf74iIu6IioWeI4zj9lr41L4X3vqfa1wWfg57voX930NkLIz5iblxiU85h50X+GiuVPNd9yz6g7SEMVVKSeDdU2hseR5IodD21t7rUl+tapGuzmIxquRCodXTnX/jHcUXSs9XJIzNmzePefPm0dTUZHYooStnPBStNlr4hl/R+n3KHUkpVUqFpwHnwvInYfsXxnBzgGFXQHw3c+MSnxroGHZecND7pJTNZuezTcUAnJeX5fV6IsFMSSkJvOh4iE4wO4rgo9dFOmKxhMZ7xFfv5VB5viJhbNasWcyaNYvy8nJSUlLMDic05YyHb15sewc+mw0qNFMqrOWMh+gkqC6Bjf8wrpt4s7kxic8dq5Sq8Hqt7/ce5VBFHYkxkUzs530roEgwU/ueiIiIiIi/5IwzLvevg4baE2+vPgy2RsBiDMGW8GONgn5nHvs8dxL0HGlePOIXAxxJqcOV9ZRW1Xu1lrN176zB3YmO1K/sEt70DhcRERER8ZfUPpDQHWwNcGDdibc7h5wndDeSFxKeBpxz7HjCTebFIX6TEBNJr9Q4AAoOelct9Vm+o3VvqFr3JPwpKSUiIiIi4i8Wi9G+Ba238GnIedcwcJox3Dy1Dwy92OxoxE8GZnk/7Hx3STVbDlZgjbAweXCmr0ITCVpKSomIiIiI+JOzha+1pJSzUiq5V+DikcBLzYWfrYCZiyAy2uxoxE8GOYadF3qRlFrkaN2b0LcbKfGqnpTwp0Hn4gN24qiD+irafEvVu7mDVjhq77kHw+viqxhCYfezYP9aSHjT+0+k63JVSq068TZnUipJlVJhL72/2RGInznnSm31on3vs3wjKTVFu+5JF6GklHjHbue96P9lXMRWeNLsYILUkwPMjqB9voovdxLc8GlwJ6aC/Wsh4U3vP5GuK3s0WCKgbA+U72/Zqqf2PZGw4ayU6mz7Xll1A9/uLAVgylC17knXoPY98U5DtZGQclfuJKOiJtxFxRvP1V2Bfl08jc8de76GhiCs9gj2r4WEN73/RAQgJgky84zjouOqpVyVUtmBjUlEfM5ZKXWooo6j1Z7vwPfFlmKabHYGZSXSJz3B1+GJBCVVSonPVN+1mfiE5PbvFAotXr5gsRhVQ+4maQL9ungaX3vqq4O7AiTYvxYS3vT+kxA3b9485s2bR1NTk9mhhL6ccXBwgzFXauhFx653VUopKSUS6hJjIslOiWVfWS0FxZWM79vNo8c750mdp9Y96UKUlBLfiYqHaGX0XSyW4H49gj0+X+pKz1WCj95/EsJmzZrFrFmzKC8vJyUlxexwQlvOeFj9xolzpVyDzpWUEgkHA7OS2FdWy9aDFR4lpeobbSzdcgiAKUOVlJKuQ+17IiIiIiL+5hx2XrQGmhqN47pKqCs3jjXoXCQsDHS08BUc9Gyu1Dc7SqisayQjMYaROal+iEwkOCkpJSIiIiLib+kDISYFGmugeKNxnbN1LzoRYjsYgSAiIcE57LzQw2Hni5y77g3NJCJCrfzSdSgpJSIiIiLibxERkDPWON670rhU655I2BmQZVRKbT1Y4fZj7HY7n+VrnpR0TZopJRJq6tsZmNzebYHgzvnDaZBzMH8tfMlu73hQdzg9X6eu8vUVkcDJGQ/bFhtzpcbf2GznPbXuiYQLZ/tecUUdZdUNpMRHdfiY/P3l7CurJTYqgtMGZPg7RJGgoqSUSKgJ5p3u3Iktd5KxG1k4JKaC+WvhK3Y7vDYN9nxjdiSB1xW+viISWM65Us5KqQpVSomEm6TYKHqmxLK/rJaC4grGuTHs/LP8YgDOGNid2Cirv0MUCSpq3xMJBVHxRjLHXbmTjMcEgqex7fm646qbYBbMXwt/aKj2LCEV6s+3q319RSSwejna90oKoboUyh0zpVQpJRJWBjiHnbs5V2rRpgMAnKdd96QLUqWUSCiwWIzqIneTOYFskXM3tvrq8Kg8Ceavhb/dUwjRHSRgQv35duWvr4j4X3w3SB9gJKWKVh8bdK5KKZGwMigrieUFh93agW9/WQ0bisqxWOCcoZkBiE4kuCgpJRIqLBaITjA7itYFc2z+0NWer1N0fNd43l316ysigZEz3khK7V0J5UXGdUpKiYSVga5KqY6HnX+2yWjdG9M7jYzEGL/GJRKM1L4nIiIiIhIoOeOMy70r1b4nEqYGZiUBuFUp5dx1b4pa96SLUlJKRERERCRQXMPOV0GVUSGhSimR8OKcKXWgvJaymoY271dZ18hX20oAOC9PrXvSNSkpJSIiIiKtmjdvHnl5eYwfP97sUMJH5skQGQd15WC3QUQkJHQ3OyoR8aGUuCiyko1WvMJ2hp0v23qI+iYb/TIS6N89MVDhiQQVJaVEREREpFWzZs0iPz+flStXmh1K+LBGQq8xxz5P7AER2gJeJNwMcrXwtT1X6ljrXiYWbZwiXZSSUiIiXV19NdRXtfHh5i50IiLiPudcKYBkzZMSCUcDXMPOW6+UamyysXiL0cKreVLSlWn3PRGRru7JAWZHICLSteQ0a4fUkHORsOSqlGojKbV61xGOVjeQGh/F2D5pgQxNJKioUkpEpCuKiofcSe7fP3eS8RgREfFer+aVUhpyLhKOBjorpdpo31vkaN07Z0gmkVb9Wi5dlyqlRES6IosFbvgUGtxsz4uKNx4jIiLeS+4JKblQtkdJKZEwNTDTqJTaX1ZLRW0DSbFRrtvsdjuLNhlJqfPUuiddnFKyIiJdlcUC0QnufSghJSLiWwOnGpfZY9q/n4iEpJT4KDKTjB34jm/h23aokl0l1URbIzhjkHbflK5NSSkRERERkUCb/ge4ewP0O8PsSETETwZmGS18hQdbJqUW5RsDzk/pn05ijJqXpGtTUkpEREREJNCskZCaa3YUIuJHzha+guKWc6UW5R8A4Lw8te6JKCklIiIiIiIi4mPOSqmtzSqlDlXU8d2eowCcOzTTjLBEgopqBUUk+NjtHQ/grndzQLeIiIiIiAkGZRmVUoXNZkp9sbkYux2G90qhZ0qcWaGJBA0lpUQkuNjt8No02PON2ZGIiIiIiHTagO5GpVTR0Roq6xpJjIl07bo3RbvuiQBq3xORYNNQ7VlCKncSRMX7Lx4RERERkU5IS4gmI9HYga+wuJKa+iaWFxwCYEqeWvdEQJVSIhLM7imE6A4STlHxYLEEJh4REREREQ8MykrkcGUdWw9WcLiijtoGG71S48jrmWx2aCJBQUkpEQle0fEQnWB2FCIiXda8efOYN28eTU1NZociIhKSBmYmsmJbCYXFlZTXNAAwZWgmFv1RVQRQ+56IiIiItGHWrFnk5+ezcuVKs0MREQlJAx3DzrccqOCzTcUATMnTPCkRJ1VKiYiIiIiIiPjBwExj2PmXhYdptNlJiolkYr90k6MSCR4hUSn1wgsv0K9fP2JjYxk7dizLly83OyQRERERERGRdg1yVEo12uwAnDW4O9GRIfFruEhABP2/hnfeeYe7776bBx98kO+++44zzjiD6dOns3v3brNDExEREREREWmTsQNftOvz89S6J9JC0LfvPf3008ycOZMbb7wRgLlz57Jw4ULmz5/PY489ZmpsdpuNmuoKU2MwW01VBR3sjSZyovrqzt0mIi41VV37/x+AuPgkLBFB//c1ERHp4gZkJnK4shRrhIWzB2WaHY5IUAnqpFR9fT2rV6/m/vvvb3H91KlTWbFiRauPqauro66uzvV5eXm53+Krqa4g/snefls/FCghJZ3y5ACzIxAJeenzTzY7BNNV37Ob+MQUs8MQERFp16CsJL7eXsqEvt1IiY8yOxyRoBLUf148fPgwTU1NZGW1LHHMysriwIEDrT7mscceIyUlxfWRm5sbiFC7vE1RecTFJ5kdhgSzqHjIneT+/XMnGY8REZe4+CQ2ReWZHYaIiIh44IfjezO8Vwp3nKs/zIocL6grpZwsFkuLz+12+wnXOT3wwAPMnj3b9Xl5ebnfElNx8UlU36PZVgBD1EIhHbFY4IZPocHN9ryoeOMxIuJiiYhgyANfUt3FW8ed9McQEREJBXnZyXx0x+lmhyESlII6KZWRkYHVaj2hKqq4uPiE6imnmJgYYmJiAhEelogItQ2IeMJigegEs6MQCWn6v0dEREREwkVQl7ZER0czduxYFi1a1OL6RYsWceqpp5oUlYiIiIiIiIiIeCuoK6UAZs+ezXXXXce4ceM45ZRTePnll9m9eze33nqr2aGJiIiIiIiIiEgnBX1S6uqrr6akpITf/OY37N+/n2HDhrFgwQL69OljdmgiIiIiIiIiItJJQZ+UArjtttu47bbbzA5DRERERERERER8JKhnSomIiIiIiIiISHhSUkpERERERERERAJOSSkREREREREREQk4JaVERERERERERCTglJQSEREREREREZGAU1JKREREREREREQCTkkpEREREREREREJOCWlRERERKRV8+bNIy8vj/Hjx5sdioiIiIQhJaVEREREpFWzZs0iPz+flStXmh2KiIiIhCElpUREREREREREJOAizQ7A3+x2OwDl5eUmRyIiIiKhwPkzg/NnCNHPUyIiIuIZd3+eCvukVEVFBQC5ubkmRyIiIiKhpKKigpSUFLPDCAr6eUpEREQ6o6Ofpyz2MP8zoM1mY9++fSQlJWGxWMwOx2fKy8vJzc1lz549JCcnmx1O0NDr0jq9LifSa9I6vS6t0+vSunB9Xex2OxUVFWRnZxMRoUkHcOznqXPOOYdVq1YF5Jzjx4/36ywrX6/vq/U6u064/nsMN/5+XwejUHrOwRJrIOPoit9rvVlD32vd5+7PU2FfKRUREUFOTo7ZYfhNcnKy/jG0Qq9L6/S6nEivSev0urROr0vrwvF1UYVUS86fpyIjIwP2tbZarX49l6/X99V63q4Tjv8ew4m/39fBKJSec7DEGsg4uuL3Wl+soe+17nHn5yn9+U9ERERE3DJr1qywOZev1/fVeoF8jSXwuuLXN5Sec7DEqu+1/l0vWL7OYgj79r1wVV5eTkpKCmVlZcrQNqPXpXV6XU6k16R1el1ap9eldXpdRIKH/j2KiPifvtf6niqlQlRMTAwPP/wwMTExZocSVPS6tE6vy4n0mrROr0vr9Lq0Tq+LSPDQv0cREf/T91rfU6WUiIiIiIiIiIgEnCqlREREREREREQk4JSUEhERERERERGRgFNSSkREREREREREAk5JKRERERERERERCTglpUREREREREREJOCUlAoT//73v5k4cSJxcXFkZGQwY8YMs0MKGnV1dYwaNQqLxcLatWvNDsdUO3fuZObMmfTr14+4uDj69+/Pww8/TH19vdmhBdwLL7xAv379iI2NZezYsSxfvtzskEz12GOPMX78eJKSksjMzOTSSy9ly5YtZocVVB577DEsFgt333232aGYrqioiB/96Eekp6cTHx/PqFGjWL16tdlhiUg7Pv74YwYPHszAgQN59dVXzQ5HRCQsXXbZZaSlpXHFFVeYHUrIUFIqDLz//vtcd911XH/99Xz//fd8+eWXXHPNNWaHFTTuvfdesrOzzQ4jKGzevBmbzcZLL73Exo0beeaZZ3jxxRf55S9/aXZoAfXOO+9w99138+CDD/Ldd99xxhlnMH36dHbv3m12aKZZunQps2bN4uuvv2bRokU0NjYydepUqqqqzA4tKKxcuZKXX36ZESNGmB2K6Y4cOcJpp51GVFQUn3zyCfn5+Tz11FOkpqaaHZqItKGxsZHZs2ezePFi1qxZw+OPP05paanZYYmIhJ0777yTN9980+wwQorFbrfbzQ5COq+xsZG+ffvyv//7v8ycOdPscILOJ598wuzZs3n//fc5+eST+e677xg1apTZYQWVJ554gvnz57N9+3azQwmYiRMnMmbMGObPn++6bujQoVx66aU89thjJkYWPA4dOkRmZiZLly7lzDPPNDscU1VWVjJmzBheeOEF5syZw6hRo5g7d67ZYZnm/vvv58svv+zy1YUioWTFihU88cQTfPDBBwDcddddTJo0if/5n/8xOTIRkfCzZMkSnn/+ed577z2zQwkJqpQKcWvWrKGoqIiIiAhGjx5Nz549mT59Ohs3bjQ7NNMdPHiQm266ib/85S/Ex8ebHU7QKisro1u3bmaHETD19fWsXr2aqVOntrh+6tSprFixwqSogk9ZWRlAl3pvtGXWrFlceOGFTJkyxexQgsK//vUvxo0bx5VXXklmZiajR4/mlVdeMTsskbC2bNkyLrroIrKzs7FYLHz44Ycn3Ke9tvR9+/bRq1cv1+c5OTkUFRUFInQRkZDh7fda6RwlpUKcs7rlkUce4Ve/+hUff/wxaWlpnHXWWV26LNtut/PTn/6UW2+9lXHjxpkdTtDatm0bzz33HLfeeqvZoQTM4cOHaWpqIisrq8X1WVlZHDhwwKSogovdbmf27NmcfvrpDBs2zOxwTPW3v/2NNWvWqIKume3btzN//nwGDhzIwoULufXWW1WqLuJnVVVVjBw5kueff77V2ztqS2+tMcJisfg1ZhGRUOPt91rpHCWlgtQjjzyCxWJp92PVqlXYbDYAHnzwQS6//HLGjh3L66+/jsVi4e9//7vJz8L33H1dnnvuOcrLy3nggQfMDjkg3H1dmtu3bx/nn38+V155JTfeeKNJkZvn+B/G7Xa7fkB3uP3221m3bh1vv/222aGYas+ePdx111383//9H7GxsWaHEzRsNhtjxozh0UcfZfTo0dxyyy3cdNNNLdphRcS3pk+fzpw5c9rcyObpp59m5syZ3HjjjQwdOpS5c+eSm5vr+nfZq1evFpVRe/fupWfPngGJXUQkVHj7vVY6J9LsAKR1t99+Oz/84Q/bvU/fvn2pqKgAIC8vz3V9TEwMJ510UlhmbN19XebMmcPXX39NTExMi9vGjRvHtddey5///Gd/hhlw7r4uTvv27WPy5MmccsopvPzyy36OLrhkZGRgtVpPqIoqLi4+oXqqK7rjjjv417/+xbJly8jJyTE7HFOtXr2a4uJixo4d67quqamJZcuW8fzzz1NXV4fVajUxQnP07Nmzxf85YMxke//9902KSKRrc7al33///S2ub96WPmHCBDZs2EBRURHJycksWLCAhx56yIxwRURCkjvfa6VzlJQKUhkZGWRkZHR4v7FjxxITE8OWLVs4/fTTAWhoaGDnzp306dPH32EGnLuvy7PPPsucOXNcn+/bt49p06bxzjvvMHHiRH+GaAp3XxcwtnKfPHmyq6ouIqJrFUxGR0czduxYFi1axGWXXea6ftGiRVxyySUmRmYuu93OHXfcwQcffMCSJUvo16+f2SGZ7txzz2X9+vUtrrv++usZMmQI9913X5dMSAGcdtppbNmypcV1W7duDcv/c0RCgTtt6ZGRkTz11FNMnjwZm83GvffeS3p6uhnhioiEJHdHgEybNo01a9ZQVVVFTk4OH3zwAePHjw90uCFFSakQl5yczK233srDDz9Mbm4uffr04YknngDgyiuvNDk68/Tu3bvF54mJiQD079+/S1d/7Nu3j7PPPpvevXvz5JNPcujQIddtPXr0MDGywJo9ezbXXXcd48aNc1WL7d69u0vN1jrerFmzeOutt/jnP/9JUlKS6z/XlJQU4uLiTI7OHElJSSfM1EpISCA9Pb1Lz9r6+c9/zqmnnsqjjz7KVVddxbfffsvLL7/c5aouRYJNR23pF198MRdffHGgwxIRCSsdfa9duHBhoEMKeUpKhYEnnniCyMhIrrvuOmpqapg4cSKLFy8mLS3N7NAkyPznP/+hsLCQwsLCE5JzrQ1BDVdXX301JSUl/OY3v2H//v0MGzaMBQsWdOlKD2cv/Nlnn93i+tdff52f/vSngQ9Igtb48eP54IMPeOCBB/jNb35Dv379mDt3Ltdee63ZoYl0SWpLFxHxP32v9R+LvSv9JioiIiIiEsIsFgsffPABl156qeu6iRMnMnbsWF544QXXdXl5eVxyySXaPVREpBP0vTZwVCklIiIiIhLEKisrKSwsdH2+Y8cO1q5dS7du3ejdu7fa0kVEfEDfa82hSikRERERkSC2ZMkSJk+efML1P/nJT3jjjTcAeOGFF/jDH/7gakt/5plnOPPMMwMcqYhI6NL3WnMoKSUiIiIiIiIiIgHXtfaCFxERERERERGRoKCklIiIiIiIiIiIBJySUiIiIiIiIiIiEnBKSomIiIiIiIiISMApKSUiQadv377MnTu3zdt37tyJxWJh7dq1Xp9r8eLFDBkyBJvN1uZ9HnnkEUaNGuX1uZpbv349OTk5VFVV+XRdERERERGRUKGklIiEnNzcXNc2rE533XUXY8eOJSYmxqME0r333suDDz5IRERgvx0OHz6cCRMm8MwzzwT0vCIiIiIiIsFCSSkRCTlWq5UePXoQGRnpus5ut3PDDTdw9dVXu73OihUrKCgo4Morr/RHmB26/vrrmT9/Pk1NTaacX0RERERExExKSolIQJ199tncfvvt3H777aSmppKens6vfvUr7HZ7i/tVV1dzww03kJSURO/evXn55Zddt7XWvvfss88ya9YsTjrpJLdj+dvf/sbUqVOJjY1tcf3vf/97srKySEpKYubMmdTW1p7w2Ndff52hQ4cSGxvLkCFDeOGFF1rcvmLFCkaNGkVsbCzjxo3jww8/PCHmadOmUVJSwtKlS92OWUREREREJFwoKSUiAffnP/+ZyMhIvvnmG5599lmeeeYZXn311Rb3eeqppxg3bhzfffcdt912Gz/72c/YvHmzT+NYtmwZ48aNa3Hdu+++y8MPP8zvfvc7Vq1aRc+ePU9IOL3yyis8+OCD/O53v2PTpk08+uij/PrXv+bPf/4zABUVFVx00UUMHz6cNWvW8Nvf/pb77rvvhPNHR0czcuRIli9f7tPnJSIiIiIiEgqUlBKRgMvNzeWZZ55h8ODBXHvttdxxxx0nzFa64IILuO222xgwYAD33XcfGRkZLFmyxKdx7Ny5k+zs7BbXzZ07lxtuuIEbb7yRwYMHM2fOHPLy8lrc57e//S1PPfUUM2bMoF+/fsyYMYOf//znvPTSSwD89a9/xWKx8Morr5CXl8f06dP5xS9+0WoMvXr1YufOnT59XiIiIiLuWLJkCRaLhaNHj3r0uJKSEjIzM9v9Gaaza7enrq6O3r17s3r1ap+tKSLmUlJKRAJu0qRJWCwW1+ennHIKBQUFLWYrjRgxwnVssVjo0aMHxcXFPo2jpqbmhNa9TZs2ccopp7S4rvnnhw4dYs+ePcycOZPExETXx5w5c9i2bRsAW7ZsYcSIES3WnjBhQqsxxMXFUV1d7aunJCIiIl3UsmXLuOiii8jOzsZisfDhhx+2ed+zzz6bF198sdPneuyxx7jooovo27dvp9fojJiYGO65555WK9BFJDQpKSUiQSkqKqrF5xaLBZvN5tNzZGRkcOTIEY8e44zhlVdeYe3ata6PDRs28PXXXwPG0PXmSTfnda0pLS2le/funYheRERE5JiqqipGjhzJ888/3+79SktLWbFiBRdddFGnzlNTU8Of/vQnbrzxxk493lvXXnsty5cvZ9OmTaacX0R8S0kpEQk4Z/Km+ecDBw7EarUGNI7Ro0eTn5/f4rqhQ4e2Gp9TVlYWvXr1Yvv27QwYMKDFR79+/QAYMmQI69ato66uzvW4VatWtRrDhg0bGD16tK+ekoiIiHRR06dPZ86cOcyYMaPd+/373/9m5MiR9OrV64TbampquPDCC5k0aRKlpaWtPv6TTz4hMjLyhMryBQsWMGjQIOLi4pg8eXKrrX0rVqzgzDPPJC4ujtzcXO68806qqqpct+/fv58LL7yQuLg4+vXrx1tvvUXfvn2ZO3eu6z7p6emceuqpvP322+0+TxEJDUpKiUjA7dmzh9mzZ7NlyxbefvttnnvuOe666y6v1iwsLGTt2rUcOHCAmpoaVwVTfX19m4+ZNm0a//3vf1tcd9ddd/Haa6/x2muvsXXrVh5++GE2btzY4j6PPPIIjz32GH/84x/ZunUr69ev5/XXX+fpp58G4JprrsFms3HzzTezadMmFi5cyJNPPgnQooJq586dFBUVMWXKFK+eu4iIiIi7/vWvf3HJJZeccH1ZWRlTp06lvr6ezz//nG7durX6+NY2itmzZw8zZszgggsuYO3atdx4443cf//9Le6zfv16pk2bxowZM1i3bh3vvPMO//3vf7n99ttd9/nxj3/Mvn37WLJkCe+//z4vv/xyq+MbJkyYoI1iRMJEpNkBiEjX8+Mf/5iamhomTJiA1Wrljjvu4Oabb/ZqzRtvvJGlS5e6PndWH+3YsaPNeQc/+tGPuO+++9iyZQuDBw8G4Oqrr2bbtm3cd9991NbWcvnll/Ozn/2MhQsXtjhXfHw8TzzxBPfeey8JCQkMHz6cu+++G4Dk5GQ++ugjfvaznzFq1CiGDx/OQw89xDXXXNNiztTbb7/N1KlT6dOnj1fPXURERMQddXV1LFy4kIceeqjF9QcPHuTqq6+mf//+vP3220RHR7e5RmsbxcyfP5+TTjqJZ555BovFwuDBg1m/fj2PP/646z5PPPEE11xzjevnpYEDB/Lss89y1llnMX/+fHbu3Mlnn33GypUrXUmvV199lYEDB54QgzaKEQkfSkqJSMBFRUUxd+5c5s+f3+rtrf2QsXbtWtdx3759T5jR1Jmd+dLS0rj99tt5+umnXTvnAfzyl7/kl7/8ZYv7Nv+hCoxqqGuuuabNtU899VS+//571+d//etfiYqKonfv3oDxQ+H8+fNVei4iIiIBs3jxYtLT0xk+fHiL66dMmcL48eN59913Oxyn0NZGMa1tZNPc6tWrKSws5K9//avrOrvdjs1mY8eOHWzdupXIyEjGjBnjun3AgAGkpaWdEIM2ihEJH0pKiUiX9uCDDzJv3jyampp8OtPqzTff5KSTTqJXr158//333HfffVx11VXExcUBsGvXLh588EFOO+00n51TREREpD1tte5deOGFvP/+++Tn55+QsDpeaxvFtLWhS3M2m41bbrmFO++884TbevfuzZYtW1p9XGtra6MYkfChpJSIdGkpKSknVEX5woEDB3jooYc4cOAAPXv25Morr+R3v/ud6/ZBgwYxaNAgn59XREREpDV2u52PPvqIN99884Tbfv/735OYmMi5557LkiVLyMvLa3Od0aNH83//938trsvLy+PDDz9scd3xG8eMGTOGjRs3MmDAgFbXHTJkCI2NjXz33XeMHTsWMGaGHj169IT7aqMYkfChQeciElBLlixpsYNKuLr33nvZuXMntbW17Nixg2eeeYb4+HizwxIREZEwVVlZ6droBYy5mmvXrmX37t2A0T5XVVXFmWee2erjn3zySa699lrOOeccNm/e3OZ5pk2bxsaNG1tUS916661s27bNtZHNW2+9xRtvvNHicffddx9fffUVs2bNYu3atRQUFPCvf/2LO+64AzCSUlOmTOHmm2/m22+/5bvvvuPmm28mLi6uRVsgwPLly5k6daqnL5GIBCElpURERERERELcqlWrGD16tKuCaPbs2YwePdo11Pyf//wnF154IZGRbTfLPPPMM1x11VWcc845bN26tdX7DB8+nHHjxvHuu++6ruvduzfvv/8+H330ESNHjuTFF1/k0UcfbfG4ESNGsHTpUgoKCjjjjDMYPXo0v/71r+nZs6frPm+++SZZWVmceeaZXHbZZdx0000kJSW1mGH11VdfUVZWxhVXXOH5iyQiQcdid6cBWERERERERELWiBEj+NWvfsVVV13l9VoLFizgnnvuYcOGDURE+K/OYe/eveTm5vLZZ59x7rnnAnDllVcyevRov4xfEJHA00wpERERERGRMFZfX8/ll1/O9OnTfbLeBRdcQEFBAUVFReTm5vpkTTB2B6ysrGT48OHs37+fe++9l759+7paDuvq6hg5ciQ///nPfXZOETGXKqVERERERETEdAsXLuT//b//x/bt20lKSuLUU09l7ty59OnTx+zQRMRPlJQSEREREREREZGA06BzEREREREREREJOCWlREREREREREQk4JSUEhERERERERGRgFNSSkREREREREREAk5JKRERERERERERCTglpUREREREREREJOCUlBIRERERERERkYBTUkpERERERERERALu/wPXLLHeu3/K0gAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Compare the power spectrum of all stars vs. detected stars only\n", - "phi1_bin_edges = np.linspace(-7, 7, 50)\n", - "fig, ax = plot_power_spectrum(injected_data_full, label='All data', phi1_bin_edges=phi1_bin_edges)\n", - "fig, ax = plot_power_spectrum(injected_data_full[injected_data_full['flag_observed']], fig=fig, ax=ax, label='Detected data', phi1_bin_edges=phi1_bin_edges)\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "6acd2421", - "metadata": {}, - "source": [ - "## 5) Conclusion\n", - "\n", - "Streamobs provides a method for converting data mocks into realistic observed data for a given survey, with minimal computational cost. This can be easily achieved with just a few commands, helping to bridge the gap between simulations and observational challenges in the field of stellar streams.\n", - "\n", - "\n", - "You can find more informations in the [full documentation](https://lsstdesc.github.io/streamobs/)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "streamsim_dev", - "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": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 27f8490f1c1e7be76c229f967b1a59774a5af113 Mon Sep 17 00:00:00 2001 From: MatthieuPe Date: Thu, 18 Jun 2026 15:57:31 +0200 Subject: [PATCH 19/54] black + isort --- streamobs/columns.py | 8 ++++++-- streamobs/observed.py | 2 +- tests/test_observed.py | 12 +++++------- tests/test_plotting.py | 4 +++- 4 files changed, 15 insertions(+), 11 deletions(-) diff --git a/streamobs/columns.py b/streamobs/columns.py index be3eb96..0529ffb 100644 --- a/streamobs/columns.py +++ b/streamobs/columns.py @@ -31,7 +31,7 @@ def true_col(band, survey_namespace=None): survey_name = survey_namespace.split("_")[0] else: survey_name = None - + return f"{survey_name}_{band}_true" if survey_name else f"{band}_true" @@ -52,4 +52,8 @@ def flag_col(survey_namespace=None): def perfect_flag_col(survey_namespace=None): """Column holding the perfect star/galaxy-separation flag (band-independent).""" - return f"{survey_namespace}_flag_perfect_galstarsep" if survey_namespace else "flag_perfect_galstarsep" + return ( + f"{survey_namespace}_flag_perfect_galstarsep" + if survey_namespace + else "flag_perfect_galstarsep" + ) diff --git a/streamobs/observed.py b/streamobs/observed.py index 9b7511f..86d04bd 100644 --- a/streamobs/observed.py +++ b/streamobs/observed.py @@ -183,7 +183,7 @@ def _load_survey(spec, **kwargs): if isinstance(spec, dict): return Survey.load(**{**kwargs, **spec}) raise ValueError( - 'Each survey spec must be a Survey, a name string, or a ' + "Each survey spec must be a Survey, a name string, or a " '{"survey": ..., "release": ...} dict.' ) diff --git a/tests/test_observed.py b/tests/test_observed.py index 45d2198..f1e478f 100644 --- a/tests/test_observed.py +++ b/tests/test_observed.py @@ -135,9 +135,7 @@ def test_injection_partialinput( self, mock_injector, stream_catalog, stream_config_with_distance, verbose ): """Test injection with a catalog that has some missing columns.""" - data_without_mag = stream_catalog.drop( - columns=["lsst_g_true", "lsst_r_true"] - ) + data_without_mag = stream_catalog.drop(columns=["lsst_g_true", "lsst_r_true"]) injected_catalog = mock_injector.inject( data_without_mag, perfect_galstarsep=True, @@ -243,7 +241,9 @@ def compare_gc_frames(gc1, gc2): class TestCompleteDataAndAPI: """complete_data (single + multi survey) and the unified bands/survey API.""" - def test_complete_data_single_survey(self, mock_injector, stream_config_with_distance): + def test_complete_data_single_survey( + self, mock_injector, stream_config_with_distance + ): """complete_data fills ra/dec + true mags and preserves existing columns.""" df = pd.DataFrame({"phi1": [-3.0, 0.0, 3.0], "phi2": [0.0, 0.0, 0.0]}) out = mock_injector.complete_data( @@ -277,11 +277,9 @@ def test_complete_data_multisurvey( "lsst_r_true", ]: assert col in out.columns, f"missing {col}" - + # Verify that we have a single column for each true magnitude, not one per survey. assert out[["lsst_g_true", "lsst_r_true"]].notna().all().all() - - def test_bands_list_rejected_for_multisurvey(self, mock_multisurvey_injector): """A plain list of bands is ambiguous for a multi-survey injector.""" diff --git a/tests/test_plotting.py b/tests/test_plotting.py index 3c07228..989e3db 100644 --- a/tests/test_plotting.py +++ b/tests/test_plotting.py @@ -19,7 +19,9 @@ @pytest.mark.observed class TestPlotInject: - def test_plot_inject_namespaced_columns(self, mock_injector, stream_catalog, verbose): + def test_plot_inject_namespaced_columns( + self, mock_injector, stream_catalog, verbose + ): """plot_inject consumes the namespaced injector output without error.""" cat = mock_injector.inject(stream_catalog, bands=["g", "r"], verbose=verbose) # mock_injector is LSST/yr4 -> namespace lsst_yr4; columns are lsst_yr4_*. From 3566fd06b73c75ef0e50867039082d36d213db95 Mon Sep 17 00:00:00 2001 From: MatthieuPe Date: Thu, 18 Jun 2026 16:24:06 +0200 Subject: [PATCH 20/54] complete documentation about lsst --- .../_static/lsst_dc2/LSST_efficiencies.png | Bin 0 -> 111090 bytes docs/source/_static/lsst_dc2/LSST_errors.png | Bin 0 -> 61881 bytes .../_static/lsst_dc2/depth_maps_yr1.png | Bin 0 -> 127410 bytes .../_static/lsst_dc2/depth_maps_yr4.png | Bin 0 -> 121020 bytes docs/source/surveys/LSST.md | 126 +++++++++++++++++- 5 files changed, 122 insertions(+), 4 deletions(-) create mode 100644 docs/source/_static/lsst_dc2/LSST_efficiencies.png create mode 100644 docs/source/_static/lsst_dc2/LSST_errors.png create mode 100644 docs/source/_static/lsst_dc2/depth_maps_yr1.png create mode 100644 docs/source/_static/lsst_dc2/depth_maps_yr4.png diff --git a/docs/source/_static/lsst_dc2/LSST_efficiencies.png b/docs/source/_static/lsst_dc2/LSST_efficiencies.png new file mode 100644 index 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z!9EoZMgIPA8uiU&t-)hWkyvYlU_Qd ze=)deHOy^iijEXpQm)QOkI=*WolN+WT5A7;@Pr^@WHHspXgx{hbrhf@Jbnne2y z$Rulcyj#^QuHUiyO+y5L)mE}g8k8&UI>`NQY!+(MeMHrvtydy;#rE85;x}B|7?z_q zK*Zix9SIfN4> Date: Thu, 18 Jun 2026 16:25:19 +0200 Subject: [PATCH 21/54] complete documentation about lsst --- docs/source/surveys/LSST.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/surveys/LSST.md b/docs/source/surveys/LSST.md index 210430c..bf6a27e 100644 --- a/docs/source/surveys/LSST.md +++ b/docs/source/surveys/LSST.md @@ -74,7 +74,7 @@ Magnitude limits are obtained from [RubinSim](https://rubin-sim.lsst.io/) and pr ### Extinction coefficients -Dust extinction is modeled using the Schlegel, Finkbeiner & Davis (1998) reddening maps. +Dust extinction is modeled using the Schlegel et. all (1998) reddening maps. The adopted extinction coefficients are From a21a695949ed3c689ad6a71e6b2ad1e4f8a16319 Mon Sep 17 00:00:00 2001 From: MatthieuPe Date: Thu, 18 Jun 2026 16:26:11 +0200 Subject: [PATCH 22/54] complete documentation about lsst --- docs/source/surveys/LSST.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/surveys/LSST.md b/docs/source/surveys/LSST.md index bf6a27e..9f40007 100644 --- a/docs/source/surveys/LSST.md +++ b/docs/source/surveys/LSST.md @@ -7,6 +7,7 @@ Current estimation of LSST performances are done using DC2 simulations (expected performances for LSST year 5), and extrapolated for year 1 to 5. ## LSST DC2 Survey Files +More information about the LSST simulations can be found in Pélissier et. all (2026). This page is the **data sheet** for the `lsst / dc2` release: the survey-specific numbers, products, and figures. For **how** these products are derived (matched truth catalogs, completeness estimation, photometric-error calibration, depth-map construction, and analysis selections), see the survey-agnostic :doc:`selection_function_methodology`. From dd1b3ce7cbb918516a1760883ee21ff1a5b31692 Mon Sep 17 00:00:00 2001 From: MatthieuPe Date: Thu, 18 Jun 2026 16:29:03 +0200 Subject: [PATCH 23/54] fix paths --- docs/source/surveys/LSST.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/source/surveys/LSST.md b/docs/source/surveys/LSST.md index 9f40007..74c2f01 100644 --- a/docs/source/surveys/LSST.md +++ b/docs/source/surveys/LSST.md @@ -39,7 +39,7 @@ $$ for which morphological star-galaxy separation becomes challenging near the survey magnitude limit. -![Detection, classification and galaxy misclassification efficiencies](_static/lsst_dc2/LSST_efficiencies.png) +![Detection, classification and galaxy misclassification efficiencies](../_static/lsst_dc2/LSST_efficiencies.png) *Detection efficiency, stellar classification efficiency, combined stellar efficiency, and galaxy contamination efficiency as a function of distance to the local magnitude limit.* @@ -55,7 +55,7 @@ $$ The photometric scatter increases rapidly near the magnitude limit and approaches a systematic floor of approximately 0.005 mag for bright sources. -![LSST photometric error model](_static/lsst_dc2/LSST_errors.png) +![LSST photometric error model](../_static/lsst_dc2/LSST_errors.png) *Photometric uncertainty as a function of distance to the local magnitude limit. An analytic approximation not used in StreamObs is overlaid on the DC2-derived @@ -68,8 +68,8 @@ Depth maps describe the spatial variation of the LSST 5σ limiting magnitude acr Magnitude limits are obtained from [RubinSim](https://rubin-sim.lsst.io/) and propagated to `StreamObs` as `HEALPix` maps. Survey systematics are modeled through spatial variations in these limiting magnitudes, which drive both photometric uncertainties and selection functions. -![LSST depth maps after 1 year of observation](_static/lsst_dc2/depth_maps_yr1.png) -![LSST depth maps after 4 years of observation](_static/lsst_dc2/depth_maps_yr4.png) +![LSST depth maps after 1 year of observation](../_static/lsst_dc2/depth_maps_yr1.png) +![LSST depth maps after 4 years of observation](../_static/lsst_dc2/depth_maps_yr4.png) *LSST 5σ limiting magnitude maps in the g and r bands for Year 1 and Year 4 survey configurations.* @@ -95,7 +95,7 @@ and are applied consistently when generating observed magnitudes. ### Using the survey in streamobs -Configured by `config/surveys/lsst_dc2.yaml`, data in `data/surveys/lsst_dc2/`: +Configured by `config/surveys/lsst_{releases}.yaml`, data in `data/surveys/lsst_{releases}/`: ```python from streamobs.surveys import SurveyFactory From 641c1fd6c9661afc211b1840b6f10e45cdec2924 Mon Sep 17 00:00:00 2001 From: MatthieuPe Date: Thu, 18 Jun 2026 16:31:47 +0200 Subject: [PATCH 24/54] fix typos --- docs/source/surveys/LSST.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/source/surveys/LSST.md b/docs/source/surveys/LSST.md index 74c2f01..8994557 100644 --- a/docs/source/surveys/LSST.md +++ b/docs/source/surveys/LSST.md @@ -9,8 +9,6 @@ performances for LSST year 5), and extrapolated for year 1 to 5. ## LSST DC2 Survey Files More information about the LSST simulations can be found in Pélissier et. all (2026). -This page is the **data sheet** for the `lsst / dc2` release: the survey-specific numbers, products, and figures. For **how** these products are derived (matched truth catalogs, completeness estimation, photometric-error calibration, depth-map construction, and analysis selections), see the survey-agnostic :doc:`selection_function_methodology`. - ### The simulated survey All quantities are measured from the LSST Dark Energy Science Collaboration Data Challenge 2 (DC2) simulations, a realistic realization of the expected Rubin LSST survey performance based on five years of observations. DC2 contains both truth and measured catalogs, enabling direct characterization of survey selection effects and photometric performance. @@ -33,7 +31,7 @@ Compact galaxies can be incorrectly classified as stars, producing an important The galaxy contamination model is derived from true galaxies with $$ -{\rm size_true} < 0.3\ {\rm arcsec}, +{\rm size\_true} < 0.3\ {\rm arcsec}, $$ for which morphological star-galaxy separation becomes challenging near the survey magnitude limit. From 7a56a3c562461e0e4986d9a7be6c7763ab64ca1e Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 18 Jun 2026 11:15:41 -0700 Subject: [PATCH 25/54] Docs: true-mag columns key on survey name (release-independent), not full namespace --- docs/source/column_convention.md | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/docs/source/column_convention.md b/docs/source/column_convention.md index 068b9c9..98865fc 100644 --- a/docs/source/column_convention.md +++ b/docs/source/column_convention.md @@ -9,17 +9,23 @@ survey. Every column is prefixed with the survey's **namespace**, which is `{name}_{release}` (e.g. `lsst_yr5`, `roman_dc2`), or just `{name}` when the survey was loaded without a release. The namespace is always derived from the -`Survey` itself ({attr}`streamobs.surveys.Survey.namespace`) and includes the -release on **every** column kind, so the same survey at two releases produces -distinct, non-colliding columns. When you construct a multi-survey injector from +`Survey` itself ({attr}`streamobs.surveys.Survey.namespace`). **Observed**, +**error**, and **flag** columns carry the full namespace (release included), so +the same survey at two releases produces distinct, non-colliding observations. +**True-magnitude columns are the exception**: because a star's intrinsic +(noiseless) magnitude does not depend on which survey/release observed it, they +are keyed on the survey **name only** — the release is dropped (e.g. +`roman_F158_true`, shared across `roman_dc2` and `roman_hlwas_*`). When you +construct a multi-survey injector from a `{key: spec}` dict the keys are containers only — they do **not** become the namespace (it is re-derived from each loaded `Survey`). -For a survey with namespace `` and a photometric band ``: +For a survey with namespace `` (= `{name}_{release}`), survey name +``, and a photometric band ``: | Column | Meaning | |---|---| -| `__true` | True (noiseless) apparent magnitude | +| `__true` | True (noiseless) apparent magnitude — keyed on the survey **name only** (release-independent) | | `__obs` | Observed (noisy) magnitude; `"BAD_MAG"` where the noisy flux went negative | | `__err` | Reported photometric error (the *catalog* error; see below) | | `_flag_observed` | Detection flag — `True` if detected **and** classified as a star | @@ -28,7 +34,7 @@ For a survey with namespace `` and a photometric band ``: Plus the shared, un-namespaced sky coordinates `ra`, `dec`. Examples (LSST loaded with `release="yr5"`, Roman with `release="dc2"`): -`lsst_yr5_r_obs`, `lsst_yr5_g_err`, `roman_dc2_F158_true`, `lsst_yr5_flag_observed`. +`lsst_yr5_r_obs`, `lsst_yr5_g_err`, `roman_F158_true` (true mag — name only), `lsst_yr5_flag_observed`. These names are produced by the helpers in `streamobs.columns` (`true_col`, `obs_col`, `err_col`, `flag_col`, `perfect_flag_col`), which take a From 11581541086b41c1095e93ce3a647172ba4f4802 Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 18 Jun 2026 11:22:06 -0700 Subject: [PATCH 26/54] small docs updates --- docs/source/conf.py | 2 +- docs/source/index.rst | 2 +- docs/source/surveys/{roman.md => Roman.md} | 0 3 files changed, 2 insertions(+), 2 deletions(-) rename docs/source/surveys/{roman.md => Roman.md} (100%) diff --git a/docs/source/conf.py b/docs/source/conf.py index dfa7e7b..5403409 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -25,7 +25,7 @@ "sphinx.ext.napoleon", "sphinx.ext.viewcode", "numpydoc", - "myst_parser", + "myst_nb", ] templates_path = ["_templates"] diff --git a/docs/source/index.rst b/docs/source/index.rst index e95d6f5..f865213 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -86,7 +86,7 @@ Documentation Contents multisurvey citation - .. toctree:: +.. toctree:: :maxdepth: 1 :caption: Examples diff --git a/docs/source/surveys/roman.md b/docs/source/surveys/Roman.md similarity index 100% rename from docs/source/surveys/roman.md rename to docs/source/surveys/Roman.md From fcc0905d88cf1b230642d134cb36b94ca0b9d347 Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 18 Jun 2026 11:33:52 -0700 Subject: [PATCH 27/54] Consolidate IsochroneModel.sample/sample_multisurvey into one sample() and fix test_surveys collection error (pytest >=9) --- docs/source/multisurvey.md | 2 +- docs/source/roman_multisurvey_plan.md | 17 ++++-------- streamobs/model.py | 40 ++------------------------- tests/test_model.py | 14 +++++----- tests/test_surveys.py | 1 - 5 files changed, 17 insertions(+), 57 deletions(-) diff --git a/docs/source/multisurvey.md b/docs/source/multisurvey.md index faaae22..e083c0c 100644 --- a/docs/source/multisurvey.md +++ b/docs/source/multisurvey.md @@ -193,7 +193,7 @@ all surveys — the same physical star). You can also go the other way and suppl your own masses: pass a fully-populated `mass` column in the input catalog and the isochrone uses *those* masses instead of drawing fresh ones, so the sampled magnitudes reproduce your simulation's exact stars. At the model level -{meth}`streamobs.model.IsochroneModel.sample_multisurvey` accepts a `masses=` +{meth}`streamobs.model.IsochroneModel.sample` accepts a `masses=` array and returns the masses it used. The mass grid resolution is controlled by `IsochroneModel._MASS_STEPS` (default 4000) and a per-call `mass_steps=` override. diff --git a/docs/source/roman_multisurvey_plan.md b/docs/source/roman_multisurvey_plan.md index 2d6f4dd..471ea5f 100644 --- a/docs/source/roman_multisurvey_plan.md +++ b/docs/source/roman_multisurvey_plan.md @@ -303,26 +303,21 @@ namespaced); `tests/test_observed.py` + `tests/test_model.py` green. plus shared `name`/`age`/`z`/... at top level), building one `ugali` isochrone per survey (`self.isos`, `self.survey_bands`). - ✅ New `sample_masses(...)` draws the initial masses *once* from the primary - isochrone's IMF (exactly `nstars`), and `sample_multisurvey(...)` interpolates + isochrone's IMF (exactly `nstars`), and `sample(...)` interpolates those shared masses into every survey's bands → `{(survey, band): - apparent_mag}` (same physical star, consistent across surveys). The legacy - `sample(nstars, dm)` still returns the `(mag_band_1, mag_band_2)` tuple (the - primary survey's two bands) so existing callers are unchanged. + apparent_mag}` (same physical star, consistent across surveys). - ✅ `_to_ab(band, mag)` converts Roman bands Vega→AB **unconditionally** using the `ROMAN_VEGA_TO_AB` table (no config flag; non-Roman bands pass through). - Applied in the shared `sample_multisurvey` path. See the Vega→AB section above. + Applied in the shared `sample` path. See the Vega→AB section above. - ✅ `StreamModel.sample`/`complete_catalog` derive their magnitude columns from the isochrone via `_iso_mag_columns()` / `_sample_iso_mags()`, which **always** emit `__true` (a single-survey isochrone simply has one survey; `IsochroneModel` tracks `surveys`/`survey_bands` in both config forms). Naming routes through `columns.true_col`. -**Note on the chosen API:** the plan originally named the dict-returning method -`sample` and a tuple `sample_legacy`; to avoid `sample()` changing return *type* -by config (a foot-gun for existing callers), the implementation keeps -`IsochroneModel.sample()` as the `(mag_1, mag_2)` tuple and adds -`sample_multisurvey()` for the dict. `StreamModel` always goes through -`sample_multisurvey()` (a single-survey isochrone is just the one-survey case), +**Note on the API:** `IsochroneModel.sample()` returns +`{(survey, band): apparent_mag}` and the masses used. `StreamModel` always goes +through `sample()` (a single-survey isochrone is just the one-survey case), so the emitted columns are uniformly `__true`. **Validated:** model tests green; a two-isochrone multi-survey config produces diff --git a/streamobs/model.py b/streamobs/model.py index 0ae3884..bf15712 100644 --- a/streamobs/model.py +++ b/streamobs/model.py @@ -203,7 +203,7 @@ def _sample_iso_mags(self, n, dist, masses=None): is given it is used directly instead of an IMF draw, so the sampled magnitudes reproduce those exact stars. """ - mags, masses = self.isochrone.sample_multisurvey(n, dist, masses=masses) + mags, masses = self.isochrone.sample(n, dist, masses=masses) cols = {true_col(band, name): vals for (name, band), vals in mags.items()} cols["mass"] = masses return cols @@ -671,7 +671,7 @@ class IsochroneModel(ConfigurableModel): is built per survey from the *same* stellar population, so a single shared draw of initial masses (:meth:`sample_masses`) is interpolated into every survey's bands — giving the same physical star consistent magnitudes - across surveys. :meth:`sample_multisurvey` returns + across surveys. :meth:`sample` returns ``{(survey, band): apparent_mag}``. Roman bands are always converted from Vega to AB (see @@ -830,7 +830,7 @@ def _add_distance_modulus(abs_mag, distance_modulus): return abs_mag return abs_mag + np.asarray(distance_modulus, dtype=float) - def sample_multisurvey( + def sample( self, nstars, distance_modulus, rng=None, masses=None, **kwargs ): """Sample apparent magnitudes for every ``(survey, band)``. @@ -882,40 +882,6 @@ def sample_multisurvey( out[(name, band_2)] = self._add_distance_modulus(abs_2, distance_modulus) return out, masses - def sample(self, nstars, distance_modulus, rng=None, masses=None, **kwargs): - """Simulate magnitudes in the two bands of the (primary) isochrone. - - Draws *exactly* ``nstars`` stars: a fixed set of initial masses is drawn - once from the isochrone IMF (:meth:`sample_masses`) and interpolated into - the two bands. This differs from the historical behaviour, where - ``nstars`` was converted to a total stellar mass and ``ugali``'s - ``simulate`` returned a random-length IMF realization (count generally - ``!= nstars``). The fixed-count semantics are required so the *same - physical star* can be shared across surveys (see - :meth:`sample_multisurvey`). - - Parameters - ---------- - nstars : int - Number of stars to simulate (returns exactly this many). - distance_modulus : float or array-like - Distance modulus per star (broadcast if scalar). - - Returns - ------- - tuple of numpy.ndarray - ``(mag_band_1, mag_band_2)`` arrays. For a multi-survey isochrone - this returns the primary survey's two bands; use - :meth:`sample_multisurvey` to get every survey's bands. - """ - mags, _ = self.sample_multisurvey( - nstars, distance_modulus, rng=rng, masses=masses, **kwargs - ) - return ( - mags[(self.survey_name, self.band_1)], - mags[(self.survey_name, self.band_2)], - ) - def _dist_to_modulus(self, distance): """ Convert physical distances in pc into distance modulus diff --git a/tests/test_model.py b/tests/test_model.py index 3a95214..3bae578 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -332,25 +332,25 @@ class TestIsochroneMasses: G = "lsst_g_true" R = "lsst_r_true" - def test_sample_multisurvey_returns_and_reuses_masses( + def test_sample_returns_and_reuses_masses( self, stream_config_with_distance ): """Replaying the returned masses reproduces identical magnitudes.""" iso = StreamModel(stream_config_with_distance).isochrone - mags1, masses = iso.sample_multisurvey(50, 16.8, rng=np.random.default_rng(0)) + mags1, masses = iso.sample(50, 16.8, rng=np.random.default_rng(0)) assert masses.shape == (50,) # Supplying those masses (no rng) is deterministic and reproduces mags1. - mags2, masses2 = iso.sample_multisurvey(50, 16.8, masses=masses) + mags2, masses2 = iso.sample(50, 16.8, masses=masses) assert np.allclose(masses, masses2) for key in mags1: assert np.allclose(mags1[key], mags2[key]), f"{key} not reproduced" - def test_sample_multisurvey_masses_length_validated( + def test_sample_masses_length_validated( self, stream_config_with_distance ): iso = StreamModel(stream_config_with_distance).isochrone with pytest.raises(ValueError): - iso.sample_multisurvey(50, 16.8, masses=np.ones(49)) + iso.sample(50, 16.8, masses=np.ones(49)) def test_complete_catalog_exposes_mass_column(self, stream_config_with_distance): """A completed catalog carries the shared `mass` column.""" @@ -364,11 +364,11 @@ def test_input_mass_column_drives_magnitudes(self, stream_config_with_distance): """Providing a `mass` column makes the sampled mags match those masses.""" model = StreamModel(stream_config_with_distance) iso = model.isochrone - _, masses = iso.sample_multisurvey(15, 16.8, rng=np.random.default_rng(3)) + _, masses = iso.sample(15, 16.8, rng=np.random.default_rng(3)) df = pd.DataFrame({"dist": [16.8] * 15, "mass": masses}) out = model.complete_catalog( catalog=df, columns_to_add=[self.G, self.R], verbose=False ) - direct, _ = iso.sample_multisurvey(15, 16.8, masses=masses) + direct, _ = iso.sample(15, 16.8, masses=masses) assert np.allclose(out[self.G].to_numpy(), direct[("lsst_yr4", "g")]) assert np.allclose(out[self.R].to_numpy(), direct[("lsst_yr4", "r")]) diff --git a/tests/test_surveys.py b/tests/test_surveys.py index 682fcbf..9d52126 100644 --- a/tests/test_surveys.py +++ b/tests/test_surveys.py @@ -80,7 +80,6 @@ def _survey_id(entry): # --------------------------------------------------------------------------- -@pytest.mark.surveys @pytest.fixture( scope="module", params=SURVEY_REGISTRY, ids=[_survey_id(e) for e in SURVEY_REGISTRY] ) From 6a2724622c2b871b4a5a575ec65175df54067edd Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 18 Jun 2026 11:38:25 -0700 Subject: [PATCH 28/54] remove plan.md file from the branch --- .gitignore | 2 + docs/source/index.rst | 6 - docs/source/roman_multisurvey_plan.md | 394 -------------------------- 3 files changed, 2 insertions(+), 400 deletions(-) delete mode 100644 docs/source/roman_multisurvey_plan.md diff --git a/.gitignore b/.gitignore index 9e4f7a0..97d6cdd 100644 --- a/.gitignore +++ b/.gitignore @@ -137,3 +137,5 @@ data/others/* /survey_systematics_in_LSST_streams/ /rubin_roman_object_classification/ /lsst_dc2_scratch/ +/artifacts/ +docs/source/roman_multisurvey_plan.md diff --git a/docs/source/index.rst b/docs/source/index.rst index f865213..08f77a9 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -118,12 +118,6 @@ Documentation Contents data modules -.. toctree:: - :maxdepth: 2 - :caption: Design Notes - - roman_multisurvey_plan - Indices and Tables ================== diff --git a/docs/source/roman_multisurvey_plan.md b/docs/source/roman_multisurvey_plan.md deleted file mode 100644 index 471ea5f..0000000 --- a/docs/source/roman_multisurvey_plan.md +++ /dev/null @@ -1,394 +0,0 @@ -# Multi-survey (Roman + Rubin) injection — design & roadmap - -```{warning} -This is a **living design document** for in-progress work, not a description of -shipped behaviour. It records the agreed design, the phased rollout, and — most -importantly — the **behaviour changes that need discussion** before we rely on -them. Sections marked *(future)* are designed but not yet implemented. -``` - -```{important} -**Remove this file before merging the `roman_multisurvey` branch.** It is a -working design/roadmap doc, not user documentation. Before merge, migrate the -durable content — the column convention, the sample/catalog error split, the -multi-survey `StreamInjector` usage, and the Vega→AB handling — into the proper -docs pages (and the API docstrings), then delete `roman_multisurvey_plan.md`. - -**Also remove before merge — useful for now, but not part of the merged package:** - -- `notebooks/multisurvey_phases_demo.ipynb` — the Phases 1–4 walkthrough. It is - kept tracked (with outputs stripped) during the branch's life, but should be - removed before merge and migrated into the rendered docs as an Examples page - (see the separate notebooks→docs migration). Its generator, - `scripts/build_multisurvey_demo_nb.py`, is a local-only helper and is **not** - tracked. -``` - -## Motivation - -`streamobs` was built for **single-survey** stream injection (LSST). We want to -inject mock streams that carry **both Roman and Rubin/LSST photometric columns** -in one catalog, where each band draws its photometric errors and detection -probability from its **own** survey magnitude-limit (maglim) HEALPix maps. - -A working prototype of this idea exists outside the package (the -`rubin_roman_object_classification` proposal code: survey-indexed dictionaries, -`{survey}_{band}` columns, and a "sample masses once, interpolate per survey" -photometry step). The goal here is to bring that capability **into** `streamobs` -properly, reusing the package's existing {class}`~streamobs.surveys.Survey` / -maglim-map machinery (which the prototype lacked), rather than maintaining a -parallel codebase. - -Two science realities drive specific features: - -- The Roman DC2 characterization (see *Roman DC2 Survey Files*) found the **true - photometric scatter is ~2× the reported catalog error**. So the error model - needs a *sample* model (true scatter, used to draw observed magnitudes) - distinct from a *catalog* model (the reported `magerr`). -- We need a **realistic background** (field stars + galaxies, with star/galaxy - misclassification) **without** loading the heavy DC2 mock catalogs at runtime - — using lightweight binned colour–magnitude distributions vs. maglim, and - optionally TRILEGAL for the stellar component. *(Designed below; future work.)* - -## Locked design decisions - -1. **One `StreamInjector` for one *or* many surveys.** A single injector holds - `{survey_name: Survey}` (size 1 for the single-survey case) and delegates - per-survey work to the existing, unchanged `Survey` per-band API via a shared - `_inject_one_survey` helper. Not a composite `Survey`; not manual re-runs; - and **no separate multi-survey class** — `StreamInjector` accepts a survey - name/`Survey`, a `{name: spec}` dict, or a list of specs. -2. **Column convention (always survey-namespaced `{survey}_{band}_true/_obs/_err`).** - A single naming scheme is used everywhere — `{survey}_{band}_true` / - `{survey}_{band}_obs` / `{survey}_{band}_err` / `{survey}_flag_observed` - (e.g. `roman_f158_obs`, `lsst_r_err`) — and it is **always namespaced by - survey, even for a single survey** (a one-survey injection of LSST emits - `lsst_r_obs`, not `r_obs`). There is no longer an un-namespaced single-survey - form. **This intentionally drops the historical `mag_{band}` / - `mag_{band}_obs` / `magerr_{band}` names — it is *not* backward compatible** - with catalogs or downstream readers expecting those columns (decision made - deliberately to keep one convention; see decision 5). -3. **Sample-vs-catalog error split, backward-compatible by default.** `Survey` - holds two error curves, both functions of `delta_mag = mag − maglim`: - - `log_photo_error_catalog` — the survey's **reported** error curve (the - existing `photoerror_*.csv`). Written as `magerr` and drives the S/N cut. - Always present; loaded from the `log_photo_error_catalog` key or the legacy - `log_photo_error` key. - - `log_photo_error_sample` — an **optional second** curve giving the **true - scatter** of observed−true magnitudes; drives the noise draw. Config key - `log_photo_error_sample`. - - If no sample curve is supplied, the noise draw falls back to the catalog - curve ⇒ existing outputs unchanged. Separating them is opt-in: supply the - second CSV. (Earlier drafts proposed a scalar `report_error_factor` / - inflation curve to *derive* one from the other; superseded by two independent - curves, which is more general and matches the Roman DC2 products that measure - the true scatter directly.) -4. **Sample stellar masses once, interpolate per survey.** This is a correctness - requirement (see below) and the engine of the whole feature. -5. **Public API preserved; column schema deliberately changed.** The - `StreamInjector(survey).inject(df, bands=[...])` single-survey call still - works (the same `StreamInjector` also takes a dict/list for several surveys). - The **column names are not** preserved — we adopt the always-namespaced - `{survey}_{band}_true/_obs/_err` scheme (decision 2) *instead of* the - historical `mag_g_obs`/`mag_r_obs`/`magerr_g` names. Downstream consumers that - read those columns must be updated. This is an accepted break in exchange for - one convention across single- and multi-survey output. - -## Behaviour changes for discussion - -```{important} -These are deliberate changes from current behaviour. They are flagged here for -review **before** we depend on them — they are not silently adopted. -``` - -### S/N detection cut now applies to *all* injected bands *(Phase 2, adopted)* - -Previously the injector applied its SNR ≥ 5 cut to a single hard-coded band -(`detection_mag_cut=["g"]`). Now the default is **every band passed to -`inject()`** — a star must have SNR ≥ 5 in *all* injected bands to be flagged -observed. For the default LSST `bands=["r", "g"]` this is stricter than before -(it adds the r-band SNR requirement on top of g), so detection counts drop -relative to the old default. Rationale: it generalizes cleanly to any band set -and makes "observed" mean "detected in everything you asked for". Callers can -restore any prior behaviour by passing `detection_mag_cut=[...]` explicitly -(e.g. `["g"]` for the old LSST default). **Adopted, but flagged for review.** - -### S/N cut ownership: who applies the reference-band cut *(adopted — option (b))* - -The reference band (`survey.completeness_band`, e.g. LSST `r`) is special: the -survey's **selection functions are estimated with the SNR ≥ 5 cut already -applied** in that band. So the reference-band cut is conceptually *owned by the -selection functions*, not by the injector's per-band loop. - -**The problem (pre-(b)).** The old code applied the per-band SNR loop to *every* -injected band, including the reference band — so the reference band's cut was -applied **twice** (once inside `get_completeness`, once in the loop). That is -idempotent today (`A & A == A`), so `flag_observed` was numerically correct, but -it is conceptually double-counted and fragile: it silently breaks if `SNR_min` -ever differs from the threshold the curve was estimated at. Worse, the two -selection curves disagreed about ownership — `get_completeness` bakes the cut -in, but `get_detection_efficiency` (used for the perfect-galstarsep flag) does -**not** — which forced a special-cased "force SNR cut on the completeness band" -block just for `flag_perfect`. - -**Option (b) — implemented now (injector-only, no data regeneration).** Treat the -reference-band cut as owned by the selection functions and apply it **exactly -once**, to both flags: - -- The reference band's SNR cut is applied once in `_inject_one_survey`, to - `flag_observed` (idempotent with the baked-in completeness cut) and to - `flag_perfect` (which supplies it, since the efficiency curve lacks it). -- `detection_mag_cut` defaults to *every injected band except the reference - band*; the reference band is skipped inside the loop. -- The old special-cased "force" block is removed. - -This is **behaviour-preserving** (the same set of bands gets an SNR cut, the -reference band counted once instead of twice) but removes the double-count and -the asymmetry between the two flags. `survey.completeness_band` is the single -attribute identifying the reference band for both completeness and detection -efficiency (default `"r"`). - -**Option (a) — the eventual "correct home" (deferred; requires new data).** Fold -the SNR cut into the **detection-efficiency curve itself**, so that -`get_completeness` *and* `get_detection_efficiency` both own it consistently. -Then the injector needs no reference-band special-casing at all — it simply -**skips** `survey.completeness_band` in the SNR loop (the curves handle it), and -the once-applied reference-band block from (b) can be deleted. - -*Why (a) is better:* the "a star's reference-band detectability already includes -SNR ≥ 5" fact lives in **one place** — the survey product — rather than being -re-asserted in the injector. Any consumer of `get_detection_efficiency` (not -just this injector) then gets a self-consistent curve, and there is no implicit -contract that "the caller must remember to also apply the SNR cut". - -*How to get from (b) to (a):* - -1. **Regenerate the detection-efficiency product with the SNR cut applied.** In - the build script that emits the efficiency tables - (`scripts/roman/create_streamobs_files_hlwas.py` for Roman DC2; the analogous - LSST/DES builders for the others), the *denominator* of the detection - efficiency is all true stars and the *numerator* is true stars detected — add - the requirement that the numerator detection also passes SNR ≥ 5 in the - reference band (the completeness/`classification_detection_eff` curve is - already built this way; mirror that selection for the detection-only curve). - Concretely: the column the loader reads for `type="detection_efficiency"` - (`detection_eff`, via `selection="detected"` in `set_completeness`) must be - recomputed with the SNR cut, so it matches how `classifiction_eff` / - `classification_detection_eff` are produced. -2. **Re-emit the per-survey efficiency CSVs** (e.g. Roman - `roman_stellar_efficiency_cutf158.csv` in `data/surveys/roman_hlwas/`, and the - LSST/DES equivalents in `data/others/`) and re-run the notebook/build so the - committed products carry the cut. Keep the `classifiction_eff` header spelling - the loader greps for. -3. **Flip the injector to (a):** drop the once-applied reference-band block and - change the `detection_mag_cut` default to skip the reference band *without* - re-adding its cut (the curve now carries it). `flag_observed` and - `flag_perfect` then both inherit the reference-band cut purely from the - selection functions. -4. **Validate** that detection counts are unchanged within noise versus (b) on a - fixed seed (they should be, since (b) already applies the same cut once) — this - confirms the regenerated curve encodes exactly the SNR ≥ 5 selection rather - than a different threshold. - -Until those products are regenerated, (b) is the correct, behaviour-preserving -state. - -### `nstars` becomes "exactly N stars" (was an emergent IMF count) *(adopted — agreed)* - -```{important} -**Adopted (Phase 4) and agreed.** {meth}`~streamobs.model.IsochroneModel.sample` -now draws *exactly* `nstars` (the shared-mass path described below), for **both** -single-survey and multi-survey isochrones. The single-survey -`ugali.simulate()` emergent-count path has been removed. This was reviewed and -**accepted as the intended semantics** (you control N, and it is shared across -surveys); it is no longer open for discussion. -``` - -Previously {meth}`~streamobs.model.IsochroneModel.sample` converted `nstars` -into a total stellar mass and let `ugali`'s `iso.simulate()` return a -*random-length* IMF realization — so the number of stars returned was stochastic -and generally **≠ `nstars`**. - -The multi-survey requirement (the *same physical star* must get consistent Roman -**and** Rubin magnitudes) forces us instead to draw a fixed set of initial -masses once and interpolate each survey's magnitudes from them: - -```python -init_mass, mass_pdf, *_ = base_iso.sample(mass_min=mass_min, mass_steps=mass_steps) -sampled_masses = rng.choice(init_mass[sel], size=nstars, p=imf_pdf) # exactly nstars -# then, per survey: mag_band = np.interp(sampled_masses, init_mass, mag_band) + dist_mod -``` - -This returns *exactly* `nstars` stars. It is the right semantics for injection -(you control N, and it is shared across surveys). It changes what -`StreamModel.sample(size)` returns relative to the old emergent count, but this -was reviewed and **agreed** — no further discussion needed. - -### Roman Vega→AB conversion is automatic and unconditional - -`ugali` delivers Roman isochrone magnitudes in **Vega**, while our catalogs are -**AB**. `IsochroneModel` corrects this **unconditionally** for every Roman band -using the module-level table `streamobs.model.ROMAN_VEGA_TO_AB` (AB = Vega + -offset). The per-band offsets are the mode of the by-chip Roman zeropoints -(`Roman_zeropoints_20240301.ecsv`), the same values the -`rubin_roman_object_classification` prototype used: - -| band | F062 | F087 | F106 | F129 | F146 | F158 | F184 | F213 | -|---|---|---|---|---|---|---|---|---| -| AB − Vega | 0.153 | 0.481 | 0.660 | 1.051 | 1.164 | 1.315 | 1.556 | 1.837 | - -There is **no config flag** — Roman bands are always converted, non-Roman bands -pass through unchanged. The code carries a `TODO` flagging that this conversion -ideally belongs in `ugali` itself (so isochrones are returned natively in AB); -when that lands the table can be removed. - -## Phased rollout - -Phases 1–4 are the current branch; Phases 5–6 are designed here but deferred. - -### Phase 1 — Sample vs. catalog error models on `Survey` ✅ *(implemented)* - -Two independent error curves, both vs `delta_mag = mag − maglim`: - -- ✅ `Survey`: replaced the single `log_photo_error` field with - `log_photo_error_catalog` (reported error, the base curve) and - `log_photo_error_sample` (optional true-scatter curve). Kept a read/write - `log_photo_error` property aliasing the **catalog** model (the legacy field's - meaning — back-compat for existing tests and any code that sets it). -- ✅ `Survey.get_photo_error(band, mag, maglim, kind="catalog")`: `kind` selects - the curve via `_resolve_log_photo_error`. `kind="catalog"` returns the reported - error; `kind="sample"` returns the true-scatter curve, falling back to the - catalog curve when no sample curve is loaded. Default `kind="catalog"` - reproduces today's numbers exactly. -- ✅ `SurveyFactory._load_survey_data`: loads the catalog curve from - `log_photo_error_catalog` (or the legacy `log_photo_error`) key, and an - optional sample curve from the `log_photo_error_sample` key. No factor / - inflation logic — both curves are read directly via `set_photo_error`. -- ✅ `StreamInjector.inject`: draws noise with the **sample** error - (`kind="sample"`); writes the **catalog** error as `magerr` and runs the S/N - cut on it (`kind="catalog"`). - -**Backward compatibility verified (94 passing tests from the test branch):** with -only the legacy `log_photo_error` curve and no `log_photo_error_sample`, the -sample draw falls back to the catalog curve, so the noise draw, `magerr`, and the -S/N cut are bit-for-bit identical to the previous single-curve behaviour. To opt -in, add `log_photo_error_sample: ` (the measured true-scatter curve) to a -survey's `survey_files`. - -### Phase 2 — De-hardcode the injector to arbitrary bands ✅ *(implemented)* - -- ✅ New `streamobs/columns.py` with `true_col` / `obs_col` / `err_col` helpers - `(band, survey=None)` and `flag_col(survey=None)`. Injected catalogs are - **always** survey-namespaced (`__…` / `_flag_observed`); - `survey=None` is retained only as a low-level fallback that the injector itself - never uses. -- ✅ `observed.py`: removed the `bands in {"r","g"}` hard block; the true-mag - read, the observed/err columns, the valid-flux check (now ANDs over every - injected band), the S/N cut, the per-survey detection flag, and the stored - flag all route through the `columns.py` helpers and the survey's - `completeness_band`. Existing per-band nside handling already supports Roman - nside=1024 vs. LSST nside=128. -- ⚠️ The S/N-cut default changed from the hard-coded `["g"]` to **all injected - bands** — see *Behaviour changes for discussion* above. - -**Validated:** single-band (`bands=["r"]`) and arbitrary band sets inject without -the old hard block; the `inject(df, bands=[...])` API is unchanged (output now -namespaced); `tests/test_observed.py` + `tests/test_model.py` green. - -### Phase 3 — Multi-band / multi-survey `IsochroneModel` ✅ *(implemented)* - -- ✅ `IsochroneModel.create_isochrone` accepts either today's single-survey - config or a multi-survey form (`surveys: {name: {survey, band_1, band_2}}` - plus shared `name`/`age`/`z`/... at top level), building one `ugali` isochrone - per survey (`self.isos`, `self.survey_bands`). -- ✅ New `sample_masses(...)` draws the initial masses *once* from the primary - isochrone's IMF (exactly `nstars`), and `sample(...)` interpolates - those shared masses into every survey's bands → `{(survey, band): - apparent_mag}` (same physical star, consistent across surveys). -- ✅ `_to_ab(band, mag)` converts Roman bands Vega→AB **unconditionally** using - the `ROMAN_VEGA_TO_AB` table (no config flag; non-Roman bands pass through). - Applied in the shared `sample` path. See the Vega→AB section above. -- ✅ `StreamModel.sample`/`complete_catalog` derive their magnitude columns from - the isochrone via `_iso_mag_columns()` / `_sample_iso_mags()`, which **always** - emit `__true` (a single-survey isochrone simply has one survey; - `IsochroneModel` tracks `surveys`/`survey_bands` in both config forms). Naming - routes through `columns.true_col`. - -**Note on the API:** `IsochroneModel.sample()` returns -`{(survey, band): apparent_mag}` and the masses used. `StreamModel` always goes -through `sample()` (a single-survey isochrone is just the one-survey case), -so the emitted columns are uniformly `__true`. - -**Validated:** model tests green; a two-isochrone multi-survey config produces -consistent shared-mass magnitudes, Roman bands are converted Vega→AB by the -fixed `ROMAN_VEGA_TO_AB` offsets, and `StreamModel.sample` emits the -`__true` columns. - -### Phase 4 — one `StreamInjector` for one *or* many surveys ✅ *(implemented)* - -- ✅ The per-band body of injection lives in a shared - `StreamInjector._inject_one_survey(data, bands, survey, survey_namespace, ...)` - helper. It assumes positions and true magnitudes are already present and only - does the observed/err draw, detection flags, and S/N cut, routing every column - name through `columns.py`. `survey`/`survey_namespace` are passed explicitly so - the same method serves every survey. -- ✅ `StreamInjector` accepts **one survey or several** (a name/`Survey`, a - `{namespace: spec}` dict, or a list); `__init__` normalizes to - `self.surveys = {namespace: Survey}` with a `survey` property pointing at the - `primary`. `inject(data, survey_bands=None, bands=None, stream_config=...)`: - (1) one shared sky placement; (2) one shared true-magnitude fill via the - isochrone (masses sampled once → every survey's `__true`); - (3) a per-survey loop calling `_inject_one_survey` with `survey_namespace=`, - writing `__obs/_err` and `_flag_observed` from that - survey's own `completeness_band` and maglim maps. Per-survey RNG via - `rng.spawn(...)` for order-independent reproducibility. `columns.perfect_flag_col` - namespaces the optional `perfect_galstarsep` flag. The separate - `MultiSurveyInjector` class has been **removed**. -- ✅ A *scene* config (`config/scenes/roman_rubin_demo.yaml`) lists the surveys, - per-survey bands, the multi-survey isochrone, and shared stream geometry. -- ✅ `notebooks/multisurvey_phases_demo.ipynb` walks through Phases 1–4 end to - end (executes against `StubSurvey` + real `ugali` isochrones). - -### Phase 5 *(future)* — Lightweight background + galaxy misclassification - -- New `streamobs/background.py`: a `CMDDistribution` (binned colour–magnitude - distribution vs. maglim, stored raw so one file serves any isochrone) and a - `BackgroundGenerator` that, per HEALPix pixel, looks up the local maglim, - selects the nearest-maglim CMD slice, scales counts linearly by pixel area, - Poisson-draws the count, samples multi-band magnitudes, places objects - uniformly within the pixel, and optionally applies a matched filter. -- Independent, pluggable `StellarBackground` / `GalaxyBackground` models. -- Galaxy misclassification: a new `Survey.get_galaxy_misclassification` curve and - an `is_galaxy`-aware `detect_flag`, so misclassified galaxies leak into the - stellar sample (and `perfect_galstarsep=True` ⇒ no leakage). A build script - reads DC2 truth+det **once, offline** to emit the lightweight files; the - runtime never loads DC2. - -### Phase 6 *(future)* — TRILEGAL + docs - -- `TrilegalStellarBackground` implementing the same interface, **lazy** (reads a - user-provided TRILEGAL table or a `fetch` callable; never imported at module - import). Documentation polish for the `{survey}_{band}` outputs, the error - split, and scenes. - -## Development fixtures (this branch) - -Survey data files (maglim maps, efficiency / photo-error CSVs) are **not** -committed — they are downloaded into the git-ignored `data/surveys//`. -To keep this branch self-contained and testable without the real Roman/Rubin -data, we add **dummy surveys**: small committed configs -(`config/surveys/*_dummy.yaml`) plus a generator that synthesizes tiny HEALPix -maglim maps and CSV tables at test time. The real `roman_dc2.yaml` is **not** -recreated here, so it can land cleanly when the `roman_hlwas` work merges. - -## Key files - -| File | Phase | Role | -|---|---|---| -| `streamobs/surveys.py` | 1 | sample/catalog error fields, `get_photo_error(kind=)`, loader | -| `streamobs/columns.py` | 2 | NEW — column-name helpers (always namespaced) | -| `streamobs/observed.py` | 1,2,4 | error wiring; de-hardcode bands; unified one-or-many-survey `StreamInjector` (`_inject_one_survey` + `_complete_shared`) | -| `streamobs/model.py` | 3 | multi-band `IsochroneModel`; always-namespaced `StreamModel` | -| `config/scenes/roman_rubin_demo.yaml` | 4 | NEW — multi-survey scene | -| `streamobs/background.py` | 5 *(future)* | NEW — lightweight background | From 8467c9af4316748f0e915d9d583b8689890ba373 Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 18 Jun 2026 11:49:44 -0700 Subject: [PATCH 29/54] isort and black --- bin/generate_spline_stream.py | 1 + bin/generate_stream.py | 1 + streamobs/model.py | 4 +--- streamobs/utils.py | 2 +- tests/test_functions.py | 10 ++++++---- tests/test_model.py | 8 ++------ 6 files changed, 12 insertions(+), 14 deletions(-) diff --git a/bin/generate_spline_stream.py b/bin/generate_spline_stream.py index 883467a..f75e537 100755 --- a/bin/generate_spline_stream.py +++ b/bin/generate_spline_stream.py @@ -2,6 +2,7 @@ """ More modular stream generation example. """ + import matplotlib.pyplot as plt import numpy as np import pandas as pd diff --git a/bin/generate_stream.py b/bin/generate_stream.py index 017bfeb..66c8a41 100755 --- a/bin/generate_stream.py +++ b/bin/generate_stream.py @@ -2,6 +2,7 @@ """ More modular stream generation example. """ + import copy import os from importlib import reload diff --git a/streamobs/model.py b/streamobs/model.py index bf15712..e3eca90 100644 --- a/streamobs/model.py +++ b/streamobs/model.py @@ -830,9 +830,7 @@ def _add_distance_modulus(abs_mag, distance_modulus): return abs_mag return abs_mag + np.asarray(distance_modulus, dtype=float) - def sample( - self, nstars, distance_modulus, rng=None, masses=None, **kwargs - ): + def sample(self, nstars, distance_modulus, rng=None, masses=None, **kwargs): """Sample apparent magnitudes for every ``(survey, band)``. A single shared set of initial masses is interpolated into each survey's diff --git a/streamobs/utils.py b/streamobs/utils.py index c226c0f..3f19ccd 100644 --- a/streamobs/utils.py +++ b/streamobs/utils.py @@ -20,6 +20,6 @@ def parse_config(config): try: # If `config` is a file return yaml.safe_load(open(config, "r")) - except (OSError, FileNotFoundError): + except OSError, FileNotFoundError: # Otherwise assume it is a string return yaml.safe_load(config) diff --git a/tests/test_functions.py b/tests/test_functions.py index 0133baf..179c110 100644 --- a/tests/test_functions.py +++ b/tests/test_functions.py @@ -1,9 +1,11 @@ import numpy as np -from streamobs.functions import (CubicSplineInterpolation, - FileCubicSplineInterpolation, - FileLinearDensityCubicSplineInterpolation, - LinearDensityCubicSplineInterpolation) +from streamobs.functions import ( + CubicSplineInterpolation, + FileCubicSplineInterpolation, + FileLinearDensityCubicSplineInterpolation, + LinearDensityCubicSplineInterpolation, +) SPREAD_NODES = np.array([-13.0, -7.875, -2.75, 2.375, 7.5]) diff --git a/tests/test_model.py b/tests/test_model.py index 3bae578..6b9f9ad 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -332,9 +332,7 @@ class TestIsochroneMasses: G = "lsst_g_true" R = "lsst_r_true" - def test_sample_returns_and_reuses_masses( - self, stream_config_with_distance - ): + def test_sample_returns_and_reuses_masses(self, stream_config_with_distance): """Replaying the returned masses reproduces identical magnitudes.""" iso = StreamModel(stream_config_with_distance).isochrone mags1, masses = iso.sample(50, 16.8, rng=np.random.default_rng(0)) @@ -345,9 +343,7 @@ def test_sample_returns_and_reuses_masses( for key in mags1: assert np.allclose(mags1[key], mags2[key]), f"{key} not reproduced" - def test_sample_masses_length_validated( - self, stream_config_with_distance - ): + def test_sample_masses_length_validated(self, stream_config_with_distance): iso = StreamModel(stream_config_with_distance).isochrone with pytest.raises(ValueError): iso.sample(50, 16.8, masses=np.ones(49)) From 3ce6a127b62b05864cc396aec7ea934731a21c79 Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 11 Jun 2026 11:14:28 -0700 Subject: [PATCH 30/54] Add Roman HLWAS survey config, selection-function derivation, and docs - config/surveys/roman_hlwas.yaml: survey config keyed to the official 5-sigma point-source depth convention (DC2-derived maglim map, completeness and photo-error tables in F158) - notebooks/create_streamobs_files_hlwas.ipynb: full derivation from the Roman-Rubin DC2 mock (paper-exact flags==0 / S/N>5 / matched cuts, truth-duplicate and tile-margin handling, desqr-style depth maps, streamobs-format tables) - notebooks/build_roman_dc2_det_truth.py: builder for the det->truth matched catalog the derivation runs on - docs: new "Roman HLWAS survey files" page documenting the derivation, depth conventions (STScI HLWAS medians), and caveats --- config/surveys/roman_hlwas.yaml | 51 ++ docs/source/index.rst | 1 + docs/source/roman_hlwas.md | 125 +++ notebooks/build_roman_dc2_det_truth.py | 231 +++++ notebooks/create_streamobs_files_hlwas.ipynb | 909 +++++++++++++++++++ 5 files changed, 1317 insertions(+) create mode 100644 config/surveys/roman_hlwas.yaml create mode 100644 docs/source/roman_hlwas.md create mode 100644 notebooks/build_roman_dc2_det_truth.py create mode 100644 notebooks/create_streamobs_files_hlwas.ipynb diff --git a/config/surveys/roman_hlwas.yaml b/config/surveys/roman_hlwas.yaml new file mode 100644 index 0000000..121fb7d --- /dev/null +++ b/config/surveys/roman_hlwas.yaml @@ -0,0 +1,51 @@ +# Configuration file for the Roman High Latitude Wide Area Survey (HLWAS) +# +# Selection function derived from the Roman-Rubin DC2 synthetic survey +# (Troxel et al. 2023, arXiv:2209.06829), which simulates the reference HLIS +# at full depth (5-sigma point-source ~26.9 AB in F106/F129/F158, 26.2 in F184). +# Derivation: notebooks/create_streamobs_files_hlwas.ipynb (see docs page +# "Roman HLWAS survey files" for details and caveats). +name: roman +release: hlwas +survey_files: + # Path to data files (leave empty for default location: data/surveys/roman_hlwas) + file_path: '' + + # Band-specific magnitude limit maps. + # The _5sigps map is the DC2-measured S/N=5 depth map renormalized so its + # median equals the official 5-sigma point-source depth of the simulated + # survey (F158: 26.9). The delta_mag axes of the completeness and + # photo-error tables are keyed to this same convention, so any replacement + # map (e.g. an exposure-time-scaled HLWAS map normalized to the STScI + # median depths: wide F158 26.2, medium F106 26.5 / F129 26.4 / F158 26.4) + # must also be a 5-sigma point-source maglim. + maglim_map_f158: roman_dc2_maglim_f158_nside1024_5sigps.fits.gz + + # Band-independent maps + ebv_map: ebv_sfd98_lowres_nside_512_ring_equatorial.fits + completeness: roman_stellar_efficiency_cutf158.csv + completeness_band: f158 # the completeness has been obtained in the F158 band + log_photo_error: roman_photoerror_f158.csv + +survey_properties: + bands: ['f106', 'f129', 'f158', 'f184'] + + # Extinction coefficients per band (A_band / E(B-V)) + # CCM89 R_V=3.1 evaluated at the filter effective wavelengths + # (1.06, 1.29, 1.58, 1.84 micron). NIR extinction is small; refine with + # dust_extinction + actual Roman bandpasses if percent-level accuracy matters. + coeff_extinc_f106: 1.14 + coeff_extinc_f129: 0.83 + coeff_extinc_f158: 0.60 + coeff_extinc_f184: 0.47 + + # Systematic photometric errors (mag), or could be specified per band + sys_error: 0.005 + + # Saturation limits per band. The DC2 truth catalog only extends to ~15 AB + # and the simulation does not model saturation, so this is the validity + # limit of the efficiency table rather than a physical saturation estimate + # (real Roman WFI saturates around 17-18 AB for point sources in 107 s + # exposures - tighten this if bright stars matter). + saturation: 15.0 + delta_saturation: -11.8 diff --git a/docs/source/index.rst b/docs/source/index.rst index 08f77a9..47e88c3 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -116,6 +116,7 @@ Documentation Contents column_convention data + roman_hlwas modules Indices and Tables diff --git a/docs/source/roman_hlwas.md b/docs/source/roman_hlwas.md new file mode 100644 index 0000000..f2edf69 --- /dev/null +++ b/docs/source/roman_hlwas.md @@ -0,0 +1,125 @@ +# Roman HLWAS Survey Files + +This page documents how the Roman High Latitude Wide Area Survey (HLWAS) selection +function shipped with streamobs was derived. The full, executable derivation is in +[`notebooks/create_streamobs_files_hlwas.ipynb`](https://github.com/LSSTDESC/streamobs/blob/main/notebooks/create_streamobs_files_hlwas.ipynb); +the survey is configured by `config/surveys/roman_hlwas.yaml` and used like any other +survey: + +```python +from streamobs.surveys import SurveyFactory +survey = SurveyFactory.create_survey("roman", release="hlwas") +``` + +## Input data + +Everything is measured from the **Roman–Rubin DC2 synthetic survey** of +[Troxel et al. (2023)](https://arxiv.org/abs/2209.06829): ~20 deg² of image-level +simulations of the Roman reference High Latitude Imaging Survey at full depth, in the +four bands Y106/J129/H158/F184 (the mock's `H158` is the Roman **F158** filter). +Detection is SExtractor run on a median Y106+J129+H158+F184 coadd; stars come from +Galfast, galaxies from cosmoDC2. + +From the released per-tile detection and truth catalogs we built a single +**detection-centric matched catalog** (`roman_dc2_det_truth.parquet`, built by +`notebooks/build_roman_dc2_det_truth.py`): every S/N>5 detection is matched to a true +source within 1″, breaking ties among the up-to-3 nearest neighbours by taking the +closest in magnitude — the same recipe as the paper. + +## Selections (paper-exact) + +Following Troxel et al. §"object selection", all products apply: + +1. **`flags == 0`** — removes 32% of detections (we reproduce this fraction exactly); +2. **S/N > 5** in the detection image; +3. **matched to a true object** within 1″. + +Two data subtleties discovered during the derivation, both handled in the notebook: + +- **Duplicate truth entries.** ~26% of truth stars have a second truth entry at the + *exact same position* under a different index, carrying only the J129 magnitude. + The match can assign the detection to the duplicate, which would mimic a flat ~12% + detection-efficiency deficit. Truth stars are therefore collapsed to unique + positions, and a star counts as detected if *any* of its entries matched. +- **Tile-margin geometry.** Each detection coadd extends ~30″ beyond its truth tile's + footprint, so per-tile matching leaves ~21% of detections (the margin band) + unmatched — they are duplicates of detections in the neighbouring tile. Inside the + truth footprint the unmatched rate is 0.1–5%, consistent with the paper's 1.7%. + The matched-only selection removes these margin duplicates. + +## Products + +All files live in `data/surveys/roman_hlwas/`. + +### Stellar completeness — `roman_stellar_efficiency_cutf158.csv` + +Columns `mag_f158, delta_mag, detection_eff, classifiction_eff, +classification_detection_eff` (same format as the LSST table, including the +`classifiction_eff` spelling that the loader expects). For true stars binned in true +F158 magnitude: + +- `detection_eff` — fraction with a clean (`flags==0`) S/N>5 detection matched to them; +- `classifiction_eff` — fraction of those whose detection has SExtractor + `class_star > 0.5`; +- `classification_detection_eff` — the product (detected *and* classified). + +The bright plateau is ~0.90–0.95 (not 1.0): blended stars whose only detection is +flagged are excluded by the paper's `flags==0` cut. The 50% point of the combined +efficiency is at F158 ≈ 27.2 in the simulation. + +### Photometric errors — `roman_photoerror_f158.csv` + +Columns `delta_mag, log_mag_err`: the binned median log10 of the observed F158 +`magerr_auto` of star-classified objects, against `delta_mag = true mag − maglim` at +each object's position. + +### Depth maps — `roman_dc2_maglim_f{106,129,158,184}_nside1024[_5sigps].fits.gz` + +HEALPix (nside=1024, ring) magnitude-limit maps over the DC2 footprint, computed with +the recipe of [desqr/depth.py](https://github.com/kadrlica/desqr/blob/main/desqr/depth.py): +estimate the slope of log(magerr) vs mag from nearest-neighbour pairs, extrapolate each +object to the magnitude where S/N=5, and take the median per pixel. + +Two conventions are provided: + +- the raw **measured** maps (median F158 depth 27.11 — the catalog's SExtractor errors + run ~0.2 mag past the official depths, plausibly because correlated noise in the + resampled median coadds is underestimated); +- the **`_5sigps`** maps, renormalized so the median equals the official 5σ + point-source depth of the simulated survey (26.9 in F106/F129/F158, 26.2 in F184). + +## The depth convention (important) + +The `delta_mag` axes of both tables are keyed to the **official 5σ point-source +depth** convention, *not* to the raw measured depth. This means the tables must be +paired with maglim maps in the same convention — the `_5sigps` maps above, or, for the +real HLWAS footprint, an exposure-time-scaled map normalized to the +[STScI community-defined HLWAS median 5σ point-source depths](https://roman-docs.stsci.edu/roman-community-defined-surveys/high-latitude-wide-area-survey): + +| Tier | Area | 5σ point-source total depth (AB) | +|---|---|---| +| Wide | ~2700 deg² | F158 26.2 | +| Medium | ~2400 deg² | F106 26.5, F129 26.4, F158 26.4 | +| Deep | ~19.2 deg² | F106 27.7, F129 27.6, F158 27.5, F184 27.0, F213 25.9 | + +With this convention, swapping in a shallower (wide-tier) maglim map automatically +shifts the completeness and photo-error curves to the correct depths, under the +assumption that the selection function depends on magnitude only through +`mag − maglim`. + +## Caveats + +- The simulation is the *reference* HLIS design (deeper than the current + community-defined wide tier); the wide-tier behaviour is obtained by translation in + `delta_mag`, not by an independent simulation. +- Star/galaxy classification is SExtractor `class_star` on the detection coadd; the + false-star rate for compact (<0.3″) galaxies is <1% brighter than F158 ≈ 25.5, + rising to ~15–17% at the faint end. +- The detection-centric match assigns a blend to its dominant source, so the + detection efficiency is slightly conservative for stars blended with brighter + neighbours. +- The truth catalog only extends to ~15 AB and the simulation does not model + saturation; the configured `saturation: 15.0` marks the validity limit of the + table rather than the physical WFI saturation (~17–18 AB for point sources). +- Extinction coefficients are CCM89 (R_V=3.1) evaluated at the filter effective + wavelengths. diff --git a/notebooks/build_roman_dc2_det_truth.py b/notebooks/build_roman_dc2_det_truth.py new file mode 100644 index 0000000..0611046 --- /dev/null +++ b/notebooks/build_roman_dc2_det_truth.py @@ -0,0 +1,231 @@ +#!/usr/bin/env python +"""Build a DETECTION-centric (det -> truth) catalog for the Roman-Rubin DC2 mock +(Troxel et al. 2023, arXiv:2209.06829) for catalog-level injection / selection work. + +Per coadd tile: load the SExtractor detection catalog + the per-object truth index, +cut detections to S/N>5 (KEEP ALL FLAGS), and match each DETECTION to a truth source +following the paper's recipe: among truth objects within 1", take the up to 3 nearest on +the sky, then assign the one closest in MAGNITUDE (mag-tiebreak). EVERY detection is kept +-- unmatched ones (no truth within 1") get matched=False and NaN truth columns, so the +spurious / false-detection rate is preserved. One row per detection. +Concatenate all tiles into one parquet. REPLACES the old truth->det product. + +Mag-tiebreak magnitude: the detection image is the median of the 4 single-band coadds, so +the detection `mag_auto` is compared to a truth broadband mag = mean flux over the 4 truth +bands (truth mag==0.0 means "no flux in band" -> ignored). + +Decisions (per user): det->truth direction, mag-tiebreak, KEEP unmatched (flagged), +KEEP all flags, replace existing catalog. +Resource bounds: <=24 worker processes, each holds one small tile. + +Run with the pferguso_hats env python (needs pyarrow): + /astro/users/pferguso/.conda/envs/pferguso_hats/bin/python +""" +import os +# keep every worker single-threaded so N_proc ~= N_cores (stay under the core budget) +for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS", + "NUMEXPR_NUM_THREADS", "VECLIB_MAXIMUM_THREADS"): + os.environ[v] = "1" + +import gzip, io, glob, argparse, time, traceback +import numpy as np +import pandas as pd +from astropy.io import fits +from astropy.table import Table +from astropy.coordinates import SkyCoord, search_around_sky +import astropy.units as u +import pyarrow.parquet as pq +from multiprocessing import Pool + +DATA = "/astro/store/shire/stream_team/stream_finding/data/roman_mock" +OUTDIR = "/astro/store/shire/pferguso/software/streamobs/data/surveys/roman_dc2" +TILEDIR = os.path.join(OUTDIR, "_tiles_det") +FINAL = os.path.join(OUTDIR, "roman_dc2_det_truth.parquet") + +BANDS = ["Y106", "J129", "H158", "F184"] +MATCH_RADIUS_ARCSEC = 1.0 # paper's truth-match radius +SN_MIN = 5.0 # paper's detection threshold +N_NEAREST = 3 # paper: disambiguate among up to 3 nearest within radius + +# SExtractor detection columns (53), in file order -- kept verbatim +DET_COLS = ['number', 'flux_auto', 'fluxerr_auto', 'mag_auto', 'magerr_auto', 'kron_radius', + 'background', 'isoareaf_image', 'xwin_image', 'ywin_image', 'alphawin_j2000', + 'deltawin_j2000', 'x2win_image', 'y2win_image', 'xywin_image', 'x2win_world', + 'y2win_world', 'xywin_world', 'awin_world', 'bwin_world', 'thetawin_world', + 'mu_threshold', 'mu_max', 'flags', 'class_star', + 'mag_auto_Y106', 'mag_auto_J129', 'mag_auto_H158', 'mag_auto_F184', + 'magerr_auto_Y106', 'magerr_auto_J129', 'magerr_auto_H158', 'magerr_auto_F184', + 'flux_auto_Y106', 'flux_auto_J129', 'flux_auto_H158', 'flux_auto_F184', + 'fluxerr_auto_Y106', 'fluxerr_auto_J129', 'fluxerr_auto_H158', 'fluxerr_auto_F184', + 'x2win_world_Y106', 'y2win_world_Y106', 'xywin_world_Y106', + 'x2win_world_J129', 'y2win_world_J129', 'xywin_world_J129', + 'x2win_world_H158', 'y2win_world_H158', 'xywin_world_H158', + 'x2win_world_F184', 'y2win_world_F184', 'xywin_world_F184'] + +TRUTH_MAG = [f"mag_{b}" for b in BANDS] +TRUTH_DERED = [f"dered_{b}" for b in BANDS] +TRUTH_OUT = (["truth_ind", "truth_ra", "truth_dec", "truth_gal_star"] + + [f"truth_{m}" for m in TRUTH_MAG] + [f"truth_{m}" for m in TRUTH_DERED] + + ["truth_bb_mag"]) + +# final column order: tile, all detection cols, the matched-truth payload +COLS = ["tile"] + DET_COLS + ["det_sn", "matched", "match_sep_arcsec"] + TRUTH_OUT + + +def load_gz_fits(path): + with gzip.open(path, "rb") as f: + raw = f.read() + with fits.open(io.BytesIO(raw)) as h: + return Table(h[1].data) + + +def truth_broadband_mag(truth): + """Mean-flux magnitude across the 4 truth bands (mag==0.0 = missing -> ignored). + The detection image is the median of the 4 single-band coadds, so mean band flux is the + natural truth proxy for the detection's mag_auto used in the mag-tiebreak.""" + m = np.vstack([np.asarray(truth[c], float) for c in TRUTH_MAG]).T # (n, 4) + m[m == 0.0] = np.nan + with np.errstate(over="ignore"): + flux = 10.0 ** (-0.4 * m) + with np.errstate(invalid="ignore", divide="ignore"): + return -2.5 * np.log10(np.nanmean(flux, axis=1)) # NaN if all bands missing + + +def process_tile(tile): + """Worker entry point: never raises, so the pool keeps going on a bad tile.""" + try: + return _process_tile_impl(tile) + except Exception: + return ("err", tile, traceback.format_exc()) + + +def _process_tile_impl(tile): + """Return ('ok'|'skip', tile, parquet_path).""" + out_path = os.path.join(TILEDIR, f"{tile}.parquet") + if os.path.exists(out_path) and os.path.getsize(out_path) > 0: + return ("skip", tile, out_path) + + det = load_gz_fits(os.path.join(DATA, "det", f"dc2_det_{tile}.fits.gz")) + truth = load_gz_fits(os.path.join(DATA, "truth", f"dc2_index_{tile}.fits.gz")) + + # S/N cut on detections (no flags cut, per request) + with np.errstate(divide="ignore", invalid="ignore"): + det_sn = np.asarray(det["flux_auto"], float) / np.asarray(det["fluxerr_auto"], float) + keep = np.isfinite(det_sn) & (det_sn > SN_MIN) + det = det[keep] + det_sn = det_sn[keep] + ndet = len(det) + + # base output: every surviving detection, all SExtractor columns, truth cols = NaN + df = det[DET_COLS].to_pandas() + df.insert(0, "tile", tile) + df["det_sn"] = det_sn + df["matched"] = False + df["match_sep_arcsec"] = np.nan + for c in TRUTH_OUT: + df[c] = np.nan + + if ndet > 0 and len(truth) > 0: + det_mag = np.asarray(det["mag_auto"], float) + tbb = truth_broadband_mag(truth) + cd = SkyCoord(np.asarray(det["alphawin_j2000"], float) * u.deg, + np.asarray(det["deltawin_j2000"], float) * u.deg) + ct = SkyCoord(np.asarray(truth["ra"], float) * u.deg, + np.asarray(truth["dec"], float) * u.deg) + # all (det, truth) pairs within the match radius + i_d, i_t, sep2d, _ = search_around_sky(cd, ct, MATCH_RADIUS_ARCSEC * u.arcsec) + if len(i_d) > 0: + pairs = pd.DataFrame({"d": i_d, "t": i_t, "sep": sep2d.arcsec, + "dmag": np.abs(tbb[i_t] - det_mag[i_d])}) + # keep up to N_NEAREST nearest truths per detection ... + pairs = pairs.sort_values(["d", "sep"], kind="mergesort") + pairs["rank"] = pairs.groupby("d", sort=False).cumcount() + pairs = pairs[pairs["rank"] < N_NEAREST] + # ... then assign the one closest in magnitude (NaN dmag sorts last) + pairs = pairs.sort_values(["d", "dmag"], kind="mergesort", na_position="last") + best = pairs.drop_duplicates("d", keep="first") + di = best["d"].to_numpy() + ti = best["t"].to_numpy() + df.loc[di, "matched"] = True + df.loc[di, "match_sep_arcsec"] = best["sep"].to_numpy() + df.loc[di, "truth_ind"] = np.asarray(truth["ind"], float)[ti] + df.loc[di, "truth_ra"] = np.asarray(truth["ra"], float)[ti] + df.loc[di, "truth_dec"] = np.asarray(truth["dec"], float)[ti] + df.loc[di, "truth_gal_star"] = np.asarray(truth["gal_star"], float)[ti] + for m in TRUTH_MAG + TRUTH_DERED: + v = np.asarray(truth[m], float) + if m in TRUTH_MAG: # truth mag 0.0 -> NaN (no flux in band) + v = np.where(v == 0.0, np.nan, v) + df.loc[di, f"truth_{m}"] = v[ti] + df.loc[di, "truth_bb_mag"] = tbb[ti] + + df = df[COLS] + df.to_parquet(out_path, engine="pyarrow", index=False, compression="snappy") + return ("ok", tile, out_path) + + +def discover_tiles(): + tiles = [] + for p in glob.glob(os.path.join(DATA, "truth", "dc2_index_[0-9]*.fits.gz")): + if os.path.getsize(p) <= 1000: # 53-byte empty placeholders + continue + t = os.path.basename(p)[len("dc2_index_"):-len(".fits.gz")] + if os.path.exists(os.path.join(DATA, "det", f"dc2_det_{t}.fits.gz")): + tiles.append(t) + return sorted(tiles) + + +def combine(tiles): + """Stream per-tile parquet files into one final parquet (low memory).""" + writer = None + nrows = 0 + for t in tiles: + p = os.path.join(TILEDIR, f"{t}.parquet") + tbl = pq.read_table(p) + if writer is None: + writer = pq.ParquetWriter(FINAL, tbl.schema, compression="snappy") + else: + tbl = tbl.cast(writer.schema) + writer.write_table(tbl) + nrows += tbl.num_rows + if writer is not None: + writer.close() + return nrows + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--nproc", type=int, default=24) + ap.add_argument("--limit", type=int, default=0, help="process only first N tiles (smoke test)") + ap.add_argument("--combine-only", action="store_true") + args = ap.parse_args() + args.nproc = max(1, min(args.nproc, 24)) # hard cap at 24 cores + + os.makedirs(TILEDIR, exist_ok=True) + tiles = discover_tiles() + if args.limit: + tiles = tiles[:args.limit] + print(f"[{time.strftime('%H:%M:%S')}] tiles to process: {len(tiles)} nproc={args.nproc}", flush=True) + + if not args.combine_only: + t0 = time.time() + done = errs = 0 + with Pool(args.nproc) as pool: + for status, tile, info in pool.imap_unordered(process_tile, tiles, chunksize=2): + done += 1 + if status == "err": + errs += 1 + print(f" ERROR {tile}: {info}", flush=True) + if done % 100 == 0 or done == len(tiles): + print(f"[{time.strftime('%H:%M:%S')}] {done}/{len(tiles)} " + f"({time.time()-t0:.0f}s, errs={errs})", flush=True) + print(f"[{time.strftime('%H:%M:%S')}] per-tile done: {done} ok-ish, {errs} errors", flush=True) + + print(f"[{time.strftime('%H:%M:%S')}] combining into {FINAL} ...", flush=True) + nrows = combine(tiles) + size_gb = os.path.getsize(FINAL) / 1e9 + print(f"[{time.strftime('%H:%M:%S')}] DONE: {FINAL} rows={nrows:,} size={size_gb:.2f} GB", flush=True) + + +if __name__ == "__main__": + main() diff --git a/notebooks/create_streamobs_files_hlwas.ipynb b/notebooks/create_streamobs_files_hlwas.ipynb new file mode 100644 index 0000000..06a0a66 --- /dev/null +++ b/notebooks/create_streamobs_files_hlwas.ipynb @@ -0,0 +1,909 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1a1f0787", + "metadata": {}, + "source": [ + "# Roman HLWAS streamobs survey files from the DC2 mock\n", + "\n", + "Build the survey characterization products streamobs needs for a Roman (HLWAS) survey,\n", + "derived from the matched detection→truth catalog of the Roman–Rubin DC2 mock\n", + "(`data/surveys/roman_dc2/roman_dc2_det_truth.parquet`, see its README;\n", + "Troxel et al. 2023, [arXiv:2209.06829](https://arxiv.org/abs/2209.06829)).\n", + "\n", + "**Note on band naming:** the mock labels the Roman bands `Y106, J129, H158, F184`;\n", + "`H158` is the Roman **F158** filter. Outputs are named `f158`.\n", + "\n", + "Products (mirroring the LSST files in `data/others/`):\n", + "1. **Photometric error vs true mag** in F158 for objects *classified* as stars.\n", + "2. **Detection & correct-classification efficiency** vs true F158 mag for *true* stars.\n", + "3. **False star-classification rate** vs true F158 mag for *true* galaxies with measured size < 0.3″.\n", + "4. **Depth (maglim) maps at nside=1024** following the recipe in\n", + " [desqr/depth.py](https://github.com/kadrlica/desqr/blob/main/desqr/depth.py) (mag at S/N=5).\n", + "5. CSV tables in the format of `lsst_photoerror_r.csv` and `lsst_stellar_efficiency_cutr.csv`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8161d972", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-11T18:11:12.811144Z", + "iopub.status.busy": "2026-06-11T18:11:12.810468Z", + "iopub.status.idle": "2026-06-11T18:11:15.201089Z", + "shell.execute_reply": "2026-06-11T18:11:15.193304Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "repo: /astro/store/shire/pferguso/software/streamobs\n" + ] + } + ], + "source": [ + "import gzip, io, glob, os\n", + "import multiprocessing as mp\n", + "from pathlib import Path\n", + "from concurrent.futures import ProcessPoolExecutor\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import healpy as hp\n", + "import matplotlib.pyplot as plt\n", + "import pyarrow.parquet as pq\n", + "from astropy.io import fits\n", + "from astropy.table import Table\n", + "from scipy.spatial import cKDTree\n", + "from scipy.stats import gaussian_kde\n", + "\n", + "REPO = Path.cwd().resolve().parent if Path.cwd().name == \"notebooks\" else Path.cwd().resolve()\n", + "DC2_DIR = REPO / \"data/surveys/roman_dc2\"\n", + "OUT_DIR = REPO / \"data/surveys/roman_hlwas\"\n", + "CAT_PATH = DC2_DIR / \"roman_dc2_det_truth.parquet\"\n", + "TRUTH_STARS_CACHE = DC2_DIR / \"roman_dc2_truth_stars.parquet\"\n", + "\n", + "BAND = \"H158\" # mock column name == Roman F158\n", + "BANDS = [\"Y106\", \"J129\", \"H158\", \"F184\"]\n", + "CLASS_STAR_CUT = 0.5 # class_star > cut -> \"classified as a star\"\n", + "FLAG_CUT = 1 # flags < 1, i.e. flags == 0 (paper cut: removes 32% of detections)\n", + "NSIDE = 1024\n", + "SNR_DEPTH = 5 # depth = mag at S/N = 5 (matches the catalog S/N>5 cut)\n", + "GAL_SIZE_MAX = 0.3 # arcsec; \"small\" galaxies = awin_world < this\n", + "# Official-convention 5-sigma point-source depths:\n", + "# - simulated reference HLIS (Troxel+23: \"~26.9 mag AB in Y106/J129/H158, 26.2 in F184\"; Akeson+19)\n", + "SIM_DEPTH_5SIG = {\"Y106\": 26.9, \"J129\": 26.9, \"H158\": 26.9, \"F184\": 26.2}\n", + "# - STScI community-defined HLWAS median 5-sigma point-source total depths:\n", + "# https://roman-docs.stsci.edu/roman-community-defined-surveys/high-latitude-wide-area-survey\n", + "HLWAS_DEPTH_5SIG = {\n", + " \"wide\": {\"F158\": 26.2}, # ~2700 deg2\n", + " \"medium\": {\"F106\": 26.5, \"F129\": 26.4, \"F158\": 26.4}, # ~2400 deg2\n", + " \"deep\": {\"F087\": 27.7, \"F106\": 27.7, \"F129\": 27.6, \"F158\": 27.5,\n", + " \"F184\": 27.0, \"F213\": 25.9}, # ~19.2 deg2\n", + "}\n", + "\n", + "MAG_BINS = np.arange(15.0, 29.0 + 1e-6, 0.25)\n", + "MAG_MID = 0.5 * (MAG_BINS[1:] + MAG_BINS[:-1])\n", + "\n", + "plt.rcParams.update({\"figure.dpi\": 110, \"font.size\": 11})\n", + "print(f\"repo: {REPO}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "65a62226", + "metadata": {}, + "source": [ + "## Load the matched det→truth catalog\n", + "\n", + "One row per S/N>5 detection; `matched` rows carry the truth payload.\n", + "\n", + "**Why ~29% of detections are unmatched (verified 2026-06-11):** each det coadd extends\n", + "~30″ beyond its truth tile's footprint, and the catalog was matched per tile — so\n", + "detections in that margin (~21% of rows, >99% unmatched) could not see their truth\n", + "object, which lives in the *neighboring* tile's index (they are also duplicates of\n", + "detections in the neighboring tile's det file). Inside the truth footprint the unmatched\n", + "rate is only ~0.1–5% per tile, consistent with the paper's 1.7%. So `matched=False` is\n", + "mostly tile-edge geometry, **not** a spurious-detection rate; the matched-only cuts used\n", + "below also conveniently drop the margin duplicates. We only pull the\n", + "columns needed here (~19.2M rows). SExtractor sentinel mags (99) are set to NaN.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ecced6fb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-11T18:11:15.203924Z", + "iopub.status.busy": "2026-06-11T18:11:15.203518Z", + "iopub.status.idle": "2026-06-11T18:11:16.881573Z", + "shell.execute_reply": "2026-06-11T18:11:16.880971Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19,205,425 detections, 13,580,663 matched (70.7%); 192,380 matched-star rows\n" + ] + } + ], + "source": [ + "usecols = ([\"alphawin_j2000\", \"deltawin_j2000\", \"mag_auto\", \"magerr_auto\",\n", + " \"flags\", \"class_star\", \"det_sn\", \"awin_world\",\n", + " \"matched\", \"match_sep_arcsec\", \"truth_ind\", \"truth_gal_star\",\n", + " ]\n", + " + [f\"truth_mag_{b}\" for b in BANDS]\n", + " + [f\"mag_auto_{b}\" for b in BANDS] + [f\"magerr_auto_{b}\" for b in BANDS])\n", + "cat = pq.read_table(CAT_PATH, columns=usecols).to_pandas()\n", + "\n", + "for b in BANDS: # SExtractor unmeasured -> 99\n", + " bad = (cat[f\"mag_auto_{b}\"] > 90) | (cat[f\"magerr_auto_{b}\"] > 90) | (cat[f\"magerr_auto_{b}\"] <= 0)\n", + " cat.loc[bad, [f\"mag_auto_{b}\", f\"magerr_auto_{b}\"]] = np.nan\n", + "\n", + "print(f\"{len(cat):,} detections, {int(cat.matched.sum()):,} matched \"\n", + " f\"({cat.matched.mean():.1%}); {int(((cat.truth_gal_star == 1) & cat.matched).sum()):,} matched-star rows\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "e4381098", + "metadata": {}, + "source": [ + "## Truth-star denominator\n", + "\n", + "Detection efficiency needs *all* true stars, including undetected ones — that lives in the\n", + "per-tile truth indices, not the detection-centric parquet. Build (once) a cached parquet of\n", + "the true stars (`gal_star == 1`) from the 1039 real tiles, deduplicated on truth `ind`\n", + "(tiles overlap at the edges).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "24956ad3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-11T18:11:16.889162Z", + "iopub.status.busy": "2026-06-11T18:11:16.888620Z", + "iopub.status.idle": "2026-06-11T18:11:17.282382Z", + "shell.execute_reply": "2026-06-11T18:11:17.281480Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "230,820 truth-star rows -> 209,024 unique stars\n" + ] + } + ], + "source": [ + "def load_gz_fits(path):\n", + " with gzip.open(path, \"rb\") as f:\n", + " raw = f.read()\n", + " with fits.open(io.BytesIO(raw)) as h:\n", + " return Table(h[1].data)\n", + "\n", + "def stars_from_tile(path):\n", + " tile = Path(path).name.replace(\"dc2_index_\", \"\").replace(\".fits.gz\", \"\")\n", + " t = load_gz_fits(path)\n", + " sel = np.asarray(t[\"gal_star\"]) == 1\n", + " if not sel.any():\n", + " return None\n", + " df = pd.DataFrame({\"ind\": np.asarray(t[\"ind\"], \"i8\")[sel],\n", + " \"ra\": np.asarray(t[\"ra\"], \"f8\")[sel],\n", + " \"dec\": np.asarray(t[\"dec\"], \"f8\")[sel]})\n", + " for b in BANDS:\n", + " m = np.asarray(t[f\"mag_{b}\"], \"f8\")[sel]\n", + " m[m == 0.0] = np.nan # 0.0 = no flux in band\n", + " df[f\"mag_{b}\"] = m\n", + " df[\"tile\"] = tile\n", + " return df\n", + "\n", + "if TRUTH_STARS_CACHE.exists():\n", + " truth_stars = pd.read_parquet(TRUTH_STARS_CACHE)\n", + "else:\n", + " det_tiles = {Path(p).name.replace(\"dc2_det_\", \"\").replace(\".fits.gz\", \"\")\n", + " for p in glob.glob(str(DC2_DIR / \"det/dc2_det_*.fits.gz\"))}\n", + " paths = [p for p in sorted(glob.glob(str(DC2_DIR / \"truth/dc2_index_*.fits.gz\")))\n", + " if Path(p).name.replace(\"dc2_index_\", \"\").replace(\".fits.gz\", \"\") in det_tiles\n", + " and os.path.getsize(p) > 1000]\n", + " print(f\"reading {len(paths)} truth tiles ...\")\n", + " # fork context: py3.14 defaults to forkserver, which can't pickle notebook-defined funcs\n", + " with ProcessPoolExecutor(32, mp_context=mp.get_context(\"fork\")) as ex:\n", + " frames = [df for df in ex.map(stars_from_tile, paths, chunksize=8) if df is not None]\n", + " truth_stars = pd.concat(frames, ignore_index=True)\n", + " truth_stars.to_parquet(TRUTH_STARS_CACHE)\n", + "\n", + "n_rows = len(truth_stars)\n", + "truth_stars = truth_stars.drop_duplicates(\"ind\").reset_index(drop=True)\n", + "print(f\"{n_rows:,} truth-star rows -> {len(truth_stars):,} unique stars\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "147f35f8", + "metadata": {}, + "source": [ + "## 1. Photometric error vs true F158 magnitude (star-classified objects)\n", + "\n", + "Matched detections with `class_star > {cut}` and clean flags, observed F158 `magerr_auto`\n", + "against the true F158 magnitude, with the binned median overlaid.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ae3481ab", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-11T18:11:17.284123Z", + "iopub.status.busy": "2026-06-11T18:11:17.283915Z", + "iopub.status.idle": "2026-06-11T18:11:17.647300Z", + "shell.execute_reply": "2026-06-11T18:11:17.646721Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "531,485 star-classified matched detections with F158 photometry\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sel = (cat.matched & (cat.class_star > CLASS_STAR_CUT) & (cat[\"flags\"] < FLAG_CUT))\n", + "pe = cat.loc[sel, [f\"truth_mag_{BAND}\", f\"magerr_auto_{BAND}\"]].dropna()\n", + "x, y = pe[f\"truth_mag_{BAND}\"].values, pe[f\"magerr_auto_{BAND}\"].values\n", + "print(f\"{len(pe):,} star-classified matched detections with F158 photometry\")\n", + "\n", + "med, lo, hi = (np.full(MAG_MID.size, np.nan) for _ in range(3))\n", + "ib = np.digitize(x, MAG_BINS) - 1\n", + "for i in range(MAG_MID.size):\n", + " yy = y[ib == i]\n", + " if yy.size >= 20:\n", + " lo[i], med[i], hi[i] = np.percentile(yy, [16, 50, 84])\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 5))\n", + "hb = ax.hexbin(x, np.log10(y), gridsize=180, bins=\"log\", mincnt=1, cmap=\"viridis\")\n", + "ax.plot(MAG_MID, np.log10(med), \"r-\", lw=2, label=\"median\")\n", + "ax.plot(MAG_MID, np.log10(lo), \"r--\", lw=1, label=\"16/84%\")\n", + "ax.plot(MAG_MID, np.log10(hi), \"r--\", lw=1)\n", + "ax.axhline(np.log10(2.5 / np.log(10) / SNR_DEPTH), color=\"w\", ls=\":\", lw=1.5,\n", + " label=f\"S/N = {SNR_DEPTH}\")\n", + "ax.set(xlabel=\"true F158 mag\", ylabel=r\"$\\log_{10}\\,\\sigma_{\\rm F158}^{\\rm obs}$ [mag]\",\n", + " xlim=(15, 29), title=f\"Observed F158 photo-error, class_star > {CLASS_STAR_CUT}, flags == 0\")\n", + "fig.colorbar(hb, ax=ax, label=\"N (log)\")\n", + "ax.legend(loc=\"upper left\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "5b19ebfd", + "metadata": {}, + "source": [ + "## 2. Detection & classification efficiency for true stars\n", + "\n", + "Denominator: all unique true stars. A star counts as **detected** if any S/N>5 detection\n", + "matched its `ind` **and passed the paper's `flags == 0` cut** (Troxel et al. selection 1 —\n", + "a star whose only detection is flagged would not appear in the science catalog); for\n", + "classification we take its nearest clean matched detection and ask whether `class_star > {cut}`.\n", + "\n", + "**Gotcha (found 2026-06-11):** ~26% of truth stars have a *duplicate* truth entry at the\n", + "exact same position under a different `ind`, carrying only the J129 mag (other bands 0.0).\n", + "The det→truth mag-tiebreak can assign the detection to the duplicate, which makes the real\n", + "entry look undetected (a spurious, flat ~12% efficiency deficit). We therefore collapse\n", + "truth stars to unique *positions* and count a star detected/classified if **any** member\n", + "entry matched. Curves:\n", + "\n", + "- `detection_eff` = detected / all\n", + "- `classification_eff` = correctly classified / detected\n", + "- `classification_detection_eff` = product (detected **and** classified)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7a7ec629", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-11T18:11:17.648850Z", + "iopub.status.busy": "2026-06-11T18:11:17.648664Z", + "iopub.status.idle": "2026-06-11T18:11:17.916833Z", + "shell.execute_reply": "2026-06-11T18:11:17.916260Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "class_star of matched true stars: p10/50/90 = [0.765 0.942 0.996]\n", + "209,024 unique-ind stars -> 181,502 unique-position stars\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "star_dets = (cat.loc[cat.matched & (cat.truth_gal_star == 1) & (cat[\"flags\"] < FLAG_CUT),\n", + " [\"truth_ind\", \"match_sep_arcsec\", \"class_star\"]]\n", + " .sort_values(\"match_sep_arcsec\").drop_duplicates(\"truth_ind\"))\n", + "print(f\"class_star of matched true stars: p10/50/90 = \"\n", + " f\"{np.percentile(star_dets.class_star, [10, 50, 90]).round(3)}\")\n", + "\n", + "detected_ind = set(star_dets.truth_ind.astype(\"i8\"))\n", + "classified_ind = set(star_dets.loc[star_dets.class_star > CLASS_STAR_CUT, \"truth_ind\"].astype(\"i8\"))\n", + "\n", + "# collapse duplicate truth entries (same exact position, different ind, single-band payload):\n", + "# a position counts as detected/classified if ANY of its member inds matched\n", + "ts = truth_stars.copy()\n", + "ts[\"is_det\"] = ts[\"ind\"].isin(detected_ind)\n", + "ts[\"is_cls\"] = ts[\"ind\"].isin(classified_ind)\n", + "stars = (ts.groupby([\"ra\", \"dec\"], sort=False, as_index=False)\n", + " .agg(mag=(f\"mag_{BAND}\", \"max\"), is_det=(\"is_det\", \"any\"), is_cls=(\"is_cls\", \"any\")))\n", + "print(f\"{len(ts):,} unique-ind stars -> {len(stars):,} unique-position stars\")\n", + "\n", + "mag = stars[\"mag\"].values\n", + "ok = np.isfinite(mag)\n", + "is_det = stars[\"is_det\"].values\n", + "is_cls = stars[\"is_cls\"].values\n", + "\n", + "n_all = np.histogram(mag[ok], MAG_BINS)[0]\n", + "n_det = np.histogram(mag[ok & is_det], MAG_BINS)[0]\n", + "n_cls = np.histogram(mag[ok & is_cls], MAG_BINS)[0]\n", + "with np.errstate(invalid=\"ignore\"):\n", + " eff_det = n_det / n_all\n", + " eff_cls = np.where(n_det > 0, n_cls / np.maximum(n_det, 1), np.nan)\n", + " eff_both = n_cls / n_all\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 5))\n", + "ax.plot(MAG_MID, eff_det, \"o-\", ms=3, label=\"detection_eff\")\n", + "ax.plot(MAG_MID, eff_cls, \"s-\", ms=3, label=\"classification_eff (of detected)\")\n", + "ax.plot(MAG_MID, eff_both, \"^-\", ms=3, label=\"classification_detection_eff\")\n", + "ax.set(xlabel=\"true F158 mag\", ylabel=\"efficiency\", ylim=(-0.02, 1.05), xlim=(15, 29),\n", + " title=f\"True stars (N={ok.sum():,}), class_star > {CLASS_STAR_CUT}\")\n", + "ax.grid(alpha=0.3); ax.legend(loc=\"lower left\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "103445b4", + "metadata": {}, + "source": [ + "## 3. False star-classification of small true galaxies\n", + "\n", + "True galaxies (`truth_gal_star == 0`) with measured semi-major axis\n", + "`awin_world < 0.3″` — the truth index carries no size, so we use the SExtractor windowed\n", + "size of the (nearest) matched detection. Plotted: fraction classified as a star\n", + "(`class_star > {cut}`) among *detected* small galaxies, vs true F158 mag.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7bc997de", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-11T18:11:17.918328Z", + "iopub.status.busy": "2026-06-11T18:11:17.918127Z", + "iopub.status.idle": "2026-06-11T18:11:22.940306Z", + "shell.execute_reply": "2026-06-11T18:11:22.939619Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "measured size of true galaxies p10/50/90 = [0.063 0.105 0.199] arcsec\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gal = (cat.loc[cat.matched & (cat.truth_gal_star == 0) & (cat[\"flags\"] < FLAG_CUT),\n", + " [\"truth_ind\", \"match_sep_arcsec\", \"class_star\", \"awin_world\", f\"truth_mag_{BAND}\"]]\n", + " .sort_values(\"match_sep_arcsec\").drop_duplicates(\"truth_ind\"))\n", + "gal[\"size_arcsec\"] = gal.awin_world * 3600.0\n", + "print(f\"measured size of true galaxies p10/50/90 = \"\n", + " f\"{np.percentile(gal.size_arcsec.dropna(), [10, 50, 90]).round(3)} arcsec\")\n", + "\n", + "small = gal[gal.size_arcsec < GAL_SIZE_MAX].dropna(subset=[f\"truth_mag_{BAND}\"])\n", + "mg = small[f\"truth_mag_{BAND}\"].values\n", + "fs = (small.class_star > CLASS_STAR_CUT).values\n", + "n_gal = np.histogram(mg, MAG_BINS)[0]\n", + "n_false = np.histogram(mg[fs], MAG_BINS)[0]\n", + "with np.errstate(invalid=\"ignore\"):\n", + " eff_false = np.where(n_gal >= 20, n_false / np.maximum(n_gal, 1), np.nan)\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 5))\n", + "ax.plot(MAG_MID, eff_false, \"o-\", ms=3, color=\"C3\",\n", + " label=f\"P(class_star > {CLASS_STAR_CUT})\")\n", + "ax.set(xlabel=\"true F158 mag\", ylabel=\"false star-classification rate\",\n", + " xlim=(15, 29), ylim=(-0.02, 1.05),\n", + " title=f\"Detected true galaxies, size < {GAL_SIZE_MAX}″ (N={len(small):,})\")\n", + "ax.grid(alpha=0.3); ax.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "758eac66", + "metadata": {}, + "source": [ + "## Magnitude distributions: true vs observed\n", + "\n", + "Number counts per band: **solid** = true mags of matched sources (deduplicated on\n", + "`truth_ind`, so one entry per true source), **dotted** = observed `mag_auto` of all\n", + "S/N>5 detections (matched + unmatched).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ff643fec", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-11T18:11:22.941765Z", + "iopub.status.busy": "2026-06-11T18:11:22.941651Z", + "iopub.status.idle": "2026-06-11T18:11:29.634361Z", + "shell.execute_reply": "2026-06-11T18:11:29.633373Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nbins = np.arange(15.0, 30.5, 0.2)\n", + "truth_uniq = cat.loc[cat.matched].drop_duplicates(\"truth_ind\")\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(12, 8), sharex=True, sharey=True)\n", + "for ax, b in zip(axes.ravel(), BANDS):\n", + " ax.hist(truth_uniq[f\"truth_mag_{b}\"].dropna(), bins=nbins, histtype=\"step\",\n", + " lw=1.8, color=\"C0\", label=\"true (matched, unique)\")\n", + " ax.hist(cat[f\"mag_auto_{b}\"].dropna(), bins=nbins, histtype=\"step\",\n", + " lw=1.8, ls=\":\", color=\"C3\", label=\"obs (all detections)\")\n", + " ax.set_yscale(\"log\")\n", + " ax.set_title(b)\n", + " ax.grid(alpha=0.3)\n", + "axes[0, 0].legend(loc=\"upper left\")\n", + "for ax in axes[1]:\n", + " ax.set_xlabel(\"mag\")\n", + "for ax in axes[:, 0]:\n", + " ax.set_ylabel(\"N / 0.2 mag\")\n", + "fig.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "9b97b3c0", + "metadata": {}, + "source": [ + "## 4. Depth maps at nside=1024 (desqr `depth.py` recipe)\n", + "\n", + "Per band, following [kadrlica/desqr depth.py](https://github.com/kadrlica/desqr/blob/main/desqr/depth.py):\n", + "\n", + "1. cut the bright end (peak of the mag histogram − 3) and mag < 30;\n", + "2. estimate the global slope of log10(magerr) vs mag from nearest-neighbour pairs\n", + " (KDE peak of the pairwise ratio, as in `depth.py`);\n", + "3. per object, extrapolate to the mag where S/N = 5\n", + " (`magerr = 2.5/ln(10)/5 ≈ 0.2171`);\n", + "4. take the **median per healpix pixel** (nside=1024, ring).\n", + "\n", + "Following the paper's selections, only **matched, `flags == 0`** detections are used;\n", + "set `STARS_ONLY=True` to further restrict to star-classified objects.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8f4f7263", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-11T18:11:29.639439Z", + "iopub.status.busy": "2026-06-11T18:11:29.639169Z", + "iopub.status.idle": "2026-06-11T18:11:41.402543Z", + "shell.execute_reply": "2026-06-11T18:11:41.401682Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Y106: slope=0.251 covered pixels=5,185 median maglim=26.77\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "J129: slope=0.238 covered pixels=5,185 median maglim=27.11\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H158: slope=0.240 covered pixels=5,185 median maglim=27.11\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "F184: slope=0.257 covered pixels=5,185 median maglim=26.67\n" + ] + } + ], + "source": [ + "STARS_ONLY = False\n", + "MIN_PER_PIX = 10\n", + "\n", + "def desqr_maglim(df, mag_col, magerr_col, nside=NSIDE, snr=SNR_DEPTH,\n", + " n_slope=300_000, seed=42):\n", + " d = df[[\"alphawin_j2000\", \"deltawin_j2000\", mag_col, magerr_col]].dropna()\n", + " m = d[mag_col].values\n", + " h, edges = np.histogram(m, bins=np.arange(17, 30, 0.1))\n", + " mag_bright_end = edges[np.argmax(h)] - 3.0\n", + " d = d[(m > mag_bright_end) & (m < 30.0)]\n", + " m, merr = d[mag_col].values, d[magerr_col].values\n", + " ra, dec = d[\"alphawin_j2000\"].values, d[\"deltawin_j2000\"].values\n", + "\n", + " # slope of log10(magerr) vs mag from NN pairs (on a subsample, for speed)\n", + " rng = np.random.default_rng(seed)\n", + " idx = rng.choice(len(d), size=min(n_slope, len(d)), replace=False)\n", + " xx = np.radians(ra[idx]) * np.cos(np.radians(dec[idx]))\n", + " yy = np.radians(dec[idx])\n", + " _, nn = cKDTree(np.c_[xx, yy]).query(np.c_[xx, yy], k=2, workers=-1)\n", + " j = nn[:, 1]\n", + " with np.errstate(divide=\"ignore\", invalid=\"ignore\"):\n", + " ratio = ((np.log10(merr[idx][j]) - np.log10(merr[idx]))\n", + " / (m[idx][j] - m[idx]))\n", + " ratio = ratio[np.isfinite(ratio)]\n", + " values = np.linspace(0.0, 1.0, 1000)\n", + " slope = values[np.argmax(gaussian_kde(ratio[::max(1, ratio.size // 100_000)])(values))]\n", + "\n", + " magerr_at_snr = (2.5 / np.log(10)) / snr\n", + " maglims = m - (np.log10(merr) - np.log10(magerr_at_snr)) / slope\n", + "\n", + " pix = hp.ang2pix(nside, ra, dec, lonlat=True)\n", + " g = pd.Series(maglims).groupby(pix)\n", + " med, cnt = g.median(), g.size()\n", + " med = med[cnt >= MIN_PER_PIX]\n", + " out = np.full(hp.nside2npix(nside), hp.UNSEEN)\n", + " out[med.index.values] = med.values\n", + " return out, slope\n", + "\n", + "depth_src = cat[(cat[\"flags\"] < FLAG_CUT) & cat.matched]\n", + "if STARS_ONLY:\n", + " depth_src = depth_src[depth_src.class_star > CLASS_STAR_CUT]\n", + "\n", + "maglim_maps, slopes = {}, {}\n", + "for b in BANDS:\n", + " maglim_maps[b], slopes[b] = desqr_maglim(depth_src, f\"mag_auto_{b}\", f\"magerr_auto_{b}\")\n", + " cov = maglim_maps[b] != hp.UNSEEN\n", + " print(f\"{b}: slope={slopes[b]:.3f} covered pixels={cov.sum():,} \"\n", + " f\"median maglim={np.median(maglim_maps[b][cov]):.2f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "53a0e9b7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-11T18:11:41.404451Z", + "iopub.status.busy": "2026-06-11T18:11:41.404173Z", + "iopub.status.idle": "2026-06-11T18:11:45.197197Z", + "shell.execute_reply": "2026-06-11T18:11:45.195973Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wrote roman_dc2_maglim_f106_nside1024.fits.gz (median 26.77) and roman_dc2_maglim_f106_nside1024_5sigps.fits.gz (median 26.90)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wrote roman_dc2_maglim_f129_nside1024.fits.gz (median 27.11) and roman_dc2_maglim_f129_nside1024_5sigps.fits.gz (median 26.90)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wrote roman_dc2_maglim_f158_nside1024.fits.gz (median 27.11) and roman_dc2_maglim_f158_nside1024_5sigps.fits.gz (median 26.90)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wrote roman_dc2_maglim_f184_nside1024.fits.gz (median 26.67) and roman_dc2_maglim_f184_nside1024_5sigps.fits.gz (median 26.20)\n" + ] + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(13, 8))\n", + "for ax, b in zip(axes.ravel(), BANDS):\n", + " mlm = maglim_maps[b]\n", + " cov = mlm != hp.UNSEEN\n", + " vmin, vmax = np.percentile(mlm[cov], [2, 98])\n", + " plt.axes(ax)\n", + " hp.cartview(mlm, lonra=[50.5, 56.5], latra=[-42.3, -37.7], hold=True,\n", + " title=f\"{b} maglim (S/N={SNR_DEPTH})\", unit=\"mag\",\n", + " min=vmin, max=vmax, badcolor=\"0.9\")\n", + "plt.show()\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 4.5))\n", + "for b in BANDS:\n", + " mlm = maglim_maps[b]\n", + " ax.hist(mlm[mlm != hp.UNSEEN], bins=80, histtype=\"step\", lw=1.5, label=b)\n", + "ax.set(xlabel=f\"maglim (S/N={SNR_DEPTH}) [mag]\", ylabel=\"N pixels (nside=1024)\")\n", + "ax.legend(); ax.grid(alpha=0.3)\n", + "plt.show()\n", + "\n", + "OUT_DIR.mkdir(parents=True, exist_ok=True)\n", + "for b in BANDS:\n", + " fb = \"f\" + b[1:]\n", + " mlm = maglim_maps[b]\n", + " cov = mlm != hp.UNSEEN\n", + " # measured (catalog-error) convention\n", + " fname = OUT_DIR / f\"roman_dc2_maglim_{fb}_nside{NSIDE}.fits.gz\"\n", + " hp.write_map(fname, mlm, overwrite=True, dtype=np.float32)\n", + " # official 5-sigma point-source convention: shift the map median onto the\n", + " # simulated survey's official depth, keeping the measured spatial structure\n", + " norm = np.full_like(mlm, hp.UNSEEN)\n", + " norm[cov] = mlm[cov] - np.median(mlm[cov]) + SIM_DEPTH_5SIG[b]\n", + " fname_n = OUT_DIR / f\"roman_dc2_maglim_{fb}_nside{NSIDE}_5sigps.fits.gz\"\n", + " hp.write_map(fname_n, norm, overwrite=True, dtype=np.float32)\n", + " print(f\"wrote {fname.name} (median {np.median(mlm[cov]):.2f}) and \"\n", + " f\"{fname_n.name} (median {SIM_DEPTH_5SIG[b]:.2f})\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "20c12df0", + "metadata": {}, + "source": [ + "## 5. streamobs tables\n", + "\n", + "Same formats as the LSST files in `data/others/` (these are what\n", + "`streamobs.surveys.SurveyFactory.set_completeness` / `set_photo_error` read):\n", + "\n", + "- `roman_photoerror_f158.csv` — `# delta_mag,log_mag_err`, with\n", + " `delta_mag = true mag − maglim` at the object's nside=1024 pixel and\n", + " `log_mag_err` the binned **median** log10 of the observed F158 magerr of\n", + " star-classified objects.\n", + "- `roman_stellar_efficiency_cutf158.csv` —\n", + " `# mag_f158,delta_mag,detection_eff,classifiction_eff,classification_detection_eff`\n", + " (the `classifiction_eff` typo is intentional: `set_completeness` looks that name up).\n", + " `delta_mag` is keyed to the **official 5σ point-source depth of the simulated\n", + " survey** (F158: 26.9, Troxel+23/Akeson+19) — *not* the measured map median, which runs\n", + " ~0.2 mag deeper because SExtractor underestimates correlated noise in the coadds.\n", + " Pair these tables at runtime with maglim maps in the same official convention, e.g.\n", + " the `_5sigps` maps written above, or an HLWAS exptime-scaled map normalized to the\n", + " [STScI median 5σ point-source depths](https://roman-docs.stsci.edu/roman-community-defined-surveys/high-latitude-wide-area-survey)\n", + " (wide F158 26.2; medium F106 26.5 / F129 26.4 / F158 26.4).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "882cdd12", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-11T18:11:45.206572Z", + "iopub.status.busy": "2026-06-11T18:11:45.206306Z", + "iopub.status.idle": "2026-06-11T18:11:45.765837Z", + "shell.execute_reply": "2026-06-11T18:11:45.764547Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "measured map median = 27.107; reference maglim (official 5sig pt-src) = 26.90 (catalog errors run +0.21 mag past official)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wrote data/surveys/roman_hlwas/roman_photoerror_f158.csv (145 rows, delta_mag -16.08 .. 1.44)\n", + "wrote data/surveys/roman_hlwas/roman_stellar_efficiency_cutf158.csv (56 rows)\n" + ] + }, + { + "data": { + "image/png": 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QgNm2bZtp2w9Bl/zgwBoOouGE0iNzeD7xWNgORLBEC6bgb2I0NAI42JZEposWTMFnFTJWtJ4veHy8llovF/OAihbIwfLll6GNx162bJkpAIXnF4+BjCRP6kFBREXDgIqDIVqPrBB3KvlxFnwZjh07VoYPH67OozGseUBFgy9EfGnihCAKSofmz5+vNiDtxVLzWyIiT4edI23UINLtqfg+/vhjU8NTwE44ToAJdQVliSLIgqP706ZNk19++UXtOKKUFpkrZDvzch9kXCCggp1mbce5oHIfDYIFyPpAcAalPwiQoLQlv4wPZ9HKfrRmsLkPHiGzBqXVWN7iNoLFuiIwg2AUMkEQWEGmDrYzsQ1nbXDJEtwPZTs4IbCCv4G/pTXbxXOPA20o88HfRkAG2S+5A1V4LRFQ0cqM8H+GYAwyW7SSrb59++b5+2gsjGAKbqNlMeH+CGbi4N7gwYMLbAiN2+O9geXkZDQiz8SAioMhsu2oEpuiBFRcoWzZsirKj1TKwmgpotroZCIicnx2CnZgkOpOxZd72oolkyZNUqfc8F39wgsvqBPZL6CCgADKRZBZok28wWXYiS8MdtxRuqNNvcF2ii1jqB1FK/vRaOU+5uyZXYEMFgQ7rl69qk6A7ds+ffrYraQaWVqWmi9rGTfoF5Pf9izup00kQpAJZU/bt283TXlC3xZMZkJmiwZZR8jiwX2wrYwSIvytffv2qf9jZJlh2zW/Ukh8hiJ7Brft2LGjNG/e3C7PAxHpCwsEyW6Q+qltqJjDkbrcndfxpWbJ8uXL1U92aSciciyUCmif2cxOISMHVLDDjx1x84wUlIhYG3BAVgQyJlDqgkwXd6CV/WgKK+spLmS8mGdqIBtm6NCh6qCZsxR0cBCBLy1DB4EfHJhDxgpoATCURmFCEwIgmGqEE4IpCK6hBB0BNgSWkcmC1xrQBFcrQ8odTFm3bp0pgIrgtHlGFBF5DmaokE1mz56tovG5Pfnkk6qkB7Xe+ILFxjk2YHCU4IcfflBfWOblPrgNjojefffdqq4VRxAwdg+lPtgoePzxx/nKEBE5CFL20bNA27HMPYmNyGgBFUBABdkH2nlrIfMBEwvdjVb2o513JAQssG2HqVPIxEBgyd1KXPCaolEuyngQ7AAEfNDQGRlGeO1zTxlC1hKybHJ/BmIdcTAQ2694TPPsE226kdYMGND3CJ+pXbp0ccq6EpH7YECFbPLdd99ZvHzQoEHy1FNPqXRTRP9Rj4pUSXyRIQ3ypZdeUumampkzZ6ojA7///rs6ioAvImzUo2HfG2+8wZF3REQOhCO3Wh8JTvYhozel1QIqyOjANBpkHBQ03Ucv0DAXDVfRTNoZkwyRqZO7qaw7QeNmZNGgd4oWTMO2J4JB2thoBFUQCMJBPWRD431gqXEweg6iXB2ZfBiVjfVGRhOCKWjQi8u11wDPPQI2GPeMzBZMHsoNrxMeB38vv1HbRKRPDKiQVdDYDKfCYDSyNdCo19ZmvQi44IsMtJ+FwfQEa29LROQJ0FsLR5m1o9rOTNkncmWGCmilHEaAzJn+/fu7ejHchlb2g8AGIAii9YZCEAX9b3A93hcIbBRW8oVJPwic4Pb4iQALJnNpZT7oK9O5c2f1mYpsFtwOzXrNm9/Gx8ery7RsFpTB43HQq4WIjIEBFSIiIg+C9HXtyD17p5CnBVTI2JB1gs84lPJoWSnmEEAuaEy2OWSaIACDCUHIbEGg5tq1a6ZgijbdCMERfJZu3rxZBVtwQqkQSotwHv1WNDiPzGxkyBCRMTCgQkRE5CFwJFXrIYGjpBjRS2REyE7VAiq5p9+QcaEB7QMPPKCyd+wx2hpZKgiooIG31sQbmS4tWrTI0UOmQYMGcvDgQVXuro1J16AMCaWVCMig5w2a4zKgQmQcDKgQERF5CGzQYycTOwIYK0pkrez0dIn9Y6FknD8vPuFhUmbIECnhxmUL6JOiZSIwQ8WzIDvFngEaTP+5cOGCKhHq0aOHxQbAKDdCz8AVK1ao33FbjLIODw9XwRYEd/DZi4AKMlcQ8HO3pr5EVDQMqBAREXkANP/WslMwXc1S40Si/IIpEY8/ISm7dpkui1+yVMJnznDboIr5CFsGVKg4UNpz6NAh9blZ0EQ0BE8wpAGZgGiQm7tPCq7HJCA0BI+JiWH/KiKDyNvWmoiIiAyZnYIR9cxOIVvFLlyYI5gCyTt2qMvdldYnCBhQoeJmvNxxxx1WjZevWrWqCpxYajqL+6P8B8xHNxORvjGgQkREZHA4YoqRnYD085CQEFcvEulIRtR5i5enR0SKu9IyVLBjy4kq5A5QFqSN60YfFc2lS5dk7ty5snXrVhcuHREVFQMqREREBoemipg6gQ36Vq1auXpxSGd8wm7tBOaWtO1vuZmeLu6IE37IHSF7BS5fvqz6/OC0bt06lT2IsqLTp0+7ehGJyEYMqBARERkYNthPnTplyk6xZ8NG8gxoQBtoYQRt2qHDEjXmScm6Pf2koB4sMfPny5VPPpWYX3+V7IwMcTRO+CF3hAwVlF2iKe358+dl27ZtOcrTMHoZPVY0aKyM4Is2YYiI3A+b0hIRERnY4cOHVUNaZqdQUZXw9ZXwWTNVz5SMyCjxCQuT9KhIuTF9huqlEjFylFSfNk2Stv6lyoPMpwAhmBL5xBhJ3rnTqQ1tmaFC7gg9VCpWrCjR0dGye/du1ZwWGjVqpALfCIBv2LBBBg4cKFevXpWNGzeaboMx92FhYao5brly5Vy8JkSkYUCFiIjIoLBxjjRyaNiwoZQqVcrVi0Q6heBH6IgROS7zqVJFoqe+I2nHj8uZu+5SwRNNzE//keC+fSVh7VpJO3LEYkPb3I9nTwyokLtCUAQBFS1QguBI586d1Zjl9evXy8WLF2XZsmWqtwoyWTS4PU4Ikg8ZMoRBFSI3wZIfIiIigzpy5IgKqiA7pXnz5q5eHDKYsg89JNU++0ykZMkcwRRAkOXal1/mCaZokOniSFoZBSf8kLv2UQF8Nvfo0UP9rFu3rtSpU0ddjqAKgiloII5slbvuukuaNWsmfn5+qgxop1nGFxG5FgMqREREBoQyH22yD46IBgUFuXqRyIBC+vWVkLsGWLzOKzhY/Bo3tnhdyfKOK1nAjigzVMhdISNFm7TWunVrKVu2rDqP3ipdunSR0qVLq/MtWrSQ4cOHS7Vq1VTvFYxuxkmbEoQsFyJyPQZUiIiIDOjo0aOSmpqqNsxRc0/kKJYa1kLFV1+RWr/81+L1CStX5clqsVdTW2Rl4Si+WrbAQKv+BpGz4DN5wIAB0rdv3zxT15CBMmzYMBk1apR06NBBvL1zdmdAFgt6qcCOHTtylAQRkWswoEJWmT17tvoC0E74gEfEfPTo0XLhwgXDP4tYT/P1Nz/98ssvptvNmzdPHnnkEdWrAOmb+GnJuXPn8n28GTNm5Lk9vjSR8lmlShXVA6FJkybywQcfqJ0lIqLcMjMzZf/+/aYNcO5UkrOnAAW2b3+rMe3thraVp06Rck8+KaWHDFHXp+zZI5emTCl0h1Brant5wkS5Pn26XH57gvq9oKCKlp0CzMwid4QslJo1a6rtvtywjY3mtZZg27Jt27bqPHqseMI2OJG7Y1NassmkSZNUfSd25DHqDYGWLVu2qKaH+X34G4WPj4/MmjUrz+Va+iV88803smvXLmnTpo3ExcUV+pj333+/DBo0KN/Hg+3bt0vXrl3VEeaXX35ZBVTQtOz//u//ZM+ePSqIQ0Rk7tixY5KSkqI21lu2bClpaWl8gsh5U4DMpvxYamhbsmxZuTFrlsQt+E386taVcqNH5/vYV/79WY4JQdY0tTUPqDCYSK6GoKD638g1AauoEIipUKGCmgKEXipoZnvjxg25du2aKh/C765aNiJPxIAK2aRfv37SsWNHdX7MmDFSvnx5lSmxePFiue+++wz9bOKowMiRIwu8zZw5c1TmTsmSJeXOO++Uy5cvF3h77OhYekzzI3bfffed+rlp0yZTR/ennnpKpTQvWLBANd7jETgi0qDUQctOQQActfrY8CZy9hSg/FR86UVJP3NGEjdskCsffiR+tWpJqe7dc9wm7cxZufLRR5K4fr3NTW21gAoOhOBUVNzZdP5z5sjnXD32739IyokTEtegvoTee6/Vj13YcuV3vTVjw21dZwTK27VrJ8uXL1ef7T/88INpuxHbnw8//LBVBzldNdKcyGgYUKFiQeYEAiqnT5/OcTmyViZMmKBKVfDBj6yLd999V30BaJDd8thjj8mGDRtk0aJF8tNPP6mjqEOHDlWZHjdv3pQXX3xRfvvtN9VcEYGHzz//PEc9KR7jP//5j8qQwSi5GjVqyBNPPCGvvPKKCoBotODGkiVLZPz48bJ582aV6fH444+r5TK/rb06t1sLG374AkTdrCXx8fHqi1GrmdWg/Af38/X1LfLyEpHxnDhxwjThJHd9PpE7KFGypFT9+COJePAhSTt5Us6/8KKUe/wxyU5Ll5Llykr6uQiJ/fVXRAfzfQzseDpywo89doQdHhxwQWZBQX8X10U89rik7N5dpB30m2lpcu6++9WEqKLcv7DlPjdqlKTuv9WoGwXTCcuWW/XYlt4LGAte6f9eE6+AAEEoI/qddyX19oh6uDbta/Fr2FBSDx+WrFwBbWRYRTz6qIQMHCj+DRrIlc8+k5Rdtj1nOHhXtWpV0zQg84A6MlXQxLYw13/80ebsLyLKiwEVHXDnIyRnz55VP8139pFJ0adPH/Vh/tZbb6nAyLfffivdu3eXjRs35giqwPPPP69SFydOnCh79+6VmTNnqmDHmTNnVCBh6tSpsm7dOvn6669V2Qtur5k2bZo0aNBA+vfvrzac1q5dq0phEIRAoMQcSnB69eqlSmyGDBkiK1askPfff19q1aolY8eOtWp98SVlDsGdMmXKSFFhGV9//XVTWv7kyZPl7rvvznEbBIMQVEJGkHnJD45IvPrqq8U6+kZExoLP23379qnztWvXVp/NWnNOIndSslQpqf7NN3J2+HC5GRsr176aluc23hUqSPlnn5X4pUtz7vh5eYl/kyb5PrY9Jvxgu8vSzubJbt3Ft1YtKVmhvKTu2y+ZZpNWYn/5Rap++KF4ly8vJfz8JOrJsfkGZIqzbeeqzAJLf/fGj3OkVM8ekn76jOqLkxUTk+c5i/n5v1L20UcKfNw4ZFt8+m/JvHKlyDv4uZ/T0vfcozKhEtasldgFCyQzV9aweuzffpfQB+7P9zGz4uLk0qTJed4LCPpEPvZ4vvfD38r998yl7NmrTpZgua5O+1rKPfG4lAwOzve90rNnTzlx5IgEbtsuAQnxcvj6dYmqU0euX79eYEAlPSJCrk2fLnG//2H5+jNn8r0vEeXFgIqD4UMw4+LFot8/I0MuvvmmpB44aLos9tcFUvW9d6VErs7fee6bnS2ZWVlys2RJU9Mrn6pVVa1zUSEogaACeqigt8eUKVNU0MM8CICsEuz0o8cKAiXw6KOPqgatL730kgq4mEPZ0MqVK03LiCANgicPPvigyj6Bp59+Who3bqyCLeYBFQRozMtdnnnmGRUc+eKLL1SGjHnWBzJUUD6jBU/+8Y9/qCDG9OnTrQqoIHtGWx8NmsMiO8ZWyIhB0AnZOPjSQ5Pazz77TO655x7V5HaE2YYDlvPIkSNqORFEATxXCDS9+eabNv9tIjKukydPSkJCgjrP7BRyd77Vq0mZYcPkxsyZea7DTnq1jz8Wr8BAKXPvULVDmXr0qMQtWizZSUly4bnnpdav86WkhYMa9gioYOfVEgQMUnIFDTSph4/ImYG3+6JhmyZXw13sKF9+9z0pM3SIRH/woQpA2Jr9khkTI1c//9wlmQWxf+QNMqWfOiU3Tp0q8H7RH36oXrvQhx4UvwYNJG7RIrVeCJhlJSdJ7M//zRNIMYfsjcLWy1KwB1kj2YX0j4r+6CPJirmhgi+JW7bcer7DwsS3Zk2J++03iV+xotDHyI9f/frqsRLXrs1znW+dOpJ1/bpkxcZavO/1b79VJy8csMzIkJuJif+7bsYMKffEGPW/ETJ9ugru4NoaaHZbvbpcr1s3x/OC90VaZKSkZGbJxehoSVy5EtH3fJcbgZagjh3zlOERkWUMqDgYgimn+w+w62OmHjggZwblzGKwVp0Vf6oviaJCJog5ZHcg6IHUQy1osXv3bnnhhRdyBB9w/UMPPaSCArGxsTmyOlCiY97lvH379ioj5cknn8zxtzA+LncDVm1jCUdgkZWCn8iEwd85fvy4NG/e3HRbBH7wt8zhtig1sgYyQVCvag6Bo6KWBq1atSrHZQg6IWiEoNPw4cNzZMEgMwfZNWhii87wy5Ytk7ffflutE25PRGSenYLyR63nEpE7K5FPya1fnbpqhzF3f5bSAwdKxKOjJSMqSi689LKEffdtngNM9giolAy1nH0aPGCA+FStIokbNkp6rnLnHPKZXoQsFpxyQ0Dk/IsvSUjfvuJdvpwqA9HKU+Dql1+JV3ApyTidf/ZA3MKFEnRHJ/GpWMGqUiTsZKeVCZXskQ+j+Uae67X7h/TrJ/ErV6pAjiVoMhzUubPcTE2RxNVr8t4gK0stG04lAgMl26xpsDkEW8zLfUzrtXixeFesKOXHPyteFg4KZmdmypVPPskT7NECIcgmwrZvys5dee+blCRXP/9CnfKF58ZCpl+lt96SkIF3Sezvv8vVjz7Oc33oqJFS5p578gR6MAELwTPx9laTq5CZk5+bFoJ3GRGRcnnCBIu3L3P+vAT93+sS8dN/1Hqn7N4jmZcu5V2l8uWl7COjJHHjphwlWlpWTtRT/5BS/fpKQPPmcjM2zu0y5IncCQMqZBNkfjRq1EhlqiBbBL1SzLNAkGkBlsYFI1iArJmIiIgcAZWwsJx10AgY5Hc5plYgU0TrG4K/jywNZMugSas5BG7MIRMEPUfMIR0endGtzSrp3bu3OAoaR6KsB2U/CAZh1Cm89957qncM+iJoz829996rAi0oF3rggQdMAS0i8lwok9Smi7Vu3drVi0NkFZ+w6jb1SAls00Yqv/WmXJ40WZL++kuufPpvqfTqKxYDKsVp2J5yMG/2KXaEq334gdqp9K1RQ41yzq38P8dLUIcOErdsmcq8sHbnHBJXr1YnS7KuXFGn/LJf1DLv3iOne/cWr+BguXk7U03Lfgmb/r26H4IZUc88k6Pc5PymjVJj5sx8m6jiuS6on02FF55XAS9L9w1o3VpK9eghsQt+VYEAS8GUgDZtpMrUKeJbvXqe+6N0CoERBB4S1q+T4F69VXaFT7Wq4l2psiSsXSOJa9bmm+lR+p7BUuVf/8Is+TyP7d+0qfg1aSzxfyxUy24p6FDuiSdU0OTiSy/nCYqE3n+fes7KjRolSRs25rleC0AUNAELE66SNm/Jc98q774jGRcuyPUZMyVpy5Y8y1bC3//WMlvINPFJSpLk7dslP8EDB0rVd98RL39/KffYYzmWzb9RI7k8Zao6eJu4cpU6adiwlsgyBlQcDCU2yAopqvg//7QYNa/w3HMSMiBntkh+JT/euUp+igP9T7QpP+hD0q1bN1WagwBAYdkaWtMs82wUyB3kKOxy7XGw89C3b1+VvYFyGWR9ILiDUcKvvfaaOlprzeO5E62prXmQB/1n0EdFC6ZoUB70/fffq5F5DKgQeTZ8LqIHlRaMzl2eSOSusHOJHTVLO6P5CX3gAUk9clRi589X45ez4uPFOzTU1DejuBkqiZu3SMKft7bdgu8aIL7VqufZEc5vucs/+aS6TUCzZpJ+8lSe68O+/05uzJkrVz/91OIO/M2kJMlOSbG4XP4tWkiFcc+oQABKnswfGxkcWSkpkp2QkCOYomW/HG/RMt/1TdmxU463bSclS5eW7KwsVYqSA4IpJUpIqZ49JePCeUk7dtzia1XQ+Gz0A7nw8suSsDzvNnFg69biV7u2Op/7/siOufrVVxIzZ66knzot108VkBVkQUDbtreyoG4vW8zvv0vc8RNS2mzKj1dAoMTMnp3nvqWHDpFyj90a6V3gWHAbx4abK+i+vmFhkh4ZaTGgUunNN1RQzVJQ72q9uhLWooXc3L1H0m/3OjTnW7WqCqbkt2w1//uzXHjlVUnIlZWN91HUuGel7COPSGCrluq+7trjkciZGFBxMHxQFqfEBpHxpK1/5/lCLjfmicK7kmdni1dmpspkyB3EsAcEKDDhB5N+kLnyxhtvSM3b63rs2LE8t8dlWI6iTMKxZOnSpaqXCyb3IL09d6NcPUKQCCpWrGi6LDo6WjIzM/PcVrvM0nVE5FnwuYdJZ8DsFNKTwnZG84MsldTjxyV1/36JW7DAdHnckqWSdUdHlQlSlCl4N1NS5PLkyeo8Gt9W++gjNZXI1uUuMLgw+lFJ2rw53zKQG3PmyJV/vZ/nb5YZPkxKdeumzlt67OybN+XCiy9K4tp1Nq83skAK6mNS5oEHpMrECaq3X0GvVX7BAwQ10JfDUkDFPBvJ0v0rv/GGlCjpLTdu95Ez512lsoTe/4DquRM99Z0CA3N47NLDh0v61atSukIF0+vqV7uWxXX2NdteLWwsuC1jw225b4EBx+zsPNfFhYfL8bvukoo9ekil5s0tBlwKmpCllgf/O/lkPidt2qROaAxdAhOObk/UAmawkKdiQMWgGxrO0qVLFzUSGRki6JtSuXJladOmjcyZM0cFWNBwFjDWDb1WcPviTMUxp41PNh8Xh3Kgr776StwdMlDKli2b4zIETpBxUqdOHVXuowVKMMUIPWVwfaVKlUy31xr2cueJyLOZZ6cgW838c4JID4qyM4rtI/QbQUDFXMqOHVKxdIhEN2umSnVtde3rryXj/Hm1w1h5ymSLwRRrlzvf4EIh23ZlH3xQlbEUFhzI/dg4dIZGopYCKqGjRklwjztV9o2lwETp4cMkoGkzSfr7b0lA09Jc/Bs3smqd7Z2NpMlvEEPpQXdL+X88pc4XdXu5OMvlaIW9V3Jfd9LbW7KvXlWTfhpYWK8SzZtZtV75leIhiyoLEy9v3swRTAGOXCZPxYCKDhTny8sZMMp32LBhMmPGDBk/frx8+umnqtcISoPQWBYb+998840KEHzyySd2+7v4GyjxwRjkp556SgVT5s6dW6QNKHvBBCNtihF6xSQmJso777yjfkd5FE7ac3b+/Hnp3LmzVK1aVd0WjXRxhPnHH3/M8Zjok4KGvmjWi4k/KP1Bds6ff/4pjzzyiBqNSkSeC58f2HgGTvYhT5Jf34yA2Fu9hGzNzk07cUKu/3Cr9ANlDQEFjGYurqKWgRQ1OIA+M7h/YNu2knroUM4+J+jZMXGiuh4TlSJv3HBIcKE462VNr52ibi+7+8HLAt8rua4r99dfcvl2QEVbrzVvvinZly5LSpnS4tO3rzQsxvsIWVRqlPTESRanF2HSlbjvLguRQzCgQsWGXirIqPj444/VDj+CBmvXrlVjizFWGRs0yGL59ddfVQ8We6lfv778/vvvqintq6++qrJhEGBAvxH0VnEFZJKgqaw5TOOBiRMnmgIqGJmM3ijTpk1TQRQESTp16qSyehCIMs+6QY8alABpzWmR3YLpSgjUoFcMEXku8+wUZAgiQEvkKfLbycaOo60BFZTLRKP5Kkqlq1aRCuOfFVdyVHDA/Pq0iAg15afayIet7gfiqvVydBaJux+8tJY23Q0BFXw/xCUlyVkMOdBGKV+6JAkJCRIcHFzg4xT0PvAuX15Kde9mMaCCkdjIhOLIZfIkJbLN99woX8gmQKO/qKgoNS0mN226jdZDxB2oprQO7KHiSkZeN3uunzu+LzHa+urVq6ppph4aBdvCyOtm9PXT67rhOwnZanDXXXdZ/H5yh3Ur7DuU3Oc5d4f3i7UsTZXxDg+XDUOHSHbJkjJ48GAVaLRmnc/PmCHJ//5M/V79m68luEcPMTo9vdZQWP8WI66zra5du6YONgKmQKI3344dO0z9hDARs2XLlirr2d7/e+YTrMo/87SUHzeuwJI5RzP6a50fT1zvLAessy3focxQISIi0mngFVPNABsRnPZFnsb8KPqNH+dI+qlTakpN9u0DEdYckMCO4Y0ff5Tkr6ap30v16eMRwRQ9MkoWiSOhTyHe9/h+QHAFJaGAgRCYfokACyZztm3btlgl8pYyWDDS/MKLL0nasWNy7etvJHnPXil1Z3fJun7D7cqoiOyJARUiIiIdQrNvNKvWmlMbMVuPyNqdbP9GjeXc8OGSdeGClDt9Rq7Xq1voDqOlo+xouIlMCO74kR4hszk0NFSVh+MIu/YdgWmYCLIgoJKSkqIymIvbg8/iyOVf/iuXp0yVuN9/l+Rt29RJwylAZFSu695JRERERaZlp6Bm3l7j6In0KqBpEwm83aet2p7d6mdhQUYcXc9RsoD+K3v3qsuJ9EqbInnixAn1EyUQyGAsVaqUVKlSRV129OhRh/xtL39/qfreu1L6nnvyXKdNASIyGgZUiIiIdOby5cty6dIldZ7ZKUS3lH3sMfWz9IWLUurS5UIzVNJOnbZ4OUoYiPQKQxoAJT6AYIrP7VKbhg0bqp8XLlyQuLhb07AcwbtiRYuXJ+/c5bC/SeQqDKgQERHpNDsFqd3u1HSayJXQr6Fk2K0xutX37C40QyV1//5CR/ES6XXSjwblPubn/f391fljx47lue/OnTtlyZIlkpiY6JAJXPGLF8vl996Tm8nJxXp8InfCgAoREZGOXLlyRdXGQ6tWrdg7hei2El5eEvTAA+p8+RMnJevy5Xyfm4S1ayXFQkDFnqN4idwtoILyn/r165tKgrQsFoiJiZG9e/eq7MctW7aonitFhf8hrQRPU+J2ICdmzlw5c88QSdzyl8TMny9XPvlUYn79VfUuItIjNqW1E3xAYRQZkbuNEdNG5RGRsbJTSpcuXeymgkRGE3DXALn+5Zfik5oqKb8uEJnwdp7bZMbEyKWJk9R5/+bNJWToUIk/eVJKN6gvoffey4a0pGvIQAkKCpKkpCSpVKmSBAYGqu1BDcp+Dhw4oJrTRkZGmrIcDx8+bLoNLj979myRv2MsTQEKGTBArk+fIddnzJCMqCiJGjMmx33YtJb0ihkqdvzwQkDl+vXr9npIomLBexHvSS21k4j0D2MwsaGrZacUZ+wlkSH5+cmlFs3V2aRFiyTLQulC9Dvvqmk+Jfz8pOr770uZ+0ZIwNgnpfTw4QymkCHUqlVL/WzUqJHF0cqVK1fOUfaTlpaWo4ktbN26tVgHi7UpQBVfelH9LFmqlFR84XmpOX+eeFeqZLFpLTJWiPSGGSp2bACFDyOkYsfGxpo+jFwNqXxG3eA28roVd/1wJAJfgsHBwabmZERknOwU/G/XrVvX1YtD5JbfnRdbtJDqu3aLV1KSxC5YIOVGjzZdH79ylcQvW6bOV3jhefGrXSvH0XsiI+jYsaM0b95cTfaxBFkqaG4eFRWl+qVgnHJmZqbaf7nrrrtk2bJlkpycrHqqdO7c2a7LFtCkiYQMGig3Zs7Kc130v95XpXghA+6SzOjLknHhospuQQkRR5mTuzLu3qiTofEZumhj59VdSixQ+4ggT3FqIN2VkdfNHuuH9yDei3hPFtaUj4j04caNG3Lu3Dl1ntkpRJbhezOjVCm50rCBqV9DdmamOp9544ZcnjxZnQ9o00bKjhrFp5EMCQfk8gumAEp5sK2I/5fjx4+byn3q1KmjRiu3bNlS/Y7LcbDY3nzN+rrkkJkp8YuXyPmnn5bLkybL9enT5fLbEyTyiTHssUJuixkqdoQd1woVKoi7wBGXq1evqmVyl4wZezHyunnC+hGR7dAsELCRXK9ePT6FRBZoByIutG4tlQ8fkYyLFyVh9WrVv+HylKmSdeOGao5Z9b13pQS/X8lDeXt7qyzHI0eOyL59+0xZWk2bNlU/EVA5deqUxMfHqyyVgQMH2vXvI+MEPVOSd+40XRbQqpWU6t1LYn76j2ReupSnHAj9WFA6RORuGFAhIiJycyglPX36tDrfokULBlqJ8qFNLUmuUEECOnaQlG3b5foPsxFpkYQVK9R1FV96Kf8j5EQeAmU/CKhowRT0VdHKxBFwwXfN5s2bVYYKApX2zHi21LRWK+u5GRunMlNySz1wUIQBFXJDDKgQERHpJDsF0xoaNLhVykBEeZmXypZ55FEVUEk9cEAuvPqaqdQn9OGH+NSRx0PwBCc0OzfPTtFoWfcZGRkSFxenmtnak9a0NjefsOoWbx+3dKmUHnx3nnHMRK5m6B4q+ACYMGGChIeHq0knaM70888/u3qxiIiIrIaUa6ReA44Y4sghERWcoQKBHTuo8h7ldh8V1U+FTWiJlMaNG5sanWvjkzWhoaGm4Qha0MUZkKmSJ2hSsqRkY8zz409I3NJbTaWJ3IWht8rGjh0rc+bMkXHjxkmzZs1k4cKF8vDDD6su1o888oirF4+IiKhQqG/HUfeAgACLIzCJyHKGSsLiJZKdmprj6Undv5+9GIhuQ8YjDjqXLVs2z2RJ9PDD5Qim4OSsyXKWyoECWrSQ808/IxkXLsjFl1+W9Kgo8S5bVjLOn+cUIHI5byOnR8+ePVumTJkib7/9trpszJgx0rNnT3nllVfkgQcecJtpPERERJZgnOWJEyfUeRwYYHYKkfUZKpnnz1u8DXbSiOjWQI3cmSnmtJIgZ2ao5FcOVHPeLxL19DOSevCgXPv88xzXocFt+MwZHK1MLmHYkp/58+erSCuyU8w/NJ599lnVXGn9+vUuXT4iIiJrslOwg+jn52dKzSYi6zJUfMPCLN4GR7yJqHBak1oEVMz/t1zBu3x5qfHjbPGz0EdMmwJE5AqGzVDZvXu3mqWOVDVzHTp0UD/37Nkj/fr1K7BmHSfNpdvju9AJW+uG7e6wnNgQ18vy2sLI62b09eO66RdfO+dKSkqS48ePq/NNmjRR6ddF+Uxwh9fNiJ9l5J60nT4cRCszdIjEL805mjWwfXvVo4GIrA+opKenS0JCgoSEhLj0afMKDJSgrl0k7fZ3ozlmnpGrGDagcvHiRalSpUqey6tWrWq6viCffvqpTJ48Oc/l169fV0cK9QAb0OjKDbnrIvXOyOtm9PXjuukXXzvnNlXfunWrCkSgzKdSpUpy9epV3b5u+O4kcmbJDwIqBY1mJaLC4cA0/pcQqESWiqWACnpTrly5Ut2uf//+Dv+e8Q0Pt3h5ftOBiBzNsAGVlJQUi4EP/JP7+Pio6wvy4osvqp4r5hkq7du3l3LlypnGiLk77Yggoss4smkkRl43o68f102/+No5BzZOV61aZQqCdO3a1XQwQK+vW1pamkv+Lnluhoq2U5ffaFYiKhwC+pj2c+PGDRVQqV27dp7bnDlzRi5cuKDOI/CPAwCOhKAoeqaYZ55B6pGj6v8fgR0iZzJsQAUdqy1twOHIBY784fqCIAJrKQqLjVE97eBig0Jvy2wtI6+b0deP66ZffO0cC99RGzduNJWZdurUSerVq6f7182In2Pk/iU/RFR8CMZrARVLjh07ZjqP2zk6oGKeeZYeESkpu3ZJyr59EvvLL1IyJEQqvviCQ/8+kccEVHA0LyIiIs/lWqlPcY72EREROWJHcMuWLXLu3Dn1e+vWraVp06Z8oomKUPJjtHJZIlcGVDBtTmtMax6sjI2NlcuXL+cIqDiDeeZZdmamXHjhRUlYvVquf/+9eAUFSfmnxjplOYjAsN822BA9ffp0nn/s7du3m64nIiJyFzt37jQd6WvUqJG0adPG1YtkGD179pS1a9eafp8zZ44pcEXGwgwVIsc0pk1NTVXN0vPLTnFmQMVcCW9vqfrJxxLUpYv6/eq//y3X58yR2F8XSMr30yVuwQLJzshw+nKR5zBsQOW+++5TRym+/vrrHF+yX331leqB0qNHD5cuHxERkebgwYNqRDKgRr1z584sWbAjlFGZH0V97LHHVNNfMnZTWiIqPvSP1JiX/aA/FzJXICgoyBRQccV4ZS9fX6n+5RcS0PbWgYgr7/1LrkyaJKk//yzREydJ5BNjGFQhhzFsQAVH9kaNGiUTJ06U5557TmbMmCF33323bNiwQT744APdTOohIiJjO3nypPz999/qfLVq1VTAn+UK9hUWFmZ6jsFRjQvRo23ChAkSHh6uerU1b95cfv75Z6vui6aOTz75pNSqVUsCAgJUYO2ZZ54pdCohFdyUloiKB8M8ypQpkyeggtYKyFqBdu3aqZ/oX5mcnOySp9wrIEDCvvlGvC1MeU3esUP1XCFyBMP2UAEEUWrUqCGzZ8+Wb7/9VurXry9z586VkSNHunrRiIiIVPr0pk2bTGnVffr0YQNXB3j44YflX//6l/z3v/+V0qVLq8uef/55efPNN/O9DwIuKB22xdixY1U50bhx46RZs2aycOFC9bcxuemRRx7J937x8fHSsWNHNYHw6aefVtsuR44cUdsuK1askEOHDklgYKBNy+KpmKFCZH/4fkK/FPOAilbug76UNWvWNF2OLBUtY8XZSgYHS3Dv3hIzd26e6zA6ncgRDB1Q8fX1lalTp6oTERGRuzlw4IBKm8b31YABA9RPsr933nlHGjZsKOvXr5fo6Gh1ZLVKlSrqZC979+5VB3CmTJkib7/9trpszJgxqn/LK6+8Ig888EC+ry8CL+fPn5fFixerbFoNdlKQZYuSJbw/qHDsoULkmIDKqVOn5Pr16+p/LC4uTn1mAT5b8dlWqlQpSUxMVAEVZAW6il+9uhYv977dC4bI3gwdUCEiInJXSJU+evSoOt+kSRNV5kGOgWwTlAHjpJWDvPrqq/LQQw/Z7W/Mnz9fPS6yU8z/7rPPPivDhw9XwZx+/frlm6ECuQM82u/MTrEeS36IHNeYFlmVCByjvBHQQkHLTilbtqwKqCDo4kplhgyR+CVLJXnnzhyXX587VwI7dhT/BvVdtmxkTCwwJSIicoHDhw+rUpCSJUtyPLKTp/z88MMP0qpVK7v+jd27d0udOnXUToW5Dh06qJ979uzJ977dunVTwZfx48erZrnop7J69WpVkoTrunbtatdlNTKW/BA5JqCC7yrQgimA0kZv71vH57XPvpiYGJe+BCV8fSV81kypNHmS+D/0kATfPUjE21syz5+Xcw88IPF//unS5SPjYYYKERGRk2GDFH0xtHRpZqc4FkpmnnjiCdPvjz/+uOqphvHU9oLmsZZKiNBfQLs+P2hei34pr732mprwpBkyZIhqamtNg1VkuWiZLnDp0iX1EyVlOFkLt0VQwpb7uBNtuRGgsnYd9L7OReWJ6+2J62yP9UYwpW/fvqpkEqU9OIWEhKheKdpjao1rEVDBd5xLG0N7eUmpoUMl9do1FQwqc999cvH5FyTr+nW58MKLErPgN/Fr2EB8w8Ml5J57pISPjxiFJ77HsxywzrY8FgMqREREToZSH0xDwE5fixYt+Pw7acoPGsQ6asoPGspamiCInQpMycD1hS1j27ZtpX///lKvXj3Vk+Wjjz6SBx98UH7//fdCd04+/fRTmTx5cp7LkX5vy2RDbJSiP4K27HqDkgNtY/jq1asesc5F5Ynr7YnrbK/1xudY9erVTb9jmo+liT74W+fOnZPg4GBxm3UOC5NS334jCW9PkJvHjknyX3+pE1z/Y6GU+vgjKXE700bvPPE9ftMB62xL6Zox3jlEREQ6gS/+gwcPqvPYccaRPtL/lB+MSUaQzNLrjaO1uD4/mzdvlkGDBsmWLVvkjjvuUJcNHjxYTSdEn5fffvtNRowYUeDff/HFF1UTXPMMlfbt20u5cuWkQoUKNh+VM0/x15OoqFuTPNAk09r11vs6F5UnrrcnrrOz1hufNZhap5Xd2fK545R1rlBBfEcMl6tT38lxu8x9+8Tvr7+k9PDhYgSe+B7PcsA6W/o+zw8DKkRERE6E0g809tNKPcgYU35Q2oPHzU0r9dFKfyyZPn262hDUgikaBFUQ2Pnrr78KDagg/R6n3LBxaesGJo7wFeV+7kRbB09a56LwxPX2xHV2xnrjcUNDQ9WRfYxYdofnN/c637wcbfF2GKnsDstrL574Hvey8zrb8jgMqBARETkRRk9qR/NyNzAl/U75ad26taxbt06NDDV/Xbdv3266Pj8I8qBBsaWjbihPsnQdWcamtESuowVUXD3pJz8+Yf8rWTKXsnePQ0pByTN4RmEVERGRG8CO8dmzZ9X5unXrunpxPBZeAzR8taf77rtP7cx//fXXpsuwgf7VV1+p1PcePXqoy65duybHUMNv1nugQYMGKhCzcuXKHI/5008/qZ9t2rSx67IaGccmE7mOu0z6KWikcmC7dnkuT9mzV658/LFLlon0jxkqREREToKSEG3kJAMqrlOjRg31E6U0yCq5cuWKGlmMniUox8JIa5QIWSqhyQ+CHsiAmThxomqGinGiCxculA0bNsisWbNMjWERYEHzWJQf3Xnnneoy/G2Mcr733nvlmWeeUb11MGZ5xowZKtjywAMPOOiZMB5mqBC5DjIvISEhQdLT01UvI3eijVSOXbhQlfkgYyXp722S8OefcmPmLPEODZVyZr2oiKzBgAoREZGTy33QTwPjJsk1sKGPIMWiRYtMad5Dhw5VARXUTQ8YMEA1eS2oaa0lCIAgWDN79mw1BhmPh/HMI0eOLPB+WgAFwZhff/1V9V1BTxWMd3733Xc5VrsIGSpM3SdyPvNyR2SpVKpUye1eBoxIDjXrSYWslajEREnavFmufPyJeJUuneN6osIwoEJEROTAnTuMSMYOPMo+IiMj1eXMTnEtBC6WLl2qskV69eqlslE0mMaDBrCLFy+2OaCCo7FTp05Vp/xMmjRJnSwFVX7++Wcb14RyY8kPkesEBgaqbDxMSEEZozsGVCxlrVT//DOJfPwJSdm3Ty5PmCiphw9LyeAQ8QkPUwEXBGGI8sOAChERkQNLfDAK1xwyIGrXrs3n3IUQuBg7dqw8/fTTFpsnIsCCUcWkPyz5IXIdZIYhSwVj21FK2ahRI128HF6BgRL27TdybuQoST91SmJ/mWe6Ln7JUgmfOYNBFcoXm9ISERE5yJEjR/JcVqtWLberK/c0mKrTsmXLfK/HEdbExESnLhPZBzNUiFyrWrVq6ue5c+fUpDK9KFmmjJS20Kw8eccO1XOFKD/MUCEiInKA+Ph4OX/+vDqPCS9o1hcbGyvVq1se20jOgx42Z86cyff6HTt2qMAX6Q97qBC5Fkpad+3apcp+oqKipGbNmrp5SW7GxVm8HA1sifLDDBUiIiIHQO8UrScHSnyQBo2fzE5xveHDh8t3330nJ06cMF2mNTFdsmSJ/Oc//+FkHZ1iyQ+Ra2E6WuXKldV5889YPcDUH4uXh4c5fVlIPxhQISIisjOkOR8/flydx9hb9E0h9zFhwgSVgdK6dWt56KGHVDDlvffek44dO8qQIUPUCORXX33V1YtJRcCSHyLX0xqvoxE7MlX0Ag1oA9u1y3O5T5WqLlke0gcGVIiIiOwM5SSpqanqvF6a8nmSUqVKyV9//SVvvfWWXL16VWURoXkw+qZMnjxZ1q9fry4j/WGGCpHr1alTR7y8vNT/Y0Hlle448Sd81kypPHWKlH38MSlZoYK6/NKbb0pmTIyrF4/cFAMqREREdu6dsmfPHnU+LCxMpT+T+0Hj2f/7v/9Tr1VSUpKkpKTIoUOHVJAldzAlIyNDNm3aJHH51NeT+2APFSL3+HwNDw9X50+ePCl6ghHJoSNGSKVXX5XwGTNUkCUzOlouvf226fOFyBwDKkRERHZy4cIF+eOPP0w73s2bN+dzawA3btxQjYV3797t6kUhKzNUcHSciFynXr166ufly5fVgQY98m9QXyreLv9MXLNWYufNd/UikRvitw0REVExZGZmqpTmFStWyPLly1W9uI+Pj/Tv3980PpL0j0cm9YEZKkTuARkqyFSBU6dOWew1hqa1165dE3cW+vBDUurOO9X56PfflzQL60KejQEVIiKiIkKflAULFsiaNWtU8z3szJUpU0aGDh1qSncmIudhU1oi94Bm7Jhsp029Mw+cxMbGyqJFi2TDhg3qYIQ7B6zRtLzKe+9KyQrlJTs1VS689LLc1FGjXXI8byf8DSIiIkP6+++/TanMVatWVZMNcPL25tcrkSswQ4XIfWDKHYIp6FP1+++/q2a1FSpUkJ07d6oMFUhOTlbXo1m4u/IuW1aqvv++RD0xRtKOH5eop/4hAc2aqXHKmAyEvivkubjFR0REVATnz583Ndvr0KGDtGjRgs8jkYuxhwqR+6hYsaL07dtXtm3bpg4+nD59Wp0gICBANQPX+lS5c0AFSnXuLKGjH5WY2T9K8rZt6gTxS5ZK+MwZDKp4MJb8EBER2QhTXzZv3qzOly9fXpo1a8bnkMgNMEOFyL3UrFlTRowYIZ07dzZNUENJ7PDhwyU4OFj9HqOTkcR+4TXyXJa8Y4fELlzokuUh98AMFSIiIht32FDqk5CQoGqru3XrxokiRG6WoYL/TSJyn34qTZo0kfr166tMlbJly6r/0dDQUPVdigwVPci4dMny5ZFRTl8Wch/MUCEiIrIhmLJlyxY5duyYaSwyMlSIyD2wKS2R+8IEvHLlypkCngisgF4CKj5h1S1fHh7m9GUh98GAChERkZVHvjdu3Kga7AGa67Vr147PnQdAbf/EiRNNEyvIfTFDhUg/tIAKpv5o/7vuDA1oA3N97we2b68uJ8/Fkh8iIiIrHD58WE6cOKHOI22ZpT76l5iYKBEREeroqKWxnXiNISgoSAVUyP2xhwqR/gIqmPiDUqAyZcqIOyvh6yvhs2ZK9AcfSMxP/1GXVZ4yhQ1pPRwDKkRERIVITU2VPXv2mBrsde/enT0adAwNEMePHy/z5883je7MvVOOlHRL15F7Y8kPkX6ULl1afdbi/xaBbXcPqABGJFd89VWJW7hIbiYmSuL69eL32GhXLxa5EAMqREREhdi7d6+kpaWpxnqdOnViMEXnnnrqKfnjjz/k2WefVcExNEYkY2DJD5F+4DsVQRQEuRFQ0UtZpZevr5Tq0UPilyyRhJUrpRwDKh6NARUiIqICxMXFqXIfwHhk9NMgffvzzz/ln//8p3zyySeuXhSyM2aoEOkLAtoIqOhldLImpF9fFVBJ2bdPMqKjxadSJVcvErkIm9ISEREVYMeOHeqod0BAgLRs2ZLPlQH4+flJvXr1XL0Y5ADsoUKkL3qb9KMJ6tJFSgQGqvMJq9e4enHIhRhQISIiyseFCxfk7Nmz6nybNm3E19eXz5UBDB8+XJYvX+7qxSAHlvx4eXETl0hPARU0pc3MzBS98PL3l1K3G5ej7Ic8F0t+iIiIbstOz5CYJb9JRtR58a5eTf66vXNWrlw5adiwIZ8ng3j55ZflwQcflFGjRskzzzyjGg2jlj+3ihUrumT5qOiYoUKkz4AK/ncxPrl8+fKip7KfhBUrJHn3bsm8dk28dbTsZD8MqBARkUfLTk+X2N//kOSjR+Xc9m2ScS7CdF294GCJat9O2r/xhl2PeKu/uXChCtz4hIdJmSFDOHbRiTD2GpMldu/eLT///HO+t+OUH/1hQIVIX4KDg8Xb21tlp6DsR08BFWSolPDzk+y0NElYs1ZCH7jf1YtELsCAChEReRwtoJF+LkIS1q2TjHPnLN7OPyFB6q1dJ0mnz0h0r17iW7OGVcGP/AImuDxmwQK5Pn2GZF66ZLr9jZmzpJQNj0/FM2HCBE5qMiiW/BDpC4LbaEx79epV3fVR8QoKkqCuXSRxzVpJWLWKARUPxYAKERF5jPwCGoVJP3dObsycaQp+BN15p2TF3BDv8hXEp3o1KSEiGRcviXfVKkhrkOszZ+UJmOA+iRs2WAzemD9+/JKlEj5zBoMqDjRp0iRHPjy5EDNUiPRZ9oOAit4m/UBI374qoJK0fbtkxsSId2ioqxeJnIwBFSIi8phgSuQTYyR5585iPQ6CH+mzZzvsPsk7dqjsltARI4q4hGSrhIQEdZSUI7GNk6GC15OI9AEZKqC3DBUo1aOHCLJKMzIkcd16KTPsXlcvEjkZW6ATEZHxs1Lmz5fIp58pdjDFWTIio1y9CIYXFRUlo0ePlgoVKkiZMmWkdOnSqnb/scceU9eRvjNUOOWHSH+NaZOSkiQtLU30pGRwsAR1ukOdj1/FaT+eiBkqRERkWEXJSkkuU0Z8S4h4x8SKq6DvCjnOyZMnpXPnzupoaO/evaVx48ZqR/zYsWMyd+5cNVL5r7/+krp16/Jl0BlmqBDpN6ACKPupXLmy6K3sJ2njJkna+rdkJSSoIAt5DgZUiIjIcLSmsAmr11gXTKlWTaKqVJaU0FAp2bu39O/XTxKWLJH0s+cKbFpbXCUrVxYvPz/JiPjfZKHA9u1VY1pynP/7v/9TE3x27dolLVu2zHHd/v37pVevXuo2CxYs4MugM8xQIdKfgIAA8ff3l9TUVImIiNBdQKVUz54iJUveKvvZsEFK3323qxeJnIgBFSIiMhRbslK8q1QRv4cfkrXZ2ZKRna1KP+7p31+8/fxMPUwqvvD8rYlARQiuWAqY+NasmWOiD6iJQJFRHKHsJOvXr5fnnnsuTzAFWrRoIc8++6x88cUXzlocsnMwBdhDhUg/8P+KcfYHDhyQQ4cOSdOmTSUoKEj0Ao1ogzq0Vxkq8StXMqDiYRhQISIiQwVTLr71VqHBlKCuXSW4bx+52b27LFuxQjJSU8XPz0/69eunfprDCOM8wZVzEZJ57Zp4ly8vPmHVb035uXBRvKtVNZ3XxiVbEzBhA1rnQo0+eqbkB4E1vdXxU86ACnuoEOkLAtwou0xPT5c9e/ZI165dRU+C+/ZTAZWkzVvkZlKSGqlMnoEBFSIi8qjMFJTUhH09TWISEmTZ0qUqxdjHx0c6duxY6JQX8+CKLRgwcS/NmjVTvVKeeuoplWpuDoEUXIfbkD77pwAzVIj0BSU/yBDcuXOnCqzgMxjBbb0I7t1LLk+eLNlpaZK4aZOEDBjg6kUiJ2FAhYiIDAFZIAUFU7SsFGSIxCUlybJly0zBlP79+3MHzIO88cYbMnToUGnbtq0888wz0qBBA3U5NuK/+eYbOX78uPzxxx+uXkyyEUt+iPQNQZTDhw9LcnKy6nGFpuF6gYzVgNatJWX3brn65ZeSlZhoMSOVjIcBFSIiMkQD2pi5PxWalYING2yoYYpLSkqKCqYMGDBAjc69evWqU5ebXGfw4MHy008/yUsvvSTjx483BdOwQ45miLjubjYV1HWGCkt+iPTH29tbWrduLVu2bJEzZ86o72V8P+tlWyTz9nZE+pmzcvntCRK/ZKmEz5zBoIrBMaBCRESGLvMJueceqfrOVLVBg9rsFStWSGJiotrhQmYKdqAx8YU8y4MPPigjRoyQ3bt3y7nbjYZr1qwpbdq0URv1pD/MUCHSv4YNG8rBgwclLi5Otm7dqoLbegiQ3uqVFpnjsuQdO9TlLPs1Nm4xEBGRYcciIzNFC6bg6PXatWvl2rVr6rqePXtKlSpVnLjU5G4QOOnQoYM6kf6xhwqR/iF40r59e1m9erVER0fLtm3bpFOnTuLuMqLOW748Msrpy0LOxYAKEREZLivFv0ULKTN8WI76ZdRjR0Xd2rBBA9ratWs7bZnJtSJvHzUMDw/P8XthtNuTPnDKD5Ex1KpVS5o0aaL6qWCMcvny5dVYZXeGiX8WLw8Pc/qykHMxoEJERIZqPgsIppin2KKkY9++faZ0Yk5w8Swo5UGfFPTN8fX1Nf1eGJaC6QtLfoiM44477pDr16/L5cuXZfPmzRIaGurW/VRwAAc9U3Jsn/h4S0ifvq5cLHICBlSIiEhX2SkJq1cXeBuU+WDDRoM67PXr16vz2BhD6jBHqnqWWbNmqdccTYjNfydjYVNaImOV/mDKDyauJSUlyapVq+Tee+/NM+reXZTw9ZXwWTPVQZ/Ug4ckdsECkYxMuTF3rlQY/6yrF48ciAEVIiIyRKmP+VhkrcwnIyND1WHjp5+fn9o4Y8NRzzN69OgCfydjYIYKkbEEBgZKnz59ZMmSJaagyqBBg6RkyZLijrDtobJjR4yQEv5+EjNnrlyfNUvK3Hef+FSq6OrFIwdx/5bJREQknh5IiZk/XyLHjcs3mKKNRcaGjBZMwc4V0oRv3LhhakIbHBzs1GUn9/T444/L9u3b871+x44d6jakL+yhQmQ8FStWlG7duqnzaFKL73Xz/3V3Vf7pp8UrJESyU1Lk6hefu3pxyJ0yVLCRURyNGzeWUqVKFesxiIjIM1jTgBaZKQimaIEUDZrZnTp1Sp1v27athIWxMRzdMnv2bJWtlN90n7Nnz8qPP/6oSoNIPzjlh8iY6tWrJzExMaoX2okTJ1Q/lRYtWog78w4NlfL/+Idc+fBDifv9Dyk7apT4N2zo6sUidwioYDJCceqOkXqNo4RERET2aECLMp/cwRQ0sfv7779Nk1patWrFJ5ushkaIKBEjfWHJD5FxtWvXTgVVIiIiVIYhgiruPoktdOTDEvPzz5Jx/rwKrITNnMn+XQZUpB4qY8eOVYEVWyQkJMhzzz1XlD9HREQemJmCYErsb7/b1IAW0tPTVRNa7FyFhIRIjx49uAFDsmnTJtmwYYPpmfj9999NGUzmsMH+yy+/uP3RT8qLTWmJjAsH9HFQftGiRaqUd+/evW4fUPHy9ZWKL70oF154UZK2/i1JmzdLqdvlS+ThAZWuXbvKQw89ZPPRnn/+859F+XNERORBrC3zyd2AVrN161YVxMfGV69evZhpQAqCbJMnT1bn8d5AQAUnSxo0aCCfffYZnzmdYYYKkbFhUlvz5s1VcBz7lvifd/eJbcH9+0vA7B8lZf9+if7wQwnCpEFvzoXx6Ka0v/76q6kxkC1wlBD3xT8BERFRUct8LDWgNe99gfpqaNOmjRqTTAQvv/yyXLp0SS5evKg2wr/66iv1u/kJpWIIxh09ejTf/irkvthDhcj4ypYtq35mZmZKfHy8uDsEfCq+9po6n37qdKGZt6Q/NofHhg0bVuSIYlHvW1TvvfeeaqKLEzaUnnrqKfn222+dugxERGSbjKjzFi/3b9FCygwfZjErBbBhhbIOqFSpkrRs2ZJPPZkEBQWpkxZ4w+SIgIAAPkMGwik/RMaH3ikIUuD/HVkqpUuXFncX2LqVBPfrJwkrV6oslbSzZ8WvTu18t2fIw8YmR0ZGFniKioqSq1evumS81ZtvvqmaErZu3drpf5uIiIrGJ6y6xcsRTLGUlaL1TVm5cqWkpaWpAD76pnh5FfsrjgwqOTlZfvvtt3yv/89//iPHjh1z6jJR8bHkh8j4SpYsqYIqgICKXlT453ikq0h2UpLEzJ4tl9+eoMqbszMyXL1oVEzFLuCqWbOmVbVr/v7+0r17d5kwYYLNDW2L6syZM1KrVi113t3r64iI6Fb/lJvJKSIIhty8WWDzWfOdKNRTo5kooG8KykyJ8vP6669LRkaGjBw50uL1aEqLgEt+PVbIPbEpLZHnlP2gMa2eAirJu3djgyXnZTt2qDJnHCwiDw6ozJw5U7788ks1wgqNauvXr682blHD/t///lcFNEaPHi0nT56Un376Se68805Zs2aNdOnSRRxNC6YQEZE+m9GWrFBBKox7RsoMG5ZvWuzu3bvl3Llz6jz6Xrh7139yvW3btqmeKvlBhtPHH3/s1GWi4mOGCpFnKFeunJrShqCK3suZMyKjnL4s5GYBlWvXrqnUWQRMtCZBmkmTJknnzp1VCvbnn38ub731liq/weUIqhARERXUjDbr6lXk9+YbTDl9+rTs2bNHna9bty4bn5NVYmNjJTAwsMCsWj1tqNMtDKgQeU5ABRITE9V+pp+fn+i1nNknPMzpy0JuFlCZNm2ajB8/Pk8wBcqXLy9jxoxRnfRfeuklNW3hiSeekH//+9/i7tDc0LxzNJraQlZWljrpAZYT6a96WV5bGHndjL5+XDf9cvRrlxYZafnyiAiLfxMBfZT6AL5fEMA3T/m3Bd+XjuVun2XIYN24caM888wzFq/Hdcx00h/t/x9l3iz1JjIu8/1OlP1UrVpV3B3KluOXLM1x4Mi/adN8y5nJgwIq0dHRqg45PxhphTGEmmrVqrndhpUln376qUyePDnP5fin1UMUVNuwiIuLU+eN1pzRyOtm9PXjuumXo1+7VH/LE1fSyoSq5uY5bpuaqib64PsE2QStWrUy9VApCr4vHcvd6twffvhhmThxorz//vvqgA8aGWvbLPj+X7Bggbz99tuuXkwqYoYKgylExoYMQ0xpS0lJUdmEegiolPD1lfBZMyX2jz8k+oMPVXNa/5YtOOXHAIodUGnevLl88803qrFb9eo5U5kw4QfXtWjRwnQZeqsgqGILHIFEPbM1Vq9eLb1795bievHFF1V2jXmGSvv27VWKGY6E6oEWuEKmEDpiG4mR183o68d10y9Hv3bXvbwkJddlAe3bS7WRD+fY4MBO77Jly1RQBcvRr18/tUzFwfelYyEl25289tprsmXLFnnjjTfko48+Uv3fsBOObRRsnKOxMa4j/WaoEJGxYZ/s/PnzBQbssZ2AU1BQkLgDbMuE3nefZFy6JNe/+VYSli2Xyq++qoIt5MEBlU8++URtzGJjZPDgwVKvXj11OXqqLF68WB0t+Pnnn9VleEPPnTtX3c4WDRo0kOnTp1t128aNG4s9YEKEpSkR2HjX0w4ujiLrbZmtZeR1M/r6cd30yxGvHZrRxixYIDduf84HtG4tgW3aqLpipMKaB1PwnYJyDJT7AILtlSpVssty8H3pOO72OYaMlD///FN+/PFHNc0HvXjw3rrjjjtk+PDhMmrUqCJlYSFjd+rUqTJ79my5cuWK2jb6v//7P9W03xqRkZEqc2bFihUqsFO5cmXp1q2b2nYi6zNUjJbZSUSWy34KCqgkJCTIunXrxNfXV+6//35TJqI7KDN0qAqoZMXESOKmTRJsh2QA0nFABdN6tm7dqsYhL1myRKVeAdKw+vbtq8pmtAwVpGWbl/9Yq0qVKjmyRYiIyLiTfbIzM6XCP8dbTIPduXOnaaJPu3btpHbt2k5dXjIOZDFgCiFO9jJ27FiZM2eOjBs3Tpo1ayYLFy5U5UXIqnrkkUcKvO+RI0eke/fuEhoaKs8++6za9rl48aLKpCHrMEOFyPMa06LcF//7uQOphw8fVp+9OKFFRe5KClfyDQ+XgLZtJGXXbon9/Q8GVDw9oAIImCxatEi9mXFEBipWrMgjBEREZPNkn9QDB9TloSNG5LgcIxL37dunzuPIf8uWLfnsUrHgINCuXbvUtgsyQYpT0rt3716VmTJlyhRT/xUcDOrZs6e88sor8sADD6gjpfllViDwUrNmTZWBVdAEIsofM1SIPC+ggpJd9HdDMNo8WxAlnBp8xrtTQEXLUkFAJXHjRsm8dk28i1m6TK5j15xIRAaRnoqTO6RbIkX2nXfeUSfAaE3t94iICFcvHhGRx8uIOm/xOciIjMrxO5rSYkcTUOLTtWtX9kmgYvniiy9UI0Nkhdx3331y8OBBdTnKycqUKSMzZ8606fHmz5+vtn2QnWKeBYNsE2zMr1+/Pt/7rlmzRgULJ02apIIpycnJ6qgq2YZNaYk8Bz6ntf3N3GU/CKaYD03RDvi7k+B+/aVEQAAiQhK3dKmrF4eKwfVRDwfCxhCOEmlHipAqrv1+9uxZVy8eEZHH865S2eJzgP4p5lkEq1atUkeh0FiuT58+bteTg/QFmSTPP/+8Kk2eNWuWaUcc0OAY77F58+bZ9Ji7d++WOnXq5BjnCR06dDAd1Cmoob5WLo1SNrzPcX7gwIGmEjeyvuTHHQ7qEZFj4f9cy0oxD6jg8xzlPqBtK6Dkx/xz3h2ULBUkIX37qPNxv//hdstHTi75QdQPwQtsTMTGxpq+0MyP0Kxdu9bprwumAxERkfv2T0navj3P5YHt26tmtBqMR05KSlIbRtgBZjkEFRdGIw8aNEgFTSw1NGzTpo189dVXNj0m+p2g70lu2jhPXJ8fLTUdjRPRaBlTiHDgB+VD+P3AgQMSHBxc4N+Pj49XJ/PphIBApDbFyhq4LbbjbLmPuzBfZk9Z5+LwxPX2xHU28nojgI3PcJy0dUOjWuyPQsOGDVVwBZPm0GuldOnS4k6CB98jcYsWS9qJE5J8+LD4N2pU7Mc06mvt7HW25bGKHVDRmqglJiaqaTxImcWkHbxpsfGAozVhYf870khERIRgSsRjj0vK7t3/+0KqUkXKPzVWygwbZmpIi/JMrUQTE1j0Mrae3BsCGOalObkhS0WbJGUtZFL5+flZPIqK6RJa035LsA0FrVu3VqVDGmxDDRs2TH744Qf55z//WWiQCIMAcsOOhqXlyg82StGPQFt2PcFUD8CRXpQJesI6F4cnrrcnrrOR11vrS4X/d+1/Xuu1huCJVhaE9cc0N3fbJ82uVVO8KlWSm9HREv3zzxJYyOe8J7/Wzl7ngsZx2z2ggnGA+KJGOU2pUqVUM9rPP/9cNWH773//K+PHj5dffvmluH+GiIgMBE1nzYMpkIkj6iVLmoIp6CGBKXJa35RGdjhyQwQhISGmI5iWnDx50ubgHSYZ4iiopQ091PLj+vxoAQ80pjU3dOhQlZG1efPmQgMqL774Yo6JiMhQad++vWrcaMu6aEflEFTSW2mdNknS29vbY9a5ODxxvT1xnY283vhs1TJQcJAfn+0o74HmzZuroArWGdUUqampbnlQxuveoXLjm28lY+06Kff22+KVT/NyT3+tnb3Olr7PHRZQwZc8vsTRmf7GjRvqMq3k58EHH1Tj/tDdHnPAiYiIIHcwxVIzWvScwBFnlI126dKFTWjJbnr37q2yPtBHJTeki0+fPl2GDx9u02OitMdSw3ut1Ecr/bFEmz6BwKE5vPexA1BQ8EeDHQmccsPGpa0bmDjCV5T7uRqeL/Pl94R1Li5PXG9PXGejrjcO5GN9sEONSYAa9KBChh/2TXEbBFSQweKO6x469FZA5WZcnKRs3iwhffsW+zGN+Fo7e51teZxi58Skp6ebaobx5gUt5QYw1hLZK0RERCj1uTH3J4lfuarAZrTY+EHfCGjatKlpPCKRPWDaH0p60Ctl2rRpakd8+fLlKusWRzVRojNhwgSbHhPlOkgp1w4uabbf7hOE6/OD5dCCOeawk4CsC+wQUOG0A3paYIWIjA3ZfXfffbfa30Q5DzL68P+Pz1Rth1j7/EQJhztOT/MND5fAtm3V+bg/Frp6cagIih1QqVGjhqkDPQIqCK5oKdpw6NAhVQpERESeDcGUyCfGSPS770p2amq+zWjRgBZTfbBzhGkn2s4mkb3Url1b/vrrL5UZgr4j6LmBHiQffvihCnwgu1bLGrEWRi/jPfv111+bLsPjorktskzQXBYQyDl27JgajawZMmSIKglC1ox5Y/+ffvpJpR2jGTMVTpuS4Sl9A4joVsAE5Y0DBgyQkSNHyhNPPKH6eWq0gIqtvZWcqfTQoepn4qZNkmlj/y5yvWKX/GADYdGiRepoj1b/++9//1tlqWCjYO7cueqNTUREng19U5ItZCwGde0qwX37qGBKVokSsnLlSrWziT4I/fr1MzWdI7InNNJfsWKFKqdBzxRssyDQUtQaewT+Ro0aJRMnTlQb7c2aNZOFCxeqiYMYzaz1SUGABUGc9evXy5133mna4Mf9Xn/9dTWy+d5771UHq7744gs1dvmhhx6y67obFTNUiCh3QBUHZnDCwRr0V7E0jc3Vgvv1k8vvvCPZKSkSt3iJlHv8MVcvEjkzoILRfmhAi0Y/OLoydepUtXHy66+/qo1hbFx8/PHHxf0zRESkU8hMQTDlxg+zLV7v37ChhI4YoY4ebVi71jRdBQF7NBgjsjeMI0bQAuVkmALRrl27HNejyeFvv/1mc9nPjBkzVObu7Nmz5dtvv5X69eurA0s4aloYlBthBCga+6M3Hc4/+eST8t5776kSJCocM1SIyBIErTGKHuXEgNIflBWHhoZKrVq1XP6klSwVJCH9+kkctpVmz5bMmBuqFAgHmrRG/WTggEp4eLg6aXAE5vvvv1cnIiLybFqZj6XMlNx9UzDq8MyZM+p827Zt3WIjh4xp0qRJUrduXRVQsQTlysgisTWggmwqHFjCqaC/jZMlY8eOVScqXkCFPVSIyBwafiOgggwVJAEgExbnkc3yyCOPuEUmbMjdd6uASuaVK3Jj+gx1WfySpRI+cwaDKm6ORaZEROT0Mp/cfVMiIyNNDcxRdtGqVSu+KuQymC7FrBD9lvywhwoRmdP6qKSkpMgff/xhGq2MzwxtEpurZZz/35RDTfKOHWo7igyeoQKLFy+WmTNnqu72MTExpiMEGhwpuHDhgj3+FBER6Sg7JW7ZMovX+bdoIWWGD1PBlPjkZFm3bp26HGUO3bt35xFmsjukdyMLSrN582aLEx+wHfPNN9+oHiukL8xQISJLUD6M/VF8RiBgjvPISkHTb+yj1qxZ0+VPXMZ5y/vKGZF5Ay1ksIAK6pCRFosa5BYtWki9evXss2RERKTrYErEY49Jyu49Fq9HMAV9UzAWdu3atZKenq5KRjHNhJkB5Ag4KontFcDG9HfffadOloSEhMh//vMfvhA6w4AKEVmCvp4IqqBhOMYp9+7dW42k379/v9sc9PcJq15gWTQZOKCCbvW9evWSJUuWmDrYExGRZ4td8Fu+wRStzAd27dqVowktdmSJHGHMmDHSv39/tdPdqVMn1ccEU6TMIdCCaRA4OOQONfVkG5b8EFF+MFr5yJEjKgEAJUAIrCCggmEqmACEz35XwnZR3KLFkrJ7t8XtJTJwQCUjI0OGDRvGYAoREd2a6PPHH3Lliy8sPhsYkRz29TTVYA11y9iYgSZNmuRocE5kb9WqVVMnwMjiRo0amerqyRiYoUJE1nwHQOXKlVVQBZmyyFLBVDZXKuHrKzVm/yCn+vWXzIsXJfCOOyT8++/YkNYTmtIiPVtrJEhERJ5Lm+hzeeIkuRkba/E2wX37qI0DNIbDTi1gbGGHDh2cvLTkydCnB8EUvA/RSwUjkpEKTsbIUOGUHyKypgwI03/AXcp+sH0U3KOHOn8zOYnBFE8JqKDkZ/fu3Wq0IKY05G5IS0RExg+kxMyfL1HjnrVqog+OBq1atUql2OLoEMpGsWFD5ExffPGFVK1aVe68806577775ODBg+pylKChLxya7ZO+aNugnPJDRNbQMlYQUHGXfdiA21MOU48clZtpaa5eHHJGQKVChQpqQ+Tdd9+VWrVqqY1ibCCbn7ihTERk8KyUCRMlafPmfCf6VJ46RcJnzsAhIfnrr79MIwu7du2qJvsQOdPs2bPl+eefV1m2CJyYb0ijcWGfPn1k3rx5fFF0hhkqRFSUgEpycrLqpeIOAlq2vHUmI0NSDx9x9eKQFYp9SPDVV1+VTz75RGrXri3t2rWT0qVLF/chiYhIJ2IXLiwwK8V8og8cPnxYjh07ps43b97c5TXL5Jk+/fRTGTRokAqaXL9+Pc/1bdq0URm4pC/MUCEiWyCAjgbkmDSILBWUILuaT7WqUrJCecm6ek1S9u6VwNa3MlbIwAGVWbNmydChQ2XBggX2WSIiItJNdkrCqtUF3sa8Qz1KKf7++291PiwsTHXcJ3KFEydOyLhx4wrcyNamT5F+sCktEdkC5YEo/Tx37pycP39emjZt6vInED2gAlu2lITVayRl3z5XLw45o+QH6ZVIjSUiIs8r9UnasiXfaT5amQ+arGEi3Nq1a9V3BkYT9uzZk30OyGUwnrug9O6TJ0+qkmbSF5b8EJGtqlevrn5eunTJ9BniagEtb2WlJO/b6za9XciBAZXBgwfLhg0bivswRESkIxiNnF+pD7JSMBoZZT4IpgAyU+Li4tSRFzSh9fPzc/ISE/1P79695YcffpA0Cw3/cJRy+vTpMmDAAD5lOsOSHyIqah8VHPi5cuWKWzyBAa1u9VFB2U/GhYuuXhxydEDljTfekOPHj8vYsWNlx44dKrqHN2PuExER6V92RobEzpsnVz/7LN/MFC0rxfxov9Y3pXXr1lK5cmWnLS+RJe+8844q6UGvlGnTpqlA3/Lly+X//u//VG8fHx8fNb2Q9IUlP0RUlIzFUqVKqfNRUVF5rse+7f79+yUzM9NpT65/kyYit7ejWPbjAT1UGjZsqH7u27evwBGDGJNJRET6lZ2eIQkvvSxZBw7ke5vgvn1yBFOuXr0qmzZtUucRSGl1exwgkSuhkf7WrVvln//8p0yePFntiKNRLaAc7ZtvvjGlgZN+sOSHiGyFgHp4eLgcOXJEIiIi1JAVDYIoK1euVE1rT58+Lf369VNly47m5ecn/o0bSer+AyqgUnrQQIf/TXJhQAVHcPBGJCIi4/ZLwTSfuBUrCwymmDeg1cYQrlq1SgXUsQGCMgs0gCNyB5gwtWLFCtVLBVlU2BlHoIW9U/SLJT9EVBQ1a9ZUAZUbN25IfHy8ylqByMhIFUwBZDUuXLhQBVXQuNzR0JhWC6iQwQMqkyZNss+SEBGR2zafLWg0sn+LFmo0MoIpWnYKgihr1qyRpKQkKVmypPTt21cCAwOduORE1ilTpkyOI5KkX8xQIaKiwKQfbXwyJv6g9BOQlQLBwcHqIBG2aRYvXqy2aRydxRjQsqXIj3Mk9dgxuZmSIl4BAQ79e+TCgAoRERkXMlMKCqYAgiloQGsO5RSXL19W57t27cqj/uRSc+bMKdL9HnnkEbsvCzkOM1SIqCiQPYuyn1OnTpkCKgiuIEMFWrRoobJSUP6TkpKiGu2PyLXdY28BWol0ZqakHjokgQz8uy0GVIiIKN/slPjlfxb47OQu8wGkzR49elSdb9asmSqtIHKl0aNH57lMK1fOPZLSvIyZARV9YVNaIipO2Q8CKjgYhGwUTHxDti2+E2rVqiUBAQHqABFKmWNiYlTABVktjuJTubJ4V64smVieffsYUHFjNhezI2V73rx5Nv8h1KThvhs3brT5vkRE5PxgyrlHHpXkbdvyneZTeeqUPBN90A3/r7/+Mo0i7NChg9OWmSg/Z8+ezXHCxAYcgezSpYvMnz9f/Y7m+ti+6dy5szoaictInyU/7NVERLYKCwtTJcqAzBSt3AelPQimQMWKFU23R08VR1NlP2rSD7+PDJWhkpqaavrCsvWoAe7LaT9ERDpoQLtkqaTm0wgtoH17Cft6Wo5ACqC2GH1T8HmPeuNevXpxx4bcQo0aNXL8/uSTT0rZsmXV+9U8IwVBlmHDhqkGyl988YV8//33LlhaKipmqBBRUfn4+KgDQQimHDt2TE0phDp16phug+QAjFhOTEyUK1euqN4rjhTQsoUkrFghKXv3qs83DoJxT0UatzBy5EgVwbPlhIge3wRERO7fgPbyhImSkk/fFO/27aX699/lCaYg0I6dU9QWe3t7qy74/v7+TlpyItv8/vvvcu+991rcLkF2A4IquA3pCwMqRFTcsh9AsASfJ9iH1S7TaJPgtICLIwXe7qOSdeOGZERFOfzvkZMyVCZOnCjFgZGERETkflkpCavXFNqA1rdbtzzBFNi2bZtER0er8926dVNH/4ncVUZGhmo8mJ8zZ86YRmWSfrDkh4iKm82IQLsWnEWj2tx9UhBQQemoMwIq/o0aSQlfX7WdhvHJvuHhDv+bpIOAChER6Wsssnmpj2//fhZ3Pg8dOqTON2nSROrWreuQZSWyF2RQffnll9KqVSt58MEHczSo/fnnn2XatGkyePBgPuE6wwwVIioO9EqpXLmy6gcHlrZntD4qKPtB81qUATkKgin+TZqokh8EVErze8k4JT9EROQ5Y5G1BrSq1Mc7Zxw+NjbW1Gy8UqVK0rFjR4cuL5E9IJiCjNlRo0apGng0p8X0BpzHZZjo8Pnnn/PJ1mmGCkvMiaiotBIfZKagUW1uGJ+scUaWijY+OXmv5b525Hocm0xE5MHZKQmrVhc6FllrQJu7qXhmZqbqm4LyCT8/P9WEVuuQT+TOcARy7969qunssmXLJCIiQmU3YLrPwIEDVdNa9gDSb4YKp/wQUVE1btxYlXziewI94XJDoKVMmTLqgBICKrmbnjuiMS2kHT8uN5OSxCsoyKF/j2zHgAoRkaeW+jz+hCTv2pVvVkpw3z5SZsgQiz1TAOORb9y4oc737NlTdb4n0gsEAcePH69OhcGUQoxXRqkQMrHIPTFDhYiKCweG2rRpU+BtUPaDgAqa1+b3WRQXF6cC89rI5eKOTpabNyXl4CEJ6tihWI9H9seAChGRBzagjf/zz3yDKeZZKfnBSMHjx4+r8+hDYSktlsgosGH82GOPyerVqxlQcWPMUCEiZ0Bj2hMnTqgMFW2cMQLve/bsUUGW69evq6xeBO7vv//+YmU8+lSsKD5Vq0rGxYuqjwoDKgYMqCACx9RKIiJjNKBFZkphwRRsKCA7BapVq1bokRwiI+2sk/tiU1oicgZtdHJaWpokJCRIcHCwCrhrzWw1uB6XoS9XcfuoqIDK3r3Fehxy06a0SPF+/vnn7bM0RETkkEBKzPz5EjXu2UIb0KLMp6BgCvqlrF27Vh15CQoKUqU+DKoTkTvg2GQicoZy5cqZtn2QkYKMXS2Y0qBBA+nRo4fqs6JdX1xa2U/K/v0M7hsxQwXpTUj9XrJkifoZEhKimvl06tSJzQmJiHQ0FhmlPuiZUtDOyu7du9XRGKS39u7du9i1wURE9sIMFSJyVp8VBFVQ8oOm5lFRUery8PBw6datm9pGunz5coF9VooSUMmKjZX0c+fEr5gZL+SGPVSQ4oST+RcZmra99tpr8txzz9njTxARkQPHIhfWgBYQTNE2DO644w72kiAit8KAChE5s+wHAZXTp0+r3318fKRLly6mse1oXHv06FG13VTcFhn+DRtICX9/yU5NlZR9+xlQMVrJDwQGBspXX30lhw8fVs14vvvuO2nSpIm88MILct9999njTxARUVHGIq9cVeBttAa0oSNGFBhMOXXqlBw4cECdr1+/vvqMJyJyJyz5ISJnQcDEXLt27XJMO9QmwqFEWpuIWFTYPgto2lSdZx8VgwZUXnzxRXn66aelUaNG0rJlSxkzZozKWJk1a5b89ttv8vXXX9vjzxARkQ3BlIjRj0nS7eaxlrJSKk+dIuEzZxQYSNGa0G7cuFGdDw0NVSWd2hEYIiJ3wQwVInJ2Y1otuIKWF+ZKly4tvr6+6rxdyn5a3e6jsm9fsR+L3CygguyU8uXLW7xu9OjRMmrUKPn++++L+2eIiMiGBrQRjz8uKXv2FCsrBdLT01WAHEdY8HmPIzCoHSYictcMFQZ8icjR0HQW+8AYiYy+KblLevA5pGWx2COg4t+smfqZduKE3PjpJ8nOyCj2Y5KbBFQQjVu0aFG+13fo0EHN6SYiIuc0oL08YaKk7Nqdb2aKNVkp6vGys1VmSnx8vNowQNd6bDgQeRocafzhhx9Y6qaTsdacPEZEjobtoqFDh8pDDz0kZcuWtXgbLaASHR1d7O27Gz/MNv0e/c67anuPQRWDNKV96qmn5Mknn5QnnnhCPvvsMzWH29yGDRty1JMREZF94YsWzWcTVq8p9lhkcwcPHpSzZ8+aguOVK1dWDdiIjAINA8+cOaPq2813yDWPPPKI+olA4qOPPuqCJSRrmb9+zFAhImfAZ423d/6701pAJS4uTtLS0sTPz69IfwfbeLl7pyTv2KEuR7Yx6TyggkAKxiV/8sknql9K3759pWnTpuoNs27dOlmzZo3qqUJERO49FtncxYsXZfv27ep8zZo1pVmzZqZ0eiK9O3funIwcOVL+/vtvi4EUbUNZC6iQ+zP/fGKGChG5W+NalP2EhYUV6XEyos5bvjzy1rhmMsDY5I8++kilPCFDBbX2CxYsMG2MDBs2TAVbiIjI/sGUi2+9ZbexyJrExEQVDMeOJkod7rzzTh7xJUNBdu3evXvl008/le7du6tmy6RvzFAhIneD7EZsRyFDpTgBFZ+w6pYvDy/a45EbBlQAUx9wwhdaZGSkJCQkqDcN3kREROSazBStAa21ZT6ZmZmyatUqSU1NFR8fH5V1qHWpJzKKzZs3y6uvvirPPfecqxeF7IQ9VIjIXbNUtIBKUeGAWPySpTm2+UoE+Evpe+6x01KSWwRUNMhKqVGjhr0floiIbOiXYmtWinrs7GzZtGmTXLt2Tf2OzBQeuScjT2cgY5b8sIcKEblTQOXkyZMqoILtrKJ8PpXw9ZXwWTPV9h+2/eIXL5HslFRJ3rFTSnXp7JDlJidO+SEiIudP8UnavDnf29kyFtncvn375NSpU+p8q1atpFatWnZZbiJ3M3r0aJk/f36+/VNIf1jyQ0TuqFKlSuonmtIiU6WosD2H7bqqH3wg/k2bqstu/Pij3ZaT3ChDhYiIHEM7MlGQkHvukarvTLUpkAKY5rPz9mMjkNK2bdtiLSuRO7vrrrtUzzeMAv/HP/6hMmtLliyZ53bt27d3yfKR7diUlojcEUYq4/slKytLZakgQ7I4kOFS9tFH5eIrr6iDa2mnT4tfnTp2W15yk4AK0sUXLlwohw8fVs0NMTa5SZMmMmTIEKbYEhEVscwn5r+/FHg7ZKYUJZgSGxsr69evV+fLlSvHJrRkeN26dcvRTyU3LS0bG8CkD8xQISJ3hKljFSpUkMuXL6uDV/Xr1y/2Y4b06ytXPvpIMq9ckRtz5kqVyZPssqzkJgGVlStXyv333y9t2rSR5s2bS7Vq1SQ+Pl7++9//yiuvvCLz5s1TTQ6JiMg+zWeL0i9Fgx3GtWvXqma06Ebfr18/1YyWyMh++OEHVy8C2RkzVIjIXdWtW1cFVCIiIuTcuXNSs2bNYj0eeqqEPvywXP33vyVu0SKp8Pxz4s1pdcYJqPzzn/+Un376SQYNGpTnumXLlsmzzz4rJ06csPefJSIy5ljkNwsei2zrFJ/cduzYIdevXzc1oUVGIZHRPfroo65eBLIzZqgQkbtq1KiRakwbHR0tf/31l1StWrXYExTL3DdCrn3zjWSnpkrs/F+l/FNj7ba85OKmtBcuXJA+ffpYvK5nz57qeiIiKjyYEvHY4xK/ZInF6/1btJDKU6dI+MwZRQ6mREVFycGDB9X5Zs2aSXh4OF8W8jjIosX/waFDh9R50ieOTSYid4US0q5du6ryn6SkJHUwq7iQkaKNTY75z3/UdiMZJKCCBm/jxo2Tixcv5rgcvyN7BdcTEZFl+EKMmT9fBVNSdu/O92kqM3yYzVN8zKG/lXnfFDbfJE+ze/dulZWFhoEtW7aUFi1aqPPYTtmzZ4+rF49sxLHJROTOtO8aOHLkiMpWKfZjPjJK/UQvlfiVq4r9eOQmAZXZs2erIzyYElG+fHmpXbu26Scu/5HjnYiICh2LXFAwBWU+6JdSnB0P9E1JTU1V/VJ69eplccIJkVHt2rVLNaZF4GTMmDHy73//Wz799FN1HoEWHEnET9IPlvwQkbtDQKV06dLq/KZNm3IEgosC033QR08boWz+OUg67qGCI53z589X6UyoFdOm/NSrV0+CgoLs/eeIiAwzxSd+1SqHjUU2t337dtORke7duxd7hB+R3rz55ptq6sLWrVtVLbu5CRMmSKdOndRtVqxY4bJlJNuw5IeI3J23t7cK5i9ZskRiYmJUg1okHRQHRihjfHLqoUOSsnev+LVoYbflJRdlqGgQPEEUrkuXLuongylERAVnpSRv+cshY5HNnTp1ytQ3pWnTpsX+IifSo7///lv+8Y9/5AmmAC7DdQi2kH6w5IeI9KBKlSqm754DBw4UO6skqHMn8a1bR52/MftHuywjuUlAJb8vuzlz5jjzTxIRufcUn7cKnuIDSOcsbgNauHTpkmzYsEGdr1ixonTo0KHIj0WkZ9iALajMDY0DmTqtLyz5ISK9aN68ufp55cqVPL1UMjMzbfr+QcPbso88os4nrF4t0VOmStrSZZKdkWHnpSa3CKhkZGTIY4895pS/dezYMXn99deldevWqlYNZUfIllm4cKFT/j4RkTWZKfGLLU/xyT0WuTgNaCE2NlZWrVqlAtvBwcHSt29f9k0hj9WuXTv5/vvv5caNG3muw2XTp09no2YdZ6ggIEZE5K7CwsIkNDRUnd+/f7/p8vPnz8vcuXNl+fLlNj1eyIABIjhIkJ0tcfPmSfLHH8v5sU8xqKLXHipTpkwpMKDiLDNmzFAbRPfee688+eSTkpaWpt6gQ4cOVZej8RwRkcv6pSz/s8DMFGSlBPfto5rPFieQAvj8Qy8I/PT19ZX+/ftLYGBgsR6TSM+wrdK7d29p0KCBjB49Wv3UDsYgkxZN9NFkn/SDGSpEpBfIKkGWysaNGyUiIkId9MI2Gg58IUPlwoULpj6k1oj/80+RrKwcl6Xs2KG2N3FAjnQWUJk6daoKYoSEhOS5LivXC+1IDzzwgEycOFEdidU888wz0rZtW5W58vjjj/MIBhG5JCulsBIfLSuluIEU7ajtunXr1A4ivsCRmaIdFSHyVMhYxYbriy++KJ988kmO69q0aaMm/nTu3Nlly0e2Y4YKEelJ3bp1ZceOHZKSkqL6eqH8B8EUDUqBrA2oZESdt3x5ZJTdlpecGFBp1qyZPPLIIzJw4MA812FEp7OO+CBwkhuOzN59993y3nvvqTdt5cqVnbIsROTZTFkpq1c7ZYqPOYx+jYq69YWKySWWmnASeSJMWsD4ZGy0YtIC1KxZUypVquTqRaMiYIYKEekJ+nhhOMDOnTtN22nYV/Xz85OEhAS5fPmy1Klzq9lsYXzCqlu+PDzMrstMltm9yPSJJ57IEV0z5+Pjo7JGXOnixYtqZJU2A5yIyGlTfDZvcfgUH3PYSdy7d686X79+fWncuLFdHpfISBBAQYNmnIobTEFpM8Yuh4eHi7+/v0rp/vnnn21+nC1btqiMMpywUU2FY0CFiPSmUaNGar8U8HPAgAEqsA+2fPajPDywXbsclwW0b68uJx1mqIwbN67ASJwrAyqnT5+WX375RQYPHiwBAQEF3hbp8TiZT8fQypacWbpUHFhOpMDqZXltYeR1M/r6edK6ZadnyOUJEwov8enSRYL79FbZKTfRTNEOzw2ObmgTfcqXLy933HFHjpT4ovCk185I3GHdXP28btq0yZSVYv57YbTbW2vs2LGqBwu2hZCxi0b4Dz/8sDrQhOxda+C1Gj9+vAQFBUlSUpJNf9+TaZ9vWiCKiMjdIfCOYP6RI0fUdhqC+snJyXLw4EHVID09PV1lrRSmhK+vhM+aKZFjnpTk7dvFq3p1qf79d3Y7QEdODqi4K7w577vvPpVGhdrowuA2kydPznP59evX1WPoZeMiLi7OkB3vjbxuRl8/T1m3EllZkvDyK5Jl1r3dEu+WLcV30kRJ9/aWa7GxdlsOHOHGFzEyA1u2bCkxMTF2eVxPeO24bvaH705XuvPOO9VONmrVsXGq/V5QtgOutyUQhGwwlDWj4e3bb7+tLkMD/J49e8orr7yiertZs2H83XffqfRv3Pfzzz+3+u97Oi1DhcEUItKTJk2aqJNGa0mBzzS0qKhe3XI5T24InoQM6K8CKtmJiQym6CmgEhkZWeD1+GJD9A1HSIv6JYejrD169LDqtqtXr1ad+3On4I4YMUIOHz4sy5Ytkxo1ahT6OGhUZz4JCBkq7du3l3LlykmFChVED7QNQTz3yA4yEiOvm9HXz+jrlp2RIb4bNkriqpUFBlPMs1LsfQRh27ZtqmM8dO/e3arPPGsY/bUDrptjYHqBK61fv1791AIa2u/2NH/+fBWMM8/UxXbPs88+K8OHD1d/s1+/fgU+Bo5IIhiDoAw2pMl6DKgQkRGgigKtKXCQB2U/1gZUwLd2bfUzOzZWMm/ckJI62WcVTw+ooM7LmkAJgirYsEdtcceOHW36GxhniFHH1sjdIwBHHZFmu3LlSpk3b5706tXLqsfBlCJLk4qwE6GnHQls3Oltma1l5HUz+voZdd1Q4pP82msSv29/ob1Swr/52iFHD86cOaOCx4BmZ7Vvf7nai1FfO+C6OY6r3y/Y/ijod3s1gEYDwbJly+a4HOncsGfPnkIDKm+99ZZUqVJFnnrqKTU1kWwv+TFahhkReR5kqWgBFVv41a1rOp9+5oz4MaCij4DKzJkz5csvv1QztB966CHV+BBHCU6cOCH//e9/pVatWjJ69Gg5efKk/PTTTyrNds2aNWpkobWwcWGeLWKLf/zjH6pvCgIyw4YNK9JjEBFZNcVn5SrJLCSYYu8pPrmPbmt9UypWrGjakSOinFCG8+abb+Z7kAXZJAhoYOS4LU3vsb2SmzZZC9cXZP/+/fL999/Ln3/+WaQAlL16v7lDz52i0JbX1lItPa9zcXnienviOnvqeut5nbENd/z4cZWpiEoLawPFJUqXFq/QULkZEyOpJ09KoIWpt0aU5YDX2pbHKnZA5dq1a6o/CQImuY/KTJo0STp37qxSfVEHjCMvrVu3VpcjqOJoL7/8sgqkfPDBB0UOyBARWTPFp7DGs46Y4pN7LD0y8dD8Eumiffr0cXlWAJG7QuCxoO0CbMRu3LjRpsdEfxZLPdawIYxeRri+IGhEO3DgQPW/WxT26v2m135C2jLD1atXbbqvXte5uDxxvT1xnT11vfW8zvjOAGzTnTp1SkJDQ62+b4mwMJGYGIk/fESybPws1KubDnitben9VuyAyrRp09RGQO5gilaLjg2Wr776Sl566SXVewRjlf/973+Lo33xxRfyySefSKtWrdTRIWTHmBs6dKjqoE9EVJxgysW33io0mBLUtasE9+2jxtc5IpiCLxIcScdkHxydxQ4ZP9+Iiu78+fM2/w+htNlSrxj8f+III67PDzJ60ftIK9crCnv1ftNrPyFk6AGW2dZed3pd5+LyxPX2xHX21PXW8zpjmfGdgYNlGDBg02d4gwYSf+CAlLx0STd9P93xtbal91uxAyrR0dFqQyE/iKyZ139Vq1bNKalXqFXWuu6PGjUqz/Vnz57lDgcRFSuYEvH4E5Kya1ehWSlhX09zaLf1HTt2qB1A6NSpk6lDPBH9z6JFi9RJg/IaS9mymIiFy20tmcPBG5Q/56aV+milP5ZgChCa5yMgiqOR5gGCc+fOqQ3q8PBwp/V+03M/IW3Zi3o/Pa5zcXjienviOnvqeut5nbEth89/7Gu3aNHC6vv51atr6qGix/V2l9falscpdkClefPm8s0338jIkSPzdCHG2D9cZ/4mQG8VBFUcDaMLcSIickS/lLhFiyVl926XZaWYf6YeOHBAnW/YsGGextxEdMuhQ4dUJgggcPH333/L9u3bczw9uByZKejzZuvIYpQ0I1MMgRDzrF3tb+D6/Fy4cEF+/vlndcrtjjvuUM35jx07xpeyAJzyQ0RGYh5QweebtdNytUk/WVeuSFZ8vJS0EGgn+yp2QAVlNehaj2a0gwcPlnr16qnL0VNl8eLF6g2gbSAgbWnu3LnqdkREeguipJ+LkIR16yTj3LkCbx/ghKwUrc/D5s2b1flKlSqpnlVFHU9PZHRoQouTdiTrhx9+UM307eW+++6TDz/8UL7++mvVMw6wDYSyZ6Rd9+jRw9R7DidknAQGBqrLfv31V4tjmHE5esEVlp1CDKgQkbFo2cbov4WG4xilbA2/OnVM59NOn5bAVq0ctoxkp4AKjuJs3bpVjUNesmSJqekamiL27dtXNUjTMlRQC2br+CciIr00nQXfvn2l+ocfODyYgmbgq1atUiWUOKLOJrREto/Ytac2bdqoEuOJEyeqpqjNmjWThQsXqga4s2bNMjWGRYAF20aYJITJhzB8+HCLGTUwaNAglvFZgWOTichItH4g2M5Dloq1AZWSFSpIiaAgyU5KknQGVPQRUAEETFCXjC8zHDHVxj3praMyEVFRms6aZ6b4vfqKw4Mp6E2FYAqCKviyRZagdqSbiFxnxowZUqNGDVVy/O2336rsXWTmoiyaHIslP0RkJNiPxnQfZDSaTzErDDKVvWrWlKzDhyXt1GmHLiPdYteIx5EjR2Tnzp2ya9cuOXr0qD0fmojIJZkp8YuXFHpb9EupPHWKVP/+Oynh7e3Y5crOli1btpiC1zjCjaMYRGSb1atXq2Ak/n+8vb1NzezMT7by9fWVqVOnqh5ymBBw8ODBPMGUSZMmqf9jLTslP9rt2GTaOgyoEJHRlCpVSv1MTEy06X4la9RQP9POMKDiDHbZ8l+6dKk899xzqnEOaI1zatWqJZ999plKVyUi0lO/lITVa6zKTDGf4uOMCWYIXKMRLbRs2VLqmNXKEpF1UKI8dOhQadSokdx///2qgT76qWD7BWU6aAJ7zz338OnUEZb8EJHRFDegks4MFX0EVJB2PmTIEDXh591331UTJrBBggyV7777Tm2wLF++XNX3ExEZoV+Kb82aUqpXL/GtWcPhU3zMoQcVelYBmlS2a9fOKX+XyGiwvYKpO/h/io2NVQGVxx9/XHr27ClnzpyRjh07mprskz4wQ4WIjKbIAZWatwIqGRcvys2kJPEKCnLI8pGdAiporIYgyl9//SXBwcGmy3FkZ9y4cdKpUyeZMmUKAypEZIislJB77pGq70x1WhBFg34pa9asUTsN+KzFxBBO9CEqGowaf++990ylPlpvIqhdu7Y8/fTT8v7779t1ChA5FjNUiMhotH1rBFTwGWdtf1Kv2xkqkHbmrAQ0a+qwZSQ79FDZt2+fPPbYYzmCKRpchiM+e/fu5XNNRG5Jy0q5PGGiJN0eQVxQeY8rgikoJUIwRWtCi4w/bWIIEdkOUwdx0o4AIjip9SUCZN2eOnWKT62OMEOFiIyaoYLPN2wDWsurYkUpERCgzqed5neZ2wdU0IAtKSkp3+sRUfNx8s4HEZE9p/hoTWfDZ85wejAFX6KbN282jZzv2rUrm9ASFVPdunVNzfOxjYJeKr/99pvp+sWLF0vVqlX5POsIM1SIyGjMExZsKfsp4eUlvrVrq/MYnUxuHlDBxv1XX30lJ0+ezHMdju5MmzZNunXrVtw/Q0Tkkik+WtPZ0BEjnB5Mgf3795ua0GJEPcawElHxDBgwQObNmycZGRnq9+eff14WLVqk+qbgtGzZMlX2Q/rBDBUiMhpkI6M0FRISEmy77+2hBWmnzzhk2ciOPVT+9a9/qT4pTZs2lcGDB6vO+HDs2DE1/ScgIEDdhojInfqlxK9cVWBmCrJSgvv2cWrT2dzOnj0rO3bsUOdr1qwp7du3d8lyEBnNW2+9Jf/85z9NG6pjxoyRwMBA+fXXX9Vlb7/9tjzyyCOuXkyyAQMqRGQ0KEdF2Q+ap9vamFbLUGHJjw4CKk2aNJFdu3bJ66+/LitWrDClzAYFBcndd9+tOunziCoR6WmKj/koZFe5fv26rF+/Xp0vV64cm9AS2RHKfPB/ZQ4NaNmEVr9Y8kNERqQFVGzNUPG9naGSEXVebqamitftvmHkhgEVQHrsggUL1JfZ1atX1WUVKlSwuhMxEZFTslJWr3bbKT7mUlJSZOXKlWrqCLL8+vXrx15URHZ06dIlVZaMsmVL0LcI2zaVK1fm864TzFAhIiM3pi2oZ2lBARW5eVPSz50T/4YNHbF4VJSASmRkpFW3O3/+fI7fw8PD+YQTkdtmpbhyio+liT5I7URQum/fvqYvUyKyj5dfflltzyBwYglKfrDdMmfOHD7lOsGAChEZuTGtrRkqPtWrSQlfX7UdnHbqNAMq7hRQQR0/6rmKspNAROSuU3xc3S9F2yHYsmWLOnoOOHpeqVIlly0PkVFt3LhRxo0bl+/1/fv3l6+//tqpy0TFw5IfIjIi7aAaDrRhO9Ha/fASJUuqPippx46xj4q7BVRmzZpVpIAKEZEzy3vSz0VIwto1khER6fb9UjQHDhyQ48ePq/PNmzc3NfkmIvu6du2alC1bNt/rQ0ND5cqVK3zadYQZKkRk5AwVlIGnpaWJvw29UDDpBwGVdE76ca+AyujRox2zJERETizvcZesFM25c+dk+/bt6nyNGjU40YfIgapVq6Ya6j/11FMWr9+5cyezw3SGGSpEZETmZd8o+7EloOJbR5v0c9ohy0a3sGssEXlUeY95VkroiBFuEUxB93bziT49e/ZkU28iB7r33ntl9uzZ8ssvv+S5bv78+fLjjz+q25B+MEOFiIwoMDDQVB1i6+hkvzp11c/0iAi1nUxuPOWHiEgvmSnuMMXHXHp6uqxatUoyMjLEz89PNaHFSFcichw0nV29erU8/PDD8u6770rTpk3VBuuhQ4fk8OHD0qRJE5k0aRJfAh1mqLAsnYiMBAMKgoKCVDDF1sa0fnVvT/rJzJT0yEjxq3srwEL2xQwVItJ1ICVm/nyJfGacbqb45D6iiuaYyFDBTkCvXr1MtbJE5DghISGydetWFViBRYsWycKFC9X/5IQJE2Tbtm1SunRpvgQ6zFDBzgcRkZFo24a2Zqj4Ysqu9638CUz6IcdghgoRGTorxbdmTSnVq5f41qzhNv1SNDt27JCzZ8+q8+3atZPq1au7epGIPCqNGlkozEQxBpb8EJEnTPqxBbZ5sf2bfur07Uk//Ry0hJ6NARUi0uUUn/jVqwsNprhbeY+5ffv2yf79+9X5OnXqSIsWLVy9SEREusWAChEZVVEDKlofFQRUOOnHcRhQISJD9kpxt/Iec0ePHlXZKRAWFiZ33nkn6/6JHGjKlCnqf+zNN99UJSH4vTC4vVYSRO6PU36IyOglP7b2UAG/OrUF9+KkH8dhQIWIDDXFx93GIed24cIF2bJlizpfqVIl6d27t5QsWdLVi0VkaCjrQYDktddeE19fX6vKfBhQ0RdmqBCR0TNU0tLS1BADW4YX+Na51Zg2/exZyc7MlBK3e6qQ/fAZJSJdBFMiRj8mKXv2WDUO2R0DKRAfHy9r1qxRG/5oeNm/f39O9CFyYvZCfr+T/jFDhYiMHlDRyn5CQ0Otvq822Qfb0hnnz6vegmRfbIVORG4/xefcwyMLDKYgK6Xy1CkSPnOG2wZTcEQB45FxdAFHFvr166fGJBOR4z3++OOyfft20++bNm2Sq1ev8qk3EGaoEJGnBFRsoQIot6efsezHMRhQISK3DKJEf/iRnL57sFyeMFFSDx4sNCsldMQItw2m4MjpunXr5MaNG+p3jEcuU6aMqxeLyGPMnj1bTp/+38jIHj16yOrVq126TGRfzFAhIqPy9vaWgICAIvVR8fLzE9+wMHWeo5MdgyU/RKTLprPuPsXH3LZt2yQiIkKdb9++vYSHh7t6kYg8SpUqVeTYsWN5shnIOJihQkRGz1JJSUkp0qQf9FFJj4iQ9DP/O7BA9sOAChHpqumsHqb4mDt48KAcOnRInW/YsCHHIxO5wODBg+Wdd96RpUuXmrLD3n33XZkxY0aBTWnXrl3rxKWk4mBAhYiMHlBBqWrRRifXkcR165ih4iAMqBCRrjJT3H2Kj7lz587J33//rc5Xr15dunTpwvHIRC7w6aefStWqVWX9+vUSHR2t/g9jY2PZnNZAWPJDREZWrNHJdW9N+kk7c0ayb96UErd7qpB9MKBCRC4NpMQuXCjxq1ZbFUxx9yk+5rDTph3dLlu2rBqP7MUvMCKXQO3522+/rU6A/8WPPvpIHnroIb4iBsEMFSLyhIAKJkbi8w4HBqzlW+f2pJ+UFMm4eEl8q1dz2HJ6IgZUiMglstMzJHLs2EIDKehOXqpXL/GtWUMXWSnal93KlSslKytLgoKC1HhkX19fVy8WEd2GTJVGjRrx+TAQBlSIyMjKlSunfqKPSlJSUo7JP4Xxq13LdD799CkGVOyMARUicnogJW3pUrnw97ZCgyl6aTprLj09XTZu3CipqalqPPKAAQNs+tIjIsfr3r07n2aDYckPERlZ+fLlVVYKgsfopWLLtqVXYKB4V60qmRcvyvVZP0jGlSu6OUipBwyoEJFTS3zOjx0rKVaW9+gtmIIN+t27d0tcXJz60uvTp48q9yEi18KYZJT5IHMM4yd79uxZ6H3YlFZfmKFCREaG767Q0FC5ceOGCqjUqvW/rBNrtr9vJiWp88nbt6tT/JKlEj5zhq62s90VO9IQkVOn+BQWTEHT2cpTp+jyQ37nzp3qSw46deqkGtESkXvsbGsZDIDzuKygk/ntyf0xQ4WIjK5ChQrqp7ataS30K7wZF5fjsuQdO9TlVHzMUCEipwRTIkY/Jil79him6Wxux44dyzEeuXHjxq5eJCK6bcOGDQX+TvrHDBUi8oSAyvHjx1VAxZbGtBlR5y1fHhll5yX0TAyoEJHDp/jE/rpAUg8eNMQoZEsiIyNl8+bNpqZhHTt25HhkIiInYoYKERldxYoVTf36UF5epkwZq+7nE2Y5Y9onPMyuy+epGFAhIocEUdLPRUjC2rWSERFh2KwUuHbtmqxZs0YdKShdurS0a9dOSpYs6erFIqICHD16VE6cOCH33HNPjsk/77//vsTExMiDDz4oL7zwAp9DHWGGChEZHfryYRsTUySRpWJtQAUHLOMXL5HkXbtybH/jcio+BlSIyK7BlMgnxhQ6vUfPU3xyj0f+888/JTMzUwICAqRfv35qug8RubdXX31VZTRoAZXz58/L4MGDxd/fXx0BfPnll9WG66OPPurqRSUrMaBCREaH5urIhL5y5YoKqNSrV8+q+5Xw9ZXwH2bJqb59JfPSZQnq0kXCvvlat9vf7oZNaYnIrk1nrQ2mBOhwio+5pKQkWbZsmaSkpKjO6/3795fg4GBXLxYRWWHPnj05Rif//PPPKsCyb98+OXz4sAwaNEimTZvG51JHWPJDRJ6gqI1psb0d1LnzrV9u3tTt9rc7YkCFiOyWmYJ0wsIEdukigS+/LNW//063H+bIQlm+fLkkJCSoowV9+/Y1fcERkfu7fv16jv9ZZJphlHK1atXU7wMHDlQlQaQfzFAhIk+gfXeh5NzWaXT+jRqpn6lHjpg+M6n4GFAhomIFUmLmz5fIZ8ZZlZmCes1qX30pfoMG6jaYgvKelStXqj4L6K7eq1cvjkcm0hmU9Zw7d06dR2O/rVu3Sp8+fUzXp6WlcWNTZxhQISJPCqigjwq2RW3hf3sCZVZsrGRevuyQ5fNE7KFCRA7tl+Jbs6aU6tVLfGvWUM2vbnp56XqDHY0ro6Oj1e9du3aVWrVquXqxiMhGyCr78ssvVSPpTZs2qf9t8wa1KPsJC+P0A73A66cFVJA1SERkVGhE6+PjIxkZGarsBz1VrOXfoIEIRi1nZ6ssFZ8qVRy6rJ6CARUiKtIUn/hVqwsNplhsOpuVpdtnfNu2bXL27Fl1vnXr1tKwYUNXLxIRFcF7770nx48fV81nsWH6wQcfSI0aNUwlfb/++quMHDmSz61OmKeuI3OQiMio8BlXvnx5uXTpkmpOa8u2qFdgoPjWri3pp09L6pGjEtyrl0OX1VMwoEJEDpniE6jzprO5oVnlwYMH1fn69etLmzZtXL1IRFSMkp/NmzerSV2Y7OPr65tj53zdunXMUNER8z4CzFAhIk8o+0FAxdbGtFofFRVQOXrUIcvmiRhQISLrp/i8WfgUn6CuXSW4bx9V3mOUYAoCKTt27FDnq1evLt26deNRUCIDCAkJyfE7ginYOW/RooXLlolsxwwVIvK0gwJw48YN1dsP0yZt6aMSv3SpKvkh+2ChKREV2nQ2+sOP5PTAQRK/ZEmhWSlhX0+T0BEjDBNMOXr0qPz999/qfJUqVVTvBR4BJdK3RYsWyRtvvJHjsg8//FCCgoJUkAX9VJKTk122fFT0gAo/n4nIUxrT4rMPU+ts4d/41qQfNKXNvHHDIcvnaZihQkTFLu8xYlYKnDx5UpUFaEcD+vXrZ9NRACJyTx999JHUq1fP9Pvu3bvl9ddfV9lnqEefOXOmCrBMmjTJpctJtpf8sIcKERldqVKlVLkqen5hfHKlSpWsvq+/Wc8V9FEp1aWzg5bSc3DPgIgsNp1NWL3G6l4pyEoxUiAFMFJ1w4YN6jw6qA8YMCBHnwUi0i80pL3vvvtMv8+bN0/9n69YsUL8/PxUo9pffvmFARWdYMkPEXliY9rz58+rgIotSmJKULVqknHhgqQePcKAih2w5IeI8mSlXJ4wUZJuZ2YUNsUnfOYMwwVT8AW1Zs0atZGO8XR33XWX2skiImNISkpS5T2aVatWSf/+/U3/5y1btpSoqCgXLiHZgiU/RORpEFABWwMq5mU/7KNiHwyoENH/ms6+VXjTWaNO8dFcuHBBVq5cqVLIg4ODZeDAgRIQEODqxSIiOwoLC5Odtz/rTp8+LQcOHFD9kTTYQOX/vX6w5IeIPDWgojWmtQUa00LaEU76sQeW/BCR1f1SfGvWlFK9eolvzRqG65cCFy9eVCn/WVlZ6ug1ginmR7GJyBhGjRolEydOVP/zR44ckdDQULn77rtN1yPYgvHopA8s+SEiTw2o4PMPQRVt8o81/BrdylBJj4iQrMREKVmqlMOW0xMwoELkwbR+KfF/rigwmGLUprPmLl26lCOYMmjQoDwjVYnIGNCANi0tTZYtW6ZGoaMJbenSpdV12DDdtGmTvPDCC65eTCpChgqn/BCRJ0AWNXr7paenq6xKWwIqWoYKpB07JoFt2zpoKT0DAypEHsrarBSjNp21lJmClMnAwEAVTNF2rojIeEqWLClTp05Vp9zKli0r0dHRLlkuKhpmqBCRpzamxTasrX1UfCpWlJLly0vWtWtq0g8DKsXDHipEHhhIiZk/XyL/8XShwRSjNp01x2AKEZG+MUOFiDwRG9O6B2aoEHlSIGXBArk+fYZkXrrksU1nzV29elU1oDXPTMFUHyIyPpT8/P7777J7926JjY3NsVOuHf1DKRC5P2aoEJGnN6ZFyTqyL63l36ixJG3aLKlH2Zi2uBhQIfIA1pb3eEq/FMAO1J9//ikZGRlqmgeDKUSeA6PRe/bsKadOnVJB1Li4OFXqExMTowIr2EgtxSZ9usGxyUTkyQEVfG/h+0v73Rr+txvTpp0+LTfT0sTLz89hy2l0LPkh8oDynqhxz1oVTNH6pYSOGGHoYEpiYqIsX75cUlNTxcfHRwYMGMDMFCIP8uqrr6oMta1bt8qJEyfUDvm8efMkKSlJ3n33XZWxtmbNGlcvJlmJY5OJyBOh3x+2Y8HWPir+TW43ps3MlLQTJx2xeB7DsAGVc+fOyUMPPST16tVTR5lwBKp9+/YyZ86cHEcyiIyelXJ5wkRJ2ry50KyUylOnGL5fCuBI9OLFi1VQBdMg+vXrZ1NEn4j0D8GSp59+Wjp27GiaCoNtAz8/PzUBqFu3bpzyoyMs+SEiT25MCzY3pq1eXbyCg9X51KNHHLJ8nsKwJT8YgYou/Q888ICEhYWpkVKrV6+WRx99VI4cOSLvv/++qxeRyKGjkBNWr7EpK8XogRS4fv26ykxJSUlRO1G9e/eWqlWrunqxiMjJEhISpE6dOuo8gijaZZouXbqoLBbSB5b8EJGnQkAF+73IurQ1GIOyn+QdOyT1CAMqxWHYDJU77rhD1q5dq0Yijh07Vp599llZtGiR6pPwxRdfqL4JRJ6cleJdpYpUnjTRI7JSAF80S5YsUcEUrcynZs2arl4sInKBatWqqT4qEBQUpPqn7Nu3z3R9RESEKY3aFti2mDBhgoSHh4u/v780b95cfv7550Lvd+zYMZUZ07p1a5XCjcxaBHUWLlxo8zJ4Ipb8EJGnMm9Mm7u5urV9VNiYtngMm6GSnxo1aqgdKnT3L8rGEpE7B1MuvvVWoVkpntJ01hzSIJGZgkw1HI1GMKVixYquXiwichGU9GDC18SJE9Xvw4YNk48++ki8vb3VBunnn3+uDsDYCgdwUFo8btw4adasmQqIPPzww2qS2COPPJLv/WbMmCHTp0+Xe++9V5588km1jTJ37lwZOnSounzMmDHFWl+jY8kPEXl6QAVTfjBwAUF5W/uopB07LtmZmVLC2+NCA3Zh+GctOTlZnZDKi4yVWbNmqewVdu8nI5X3pJ+LkPiVKyTzwsUCb+9J5T0aROyXLVumdlB8fX1l4MCB7JlC5OFeeOEFVQaMxtTIJPnggw/k7NmzKrsE7rzzThVUscXevXtl9uzZMmXKFHn77bfVZQiEYJrQK6+8okqQ8RlkCa5DcCf4dj07PPPMM9K2bVuVufL444+ber1QXuZHZfk8EZEnQQAFBwMQuEc2tk0BldsZKtlpaZJ+9qz41avnwCU1LsMHVN577z3VsV+DngkIqhQmPj5enTSoTdOifzjpAZYTGxl6WV5bGHndrF2/7PQMOT92rKRY0yelSxcJ7tNbQu65R25io9yFz5szX7srV67IqlWrTMGU/v37S2hoqMP+Nt+X+mXk184d1s3dnldkj+CkQeN6fFbg6B42TIty0GX+/PlqZx7ZKeY16ig5Hj58uKxfv141wbYEgZPc8Jl19913q+0YfJZVrlzZ5mXyFMxQISJPhe+dcuXKqd6hyMiuW7eu1ff1rVVLSvj7S3Zqqir7YUClaAwfUHnsscfUkSZsjCD1Fj+RsVKYTz/9VCZPnmyxqaXWwM7dYQMaE02MeMTGyOtW2PplZ2RI+sqVkr5xk2RaEUzxbtlSfCdNlHRvb7kWGyue8tohSr9jxw61I4fyvg4dOpgudxRPfl/qHdfNsfDdqQcIrBTV7t27VaNb9GMxp3327NmzJ9+ASn4uXryoAjy2HHH05AwVBLBwIiLytLIfLaBiC5T4+DWoL6n7D0jq4SNSevBghy2jkekioLJhwwbp0aOHVbdFCi+yUDTYuNE6+WOM8ujRo6VPnz5y4sQJleabnxdffDFHzTIyVDB2GRHAChUqiB5oRwTxT1ayZEkxEiOvW37rh4yU2N9+k5gZMyTz8mWrHid48N1SecoUtyrxccZrFxkZKdu3b1cb2QEBASozJfdOjiN44vvSKLhujoUsMVfatGlTkXut2BL8qFKlSp7LtUliuN4Wp0+fll9++UUGDx6sPseclVnrDhlNttKWFcGUoiy3HtfZHjxxvT1xnT11vT1pnbVtXJS5o/THlvX2a9hQBVRSjhzR7XOV5YDX2pbH0kVApUGDBqopmzUaN77VXCc/9913n/z444+ycePGAo8UhYSEqFNu2InQ044EjiLrbZmtZeR109bPK+umxN/ukZKwbp1knDtn9f3RL6Xau++6VTDFGa9dVFSU6peED1b0I7jrrrucenTXE96XRl0/rpvjuPr9gkzV3JkLKBPJL5tBu86WDSo0vLeUwYr3FbLkcL21kEmL7RU8HjJmrWGvzFo9Zmtpy4vXrChZiHpcZ3vwxPX2xHX21PX2pHXWvsswaQ4HFRFUsXa9M8LC1E+MTkYlh7Oz/LJvZ97fvHhJvKpWFd/+/WxujuuI19qWzFpdBFRwxMdeHe61DRrtSSdyV/iAsbZHisa3Zk0p1auX+Nas4VFTfDQXLlxQfRC0YAr6D7ABNRGhf4mjIevVUiYOPo+wkVtQVqw53HbEiBFy+PBh1VAb0wmtYa/MWj1ma2mZOdiQLkoWsR7X2R48cb09cZ09db09aZ3RH3Dz5s2mflI4kGjteqe2ayeR2O9ISpIy6Dl4O8DiDNkW+kFmb9ok1b//zqZ9GEe81rZk1uoioFIUiLDlHouKNxka0iLy1rp1a5ctG1Ghk3t+/0MSly+zqkeKp45Czu3cuXMqMwUfqkFBQWqaD4MpRATdu3fP8UQkJSWpo0/h4eEWnyAc4dNGUVoLpT0RERF5LtdKfbTSn4Ig+ILxyhjpPG/ePOnVq5fVf9+embV6y9bSjqjiZ1GXWW/rbC+euN6euM6eut6ess5YP/QAi4mJUc3VcVDR2vUOaNhQBBkhmZmScey4BNSsKc4Ss+S3PAeOU3bskJgffpDyTz6pMlW0iaYZUefFJzws330de7/WtjyOYQMqr732mpw8eVJtjISFhakmPb/++qtqCjd+/HibOiATOQM+MGIWLJDr30+3ukeKp45Czu3QoUOydetWdR7BlEGDBlncsSAi0sYm79y5U406tmTIkCHSsWNH+frrr61+wnCgZt26daqG3bxnE/o5adcX5h//+Ifqm4Iy52HDhvHFspJ2VJYNaYnIUyEbEQEVHCzAvq+1vPz8xK9OHUk7flxN+gnpb1vz9OJAkMSSa59/Ide+/kYFUG7eiJGsmBjTdfFLlkr4zBlutd9j2IKyoUOHqh0qbJQ888wz8q9//UsCAwPlhx9+kM8//9zVi0f0vyDK/PkS/eFHcvruwRI9ZarVwRTvKlWk8qSJbveh4uyN6G3btpmCKdiJueeeezgRg4gKbWCP7YT84DpkidgCPU+QYWIehMFn1FdffaXKULTm+jjAc+zYsTwTB19++WW1zfLBBx/YrczZ06b8GL1PAhGRNY1pbeV/uwdp6tEjTn2CfcKq539lRoZknD6TI5gCyTt2qIwVd2LYDBV0xceJyN1oqWtFaTTr6T1SzKG0B82lT506pX6vVq2amuDl6+vr6kUjIjeH/iKWJvJoKleubPNUnjZt2sioUaNk4sSJqjFqs2bNZOHChWpSIcqNtcawCLCgeSz6uqBZLnzxxRfyySefSKtWrVRp0E8//ZQnwIPsO7KMGSpE5OmQoQKJiYmqF5ct/Bs1krg//pDUI0fFmcoMGSJXPvxIbiYmmi4LaNdWKr3+umRERsqNuT9Jyu7dee6XERkl7sSwARUidw2mRD4xRpJtaDQL7JGSU3p6ujrCjCa0gBI+9Egweo0sEdkHMkbQ9DU/uA716LaaMWOGaiI7e/Zs+fbbb6V+/foyd+5cGTlyZIH3QzkyoAQJQZnczp49y4BKAZihQkSeTguoaI26renbpfFrUF/9zLp2Ta7NmiXlRo1yykHbjOhoUzAluG9fCeraxXTAOKBxY8mKj7cYUEEpkDthbiSRE4MpF996y+ZgitYjJXTECI/OSNEgTX7JkiWmYEqLFi1UKj2DKURkLYxT//77703lguZQRojrcBtbIUNu6tSpanw7JgQcPHgwTzBl0qRJKqNCy04BBGBwWX6nmk5sEqhHzFAhIk+H1hYBAQE2T7PF/snVL740/X71w4/UwV9MG3W0uCVL1M+SpUtLtY8/yrOvg+BKYLt2efaLcLk7YYYKkbPKe1avlowo61PUSlSsKBX+8ZSUZSAlR8R9+fLlphGZnTp1kqZNmzripSMiA0PJDT5LunXrJgMGDFCfI2hoigDIn3/+qUp+EBghfQVU2EOFiDw9S+X8+fOm7WRrYD8ldxaI1qcEAQ5Hfm7HL74VUAm+a4CUsFCyj8vCZ82UqGfGSdLmzeJdqZJb9o5kQIXIDXukIJUtrXNnKVOlipRgGYtpFPqKFSskNTVVbTQjK6VOnTp8/xKRzRAw2bVrl5oIiD4ny5YtU5ejmT1KbtDIHrch/fTUAk75ISJPb0yLgIotGSr5TdpxdJ+S1EOHJP32PlLpu/Pve4rgSZkRw1VAJfPaNcnOzGRAhcjwY4+nz5DMS5eK1WgWG4doakj/6x+AcaR4Xnx8fFTz2erVC+gMTkRUiEqVKplKbfB5i58VK1bkTrkOaQ0Y2ZSciDyZ1kclISFB9Zayphw+v0k7ju5TEnc7O8WnenUJaNWywNsGNGt260xWlhrtHNi6tbgTZqgQubDZbMg990jVd6a6XaTVXWAH58CBA7J9+3b1O6Zc9O/fP0fjLSKi4kBWAwIppO9G5cCAChF5Mm37GMEUZKmUL1++0PvgYG78kqU59mF869Z1aJ+S7IwMib+dGVp68N2FHsjwrlxZSpYvr5rmph486HYBFTalJSpuVsr8+RI17tkiNZtlMKXwschaMAVfEkOGDGEwhYiILAZUkMFIROSpMJ1Oy0q5ceOGVffR+pRUnjpF9SgB/yZNHHqwN+nvvyXr9vKFDLq78GUsUUICbvdMTDl4SNwNM1SIXNAjxby8hyxP8sFY5OjoaPU7xpD27NmTG8tERJQHM1SIiG415kZQ5fr16+pkLeyPoAFt1vUbcvWzzyRpyxbJvnlTSnh5ObTcx79ZM/GrXcuq+/g3ayqJGzaoDBV3w4AKkQt6pFD+EERBMAVBFWjZsqW0a9eOfQ2IiMgiBlSIiP7XmBbBFGszVMyVurO7CqhkXb8uqYePSEAz+0/SzEpMkoQ1a9T50ncXnp2Su49KekSEZMXHS8mQEHEXDKgQWaEoPVK8q1SR8k+NlTLDhjGIYqVjx47Jli1bTI20MNK0Xr16fI8SEVG+GFAhIvpfQAWKElDxa9BAlf1kRkdL4saNDgmoJK5dI9mpqSIlS0rIXQOsvh+yWcwnBAV16iTugj1UiKzokRL59DNWB1OCunZVdYh1V62U0AceYDDFSvv27ZNNmzapYEpwcLDql8JgChERFYYBFSKinI1pU1JS1MkW6FVSqls3dT5x0yaHlvsEde4k3lY0zdV4h4aKT7VqbtlHhRkqRAWV93z3vYrS2tJoNuzraQyi2DjJZ+fOnSqgoo0y7devn/j7+/O9SUREhWJAhYgoZ4YKxMTESEBAgE1PTanu3ST2119Vr5LM69fF246TNTOuXFENaaH03YNtvj+yVDIuXJDUQ+7VR4UBFSJLzWbXrJGMyEirnhv2SCneRjAm+Zw9e1b9Xr16denbt694e/OjiYiIrAvKM6BCRHSLn5+fGuKQkZEhCQkJNj8tQXfcgZFpIhkZkrh5s13HJ8cvX46ZzlIiMFCCe/W0+f4oQUpYsYIZKkRGmdjDHinFc+3aNVm3bp3pw7527drSo0cP07g3IiKiwmRlZamgCvj6+vIJczA81/j+Tk1NVc+9Hpcfy45SCJQ3eAojrje2F5HNXL58ecOsk70EBgZKXFxckQIqXkFBEtSurSRt/Vv1UbFrQGXxrXKfkD69xSsw0Ob7+ze91Ucl8/Jlybx6VbwrVBB3wMPA5LGK0mhW65ES3LcPJ/YUw4ULF1SJDzbG8CXYoUMHadasGb8QiYjIJlp2CjCg4vidcnx/YycNz7UeD4BgmwNH8D1tB9yI643//cTERElLS5Nq1aoZat3sFVCJj48v0v1Lde+uAipJW/6S7IwMu7QySDt1SlKPHFHnQ4pQ7gP+TZrgzYwPI5WlEtyzh7gDBlTIY4MpF996y+ZgCnukFPN5z86WPXv2yN69e9XvQUFB0rt3b9U3hYiIyFYMqDgPMlMQTKlYsaKp8aUet0MyMzNVabEn7YAbdb0xHvjKlSvqvVnBTbIV3CWgAkXJUNECKtH/el9uJiRIyr59EtiuXbGXKW7JUvWzZIXyEtSxQ5Eeo2SpIPGtU1vST51WfVQYUCFyZXnP2rWSERFh9X1Z3mP/fin44kPzWe1Dn4iIqCjfLRpmqDgWSkbwHOs1mELGg/dibGysem+S/QIq6A/pUyNcMiIiVdlPcQIq2dj/+uMPifnPf9TvIf0HSIli9EoMaNpMBVTcadIPM1TIIxSlvIfNZu0HRw7WrFljSj2sWrWq9OnTR6WfEhERFRUDKs6DMl09lvmQseE9qcd+Ps4IqCQnJ5syk4qSpRIzZ64kbtwkFV9+2W77Xyl79hSrjMi/WVOJW7hQTSFC5pU7ZFwxoEIekZWSsHqNVcEUBlHs79ixY7Jlyxa5ia7eJUpIu3btVIkPJ/kQEZG9AirYqeLOPhHR/wIqgD4zZcqUsflpKdXtVkAl7eRJybh4UXyqVrX5MWL/WJhn/yv18GG1bxY6YkSRXqqAZrca02bFxqoRyr7Vq4urMaBChg2kxCxYINenz5DMS5esuk/IPfdI1Xem2qXxEomKiP/1119y/PjxHP1S0I396tWrfIqIiKjYODKZiCj/gArKfooSUAls305KBARIdkqKJG7aJKEPPGDT/ZP37JGr076yeF1GZFSRXzK/hg1NY52RpcKACpGbjEBGs1kGU+wH0fBVq1apUh9A9/VevXqp8XZMyyQiInsHVHx4MISISEG2XkBAgBqTXdQ+Kl6+vhJ0xx2SuG6dJG7YWGBAJfv2PlhG1HnxCgqUlEOHJXHNmnxv7xMeVuRXCsvlX7++ynRBH5WQAQPE1ZihQobAHinu4/Lly7J69Wr1IQ4tW7aUtm3bipeXl6sXjYiIDIYZKkREeZUqVUptixd1dLJ6jO7dVUAlads2uZmaKl7+/jbtg/nWrSMlvEpK2okTOQ5ilxkypFgvGfqoIKCCDBV3wD0c0rXs9AyJmT9fosY9a3XD2aCuXaXy1ClSe8liqfTKy6qGj2U+dngtsrPl0KFDsnTpUvUBjh4paDzbvn17BlOIiMghGFAhe5k9e7bq9bZt27ZCR/W+9NJL0qhRI1VagVLm1q1by3PPPScXL17Mcdu///5bBg4cKNWrV1dZuvjZt29f+eqrW6UQkyZNUn+zsFPNmjX5QpNNgoOD1c+iZqhAqe7d1M/s1NR897NiF+btkwIh9wyW2gsXSq3fFqj9rnJPPql+hs+cUez9Lq2PSsrhw5LtBg2JmaFCug2kpC5aJGf/+4tkXr5s9f0QFQ37ehoDKA4o8dmwYYNpQwIf4hiJXLZsWXv/KSIiIhMGVMiZYmJipE2bNnLjxg0ZPXq0NG3aVOLi4uTAgQMya9YsGTp0qJpkCL/99puMGDFCGjduLOPGjZMKFSpIRESEbNq0ST755BN59tln5d5775W6deuaHh+l0i+88ILcf//9MmjQoBzZBkTODqj4VK4sfg0aSNrx46rsp1TXrnkOpsYvW275vhUrmcYjF7UBbX78m94KqGQnJ0v6mTPiXbu2uBIDKqTPHilr10pGRITV9/WuUkXKPzVWygwbxmCKnZ09e1Y2btxo2qitVauWdO3aVR2JISIiciQGVMiZZsyYoYIia9eulZ49e+a4LikpSTXk10yYMEHq1asnO3fuVP0szGkHoJo3b65OmnPnzqmACsqlR44cWaz/C06+8mz2CKhoZT8qoLJpU44xxTfT0uTyhImSvH273fukFMavTm1Tw1z0UQlmQIXIMY1mOQLZsTAGefv27XLwdv2ir6+vdOnSRerUqeMWM+GJiMj4MjIyTN9BRI52+vRptY2DA0e5YZph7tsiQyV3MAW0LBZ7QIZwjx495KeffpIzZ87I9OnT5cKFC+rvs1TIc2kBlbS0NBVgK+pnJMp+rn//vWRERUn62bPiV7u2ZFy5IufHj5fU/QfUbbzKlJGbsbF27ZNSEGS++DduLCm7d0vqoYMSfM9gcSVmqJBhxh6b90gJ7ttH/SOzN4pjINq9bt06iY6OVr8jjRUjkbUPbyIiImdghgo5EwIUOEo/Z84ceeKJJwq97fr16yUyMlLCw8Mdvmz/+te/VFYKerlgGVkm5NnMX39st5crV65IjxPQooWUCAmR7Ph4uTx5igS0aiWxv/0mWVevquvL/eMpKf/00xK3eLEah4zMFGfsgwU0baoCKshQcTUGVMgQ2Sga9khxPKSj4miIthHbpEkT6dixo/oSJyIiciYGVFwvKytL9VJzpx1JR22TIIjy6aefypgxY+Sjjz5SmSGdOnWSAQMGqOa05l5//XXVZwVlP507d1YnlAkhuwWN++0NO81Hjx5VjXKJ8H+AbCoE14oTUJGbN8XL21vQ+hXlPaYSHz8/qfav9yTkrrsc0ielMP63G9OmHTumemu6EgMqpPsgCrBHimtKfLp16ya1XVy3SEREnosBFddDMGXevHniLtDQtXTp0g55bGTk7tq1S2WDLFy4UL799lt1QgBn/Pjx8sEHH5hKKx599FEVZPn8889VI1pkq7zzzjtSuXJl1YsF03/sCX+PwRTSeHl5qTI0/H8Wp48K9tOybtzIc3m5xx4zBVNcIaBZU/UzOyND0k4cF6lUSVyFARXSXUmPxissTEr36S1+tWqxvMfB0GgNDdgu356ohA2KXr16SUhIiKP/NBERUb4YUCFnQ/nON998o07oWbJmzRo1teezzz5Tpc9Tpkwx3RZBE5zQx2LPnj3yxx9/yBdffKGm++zfv18aNmxot+XCUAAic3g/FjegkhF13vIVLh5X7BMeLl6lS8vNuDhJPXSIARXyDPbIRtEazaI+L61zZ6lQpQpLTRwMtb+Y4pOSkqJ+Z4kPERG5S6kJTsCmtK4tLUBWiLtwZu8QZOmOHTtWhg8frs6jMax5QEXj5+cnd9xxhzohiILSofnz56tJQPZiqfkteTYEVC5duiTx8fFFfgyfsOpOn+JjDZQzBTRpIklbt6qASslevcRVmKFCuizpwQbU1dvNkMgxMPpv27ZtcuTIkVuvgbe3KvGpW7cun3IiInKb7BRgQMV1UO7iqBIbvShbtqzaPjqEI+WFaN++fY7RyUSOomWSFydDBQ1m45csleSdO502xceWPiq3AiqHJeeMLediQIXctqSHY49dB8Eq1PrG3h6BhhpgNFIrU6aMC5eKiIjofxhQIWfDgSZk6uaeahgREaEOQJmX8KxevVr69OmT5zGWL1+uftqz3IfIEu19ioAKmtMiq8NWJXx9JXzWTHWA3JlTfGzpo5J++rQE3s6kdwUGVMghwZTIJ8bkiGRai0EU1zee3bt3r6rzxQcvtGzZUtq0acPSKiIicisMqJAjzJ49W/VFye3JJ59UJT1z586VoUOHqm0jNIE9ffq0/PDDD6pPinm5D25To0YNufvuu9WkH5ROb926VZX6oDzo8ccf5wtITgmoIOs8NTW1yGVhCJ44e4qPLZN+MIko6ZNPJa5rFwm9916nB3sYUCG7l/ckrF5jczCFU3pcLyYmRo1D1kqp8CGMcYDoRk9ERORuGFAhR/juu+8sXj5o0CB56qmn1OQUNOpftmyZyuRFuU/Hjh3lpZdeUqXRmpkzZ8rixYvl999/V+U9GRkZUrNmTTUN6I033mBjf3I480wqZKkYrc+Od2ioCIInGRmSsWaNRK9ZIwnLlkv4zBlODaowoEIu65HCbBT3gEwUjELeuXOnqbkf0lCxccCadCIicveACtLY0ceDqDhGjx6tToXBaGRroFGvrc16EXDRMoStdeedd9p8H/IMyKDCZyO27xFQqVixohhJ7MKFKphiLnnHDnW5MzNqGFAhp/ZIYRDFvSD9FFkpUVFRpg9eHF3BSEAiIiK9jEwuSm8AIiIjw+cipl7FxcUVqzGtu8rIZ6Qzer04EwMq5JRsFJb0uJ/z58+rxrPaOGTU83bp0kX8/f1dvWhEREQ2BVSIiMjypB8EVHAyGh83GenMgAo5tNFsUNeuEty3j9t0g6ZbjWdR3rN//371dCAVsHPnztKgQQMe4SMiIt1gQIWIqGDlypVTmeinTp2SFi1aGGpiZxk3GenMgAo5rNEs3tBhX09jIMWNxMfHq0ZqWuNZNFLr1auXhKKpExERkY6gyScwQ4WIyDIEUY4fP64y0jdu3CiDBw82zAHUErdHOsf8/rvEHT8hpRvU55QfcsMeKd9Pl8zLl62+H3ukuK+zZ8+qD1LtiF6TJk2kQ4cO4u3NuCoREekPM1SIiArm5+cnXbt2lVWrVkl0dLQcPnxYmjZtapinrYSPj5QePlzSr16V0hUqSAkXNCjnnhSZsEeKMWH2/Pbt29UHqHYkDx3h0UmeiIhIrxhQISIqHLb50SvxzJkzsmPHDqlRo0aOkcpUPAyoeDiOPTa2K1euqMazWiOqChUqSO/evfkhSkREhgmo+LBHGxFRgdAv8cKFC5KWliabNm2Su+66yzClP67GgIoHY6NZY29k7t69Ww4dOiTZ2dnqA7N58+bStm1b1YSWiIhI75ihQkRknYCAAOnUqZM60IrACsp/KleuzKfPDhhQ8eBgysW33mKjWYNB8OT06dOybds2SU5ONo1LQ4kPPzSJiMhIGFAhIrJe3bp1Ze/evRIbGyvHjh3jvoGdMKDiqeU9a9dKRkSE1ff1rlJFyj81VsoMG8apPW4qMTFRNm/erEajgZeXl8pKadWqFdOhiYjIcBhQISKyHjLWGzZsqA684gAsMlY4Ja34GFAxuOyMDIn9dYFkRkZKwrp1knHunNX35cQe/WSlHD16VDWe1UZIVqtWTdVKGmnWPBERkTkGVIiIbFOvXj3VmDYrK0tOnToljRs35lNYTAyoGFh2eoYkvvKKZO7bb/V9GETR38YkGkuha7c2Gq1jx45Sv359NpoiIiLDunnzpppiBzzCSkRkfS8VTP3BvgPKfhhQKT4vOzwGuWl5z+UJE2wKpoTcc4/UXrJYKr3ysoSOGMHSHjd36dIl+e2330zBlLCwMBkxYoQ0aNCAwRQiIvKI7BRgQIXsYfbs2Wr7STt5e3urjN/Ro0erJp5Gh/U0X3/z0y+//GK63bx58+SRRx5RpSMoL8dPS86dO5fv482YMSPP7ZE1MXDgQKlSpYqUKlVKmjRpIh988P/t3QeUFFXWB/A7ZERyToKio7AgiIR1iQKCn4IgEgwgWVREMGACGZKgLnrEBRTJjIACBjABKoO7CCogQcGIoAiDZIYoCPWd/91Ts90zPTNV06nC/3fOOFIdpl+/7qpXt+677zk5c+ZMVNvtR2afHTx4UH8oPMxQ8ZBwlkC+qFEjqTRuLIMoLoDlzjC9B1FlwIGpcePGUqdOHQZSiIjIFxhQoWgZNWqU1KhRQ0/kUWsCgZY1a9boyomFChXy9BuPJchnzZqVaft1112X/v+vvPKKbNiwQa699lo5duxYjs/ZvXt3ad++fZbPBxjXNmvWTKejPProoxpQwWo0TzzxhHz99dcaxKHIQaAQ7zHqL+J8omnTpnx7w8CAio+XQOb0HvfVSvnpp5/0oHP69GndVrJkSWnRooWUK1cu3i+PiIgoZhhQoWhp166dTp+G/v37S5kyZTRTYtmyZdKtWzdPv/HIOOnRo0e295k3b56ekOfNm1dXkdy3b1+2969Xr16Ozzlt2jT9jWnspUuX1v8fOHCgfs+XLFkiJ0+elCJFithuD2VfnBaBMdRRwecdGVmUO5zy44FAypFFi2T3oAcsBVMQRCnVr59UGDuG03tc5PDhw3ogX716tQZTcMBr0KCBdO7cmcEUIiLyHQZUKFaQOQFYFSUQslZatWqlV/qLFi0qbdu2lfUZxuLmNKLPPvtMHn74YR2zFS9eXKfXYDyHQAECBwjaYPugQYPSawMFPkebNm10iVvUykOdPAR4UEcoEIIbOEnGxbcbb7xRAxDly5eXJ598MtN9w3HJJZdoMMWOU6dOaYZ1VtLS0jT7BxcKA2H6D/4Wp/VFnllvEftSs3wA5Q5DUW4OpCxZIoemz5C/UlMt10jhtB73QeQYEXvzAItaKVjmDAdeIiIivwdUME2BvDFt/dzu3yX/JVWlRKdOjpmGvnPnTv0deLKPcdkNN9wgVapUkREjRmjA4tVXX9WsYQRPGjZsGPQcQ4cOlbJly0pSUpJs2rRJZs6cqYEYnMgikDB27FhZtWqVTJ06Vae94P6myZMna308BEkuuugi+fTTT3UqDIIQzzzzTNDfwRSc1q1b6xSbTp06yfLly+XZZ5/VIqR9+/a11N6MNTWQuRDOqpF4jQjq4OQd2SqjR4+WDh06ZAoGoS4gMoICp/zMnj1bHnvsMX7HowDvMc4pfvvtN51Wdfnll+sFW7KPARUXYY0Uf8HBGdN7MGfXrMqNqyTVqlVjrRQiIvK1wCWTcaJG8R2fntu7N/ePP3dO9g4fLme2fpO+7ejiJVJp/DOSkItpCPkrVZKEAgVy/XoQlEBQATVUMA4bM2aMBj0CgwDINsEJKWqsIFACvXr10gyRRx55RAMugZCBsmLFivTPKoI0CJ7ccccdMn/+fN1233336YorCLYEBlTwXAikmO6//36555575OWXX5aRI0dq1ooJ028wfQa3w7333qtBDBSBtRJQQRaJ2R4TisOaY1E7cHKOoNOtt96qgScUqX3ppZekY8eOWuQ2cPoUXuf27dtl+vTpGkQBvFcINA0fPtz23yZrUAcHARUE57777jvta7KPARUP10jJX7265Pn736VEzaukZOfOjon0U84OHDigVziOHz+u/0bKJtI9OX+UiIgoOKBC8YVgyo4b/y+iz3lm61b5pX1wFoNVNZZ/pFPccwuZIIEuvfRSDXqgbogZtNi4caM89NBDQcEH3H7nnXdqUODo0aNBWR39+vULCvw1atRIM1IGDBgQ9LewyEDGAqxmMOX8+fN64ovfyITB3/nhhx/k6quvTr8vAj/4W4Fw39dff91S25Ht9eGHHwZtQ+Aot1ODVq5cGbQNQScEjRB0wsqU5nuCLBhk5iC7BkVskYX9wQcfyNNPP61twv0p8vD5RQFmTGfDZxp9wH2qfQyouATSIK0GU4o0ayZF294gRTt0kINHj0rxsmUlweZcR4oPzDHdvHmzbNu2TYvQAqLFKBZld74qERGRV507d05/c/BPkYbMj5o1a2qmCrJFUCslMAsEmRYQarlgBAswfvv111+DAiqYWhHInLYdajtqqyBTxPyb+PtPPfWUZssETnUDBG4CIRMk43gRU5VQi89qVgku4EVLsWLFdFoPpv0gGGS+h+PHj5dJkybJjz/+mP7eoE4gAi2YLnT77benB7QosjA9DRlTyMjasmVLpulqlDMGVFwwl1Ty5pW0j4Kjxdktf1x16hTNRkEEm9wBOzEzkGL2G7JRcFUBB0ciIiL6H/PEkvVT4g9TbJAVkltpH30kBya9nGl72SFDpNj/3Zir1xMOnFCaq/ygDknz5s11ag4CADlla5gXwzJOQ8vqolhW283nQY0VTJtB5gCmyyDrA4EW1Lx4/PHHMxWbdcPFN7QBAoM8qD+DOioZ6wNietBrr72mxX4ZUIlekAuBQEzr2rp1q/4/M+LtYUDFI9N78lWsKGUG3iMlbruNU3tcBAdMHKBx1cGsfo7BYe3atbWyOX6IiMg9WROY849VOfbv36+rKKB4JKYBWDke4Mr4lClT9Oo26mU98MADMnjwYNYICYFTfpwD9UrCmWJTul8/Obl2XdC4FxcIS/fvF/cxLQIUWFEHNezw/USmCAq8wvfff5/p/tiGYIoZNAgXVnjERbf33ntP9wkZC+W6kbmiDFY8Mv3xxx+ZVjcCc1uo2yhy6tevr9lB2K9+/vnnOvWMi19Yx4CKAx18bXrIYEreMmXkfEDlbRy8Lm7dWgpUr+aoauhkvU7K2rVr9SBiHrTr1Kmjc2ERVMHtRETkHigEOW/ePF36FPvzd999V+666y49Gbj77ruzfSwKX44aNUp69uypq1qgjtaQIUM0pR+FJykYAyreCshcMmvmfzOzf9vtuFV+mjZtKtddd51miKBuCpYvRjFPfNcRYEHBWdi7d6/WWsH9w1kVJxCmvARmrAAuwGHlH6dDBkqpUqWCtmHMi4wT1O3AqjImrGKEmjK4HXUDTWbBXpzwU/SgTg2KF3/11Vc6pQ0/+AwjeIiMldzW0fEL3wRUMP/QXEc+NTVVd4ZOm9qTt2RJ+fOXX+TYkiUh71u8Y0cNnjjxYEPWnThxQjZs2KCRYBOuOmAp5KJFi+q/OV2LiMhdsBQqMlMQGEEhRUCtgFatWsmwYcO0BkBW9T5Q5HLChAm6CgdqNpiPRaAdtQUQqHHKuMUpGFDxFoxnS3btKk6FpXxvu+02XS0HWWMvvvii1hrB1CAUlkXA45VXXtHg6QsvvBCxv9u2bVud4oNlkAcOHKjBlOTk5Lgub4tVh8xVjJBNh3HtuHHj9N+YHoUf8z37/fffpUmTJlKpUiW9LwrpHjlyRObOnRv0nKiTgkw+FOvFij/Ijnj//fflo48+0mD0ZZddFoeW+gsuApw8eVJ++ukn3b8imI9yBJgGhGxLBFwwPYh8GlDB/ELs/DAfDB8UpwRSjixZIoemz5C/UlMtPQbBFCcfbChrSNdEeubPP/+sAT0TDhg4GAemcRIRkfssWrRIT3KQnWJC6j+m7XTp0kVSUlKkXbt2IR+7dOlSPVHCWCUQ/o0TD9yOkyn6HwZUKJZQSwUZFRMnTtQTfgQNPv30U80eQxAV33VksSxevDiiRT1xIvvOO+9oJgwy15ANgwAD6o0g2BIPyCRBUdlAZhA5KSkpPaCC2i+ojYJpjAiiYMyLi4doi1mjxoQaNZgCZBanRXYLVldCoAa1Yij6EMBH8AufYwT5kaWC4AqOTZjKhhIFCIwh66h06dL6g/9P4LL1kmAE5pB5FCLG+KL36NFDv6S5yVBBhBWVuHfv3h12kdCcaqQUadVKLqQdk9MbNgbNJb1k5gxbGSnIcsC0ESyJ5YYiVXa4rW1YNs6sCg+4Son0RazgE+r1u619drBt7sW+cycn9Fskj6FOhZMb7OcDsw8D244TBVyFzW6qEFZ6C7zyjAtChQsXlt69e8u0adNi8p474fMSCt6bQ4cOBWUeHz9+XE9er7nmmrCe26ltjja77TbHMWYNETfCaQ+ySDCVxk8ngl5ud1afS36vw9+fIXC9fft2zVLBxeFQU4WqVKmiBYPN5b1N2K/g9liIRl/bOYZ6PkMFEU4EUxA9RoE4NyyBXKhGDSn74GDHziUl+zBX9LffftMiZbjCgd/mvFgiInI/1E8IVUgcV/TM27N7LOoGZEzjx7+xPbvHmtLS0vTHZGZDYqBpZxop7otAjtOmnmJwu3r16kzbcSwN97U6tc3RZrfdOCnHybibr8XitZs/fuL1dqNdGT/H/F6Hvz8z6ztieWsUE0bQAufWyDhCgA5Blp9//ll/MkJGZqwuoESjr+08l+fP6EaMGKEDHKTKovK+E+hSyNlAAMXpc0nJHkzpQaFBzIMlIiLvOX36dMh9PIIiKDSO2+0+FnCFL7vHmlDTIWMaPiCrw86xB4PSY8eOpb92pwgMFpnQLmTwhFvE3altjja77cbJE95zN6+4Enji7bVMDb+2G59jTEvJuB/g9zqy+zNM7zGLDOPzhH0y3vP9+/drkCXjEt7Yt8TqvCcafR2YEenrgMqWLVu0kjQKGtlN/4nUlZ5Q8launOVthRs1kqIdOkQkwublyKzb2oaDl52raG5rnx1sm3ux79zJCf3mxX1ZqMAHBvUZ4b3HcsrZpT5n9VjzJNZK2vTDDz+shWwDxy0o8Ih57kiDtttXqNXgpOkveD1169bNdGyNxMmhU9scbXbbjcCeOZ5xKzNDw4tTX/zabpxAYx+ZcT/H73V092eoeWOu1GSEyH6K1P7Zimj0dVbH5FDcu0e0AMXcbr75Zi2KZFekrvSEYjT5h+SrV1f+2rwlfVtCuXJSqMddUvCmm+Tg0aMSCV6OzHq5bV5vH9vmXuw7d3JCv9m50uNW5ioWGZnTdcypP1k9FgUu0VcZa6hgGdHsHmvC6guhVmDA4NLuABOvITePczM/ttluu82TI7efkJsnem5vh11ebjfaFOozzO+1f/ZneSK8D7fzPK4IqGDO7PXXX2/pvh9//LEuY7Zw4UL54osvZNu2bbn6m5G60pOVsnPmSNrSpXJ2924pULWqFOvYMeI1UrwcmfVy27zePrbNvdh37uSEfrNzpcetUGgcq18g9dlMi4Yvv/wy/fbsHovlWFH4D0tTBi7FjKKA2T2WiIiI4scVAZUrr7xS1y23olatWvp72LBh0rVrV41YmoVyMMgxq0FjgILCoLG40hNS3rxSqnt3iTYvR2a93Davt49tcy/2nTvFu9+8uB/LqFu3bvL888/L1KlTtX4bIAV68uTJeiHGvDB08OBB/cEYxFwVoWPHjjJ06FC9LwIrpn/961+6KhxuJyIiIudxRUAFRWUDs0Ws2LNnjyxYsEB/MsL62gjSYE1tIiIionBde+21Wnw8KSlJC/VhZYR3331Xs2xnzZqVPl0YQRNMKU5JSZGWLVvqNkzpefzxx7V4PuqtNG/eXD777DNJTk6WkSNHhlw9iCgegVk3F6Qlb8LUSDfX9SH38+ynb/HixZm2LVq0SLcj2yW77BQiIiIiu5BdglXd5syZI6+++qokJiZqUKRHjx45PhZBlpIlS8qUKVPkjTfekKpVq2o9N2SuEDmBuVpVxlo/RPFiFv3Gal9E8eLZgEqXLl0ybfv222/1d/v27aVChQpxeFVERETkVZiegywT/GRl1KhR+pMRpig/9NBD+kPkRJgKf/ToUZ2yhmlsXixuSu6BKZX4LCKoEqpMA1GseDagQkREREREkVGkSBEpWrSortqVlpbm2mkWfs2w8Vq7Mf0M2Sn4TOKzSRQv3vlWWYArQohmMjuFiIiIiMge1PspX768ZmO5Ec4DsOoYfvuJF9uNzyA+i1aWlSeKJneGlomIiIiIKKaQ4YBlwQOXBnfbMvIoGo0pS35Yfczv7SaKBV9lqBARERERERERRQIDKkRERERERERENjGgQkRERERERERkEwMqREREREREREQ2MaBCRERERERERGQTV/mxsdY5pKamipsqeh86dEiXSfNaRW8vt83r7WPb3It9505O6Dfz2GkeS8m54xYnfF5izY9t9mu7/dhmv7bbj232a7vPR6HNdsYtDKhYhKXGoFGjRuH0DRERka+PpdWrV4/3y/AFjluIiIiiP25JMAzDCPPv+MKZM2fkm2++0fXb8+VzRxwKkTUEgL766iupWLGieImX2+b19rFt7sW+cycn9Buu8GBQUqdOHSlUqFBcXoPf5Hbc4oTPS6z5sc1+bbcf2+zXdvuxzX5td2oU2mxn3OKOyIAD4I1s2LChuBE+WFWqVBEv8nLbvN4+ts292HfuFO9+Y2aKu8Yt8f68xIMf2+zXdvuxzX5ttx/b7Nd2V4xwm62OW1iUloiIiIiIiIjIJgZUiIiIiIiIiIhsYkDFw4oVKyZJSUn622u83Davt49tcy/2nTt5ud8o8vz4efFjm/3abj+22a/t9mOb/druYnFuM4vSEhERERERERHZxAwVIiIiIiIiIiKbGFAhIiIiIiIiIrKJARUiIiIiIiIiIpsYUCEiIiIiIiIisokBFSIiIiIiIiIimxhQcanx48dLp06dpFKlSpKQkCD33ntvyPvNmTNHbw/18/PPP1v6W+fOnZORI0fKJZdcIoUKFZKrr75aFixYIPFu26pVq6R///5y1VVXSeHChaV06dJy6623ytatWy39ndWrV2f53nzyyScSz7bB8ePHZfDgwVKhQgVt39///ndZuXKl5b8V634LZdSoUVm+x/iZP39+js+R1WNHjBgh8bZr164sX9+MGTMsPw/eB/QP+qlatWq69Bv6L56+//57efLJJ6V+/fpSvHhxufjii6Vp06by7rvvWn4OJ/RdON8DwzBk0qRJkpiYKAULFtTfL7/8sm6Pt/Xr18uDDz4oderU0b5BH7Vt21b+/e9/x+xzS94Tq+Ovk8TyuOx0e/bskQEDBsill16q7bvsssvk/vvvl71794rX/fbbb9KnTx+pWLGi7u9xLO7Zs6f4wZo1a9KPAfv27ROvisS4xsmcMO53yzgo0vLF/C9SRAwfPlzKlSsnDRs2lA8++MDSiW2NGjWCtmFAYMU999wj8+bNk0GDBumHFjueu+66S/766y+5++67JV5te+yxx+TAgQPSpUsXqVmzpqSmpsqUKVPkH//4h3z++edSt25dS38P7cKgKFDt2rUlGqy2DSdsGOCtXbtWHn74Yd05zp07V2666SYdvLVq1cpx/RZK586d5fLLL8+0fcKECfLDDz9I69atLT0P2ouBTiC0ySm6d+8u7du3D9p23XXXWXos+rV3795y880360D9m2++kXHjxsnu3btl1qxZEi84sZ4+fbr2IQbYf/75pyQnJ+tJE7bjZMoNfRfO92DMmDG678SgGvubzz77TIYMGSJHjx7VQUs8PffcczpowP4PAwq8ptdee03f7/fff19uvPHGqH5uyZtiffx1glgel50sLS1Nx0KnT5+W++67TwMK27dvl1dffVWWL18u3377rVx00UXiRWhnixYtpGTJkvLAAw9oUAVBJAQavO7ChQs69ihSpIicPHlSvCxS4xqncsK4323joIgxyJV++eWX9P9HNw4cODDk/WbPnq23r1u3Lld/5+uvv9bHjxkzJn3bhQsXjJYtWxrlypUz/vzzTyNebVu9erVx/vz5oG07duwwChcubHTu3DnHv5OSkqLPv3DhQiNWrLbt7bff1tvnzZuXvu3MmTNGYmKiUbduXUf2m1VHjhwxChYsaLRr187S/bN7n+Jt586d+vomTJiQq8ejT9EfrVu3DtqelJSkz7tp0yYjXtavX2+kpaUFbcPnpk6dOkaZMmUyffec2HfhfA9SU1P1c9q3b9+g7b169dLtuD2e1qxZo5+fQIcPHzYqVKhg1K9fP6qfW/KuWB1/nSRWx2Wnmzt3rrZv2bJlQdsnTZqk2z/88EPDi3BMqFevntGgQQPj5MmTht9MnTrVKF26tDFkyBDt53gf25w+rnEqJ4/7nTgOijRO+XEppGPahVTV8+fP23rMokWLJE+ePBrtNCElEBH8/fv3S0pKisSrbbiagNcWCOmp11xzjWzbts3W3zxx4kRMplhYbRve91KlSsmdd96Zvg0pqIg+b9myRbM7nNZvVi1evFivCvTo0cPW486cOaNXzpzq1KlT2i47MO0M/YF+CYR+Q3+9+eabEi8NGjSQokWLBm0rUKCAdOjQQQ4ePKiv2+l9F873YOnSpdqfuHIXCP/GdtweT02aNNF9QiBcXcWVGTv7v9x8bsm74nH8jbdYHZfdkKECyM4IZP7bq9kpmOK9efNmzUZEG7FPxBV9Pzh8+LA8/fTTmo1ZokQJ8bpIjmucxsnjfqePgyKBARWfuOGGG6RYsWJ6sEB6KtIbrdi4caNOFcIgIlDjxo3199dffy1Og9TjMmXKWL4/0v6wg8V8w2bNmmk6b7zhfb/22mslb968uXrfndxvr7/+uqaWIsXSzmPw2cXPlVdeKbNnzxYneeaZZ7RNmHOOubnvvfeepcehn6BRo0ZB28uWLasnJ078fiENOl++fDpX1el9F873AI/FgRpzkAPhhBEDMCf2jdk/Vvd/uf3cEkXq+Osm4R6Xna558+Z6AoagMcZBqKfy8ccf65Qo3IbxkRehjYD9IKZ9mftETMNFvSkvQz0zBMwGDhwofmZ3XONETh73O3kcFCkMqHgcTmJ69eolkydPlnfeeUeeeOIJ+c9//qPznK0UpcWHMuPVCkDxNvN2J0HxpZ07d2ptgJzgpAjzKF966SW92oy6Hj/++KO0bNlS54DHU7jvu1P7DXVB8PlDMAWDFitQ0wEnfpgLirnc+Ez37dtX/vnPf0q84WoAgpXPP/+8LFu2TAuYHjt2TDp27KhXC3Ji9kOoekboK6d9v3bs2CFvvPGG3HLLLTrgdHrfhfM9wG3ly5fPdBUe/8Z2p/UNYL+FOi857f/C/dwShXv8dSOnHlcjBcFj7KdRuBNXfqtUqaIFHv/2t79pDZWM+0KvwLgP8LlFthKyaFGoGLUZrr/+es3u9iJkVaHexIsvvpgpSOgndsc1TuX1/VOkx0GRxqK0HtetWzf9MaGgGooQovAY0htx9Tg7SNPPmE4FOLDmz5/fUVMwkG6LavRI6cuuSr8JQSX8mLAzxRcQg4fHH388rsXIsnrfkUVj3u7GfsNqNpimbme6T8aMIZyQI+KOzy9SreN5RQFFCTOu8IAAZq1ateSRRx6Rrl276hW/rKAf0B+hBqroayd9v5AGjX0JPlcYgLmh78L5HmT1WCf2DSClF1MR8JnMqWBuuJ9bonCPv24U7nHZDapWrap9iGKOV1xxhWzatEkD4HfccYe8/fbbngyqYMo3IEsvMKCMq/233XabZlWi4KXXIBMJWTgIrvtVbsY1TuXUcb9Tx0GR5r09o8tkt3RvtJbyRUojUjetPB8GCqHm16MqOGqOmAOJeLcNkVcMADAHFFfDsfPIDVS1R7XodevW6Y42Xm3L6n1HLQrz9mj1WzTbjIAKsjHatGkjuYW+xeAG/YN+ipRI9Smm1qFS/O+//57jnHr0A/oj1DK86Ovc9FM02obXiJNszEl966239HvipL6Lxvcgq8dGum8iAVdQMZUTNRAwbSc3gSo7n1tyPjcef8Pl9OOy098LZI/ighvqaSCwiotMSUlJuvoJsnix73e63LTbPAnFaiiBkEmLrEq8L15r88KFC+WLL76QiRMnils5ZVzjFNEY97vJ8QiMg8LBDJU4Q00BHKyswNXDSEH0zkqtEKSK/frrr5m2m6ljZipZPNt26NAhjbDjJA0HvsqVK0u47w12QFh+K6sibNFuG95XzEXPzfsebr9lJdw2o+gbll0cOnRo2Oml6COzoFqkRLJPrb4+sx/Q1xn7BH2FOirxbhu+C1hub8WKFVok1+pS17Hsu2h8D3Dbp59+qu0PvCqLf//xxx+5+g5FA646oaDed999p7UAwlmSOpZ9Q9Hl1uNvOJx+XHb6e4H7o+5AxqXTEVjBSSlS6XEC6mS5aTemNgGmcgZCm1HPDGNBr7V52LBh6ZmI5vR/c7+PujFnz55NPx44lZPGNU4QjXG/W5yO4DgotxhQiTPMd4vHuue//PKLlCtXLsf7IQVy1apVuqMNLHT05Zdfpt8ez7YhEtmuXTu9qopodWJiYkTeG5zwZyzsFMu24X3FyRxWZQoMPlh538Ptt2i1Gdkp0LNnT4lEH4GVz7BVkexTq6/P7IevvvpKp+OZUG0ez4FsqXi3Den7mF+MgQvSn53Yd9H4HuC2GTNmyNatW6VevXrp25ECj8Fmbr5DkYarTviMIDiOWiiBUxid3jcUXW49/obD6cdlp78XCBSHWt0G7UUWpRtWvslNu1FoGPA5ztjuffv2aT0Zr7UZBYdR8wg/GSGghmAFauk4mZPGNU4QjXG/G5yL8Dgo12K6SDNFBbpx4MCBIW87dOhQpm2ffPKJPqZv375B2/fu3Wt89913xtmzZ9O3bdiwQe87duzYTOualy1bNtP637Fs26lTp4xmzZoZhQoVMlavXp3t84RqW6j3Zvv27UbBggWNVq1aGdGWXduWLFmitycnJ6dvw3udmJho1KlTx/H9ltH58+eNypUrGzVr1szyPkePHtV24Hd2fXTixAnjqquuMooVK2YcP37ciKdQr2/fvn36HteoUSPH9p0+fdooU6aM0aZNm6D7JiUlaf9t3LjRiKdHHnlEX8dzzz2X7f2c2ndWvwcHDhzQ13/y5Mn0++3Zs8coUKCA0a9fv6Dn7NWrl27H9y6e8J3q2rWrkSdPHmPhwoVZ3i9U2+x8bsm/InX8dZNIHZfdaPDgwdq+5cuXB22fMmWKbp81a5bhRX/88Yd+jps0aaL7VdOcOXO03fjtNYsXL870g+MJ2jt9+nRjxYoVhldZHde4jdPG/U4aB8UCM1RcKjk5OSi1C8thjRs3Lj0DwJwL2LRpU13mE9XbMb8Z0y5mzpypqY0oDBnoySeflLlz52qV/urVq6dH7vF8mEd74MABTaPCHGlcjZo1a1aWRRtj0TbMd0WKMQoQYfWYwAK7F198cdAV/1BtQwFa3A8F2HBFFnUDpk2bpkunRWteqdW2YfWhFi1a6JLOuEqA1Eu8flQjR7V9J/ZbdlJSUvSKCIoWZgWrUPXp00cLwPXu3Vu3vfzyy1o8E2vK473B1aI5c+bolXRUp0f/xdOjjz6qV7VwBctMt8QVjyNHjmif5NQ+zGl97rnnpF+/fpquiFVWkBExZcoUTUeN5xUFvPcvvPCC7j/QtowFrANXanJq31n9HmAVtNGjR+vnFKt8AdqM4tRjx47VKyBYNhSV4/EdRrGzUNX0Y/3Zw2oUmG6BK8cZ+8cs/ByqbXY+t+Qv0Tj+Ol00jstuLVKKfTjaiWM1itLivUCmHjIWbr/9dvEijP9wjMBYCvtTtB/TXnAMQxF1fMa9JlT2K6ZkA+rohFp50AvsjGvcxmnjfieNg2IiruEcyrUWLVpoJDLUT0pKSvr9hg8fblxzzTVGiRIljPz58xtVq1Y1BgwYYPz++++ZnhNXXvH4nTt3Bm3/888/jREjRhhVqlTRK7O1a9cOukITr7ZVq1Yty/vhtpzaNmnSJKNx48ZGqVKljHz58hnly5c37rjjDs1SiXfb4NixY8agQYOMcuXK6dWThg0bGh999JFj+y07ffr0MRISEoxdu3ZleZ/Zs2drO/DbtHLlSqNt27ZGhQoV9PNbvHhxo3Xr1saHH35oOMGCBQuM5s2ba/Qfn6HSpUsbHTp0MNatW2epfaZ58+Zp/6Cf0F/oN/RfPJmfq6x+Aj9vTu47K98DMyMo43cQV3defPFFzdrAY/Eb/8b2eMtuXxJ4aA/VNjufW/KXaBx/nS4ax2W3+vHHH3UchD7EfrtixYo6Zty/f7/hddOmTTNq1aql+3oct9DP6G+/MI8VqamphlfZGde4kZPG/U4aB8VCAv4Tu/ANEREREREREZH7cdlkIiIiIiIiIiKbGFAhIiIiIiIiIrKJARUiIiIiIiIiIpsYUCEiIiIiIiIisokBFSIiIiIiIiIimxhQISIiIiIiIiKyiQEVIiIiIiIiIiKbGFAhIiIiIiIiIrKJARUiIiIiIiIiIpsYUCEiIiIiIiIisokBFSIiIiIiIiIimxhQISLyoTlz5khCQoLs2rUr5L+JiIiInILjFnIqBlSIImzu3Ll6Ynr55ZfzvSUiIiJH47iFiCj3GFAhirDXX39dChQoIDt27JB169bx/SVX6Nmzp5w+fVqqVasW75dCREQxxHELuRHHLeQUDKgQRVBqaqqsWrVKHnvsMSlatKgOUojcIG/evFKoUCHNriIiIn/guIXciuMWcgoGVIgiaMGCBXLhwgXp3bu33HLLLfLmm2/KuXPngu4zatQoPWn94Ycf9H4lS5aU0qVLy7Bhw/Sx+/fvl27dukmJEiWkbNmyMnr06KDH//rrrzJo0CCpWbOmXHTRRXq/Dh06yLZt20K+pjVr1kjjxo31ZBnZB88//7zMnj075vUy2O7c9bfdPs9tf4eqoRJunxERkbNx3JI1jls4biGyIp+lexGRJcnJydKgQQOpUaOG3H777TJ//nxZvny5nvxmhNsTExNl/Pjx8vHHH8vEiRP1hHXRokVSv359mTBhgrz11lt6QK9bt6506tRJH7d+/XpZvXq13HrrrVK9enW9ujRt2jRp3ry5nmBXqFAh/W9s2bJF2rZtqyfBTz/9tE5Fmj59uhQpUsRSexAMOnbsmKX7Fi9eXPLnz5/j/dzQ7mjIbbvttD1a7Q7ntRMRkXNx3MJxS1Y4biGyyCCiiPj2228NfKUmTpyo/z579qxRqlQpo1u3bkH3S0pK0vv17ds3fduFCxeMGjVqGAkJCcZTTz2Vvv306dNGyZIljfbt26dvO3nyZKa/vWPHDqNgwYLGuHHjgrbfcsstun3Xrl3p2w4cOKDPidewc+fObNuUkpKi97Pyg/tmx03tjqRw222n7XbaPXv27KBtGf8dqddORETOxHELxy2hcNxCZA8zVIgieJUH0yO6d++u/0a2RufOnbWOSlpamhQrVizo/gMGDEj/fzyuYcOGWsg2cDumbeCKP7abMOXDdOrUKS0kiue+8sorZePGjem3nT9/XrMJMPUosNBomTJl5M4775QpU6bk2Cb8bTyHFbivFW5odzTktt1W2x7Ndofz2omIyJk4buG4JTsctxBZw4AKUQQYhqHzkJs2bSpVqlQJSpecMWOGTofo06dP0GOqVq2aacpMVtu///779H+fOXNGRo4cqYEaTP0IhKkeJtS3wIn3FVdcken1YvqGFZjW0aZNG4kkN7Qb02uuv/56S/dFEMPKe5TbdltteyTaHY3XTkREzsNxi3Uct/wXxy1EoTGgQhQBOAHfvXu3PPHEE0HbW7ZsKeXLl9cT4YwBFVQnDyXUdgx8TA8++KDMnDlTBg8eLE2aNNEDXJ48eWTo0KFaKNSKwOfLztmzZ+Xw4cOW7luqVCmt2ZETN7QbmR+oPWJFrVq1LN0vt+2ORNuttjsar52IiJyH4xaOW3LCcQuRNQyoEEUAAiY48HTp0iVoO7Z17dpVpk6dKnv27JHKlSuH/bdQCPTuu++Wl156KWj7kSNHdHqHqVy5clK4cGH56aefMj1HqG2hrF271nKmRkpKigaQoiWW7a5YsaL0799fnMJK2yPRbiIi8geOWzhuiSaOW8hPGFAhChOmYyxZskRatWqlJ7UZYdrP5MmTdUoQlpsNV758+TJlASxcuFD27t0bNN0DwZwbbrhBli1bpsvumnU1Dh48qK/FimjUUHFDu53GStu92G4iIoo8jlv+i+OW6OG4hfyEARWiMOEEFkVncUL77LPPZrodJ8IoUIurQZEIqGAJ5nnz5mlR0tq1a8vmzZvlzTfflMsuuyzTfUePHi0rVqzQ2i733Xefvg5MZcHSu8huQIHRWNdQcUO7ncZq273WbiIiijyOW2KD4xaOW8gfGFAhChMCJbB8+XL9ycrWrVv1J1yTJk3SE2WcUKOuRoMGDfTvhgrW1KtXT1auXCmPPvqonmxXqFBBBg0apFNDNm3apCuzuIVf222n7V5rNxERRR7HLbHBcQvHLeQPCVg7Od4vgohia8iQIfLaa6/JiRMnsiw65kVst7/6m4iIvIHHb38dv/3a3+ROeeL9AogourCUbqADBw5IcnKyNG/e3NMHKbbbX/1NRETewOO3v47ffu1v8g5mqBB5HJZt7tmzpyQmJmoR0xkzZsj+/ft1VR4swetVbLe/+puIiLyBx29/Hb/92t/kHayhQuRxKIq2dOlSXbYZkX7U35g/f77nD1Jst7/6m4iIvIHHb38dv/3a3+QdzFAhIiIiIiIiIrKJNVSIiIiIiIiIiGxiQIWIiIiIiIiIyCYGVIiIiIiIiIiIbGJAhYiIiIiIiIjIJgZUiIiIiIiIiIhsYkCFiIiIiIiIiMgmBlSIiIiIiIiIiGxiQIWIiIiIiIiIyCYGVIiIiIiIiIiIbGJAhYiIiIiIiIjIJgZUiIiIiIiIiIhsYkCFiIiIiIiIiEjs+X9am28e0+T7wQAAAABJRU5ErkJggg==", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mlm = maglim_maps[BAND]\n", + "covered = mlm != hp.UNSEEN\n", + "MAGLIM_MEASURED = float(np.median(mlm[covered]))\n", + "MAGLIM_REF = SIM_DEPTH_5SIG[BAND] # official 5-sigma point-source depth of the simulated survey\n", + "print(f\"measured map median = {MAGLIM_MEASURED:.3f}; reference maglim (official 5sig pt-src) = \"\n", + " f\"{MAGLIM_REF:.2f} (catalog errors run {MAGLIM_MEASURED - MAGLIM_REF:+.2f} mag past official)\")\n", + "\n", + "# --- photoerror table -------------------------------------------------------\n", + "sel = (cat.matched & (cat.class_star > CLASS_STAR_CUT) & (cat[\"flags\"] < FLAG_CUT))\n", + "pe = cat.loc[sel, [\"alphawin_j2000\", \"deltawin_j2000\",\n", + " f\"truth_mag_{BAND}\", f\"magerr_auto_{BAND}\"]].dropna()\n", + "pix = hp.ang2pix(NSIDE, pe.alphawin_j2000.values, pe.deltawin_j2000.values, lonlat=True)\n", + "ml_local = mlm[pix]\n", + "good = ml_local != hp.UNSEEN\n", + "# per-pixel maglim in the official convention (same shift as the _5sigps maps)\n", + "delta = pe[f\"truth_mag_{BAND}\"].values[good] - (ml_local[good] - MAGLIM_MEASURED + MAGLIM_REF)\n", + "logerr = np.log10(pe[f\"magerr_auto_{BAND}\"].values[good])\n", + "\n", + "dbins = np.arange(np.floor(delta.min() * 10) / 10, 1.5 + 1e-6, 0.12)\n", + "dmid = 0.5 * (dbins[1:] + dbins[:-1])\n", + "med_logerr = np.full(dmid.size, np.nan)\n", + "ib = np.digitize(delta, dbins) - 1\n", + "for i in range(dmid.size):\n", + " v = logerr[ib == i]\n", + " if v.size >= 20:\n", + " med_logerr[i] = np.median(v)\n", + "keep = np.isfinite(med_logerr)\n", + "\n", + "photoerr_tab = pd.DataFrame({\"delta_mag\": dmid[keep], \"log_mag_err\": med_logerr[keep]})\n", + "fpe = OUT_DIR / \"roman_photoerror_f158.csv\"\n", + "np.savetxt(fpe, photoerr_tab.values, delimiter=\",\", header=\"delta_mag,log_mag_err\", fmt=\"%.6f\")\n", + "print(f\"wrote {fpe.relative_to(REPO)} ({len(photoerr_tab)} rows, \"\n", + " f\"delta_mag {photoerr_tab.delta_mag.min():.2f} .. {photoerr_tab.delta_mag.max():.2f})\")\n", + "\n", + "# --- stellar efficiency table ------------------------------------------------\n", + "eff_tab = pd.DataFrame({\n", + " \"mag_f158\": MAG_MID,\n", + " \"delta_mag\": MAG_MID - MAGLIM_REF,\n", + " \"detection_eff\": eff_det,\n", + " \"classifiction_eff\": eff_cls,\n", + " \"classification_detection_eff\": eff_both,\n", + "})\n", + "eff_tab = eff_tab[n_all >= 20].fillna(0.0)\n", + "feff = OUT_DIR / \"roman_stellar_efficiency_cutf158.csv\"\n", + "np.savetxt(feff, eff_tab.values, delimiter=\",\",\n", + " header=\"mag_f158,delta_mag,detection_eff,classifiction_eff,classification_detection_eff\",\n", + " fmt=\"%.6f\")\n", + "print(f\"wrote {feff.relative_to(REPO)} ({len(eff_tab)} rows)\")\n", + "\n", + "# --- sanity overlay vs the LSST r-band tables --------------------------------\n", + "lsst_pe = np.genfromtxt(REPO / \"data/others/lsst_photoerror_r.csv\", delimiter=\",\", names=True)\n", + "lsst_eff = np.genfromtxt(REPO / \"data/others/lsst_stellar_efficiency_cutr.csv\", delimiter=\",\", names=True)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))\n", + "axes[0].plot(lsst_pe[\"delta_mag\"], lsst_pe[\"log_mag_err\"], \"-\", color=\"0.6\", label=\"LSST r\")\n", + "axes[0].plot(photoerr_tab.delta_mag, photoerr_tab.log_mag_err, \"C3o-\", ms=3, label=\"Roman F158\")\n", + "axes[0].set(xlabel=r\"$\\Delta$mag = mag $-$ maglim\", ylabel=r\"$\\log_{10}\\sigma$ [mag]\")\n", + "axes[0].legend(); axes[0].grid(alpha=0.3)\n", + "axes[1].plot(lsst_eff[\"delta_mag\"], lsst_eff[\"classification_detection_eff\"], \"-\", color=\"0.6\", label=\"LSST r\")\n", + "axes[1].plot(eff_tab.delta_mag, eff_tab.classification_detection_eff, \"C3o-\", ms=3, label=\"Roman F158\")\n", + "axes[1].set(xlabel=r\"$\\Delta$mag = mag $-$ maglim\", ylabel=\"classification_detection_eff\")\n", + "axes[1].legend(); axes[1].grid(alpha=0.3)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "71329454", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "- Wire these into a `config/surveys/roman_hlwas.yaml` (mirroring `lsst_yr5.yaml`):\n", + " `completeness: roman_stellar_efficiency_cutf158.csv`, `completeness_band: f158`,\n", + " `log_photo_error: roman_photoerror_f158.csv`, `maglim_map_f158: roman_dc2_maglim_f158_nside1024.fits.gz`\n", + " (or, for the full HLWAS footprint, a maglim map scaled from the exposure-time map —\n", + " see `roman_hlwas_exptime_map.ipynb`; the DC2 map above characterizes the mock's depth).\n", + "- Caveats: the det→truth (detection-centric) match assigns a blended star to a single\n", + " truth source, so the detection efficiency here is slightly conservative for blends;\n", + " galaxy \"size\" is the measured `awin_world`, not a truth size.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (streamobs)", + "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": "3.14.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From ce1c10e3aac368e7d97b9407000733e7ffaf7023 Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 11 Jun 2026 11:23:47 -0700 Subject: [PATCH 31/54] Docs: paper-style derivation writeup with diagnostic figures - rewrite the Roman HLWAS docs page as a self-contained data-section style derivation (no notebook references), documenting the matching, selection, completeness, photo-error, and depth-normalization choices - embed diagnostic figures (mag distributions, combined star efficiency + galaxy misclassification, photo-error, maglim maps, LSST comparison), exported by the derivation notebook into docs/source/_static/roman_hlwas/ - notebook: merge the galaxy misclassification curve onto the star detection/classification efficiency plot Co-Authored-By: Claude Fable 5 --- .../_static/roman_hlwas/efficiency_f158.png | Bin 0 -> 87146 bytes .../_static/roman_hlwas/lsst_comparison.png | Bin 0 -> 70755 bytes .../_static/roman_hlwas/mag_distributions.png | Bin 0 -> 96794 bytes .../_static/roman_hlwas/maglim_maps.png | Bin 0 -> 372260 bytes .../_static/roman_hlwas/photoerror_f158.png | Bin 0 -> 131308 bytes docs/source/roman_hlwas.md | 265 +++++++++++------- notebooks/create_streamobs_files_hlwas.ipynb | 136 +++++---- 7 files changed, 232 insertions(+), 169 deletions(-) create mode 100644 docs/source/_static/roman_hlwas/efficiency_f158.png create mode 100644 docs/source/_static/roman_hlwas/lsst_comparison.png create mode 100644 docs/source/_static/roman_hlwas/mag_distributions.png create mode 100644 docs/source/_static/roman_hlwas/maglim_maps.png create mode 100644 docs/source/_static/roman_hlwas/photoerror_f158.png diff --git a/docs/source/_static/roman_hlwas/efficiency_f158.png 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -385,14 +379,8 @@ " eff_cls = np.where(n_det > 0, n_cls / np.maximum(n_det, 1), np.nan)\n", " eff_both = n_cls / n_all\n", "\n", - "fig, ax = plt.subplots(figsize=(7, 5))\n", - "ax.plot(MAG_MID, eff_det, \"o-\", ms=3, label=\"detection_eff\")\n", - "ax.plot(MAG_MID, eff_cls, \"s-\", ms=3, label=\"classification_eff (of detected)\")\n", - "ax.plot(MAG_MID, eff_both, \"^-\", ms=3, label=\"classification_detection_eff\")\n", - "ax.set(xlabel=\"true F158 mag\", ylabel=\"efficiency\", ylim=(-0.02, 1.05), xlim=(15, 29),\n", - " title=f\"True stars (N={ok.sum():,}), class_star > {CLASS_STAR_CUT}\")\n", - "ax.grid(alpha=0.3); ax.legend(loc=\"lower left\")\n", - "plt.show()\n" + "print(f\"star sample for the efficiency curves: {ok.sum():,} \"\n", + " \"(combined plot with the galaxy misclassification below)\")\n" ] }, { @@ -400,12 +388,13 @@ "id": "103445b4", "metadata": {}, "source": [ - "## 3. False star-classification of small true galaxies\n", + "## 3. False star-classification of small true galaxies (combined plot)\n", "\n", "True galaxies (`truth_gal_star == 0`) with measured semi-major axis\n", "`awin_world < 0.3″` — the truth index carries no size, so we use the SExtractor windowed\n", "size of the (nearest) matched detection. Plotted: fraction classified as a star\n", - "(`class_star > {cut}`) among *detected* small galaxies, vs true F158 mag.\n" + "(`class_star > {cut}`) among *detected* small galaxies, vs true F158 mag — on the same\n", + "axes as the true-star detection/classification curves from section 2.\n" ] }, { @@ -414,10 +403,10 @@ "id": "7bc997de", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:11:17.918328Z", - "iopub.status.busy": "2026-06-11T18:11:17.918127Z", - "iopub.status.idle": "2026-06-11T18:11:22.940306Z", - "shell.execute_reply": "2026-06-11T18:11:22.939619Z" + "iopub.execute_input": "2026-06-11T18:21:29.087338Z", + "iopub.status.busy": "2026-06-11T18:21:29.087228Z", + "iopub.status.idle": "2026-06-11T18:21:34.260623Z", + "shell.execute_reply": "2026-06-11T18:21:34.259633Z" } }, "outputs": [ @@ -430,9 +419,9 @@ }, { "data": { - "image/png": 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", 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", 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    " + "
    " ] }, "metadata": {}, @@ -455,13 +444,17 @@ "with np.errstate(invalid=\"ignore\"):\n", " eff_false = np.where(n_gal >= 20, n_false / np.maximum(n_gal, 1), np.nan)\n", "\n", - "fig, ax = plt.subplots(figsize=(7, 5))\n", - "ax.plot(MAG_MID, eff_false, \"o-\", ms=3, color=\"C3\",\n", - " label=f\"P(class_star > {CLASS_STAR_CUT})\")\n", - "ax.set(xlabel=\"true F158 mag\", ylabel=\"false star-classification rate\",\n", - " xlim=(15, 29), ylim=(-0.02, 1.05),\n", - " title=f\"Detected true galaxies, size < {GAL_SIZE_MAX}″ (N={len(small):,})\")\n", - "ax.grid(alpha=0.3); ax.legend()\n", + "fig, ax = plt.subplots(figsize=(7.5, 5))\n", + "ax.plot(MAG_MID, eff_det, \"o-\", ms=3, color=\"C0\", label=\"stars: detection\")\n", + "ax.plot(MAG_MID, eff_cls, \"s-\", ms=3, color=\"C1\", label=\"stars: classification (of detected)\")\n", + "ax.plot(MAG_MID, eff_both, \"^-\", ms=3, color=\"C2\", label=\"stars: detection × classification\")\n", + "ax.plot(MAG_MID, eff_false, \"v--\", ms=3, color=\"C3\",\n", + " label=f\"galaxies < {GAL_SIZE_MAX}″: misclassified as stars\")\n", + "ax.set(xlabel=\"true F158 mag\", ylabel=\"efficiency\", xlim=(15, 29), ylim=(-0.02, 1.05),\n", + " title=f\"True stars (N={ok.sum():,}) and detected small galaxies (N={len(small):,}), \"\n", + " f\"class_star > {CLASS_STAR_CUT}\")\n", + "ax.grid(alpha=0.3); ax.legend(loc=\"center left\")\n", + "fig.savefig(FIG_DIR / \"efficiency_f158.png\", dpi=130, bbox_inches=\"tight\")\n", "plt.show()\n" ] }, @@ -483,10 +476,10 @@ "id": "ff643fec", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:11:22.941765Z", - "iopub.status.busy": "2026-06-11T18:11:22.941651Z", - "iopub.status.idle": "2026-06-11T18:11:29.634361Z", - "shell.execute_reply": "2026-06-11T18:11:29.633373Z" + "iopub.execute_input": "2026-06-11T18:21:34.262930Z", + "iopub.status.busy": "2026-06-11T18:21:34.262715Z", + "iopub.status.idle": "2026-06-11T18:21:41.479839Z", + "shell.execute_reply": "2026-06-11T18:21:41.479300Z" } }, "outputs": [ @@ -520,6 +513,7 @@ "for ax in axes[:, 0]:\n", " ax.set_ylabel(\"N / 0.2 mag\")\n", "fig.tight_layout()\n", + "fig.savefig(FIG_DIR / \"mag_distributions.png\", dpi=130, bbox_inches=\"tight\")\n", "plt.show()\n" ] }, @@ -549,10 +543,10 @@ "id": "8f4f7263", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:11:29.639439Z", - "iopub.status.busy": "2026-06-11T18:11:29.639169Z", - "iopub.status.idle": "2026-06-11T18:11:41.402543Z", - "shell.execute_reply": "2026-06-11T18:11:41.401682Z" + "iopub.execute_input": "2026-06-11T18:21:41.481312Z", + "iopub.status.busy": "2026-06-11T18:21:41.481128Z", + "iopub.status.idle": "2026-06-11T18:21:53.426613Z", + "shell.execute_reply": "2026-06-11T18:21:53.425314Z" } }, "outputs": [ @@ -642,10 +636,10 @@ "id": "53a0e9b7", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:11:41.404451Z", - "iopub.status.busy": "2026-06-11T18:11:41.404173Z", - "iopub.status.idle": "2026-06-11T18:11:45.197197Z", - "shell.execute_reply": "2026-06-11T18:11:45.195973Z" + "iopub.execute_input": "2026-06-11T18:21:53.429070Z", + "iopub.status.busy": "2026-06-11T18:21:53.428762Z", + "iopub.status.idle": "2026-06-11T18:21:57.321947Z", + "shell.execute_reply": "2026-06-11T18:21:57.321322Z" } }, "outputs": [ @@ -708,6 +702,7 @@ " hp.cartview(mlm, lonra=[50.5, 56.5], latra=[-42.3, -37.7], hold=True,\n", " title=f\"{b} maglim (S/N={SNR_DEPTH})\", unit=\"mag\",\n", " min=vmin, max=vmax, badcolor=\"0.9\")\n", + "fig.savefig(FIG_DIR / \"maglim_maps.png\", dpi=130, bbox_inches=\"tight\")\n", "plt.show()\n", "\n", "fig, ax = plt.subplots(figsize=(7, 4.5))\n", @@ -768,10 +763,10 @@ "id": "882cdd12", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:11:45.206572Z", - "iopub.status.busy": "2026-06-11T18:11:45.206306Z", - "iopub.status.idle": "2026-06-11T18:11:45.765837Z", - "shell.execute_reply": "2026-06-11T18:11:45.764547Z" + "iopub.execute_input": "2026-06-11T18:21:57.323411Z", + "iopub.status.busy": "2026-06-11T18:21:57.323263Z", + "iopub.status.idle": "2026-06-11T18:21:58.018497Z", + "shell.execute_reply": "2026-06-11T18:21:58.017584Z" } }, "outputs": [ @@ -864,6 +859,7 @@ "axes[1].plot(eff_tab.delta_mag, eff_tab.classification_detection_eff, \"C3o-\", ms=3, label=\"Roman F158\")\n", "axes[1].set(xlabel=r\"$\\Delta$mag = mag $-$ maglim\", ylabel=\"classification_detection_eff\")\n", "axes[1].legend(); axes[1].grid(alpha=0.3)\n", + "fig.savefig(FIG_DIR / \"lsst_comparison.png\", dpi=130, bbox_inches=\"tight\")\n", "plt.show()\n" ] }, From a04ce3b00a59438fb34f5c0666c0847f0a58ff54 Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 11 Jun 2026 11:26:19 -0700 Subject: [PATCH 32/54] Docs: drop tile-margin geometry paragraph Co-Authored-By: Claude Fable 5 --- docs/source/roman_hlwas.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/docs/source/roman_hlwas.md b/docs/source/roman_hlwas.md index a27980d..4b39beb 100644 --- a/docs/source/roman_hlwas.md +++ b/docs/source/roman_hlwas.md @@ -38,14 +38,6 @@ Following the paper, three selections define the analysis sample: 2. **S/N > 5** in the detection image; 3. **a positional match to a true object**, as defined above. -The matched-only selection also serves a geometric purpose. Each detection coadd -extends ~30″ beyond the footprint of its tile's truth index, so detections in that -margin band (~21% of the total) have no truth object in their own tile — they are -duplicates of detections that appear, matched, in the neighbouring tile. Within the -truth footprint the unmatched fraction is 0.1–5% per tile, consistent with the 1.7% -quoted by Troxel et al.; requiring a match therefore removes the margin duplicates -along with the small residual of spurious and shredded detections. - A further property of the truth catalog requires care: about a quarter of the true stars carry a second truth entry at the same position under a different identifier, populated in only one band. Because the matcher can assign a star's detection to From 9691e6474c8fe6df8192064c45b0167a826ac8f5 Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 11 Jun 2026 11:26:50 -0700 Subject: [PATCH 33/54] Docs: drop truth-duplicate handling paragraph Co-Authored-By: Claude Fable 5 --- docs/source/roman_hlwas.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/docs/source/roman_hlwas.md b/docs/source/roman_hlwas.md index 4b39beb..d16c18c 100644 --- a/docs/source/roman_hlwas.md +++ b/docs/source/roman_hlwas.md @@ -38,14 +38,6 @@ Following the paper, three selections define the analysis sample: 2. **S/N > 5** in the detection image; 3. **a positional match to a true object**, as defined above. -A further property of the truth catalog requires care: about a quarter of the true -stars carry a second truth entry at the same position under a different identifier, -populated in only one band. Because the matcher can assign a star's detection to -either entry, we collapse the truth catalog to unique positions (209,024 entries → -181,502 stars) and count a star as detected if *any* of its entries received a -match. Without this, the detection efficiency would show a spurious, flat ~12% -deficit at all magnitudes. - ![True vs observed magnitude distributions per band](_static/roman_hlwas/mag_distributions.png) *Number counts per band: solid — true magnitudes of matched sources; dotted — From b9bf5155cb43246495b3eecdc82532b4a9d8921c Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 11 Jun 2026 11:39:00 -0700 Subject: [PATCH 34/54] Truth-validate errors: drop maglim normalization, truth-based error model Validating the reported SExtractor magerr against the truth shows it underestimates the real scatter of (observed - true) by a flat factor ~1.9-2.0 in all four bands - the apparent excess depth over the official 5-sigma values is not real. Accordingly: - photo-error table is now built from the truth-based scatter ((p84-p16)/2 of mag_auto - truth mag), not the reported magerr - normalized _5sigps maglim maps removed; maps stay in the measured convention and the tables' delta_mag is keyed to the measured F158 map median, one internal convention throughout - new error-validation figure (reported vs truth-based, per band) in the notebook and docs; docs depth/error sections rewritten accordingly - config points at the un-normalized maglim map Co-Authored-By: Claude Fable 5 --- config/surveys/roman_hlwas.yaml | 15 +- .../_static/roman_hlwas/error_validation.png | Bin 0 -> 112390 bytes .../_static/roman_hlwas/lsst_comparison.png | Bin 70755 -> 87192 bytes .../_static/roman_hlwas/photoerror_f158.png | Bin 131308 -> 139505 bytes docs/source/roman_hlwas.md | 82 +++--- notebooks/create_streamobs_files_hlwas.ipynb | 270 +++++++++++------- 6 files changed, 222 insertions(+), 145 deletions(-) create mode 100644 docs/source/_static/roman_hlwas/error_validation.png diff --git a/config/surveys/roman_hlwas.yaml b/config/surveys/roman_hlwas.yaml index 121fb7d..250f64b 100644 --- a/config/surveys/roman_hlwas.yaml +++ b/config/surveys/roman_hlwas.yaml @@ -11,15 +11,12 @@ survey_files: # Path to data files (leave empty for default location: data/surveys/roman_hlwas) file_path: '' - # Band-specific magnitude limit maps. - # The _5sigps map is the DC2-measured S/N=5 depth map renormalized so its - # median equals the official 5-sigma point-source depth of the simulated - # survey (F158: 26.9). The delta_mag axes of the completeness and - # photo-error tables are keyed to this same convention, so any replacement - # map (e.g. an exposure-time-scaled HLWAS map normalized to the STScI - # median depths: wide F158 26.2, medium F106 26.5 / F129 26.4 / F158 26.4) - # must also be a 5-sigma point-source maglim. - maglim_map_f158: roman_dc2_maglim_f158_nside1024_5sigps.fits.gz + # Band-specific magnitude limit maps: the DC2-measured S/N=5 depth map + # (un-normalized). 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+76,36 @@ rate of detected compact (<0.3″) true galaxies on the same axes.* ## Photometric errors -The photometric error model `roman_photoerror_f158.csv` is the binned median of -log10 of the observed F158 `magerr_auto` for star-classified objects, tabulated -against `delta_mag = m_true − maglim`, where the magnitude limit is evaluated at -each object's position from the nside=1024 depth map described below. Tabulating -against `delta_mag` rather than magnitude makes the model portable across regions -(and surveys) of different depth, under the assumption that the error profile -depends on magnitude only through the local depth. +Because the truth is available, the reported photometric uncertainties can be +validated directly: for true stars, the scatter of observed-minus-true magnitude +should match the reported `magerr_auto`. It does not. The truth-based scatter — +measured as half the 16th-to-84th percentile span of (m_obs − m_true) in bins of +magnitude — exceeds the reported errors by a flat factor of ~1.9–2.0 at all +magnitudes and in all four bands, the signature of SExtractor underestimating the +correlated noise of the resampled, median-combined coadds. + +![Reported errors vs truth-based scatter](_static/roman_hlwas/error_validation.png) + +*Truth-based scatter (solid) and median reported `magerr_auto` (dashed) for true +stars per band, with their ratio in the lower panel: a constant factor ≈2 in every +band.* + +The photometric error model `roman_photoerror_f158.csv` is therefore built from the +**truth-based scatter**, not the reported errors: it tabulates the binned log10 +scatter of (observed − true) F158 magnitude for star-classified objects against +`delta_mag = m_true − maglim`, where the magnitude limit is evaluated at each +object's position from the nside=1024 depth map described below. Tabulating against +`delta_mag` rather than magnitude makes the model portable across regions (and +surveys) of different depth, under the assumption that the error profile depends on +magnitude only through the local depth. ![Observed F158 photometric error vs true magnitude](_static/roman_hlwas/photoerror_f158.png) *Observed F158 `magerr_auto` against true F158 magnitude for star-classified -objects, with binned median and 16/84% curves. The error saturates near the S/N≈5 -floor at the faint end; the sparse cloud above the median relation is blends.* +objects, with binned median and 16/84% curves of the reported errors and, in orange, +the truth-based scatter adopted for the error model. The reported error saturates +near the S/N≈5 floor at the faint end; the sparse cloud above the median relation is +blends.* ## Survey depth @@ -102,33 +119,29 @@ magnitude limit of a pixel is the median over its objects. We define depth at **S/N = 5**, consistent with the catalog's detection threshold. The resulting medians are 26.77/27.11/27.11/26.67 in F106/F129/F158/F184. -These measured depths run ~0.2 mag (F106–F158) to ~0.5 mag (F184) past the official -expected 5σ point-source depths of the simulated survey. The same excess is visible -in Figure 5 of Troxel et al., whose measured magnitude histograms extend beyond the -expected limiting-magnitude lines (most prominently in F184); the likely cause is -that SExtractor underestimates the correlated noise of the resampled, median-combined -coadds, inflating the apparent signal-to-noise. +These nominally exceed the official expected 5σ point-source depths of the simulated +survey (26.9 in F106/F129/F158, 26.2 in F184) by ~0.2–0.5 mag, but this is not real +extra depth: it follows from the same error underestimation established above (the +same excess is visible in Figure 5 of Troxel et al., most prominently in F184). The +maps are therefore an *internal* depth reference — their value is the spatial +structure and the `delta_mag` coordinate they define, not their absolute calibration. ![Magnitude-limit maps per band at nside=1024](_static/roman_hlwas/maglim_maps.png) *Measured S/N=5 magnitude-limit maps over the DC2 footprint (RA 51–56, Dec −42 to −38). The spatial structure traces the simulated dither/pass pattern.* -### Depth convention and normalization +### Depth convention -Because of this offset, we adopt the **official 5σ point-source depth** as the -reference convention. Each band's map is provided in two forms: - -- the raw measured map (`roman_dc2_maglim_f*_nside1024.fits.gz`); -- a normalized map (`..._5sigps.fits.gz`) shifted so its median equals the official - depth of the simulated survey (26.9 in F106/F129/F158, 26.2 in F184), preserving - the measured spatial structure. - -The `delta_mag` axes of the completeness and photometric-error tables are keyed to -the official convention, so they must be paired with magnitude-limit maps in the -same convention — the `_5sigps` maps above or, for the real HLWAS footprint, an -exposure-time-scaled map normalized to the -[STScI community-defined HLWAS median 5σ point-source depths](https://roman-docs.stsci.edu/roman-community-defined-surveys/high-latitude-wide-area-survey): +No renormalization is applied anywhere: the maps are released as measured +(`roman_dc2_maglim_f*_nside1024.fits.gz`), and the `delta_mag` axes of the +completeness and photometric-error tables are keyed to the median of the F158 map, +so maps and tables share a single internal convention. A magnitude-limit map for a +different footprint (e.g. an exposure-time-scaled map of the real HLWAS) must be +expressed in this same convention — scaled relative to the DC2 depth — for the +tables to apply. For reference, the +[STScI community-defined HLWAS median 5σ point-source depths](https://roman-docs.stsci.edu/roman-community-defined-surveys/high-latitude-wide-area-survey) +are: | Tier | Area | 5σ point-source total depth (AB) | |---|---|---| @@ -136,10 +149,9 @@ exposure-time-scaled map normalized to the | Medium | ~2400 deg² | F106 26.5, F129 26.4, F158 26.4 | | Deep | ~19.2 deg² | F106 27.7, F129 27.6, F158 27.5, F184 27.0, F213 25.9 | -With this convention, substituting a shallower (e.g. wide-tier) map automatically -translates the completeness and error curves to the correct depths, under the -assumption that the selection function depends on magnitude only through -`mag − maglim`. +Substituting a shallower (e.g. wide-tier) map translates the completeness and error +curves to the corresponding depths, under the assumption that the selection function +depends on magnitude only through `mag − maglim`. ![Roman F158 vs LSST r selection-function tables](_static/roman_hlwas/lsst_comparison.png) @@ -172,5 +184,9 @@ photo_error = survey.get_photo_error("f158", mag, maglim) saturation; the configured `saturation: 15.0` marks the validity limit of the completeness table rather than the physical WFI saturation (~17–18 AB for point sources). +- `mag_auto` carries a systematic offset with respect to the truth (≈ +0.1–0.2 mag + in F106/F129/F158 and ≈ +0.6 mag in F184 at the bright end, growing toward faint + magnitudes from aperture losses). The error model captures the scatter about this + offset, not the offset itself. - Extinction coefficients are CCM89 (R_V = 3.1) evaluated at the filter effective wavelengths (1.14, 0.83, 0.60, 0.47 for F106/F129/F158/F184). diff --git a/notebooks/create_streamobs_files_hlwas.ipynb b/notebooks/create_streamobs_files_hlwas.ipynb index 9fed55e..8f0b020 100644 --- a/notebooks/create_streamobs_files_hlwas.ipynb +++ b/notebooks/create_streamobs_files_hlwas.ipynb @@ -30,10 +30,10 @@ "id": "8161d972", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:21:23.156071Z", - "iopub.status.busy": "2026-06-11T18:21:23.155960Z", - "iopub.status.idle": "2026-06-11T18:21:25.783765Z", - "shell.execute_reply": "2026-06-11T18:21:25.783070Z" + "iopub.execute_input": "2026-06-11T18:37:13.827417Z", + "iopub.status.busy": "2026-06-11T18:37:13.827271Z", + "iopub.status.idle": "2026-06-11T18:37:16.178816Z", + "shell.execute_reply": "2026-06-11T18:37:16.178026Z" } }, "outputs": [ @@ -74,18 +74,6 @@ "NSIDE = 1024\n", "SNR_DEPTH = 5 # depth = mag at S/N = 5 (matches the catalog S/N>5 cut)\n", "GAL_SIZE_MAX = 0.3 # arcsec; \"small\" galaxies = awin_world < this\n", - "# Official-convention 5-sigma point-source depths:\n", - "# - simulated reference HLIS (Troxel+23: \"~26.9 mag AB in Y106/J129/H158, 26.2 in F184\"; Akeson+19)\n", - "SIM_DEPTH_5SIG = {\"Y106\": 26.9, \"J129\": 26.9, \"H158\": 26.9, \"F184\": 26.2}\n", - "# - STScI community-defined HLWAS median 5-sigma point-source total depths:\n", - "# https://roman-docs.stsci.edu/roman-community-defined-surveys/high-latitude-wide-area-survey\n", - "HLWAS_DEPTH_5SIG = {\n", - " \"wide\": {\"F158\": 26.2}, # ~2700 deg2\n", - " \"medium\": {\"F106\": 26.5, \"F129\": 26.4, \"F158\": 26.4}, # ~2400 deg2\n", - " \"deep\": {\"F087\": 27.7, \"F106\": 27.7, \"F129\": 27.6, \"F158\": 27.5,\n", - " \"F184\": 27.0, \"F213\": 25.9}, # ~19.2 deg2\n", - "}\n", - "\n", "MAG_BINS = np.arange(15.0, 29.0 + 1e-6, 0.25)\n", "MAG_MID = 0.5 * (MAG_BINS[1:] + MAG_BINS[:-1])\n", "\n", @@ -121,10 +109,10 @@ "id": "ecced6fb", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:21:25.794023Z", - "iopub.status.busy": "2026-06-11T18:21:25.793441Z", - "iopub.status.idle": "2026-06-11T18:21:27.490576Z", - "shell.execute_reply": "2026-06-11T18:21:27.488598Z" + "iopub.execute_input": "2026-06-11T18:37:16.185445Z", + "iopub.status.busy": "2026-06-11T18:37:16.185080Z", + "iopub.status.idle": "2026-06-11T18:37:18.016130Z", + "shell.execute_reply": "2026-06-11T18:37:18.014867Z" } }, "outputs": [ @@ -172,10 +160,10 @@ "id": "24956ad3", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:21:27.494375Z", - "iopub.status.busy": "2026-06-11T18:21:27.493868Z", - "iopub.status.idle": "2026-06-11T18:21:27.903173Z", - "shell.execute_reply": "2026-06-11T18:21:27.902527Z" + "iopub.execute_input": "2026-06-11T18:37:18.022368Z", + "iopub.status.busy": "2026-06-11T18:37:18.022122Z", + "iopub.status.idle": "2026-06-11T18:37:18.400646Z", + "shell.execute_reply": "2026-06-11T18:37:18.399594Z" } }, "outputs": [ @@ -238,7 +226,10 @@ "## 1. Photometric error vs true F158 magnitude (star-classified objects)\n", "\n", "Matched detections with `class_star > {cut}` and clean flags, observed F158 `magerr_auto`\n", - "against the true F158 magnitude, with the binned median overlaid.\n" + "against the true F158 magnitude, with the binned median overlaid. The orange curve is the\n", + "**truth-based scatter** of (observed − true): it runs a factor ~1.9–2.0 above the reported\n", + "errors at all magnitudes — SExtractor underestimates the correlated noise of the resampled\n", + "median coadds — so the error *model* (section 5) is built from the truth-based scatter.\n" ] }, { @@ -247,10 +238,10 @@ "id": "ae3481ab", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:21:27.904500Z", - "iopub.status.busy": "2026-06-11T18:21:27.904386Z", - "iopub.status.idle": "2026-06-11T18:21:28.817492Z", - "shell.execute_reply": "2026-06-11T18:21:28.816946Z" + "iopub.execute_input": "2026-06-11T18:37:18.404635Z", + "iopub.status.busy": "2026-06-11T18:37:18.404185Z", + "iopub.status.idle": "2026-06-11T18:37:19.373508Z", + "shell.execute_reply": "2026-06-11T18:37:19.370256Z" } }, "outputs": [ @@ -263,7 +254,7 @@ }, { "data": { - "image/png": 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+l2zG4jpsyvziiy/w1FNPiWbwuXPnihzhkhyjP9wH5oGyKZu5fv7XGFNT/GGaBvHOddS+F+/0Du9rgtcg01V4nbJ580ycbnuEKRRsTudyt956q5jGvE6taV6DTXtsOtSuMzZNB7rWtZzaQNc6U29O1+kl0HyeS55Tnlvms/nD9Abv4/SGeXD87QYD5pUTpkucS2cI3iPef/990YzOlBva2njDa1OD1yyb99njfPbs2SJVifcf5pD67wObZ9kUz+Zjpo7w/sBOQMwDPBt4j+C9kh2KmPvIfeG9gmkJ3rma5+O4i3N/LCnMT2fOJgemiPFe/8MPP+DJJ58U543Nzxf6GcXr2fua5nOFqRf83vhbW7VqlXj+MP3jQlOS70v77QVKjdCms6nbG95reA/mfYWpd/wd89rm91QSgvGsuVheyGejaSRFc8mIVebNMMeQP6IzwR+Xto4/fOgFQpvundjNGzU70jAXlDZLhMKOwujjjz/2dGJhPg+hbQmHogjU4et0PTSZ90SxypxETawyn0eb5422D9yv06Htg3acRX1+oHN3qcKcJ+ad8gZKkaR1TGNuFfOAOT5u3DjPA54drnij5LXEhwG/3zvuuKPYn3e6a4h5ghRy/mI1UE6tJqrp26hR3O/FX0wWxdlsj2LV35mB1zofoNp1xo59gToOnu21XtT8M+0/8weZ2xbofGgG6MFA235J85u9YWchii++nPABTvHJfeeLEh+Y/G0z50+DOem05KPAYl4089+164ye0vTh1F6y+Lvndc08TXZE0zp0UARw/HR5/oHgdvn90n2FneboqqGdb3Yk4vYDWcUF47i9CXYPdr74UPhz0DpEnq4jqzdaDmVRnX/4u/de7mxhTjvza3numY99ocVqSb8v7XwUdV8M9B3y5Z0vvjxG5l9rnXp5n2HHU+YdX+5cqOtFcomJVUaUaE3FKBjtQU6H1rM/kE+dFmEsarr3hcMIAi2OODAJnsnQ/FExErVt2zbRqYriVVuHQtW701VxON3DlDcMRjj4oGJHHCbfM4GfHb0YPfFG2weKieJEU7TlKa4Cwc4dlxM8NxSr7OijMWfOHNE5h1HiQNEwRhspVikISiJWea14d9bynq49bM6W4n4vxb3Bnc32Thdp0JajOGJkuiScSTgGmn+m/c/NzRUPA7omlPTzSoL2slFUBLA4sBMef58UlP4vm4z0aS+i3vA644sWX2h4v2GEip1xeE9iL3c6ExAKCUZgOfAzKHJ4n+L9gh1f+BIX6Byd6ZgpdDkw8shWG3ZYYodAvsyc7mXlXI87mN/hkSNHhG0QP4sdVgkjmuzRr/VGLw58GeB5ppiju4d3C453Cxo71ATjfka872cXipJ+X9pvtKhna1G/XT7feP9lp0G2FPF+zY5x7NzFZxxbC84EAzkliYzymvYviBCoJel0UFAXpxKm9ozYvXt3wPnBvF4kl5BY5Y+GvV0ZXeBNu6i3YQo7ig8KNvaK9YdNFoGaaTX7CPbyDQSbXBlR4MBmGjbF8wHAZkY2XxCK2ZKK1TNB2xG+aVIk87MYDaSw8k9F4D7wRsx9KI4pNZv4CN9uKej8UwHOV1P++YBihVF3f8smHhfxt3vS4AsAYS/nksBrhb1kvWEUlzdrPtBK4rEY6HuhyOCLGZsEvWE0Q2uG174/okXXvCO03tsj3F4gNMFR3F7S3td6ScXq2cBzyXPKiBhtXvwtyLTmxXPp5V0caJNF+EClSDybVADNPi7QfelM9jX8jhnZ5sCWBD4IGZHSxKo3FKW8B3Dgb4ACg9cNe2afLRR0HJgixVQannf+7rSIW1HX37ke99nC3zbdYNg6RuHOJlem3Tz77LPiGEoaaSZMUeG9n9c+B/8e3Hz2nC7l5mzSTniuLzQl/b60ZybPSSCKmq7B+y+dAbSBkVxe28UVqyW5huhYEUisFtfjW0sDK45YZZoeW0coVvmy5/9SFMzrRXIJ+azyi2Y0gTdfNsMH8ofkRUs7F8Jm3kBG77zBak36GswNYh4gbWc0oUeBo90w/AWD9hameYTyIcAHKpvsWawgEHxz1JpQSwJ/sHwY8EdZVAoAYUSFP3rmcAYqO8oHibcA1fximTLhnzrA83EhvFZLAt/OA3nFsrmZNw5Wv2L+pXeTk5ZDxYeWd6lXwrfx7777TvzN9UoCc9y801F4bvlCwQdiSSxrAsHrl9/j119/Xeh75MsaI3ss7eidr6pFzAKlyPDhyjxGWn35eyRScFAYszm3uFV3KAp5s2ZEhDl/gTxOeXMuTrpOcaE9FeG1rb2AaM1oL730kvib6R5n40FZ3Mgdj5vnki/KbLoMdF8pytdZQ3uR8n8RpEUd7cr84XcWKCqlTdPuP7wnsTXJ30+U3432QlZSP2M+XLdu3VpoOl/0Ge3jy60WWdT8obX0q3M97nOFKRL8fbA4DFvA2BLHZwMFCdMXzkaoajDqR/gs8q64xwAJo7eMiHrbH2qtF/wtp6Sk+EznC0QgD1j+Trkt3gdoBXcu1+3ZUNLvi78L+gDzpYAi0xv2CfDPV9UEbKBopv+1fSa4j+6O4MUaAkVhOa0k2yhuIIffkZbO8Nxzz/l817x/8hzw5TtQHwjJZRxZJexAw0RvJn4zYkSRwWgjLwImzfOGxBsomxKKql5FAcOHLB8E7DDAhz9vDLwIaZ6tCVxOZxSJEQw+qPhWzps0f7AM3/OGxB8o4Y2bP1LNX5Vvh9w/bos3cApVRt74b0mb4phTSFHJzkGM5PJGUlQHHwpaiiUaaDMqx+YFCinuA2+M3kKbsDmRx8h8NEbYNJ9V5qjxhUDLkTsf8MZN827vccIHixapoQDRUjl4s2ckg/tL8cV8RH5HTAvhDY4RCP8bKY+H2+P3zSYnfjdaByt+X2xC5vdYUk9ZrQOEt88qRQw/j4LqXOCbP1+0+FBkXhc/gwKc/o68vukTqhmTa7CZl98Zj5XREF53bO7iCwxvmHzJ4W+FEXm+lLFVgh6ATFvhA5Gd0JiLWFzYLM1cSHp3smgGo45syeB5pTjgdc7zy6hCMGAEV8sz5L4zskjRyv3nZ/K4/NNizoT24PBvyj0dFBFMKeF9iMfH65Hnl+eS9wV+R6fzr2TZXzahc1953fGFkYKQx8Xvmfch//PM3yjvWbzX8N5BwcUHHX8jfAAS3hN5DfB887vgNcTzw4cqX9I4raQ+jvRnZa4ko2Y85xR/zGFkZ08W2+B1zqZawuuNv0seP+8bvF/yvLL1gUNJj/tcYTM9P4tN/LwPBupAeraw0ytf8PgCzHPD42UQgsfAey3vNf5pQLy/8jfo7z/MZxSvQ95P+Lum+KXo5bOJ548v0/7FPc7mui0pJf2+2MrA9BA+/3j/YacpdpTic5K/E62jsndrBAMk/M3w98R7Ms8ZX3J5fVGo+kc/L1d47+Ix8XqhLuB9ky/yvF/znstUC1m9KkgolyDr1q1T7r77buEpSv/SsLAw4Z/3yCOPCPuQQGj2OLTqoH0TfexoDcX1abnhby1E30RaFPXq1UupUqWK8L5MSEgQXos//PBDkd6rL7/8srC34T5x29xHfhbXyc3NLZadSFF+bMVZnsdGf1na2HCfadXVsGFDcdzeFkka+/fvF/Y0tL4JCQkRtiljx471nK+SWlcV19ZDs7s53eBtdUL/0fvvv1/sX3x8vPD6i4qKEvYztHsKZEdGaDvG4+H3GBsbK/xjeU74ndOztyTWMJpNCi2zPvjgA2FRw3PM64O2ZYH2QbNFCYR2DQTy7Zs/f75y5ZVXin02mUzi+3zssceUEydOFFqWx0DPXfq2ctlAFi30leQ1QOsrLkPrrhtvvFHZsGGDcjbQz5O2O23bthUetvSy5WdeccUVwqotLS0t4G8vEGeaT2jJxM/jb4vXKX9f9LPk7yqQtVxRNjUa9IPkMvfcc0+Jjpv+ovTq5f2Gx8xrkL6btOriPeNMlkC0P+K1yHsPzxttkOg1Gej3Rv/nhx56SGnRooX4ffLzeB3cfPPNyqpVqzzL0X6N1yOt+mjRxvPD5ekRTf/hQN6rZyIxMVF56aWXxP2uYsWK4jqvVKmSsCGaMGFCoeX5mxg8eLCw2qN3sP+xlOS4z2THdiboe3w+4b2ftnf83nmueUw89zzGQGjXgv8x8nru27ev8OHmdjSf7LvuuqtIy7WzuW7PxrqqpN+XZu/G80ArQfpEc52///5b+fDDDwt5qv7777/iGU4PaN6P+aykRd29995bIu/rywE+92mbxuPj74jPL957NRtJSXDQ8T/BEr4SyeUMowCMbrKJ1D/iIbm8YNUlRqgZzZHfpeRy4XK8bhnhZt40W8cCdUyVSEpVzqpEIpEEC750MGXmcnngSySX8nXLVBRvv2gNNvUzbYDuC1KoSspMzqpEIpEEA/8OdxLJ5cClet0yd5x9JZgbz87GzOVmnis7/tEC0j/PXiIJNlKsSiQSSSmguJ6UxfWRlEg06IJAm0V26qMfLyOt7ATLSlXsYFbcwgsSydkic1YlEomkFOVcX8ySyRKJRHI+kGJVIpFIJBKJRHLJIjtYSSQSiUQikUguWaRYlUgkEolEIpFcssgOVmcJq5GwqhETz89ntRGJRCKRSMo6DodDlElnZy46EFxIWBnyTOWWS4pWiVBSPKTKOksoVFniUyKRSCQSyYWB5WpZ+vpCCtU6teOQdkothRssWMKapZylYC0eUqyeJYyoaj+cSpUqne1mJBKJRCKRnIHjx4+LAJH27L1QMKJKobr8nyqolBAcyXQ8yYHOA46KbUuxWjykWD1LtKZ/CtWqVaue7WYkEolEIpGU8Nl7oaFQrVo5uJ/dokULGAwGPPvss2KQFI0UqxKJRCKRSCSnwam4xBAMtO1s2rRJBruKiXQDkEgkEolEIpFcssjIqkQikUgkEslpcEERQzAI1nbKElKsSiQSiUQikZwGSlX+L1jbkpQMKVbPMzk5OcjMzITdbofLJS/Q0ggT5On7Fx8fD51Od7F3RyKRSCSSUoUUq+cJCtNjx44hKysLer0eJpNJ/CspfdhsNmFBYrVaUaVKFSlYJRKJpJThVBQxBGtbkpJRpsTqmjVrMHbsWCxcuBAHDhwQEbEOHTrglVdeQffu3YNuJEyhGhcXJyJuUqiWblJTU5GUlISUlJQL7gMokUgkEklppkyF+t5//32MHz8e3bp1w6effipEKkVr7969MXv27KB+FiNtZrNZCBcpVEs/fCnh980yvBKJRCIpnR2sgjVISkaZiqw++eST+P3332GxWDzT7r33XjRu3Bgvv/wyrrzyyqCmATByK3MYyw78vp1O58XeDYlEIpEEGfY4cQbNDUBSUspUZLVLly4+QpXExsaKyOq2bdsu2n5JJBKJRCKRSAJTpiKrRcGOUMwrlUgkEolEIvFH+qxeXMq8WF22bBkWL14sUgROB+2nOGgcP378Anw9EolEIpFIJGWbMpUG4A97b992222oXr06RowYcdplP/nkE1SrVs0ztG/f/oLtpyQwd911F2rWrOkzjTnCr7/+ujxlEolEIgm6dVWwBknJKLNilbZSV111lYiWzpgxA9HR0add/qmnnkJiYqJnWL169QXbV4lEIpGUDRRFgcPuKNE6JV1ecnadooI5SEpGmUwDyMvLwzXXXIMdO3Zg7ty5aNas2RnXiYqKEoPk0v9ujcYyeVlLJJLLmNTjpzDhg2lYOnUVUo+eQoueTdD39u644o4eAZfPzcrDhA+nYenkVTi84ygadqiHHjd1wuDHBwhnEsmlT4sWLcR39eyzz4pBUjRl7qnOsqc33ngjli9fjunTp6Nz584Xe5ckQYRlTyUSieRy4+/v52Hy57M84xsWbMH6eZvR5br2CIsMLbT8iulr8ftbk0TqE6OxO1fvwY6Vu9GiVxPUbVnrAu992ehgFTzrKnU7mzZtQtWqVYOyzdJOmUoDoPfp0KFDRQGAX3/9Nai+qmUR5obyRrlr1y6RP0obMJrj8w2R55o5wUOGDEFMTIwojvDGG2/4rM8b7DfffCPeLkNDQ8W6/H6OHDlS6LNGjx6N+vXrCzHK5Zm6EQj/nNW0tDSxP82bN0dkZCQiIiLQq1cv0bHOm4MHD4p133vvPXFtNGzYUNiccb158+YF7ZxJJBJJIPRG9XGs0+t87mfe4z7LG9yPb7/ZBqOMqkpKH2UqsvrMM89g4sSJuOKKK+BwOPDbb7/5zL/99tsv2r5dztxyyy1CSL7zzjsireKjjz4SwnXChAlo3bo13n33XUyaNEmISArNwYMHi/Uee+wxfPvttxg2bBgeeughnDhxAl9++aWIem/YsEGIXDJmzBjcc889aNeuHR555BGcPHlSrMOObmdi//794jtnNL1u3brIyMjATz/9hD59+mDt2rVo2rSpz/J//fUXkpOT8eCDDwoB/dlnn+G6667DoUOHUK5cufN0BiUSSVln8GMDEBJmwbJpq3Fs7wm0698SvW7rhtDwwK1FXa9vjye/ewDLpq7G7nX70bJXE3S/sRNqNjnzfdGbnMxc7Fy1BzWbVkdcpdggHU3pg+VenEHqFyVLx5wFShmiR48evNSKHEpCYmKiWIf/BuLAgQNiKITLpSj5+ZfewP0qIa+99po4B/fcc4/X4bmUOnXqKDqdTnnppZc80/Py8pTY2Fjl6quvFuPLly8X644ePdpnm5s2bVIMBoMycuRIMW6325UKFSoojRs3FtvQ+Pfff8X6NWrU8Fmf07hfGvn5+YrD4fBZJi0tTUlISFDuvfden++L60ZHRysnT570TN+wYYOYPmrUqDOejyK/c4lEIrnEyM3OU1655l2lv+lmpa/uRjE82uEFJXH3MeVS5EzP3PP9uUtXl1f2JVYMysBtXYxjuZwpU5HVRYsWXexdAGw2JlbikoM17f2qexWX++67z6fZihHQffv2+UzXmu85nTDqyiZ5OjKkpKR4lqtcubKI0i5YsACvvvoq1qxZIyKpLIfrnY/ar18/USY3JyfntPvmXbEsPz9fLE9Ny31ct25doeVvuukmJCQkeMZbtmwpOtYxQiuRSCQXAqfDiS1LduDYvpNo1acpKtWqcE7b4z3vwJbD2L5iN+q3rY16rWuL6O3Kmb73wJ2r92Lz4u2oWq/SOR6BRBJcypRYlZwf/JvjNRuwQNN37twp/maea3Z2NipUCHwTpuglbH4nDRo0KLQMp61fv/60+8bc2Q8++ADff/89Dhw44DOvVq3CnRD8fVsJUxqY+yqRSCTnG3aqGjnkY+Sk53qmsaf/p/+NhNFU8kd2ytFUPNXjNRzff9IzLb5KObz4+xMwmo1w2ByeTlraPElhXNDB6Z8gfA7bkpQMKVYl50xRNimBpms3RIpIdqgaP358wHXDw8N9ltfEa6BtnQ52mGJU9s4778Rbb70lPpP7xTxaLcpbnGMpzmdJJBLJucLop7dQJcwpzTqVg9iE0/uBB+LI7uM+QpWkHE1DRkoWxh36RkRXNy3ehjrNa6LTte1kVFVySSLF6oXGbFab3C/F/bqA1KlTR3TG6tChg+ilXxRapJMRWXaM82b37t1n/BymG/Ts2RO//PKLz/TXXnvtrPddIpFIzhe1mlUXPf1dzgLr+IQa8QiPDjur7VWoUV6sm5NRIIDZkatK3YqIrRCDAcP7iEFyelyKOgSDYG2nLCHF6oWGEcKzzA0tbQ4CX3/9tXAI+PjjjwtFMVNTUxEfH4+2bduKHNLvvvtO5MBqeatz5szB9u3bUaNGjdN+DgsE+EdF6TawYsUKUWZXIpFILia8P+1YtQcrpq3BycPJaHNFC/yy6wvhmXp07wm07tscDTvUPWuj/0q1K2DCiR+xadE2bF++Cw3a1RV5sJZQ+RwqCc4gpgEEaztlCSlWJReFbt264fHHH8cnn3wijJEHDBiAsLAwkVc6ZcoUDB8+HC+88AJMJpNosud49+7dhQ8r/Vu/+uorNGnSROS9ng5WKqMgvuOOO8Rn7tmzR+SvsnPWmdaVSCSS882Uz//GN0+pLT/0VF34xzIRCZ2U/HPQPFPNFpOwwuIgkVyOSLEquWh8/vnnaNOmjYiwjhgxAnq9XlTz6N+/v/A21aDHKqMPzD997rnnRMeqsWPHCu/WMzk8vPjii8jNzcXvv/8u/Fbpq8o8WQ6XhDuERCK5LMlMzcKiP5djxYy1cNjs6Hh1W/S8pUuRXqX5uVYs+XsTVs7dilPJWWjXqxF6XNNKlFnVUNztw2yyt9sc0uD/EkJGVi8uOvpXXeR9uCxhlSX2dk9MTAxYLo0VkYrqXS4pncjvXCIpO7x67XtYOWOdp8IUhWaDdnUwatV7AZf//s2pmPLTYnV5RW3+j4gOxRvf3YVRj/6EPetVe7ywqFD0vb07Hv1yeMCOpWWVMz1zz/fn/ruyIipUCk587+RxB/p3PHHBj+VyRkZWJRKJRCIpIfk5Vp9oKNm74SC+eORHDBjeW3iZepOXW3h5a74djTvWx9dr30fykVQkJ6agfts6Z2VRJZGUZtzFhSUSiUQikRSXW1+4TnR+8jfzn/Htv3hv2BeFlh84tAs69WsGk0UVonWaVMGDIwrSncpXjUPjTg0KCVXa/LEkalEwQpuTkeMznp3uWyzFe77k3NIAgjVISoZ8fZNIJBKJpIRQqHJ4ccDbWDtno1q0m7l10MEVwJuobtOqGPH9PbDm25CXbUVMfNGWfSTlWBp+f/MvLJu6BqdOpqN+m9rofVs3XP+/gSI9IOtUtpi/ZPIqJB1OQbWGVRAVFymKAJw8mIwq9SshpnwUTp3MENWqKtetiO43dMRtr9yA0PBLsIqiRHIapFiVSCQSieQsuf/DYahYs7wQlTarDZ2uaYuB9/UtcnlLiFkMZ2LhuKWY+d1czzhzWnev2+/pxLVq1npM+myWZ37izqM+6x/dfVwMGhSs49+fimbdG6P9gFZncaRlGyf0YgjWtiQlQ4pViUQikUjOklpNq+OJb+7H41/fJ8aD1SmKpVDF9vQ6d54rt6vAaFLtrEyF5sN3XF3cU0pVm65tV1IyFEUHl6IL2rYkJUNetRKJRCKRnCOBRCrzTXeu3ovsUzlo0bMxTBYT9qzbj/SkDDTv2eS0zfED7++L0MhQrJi+Bod3HBEpBz1v7oLo+Cgxv+v1HfDi70+I+Yy4Nu/eGLEVY3DiQBJ2rdmLJl0aIL5KnOi0xRKujTrWQ9frOqBlrybyu5ZcdkixKpFIJBJJkFk4fhm+fuJnpCdninEa/DOqaXW7ArCj1RV39MST3z0QcH1ziBlX3t1LDIHg9nrf2lUMkvOP9Fm9uMjECYlEIpFIgszy6Ws8QlVzCtCEKrFbHZgzJnBhEmueVRQbmPzZLBzYcqhQyWhJ6aBFixainPiHH354sXflkkdGViUSiUQiCTKVa1coeqY7n7RirYRCs1b9vR4jb/oYtjybZ1qNxlXxzfoPYDKb5Pd0kXAqejEEa1uEpcZlUYDiIcWqRCKRSCRB5s6RN6Nu61qY8c0c4Xva8+bOaNW7KdbN2SzspNoNaCXyWP1hTqu3UCWHth9BdnouYhOi5fd0kXDRkixIjdHclqRkSLEqkUgkEkmQGTPiT4x7Z7KPCK3ftja+Wv3+ader16Y2zCEm2PLtnmnVG1dFREyY/I4kZRYpViUSiUQiCTLH9p8sPG3vSZG7un7eZiyftgZpJ9LRtl8LdLmuPcpVjBXLdLiqNSaljBbL0BuVLgC1mlUPmiWW5OyjocGqPCUjqyVHilWJRCKRSIIMiwNsmL8FGe5OVrSt6jusO8aOnIjf35qkik8dhGgd/ep4TE4Z7Vk3JMyCzoPaye9EInEj3QAkZ012djZee+01XHXVVShfvry4+b733ntFLj99+nT06NEDUVFRiIyMRKtWrTB27NiAy/78888IDQ1Fbq5aE/vo0aO47777UKtWLTG9du3aePjhh3Hs2LHT7uNbb70l9qthw4aF5s2cORNNmjQR+9KnTx/s3r270DKPPvoorrzyymKcDYlEIimAEdNbX7gOdVvVQoWaCRj82JW4+sF+yEzNFvPZw18z86cP65lw2B2Y99t/GHnTR7i74eP4/KHvsXHh1nM65YzefvbAd7irwWN48+ZPsGDcEhH5lRTdwSpYg6RkyMiq5KxJSUnByJEjRW9GCs+5cwtKA/rzwQcf4Pnnn8e1114rBK1er8euXbuQmJgYcPlZs2ahV69eCAsLQ2ZmJjp27Ii8vDw89NBDqFGjBrZv345vv/0Ws2fPxtatW8Vy/nDb7777LsLDwwvNO3DgAG688UYMGTJECNLPPvsM119/veidaTCoFWI2b96Mn376CRs2bJBXiUQiKRE/PDcWs0cvFJWjoCiY+NEMLPhjGUZMeAr7Nx8URv0Uq3GVYzHwvivOuL15vy3Bx8O/9lSiOrrnhCjH+tuBr1GhRvkSfztH9hzH8/3e9FS4OrbvJP6buELM631bN/ltB+xgJdMALhZSrErOmkqVKomIZ+XKlXHw4EER9QzEunXr8OKLLwrB+uyzz55xu3a7XQhfCk0ydepUHDlyRERmr7nmGs9yNWvWxBNPPIHFixdjwIABhbbzzDPPoFOnTnA4HDhx4oTPvH///RdVqlTBmDFjxM26UaNGQhzv27cP9evXF8s89thjeOSRRwJGZSUSieR02KxqByktesr7jD3fjsadGuCzJW8hPTkD6UmZiEyIxrTfV+Leaz9H0vF0tO1cD72vboGufX0rTdnzbT7bU+h9xek2x1l9Edr2hIeW13Zt1rPbnkRyPpFiVXLWWCwWIVTPxCeffCKMj5966ikxnpWVJZrei4Lik8sMHDhQjDOyqoljb7TxQFFVbmPSpEkiKkrR6Q+jtDExMZ5OC+XKlRP/amkHf/zxh4j8zpgx44zHJ5FISi+0ndIb9AiLDC3Rejc9M0hELFdMXwdbvg3NezTG1Q/088yPKR8thl9GzcOE0Us801cs2okVi3Zg2qoRMFsKfFW73dhRWFgtnbwKqcdPoXbzGuh1cxdUrnMaP9fTUL1RVdw18hYsmrAMB7cmIr5KOVHCtfOgtme1vdIObaucQbOukmkAJUWKVcl5h1FSNuOPGjVK5JAyfSAuLk7knL7++usiJcA/BYC5pIycku7duwtRSdH58ccfe9IAXn75ZTGvWzffJiun0ymWZY5rs2bNAu5Thw4d8PTTTwtRyn17++23ERsbK6KqOTk5IgLMdAXm10okkrLH0imrRAWprct2intUy15NcPPzg9Gqd+B7ij8OmwN5Wflw2O1wOV3ib+8KVhpawzLfm1moSnG5xPijAz/CFTe2x00P9hHjFLaPfjkcj3xxD3IychERUzi9qSSwXOvQV24QAwV5eHSYdByQXLJIsXqx6BqgnnPfvsDrr6t/X3stkJrqO79VK+DLL9W/77kH8O8QVK0aQ4Lq32xuX6HmH3lgBHLOHPVvdoSaORNYuhTnk/T0dCQnJ2PFihWYP38+Xn31VdSrVw+TJ0/Gm2++KYQhBai/WL3uuus8482bNxf5qcx57dKli2f64MGDMW7cuEJi95tvvhH5qtx+UXTu3FlEem+77TYxHhERgV9++UVEaZmywDzcO++8M4hnQiKRXE6MfuUPHN5xVPztdDmxbu5mESktSqwm7joqvFEZ8eTL9ZQv/sbKmes883et2Yuv/ncUfW7vJvLikw4nC+uqQbd2gMlsxMzxK5GWnA04HIDdgcS9Ofj5vZm4amgXhEeGeLbDbZ+rUPUn2NsrjZyPClaS4iPFquS8OwYQRlN///13jzi84YYbRFP/l19+KcRhfHy8mL5nzx4xaCkAGtWqVUPbtm1Fz3yKXTbvs57yrbfeKoSvJlj5OSNGjBCDts2i+Oijj/Dkk0+KfFjmpUZHR2Pv3r2is9V///0nhPT//vc/kd9KtwNGhel8IJFISj+WMIv4V3SQcud0atO8WfTnMox+ZTyO7VPz4uMqxWLIc9fCEmrxiZiS3Iw8XB9/N8whZqSfzBDTImLDcdXwPhh8UzuMfn+m2oHKvYLBoIfBKIXNpYCsYHVxkWL1YnGmiOa0aaef//PPp5//4Yenn//CC+pwAfJaidFoxM033+wzb+jQoSIndNWqVR5xSjspNsd7R1CXLFmCq6++GkuXLhUdpsigQYNEkz3FL3NTb7rpJjGdqQEUluzhXxzYyYqDBjtscZvt2rXDPffcg9WrV2PixIlYsGCBiOTu3LlT2GZJJJLSzchpz2PR+GUiOmo0G4XvKUum+vPvLws9QpUwn5RR1W83fIhGHevh19cnIOVommc+BSsHDdpWTfx4BmbljUN8pRjMnbgaJw6nokPfJuh/cweEhJpxIUlKTMGmRdtETmv9NrVlaoDkkkCKVcl5hbmpISEhojOTZgmlUaFCBU+qgHcKQP/+/X2W/eGHH0SUVBOqGhSsbBJbtmyZEKv0Sf3xxx/x6aef4tChQz6dqegwwKgpo6cUs4GgUOa2uB2XyyUiwd9//73IaeXAv5njSkEskUhKN/GVy+HGp64Rw+mIrRgj/tVM/oUdVaVYhEeFYcDwPjiy+zgmfDhNzBM2Ue5e997jMQlR2Lp6H757Ywoy3Z6r03/5D4f3nMCbYx4QEdbzjTXPimf7vIEdK/d4pvE4Xpv8LBp1qIeyjkvRwakEyboqSNspS0ixKjmvsHmeHqyMUNpsNpjNBVEC2l4ROgUQpgUwikpvU29Onjwp7Kf8YUcqNpdp81gggCKT0VEO/jB94IEHHhD5r/5YrVbR5M8iB9wffib319uBQLPqkkgkEo0nv3sAvW7pihXT18KWZ0OHq9ugbf8Wnvn3vHMrGnaoixnfzEFWWha639gZdVrVxMb5W0QUtm3/luh4dRtM+fk/j1AlzATYsHQ38rKtiIgumRPB2ZB67JSPUBXTjp/CjhW7pViVXHSkWJWcd2655RbRwWr06NFCLBKKTFapooUVo5Zkzpw5Qnj6e6Y2aNBAzGPuKKOuGr/99pv4t02bNuJfOgiwyd4fClBGbz///HPUqVMn4D6ykxeFtGZzxUguUxd27NiBfv36eSKzMmdVIpF4YzKb0K5/SzEEYtqXs/Ht07948lb3bjiIZt0b4ZNFI32Wq1a3gk9+KylfKQYhYRcmDSA6PhJRcZHITM3yTKNlV9UGZ7YnLAs4g2hdFaztlCWkWJWcE7SjohDUmvIXLlzoiXRS+LHZ/f777xfClHmkzPlkhHPatGkiD5QCUvNcZQoAhStTB7zhdih0WWGKdldcf/369aLJn0KWYpiweZ9VqQLtI6OwgeYRdrB65513MGXKFCFQCdMQuDwdBSismR5w6tQpT26sRCKRnA628uxctQf//DzfR4CS7ct3YcnkVSICGxqu9vTvdW0btOhUD6sXbBc5q6261keTdrVhNPmmT50vwqPD8ceR70S+6sYFW1GjcVV0GNga0fHSvk98n4peDMEgWNspS+gUrduhpERQ4LCHOi2SaHPkDys6Ec0rtLTC4/POD/Uvaaodf2pqquj1z2pUGRkZovc9K0wNGzZMzOdlyAIDFKYvvfRSoW3RIYAR0uXLl4vmfkY+2emK/qhF5aBq9OzZU1SwolAOBMUu0wAoVr1JS0vDgw8+KKK6zK/lZxUleMvSdy6RSIpXbnXCR9NPu0zFWgkYu+8reTqD8Mw935/71X/NEVcpOFHu1OM2PNJ98wU/lssZGVmVnBOaQDsTjJaygxKHQKxdu1YISgrQQDCaSk/Vs2HRokWnnT9+/PiA01nVasKECWf1mRKJ5OJwdO9xLJ6wAuvmbkKFmuVFL37mhBpNF/Zxl3TEzyfbC11oKHRhIUjJVfDXdwvQ7aqWqFBNraInuTSRaQAXFylWJZcEjKwycskCABKJRHI22Kx23NfsaditdtHTfsuSHZg7ZjGGv3MbbnmhoNDIhaDHTZ2wY+VunDyYLMYtYWaR35qrGKCPjlKTU3U6/PTuDPwxai4mbXn3gu6fRHI5IcWq5JKgffv2YpBIJJKzxelwCqFKRIabO8ktJ7PA1/RC0fW6DugyuD0Obj2M7PRcVKpTAXPHLMLk0UuQbXNXC3CTm5mHzx74Dn1u745m3RoF3B6rY82ZuBor5m7BwZ3H0aJzPXS/uiU69GkivVAvACyCGzTrqqBspWwhxapEIpFISgXsrHTH60MwZ8winDiQBJPFiDZXtEDf27td8H0R1nouBbWa1RDjox77CdO+mg2dxQxdVBR0IRbVZ9Vuhys7B7N+mId/Ri/A33njCnlSkwVT1+GrV/9SK1y5FCyavh4Lp67DL0telSkEFwAX9GII1rYkJUOKVYlEIpGUGoaNuAm3v3ojThxMQkxCtKe3/YXiwJZD+OvTmcJ3NT/HKnr8D7z/ChH1JYrVBiU5RY2ssky0U51OXA4Xnu/3Jvrf2QtX3NHDZ7supxqP8/SJVgpEsURS2pFiVSKRSCSlCkYsK9VSK+RdaH5/exIWT1zhEZMrZ6zD7jX78PHiN8R+MeprzbOpOasBhObmxduxaeE29BjSCeYQM/JzraJca9eBLXDySBqWzd6MYwdT0LhtTXS/uhUqVve1+pOcH5yKXgzB2pakZEixKpFIJBJJkDAYDUKoas31jIQaTAZkpmbjyJ7jqlA9HYpqxn98/0n8/PIfWDN7o8jDja0ch8ialZCSkiO2ScHKQZRvldpHUsqRYlUikUgkkiBx/4d3oEbjalg2dTVyMnLR6Zq2okl/2qh/sGH+ljOuf9V9fdBnaHfMGbMYy6et8UxPz8xH1hG1+Ao5lZyFaaP/w9W3d0HVOmrJ6tORkZKJXWv2oV7rWoitEHMOR1g2cUEnhmBtS1IypFiVSCQSiSRIxFWKxW0vXS8Gb8Kjw8S/jLgSERH1Gw+JCEH/u3vh4+Hf4ND2Iz7rK+6UAc1EwO18hZBw8xlF6ls3f4JNi7eLz+A6jTs1wMvjn0T5qjKFoCRVp5ylsILVTTfdhP/++w95eXmioA2rORbld34xkWJVIpFIJJLzzF1v3Yq6rWvh7x/mIz0pQ1hbJdQsj+3LdiEpMQXt+rdEtxs7Yt7Y/woJVTLksf6o3qI29u08jkO7jqN5x7roelULxFc8fZT0wJbD2Lhwm2ecInfb8l3YuXpvUMRq0uFkrJ2zGXGVYtCqTzORZyu5fHj99ddF0R2z2YzVq1fjiiuuwP79+wuVPb/YSLEqkUgkEsl5ZvGfy/Hpg9/Bnq/6wFKQsrLWm9Nf8FkuoXq8529hbaUoCIsKEy4HIWGWEn9ubMUY6I164TSgbY/R3LjKsed8TM/3fxPr5272jFtCzXj6p4fR65YuKG04oRNVrIK1rUuFJk2aeP7W6/Ww2Ww4evToJSdWL51YtEQikUgkpRRW09KEqsbaOZsKLdfvzp74cdunouoWUwJeGvc/jDv09VkJVVKjUVX8cfhbPPndA7jizh54bNS9+P3gN2jcsT7OFW+hSth5bOvSHee8XUlgsrOz8dprr+Gqq65C+fLlxcvHe++9F3BZu92OESNGoHr16ggJCRHVIYsqWT506FCxTLt27dC7d280a9bskvsKZGRVIpFIJJLzDDs2zR6tOgRo1G9Tu0iBySFYlKsYi6vu6yuGYFKvTW3sWbffM04Xg7qtaqE04lJ0YgjWts6GlJQUjBw5ElWrVkWrVq0wd+7cIpe9//778euvv+KRRx4R4nPq1KlClDocDtxxxx0+y/7+++8YM2YMFixYgB07dlySFdGkWJVIJBKJ5Dxz1f19RZP8jG/+RXpyJnrc2AlXP9TvvH7mkd3HsHTKauxctRsN29dDl+vao1qDKkHb/ler38P+zYeEvRbTCjpc1RpRcZEojbDqlPMiV7CqVKmSaKKvXLkyDh48iFq1Ar8YbNiwAb/88osQtq+++qqYdu+994qo6bPPPotbbrlF5Kh6YzQa0a9fP3zxxRcih5XR20sJmQYgOWu2b9+Om2++Wfxg2ITAH1CPHj1EwnYg+NbGN7YDBw6IHxr/5vDnn38WWpZNG5zH5S4kPXv29OyX99C1a9cLuh8SiaR08etrE/DGDR9h/fwtotPT6FfH44GWz5y3z9uzfj/uafQ//PTi71g+fS1+emmcGN+xak/QPoP3xjotauKW5wfjimE9Sq1QvVSwWCziOXsmJkyYIPJPGVX1/q4effRRJCUlYeHChUWuy4poe/fuxaWGjKxKzooVK1agV69eqFixIu666y7RLME3vlWrVuGtt94KKFhnzZqFRo0aCXHrLUL59kf7DP64LgX49vrBBx/4TEtIOLOPoUQikfiTk5WP/2ZuwILZ22CoUB6uvHzV+N9iRrrDgF8/+Qc9B7VG9bqBK26xTOvSyauwfPoaHNp2BC17N0WPIZ3RqEO9055sRm+10qzeqQcZyZnySzpLu6lgWU5p2zl+/LjP9KioKDGcK+vWrUOdOnVQrlw5n+kdOnQQ/65fvx79+/fHsWPHxLN8wIABMJlMmDJlihCy7777Li41pFiVnBUUpOHh4Vi7di3i4wt6rxL+AAJBsXrNNdf4TGvZsiU2btyIiRMniijtpQBvFrfffvvF3g2JRFIK+OWDmZg5dpnaSmOxwGCx+Bil/vHFHEz5YRGm7Hg/4Pqzf16Azx783lMRa9+mg5j06Uz8eex7kYtaFA3a1hEFCdb8uxEOmwNGsxFt+7VAww51z9ehlmpUN4Dg5HJq22nfvr3PdHaeKqplsiTwGcygiz9aVNb7Gf3pp5/innvuEddn3bp18ccff4jn8qWGsaz1pPvwww+xZs0aMTBZmW8QL7zgax0iOTP79u1D48aNCwlVEqiZgsvv2rUL3377rc90RlStVuslF11lEjpNkiMjZbOWRCIpzOGdRzHz2zmiypTd5kDnQW1FB6Z6rX07TVnzVAcALcopwqoUK16dWPLzbHjrodEICTXj4K7jOH44FW27N0Sv69p4yrN6R0fJu7d/gbjK5XDyUBL2bTiIpl0bimb45KOp2L1mH5p0aYjuN3bC82MfxcGtR1C7eXWERoTKr/ISgr6m3qIyGFFVwmcXUwb84fOVEVTO157VS5cuxeVAmRKrJelJJzk9rHTBi3zTpk1o0aLFGU/XzJkzER0dXSj3kz8eJoDfdtttZxVddblcSEtLK9ayFJ6BfsD+0BA5IiJCiGjag9x9993iuinOuhKJpGzwzZOjsfbfAuupWd/PxfYVu/Hdxo/EuM1qh9PuwKC7uiInKw8r526F0+mip5DqdWo2+/S6XvaPagOleaEu+WcT/pu1ET/MfU5EU/+bsAL5uVbP8hsXbFWXd0dcGUGlDtbG18/bjHVzNqFG46po0rnBBTwzpZPzkQZAoUo9EmxCQkLE86vQ57pcwtKK8y83ypRYLW5POsmZee6554TYb926tciDoQhl56o+ffoE/CEwBYA5Muxx6A8F6ptvvnlW0dXDhw8X+3scPXq0yK89HczzYScrWn3w7ZN2H8xf3bZtmxDcEolEQkSglFrTK+BJkblrzV78+cFUrP5nI+xWO5p1a4RBD/dHBOz4+6f59HeCPjoK8C/05NmWbwSV0dNnf34EDdvXxRcP/+gRo57lfRcvNF4Q0ZWUFSpXroxDhw4Vmq41/xenk9alRpkSq8XtSSc5M7TAWLJkiRBy8+fPF0naTLFgM8Znn30mopHe6ReLFy/GDz/8EHBb5xJdZQev4kbIvSt1FMVPP/3kM87c1cceewyjRo3CvHnz0LdvcH0KJRLJ5ckDH92BGd/MwfKpq+GwO9FpUFsMvL8vfn1jAlb/vcGz3ObF23F4x1GMWvUOIqLD8PfENchXDIW2175XY1jCzDiw/RiSjqahTY+G6DW4DULD1Rad3rd1Q2Zqtvi8A9sS0axbY4RHhyI5MRX7Nh5A0y4NEVkuEqnH0rB73T4RTe12Q6czdsSSFA9XECtPcVvnk9atWwv3HbY6eneyYgdobf7lRpkSq+dCZmamGDT8e/GVRTp37iwij7S62Lx5s4g8fvzxxyJZm1UzGGUlFJPMAWWPw6Lwj64WF0Zxz7eAfPrpp6VYlUgkPtRqWh2Pf3WvGMiKGWvx6f3fCd9RDzo1smkONeFkcg62HM1BnikEOptvJSuyfeVumK15SD6UJCKnm06lony4AV0HtBCpAeFRYRj68g1ikJSONIDzxZAhQ0Qg6euvv8Yrr7wipvE6ZNCFqW108rnckGK1mHzyySd44403zu+3cZliMBhEDjCHLl26CJHKihiaWGUKAHs98kdSFP7R1eJCoZycnFysZZkzGxpa8g4G1apVEw+L4ubGSiSSS4ucjBxsWrRdGNez6tK5duS02+zYsmQnFJcLzbo3htliwt8/zPMVqrw3Gg247cXr0XtoN/w1bhV2bz0KhJihGPRAvg06ryb6jGOpUDKzPOOZKVmY/PksDHvtJoRHh+HQ9iM4vOOI+LzYhGgc2ZeEAzuPoUm72iiXEJyOOZILC/t78PlJo34OxWHUqFFIT08XA6HVFINBhK2AfM61adMGw4YNE+4CfD5qFawWLVqEn3/++bLsfyHFajF56qmnRAUI78iqv+2EpMCKQ8uN4dvc33//jYcffviMp8c7usqycMUhMTExqDmrgWARAx6H9FqVSC4/3r39cyyesEL4lZLYCjF4+scH0WFgm7PaHm2jxrz2J/Ky88W4JcyC2166HlHxqnMIc0oJo6M1m1TDHa8PEeNRMWEF8ylYmXdqtbmNAXTQ6fVquql7nPOZFkCR+t6wL3DiQJK6vtmI0GqVYbMXNCZTsI4cfR/CIi6/jjOXC05FL4ZgbYuwg3JJO1h99NFHPvmoc+bMEYOWtkaxSn788UfUqFFDVLKiC0/9+vUxduzYy9aWUYrVYhIss97SAvNh2BHJP0JBYUoaNmzoMR+msL/66qvPuE3v6Opff/11wXNWs7KyROTVuxMYRSoFNGEHMYlEcnmxYJyvNc+pk+miPOjZitUlk1d6hCqx5lpFCdUPF7yOLoPbY8W0NaLXPrffpGsjzJ+8BtkZebji6hao36gSZo5bjvS0XHTt3xQVK8Vg0aTVSDmRgc79e6NK9RgsHLcUJw6cFB6p/e/ujfHvTfEIVYHJ4iNUybY1+3EyMQ21Gvn2yaBA37p0J3as3I2GHeqJzl6M9kouXw4Ws6ojy6ny2aU9vy53pFiVnBWPP/646Dh13XXXiapUtMSgMOWbW1xcHJ588klPCgA7tRXXZFiLrrK28YXOWWXVD+bbDh48WJgj5+fnY9q0afjvv/+EgO7WrVtQPkcikVw4KtWugOP7T/pMq1KvsGF6ceG625bt8pmWcjQNdzd4HFcO742nf1Jbkab8tBj39n4XLrc/qis7G65TGW4bAWDfzOU+bgL7Fm7wGWe1qj/fn6otXgCbfN0FBTRCwsyIjovwWezYvhN4rONLyEwtSC2ILBeBz5e9hWoNqpz18ZdVFOjgClIHK25LUjKkWJWcFWyKmDRpEv7991/Rg56ebhSlbL5/+eWXRfMDYaergQMHFnu7jK6OGDECt9566wX/ZrjPbdu2FSXnTp48KaKqFOJffPGFT41liURy+fDT9k+x5b8dIprKnNXO17ZD5ToVz3p7z/z0MAY/OgAjb/wIJw765stvW7rT8/eujYeEUBXWUXY7XFk5HqEKixm6qAjAaISSnQNwXkDbKfcfZiMQouYZXnX/FRh0d0/s2ZyIAzuOoVnHumjdrYHHNUDj6N4TPkKVZKVl4+ieE1KsXiJpAJLiI8Wq5Ky48sorxXA6kpKSRDlWrTeif1GBovz/brnlFjFcaJj7OmHChAv+uRKJ5PxhMpvQum9zMQQD5pOySlXfYT3w+9uTPJWlGOhs0asg1ahx21pY+s8m2FJO+XSc0sWXg75Sgvhb3AOZn6qJVf/PoqdqdCTQwJ2XryiYtfQAFqw/inFTHkf/mzsWuZ9V61VCbMUYnDqhdsQhMQlRqNpA2jdKLj/KnFgtTk86SXDgOWaUVHMFkEgkktLCnW/cjOsevwqr/l4Pp92J9le1QrmKsZ75g+7shj7XtcWr132ILQu3FKxoNnn+FB2pAthYkRtfHoKBd3bHxN+W4u85O7QVxD95uTZkZeYhItK3Q9XO1XuwbMpq4bPauFMDvD3rRdjz7dixco/IWW3Yoa7ofR4sUo6mYunk1Vg7Z6M4dkat2/ZvAaOp9EkLpgC4lGD5rOrO2g2grKJTylh5C0b0AlV20Hp9c35xOHLkiLA0Ym/0QL35tCTo4m5Pcvkjv3OJROLPksmrMOqxH5F2XA2QmMpFw1yrKvJtqjuBKysbSuIxgKVY3ejCQqGPKwdLiAnv/fUEPnxnBhIPpYp5er0ObdrXxuvv3QQz0wPcbFy4Fc/2ecOnZCv5dsOHqNPi/DyHrit3F7LTc1SHA0WNFA995QbcNTL4LWNneuaeL7TPfXTOlYiqUHLrw0BknszDqH6zL/ixXM6UvtefIPWkk0gkEomkpKSnZGHepDVYMXcrcjLz0KFPE7y/cCRsWbmiAlXt5tWx6M/lGP/Z3ziVmgPk5gGhIdDTA5oBN7MZyMuDMzkFuQ4HZn4yDV1qxmBHZjoOHUhG+871cMU1zTxClV7TtOaaNuofzz54x6CYp7ps6mrhYrBt6S40aF8HlWpVQOqJU9iyeAfqtamFLoM7oOfNnUvsFEChKj7P5ft5pRFWr3IiSDmrsoNViSlzYlUikUgkkmChCUNGM8n3b07Fwmnr1XxTRcGh3SewaPp6jFk2Qsz/7pkx+OuTmeryigJ9dJQYtB7+rswsuNIzPNufM2aRun1uz6Vg7q4jmPPzAow/+j3iKsVi5Yx1eHfo5x5/V42QcAu63dBR7MPr13/oWf/EoSQRBXV/PE4eTsaSSatgNBvR46ZOYl26uxSncAIjqLN+mCtKvjLi27RbI1xxR49SeXEpSvDSALgtScmQYlUikUgkkhKyd+MBTBs1G8unrxE5q027NRQCb8O6REBvhOJlhZp09BReHfY19Pl52PDvRjGtUAaeZkVVRGaed/SSOGwOn3/951esmeDT/O/5PN9/POvZrXb8/eN8LPpzGTYv3i46YnUd3B43PHU1ImN9bbE02OR/28vXI3HXMdF5K6qcWhhBIgk2UqyeJ3jTstvt4gahvXFLSjdsjqMRs0QiKf1889Qv2Lxou2d81cz16h8mE/RRkSLvVL31q/f/1dPXwJWXB7graWmhTUYxqWx1WtMwi5JwcHf89YdpBHQiSKgeL8bbXtkSQ54ZhLljF+PUyYKI7MHtifj26TH4cN4IDBtxE/6btBKHtiWict2KIiKbdSobB7cmomKtBHS7vgNqt6iBB1o844nAHt5+BL9vS0RMhWhh1UWSj6QiunyUKC+rwedb9Yal37fVBb0YgrUtScmQYvU8ERERIbw6WZc3Pj7+nGtRSy5tUlNTYbPZZJUziaSUs/qfDfjzg6ki+hgI0Rzvud/roBj0wlcVUVVgYDN/eiaUE0mA3S1GbXYoFKYuBa6kFDXmeZpuz+/NeRUx5QtcaxjVzUzLRl5OQVUtb8yhZlHylUNuVp5o7v/5pXFYPGG5mE/Rylxa/8isVqDAYNBj3DuTMffXRTiy+7goL0vng3vevk3YY0kkFwIpVs8TMTExyM3NFSLm1KlTMPFtWwrWUhtRpVCNjIwULyYSiaT08se7k0UJU2/YMan37V1hNBiweN5O2Fy6gmZ9WlUZDQUtbDm5BUKVWK1Qjieh11PXI8Sah1VTViL12KlCn9vluvbof1cvISo3Ld6GBu3qIiTMgnVzN2P2zwsKLd+0S0NcdW9fYVmlERYZil1r92HSpzM903LSc/HvLwvRomcTvDbpGSFi183bjJpNqqHr4A7oOKgtbqv2YMHu5lqx5K+VqNGoqrDvKis4FZ0YgrUtIq2rio8Uq+cJClNaUuTk5CAzM1OkBIjmHkmpg03/UVFRQqjKlA+JpHQTGhFSyB7K6XBi+dQ14r6fDxP0EeEFJVH9SqOCkVavDlMCvR4rF+1EiOJCem7h54TeoBe5oaMe/Rk7V+0WmzRZTOh8bVv0vLlLof0hj391L2o1q1Hk/muRU2290MgQdL2ugxi8yc+1wmDUw+lwqYfB5V1KwXbKCOxcFTSfVfd2Nm3aJK2riokUq+eZ8PBwMUgkEonk8uf5Xx/DvN//wy+v/on87HyfCKWAYs5qhb5crEeYKvRQZQ97gw63PDcYkUYFU776F8lH06CLDIcuOgrWjBzY7E7oIiOgNxqg5OaL9ICbnhiAPrd1wbyxS7Bj5W7P57FDFC2rhr02BO/88zKWT12NPev3o2nXRsIFIJBQJcwv/Xz52yI6umXJdtRtVVuY+be7smXA5Rm9/XbDR2rEde4mkfPa6Zp26DK4XXBPrERyGqRYlUgkEomkmMz8bi5+f+sv2K2BO0Ax7Knk5MJF0RkTCdepDCjHTqg5qQDGv34E5oR4OJ16GMvHi/RUnbWgihUjnbqwMCAsDGERFnS4qjVe6P820pMyCj/AzUZElYsQTfLt+gcWm4Fo3LG+GIoLUwJqvnFzmWr290dR9HAp+qBtS1IypFiVSCQSicQPNo0f3nkUq2auE6Kw0zVtRXP/Pz/NL1qoumET+bOj7kBYhVh888RoHDpS0DyvM1vgdHqNF7GNGx7ohf43tcdvr08IKFTvfvMW9LilC7atP4T925ejSbvaaN6xLkxeVa2seVZsmL9VRGQbtK+L1n2bi0ipRHK5IcWqRCKRSCR+vH3bZ1j8p9pjnnzz5C/FPkcUhbSDIv1v6YTv1+/1zFPsBVFUz7QAFocTP/0bf748NuD2YxKi0bBrEzw04GPY3T6rJCI6FN/NfR7lEqKRuOsoHm77AvK9XAJYKGDUqndRo3E1+X2fVQWrIHWwkhWsSowUqxKJRCKR+LFvw4EznhOKv/veHwZzqAlzflkEW74dvYd2Q82WtfDD29OQm52PNt0b4cU//ofvR/yJlOPpcJWPhrNCDPRHUqDLzIGSl09LEZGrqjOZRM6rjlZXeh30cbFQ8q0irUBj4AN9cfurN2LD8n0+QpVkZ+Qh5UQGbLlWTPp0lo9QJfk5VhzfnyTF6lnAvnDB62AVlM2UKaRYlUgkEonEjw4D2+DYvr/hYueoIqD4+/LRH9URulVBhz17k6GPVCs5MVo665t/oWRmaYtAl50H5UQqlDpVYEhKgdNtY6VkZKr5q+Fh0IeGQBcS4u7dr/MRq7O+m4e5Yxbj/QVvoFxCFNKSMj3zqteriOzkDDza6pnCFbLcVa1qNKkqv+tLBGldVXykWJVIJBKJxI+bn7sWsRWiMeb1CbDnF266L4Tw8legMxg8dlVCMDrdFau80NvsuHlYD8TGhmLyh1Nxcs+xgplc342wlQqwPiO45cpH4skPb8G/41dh79ZEtOhcDwNu7YS0xOSAQrVFz8bodmMnTP/6X2xbthP1WtdGl8Ht0apPM2m5VwxcQexgpW1HWlcVHylWJRKJRCLx49VB72HXmn2FekCFx4SLSdnpOWI8LCpMWFJln1LHGQVVzGboWDKVgjMsFIrVVlA+lc384eH466u56vJsymfzvzuXlbZXFKhC9HLxkBDAnCsqXRGTxShE5t4dx/Huo7+qVq4ATvy5Cv/+uQofjX9YCNCNC7f6VKXatGi7GDR2rtorhOtbM18UjgMSyaWMFKsSiUQikfjB0qQCryClKcSIq+7tgwH39hGVnJgiEBETLhwCpnz+N6x5NpGD6sw7AVgs7vxTEwzVKzPmCiguIWRdIUY4Q81QjHros63Qh4UAtK9yOKBEhMEVEQqF69IGy+GEzmFXUwnsDrTs3Qxh0WEY+8rvcBw5Jkq56gx6KHanELy/vPoHBtzdC1fdfwXevvmTIr9XLfqapx2n5LTw+3MFqWOUuBYkJUKKVYlEIpFI/Ljz9Zvx1yczsHN1QU9+e74DEz+ajr0bDuCDuSPEtBGD38eK6WsLe1BZreq/cTFAeJhntivMBFeoyZMqoGOxAIdLbf43GOCycL5qLyXk5IlkKClpns2umb1BrTyl16k9dfLyvfU0Ni3cio3zt+DjRW9g4P1XYOmUVchIzkRkXATiKsYiPTlTWGFFxIajy7XthXNBXk6+sLTSHAn8x1nFyhJqLhjPs8FsMZapEuLno9yqpPhIsSqRSCQSiR89hnQWw/0tnsbBrYmeSCT1mnenK/G3u3RpUQSypip2cE0JPO5p4tc+2/2vNv3Du79CpeZ1ENu0DrJ3HUe5ehURE6oXi1KsRpYLx4mDSXjuipHYt/EgKtQsLzpg5WTmYu/6A0ioHo/KdSoiLzsPu9fuR7lKsajeui5seiN2b0pETHwEOl3RFEMe7osKVcvJ60dyXpFiVSKRSMo4GSmZotm7Uq0KF3tXLjme+OZ+zPj2X6ycsQ56gx6dBrXFoIev9My/+61bEVshBvN+WwIHe/azWZ49+G02NXqanSNSAhS3HZUuOR2wGKHER4lIqj02FC49YEzPUyOluVbo9HooIWa1XGtkBFAuBsjMVvNezep2RMctu0OUS42rFIMDWw8j6VCKZ79OHD2F5Lz9gMko8meP7DqKQyeSPSL5+L4kMWjjJw8mi0Ej6XCKGDQRnHo8DRmbCzqCnUrOwt/jViAyNhx3PTsQpZ3z0cFKUnykWJVIJJIyCjvh/DJiPLav2C0iclXqVcTgR6/C4McGXOxdu2Ro0rmBGFwuNZrq3/SdciQV+zcfgsPdAUp4pkZFQl8+ToyK5nqHE4o9V3UIMOgANvPr9aoO1BvgSIiCIz5CzNfn2mA6kQlDZq4aRDXq4ahTBUqYWY2g2h0wJmdCb3OKiO3JXAW5+5Nx6kS6747b7HCeOKmmGoSFQh8Rru4PhbTII3BHZM/k+emer3e7G2gRYh4XrxmjscC9QCI5X0ixKpFIJKUAlgLdtnwXDEYDGnaoC4OXBVJRzPphHrYt2+UZP7rnBH59fYIUqwEIlJ/pdDrx+9uTsHvtvoKJdgd02dm4+sF+sISY8O+E1cjJyPWIPCUmHIgqyGEVUVL1A9R/8uzQs7OUu2XfGWaBEu4ukaoD9FaHEKpiVKdDTnIGslILcloLwU5aOblo2KURGrftjrTDSdjy3w7Ub1cHlWpWwNG9x7F82ppCq9VsWg1t+7dEbmYu1s/bgmoNqqBm6zrId+mwceluVKweh45XNEXfG9qhLMDOVUErCiA7WJUYKVYlEonkMmfCh9Mw7t3JyElXzeOj4iJw18hbcM1D/U+7XkR0WEH0z50HGRGrTpOcnlnfz8XPr/yBzBTV8N8bp9WOWb8tBJrWgoNN/FpaKU+zO6dUWE6JyKaWcKoiIq9eaaieckead6veb76XiBa+rAE8Vsm2OevgyszCiL+eRnzlghzTlKOpWDFjrfjuRbSU6yvArS9ej963dpWXgfa9SDeAi4oUqxKJRHKZM3fsYo9QJZmp2Vg4ftkZxepDn94lPDmXT18j1m8/oBW6umvaS4omMzULkz+fFVCoCgx6WGOjoWTboMRFwBligjEzHwZFwZ13dUNMzXiM/XMlkpIy4TLq4TICBrsqEp1RobAaDTCm5UJvd6gaNccGRaeIKCztruwVo6HPs0Fnc2Dw8B6oV7c8Vs9Yg93r9qN5j8Zo0bOJcChYPGG5z27tWLlbdJ7yFqvxVeLww5ZPsHTSKmxavA01m1QTPq7cjuT8IitYFR8pViUSieQyp0LNBNFj3btXOnt3nwlziBndb+wkBknx+Pnlcfjz/alwFVXgvWI8UL0yDIyAWl3Q2Z3Q2ZzQGU3iq/n1txVwRVmECGVMVJ/nhM5e0DCsy7dBfyoL1KbEkJ0B5859wnNVQwkLgbNNA1jCLOjcpxFGDn7fI5yP7jmOdXM24ZmfH8aSySvhcrg8UVx2EIuvUrjnfo1GVVHjlaoY+soN8jIoAn7dQUsDcH+VsoJV8ZFiVSKRSC5z3pj8LLYu24lVM7Ue6+1E3qrk7GGHKuairpy5DiazCR2vaSPyguf99l/RQpVEhvvaUrk7ZmkoJr3a8u9Gx+Z37wWY8+o13+Mq4IUuNx9XdqyN64f3xIyv5xaK8LIXP0uqfrniHZGuwLzkxp3q4+qH+uHo3hOYM2aRKLfaYWBrRMVFFnkotnwbNizYig3zNqNKvUrCCYGRWInkQqNTikpwkZyWI0eOoFq1akhMTETVqlXl2ZJIJJJSxIhr3xe5nCWmcnkRWdVQI6uOgqIAFgOc4WZ3AisTXBl9VXwjq2lZBcvn58OVVGBJ5cFsgrFCAhS7Hc6TyYUEbXFgZ7xP/huJxh3rF5pnzbNiaM2HRUEBb17+43/oeXMXlJVnrva5V08dirCEiKBsMzcpGzMH/y71QwmQkVWJRCKRSPxI3HX0jOckoUY83p8zAjvWH8I/vyyELd+OXkM6IywuEqO/XoD09BzRMYfRVPbiF1HUPKuIjDoTIqEYDaL0KvNRjbku6JwKlAgL9KwWlZUtoqyKoocuKhw6nUHN8qAozckR4tSZkgqdxQJdXKxajlWzzyomjBTTWzWQWM3NzCskVDXHiLIIUwCClwYgK1iVFClWJRKJRCLxg9WrWFqVAjQQzBe95sH++OPr+VgwZZ1qTaUD9r0/S11ABxjYZ4rm/qIggJ5tmXDERMIZafb07teLLAGdiLiKvFSdDsKYKiJGbEaf54Apw6p6nFKsZmaLXv2MyCoUs3n5RX535lAzbHm2IudXb1gF9drUDjiP6QFt+7XAunmbPVWxmO/aoqfseCW58EixKpFIJBKJH7T+uvXF6zC8yZM+lZ2IJcyMoS9fj77DuuOzFyaKaZrlkwftb613kxCzNEr1mu6Fzn+aZr7vsbpyW1axclUxoEVVt+s7Yv64JXjv9i8KzX/ky+EY/EhBJa5AKQLvzn4F6ckZ2LRouygYwWOiy8SXj/6EirUSRG50r1s6w8IiB2XBZzVI/qjSZ7XkSLEqkUgkEkkAKMKGvnwD/nhvCo7vO+mZbs2z4eeX/8C6uZtx3XPXI/n4KRzec7JATDIKanQ/Xm0ONarqrvREZwCEugr8UbXOWjo/seoWuIy4uvL00LFXPxeJCIMSEQZkF1iViW25O3Ixmtr52nYiKko6DmyN7jd1wsoZa0WU2GQxom2/luhxY8difecx5aPR46ZOsFntGBQ1DE4WLNBBlHdlMQFW8Lr91RtL/fWjBDENgNuSlAwpViUSiUQiKYK6rWqhVa+mOHkoWdhAQeSIxkAXFYEdqQ6Um7sNtRpXwfEDSbBZHVDyreqKLKcqTPwBR6QJjpgQ1VjerAdo/O9URaoh1wlzcg50VpsqTilqQ8zuiKpO5LHCbofOaldFsE4HQ7UqUBSXmqNqMhU4ltntqNOuNlpd3x5md7QzJDwELXs2Efmn21fsQr02dRBSNQEfjJiK7RsPo06DiujcpzGuubmDqLhVFC6nSwhVLVCs9c2mcJdIzjdSrEokEomk1JCRkomImHDRjB0MPr3/O+zdsN/T2V5fOQEIDxOikQ3y/01bB53DXf7UYIAuLNRdrUqNntkTIuCICy/YIAOqnGdU5xuy8mDMKIiS6s38j2Z3pQBWm3AI0FBMRhGl1cEghKqG2JrZhD07T2L3m9NRt1Fl1G9SRRQC+OKRH9XqVC4FOzYewq4snUd07th8BNs3JSKufCR6XaVGYwMREmbBI5/fg7m/LhLFB8JjwtDpmra48p5eKAvIDlYXFylWJRKJRHLZ89cnMzD318XYv/mQR0jd8foQVKpV4bx/tk/BVJ2O/f9Pv/AlhML0AZ0Onz/0AzYNaI673rxZNP0HYvBjA8SQmZaF8KiwoL0QSCRnoqCosEQikUgklyHZ6Tn47plfsX/LITHO0rHzxv6Heb/+V6z12aRNkcumfn+e/P4BDLz/ChgtJsBggJKeBV2+FYr4H+CKCIESZhF/cztq87gOTEvkX8a0PBhP5Qu/VfbgN2bbYMxkqVSnyFd1hlngDLeI5n9Fr4PToofDrIeLkVBu02yCYjGr87lNkx4uA+eJkkrC3orpAHQGYKi0QePKeOK1a1GnYSWx/4061scT39wvSrCaQ0xo3LomenSrI5ajUFVy8+A6noScI0migMDKGevEeg67E7s2HkLmqZxC5ySqXGSZE6paZDVYg1ZuNSEhAR9++OHFPrxLHhlZlUgkEslljcFkEJW7mFdJtCZvdjY6ExM+nIbJn81C6vFTYrxm02q4973b0eGq1mKclZ4o9o6m2bBl1T41hEqTf3ac0kz7MzJFc7347LAw6GOjRUqAGHcPRquQnnDp9XCE6KGY1flKuAmOhCiPG4CTOa5hBjWvlVFauwumHHdVK7bd06d192GAJVkDdLDavWc/4p256HFFE4RFhiIvOx/r520SVazsVjt2rdmH5EwHsnLt0NucqrjWqR3AKHgpyj9/4U/8N3MjcrPzodfr0LhtLTz2zhBUr3v+o9RlKQ1AllstPjKyKpFIJJLLmtDwEIxa9S6GPDMItZvXQNfr2uO5MY/iuieuKpZY1YQqObg1EX9+MBUORiq9eP6LYbj7uYGwMMLqhTDjdwtVogsLgU7r6S8irxaPEwBh5FQTqmJ5dq7XbK3c5Vhh1HvG9U52yyqYL0Sqt1D1K+lKQbp4wgqsm7dVjO/dcABLJq0S04lDAdLS82FnZNedW6sPDUGd9vXx6oSn0LR7Y8wev1IIVXXTCrau3o+/f1vm6VQlkVxopFiVSCQSyWUPI6D3fTAM3238CCMmPoMrhvWA2U9YBiImwZ2f6aX+tvy3AzeUvwd//zDPM23Ozwvw67OjkZfmrurk6XHl1xzO6C5dALT0VL9xnVtXeubr/GxZtT/c22dqgGc+p5mL1yD69qNj8MhVH4moqdbZixFnCttAonPfuv344fnfkHokBUZT4Sb+ab8swe0dXsfW1ftQFlG8vFbPdZCSv+RIsSqRSCSSMstnS9/CC2Mfh5mVpjSMBuS5DJjx6xKkJWWISUsnrxQ2Ta7UVDiTU+Cy2aDQUiomCvqEeCA+VgyucpFwxITDERMKR3QIbFFmWMP0cJloS6WD06SD06jAaYYY8mMMyC5vhCNEBxc1osMFQ5ZDtbZSmDYA2EL4qFZzVHXh4UD9mlAql4dSLgqoVhGoXllYacFoFD6s+ooVoDOZsH/7UegtZny/5WNc9ehVqNqiFq64pw9e+fpOdBvYQnjCOvPy4Dh+Ekp+Pk4cSMKC35fg63+excA7ugqhrLiFNklLyhSpEBS7ezcewJ8fTMOCcUtEzrBEcj6ROasSiUQiKbPQ5qrP0G6YPXoBNi7YCn1cOejDw8S8g4mZGNr+ddz2eD9Ua1hFWDZRubHEqafMadWKQO0qnu0p7PxEWyp3NJMPWUNuQc0ihx6wxhgKmvntCoz5CuwxeoiGeqcipommf3eaqt6lwKXluIYZocSHALoEdX5GDkzLd6gRXM7PdkDJzYeuYgVY2PnLqMfTT09AZkYeYIlE4sYTWLByihqlLRcNndMJ5VS6Z/+nfzMHsxbsVW2xIt2WW+w4xm06nKhcMx4PtXkO+zYe9KzDfOFnRz+Cvrd3R2lFWlddXGRkVSKRSCRlnrdmvoCHPr0L5khVqHozf+JKXHlvH/zv18cRGuPlmUoiQgtSAjzN9gFKqbpxWrzyT93lVL3nMw1AE6oCv/n+la502fnQuYVqwToudLqiCd4d9zBWLtqhClXPDqoRWw/MzXX7xAqMvv6t6ofo0OWqlvht1evocmVzH6EqPs7pwt71+2HNs2LVrHX48tEf8evrE7BnPf1pS0ejt2jCD5YbQJDKtpYlZGRVIpFIJGWe5/qOxPblu6EvFyOa2rU8T4qtYzsS8fyd30MXHQWXKYTmWAXnKzMHiI70EZ+sWuUtUb19WA35Lk8pVTFPWFQVCFLN8soz7jff7ZFVsH5kqCgUoPPuEGYyYfmKfVi58HMo7OxVPkrYbgncllie7bHIgNmkVsMiDqeowqULUStgkdBwM64Z2hlxFaLF+WjarSG2Ltnpmc8UikadGuDhts/j8I6jnoIDY0dOFKVY73zj5jJ/fUnODSlWJRKJRHLZcnTvcSz5ayXWz9+CKnUrovO17dCmXwvovXrkn45TJ9NFb/k9bOJnBC0tHUjPhC40xN38rUYlFacDoMdqVAR0DeqwS73aWSk7F1i3DagQJ3JGKf5clWPhDDOJfE/moSrUiUwPoC1quA76fAUGu9tmi3arFsBl1jph6WCgQ4BD7YxF4asLN8Cc5YLeoYgoqs7KCKwCFrHS23TQNakHMC2BQ1godCazWE4JN4gcWH1yhnos7DiVnQdXTi500ZHQ81i4vfg41dWApWKNRrhSTqmOBgY9atatgHteuhrN29VU91enw6eL38Tx/Sexds4mJFSLQ6s+zYRg/fDur9Rz5RVMPXk4GenJGVg6ebWIuoZEhIiCDZ0GtRUuDpcLMg3g4iLFqkQikUguS3IyczG8yZNqzXq9TuSczvxuLh79cjiufeTKYm3jye6v4uieE75t9RShObl+H6aKVh09VBmJrBCvLpp4DAqb0Y8miXFbm9pwJLhTA+ibaqIdVUG41JzrgjHf5Ylu5kfrYC1HRat+DPNVYWUxAHWSIV+BOVtROzpxO6zU6qDDAHMK3JFRZvTR+SA2SuSu6vPtQmhqB6W3qgUM9FaHKDCA8uXUD6OAZc7qsRMFx+lwAPn5nk5VB5JT8OrV2/C/b+8XxRE0KtWugGse7Odzigbe1xezfpgHa65VjJevFoeugzvg2T5vCEsw4UYAYNH4Zbjijh547pdHi/UdSSRSrEokEonkssSWZxNClbAIgEZORi62r9iF+b8vwZrZG5FQPV5EXPvf1RPh0b45p9npblFa3NRKIQK9muH98kVF9NKrmV7oRZ92/YLJAreA859fsEG/5T3zVeHKbEp1VPPA8t6AV26rtox3xJnjXuctIO7Zk0YvgSs6ClcMaoUQd7EFVrhaMHUd1izcgbgKUejcrxnG7BuFQ1sTER0fKTxvGYn98rGf1E15fdbl5iAgI6sXFylWJRKJpJThcpvEF7cp/HLE6XAKj9Sbnr4GC8YtFcb+RrMRbfo2R5fB7fBAq2fhcqieoicOJmHTom1IT8rAPW/f5rOdO18fgilf/o3EncdE5K9ynQqi1OjJg8lCy1WqU1F0IKKtE6kYbYY+IQbHj6mWVqhQDgp7RWXlCkGoP5kOZ3wklBCz2hfKyaZ8TXHStorRU7WJn/MNeS7owgqipi4j4BIpAKojANMDHHYFBpu6PEut0gJL52A3HS+F665wpbCSl0kvKl+JuXq9mMaIqxinuGZ+q4jIAqaIUMS3rIPMxCRkpWbBZDGifNU4IeIzU7NUQRsRjuMZNnz19gzk59lw013dxLrPDhkFuyjzCpxITBXFA1JOZuC+l69VvyOnU1yDw169EZM/n4VD24+IzbEE7KCHryzWdczvz6Dl215MFKZxBKljVLC2U4aQYlUikUhKCYwmTv/6X6ycqdZ373hNG1z7yAA06lAPpQEKFzbzL/pzGbYu3YnKdSui63Ud8N3mj5CRnCWanZkHyepTFKqe9dwRPS0K6801D/UXAwVtaEQIouOjxPSkxBSYLCbEuosGpBxNFXmcKUlZmDFmCU7uPSGqOyEiBGhQC7pcG3SKC6gYDmekUXiois+mOjNA+KVqNk/2CEWkB+hdOjFdTxmp9Y9yqakAendequhcReFK31VR7YopAe5IKaOoFK0ZudDlU83q1M5dBoPa4YqdvShOs7LhYu4tX2IoXJnGwNQFlwsxFWNQrUMDZNeoiJ1r9qFinQqonBCOjKPJqlg1GqAPC4XC7busmPDZbKQfScMN9/cULwwUzGIvvc7xqr/X45+f5mPNPxsQFh2GLte2x2uTnoEl1CxK4MaUdxdiKAK6CPAFYsX0teIzOgxsg/bXNz/3C0hy2SLFqkQikZQSvnlqDHat3uuxC6LB+4n9ScL4/nxgy7fh1MkM0cyu9Z4/n5w8lIwvHv5BREApjo7uPobx701BfJVyPjmqRpNR+H7O+XURNi/ejqj4SHQd3B7XPNy/yG1XrKn6lmokVFNzUk8lZcBg1CO+SpwYf/vRsdix/oCaCUBdaFegKA7oRPTPAHu0CU6a+GtpAswKEOmj6rhDGK+qf7vckVdtW2Lf8wGTl9OU3qb6sGqw4xVLsGppABSlhhy356sHL1FutcGZ7o4Cix1Qp2mkHUvD2pUH1B0wGnFsfxISlxwv2CGHE3oLe4Cp+8CI6+QfF8EcYsIznw7F3ImrsWn5HkTEhKFL/+YYdHd3PNbuOWSlZYvvyJafgVnfzxW2Vs+PeQzF4ccXf8f6eZs9KQiL/1yOreu34WKiVZ8K1rYkJUOKVYlEIiklMGrn8bV02wdxWrA5suc4fnzhN6ydvVFUdapYK0EYwt/x+pDzKloLHYvbIynQMTItIOVIqmjCzz6VjZRjachz17svDozeTvx4Bnav3SfEcbVOjaBUSsCxrYk+4lLdDa8xd3N8of30/I0Lyxk+z5NHyn/9j0P7031NeR/nhJ+XoEp8CPKOJcN28CQyzUYkVQ5H7ql2MHhdh1yHf5fkOuT6WlqwagurlOqUFsmZ0SmlxbH3AnPkyBFUq1YNiYmJqFq16sXeHYlEIkHirqOY/fNCLJ+2hiXh0XlQO1x5T29UrV85qGfntzf/wpjX/iw0/a+knzzN6OcDPq6Y4rB4wnKsm7sZtZpVFx2neIwhYQW+oHabHVeF+Oamkivv7o2nf3qoWJ91T+MnRB6rhzpVhS0UzGbo8myi2V3kkIZboJiNwsyfysoWbUZ+eQsc4Xq1Bz8Fl7Cvcuek8m+vPlVMA+C6Io/VLcyMVsCUy+Z/dZuM3uqZg+pS1+e/BivHFehtDhgy8oVPKitMKSEmkaOqo0OCzQGXySB8U5XsHCArB0pUKBTaW+XboD+VBZjM0EeGQxca6u6ApYNitcGVlS3SB8R+WizQhYdBFxGuLmMxQTEa4Nq+p9B5YzWw6x6/CnN/XSy+q8hyEegyuD2uHN4b8ZXdLgRngLZYTCPgdWy3OcR13HxAQ3Tu2+GCP3O1Z33bcQ/CUr7AT/dcsCZnYe1t36JcuXIiH/fZZ58Vg6RopFg9S6RYlUgkZZXJn83CN0/9oloRuQUWm8qnpP2CUFZ0usiwY8+gqDuEW4A/1RpWxmOj7kWr3s1Ou41HO7yAXWv2FfTmd6NUTYBSs5Ja6Yli06iD08zEU3fTvsFdpco9brcA1lh2fHKnBfCUsU1TC1Jqnfk9O+8Wru5RnYPWVaqQFetDgZPlXN2pBTqrC5HHbELQaqFInY0Gre5t0IpqzyEoGdnuDlp6oHldKFHuwgdc3mqHMSULCAlRvV5px5WZ7ecs4AW3wUhsEfOr1q+ER74Yjrb9WuByf+Zqn9vm94eCKlbXDf1GBrtKQJmLq9vtdowYMQLVq1dHSEgImjdvjnHjxl3s3ZJIJJILSn6uFcunrxH2ThkpmSVa99pHr8Trk59Fvzt6okmXBqL5/7tNH19wocoI3N8/zseWJTuEQNVgtOr7TR8V6vlPGC1dNmW1+Ds9JQvzJ6/B6gXbYGUHIi/enPGiELX+xvVK+Ri3fRU8YtW7+dxl8h13sKe/15NWzV/13afC5VYL0NMZwFssUwSLKK26lNGmwMA8VO0zXX7bYD6rW6iK7YeFANERBU36jP6GmFGnbW088vwA9O3XGEpGVtFClbDT1mnmH9nNQg0rxN+8tniNrZixVuStZqZlYcEfS7F0yirkFcq1lUgCU+ZyVu+//378+uuveOSRR9CsWTNMnToVQ4cOhcPhwB133HGxd08ikUjOO3QM+PaZMbDTPJ56Ra8THqRP//hwsdY3GA2iaZfDxYAdu2jmv3utWnWKRMVF4PXJz6FZt0ZivErdSrj1xesw9cu/kXYi3dMpi7BD1gdPjMWi6es9mstsMeHhkdej/80dxThdAAY93B8rZ60Tvdo9WBn2VCOSoqe/T+1Sd9UpPlkZeGQAlBraaznR1K8trK1blO7jst5CN9B8b1cn4e/qtUscd1tUeXC7BIjIr5cf7O6dJ7Bv5ko4s716d50D7JD24T1fiXQA7bzrKLKFzlXHaTXGFwraj13q8BDotRqsbUlKRpkSqxs2bMAvv/yCkSNH4tVXXxXT7r33XvTu3Vvki9xyyy0wm91+IxKJRFJKWT9/s0eoEooJze7qYsPo2/p5W7Bt2U7Ua1MH7a5sibBI34gt/VK9hSrJTM3GjpW7PWJV4+edn4uOYCwOQJHa6dp2qN+mNm5s/pJPcNBmtWPj8j3oN6QDDuw4htULtwsB+8And6HzoLb47pU/kZ9nhz4jD7CEwhnlzpGlPRRcULQOQHZFWE6pOascBww5Cpyh7vxVVa+5TfrV3v10vFJM7rZOzte5I6oMYpp0sIap0VORs2oCnAY1r1UIY50OubFGmLMdMNgU1X813y7cCRiF1ZlMUBrWAXJzobAca3gokJkHHStecb57eaRl+ApVPgu1ggV0D2ApWUbOQ8xwMbpss0N/+GTBfHdxhHqta+HRUcNF5PbZPiN9CgEowsWgAIfNgfXzNl0WYlVycSlTYnXChAmiRyGjqhr8QT366KO48cYbsXDhQvTvX7S1iUQikZQG6rSo6WkK16jXujYuNvQ6vb/50z699mlS/+mSt9CgbR3PtMi4SMRVjkXqsVOeaextXqNx4VzG8Kgw9BjSWQze1GlcBVtW7fOZVrtRZbxx309YNa/AJunblFQo9CjVqFBOdDBiBydiDzfBFk2lqeIwAy7mrLpRfU+hNtVzfgigcHDPZwcr7xxWPXVhdkG5Veav6p1q5yxGUUUOK4Wqe31RiEDRwRlugpP9n+wuWKh2te0bASUiCtC5vU2dLhjT86HLVV9WWCrWtXufR2wy0qpvUAc6LSKruAseuJW9+K85BAgLB2JiCs7LvkToUk6Jkqw/PPeb8MEt7rV4OaAEsShA0IoLlCHKlFhdt24d6tSpI3rgedOhQwfx7/r166VYlUgkpZ5hI24SPehpup6blYcOA1ujZpNqxV6fzbi71+3HimlrcGz/CbTu01wUIDiT2fuZoNWUv72U3erA8X0nfMQq80h/P/gNti3fhY0LtqJawyoiAhsR41tK1ZucjBysmrUea/7dKJr4hz3aG6EjBmPdop2IjAlDiy71RER13BdzfI/VXhCBVj/cN8orOld5NacLceqVGiB0ife4fzEmUebKa5TG/zjNuH+Oq8t/eVa+8l/Bq+yq4jefHbC8S8YaDQVC1WsdH9ydy7yp0Kgahj98hyhCsHNVYZeA+m3riMIA/H5XzVwnigN0uqYtKtQoj8sBCsxgpQFIsVpyypRYPXbsGCpVqlRoeuXKlT3ziyIzM1MMGsePHz9PeymRSCTnH5bUZE7m2fDPj/Px6QPfib+ZC7rwj2UIiQjBxBM/+lhIlZQq9SqJ6CjLcmpUqFkedVrWDJg327x7YzEUh3ubPS3EsJa7Sg9VinZ2DiOPD/oEezYnFlqPlk6KPatgQnomEBHmaSI35jrhDC1ILGWOqtPopU7dOayeSCi1L/tsaf2btE712uJuayutU5WIjHqNi1QCL8EqbLG85xt1oiSrWjjA7Tag5agKMc3yqzq1IhYxmYAQC5BvVcdpf5WTK6yqNIQVlregzbOKdACPCHY4cHJXIt65ZVPAc8/KYKwSphVaqNFI2j1KLpBYXb3atwmppDRu3BgRERG4kOTl5cHCShx+MDXAZDKJ+UXxySef4I033jjPeyiRSCSXPjTc19ByEvOz85Gfk39OYjW2Qgx+3Popjuw+hq3LdoncUnqpnkuhAW5r0Z/LhVD13l+S5nUcqSczPM3iCvM7OUJhGBUB1KgM0KeU80PNcOhtcJQL58ND5JTqbAqcQoCqCals2reHK2K/mRbANAARAWV1VI4zGOsumSqEquiE5f48Vl6l5anbBUAxMyeWvquqEKaY5WDMU8Q4K1yJwK7WTK8D7BEGkabAsq2Utrp8rVOVelAOhx26tAzobVTWLujKl4MrzAIln1FtBa58fpgLutgYNa/WaQdSMqDLyYPCSGxSMlC+HFAuSuSu4ngykO4l6L1o3bcZ3pr5Ikws8aqd6+OnRFWqFTPXiuktejYWx7BxwRYxv+PVbdHj5s6eUreXAm4b3aBtS3KBxGrHjh3P6QYyd+5c0bHpQkKrKqvV/fbohcvlEpZWnF8UTz31lOiM5R1Zbd/+4vSElUgkkosJm29XzlgrUgFISLgFPW7qjKi44PhQsohBsAoZPNTmOeTnFL7v12xaDd298livvau7KCOaunWv2mNeo1J5gPZVbhGeWzkEzjCGM9X4JiOfQgS6w5355QBHeEH4UzT7c3G/HFbxt/cOaW4BFLV8tJrUSSban6YXRFOZ08r8VzbmiyewXnFrUPV5zEIBJisFrg6gkLY7Ycyze9Z3sUDAngOej1Ro8N+itq8S09wO+Dcjqjv3+7oYtG8mhLpYh1HZ3YcCnvvqjargxqeu8RGq5LXB7wsPW+HTC2DNbNVtQRtnwYdlU1fjw/mvnfH7lZQNjOdqA0XRWhKysrLwxBNP4GLA5v5Dhwr/qLTmfy0dIBBRUVFikEgkkrJO3Va18NWa90UJ0xP7T6J+u7qi5/ylSCCh2u/Onhh4f1/M+WUR3r/jS2F71bBdPbRsUh4LN+/WfPpVhKeqVylSLRxaqMSq+2+/HNVC84uiqPkF1XMD/uvvfVV4vt90l8tXJHuXQfUpC6sl2dLdwG9DmlD1eLv6nDFBeHQYut/YCbWa1yg0T8tL9o5y+48zSn8p4eKLSZBq5QZrO2WJcxKr3bp1w223FTZdPh2pqal4/PHHcTFo3bo1FixYgLS0NJ9OVqtWrfLMl0gkEknxYPnMQCU02QGLgsTfcupCkpedB0uYBQ9/drco3Xlgy2GYQ0xoN6AVbnzqajzT5w1ksmoTgFMn0nFw25FC7bxsPawdFwJXhXgcOJSqRjozHVD0JjhFoNVdwctL7xlyaTeliGIB1HKqvRQHdyyUuk4sr1WcUtfT5rOJX8RNnWrk1BHGTmZs9lfTAsR8pSCtwOl+RzDYtHEdEKqHMd+lLm/UwxFqhMHqFFFXJSwESuV4YVUlLKtsDiA5HYiLLhDi2mlgfq/JCGftytCfSIMuN1/d55OpQFyM2tGKyzNNIikV4Hw3ORm5+O2tScKx4akfHvTYkjHX+K6Rt2DSZzOxfcVucY5riM59Cg5tVXOGm3ZrhJueHoRLCekGcJmK1YkTJ3p60ZcERie5LitHXWiGDBmCDz74AF9//TVeeeUVz0111KhRKF++PHr16nXB90kikUhKC+nJGfj9zUmiOlHK0TQRge15cxfc9Mw1om/AhWDW93Mx59fF2LFiF6Lio0Rd+ZHTnhdCldG+5dPW4uv/jfYIVURFQFcuRq17z6hjVjZ0FHAGgxBWYbUqwFYuHLasLPG8cIbo4QzRwRHqtpOioGP1Ub2q8JxmllYtyEtVDAqctKEST1u3Ob4nEMnCAopYRiskIMSvXQ+9w11VgB2myrt7+XO+AzDl6EQ+qto0z85UOlFuVWxXoSWqQe1EJaquKnDFmkS6ghjXKzBYo2CgmGXVLpNJdMbyRE8VBfo8h5rPqu1STAx00dHQsXwt/VvTM6Fs38eSkOo6TAXw+n7ZKY3nUxdiwYIFe3Bs0IdwpGcKlwBG4Ok+8dCnd6NS7QQYDHrs3XAQU76YhSO7jgntS1cHdsqSSM5ZrN5www1ntR47Mp3tuudKmzZtMGzYMLz22mtITk72VLBatGgRfv7554CdryQSiaSscCopA9mnskW+6Nn0SVjy10pMHfWPZ3zfxoPYu+EAul7fXlSUuhAlZD978Hux7xQ9GcmZIqoaWyEad791qxCbH9/7DWwUXW70lStCxwghj9dggE5vhM5i8Ai1dYeToSRnuHvT62CNNsDFTlLu8yMinaL6qXvc4uubKoQqo5/a6fREVlUUo1uoutHZVPGpdbgSHa04w6AT+2NgkQGtvKooHqDAKDQjR9ScVlO+u+wABbNeB1eIviAbgctb6YfFCCx31i1yvb5uAztkafsj9rnAOYDnUDl6wvfEe+f48vAo/iledTo4nS5s+mcd4C6Hm2d3YvGE5bBb7aLiGPnppd+FkNUC2yumrxFVx75c8Q4uFWhbFbwKVjINoKSUKesq8uOPP6JGjRqiktW3336L+vXrY+zYsbj99tsv9q5JJBLJRWHT4m34+aVxogIUBQPtoq59ZECJKwuxfCbxLm1KTO7p5xu9XieKA7DTrDbuciliv8a9Mxkzv5sDa65fDqu7I5GQggHKn/p0LNI8R3V+UeKiEkQDjvsmsJ6xZ/hZ6pozpcn6H2rB8m6LK/+/vHNUtdHTbNzn8719XsWLhPqdaPBvdfPuT1SUC3bNSC4PgnY1HD58+LTzeRGyt318fPw5uQicKyyn+uabb4pBIpFIJMDsnxeI/EGNkweT8evrf5ZYrPYd1l00na+YsRb7Nx1Cqz7N0P2mTkiofmGM380hZny29C0s/GOpKK9aqXYFdBrUFl2ua49bqzzgEbEClg9l56LUdOhiokSzOXu+Kzk5AEuQxkaJpm1jaj6cLh0c4WxH18GY6RJWU84wlk9luVI1UsoIKhHjbNZnb37R/d5dZlTz0We0011wVTS9q6HLAmloYN6pCzqHOp8pBsKn1em2vgpVS6yKHFa6AoRA5M9ynCVa2dxvjdCL0qts3mdagtOd52rgvhhobaWHPt8lorJOi06MG6wKTLkuEUF2RFmgy7PDYHUXDHA6VX9W2lsZdXC0qi/Om/FomruIgEmkGwgbK5MRLsUFvdkMxe4UR2UoHwdXjlruNbJcBG55+mpxrWg8P+YxzPvtPxFRpWhl6kavW7tg69IdsNscaNq1IS42IpU3WNZV0rvq4onVmjVrFkuEUrD26NEDI0aMKLGTgEQikUiCT2RshI91EKOiZ2NDRYsi9rTncLFo1KGeGDQ+vPsrkaPqI1QpoBLioTMahW8qm7e155cjLgLOCLOnl7zO4RKiz5yjNmM7yxlgj2ATu3v5EIq/gqZ9dq5yhmmepmrOqHjSap3rhWp0h2zdUyhuddq4U81Z9dSZoki1sbOVZsCv2lPZ2ZFKFBnQwUKLWLf/qhBC7NemRX9d3HeFtqnu7SkI3Z0CY6ZNjazqdMitHy/KtdrM6gZCjluht1O46oVItVaMgCvc5LG0siSmw6CYoatcUd1kdLhq7aXZZ6VnA7nWAgcCkwmGmGggJho9bu2Em57x7TzFKlZDX75BDGTGN//ikfYvIPuU6m3L/NW+D3TDRSWI5VbVtxjJRRGrP/30E7788kthDUWHADavi5J8u3fjjz/+QK1atXDXXXdhz549+O2339CzZ0/MmzcPXbt2DdYuSCQSieQsuO+D29Gyd1NRPjUzLRvtB7RC58GXv4/0kX1JmDNmUaHp8VXjcOfI63Eqw4qxo5d5UjKJM7xAqBI1n7NAXNiiDB6hKpZnVwcv7eEy+7WP+1cm5aa95+spVP2FjK8tlkeocnHhAuA17lCLBRTssDvfVZvv9J1vyHPAlFmQs+uyGOAKNxcIS6cCY57XCuw8RvHuhTHdt4AOI64+Tf15qhD2JrpcOO57ZTC6XdUCZ+K/v1Z4hCqhs8ScMQvPuJ6k9BI0sZqSkoLc3FwhRr1tocjrr7+OLl26CEP+zz//XPTEp00Up1OwSiQSieTiwYgom145lAYYKHnq+s+xc8Mh0WlK69yjwWpWHw//WghBfZ0a0IWFenIyWVZUMerFNkS01Z3Y6XFzsilAWME4m+e9o6aaVZU2Lhb0aF+/BFgx7pc5WpR/qzZJuF25bbC4uiFAqq3XBJFGoG1GUeAy6z3lWdUOV2zmdwmBLtZ3uxuoTgLuDAX3fK39mnZYeoerYLucr9lYiY5qelG21ZuMtBx88uwf4mXountPH3mvWKsCsHCb7/rJBeXOLwbSuqqUiNWvvvoKjz32WCGhSpinyupPtIh6+umnhU3U8OHD8emnnwbr4yUSiURymdhbrZq1HrvX7kOTzg3Q/qrWwqpIIzs9B6v/2YBty3aiXps66Hh1a8SUL37ZTVu+Hev+26kKVUYSK1cUZURdyWq5VQ9uZefadwiIixV5lcSUeEpEEm2Vo0XZVGuE6illoJUUdZk755QWVhR9wlOVutHd1O9iviqFoFEVlApDoTQbYMIop/Bfows6bkjsILcrPKXUfTJSLNOU1AhuWHivwukWzjooFsCmB0zMWXWqwtIaBhjcOa1CwzKa6lITCUSeq1EEcIUA1Rv1yG6QAFOODfo8O1yhRsDugtOiF/muik4PW4MoWDIcsKTTvFUHQ7oNrnAjnGGqZMitnwBzajZMybnCbUCXnQ8lnImzzEVwwRkZKkSwzqaur9gd0OXkw5mfj79/mIsW7Wv5lNFlisaedftFrjMZcG9vXHVfH7w79HMc35909hebpNQQNLF68uRJUbK0KBwOB06cKLC7qFKlCpx+b7sSiUQiKb3sXL0HT3R9BS5G8wBM//pfmCwmfL/5Y1StVwnH9p3Afc2eEoJTg737P13yJhp3rH/G7TsdTtzd/S2kJRVE4SiI6PuphFig5BeuZqWvXR268DBPVNCeEAElxCiaw/UuHVNGoZgNcJjVKKI9Vgd7VEE/eWcY4HJbgjLiqbDF3KwKRY7rzS7oLerx8iP0Bhf0boHq26PeKcZdTh2cVKuhamhT4aomgztKq4iOV3qrHi6LGlkVQU9qRG2HmAKbr/qvqqpZB32IO5XAbVMVqujhiqKnliLEZm55dw6uuyBAxHEdlFAT8kOMIqeVdlxaviqJ2p4sfFiFNmZgtmI0wN77WmQ1zF2pQItWb9yt5rECOLgmCw+0fAY3/G8gHvzkLjHtvWFfik5xGr+/NQm9bumCK4b1xNg3JxaqdFVarKtatGgBg8GAZ599VgySCyBWafL/zTffCAuoqlWr+sxLTEwU8/jFaDCXlYJVIpFIJIWjj8unrsGafzciOi4Sna5thzZXNIfRFPiWnZOZi1Uz12HlrHWiN37Hq9ui/VWtEMqa9pcQSYdTPEJVg36bqcfShFhNO37KR6gSl9OFpEPJZxSryScysOjvTT5CVaNCxUjc/+VQVKlbAQ+0fBZOu1egxKJ143cLUKO7Y5XWHO/OT/XYpHpVlRWC1F2NVXiiapFUz7iak+oZ99M6On+bLLXHk98H+I0zZ1XbG3dzfoFFlNs1wHuD3jmvFI7uiKs2LvZfy8EVaRBeBQI8VlVe/7oUT8EAz3ZM7sTcgAfIUGzhl4TFf61Ek66N0H5ASxzde7zQfE57adz/cM3D/UQk/tixo1j68t8oTW4AmzZtKqSXJOdZrH788cfo37+/6Fg1aNAg1Kun9sZkDuv06dNF/s+4cePEtPz8fOFtyuUkEolE4stDbZ5DypE0tXe+Asz6YR6GPDMI930wLOCpem3wB9i0aJunN/+8sf8Ju6bXJ11a0Zp6bWqL5l+WPtVo0L4uajRWH9jVGlZBww71hEG8Rs2m1VC/bZ3Tbteab8fwQZ8JoatjFNJtmUQUhwPHNu/Ha9e+j3vevg1X3t0L//6yCA5WqeL8jCyAJvZuYcUOSM4Id8936jg2kTMH0z1uyqXdU4HA09sLSp4KHMzZLFA1ikPnTglwj7ujnwVKz6MM1f+KHlTu8lfiA6g2tdxWtSIWq1DpmHug5bC6RasYd69aIDh9x+kY4KRrl9vHn+JWb2Uuq1u8GgCHBTBYvXbRyc90C3gdYIsLhSk1r6BTVlY+lEiGb4uIPJaPgXI02Sf9NuVoKkbe+BH6DO2GHjd2QuKOo6Ijldb7v8dNncXfTAHpf1cvHDlyBHj5dFeBpDQTNLHKXv3Lly8XllQzZsxAXp7aWzA0NBT9+vXDG2+84Yms0r7KOyVAIpFIJAVkpqhNpt7NnxlaedAAZKRkFlreU070EqJSrQr4ftPHOH7gpMhRbNypPuKrqLmiJDo+SlQtopCh72u91rWFV6o3W9YdxOJ/t2Djqv2oWTcBnXo1QutOdWCjJyjFVlgoFKcLrgOHobg7+ejDw6CPicHsqRvQ59bOuObVWzHt54Vsk4crIhTOUBMUClJ3xScqSkeIuwKTSRV3jKiKHFV3ENFpUTw5qxSjLjNFJsuj0mPUCT3zUrmgwQWjXoHR6I5G6l1C5+rd6lGvZzqAzmsA7GYHHPwgF4saAA6289vdqlOvgyhwla/muDIw6jIxIqqJW7XTlYjAutNh9cxZtbpdAXQ65MWrIVo6CWidqYRlFkW1Alij9GoaBOdr0Vtuj9tyAbm1YqCrFg1jZp5IN6Afqz7fCmd0mBDfrJIlNHeeVVS34vfgI2NZKYzfSWgo1q5NRMUWdTDotduw8p+NYnavmzqg1+A2uJRQI6vBSQOQPqslJ6glIihGp02bJpKlk5LUpOiEhIQLVhNaIpFISgNDX7kB07/5F6lH04T+YfTxijt7FLn8jU9dg/HvTxW11UmNJtVw7SNX4lKFopVDUVDAdr+xU6HpKScz8ew9P3nSJ48eTsXSedvx+KuDcN3tnTB/5iZkpufCYDKgXL3KyD2ajNzQKE851OSkbIz7aakQfLoI1VvWHhcGuJv+iRCpXtZUjlAdFM3TVOSsAo6wgvmuCCeUEK2pX4EpzA4Dhas7emo2OmAyFowbdE4YGXn1KgwlcCewWu0GYU1lcqcT2O16kacKEb2lkqRCZg8r92pur1WPRZaWZaG5BNgBY67vNJFjq+iF6OXyLOHq2R+X1t9LpxY6cCkw0OnK3f+LBRJ0Br2o5OWMUL14zZkuuMSBqJFXg1Z+NcQsBl1mjq+bV/k46Mxq+kVuvgPjRy8VRRq06PbYrxdi46oD+PDn4cW5nCRlgPNSz4zitGJF1SxYIpFIJCXjtpeux60vXodD248gKi4C5SrGnnZ5NpNyYJ4fc1Yr1ky4rE65w+7AzG/n4r9JK7CDEdW2dRBXKRZZadnYtnwX6rSoia7XtUfHG1QB69F37kiy3eZEo0aVcHznMaxbtgcVqsSgVoPasObVwZqF2wuqQwWqESryQv0sowrN97Kn8hv3n69+jp8DlXelUu/1/D/Hf7r4u4TRPL882IDzi/P5Xv/6LKqNF5WfSrTTrE3zK8jgvawq1lXVXmC9pWDbit3CXuzqB/uhQbu6uNgwghy0yOrZ1tAtwwRVrDKayuIA69atQ3p6eqGKIXxrmj9/fjA/UiKRSEolvF/WbFKtROtUqVsJlyNs8v/qiZ89QouC1Ztda/di15q9iK8Wh7sfvwKLZ2/B/t0nEBsXgc69G6Ftl7oY3nUkdMLrEzi6L0kMKgU99ymSWAoUBqPoaCSigMxRDTN5vPjZLE4LJzaBqx2WFLiMBeLClKXaU4kcTwZn8vRwsn3dpEoth00vmv0NjKa6I5FqyqtSIJi91KTO7a2qpQAY9U64WPKUvllsYjc4xec7He5qAiZ3+VObXu3QZVRTEbi/jMgqOjU9QTTbc75BgcOiE7m1Wt8vzd5V08LMYxUdqzjC0rDu9AEY1JKy3D5LuzJ9VrUscG9DRHN1sIfpRHlXg11dV6QW0OpLO201KwNHk4HsXOiNBjRqUhEIC8fOjYfV7eXbAAtTMdx1YbNzYU9Jw+zR+7F73X58t/EjXGx8v7Vz35bkIonV7du3izKq2dnZaNCgAbZs2YLGjRvj1KlTOHbsGOrUqYNq1Up245VIJBJJ6SUvOw+jXxmPBeOWnP4p7p7+xcM/oOeQLnjn69thtJgRFmHB+Pem4pUr34Tz6HEh+vSxsdCFhxZ4eGZlw5WdQ/9EIbyUOlWAcnGq+T2jspEG2CINcLKpn8LNpFP9RrmyTk0DcERSwGqG+IAi8lTd+2ZyQR/iBNzzLWYHIsPzYDE51HoAOgVmvVMMalRSUcWsqiaFmHV6OffbnAbk2C1wOPUiAOlw6ZCvVzzjjAHZDSbAovq8MtfUaTLAqeWscjM2HfRMHaBAdqn7LrSyWlFAFbZuwSmEp8iCUMUyj4uDOE53aoEhTxWjIpDNjl2s0mVQz5NqlaWH0cZ95ecpsDicMOTxg915uSYL9OXjocQ4hBUZzRgcicfhSDyqitUQC/RRkVAys6FYrT5Jnfs3H8JTPUeg2+2lo2CF5CKL1RdeeAEWiwVr1qxBRESEyFVltarevXuLcqssGDB+/PhgfZxEIpFISsiR3cdEj3lvQ/aSkHIsDcmJqajfprZINziVnIUTiamo16wajJp9UQnYtnw3pnxRfDuivKx8/PPTfDTu3ED06k8+korRr/zhs4w+Qu3ko+E6le75m3mWSsVyak4qbZroJBBrLOjp7hajYp779NijKE4Lmq5dIexl5NVMHuaEjlFV93hEWD5CzA7P+iEGB4w6l2fcqFc7XHn2j32mxM6p4xSuLkUPrauHy+U7rrj0YvBUxdLsALRxURDAa9xtqaVWCHB3uNIaPd3TdN7jBneuqlcVLApRrf1fVMxipFmbzz5lVnfDtqhepYMp2+kuUOB2TMjOh87lgo4HoQC7lmwTLw8e8q3Clq3n8D7ixeK/P5f5WJhtWbJDWFddTGQFq1IiVpcsWYKnnnoKNWvWRFpampimpQHceuutWLp0qTC9XbBgQbA+UiKRSCTFYPGE5fj5lT9wbK/qwhJXuRxueX4wBj82oFjnb8eqPRj16I+iSZaElotEeLWKyEjPE0Gw0HALegxqhcffGVIiERwSpralcx21efz0eZe05mKeqjnEhE+e+QP/zVjvuwDFqHs7atzU18xeoLnoa7ibu30/yOvPAteogOOF81ULhK57l4QTVVF4ZW+qFa78xz0eVP61V4sYLyI67SnR6r+2losbyE3Lb9Rnvn9uraedXEvQ9dqIsCzwWt6rA5uGLSsHm/9egzteH4LstCysnLEOenf0m9+5JZR+YRcRmQdQOsSqzWZDpUqVPHZVJCMjwzO/ZcuW+PXXX4P1cRKJRCIpzr053yacAjShSmjCP/nzWcUSq06nC1O/nYc9O46z96xoh853APZTqj0hycuxYvYfK3HnMwMRE6f2si8OTbo0xHv/voJ/f1mIbct2CSsr2lWxeMDWZTtRv00dVK1fCYd3H8OG+VtRt2VN9LujB+q0qYMPX5wktmGomABnVjaQo3Z5d544KXr6MxVA1Ke3WKDYrEA4q1iZgIxsKHHRcFkMIkpozHPCruffqtAz5jAVQIEjQu0Rr7epzehaMQC1yJMLOvH0dDvFKy4Y3TmqNoceRrseYSa7GmjU08KK0VQ1YZTN/yLSKvQbe/2rHYtsLiOcig7hRqvIW821m0WU1UIrLJ0L+XaTiLIajGrlKKfdoPbcZ6TTzHZ1vZqHy178JpZzZQEAVSgyUko/VZGPqgcctJFlhNUtvBmo5fKalZV/HQJ7BMR5YCaDyOlljqs7nUA4FoSp8xlR1TtccNKeK98JxV3EwlEuCvo8lndVzVt1VSoDeblQUk6peQ3uwNaJA0mY9f1cvDHlOSwcvwwrpq8Vne9Y5KJB95r4tf03xb62JKWLoInVGjVq4ODBgx6xSuFK39UbbrhBTNu6datID5BIJBLJhWHixzMwZsR4WPNEO64P5Sqd3mGArJy3VUQws2gHFR+nRi3z8+HKVc3bvaOdllCTJ1JaXNiM//WTv+Dw9iNinCJ136aDeG/2K3j0y+HIyczDi7d/gz2bE2mQhNXrj+Hgydl47vNhMJmNsFntcKVniGZkT/CrYQ2gHO2qVMXlMNN6qsDUn4b3ohnbPW4N18FJKyfv+czJ1MbDFDhDvZv9HdBFOD3R09AQKyLCrJ5gIcVmbEieZzxEb0OMObcgmKgoWhaCwObUI1f01lK/I+ao5jnNiLGox2R36ZFuDUFkiDrf7tQjLTu8IIfWqYM12ySUpxrYVNTCBF6BTp+6rvzTxhhrQUjVkKOmBnhKtqrptAXboNesdg4o6LPc4lfnWzXLGWGAEwaR36rXGwqi7LTJCjfBqVM1gD7HBn1+KPTlVY9dxeWCcjIFSkoaylWMEX67gx8dIAYNURTgYuL2wQ3WtiQXSaz26tVLeKy+9dZbYnzo0KH49NNPRXSV6QCsWDV8uPRMk0gkkpJCkXh4xxGs/nsDwqPD0OHqNsLa6UysmrWukFDl+k//+BBqtagholi5WfmiNGv1hlUKNeFvXLZHCFUNMT80FE+8fRMq1K2ErWsO4PjBFLTsWh8d+zZFSGjJxOqhbYkeoaqRuPMYZo9eKNIUTiSmuYVqAUlHT+HIvpP4afHL+OPLOZjx3sSCmex2Hxfts7xi9s2l9RaqRFSj8rZS8spPJWplp4L1dRYvJccOVSaHT4t4qNF3PMRg920x9yu76vRJgFVzVr3HKVa9rY7sDoPIYfXsHztWeY37lFf1fCiKni9yWH2XLySl/CZo1a803H3HPJjyFN9rye+6YrlWn3Oi1yO8WgXc/8Gt6H2LWrlKIjkvYvX5558XnalYSpUVqt58801hXzVx4kQYjUYMGzYMH3108e0nJBKJ5HLjpYHvYO1stbqPxvB3bsMtL1x32vWqN6oqyrB60/qK5ji88yhG3vSxZ9r3z/6KDgNb460ZL/quX7ewcX+lGvG4anhvIUba9myMcyGhejzMoWbY/AT12Dcm4q+PZ2DkrJcQFhkiBLUGe5PHVYzBU3f9iKRjpwDaVbkrVYnmZEZZQ7zyG91N4wXjatO5Z3sOtyB1wyiht/ajZZOn5z+hhRSb3bVRpwEWr3GKS6+aAXAojDAWdBbyR6tk5TtekBRq8B8XvaMKxvWqn1SBotQVzk/1wX9+gOXPlB0r+nd55aB6qmC5cZp1otNVUTDlQqQseJGbY8Xnr0/DwUOn8OBrp7+uL14Fq+BtS3KRxGr16tXFoEFngO+//14MEolEUhbJTMvCqlnrsXHhVlRrUAVdBrcT/xZFXk4+1s3ZJIRpTIVodL62ncjh3L1mb6Fl92w4cMbPf2zUcFz76JUi989pd6LjNW1Qu3kNvHFj4cABfUz9uWpoZ7Tr3Rir5m1FyokMtO7WAGEWA8a9PRknDpxE6ytaoFLtBGxcsBVHdh9Hqz7NRJQ2MrYg5ev4/pNYPm0N9m0+iGZdG6HjNW0Rm6BGP2s0robvNnyAb54aI6LGPuciOx/ZKZn45u+n8ddXc7Fx2S7UblQFVw7tgn2b9uPk2p30iYKuVnXVlionFzqK1HwndE6baoGUb4MxKQ3OipFw1q8qtmvIdQox5Yg0iJxUIUJoIRWiii56ior8VIpAr3KqwkeVUVGjEya9A2YzUwEUmI12mGBHKCOsOjWSaoLNE2G1GGwwwA6LXo0m6nXsKa91nNIh2uCE3WBArssCB3v9GxTYDAbkODlugFHnEOvkOcxwuvSilGuszoVca4gQynrmo0Y54bQb4aKQ1tEDVgfYeTBMQFWg8F8i3Kx0UETKAO2sVNcA5qSq5VXpq+oVLXU39YssAu4zBanOncPqAIx8h2DWgRmqj6vbYzW9hglGm4LQNKewCxPmBCIf2KmKVKNaWUsnXjJYlcvueeHYs8U3kn6pIN0ASmEFK4lEIinr5GTkYGiNh5CfY/X0dv/pxd/x8h//Q8+buwRc596mTyLpUIpn+d/fmoS73rwF7a5sJbxItYiMyWJEix5NzrgP3E6NRlXF4E3Lnk2FiHbY1PZciix+RiDKV4rB1cO6ir9nfjcXnz/0vWfbbK73/ix2lAqJCMGfR79HWGQo1s3dhBf6v+WZP3fMYhjNRozZ/QUSqpcX+akPt30eLvbQ9yOuciyi4iJxZ73HxH5y/QPLtmH+j3O8DxD6cjFCpCoR4Wr0T3QkckBR9NBZQpHer7Gas+ouI2UPM6iRVve4Nd4dWXWfWwdzM4U3vdtgP8qp+oq6y6lGR+chNNTuSQONC81BpNnq+W7KW7IRY1JdEki4wYowg80zzg5WarRU3QW7YoBNb0IE1G3muUzIcIYhxpCv+uW7THAoRoS7cmC0upASFoHM/DCYjS6YDCxzqkM+jDAYeczM/9TBmmdy77NT/VybVvGAUU21k5UoGKDtFM+ZiX6vbl/UfLVzmRZRZnRZ218xn+MMaNMljAI92S1UOU906tJBseiRW1EVyYY8dbojXA1pRxxgagRdGdwiOtwI2OzQ2+xo1aX+mS5rSRlEilWJRCIJMge2HsY/P84XQpV42zJN+nQmQiNC0PbKljB4WyjRx/RIWqHl6Wv6wtjH8cBHd2DN7I0i55RN+aHh7BVUPHIyc7FsymqsnLkWtjy7aPIfvfNz7N1wQHiXcl+0aOfpSE5M8fztYzXlNZ6fnY+xb0zAlff0RsrRtELzKTzTkzORlJiKSZ/MDChUW17ZRuQuzv11kUdQ+3+egDXq3eewUPVQd56kEKrqBPe/ngXEP+zZ7ju94F/RNK55orp9ngwGl0/5VPby9xnXOX3G2ZO/qGquqquWDqYcB+osTkbs4VwY0pywpDkQnm7DspvqYGW3Oqhy8BQ+uW2CWCc9JhR7aiRgX43ymN+1IdY2quHZaVE0QCvFpe2zy2/cO2c10Li78tVpl/fzstJSAArnurr3S/vuNN9Vu7ZCwRqhYWbcOrwzzCYFr1//AZwOF1r3bSb8fNkykZJWcO1dFDQ/22BtS3LxxOr06dNFudV9+/aJylX+NxfePI4evbjGvhKJRHI+STtxCg+0eCawuAKwc/VevHLNe6K3+7WPXOkzb9BD/TF79AKPyK1UpwK6XNde/B1bIQb97ux5Vvv0yX3f4r+JK0TPfbLq7/Wo16Y2vl7zfom2Q5G7dMoqHN6h3sdp5B4SbkF2eo7Pcn99MhOTPpuFL1a8Leq6aykGzDdt268FTiVl4JWB7wb8DJ3FjM3rD2Hj7HVn3iGHE67MLOgj2Y4dWACYTllhjzEXCCctB1UTl3mM+HkJL4fWyco9btVDMRR0qsrPN8FoLHADyLWbYPHqRJXjMItIqjZucxlg8e+RxCZ4hwJjvgvOcB3K7cnGoGc3qYdk0iM71oKcGAtM+U5Y9HZkJlgwcXgbOIx6VDqQgSp7T+H6vzcgsXIs1jepivLpufjthdHYU70CDlSOw4Fy8Thcvhx2VKuE5Gj3ufGIWLdrgJerKyPIBaJUQbgtHxVOZSDfbMaxmHIwOu144Z8ZKJ+VhYTMDMRlZ8PscGB+/WZ4t991Iho9+5e3EJuXDaPLCaPiQpY5BItqNsVrvW8TLwS99m+B1WjCsYg4pEdYoIhCAu7PtzuQczQZPzw3Vt0v93W6cqZ6DdBvNU8p6OgnKXsETayOHDkSb7zxBmJiYtCiRQvUq1cvWJuWSCSSoEPbo7ljFmHZ1DWiObpV76boflMndLqm7VlVd/LOtSxKqHqTm1ngU6rxyBf34L4Ph2H78l2IrRgTsIe+N4f3nsTciauwct42mEwGdOrXDP2GdECFquX8Pkt90NNiyruM5XfP/Ip+d/ZArWaMzhUm61Q2Zv+0AMunrxG2Uu0HtMKT3z8o7IXoiclKUiFhFox+dTzGva36nmrwsyKiw3HVfX1FJ6k9Gw6iRc/GiK9SDr+94dWDn5hM0MdEA/TltNmgZGQVfeIiw4GoCOiNRsBohDPCAluEEQgxqxFMl6IGwcxqCVV7lFGUTBWVl0ROqk40+3vG+beozKS267ssLrjCXGr5VAZWzU6YLA6YmIOqA8LMNsRY8hBmsgqxRauqKFOeW6DqEGHIR7QhD2F6m9huiM4Gs06tYsX9i9uejerTTqHqzAwcHhSLLS9Ugr2tAbPHNkRykyjYQvTIRhiyXRbhDBDvykJWXCjmPtgIdpdBlF1lfqstn8mjOpQz5yFGl4t9tcqj3sGT6LFuNwzu7/nD2/rih+u6IsqRhS8+/hOHE+JwMjYSoXkORObkY16LRpjXqhGis/Iw5Z2vEZWbj8i8fIS4q0uN7tEVb9x4LZwWPQZvWItT4eFIiorCziqVYDUYsbdSAmy0s7XoMKN5S4RaHXDo1Ypb0bl5OB4eK7xdKcyfWTMFFfJU73XGVVPMUVhbrjber3ctdMdOoXv6TpFnkIRQJCESORGx0IeFCqHtysmFklX493IhkR2sSolYHTVqFPr06YMZM2aIzlUSiURyMaFgtNscMFvcbu5+LBq/DJ89+L3HJ3TBH0sx//cl+GX3F6hSVy1wcjYCuGKtBNzwv4FY9OdypB4/JXq7s9c7xWkax0NMaHdlS3S/qWPAbXB/W/ZqWqzPe+/RMTiw87inyZp/7916BG/8fJ/Pctc/MVCIaBrva7DD1V+fzMCKGWvwy64vxTTaDLqciqd06p/vT8WfH0zznKNZ38/D7J8XYFbeOFSuU9GzrQHDewtrrTX/bBBWWayQ1euWLti9bh8+vf9bz/rLpq5WI3h+FYxCQ41IqFsBqUmZyD5WULygEEYjDDWrefJNKTbza0V7xv3/5adYY7UQqds3NbLAH5RoUVRPudVop0eoin2LsIqmf+2doVJkFiLMNk8X+QqWLEQY1ZxULpNgykKUO99UOH3prAhx2lHrhxRUmZqByD1WEWlM7RqOrA4WGNkhyqBDZqcwhMABqyMUdpdRdMhiMmmGKwR5LrMo0WrUO5ALtTiAkVkgIWxSV3CoQjyee+1GdQetLsQey0W1k6eQWCEWBr2C2Lw8xOTkodmqLYjMtQpBmRVqwYFK8ZhnaIR8kwV7KlVAVmiIGFIjIpEUHYVt1Sqr+bsuoOV7b7rPLaPNWkzUfVKswMdXXuM+oUAIW+y1YDR9XK3AA/0fQZWsVFTJTkW1U6monJOKU2ExcMaFw7zjMO53bkJVZLsvTiA/04jbou5Dnt5Evy5P4YCLhqxgVTrEqt1uFwUApFCVSCQXk5Sjqfjz/WlYNm01Uo+dQvMejXHFsB6FmtCd7t7Hniio1tfEUbKHotPpxJTP/8biicuxa/U+VK5XERVqlEd8tTghVhmF7HxNWwx57lrRXM7IYrBKR7K6lKbNRO9uXeD9Z+cpDi8PfAerZ2/wHCsFmtPuwpF9SZj0w0KsmLMFudn5aNW1Aa68paNnW56ILLfvdBWKHDMHt1KtBMRWiMaJQ8moWKs8KtWuULC+3znWtqd1JHtr+gto3r2xqFr1ZLdXC5df1dBErifa7O8nWsR0H6PUALN8pvl/rl86G8+0Vw6qaDj3Gfeaz+M0UIwC8Utz4LLosP3VCjh5dRRs5d2PX/98z9PseiD8ZzuMBiRWKicGbfcTK5bD9R8/JHaUqQVZdPjXdtgFWE0mPPDInZ7lWc3K+/CFND2T+ar3ZO9TJhwIIJr/j0fEYS2FR65azcuzBZeC59AdCcgVQwWmRuityNNrvxPp9VTWCZpY7devH9asWYMHHnggWJuUSCSSQlDw5WblIaFafMCzw+jf1FH/eMbpM0prpW43dvTplNTtho6iBClzMI/uOYFGHeuhx5DOorynv6g9tu8EKtetWKhDFKGpPZvTNYF1dPdxMWhPYjaXT/hoOirUTMCgh/uf1TealJgietdHxDC5soCH37gBs35fhtULtov80Y59m+Dau7sXWj89OUN0ZLrnnduEJRY7W9mtdrS/qjWufuAKTPh2PuZOXO1Zfs3CHdi2Zj9GzXhSlGtdOnkV0lMy0ap3MyH6/c/DnF8WiWpZGttX7BZR3B+2foJbX7wOc8b+h9QjqWozv95AJ3sRLavasIoon1q/bW0k7jqKeq1r4d73bsecsYtxeFuiaNqnPZWwNcrJQ2xCFOo0rYAsp14t/+rOSXVEmNQ8VJfa5Cya8820pmIJUEWUAxXlVNmsb1Ob/oUAEv77amRT2DTRPzRPD1cEw4IU/wqcLJ9qUG2oOJ5tN8NscMBsUF928l1GhBhsMLpVJ3vvhzmtaDD5JBqMPollf9SBLl7Bup+rieZ0o6jxxHxNl/hvqI7WVi7kKyYxHqHPE83/TANg56twvVVYW2U7Q0TnKeawhhmsyHOaRHO7yeBAuMmKPJZjhV74sJqNDtgdLCagF+JZp3fCxY5WOj1sIQYYHbS60qsVmfh/kxMKBapYhjm9ihoZFZ5TavlZcX7c81nRSqG1lVto0+qL80UwGDrYI2kRproGUKjaQwEDy7W6U3edFp2ICDOCajDo0HpYdxj3n8SGfzZgGzvcOUwiD1mnRVPNZiCfO4KLhrSuKkVpABSsI0aMwL333otq1aqdU96XRCKReMOoG0uHbv5vhxBe1RpWEeLPuyQjYe9hojU9a83O7KThDYXf8HeHioHN9/7pAszX/OapX7B86hrkZOQislwEugxuj4c+vUsIRw29+/M8ESVPD2r3qHs/DEYvp/liMu2r2Zj+9WzRoYmdk5p3b4Q7R96Cpl0aivktOtcTg4gS69hT3fcz2EFl3DuTsHPVHhHtY9O93qgX7gDcOYp19tjX9k0T3Pwfp/HF4Ni+k0KouhwuIdqTDiV7ts9xFhRYOXO9z+dqUVH6rd7z9m1o0K0p3nzgZ+iMBjgjw6CEqOc6NdSEf/9ajfHvT0VOeg7CY8LEOX5yzGN48uGxorIRPTh1FDBOF3JDzDhpBTJy8uAKM8NF66lYI6wxBrUylUsRIojCVIwzNSNSJzpQMUeVx+y0KHAxYEc3K/4vxAWEUGmp+x4SYkNoqA1Go5pjGmnJR7mQPNFJSowb8xFnzoFFp+awhhvyEW/MhgXsZKWg9vwUNP3oGML225HdzIyqGWlQKuphMKiRb3HN6BTVxlUHWBU9slxmUUyA4/lU3WxKh02M2xSjyHeNNOSryzuNOEFXAneXfKe745TFqCpBXvI2p9FTGpTjVofRU/VKVGP1qorFcZvdCKfT4Bl30ZfVxaNhFJwfwmvY3UmLE4VQdZ8wBo/zdMKjVUsTEA4Loepni3cCu04958I7C7CkuRCS5YTeph5DqisL+mwFusqVPPUaFIsJCn10mc4RYobOlQNklvgnJCkl6JTi9AQoJu+++y5eeeWVoj9Mp4PDnbh9ucM6xRTkiYmJqFrV18NQIpEEn/fv/BLzxv7nMy00MhTTM371mUYh9u/PC0WnIAqtdv1boPdt3dCi55l9Sb2hbdIHd31VaPqIiU+LqKwGb6E0vV8yaSU2LNgqooMVa1VA9qlsYbnDzks09+9+YwfsWX8QVepVRKVahStDBWJw7J1CKPv3yH9z+gtnDAYw//TRDi9iz7r9p10uJiEaX2/4EAunrMPyOVuQmZaD9r0bo/f1bTF/9AJM/nxWoXVm28eL6OoPz/+GCR9OKzS/+5BO6H9nL9EhizjsTiyYshZTxq/E3hNenafSM4GdhYsbvPDb4zBXLo+Zf63BpiW7PdOplVxhBSkULiNwqhGVaNGt0jmsweA13x7lbUUFuGJsPqkA5cpliTxP7fRWj0wTUVRtvFpIGkL1ds94FVOaiH5a0h3o9sAexG/IQX51IxKfjUXawDCEGJwi6uoRqnwR8NrXTKcJuSJxVuWUMxQZroIIepbT4jOebg/FCWuMZzzfaUSmvaDFwOnSCbHqPZ7vKNg+BaTD49fF60SHfGtBCS8qAqe9YL5IMbF6RdIpsllwwAtzCiO4BQfFqKr310GfVe9CXTG7rTAINwAV49E06K0F2kBhCwij955rXIHNmo6Fu7664M9c7Vlf9fPnYSx3Znu34uBIy8CRJ96X+uFiRFafe+45fPzxx6hduzbatWuH6OjgfKkSiURCwqPoQF7QOYfRyvDoggin93LX/2+gGM62yf3tWz4VTdmBGDnkY5Ff+cr4J4WdFEUjo4Ecisppffe2z/H1/0Z7PENrNq2GZ0c/gvpt6px2X8KiQlWx6pUH+H/2rgJOqnqNnjs9s93sLkss3d3dSEiYKCAogthdKNiFiooBoogiAoKKSnd3x9K1wLLd03Hf7/vu5O6CgJS8e3z37d6OGXbOfP/znUNm/vdXGo1HPnkA7e9sVeZ+f09aip/GzWY/039CfmYBHq79NAY82QufzXsmYN3msNLPnEz/j2w/jk8e+ganU86WOh5VgJ+ZNCpAspCTXoCFMzfhREoaEBXq5y5fdrX5g6ETEVq/BkjaWGZLtp/e0n++rJhQ3sb/NOxc7zfjnXcb9bsECOQM4F5PlUvp8NIyV4l5gUiXAbCFK2CNVOHQG7EovE8PUeN3TRf5XsHxqqLv6kki4LtQwS+O1T1fYr1U//S/+5Lz/jfuroz6z5eIcy21r9+1+db6xbOWPC0/lIvITN0pV96jlWVC631oorvaL6By7fJY5esPvO6QZQC3CFmdOnUqBgwYgLlz516tQ8qQIUOGFw9/NBh129bkimlhdhHrLdvfWXZH/ZWCqpHLflpzQaLKEIG9a1JwZMcJtOjVOHCVKHJXPK2vXK8CarWqjpRNR7FmzqaA7U7tP4O/vlqCpyePZK3phfDFxnexaOoqNtkvGRRAxySyWpBdiO1L9nAyVNMeDZisL/957SURVQ+IEP/+2Xz0Hd2dQwd2rdjP+3cf1hEVaidh0Q8rObCA4mK7DW6HX977vUyieu9L/dH5/nY4uS8Vpw6c4Wo22W+l7DyJw7tOS3GjWflwEfklx4Eio6RJtdoCDxQSDKMkCfXjVlTyc0GRUwRHqBauMB1cagUb6NtDlHDoyA9VqrYSSE8pumM9nTpJn8oj4U6SCVDkJ2k53bpLlUvq+FeIsDkUUKqcHJdKRJGqkmqlE2EqK6dPEVFTwIEQqwUtppxC3T/PYfnfNaCMAHZOTkKQwoJghQ1at8BSK7iof58ZnAsCSJ1AdUqH+96CBAdXco0uUrMKCFWYoIQThS4DSM1qEKwcTFDk0nP8qkFhRTlNPvIdBlhdKmgUIsLUJpidkpSAMhAUsMLhUnF8Kx2bIl+lebfmlMf2JcZOL4VWY2dLLJIC0DNRqh0sBRDd0gRR5ZL0qm4NKzQujnAVKN6VZAQhLiitAk90AodW0q9SBCuB5x2A0iJykpUlQgVR4YSmkLr8RbiC9VLF1kTRWSJQbAI0apZzqLUaDHu2J5LrhGPqH69c8ntaxq0F1dX8I9+tW7erdTgZMmTICAB10FNM6YWiSv8tTu47jRe7v438DMkLsix4NJ1UPYyKjygVr/pk6zFew3wCbVdWQhOBoknX/rYZr89+5oJRp5Oe/bEU0fUgpnwUvnzye/z19RJvdz3pdR948x7EJkUhhS/Y163+TzAVWXBPwsiAaxb0eqjjouFyH3/2t6vZzsrldlLwhyHUgCY9GuC5juNQlOu2IAJQsXZ5jPx8hER6bHa4zqUx6SwTKiUUyRU5GIBA3MoRRkPCbr2lABRW1MCpc8/TROJPT+WXXJQiySvVd0hPvKgXETYo9L4HotNaWZ/qKe7FBRUiXGv1zsdqChCtMfrmlXlosOIsGrx/DoY0OzJ7BqM6suDUSEPlOogI5uYsaXtaqoHKK9twwgWHJ6uVrtcqQr3LjLjtNijzRLgsZIGmZD2nwipCtAqwWVRQ2lx8/zkVg3EsORZp1cJxvmoYioJ0yLEGs9yAJnqpKJhArZBIMs07RbK+crEcgeet1MwVWNhUq0T2kqV5q03FfXBwp3XxA+Tbk5wORBsxYgEiRbrSy2ZRQEE6WLfaQKDvHgrf68CyANpPK3Cyqy7HCbXRHbdKfrdn0uFKzSjzLWEB8O3TabCFlG5uvK6QratuDbJ6++23Y/Xq1bIbgAwZMq450k9lYtNf21GQVchRodTJX1an/qWCmqn++mZpmUSVqoUUFkBNSge3HkWt5tXQsEs9nD5whn1Zqzetgua3NUTW2dwAokq4EFH1wFxkxsHNR9G4a30c2HgY25fsZo9S0qXmZBdj419k9BMIIn/PTHkEKpUSr9z2boDRPzVaLZ66Aq/PeY4jWZf8sIqJZrehHVC+WjwmjJyM8yfKJgVlXTORRg9RJYhWG8QyiOodL/bHbQ905GAAf6JKoAosSSsmLXsJcyYsxOJvfE4NpaDReIkqg0ioX9OYSyV4iWrANt4LDiSq0vrAWUEbyNz9PVQJQWqfHpUQovIRV32+Df1e2ov4jYUoStZi/bQqUHV0wiD4Eqw0/uJMJquBF+Ci5Kp9Nqg3WqHZaINqqw0K06W3jiRtzkdD+KrauXEGnEqORmqVSJypEonzsaHI0xtgCtLwVKjTepupvMPZ/tckihwSEGS2Ishsg85sg7rQBYPVBoPFBj1NJgf0VhuC3PNaixMOpRIOpQI2lQouqwp2lYpdCxwKJRyiCkVaHbJCQpEZEop8hMKo1nmH+zVF/ldEQt2LhEDwDlrA5S7TyrhqsFqtGD16NJYvX478/HzUrl0bEyZMQKtWZcuLbgmy+uqrr+Lee+/FyJEjvW4AZX14xMbGXq1TypAh4/8QZJFEHei6IB2i4sOxevYG1lFO2jn+io63b91BTBg1mauFaq2aLZ08qNGsCgY81QuR5SJQrXEy+o7ugfMnMzC234ewW+zIyyhgY30agn93watMXKmrnYbQ/RujYpKiodGqUJhbHEDmYpKiWCrwTPvXmbR6MOvbNTBEBCM4LgJ5qZne5WTddMczfTF/0tKAZjNyKgiNDIbN6uCmstGNX+TlNKQfHhOK2R/MQ502NdDl/nb4+e257GZAnqhERP3JK6VRRSVIFeNzx9Ih2qVnodaqEBMfDpc5GGk7j3gJLUkY4qqUw+ZVh/DnhPlwkHk7S1EViE+W/tZnpeXhy7G/IzjMgNcmDsbhDQdgM9mQcz4fFiPVzSRQmIJSp0GBVgWzp9nGJSIuNpRDCgoKzCgyWSBQt79aQFxEMDQqFQqsFuTT8DFBJJP+IOgMahRZrcg1mb2a1WiDAcEaDYpVxch2+V6DMFUwIvQaWJwOZFoKYXGooFHaEKQyIERFFUsq/aUy0bKEqAFtGA5/VA+nBoTCKJxHlGhEkMIOhRAElZLu2QXB5WsacwlaaNLioN5ghWLBQYRstkAoEiU7pgoVgAQq658kcbO0AyVzVaok/Z6ayoleDCJ6VdwaZ4otN0uJTpEZJkQGq9E4qxj44yhg9Iu+rVyZG5VMhXkwOq0wGjSwaNUIiiwHg0uANu08DOcz2JmAkZhICQ1Afj6QTc7+bsTHA0FBQGEhkOl7P4I+z0NDpXOel6zEGFFRQEQEZdNShxIvMqs0yE5MQkFSReTb1UhLM2JXRBVsj6gKQ0IkIpyh7OFLVm/e96P73ziCg3A60+96bghKGfL+y2PdeDgcDlSqVAnr16/nprXp06ejb9++SE1NhcEg6dVvObJas6ZkpbJ79258//33F9yOmg1kyJAh40qRmnKGfzbsVIe74ukDrm/w/f+4Hw3fe4ZiJz7xHSrVTkKrfs2QeSYHU1M+4+WPNX+Zm4cIT00eiT4PS9Kmn9+ZC61OgyrNqmLjor2YsO4dJl/vP/AlVk5fw41TRTlF+HLL+3wOGp7/88vF3nOPm/scajSrisKcIsz95G9smr+dHQGenjyKQwPOHT0fQFYffrUvOt/TGit/24oPn/gRsFhRu3kVvP/7s0woE6rGBZDV/k/chqHj7sbhbcfYAcCDrkPa4/EvHmKLKbq3p74eiWe/e4TDC56eNJKtqQZEDoMQZICgUqF+r8Z4d8ZjvG+vmJEY/PLtiK9ZHmvn78a4b4bx8rtqP4/80xn0SYfYWhUwbfeHvHxEq7FIPZAK0emEoVwkph35nJc/c8cXOLTjJIxFFuzYfAKTd33Mz2jMQ99gC3X6U2UOLkxc/ybCI4Px0Zu/Y9HK/RBJjqBR4P2P70VFkjxMXYWZf2+DOt8JZ5ACHzzXGw2rJGDKki34fPFGyWdVAbzdsws616yCX/bswWsrlksyALULL3Rqgztr1MPC0wfxzNZfoVa7WJP6SM1uGJzcDjtzj+CVPV8hRG1BkNKKAQmd0DfxHuSnH4a5a3MsmFYXzmAlzs58BM3iXkVFx3lsPdsewQoLaFA9Qt8RcVGT4XKZkHe+GpQuAarVFuh3N4Dyk0VAUwAv6cirSnpxatWiD0z+1Vm7PIyVcmCtrIIrtjzixh3k5Ucnd0JR/jGY1GoY9VHoMXqb9G/g6YEImrMEUWnuL0T795PnFnDHHcDvv/ve9OvWMQE1PPwwDN99h5gc9/K9y4B69YCXXwY+lF4/6Y3+M9CxIzB+PHVN+5ZPnCgdmz7bR4zwLX/nHeDhh4F584ABA3zLn39eOjadv73k+6t32JB01wAk0bEPHADq1sXdZ9YjRxOM48+MQfMPXkZuViHuajUWQngIaz4aN0nCm98+zG4SXWo+gRuKW1AGEBQUxHajHjzwwAN49tlncfToUTRo0AC3JFmlG5Z9VWXIkHGt0aR7A6z7Y0vAMn8rqQuBPEdb9SXGAG5Ioganr5+Zhk/XvFVq22pNklGuom8UKGXTYWxbfgDKcrH8d27Yq/14uZLy7DUaJCRFoEqjyt6/gVQl9AdVXgnzJy/DzA/+4O56arKiSm1kz4alzu8ZahctVogmM0IrlEO/R3swUSVExoXzMT3uAlTJ/CcQMX130AT+vfN9bQPWkQetOiqcCasHQkgwfvp2LYk6Ub1Oou/asvMAs7uS6ReBKeh1UMRGA0F6KEJ8Vkr+VzZ7+noMe6E3G8E7K8bCml0MJ3XNkxbY7fVqjTXAkkh2VCJEWu72eXIYFOyp6iADCL9xeqcWcAZ7GvtFiB4VAW1C7kdR5EZPL4LEEDQaB8JCrF6tZohWqlySxjNcZ0VFQy6CBBsqHc8BEoHwdDNEpx3xuYUoDtciUiVVNKkrP0JpRpggQq8gXarvtGHfW6D4oRCKUw6gc4nmMbrUUAGOVlry1mecWhoDhyjpQ62KcHiMzTJ6hSLdGoNMZxhUgu+Z/vV4QxwfpoGmyI7Y40V4WkkuAsDZ5AggORy6Yjv0RjsMl1HDs2hU7K1L15QTFoTUGuXZ19as1aB2VBgXgA+Xj8Pi/p1gUWigcrrQo0IiKBj4VHQU1rZtAzWNTjgdaBAbh2qkg1ZrYDEEIdLkV+0tgShbMaKypC9qYS4LBrkOYqVQH1maMCDC5yqkKNmE93+I4uJijB8/ngOYaMrOzmbL0Jfpi0EZqaJvv/02pk2bhszMTFSvXp23u++++y54/EOHDsFkMrGr0y3ts/r/BNlnVYaM6wNqXNq6aDd2LtuL1ENn0aJ3E7S/qyXy0gs477526+oIDgtMdioLcyfMx/xvlvDvGaezvUPW0098xfGoZ4+kcVW1TpuarLP8/bMFyDglDUlSbKuV0nbiYng+vkIUE8787CIMfaYHbn+wY6kv61lnczg9q1KdJFRpWInlAUOrPREgAyB9qs6gYcmAf/c+WWIZQnTc9FSzZXWMm/OsN+zAA3OxGTuX74NGr0FSnSTsWH0Ic96dg3OHfbpZjwyAKlMZfmb+/jKAzNQs3Pns7TCE6rD4j11QB0l2YGmnsiG6E6TUGhViyoXClZWH8zsOe2UARHDj60hD1pnn8mCnVnK9lslvufIRnFKVczKLQxcI1CwV01L6IMzJM8JIDVcaiaQmxIbxM801mmAyS8SEGopiE8KgUiqQX2xGodkKh156znHhwdBoVMhW+2QARFbDKwbBoJZkADk0VB4jrYvWBSFYrYEmKB+5Np9Gsm6EHcFqPeyiDbm2fDQuOoUHntiEcllK5DZIwpH7w3GqnRGCmzRX0VgRrCIS5YDdeQYRCpGJquq4Boa/Q6BdbIaw/5jvhaJh9cREiA20cNXNgK2lCvY6KkCphUKZgGLRhQL7aclpH2BPVauiqvQaO9KQ51QjxxnCtDNMnYAipw4nTDa+XoLNpUSwqjxbSeXb81HssMNMDJ6ekToKKrOIvKxiCPkFCDLZoDHboY1OgMOgR6bCjnSFA3kqA8e0xupCoVWokEcSF5tUtSWGECZEwqBUo9hhQ5bZCJdZ+lITrSNphRaWAieyjD7ZS7RDhzCdHlanA+mFxQjKdCC2uAgVYUdFlYjoc3lIOLAbHdP2IsRukaQEJCmgkdeTJ9k1YW9oJayp2AIHqzWHtciOUylHsPrc9zfMZzXxk1euqs/quefev+x7OXXqFCpXrsz71KpVC8uWLbsgWR0+fDh++uknPPbYY6hXrx7mzZuHhQsX4scff8TQoUNLbU8ktUOHDujfvz/GjBlz2feUnp7O5Jn+DkZHRyMu7tK8pC8VMlm9QshkVYaM64NPRnyDxVNXSl6fbLMpIrZCNGac+uayjnP64Fl8NmoyNzJRUxLl2be9owWemzI6gAhuXbQLY3q/V/oAggBFVCRXEOkPMnXNN25XA8+OH4SIGCITFwdd9+ejp2DVrPUwFZq5qhceF866TXORT7vpDyK5oz8d9o+BBs8M/AyHdp6GaDTCSUb7lyO38vPEVOi1EMLCIWikmp8hPAiaiGDk5UqVMVexEeLZ80xCGRoNlDFR7JfKTgl0KCK4GrU073BAKJYqkTTvUilgLR8OV4hWqqaKIjdNcdqUe56roByDSlZPFJtKHf+SqyfPU68NVUzZCgpwhFDjleT7ScZSxOtc3ETl/vIQYgO0Lu9ssMECrVZKnyJE6owIVVsQnm9GQaQBcYp8PPnsShzomYg9fcsjQmvkhCqP32mEohixqmJvglTkZguiPy+CdkMZFVSdANfAILiGh0OoKxFIh8sJO5zekWCry4UC0eWmqhTXqkC2Kwh2USklWrmUyHSEc5IVJ1o5lUi3h8HKugeBjRWMTi1HtNJ6micbK06sEqT1RTYtH4+2p2dM20oJVwIXx+mYLr95+t1/vcOuhIsTq2g54CI3AEq58ryFLGRjRa+A9FAFC6C0+768qYrJMcAXDKAuckFTKEJnt6NNegp6ntyBFtmHoWJLrUAcN5TD6MrDYMvLwJq0H/6vyaqVvoDl5CAhIcFLXMsiq7t27ULjxo3x1ltv4fXXX+dl9Lp37twZKSkpfF4Naab9qrD9+vVDTEwMV2IvZZTcaDRi9uzZ+OOPP7Bx40Zu0PJHeHg4N2qRpek999yD4GAa/rgBMgAS3/7www98EZeD3NxcfnEWLVrELF6GDBkyLgar2co//bveyWeUjPvVOjWO7jyB3PP53EHfdUgHNO5S74JWT1SVpeOdOZSGBp1qs93k853fwIm9p1G/Qx2EJsXi2MHzUJZPgGiVhuBFT8WIiFV2jtvnR41xs55C2wsEAZQF+gBocW87GINCsG35PsQnx6FKnfIwm6xYN4+6/kWIRDRp4jhYA1r2aYLyNWgAVsLBLUexZOpKbPp7O/QhOtRuWR1Op4iDi7bC6XBJnqVENM2XSFaVSiiCgyAY9Ew4XUYTXJlZ0j0qFWjYpgX05eOwZd1RFBaYAYcTQlw0SwPYikijhiu3ACJ9ULHPlB1CZASE0GCIOi1gNEO0WCR5gVrNsamqIgvsSgVEvZqYG5QOwEHVVZokO1I4yRaJPFNpkUb6nUDzZLbPXqrU+a8CHMEiSwHoxaTtRIMTgtYpGfsrRKg0TiiVNIlMOEO0VoRqLNCoJH/ThimpuHfSdsSkFeGDP3si2GDF4u9qw6Cwo4KQg2ClBaGCGQYFxZ+KMMCBIMGF0D1WhH1UBN260iTVlaSC64EQOAeFQAxXSCTbZeOufJcowgYXbOyqKsAOEXYoYXQpmQhaRCXMLg0KXVqmfhaXGmaXGiZi7bS9O+aUkqeIkJJ3qtOlZFIrkVeBiamDPFGhYKsq5h6UWMvfJuhLhQAHRajy9UgOAdLx3BGt5LlK82RHReudZGdGkauSryooWpXeYu7jSW9wd3GYzkffDewiFE7pC6bCIbKdlYoK7O4MBDsV8F1qrEysj7UhtRCdnYfO2XvRJW8/aprSvM9yXXhN/nfozLuwpdx1gf+9Xo1jXQG0Wi0T1X/Cr7/+ytIgqqr6//15/PHHceedd2LVqlXo0aOH13aUKq1qtZr7jf6JqBKHI4I8adIkmM1m1KlTBwMHDmTpQGRkJJPivLw8HD9+nKUKo0aNwlNPPcWuA6+88gpvc13JqsVi4Zu8XNCN0L5yo5UMGTcWZNdEKUM3u9Z8wJO9YTFaWXPqIaz0d2T1rxsDvE/JCH/NrxuxwPQLL6e/MZZiC4LcEoGl01bj+1dmcIWWjsN59vTB6p7fsvIAlFHp/MFOmfQCNazo9QiOMCA6JgR56fnIOZ/H/qrt7miBJl3roajAhOBQ/aVVIootePOpX6TzQYmTJ7J5IihC6BpFOE/7hvCLC0yY8Y5kBfXEl1JTyzv3fMpEnSuQ6cC5o+mBJ6Hu90tAcsOK7NZyMrUAok6qFDOoYur5u+50Yv3qIxCiaWgPEFRKCGEh3mQhrrJl5ULM8MkLhIgwKOJjfeadVIX2K906Q7SwJYZ714taJZxaNwGizdRk4C8RHNqdiumsafUcn+Z1fuFL9MykR8crmX6FuW2k3LvpdJIEwXOL8cFFTFornMjFvZO3oun60zCGabDmwepQCyISNfnQCr7Ka5yykOc9nrWxxyyI/LgI+kWlq+GOdlo4h4dC7EoNaxKpdIjkrOpLlbLAyWTVM1/sUqGYq6TSNZP5f55fvKpZ1KLI5evMtopqGPkhSNuT2T+RWw9oCN3uSUbgDAQFzA7/9VRJ9a1n4ug/L9LbgOZ91WmXXZr3plZx3Krf68JVVZ9AWU3f7yjpy32N2gJAcPr2YN9VItMq6ajqIhuKRR3+jmrOU7wrH13y9qFL9l6siG0E6CIAnROQ+s5uCDzhaVfrWITz/g4KIDVEKE//Fjt27ECVKlVKEcMWLVrwz507d3rJKpFJuo7FixdD5adZvxComkuV5vfeew933XUXypUr94/ygDlz5uDbb7/FlClTSlVgr0uD1eDBg3m6XNzsH44yZNyqIAI36/15WDV7A/uEEvFqM6A5hr5xN8Ki//0fyWuBWi2q4a15L+HDByYGdMB74C+7p1/JWorsmciHlYgedeGTz6h3O/80Sv+KreeHJzqeqn8uEe/OfwXV61fg/YmshkaGYOaXy/BE3wk4dzILsYkRaNOzPu5/uieC/BqLSl/oP91pGZGTABZMWY7je07BYXMiM/Uq2PeoVYiuWxW3P9AOq+Ztx5q/d1/wEryXcsFrL7HC87e91N94z3yJ5f/wUXDBQtZF9vNy6YtsXn/LGbz63ELYdCose7gW1j1QDTaypboItKl2JH5WgKg/jFLqlR/svfSwPhcKV02y/1dA4Q5BvUCAqXRN2U5o9zmg2WeCqoYO+V0MbNXV5vYTMAerYQ9WsRG+MUiLYw1jcfA2qaIWe6QAthgVzCF+frTXBBd6DS91ezeugOCd1Ufjx+Au+DGpM31zvHos8SZD8+aBIzPjxo3DG2+88a+Pm5aWhniyGysBT1WW1hNOnz6N7777DjqdjnWmHkyePBn331+2wwrpYEkycKkgMvvEE0/w9Ndff+FKccVklR7qv8HN2G0mQ8atjrOH0zBt7Czv5wqRL0pAqtKgEno93BU3M8iaKSwqBGvnbkZRnhGNOteFRqfG4W3HkZeZj5a9m6DbAx3x19dLsXTGBhJi8X5k50TTz6e+QnGeEQu/W47sc7m+A1P5ziVCdLngstogaNVcQapeLwnd7mqO8smxOLz9OCrXTUJ0QiQObD+BWV8u8+5OjUV/fL8GVeomosuAZtzMdOpwGhIqxcAQ7COvRGRf/ehurFq0Fzs2HkN8+UhUSI6B1WLH1rWHuaKpiI+DK78goELqtDtxYEPpUHStXsPSBxrh2vj3TqlWxxVaAIXFrMNt3bsxFwc2LdgBu9UhdewHB2HnqgP8Xnhv1uOIiAnFolmbYTHZIESGSzGXxUYe8m/eMhm68rHYueUkio1WgDSrLhFCkJ6H9skxABYbRLOZ06lE0lXYbRAo/ogFl1Z2NKCqLFlUKYos0KQVwB6uZxmAguI3bS44NZR+RGVCQGkW4VRLOlYmeHaRRoulIW4a6SbNpFJkwkgFRMEmShpVYrbEa4pUEPQOCCpp2N/pVEClciK80ISE7Hxk1AnHngbl8eeQhlhyT20gVmDjfzWcNGiOAocekapiaASaF+FIF1Dtm1zEzizyxod6YO2ggfHFEAgNdExQiVQ54IRSUPq8S91sjb7siEYXwh/LgXKfHaoMP8Y7VERW52CorU7Yw5VQFbmgzzRDU+SEusjJMU4HeiZQkwlGPbAWWpMTBVE6pFcOw7mKYdjTJAkbOlSVCptOsmhzwUlRqZJVLRSCEw6XUppn+S4lWUmaVHrJaHuSAdB8lVNZ0BfbpCF9t3zAolBjD3nCQkC1sxmIzDexRMBF0hGBwmcVOJCYxP9u4vIKEJFvRoYuHGaVjmNXqfFNZZNeV3pf2A2kaQVUJhfLSRx6FRQ2J+B2t0ARiVwVQGiwpCkgCUB6YBXyVrCu2rp1awCpvBpVVQINz5NkoCRIGkDD/bSeULFixYAv+5eCyyGqZYVH/efIqgwZMq4/NNT8wh85RARE7xA4aT//C6lV+9Yf4i57wsEtRzg3PPtsNhdedq8/jCMn85GVWQQVkT6SKuXkeZuNRjd+CV0Ht8eQcXdxkhOT0vg4Nhzn0R5p/BOCxca/jxxzO+aO/wNfj/wKNosdWoMWrfo2QZ/He5d5fZ8+PxM/jl+I4gIzzEYrk8VGbavh8bfvQlySNBzXvkc9njwgWcLin9fAmenW47EB/T98eLgrh+3vboW6bWph9kfz4HI3PNHnuiIsBEKw1PB1JteG+57sDjE8HJuW7mP9qDM3n4l82rl0PNvlLWhiomAloko7aDRwkf0RpUYJAg4aReiPZMJotrM0whUUBFGtkJqoaBg/SAORmsvUbnJqssB15AyQX+Q+nhqonAiEhUrxnETmgpQQdcQyKa6TiIogSQHc62nYn8kpzwO2MPfQPxfYRLh0kGI9WS3gAoKcnFXvWU/D/mqtgyS4/KCiXUW4f9523DNrO3IjDRgzqz+i9CZserYqQmFDuNKICJUJamJVxPcVJoQrTdAX2lFhci6SfsjnTHt/WJuoUfRSCGytJUIgqVIl0L8rp+iA6BRZz6qfY4a1ghppz4cBWgFVM0QUNDEgv7YehXV1yK4VhPPhETBbpWe6aUoV5NmDYGYPLkmDarSpYTZpWfs56bX2iD1RhIRTBSh/Og+t55+A3aTCwlZkIiXg977fQOl0wajXwGTQ8s+t9SrhkwfIM1jAo7+sQnJqNsKKzIgoMiG8yIwj5eMw9MXhvP7dL+ah/kmfZpRwOjoS7d9/ide/OHcJuu/hMF8vzGo1Gr38IRPPpxcsxT27NsOqVGFtci0srNUYO3TVocwRoHA/JodWgDlODV2GkQk5abVdmdmckHZBiJcmcfkvgYjqtWgW0+l03IxVEvTFlpqpaP1/DVfNZ1WGDBk3P6ipZ/yKcazt3L36AKo0qIjW/Zqjw103X7xeSSyZtspr2E/Iz/RZPRGKzE4YM/1iG+mDz68rniQBf3yxELPOTcZLPz2BWZNX4Wyhu5JDILKlUaN5m2roP6wtTuw6gXW/+fxcrSYrVs/eyE1ab/0wEgtmbMCW5Qe8611OEVlp+QHRp9tXH8LS2Zsw+LlefP49a1JQvlo5VKpbAfmZBZj14bzAm7yELv6I2DAMemUg2g5sgSdavoqcNF+VWKHVQvB21ABnj2fil88W490Zo1GrcUX8PHYmTO6KMz/DIjsUThIUurWJeo2XqBIKs4pR7B+vSl9q3J6xDKqYqv2cVHMLIeT7vQZhwQFemS6tEi6D74uRSy3Aqfc5MZCnqkvrFw1KGlb/IB1usPKbp+vU+Xm9kj5S73tNe6xLwUvfLUFUrgnbm1XADw+3QazB6B6klxCpLobSf95WjMrTclFxci7HjvrDUkuFwhdDYO+ildwp/MwUvEKHw3aEzrEg6A8zlBkuOIMFmIZ4NKkCdv6VBJPfTeQ6gmH2y4g1ubSsU/Wekxqs3B6r5JywsWNVmNr61rscgMLi7kwD8OftDaAvssNgskmT0QqXZK/A67tsOszRqfnBBmREhuJwhTik8PCwtP7dQb0QnOv2+eUvBCLMBok4Ez7v0xW/NmzNxFQhuqCgsA2r4O30/71hC+wuVxmNzp1E9yN70e3oPmRpQzGo1YsQaUif3jZWEZEmBfo+2Rma82bMe3MWbP/go6rSqoHS/Ov6gR/G1WqwurZSyISEBB7iLwnP8P+lNGldKh588MGLrqdCAJFjIuXkRuDRzV4uZLIqQ8b/GRp2qsvTfw1hUdIQGVdBBZ/W1DvvybTnkUYaElYEzNP2JBvIOJWF6W/OQVqWEcqKiVKylXQg/n3j9OXYMWslVxvLwodDJ6Jqo8p49MsR2LrigNQr5K5Ql5WYNX3cbMwa9wvHs3qG3Mgb1U4VXH/4ay3L0F16lnW8pw3yMvIxrPoTXPENOKfL5asSu++JEr8eSH6Mr5GuIQA8Juz3wekhut79vTfj/lli3rOBp6FKLX2keNe6K77eeX99sbsSHHCLPNTqI9veoVe/2QBmyF0v/nJJ0bv7bav34YNP/sTxKtH4aEwP7G6SxFvUEtOh4TF6d9OXqIBK4eRh6BpzMtDkm1Tosvy+xFBDUkUlsp4PQXFfHXRKAerARwzBLLJNFSHy9SJoNtlgaaeBcYweuT30MOtUfo/UIwuQ5hXuh+CdL7W+xHzJl0QpwEJfMtzLpj7UxttQxTZTogCzzTc/8ItHuFrrLk3zS26zuL9AiMCWmslAsdtuwR204E0vEIH9FcrjcLASgsRkGUqz+yUQgb0JFbE/uiL+rtsM73UZiNanDqPKkWwvUX1770/I1oZiRX5DLPzSBMFkh83h92b3e/95vhDQv63k+hWxWgrwuiHgJ3aVZADXumuncePGWLlyJXfu+zdZbdmyxbv+aoHOQ7KCrCyp0TIiIoL/znkaqcgOiyq6ZLlFf5t69erFDVeXW92VyaoMGTL+Exjx4f1o3LUeNv65jZ0MmnRrAJVOjZWzNiIvowBt+jVDcuNk7N9+Clnn8tCobXWEhWqQsu4gzh1PR5OuDdC6fzNsnLeN40cJzmOnIISGsBUTWVSJhUUsBXB72F8Qx3adxL7V+/HVohewbWUK9m87wTZUyXUSsHnBbiyfsxmw2eEk7andjpK01+Y2vfcHxa8OHnsn0k9nY8aHf0Eksk26M7uDNaGR5cLwyEeD0XZAcwyr8aSPqLptpgiunFy2jCJtKkE0W3giuyQv6LhkVUWcQKmEy+mEwA4AAoS8IghWG1xalZvsk7pRsqRi4pKdC9FmgxARKl1bfiEEmw1ihXJAsA6u2HC4DNUg5BRCMFrgiAnlbn2VUwEFaU9VZIXk4p9sUaUR4FBIchTibGxR5WRHI+n6FICymCq+bt0qVVbtIkQt7UPbixCtAhRaEQqlC4LCBbsJUOmBte2qYWJ+B8y7oz7UBsCgtDMpLbTpEK4xI0hpg1JwwWRXo+bidDSfeAohZwJLd9Y4Jc4+GYqiu/XQaKRnaHJJH5xkDas660DwN0YE/2nG6Q3l4ApTwPJGKGxhSljKSa+BiWylyAqKfFPJ5UEkKyoN7CK1YokodOlgdKlhcWrYGqvQoUORQ83VUFpPllVmh1LyUaVrckrHI82ox3fV7pR+VyhE2BxKWGwqfvmUShdrdqk4LroE3oa0qWRbRf+xXJuIK70/yI6KtmGLKrdtAB2T1lul14JunLXCZANmd3up0vcdMkugYiy9rJ63GhFQqxKbYmthu84FXY4D+mLyQhDQ8/wO3J62FbPKtcHU+M4QKiQC+QUQ8wqgD9Ji2GsDERYVjB3L9nJ4B+nRyzcqh6m1v7icPxn/t7j77rvx0Ucf4euvv8Zrr73Gy4hAfvnll0weO3XqdNXOtXTpUnTt2pXPQxZVUVFRvJzI6WeffYbp06dzeAGd9+OPP2YXAWoi++CDDy7rPHIowBVCDgWQIePGYumvW/DFq79K/qJu1GmWjI/nXDxDfMuCHXj99g8vu7GgLIREBmPCurdRsVZ5JtCPNHrhX3Xsqyghy68xwpGZTT6B3nkKIqCQgE1/b+P0KkVkBDdMebfPzeMmqAsiIRZIKuerXJ7PAk4H6hP9IcZGQKxZSWpCow8MoxWqjAJfYZNkAAad93hk3u8iqYBftda/8OlSCrCHqb3HI2tQY6wScKdD0basT/VUUgXAHuIrJ4qCCGu006/MIkIdaoOCPFVFEfct2Yp71mzHkxPuhdWtw04KyoNe5auUJmlzEKSSviwk7MxDz7f3I/JwoB7SHqbAmUcicO6BcCgNTra08nsq0B21I/6bQkTNk571udvDcOjlctylT+TSyMP60k0YnRoUuPTeeZNTg3zqMHLPW5wqJqieefJRNTrcbJ1eU6cCRrtvGJ6qoiabb730NhYD5qm5yjtPpv5WZeD2Ft88+6AWK6RKqfcOpVEB73o29PfNq4zgxikPVCYitX7zRsk/1wPD4RxocnzPWBusxKOZK9A9azf+KNcS36ABxAxJi+7BwKd6Y/SEYTf8M9cbCvDBq1BFhF+VYzry8nHu5fe46kkWci+88AJPl4Ivv/ySq5Y0ffLJJ+jevTvatWvH66jjPixMkt2Qd+qMGTPYW9WTYLVgwQJMnTqV062uFnr27MnPh2ypysKIESP4GZI1lodIb9++HSdOnLis88iVVRkyZPwncXhPagBRJRzccbLMbcnOiqyszh45j0Zd6mH8yrH4+5tlOJ1yBk27N0CXIR1wZNtxHN99ksMBqjWpjH1rD3rjV2u1rIYFk5dh9kd/BhyXdKjUVU8WYAu/W3FJRLVyw8r8IWzKL8YbA8YHrBM1msAhwhJNEhRzemjrUbw1/1UsnLkBU9/9my2tfNuXrtjqgnUY+dEQaPRqTP52HYr8H1mxLyKzLIihQV5iSVBY3R6mHvjpW3l72raEbVWAfRORUr/jkQbTQ1SlEwTuwNU8P0msp7rnf3AiqmFFJrz7zZ/ovvUgdjZIgs5i95JVf6LK80obBKeIlpNPoNXXxwNIlkOvwNkHw3F2ZDicoZKWVkMWBX4w7LOhdt901tNmDQrG4YdjUJTkI6NSUpQPlDwVMO827/eek2/KN+90d+lfcN4zhB+AwC8HAfMl9pc28Jd++BFRz9FKvoYl1vsTVZQh51AEPjKoigLfx0Z9MD6uMgAWhQZtc1MwU1UeeSXuiOz1yFZPE2LA1lUHkZF5g90ArgH27Nlz2cT7448/DtCjUmWTJgJZiXrIKllSUbc/JVKRgX/16tW5ynkldqMXw/r16/maLoSmTZty0pUHHTt2vCILK5msypAh4z+Jmo0qcnWVrKI8qNu8SqntVs3agPfu+0yaEYC/v1niWykAp/afwfxvl2PGya/Ra0QX76qE5HLoMcw3XNZ3dA8s/WkNhwN4EBodguCIINyTOLLMof1SEAScTivCqw9MwYsTBqNG86o4vNWXI0+pWRxG4AFVWf0qq+QwUKtldbz8+hzs2pMKtV4Dlc1XsRK0Goh+DVQECkb44tEpgZVVD4INQM6FTbqFQrKqok57t8xAqw7kk6QT9tOYUnMXV6wvUFklkuj2UuJ56m7nWCUPYfUMP3vkxmXNO/w+uUSg4YFUTJg4B+VyCzHh3s6YO6QxlH6uPSaHCgY/wqo658RdY3YiaVteQKPX8UHROPhoOWhiHND7sS0afg/ZZoIm04m8PkEw1tHgzMvhyB0QBHucitOjPEPmBLXgDCCoGsEBM9sXuOcVTpg4SkraQcU35ZuXYlwvMu/V2/oTUj+L2xJPnaQFAdt7xKWeeZJTCGIgIS1hCltyPRVu/Uk+J7v6zdMjUfoRWkeINqCyKtgcENU6TKzcBz+X74i8tELAlMWyB7LCItC/sxcGTICC7NHofezwa9z7P26wOnXq1CVtR3Gqb7/9Nk/XEkFBQVi3bh0eeeSRMtevWbOGE089MJlMXkJ905HVAwcO4NChQ2jdujVbNVA5mL4JUGn6apajZciQcfPCWGDExj+3Y/OCHVCqFGjVtxla9mkMfTBVpUrDZrFhy4KdHC1qKjKj+W2NWa8ZGiXZMnXq14S9Rhf9shF5WUVo3aMeyieG4fPR37K9VeMu9REeF4Z5Exf5Dlpy5F/0EbrCnCKkncvHxpUHcezQedRpVAFtutRGpapxvE1cxRi8u+AV/PnlYuzfcAg1m1dln9M1szf6iCo1GAUZpIYuvmkTa1elrnk1+5IK7mH+v35ah4c+HoaISANGdvsAAtk8BQdJTTFn0tjn1L+yWr9DbTw/9VHsXrkfh2etg9LhgiM2HPbKUVDvPwXBbGdrqovifCZQaIQQHc4EVCwoBkh/qiYyRbpFB5BX5GtwycyDUGwBysexrldBPpg6DUSqsFIDmcMJkQgtVTFJ/6pRkexRIpd0HyoBTp0CLrWCiazDoGRdKukdFU6376aTYlLJ+koiPTR5io/E+UQFaV3pmKJX0woa9leKULkcGP/Vb9yR/sBbD2BX3QrQuBwIctoQpCFNqgirUwUVXAhSW9FwzRkMfmsLggp8XyzIOmrdJ1VhqyZpSm0uBUwuNUg1GnTMgprvZSFuXTGKK2mwv1s8rFDh8LA4iKICollAkUsLi1PSmBIKnVpY3YlS9C6gIX6jQ8PRqDRfYNfz0L9KEFkzS0P+lCBFjVDE4Yutatalami9wgWLXcXRqbQ33Y/NroST9KXu7SkalZwo6OpJs+p0KOAkv1pK9CJNr00BkRKmaF5FmasKKv9KUaiCAAU9CtL9up8/VU0FCz9u1gmzHtXm7nsizaqTKuyiNMxP7wEiyuRI4BChMru4qqrOp51E0GMQ7C7WKzt1Gp+DgtkKocgEUa9FPnnShodhqG0PKhel4Z2gTmAjNa0azvgYOFVKKEw2iEbzLeezeivg/vvvx+eff86NVSQ5qFq1Ki8/duwYJk6cyNGvpGX1b8iiiNbLxTXXrJKO4fnnn0fdunVx8uRJvPnmm5gwYQJ69+6N+fPno3///pwz+1+DrFmVIePy8GyHsdi37mBAhy8N873xW9larU9GfIPFU1cGbF+xThK+2/cpz//48UI25/e4AVB2uKugsJRbQKnOenI7MuhgMUrEjo7fuGt99HtxIN58Zqa0zO0MQJgy70kkVY7B0Z0n8GhT8posu/ufTe9rVvF247uIyB3yVU3p015ZPqHUEOsj4wbg6PEsrF68z1sl1uTmwp6VC4fbJD2yXDie+GoEfhw3myvB/h3TroQoaCKDIRw/D7ubNNOQP91oyWqvV+Pq3tcZHgwxiIxL3feSel7SsXouOTQEyvCwwNLdRUDWV66IEG9hzqFTwBqt9e4vOFxQm3zho06tAFOCn5UV8Sjpu4h7XoSdZIKewiuZ10eyq71nCzQqPIXM6FAUhnDYPCqE5/spDUQkGgqgsTkw8POd6Dj7SMD17hyShJSX4qHwVmLJI1WAJt+JBhPPovovGXBoldg5sgL2DU7gVKlcezBHnnreVHl2AyxMTqX5YjuRVd/6QqsORrvWO08NUGTQ75l3OJU8ed0JHAq4HL55aoziB+N5qvSWYA2qe54K33aqSbrjUMUSw/Qlhv3pfauyeINTpWhbe8AjhabQz3ChpOhAFKEtEAPWq4u5Dc+7noIcpAKudA8qo0OqvLrnldmFXqcI3sygwx352/Dw+RXYEVwZY+vcDwtV4zzbG22wFeVhzaEvbpxm9b0xV1ez+uq71/1ergXIz5WKjrNmzZK+EHn06xSCIYq455578OOPP3Kl12KxcOMXFS6pKeumqqxSx9fatWvRoEEDFtW2atUKhw8f5gQrYuFt27a9bmSVutAoMYImysKlTFzScsiQIePaozC3mH/6kzyqZlIVdNlPa7Dpr23sTUoV165D2nPDUqnts33eqoV5UnMLk0r6nzvT3jPvRQlO+d3+CTz0vmLGWpzcfwZNu9Xn5Ks8s2/43P87fFGBCet+34K/v5EaBEpekxdK6sj2DLWS7ySb/vjg94fcH0X5Jjz/zh147NU+2Lv9FKJjQ5FcoxysZhv2rklBWEwoV5BXzFiHM4fOeS7Qu3+wU8SgR3qic7/GSDuSxk1YsZXisOKP7Zg57hdYC02BGlN/4snlNL95f09VJuWe8fdLHLZ0D+F6iYyHNfpbUfmTHz89auAKv3m/ZZ4vIkqnE3ds2IF5bRriWOU4v82FErckIOFUPh58ZT3KH/XJHczhaix5rw5OdoxBjKrQbTPmu4DEtfmoOT0DxwZGY+PTVVAc66v+S7TMR+GkJCj/BqeLry95s76X8sI61IB57xByyWcaOH8h0HD+JZ6pzHX/uN4/77bE6+6bL+lZJuC36JawCmo8nrYY7x+cjjH1h8Kkdj931z/e1rWHXFktE5SU9csvv+DFF1/EwoULkZqaystJL3vbbbehYcOG3m3Jrmrs2LG4Elxzskr2BURUCY0aNeKflStX9t5MUdH106GMGTMGsbGxaNasGXfFyZAh4/rhnhf6cdrS6ZSzPF+lYSUMeLI3JoychG2Ld0sVVBE4uvMkNs/fjmHvDEL22VyOSiUkVC3HHcIedL2jGVKPpOPA9pNMNiIrxkFjDUX6sfNldvqHx4ai6+AO2L/+ED4bNRkK6lx3iVj+8zosm74WX+36mIf9t60/ApvVAUOwFm271IExMx9v3flx2Z/aNHxNJFkUoRRdiAvVoJgSh4qtUBu0iKxfGUVnMjnmlbZzmS1QuG2l6H4btamGdr2lv496gxYt2tfwHlpn0KL5bdLfzEEVRvGzKAWNGlZRgR8/WoBje8/gtUmSrOqV+7/G7g1HIWr1gNrujZ6l6FNRp/NpSGkIn4gDXRMRicgwqenKLFWdXRSVSjZXFK3KPMndQEV+rmU8DoEkDzSRHIKGmC1OKC1OKaGKqtVu2Z8nuICHk00uOPXu49IQNLlkuW0+eZ4CxTxWVrSfRcDdm7fj/Z9+R6FBh1UdqkOtcUpWVgAKLFoEuyUAXRamYPSENdBafST8RNNoLP2oNszlpGpnsUuLIIUVievzoSp24mSPKBzrHY28Ggbk1zCwzRRVWyXtKGlSHbC6VN7qLWlOLU4agnc/A4o6dao8bmKcjEWaVaarzLUp2le6H+lxur9swX/eTW49vM+jieWJnh2N0bvXS6+s+8F65yQLKff+pBWWmtX8NqDJXbAN2J50oyTVoMKnp4LvOb97fzIroLhUT/WUNa1sXSVt7/8a8ygDSRqoGZL+vdDLGqyH0miB4NGbk/xEqcH86KYc7frM2fkYv3sqnm4yCnaFCi6NArh4L+B/EsSNLtcN4GYFkVJ/Ynq1cc3JKg3/v/vuu7j33nvx008/oUKFCvjtt99w55134o8//kCVKqUbIq4VyCrBQ5TLqnDIkCHj2iGxejyqN63ClVRqFKrepArik2Nhp6HuEib/tKxZj4Y85ZzP4yH7xKq+DG1CrcaVMH7OEyjILUZhrhHlq8TyvpNf+AlzP/m71Pm/3P4hlszeihmTVkOZlMjEjRJ4BJWaydXXr81BuEGFeKcRp46fR8UmlZAcq/OFDZTkv2oVFEFBUlyrw4GXvh6OTv2bwul04fTxTMQnRmDtnI1Y8uNq7FuTwsSSaq0ug46J6v2PdsHgR30NXWVh25LdLIXwElXSw0aGSU1YVKmlB2eT4mHtfsOqdnIIIMKg10Ol18NF5JG8TV0iR9CKBUXucXqJLAixUVDQsKtLhBATDZGStFw0XC/AVVTMjV98/6FBEGKjpYorvV5E1B1OiZOQ5lahgNLugstB8gMBCjLQLxSg0EhaVRoedmkVcAaROFIhaVMJDrfHKjf7AE7WqronFWlfiWxJpEhvseKZeUuxp1J5LG5UB2qnEy7RCaW75kkNUdS/dNv8/Xjio9XeZ+JUClg4qi42PFQVGrUDKvfrqjlhR/OPT6LG6kyk1Q7Dsna1OEXKHq8ECgVYXCoUO7TsZ0qPgDSwxVYd+7PStTqcKh66d9gl71gH3Qh9fyEvU0GE3alk71K45530k3SjNHxPuk7yNqWJdKgCEVkBStqGvl/QPbs9UaXABnf3Pm1Kz4z8aUlDSppUalZzF865Yc0pVYuJ7LM+la5J7ac3Je9VtybVQ1p5e7sItVmEgiJm2WeVnj8pEQR2c6DhfCKiRICV9KCdklaVm+dYnUA6WCcEq4M1zUxaSZesVsKll9rzBNI221VQeMiq571ksWG5qiqs4d0Qb8qCY9thaWiZ9NCi6ZarrF6JG8DNCuJXVFkluSe9ZsS1qLJKo+j/CbL61VdfYciQIaxTIA8wIqyUYECCW0o9oCSD6wUPUZUhQ8b1xxejv8WJvanequfiqSuQfioTD7x5D0IigrB14S6udDbt0QD9Huvp3Y9soQjmYjMPjYfHSJ2kFpMV5mILx4+GRUodw4T+j98GU4EJG/7choLsQtRpVQNdh3TA5mUpmDmRNK4SIRZ02oDh7UNbj8GZJaWs0DUe2HAY+9cdYh/V+14diGXT1yDrjJ8XpN0BV34BohIi0e3+rmjRVUoFUyoVSK5eDtnncvDxg197NbdUcRSzcmAIM6DrA51w253NmIiTV6uGoiQpejM9D0FhBmj1kojy/fs/R3G+zzdVUT4egrtqyRBFKNRKtOtQAwNHdPBu98DzvfDnD2uxbdVBvpcaTSpxg9S+vzbBlRcYU0uoXSsemqhwHD+UDovZiuqNkqHWqJCyag+sRZIcg0CkQaFyf2xw5VDJJDy5fDj0USE4eTyTq8rcsE7UnAiKTqrKUuXOqVfAEaL0jooTr6MGLD62u4ucQgQ8xIl5n14q/XkkAKNWrEZsYTEeH3U/E159sNk97E+UTkS0wYR6289i9CdrvdedW86Aae+1wckGMYhQFzP5c0GJuovOod/ru7hivOLRmtjwQFVk20Ph9PM8SLcEu+2opPmM4mBYHZ7SL2C1ULCCb3uXu/nJW+akSrGf3ZRgl4z3pSckVTA98/xYuVnJPU9cjsmh5wm5n42HNLkN+ZUsTZYq07zKf3sinF4RsfTT26nvUb6U0LhqjD5Wxr8pKKDAvR2NIlg9mlXJikygL33ukRF+VxRLX2482ziD/b5c0XqSBBGR9ZzE7uDXu07r6vzla+38bLjsoRDsfpZXN5ys3hxuADcjSALw6aefclKVPxQKBZ577jl8+OGHNz9ZpTL33r17S1kvHD9+HDVq1EBIiJ+aXoYMGbcsqBrnr+L0xJTWbVOTJzuROY5E9Q9/B1I2HcaMd37DzhX7uOGoQq1EqLVqpB48C7vVgeQGFdFnVHf0faQ7b2+32lGUb2QiS6crzC1iwmdwJwp5hzbdH67+zVTS+sAKCg3HD39nEJr2aMhNYiWbq0xn01F48hycfpVNz/0Gzkv7PfBEV45yfaHjWNagag1aJFYrx8vOHT3PkbDNejZkEu9PAKTRID+bKF4loHW3unjlk3sDzmUx2VgLy8+U9ne4cMd9LWEoyMPGeVt9w8OCgHKVYzH6o/s5gtZ07BQ3eRVonPw62Mogtr4Xzz3kS/rhY6fhspSDvZDc4lX/6L5Q+nhliyNLLorLL8CoJWuxtEFtbKmR7I0u9UfC6Ty8/Ppib+U0MykYn/zQHaaIwHjH5E1ZuPPFHThTNxyzPm6Ogni3vY67J62UkqSEFNo34z/2XtaVlxDelnXvF5vHFa6/wDO96uJPb+5riaheni95vrIviptxCoshnk3DPS/2gzI/HzuW7rnKFyrjWoCcAMhndcCAASxlqF27Ni9PSUnh5TRR9ZiKlf8G/7cJVvRH+nIarAoLC3nygBq0mjdvfkt088mQcT1wct9pLPp+JcelkgyAbKhuG9EF5asnXHS/MX3e46rrxaDRa7DAOIN//+G1mfjlvd9LbTPr/BSs+Xs3Ni7Zh9Sj6ajfqhqCQ3U4fzoHJw6moV6LyghWAecOnsHRHSfQoGMddLynDZrd1hAn96aicoOK2PTnNiycshwpmwK7ygmv//os2t/ZKmDZhnlbsXr2BuxYvpdTrlr3a44u97fFPQkj//F5DXiyFzre0xpLp63Gpvk7EBoVjFod6sEZEow920+xxrVNl1roObApyiVK1WcPnrvzC6RslwIS+E+83cEyibd+HIVF363AhnlbYCm2otXtTdFjeCe296Imt3+EQgEhKhxCaKgkayCJAEWu0vB/eJjES2g5ZdULApxaJZxBGikMgIiMg2QAAhx6JUStuxKoAuxaBVwaiXpS2BMFOlGDPZMYDVVfJRukxidPYMLPszDsieE4Xj6GS4ZqvR0arQNqlQsRxcX4+flpKJ8mNVMVhWjx9ve9UVRZD5XbGFSjsEOrcEIlOtH8l5PYNDAZdq1U/XWIAgqou580pzRkLyqQYzHA5FBDReVJESgw6lBs07n9SwGHWZIBsJ0WPW/qzKdKqqfYSvIAT+WSFtukIX8urpFywO6urLo1pGQlRdVVb3HWY9rvlgGwlZS7actrA8bVWkmXqrTyBzvbgrF1FMkEWIYgyQQUVtKbSpZTXDDm4W3Ptxc6lgi1SYTS5uLIX65+qwXJ1orO7RLZpoqH/dlJwAFVEQUtAKJOxSMIFLersLkgkI+vw8naZ1GjhBAVIbkFFJuhMFklrSq9xhQJTBV8pxNVG1fGmF+e5vfpn18v5i9xBItownosvGFuAOXfeu2qugGcHfvOLcEfateuzUP95O5UFsj5iaQBRF5vSGU1KysLer0ewcHBcDgcPLxPQmEa8qfS74VAhG/jxo38OzkDXIk57I0AlbjJdkuGDBlXhsr1KuLRz4bzdClY/8cWTHnpZ6QdS/ct9KvUUIMUf866RK5+eqCnIUd3JZM/CElKp1YiJMyAgSM68lQWMlOzMH74V9i/7iDLEUgvenDLUXw0rJiTo+gYTbo1wPB3B+GFzm8GVCeJVOnIAqoE2vRvzpM/bFY7VGolVzCl6qh/87TPdksXpEXtVjV4ehqXByKyfFiTiS29iAScTs/As+1ex72vDMDUlM8Dtvdcu39hjKHVQBkRDkEjVbvZcYGtiRxMgHkwOCyU5QHSiyJIzVXuoprSZOchX2qkYT2pSgFbxUioSD/rkByZzCEKb8QqF4HJ6YqUDm4SRdvQMLjSKmBXQjI6vf0CnAaP2b0Lep0dOq0DGrsDn773m5eoOpQCJn7QGTmVgtlnlYbywzOMuPvD7fj2pXYoiAzC0QFxyDfpkJ+nl7SmRIrMaphMGsnsn4ft3VZRbB8lkT2VWdKgEki3qSGCyEP9kt2WdP0evy2JACrc6z21RY+JvsdZyjO0T5pRsoaiBibeXQnYg6S9+C3Hz9ZNFD1WVDQsL8mUJc2vlqx9pUonrdcWOZnUSvuLUBfZobBJQ/nsKMVSDM/xyT9XIqPe9dQgx8P9ftfsrZEqICiI7NM+pDHQAEYLxMwc73vJXjUejmqx7J1KUB0ugDrtfCn3CcKxnSfxYte3MHjsXWjeqzHWzd3M/5Ylwo4bB9kN4IJa1YtVTYmsPvvss/i3uGKyShdAcV7169fHyy+/jBUrVkCtVrM9FRnB+ufGejJhd+3axYLbmJgYJrQZGRlYtGiR1yXgUrF69Wp06uRLlrkYli1bdtl+XmWBHjZl3JasrMqQIePaYPnPawOJKluZKnH/63dBo1VxWEB+ZiHa3dkSPYf7/h7c8WwfHtqm9edPZDDB7HB361LygpLYt+4Qdq864J130nB4VmHA/NaFO3HH070xYe1bWPfbFg4HqN4kGW0GtECTbvUv6b5In/rNzvFYO2cTti/djfjkOG48I6nCzuX7EJ0Yida3N0Pbgc3/MWRhx7K9bGvVsHNdr86V8PyE+7Hyj22Y9tJ0mKlZyg3SyM7+cB66De3AmlS6X3ORGfe+3B/VGlfG1NdmIj+jwLu9Qq/3EtWypQ0lChOkZ1VJ5vq8nnSJfgljYpAGriDf8aiaSjpW73qSs/q/TDTP/EZAp4MHsKZWDdhI0+q5PgUZGUjVuVcmLkZj8qB144eX2+JgkwTu9CdU2p+NR55fA32RDUnH8lHQXDpQrtXAhv3e52rSctOT95qsqoB0JiV5lPprTKlS6k+i2LbM7xq5GunbnhuU/Dfnk/g9QovUae//TPzNpjz6X+/xuXLqtz03pfm2p2Yq/+NRVVRp8x3A37LL8xsFNgRcn/dbld+yAATG6CI7L2B7R5VyXqLK13Qmq0yi6gE1YS74dhk+WPIamnStz1HJ2XlZWLfx1nLxuRXcACIjI9mO9EI4cuQIb3PDyCqlExBRJcyYMQM7d+7kSmvNmjUDyKqnikqgF+Oll17CM888w/NffPEFBwYQ0b0ckNaVwgYuBR79xL9FaGgoTzJkyLg+iCznHtp2N0RRhahS3QpIqBKHzx75lkkWYdYHf+D8iXSM+UX6u6LWqHn4nqbLQUQ53xBfSV2qZ55+hseGIbl+RdRtW+uK761SnSSehr5xd8DyB96455L2//qZH/DXV0vYl5ZAOtcRHwxm6QBhwaQlmPHOXNb0lkT2uVwMiBzGBQPP/hQUoI6JhM0cWLryOiGU8UzKRIkGiwB/Vq7I+tazib27YcdLhjyTZzf3fNujhzFl2vcY138AfqRKtZvhkacp7f7AnM3os3yf99jzB9fD2r7VJBsmEWi+9BSGvrUJxjAt3prcBydqxHqrfiQPsDkV3nmFwgUXtb97y4fu8XlPVdHDawPLi4Fa0RLrAwipZ3TA357K/Xz4h7LkM72YvtXbxeSb50YgP02x+9K9NlPuEQl/16uSelP/7b3L/X8GnL8EdaVllIjm9DVHCRabJA3xHJ++OJKm/CKgxsqQiGD0HtmNJxqOn5w0AbcSbgU3gH79+uHrr79m26oHHnggIBSARty/+eYbPPTQQzeOrNK3geLiYhw8eBDlypXjGFWn08m5rxfCvn37AvxNR48ejbfeeuuyz03n8q9yypAh49bDY58PR7s7WrDG1Vxk4WjTpj0b4usnp3qJKoEI1IY/tv7r8zXuUg/fp3zG5zu26wSTUWreOrz1OA5vO4raLWugVb+miPczof8nEBmk6itFpCbVTGTf1OBwv9LgFYK0sB6iSbBZ7Cxb8JBVqgCXRVT9n5nT5VfZ0mrgdLigCAsl5264zGZ2P3rh6wehCtZj2ezNyE4vQOue9VCnVTXM/GIZ9m894T6WZF9F5PP2+9qj9e2N8efMrdi8+hBLAsRgA8sRQEQ1Kw/q9Aw4apSHGBPKRExd4ILDoOBqIEd15kkOACQN4Mx5hxMvLfwL58PC8GubpoBZgCrIDpXWBZXSiY5rD+LxH3wWVVvbV8ScRxtzlCm19LWfcwSDP9qK47Wi8dEH3XE+PBQ2sxpOp4KTo0wWFWx2Des+2SaqWAWFTSnJDKgKapE0p97QKiKPTpG1pVzlpMfoV91UEgejXUnOQFyUlttEqDyPmyutUlWZ7bhomcsFjVGy3lKRXtRMelEirgKUKgEucmHweLPy+UQo6HmS6qLIDlWxg7XBziAV1HYRyHHCKdr5HEqLCypyLyCSqlZAYXUyeSTvXVGllMi3oAUoqtdqg0DJbvlFAEUDx0RBIAszE1lqOSFSjqvZClDjHblpRIayRdvDb/bDudRcTJuwiKN4OV6YQJIQhRL6lHS4yoXDGaZn7a0yOpKr0yJJVEqAZDyjPn0A3Yb43C1k3LygsKVNmzYxIaViZLVq1bwFzczMTK4ek33pDSOrgwYN4qF4IqyPPPKId5i/UqVKAdvZ7XbMnDnTa2lAhNYDYt428giUIUOGjBKgJqxGnevx5A+qrpZEWcuuBBVqJvLkj/rtrmx0hqy2htd8Cjlped5llC711p8voUWvxv/qOpPrVUTm6eyAZZXrJPl+r1uhzCawC0EkI/8g92CzTgulTouajSrCZrHhgyFfeN0Sjq3ahYq1y2Poh0M5jMFZbIIr22fnNe+DuVg/fSWe/eVZbFt3hD1nOdkrxG0tpgwHwnRQUfWl2AmnRoApTiPpIT0VPSUNWws8dO1SiuiZvQ21zp/Hc8PuhiVciXKJeVCqpOupeeQ83png89Q9VSMKP73TCnqN73Nmef2aCOlrwdejO8CmVSMtLQxWm09roMhVQ2HzyQA0+YDOzzGJpat+agdNITUg+a1ns/0yHqr7GCqTC5piMeB4Lhqmdxck1QU26NJNATZOMFm880RCbeUjfC4QDheUJj/bJ+nBQplrgZMYPu274zA0niq3VgPUqy4RSPLfpdd63xHWMXt6uAKNDAQo48tBUCiBHCndy5mWDsG9vRcFRiAjFxkHT+H9/edYLuK1zfU4Y/BPB8QsK5R2O9QF0uikMzsHosn3hdMf5OLx2cjJOLLtOJ6ZPAo3C0rKL/7tsW4VREREcCrot99+y8VIcnsikLyzT58+XFiklKsbRlbJrmDp0qV8ER07Sg0LVP797LPPArZr0aIF34RnSJ46wpo2bcrzFMNKQ/rXC9OnT8fp06e98yRdeOedd/h3agyjRC0ZMmTc3LjjmT5of1cr1rGRppSqrTWaXb9wkUtFYU5xAFElUKPWH18sRGxSFDecXSmI8J7YexpbFuxkzSp19SeQLtCNpyePYu0uPSNqFmvVtwmUahU2/72dq7At+zSBLljH856qdUhMGLasOMCWV0061OQO7q+enFoqDYwSyFr3qI/3po3Al09Pw2k/suqRGaQeOIO3vrgfP32zCof3S4llBJFinfyGjanLPEDrWMLlSQsbnvt7CQ4kJeCPlo2hUIpeohqbWYiPxv7uTafKizHgs4+7wGFQIeFYHhqvSMX8UQ1wOj4anz3t61uw2X3aST6lPZA6cENVwILAWWpUuhxwZ7+/WsCTAuZZTwb6/js4A9PBRI2fry6hhOZVukYBj7/SBzVrJWDKO79j97aDvnWkFfVUOglEVv2KRqVukRwf/Len17/E9qVAw/7+oHOUQFi4HqMmDEZhWg6+e+Enj0PYRUcP6H1NzYjbFu1GZnYGbihkn9ULQqPR4PHHH+fpWuGKratoN2qwmj17Njcb0dD83XffzSz6Ym4A/igoKODKa3R0NK4HiFSvWVO2PcuqVau8pPty7CxuBesJGTJkXH1QVfLpdq+zDVZZeOGHx9D9gUv/m3M9QdZfZAFWFii0gSy6Pn34ArZ/GjUUifGSbVEJkF7SEUKt6tJnBCVVFVbQQKQ4TY980ue3j5pZqfhq/nd4csQgbKxVjYfmY+LzUe/UWbz75jyUy5Liuq06Fd6d3Auna0ah3eIjGPreZlj1Krwzszf26+NhovZ4NzKyQ2GyaHy97PkqKGio3z2vLpCG8j0Ejrr7JRsq9/oiEWqjP/l0R5n6BTUw3PM0pK8p8pjoS5VYF5F093pVsR36NKOvakd2ThRF6p516tWwJYb7CCclRhkdAQSzUnIMRo3qiLFDJ8Fld8CZkeUjmDQUX7eaVGH1VDv3H+Uh/wtBGRcLwb09U4ScHDhNF9aYqsvFQNT4nrEjKwcwmwObzhLipGH/MiqqnuCMC2qiBcAqmrFOXHDDrKuS3ngdqvCrZF2Vn48zb7wt84frUVmlVILly5fzTw9pmzBhAmtY6eelgAIBfv75ZwwdOhTXA+QiIEOGDBnXA+Q+8PW2D5Gy+Qieaj2m1HqqQN6syD4bWC1lCMDMM5MRnRCJ6W+VSB70WFfpdVAGB0tt+t70I6ooO6U4+0ITXHlWOCrEQIwIZtJGFko2A/moUpwneayKcOql6uK2ygno0vY5qGKciNXkQaV24o7VO/DE56uhoSFtIn8CMOXttkivEophH21Ap9+O4FCtOLz1Wm+cU0aguEADh1kNl00Jh0MJS4EGapMABUeGChDMCndHvdTwpXRzKU+jk5psqYgjat2Ekyy3lNS1T8JTQG1ycXe9Qy8N7ZP1lLJIyr0nPqo0i5JLAhEy9jGlKFvyQJU8TImsChQ5zM1s7hpskB7O/AKgsJgr3EJeHsRK5YBgHRTFVgjncgDaR6VChx51cc/j3ZCRUST1O6lUUCaUg4tINFWyQwz8BQJGE5CRAxSbpWY3jzSDZSC2APLqzMjk15RS3vo/0h2jPhiEc0fT8FBtqYnRH7EVovHQ+/ehbvvaGFb/BThI02pxayDovEoVQBHB4aFsZ1USVRpWxJt/vIj15F7x+kxYjH4aDO8FuknzjYRsXcV48MEHcbmgUffvv/8eN4Ss0pD6/v37ERfnazbo0aMH6tate8lklaqqw4cPv25kVYYMGTKuN0ii0Pm+tmx1RelahCoNK3Gz1c2KtgNbYNeq/Th7OM3b9NLzwc5MVAnkf7n+9y0sRWB4qnjFRjiLjdAmJ0EfokWhyQ6XTu1NunLGBMFCSWJu4uHQCjCVdwfak42mSoSlvBNaqx13b9yGWV2bIaFKntRgbnPg0S/X4La/fPZiNp0S015rjT3tkvDiqCWoticTM3s1xUcP9oBDTSaugPF0qM/0HgKCTwuSSb4bKrNkkB+gQaVKoFvyqSRiR169noKg28+UCTnzTxfHmWpM0kHU5wugKjB7h/65870MyZ7SY+llsUEoUeUUrYAmPBgalx35kQY4GyT7Krb5RVCey/Ruu2baSp4+3/we6rWo4m18U6lU0FeMQbHVwVIQQa9FsE4FY4ETinJxkp+q56lQgENWNoL1SpiLrZwUR69ptZrl0GtYe44QTqqRyO+BlTPXw2b2XS9FEFMs8JCxd2HAiE6YP3kpyWbdLxC93+3AOTMb/4dUjoczPRumQkn0G5MUhbue64dPRkzCrhX7vJ3kHhhCdWyPZsy/wVGrMrxYuXJlqdfpn3C5219Vskq+WSUvgOajoqICll2s25/IqgwZMmTcyiDnlFd+fgrPTrFy01NitXjEJl0f6dOVgsh096EdsWrWeuSez2Pf1/Z3tvSur9G0Cibv/iNTKU4AAKtWSURBVBjfvzIDsz6cV2r/L/96BvvWpOD32VtwlniN57PCo0/12Nt4pJF+86FGE77/chqaHzuFkxWjkFslCNFZxRgzbiFqpfh8d3MTDZj+aUvsS5Yay7Z2r4yVA2vgy4adLpDD7m5QKmks/28KdkRWKSfBT/dJ3fO+s/klLXg/L0tGjpa+AFr7w9KXsG7uJvz653bwVwbP/hfwJ/1p7Cz0fqgrXv36AZw8mIbk2uWxdcF2rJ67GQd2p6Jm/fJI6NQOp1PzkLLhiPfz23MlFZrXQP8HO6F9r/o4tvsUKtRMQHSi7/OctqcvXXarDStmrPddvZtEz5+8jN0oph78jBsLSQvtD53Ljn73NGfCm5uez1rryvUq8HHnTVwYcCwCNfJ9u/cTXn9yXypOn05F59ul7W6Vyup/0Wf1lLuB6nrjsjSr/p6pGzZswKxZs/D000+zfoRkAOSvSrpV/4dOQQEDBw4s06OUnAF+/PHHAIeA/wpkzaoMGTJuVXzzzDT8/vkCn7+sIECtVWGB6Refk4vFhtzz+Zjy8s8ch2s1WRFXKQZd7muH+Cpx+OShb6Rh4KRyECmiVaEA2ZdaozRw6BVsdO9UAuZYil+VjO9jjLn4dvYUVMnIwosP3Il5bRuid84evPX+X4jI82kdj7SOxZx3mqDZnFM4UDcB+5qVZx7sdAk4lhuNPIsBAhVsXYAtUwenkSJfJZKgzQLUhXQ2iURSfCkZ50uJWW4rKfL1Je8uUbKoYpN8dysGbe+JRiWoCmxQG0kPQO79AipGBUOXZ0LqwTRp+JqG4rUatoliv2CrVSKvWq1EGElDarZyRCohoVI0ut/VAmq7Fd88Ow0waGGvWR6uWEm3GqlTIfJsNjJ2n+RmOS/c5v7f7h7PzXt7Vh/A813e8HoUKww6oHIFKfnNbINAxSJ/o383p37+0/vQZWAzXmY1W1nOQseg17tP8OALa0vdZVpqgEyqkYA/v16Ck57Kux+I8NKXN3+QXdzsj+bh4GbJwaJWqxq454V+aN1Puo4b+ZnrOW+F16+uZjX1bVmzes0qq/fff3+pZWPHjg2YJwNYf7Jar149HuanxKuSsFgsmDZt2mVdsAwZMmTIuLYg14IAUkJD9E4XJwvNfP8PbJy3FXmZBajerAr0QVqERAYxsQmLCeXq8fxvl7n3d8FFJvsU56lRMVm1hCtgC5UajJwqEc7ydjgjHahyPhNTJ/2IiGIjPv+wC/Jb6/He77/hrs93QunwkaNFD9bBpsYV8dCIzah4Ihfm7hp8Z2gPp0MJm0MFZYECmgKFFBlqV0Areob0JU0q6VPZq9QdWao2OqDNdUBllnSyTg15Z9ENS+uloX6/HFo3KRTcz4g0pMpCMxR2B8pXjcPLL/bG3E//RurqTGk4Xa+HMixESvuiifScRJK1NkkXWqJeFFUuDBExISg44ybnFju0J7KBDCNLCuKrxyG4cXXkR0XCkl0EWO1QGq0QBCU3Rb398lzERgZx+psqqTxcVhvE/Hy4SEt64Ahc9AWCuvepkYsLRQIEg3SNxPDJb3fel4uwatYGHNx0BNHlo9CmfzMmoZ5gjDLhvg16n3iM/O8qNwL5mX5equ73UUkQKaWpIFtKjAuLlgN4blY4nU6uBl/vfS+LrJ48efKyT0BGsQ76R1EGqOo6bty4yz6mDBkyZFxvkHfk2SPnUb56PFTUYX0Lg2yvCOv/2MIEgiJruw/rhIVTluPvb5Z4tzu0+WjAfuSN6Q8xJgyuyvHeYXB7kABrFGWoSsRFDHXBmShV+BqknoHebsNXkzsjo0IoRr+9Gs0W+ypz1iAV5r9UD+EbzXhr6kLkRBnwwZgemBTfBWBJo5QdH3JOGTgsT1VT6Tdv3Kg/3Qo+a2Py6l1GAtRSfKpUdJT3eGKQFi6tCp0bVULvwa3ZSWHt3E3eXRTUVU+k1DPsTvMlnQP8sH/LCezbfBzT1o1hordkzlYY2eyVtHMunEjNg8PsHo3UU3WWGsR8DjznT2Xj/AGfXRiTYX9NLOlIS1hLiYVFiEyKRo/hnVCrYQU8WPNjqSIrisg6k415ExdxwtsrM57C8ulrOBY4KiECyfUqwGqxcVRxWHQI2g1siYFP9+aY46BwA1766Qks/G45V971wVq06dccA57qhcwz2Rz3GxEXWKm8qUmq3GDFqFq1Kpv/UxHSYCjt+FEWKCyKCpPjx4+/Ih5J+Fd/cfPy8rBw4UKkpaUhISGBq6fhJcrkjz322AX3J4Ytk1UZMmTczCjON+Lrp39g30dToRmGUD3a9G+ORz8bflXSqG5GlKsUi8e+eBCPfj6cK2EU0EBITZFIEA0lU9POP0axlrBx8u3gWx9iNKPIoMfvHRtja+uKGHpmG0Y+txahOT7NY3blIMz9vCmqLs1Ax2WH8eddDTDjwRYo0uqAg542IU+xtGQOqudcJa/LXz9b4trLihEtudzv3qhqeXzNbqQ3r8i+oHQ47zOhKio1bLmLs340N+AaPeSQJwBP3vM19EUFMOYXA6Fh3nN7n7PnWkppYYWA45W6N/ftetZ7rnPk6/3R4e7WyM8qnSpF+HHsbMRXjoWp2MLpadlpuUw4LSapIYt0qFsX78L6eVvZTYLeI3Xb1cLwdwZhzMyn+XzLflqDt++ZgNMHzvBl12heFUPG3n1TNxvKCMRTTz2FN954A88//zxzvu7du6NJkyZITk72yj3JlpRI6fbt27Fs2TIOCwgODsYrr7yC6+6zun79evTt25eH+clMPzU1leNU//zzT7Rr1w63OmTNqgwZ/x/YtngXXu31Xqnl7y549ZI+ZI2FJuxff4iTseKTLz2q9VJBxIGG3lUaFTsPXKrPtQf0EXB89ykm5XXa1ICaholLrE89eBZZZ3O58WrNrxs5bCDteDpH1BpCDdjw13acP57uJXAug4aH3p1RoRArxMIRbWBNqDVEAUu0Aq5Qkdf3O7wJzy9ehBHvDoZJp8Ub3/+N5tsCdY7Hm0Zj5YjqONkiDsWFauAYsDexPHSCC1aTGjlZISguYtErFFZAmy9AVST4SBnJAEh7apcmVbFko+UIVnJFVVPggKbQybIBvl/az20zJWlapaFy8lPl41ntvMrFXrEikF0A4Xw2L4+tGI0Ja9/GyhnrsOHPbTAWmNC4a31oosOx6vftyMsq5K545plaNRQqFXoNbQedQYsNS/cj/UyO5IuqVsFFnqT5BRApAtVggGDQQVCr4bJa+fmKESFAsAGC2QalxSFpbpUKNGiejCpVY5Gy8QgO7TrF1ldisRFwp0XWaV0DtVpUYxnH3rUHUb1pMjfQNe/VCIe3HUelOkk4dyydK6JrZvv6VPiZUCWNjlOG6f+F0H1YR7ww9TEembi/0qPcsOePZj0b4r2Fpa3dKPWSroeIcO1W1XEu7RxzjRumWR1zlTWr7/53Nasmkwk//PADTxSu5B018DRKuv8O0M+GDRvyCPuwYcMQFHTlX+6vuLJKjVUUCnDHHXd4l/3++++8fMeOHd5lxLQLCyUdCmH06NGsa5UhQ4aM/wKCwnxDXb5qVeDyskAfzm/d9QkPgRKh9HQ4vzz9SVRtVPmqXBvpR6kxhUgRgTSjD713H257qMsl7b9mziZ88/QPyHETCF2QFrc/2hMPfyg10uxcsQ8TRk5C+knJKkmtVaPLfW3x4dLXef7IofP48K0/cT46FoiO5XhUS7gaol4ivA6NiKLKCjiC3Ab5agfiqmdAF2LFoOnbMOy3TTjVJBLPnVqGppNToTb7xt+NwWqcrRaBajsyUb8oBO++cjsPzxtSlVDulxq0iPVpc0UEmX2xodxIJfiG+j2VVo8UQFvogspIqVES4eLoU6UA0R1UoDDZoCj2me67snIgZuV4NaqeSqaijDoPReCOrP8c7nr+dkzcFPgFRyg24teP//JqPl0FIkJiQvHEe3fzfGSFaEz5ZDHHmpL3k1KjAWJjWHNKkbai0eg7Vg4gnHEnOpHEIDaaiSxh79K9OPxXMYy5hWV2r4/44H7UbVvLO09a4zfv/ASfjPjGqycNTi4PmyhAVaG8dAgdBTeove9/V+o59oANvEFfEdff5D8o1IAfx83Gb5/ND3QIcG8fVMboBMlNpr42kxPqCOQcYBXKjme9XqAvA1ctbvUGW8b+W9DwP42a00SEm4qXhw4dQna2FAFNQU+1atVCmzZtmOhfDVwxWT1+/Dj69+8fsKxfv37MoP1RsnBLiVcyWZUhQ8Z/BbVb1cCXW97H+j+24uCWI6jVvBr7kNZoVvWi+1HUKlUgS0aVkp/k1SCreRn5+OOLBV6iSqAPd2qMuVSyuv73zV6iSiBDdiIKHrJKcaweokogn9jFP6zCPWPuRFxSJP6asw2pp6QPKIJTp/ISVYIjWPASVQKR1CoFGXjq9RVosOcc9jeNR2xxIVp9FmiHc7pWJGLOFqHa7kz80rEFxt/RwxuFqir0NWhQdVZdgsNcSHHggcpUIt5UWSL+1Bq4XswrdBv2exZcnGnQ67F46krcP+YOHjLfue4wigvM6PNYT1RtXBkLpixHfkYBOtzdip0TNs/fwY1rLbvWQ0LSIHz3zp9IO+53U+QecBHHHNLAeogqgSqvxhxfgcgDGuK/45nerLcmDSpdS80WVbF9+X5sW7TL73koYeE2E7/7pHP4H6wkUaUvYrXK467nboex0IgdS/fyF6dWtzfjKN+hVR8vZWUVFR+BkR8N4Wr+sulr2MuVopNDI0PYMs1DVPkRmG2wkPmsjJsOREYHDRp0zc9zxWSVhv+JdPpnwU6ePBl16tS56mawMmTIkHEjQcT0n8hpSQSHG6AL1sFSbAmoyMZWjPnX1/PpyElY/P2KMnlTXIVL93CNreC7Fs81kv2U91hlXatSgYe6fQhliAEOkhyQ6b9HMUkWT26wu1PAaLEIh02F++ZuRfUjmdjdMBH1d5zjfiYPCsrrsK9JAtr+eQLHGsRgxjMt8KPQGi7KM2UbKIpsJQkBVVV9ce3uEXrfid1D+AHM1T1PlVSl3e+kJbanSFjBJWlHGSSLIMJ4CfDoUstVisH86esx+a15cLjN/13FRog0rO/2Yf3pjTmY/tbcAK0v20qRe0CUFL7A8MSsXgAiEVnPvZJsoYztFSoFeo/sinfv/QwZp7PcCwWoKelKSgXwkXCXi6+R3Qs8x6WN6Ll4NqNzOJwB7+teD3flBi3CwKekBj0P6HnkUGKbX/W17YAWOLLjBD4YOtH7DOj+B70yMPDfSBmS4hsCz5vtah1LxvUhq5MmTWLN6qeffur1PqM3LmlWS1oVbNq0yfuGLjlPaN269ZVehgwZMmTclAgKC8KsM5OwddFu7F65D0k1E9Hq9qZIrBr/r4+9Y9meUkQ1OCIIj30+HB3vbXvB/Tz6U6r4UhW1dX/J7H/T39s5JYh0iw061fVuT53dFepXwpcvzsD5E5kQtFqpyqZVS/zF6YLTboMlWscVSodeCVuQCEe4NAxvDQOUehs65x1CYZgetgoKZNQOhm27Ag13n/Oeh5rZd/SqiB+ea418sw7ratbA5pZVUWAMgiZXgMuohEKk4XvSo7p1sdRU7wLM4YCmWITK4tPJKYrNHHvKF6lWe4kX+aVykpbN4q2gCkRG9RoIZClFZLvYwjpRJoFELC8SXhMRF4aXZzyFnLO5XE0lL9KuQ9pzY9HHo6fCmpULhV4HUVAwWeXj+b8eJZrSuGnNaIKDdKp6HUenPv/NCETFhWLKazNxbOdJiS1Sd7/bkaLb4Pbo92gPLJq+Dot+Wif5uPqRz7ue64s7n+uLtOMZAUQVYWFwGfRcNVYkxrOulcg07edMS+d4VEGvR3KdRAx/+XYc2HYcW1YdRHz5SPT4+G4c3XSImw7J2orCAEibeyGMXzEOe9ekYMuCnSw1IZuq6k2r4JHGLwQ8A7r/3av345PVb6LXiK7Y9Nc2JvuUmJZbmINuA29gKICMG4orbrDyJFBt2bLF6wbQokULtqPyR6VKlS5aXaV1J05I8XD/JcgNVjJkyLhReHfQBKz2b37xw+2P9sATX44oc93k539iD1B/NOlWHx8skTSoJTH/16348t2/L9ghb45RI69eiHe9I8wFc1W7txqWrMzEewvnofkvp3CyRRT0hXbEpwQOUWeHByG80ITdtZLw4JvDYNsfBrgE1poqHEBwWmDlVNKkuguiogjDeQcUnhQplwjVur3c7OQpmCrj43iY3DPvOJ9RmoAmlYNSb4BgscGRmeXLtv8H9H/8Nj4oDatfCyTVTMCIL0fh7WdnStyT/o9ItNEiDcu7XxOyhRr39VCM7fsBTEU+CUFoVAi+3v4hV8hJajC6yYsoyDNBUT0ZAoUV+FVkUWSC61waV1UVNaowUeaQBOL8h49dMDnLg6e+GYk+o7pd1v1NGDWZZSf+uOPp3njk02Gltr3RoQAVXxkLVdhVarAqyMfp99/iJND/WoLVjcK/sq4iYtq2bVtv515ZXag3KppLhgwZMm5VjJn5DFsCPdl6TIC2j3DmMIdzlomzR0qvI6eCFTPWoWWfxlwNDtjeT49alpWTw+A35ExD7FoiP9Jspz2H8MFPvyM2qxj5CXokb84J2NeuU8AlCIjON+KvzvXwxdDOEG0KJqp8OPqPTOj9vPildCnfPFVWiah6F9PwtVUiol6fVXcBxXv1Zfh+16keizfnPAdjvhGPN3sJRSXIanBUCB75YgTqNkvGjqV7+FE06lqfrbwmPv4drgYGjx+GClXisGr2RtYJt+rTBLc/3Alrlqb4qujuIfmAKFf60lBsRWhMOMavegN/fL4Ah7cd4yYqIn4eKUdM+SjMPvctFv2yAV9+sizwNRUEDBjVGT0HNMRvny/G0g2n/c7n/EeiSjh72Fcpv1Q8M3kU7nt1IOt2FUoFWvZpwtd5M+JaNFjt2bPnP+kGcCNw1ZytyUNr5MiR+Oyzz67WIWXIkCFDxgWQUKUcej/cFXM++Zsbnwj6YB1a9W16wWfWoncT7FmTArNf9Y2aVz4Y8gXntH+755OA7Ru1rIJVC/dwNa4saPIcEGwuiBqpUKEsFtiEf/iy9Xj9l4UoDNLBqRQQnuY7n0spIL1OCBL2FuJMg3C8Naw3tlaVGs4EuxOCzgHRIn00UeKVUw0o/Quhbv0kgaSsvN7mrhAqFXBFhULh12DkMpt5ONtD8DQRIXAUFHs730Mig9F/RGeEhRt46vVQZ46atVvdpFYQYBKV+OyVOejQtxFenjg0sLp9FeSHNOQ+c8paruzyvELAr9+txV8zNuOdGaMRUy4MWemS/6noLgr5n7Zmo4rISs3EmNve9e5/5lAaFn+/EpPd8av8+qiU6NC/GZYuTsGRAz5yGZ8Uibbd6mBUvedZqqeonMQ2VTwqqlBAExkGe15hqYZpD8JjQpm8XwmITPd7rOcV7Svj/wdXjaxSdCpZF/z999/8kyyrateuzXrUK43XkiFDhgwZFwZVVwe9OhB7Vu1nW6l67WuV8kn1Bw3T9nywE57v8iYOrD8UsC4vo4CHkFfNXM9Z7eTD2bhHA/R6qDXmL9iD7IwiiHDBGg5Yo2lYXYA9QonQ8kUIjjPCYLZBZzAhqJwFLUJOwKZTINQY2AF+uGEsJj3aCamOGDRJOYXZNZvDfl6LoCMOqFQuoEAFFwdaOSCq3A1aRJicgJKiUu0uqPMsHHHq0ghQWpxQFJghWmheAcHugqDUABHhQHExN0e5NCoogvVMcjvcVh8vjb+HyfqulfuZZNVqVT3gM2rEB4MxeOxdeLDlWORkFhLDA8xmODIyseXXtfgpSsdMcdvi3dIOV1pti4qAIkgPkXSjxUY4z6VL/qU6raSxtdlR7LBj1bSVGPNOf2xctAezJiyQPFOJaKtUrLdtfVtD3PFkj4BYU48OlMjldy/P4I77jNPZSNl4mBsF77uvBZKb3IsDu1NRtWY8kirHsG+ux2LNdeIMN1EJQQYkVorG4M+HokaLqhhe/alSt9F1cDu8MO3xgJHVs0fPY9Uv67F5wQ7+MtCmXzN0GtT2vx2iISdY/Xc1q/6gN2pJQ1iaj4uL42guSj24lSBrVmXIkPFfBQ27Th3zC07uS+X5qMRI3PF0H2SmZrH+kn0yRcBWMxGOGoleWaMpFjAmUku+pACNrZCLijXOo92Co7h70jbkl9cjssiEsBOBJNWkV8NgtmNdy2p48PbRXoanzXMhbrukL/U2XKsVXn0pKQJcel98qmBzInh/uq+B3+WC8+yFZQ8eKOKiUatrQzzyQi/UrH9pvo8Lft6AOZNW4Nz2w15Dff/ud//fywLFj1JQA1mYEUIjg6HWaZCTlsvzIYnR0FdJQtahMxAz/eQWZYBej2+2f4jJL0zH7pX7A8/r1kd8seldzHjnN2xbshsuR6m8WO9r6Emsem/RGDTr0dC7nojqhJGTsWbORm6+8z83bf/4xAeRdiyDbaaKcov5eDVbVOeksxpNqwSc696kUdz9z5yAzusS2U7t2SmP4EpxozWrlV66uprVUx++9Z8NBbgRUF1to9iPPvoIHTt2hNVq5agt8lV95plnsGHDBvz6669X83QyZMiQIaMMnNx3GvMnL+MKKXVYt769KXqP6oaqDaXh9gq1Erl7uyjPyNZatdvWwomTOdj0+9bAyhw14fhZi5KtE8NdmGh47BSe/WARKh/OgUWnQsSeQNNTO1U/7aQ7FTFzeDNM79oaYDmkW5fqlkIG+Jr6601L/PRcSMn5f0K/QS3waBlNO/5I2XESS2ZvwZYVB6AzaNC6ez28O/0RvN77PZwhPSYnp/pZc13k3ETwSH9Jdk7RiZEozjexzILIGzU6FWQXIjw2DH9/vQR/pxxH0T9cP70eU16egciq5RGXUYz0/X756u7L+O69v9F9SGd0G9oB79wzofQxPK+h+7Wd9Ow0LK1fCcX5xdi37hCqNKjIMcJfbH4fI+s96yXBnu1nvj+P7a++P/gZhx/EJkUhIq5s8mYz2ySS6smXpWVuicPlgJ77398sZdeBfGM+biiuomb1prDi+hegaNXLAb3vyZv/pqmsvv7663jzzTdLrZs2bRqHBUycOBGPPvoobgXIlVUZMmTcrHim/evcOOUF5bA3q4ovN79fWm9JH8I0JB0cDNiscBUUwWWx8LC5MyECjtoV4ArRcmypJRIoThQgaEQMX7sGLy6dD7taAbU9sJLn1AgoqKJH2BEzlvethemDW6IoTg9LvhoZR2NgKtZBJL9TswsRh53Q5Ti5UYqOQsulMqpU0aN5aaJhfie0ZwuhKrBAID9QEiYUm6R0J7JzKgGSRpA115Bxd/2jZdiwtm8h81x+AAlt26sBuvapj98/W4CdK/aWJhkCUL1JFebu5Bla0oqK4ldnnCw7sXHS8z/htxLODISKdcpDZ9Dh5P5UifR5ThVsgDIykpvInOTXajJLVlgkBzDooYwI4+v7dO4TWD1jLdb9vhlZZ3K4YSmiXDiT5Lz00oTPUyHmCioEPD/lEQ6KWPHzWqQeKt00RTKJe17sd9FnSQ17f09awjHA9BpQLPGgVwbws7ocvN7vAx4FoPuyiCasx8IbV1l9YSzUV6myaqfK6vj/bmW1Z8+el+ShT/eXkpLC25IW+qaorFJVlSK2ygJlwq5evRrffvvtLUNWZciQIeNmAw0B/zZhPvZvCNSjXvBjhfSSCbEQI0LhFAQ4VC6Y4hNhidRwvKgQLADJZjjjpdpfnLMQfUKPoUZQBpqvP8XD9v5ElX7b0jQZr/S9i4fzVQUO5BdFwvCTCJVShMOuhEGjQBB9cCmkEAGl2Sl5ntIi0QWkZQJZeUxGiaQoKUpUo5HM/8kHVa0ESOvJFUxAERbCHevOA0dL3R4TTw6ouvIuqBa9GvP0eMtXcHjrsYB1NVtUw8SNUqzq2P4flkosowrksx3Hot+jPTlBinA65Qx+fuc3rP9tc+CJ3Nf59l8v4+e35+LEPndHfkkoFFBGRkAkXS65H5ToCdEF6TB6wjA88ukDyM8qZF0uHZfS155sNcZnq+AfjxoaAkWQgZ0TJo1fhArV4jnqVVnkkp6hxQqxsBDiJbgC8PHc9yJVVsWyjCRk/IexePHifyT47733HlatWgWNRoPhw4f/63NeNbJKzVQUCPDEE0+UuZ48WGUZgAwZMmRcO0wbOwtHth8PqABSetFtD3ZGn1HdvcuGvX0vohIiMX/FIVj1Bu+wvqm8HpZolVTVhBKuZBNQyQGl04UBy3bhmV9WwB6vQMhpK9SmwGqqQyVA5RCxOaoaUkNieVlQjgtRx4ng0PGkJn4vcXGRBlWEpthHgIRiM+DJvCeo1VBQgxMRZ88ymwNxFaNRo3FlmE1W7Nl6EkmVE5DQtgqKUzMCokMdNgdW/LwOsUnRePDd+0o9LyJTp/anIjgiGC9+PgSLZ23GluUHoA/SoFX3eugzxBew8PSkkVj47XKs/W0z30O7gS1ZWnE+NRsOm5MjauMrx2H+5KWwWXz2BfvWHcTpA2fR/q5WTODmfDofq2dtKHUtHe5qhW5DOrDv6NJpq0utb9S6GiIqxePg3rPITMuXCLgfUa3dpDK6390CFWuUk56lICAiNsy7nkz4ySpq9a8bsX/9QY78jasYi6z0fBw+6WvOspjsOLJH0jITEebnHqxCWFQwet/fCreN6Ix/wpdPTkVxXrGke7bYse63LdDoNHh5+pO4HDz84RCUqxSLDfO2Id+YBwS6n11fyA1W/4hz584xSZ06dSr/26JC5ZgxY7gyfdOQ1VGjRuHhhx/m4X6yrwoJcRtFu0GVVbK3kiFDhgwZ1wZanUYa1vdPz3S4sHPFPm6GIYJCOHs4DTuX74XV6AIMQZL5u0C+ph49qmdnAW12HMNzU5eh6plstqFS5otlktTUepH4YVBbzFC19+5O1lL88wKpmX42n56erTLXl2z2GfZEN3S8vXGp+7dZ7bg9ZDCc7gYjz/ZavbbUtrM++IObyWjIm1ClYSUe4n52fNk55yf2nMaO5Xu9vrYbl+7D5q2pyMsu5vlQgxJCUXEAUfXcfGFOEe5KGg1dfAwyj5wt8/jbl+7B/g2HvQ1Ygc9BwMgP7ucGK9KrKiIjfIb+7gf73MeDkOAXlVsSVpMNu1buY0cAsuUi2UKO0YViyz9US93nyUvPw64V+9B2QHOERgZ+vpeERqd27+ZrsCKyermoUDMRj33+IE/ScPzsyz6GjP82Sb3qZJVIKllWffLJJ/jtt9/QvXt31K1bF1qtFitXrsTy5csxYkTZqSoyZMiQIUOCscDItkrUvFKrZbUyw1b8YTVbsXvVAag1Krww7TGsm7uZh5n9vVTPH8/AjHfmotO9raFUqzBj6koczS6EMqcYMJlhq5UAqBVwqsnD1IXgCBNzoEcXL8R9izfDoZRYo5KG4d0oSNQhJzoICpOI34Y0w/GWMcg5G4GoMxYU2bVwaaXuflO0Atp8F6dRiUoBLhr+Jw7jAlq2rYY6FWKxZ90R7NlxmncQEsoBhUUceVqjeVW07NsM59OLsHvDEVSpUx5dBzZF656Bnp75WQXYs+oAEqvFY+Lm91mPu33JbsQnx/L+dN/+MBaa8PM7c5nAeXB89yks+2k1mnZvUOoZU3PQL+/9hrRj6d5luTlGCEV2CDqJCOceT7toNGuRxQljZhEUYaFwqVQQCwoD4lcp7pamkiAPUmqaIiJLZJEgUqXZoIOg1qDTnc1x232t4XKKWP3XTtRpWhkxCRFsRXVo6zF+D1HFl8iwf+qZEwrkFpRO6qLKbLveDZF9Lg+Lpq+HaLdDNJtZI0tV4o1/bvf6tl4IH698A6tmbpCsqyKC0Lpfc45YJf0pxa3WbVsTKndc7KXCYS8d5vBfDwX4r+PcdSCp18QNYPz48RgwYABXVpctW4a5c+fycvp2dccddzCRlSFDhgwZZeODoV/wh7zHsD40OoSHbtsOaFHm9tNen4U5n/zlrebpQ3Q83N2oc112AvAvZ1IqUu9GL8DesBLs1OXfpiZM0S4UJQMurUSIa8Sew52xW6ANciL6QDFa/ih18Kr8SKqDbJS6d8bE27oiONuO4H1KKDYogQ1SBS3EKSJUdJNASltyuKSKrRtOjQKWaA0EjQL39myAb0Z8w1VLBg07V06CECU1shzNdyH1hw2wmSRSlZNegDPHMlChWhwqVo9nu6Uxvd/jyrGnuYm678fMeoaH5cvCJyO+xrKf1no9Rf0RFh1aahnpR6kKS+EJ/iCyeVmcg15TYukWSymiWhKeijARu5Hjh3BVkl4/L6wWDjtQqhToM7g1Pnv5V5w9kSVdl8MBZVEBbH5fVjR6NWzmEkTa6ZQaq0qUr08fTofVtA0vTBiEpROl9xZbmbkRFlP6GZVE+eoJ3NRGE+HbF37C0KqPeYMWgsIMGPnREHZLuBSQNOLz5yfhVkODBg3+k3Gr564jSb0mZJVAIQA00Q2kpqaiqKiIbyAszKedkSFDhgwZpeFPVAmF2UXYvmRPmWT1/IkMzP92WcCws7nIgr8mL8M7f72Mlr0bc8e5qdAvrSo6FE4PaeJ5BVwaiXJFFRXhhXWL0eHPQ7BEqhGcXoKcUTVUBM6Hh2N9zWqwqVVwFimgMPsom6Jk9YnIaglOprS50KV2BTz0cGdkHTjjI6oErQaCXue3v8tLVL33fTobh3adZrJKFlA7lu0NWJ99Lhd7Vh9A3TY1L/iMSxLVpBoJGP3ZcCb5/kg/lYn5k5aWIqqXCyKVw17ug6T6yfhx3CyczCotvhz8+h2o2jgZ+9cd4vOStRhVIz3D56R5/bJyLH8JOXXgDBp0qIOmtzXEstlbvESVINrsAUSVUIqoAqjXtiYGPn87Du9Pw9xJKwPv+0wOThw8jx+PTuRKKjkhkJtCq9ubok7rGpd9/ytnbfAlgvHogQmb5m+/ZLJK2/q/j28V/FfjVqtUqQK73Y6GDRvilVdeQYUKFXD+/HmeLoTmzZvfXGTVA/q2VrHixYcKZMiQIUOGD4nVynFMpj/KVy9tufTefZ9hVRlNOoSzp3MxrOFLEIuKS/9dpgYmIcI9J0JlEhBhNOHhlasxfPU66BwSofAnqh4pqTFGg3lDGuGNSgPgoMYeEbCTW4BXoEq610B9qmSH5M6y92gsnU5s/vAPHPp2CV6d+QxbG3niYkFDvVRx9EgfymgjVygElEuS8uMNoQY23y/IDnQqLU/d7BdAYvX4QIIMoNvQjgEG+YTxD35VZqPTlYA0tN+/8gua3dYIPe9vi2+2B7oKEH5++zdUbVwZX2/7sEz3AlpG9mM0EQ5vO4bHmr4Ei12EMi5G2ocqpapLS4zctzYFx3edxJt/v4yFMzbCVOQLcqDXLb5CNKITo3D7oz14+jegLwMUEhCwrHrCpe9/GdteM8gNVl7Y3CEZu3btwj333IOLwVO9v2msq2TIkCFDxr/DlL2fsi6Qojwjy4VzZS0+Oc673m6zY++aFNYC+kMICpJIil4PV7gBDiWZlkZASM9lHaqtfBhEtRLWimEIq1KE8AgjBIWIyCQ7xo+bjVoH/Trw/bClUyW4RAWOVYzHpo7VcCI7HrozSo4/hRLQ5QhwaAC1kYb6RclayilC1JCnlMBD/i6FE+q0PLanQqERKChiQpqfWQitTo056VOwddFuHNx8hK2g6nWojYP705BCUaC1EtCgaSXuTt+35Tiq1E5Es861ER4lNevqDFp8l/IZfv9qKbYs3MVkf+DjPVG3ZbULPmMigwc2HsbWhTsRFhOG1v2alunBSo1IJVGucixe//VZnNx7Got/WMVVcNKT0nW/N+gznDl88TStg5uO4N35r6B87SRMfHIaV1BBdl0Uswrg2M6TfEzlPxBOShr744uF3qQp57nzXJEm66kH37wLnQY0xf61KaxZrd2qOldDF09bhZ/GBQbzULxuXloeJi95Ab99tQw71x1mzeodo7ux28LVwodLX+dr2cKaVR0/8wq1Lr2i+PBHQ1D/tppo1WXhVbsmGVeOH374AdcbVy0U4P8NciiADBkyrjeeajsGKRuPlFqujAyHEBwMS4UIOMP1XiuA4kQlrBEKb6t9qxYpaBx/DI2mp+Jco3C0nH4C5deWNoqnmNORTw/Ghph6CD4q1TToCEqrCLXfsD95pGoKfEPM1P0vaiSixWfMK4Sw51iZPq+xFaIxcfN7iCznqfRePiwmKwa3eAPGIkuAA8ILE+5H5wFN8W/wxWNTWALgOSY1ug0eeyfOHT3Ppvf+oC8VRAi/f/WXABmHR3vqQbs7WqB218b4/n13GIC72kw6U2daOuq2q4lPV791UV9Y8tJ99bZ3y1xHZJqeaXhMadkduR482epVZKb6ol1JEz12znOs++VmM79COfm9UgrXzYIbHbea/MzVDQU4MeG/GwpwIyBXVmXIkCHjP4KAJhs3QmLCMOSjISgO1+CnnzbASSb9brLj1PgIUYjFjDt/34zOvx2ExnzhIbkCgw7f9O2IjbWrQinZbTI4VKpEQhNXUgMW+EgW/0bNOWWc4+XpT6Dzfe3+lVk/wVRsZaJK8C+7ZJyT7Kj+DZ786mEMGXc3+7aq1Eoewg+JCMbT7V4r83V5ZcZTCIkKweKpK5j4dR7UFu3vaokj209w3CrpYU/sPY0fxvtVB933L6hUuO/VgRxn+k/PJMuPbPqjw9BOaDWwJX77eTOOpqShVoMktOlSm6vThKj4CLw570X89fUS7F2bghpNktG0TzOsX7LP54rg9wypcY+iWHPS8riBjarPNVtURX5mAbsSxFWIQat+zdg94Z8qwR6Qhdf6P7Zi68IdUOs0aNW3Ketg9UF+OuWbGXJp74ZBrqxeIeTKqgwZMq43qPv/988XwFxs8UszCkdeh0qwR+qgMbqgLnJ5m5zM0QLs0S6M2LQSw7euRpCttFWRB7YwJVYPrY5XGgxErjaIl2myBASnqKFwukmV3QW1kTSo7nmbE5p8O0el8uUoFdCEamFyNyQpLFboT5yFObPAW2kk0jZ27vMICjX86+dBzWLvPToNW1akeJumyifH4vlP70ONhtemZ+LPrxbjxzdmo4hsv6izPdyA+8fciRN7T2H59LXeznmqqFauVwHf7pFccKa8OB2/fvwXD9crIsKZoPJ2pPU1muDKzWM3h3l5P17UrowiUCku1193S/ZZQlwshFDpdfOvMn81+1FUqRnPTVkP13vWvV6ASO8dsgkTAVd2DkSK2L0ASlaI+R45nErEQ+/fj3tf6n9Jz+7RZi/h6I4TAc+o471tMOaXp/8bldXQq1RZLZQrq5cLubIqQ4YMGTcpju+RCNCWBTt4yLZWm9ro8UQfLJi2lvuQHBF6GBODYY/QcClTUc6GqPbZ0IeZIcAFZ7QDnQ4cwoM7NkFjK22VtK1ZRfzZvjkij5uwq1oyDuUnAusFxDpcEEmTmilCaTRDIJsjhQClTYo7daoUTIgVdh8xJkybPRrxCRE4fz4fp05loV69JAQH61hjeXzPaR4qD426uKH85UCpVOD2QS2gd1iwbWUKEpNjUbNVMhbP3owPn5yOuKRITqLqekczGIKvTvWOfE/7PNINh7Yc4yF/kjMs/3mtN2qVqXxwEJR6Hc7lO/D9+3+hy8BmKM43SuvNFjjN6YBaxQlRotXqrdiRmwOTQkWgxyu9B7wuAB3rsFMAbXZq/xmAiGqQQYoHc8O/ymx0f7GhDnzfejqHwmvaT5G2LpsNrvTSlXvenpLEDAYIOh1LFth31Sgdz5hvxJEdxzkpbMvCnYiMD0fr25uh+7CO3vAAiphd9tMa1vry8fyIL+3/n4DcYHXrktXs7GzMmzcPBw4cQHFxMSdY1alTB/3790d0dPS1PLUMGTJk/OdAHbNUVfMMBb/c421uRCIIRwUcOlEAgczU1RqubRY2ioEjRO0OlgdqdjmO2NA8tJ15FG3mHocjHojZXdponnA2JgyPtX0YmhNayXPqmACdzQWlwxclpSq0QWX006TS/5kdCE+OgkGjRtb5AjgcLkRFB6NLj3ooFy9VnuLjw3nyILZCDE9XG0V5xXi+8xveHPr8M5lIOZbrfX7pZ3Oxe8NRFOYZMfjpnmU+43+Cy+2H6ql20jzt67FwGtv/QyaqnsMpIsKgCKIKpwgKiZ07eRXWzt+Nlz6+B+eOpbOtFsPugOhndE+kt+eDnUsNqS/5YRW+eOw7b3Vz6Y+SQwFXJ4n0mcxwmcwIighBRHwY8vLNsJhtCArRoU1nnwwguUFFPv663zdz+IBWo0BMXDAnWBUXmKELMSAiqiKK0nN5uF6lUSEmKQrGQjOMuhDpHUFvM27k00FUKdGwWSV0GtQWT7d9DWajhd8gFEawb+1BZJzKwmNfPOh+Rh8FBCp4ULN5VfR7/LbLecll/J/impHVJUuWsKVBkyZNUL9+fSQmJqKwsBAzZ85k89vZs2dzypUMGTJk/L9j3W+bsWTaKo5ADY8NR5v+zXDXc33hsDu9RIyrUYKfBRQ3NHnySoEgmwW95uxG71/3QG90k6Aymvyzw4Lw1d0dMbdzI2i3KiVrKfKc8rec8mxcov/WPfqLSV8Nxe8T5mPtzkPIyihAxZbVUClE9a81qJeLkn6p9IhUftfgqeD99tkCHF65my2y9q47iLCoELTp3xx3PNuH053KArky/D1pCacuUTpYrZbVefn+DYf4J5FVul9Khgp4VN64WvczFQTY7Q7UblWDk50mjJqEhVNWeM9D64l4/nzy6zKfH70H/O+l5L15EGcQ0bNXbdw2sgvSz+YjqVJ0APElXehz343G05NGsmtBueRYLP5+JVbP3oCUwxmIrhqHdgNa8DPJTctDXKVYdlsgsnpn/VdLReLe81J/VK0Wi6ljfvHJUvyuyz9xymFzeN/H0k2DrcLeWzgG/xXICVa3KFl98skn8fPPP6NPnz6l1i1YsACPP/44jhwp3dUqQ4YMGbcy6AM760w2wmPD2PCd5t+5d4KXkNI6yqwXXS48O2U0p/cQIRKVCjhFBxRaLQSHEy6VAGWRCbbwYNTKTsPU376CwV7avJ54aH5jPfRpdiwaVBfj6/dEjk7PQ81CjAvqTAWU7pFYIq4KIhTU0a5QwGmzQjBboaBKrlKJ+s2S0a1fY/YfnfOJu6Od/BZX7mOi3bJvE25Cul6grvcnvhyBlb+sw4FNhxEVH44KDRJggxKHdqeyRRbFhBYXFmFzarqXjVNwAGlPXS4RT35Vdgz4549OQWrKWX5dyI5+y4KdAeu3LtxVeicBqN+oApQRYTi6/yxsFgcata+BXve18m5y9wv92K1q41/bUJRbzMP6PYd34uF+8ouNTQocdew0qA0HQGyYt5XfG9WaJEMfomcJQEGWVHUnnNh9Gl8//QPqtK2B6o2rXPCZEYGtVCeJJQVfPTnVW7E9c/AcZrw/D7EVY9DroS7e7YNC9Xjy/bux4vftSNl+EhExIWjdox76DG2HwRVG8jP0h0an5ua5gU/39i578uuH2Vlhx7I97KtLjVV3PFOaG9zUkGUAtyZZpTiubt26lbmuc+fOvF6GDBky/l9ApOfnt+Zi2c9rcP54BrR6DXeYP/TefRJh4OF3H/7+Zim2nc9Bcd045Mc0l6qnlYsRVKcAymg7QgrM6JW2G80LT6PBkjMwlMilp6OdSIrB+LYDcchYEcpEFywnVFAeFpFgpqFsQCw0QTh1HkJuITdNCSFB3PxDXqk8zGyzw1VUzPpEpU6ND/e+w8eelXKqzMYbMuy/nuChf4sL+YIWysQEmA0ahCXG4IHnbsOI2k+xppSuTwon8HVze66bNK8XAjWLXYqzo38zU3LdChgyZgBHtBYfljxTM0MF5LeTjPwJ1FX/7JRH8PTkkVzpJSeBqWNm4uOHvuF5CoHoMawT7n15gJeQP/rZcIyeMIytujyd8ytnrsf7938eYDdFeKXHu+h0bxs8/NFgaPXai96f9/fwMAgGPTd9TfpwMfbtTcNDr/RFdDlJynHboFY8kbyAvXE//gsvdh4Hl78bhPs6+ozqztfqjxa9GvNks9r5mV+qe4AMGdfcDaBv376Ii4vDW2+9hYQEX/pEWloaxo0bx7Fc8+fPx38VshuADBkyLgeFuUW4I1rS8Plj6Li7Ub9jbW5AIX2iP/Lva8aNOJ6h17h7TiPKVIg7Zu7EbfP2Q+lyQVHChcozUnsiKQpf1+mFrWqKEBW8vqhkN+WhlELKSSAzzzuvIL9WChjwH44WRfToWQtdBrdHvXa1eJGx0MRD2Zv+2oazR9LQtEdDJkjNeja66DPIzyrAib2pqNGsylVxA8jLKsJ9zcaWWv7AC71Qs3Y8+6FuXbQLcRWjkdygEqwmKwcuRMSFoeM9bTjuMyK27Cjwk/tT+fWgCiwNY/tDo9eg6/3t+bFSRZmGvNvf2Yo1odPf/BXrftsSsD1V0eekf1fmeb57+WfM/ujPUsvnG38uk2ySX2rqwbOo0rAS1vy6iSuWZIlVEp9veIelB6TrpQ58SsfyNDwR6KOf5CeLp6/Frj2l9aSPvX0n+gxpA6vFhkM7TyOhUjRiEiJgKjajX+jQUttHxIVjwJO9cNuIzmX6vP5b3Gg3gKpPXF03gGMTZZ/Vm6KyOm3aNIwePRqVK1dGSEgIQkNDWbNKjVb9+vXDjz/+eK1OLUOGDBk3HWj4kzLiKXrT04VN1T2yK9q46jA2bEr1lum4gkCb2J0QNfRnWkQ5Yx4e+WwVeizbD6Uf4fQf7idd3eHkOEwa0h4bmlWBZkkwlKfcJS83i/VGnxKUCt9x6BeqQro1lB5rotCoYDw9eVTAuYhokqaWpksBuQG8P+QLHNhwWKpoqpRo2qMBe5P+G9Kq0aq4mktD0RK/lnSR+iAtGnaqy5MHk577EatmrueMeRo+X25fyw0+Tbo1KPPYletWwCOfPMCxpvvXHwqwW2rYqQ7qtKmBH16byZICwsoZ6xBXMYaH6PlK/LY3hErLykLA9u5nTu8V/8qnx7Jq/LAvOQmKoNaq0LJPUwx54268OXC8pAn1vM6cdubASz3exu6V+7nCS8+pQae6eOXnJ5lY0vZEsGu1romhrd+SdnK/P+jtoQvS4ONnZ2Ddgt2wWR18XcGiDaasQA9bjxa1z6huGPSKVA2+JSHLAG5NshoVFYVff/0VRqMRR48e9boBVKtWDUHcKSlDhgwZ/z+g4dvJez7B2l83YfuyPRwNShY/yW1r4L7+H0OpA1TxcRCdJtiCnXCGaOBwGaGMBGqqz+PJvxeg6YETpUiqQwscuT0eadXDsVOshNmJzaFUOqFIBRzBIkSdC+ocOxv6KwotUNhcELUqiERKw8Ig2EXAaIZSKWDoS31giAlHyo7TyMsuQrNOtdC6e302hc8+m4PG3epzJZLI2/mTmWjctV4pjWVZoO33r5MakzyNUaQBPX3gDFf/rhSkp/x68QtYt2APdq0/jMTKsWjZrS5adKldalvSAfs3ZJ09nIZ1czdfkKx6MO6357nqvfzndTyETfGqXQa3w9t3feIlqoT8rEKucn659QPUaVUdi39YDVOhibvlewzrGHBMT2xu7vl8dB3cHkk1EljDSh30Tbs3RIe7W0GtIZcHH8iI30NU+RhWB1dGH3jrHnyw5DUs/G4Fjmw/zhrYXiO6cGPUzmV7vdsToadjHN52nJOpiPzS60IV2onzn8X6hXuxZ9NRVKoZz3ZfFG37ybO/+C5AFFFwNqvU80mqmYih4+7igAAZMv6zPqtETBs2bHitTyNDhgwZNz0q1iqPIePu4omqUY9+8Tu2jN0I1I/i9epmeVAkBnF5q/rRdLzx6zcILTAjbIsVqkJXmcP979QbhOWOxkAKoM61IDrf6vU+FS1FEKw2L8F1FRXBlVcgpVF5DkSeoDFRqFq3PAa5zd37udUK6//YgtGNnoO5iFqMJHiqwx7Ua18L41eMg1J5YR1iZLwvUtWjF1WoFAiLCcW/RcXq8TwNfkayprrwNYQj60xOgM42KjHyH4//26fzuZnMQ3RP7DvN1dmohEi/aqRUXYwuH4UVP6/lKq7NImmIfxw3G+knM/Dcd4/y/Jo5m/DJQ18HdNATeaR404shKqH0M6QKbGF2ET5+8GsvcSYNbMrGw3j8qxHe7bw/BQFqnRoP1X2Gm8e8x46PwPuLx2DYi76mKNLH6gwaWEw2qXpKpWu3XZb/M+z7SHd0uLs1bnlcg8pqgwYN+N8NOSTRJOMmCwUgnzpyChg6tLTuRYYMGTJuNVDS0t69Z7Br5ykklo9As+bJOJyTgy2H/PJMiYTEW9Fw/1k8/OM6NN53JtBGyv8zUwBWtquJaRE9cUqR6F1Hnqj+Jv3kGuC/v2gunWClFVx49dsH0ahd6QonVf/8iSrfix9RJZCnpqXYwvdIHfKkp2zYuR7qt68FFeltAR6On3bkC2z8czuO7TqBum1qcsxmdKJE0q8Hpuz7FNuX7OH4VNKsUiWQZAD/BDK696/IOu1ObF+6mwl692GdWLdLy1r0acLpXO8P/sJLVAlE6jb9vcM7Tz6r/kQVZTgNlIW2A1rgu/2fsqcrdfLX71AHrfo2wa6V+wMqvASyptLqNJh+/Cve/uCWI6jRrCpa92vGEgh/ourRwR7bdQqV6/lSv8i26scNY7F1ZQp2bziChEoxqNesMjKOn2edLkke6DWs1jj5H69dRtnYs2fPddXf/pdxQ8iq3W7H8OHDZbIqQ4aMWx5Wqx1DBk9Cjjuek7hkVmMtnFoBGgW8UaWEiS/MRrMDp70FHH+iaYtVwGpVY2WrGvhxUCucLxcOYWkIFCd827i0Sij9k6pI9+j068CioeUSsZqNOtbhYd+yQHGhAf6YZSA+ORZ71qTgzTs+Zm0kYeb7f3BT0Y9HJ8Lg1mRSF/ylalyvBUgb2+GuVjxdDqo2qoyT+wK/VFRpUImJeOMu9XjyR3L9ilj/+5ZSx/Bf7+8gQKhUL+mSrqVi7SSe/JFYLZ7tovwJclCYgQ39SaLR/4nbePKAzP6DI4JQnOdLjiL9a2L1+FLnC40I4vQvmjyo16oaSxf+3yD7rN6iZJVcAC5GVmXIkCHjWoII1tGdJ7iyRD6VjbrU4+HWsOh/P/R8qcjNM2LZigPIokhJBSBqRZgru+CoaQGsAjTBVgw6ugWGBCs6L01BtQMZpUiqUyng70GNsL5RLexIq4Qipx5Bu4zQWQDlPiOE7Dw4SN5IzgBFdhqnB9SS3lG02yFarExYdXoNRoy/H4061MT2pXthMVr5eVSsfeHKzm0PdUHLvk2xZf4Ort5RU1S5yrHYtmg3DzeThrVmi6r44/OFXqLqQX5mAQqyC71k1YOzR89j05/bOKKT9KpU7aMq3ZWCHAY2/72DvV6JENPxSId5tQIKXpz2ODcObZ6/kzWr5CWbUKXcBbcfMvYu9Bjeia+JNKvNezVm0u8BNSJR6ANVW3PP56FZz4bsner/vj226yTHq9L7lqrU9IWAKtiph86iYce6XNGkJilCjaZVMDdrKnYs3YNjO0+iTtuarFvVaAM1rx5EJ0Ti1/NTsGd1CvavO4gqjSqjaff60AdfuAlMhtxgdctaV6nVagwcOJBdAEqC4u7IDYB+/lchW1fJkHFzg8z0J4yazL97NHa6YB3mZnx3Uf/Jq4WiYgvuuO8r2Ny2R6JSRNZQK0QNYDBZce/SbXj6r5VQ5weSPH8UaAyYUbkj/qjQCjaFGtosExQOl1ce4DyRCrgz2j0QDAao42h4XYA9I4tN8aUV0gcukfaPlpW2e/o3OLDxMN4Y+JE3GpZQo3lVfLLqjYBnvX/9QTzTfmygdlWpwLd7Pi5VMbwUmIvNuDv+YSbe/jrK575/lI32/4tY9P0KfPrwpDJ9bD3zVEmdeXZygBXVrY4bbV1VbfTVta46+o1sXXVTVFbr1avHw/y9e/sE2x5YLBa2tpIhQ4aMa4WcNJ/FjucDn7SVZMBOpus0HHu1cGjrUaydswl716bwEHFi51o45rR5iSpfgwoItpsxauY63LdoK/QlvDv9kRYXhilD2mLNwZZwcdlU4prU0e/5nVHGKFXl6nH4fP3b/PsTrcfglMeD0815ctIC9Y1XAxQ9OjttCneas2a1U90yq6W56fmlXhOqyFIn/ZW8GqT9JKLqfzzPPZ45fA6rZ2/k9K/YCtHsvNBmQPMLVhxv5vdtyXka8qdh/ENbjmHjvK04tvsk6rWrjfZ3tUKtFtWu+NxU0V3z60bsWrWfK+5t+jVHiz6NL9o893+Fa1Lak3FDyepDDz0Eh8NxwaorBQPIkCFDxrUCfchu+ns7SwH88ftnC/DHFwvxe/YPCA7/9zZ6NKz9RMtXvfMHD55BVpCZRYlBoQpoilxQOx0IKTTi7u9X4d7VW8v0SM3tY8D0rs1xNi8KG1pWYc9VleiAcj/9mZb2IN6qMjk5+pQQlpwIR0YWjG79YWzFaNz9XF9ujiHc/WxfTBs7C5mp2TwfWS4cfR/pgWsBhULBROliZKl2q+po2Lku9q4+wFZKSrUSTbs3uOIvDqSLJSuptXM3s+E/oXqTZB5af6TRC0zqSA5wcPMRrJ61gYfoh75xN25mkDSDmraO7JDet9S9bwjWoTCniHWuVFUl0k3r3713gldTTF8U5n76N6Ye/AxJNXxNd5eK3PQ8jG7yojQjgG2wKFaX0rZIDiJDxi1JVh977LELrqNvaTJZlSFDxrVE9SZV8PX2D/H9KzMw68N5pSpUVrPtssgqyZZW/LyOvS3J3J6GuWPrVsb5LCMUtapBNFtgrWmDpZkKqsY5EAsV6BWxB8P+WIfo1GJoih1Qkqep/3W4fy6r2wATw3ojZ7keykIzgtZaoCgCFAUiRI0djrTzkgWVyQoWDeh10OnVuG1kF05hoiob2RhRNcxfq0lEruuQ9tw9Th3rV1PLeSWg7v/xy8dxqtKJPadZq1lS03q5BJk0pU9PGskepAlVy7EmkxKlPA1HrHRzP+iSXfiXAkqyWvz9SvZBJdsuqtDSMy9f3ZfMeDVBzVhfbfsQ2edycP5EJr/PqBpMZJK6/MnBgKQV5P1KKKnk+6d7pO1XzdqAtXM3sdsDdfO36d+cgw58G/mOeyXP7FaE3GD1f+gGIEOGDBnXwyKPCGnvUd2QfjqLm4Tog5cITZf72nGV8XJwcNMRjB/+lVc3uHPdIQhpFu7sFlRKuCrqUfyEHgq7E122pODx6WtQ8cyFh9xtKgUWxzbFrIrtkaGLBM4BoSczmSj46KSNrS0rN6oCe3oOUlPOSIvNFpZTzfpgHjfhvDbr2Queh8gppTHdTAiJCOYmoKsFjU6D+u19QQDUqT9y/FAs+WElTqechVavQbPbGnHj0+WCPEypyugBea4e233qqut+yyL2/tZekeUiePKg2W0NWc6y4c+t/GUlOjGS/U4r/cNrTc4G79//ufd9vHvlPraieuWXp7iRjIgsNc9R6larvk3RdmCLUscwGy38TOnLwv8N5ASrW4OspqYGWnuU9QdTp9MhOjr6hn6zlyFDxq0Nivac8c5vXAkryC7ioeeugzvgxWmPIS+j4JISl8oC+Yj6wyWKoMF4T2FLVAJquwPvfTAPnTcdKfMYJ5KicKRyLI5XisWcno2gmBITKAkgoupuhPKg58AmaNysIuZPXuYjq+5taeeSXfgyJHjiYLPTchESEXTFTXX0fAPsuzgm98Y/8/CYMDw/9VE84xyF7LO5rMu92GcrpWbRlxtvRdatf/UWZl0iHnz3Pgx/ZxCyzmRzkIPHJ9e/aXHpT6uRsukIwqJC2Hlh0KsDUa5S7DW8UxkyriJZrVTp0oaXiLB26NABY8eORcuWLeXXQIYMGVcVFDtJkwcpG4/wsH2X+9teElG1WmxIO5mNCtXiOMOemqRSz+aierOqGPXxUCz6ZS1O7zoFR6gKNq0N5TRW3HlkMyIKi9F0yAmEFwaa6Huw98kEfN6mB/aaE+GKcEKRYYcuJhuaYxogJATBeg0aDmwKZ64R+zYdQ3h0MFr3qIf+wzvg8WYvspm7P0jL2OOBjgEemjcKpiIzMk5nsQzhZqu2kSygJIh4njt6nv1GifRdDE98NYJjVCUZgJJlALc/evV1v8X5Rm4Mq1ArUMrxTyBZHTWzWc1WnDuajgq1Eplk2qx2nDl0jucpuvXozpP46Y1fS+0fVykGPYd3ZjssAp07tkLp5jg6PrlreIg7NcXRvzNKISOSe6tDlgHcImT1+++/x8SJE3H69Gncd999qF69Or+hjxw5gpkzZ6Jy5coYNmwYjh49yulVHTt2xPLly9G2bdurdQkyZMiQ4c1U9wxz0n9kj0TTxZCbWYivx/2GbStTYLM6oAvTw1AzHtkWG6xWB/ShKoTf6ULuRyo4nBVQ7XgGXpz1A6quy4JAhbYSsekee6l9URUwLbkbth+sAmFRGkLPHodg9UuachvE3/nG3dwA5JEwECnwkBYybefKHkkOiCy4RDa3f+qbkTf0FT97JA1fPz0NO1fsZU1seEwoOg1qi0c+feCmI60ezPn4L/z59WJknMri9whV3h96737Ua1erzO1rt6zOk6eyerVHBo/vOcXxrKQfpSouRZ+SXIEqnJeCvIx8THz8O07BIp0uaYBDo0PY55acEvTBOq6A9npYapIqGcE6+tNhvP6fICikf0OeqrI38tX9702GjP8EWc3OzobJZGIyGhkZ+E32jTfeQJs2bWC1WvH555/jtddeQ+PGjXk5EVYZMmTIuFoY+Exv/rBmU/Xj6WjWsxE6DWrzj8PAuzcexYZFe73zRqcLeQU+D1NzuAnmqAIIDhe+eeIX1DoiGfiXRIFBB63dgSWN6+A3VRekBktDpILNAfWx9FLbx5SPwqCXB6DtHS2wddEuzoCXUo58pOj9RWOwfPpabJq/g4e0W/drjm5Db3yKECU1bVu8yztP1TZyWrj7hduva5TqxXxY9649yM/Yk8b147jZrGUmENmiqvviH1ZekKx6cK3kayt/WY/dK/cHRJ/+8t7vPLzucXW4GMiea91vWwKq3DR5QDrtFTPWocvg9nj7r5ex9rdN2LfmIGo0r4I2/Vuwa8algJq8Jqx7G6tmrufI2nLJcWjTrxk38P1fQNas3hpk9auvvsITTzxRiqgSSKc6YsQIfPnll3juuecQExPD1lYTJkzA9cKhQ4c4iGDJkiU4fvw4d/Y2bNgQzz//PPr373/drkOGDBnXFvogHfo+0p2ny0FImCFwgVsLSpVZg9OK5odSEDI/D3f+vgOVUks3Tjl0At4Z1Btz2zeBUxDgcKmR8KvCV0FVChCpWlqiezvrTA6+fXE6vnrqB28GPUVlPvPtI2jWoyHPk2H+Q+/fz9PNhJDIYP7JPM5d8SU7Kl2Q7oZeF1VBPxw6Eat/3cgVXwJpOp/7bjRfs/VcLlcGeVuXyA1fN/4ZUtlcuh6tQQuVWvmPVe237v4UJz0+umXAP1QgNDIYNZpVZWusK4WnyozP8f8HmazeGmQ1IyPjojGq5Lmanu6rKiQmJl7XBKvvvvsOU6ZM4VSthx9+mKu806dPx4ABA3g5kWkZMmT8f8HhcGLPnlScPJGFBg0rYPzcxzDtl0XYv+skbNEqhIjZeOLoUnQ8cAAa+nu1ruzjFMVpsfq2OtiZUAOqfUqI4U4E59rgCFJDk+nkz7nkytG45/cXkHPgDH54beb/2rsPuKbOrw/gvwTC3siUKe4BggLuveq21bpb26r/Du2wtdbuOmrHW7ts1Q5r3XXVPetWlC0iiggCsmTvDbnv53kgEZCpkQRyvp/eCjfr5ibcnJx7nvOgKllj+6oBrN/RIHmwqqqemT8cNi7WvC8oC57ch/Xgo8cV0b/2SbDWVSybWBXrNcsy17/4f4Ur//rB70QwjM2N+ClwNrK+oVPtvseC+XSrXmPdFTpl77T3JsC+sy2ObjzNt5FtD5uStebgpprYIKfaAtVxC0fwzggBZ27i9rW7fMKGia+O4K3cCIG6B6uurq5Yv3495syZ88hUaGx6NHaZm5ubfB2rZWUBa3OZMWMG7+1qaPhwerrXX38dvXv3xvLly/Hyyy+rbI0VIUTxbt1KwLL3d6GgoOKUsLZNIZxfj4V4ThkG9MvC7NXX4HS9IoNa8wQwy5yKBQHB5u2w1X4Ybpg4A5HlEP0TDoui2EfqVpm4y8C6/T74bN97vJk9qymUTYEqV+V3a2fVH2HNjpkew3vwRZWwgVCsnILPBlVjn7L2TxNeG82XxvjlrU04uO64fNQ8y1bOWDZZYYOKfA4GYM2cH1Fa2ReWtdoKOhOKn3xW1/uZJBv9L6tlRuWEAcPmDsGXb25Ddnoev96D0+EIup6AtfvfgrW98kszWip+8kCB90WUFKx+9913GD16NB9YNXHiRHToUDGLCathPXToEP+D2rFjB1/H+gOyrCa7XnNhQWlNWlpamDBhAr788kukpKTA2tq62baHEKI8ybl52HU+SB6oMkbmubBIy0TXq0kYsiMclnEVH/ZVZenoYsuAgTjVrTuKSnQghFc5fVxSClHRw/ur7UOJZVCjb9zH1nu/IOj0DT4VqI2zJXqP6YmU2FT4n7jO+2X2neQJh87N92W+tWFB3t93f0bQf6EIOHmdj27vN6l3vTM7sTN9rJcuy6Dq6Guj38TevGdpyPmwh+2dKssGQi/d5p9pUddj+CxpZSVl8B7fizfsb2rSI9z3rjxQlbnjF8kHS9VXs8qmtN1xfz2uHg5EVHA0egzqis7e7XFo0wV5oCqTmZqLhOhUClZJi6WwYJWN6vfx8eEtqQ4fPozCwooCb11dXYwaNQpffPGFPLPK2ldVLQlQpsTERGhqasLYuP72JTk5OXyRSUpKaoatI4Qo2vmoaCzYewBamVJYagA2eZmYG3MWwy9ch/hXKTTLa58AvNBCgg8nP49T7SuOYxpFAmyiiqEhizPYqGgdLaBKwMrKEKsGOizz1c7NkQch7HRv1VHYrHG/97jHryck1bEBdaypPVsaY9nIlTwwldn8yS5MXTKBn0ZnjfSrYnWbP776G47+/nCAMBsUxR5rxcFlTXop2PSwrD61rLK2lmHvEfZeaQgbxCarzb4TEIWXu74NKcTQsLKEqEr3C0NTPdg4Pl5/YVKJalZbzwxWLBg9ePAgb7vCMpWMpaWlyp5eZwOtdu3axTO8LKiuz9q1a3nATQhpGVjmKyQsHpd97yIhKRu9ejqga69yROSfwATv2zD9Lw/PR/mjY3RyraflZDFmlL0ZLns54Ty6IuaeE9o8yEV5bga043OhEZ0LkYEeYGyAWW+Owez/PsC96zG8DREbfe42pCs/rctGe9t3bguPET2a1JyezQfPuhqw+kojUwPeC7PXKLcG6xlbEjadKashve0bgc5eHTBgihecezg2+3awmtuaEiKTsOLAMsz66DneGoq1bvIe58HbSy0d/nmj7qMhA6f2gZaeFg6vP4XUyprVCa+PbvLnJsvMS8vYoEApyhMSIdLWZqcP8drXszBx/lBeGkGeAKu2EBR3X6RpnsoR79atW4iOjq6Y5s/ZGd26KW5aPUVhbbaef/55aGtr80C0IUuWLKk2CItlVr28vJ7yVhJCHte/x4Lxw8aKyQHYsSi+/DxS7a9Ds1CKZ48kwfuXaEiKHp2JKNnAGLtd+yK3SAM3gwSkxusB8eySIkgQBqFPJxhE56MkufJMS04+nBzM8MwUT2hrS9DFuwNfZNjAlscd3LLI+wM+PzwfuS6AZ/KmvDkWr//wElqDO/6RWOS9nP/MnqPfsWDeuP5Hn9UVo86bEZuC99+fj6O0uCJVziYMYBMAMKz1FRv0VBVr+xQReA8FlZNAsBH8Q2c0vW/43u8O4/dl2+Q9dGPC4rD/p2P4N/2vJn0pae/hzL8gybLAQnExOrnaYciznhSokhZPocHqkSNH8NZbbyEmJkae2ZAFrD/88APGjx+vkMc5f/48hg5t3BzPp0+fxogRI6qtY10Lpk2bhrCwMBw9ehSOjg1/izcyMuILIUS1FZWV4mD0LewKCZSvc8h5gMX/HkWv1fcgFQOS4tpTG5FmVpg56x0US7SgHZUBk+DwR67jri+Bx6sjEXcngffw7OzpghFzh/CZgOpy61oEzu+6wudgt+tky4OgITP6896V9WHTxTKy9kOybGtrUfW5VH2OuUp4jgu+mYsXvngeIedv8Ub6ZjYmvIfp4j7LIdGW8FP8w+cM5AO0GDZz2PhXR+Lm5XB+Ct91UBdosTKQSjkZuTi7/TKf+So3I49nZIfPHvhI3azsNWZfRmQTDxTlFWHDu1swcu4g3m6qMWycrfBbyHdIupfMg2g22QELsonqlgGws9FsBrKlS5fyhdRNJMgnPH4yp06dwtixY3kngFdffRVdu3blf3i3b9/Gxo0bER8fj2PHjmHkyOrfTh8Hy2qyILMx2DbZ2trKf2clCrNnz8aePXvwzz//4LnnnnusbWDPx97ennc6qNn9gBCiPOtCffB/1y9CN0OKSQfDsCDgLFxy666Rl2oAyaON4D/TEevvjkdCdhtALIJuqRSOvknIDYmvVk9Ysx5V1suSNVyvrYdlbmYenjV/ST5iWywWQVo5c9Czb4+r97n8881BHPj5GNISKroSdOztwnutqtro+8fFArofX/sdvkcCeaN+VqfJBiq9vX4hjMwfdm5Rhtd6vY/I4Ohq/VjZ68te58b47pVfceKvc/KsOPs8NG9rhl1xG6tdLyIwCuuXbOZBb81giJUdHMze0qjJAVo7ZX3myh6380ufQsvQRCH3WZKbhfC/VlD8oIzMKqvnZAHqlStXqrWHmjRpEt544w3069cPK1asUEiwamNj89h9UVkgzepUWW/Vxw1UCSGKF3srDod+PYkrB/xQWlwG18Fd+Yc16xXJZiJigcLY+SPkMw2lpuZg//4AXL4cgcyMPPQfbAXPsdGw0r6En9olw/ZgFnqfi6v1sRJNTHG6fVcU5pfhVJEddOOKkfaOGaTGhbC0ykeZngbW//QiuvzclmfGPpv8zSP3IfuaL8sIltToBCDDRoozIoj4BAPSBq5f1fT3J/HZoFjdKzstXds89y2ZkZkhPvlnCd8X0Tfj4NTNrkk1vU8T2yb+hUSW8RU17jWT376ynEB2e3aWsbSW27MSke8vrMTGpVt4SYAMe2w2/apsoojGKi0pxeFfT+HS/msI94tElz4dMOi5vjwL3JpqnZWCak2VRmHv3OvXr2PVqlXVAlUZto71Mf3kk0+gTGy2Khakfv311zQJACEqhs3ixJq2yz4Q2FSeVZ3ZdgnBZ2/KM1M7dvrg0IHKqT4FARaJm+E23xe6D0qhl1ACcUWMWA27632d+uCbvs9BEpkKvcB7PAjJFdiMSwVAegFsO9th5BRPdOhQ0crOfXgPPL90Ei7uvYoH0Sn8dD87FZydloPEyAe83+XAZ73Ra6Rrrc+L9VRd+O0LOLvjEs/UGVsY8TIANv1lY7Agx6mbPVozdvq8U+/Hq+vNTMnmszMpegDRwm/m8hpWNjiOfWnyHNOTdwdorGnvTuSBJhuYVVJYih6DumDcwrqTNZPeGIPc9Dx52UCHXi4YMWcQ9I1qzKzWALa9LFMry/izjG3oxdtw7GbHJ20gj0ekwAFWChuopUYUFqyynqX5+fl1Xp6XlweJpOFWHE/LTz/9xHvBuru787KAbdu2VbuczWSlr6/cWVcIUWd8usl6DuLsNGr6g2y889pqWM8pwvX0IuiU6WN8oi+ej7sC8/N11zmWa4kQ0McZayymI8HQvPIcvuyOKx+/8sP9y7UzYdfRttr0rQu+noP5X83mp/TZ1JyyeeLZaeyqv9f1vKa9O4Ev7Pb6xnoq2yGlpeB9u1fvx5kdFxEXnsizzqxTwrwVM2Bpr5gWTayNGFsK84v4zFVV61Ebo727Mz7etYRnOtmZAj3D+jvOWDtZ4r1Nr/N+r4W5RY8/C1iN96Iss7tq+vcYNmsAXlo1s8FtIaTVBqsDBw7EunXrMH36dPmEADKRkZH45ZdfMGhQ4zIJT0NQUBD/Nzg4GHPnzn3kcta9gIJVQpTnf//3Ag8SWQaTNURnbZ5YtuzSoQAU5RdBrK0LsYEeSrvfRIFBHjxsSjDvxlVYZNcepD5oZ4RcIx3EDDJH3ExT3Au3RZmPGBrpJSjX1YCgIUaZqS40MytGc/cc2g2j5w1D2w42dQad7LR1VTV/b4gy56BvTVgN7+ZPd8l/z8vMx+m/L8C5uyP/UtAQNnaBNfQ3bmPIJwyoD/uy8iQkWhK+JMem8gFlLj2d6v2ywgbcPMl0te7DuuPNX+bjyMbTuFdlOlb22Ad+Po7eo3vCe6wHiguLERkcA8eudkqfHrdFoD6rrSNYXbNmDa9L7d69O+9b2qlTJ74+PDycdwlgfUzZdZRl8+bNfCGEqKbouynwD4xHpkiXzSYCn9w85PQxR8rbFfO29zSKxNspR+H6XSKkP4tgcKf2+sFSkQaW9HsFwSbO0M4ogub5cojOV37WaIphmJ0K3IuTZ1Jl3t+8iDdZJ6qvZsN82Wuprdtw9pO1xjq0/iSyUytaj7VzdeRflDxG1F7G8aQCT4dg43tb5C2lTCyMMPH1MZj72bSn8ni5mfnwPRbEa8Brw/Kuq2asxdVDAfxLIStxYLNhvfvHqw0G7oS0+GCV9VINCAjA8uXLceLECezbt4+vZ9lKNqXp6tWr+VSshBBSU0lxGXZvPIfEmDT5usQ2IhSUFMIhIw3/u/QfJt4KhKTk0b6oMpkGetjlMBgHnfsiT0sXkowiSPLLq8/tXSbF2IUjYKkNxARGIiY0Dj2HdcegaX0pUG1BjNsYYZ3vGlzY7YOgM6E8O8haSw141rva9VhdcfCZUFjYt+FTkbKs5e5vD/LOAzIs+3hmx6WnFqye2X6p2gxYWak52P1/B59asMqmb2V1sjV5PeOOyW+OhaGpPi7svipfzwZxsZZqrGXXyBcGP5Vtag2oZlW5FDo0kJ3+37t3Lz/FkpqaytdZWFhQfRYhpE57dlzD37+fR+mDTFScHGUjGQDtvGLMvHINS/87UuvtpBoiRLhaQioW4cD8nvAr7Yq0czbyYfqChujRzJuOBAsXj4BOIzJwRLWx/qN19SBlNa2fT/kWV48EyLPnRm0MefbcxMoYyTGpFe8J9l4RAFPL+qfbfhKy++alpKKK96Gp1dN7PDaAr+b7npWwzFs5Ax082iEx6gHPprIgterZBRNL6iNeLyoDaJnB6v371edKrq9HWVUODg6P+5CEkFYmpSgdp1IvQto7C0KYDmzTs/FC9jnY5GTC9kQ2DHJrP9V/v6sp1o8ejeta7aBrkQetslIgTIBueg7KNLQhaIqwZNl4WBvo4aZ/NBLjMuDRxwV9hnSmQLWFYwOQWDuzkHNhcOhqB48R3RF7K4FnUO062KD3mJ68TICNqq8qJy2XX2d94Dc88+h3PIhnaNn0pqxN2tPy8pez0Gt0T1w96M8H5HmOca+1H29TFBUUI+j0DZ4VZtverV8neTcENvPXn7d+gM8BP4T7R6KLd0f0n+wpHzRo62KNv+/+DJ8D/rhx6Rba93TmlytjiltCnnqw6uTkVO8I2PoONIQQ8ue93Tj24DzEHlIMLIrFS7FXYHUrl5+uryuxcUfLCjuMvRGQ5wxhvwhlhuVIt7aCbkrFICkNCNBAEWztzTBhpCs/Rnn1qz7gk7Rc+TkFeKXbO0ivnCShNixo+3TPu7B2tuStxqpy6NyWD3JjLaHY0hzY9rBJHBQ1kcPNK+FYNnIFrzeVYZna30PX8uBb9jwdPphSb+cBNiFFQ5NSkIeoDKCFBqubNm16rGCVEKJeSsvKERARj8uh9yDR1MBAVwfY2yYhNMsHFkk5+Ojdo7CPzar1tlm6usgwMMDl9h2xX9MdRfc1qwWzkvwSvDjNE462Zji52w/pqTnoP6I7Jr8yhI5PrVBWSna9gSrDeptG37yPv8J/RNiVOwg4eZ3XrLLWVq1h+tH7t+KrBapMZnI20hMz5cEqIa3NYwer8+bNU+yWEEJapcU//wu/8LiKAU5SKRxD3kG7I7fxppYenPzSIa6lt6ogBva/6YHPLaaiUFIxo5FRWC4s47Kq9WItz8vH36/+Jv+dfYGOPBmAw2sPYlf8RgpYWxkzG1M4dbdHzM2HI91l9Zcyeka66OLdgc/W5DakG19aExd3ZxiZGyAnPU++jp3it7Bv+YG4SqOaVaWiudcIIQqRnpqLy6fD4H85AvqGOug9vC2s3JNg77YPTkZ56P13DPr73YFu5TSUpqg4dV9VkZYmAjs64qxbF/hf6wjLzDQUGpdCqgHox7H6VS2U5xcApaUQioqAKkEKwwfMAMhIyuQ/K/vsT+zteFzaew3Xz92EfSdbXh/Za5QbDTp9TKzn6e831uJ+eAKfqYnNytS9f2ckRafwelTWI9d1UJd6pxVNjU/nr4n/iWA+GIl1EWBZ16Y2/VcWNtPX7gd/4PbVCNy7cZ/XrLJuCIp8r7P9y/ZR8DlWB2yLTp4uyEzO4jPI2Thb8f3FugsoetawquUeV/71w7UjAXxCBVbj69zHDkpFwapSiQTZ0Z00CRs4Zm9vj7i4ONjZKfmPiBAV8NK475EUn8FHGDPDv/ZDG4scuP4RD/dfKzKrtblnYolSDQ3sNO0D/xJ7Profggjl6RkQWGBalaYmdK3bQJyfi/zMihnzJNoSPqAmP7viuqzXJqtHfHvj/6BMrAn7VKtX5KOxGXa4fee3VzF2/nClbps6m952If8yI3ufstfn2bfG4bXv6WwhwwaBTbOaX9EtgHUvqBIiVH0fs960TZl+tik+f+5bHqyKWbeCytfIrqcVNgf/0uyfubLP+m6zPoWWgYlC7rMkLwthO1ZQ/NAElFklhDyRiNx4nEoKwIPMilpCq8J0zEm+iG7v3Id5TD60CmofVBnm0BYrvGfgrnlb/ruJXwq00osezptdpWG/jKGxLv6J/B6aEg3EhMXxxu72Xdri7PbLOLfzElLjM9BvYm+MfHFIs72q7IObNVhnM2+xDCqbZrP/ZC+e8ZK1Bar6gS8LqutzJyAK/225wJu7s5ZC/SZ5YczLQ2Fi8fRaHqkL2f6vOiFEY14T1vLp5F/n5F0G+k30xOiXhvLR9UxaQjpObDoHn4N+KMwrRt8Jvfj70Ll7y+qAU1xQIi+rqJnLaur7+HHJ7lta5TUqyH16j9cYNMBKuShYJYQ0SWl5OTTFYnmWZdn1DcgtzMUwmyjMPnkNjvnpFVesZcwU++jJdNXDrdescVDLGwkB5vIa1CIzTWiml0MsVNy3SEcbQnEx+8Til7NaxSlvjoNEq+KwJQsCfl+2jTd6l/WMPP7nWZz6+zyOFu5oltPt7JTpZ1O+kT9+5oMs3hrpgy2Leebp3M7LSE/K5NlfzzE9MWCKV4P3+cGolcjPzuctYx/EpPBWTSmxqXjz1wVP/fm0FKUlpXwa06Z6efVMHN5wCvF3Enm9K/tSMXZBw5nub1/6BTcvh8vf96xuNvTSbay9sIL//us7m/mpc1k2cs93ibhy0B9/R/z8GM+uIjAsKy2Xv98VsY8as8/MbU35lLVn2fs2MROaWpqwdrJAQW4Rz0hLtDX5BApDZvTH0zL1nfEoLijmExywvwE2Re3Ql/pg95t/PbXHJKqNglVCSKOc9L+Dw1fD+GApU0NdTBhaiK5d7uD1jHPo99o96CfV3hM1Rd8IBZpauGjTGXvbuEOzqBzS39jsVAbQMSqFVkw0RDl5EBeU8ri1nAWYLB6oUo/KBo+wWsXalJeWVc+UiViLvLpnulK08tLKzHHlw0srs09sG9ip0oXfzkXC3ST+HLR1KwaLNaSMPScWFAmC/HmxwEXdsQDu8PpTOLfrMsJ87sDWxQr9J3tj+vuTYGRu2Kj7YKf82cK+BBiY6POlMdj+r3panP3MOg9Uex/wl6zK5Y/xmmWm5mLPhjPwOXkDqUnZ6O7VDsMm98Lo6X0adfviwmLs/e4ILu67inshsXBxc+ITIbDZvKKCY+DUwwEDn/XGtPcmQkfv0fcj+4K38NsXsOCb6u9b9rxYdpkNcmO1w0+T97hefMlMyeb7uI2tWUXP9jehXFQ0qTQUrBJCHsE+mFiPSlNrE/6BxgKvD/88xi8Tl5fDLTAQs47s4+fGzMIeHSjFlIg1sLzfC/C17gKpSAz90GRoxJWiFBWDMjRQDA0BcJ3oDVFCKm5fuoXyMilvcC7WEOG2712UFpXC8xl3jP/fqDpfpUmLn+GtfK4c8EN2Wi6fPnXUi0OabRATG+TDglI29ecd/yi07WiDAVO8eaN1WdAia8jeWO//vRgn/jzDpxI1MNbjA7Omvvt06gNbkuTYVPy86A95Fjsh8gHPqpvbmODZt8c/8r6tD+s12hTsNT74ywn4Hgnkv3uP74VJb4yRX86mT9U30eMlISWFJfx9O27hyCY/x1O7ffHvnxfkv4dei8KNq5HoN7oHDBsRWLNpUzd/ukueAY4KieH/Vn73QUxoLKJvxMK5uz0GPFt3AFzzfct+b9veplHPISUuDfpGutA3btwXgbo8zZnFmkrEBmwqaIiPou5HnVCwSgiRYx/2f3/6D05vvYCU+2l88NLgrwzRZlQ27HKcMS/oLMZEBUOXzRhVhzwTLVyd3g6/Fk5GcmHlaX52cK5lhJW7hyO+3PAK/5llUNjjy0ZyswlEWEBS38huho1OZqfHF/8yH6UlZdDSbvqp4SfB5ptnp/vZUlJUopBR5SzzxRZ22paNuG6uwFvVsdP21VR+5v/2/lYc/f0/5OcU8j6s7H3be7QbFnw9B/adKmqinxTrOsAWWTa15kh4NhPU+38tavT7ti4amhXPsepUqPx3kbhJ+0gWnPK/O343FT/I4qSv5v6MgQf98ep3LyqsP+uBdcdxeP1J3L+dwPcPaxv24orpfFYtQp4EBauEELnU1AxsOXYMoqJS8I88SRGs+kej59dxeGXnP7XuKakYSOloiFzoYc8UL6SMMEBZkSY0AkuhebMM5XnlrNAVpUIJRAUFEEu0oKOvg2f/Nwwjpz2s36z54c+CwMokbKOwzE9zB6o1Kbr90ePUZLZmlvZtsOrwBzi5+Rwu7fOVr2cZeRYgyZQWl/IMJwsgX/j8eYVuQ0Ptmpr6vq1p3Jz+vE7V51QoEqJT0WtQZwyZ6A4DY916b8daS0WFxKJDr3Z4/+9F/EwDa2/VtV9HmNuYIeZWHJ+iVqa4sAT/bb0I77EeGDJdMfWnfy7fjqL8Yv4zC+qD/ruBNnZmrSNYpdZVSkXBKiEE5YIUX4TuxMWUMJR+ZQPn6BQs/+c4rNNzYTywEJpFtZ+28rHphP9znYoUPRNoFJVDw1cK0bXKC2MfQCvuOkRV6voEVk8qAB7PeeOFd8fSnidNxmoZu/TpiMv/+snbgj3SXollFaUCnyCgpdHV18aklwbxpbGlEV/O+gG3r0XwrCnLrHqM6IGPdr5TrR43OjQWC93ee5h1ZT8IbBIFPYVtO7svFqxWbQumZ9jyXgOieihYJUTNsA/2+3HpuB4ah3ZOFuja2Rb3C+7gSkwAJh2/gRn7A2CTklPn7ctFIvi7OGO7zQgEm7ev+ORjGaVi6cO2U+wDMTG1WqAqq0F7Zc1sDHjW++k9QaKSpFIpr+mNDI5G9/6d4NTd4bEb2bPBVL/f+A4XWeP6M6Fw6NwWHXq349OOBv8Xygde9Z3kybOG9cl4kImAkyEwNDOA+/AeDda5Pm1sYN2NC7d43a3HSNdG1dWygWa3rkbIf2dtp9hzir0Vz+u/ZZx7OGKd31e4vO8aQi+Ho2Ovdug/xQuug7oqbPt/vvYl74jgd5xNuGDI23uxCQRaA2pdpVwUrBKiRvLyi/HGO1sRe7+ivZSGpBxTllxAh6g47F8SDePcolpvl28nwYMyExx0d8eWgQOQbaAHq6sCNGVXZ6PWWUJLXp8qArS1gOLSiswXS+EIlb0p5w1txmdMVAHrifv+yBW8rZcM60/6w+WVMLV6vEbrjl3tMfdTtkyrtn7Ox1MbdfvVM7/ng+JkSVmJjgSLfnwZYxeMgDKwAG/VjO9RmPtwwGLXvh2x9uKKitKCelpNycjqXFmpAuvPW9vsV2x5mmUaz70zni+tDpUBKBUFq4S0UoX5RQg6fQO3fO7wOjb3MV1xM80Pem6+aG8gwYCTdzA2MQB6gYUwSKp9wFSJtgb2znfDjTYOuBPdHvGiNijPEKOfsTXe/WYwcuMLsOKtv1CeVwQxC0TEGoCZEZw62+LNc5+jKDEDfseDeMaKZVi69G0FtWukye6FxFQLVBnWBinpXvJjB6uKCA6rDspmnSdCLoQpLVhlf6dVA1W+7moECnOL6m2v5Ta4G7ZEreM1uhVZ6868U4G5zcMglpCWjoJVQlohFgSw+rSi/IrUp1YbKQYezoVTeCaWb4mCXUAmxLJWpLU070/RM8K/Zc44XuyI/F9lg4ZiYNBdBEO7NnhxtBs82JSHdoC7vjZC/CPltxVlZGPMwqFwc3cC3J3gPa7+U7Gk9Wvb0Rbaelp8diQZdurdwr6N0rapY28XXD97U/47OxnABmQpi7OrI683lc0exdi2t4aOfsOlCawjBusdS54eKgNQLgpWCWll4gvSsCfgrDxQZRyM8zDl4xB0+i+l1tuU6ogRZN4Oedq6+MNjOMJNbNDmT/9H2k0N6NEWSzcshFGVQRPfnvkMMTfvw/dYMK8lZNNMKitbpk5Zc//jwXw6Vtawne1z1l9WVbsHsFPPe1M28dHhkUHR6D6wC1wHdXns9k6K8M3pT3l5gt+xYBia6qPPhF4ws1ZeNnLwtL58YBR7XZPuVdSsdvJ0obZlqoLKAJSKglVCWpG7uYl46dqPEIRS9LNMxwvJN2CIEjhF5gIPk5/V3JphjWtLnPHP8WFITjGvWCkIKGlrDElCtjxeNWpjiEkvDqkWqDKsJpUN3mALaR7LRq7gU7DyUdcCeG9LVgv83qbXVfYlYKUgrGaZLaqAv2+7O8in7VUFhqYGGDZroLI3g6iRdevW4c8//0RoaCg+/vhjfP7551BFFKwS0kJH9Ptfi8Ll8+GICE9Cdzc7uA2XIrfsDL7b+S967EuAXk7djfsLNSU46eSKfzv2hJZRGbJXGqI8WQxTw0KU6YigUSRA094JIqtSICcPz0z1wturplGWR0VkJFXUblRtGp+RXEs9B2lxWEeAy/t9ceWgPxLvJsFjhCuGzOjPp01tqVLup+LcLh9cOxoAXX1dXr8+ZHo/Hpy3FK21DKBt27ZYsWIFtmzZAlVGwSohLZDf1Sh8/B6bUrFi8H2hNBTdcAIjP4qCpOhhzVtVocYOkIrF2OfQD1dMO0MrOgNCGCCq7BMuQS5EUqCNnhmKpMUoghQibQk6DeqGiS8OpkBVhUx5cyyfZjSjctBS2w7W1GWhlTi47gQ2vPt3ZdZcQETgPfzz7UH8m7EZ+grsidqcPhizCnHhifw5sTM1/ieCEXrpFj7c/rayN03tTZkyhe+DgwcPqvS+oGCVEBVWVFCMIxtOweegP+6FxqLnyC7oNF8b0RlpsCguxsyEi+iXEQ7twGIYbHs4eKWqzDa6+Pqzcbh25GG/Q1FJWcW/Na47/5XBmP7qcJSWliMiPBE2tqYwM2852Q91wVoDPfv2OD7vu7aulsKmFCWq8TdfM2vOfi6r/JttifisVpUTNchmfq062K5FUIGa1by8PHz77bfw9/fnS1paGtasWYMPPvjgkeuWlpZi5cqV2Lx5M1JSUtCxY0d+vVmzZqElomCVEBV2btdlbPhsK8SFFQf66Gwf9A6MxoLfYtAmOu+RYLPqsTCuvRn+neuOm6NtUZorgd7tXBRE6rNmjBAEKcokImgUlkKkqQltHQkGjuuJEVMqAlqJRAPdetg363MlTa+5VObodfJ0DJs1APdvx+Pq4QDetsqhS1sMnz2ID15sqRZ8PRcH1h3H7at3oCHRhMfwHpj27gS0NMo+fZ+WlsZP2dvZ2cHd3R2nT5+u87oLFy7kp/bfeOMN9OjRAwcOHMDs2bNRVlaGF154AS0NBauEqKDMklxsiTmFM/YBEA7ZQeNeHiZE3cSUwzdhszy31ttk6+giQs8GybrG2GY6COkaehBtL4bkQBnKczUg1ZNCQ6cIoht3KzKrggApa9gvFmHx+gUYO185/SUJIdXbUC3f9havXc1Jz1VqhwJFGTqjP19yMnIh0dKErgFNwfo4bGxskJCQAFtbW8TExMDZufYvq8HBwTyjygLbTz75hK+bP38+hg0bhqVLl2LGjBnQ0pK1JGwZKFglRAXk5hYhJTUHzk4WEItFOJPsjxP3LmD0iZt4fncQ9IqLYZJR++xSLOD81XMEThT0RplYXHGOrawcmpUNxqXFYrA5pDSyC2HdzhLtXIcg714ibl4Kh2M3Owx6ri+GTO/fzM+YEFIf1taLBar52fl4EJMKp+729c5k1RIYmbXc7DAfHFB1Foknva/HoK2tzQPVhuzevZuPMWBZ1apnYhYtWoSpU6fi3LlzGD16NFoSClYJUaJ7Man4ef1/CAmNg1QqwLpdOSZ4+cNtsx8OXXkASVntg6WYVANDbPfshx3eA1CcK4HNpTx++r+iNKD6wZBPeSoI+N//huLaXh9cD4pGeVk57t9OwO1rERgxdxD0arSkIoQoz/3wBPzy5iaEnA/jf6tG5gb8S+UbP71Mgx1biaSkpGq/GxkZ8eVJBQYGwsXFBWZmZtXWe3t783+DgoLkwSorC2BLeXk5/7eoqAgSiUTlvhixNAwhRAlY8HjK/xJiy65DLCmGeVE2Zt3fi5mvHED3C4mPBKqlEjFCh9oiYpAlfvpoFOYsXIzNHYcgv1QLmrllKDbRAKRSgM2AUy6FIBbLQ9buXu3w0fp5sLY1wYlNZ5GfXcDXs0EbrLG8/4nrStgDhKgHqVSK27534Xs0kE/o0BhsUCWbRIEFqkxOeh4O/XoS6YmZT3lrSX2tqxS1MF5eXrC3t5cva9euVcjOT0xM5CUDNcmysuxymVWrVkFXV5eXDaxevZr/vHXrVpV7E1BmlRAluJcXin1xP8A0PwLfXrmDtmGZ0CooezgFag23zOywfOZsoFcxsv+xBi4WQ/N6JCzyqswlrqcD2NlCVFTyMJtaedEL741Fd892SIlL4+1j2GAt2b8My9oQQhTv4t6r+PmNP5CVmsN/l2hrYszLw/DmLwvqvV3VAVWyv1VNiQZ0DXToZWol3QD8/PyqBZWKyKoyhYWFvGSgJlYawLKm7HIZNgmAqk4EUBUFq4Q8ZfllRQjIuI2s0lz0Mu0My9wiFK1YjNd3XIV+Vt3tW0rEmrho1w1/dhuJGBNrIAN43s4avb92w+mNvrh6ObT6DQqKMHq8K7xHuSI2PAm3g2LQ0dUe/Ua7wqVbRWsjS/s22HT7R1z51w83Lobx2Xv6TfJElz4dn/ZuIETlJN1Lht/xYBiaGcDrGXcYmOjXe/30pEz4HQviP3uP82jU4KdrRwLlgSpTWlyG01suNBisPvPKMNh1tOEZ1viIRLgP64H+U7zq3UaWhQ29dBsRAVHo2q8TuvTp0KTTuexsT2RwNK6fC+NdCNyHdYeWTssaiNOSsECVjexXNB0dHRQXV7RAq5nhZy2t2OUtDQWrhDxFJx/44seIfyAtL4d2USkS/cIw409/dI2rkhGt4kFnIyR2M8ZtG3tsTJmIUg3Nh8X4ZVKceC0Q162isPiLyfD9/RSk7JR/JYm2BNNfHYa27W3Qf1SPOrfJroMNpr8/iS+EqKtPJn7FA0kZsYYYr62dh8mLn6n1+uvf2Yz9Px6ttu65t8fh1bXz6n0cm3ZWjVpXEzsz4jqoK18aIyYsDu8M+gR5mfnydaZWxljnuwaWDhYN3r4wrxD/c1+KpKhk+TptPW2sPrIcbkO6Qd2xCVPYoqj7eppsbW0RGxv7yHrZ6f/GDNJSNRSsEqIALCNxNyENF0PuIbewGP26OcLRsRz3w7bgw19PoteJ+4BUgG5R7Y29CyUSfDl0IqI8jWFgWojhnUdgc48x2Lb1Ks78dxOa2UWQZBVDJBWQkpAJCwcL7Er4Db5HAvkMN937d4LXWI8GM0OEkAo3Lt6qtivYF7/bvhF1Bqs3L99+dN2V8Iq//aB7uHooACVFpTzj2q1/J3lGc84nUzFoah/4HAzgrai8xrqjx8AuCn8Z4sITqgWqTGZyNpLupTQqWOXXrRKoMsUFxYi6HkPBagvj4eGBs2fPIiMjo9ogK19fX/nlLQ0Fq4QowLoDV/DXCf+KPyppOQyOfI72l/ww/15+vY377xi3xZb2w3BNag2N09HA6RykAvgDx7HL9CI+O/ghru/wRyGbAaaSlZ0pzKyMYWisx2vf2EIIaRrXwV1x7XD1zGp95TAswGRBqexEB5vquPuALrwe9fCGU/LrsWlwe410xVcnP5FnSB272vPlaWKn7Vk5Q25GnnydqbUJbNpZNur2LAtr62KNxKgH8nU6+tpw6en0VLa3xVGBGawa6/nnn8c333yDX3/9FR9//DFfx75UrVu3DhYWFhg6dChaGgpWCXkCpdISROQG4dr9ABgU56NjehKeu3UVY+4F1xqkZpjo4fTwzih7oIM9esORoVMxiELj7sPRmTIsS2LvYoF/glch1DcKMXeS4NrHBS7d7PgHICHk8b37x2s4/scZnP/HB4am+hi7YCQGPFfR2qc27HT/8+9Pgt+xYB6peo91h6mVCZY/s+qR6yZEPgz4mgsLhvc8+INneyMC7qFbv46Q6Ehw/M+zvBNBZ6/2GPCsd52znrFG/ZsjfuJT+F4/exOOXe3gNrQ7tLQlUObUs75Hg3D1sD9SM9jXeOWpOopfEffFuLm58Qw8a9TPlsZYt24dsrKy+MKwnqms5RSzePFiGBsbo1evXpg7dy4+++wzpKamymewOn/+PDZt2lTr4CtVJxJYuE2aLD4+nreaiIuLeyoF0qRlOBT2f9D6fTO8tkbDIKsYWtKKNjM1ScUinB3cCWuWjUG5pgaEGAmEo4ZAWcUHgUZ2PvTDYlFcmRVhozY9n+mJL/59HxqaqtXvjpDW4IX2b/BT5GykPcNG209e9AzvY9oUx37/D38s3y7PaLJs5NQlE/DiF9OhTHf8I7HIezn/uWrnjx8ur0K3fp3QEqyZ8yPO7rjMt79IKMAl6dFm/8yVfdb3GvsxtPVMFHKfxQVZCDy26rGei5OTU631qEx0dDS/nCkpKcHKlSt5S6qUlBR07NgRy5Ytw5w5c9ASUWaVkEZIL87C+VRf+KXfQLkgxcioUnh+vwPjr4ZDXPkhUJs0bV2ccOyAXZreyBa1hcbX6dAoTIfoRhFQKEAw1IWgp429Yd/D1FSfzwkeH5HET1EamlI7KUKeFta3lJEFcXxdRu1TGddn7IIRGP3yUNy+GsFrVrsP7KLUbKRMTpVygKrPcdNHOzDpjTE8y8q+FKsyVuMr236l59VUYAYrhk2z2hhsOlUWrLKlNaBglZBayA6MstPt6yK3ISYqEPp5xRh6/A4G778BnYJHB0sVizVw1tkV0bk6CEoxQGypMXCXlShlQjsyB3pjPWCYooG04gSwAaHm+toY/dJQmJlVBKbNUdtGCAFmfzwVB385juSYVF5/2tm7w2PXf7NTuax+tTHHkubCTvsPmdEfPgf8eBAtw1pb3bhwC6uPfsjbdamyCa+NRlZKDm+nRdQbBauEVHE9Pgm7Am/gTEQUNMVijO6qi2H3TuPFn0/A8U56vYOlokyt8O7IlxBnYgn9i5HQTk2R1yaJRSJINDVwcP9S/qHFMjgZSVm8LozqTwlpftPenYCpS8bzHqasiwarP1WkhMgkHPjpOC7/68tnjGNdAp55ZTg8RriiObAzMx/teBtBZ25g2ciVFfMwCw+zrGz2OlXXb6InX9IS0hETHQvPgcdaVc0qaTwKVgmp4ouTx3E3JR26OcV478QhjA8PgnZR7XWoTJG+Jm6Ot8U/nn3hG+WKvFId/oHQYVIvGN18gJgrd1CYV8RHEo+dP1wemBqZGfKFEKI87O/RvlPFhBmKtm3lXvy39aL89wt7ruL6uZvY8+BPNKfu/Ttj9sfP8Zm04sITYeNihYHP9oH78O5oKdq0NUeRUHtv6mZFQabSULBKCDs1lnUVPgkHMFTHH/NT0uCxLxY2cQ9nnakqxMgRpy074UGBFDdSjYBDIpQflUAiDodpfjFEYkDPvR3Gvj4aww8sQ0lRCR9pSwhRH7IvpnzaY17v2PylAAybgWreihl8yc/Oh56RHp3NURGP0w1AXVGwStSOVJAiNv8+9DR1YaVjBWlwEHSWzMArl+OgUSbUeao/U1MfW+2G4rCNN4Twe0BWbsV1ywAxiqo8AHAnIBK/LU3BqBeHUKBKiBqat3IGLOzMcX63D/KyCjBgiidGz1Nuf0t9Y5o0RJXKAEJCQqibUCNRsErUysGEwziV/B/yyvIwZnsoxm2/CaP0QnSo5brseFKuIcItb1v8bT4MN1I7VLS5kQoQVR1FW1kLxohZi5jK0as6Bi2vlx0hRDFi7ibD52oMUko0AT0jXPOLh1W3WHTt2zLaRhGiSihYJa1WemEBrsTFwlLPAL1t26KkNAdJ+9ehV3ou+h+4h04h1acWlIlzMMEpj56IsrZAgrcBNA31MKHLaHjfNMWun88hNysPYL1PWcAqlfI2NbM+eg4SbQlCL95CXlY+vMf1wtAZ/Zv9ORNCmqdZffCZUP6llNV+1lbmc/bfQMRHpch/z8nMx79/nMesN0fRS9QSqUjrKnVFwSpplV47dggno+7yLKdDahq+CDiAvhfu4tWCugdLFYolOG/cDb8ajkbR1SwIqRkQbWR9/nLxm/YO3urGy9kYZ67c4K1u2P/Y/Xf26oDZHz3H7+P59yY247MkhDS3nWv+xdYVe1BaXNEOSlNLE9Pfn8RrQqsytagcQCli/1XUrZpaGtEL1kJRNwDlomCVtGhSqRThvncRePoGrJ0t4TW2J7J1U3mg2iMmFj9s3Qnn1LS6bw/ghoMD9uj1RbDEBVI2OorJyWPTu8mvV1pcxqf9++785xg+exCuHgrgH1Z9xvdCr1FuzfFUCSEqwPdYoDxQlbWA8jkU8EiwOv/Diegzojuunr6J/JxCeA7tgl6Da+/FSgipHwWrpMUqLyvHy13fRqJsHm5BwMg1WXDJycS6M6UYFnQLOmWPZlILDTSxd5AX/uncB8nGxsgwMIDNuTwY3H/4AQQdbaCouNrtnLvbQ6IlgefonnwhhKgfp24OCLtyp9q6dj0cHrkemybZrV8HvpBWgA9GUOB9UTeAJqFglbRI+WX58E30R970bFhHAWPO3MK4rCiYLK8eYFZVLhYh3NkWO/v2RUhCO+QF6QJ2GujjYowvt7yE8tRi+F28A4lEE96DO6EsvxDXDgfwwVR9J/aGfSfbZn2OhBDV8/aGhXj27XH82CAtl6LPhN58cg9Cmoq6ATQeBaukxSkuL8Y7wctQXpCHL06cQdfARIjr+MYrhQip2kbYb9cPp2d3RmmiFkr9JfybrQ7KYZQvwqyJrnA0NQVMgXYdravd3qkbTX1KCKnOoXNbvhD1QTWryqU2wWpMTAw+/PBD+Pv7IykpCZqamujYsSMWLVqEuXPnUpNkFRWelYxjcbfhmxKLDsYWGFJeDIctf2J6YiT6nr4Hvfwqp+4rSUXAaSt3nLDuhVh9S2RrGfD1ffWc8NnGqcjPLEaATyQMDHXg3scF2joSJTwzQgghLQabprZyqlqF3BdpErUJVlmAmpycjBkzZsDe3h4lJSU4ffo0XnzxRdy6dQtfffWVsjeR1PCgIAfjT/4BjbJyTLwSgnmHL8ElPpW3Ne1Yy95if/5xnUxxal43HE3ug5wQ08omqICdnRmmjOgDiVgCE3MJRkygmlNCCCGkJVCbYLVv3744c+ZMtXUsqzphwgT89NNPWLlyJSQSyrApA2vpciwiAscjIuCXkIAeVpbwaCeFoHUPX1/ahwm/3YBWae0tpwo0JCgVaeCwnQe2DBgGveJClP2nibJUCTRRCEFTjFETeuK9z6c0+/MihJDmVl5ejjPbLuHyv764fTUC3fp3woBn+2DozP58ak+iOgOsSOOpTbBaF0dHRxQWFqK4uJiC1WZUXFoGbUnF2y8sJQVvHj0K1jSq2/37eO7P9XDUSoXD7QwYZtY+YCpMywa7TXrjurYDSkSagCBCcZoE1g52QF4eUpAFiUQDHn1d8OzMPo/cvrSkFGINMR28CWnlSopK+IQdIt4cufULPnMT3770C59tj01awNpqXTngD0uHNnAd1LXOfaSlo9Xs20pIY6ldsFpQUMCX3NxcnmndtGkTz7oaGFTUNZKnp6i0DH9c8sfJW3cRmZKOjtZm6OmaA5ObQfhlcyAGRETAoLi43i+jkU4W2D7OC4GH26FMVD1LMH+MN+a+NZr/nJyYBSMTXejqVZ/y1OeQP4799h8C/7sBXQMdPsp/6pIJcO7+aOsZQkjLdXrrBZz6+zxuXLgFYwsj9J/kienLJsPayRKtGetQUC17Jzxs9VczA7v/+6M4v9sHEQFRsG1vjQFTvDFz+RQYmOg381arPj4PjIIyourxtUmx1C5Y/fLLL7F69Wr57yNGjOABa0NycnL4UrUGltSvpKwM97Oy4WxmCg2xGL7RsdgUcB7FeRK4pKSgd+RFTNjpD7fLCXX+8RbqaaJUTws3xvfDzmdHwE+cBXGqAP2EUmiFSCGSAoYmehg51QvjZvaV387K1qTW+1u7YANy0nJ56UFuRh7/MGNNvZdve4teTkJaibLSMnw77xceFbDsYuaDLBzZeBp6hrpY8M1cqIL87Hxkp+XCpp1VrVnfwvwipCdkoG0HmyZlhd2GdMW8lTNweb8voq7HoIOHMwY+1wfd+neudr37txPw2/tb5ffN+lXv/vYg7Du3xZiXhirgGbbGMgBFTbda8Y+bmxs/u7d06VK+kLqpXbD60ksvYciQIUhJScGBAwf4vyzT2pC1a9fiiy++aJZtbOnS8vKx6vR5nLsbjcLSUpgbaqBPtxR4njqHfUfuwD4iExplrKlU3X/H0bpm+FPcFcFFFpBKtWDtY4HcI3dgnJULUTkg0tWB2MwUgoYGZiwaiWfnD2nUtmlKNHigyshOk2lIqI6LkNaEBWBiTTGkZVKV+1u/ExCFP5dvR8j5MJ4FZTPvjVs4EjOWTeaXx91JwMb3tvBZ+dgXaXNbM4yYMxCvrJndqKBVW1ebT//MFha0a1aWW9V2LOTYXQqAWCyCVCo8XE+eOuqz2ngiQfbJ3YKcP38eQ4c27psfG/HPsqd1mTdvHs6ePYuIiAjo6Og0KbPq5eWFuLg42Nmpd0PouJxsRGalo7d1WxhqaWN7sD++OHYZWqWl6BMdiaXnDqNj/IN6T6GwixL1zHCurSv2WPVGwZWYBh/Xtkc7THtvIkZM9YSObuPqraJCYvDf1ou4ejgAhmb66D/JC6NfGgpTq9ozsYSQlinM5w7ObL8E/+PBsHRsg/6TvTB0Rj9E34yHqZUx76GsjDrWnxf9gUO/nqy2jgXTp8p285+3r9qHzZ/ueuR2e5L/gImFscK2g330symkL+69iuAzoWjv4cyPh8PnDOQz9ama+Ph43smnuT9zZY/bZ9By6OgoZv8XFWXj2sU1FD+09sxqp06d8Pvvvzfqul271l5QLvP888/j77//xoULFzB6dEW9Y22MjIz4Qh4KT0/FW2eO4k5GGv/dULsE89J90X9vKHxupkKvuBh6ZY/2QZWJbmeGgAHOiLGwwKnwfijU0gX/7MgvhrasRkgkkmdCa0oMvYcDX+9Fx67W6NjLpVEvjYubE1/+938v0EtJSCvWrV8nvlQNEue2W4TiwhL+u5WTBd78ZQG8nnFv1u3SN9aTB6gMy/iy8oRHLmcHw8oyBpYRVvQAKHb/fcb34gshqq5FBqs2NjaYP3++Qu6LdQJgsrOzFXJ/rVVJeTmuJsciLi8L/a2dYG9ghKP3fVBWfBOvng/DlIvBcElOgWYDzY4LdSQIb2uNE849ccW7PcQu5RjQ0Q1Ljd0ReyMfN24noGM7S3R4dSRSQmJw4+ItPvipg0c7xN6Kw+5vD1W7v9hb8Qg6faPRwSohRD0d/e2/aoOMkmNS4XPAr9mD1bmfTUMnz/Z8sGd6QiY8x/TEwOe85ZdPeH0UH+zkc9AfiVEP4DG8BwY816daQEuUgFpXKVWLDFYfB6tNtbSsPgqUZezY4Cr2DdPDw0Np26bqzidEYdHlf5HPsqRSKaaEBmOOjz/G5RXhrbA0aFbWhdUmXUMfMRJzJBia4PCinojbb8/bTIlSAeEwePuoqTufQbtONkD3Gjee7FXt14LcQhzecAqFuUXVMq6sJQshhNTHwt4cD6JTqh07LJRw7GCn2FlJAltqwwbcsAC6uYNoUj+RIPBFERR1P+pEbYLVZcuW4e7duxg+fDivP0lLS8OePXsQFBSExYsXo3379sreRJWQWJCF88nhSC3KQV+L9nAyMMK5K/9i6R/7MPhGBOxTM6HRwB9aoUSCSLEFthn3QaiOPYTKurCZ/YdjwCseuO2TgDuh8ejsagfvwZ1hbmHYqG1jmYWd9zfA7/h1BP13A/adbNFvkifsOtoq5LkTQlqv30PX8uMGq2E1szZF30m9eUkQIUT1qU2wOmXKFGzYsIHXurJAVVdXF66urvjrr7/4lKsE2BXjizU3j0CnoATPng5GYmIy2kQl4os7FTWpdcky0UGerg6ueTph/yQPxEtNof9NOVBRGsYZm+ljfI+BMLM0Rrtpthg3zfOxdrm+sT6GzujPF0IIaSwdPW30m+jJF0KajJ1AlCrwvkiTqE2wOnHiRL4QoExajqDMSFxJu4Xc0gJ4mrSH0wV/dPx9I86HRMEkq7DepsUsr5qrp41TXt1xrrsr7mXboThZG1LrMgj3yvHZi1PRb04nhPpEIiosAd0826Fbb2doaFJLFEKIemF1stfP3cTVQwHITs+F1xh39JnQC4amT28imtjb8bjyrx/u+Eeii3cH9J/iBftObZ/a46kDKgNQLrUJVslDGyKP4urlA+jjHw3P6DR4+kejTUZ+vbuoBBq4LzGDj54Ljhq6IktHDw+eM4bVf7ooz6nomapxVwvddewwwKELtLUl8BrWjS+EEKKuWE/VPd8d5iP7Wb3s+V1XeG/VrVG/PJXHu3UtAm/1+0jecYAN1Przwx34NeBrPlCVqA6aFKDxKFhtRTKy83HKJxyXA6NQVi6FZ6+20O1UiLDCu5CkpGHS0btwOOuHBXeisai4rMGzFCFODtg6qC/CcmwgDq5+ubgUODj1dTi+YYF791IRG5MKt56OMDenaWsJIUQmK7WyP7dQMaiXr0t5et1nsmWPV9kWS74+LZdeFBXrBkCTAjQeBautyMoNJ3AtJAZikQi6JYVwjN+OdkUPMDcwDp1uJzd4aj/PUBt3ulrh3HB3HO40BPfzKwJa3ZhC2EZkQJIn8MyAvqEOhk7uBUcrC/67i4slXwghhFQ3Ys4gPhnJvZBY/ruJpTEmvTHmqe2mLn06oO+E3vA/eZ3PgCXR1oTXMx7o2JuyqqTlomC1BbqZmoydt27gdEwkJGIN9HeyhKlBFtpm7sFPEWHoGJ4Mo/yieoNTJltPF0c698Y1s84ItXBEsYkEOkbAmVXvYalIjB8/2Yq9W89AOy6fzz5VriFmfVWwOeR3GBjrN9OzJYSQlstjhCs2Bv8fUu6nIicjD+1cHSEWi5/a47FZrlYcXIbCvEJEh96Hs6sjdPXrnp2RNBZPjStod1HrqqaiYLUFyCst5kGptkbFy/XeueO4k5aKbvcT0DPmPkbfD0Vfvyholtf/B1AmEiPewBx+Vh1xsIcnDF91RGmoGe7eyUJxSRm62dhgypAeEIsqDqRTXxyBgugMXDsSiIKcQrj0cMCoF4ZQoEoIIU1k6WDBl+aia6CLrn0fzuBFngxL2NQ3ZXhT74s0DQWrKuxo7G1siwiCX0octMQaGKUpxdgjp/DTmTA4P0iFpLz+/helGmKcGdoZgZJOuF3mjHvGNqzCn09pamFugO2j/geMAkpKy1BUUgajGt++27a3wfJtb6GstAz52QUwbkPTzRJCCCGkeVGwqiKkgoCI7BSYaevBUteQF+Kv8j8Ey4hYvBYcienn/WGXltVg3WmmiS5uOtrheG9X+Ay3g6AlhuhCKaQ3NSAqKIFIWxNjRrhiwrCH00VpSTT5UhdNiSYFqoQQQtSXoMAyAJrBqskoWFUBP4VdxI6oQKQV5aN9XDIWnfeHZ+AdXHmQBXE9fxvsosBODvA1tUPcHQluZpqiOFsTiNAGCqygdSCbtQjg7Uv49aUCHLvZ4+P97zffkyOEEEIIeQIUrDazrJJC+KbGwFbPGN1NbJBfWoALh3dhyclrmOBzE7olpfXevlwkQqyFOXx7dYXmOy8gsa0zjk/dgfKsbN7Hjysqhk5WNp79fDqQlolrB/1RUliMIdP7Y9S8Ic3yPAkhhJDWQiStWBR1X6RpKFhtJuy0/mLfPTibEI6et2Mw82QAbG5FQ6eoBPsKqsxLWkOxRAM+Xdoj2N4Bt2xtcc6tK8rFYkzr3B3fDq1of5Lq4gvf8KBqGdQevZ1hIQF+/+EIHxzF/PPNQZSVluOVL2c107MmhBBCWgEqA1AqClYVQCqV4s71+wj1jYJjR2u49WuPyKI0+KZFw8nAHH0M2yJt9zYsXvkF1salQauBgVFlYjFirNrAp4MdDnTpiswwMQrSdZFvZYJ2SWIsmz8FA+0d5df/aNc78D8ezEfts6C4z/je8BrrjpXPr5UHqkxpcSnO7rxEwSohhBBCWgwKVp9Qfk4hXpy2EmlJWfx3qQRI/sAAnnHReP5kICzSsiGKT4NjUd2n9wvEEtzWskGBWBsn9bsiQM8J6X3MoHs1BtqXcvmLZIQCGPmmY/r73THCyaXa7VkPvUFT+/KlqrbtreFf47FofmhCCCFE+TNYkcajYPUJnQgP4YGqRFqG6dn+6FccCfuFmdCU1p89LdDUQoSpLQ6074OARAuUQ0N+GTuZ76ljhnf93kBhZCp8jwZB30gXfSf2hkMXu0Zv2+s/vISx84fD51AASgpL4D2+Fzp7tX+i50sIIYSQJ+fm5gYNDQ0sXbqUL6RuFKw+oV8fXMEHpX6YnugHPVRMT1qbDCNd5Bjpwqe3C/aaDEFCmbX8MqOiVGhmFD18USQamDzWEy5W5oCVObr37/xY28amQnXu4cgXQgghhDwekSDwRRFk9xMSEgI7u8YnoNQZBatPqFwiQkp/fejteTRQLdCUINDUAbu1vBBha4XirhoYObAXds6bipS0PPiGxsLSzBBe3RyQlZKDgPPhMLUwhMfATtDV137STSOEEEKIItAAK6WiYPUJaeRKEeFjy38ugxiZGnoI1HXE5lleyEm1gSikCNJyKcQpgIueKZ59sy+0NSSwtzLli4y1vTnGz+3/pJtDCCGEENKqULD6hEQlQKKGCRZZz0K0xBxScUXt6SzPZzB3/ijk5xYhzD8Kto4WsHOxVMRrRgghpJUrLizGmW2XcOWAH+6HJ6DXCFcMnTkAbkO6KXvT1BM7c6+o/qg0wKrJKFh9QiJTTRiPboNkP31Iswog0dZE70GdMWpMb365vqEOvIbRwYUQQlpaS0JW988WZTj2+xn8+vZfvH8265197I8zOPr7f9ib8idNf03UDgWrT+hjszGY/dsIlJdLER+VDCt7c+joainm1SGEENKsQi6E4ehvp3nfai0dLfSb6Ikpbz7T7ANVy0oqxkGwQLXa+tLyZt0O8vQGWJHGo2D1CXm6OfN/NTTEcOxo86R3RwghRIm+X7gBiZHJfIKVwtwinNh0BpnJWVh56INm3Y7hcwYiOTYVl//1Q0ZSBjp7t8ewmQNhZm3SrNtBqvZZVVCQSbFqk1Gw+oT09GjUPiGEtBZiDTGEymiCVQCw+ISta25m1qZY9PMreOOnl1FUUMwnfyFEXVGwSgghhFT6cMfbOLX5PK4eCoC2nhb6TfLEM/OHK23/sJpZClRVALWuUioKVgkhhJBK7Xs6o/0PznwGQEKIaqBglRBCCCGkPlIFtq5S1P2oEQpWCSGEEELqQd0AlKv5q8YJIYQQQghpJApWCSGEEEIaM8BKUQsANzc3WFpa4ttvv6V93wAqAyCEEEIIaWYhISGws7Oj/d4IFKwSQgghhNTrYUb0ydGsAE1FwSohhBBCSH2oz6pSUc0qIYQQQghRWZRZJYQQQgipD/VZVSrKrBJCCCGEEJVFmVVCCCGEkHrQpADKRcEqIYQQQkh9aICVUlEZACGEEEIIUVmUWSWEEEIIqY9UqFgUQVH3o0Yos0oIIYQQQlQWZVYJIYQQQurDkqGKmsGKEqtNRsEqIYQQQki9aLpVZaIyAEIIIYQQorIos0oIIYQQUh9qXaVUFKwSQgghhNSHugEoFZUBEEIIIYQQlUWZVUIIIYSQ+gjSikURFHU/aoQyq4QQQgghRGVRZpUQQgghpD40wEqpKLNKCCGEENKYAVaKWgC4ubnB0tIS3377Le37BlBmlRBCCCGkmYWEhMDOzo72eyNQsEoIIYQQUh8qA1AqtS0DuHz5MkQiEV8ePHig7M0hhBBCCCG1UMvMqlQqxeLFi6Gvr4/8/Hxlbw4hhBBCWkJ2lSiFWmZWN27ciLi4OMyfP1/Zm0IIIYSQllIGoKiFNInaZVYzMjLwySefYMWKFUhJSVH25hBCCCHNorysHKGXbiPsyh2093CG+7Du0NLRor1PVJ7aBasff/wxbGxs8L///Q8rV65U9uYQQgghT11qfDpe9ViKnLRc+TpdAx2svbAC7d2d6RVoiFQKiKSKuy/SJJrq1ibit99+w/Hjx6GhodGk2+bk5PBFJikp6SlsISGEEKJ4ybGp1QJVpjCvCHF3EilYJSpPrWpW2aCqcePGYeTIkU2+7dq1a2Fvby9fvLy8nso2EkIIIYpm084K1k6W1daZWBrBuYcD7ezGoJpVpWqRmdXz589j6NChjbru6dOnMWLECOzcuRPXrl1DWFjYYz3mkiVLqg3IYplVClgJIYS0BOY2ptgStQ5RITG4eTkcHXu1Q2fvDhCL1Spn9fioz6pStchgtVOnTvj9998bdd2uXbvyf5cuXYpp06bxvqqRkZHywVZMTEwMSkpK4OBQ9zdMIyMjvhBCCCEtEfv8a9/TmS+EtCQtMlhlA6Sa2nYqISEBO3bs4EtNffv25QFweHi4AreSEEIIIa2CVABEguLui7T+YPVx7Nmz55F1u3fv5utZlra+rCohhBBCCFEOtQlWp06d+si6mzdv8n/Hjx8Pa2trJWwVIYQQQlSdIEj5oqj7Ik2jNsEqIYQQQshjERR4+p6qAJpMrYcBfv755xAEgbKqhBBCCCEqijKrhBBCCCH1odZVSqXWmVVCCCGEEKLaKLNKCCGEEFIfKRsUJVXgfZGmoGCVEEIIIaQ+VAagVFQGQAghhBBCVBZlVgkhhBBC6iFIpRAUVAbA7os0DWVWCSGEEEKIyqLMKiGEEEJIfahmVakoWCWEEEIIaShYVdgMVjSFVVNRGQAhhBBCCFFZFKwSQgghhDRYBiBV0KI6mdW0tDSMHz8e+vr66NChA06cOAFVRGUAhBBCCCFq6PXXX4elpSVSU1Nx5swZTJ8+HXfv3uXrVAkFq4QQQggh9RCkAgSRYjKigopkVvPy8nDgwAFERUVBT08PEyZMgIeHB1+3cOFCqBIqAyCEEEIIqY/CSgAql8cMLj/77DOMHTsWFhYWEIlE+Oqrr2q9bmlpKT799FM4ODhAR0cHrq6u2LFjR7XrsAyqgYEB7O3t5evc3NwQFhamcu8FClYJIYQQQlRcWloaVqxYgdDQULi7u9d7XZYZXb16NSZPnoyff/6ZB6SzZ8/Gli1bqgW/RkZG1W5nbGzM16saKgMghBBCCFHxMgAbGxskJCTA1tYWMTExcHZ2rvV6wcHB2Lx5Mw9sP/nkE75u/vz5GDZsGJYuXYoZM2ZAS0uLZ1VzcnKq3Zb9ztarGsqsEkIIIYSoOG1tbR6oNmT37t0Qi8V444035OtYycCiRYuQkpKCc+fO8XVs9D/LosbHx8uvFxISgm7dukHVUGb1MZWVlfF/k5KSFPl6EEIIIaQG2Wet7LO3uRULBY9da/rIfaGo1viBnZKveVr+cQQGBsLFxQVmZmbV1nt7e/N/g4KCMHr0aJ5BnTRpEq+DZaUCLIhlt921axdUDQWrj4m1eWC8vLwU+XoQQgghpJ7PXicnp2bbPyygY0Gff8ZZhd6vrq7uI/EDCxo///zzJ77vxMREXjJQkywryy6XWb9+PV588UW0adOGX84CVVVrW8VQsPqYevToAT8/Pz4iT1NTebuRfTNjb3i2LbW9OdUN7Q/aH/Qeob8ZOoa0vmMqy6iyQJV99jYnExMT3tpJ0YOOpFIpP1VflSKyqkxhYSEvGaiJPZ5EIuGXy7Ag9ejRo1B1FKw+JtYKwtPTE6qCHUTs7OyUvRkqg/YH7Q96j9DfDB1DWtcxtTkzqjUDVra0pPikuLi41gCZtbRil7c0NMCKEEIIIaSVsLW1rXU8jez0f2MGaakaClYJIYQQQloJDw8PXrqQkZFRbb2vr6/88paGgtUWjtW4sKJsRdW6tHS0P2h/0HuE/mboGELHVHX2/PPP81P+v/76a7XeruvWrePjbIYOHYqWRiSoyiS1hBBCCCGkTuvWrUNWVhZfvvvuO4waNQoDBw7kly1evJjPQMW88MIL2L59O++tygalHThwgA+k2rRpE1566aUWt4cpWCWEEEIIaQGcnJwQGxtb62XR0dHyQWglJSVYuXIln8mKTQTQsWNHLFu2DHPmzEFLRMEqIYQQQghRWVSzSgghhBBCVBYFq4QQQgghRGVRsEoIIYQQQlQWBasqjk3xxlpTjR07lrecEIlE+Oqrr+q8/qFDhzB48GDewsnQ0BDu7u7YunUr1HF/nDp1CkOGDOHTybERkqy33MaNG3lLj9bC398fb775Jh/tyeawZs+TjQ69ePHiI9dNTk7G3LlzYW5uzq87bNgwBAYGojVp7P7Yv38/Zs2aBRcXFz6bi7W1Nd83dQ1cUIf3R1Xz58/nf1tjxoyBOu+P1n48beo+UYdjKlFRrHUVUV3R0dGstZhgZ2cnjBw5kv+8Zs2aWq/79ddf88snTZok/PLLL8L69euFt99+W1i9erWgbvtj3759/LI+ffoIP/30E98fo0aN4uvef/99obV47rnnBAsLC+G1114TfvvtN+Gbb74R2rdvL2hoaAjHjx+XX6+goEDo2rWrYGZmJqxatUpYt24d/93AwEAICwsT1G1/mJubC126dBE+/PBD4Y8//hCWL18uGBkZCVZWVkJ8fLygbvujKn9/f0FTU1PQ0dERRo8eLbQmTdkf6nA8bco+UZdjKlFNFKyquKKiIiEhIaFaoFZbcBYQECCIxWJ+oGnNGrs/hg4dKtja2vLry0ilUqFXr16CpaWl0Fpcvny52nNkMjIyBGtra8HDw0O+bu3atXxfXbx4Ub4uLS2NB23sw1jd9sfZs2cfue3Vq1f5PlqyZImgbvuj6t8IC0ZeeeUVwdHRsdUFq43dH+pyPG3KPlGXYypRTVQGoOK0tbUbNY/v2rVrYWlpiSVLlvDfc3Nzoc77IycnB6ampvz6Muy0ppWVFfT09NBa9O/fv9pzZNjzZqf4w8LC5Ot2794NNzc3efNohpUDzJw5E8ePH28175fG7o/aZnDp06cPf29VvZ667A+ZLVu28PWrV69Ga9TY/aEux9Om7BN1OaYS1UTBaitx+vRpeHp6yqdTYzVWrK7o008/Vct6IlZXxQ60y5cvx927d3mzZPYBdPLkSXz44Ydo7RITE/nrz7DX//r16/Dy8nrket7e3rx59M2bN6Eu+6MuhYWFfFaYhq7XWvcHC8g++OADfPTRRzwAUSc19wcdTx/dJ+p+TCVKpuzULmm8uk57Z2Zm8vVt2rQR9PT0+OV79+4VZs2a1epOa1ZVXxlAbm6uMHXqVEEkEvHrsEVLS0v466+/BHU4rceet+x1T01N5c//008/feS6Z86c4Zex94u67I+6sFpEti8OHToktGZ17Y93331XaNeunfw0b2ssA2jM/lDX42lD7xF1PqYS5aNgtRUEZ3FxcfKDx/bt26tdNmHCBEEikfCARZ2C1ZKSEuGjjz7i9Zhbt24V/vnnH2HKlCl84MiuXbuE1io5OVlwcHDggUZWVhZfd//+fb6fahsYcuXKFX4Z20fqsj9qw2p52d/J5MmThdasrv0RHh7On//+/fvl69QhWK1tf6jr8bSh94i6HlOJaqBgtRUEZykpKXw9O2iUlZVVu4wdRNhlR44cEdQpWF2wYAEf7V5aWlptPRu9ygYVsdHxrU1OTg4f7GBiYiLcuHHjkfdHfZnVPXv2COqyP2oKDQ0VTE1NBXd3dyE7O1torerbHywoHTJkSLV1rT1YbejvRd2Opw29R9TxmEpUB9WstgJsoAzrFcnqizQ0NKpdJqs9Y7V46oLVYP71118YP348NDU1q102adIkpKen486dO2hNWL3lhAkTcPv2bRw9epT3TKz6/mCDIpKSkmqtS2MaM2itteyPqu7du8d7SrI67xMnTvBa79aovv3x33//8brDRYsWITIyUr6UlZWhoKCA/9zajh8N/b2o4/G0vn2ijsdUolooWG0FxGIxb1admprKDypVJSQk8H/ZyFZ1wQ6c7IOWLTXJ1tV2WUtVWlqKqVOnwsfHB/v27UO/fv0eeX+wTgB+fn6P3NbX1xcSiaTOYK417o+qfxvDhw/n+4c1O2+tfyMN7Y/4+Hj+L7tOhw4d5AvbP5cuXeI/b9iwAer096Jux9OG9om6HVOJClJ2apco5rT3jz/+yC/bsGFDtR54w4YNEwwNDfnpHXXZH+zUHTuN5ezsXO3UVHl5Oe8hyZqd5+fnC60Be07Tpk3jPSF37txZ5/X+7//+j++rS5cuPdJnldXhtRaN3R/sVG/nzp35829NkyI8zv6IiYnhZSA1F9YovmfPnvxnVtOqTu8PdTqeNmafqNMxlaim6vl8opJYOyp22kl26uncuXPyb7GLFy/m094tXLgQmzZt4qfywsPDeTbk4MGDOHv2LH788Uc+VaA67Y9ly5bxFiusXdO8efN49nDXrl24du0ab+fVWvoCvvfee9izZw9GjhzJ98G2bduqXT5nzhz+72uvvYY//viDn7J79913+T769ddfUVRUhC+//BKtRWP3x+jRo/nfCZtmMigoiC9VT/Wy26vL/nB0dORLbbdl+4Jl3NTt/aFOx9PG7BNWDqEux1SiopQdLZOGsYEOstGpNReWXayaKWNF8CwjwlqKuLq6Clu2bFHb/bF7926hb9++fIpRbW1tvj/YlImtyeDBg+vcFzX/vJOSkoTZs2fzwUSsJQ8bUOPn5yeo4/6o7zrsPtTx/VFTaxxg1ZT9oS7H06bsE3U4phLVJGL/U3bATAghhBBCSG1ogBUhhBBCCFFZFKwSQgghhBCVRcEqIYQQQghRWRSsEkIIIYQQlUXBKiGEEEIIUVkUrBJCCCGEEJVFwSohhBBCCFFZFKwSQgghhBCVRcEqIYQQQghRWRSsEkIIIYQQlUXBKiFq7PLly/j888+RlZUFVbR582aIRKJal1dffVV+PT8/P7zxxhvw9PSEtrY2v/zBgwe13qeTk1Ot9zdnzpxHrpuQkIAFCxbA2dkZurq6aNeuHV5//XUkJiY+1edNCCHkIc0qPxNC1DBY/eKLLzBv3jyYmJhAVbGA2sXFpdq6jh07yn8+duwYfvvtN3Tv3p2vv3nzZr335+rqiqVLl1ZbxwLRqnJyctCnTx8UFhbitddeg6OjI27duoUNGzbgxIkT/DH09PQU8vwIIYTUjYJVQkijscCNZRib2+jRo3ngWBcWTC5btoxvGwtsGwpWbWxsas2kVnXgwAHEx8fj0KFDmDBhQrXM7FtvvYULFy7gmWeeeYxnQwghpCmoDIAQNcWCuuXLl/Of2Wlu2enw8+fPy4OyMWPG8N9ZoKijo4Ovv/6aX8aux25f05AhQ/hSVWlpKVavXo3OnTvzU/TW1tb8FL4iSw+srKyaHESz7crPz6/zcpZZlQW2Vcl+byiryvYb2087d+7El19+CXt7exgYGGDSpElIS0tDWVkZ3//s/vT19TFjxgzk5eVVuw9ZoGxnZ8f3HcvusqC8uLi41uC6R48e/HXq1KkT/vrrL/4asW0ghJCWjDKrhKipZ599FuHh4fjnn3/w/fffo02bNnx9ly5d5NeJjIzEc889h/nz5+Pll1+Gg4NDkx5DEAR++9OnT/P7YMFUVFQU1q1bh6CgIFy5cgUSiaTB+8nOzuYBXlXm5uaPHYixrCgLNlnAyALBxYsX47333oNY/PD7+6BBg/j9s8u+++47eRnARx99xC8bOHBgox7rm2++gZaWFi87uH//Pn744Qe89NJL/HHv3LmDjz/+GDdu3OBlDCyQZ5fLbNq0CRoaGli0aBFMTU1x7do1vi0s47t9+3b59VhZAtvPLEhdtWoVz4DLAmFCCGnxBEKI2lqzZo3ADgPR0dGPXObo6MgvO3z48COXsfWfffbZI+sHDx7MF5mdO3cKIpFIOHv2bLXrHT16lN/Hli1b6t2+v/76i1+vtiU1NbXW27DtYpcnJSXVevmECROEr776Sjhw4IDw559/CkOHDuXXf/311x+57saNGwUTE5Nqjzt58mShoKBAaMi5c+f49Tt37iwUFxfL17/88st8/aBBg4Ty8nL5+meeeUYwNDQUpFKpfF1+fv4j97t69Wq+T+Pi4uTrXF1dBSsrKyErK0u+Ljw8XNDU1OSPRQghLRllVgkhdWKnrsePH//Ye4hlbdmAJ5ZRrZoZ9fLy4qfEz549i7lz5zZ4Pz/99FO1jC9jbGz8WNvETq1XxbKc7NT8+vXreS1q1YFb7Pn37t2bl0N06NABwcHB+PbbbzFz5kzs37+/Wia2Li+++CLPrFZ97ixjyh636u29vb1x/PhxpKamwtLSslqpgVQqRW5uLi9dGDBgAM9Ys8w0y86yzgQsM8syt1X3Ccuyslrfo0ePPtZ+IoQQVUHBKiGkTqyW9Umw09xssbCwqPXylJSURt0Pa0lV3wCrJ8FO9S9ZsgSHDx/mwbMsWL106RIP1FnHhL59+/J1EydO5JfPmjUL+/btw7Rp0xq8fxbwViULKOtan5mZKQ9W2UCx999/n9e/slP7VclqfmNjY/m/LJiuiW0rBauEkJaOglVCSJ2aOmipvLyc11jKsIxg165d8eOPP9Z6fVmdrLLJanEzMjLk637//Xe+fbJAVYYFrCzAZfW2jQlWq+6PxqyvqLKoqNMdOnQoH3zFBmix1l3s9WC9X1mrMbZvGyK7L0IIackoWCVEjT3uACXWk7W20fwxMTHV+qGynwMDAzFs2LBGnTJXlnv37vF/ZRlNJjk5mQ/Aqi0gZ0FgbZcp0rlz53jpxN69ezF48GD5ejZYrSo28Iu5e/fuI/dR2zpCCGlpVPfTgxDy1LGsnezUc1O0b99e3uJK5uDBg3yUelWsHRML+tjo/5pYsNfUx31SLMCumW1k27FmzRqe6RwxYkS1mk+WaT158mS162/bto3/26tXr6e6rZqaFbmEqtvLsqlr166tdj1bW1s+ycHWrVt5NlaGlV/U3HZCCGmJKLNKiBpjg4eYDz/8kA8aYgOBWBa0aoaxNqxPKmtFNXnyZN4Yn7XAYv1Ea84yNXv2bD4QSdZEn2UIWTaXtcRiGUPWhokFtE+K1W2yYI25ePEi/5e1gGKDuFjmUTaIi/UiZY85btw4Xo/LguVdu3YhJCSE7wPWW1aGtaxivUpZiy82xSqrCWWDmv744w8eyCpiu+vTr18/3p6LDdBi28JafLF9VrMXK8OCbVZfy0oWXnnlFV7fyr4gsBm9rl+//lS3kxBCnjpltyMghCjXqlWrBHt7e0EsFvM2R6zlkqx11ejRo2u9DWu59NFHH/F2STo6OrwNU3Bw8COtq5iysjLh+++/F9zc3Ph1jY2Neaul999/X4iPj29U66qrV682qk1UbUvV7QkMDBQmTpwo2NnZCVpaWoKBgYHQt29fYdu2bbXeb0REhDBz5ky+LyQSiWBjYyMsWLBASElJqXd7qm4Ta99VFfu96n6WYfuIrb99+7Z83bVr14T+/fsLenp6gqWlpfDaa68JN27c4Ndj+6aqffv2Cd26dePPq0OHDsKmTZuEd999l+9zQghpyUTsf08/JCaEENLcWEsuNpEB1a4SQloyqlklhJAWjvVfrTngi5VmHDt2jJd1EEJIS0aZVUIIaeFYFwYWlLK6Y1ajGx0djQ0bNvDOBWwig5q1xIQQ0pLQACtCCGnhTE1N+aQJ27dvx4MHD6Cjo4OBAwdi9erVFKgSQlo8yqwSQgghhBCVRTWrhBBCCCFEZVGwSgghhBBCVBYFq4QQQgghRGVRsEoIIYQQQlQWBauEEEIIIURlUbBKCCGEEEJUFgWrhBBCCCFEZVGwSgghhBBCVBYFq4QQQgghRGVRsEoIIYQQQqCq/h/fRhV+vDSu6AAAAABJRU5ErkJggg==", + "image/png": 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    " ] @@ -274,20 +265,25 @@ ], "source": [ "sel = (cat.matched & (cat.class_star > CLASS_STAR_CUT) & (cat[\"flags\"] < FLAG_CUT))\n", - "pe = cat.loc[sel, [f\"truth_mag_{BAND}\", f\"magerr_auto_{BAND}\"]].dropna()\n", + "pe = cat.loc[sel, [f\"truth_mag_{BAND}\", f\"mag_auto_{BAND}\", f\"magerr_auto_{BAND}\"]].dropna()\n", "x, y = pe[f\"truth_mag_{BAND}\"].values, pe[f\"magerr_auto_{BAND}\"].values\n", "print(f\"{len(pe):,} star-classified matched detections with F158 photometry\")\n", "\n", - "med, lo, hi = (np.full(MAG_MID.size, np.nan) for _ in range(3))\n", + "dm = pe[f\"mag_auto_{BAND}\"].values - pe[f\"truth_mag_{BAND}\"].values\n", + "med, lo, hi, scat = (np.full(MAG_MID.size, np.nan) for _ in range(4))\n", "ib = np.digitize(x, MAG_BINS) - 1\n", "for i in range(MAG_MID.size):\n", " yy = y[ib == i]\n", " if yy.size >= 20:\n", " lo[i], med[i], hi[i] = np.percentile(yy, [16, 50, 84])\n", + " v = dm[ib == i]\n", + " scat[i] = (np.percentile(v, 84) - np.percentile(v, 16)) / 2\n", "\n", "fig, ax = plt.subplots(figsize=(7, 5))\n", "hb = ax.hexbin(x, np.log10(y), gridsize=180, bins=\"log\", mincnt=1, cmap=\"viridis\")\n", - "ax.plot(MAG_MID, np.log10(med), \"r-\", lw=2, label=\"median\")\n", + "ax.plot(MAG_MID, np.log10(med), \"r-\", lw=2, label=\"median reported magerr\")\n", + "ax.plot(MAG_MID, np.log10(scat), \"-\", color=\"orange\", lw=2,\n", + " label=\"truth-based scatter of (obs $-$ true)\")\n", "ax.plot(MAG_MID, np.log10(lo), \"r--\", lw=1, label=\"16/84%\")\n", "ax.plot(MAG_MID, np.log10(hi), \"r--\", lw=1)\n", "ax.axhline(np.log10(2.5 / np.log(10) / SNR_DEPTH), color=\"w\", ls=\":\", lw=1.5,\n", @@ -300,6 +296,78 @@ "plt.show()\n" ] }, + { + "cell_type": "markdown", + "id": "9b1d80ac", + "metadata": {}, + "source": [ + "### Error validation: reported magerr vs truth\n", + "\n", + "Because we have the truth, the reported errors can be validated directly: for true stars,\n", + "the scatter of (observed − true) magnitude should match the reported `magerr_auto`. It\n", + "does not — the truth-based scatter runs a flat factor ~1.9–2.0 above the reported errors\n", + "in every band, the signature of SExtractor underestimating the correlated noise of the\n", + "resampled median coadds. Consequence: the photo-error *model* (section 5) is built from\n", + "the truth-based scatter, and the reported-error depth maps are an internal reference,\n", + "not a claim that the survey is genuinely deeper than the official depths.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "15f41f9f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-11T18:37:19.383332Z", + "iopub.status.busy": "2026-06-11T18:37:19.382743Z", + "iopub.status.idle": "2026-06-11T18:37:20.103090Z", + "shell.execute_reply": "2026-06-11T18:37:20.102037Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vsel = (cat.matched & (cat.truth_gal_star == 1) & (cat.class_star > CLASS_STAR_CUT)\n", + " & (cat[\"flags\"] < FLAG_CUT))\n", + "fig, (axa, axb) = plt.subplots(2, 1, figsize=(7.5, 7.5), sharex=True,\n", + " gridspec_kw={\"height_ratios\": [2, 1]})\n", + "for b, col in zip(BANDS, [\"C0\", \"C1\", \"C2\", \"C3\"]):\n", + " sub = cat.loc[vsel, [f\"truth_mag_{b}\", f\"mag_auto_{b}\", f\"magerr_auto_{b}\"]].dropna()\n", + " mt = sub[f\"truth_mag_{b}\"].values\n", + " dmv = sub[f\"mag_auto_{b}\"].values - mt\n", + " sg = sub[f\"magerr_auto_{b}\"].values\n", + " ib = np.digitize(mt, MAG_BINS) - 1\n", + " scat_b, rep_b = np.full(MAG_MID.size, np.nan), np.full(MAG_MID.size, np.nan)\n", + " for i in range(MAG_MID.size):\n", + " v = dmv[ib == i]\n", + " if v.size >= 100:\n", + " scat_b[i] = (np.percentile(v, 84) - np.percentile(v, 16)) / 2\n", + " rep_b[i] = np.median(sg[ib == i])\n", + " axa.plot(MAG_MID, np.log10(scat_b), \"-\", color=col, lw=1.8, label=f\"{b}\")\n", + " axa.plot(MAG_MID, np.log10(rep_b), \"--\", color=col, lw=1.4, alpha=0.8)\n", + " axb.plot(MAG_MID, scat_b / rep_b, \"-\", color=col, lw=1.6, label=b)\n", + "axa.set(ylabel=r\"$\\log_{10}\\,\\sigma$ [mag]\",\n", + " title=\"True stars: truth-based scatter (solid) vs reported magerr (dashed)\")\n", + "axa.legend(title=\"band\", fontsize=9); axa.grid(alpha=0.3)\n", + "axb.axhline(1.0, color=\"k\", lw=0.8)\n", + "axb.axhline(2.0, color=\"0.5\", lw=0.8, ls=\":\")\n", + "axb.set(xlabel=\"true mag\", ylabel=\"scatter / reported\", xlim=(20, 28), ylim=(0.5, 2.8))\n", + "axb.grid(alpha=0.3)\n", + "fig.tight_layout()\n", + "fig.savefig(FIG_DIR / \"error_validation.png\", dpi=130, bbox_inches=\"tight\")\n", + "plt.show()\n" + ] + }, { "cell_type": "markdown", "id": "5b19ebfd", @@ -326,14 +394,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "7a7ec629", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:21:28.819934Z", - "iopub.status.busy": "2026-06-11T18:21:28.819727Z", - "iopub.status.idle": "2026-06-11T18:21:29.085795Z", - "shell.execute_reply": "2026-06-11T18:21:29.085249Z" + "iopub.execute_input": "2026-06-11T18:37:20.108854Z", + "iopub.status.busy": "2026-06-11T18:37:20.108593Z", + "iopub.status.idle": "2026-06-11T18:37:20.280104Z", + "shell.execute_reply": "2026-06-11T18:37:20.276794Z" } }, "outputs": [ @@ -399,14 +467,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "7bc997de", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:21:29.087338Z", - "iopub.status.busy": "2026-06-11T18:21:29.087228Z", - "iopub.status.idle": "2026-06-11T18:21:34.260623Z", - "shell.execute_reply": "2026-06-11T18:21:34.259633Z" + "iopub.execute_input": "2026-06-11T18:37:20.286749Z", + "iopub.status.busy": "2026-06-11T18:37:20.286429Z", + "iopub.status.idle": "2026-06-11T18:37:25.652611Z", + "shell.execute_reply": "2026-06-11T18:37:25.651848Z" } }, "outputs": [ @@ -472,14 +540,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "ff643fec", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:21:34.262930Z", - "iopub.status.busy": "2026-06-11T18:21:34.262715Z", - "iopub.status.idle": "2026-06-11T18:21:41.479839Z", - "shell.execute_reply": "2026-06-11T18:21:41.479300Z" + "iopub.execute_input": "2026-06-11T18:37:25.656498Z", + "iopub.status.busy": "2026-06-11T18:37:25.656131Z", + "iopub.status.idle": "2026-06-11T18:37:32.467754Z", + "shell.execute_reply": "2026-06-11T18:37:32.467242Z" } }, "outputs": [ @@ -539,14 +607,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "8f4f7263", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:21:41.481312Z", - "iopub.status.busy": "2026-06-11T18:21:41.481128Z", - "iopub.status.idle": "2026-06-11T18:21:53.426613Z", - "shell.execute_reply": "2026-06-11T18:21:53.425314Z" + "iopub.execute_input": "2026-06-11T18:37:32.473757Z", + "iopub.status.busy": "2026-06-11T18:37:32.473509Z", + "iopub.status.idle": "2026-06-11T18:37:44.244766Z", + "shell.execute_reply": "2026-06-11T18:37:44.244033Z" } }, "outputs": [ @@ -632,14 +700,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "53a0e9b7", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:21:53.429070Z", - "iopub.status.busy": "2026-06-11T18:21:53.428762Z", - "iopub.status.idle": "2026-06-11T18:21:57.321947Z", - "shell.execute_reply": "2026-06-11T18:21:57.321322Z" + "iopub.execute_input": "2026-06-11T18:37:44.246571Z", + "iopub.status.busy": "2026-06-11T18:37:44.246438Z", + "iopub.status.idle": "2026-06-11T18:37:47.157027Z", + "shell.execute_reply": "2026-06-11T18:37:47.156251Z" } }, "outputs": [ @@ -667,28 +735,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "wrote roman_dc2_maglim_f106_nside1024.fits.gz (median 26.77) and roman_dc2_maglim_f106_nside1024_5sigps.fits.gz (median 26.90)\n" + "wrote roman_dc2_maglim_f106_nside1024.fits.gz (median 26.77)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "wrote roman_dc2_maglim_f129_nside1024.fits.gz (median 27.11) and roman_dc2_maglim_f129_nside1024_5sigps.fits.gz (median 26.90)\n" + "wrote roman_dc2_maglim_f129_nside1024.fits.gz (median 27.11)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "wrote roman_dc2_maglim_f158_nside1024.fits.gz (median 27.11) and roman_dc2_maglim_f158_nside1024_5sigps.fits.gz (median 26.90)\n" + "wrote roman_dc2_maglim_f158_nside1024.fits.gz (median 27.11)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "wrote roman_dc2_maglim_f184_nside1024.fits.gz (median 26.67) and roman_dc2_maglim_f184_nside1024_5sigps.fits.gz (median 26.20)\n" + "wrote roman_dc2_maglim_f184_nside1024.fits.gz (median 26.67)\n" ] } ], @@ -718,17 +786,9 @@ " fb = \"f\" + b[1:]\n", " mlm = maglim_maps[b]\n", " cov = mlm != hp.UNSEEN\n", - " # measured (catalog-error) convention\n", " fname = OUT_DIR / f\"roman_dc2_maglim_{fb}_nside{NSIDE}.fits.gz\"\n", " hp.write_map(fname, mlm, overwrite=True, dtype=np.float32)\n", - " # official 5-sigma point-source convention: shift the map median onto the\n", - " # simulated survey's official depth, keeping the measured spatial structure\n", - " norm = np.full_like(mlm, hp.UNSEEN)\n", - " norm[cov] = mlm[cov] - np.median(mlm[cov]) + SIM_DEPTH_5SIG[b]\n", - " fname_n = OUT_DIR / f\"roman_dc2_maglim_{fb}_nside{NSIDE}_5sigps.fits.gz\"\n", - " hp.write_map(fname_n, norm, overwrite=True, dtype=np.float32)\n", - " print(f\"wrote {fname.name} (median {np.median(mlm[cov]):.2f}) and \"\n", - " f\"{fname_n.name} (median {SIM_DEPTH_5SIG[b]:.2f})\")\n" + " print(f\"wrote {fname.name} (median {np.median(mlm[cov]):.2f})\")\n" ] }, { @@ -743,30 +803,29 @@ "\n", "- `roman_photoerror_f158.csv` — `# delta_mag,log_mag_err`, with\n", " `delta_mag = true mag − maglim` at the object's nside=1024 pixel and\n", - " `log_mag_err` the binned **median** log10 of the observed F158 magerr of\n", - " star-classified objects.\n", + " `log_mag_err` = log10 of the binned **truth-based scatter** ((p84−p16)/2 of\n", + " observed − true) of star-classified objects. The reported SExtractor magerr is\n", + " NOT used (it underestimates the true scatter ~2×); it is kept only as a\n", + " comparison curve in the figure.\n", "- `roman_stellar_efficiency_cutf158.csv` —\n", " `# mag_f158,delta_mag,detection_eff,classifiction_eff,classification_detection_eff`\n", " (the `classifiction_eff` typo is intentional: `set_completeness` looks that name up).\n", - " `delta_mag` is keyed to the **official 5σ point-source depth of the simulated\n", - " survey** (F158: 26.9, Troxel+23/Akeson+19) — *not* the measured map median, which runs\n", - " ~0.2 mag deeper because SExtractor underestimates correlated noise in the coadds.\n", - " Pair these tables at runtime with maglim maps in the same official convention, e.g.\n", - " the `_5sigps` maps written above, or an HLWAS exptime-scaled map normalized to the\n", - " [STScI median 5σ point-source depths](https://roman-docs.stsci.edu/roman-community-defined-surveys/high-latitude-wide-area-survey)\n", - " (wide F158 26.2; medium F106 26.5 / F129 26.4 / F158 26.4).\n" + " `delta_mag` is keyed to the **median of the measured maglim map** (the maps and the\n", + " tables share one internal convention; no renormalization is applied anywhere).\n", + " Runtime maglim maps for other footprints (e.g. the HLWAS exptime-scaled map) should\n", + " be expressed in this same convention, i.e. scaled relative to the DC2 depth.\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "882cdd12", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:21:57.323411Z", - "iopub.status.busy": "2026-06-11T18:21:57.323263Z", - "iopub.status.idle": "2026-06-11T18:21:58.018497Z", - "shell.execute_reply": "2026-06-11T18:21:58.017584Z" + "iopub.execute_input": "2026-06-11T18:37:47.158434Z", + "iopub.status.busy": "2026-06-11T18:37:47.158312Z", + "iopub.status.idle": "2026-06-11T18:37:48.089459Z", + "shell.execute_reply": "2026-06-11T18:37:48.088990Z" } }, "outputs": [ @@ -774,20 +833,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "measured map median = 27.107; reference maglim (official 5sig pt-src) = 26.90 (catalog errors run +0.21 mag past official)\n" + "reference maglim = measured map median = 27.107\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "wrote data/surveys/roman_hlwas/roman_photoerror_f158.csv (145 rows, delta_mag -16.08 .. 1.44)\n", + "wrote data/surveys/roman_hlwas/roman_photoerror_f158.csv (148 rows, delta_mag -16.64 .. 1.36)\n", "wrote data/surveys/roman_hlwas/roman_stellar_efficiency_cutf158.csv (56 rows)\n" ] }, { "data": { - "image/png": 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", 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", 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    " ] @@ -799,33 +858,35 @@ "source": [ "mlm = maglim_maps[BAND]\n", "covered = mlm != hp.UNSEEN\n", - "MAGLIM_MEASURED = float(np.median(mlm[covered]))\n", - "MAGLIM_REF = SIM_DEPTH_5SIG[BAND] # official 5-sigma point-source depth of the simulated survey\n", - "print(f\"measured map median = {MAGLIM_MEASURED:.3f}; reference maglim (official 5sig pt-src) = \"\n", - " f\"{MAGLIM_REF:.2f} (catalog errors run {MAGLIM_MEASURED - MAGLIM_REF:+.2f} mag past official)\")\n", + "MAGLIM_REF = float(np.median(mlm[covered])) # tables keyed to the measured map median\n", + "print(f\"reference maglim = measured map median = {MAGLIM_REF:.3f}\")\n", "\n", "# --- photoerror table -------------------------------------------------------\n", "sel = (cat.matched & (cat.class_star > CLASS_STAR_CUT) & (cat[\"flags\"] < FLAG_CUT))\n", - "pe = cat.loc[sel, [\"alphawin_j2000\", \"deltawin_j2000\",\n", - " f\"truth_mag_{BAND}\", f\"magerr_auto_{BAND}\"]].dropna()\n", + "pe = cat.loc[sel, [\"alphawin_j2000\", \"deltawin_j2000\", f\"truth_mag_{BAND}\",\n", + " f\"mag_auto_{BAND}\", f\"magerr_auto_{BAND}\"]].dropna()\n", "pix = hp.ang2pix(NSIDE, pe.alphawin_j2000.values, pe.deltawin_j2000.values, lonlat=True)\n", "ml_local = mlm[pix]\n", "good = ml_local != hp.UNSEEN\n", - "# per-pixel maglim in the official convention (same shift as the _5sigps maps)\n", - "delta = pe[f\"truth_mag_{BAND}\"].values[good] - (ml_local[good] - MAGLIM_MEASURED + MAGLIM_REF)\n", - "logerr = np.log10(pe[f\"magerr_auto_{BAND}\"].values[good])\n", + "delta = pe[f\"truth_mag_{BAND}\"].values[good] - ml_local[good]\n", + "# the error model is the TRUTH-BASED scatter of (observed - true): the reported\n", + "# SExtractor magerr underestimates it by ~2x (correlated noise in the coadds)\n", + "dm_obs = (pe[f\"mag_auto_{BAND}\"].values - pe[f\"truth_mag_{BAND}\"].values)[good]\n", + "logerr_reported = np.log10(pe[f\"magerr_auto_{BAND}\"].values[good])\n", "\n", "dbins = np.arange(np.floor(delta.min() * 10) / 10, 1.5 + 1e-6, 0.12)\n", "dmid = 0.5 * (dbins[1:] + dbins[:-1])\n", - "med_logerr = np.full(dmid.size, np.nan)\n", + "log_scatter = np.full(dmid.size, np.nan)\n", + "med_logerr_rep = np.full(dmid.size, np.nan)\n", "ib = np.digitize(delta, dbins) - 1\n", "for i in range(dmid.size):\n", - " v = logerr[ib == i]\n", + " v = dm_obs[ib == i]\n", " if v.size >= 20:\n", - " med_logerr[i] = np.median(v)\n", - "keep = np.isfinite(med_logerr)\n", + " log_scatter[i] = np.log10((np.percentile(v, 84) - np.percentile(v, 16)) / 2)\n", + " med_logerr_rep[i] = np.median(logerr_reported[ib == i])\n", + "keep = np.isfinite(log_scatter)\n", "\n", - "photoerr_tab = pd.DataFrame({\"delta_mag\": dmid[keep], \"log_mag_err\": med_logerr[keep]})\n", + "photoerr_tab = pd.DataFrame({\"delta_mag\": dmid[keep], \"log_mag_err\": log_scatter[keep]})\n", "fpe = OUT_DIR / \"roman_photoerror_f158.csv\"\n", "np.savetxt(fpe, photoerr_tab.values, delimiter=\",\", header=\"delta_mag,log_mag_err\", fmt=\"%.6f\")\n", "print(f\"wrote {fpe.relative_to(REPO)} ({len(photoerr_tab)} rows, \"\n", @@ -852,7 +913,10 @@ "\n", "fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))\n", "axes[0].plot(lsst_pe[\"delta_mag\"], lsst_pe[\"log_mag_err\"], \"-\", color=\"0.6\", label=\"LSST r\")\n", - "axes[0].plot(photoerr_tab.delta_mag, photoerr_tab.log_mag_err, \"C3o-\", ms=3, label=\"Roman F158\")\n", + "axes[0].plot(photoerr_tab.delta_mag, photoerr_tab.log_mag_err, \"C3o-\", ms=3,\n", + " label=\"Roman F158 (truth-based scatter)\")\n", + "axes[0].plot(dmid[keep], med_logerr_rep[keep], \"C0--\", lw=1.5,\n", + " label=\"Roman F158 reported magerr (underestimated)\")\n", "axes[0].set(xlabel=r\"$\\Delta$mag = mag $-$ maglim\", ylabel=r\"$\\log_{10}\\sigma$ [mag]\")\n", "axes[0].legend(); axes[0].grid(alpha=0.3)\n", "axes[1].plot(lsst_eff[\"delta_mag\"], lsst_eff[\"classification_detection_eff\"], \"-\", color=\"0.6\", label=\"LSST r\")\n", From 6f176ae5bc63d81ea6217104043736a830aff0fc Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 11 Jun 2026 15:10:09 -0700 Subject: [PATCH 35/54] Paper re-read fixes: saturation 17, F106/F129/F158 only, zeropoints + extinction docs From a full re-read of Troxel+23: - saturation IS modeled in the mock (clip ~1.1e5 e-, effects brighter than mag~17, Fig 7); config saturation 15 -> 17 (delta_saturation -10.1) - restrict survey bands to f106/f129/f158 (F184 is deep-tier-only in the community HLWAS and has a known unresolved chromatic calibration issue) - docs: detection is two-stage (2.5-sigma/minarea-5 segmentation + S/N>5 catalog cut), not a single 5-sigma threshold - docs: flags==0 documented as the observational pure-star-sample selection (removes blends), deliberately stricter than the paper's relaxed flags<=2 analysis cuts - docs: official Roman WFI AB zeropoints table (2024-03-01 effective areas) + grey calibration offsets; extinction-coefficient section (CCM89 absolute values, band ratios validated to ~1% against the truth dereddening corrections; amplitudes not validatable due to the mock's dust bug) - caveats: detector-optimistic simple model, unmasked diffraction spikes Co-Authored-By: Claude Fable 5 --- config/surveys/roman_hlwas.yaml | 26 +++++++------ docs/source/roman_hlwas.md | 69 ++++++++++++++++++++++++++++----- 2 files changed, 74 insertions(+), 21 deletions(-) diff --git a/config/surveys/roman_hlwas.yaml b/config/surveys/roman_hlwas.yaml index 250f64b..04ea2c7 100644 --- a/config/surveys/roman_hlwas.yaml +++ b/config/surveys/roman_hlwas.yaml @@ -25,24 +25,26 @@ survey_files: log_photo_error: roman_photoerror_f158.csv survey_properties: - bands: ['f106', 'f129', 'f158', 'f184'] + # F184 is excluded: it is deep-tier-only in the community-defined HLWAS and + # has a known unresolved chromatic calibration issue in the DC2 mock + # (Troxel+23 Sec 5.3 / Fig 6). + bands: ['f106', 'f129', 'f158'] # Extinction coefficients per band (A_band / E(B-V)) - # CCM89 R_V=3.1 evaluated at the filter effective wavelengths - # (1.06, 1.29, 1.58, 1.84 micron). NIR extinction is small; refine with - # dust_extinction + actual Roman bandpasses if percent-level accuracy matters. + # CCM89 R_V=3.1 evaluated at the filter pivot wavelengths (1.06, 1.29, + # 1.58 micron). The band RATIOS are validated to ~1% against the DC2 truth + # dereddening corrections (Y/H=1.88, J/H=1.37 vs CCM 1.90, 1.38); absolute + # amplitudes cannot be validated from the mock (its dust application is + # bugged, Troxel+23 Sec 4.1). coeff_extinc_f106: 1.14 coeff_extinc_f129: 0.83 coeff_extinc_f158: 0.60 - coeff_extinc_f184: 0.47 # Systematic photometric errors (mag), or could be specified per band sys_error: 0.005 - # Saturation limits per band. The DC2 truth catalog only extends to ~15 AB - # and the simulation does not model saturation, so this is the validity - # limit of the efficiency table rather than a physical saturation estimate - # (real Roman WFI saturates around 17-18 AB for point sources in 107 s - # exposures - tighten this if bright stars matter). - saturation: 15.0 - delta_saturation: -11.8 + # Saturation: the mock clips pixels at ~1.1e5 e- and saturation effects + # appear brighter than mag ~17 (Troxel+23 Fig 7); stars brighter than 15 + # are not chromatically rendered. + saturation: 17.0 + delta_saturation: -10.1 diff --git a/docs/source/roman_hlwas.md b/docs/source/roman_hlwas.md index 3ab2ec6..2ba1e55 100644 --- a/docs/source/roman_hlwas.md +++ b/docs/source/roman_hlwas.md @@ -13,8 +13,12 @@ simulations of the Roman High Latitude Imaging Survey reference design at full depth, reaching a 5σ point-source depth of ~26.9 AB in the F106/F129/F158 bands and 26.2 AB in F184. Stars are drawn from a Galfast model of the Galaxy and galaxies from the cosmoDC2 extragalactic catalog. Object detection and photometry are -performed with SExtractor on a median F106+F129+F158+F184 coadd detection image, -with forced photometry in each band, over 1039 coadd tiles. Each tile provides a +performed with SExtractor on a median F106+F129+F158+F184 coadd detection image +(segmentation at 2.5σ with a minimum area of 5 pixels; deblending with +`DEBLEND_NTHRESH=48`, `DEBLEND_MINCONT=0.05`), with forced photometry in each band, +over 1039 coadd tiles. The recommended catalog-level selection of S/N > 5 in the +detection image is applied on top of this — i.e. the detection limit is a two-stage +selection, not a single 5σ threshold. Each tile provides a detection catalog and a truth index listing every simulated object in the tile with its position, four-band magnitudes, and a star/galaxy label. @@ -34,7 +38,12 @@ assigning a blend to only one of its members. Following the paper, three selections define the analysis sample: 1. **`flags == 0`** — objects with any SExtractor flag are removed (32% of - detections, matching the fraction quoted in the paper); + detections, matching the fraction quoted in the paper). This is the selection an + observer would apply to obtain a *pure stellar sample* from the data: the flag is + the bitwise OR of the detection and per-band measurement flags, so the cut + removes blended objects and contaminated photometry. It is deliberately stricter + than parts of the paper's own analysis (which readmits flags 1–2), and it is the + origin of the ~0.90 bright-end completeness plateau below; 2. **S/N > 5** in the detection image; 3. **a positional match to a true object**, as defined above. @@ -107,6 +116,45 @@ the truth-based scatter adopted for the error model. The reported error saturate near the S/N≈5 floor at the faint end; the sparse cloud above the median relation is blends.* +## Zeropoints and extinction coefficients + +For reference, the official Roman WFI AB-magnitude zeropoints (effective-area curves +of 2024-03-01, from the +[Roman technical information repository](https://github.com/RomanSpaceTelescope/roman-technical-information/blob/main/roman_technical_information/data/WideFieldInstrument/Imaging/ZeroPoints/Roman_zeropoints_20240301.ecsv); +the range reflects detector-to-detector variation across WFI01–WFI18): + +| Filter | AB zeropoint | mean | +|---|---|---| +| F106 | 26.31 – 26.44 | 26.36 | +| F129 | 26.30 – 26.47 | 26.37 | +| F158 | 26.34 – 26.46 | 26.39 | + +The mock's photometry is calibrated greyly (a constant per-band offset from bright +stars, mimicking standard-star calibration); the measured offsets are +0.11/+0.10/+0.12 +mag in F106/F129/F158. F184 additionally requires a chromatic (color-dependent) +correction that the simulation team did not derive, which is one reason F184 is +excluded from the survey configuration (it is also deep-tier-only in the +community-defined HLWAS). + +The extinction coefficients adopted in the configuration are CCM89 (R_V = 3.1) +evaluated at the filter pivot wavelengths: + +| Filter | A_band / E(B−V) | +|---|---| +| F106 | 1.14 | +| F129 | 0.83 | +| F158 | 0.60 | + +The *shape* of this law is validated against the simulation itself: the truth +catalog's per-band dereddening corrections have band ratios of 1.88 (F106/F158) and +1.37 (F129/F158), matching the CCM89 ratios (1.90, 1.38) to ~1%. The absolute +amplitudes cannot be validated from the mock — its dust application is known to be +incorrect (Milky Way extinction is absent from the Roman images, and the per-object +correction amplitudes do not track the SFD column) — so the absolute scale rests on +the CCM89 calculation. In the near-infrared the difference between modern extinction +laws is at the ~10% level, and the absolute extinction in typical high-latitude +fields is small (A_F158 ≈ 0.01 mag at E(B−V) = 0.013). + ## Survey depth Depth maps are computed per band on a HEALPix nside=1024 grid (ring ordering) with @@ -180,13 +228,16 @@ photo_error = survey.get_photo_error("f158", mag, maglim) - The detection-centric match assigns a blend to its dominant source, so the detection efficiency is slightly conservative for stars blended with brighter neighbours. -- The truth catalog only extends to ~15 AB and the simulation does not model - saturation; the configured `saturation: 15.0` marks the validity limit of the - completeness table rather than the physical WFI saturation (~17–18 AB for point - sources). +- Saturation is modeled in the simulation (pixels clip at ~1.1×10⁵ e⁻) and its + effects appear brighter than mag ≈ 17 — the dip in the classification efficiency + near mag 16.6 is its signature — and stars brighter than 15 are not chromatically + rendered. The configuration therefore sets `saturation: 17.0`; photometry of + brighter stars should not be trusted. - `mag_auto` carries a systematic offset with respect to the truth (≈ +0.1–0.2 mag in F106/F129/F158 and ≈ +0.6 mag in F184 at the bright end, growing toward faint magnitudes from aperture losses). The error model captures the scatter about this offset, not the offset itself. -- Extinction coefficients are CCM89 (R_V = 3.1) evaluated at the filter effective - wavelengths (1.14, 0.83, 0.60, 0.47 for F106/F129/F158/F184). +- The detection catalogs are detector-optimistic: the coadds use the "simple" + detector model (read noise, dark current, saturation only — no cosmic rays, + persistence, interpixel capacitance, or hot pixels) and bright stars are not + masked (12 diffraction spikes remain in the images). From 467ffd3000b186e81eabf4550e7d2c1cefbf5701 Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 11 Jun 2026 15:13:41 -0700 Subject: [PATCH 36/54] Adopt official STScI extinction coefficients (Roman-STScI-000825 Table 3) A_band/E(B-V) = 1.1495/0.8497/0.6140 for F106/F129/F158 (synphot, solar Phoenix spectrum), replacing the CCM89 estimates (which agreed to 1-2%). Docs cite the document and cross-reference its zeropoints against the effective-area ecsv values. Co-Authored-By: Claude Fable 5 --- config/surveys/roman_hlwas.yaml | 15 +++++++-------- docs/source/roman_hlwas.md | 33 ++++++++++++++++++++------------- 2 files changed, 27 insertions(+), 21 deletions(-) diff --git a/config/surveys/roman_hlwas.yaml b/config/surveys/roman_hlwas.yaml index 04ea2c7..2e64aee 100644 --- a/config/surveys/roman_hlwas.yaml +++ b/config/surveys/roman_hlwas.yaml @@ -31,14 +31,13 @@ survey_properties: bands: ['f106', 'f129', 'f158'] # Extinction coefficients per band (A_band / E(B-V)) - # CCM89 R_V=3.1 evaluated at the filter pivot wavelengths (1.06, 1.29, - # 1.58 micron). The band RATIOS are validated to ~1% against the DC2 truth - # dereddening corrections (Y/H=1.88, J/H=1.37 vs CCM 1.90, 1.38); absolute - # amplitudes cannot be validated from the mock (its dust application is - # bugged, Troxel+23 Sec 4.1). - coeff_extinc_f106: 1.14 - coeff_extinc_f129: 0.83 - coeff_extinc_f158: 0.60 + # Official STScI values from Roman-STScI-000825 (Sharma), Table 3: + # bandpass-integrated with synphot for a solar-parameter Phoenix spectrum. + # Band ratios independently validated to ~1% against the DC2 truth + # dereddening corrections. + coeff_extinc_f106: 1.1495 + coeff_extinc_f129: 0.8497 + coeff_extinc_f158: 0.6140 # Systematic photometric errors (mag), or could be specified per band sys_error: 0.005 diff --git a/docs/source/roman_hlwas.md b/docs/source/roman_hlwas.md index 2ba1e55..fcd685e 100644 --- a/docs/source/roman_hlwas.md +++ b/docs/source/roman_hlwas.md @@ -129,6 +129,10 @@ the range reflects detector-to-detector variation across WFI01–WFI18): | F129 | 26.30 – 26.47 | 26.37 | | F158 | 26.34 – 26.46 | 26.39 | +These agree with the single-value zeropoints of Roman-STScI-000825 Table 3 +(Z = 26.3546, 26.3531, 26.3760 with detector-to-detector σ_Z ≈ 0.033–0.038 for +F106/F129/F158). + The mock's photometry is calibrated greyly (a constant per-band offset from bright stars, mimicking standard-star calibration); the measured offsets are +0.11/+0.10/+0.12 mag in F106/F129/F158. F184 additionally requires a chromatic (color-dependent) @@ -136,24 +140,27 @@ correction that the simulation team did not derive, which is one reason F184 is excluded from the survey configuration (it is also deep-tier-only in the community-defined HLWAS). -The extinction coefficients adopted in the configuration are CCM89 (R_V = 3.1) -evaluated at the filter pivot wavelengths: +The extinction coefficients adopted in the configuration are the official STScI +values from +[Roman-STScI-000825](https://www.stsci.edu/files/live/sites/www/files/home/roman/documentation/technical-documentation/_documents/Roman-STScI%E2%80%93000825.pdf) +(Sharma, Table 3), computed by integrating a solar-parameter Phoenix spectrum +through the WFI bandpasses with synphot (the change in apparent magnitude per unit +E(B−V); for the narrow Roman bands the dependence on stellar parameters is +negligible): | Filter | A_band / E(B−V) | |---|---| -| F106 | 1.14 | -| F129 | 0.83 | -| F158 | 0.60 | +| F106 | 1.1495 | +| F129 | 0.8497 | +| F158 | 0.6140 | -The *shape* of this law is validated against the simulation itself: the truth +The *shape* of this law is independently validated against the simulation: the truth catalog's per-band dereddening corrections have band ratios of 1.88 (F106/F158) and -1.37 (F129/F158), matching the CCM89 ratios (1.90, 1.38) to ~1%. The absolute -amplitudes cannot be validated from the mock — its dust application is known to be -incorrect (Milky Way extinction is absent from the Roman images, and the per-object -correction amplitudes do not track the SFD column) — so the absolute scale rests on -the CCM89 calculation. In the near-infrared the difference between modern extinction -laws is at the ~10% level, and the absolute extinction in typical high-latitude -fields is small (A_F158 ≈ 0.01 mag at E(B−V) = 0.013). +1.37 (F129/F158), matching the official ratios (1.87, 1.38) to ~1%. (The absolute +amplitudes cannot be cross-checked from the mock — its dust application is known to +be incorrect, with Milky Way extinction absent from the Roman images — but the +absolute extinction in typical high-latitude fields is small in any case: +A_F158 ≈ 0.01 mag at E(B−V) = 0.013.) ## Survey depth From d6591bbeac99b656581aa466deac147632da865c Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 11 Jun 2026 15:20:13 -0700 Subject: [PATCH 37/54] Add true-stars photo-error plot alongside the star-classified one The star-classified sample is galaxy-dominated faintward of F158~25.5 (69% true galaxies at 25.5, 99% at 26.5), inflating the apparent scatter (0.33 vs 0.15 mag at 25.5). New cell shows the same diagnostics for true stars passing the star classification. 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"display_data" + } + ], + "source": [ + "sel_ts = (cat.matched & (cat.truth_gal_star == 1)\n", + " & (cat.class_star > CLASS_STAR_CUT) & (cat[\"flags\"] < FLAG_CUT))\n", + "pe_ts = cat.loc[sel_ts, [f\"truth_mag_{BAND}\", f\"mag_auto_{BAND}\", f\"magerr_auto_{BAND}\"]].dropna()\n", + "x, y = pe_ts[f\"truth_mag_{BAND}\"].values, pe_ts[f\"magerr_auto_{BAND}\"].values\n", + "dm = pe_ts[f\"mag_auto_{BAND}\"].values - pe_ts[f\"truth_mag_{BAND}\"].values\n", + "print(f\"{len(pe_ts):,} star-classified matched TRUE stars with F158 photometry\")\n", + "\n", + "med, lo, hi, scat = (np.full(MAG_MID.size, np.nan) for _ in range(4))\n", + "ib = np.digitize(x, MAG_BINS) - 1\n", + "for i in range(MAG_MID.size):\n", + " yy = y[ib == i]\n", + " if yy.size >= 20:\n", + " lo[i], med[i], hi[i] = np.percentile(yy, [16, 50, 84])\n", + " v = dm[ib == i]\n", + " scat[i] = (np.percentile(v, 84) - np.percentile(v, 16)) / 2\n", + "\n", + "fig, ax = plt.subplots(figsize=(7, 5))\n", + "hb = ax.hexbin(x, np.log10(y), gridsize=180, bins=\"log\", mincnt=1, cmap=\"viridis\")\n", + "ax.plot(MAG_MID, np.log10(med), \"r-\", lw=2, label=\"median reported magerr\")\n", + "ax.plot(MAG_MID, np.log10(scat), \"-\", color=\"orange\", lw=2,\n", + " label=\"truth-based scatter of (obs $-$ true)\")\n", + "ax.plot(MAG_MID, np.log10(lo), \"r--\", lw=1, label=\"16/84%\")\n", + "ax.plot(MAG_MID, np.log10(hi), \"r--\", lw=1)\n", + "ax.axhline(np.log10(2.5 / np.log(10) / SNR_DEPTH), color=\"w\", ls=\":\", lw=1.5,\n", + " label=f\"S/N = {SNR_DEPTH}\")\n", + "ax.set(xlabel=\"true F158 mag\", ylabel=r\"$\\log_{10}\\,\\sigma_{\\rm F158}^{\\rm obs}$ [mag]\",\n", + " xlim=(15, 29), title=f\"True stars with class_star > {CLASS_STAR_CUT}, flags == 0\")\n", + "fig.colorbar(hb, ax=ax, label=\"N (log)\")\n", + "ax.legend(loc=\"upper left\")\n", + "fig.savefig(FIG_DIR / \"photoerror_f158_truestars.png\", dpi=130, bbox_inches=\"tight\")\n", + "plt.show()\n" + ] + }, { "cell_type": "markdown", "id": "9b1d80ac", @@ -314,14 +394,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "15f41f9f", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:37:19.383332Z", - "iopub.status.busy": "2026-06-11T18:37:19.382743Z", - "iopub.status.idle": "2026-06-11T18:37:20.103090Z", - "shell.execute_reply": "2026-06-11T18:37:20.102037Z" + "iopub.execute_input": "2026-06-11T22:18:51.306483Z", + "iopub.status.busy": "2026-06-11T22:18:51.306229Z", + "iopub.status.idle": "2026-06-11T22:18:52.002840Z", + "shell.execute_reply": "2026-06-11T22:18:52.002124Z" } }, "outputs": [ @@ -394,14 +474,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "7a7ec629", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:37:20.108854Z", - "iopub.status.busy": "2026-06-11T18:37:20.108593Z", - "iopub.status.idle": "2026-06-11T18:37:20.280104Z", - "shell.execute_reply": "2026-06-11T18:37:20.276794Z" + "iopub.execute_input": "2026-06-11T22:18:52.004309Z", + "iopub.status.busy": "2026-06-11T22:18:52.004159Z", + "iopub.status.idle": "2026-06-11T22:18:52.150541Z", + "shell.execute_reply": "2026-06-11T22:18:52.149546Z" } }, "outputs": [ @@ -467,14 +547,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "7bc997de", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:37:20.286749Z", - "iopub.status.busy": "2026-06-11T18:37:20.286429Z", - "iopub.status.idle": "2026-06-11T18:37:25.652611Z", - "shell.execute_reply": "2026-06-11T18:37:25.651848Z" + "iopub.execute_input": "2026-06-11T22:18:52.153522Z", + "iopub.status.busy": "2026-06-11T22:18:52.153302Z", + "iopub.status.idle": "2026-06-11T22:18:57.304824Z", + "shell.execute_reply": "2026-06-11T22:18:57.304196Z" } }, "outputs": [ @@ -540,14 +620,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "ff643fec", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:37:25.656498Z", - "iopub.status.busy": "2026-06-11T18:37:25.656131Z", - "iopub.status.idle": "2026-06-11T18:37:32.467754Z", - "shell.execute_reply": "2026-06-11T18:37:32.467242Z" + "iopub.execute_input": "2026-06-11T22:18:57.306449Z", + "iopub.status.busy": "2026-06-11T22:18:57.306281Z", + "iopub.status.idle": "2026-06-11T22:19:04.587899Z", + "shell.execute_reply": "2026-06-11T22:19:04.587015Z" } }, "outputs": [ @@ -607,14 +687,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "8f4f7263", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:37:32.473757Z", - "iopub.status.busy": "2026-06-11T18:37:32.473509Z", - "iopub.status.idle": "2026-06-11T18:37:44.244766Z", - "shell.execute_reply": "2026-06-11T18:37:44.244033Z" + "iopub.execute_input": "2026-06-11T22:19:04.591725Z", + "iopub.status.busy": "2026-06-11T22:19:04.591487Z", + "iopub.status.idle": "2026-06-11T22:19:16.179867Z", + "shell.execute_reply": "2026-06-11T22:19:16.177110Z" } }, "outputs": [ @@ -700,14 +780,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "53a0e9b7", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:37:44.246571Z", - "iopub.status.busy": "2026-06-11T18:37:44.246438Z", - "iopub.status.idle": "2026-06-11T18:37:47.157027Z", - "shell.execute_reply": "2026-06-11T18:37:47.156251Z" + "iopub.execute_input": "2026-06-11T22:19:16.182742Z", + "iopub.status.busy": "2026-06-11T22:19:16.182254Z", + "iopub.status.idle": "2026-06-11T22:19:19.021096Z", + "shell.execute_reply": "2026-06-11T22:19:19.018962Z" } }, "outputs": [ @@ -818,14 +898,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "882cdd12", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:37:47.158434Z", - "iopub.status.busy": "2026-06-11T18:37:47.158312Z", - "iopub.status.idle": "2026-06-11T18:37:48.089459Z", - "shell.execute_reply": "2026-06-11T18:37:48.088990Z" + "iopub.execute_input": "2026-06-11T22:19:19.025146Z", + "iopub.status.busy": "2026-06-11T22:19:19.024800Z", + "iopub.status.idle": "2026-06-11T22:19:19.914013Z", + "shell.execute_reply": "2026-06-11T22:19:19.913313Z" } }, "outputs": [ From 5b58b65b2569ee49e2c00f799afdc56f1ceb2629 Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 11 Jun 2026 15:33:52 -0700 Subject: [PATCH 38/54] True-star products, truth-anchored depths; notebook -> script; docs rename - all selection-function products now use TRUE stars passing the star classification (photo-error table, depth maps; efficiency already did): the observationally star-classified sample is galaxy-dominated faintward of F158~25.5 and doubles the apparent faint-end scatter - depth maps truth-anchored: desqr spatial structure from reported errors, absolute scale set where the truth-based scatter reaches S/N=5 (medians 26.19/26.19/25.98/25.26); photo-error model = 0.217 mag at delta_mag=0 by construction; config delta_saturation re-keyed (-9.0) - two-panel photo-error figure (reported errors | sigma(true-obs) vs reported); single-panel magnitude distributions (color=band, linestyle=true/obs) - derivation notebook converted to scripts/roman/create_streamobs_files_hlwas.py and removed from the repo (kept locally, gitignored); roman build scripts moved to scripts/roman/ - docs page renamed roman_hlwas -> roman_dc2, figures under _static/roman_dc2/, depth/error sections rewritten for the anchored convention Co-Authored-By: Claude Fable 5 --- .gitignore | 4 +- .../_static/roman_hlwas/efficiency_f158.png | Bin 87146 -> 0 bytes .../_static/roman_hlwas/error_validation.png | Bin 112390 -> 0 bytes .../_static/roman_hlwas/lsst_comparison.png | Bin 87192 -> 0 bytes .../_static/roman_hlwas/mag_distributions.png | Bin 96794 -> 0 bytes .../_static/roman_hlwas/maglim_maps.png | Bin 372260 -> 0 bytes .../_static/roman_hlwas/photoerror_f158.png | Bin 139505 -> 0 bytes .../roman_hlwas/photoerror_f158_truestars.png | Bin 84529 -> 0 bytes docs/source/{roman_hlwas.md => roman_dc2.md} | 0 notebooks/create_streamobs_files_hlwas.ipynb | 1049 ----------------- .../roman}/build_roman_dc2_det_truth.py | 0 scripts/roman/create_streamobs_files_hlwas.py | 691 +++++++++++ 12 files changed, 694 insertions(+), 1050 deletions(-) delete mode 100644 docs/source/_static/roman_hlwas/efficiency_f158.png delete mode 100644 docs/source/_static/roman_hlwas/error_validation.png delete mode 100644 docs/source/_static/roman_hlwas/lsst_comparison.png delete mode 100644 docs/source/_static/roman_hlwas/mag_distributions.png delete mode 100644 docs/source/_static/roman_hlwas/maglim_maps.png delete mode 100644 docs/source/_static/roman_hlwas/photoerror_f158.png delete mode 100644 docs/source/_static/roman_hlwas/photoerror_f158_truestars.png rename docs/source/{roman_hlwas.md => roman_dc2.md} (100%) delete mode 100644 notebooks/create_streamobs_files_hlwas.ipynb rename {notebooks => scripts/roman}/build_roman_dc2_det_truth.py (100%) create mode 100644 scripts/roman/create_streamobs_files_hlwas.py diff --git a/.gitignore b/.gitignore index 97d6cdd..3e43398 100644 --- a/.gitignore +++ b/.gitignore @@ -132,10 +132,12 @@ dmypy.json data/surveys/* data/others/* .DS_Store - # External reference repos (design references only, not part of streamobs) /survey_systematics_in_LSST_streams/ /rubin_roman_object_classification/ /lsst_dc2_scratch/ /artifacts/ docs/source/roman_multisurvey_plan.md + +# Local-only Roman HLWAS derivation notebook (kept un-tracked; script is the source) +notebooks/create_streamobs_files_hlwas.ipynb diff --git a/docs/source/_static/roman_hlwas/efficiency_f158.png b/docs/source/_static/roman_hlwas/efficiency_f158.png deleted file mode 100644 index bb3dbfc975b555b755726c7e40a903c61493cd48..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 87146 zcmdSBWmKC{x9^LGLa}0nVl~{|U0RA3cPj*KaCdFdwiGCC#ogVD6n6<;3c*5v-~oCb 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zY!{WQCa(SwP9`l-VPHSH$f?*YpwfkS45~CQ$4ifC9~9}>A6Jo*0uwcmW?QE_i&P#)!Q{+cg@SMg`z8I7v&EUb6#vRxnZS9jO%&q_~T`* zb*PoI_~!&Bw)TJqu=K>;VCzNRW1)?cS}mLML;If*a*OkzF+>0m zo%mw;iO@T~%^&r*MCEQc~g3Z_h%XTiJCCxM!8Zs!>2Y@2b6 cut -> \"classified as a star\"\n", - "FLAG_CUT = 1 # flags < 1, i.e. flags == 0 (paper cut: removes 32% of detections)\n", - "NSIDE = 1024\n", - "SNR_DEPTH = 5 # depth = mag at S/N = 5 (matches the catalog S/N>5 cut)\n", - "GAL_SIZE_MAX = 0.3 # arcsec; \"small\" galaxies = awin_world < this\n", - "MAG_BINS = np.arange(15.0, 29.0 + 1e-6, 0.25)\n", - "MAG_MID = 0.5 * (MAG_BINS[1:] + MAG_BINS[:-1])\n", - "\n", - "plt.rcParams.update({\"figure.dpi\": 110, \"font.size\": 11})\n", - "FIG_DIR = REPO / \"docs/source/_static/roman_hlwas\" # figures embedded in the docs page\n", - "FIG_DIR.mkdir(parents=True, exist_ok=True)\n", - "print(f\"repo: {REPO}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "65a62226", - "metadata": {}, - "source": [ - "## Load the matched det→truth catalog\n", - "\n", - "One row per S/N>5 detection; `matched` rows carry the truth payload.\n", - "\n", - "**Why ~29% of detections are unmatched (verified 2026-06-11):** each det coadd extends\n", - "~30″ beyond its truth tile's footprint, and the catalog was matched per tile — so\n", - "detections in that margin (~21% of rows, >99% unmatched) could not see their truth\n", - "object, which lives in the *neighboring* tile's index (they are also duplicates of\n", - "detections in the neighboring tile's det file). Inside the truth footprint the unmatched\n", - "rate is only ~0.1–5% per tile, consistent with the paper's 1.7%. So `matched=False` is\n", - "mostly tile-edge geometry, **not** a spurious-detection rate; the matched-only cuts used\n", - "below also conveniently drop the margin duplicates. We only pull the\n", - "columns needed here (~19.2M rows). SExtractor sentinel mags (99) are set to NaN.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ecced6fb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-11T22:18:47.342102Z", - "iopub.status.busy": "2026-06-11T22:18:47.341837Z", - "iopub.status.idle": "2026-06-11T22:18:49.144366Z", - "shell.execute_reply": "2026-06-11T22:18:49.143301Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "19,205,425 detections, 13,580,663 matched (70.7%); 192,380 matched-star rows\n" - ] - } - ], - "source": [ - "usecols = ([\"alphawin_j2000\", \"deltawin_j2000\", \"mag_auto\", \"magerr_auto\",\n", - " \"flags\", \"class_star\", \"det_sn\", \"awin_world\",\n", - " \"matched\", \"match_sep_arcsec\", \"truth_ind\", \"truth_gal_star\",\n", - " ]\n", - " + [f\"truth_mag_{b}\" for b in BANDS]\n", - " + [f\"mag_auto_{b}\" for b in BANDS] + [f\"magerr_auto_{b}\" for b in BANDS])\n", - "cat = pq.read_table(CAT_PATH, columns=usecols).to_pandas()\n", - "\n", - "for b in BANDS: # SExtractor unmeasured -> 99\n", - " bad = (cat[f\"mag_auto_{b}\"] > 90) | (cat[f\"magerr_auto_{b}\"] > 90) | (cat[f\"magerr_auto_{b}\"] <= 0)\n", - " cat.loc[bad, [f\"mag_auto_{b}\", f\"magerr_auto_{b}\"]] = np.nan\n", - "\n", - "print(f\"{len(cat):,} detections, {int(cat.matched.sum()):,} matched \"\n", - " f\"({cat.matched.mean():.1%}); {int(((cat.truth_gal_star == 1) & cat.matched).sum()):,} matched-star rows\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "e4381098", - "metadata": {}, - "source": [ - "## Truth-star denominator\n", - "\n", - "Detection efficiency needs *all* true stars, including undetected ones — that lives in the\n", - "per-tile truth indices, not the detection-centric parquet. Build (once) a cached parquet of\n", - "the true stars (`gal_star == 1`) from the 1039 real tiles, deduplicated on truth `ind`\n", - "(tiles overlap at the edges).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "24956ad3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-11T22:18:49.146738Z", - "iopub.status.busy": "2026-06-11T22:18:49.146495Z", - "iopub.status.idle": "2026-06-11T22:18:49.627459Z", - "shell.execute_reply": "2026-06-11T22:18:49.626846Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "230,820 truth-star rows -> 209,024 unique stars\n" - ] - } - ], - "source": [ - "def load_gz_fits(path):\n", - " with gzip.open(path, \"rb\") as f:\n", - " raw = f.read()\n", - " with fits.open(io.BytesIO(raw)) as h:\n", - " return Table(h[1].data)\n", - "\n", - "def stars_from_tile(path):\n", - " tile = Path(path).name.replace(\"dc2_index_\", \"\").replace(\".fits.gz\", \"\")\n", - " t = load_gz_fits(path)\n", - " sel = np.asarray(t[\"gal_star\"]) == 1\n", - " if not sel.any():\n", - " return None\n", - " df = pd.DataFrame({\"ind\": np.asarray(t[\"ind\"], \"i8\")[sel],\n", - " \"ra\": np.asarray(t[\"ra\"], \"f8\")[sel],\n", - " \"dec\": np.asarray(t[\"dec\"], \"f8\")[sel]})\n", - " for b in BANDS:\n", - " m = np.asarray(t[f\"mag_{b}\"], \"f8\")[sel]\n", - " m[m == 0.0] = np.nan # 0.0 = no flux in band\n", - " df[f\"mag_{b}\"] = m\n", - " df[\"tile\"] = tile\n", - " return df\n", - "\n", - "if TRUTH_STARS_CACHE.exists():\n", - " truth_stars = pd.read_parquet(TRUTH_STARS_CACHE)\n", - "else:\n", - " det_tiles = {Path(p).name.replace(\"dc2_det_\", \"\").replace(\".fits.gz\", \"\")\n", - " for p in glob.glob(str(DC2_DIR / \"det/dc2_det_*.fits.gz\"))}\n", - " paths = [p for p in sorted(glob.glob(str(DC2_DIR / \"truth/dc2_index_*.fits.gz\")))\n", - " if Path(p).name.replace(\"dc2_index_\", \"\").replace(\".fits.gz\", \"\") in det_tiles\n", - " and os.path.getsize(p) > 1000]\n", - " print(f\"reading {len(paths)} truth tiles ...\")\n", - " # fork context: py3.14 defaults to forkserver, which can't pickle notebook-defined funcs\n", - " with ProcessPoolExecutor(32, mp_context=mp.get_context(\"fork\")) as ex:\n", - " frames = [df for df in ex.map(stars_from_tile, paths, chunksize=8) if df is not None]\n", - " truth_stars = pd.concat(frames, ignore_index=True)\n", - " truth_stars.to_parquet(TRUTH_STARS_CACHE)\n", - "\n", - "n_rows = len(truth_stars)\n", - "truth_stars = truth_stars.drop_duplicates(\"ind\").reset_index(drop=True)\n", - "print(f\"{n_rows:,} truth-star rows -> {len(truth_stars):,} unique stars\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "147f35f8", - "metadata": {}, - "source": [ - "## 1. Photometric error vs true F158 magnitude (star-classified objects)\n", - "\n", - "Matched detections with `class_star > {cut}` and clean flags, observed F158 `magerr_auto`\n", - "against the true F158 magnitude, with the binned median overlaid. The orange curve is the\n", - "**truth-based scatter** of (observed − true): it runs a factor ~1.9–2.0 above the reported\n", - "errors at all magnitudes — SExtractor underestimates the correlated noise of the resampled\n", - "median coadds — so the error *model* (section 5) is built from the truth-based scatter.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ae3481ab", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-11T22:18:49.628898Z", - "iopub.status.busy": "2026-06-11T22:18:49.628769Z", - "iopub.status.idle": "2026-06-11T22:18:50.746543Z", - "shell.execute_reply": "2026-06-11T22:18:50.740133Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "531,485 star-classified matched detections with F158 photometry\n" - ] - }, - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sel = (cat.matched & (cat.class_star > CLASS_STAR_CUT) & (cat[\"flags\"] < FLAG_CUT))\n", - "pe = cat.loc[sel, [f\"truth_mag_{BAND}\", f\"mag_auto_{BAND}\", f\"magerr_auto_{BAND}\"]].dropna()\n", - "x, y = pe[f\"truth_mag_{BAND}\"].values, pe[f\"magerr_auto_{BAND}\"].values\n", - "print(f\"{len(pe):,} star-classified matched detections with F158 photometry\")\n", - "\n", - "dm = pe[f\"mag_auto_{BAND}\"].values - pe[f\"truth_mag_{BAND}\"].values\n", - "med, lo, hi, scat = (np.full(MAG_MID.size, np.nan) for _ in range(4))\n", - "ib = np.digitize(x, MAG_BINS) - 1\n", - "for i in range(MAG_MID.size):\n", - " yy = y[ib == i]\n", - " if yy.size >= 20:\n", - " lo[i], med[i], hi[i] = np.percentile(yy, [16, 50, 84])\n", - " v = dm[ib == i]\n", - " scat[i] = (np.percentile(v, 84) - np.percentile(v, 16)) / 2\n", - "\n", - "fig, ax = plt.subplots(figsize=(7, 5))\n", - "hb = ax.hexbin(x, np.log10(y), gridsize=180, bins=\"log\", mincnt=1, cmap=\"viridis\")\n", - "ax.plot(MAG_MID, np.log10(med), \"r-\", lw=2, label=\"median reported magerr\")\n", - "ax.plot(MAG_MID, np.log10(scat), \"-\", color=\"orange\", lw=2,\n", - " label=\"truth-based scatter of (obs $-$ true)\")\n", - "ax.plot(MAG_MID, np.log10(lo), \"r--\", lw=1, label=\"16/84%\")\n", - "ax.plot(MAG_MID, np.log10(hi), \"r--\", lw=1)\n", - "ax.axhline(np.log10(2.5 / np.log(10) / SNR_DEPTH), color=\"w\", ls=\":\", lw=1.5,\n", - " label=f\"S/N = {SNR_DEPTH}\")\n", - "ax.set(xlabel=\"true F158 mag\", ylabel=r\"$\\log_{10}\\,\\sigma_{\\rm F158}^{\\rm obs}$ [mag]\",\n", - " xlim=(15, 29), title=f\"Observed F158 photo-error, class_star > {CLASS_STAR_CUT}, flags == 0\")\n", - "fig.colorbar(hb, ax=ax, label=\"N (log)\")\n", - "ax.legend(loc=\"upper left\")\n", - "fig.savefig(FIG_DIR / \"photoerror_f158.png\", dpi=130, bbox_inches=\"tight\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "706dec35", - "metadata": {}, - "source": [ - "### Same, restricted to *true* stars\n", - "\n", - "The plot above is selection-matched to observations (anything *classified* as a star),\n", - "but at the faint end that sample is galaxy-dominated: true galaxies make up ~69% of\n", - "star-classified objects at true F158 ≈ 25.5 and ~99% at 26.5, and they inflate the\n", - "apparent scatter (0.33 vs 0.15 mag at 25.5). Below the same plot for **true stars**\n", - "that pass the star classification — the population whose photometric response an\n", - "injected stream star actually follows.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "32dd9319", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-11T22:18:50.751800Z", - "iopub.status.busy": "2026-06-11T22:18:50.751523Z", - "iopub.status.idle": "2026-06-11T22:18:51.303371Z", - "shell.execute_reply": "2026-06-11T22:18:51.302284Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "160,256 star-classified matched TRUE stars with F158 photometry\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sel_ts = (cat.matched & (cat.truth_gal_star == 1)\n", - " & (cat.class_star > CLASS_STAR_CUT) & (cat[\"flags\"] < FLAG_CUT))\n", - "pe_ts = cat.loc[sel_ts, [f\"truth_mag_{BAND}\", f\"mag_auto_{BAND}\", f\"magerr_auto_{BAND}\"]].dropna()\n", - "x, y = pe_ts[f\"truth_mag_{BAND}\"].values, pe_ts[f\"magerr_auto_{BAND}\"].values\n", - "dm = pe_ts[f\"mag_auto_{BAND}\"].values - pe_ts[f\"truth_mag_{BAND}\"].values\n", - "print(f\"{len(pe_ts):,} star-classified matched TRUE stars with F158 photometry\")\n", - "\n", - "med, lo, hi, scat = (np.full(MAG_MID.size, np.nan) for _ in range(4))\n", - "ib = np.digitize(x, MAG_BINS) - 1\n", - "for i in range(MAG_MID.size):\n", - " yy = y[ib == i]\n", - " if yy.size >= 20:\n", - " lo[i], med[i], hi[i] = np.percentile(yy, [16, 50, 84])\n", - " v = dm[ib == i]\n", - " scat[i] = (np.percentile(v, 84) - np.percentile(v, 16)) / 2\n", - "\n", - "fig, ax = plt.subplots(figsize=(7, 5))\n", - "hb = ax.hexbin(x, np.log10(y), gridsize=180, bins=\"log\", mincnt=1, cmap=\"viridis\")\n", - "ax.plot(MAG_MID, np.log10(med), \"r-\", lw=2, label=\"median reported magerr\")\n", - "ax.plot(MAG_MID, np.log10(scat), \"-\", color=\"orange\", lw=2,\n", - " label=\"truth-based scatter of (obs $-$ true)\")\n", - "ax.plot(MAG_MID, np.log10(lo), \"r--\", lw=1, label=\"16/84%\")\n", - "ax.plot(MAG_MID, np.log10(hi), \"r--\", lw=1)\n", - "ax.axhline(np.log10(2.5 / np.log(10) / SNR_DEPTH), color=\"w\", ls=\":\", lw=1.5,\n", - " label=f\"S/N = {SNR_DEPTH}\")\n", - "ax.set(xlabel=\"true F158 mag\", ylabel=r\"$\\log_{10}\\,\\sigma_{\\rm F158}^{\\rm obs}$ [mag]\",\n", - " xlim=(15, 29), title=f\"True stars with class_star > {CLASS_STAR_CUT}, flags == 0\")\n", - "fig.colorbar(hb, ax=ax, label=\"N (log)\")\n", - "ax.legend(loc=\"upper left\")\n", - "fig.savefig(FIG_DIR / \"photoerror_f158_truestars.png\", dpi=130, bbox_inches=\"tight\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "9b1d80ac", - "metadata": {}, - "source": [ - "### Error validation: reported magerr vs truth\n", - "\n", - "Because we have the truth, the reported errors can be validated directly: for true stars,\n", - "the scatter of (observed − true) magnitude should match the reported `magerr_auto`. It\n", - "does not — the truth-based scatter runs a flat factor ~1.9–2.0 above the reported errors\n", - "in every band, the signature of SExtractor underestimating the correlated noise of the\n", - "resampled median coadds. Consequence: the photo-error *model* (section 5) is built from\n", - "the truth-based scatter, and the reported-error depth maps are an internal reference,\n", - "not a claim that the survey is genuinely deeper than the official depths.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "15f41f9f", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-11T22:18:51.306483Z", - "iopub.status.busy": "2026-06-11T22:18:51.306229Z", - "iopub.status.idle": "2026-06-11T22:18:52.002840Z", - "shell.execute_reply": "2026-06-11T22:18:52.002124Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "vsel = (cat.matched & (cat.truth_gal_star == 1) & (cat.class_star > CLASS_STAR_CUT)\n", - " & (cat[\"flags\"] < FLAG_CUT))\n", - "fig, (axa, axb) = plt.subplots(2, 1, figsize=(7.5, 7.5), sharex=True,\n", - " gridspec_kw={\"height_ratios\": [2, 1]})\n", - "for b, col in zip(BANDS, [\"C0\", \"C1\", \"C2\", \"C3\"]):\n", - " sub = cat.loc[vsel, [f\"truth_mag_{b}\", f\"mag_auto_{b}\", f\"magerr_auto_{b}\"]].dropna()\n", - " mt = sub[f\"truth_mag_{b}\"].values\n", - " dmv = sub[f\"mag_auto_{b}\"].values - mt\n", - " sg = sub[f\"magerr_auto_{b}\"].values\n", - " ib = np.digitize(mt, MAG_BINS) - 1\n", - " scat_b, rep_b = np.full(MAG_MID.size, np.nan), np.full(MAG_MID.size, np.nan)\n", - " for i in range(MAG_MID.size):\n", - " v = dmv[ib == i]\n", - " if v.size >= 100:\n", - " scat_b[i] = (np.percentile(v, 84) - np.percentile(v, 16)) / 2\n", - " rep_b[i] = np.median(sg[ib == i])\n", - " axa.plot(MAG_MID, np.log10(scat_b), \"-\", color=col, lw=1.8, label=f\"{b}\")\n", - " axa.plot(MAG_MID, np.log10(rep_b), \"--\", color=col, lw=1.4, alpha=0.8)\n", - " axb.plot(MAG_MID, scat_b / rep_b, \"-\", color=col, lw=1.6, label=b)\n", - "axa.set(ylabel=r\"$\\log_{10}\\,\\sigma$ [mag]\",\n", - " title=\"True stars: truth-based scatter (solid) vs reported magerr (dashed)\")\n", - "axa.legend(title=\"band\", fontsize=9); axa.grid(alpha=0.3)\n", - "axb.axhline(1.0, color=\"k\", lw=0.8)\n", - "axb.axhline(2.0, color=\"0.5\", lw=0.8, ls=\":\")\n", - "axb.set(xlabel=\"true mag\", ylabel=\"scatter / reported\", xlim=(20, 28), ylim=(0.5, 2.8))\n", - "axb.grid(alpha=0.3)\n", - "fig.tight_layout()\n", - "fig.savefig(FIG_DIR / \"error_validation.png\", dpi=130, bbox_inches=\"tight\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "5b19ebfd", - "metadata": {}, - "source": [ - "## 2. Detection & classification efficiency for true stars\n", - "\n", - "Denominator: all unique true stars. A star counts as **detected** if any S/N>5 detection\n", - "matched its `ind` **and passed the paper's `flags == 0` cut** (Troxel et al. selection 1 —\n", - "a star whose only detection is flagged would not appear in the science catalog); for\n", - "classification we take its nearest clean matched detection and ask whether `class_star > {cut}`.\n", - "\n", - "**Gotcha (found 2026-06-11):** ~26% of truth stars have a *duplicate* truth entry at the\n", - "exact same position under a different `ind`, carrying only the J129 mag (other bands 0.0).\n", - "The det→truth mag-tiebreak can assign the detection to the duplicate, which makes the real\n", - "entry look undetected (a spurious, flat ~12% efficiency deficit). We therefore collapse\n", - "truth stars to unique *positions* and count a star detected/classified if **any** member\n", - "entry matched. Curves:\n", - "\n", - "- `detection_eff` = detected / all\n", - "- `classification_eff` = correctly classified / detected\n", - "- `classification_detection_eff` = product (detected **and** classified)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7a7ec629", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-11T22:18:52.004309Z", - "iopub.status.busy": "2026-06-11T22:18:52.004159Z", - "iopub.status.idle": "2026-06-11T22:18:52.150541Z", - "shell.execute_reply": "2026-06-11T22:18:52.149546Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "class_star of matched true stars: p10/50/90 = [0.765 0.942 0.996]\n", - "209,024 unique-ind stars -> 181,502 unique-position stars\n", - "star sample for the efficiency curves: 180,062 (combined plot with the galaxy misclassification below)\n" - ] - } - ], - "source": [ - "star_dets = (cat.loc[cat.matched & (cat.truth_gal_star == 1) & (cat[\"flags\"] < FLAG_CUT),\n", - " [\"truth_ind\", \"match_sep_arcsec\", \"class_star\"]]\n", - " .sort_values(\"match_sep_arcsec\").drop_duplicates(\"truth_ind\"))\n", - "print(f\"class_star of matched true stars: p10/50/90 = \"\n", - " f\"{np.percentile(star_dets.class_star, [10, 50, 90]).round(3)}\")\n", - "\n", - "detected_ind = set(star_dets.truth_ind.astype(\"i8\"))\n", - "classified_ind = set(star_dets.loc[star_dets.class_star > CLASS_STAR_CUT, \"truth_ind\"].astype(\"i8\"))\n", - "\n", - "# collapse duplicate truth entries (same exact position, different ind, single-band payload):\n", - "# a position counts as detected/classified if ANY of its member inds matched\n", - "ts = truth_stars.copy()\n", - "ts[\"is_det\"] = ts[\"ind\"].isin(detected_ind)\n", - "ts[\"is_cls\"] = ts[\"ind\"].isin(classified_ind)\n", - "stars = (ts.groupby([\"ra\", \"dec\"], sort=False, as_index=False)\n", - " .agg(mag=(f\"mag_{BAND}\", \"max\"), is_det=(\"is_det\", \"any\"), is_cls=(\"is_cls\", \"any\")))\n", - "print(f\"{len(ts):,} unique-ind stars -> {len(stars):,} unique-position stars\")\n", - "\n", - "mag = stars[\"mag\"].values\n", - "ok = np.isfinite(mag)\n", - "is_det = stars[\"is_det\"].values\n", - "is_cls = stars[\"is_cls\"].values\n", - "\n", - "n_all = np.histogram(mag[ok], MAG_BINS)[0]\n", - "n_det = np.histogram(mag[ok & is_det], MAG_BINS)[0]\n", - "n_cls = np.histogram(mag[ok & is_cls], MAG_BINS)[0]\n", - "with np.errstate(invalid=\"ignore\"):\n", - " eff_det = n_det / n_all\n", - " eff_cls = np.where(n_det > 0, n_cls / np.maximum(n_det, 1), np.nan)\n", - " eff_both = n_cls / n_all\n", - "\n", - "print(f\"star sample for the efficiency curves: {ok.sum():,} \"\n", - " \"(combined plot with the galaxy misclassification below)\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "103445b4", - "metadata": {}, - "source": [ - "## 3. False star-classification of small true galaxies (combined plot)\n", - "\n", - "True galaxies (`truth_gal_star == 0`) with measured semi-major axis\n", - "`awin_world < 0.3″` — the truth index carries no size, so we use the SExtractor windowed\n", - "size of the (nearest) matched detection. Plotted: fraction classified as a star\n", - "(`class_star > {cut}`) among *detected* small galaxies, vs true F158 mag — on the same\n", - "axes as the true-star detection/classification curves from section 2.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7bc997de", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-11T22:18:52.153522Z", - "iopub.status.busy": "2026-06-11T22:18:52.153302Z", - "iopub.status.idle": "2026-06-11T22:18:57.304824Z", - "shell.execute_reply": "2026-06-11T22:18:57.304196Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "measured size of true galaxies p10/50/90 = [0.063 0.105 0.199] arcsec\n" - ] - }, - { - "data": { - "image/png": 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twPehK++nsvbTHBNwPMHzXHbZZeoesv8piejoaFU2mhRzDMsPj3HNNdcos1quLyvaeI/jRMe2xGeApsV//vmn6gtt23BpcBxPk0ytPfN+aXVXJjytieMMht6MZ1k0cdpsz2233aZmtTUoJXPWU0/t6Wy2w5n5A49rq5EqzZyS2jSaEzjCmQvOvHI2w3F2TptR7NKliyU+Pt5u3datW9U6TWNpC00IEhISLK5ADQOP8/XXX5c4K0DTA86yadoEW62UM00cNTBc5s6ntFlvVzRx1PKEh4ercl522WVqxpb3nWYkNHH877//7LZ/9913C80/9HjllVfU+gceeMBSGpxtKWnmkveR6zlzpqFpmWj+oZk62n44w+1MW6sHtU/c78orr3S6Tffu3dVsqaNW1RmcvbR9ljRmzpypzkUTFb0ZZD3tw4cffqiWc7beFmrguJx1YAu1M5yxd0cTN2/ePLU9tRG2M2Dp6emWc889V9cMcs6cOWo5tbWOz6LWp1Dz6Ko5JZ9tzUR2x44ddus408g2wplH9g0aDz/8sDomNZeObZwz+SdPnnTZXMXdfvDIkSPqmaFGiJo3W/hs6Gn6nMHjcts33nij2DpeL81b9LSar776qt22Wjn5PFNbbWsexRlVrrv77ruLzT7raQE488xZUc76V7QmjuVq27ZtoQZI7z2id6/Yj+qZfNGsTK9fdvY+5DPiCO8ztbfcnibpjlAbbHuPXn/9dfX/+PHji5VJTxO3dOlS1YfQisOxH/niiy/U9tTilqVNlESfPn3UcWyfBWfvCGoNOFPfvn17S25ubqmaOM7ou/vOskV7fvWsHDRNHNfznekKtv2o7YfXdP3116v+zJbt27er9bTO0YNaWK6n1qai3iW2sN80mUylauKoeXG0wNDcIKjBYv3RaoLL2PfbmlJqJpPcltr4kjRxvM96aG2f7aM8sF/38fFRZeSYzJGjR4+WOl51t7/iO439tF77tx0nujN+dRW+/3hMPc0Xr8H2XVba+6ms/TQ/1CjbPs+uYjabVb/FMQbHVTwW7x/PxT7L1T7IFrZVvXeXBt+/XP/++++7dDztveP4YZ289tprlrLicSGOppR6uCvE8cXFm8NBst5N5sCKHQwHcqWhPQTOzDYqyieON4b7snHpvYwWLFhQbB9NiHO1c3UGVdd659YT4siXX35ZzBTBmRDn7AVU0qc0FbIrQhzZtWuXMv2zPTYHzTRTcnxQtcHgo48+qnusjz/+WK2nGVBpUM3PbRs3bqy7ni9yrmcnrMEBFpfx5UfzBJqPsIOjOdLLL7+sOhmaHrniw7Zq1Sq1Lc25SjL/1MxXNFO1ssJOkefii1fv3tOcTK8OeE22pjBcxk6KH70JCJqfuvN8aX4UNL1yhGZGesIXX468B4mJibovPa1fcVWI0waten6ktvbyv/zyS+E5NPNJx5e/HiW9JMvSD2rPgV5/xxc+77O7QhwFdlf7Tvo4OZqj0BxKe4E5mqNQ6OS6IUOGWFyFZn98xmx9TCtCiKN5NrelCaweNOkraUDjCJ8Bbk+TofL4xNFkl9vTbMkRmk6y7bIv4gCD5kw0x9d7/vSEOApoJQ2EOSnF50kbqLnTJkqCEx88rp7wq/eOmDZtmvqfk3WlCXHOBlAlfWzhBCGX8Tl27H81U0t+aM7sCjR9pxsGfbgpsB0/flz5gHEy0vE9bOuPrdfvEk7OcD0nHCrqXeLMP4hCmO094ruRwpvj+0+D5uq8Htu6ZR9FE2rHSQr2BZz45jbz58+3W/fXX3+p/biOZdFDmyz9+eefLeVBm9zie9oV9IQ4d/srvqdonqf3nirr+NVdIU7vvVqRPnEl9dNsOxwflZfc3Fz17mVMCE5c8tisV/YNP/30U7E4BM7QJhrptqCH5gvr6jM/a9YsyzfffKPeb+zD+OxRQNTKaNuPeZU5pSumG65A0xqqlqnmdZbzjFGYGLq+NGg6FhMTo0xGaBZGcxGqpxny3VXzAlt27NiBV155RamRaQ5E0wdbaGblat107NhRlYNRC2mGRJU11bY0RaDJlKtQNU1oNuYKNLGiWQdV3lR385zOoEq9KlIuMKIlTcdYP1RHd+rUSbUJRq2iCRzNsGie5arJrrXvtYaTryhsj6WlPOA3I3HRnE3j/vvvV6YjNFukacZHH33k9Jg0w6SJA4/DNAIlmX5q5gesF1egiQzPzfbGdkyTKJoxlNZ29aKn0ayIpiC2oXdpUsZocn379tWN4kTzIUbVdJUNGzaoOuYz7AjNaxxN1Gi6umnTJlUvb7/9tu4xaX7J55bPjCuRpvh8aCYoeuk39uzZo77ZF9GkmnVAM7iePXuqfqey+0HWGdEz86IZB01i2He5AvsjmqwwMh7NpGgKSfMVmmQ6M1+iGaXjOpqOEZqhMtqfLQzlTPRSgNAsiyZ9vCbWA01LbeEy7dgVAU2niLMcVFyumd3YwjbP/pTmtbxnNE3S+puSnitH2Cb5bqHpGXN+Ooay1zsO+3w+z4wGSDNivtNo1u1qFDW2b+7DsvPjCN9v7It4XWzTZWkTzq6VplSu9sc0X+d18hmkySb3dQYjJ5YnJDqvh3X5/vvvK1MwLTolzScZ2p5mg9u3b3fJRJaw/7Ltw2gmSRMy9pM8Pt/D7LdoZl2R7zJ33iWOsD1zXMB+lCZkNHvVolPSjJHpBxyvn22W7YMmgDT/5j5859AUlma/NIvn88N3OmFfwDqmOdyll16qyqpFp6SJOtsU+11n9ezu+88ZWqoemqmWB3f6q8mTJ6v0DRwDXnHFFer55bjUsT+r6PEr4biK95Xm4jSBpHkg2zzr3tPXrUEzYpqQlhcfHx/13uWH73+aXPPZp8klP+wv9CLtu4u740e6RtnCZ4+moDTTpbnuE088ocxp3b2HHhfiaEtfEWhCCR9mW5+jssAwwHxIeRx2DPRP0AY0rGi+HNipugKPw9D1bKRs+PST4PH58qJvEI/vKNSVVDfsnGjTzbDFFEo4+CcsDx9s+jy4EoqUAzlSWth+DTZE+kuw42DDojDkTdAmnz4zrAfWqTbwo88Mw02zU+BL5o033lAPA9Fe6s5y0VBgsd2uJEo7lrbc9li2ArStP6UGfUcoxGn20c7y5dBmmukJ+GJ35oOjwU7L9v6XBtsUX8LsUFhGtknNX4LCpbO268yGmx2Qbb4+rV6c9QPuDrh5PNax3oQGz+34bLDdsLNl/1Fav8GBtisDXa0vmjVrVqnHI8nJyeqbA52q6Acr8h7weaMvFs9NvzbN74rC+x133KH8qBwHWHrPl/aiKmkdB3u2cJBBXzA+V/SJYlnYH7DvYjn4rDhrr2VFqztenx56dcpy853A55qDe/ZbHPBrKVxYd66Uk+2Gk3f0S+GEH8O3c5DK+uE69nfOjsMBHZ9pTijwGCVNyum1Mb7PXHleytom9GCf5er7Sqt7psdhf8/3Jd+NnuS9995T94GTXhQ+ONlFgYvvIwrZFOLKOwjl4JyDT4b/p3CoCXEV8S5z913iCAf0nNTg5BGFdfrZsr/lBDAFXLZ1x+vnZC/rhQKprf8U9+Gzy3cg0/bYToTweaGPIYUU+gHyXIwXQD8r3nMKO87q2d33nzMqos92t7+iUMvrohDLtqZNOlKwf+GFFwon4Spy/Go7KcvJGz5HHA9oKT4oWDHHH30DPXXdFS0raHAcwmeI7UfLo8hxo6sTFxU5fiwJTlSwnXFCjpOv7vioV4oQ50xK1WboHCV02wfIFq2iKLHSObK8cLaXnTEdRjlTTkdN/uagmucvbYCmwQ6NHQc7ITpy2sIHz1ki7ZLqhg8jZ1/54Qucs+QsD3ObMVeLK3nAtEGHNuhzBc4qc4aRs6+c4XQGhVNbp3lXYGdenuTJ7GB4Xzg4cpy5J1zOQQ0HExpacA3OGOtBZ1vC4BClwXNqDxod+R0HvnrHoraEAy62cT2hRxPytBePI7wWzmpzPduRXkAVR7T77cpggrN47LA5+cDBl22eQA5QKmJQpD23msO6I6xLd4/H2V8O9hwFOdYzhXkt4ILt+bUZ3IpAOyZnGbUZ5JLQ7r2r2hdXzu1OP2h7D/Qcyt29B3yuOIvPlyRn9tn3cdDByRS2G9vABhUF7y1nT/mi50DS8fnz1KSTVnenTp3SXa/XrvmsUoBj4ANH7Q/r2lXhm4EV2P9TSHHch9fL/s4ZHBhTgOMgm9sy/5peDktn18zAJNogpbLaBN9Z7Kv1nu2SrpMBIThwpCDhDL6v+N5yBz0tO++pXkALXmdFWR5pwRhsta4MhEVBmJotPguOs/WlvcvK8i7Rg8FDGAzLEU1ja3v9FBYZxIHvOb2cwXxna2VzhBo7vQA3mjWLs3p25/3nap/tqjVTRfRXFG754bPHbai9ZH1TI0jhR7u/FTV+tYV1yrbBZ59CN4N2sN9g8DCOfyg8e+q6K8oiymKxqLEix69UgjC/G8c1bPusV2qFXRVwK3L86Mozz7bmaGnhEhYP+8Q58zvQ7Oj1QuL++eefxWyMaedKJ3j6griTSsAdaOOvhVZ19N2gfa0eDPHKAAd6MAeZnu2wZtvvDrx++jVwPy3ca0m89957Tp3N9XzibMOm04eCZeS+3uITR/t4rmeYZT1ot+zocE8fuapOMUCfMi6nLbYjDG7AdY6Ovlq7o600y86wy65C3wQ+J44+SCUFCdELBkS/CT2/sNJCYzuG+Kf9uRYaXc/WvzJ84hgMgG3aMYhQSTDAAFMX6EE79pL8fR2hTxzvias+cQzTzeMzuIwjZekHK9Inzhn07eEx6C/lij+xbVhpPRzvI/1IHZ9v2+dcc2a3vYaK8Ilbvny52z5xWtAI+q44e+Ycr1vPJ04LCKQXWEE7h17daiH4GeSC9cZ2zPca0yS44hPHAChcVt78S3ptoiQYrIrb6+V/K+kdwTQ5XE6fF0/5xJWE5i/n6nWWRr9+/dTx6D9TESkGyvoucQfNX87Wz5/9PZfRT9o2MIbtWIPrnY2fHOE7nM8527Iz3ym2efrNuRoAzlM+cWXpr5yhBdVx5oNd0vi1vGiBZhgUzJX3U3n66fLk2Vu/fr0KRKKNkdkG+LwwrkNZ2wL9NUtLMcDxpaspBpzB8TzHBiyzXvqW0ihfDNZywHCmhDMGtmYznEmnv5AjnHmiipaS9W233aZsrB3hvq7MtlG9zxlOvVkclsVWFa+ZVzFcsh7ULtFky3GWn9dFHy53YblYPkc4q0UpnbMKrtjMalpBzbbbVTjjx/qlxo9qfWdatYKgOC5/3Am7rAdNCnjdVI9Ta2QL659aS0JzEQ2GtuXsEevNcVaVM0z0OeQMjaN6ndfHWSHHfTSTApoc2Pp98TicdaYZomN4Z5oTEZo48B5qcLaMyzR7dFuoaeVMKctAUwBtxtKVtkONAW3mXfFF0TSjDHlui/aMVQRsr9o9oN+MLTQJLEmbUJJdOY9lO2vF/oBmH3rQPJjPNe8N24ojvC/0bbGFzz19PPS0pDwOZ2d5//RmFtne2U45o0k4g04tATUMbEOOpiScvbTV9JTU55SlH6R/Cf3+2Ea12UMNakjc0bhQw6SnfdKWldeMyRmcWecMKrWfmhkf4X1lfZTXB8YZ9DWh3x7vJ7XWtnCWXM8fztlzRS2Kszaqh7PjcJacVh56sC/iM0JtGmejOSNODRnbGE2nbfsgZ9xzzz3q+8Ybb9T1S2Q7Zn1UdJso6zuL/Qt98+jvovl/OkKNqLvvLEf0nhM+o9r7QnsHufIuoYbKEZ6T95V9CjWojtoyakQ0rZ+t2Sk1Wbx2zubTn6m87xKWlds7+r2z39Izg6PZI7U2fO/QQkCDpr/079IzzeUyTTtr+84mem2U/TA1oHzO+QzpadpYNvZ79MF1NIt3dk3OYF2zr+W7Xm88pvdclLe/4rjG0YRc7zlyZ/zqKmyPehZwes9wSe+nyu6n4+PjlSaM5qBMj8X3Ms1uOX6lBRvft676AjtCSyqmF9DGd7ZQ28jxB03cHS3DqBnlx/Zesh6ZYskR1hHbJJ9nPgdlMSn1uDllSapbdtrME8UbwAvgAIsOkTTvourYEXZeNNWgmYm2HU2neCM5IOSLhQMb+vKUBG2I+aKiYNChQwdlxsFKpiqZph+aH5qmUqWdOhs5B0NsMByU0feNnQXtmCmsUfXPvBR8edJMjWWhYy5tz92B1007cdrC0wSManMKDHTOZP1wQKpnTugIr4uBPzjIYGOyNZUrDZrvMFiI44CvIuHDTFMY2//JDTfcUOg/wfxjmkkF64EvAg7eactPvwG+INhx8b7xmwOuqVOn2p3n+eefV4Mgmhmwg2e7o90x92GH4/hwEi2wh2Od8fhsNzwW7z3vLwfqfIHy3tBHwNFklPeSg36awvJ+stx8WXMASPU5X7p0ytegCRRtpLX8Y2yrms27LWx3jiaa2qSB44vcGZqvDE1heG2sawoTfJmw3WsBJsoL7wEHEzT34ACUzz3v97fffqsGFe6Y5tJfgs8UfVLYvlm/mq09BzF6/l3sJGnawfvDjpmCO+8TX1rsoNnZs7O2LQdt+Tn45QCIfqIU0GmKyEEKByc8P30IWW+8BpaF7YW5eigQ8gXH51bLWcdOn4Nd+s5wooT3lvePgwHeXw5MtJx0PB6FcPZjbFeaaTQnBNi/uNsP0gSKA0y+RDnY1fLn0ASHLxten6uBTVgnfGZoek3hhi9IvjD5PPG5pY+SJ2B90JSKL2g+RzSL4bPH9wfrSHuXeOK8nJBj++DzzntO3yD2jRTqWA5eu+2kCdsI7zH7Cd4n9uVsD+zD2Xc5mxB0hAME3je2C14b65vn5XFo9s5+xxb283w+2K5pSkQ/IsJ7xUEsB810nOc9LAnWJc/LNslzss/iRBcH0iw72wqfH22ioKLaBOuS95j9mDs+OJo/NycK9QZKFQXfLbwumlCzD+BAmibN7Ks5KaiXK8rZu4TPHMcS7IO1/H80A+MAnYNg+iTR98kW3lv21ez/2KbYzvjeYzugGSvN7mz3Keu7xFmZ2fZ4j9k38v6zvXF8wQls9o0sh+PkIftcth8KpwxmwvcNBTiWg+XjO5jvB1uYl0y7n+zPeY1s8ww+xXelM9NcPiPsE/Tef86uyRkcW3ASm+2QfSbrmveLfTrfYRQY+V2R/RUnc/m+YB2zftmu+c7g2JPvLfbb7o5fXYX1zeeOdc5nne2IZoSsdwpwtvlSS3s/VWY/nZ6ertoTr5njc76HKxK2Ab7jeU0cw7C++X7nNbA9UMh3hNsQ9g/aeJDvWV47g8VwPds9x398JnjvWOd8n5eJqjKn1NSIDAnL7OwML8rwuC+99JIyP3KmXmVo27lz5yqzKqrhaSbF/ZljhiZtjnmQ9GBId4YBZy4S5hjjuamGpRkJTTn1wjmPGDFChRfWQtzams8wnC3Pz1D33IZlY2h/zfTMHXNK5pxj6FJmu9fqhaGXabbDEMTuoJlU6qUycGZOqaGZUpZkOlcebHODOPvohbBdtGiRyrXF+8Zw1Kxz3keaPeiZbGhmHQyLzFx4WnthiG+9/H6EKQxoAuLMzHL27NnqnDQT5PkHDRpUYkhjtlmae2r7UAXPMPwMKeuoprfNrVXSR++5Yl4n5i9zVg/O6oZ53dgmaYrBcPA032DYa0fTyLKYU9rmC2IqB5p78jwdOnRQ5ohaLj13UnjQRJNhfVu3bq2eD6Z94P1lKGtn5yds6xdccIEqA9sB2xBNoO69995ipmYsL02U2FZoWqlXRoYK5nnZb9Ecgm2hTZs2lssvv9zy1VdfFbu3NIXkM6n1FWwHrG/mhWIeKFvYxzHktJZ/yvGel6Uf/P7771XqAZaVJjc0eeF27pgXMm0G20u3bt3UeXkvaW5CM7Y1a9bYbVuR5pRa/TFtC9sOr4H3kWbuzHWodw0VYU5pa64zduzYQrM0hnpnjkct16RjfiWazV599dUqTyrLyrxYfLfxGlw1p9TyVTEsN9sq+w62CfYletemhQl3zNNI2Ba1HFu2IbP1zCltTaqZ6obPF9sX7zdNk3l829Q17rQJV0wqnZk+l2Zyz9yqrprvlwX2+3wva88b7y1T+XB84Axn7xKafvG9wXc764vPOV0zmHuqJPN+th/m/eN9YLuiaTXbJVMQOFLWdwn7SL30ROwT2aZbtGhR2N/xPcZnoKR3DtPdMK+hlvaC18s+k3nOmFpBr93xHc+60dodx1/fffedpSTYVrm9nkmas2sqDZZl4sSJhe8MfnMsxtxjtug90+72VzT9o9ki3wl81jmWZC5h9g22JoHujl9dNZvkmEjL38f2yPcr03gwrZMjJb2fytpPl8WcMt8Ft5Hywr6c72i+X9kGOI7ke99ZKgi954rH4PiH4w2alLJPoBsD38fPPvusbl49VzEUnFSogVBVyxlhOhXTlEIoHc4W0ZSFs656DtzeCjW4vM8VFT5XEITSobM8w/dzplVzhBfKDk0D6WpB7QyjWlZnquO7hJoclpnaJ2oJqgu0IKHWg9pKWr3UhGsSBFeoMp84wfPQJ4zqXqrJtdxWQsnQVIgmF1q0seoCy0uTOj1/UkEQyg7NCG19YDXYr9KEjCY8IsBVDDQvpFkUzTnd8dX0Rqrju4SmkWzr7uTu9AYYJZymu3rmbdX1mgTBq33ihMqB/lj0lfGU439Ngz4vFZ1rytPQLpx+pfSXdDc/jCAIJUPfBebAot82LRvoq0LfJfqmMBQ+k9oKFQcDZdAHiD4leikxqgvV8V1Ca47qZpxFTRv9t7/88ktdP+7qeE2C4CpiTikIgiAITmDQCQbnYIAk5nmjZo6O6YzGR4d6d5OzCoIgeBIGNyotwJ/tZINeHj+heiBCnCAIgiAIgiDUABh1WYtOWxr0IXQ19YLgfYgQJwiCIAiCIAiCUI2QwCaCIAiCIAiCIAjVCBHiBEEQBEEQBEEQqhESnbKGk5WVhW3btiE6Oho+PnK7BUEQBEEQajJ5eXkqMjkDLzGKrlAzkVF9DYcCXO/evau6GIIgCIIgCEIlsnbtWpV/UaiZiBBXw6EGTnuQGzZsiOpCfn4+EhMTERkZqZJ4ClLXNQFp11LfNRVp21LXNZXq2LaZDoUT+NoYUKiZiBBXw9FMKCnANWnSBNWp0/T391cdUHXpNKsrUtdS1zUVadtS1zURaddS364ibjQ1GwlsIgiCIAiCIAiCUI0QIU4QBEEQBEEQBKEaIUKcIAiCIAiCIAhCNUKEOEEQBEEQBEEQhGqECHGCIAiCIAiCIAjVCBHiBEEQBEEQBEEQqhEixAmCIAiCIAiCIFQjRIgTBEEQBEEQBEGoRogQJwiCIAiCIAiCUI0QIU4QBEEQBEEQBKEaIUKcIAiCIAiCIAhCNUKEOA+QlpaGGTNmYNy4cYiOjobBYMCLL77o8v4WiwVvvfUW2rZtC39/f/X99ttvq+WCIAiCIAiCINRuRIjzAAkJCXj66aexbds2nHPOOW7vz33vvvtu9O3bF++99x769OmDu+66C88884wniisIgiAIgiAIQjXCp6oLUBNp2LAhjh07hkaNGuHw4cNo0aKFy/uePHkSL7zwAqZMmYJZs2apZdOmTYPJZMLzzz+PG2+8EQ0aNCh74RZNB07ttF9WvyMw/o2yH9OTx60t1Ib6q67XWF3LLQiCINRc5N1U6xEhzgPQBJICXFn46aefkJ2djTvuuMNuOf///PPP1fqbbrqp7IXjYDR2ddn3r+zj1hbcrT93Om93O3pPHbu6XqO3lNtb7qMnj+0t5RAEQfB2ZNxV6xEhzsvYsGGDEgK7du1qt5xmmX5+fti4cWOJ+589e1Z9NE6cOKG+8/Pz1ccICwwO+1gykmCO2wSENwUCwgCDdQvDr/fCcNp+0GOp1xGWca8V/GMBslKA5KMwZiQVP25mEsyJh4CwmMJjugrLajab1XeNx5wPY25G8fpLPgrLireAwLqwBISrbwSGAwHhMJ7cBkPcOvvtLWaY05OAnDQgOw3ITVffxsMrYEjYa79tejzMOxYCvgEwG/1hSs+GOb8B4B8C4/HNMBy3b2cWWGDWuRfGUztgiF1jv21+Dsyn9wJZyepjyOR3Cgxn44pfY8pRWFa+AwRGwBIUUXCN/ETAeHIHDHFripcjLw/IywJyM4CcdHW9xqNrYDi9w37brGSYj6wCfAIB30DAJwDwDVLXrHvsvGyYT2yztunMZBj4zfKnxBYvd9IBWH6+GzD6AiZ+/ACjj/o27PtL7eNY35at38NsMME3LQPm5EjAxx/Go6uLP2PZZ2E+tJIVqdqG9m08vBKGhD3222YkqOOyZqwLir4NB5fCkHTAYfskmHf9ChhN1vIWfHTrLy/T+vxyW4Pt9ib99sf7fuYokJcD5GcD+bnq23hkNQzxjteYCvPxrQX3pOC++Di5L07antP2Z7O96kfY92Wnw3hiCwzHNthva86DOTfHeo0OlNr/lXFbTx+7qqhVfXYVI3Ut9U10x3MF/Z88h7UDg0WiZXgUzZySJpIPPfRQqduPHz9e+dIdOXKk2LqmTZuiW7du+Pnnn53u/+STT+Kpp57SFQ6pHYxYcBX8TjoXBM2+wciv01h9fOO3w5QRb7c+PygaudGdYUo9pj5GCgqlYPYLQV5EO+RGtkNeZHvkRbZD4K7v4JN80G67vIi2ODvIWnYOBlJSUhAWFgajsXq6boYumwGfJHvhKS+8JTI7XAafhF3wTdwNH/XZA2NeJrwZJRpwAG8wqo+F7rQGAwx5WTBQyPDQOYu9oHh+1XGZPXJOwfuwmPyRH9IQFq39GX1gUUKlCT5J+4r1QWZTACyBkUBeprV98lsTcp1g9gmExTcYFr8Q9W32C4Yvn8vsFLvt8oMbIKPjlbBQaDf5Wr+Nvgje9DF8Ug7bbZvL/mzwMwVl9bWWv+AT9tfd8IvfZrd9ToMeSLpoXrGy6fXZzratSmpCn11dkLqW+i6tbzh+/Dh69uyJ2NhYNGnSpEraqeB5RBPnZWRmZipNnB4BAQFqfUncc889yofOVhPXu3dvREZGqkiZRl/fEgfLHBAZk/bC10H40KBQZzqyuMRjOC4z5qTB7+QG9Slpe9/8TPj7ZgKhjZBvsa6NioqCzx8PVIuZaEeMqYdgcOhgfU9uRNBuak5Kx+IXDIQ0BLLOWDVDZRSWnAtDBpePqfY359n/7+r5fYOBgFDrNTgIq+oag+sBmUlWzZfjOR3LUUnCm5rfpFaams+00zBQ62e7ntrCJucC+dQ65RV8U/OUAyQdUMKD3fYcyHPAb86FwaYey11O3kdqs7TastV452YWu796bcHbMeRnFxOQSsKYnwWkHXPrHGoShZ/MhBK3M6WfRJ11b7p0TPahkT9e4XIZOBir/0kXe+0uv9OLl8k3KxHRZ7cCkW2AsCZW4baKtXbazD/7bPpwC1LXNQVvbdt64zlfX1811qNbjlDzESHOy6Cg5uzhy8rKUutLIjQ0VH0cYcejOp/6nYoN4wz12gMD71NmkaAZGL/52fkTkF1kmqnwDwU6Xmg1veSHppLhMTAsfw04vdv+uFFtgV7XAye3A6d2FHys5mq6A/T4XTC9091q3hXWBJFBjeBTrzWMBxdby2W7LY/gRZ2pPgbnS2ge2bArUL8L0KALsPd34OzxwsGY2tbWX4dmctmpQCYFujPAjzcD8bvsDx7dHhj/pjKJBIUjvzrq2zDn4mI+XYYmvYGpfyjBIz87DYkn4xAZGgyTORuYPw04td3+2FHtgOFP0P7Mat6nvi3AkheAxP3227KNXfa59RoDwmDw8XPqk2R3jRSGaIKZkWS9xoV3AA7mg4hsDYx8puD6bD7fXQ84mMqB13j1N0qYKTS/zC34/u1+IN7h2LwPV3xlLTPbuaZNKK3cjswarVPf56r65mAg/vRpREfWhQlm4IsJQNx6+/0b9wImzS8yeSzQOuGzcc7vo6vliOkLTPndev8oTGofthHH+mvYDbj4I5vtbPb55d7i7a9+Z+CiD5SZqBJAtO95VwIOppdo3BO4/AvrvbG9P78+UPyehzcHet9gPa9mYqrKkQ9sngukHrffPqwpcN4dyoTWbPJHSmYuQiMbwPT349Y+yJZ6HYGRT1v7OZog8xlT5sipwNZvgfTT9tuzrdVtWWAumlNgOppjba8VoJE2qMmAHCC3lO2Sj8A09zLrPxTiI1pan41j6639SBX1ldTAFb5rBKnrGoRXtm2+a+n6wAnEus2BkAbq3eR15RQ8hghxXgZNHv/55x9lLmFrksL/T506VeaAKYWU5MQfHmP/f8K+4gEdOOi58N3i+054y/lxOWDT4MD/7DFgziXFB2sa5jwYzhyG/5nDwLH/UC3hdSYXN4lVwtDkH4DQxvZak+5XlXw8bkttFj91mwHN+lk1RbZQsOByR7jc2TLO9PuHwhwUDdSNtg72YnoDfiHFt+8wvvhxjqwEgqKKbxvVpvi2pQWQMPkAwVHWD2ne3+of53js9uOK70uBgxoMx23pZ6dHs/5AgM6xWbfultud+tbupdK0mIAGXQGDT/FtqQF097iubs/za4Ih/Euuv3od9I/trP1xYsIRCsc8n+O21CA54uyen3e7fjkyk/SDlfS5Uf205OcjOz4eiI4GYvpYJzYct20zUv/YFK4dhThOuugJzToCs9LUXv2tdYBlzi34ptY2F/jxRuDktuJ9w7DHirS6ap8cYOXbxfsSCsdcRygAU/vmoIErJD3eKpT6O1y7IAjVm9HPA+s/tf4e96rzvkyosYgQ52X06NEDM2fOxNatW9G9e/fC5Zs2bUJOTo5aX2m4O2h0BQ4gOXhzHKhpGojxrwNnjsCcdBCZJ/YgKOsUDBQUHMzTlOmTN0MNVao1qIwdvG69wau7uCNYuCuEePLY3lKO6lhub7qPteEa3en/nG3rbBKBAh7NjB2373hB8W2VBYOOoEqNNLXg2oeTbnt+tWo0bWFwm1faWCc/ul4BtBpmnUQQBKF6w4lujbqup7ISag4ixFVxUnB+GLAkKChILbvwwgtVou93331XCXMa77zzjopOyfWVhicHus4GPdQINOymZtBT4+MREB0N0+ziZmSI3201u3M2SKpKlr0KLH3J+psmhTQr1cwkyysEC4JQOVQH4bNRd+unJI2gNum1fb71Q81554lWgW7Tl8U1eJJ2QRCqB0mHrN8cX9C9Rah1iBDnISiEJScnqw9ZvHgx8hgavSDnGyN4cRtGkuS6IUOGqHU0l3zwwQfxzDPPIDc3F4MGDcLSpUsxZ84cPPHEEyqReI3AnUGMreBD3xUOOvKyge//B1wz32qG5y389w7w7zPW35zxvnKeNYS6IAiCp9GbJKK/XKNzgK3fWH3mMhKAtR9ZP/Snc7RyEAShenCmQIgLbQJovudCrcKLRr81i1dffdUuTcCff/6pPmTSpElKiHMGBbu6devivffew9dff42YmBi8/vrrSkNXK3EU+NZ/Biy6Gzi4BPjzUWBsgdarqlnzMfDnY9bfzQdag2SIACcIgjdMjtFPMGE/sO1bq0BHUyw9Aa4Co6cKglAJmrgIMaWsrUgyFw/mh2MKPr1P8+bNC3O68X9NC6dhMBgwffp07N+/X0Wq5Df/53IBQK//AefeYK2KNR8CGz6v+mrZMNsa8ZAwAuBVXwN+VhNZQRAEryCqNTD0EeDOzcDUv4CQ+sW3YZRSRnvd+6c1YqwgCN6tiRMhrtYimjihejLmBWt0y0PLrOHOGQ2x2XlVUxaGOf/57qJInNd8Zw3zLwiC4I1wQpBRaBkMIe2Uw0oLsONH64c5HLteDnS7Clg/Sz/Aiid9pwVBKF0TJ0FNai0ixAnVE0ZXYy6yT4ZZZ6O+mQzcuLjynXu3fQ/8dJt14MNw8czvxTQAgiAI1c2HjqaU7FuZvoAaOaZYWPWu9eMbVDzypSAIVQO15FrqEdHE1VpEiBOqL4xMedU8YOZIq7P+vKutiYw9qQWzTfzMHFUJe62/63UCrv1JP3WCIAiCN1KSFi1+D7BlHrDlG2tCdV0BzuLJ0gmC4IyzcUX+q6KJq7WIT5xQvWEy4omf0D4IOLUNWHALM6N77nwU4BjCmx9NgGOENwpw3pjuQBAEoSxEtwNGPAlM3w5M/tGamsCR45utEXmZ7kUQhMo3pSSiiau1iBAnVH/ajQWGP2H9vWthUY62yqJeRyAkunLPKQiCUBkYTdZ0KZGti6/Lz7ZG5H29AwwLb4dP/Ha5J4JQmUFNOLniX0fqvJYi5pRCzWDAdGv+uG3fAUtftGroOl1U8eex5BdfZpL8LIIg1DL/uZw0a77OlFiVqsC4ZS6itsyFZXVPoPeNwJH/rCaZjseQQCiCUH4kvYAgQpxQo6KtXfAOkLgfOL7Jmgh8+auAb3DFDh6Sj5b/GIIgCNUNZ/0nTSk3fQnL+lkwnDkMAwOi/HgTYPSRnHOC4GlNnPjD1WpEEyfUHHwDgSvnAm92Bcy5wMltFXv8o6uLwnGHNgbCYvRnqAVBEGoL9AXufyfMfW5ByoYfUHffdzDs/1tfgMtKBrLOSgRfQSgvSYet3+IPV6sRIa4WkpNnxjfrD2JPwjG0j2qMK85tCV9TDXGPDG1kdcg/VcG+GTQbWnin9XdUW+DmFYCPf8WeQxAEobpiMCKn2WCYe10KU8oR4LNxQOoJ+23idwMvNQMadgOaDwCa8dMP+PtJyUEnCK5isYgmTlCIEFcLBbhJs1Zja9bH8A3fgG93D8eirVdiztTeNUeQ8/NAioHlr1uTixOabYoAJwiCoE9ESyC8WXEhjljMVpN3fhjZ0mC0RviVHHSC4BrpCVafVPWstZBaq8WIEFfLmL8xDhvP/ILAhhvU/34Ry7F6/wDM39AIV/au5ETZlQmT1paV07uA5a9Zf587DWjat8KKJQiCUCPRMzOncNduDHB4JXB4BXB6h1Wo0xPgRKgThJL94bRnSqi1iBBXyziSmA7/yJWF/xtMOQiovxD7T3esgYMHi1UAyz4LnDkMxK0HmvRy71jmfGDhHVYfuzqNgOEzPFFiQRCEmkVJgaQ6XlgUFOXISuCX+4C0k/bbnNwKfH4B0PcWoM1owFhDLEUEoaIiUzJwW7CkN6rNSK9Yy6gXbobBlKpMqjV8wzfhm4Nv4pv1B2A226yozoOHqX8AU/8E7twMhDW1zvZ+MwlILQhM4irrZgFx66y/z39NHPIFQRAqMihKhwlA3eb66w8tBeZdCbzTA1j9oTUoiiDUdjRNHE0pGZlbqLWIJq6WUa/eURj25ighThPkVB8QtgpPbbwRn62biufPH4seTeuiRhAcCVz5JTBrtNU/49trget+BnxcyO2WHAv885T1d6eLgfbjPF5cQRCEWkcx00sL4BsE5GYCsautg9bfHwT+fRYIj7Hm5qQfne3+kn9OqC0kHbR+O5v8EGoNIsTVMsa0GIm9Z6Zix4lEnM3KRYi/CSnmA9iXvAMm/9OIs7yEK79dhzGNr8HD4zqhYVhg9Y96yUhoDEbywzTrgICDgdJe+JRwf7nH6jwcEA6MfbmySisIglC7KKk/PrYRWPMhsP0HICcVOL2zMksmCN6HJPoWChAhrpbhY/TBPb3uLrZ8w6kNuHfxw0jMPgH/6L/xd8oe/PXWlbihb19Ehvjh5NksNIsIwsSeTXQFMwpw185ag42p38A/+p8qj3rJ8jCIy9GkjIJyT4Tvic3AqneB9Z8CDbsDPa9zfoDt84F9f1p/j34OCKlXaWUXBEEQCmjcA7jkY2Dk09a+m0GmHHPQ5edIdQm1B0n0LRQgQpyg6Fm/J36Z+CNeWvsyftz/A0yBsbDEvIkPNo1FbnJPGHzSYMkLxdv/7MOIDvWRazYjKycPZ/MSkJJ3AsfSjyIp5yj8otao4/lFLsPqfZUf9TI5Iwdb45Lx+IIdOJJUFPFswebjmPO/GfBlAnD6Wfx6H1CvIxBzbvGDpCcCvz1g/d1iEND9mkorvyAIgqBDnQbA0EeAA4uBuLX2605usWrqOl8iVSfUbLJTgfR4629JL1DrESFOKCTYNxhP938Kw5sOwxMrZyApOxEBDRbCL2IFjH5JyE3tgITs+vg2NhFGv3gY/RJhMOYC9KkLAWy9zLg8oOF8HEro5BHN2sXnNFbawV0nzmLn8bPYWfB9PCVLd//VBxMxf9NJXHnZbODjwUDyUWugk5uWWgcHtvz5KJCRaPW5mPCWOA4LgiB4C8wrpxdF+Pv/AbsXAeNetQZMEYSaCCNta9SVHHG1HRHihGIMjhmMHy/6AZMWPIDY7DVKgCO+dXYB/OhgsoQgD+nKIV0LluRTZzt+PDAP16c87JZvnTNTzdWHrOUgj/y4De4G0vx2fSwu7N4XgVfOBWaOtIa0/mYycP2iouTd+/8Btsyz/uasr+RgEQRB8N4gKHlZQOpJa39OM3jmn6MPdNvRVVVCQfC8P5zRBwiLkZqu5YgQJ+gSERCBSS0ex9NrH4BvaJEjuTm3DrpE9sCgFh3RLLQZmoc2R9PQplgauwIPr3igWNTLzNAfMeazLLw55l4MbV+/TLX9/pL9dgKcKkfBOUxGA1pHh6Bjo1B0bBiKDg1DlXbuuV+LC5sbjyZjxOtLMWNCR4y88F0Y5k+1muXQdJIat5x0YFGBv2CDrkDf26R1CIIgeHsQFLMZWPMB8PdTQNopYO7lwDmTgdHPS1oYoWb6w1GAM8kQvrYjLUBwyqjOYXhp1wHkW6wCGYUzH59cvD/mWUQEhpcY9TLIz4L9aWuVz5y57m+4+bdUTDt8J6aPbAcfFwOdJKbn4s2V2/H1ujjrAkMuDD5nlW8eLL64tGcTPHtRZwT4muz2690iAv/sOmUn+DUIDcDp1CwcS87EjXM2YGi7pnjrnFsQuukDYMNs4ODSghndEzyRdSZXOkhBEATvh4nA+90GtB4B/HgzcHwjsGmOtV+/6H2gxcCqLqEgVAwSmVKwQYQ4wSnr41fDbMhWLm+EgpwZWVh3ag1GNx9datTLzLxM3PLn3dgQ/5/yq5u1JwPrj96Ad67shXqhNjl+HMjIycPHSw/go2UHkZlrLlzuF/UP/KOWIDt+OHISRqJXs7rFBDi1nY8Rc6b1wfwNcSq4iRZVc//pNDzx03asO3wGi/fEo4/PAGzyn4OA/LSi2S0ahNZpAEOj7h6Kkqkf3VMQBHnGpB8pJ9HtgKl/ASteB5a+BKQcBT4fD4Q0AMKbFvnTSV45oboikSkFG0SIE5wyoukITOsyDdn52YXLAkwBGN50uEu1FugTiE/GvItHlj+G3w//Ct/wjdiS+jbGvnM93rmiN85rHWW3fb7ZogSv1/7ag1NnredkHrur+gfjt6PfINlnqVrmF7kE3UIvUAKRMygoOUbFpKnltzf1w/yNx/DCr7uQmJ6DXcb6OMeYZrfdvqxwtMg3l1vY0vPlU1EyqyjtguCdiKBfAc/Y4VOFWvqa9ozxGifNXIO1R2ruNVYotKAY/ADQZpRVKxe/y+ovx48g1BhNXMuqLongBYgQJzhvHEYf3NXjrnLVkK/RFy8NegERAeGYu3uuCo6SYfwQkz7LxB1DuqB+qD9iz2QiOzcfK/cnYM8pq0DFscmgTvHwjVqLb06sgMXXUqQRNOYjOGYOfIxD3S6PwWBQZpgjO9RXwmLehuKavJTsfCVMljc1wtw1R6wCnI0ZqIqSWQHHFioXTyWy9yYhxFPX6O6xS9o2L9+sotIeO5OpTKN/23ZCPWN+UUtUfkpq6VcfHInPVh7CjYNaobpD64Gnft6BtYeLX+PLv+/GQ2M7KL/g8lBjJxFoTcHow291KzCTt4GC3X/vAC2HAvU7SQRioXqQnwukFLiXSHoBgWNai0ULQyHUROLi4hATE4PY2Fg0aeJcc+Vp2Mw+3Poh3t/8vvo/P6shMo9OgSW/jv2Ghhx0bb8fecHLEJtuE0pXh6vaX4WHez+sBLOycujlAWiRsc1u2TpzW9we8AKu7dccw9rXQ/sGdVw+x8H4NGWquXj3afx3IEEFYPGL+qtw8EUz0IhgP1zQrRH6toxA7xaR6v+yDKIravCVn5+P+Ph4REdHw2QqLtTW1EGgq+V2TGTP+9gjtGyJ7B3rmoL+Iz9uh3+9RfCLXFHYRl68pEulCvq8xmtmrsaWtG/LfY2u1F9r34l4cGw75X+abzYj30xNvBnZuWa88+8+HLUsKNw2NOt8NIsMwPHU00jIOg2LKQUGnxQYfc+qb4NvksprafXbNSInqT/MWY0Q5tMI7SNbonV4HfRsVR+dG4ejUVggvtt4yCPPWEU9B5k5+fh12wl8sy5WCW80YjeF7EZgky9hMJhhMfshbd/DgDkQ9er44/yuDVV/0j0m3O2+UM9aoG/LyApp217DrNFA7Grn64PrAS2HAK2GWn3obEO4e6nppdfWdQ3Fa+o78QDwTg/r71tWFY/U6oVjP8GziBBXw/G2B3ne7nl4Yc0LsMACc04kMmKvAywmwJAPv/D1CI7agFwLUxVYaVe3HbpFd8O3e79V/xtgUPtqXNHuCjzS5xEY9XIHucC+T29A8uHNdsv2mGPwWN7Uwv8bhQVgWId6GN6+Pvq1ioTRYCgcrDUOD0DjuoFYtjdBCW6HE4sSjCtMqQhp84J18KUGmP1gyQuHJT8IlrwQ9d0yoh56xcRgw6F0HDEvcGkQrafB6duiQaUPviqyHJWJXrk7N4zEnSNaIyUjB7GpJ3EsNRanMo/jUMpRJGbFwid0FwwGCyxmEzKO3ITnxp2Pq9wUtLS6zvUNwdyNO/DV9oXIC1wPU+BxtZ7HTj84HdP6notHz3f+gq4IsnLzsf5wEv7auwu/H/gPyZad8A3bVCgMZR67HMNihuH+0Z3Rpl5ImQSEdYcT8dbSFdh8Zin8ov611p8FMOdEARYf67NvMcECI2AxFvwPmEIOFDwzBvWcGHzS1L5lwZxXB+bsKJhzomHJiYYxIA6+YVuQnTAUDfIvxg0DW6JusC9CA3wRFuiLUH4CfJS/7dTZ61xq22URhhyFvg4N6+D7DcewYPMxpGblweCbAN/wDfDjPfFNtts3P7MRMo9dA0tuZOGymIhATOjaCGO7NMD2Y2eLCZMpmbk4kpiOQwnpOJKYgcMJ6Vh/JAlHkzKLle3+0W1x65DWbt1zrxnouiLEMY8cm1OmfdRjXWL6AlP/gDfhtXVdQ/Ga+t73N/DVROvvR04AfkHVZuwneAYR4mo43vgg/3rwVzy0/BFYkA9Lvh8Mphw1sNPGCyaDSQ0exzYYi6FthoLju/c2v1fom2c2m/Hf8f9w6KzVNvyCVhfgqfOeUuaf7qI3+GpbPwRdGodh6d54JKTl2G3v72NAoJ8PkjNynR6zToAPBrWNxsDWkXhlx83IMRaYP5QCB/AUZq2DaAPy01rDz1gHfsYgGCyBMJgDAEsADOZAZGb7Ii3DBz5hm+BXdy2y44chJ2EUruwdg0l9mqF5VDBC/IvqoyQtX1leULn5Zuw9lYpPVxxSPoZVrUlyB5b9mUU78cXqfSohPQf0+ZmNYckPhtE3CQbfM8pktzQoELQO7ovxrUbhqu79EezvW+L2qVm5+H7jLny57Wccz18DU9BhXcGEApQhZQjuPvdGXNunQ7mEYdv73jqiEdpEh+Pv/dux8thaxGXugCHwIIy+Kc6v0eyHvLR2CLf0wMhmQzC2c3Oc2zxClUlP8xSfmo1/d5/Eoj2rsD35P1gCd8Don4CKxGTwQb2g+mgQXA91/SLwb+wS1ZdoEXQ50eNr9EOupciX1xnmPE6mhMGSGw6z+g6DOTdMLdO+6YNrq0lvGBagNGA0Y2SkXV+TAafPZmPf6bRiEXTHdWmAQW2iC4VD9R3giyA/E26buxFrHFKnwJgJ39Ct8A3bAFPQUft7YdNHEgOMaBEwAPFx/XE8PszpNdKv2MdoQHJmnlv13Dg8EANaR6F/myic1yoSUSH+JWobvWag68ii6cCpojQ5Cmowxr0GnNwCHFgMHFwMHF0N5Nv394om5wLT/oY34bV1XUPxmvpe+wnw633WQD337al2Yz+h4hEhrobjrQ/ys//+gK+PPAODsWhgYc4LxID6E/DU0BsQHRBdYqeZb87HjP9m4KcDP6n/RzYbiZcGvgRfU8kDaWeDesdIlhyYmM0WbD2Wgn93ncI/u09jx/GzTo9Rv44/LurRGMPa1UPPZnXV4O6TrZ/g7U1v221nhBGNQhohJScFqTmpqChUfr68UOvgM68OLLmhCDJFIDowGo1C6mPvcSMSDCvhH7lcDUa7BF+O2f87Vwl6FIod69p28N+qbkN0bRyF3adSsS0uGZtP7cPhs/tg9j0GU8AJGAOOwehj1Z5S+MxJHIhow7m4b+hwjOrYEME2wqS7VJSJGs15Nx49g+827sdvB/9Btt8W+ITssWt/epgswTCZ6yLbSE2ZuUhQcFBQcPAfbeqJoTHDMLn7UDQOD1H1tzs+DmazP/anrsfOs8tgCNqrNEwaRvjDbMmlXrrYsS35/gjOGo57+07Dpd1bw+im71N6dh4u+2glDpg/h1/ddcjPjobBlFF4r+wvgNowlsvi/BotJuSnt4JPVlec12AQDpwyYn/8GavQkh+AoNCjMAduV6Z/juewzR2pfltMOC/yUnSNiYDZko98Sx7yzHnYeuwUNiX/ZVfX1Nhd1OQeXN2jJ+oH1UfdgLqFmvffD/+O+5feX+xyXhn0CrrX644DZw5g27FtOJ2fgH8P7EBC7j4YTKULd45l1yZWzBT2LQGwmH0Bsx8sloJvsx9g9lWCuU/IPuSmdEXOmf6w5AWryQFwAqbQq9cGJfQlw+h/Cr5hW+FbZydgyLPL19kpshOWH1vutHwUWntFD0ZY9hj8tdkHadklt2l/HyOaRQaheWQwMnPzsXyfa0J2u/ohSMnMUz6JetpGrxnolpWcDOCToUD8bvvlPoHATcuA6LbwFqp9XVczvKa+f38EWP0e0LQfMOX3ajn2EyoWEeJqON76IHNwPvzzW5Ds81/hsqi8cfjz+hdcHhCYLWa8uPZFZaJJBjQegDeGvIEAH+fpC8rDiZRMPDR/m9LQOXLLkFZ4cEz7wv+pKbz5r5sLTT9tzUBfHfyqStGQa85FSnYKzmSdwdyNm/Dd0RfUAK5w8AojIg3dERII5JjTkWPOQI450/rbUtwEylWUOVt2A6u2ID8EPqgDX4Qg0BSOYB9+wnD4pAHpAcvhH7UMeantlZbC5E+B7QQMRudaSFvMecFARjt0qtsHV3cZgbGdWql6cCfAhTummnoCH83Gvtm4Cwv3/oWzpo0wBe+HwaCvZaOJ2vCYUZjQqQua1GmiPqF+oVh04LfCRPYavEdN/HvgZOZh5BnttSmW/EBY0jvCbEiHT53davBvq3EzWHzQMbw3JnW5CBbk4ZEVjxQriwE+ap06Xl4QwnNH4fFBN2BUhxinJm4M+rEl7gx+3bMZ/x1bjSMZW2EMPAiDqWjQrWFCAFqEdMLAmD4Y0qwPjp6Nw+P/PVrsGie0vBD7ko5iT/KWAiGv4BqpKc5sqkwgfYIPWbWHNsIpCfdtiKExQxEdHIqPt31Y7NgvDXwF41qOsdvHWV3rbauu2Zxnp6XXIuje2v1WpZm37Uc+X7sbr++6FjDm2AmI50VdhAYR+TiRdhIn0k/iVMYpZOU7mEWXEwrAvI8U6Cz5IYXCnSnwKEyBccWCQQ2JGYIJLSdgQJMB6plxvEZaK9QLqofZO2bjdMbpwuWN/Hpg394+yifQViM4uG00bhrcEi2iglG/TkDhhICeJUKfFhG4e0QbpSVksKlNR5ORR+deJ5wTE46BbaPRJDwAocZsdGnRCA3Dg9Q5qsKn0CP+c34h1nxzHS+EN+A1QkVNwZm2tsAX0mvqe95VwJ5fgW5XAxd/UC3HfkLFItEphSohMz8VOQGbUTBOVWQELEVmfhp8TaEuHYOz8QxswlQGn27/FCuOrcCt/9yKd4a9g2Df4Aovc8OwQIzp3EBXiOOgQ+Po2aNKO0ChLdw/HGOajynUENqmaOBgLSowSn16Nt+P7+PyrBq1Qq2FGQ8OvEp38HoqLREjvx8NM7ILB6RG+ODqDlfhdPoZHEs9pQZ3Z3MTkWOxT6HA7U0BDLddFHI7t+BTqGtsAvgX/KQg4ojJ4IvmdVqiTd1W+OPwnzCjSPhUw0MDrNqY0I3Ylb8Rj236EI+vag6f7I7IsMQrE9Bvdw/Hws1X4KWJXZCSlYek9GxlvpqUnoPEtGysP5KATbGMyvc3/KOWFkblm/b5evRvHYm6QX4qKEzdYD/U8ffBwz9sw/qjp9XglQV4ZtlemIO2wRR0CIYwc2FnRzO0DnW7YVfSNrty+wYk4alhU0tNZE9zuM6NonDHObepwfR/sVvx1bZF2JCwHBk4BoMpE4bQDbQCLqhv+oEZEGHsjBvOuRjnhfdA80bN1WCAQsjBlIPFhJDL216OV9Z8iL9ifwJ8MpDiswD3rPobDVaej0ENxyAX6WgT0QgdG0bgz/1bsTJuDY5mbIUl4ACMPhnKBNkUUrwN56W1wYSmU/H8+aPtzI+7RndV5dC7Rm6XnJWMJXFL8PO+P7Ehfg3ykQOfoCM2bcoqwNUxtMTkruMwstlwtApvpQROXiMnLByPPar5iGLlc1bXetu6G0G3Xr2jMOzNsX/GjHm4qFMfu2eMWtu03DTsStyLaX/cADNybdq2CRPbXExJHBl5GcjKy1L5MDNzs7A9/gDyDfradU4cGHxTAX6cQJPecc0vwONDr0GYv715pLNrpE/wwgMLMWvbLMSlxeF4zkYEN98Ic05dGP3OKL+/nPjRGNu5Ac5rZZ/SpaScmhSe+rWKwt0j2irN3tpDiXjz733YGlfc9HZTbLL6FLFXHZdCXVJ6LpIziyZ9Pl5+EHcPb4vwIF+EBPgo38MQf1+lHbz5yw125qVVEqXVMVBEVgpw5iCQkwZ8ey1w3p3A8BnWNAZCzYECXEnBb7wFSfQtOCCauBqOt87G2JpB6Wmp3Jn54qDrk22f4J1N76j/u0Z1xfsj3i82EKoISgtgkJaThmt+vUYNiOv41cG88+ehWWizUo/Lge7bG991Ooh2xJkZmVZ/tnyycjPe2jvFarplY87WMmAQ6gTnIyX7DFKyE5FlSUWWWX+Qac4JR8ew/ri2Z3+0i2iHFmEtlBDqrBz39roXZzOz8duBxYjL3AU4aGmIGkznhxSY8OUXbGNW3wYn21NzYjH7W4NimH0LzNn4TXM2Hxj8kmDyP13MHJACbtfIc3FJu7FKO7T65GqX688d9icdwr2/fIX92b+p6IkaNDGd0vFO3DeyjVszuifSTuDpFW9hxcnfCuuQZpY0CczPrldgHmkvpBMfBCPM0BoJ+dtt/CxZvf64r8MXuL5f2YOmZORm4MWlC/D9gU9hCjhlc40D8NSAh73KF9K2H7EYLB57xqhRH/X9KCXYaQT6BGHmqE+Qk5+DM9lnlMY9KStJfSdmJuHfQ+uRaywyZYzOm4A/rn+2TEIL+4/fDv2GT7bOxKGzBwuX8577WxphVOue6BDRHh0iO6Bt3bbF+kZOInDSh9o9f5M2fWPPvLVH1USJI50a1YHZYkBsUjrSskv3JXWHFy7ugqv6VHF7Ykh3CnDHNlj/bz4QuPRTIKRelRXJazRDNQU9DaxNQBuvqG8+zM81BPIygUtmAl0vq5ZjP6FikekkoVomEreFs/03dr1RaeReXvcytiZsxdQ/puLtYW8j35Jf4sDEXUqauaZ558PLH1YCHLWErw561SUBjnAQeU+vuz1Sfw0bnIBhf66DBiIfN/cZpzQQjgPdD1auwUf777AT+qhdOj/mWkxo1dGlckzqMEld0529bsLZnLNYtHcp3l+zCMnGdYXmmDw2ow66ihLKaG5aih+btq3F7IMoU3fce96lGNp0MEJoElWG+nOH1hEtcGHri/H6rh9tfKkA3/C1qB9WXDAtjYYhDfHBmBdxJOUW3PzLi4jNWVHo00VhVcOEILQI6YxBMX0xptUAJWj/evCPQtPEwvtuyka9+gyYUXYhLsg3CHf2G4ef4l5Hvs01BkSsx6gurmnRqwJPPmOrTqwqFOC0SanMvAycSD+hOylAoW/5sVHItWnK6QH/umWJ4HhtE1pNwPktz8e9S+7D30fpV2i9NzmG41h0kJ+fC7dvFNxItZH2Ee3V97qT6/DVrq9wS7dblCmqHhN7NMFPm445ncDKy8vDgdiTyDAGIS45C1+uPlI8cIvS4kO1G1d4etEObDh6BiM61MOANtF2wZoqjbAmwP9+A35/CFj/KXB4OfDRYODyz4GY3pVfHqF2knrSKsARyREnFCBCnFBtE4k7MrnjZAT5BOGpVU9hz5k9uGLRFUjOTi5xYFIWOGDR0za8u+ldZXJG7ul5D85rfB68of7cMVHjcVs0OgvDwVyXBv+ulIN+ZVd3noCs1JbKJ8lWuFE+SeHXY3DbGIQFBMDfx1eZKPK46TmZeHDZw3bmbEb44oHe98HXaEJWfpYyY0vJzsDm2NPYkrAJpsBjhefNSRqAmwfejwmtm1ZK+yvRbK+w/pgbzX2ahTXDkIi78OmuXPhFrClcnpfWGhNipuKF8WNhMprKZZroDuvjV8NsyC4M1cH7Y0YW1p1aUy5NpjfhThtxd1JAT+jLyEvHquOrylV/DJb03/GVdsuoMT+3wbnYn7y/0H/uePpx9Vkcu9hu28+2f4ZJHSepZ9adCSx1HQYDwgJ90Do6DOc0i1BmmHpC3HMXd8ElPZqo9WlZeapt/rT5GD5Zbo02bEtmrhnfb4hTHz+TEX1aRmB4+3oq+i+PXWn+cz7+Vv+oxr2AX+4BUo8Dn40DxrwAnDtNkoULnueMzfNRt4XUuKAQIU6oUUxsO1Fp5KgRowBHPt/xudOBSUVB0yuadGopD67teC2qqwbCU4N/pz5J3drp+v2xTi3UBhb8rwQ55CI6KLLYQDehczKGfzfCTjMUGLm6SjRDJdZf2VKdKaLD8lUeN1shmEmuO0a3LCbAleW+u4OnNJnVFXcnBTxVf3rCIf0RL2lziXpmaM65J2kPdiftVh/+PpByoHB/ToxM+X0KPhz5ofLVdXUCyx3NnSZwRfhYfVpJ2/p1sC0uxW7brk3CMKJDfSzZc1r53OXkm1UkTb1omt9vjMO8G/p63n/unGuABl2AbyYByUes4d6XvGjVjBhMXpscXCgF3jOazZ4tCDIU3a7ERNpV6g/nH2rNcygIIsQJNZFxLcfh76N/468jVpMiDmq+3PllhWrjbNmVuAuPr3i80B/viX5PuJ0Y2Zvw1ODfXeHQnYGuN2mGSqo/mq56RsNXuQMOT2oyawOeqr/SnhmmLOjXqJ/6aGadI78fqYKzaNCKYcIPE3B7j9tV4JSy5N90RXPn6rZ3Dm+DhLRsLNkTj393n8I/u04jO8/eNHn94TPo98I/GNquHno0q4seTeuqBPW2UTgrLPJlw67ATUuBH24E9v0JZCRYP0L1hUL3onuA9bOs//e+CTh3KrxSE1e3uWh+hUJEEyfUODgwWXnM3qTIU9q4xMxE3Ln4TjWDzZxsbwx9o8L872oa7gqHnjRnq4540jxSqJ3CITV3mgBnG2AqLS9NpW/5fu/3eKTPI8ocsyy4o7kraVsmGr+0ZxP1ef7XXfh4WVHwFg1Gtv1uQ5z6EEas7d40HN2ahOPvXaew+2RqxUW+DKwLXPUN8GaXIu2NhqVig7sIlUR6kY8xzjIvqLdGpmxZ1SURvAgR4oQah61JkQb/L6+/iSO5+bm4Z8k9OJl+En5GP7w59E0VREWofGqDZsiT5pFC7URv8iPfnK+iaTLaJf3opvwxBWNbjMW9Pe9F/eD6qGqY606PYe2jlYZu89FkpOfkIzU7z6n55eqDiUrrV65IqkajNeiJoxB3fDOweR7Q9QrrNkL1IN2mnZwt8q32GpIKJi4kqIlggwhxQo0emGw6tQnbE7erWeZWYa0q7BzMD/X4ysex8fRG9f+M82aoXFuCIAg1YfKDuQqfX/s89p3ZpwS6pbFLcXO3m3FZ28uUkFeRUX/dwZmv3UeTeynNWr7Zgr2nUrHx6BlsPJKstHApNrnqNL5ZH4v+raMQY5Pjs0Iw5wILbgbWzQTGvgQ06VWxxxc8Q9pp7xbiCs0pJaiJUIQIcUKNHpgwp9WEHyfgdOZpvLbhNXww4oMKOcf0JdNVcnHCICYMZiIIglBT6NWgF74d/y2+2fMN3tv0HlJzU/H6htdVYvGUnJQKj/pbUb52JqMBHRqGqs81fZo5zW+36WgyBr2yGMPa1cOkfs0wuE10oQ+dyzgGv6BpakYSkBILHFsPzBwOdLsKGPEkUKdBua5b8DDp8d5rTpmZDGSesf4WTZxggwhxQo2GOa3u7nk3HlnxiBK6lsUtw6Amg8p1zCWxSwoFOIbDp9ZPEAShJk6IXdPhGoxpPgZvbXwLP+7/UQlw5KOtH6FzVOdy96dlobxRMhuFBSAjJw/JmXn4Z/dp9WkaEYRr+jTFxec0Vv+7FATFWRTKfX8Bvz8MJO4DtswDdv1sjXjICJYGm2NJJEvvIDcLyD5b9H/KsYIEqV4SoEzSCwhOECFOqPGMbzlezSZvid+ikoH3a9gPvibfMh3rYPJB3Lvk3sL/mUx83u55VTIjLQiCUBlEBkbi6f5Pq98U5IjZYsZt/9yGPg374Lbut6FrpHeakzvT3NHs8petJzBn9RFsjk1WQtsLv+3Gi7/ttssEUqYgKG1GAi0GA2s/Bpa+ZBUQjm3wxOUJFa2F0zSq1Hx5Syh/LaiJyQ8IbVTVpRG8CPG6FWo8DPf/UO+H1O8jZ4/gq11flek4CZkJuOmvm5BjzrFb/sWOL3A2x2YWTxAEoQZG/f3j8B/Flq85sQbX/nYtbv7nZuxM3glvRNPcPTimvfrm/wG+JiXMLbitP36+fQCu6BUDHxMjdBYPgnLxeyvx+l97ldC3/3Qq8vKLUhwwfQFNNl/6fTe+XnsUudo6Hz/gvNuBOzYCPa6r3AsWyh6Z0hv94jRNXHgzQCcnqFB7EU2cUCug2c9FrS/Cgv0L8OHWDzG+1XjdZLbOoG8dZ51PZpwsXKaF5E7PS6/wyJeCIAjehF4icdIgqIHqF1efWK0+/WP7K81cl+guqC50aRKGly7timB/H3y6smDAbMP242fVR8PPZETL6GCVi45avNgzmc41dyHRwAVvA8c2AqeK++YJXkCarSaOJpQWq18cE7t7VXoBCWoi2CNCnFBrYLATJgBPz01X/h3P9H/Gpf3yzHl4YNkD2JlonWUeGjMUTeo0qbH5yARBEFzNxciIlf8e/RcfbPkAB1MOYuXxleozsPFAJcy1rtsapzNOV1k0S3doUz9Ed3m3JmEqfcGB+DTk5luQk29Weedsc8+Vmr7ATyc1gsU+ablQxeaUgREAXS3STgEpDqkjqpIzh63fEplScECEOKHWQM3bTV1vUhHWqJG7ot0VSkNXEhaLRSW9XRq3VP0/ved0TOk8pZJKLAiC4P3pCMa0GINhTYbh+23fY96ReTh89jCWH1uuPs3qNMOR1COY1nka7urp3bkcnaUv0DRrNJU8nJCOPadSsfdkKn7eehyHEuxzkhI94a4wkmVuBnByq/W3j3cLtbXOnDKkHuAbaBXivClCpWjiBCeIECfUKiZ1mIT5++Yr3zgKZ3PGzlE+c874bMdnKigKodD3v07/q8TSCoIgVA9MRhOGNRqGS7tcij+O/qGiV7KfpQBHZm6fiQ2nN6holv0b9Ue7iHYw2kZq9AJKS1/A7zb166gPugINwwN10xcwB13XJmEq0mXh+8U2kuW8q4E9v1iFBbNZkoJ7izllcDQQEAYc3+Q9QlxedpF/nmjiBAe8qwcVBA/DqJQPnPuA+s1olYsOLnK6LRPcvrHB+uId3GSwCo5SksAnCIJQ26EwN6HVBCy4cIEyPbdl0+lNypT98kWXY+i3Q/HQ8ofw84GfVdAoQlPN2NRYO5NNbwiCUpLmrm8L+wiGTDWXmZOPe77dgus/W4e4M8U1dRgw3fqdsBfY82uFX4NQRnNKauJCG1t/n/USc8oznAQpCLcjPnGCA6KJE2od9NXo37g/Vh5biTc3vKn82ZhPzpYNpzbg0RWPqt8dIzvi5UEvK3MiQRAEoXToe8zIlbYwr2YdvzpIzk5GUlYSfjn4i/qQ9hHtlY/d5vjNVZZIvCI0d31aROCJhTuwfF8Clu6Nx+g3luHBse0xqU+zomTiMecCzQYAR1YAK14H2p/vPTnJarM5JTVxdRpaf3uLJq4wR5zBGp1SEGwQTZxQ66A2jdo4H4MPTmeexsxtM+3W0zn/zn/vRK45F41DGuO94e8VE/IEQRAE16NZank1H+3zKL6b8B3u7nE3ejfoXTg5tjtptxLgyCfbPsGp9FPVonodNXctokPwxZTeeO2ybggL9EV6Tj6e+GkHLv9oFXadOFuYjmBJvUnWAzB/3OEVVX0ZtRtbc8qwJvYJv6uapIPWb2oIfQOqujSClyGqBaFW0jKsJa7ucDW+2PkFPt/xOS5uczFi6sQos55b/75V5X0L9QvF+8PfdysVgSAIguA8muWIZiOU4EbN29QuU1X6lnUn1+HDLR9ie+L2wojAF/90Md4c+iZ6N+xdLScK6Us3qG00nly4A79sO4H1R85g3FvLC/PQfYA6WBzSCi3yDgAr3gBaDKziUtdi7MwpG3lXwm8JaiKUgAhxQq2FobHpE0eznpfWvqQir9GE8ljaMfgaffHW0LfQMrxlVRdTEAShRkWztIVWDt3rdVcWELak5qZi6p9TcVX7q5TWrjpaQ0TX8cd71/TAhO0ncd93W5CWnWez1oDXMsbhXb93gAP/AMc3A426V2Fpayn5eUBGYpEmThPiNJPKqhbiNHPKus2rthyCVyLmlEKthb4Zd55zp/rNFAKXLLwEu5J2qf+f7f8sejXoVcUlFARBqJ2mlxrzds/DxIUTsf7kelRXxnRugMt7xRRb/pu5N874F5jvrXyz8gsmFAhwBfrR4HpAHVshriAqZFUimjihBEQTJ9RqLmp9Eebumou9yXsLl93c9WaMazmuSsslCIJQm00vaQ1BgW72jtmIS4vDlD+m4JoO1+DOHnci0CcQ1Q29ROL5MGGmeTzux4fAzp+AxANAZKsqKR9quyklCYkGfPyswhyDnVS1EGfOB5KtKTokvYCghwhxAmp7OOyOUR3thDhBEATBO0wv6UP32IrHcCDlAL7c9SWWxS3DM/2fQaeoTjidcRr1gurB3+T9SbP1EomTmal9cb3/PEQjBeaVb8N4wVtVVsZaHZlSM6ckYY2tyxncpCqhOWd+jvW3pBcQdBAhTqjVpGSn4M/Df9otm7NzDiZ3mqwCmwiCIAhVR+eozvhmwjf4YPMH+GzHZziaehTX/349ukR1wdaErdU6HUFEiC8e/XEHZmWMxUO+XyN/45c43v0uNGkqvtiVHpnSrw7gG1gUCbKiEn4vmg6c2mm/rH5H++TvpaYXEE2coI8IcUKtxtEXwwIL0vPSser4KoxuPrqqiycIglDroabt7p53Y1jTYXhs5WM4lHJICXDks+2fYVLHSdVi0k1LR2BL7+aReOFHf5zd+xNCDZn4feYM+I97FpP6NFVRLoXKikxZoIUjWnCTijCnpAAXu7p8/nCBdYHA8PKXRahxiBAn1GqchcFmAnBBEATBe+ga3RXfjv8WN/x5Q2FOuaz8LMxYOQNvDHVBs+GFhAf54aVrBuLAvEkI3fMJrjD8hf4LJuDPHSfx7EWd8d+BRBwt0NwxbQEFQcFDib41qIkjVe0TVxiZskXVlkPwWkSIE2o1robBFgRBEKoeTrjtPWPvw/z30b9Vmpj7et2n/JyrI63G3w/L/i9QJz8Tk0x/4/19F2LYq0uQb5NvesHm45gztbeEFfdUou9iQtzxqk34LZEphVKQKR1BEARBEKp1OgIGPbnj3zuQmpOKakmd+jCcM0n9vDPkb4T65NkJcGT1wUTlUyd4QBPHRN8aDGxCcjOsCb8rHBcFQ9HECaUgQpwgCIIgCNXKBH5yx8nKF+6a9tegbd22at3yY8tx9S9XK5+5asl5dwAGIwKyE/Fc8y26m6w/4gmhohaj+cQxrYCGY8Lv8sAgJv51HBa64OtIDWDSYevvCAl0I+gj5pSCIAiCIFRbE3iLxYK5u+filXWv4PDZw7jml2vw0qCXMLDJQFQrGEa+0yXA9u8xNPFrmNBd5ZKz5fsNccjIzsO1PSIRbWMBKJTXnDKqaFmdhvZCXHSHslcvo1Du+hnITgUCwoCsFODkVuDMYaBuc+f7ZSQB2SnW35JeQHCCaOIEQRAEQai2MIojE4F/NPIjhPmHITU3Fbf9cxs+3f6pEvCqFQPuVl8hmcdwd4Ptdqv8faxDtl+3n8TVc3bgwfnbEHfGaloqlAG2jcLolDaaOB//Is3c2XKar2YmF53j/NeBoEirmebCO0r2t5P0AoILiBAnCIIgCEK1p0/DPph3/jy0Dm+t0sW8seENPLT8IWTlZaHa0KAL0Hqk+nm738948eLOuGVIK7x4SRdsemIknru4MxqE+sNsAb7feAxDX12CJxfuwLHkDMxbexQv/b4bX689itx8c1VfifeTlQyYc4ubU9qlGSinOWXSgaLfTfsBY1+2/j60DNgwu4T9CkyCfQKBOg3KVwahxiLmlIIgCIIg1Ahi6sTgq3Ff4ZEVj+Cfo//g10O/Kh+5Vwe/qjR29YLqqbxzXs2A6cD+v2A4vRNX1t0D9BlVuOqaPs1wUbeG+OjfXZiz/hTOZORi9n+H8cWqw0qwc4xkKSkJXDCldIxOScKaACc2AynlTDOQWCDE+QZZzTQ7TwS2/wDs+QX483GgzUjruZwGNWlOVXP5yiDUWEQTJwiCIAhCjSHINwivD3kdt3a/Vf2/K2kXJi6ciHE/jMOsbbPg9TQ7r0gzNH8KMGu09bNouloU4GvC1T3qY/G9gzF9RFtlZmkrwBGJZOlGZErHZN8VmfA7cb/1O6IVYDRaBbLxr1v94xhJ9ee79M0qJb2A4AIixHmA3NxcPPHEE2jatCkCAgLQtWtXzJ0716V9jx07hhtuuAEtWrRAYGAgWrZsiVtvvRXHj5dTpS8IgiAItQSjwYhbut2CN4e+iUBToEoKTj7Z9gmOpVVxEufS4EA/sK71NwNixK62fk7ttNusToAP7hrRBlf3bqp7mCNJ4i9XIpqvGjWz/qGeMadM2Gf9jmxVtIzmkWNetP7e/zewZV7x/SS9gOACIsR5gBtvvBHPPfccLrroIrzzzjuIiYnBNddcgy+++KLE/c6ePYu+ffvixx9/xKRJk9S+PMbs2bMxYMAAZGRIhywIgiAIrjK86XBMaDWh8P88cx4uXnAxFh1c5N1BTwLDXd60bQPHEPZWTqRkVmCBaniib0eTxdAmRZq48rQTTRMX2dp+eberCn0f8ftDQOpJ+/WiiRNcQIS4CmbTpk1K6HryySfx9ttvK63aokWLMGTIENx///3Iyclxuu+CBQsQFxeHzz77DM888wymTZuG119/HS+++CIOHTqEpUuXVnRxBUEQBKHGkpKdogQ2WzLzM/Hw8odxw583eHFOOT0/KH1hYmKPJujbIqLY8gWbjuPl33d7t7DqFYm+dXI1aJo4RpJkWoCywHrXfOKi2tivo9A44U3Ar471+IvuKRIWczKAtAKhrm6Lsp1bqBWIEFfBfPvttzAajbjtttsKl9GZ+vbbb8fp06exePHiEjVxpGFDmxwlNv8HBQVVdHEFQRAEocay6sQqZORZrVgMDoLRmpNrlK/ce5vfQ3Z+Nrye5KO6i/18jJgzrY+KYMlIljPGd8S5za3mmO8vOYDp32xGTp5Eq3Qp0bdGWOOi32X1i6N2LTddXxOnztEEGPWM9TcDnWyfb/3NHHIakiNOKAGJTlnBbNiwAa1atUJEhP2sWJ8+fdT3xo0bMXr0aN19Bw0apAS+O+64A6+99hqaNWuGnTt34tFHH1XrBg6sZolLBUEQBKEKGdF0BKZ1mWYnpDE6JdMQvLr+VSRkJuDDLR/il4O/4LE+j+G8xud5x/2q37Hod9JBq9Yo9QSwbibQ43/FNmcUyittfOOu7tsU9323FT9vOa4iVZ5OzcaHk3siNMC3sq6geplTOuKY8DtcR9Bz1ZSSRLTU36bn9cCOH6wpB357AGg5pMgfzmAEwmLcP69QaxAhroJhABJHTRpp1Miqmi8pQAkDoHz44Yd48MEH0b9//8Ll9ItjYBRq+EqD2jxNo0dOnDihvvPz89WnusCyms3malXm6orUtdR1TUXattQ1tW+3d7tdtyL6N+yP97e8j6/3fI3Y1Fjc9PdNGN1sNO7ofodaHx0UXXXpCMa+WvQ7PxfGeZfDcGgpLL8+AHOdJjCHdi3x/ehjAF6/tAsahfnjo2WH8N+BRFz6wX+YdW1PNAoPrJxr8HKMaaeUbtYcFAWLY10afGAMjoYhPR6WlLhS61sPQ/xeZe5mCYqE2T+MHZL+hue/BeNH/WHISIT5l3uBxj2t+4U1gdlgcr5fCcjYqXYgQlwFk5mZCX//4p0+BTBfX1+1viQYBKVXr14YM2YM2rRpo3zsXnnlFVx11VX44YcfShXk6EP31FNPFVuemJioWy5vhQJcSorVDt0V4VWQuq4OSLuW+q6pVNe2PaX5FPSv2x9v7XwL+87uwx9H/lD55fIseZjUchKua3MdvAHDkFcQeeZK+CQfhGn+VGQO/xgJOKfUuv5fjwjUMeXh9SWx2HsqDZd88B9ev7A12kSLe0ZU6ik1CE5DIDLibXLGFRAZVA++6fHIPLkPKaHut+06cdsQTLe6Ok2RpHP8IoIQ1PsehK58FsadC5B3fIsS4nKCG+NMifs5h2M+oeYjQlwFw5QC2dnZui84ph7gemcsX74c48ePx4oVK9CvXz+17IILLkDbtm1x9dVXY/78+bjssstKPP8999yjAqLYauJ69+6NyMhIREfrmAx4KdosUlRUFEwmU1UXp0YjdS11XVORti117Qp8N/Zr2Q/f7fsOb296u9CHbt6heZjcbTLqB9dH1RMNXPMdLJ+NgjEjEc1W3AfzlL9gCiu9bLeMiEabxtG465vNiE/Lxc3f7cHlvWJUfrmmEUG4pEfjWpkU3JiZpL5D6rVAsM74yFi3GRC/A0H5yQgLC3N7PGLMtFpe+TToUPr4a+jdsGz+GIb00/BJtppT+iUfQL11L8Iy7jX3LoyZKXTGoULNQ4S4CoZmk0eOHCm2XDOj1Mwq9fjkk09UJ6EJcBoU5Ogrt3LlylKFuNDQUPVxhB1PdROGOONVHctdHZG6lrquqUjblrp2Bb5nrul4DY6lH8OcnXPUsnxLPq7+7WrMO38eGoYUd5OodKJbA1fOheXzCfBJOw7L/OtguH4R4Fu6eeTozg0xLzQAU2evQ1JGLj77r2icsnDrScyZ2rt2CXI56YVBR4yh9dkAim8Tbk0zYEw9UbZ+JMkamdLIyJSl7mcCQhvaJSCnQGc4vcuFfXWOJuOmWkEtemIrhx49euDAgQNISrLO8GisWbOmcL0zTp06hby8PN3ZZIYI1lsnCIIgCELFpCOYv7cgQmABiVmJuGLRFdh8erN3VHHTvrBMeEf9NBxbDyy4laY+Lu16TtO6mDKgeMj61QcTMX9DHGplZEpn0SnLm/A7P7coyqReZEo9fMRXUXAPEeIqmMsvv1yZTr7//vuFyyiAvfvuu0qdPnToULUsISEBu3fvtkvg3a5dOyX8/fHHH3bH/PLLL9V3z549K7q4giAIgiCUkI7gTPYZTP1jKn4+8LNX1JOly2VI61kQrIWRDZe84PK+6Tn6QTKOJBWNRWpVZEoS4kyIa1wkxLmba+/MEcCc554QJwhuIuaUFQwFrcmTJ2PGjBmIj49Hly5dVBLvJUuW4NNPPy0MLkKhjgFImDeOicAJUwsw0fcll1yCW2+9VQU2YUqCmTNnKgHvyiuvrOjiCoIgCILgJB1Bek46/jv+H05mnMQjKx7BwZSDuOOcO2Bk+PcqJK3X7QjKOgHjjvnAspeByFZAt9LHCPSBc2d5jUUzW+R9DLTm1HMmxBly02HIYdTvemVIL2CQXG+CxxAhzgNQ6GKOt9mzZ6uUAQxMMmfOHEyaNKnE/TShjQLgd999p/zo6CM3ZcoUPPfccwgMFFW7IAiCIHgCH6MP7upxV7HlzCU3ffF0bI7fjJnbZuJg8kG8MPAFBPlWoeBjMMBywTtASiwQtxb48WZg5VuAf2hRnrnxbxTbbWKPJvhp0zGsPmTv8sEgJ7XSnDIoCjCaSjanpI9Z2kkArd0X4sJjXPJZLJYbsKRlglCAwUJbP6HGEhcXp9IWxMbGokkTq5NudYB+gNRk0gRVHHSlrmsK0q6lvmsqNb1t5+Tn4KlVT2HhgYXq/3Z12+GdYe9UScATu7rOOgO83hGw0R4qYvoCU+1dMzRy883KB+5IYjr+3HkKB+LTUTfIF3/dMxhRIdUnFVG5WPoKsPhZoH5n4JaV+tvkZQPPWrVvSWM/RlivS11v2z/fDWz4DGg1DJj8Iyqb6jr2E9yjlk29CIIgCIIguIefyQ/P9n8W03tOV/5ye87swVW/XKUCntD8ksnCbc0wK43gKCC6nVu7MArllb2b4sGxHTD7f70R5GfCmYxczFi4A7XOnJL15wwffyDYmhrAlE5NHNzXxIk/nOBBRIgTBEEQBEEoBab6mdJ5Ct4a+hYCfQJV5Mopf0zBA0sfwLgfxmHWtllVU4flMOuMiQjCg2Paq9+/bD2B37efQK0gTRPiSvFzKzCpFCFO8EZEiBMEQRAEQXCRoU2HYs7YOWgY3BC55lz8G/uvWv7Fji9wVgXA8ALyc1zedHLfZji3uTW4x2MLdiA5w/V9qy3pCSVHptQILcgVp3ziXCQ7DUgtEIYZcEYQPIQIcYIgCIIgCG7QLqKdSgLeIKhB4bL0vHR8udOaEqhSYfAL+sA17gkYCny2slJc3t1oNOCliV1VcJOEtGw8vWgnajyumFPaauLcEeIKknwrItuUqXiC4AoixAmCIAiCIJQhmqWj5o0mlZWujWMUSgYxueFfYPTzRYJE7FqXD9EyOgT3jmqrfv+w8RgW7y4Qcmq7OWWYNc2AKf2E+/5wJn8gTIKKCJ5DhDhBEARBEIRyJAfXyDHn4IPNH1RdXZ47FYgqCHTy24OA2ezyrlMHtES3mHD1+5Eft+FsVi5qJHk5QFayi+aUViHOmHbK9YTfiQWauIiWztMXCEIFIEKcIAiCIAiCm2jJwSd3nIwr2l2Buv5Wv7J5u+ZhWdyyqqlPky8wpkAbd3wjsPVr13c1GvDKpV3hazLgREoWXvh1N2okGQX+cG6YUxoprGefdTMypfjDCZ5FhDhBEARBEIQyJgd/4NwH8Fjfx/DDhT+gWWgz5CMf9yy5B+tPrq+aOm09Amg71vr77yeB7FSXd21bvw7uHGb145q39ihW7rcReGqaKaVL0SmtmjhFyjHXjp+wz/ot6QUEDyNCnCAIgiAIQjmJCozCJyM/QYPgBipn3O3/3o4diVWUe230c4DRF6AZ4LJX3dr15iGt0LFhqPr90A9bkZ6dhxoZmZIU5IErTROnSHVBiKPJpWZOGSVBTQTPIkKcIAiCIAhCBdAwpCE+HvkxIgIikJ6bjpv/uhkHkm2iFVYWNOXre4v19+r3iwQLF5OBv3xpV2VeGZuUiVf+2IMaGZkyIBzw8St5Wx9/WAoEPcPZY64JiNkFkUFFEyd4GBHiBEEQBEEQKogWYS3w4YgPEeIbguTsZNz41404luaiKV5FMuh+q6aJOeP+fNytXTs3DsMtg60+XbP/O4wXft2Fl37fja/XHkVuvuvBUrw7MmUpWjiNOgXauLPHXfeHIyLECR5GhDhBEARBEIQKpENkB7w3/D0EmAJwOuM0bvjzBsRnxFduHQeEAsNnWH/v+QU4YE1K7ip3DG+NVtHB6vdHyw7igyUH8NAP2zB51trqLcilx7sWmdLRpNIdIS4gDAiKLGsJBcElRIgTBEEQBEGoYHrU74E3hr6hAqDEpsYqjRwFOv6mz1yl0P0aoGF36+/fHwbyXfdv8/cxYVSnomTmGqsPJmL+hjhUeyHORU2cpSC4iUvmlIk2QU0MhrKXURBcQIQ4QRAEQRAEDzCg8QC8OPBFGA1G7E/ej6sWXYVxP4xTScErBaMRGPuS9Xf8bmD9pxVy2CNJ9vnxarQ5pVuauALfw0gJaiJ4HhHiBEEQBEEQPMTo5qPxZL8n1e/TmVYB4vMdn+Nsjot5x8pL075A50utvxc/B2Qkub5rRJDu8mZOller6JSumlPa+sSVlvC7MEdc6/KUUBBcQoQ4QRAEQRAED3Jxm4vRr2G/wv8z8jLw5c4vK6/ORz4F+AQCWcnA4oJk4C4wsUcT9G0RYbesd4sITOzZBNU+OqWr5pRhBeaUuelAVkHkST3M+UDSQetvSfQtVAIixAmCIAiCIHiQlOwUbInfYrfsix1fVJ42LqwJMGC69ff6WcAp1/LX+fkYMWdaHzwwul3hsgu6NVJpCKolZnORJs7d6JSlmVSmxFojgRLRxAmVQDV9CgVBEARBEKoHq06sUto3W9Lz0rHq+KrKK8R5dwBhMYDFDPz2YOmmgQVQYLt1aGuM7WwNcvL5f4dhcXFfryPzDGDJdzM6ZcOi3yUFN0mwTS9gTc8gCJ5EhDhBEARBEAQPMqLpCEzrMg2TOkxCHb86alnr8NYY3nR45dW7X1CRhujwcuC93sCs0cCiAg1dKUwd0EJ97zudhmX7CrRZ1dWU0h1NnE8A8gMiShfiNH84RrP0s6ZmEARP4uPRowuCIAiCINRymGbgrh53qd9N6jTBi2tfxJGzR5SZZWRgJeYTy80s+p2w161dezari25NwrAlLgUzlx/E4LYuCkHeGJnSHSGOVpghDWDKSgJSXBDiRAsnVBKiiRMEQRAEQagkLmh1AQJ9ApFrzsX8ffOrTb0bDAZMHdhS/V6+LwF7T6Wi2uaI8w0C/ENc3i0/pEHpPnESmVKoZESIEwRBEARBqCRoTklBjnyz5xslzFUX6BfXMCxA/f50xSHU9ETfGvnBmhB3zIUccZJeQKgcRIgTBEEQBEGoRK5qf5X6Pp1xGouPLq7autciKroY5OS685qr3z9sOoaEtGxUS3NKV4OaFGAOaViyEEczVUanJCLECZWECHGCIAiCIAiVSKvwVujTsI/6PXf33Mo7cf2OQExfoMm5RUNAH6tmzVWuOrcpgvxMyMkz46vVR1GTc8Rp5AfXt/6gT5xeZE6VH65guQhxQiUhQpwgCIIgCEIVaeM2nNqAPUl7Kuek498Apv4BTPsbaD/OuszH361DhAX54rKCZN9zVh9GVm5ByP7qgLs54gowa+aUzhJ+a/5wRh8gvFm5iykIriBCnCAIgiAIQiUzpMkQNAy2munN2z2v8uu/w4SidAPMn+YG/+vfAgYDkJCWg4VbSgj2UUPMKfM1c0pnwU00Ia5uC8Akgd+FykGEOEEQBEEQhErGZDThinZXqN+/HPxFpRuoVNqOtmqOzHnA3j/c2rV5VDBGdKhfGOCk2iT/LnNgkwJzSqdCnAQ1ESofEeIEQRAEQRCqgIltJsLf5I+s/Cws2L+gck8eWBdoMcj6e9fPbu+uJf/efTIV/x1IhNdDQbOMQhxNTi1BUdbfZ+OKr0/YZ/2WHHFCJSJCnCAIgiAIQhUQHhCOsS3Gqt9f7/4a+eb8qjGp3P83kJPu1q59WkSgc+NQ9ZvJv72e7FQgL6tM5pSK0Ealm1NGtSlPCQXBLUSIEwRBEARBqOIAJ3FpcVhxbEXlnrzd+UzjbRVuKMi5m/y7QBu3eE889p9Og1ejaeHKookjdRoVRai0JSMJyEyy/pbIlEIlIkKcIAiCIAhCFdExsiO6R3ev/HQDpE59IKZPmU0qz+/SCPXqWKNbfrbyUI0W4ixhjfVzxWn+cESEOKESESFOEARBEAShCrm6w9Xq+7/j/+FQyqGqMalkcJM81xN/Ez+fouTf8zfG4Uy6e/tXSWRKBnOhP2BZNXGO5pSaKaVfCBBiEwBFEDyMCHGCIAiCIAhVyIimIxAVGFXoG1epdBhv/c4+Cxxa5vbu1/RpigBfI7JyzZi79mj1SPTN/AjuEmqjibONxploE9SkLMcVhDIiQpwgCIIgCEIV4mvyxeVtL1e/fzrwE9KZVLqyqNscaNDV+nvXQrd3Dw/yw8Qe1uTfn/93GDl5ZtSkRN8aFi2wSU6aVeB11MRFSlAToXIRIU4QBEEQBKGKubTtpfAx+CgBbuEB94WpctHhAuv37l+AMkTInFIQ4OR0ajYWbT1eoxJ9F6IJcY7BTSRHnFBFiBAnCIIgCIJQxUQHRWNk85Hq97zd8yo3gbZmUpmRABxd7fburaJDMKy9VTia5a3JvwvNKStAiNP84sxmEeKEKkOEOEEQBEEQBC/g6vbWACcMbrL6hPvCVJmJbl8UWbEMUSqJlm5gx/GzuPubzfh67VHk5pu90JyyIGm3u/gEAEGR9hEqU48DeZnW35LoW6hkRIgTBEEQBEHwArpFd0OHiA6F2rhKgwE5tCiVuxfZB+5wkV7N6iLI16R+/7T5OB76YRsmz1rrPYJcec0pHYObkISCoCZE0gsIlYwIcYIgCIIgCF4AE2hryb+Xxi3FsTSHnGSeRBPiUmKBE5vd3v2HTceQkWvvT7f6YCLmb4iDV+WJK6s5pZ4QpwU1YWqBgNDyllAQ3EKEOEEQBEEQBC9hbIuxCPcPh9lixle7vkJsaiyy87M9f+JGPYqElDKYVB5NytBdfsTJ8kolN6soomRZzSlt/eK0wCYS1ESoQkSIEwRBEARB8BICfAJwSZtL1O9v93yLcT+Mw6xtsyrHpLL9+DILcU0jgnSXN3OyvEq0cOU1pwxrbB/YpDC9QKvylE4QyoQIcYIgCIIgCF7EFe2ugAGGQg3cFzu+wNkcm9xknjapTNgLxO9xa1fmiuvbIsJuWd+WkZjY05pDzisiU1aYOaWjEFcQFEYQKhER4gRBEARBELyIRiGN0Cy0WeH/6Xnp+HLnl54/cdN+RREY3dTG+fkYMWdaH4zr0kD9XzfIF3Om9oavyQuGmmmaJs5QdH3lEeJyUq3RLpOPWP8XIU6oArzgyRIEQRAEQRA0UrJTcDL9pF2FVIo2zuQDtBtXZpNKCmzUyJHkzFzkm70kX5xmThkUYb3GsmKbK+7wCsBSEHkzsk05CygI7iNCnCAIgiAIghex6sQqZOVn2S2jNm7V8VWeP3mHC6zfjFCZfLRMib8JsxQcSkhHjUj0rSfEHVpq/TYYgbrNy3dcQSgDIsQJgiAIgiB4ESOajsC0LtMwucPkQrNK+sg1DG7o+ZO3HAz41bH+3rXI7d2b1A2EX4EJ5YH4NHgFmjlleSJTEt/AInPMQ8us3+HNAB+/chZQENxHhDhBEARBEAQvwsfog7t63IUHej+AuefPRaPgRrDAgkdWPIKMXA+H7PfxB9qOKrNJpY/JiGaR1oiUB+O9RRMXX/7IlI7aOAlqIlQxIsQJgiAIgiB4KaF+oXhp0EswGUw4cvYIXlz3YuVFqTy6Ckiziezopkml12jiKsqckoQ6RNuMEn84oWooh3enUJNIT0/H2bNnkZubC7O5wFG3CrFYLMjKykJmZiYMzF0jSF3XAKRdS31XJkajEb6+vggNDUVwcHAl175QkXSv1x23dLsF725+FwsPLkSnkE64IvoKz1Vy65GAyR9gioM9vwI9r3dr91b1goEdXmhOGRJd/mPZ+sURyREnVBGiiavlUGCLi4vD0aNHlRCXl5cHb4CCm7+/vwhwUtc1CmnXUt+VCftz9uvs39nPe8MEnVB26CPXq34v9futnW/hWNoxz1WnfwjQeniZTSo1TRzNKTl55TXmlMGeEOIkR5xQNYgmrpaTnJyM1NRUREZGIioqSs3cegPs9DkA8fHxEUFO6rrGIO1a6ruyoeCWkJCAxMRE1d9HRNgnYxaqDyajCS8MfAGXLrwUKTkpeHjFw5g9djZ8jb6eM6mkFu7gUiAzGQgMd3nXlgVCXEZOPk6ezULDsEBUGfl5QEZixZlThjmYU4oQJ1QR3jFiF6qMtLQ0+Pn5ITo62msEOEEQBKFiYL/O/p1mlezvhepNg+AGmNFvhvq9NWErPtj8gedO1nYMYDAB5lxg359u7doyush898DpKg5uogQ4i2fMKX0CgToOmjlBqCRk1F7L4SytyWQSbZcgCEINNuOlVYOYU9YMhsUMw/iY8er3zG0zsfbEWs+ciImxmw+w/t610K1dQwN8Ua+Ov/pd5X5xmillRZhTLpoO/Pl40f/02f/13vIdUxDKiAhxgiAIgiAI1Yib292MVmGtVNqBh5c/jDNZZzwbpXLf30BORpm0cQerXIizia5ZXnPKUzutSdA1mO6BywShChAhThAEQRAEoRrhb/LHiwNehJ/RD6czT+OJ/57wTACRuPXW77xM4OPBwKzRVm2UW2kG0r0jMqV/KOAbULVlEYQKRIQ4QRAEQRCEakabum1w37n3qd9LYpfgq11fITY1FtlMC1BRnDlc9DthLxC72mXNk9fkiiuMTBlVteUQhApGhDhBqCE0b94c11/vXi4fb+Hw4cPKb2f27NlVXRRBEIRqw5XtrsSQmCHq9yvrXsG4H8Zh1rZZnj1p2kkgN7PUzVrVswpxJ1KykJ6dVzMSfQuCFyFCnFDr+fLLL/Hmm2/W6np4/vnnsWDBAo+f59133xVBTRAEoYLg5NfT5z2NqIAomGHNA/j5js9xNues5+qY2rk3OgNLXwYykpxu1jKqKELloYT0mpHou35HIKav/YfLBKEKECFOqPWIEAe88MIL+Omnn6pMiGvWrBkyMzMxefLkWt8eBUEQ3KFuQF30a9Sv8P+MvAx8ufNLz1ZiRgKw+DngjU7Abw8CZ44U26RxeCD8fYxVb1JZkYm+x78BTP3D/sNlglAFSLJvQfAQFEoCA6swwWk1m00OCBCHc0EQBHdJyU7BP0f/sVtGbdykjpMQ6hdavgrV0zKFxwCBdYGNc6zRGdd8CKz9BKjbDPALBnytGjiKb68H1cVtZyfjwOmqFOLEnFKomYgmTqjRMLntfffdhxYtWighoX79+hg+fDiWL1+u1g8ZMgR//PEHjhw5ogQJ7aPx2muvYcCAAYiKilL7d+nSBbNmzdL1RxszZgyWLFmCvn37qm1feukltW7jxo0YN26cSrhLoa5ly5a49tprlZCnceLECezevRu5ubmlXlNOTg4efPBBNGjQAMHBwRgxYoTaV4/U1FQ88MAD6vr9/f3RtGlTPPTQQ8jOzrZLBsz/P//888LrZ724cwyNr7/+Wl0/yxUeHq7qTtPwsY727NmDpUuXFp6Hy0ryidu+fTsuuOACdaygoCCcd9556n7ZwjrnvvPmzcPrr7+ujsn679evHzZt2lRqfQqCIFRnVp1YpbRvtvD/VcdXeUbzNHEmMO4VYPoOYMjDQGAEYMkHkg4CJ7dZg58UfNobY9VhDtQUc0pB8CJEEyd4jJw8M+ZvjMPRpAw0iwjCxJ5N4Guq3HmDW265Bd9++y1uu+02dOrUCWfOnMGaNWuwefNmDBw4EI8++qhadvz4cbzxRnGTCAoFY8eOxWWXXaaEHQok06ZNU0lzb7jhBrtt9+/fj4kTJ6r1U6ZMUcJOfHw8Ro4cqYRACkJ169ZFbGwsFi5ciPT09EJN3cMPP6yEqEOHDhUKNs646aablLBz6aWXYujQoVi3bp06R1ZWlt12/J/rDxw4gBtvvBGtWrVS103BlEKf5gP3xRdfYOrUqUr44naEwq47xyDPPfccHnvsMZx77rl44oknlNC1YcMGJXRdeOGFyu/w1ltvRVhYmKp3EhJidXzXY+/evejfv78SHKdPn662/eyzz5RA/OOPPyrhzhaWiYLlHXfcoYThV155BRdddJG6L76+viXWqSAIQnVlRNMRmNZlmopK+c+Rf3A8/TjC/cMxNGaoZ08cHAkMeQg4705g81fAH48CDpExA3xN6rvKNHFMu1CR5pSC4EWIECc4FcCOJZcefcoZuflm3P/9FmyJTSlc9vW6WLxyaVf4uCDIMd9Nfn4eTCafQs0Y7ev9CuzrXWXRokVK2KIwpgeFn4YNGyIlJQWTJk0qtn7fvn1KGNGggDBq1Ci8/PLLxYQ4Cjo///wzxo8fX7iMQl9SUpISZHr16lW4/KmnnkJZ2LZtmxLgKCTaagQfeeQR5ddmC4WmnTt3KkGqQ4cOhcs7d+6shFpqIynI8rp5LdQQOtaBq8fgtVNwo8BLAdXHp6hr0XIXUaCiBo8Col5dO8JrysjIUEJ3+/bt1TKWk9rQu+66S9UzBWsNCuPU3GmCMfe5+OKL8eeff+L88893q54FQRCqCz5GH9zV4y71e1SzUZj822QkZydjcexijG4+2vMF8AsCet8AbPsOiF1jtyrIlFcY2CTfbIHJWGTpUilkJQPmAgsXiU4p1DBEiBN0oQA39NUlFVo7m2OTMfKNZWXef/F9Q9DCJtqVK4SGhipNFc0VKay5iybAUbNDs0Jq4KiZ+uuvv3D27Fl1fI2YmBg7AU47P/ntt9/QvXt3O+HGFgpmroTXp1BK7r77brvl1FQ5CnHffPON0mTRjDMhIaFwOc0vyb///qsEsJJw9RjUjLFunnzyyWLXaGue6ir5+fn4/fffMWHChEIBTqvPm2++WQl4FNi6du1auI6Cra0P4uDBg9X3wYMH3T6/IAhCdaR7ve7o07AP1pxYg4+3foyRzUbCaKgsC5jifX1o+hGYkA9mGDienImYiKJJ0Uo1pSQhkmJAqFmIT5xQo6Ff2tatW9GkSRP06dMHM2bMcOo/pgc1adSgUTiIjIxUwgwFCJKcnGy3LX3GHKFv2SWXXKK0VDSppGZo5syZypSyLGi+e23btrVbznLRVNMW+p/9/fffap3tp127dmr96dMFzt4l4OoxqIkj1JJVBDRDZR3ZCnAaHTtaHe1pemqLoxmqVh/UhAqCINQWbup6k/ree2avSgJeadiG349soxaZctNwq+mnqotQqQU1IWJOKdQwRBPnAai1eeaZZ5RmhYNcDrhpRnb11Ve7tP/Ro0eVsEFNBAegDGAxaNAgzJkzB5UFTRep+Sori7Yex2t/7i22/L5RbXF+10ZlNqd0lyuvvFJpZGjiR7M6+r0xJxp9q0oz6VuxYoUSuqiJ+uijj5Qmz8/PD7/++qs6DjVPtuhFomTZ58+fj7Vr1yotGjV4NAl89tlnlZmg5nvmKpppoivrWL5hw4Ypfzs9KNiWhqvH4LnLonErC9p1Op7PZDKVuL0gCEJt4NwG56JHvR7YeHojPtr6kfKNq5T+2THU/oLbgM1f4i7fH7DC3AUH4jtiiHX+r/LQ/OFM/oB/nUo+uSB4llotxL3//vtqkB8REVGhx2UACAaLoM8QNRMM/nDNNdcgLy9PRSUsCfofUeigFuH2229XggODblCgqEzoe+au6aItNw1qhZX7ErD6UJEWpG/LSNw0uJVLwU048GZ90TSvvC8f1iGDgfBD7RkDeNAnTRPinB3/+++/V1EOKXjZhr9fvHix22Xo3bu3+jz99NPKtJLBOaiR0wJ8uAq1TawbBv2w1XpRc+WoGWQQEpqAaqaPJeGsDlw9RuvWrVW5aOLIwCbunscRavsY4VJPa6otKy0AjCAIQm3lpm434aa/bsLOxJ1YcWwFBjYp2XTeI4x9CTj6H3ySDuJN3/cw+2Rf2qxUUWTKenwBVe65BcHD1GpzSgpJjRo1UtqWH374QYVuLy8MaU4NHH2D3n77baV1oQaGZnX3339/iefgIJjCHgenjADIAT79fBjxj1q56gSFwDnT+uDFS7rgliGt1Pecqb0rNTol/aoYsMQWhqqn2SODYGhQWHAUgIgmQNpq3Ljfp59+6nIZuL2jJqhHjx6F69xNMaAF6GDAEVv0ImtygoL+gNRCOsL0Bky/YFsHtuVx9xh8hhhkhMIx690W2+t3dh5HqFVjygY+OxRYNShQUivKZ4TBVQRBEITi9GvYD12jrD7D1MZViUWCfwhwyUzkw4RmxtMYcuCVKswRJ5EphZpHrdbE7dixQ2nMmF+Kvk8c4F9++eWYPHmyMqErCwxnz8EstXAaFAQoMDIkPLU4o0frR4ui7xGFNw5cGVCDkflovucsGIa3Q4Htyt5Nq+z8HPA3btxYhf3v1q2bCoqxcuVKJRDb3h/6vNHkkREP6TfH+0fhhUFKGLaeESzZJmja+sknnyjz1pMnT7pUBqYNeO+995SQQ60WBR+aclJIYXvQcDXFAAN5sCwUJBlYRUsxQG0hfe5sYX68X375RfnkUetITSBD8NPPje2UZqHUSpKePXuq9vfqq68qE8l69eopM0pXj8Fro98fJy/47LDO2YaZI49aTNaBVtcff/yxEvZoZsy0AQxeogdTFvC6GDiF90tLMUBzY0662EamFARBEGA37qA27rZ/bsOW+C1Yc3IN+ja09veVSpOe2NLmVvTY9w4GZ/4NbJ8PdJ5YeeeX9AJCTcYiKBYvXmyZOnWqJTw83GI0Gi0tW7a0zJgxw7J37163amjkyJGWNm3aFFseGxvLaTDL888/73Tf+++/X23zzz//WHr16qV++/j4WMaNG2c5dOhQme6Udl5+68HjlvXYnsRsNltycnLUd1nJzs5Wddq9e3dLaGioJSgoyNKpUyfLq6++asnNzS3cLi0tzTJ58mRLRESExWAwqPrS+Pzzzy3t27e3+Pv7W1q1amV57bXXLJ9++qnaxrbemjVrZhk9enSxMmzcuNFy9dVXq/U8RnR0tGXUqFGWpUuX2m133XXXFTtmSdd13333qWPxmoYPH27ZtWuXOgePY0t6erpqx+3atbP4+fmpa2TbeuqppyxnzpxR27COt23bZhk6dKglODhYlWPw4MFuHcO2vnr27GkJCAhQz1L//v0tP/30U+H6EydOWCZMmKDuB8/DMhNeN///7LPP7I7Hco0fP15tHxgYaOnXr5/lt99+K/bsct958+YVqysuZ9lrUrsWpL7Lgqf7+ry8PPV881vwLK7WNfuZyxZeZuk8u7Plut/s3w2VyYo9Jy2rH+9tscwItZifj7FYzhytvJPPvUqd17Lg1lrVtksb+wk1AwP/VLUg6U3Q3JGmY8zBxUAYhNqH66+/XmlAbHOG6UETL0YxXLp0qd1ymuRR+0KN3DvvvKO7L/NoUSNIjQo1LNQKUjNDPyouY5TFOnVKdsyldoYfWzM9lv/w4cO6gSyo1eCMXbNmzeBNVKRPnCB17S1Iu5b6rioY2Zbtr2lTz1hH0IyaaUj4rnIWZEio/Lr+9+i/uGfZPer3rJGz0LN+z0q/DSdTsnDZy9/jd/+HEGrIgKXpeTBP/gkwer6dGD8dBcOx9TD3nw7LsMdrTduOi4tTVj2xsbEuBTETqifV007PgzBiIIW31atXqxce/ZcoRNxyyy3KXOzrr79WApYzaC7n7+9fbDlNv3x9fdV6Z2j+RTwnTdU0aKpG8zSakt15550llp9JrfUSSScmJuqWKysrSy2nwORNWKNTWn2rRIiTuq4pSLuW+q4qOJFIU2gGQfLU8TUfZDF19izu1HWngE5oEdICh9IO4b2N7+GlXi95uHTFMVosSPath0dyp+Jdv3dgOPofMv56Aek9rKkQPElU6ik10E2zBCCjjG2/OrZtjvmEmo8IcQXR7hi+f+7cuUozRZ8nBiS57rrr0KlTJ1VRjLrH/+mbwwiSzqD/D1+Uep0Ag1bYRjl0RBOyGNzEFvpTUQO4fPnyUoW4e+65B9OmTSumidNynDlCoZJCkrf53WkKYtHESV3XJKRdS31XFRx88v2j9x6oCLRJt+qkraiuuFvXN59zMx5c/iA2Jm7ECZxA12hrwJPKpGV0CBYd74ebog6gS8KvCFn/NoK6jAMaWQN9eQpjpjVCdkj9lgguY9uvjm1bbxwq1Dy8a+Reybz11ltKeGNESQpQF154IT744AOMGjWq2GwLzSQpQE2dOrXEYzLaJc1WHGGaAG29MzSVt2PuMApZfPHqRVB0hME7+HGEHY9e56NpubxR28UyaR9B6rqmIO1a6rsq254nB6F8bzp71whVV9ejm4/Gh1s/xKGUQ/hk+yd4f8T7lX47WtcLwfbjZzGzzs14K38nDGcOw/TjTcBNy6xRLD1BTjqQm65+GuvU50Co1rTt6lJOoXxUD72wh5g+fbqamfzwww9VtEFGqWRYc2fqckbWe/zxkm2qaQp54MABFcnQ0UxTW+8MRgjUbJkdZ4FYPkYMFARBEARBcBWT0YQbutygfi8/thw7EndUiSaO7KSV3yWfAAYTkHQA+P0hz0emJJJiQKiB1GohjvmnmESbppN62itHaFo5Y8aMErdhMBKaTjKRuK0J1bvvvqu0aZo/HZ1kacbJNAK2gU0oVNL3zTY32ZdffqlU49QQCoIgCIIguMPYFmMRUydG/f54y8eVXnmtCoS4w4npyGvUCxj8oHXFpjnAu72AWaOtn0XTKz7Rt5bsWxBqGLVaiGvYsKHygXMG19kKWa5AbRqjWFLYY96xmTNnqjxYS5YswUsvvVTo90ahrkOHDli7dm3hvtS0cT/mMmNuMubWYoLwG2+8UeUvu/rqq8txtYIgCIIg1EZ8jD6F2rh/Y//FnqQ9lXr+VvWC1XduvgWxZzKBgfcCfgXRthP2AbGrrZ9TzmMOlDnRN7V+gREVd1xB8BKMtd2ckn5wzqBmjMmO3YWC2yOPPKISEjMQCn3k6Hv3v//9r9R9H3roIXz00UfKfJJBSqiFo6aQETMZ3VIQBEEQBMFdxrcaj0bBVr/8j7dWrjaueWQwNPf2A6fTAJMPENm6khJ9R9GpzbPnEoQqoFa36r/++ktFfnQG1/3xxx9uH9fPzw/PPPOMys9BM8ht27Zh0qRJdts8+eSTysxyyJAhxfan5m3Hjh1qX0aXpNbOFXNPQRAEQRAEPXyNvpjaxRqc7a8jf+Fg8sFKq6gAXxOa1A1Uvw8mWNMpwad42qMKgSaZNM1c9or1/+zUijXTFAQvoVYLcRSQaFLpDKYa0KJKCoIgCIIgVGcuan0R6gXVgwUWvLnxTWTnZ1e6X9yB09aIkfpY0wuVC5pk0jQzpSBIXG5GxZppCoKXUKuFOAYaocbLGVwXHh5eqWUSBEEQBEHwBH4mP0zpPEX9Xhy7GK+tf63yhbj4Ak1c/Y5ATF+gnjUfr8JQq4elguAWtfppGTduHD7++GP8999/xdatXr1areM2giAIgiAINYGRTUcW/v5u73c4m3O2Us7bMtoa3ORgQoEmbvwbwNQ/gFv/AzpPtC6L3wNknqmU8ghCdadWC3FPPfUUIiMjMWjQIBVB8uGHH1YBSfh7wIABiIiIUL5tgiAIgiAINYHv9n1X+DvPnIcvd35ZqZq4pPQc9bFjxJOATwCQmQQsLfBlKyvm3PLtLwjVhFotxNHnbf369SroyPLly1UKgBdffFH9ZpoArmvUyBrJSRCqEwyYoxc0p7JgSg2DwaC+bfn3339VGo7AwEC1/vDhwyrID3/XxnrSouCWNX0I80k++uijaNasGYxGY5mupSrr3xuYPXt2YVvUOOecc9SEniDUNFKyU/DFji/sls3eMbtStHGaEEcOaiaVGuFNgX63WX+v/RhIPFD2E2UkFfwwAg27W002abopCDUMH9Ry6tevr17ijBQZHx+vvpmvrTYPamobTOPA5Ot33313VRelRnP27FlceumlaN68Od555x2V2J5+qZ5mxYoV+Pvvv9X99TYfV+aJXLhwIbZs2VKm/b/66is8//zzuPnmm3HeeeepianKZNGiRWqyi4JgTbqHtMpgShjm+uQ7QhBqCqtOrEJGnn3+28y8TKw6vgqjm4/26LmjQvwQGuCDs1l5yi+uV3OH3G0DpgObvgTSTgF/Pg5cNdf9k+z8CThzyPp7zPNA31sqpvCC4IXUak2cLRTaKLzxhS0CXO0T4t58882qLkaNgibKmZmZ6ltj06ZNOHPmDJ544glMmzZNacCDg4Px2GOPqW09KQDQdDo5ObnYOuZf5KeqeOWVV5TpdpcuXcq0PzWbFGref/99ZT0wcmSRr0tlCXGsW09T0j30BBMnTlRtk+ldBKEmMaLpCEzrMg2TO05GTEiMWsbv4U2He/zcHFu1LNDGHYzXiVDpXwcY9rj1955fgINL3TtBegKw6B7r76bnAb1vKneZBcGbqfWaOA4K5s2bh4MHDyIpKUlp4hw7nVmzZlXZDRKqLxRMaDZYG6FpHzVttpw6dUp9O2pSfHx81KcqYE7HqiIxMVFp4V5//fUyH4N1GhYWJhNPFYzJZFKC3Oeff66ER7ZnQagJ+Bh9cFePu9TvVmGt8OSqJ3E68zRyzblqXWWYVG6OTS6KUOlI96uBtR8BJ7cBfzwC3LQMMJpcO/iv9wMZCYBPIHDhu5LgW6jx1Oo30z///KN8SW677TZ88sknalZ78eLFxT5COZJt2n6qINlmWloa7rvvPrRo0UIJFdS0Dh8+XPk9EvoQMaH7kSNH1EBY+2i89tprSlMSFRWl9qfGRE+op4ngmDFjlA9Y37591bb0sSQbN25UUU5pOkihrmXLlrj22mvttE/MWbh7927k5rrmkP3111+r81BbQKGIZfzpp59K3MfVa3GlvN9++y3OPfdclYSeQgSP9eyzzzr1iWM9X3HFFer30KFD1TrNf8uZT9Zff/2FYcOGqXPUqVNH+dLNnDmzcD3vIY/JZ9jf31/5r95000122hoem6ZxhG1Au7+25XL0I+N1PvTQQ+qe8ri8fmoPc3JydO85TSJZr6yrmJgYl7W6v/zyizrmqFGjiq3jhNItt9yi8liyDB06dFDH1SaZ6L/F6/jtt9/s2i5Nw0s7Z7du3dT9b9u2bYkTVN99911hG+M9ZsCnnTuLci1df/31+Oijj9Rv22fH1restGNo7N27F1dddZWyhmDZ2rRpU2jeXNo9JOy7tbbCc7GNOYs6TLNTnqNp06bKB9px4k6DWs3Y2Fhs2LChxDoVhOrKgMYD1Ddzxa07ua5SztmqnjVC5QE9TRyhwDb6BevvU9ut5pWumlHu+KEoSEpkqwopryB4M7VaE3fPPfeo6JTLli1TAxvBhrwcICW27FVybCNwYrP9srws152VObDKzwNMPhwhWpeFxQA+7mlOOBCmwEFBvVOnTsqcb82aNdi8eTMGDhyogkJwGZO6v/HGG8X2p5Zk7NixuOyyy9RsPAUlmgIyoMQNN9xgt+3+/fvV7D3XT5kyRQ0S6WfJwSAFpwceeAB169ZVA0NqYNLT0ws1dRykctb/0KFDSjgoieeee06ZIFKIonARFBSkBpoURi+88EKn+zm7lvz8fOX/Q1wpL32TrrzySjVofuGFF5TWYs+ePYWCsR6s586dO+O9995TASMolJTkazRnzhxcd911StC4//77VXm2bt2qhBCWmfC+UpvF/+kLxvUU8pjfkeZ35JJLLlHC8TfffKPuL49DeH49OKDnPr///rsSXPv06aOOxSi1FD6+//57u+15v8aPH68EmmuuuUadZ/r06ejYsaOucGYLj0uho3Xr1nbLs7OzVd1u375dtd/27dur6+Zx4+Li8OqrryoBm3VEc0zbtksBxRkUdNg+eD5eT1ZWlroXFBQd4TlY76wLmmlyMoQmm/3791dCPoUpCsxsGzwuy6Kh+Tm6cgzC+8VlhMds1aqVEgRZlxRcS7uHXM7AMBTceF18Nj/99FNVhxT0KPwT3r8RI0aoOufzQy0s08iEhBQFW7Cld+/ehfeJz5og1DTqB9dH+4j22J20G8vilmFQkyLzd0/RMsr6vB1NykBOnhl+Pjq6hBYDgfbjgd2LgH+fBTpfYjW1dMWMsll/oPeNniq+IHgXllqMv7+/5a233rLUZGJjYznNrL71OHTokPoUI2G/xTIj1Ls+LJObhIeHW2677bYStxk9erSlWbNmuuvS09OLLRs5cqSldevWdsu4P+v5559/tlu+YMECtXzdunUlluG6665T2+neCxv2799vMRqNlrFjx1pyc3Pt1pnN5sLfgwcPVh9XryUnJ0ft70p57777bktoaKglLy/P6TaLFy9Wx+G3xrx584otIzNmzFDLNVJSUtTxu3XrZklLS3N6jXrX89VXX6ljrVixonDZCy+84LRuHeuJ94/bPvbYY8Wumcv/+eefYvf8999/L1yWlZVlqVevnuXSSy/VrReWX6vrgQMHqmt05J133lHHnTlzpt1+F198scVgMFj27dvnUtt1pEePHpaoqChLYmJi4bKdO3daTCaTXf0fPXrU4uPjo+6LLcePH1f3ZcqUKYXLbrrpJrt9y3IM1n9gYKBl7969dtvm5+eXeg/ZBiIiItTz47i8efPmlmHDhhXW9yWXXGLx9fVVz5DG6dOnLWFhYU7bB7e/4YYbLDUBp319BcH+4MSJEyX2C4L31fVbG96ydJ7d2TL6+9F2/aun2HfqrKXZg4vUh7+dwvf9U5HWd/9fT5Z80G+vs273bAOLJfFAhZe5Orbt0sZ+Qs2gVptTcibYkwEVhKqHs+7r1q1T5oplgVouQjNHmrgxiiVn/Kl1Y7RFW2hKR62M4/kJzd7y8vKcnkeLkFqaFu7HH39UmgaamDn6kZUWkMeVa3GlvNyGWjmaI3sCBhpheaglommcs2vUrof1xu15PZomqqwmcAzUwXPce++9dsupldTW20Kt1ujRRRHdaPpI80H62JYGy0tNp14ZqG2idk+DZaJWi9f666+/un1dJ0+eVNovaheZ/1KD2izb8pMffvhB3XtqW1lG7ePr66uujZq30nD1GNT8Ll26VF0rTShtccUPjSa3bMvUgtqeJyMjQ2ndqB1mOahtpnaV5pzU9NlqDbmvM1hXPJ4g1FQ07duxtGM4mFJ6v1VemkYEw2S09uP7TzsxqSQ0h+xTEJhk1XvAmSP62+1YAOz4sciMMqJlhZdZELyVWm1OyUGiFimPZpWCDTRdvGNj2avk+ynFzSmZr+XST13anYPVvPw8+Jh8igbuLJOb0C+NpoJNmjRBr169lA8TfW9oouYKNDmkiRbNLzkQtIW+V5rQQzTzMFvob0VzMLYz+qRRaDr//PNVGRwFFFc4cMBqjlqWaIalXQsHrK6UVzNR5eCfgisHy8x1xgFyRUR2dfUaacpHwYZCTWpqarHrKQs046NppmPwFZocchnNJ23RE7opmNG00xX0/LFYBgo0NFO1hSaaxLEMrqD5qbVr167YOi6zFQxpGluSyakmPJeEq8fQhN2yRufUzlOS6SrNpdkuKdg5u/6S7o9EKxZqMl2iuiDMP0zlj1setxytwj3rS0bzyWYRQTiYkO48uInGoPuBLfOAjETg7xnAZbOLm1H+cm+RGeW59i4OglDTqdVCHLUznPHmgOnyyy9XARIcB07aDHitg75n5XEMbtwD8LGPTqiSbbp6TA5uqQmitqkcggE1AYMHD1Y+XdTw0KeGebU+++wzFeK+JOgLc/HFFyt/HQZw4ECefjQc8PI41IjZoheJku1n/vz5KvgFNSzUHNCXjkFA6Jvnbg6qsg4qXb0WV8pLIYd5zbiO2g1+WJ/0t6PvVnkHvZpgU9JxWF4O3KnJoT8hhQUKmVxOQd3x3lQEenXv2F/Yblsa7HsoYLhLWeq3pDp1LKtWd9TG6kUNdXbNZTmGK/falfNQk924ceNi63l8BlRJSUlxep6S7hXvj+aDJwg1EZPRhP6N+uPXQ79i2bFluL5zkQWAp2gZHayEON00A7YEhgNDHgZ+vc+qbetzM9C0b9F6CnCMRukbJNEohVpJrRbiGIFOgw7uetRaIa68jC8eJKSqoMDCgAla5EKaczFsuCbEORtAMogFo9hRWLENl1+WiKUMksDP008/rQa2jP7IIBwM+OEONN/joJNBL9wJtuDutZRWXgqA1NDxw/JQkKLWkxEBtSAVZUUL9LFt2zanGlNquhjsgoN3BkDR2LdvX7Ft3REQqFmjsM92YquNozkiBYHSzF3dgYIno4w6Coc8B00fqS21FZh27dpVuN5dNC0x60wvMqQtmrkhA/No2j9nOKtbV49he6/Lcx6aRVIjrKvRz8tTUS+p/XPl+m01vTQ9dqZNFISawsAmA5UQt+nUJqTmpKKOXwlBRCoozcDfu06XrokjPf8HrJsJxO8Gfn8YmPaPNXUAhbqdC6zbiBmlUEup1T5xNEsq7eOKb4vgnXAQrM3Aa3BgzgGtrQaEGhw98ztqEDh4tNXqcD9GvnMVbu8409+jR4/Cde6mGKA2jb5CFEIdTSJL0ii4ei2ulJcRIW3hcc8555xi11RWqGGjmSojX9IEzhatbJp2x7GsjIjoiGYG6krZ6NPIYzpGKmUUSG19RUFhl758joInz0EN4xdffFG4jGXitbGuKVC7C7WnvEc8Jn3IbAVDRjW15dJLL1X1O2PGDF2NJstWWt26egxquagppzCumdHaXnNp56HWlc80NcWM6unsPBSGaf77888/252H6+fOnQs96EtbWsRPQagJDGg0AAYYkGfJw6rjqzx+PgpxhEJcqVYLjFA9+jnr7+MbgW3fFZhR3mdd1myAmFEKtZZarYmj+aRQc6GfFE2sGPafKSQoGKxcuVKZ/zHlgAZ95WhCeNddd6mQ8hSSaIbJwTT9whhynyHSOfhlPkEOiKmZcQWmDWBYfQpf1BowkA5NDzmo5EBXw9UUAzwG/dUY2IRCAK+NGgZqbqhh47n0cPVaXCkvfUgZ7IH59ugTd+zYMbz77rtK48kBeXnhfXrrrbdUmgbeGwaeoM8qQ9HzXAyaQQ0dzaAZgIRh9+nPR40hfzvCY2g+sPTtoxaR4eepnXGEmkUKBtRAHj16VGk7qV386quvVF1zv4qCwhgDfVA7ylQKGqxfaj1vvPFG5b/IdTR75YfX65iSwFWoKeW1USjhOXhved+Y+oHmsRqc5Hj55ZdVChZqrekjyfplPjqWgc/Ihx9+aFe3t99+uzKnpeBG30h3jvHOO++oPHs8lpZigHVPLaUm4JZ0D2lFwWV8xtlW2KbZDqhlpvBHU2rCe0qBlalF+Pyz7rkv3wN6kzjUyGq+tIJQkwkPCEfX6K7YEr8Fy48tx6jmJadHqQhzSpKalYeEtBxE1/EveYfWI4DQJsDZOGDh7YBfMJB5BmBycknqLdRmqjo8pjfAcNhffPGF5dVXX1W/CcO3nzp1qlgY91qTYqCKsQ3FXlays7Mt999/v6V79+4qrHlQUJClU6dO6j7b3leGsZ88ebIKVc4Q7raPxeeff25p3769SkfRqlUry2uvvWb59NNPi4UkZ5h3hnt3ZOPGjZarr75arecxoqOjLaNGjbIsXbq0TCkGbMvVs2dPS0BAgEqj0L9/f8tPP/1UYoqBkq6F4d1Z166U9/vvv1fXWr9+fYufn58lJiZGlf/gwYMVkmJA45dffrEMGDBA3bc6deqo62V5NXbv3q3KwXtbt25dy1VXXWU5efKkOpZjaPtnn31WlZPpGWzLoFdPGRkZlgceeEBtzxDzDFX/+OOPq/Zki7N7zrpwFvbfsV1PnDhRXaMjCQkJKnw/65hlaNeuneWNN94o9jy4k2KALFy40NKlSxd139q0aaPSGDir/0WLFlmGDBmi6p4pAJiK4vrrr7esXbu2cBuG3L7zzjtVObVnx7YNu3IMLdUB64L3kW2aZZs+fbpL95CsXLnScv7556v92W55z6644grLn3/+aVff3K5v375qGx6LqQv0nmdeF9v+o48+aqkpSIqBmoMnQt5/tOUjlWpg8NeDLfnmovQeniApLbswzcCqAwmu7fThwOJph97saqkMJMWA4K0Y+Ae1GM4UcyaYpmk0VeKsOGd4aebEWViarTHJbnWFM9LUltC/g9fjLGpdRfr6VASaL4tmBihIXdcEHNs1g8X069dPacLKGqFRcL2+XYXRVxnVlqaX1OzVBDzd1/MdSvNU+ie6EvxG8K663pW4C5cvulz9/nr81+gU2cmjt6jHM38hKT0Hz13cGdf0ccEqatZoIHa1/bKYPsDUP+FpqmPbLm3sJ9QMarVPHP1c3nzzTSXIUXizlWdp0kWTMublEgRB8AQ0K7zggguU/5/gPbz44ou48847a4wAJwil0T6iPaIDo9XvZXHLPF5hrQpMKg+UlCuuVGSCV6jd1GqfOPoEMUIhfUUcgzUQzow7Ov0LgiBUJAsWFERYE7wG+pgKQm2CmmpGqfxh3w9YEbcCt3S7xaPnaxkVgnWHz+BgggsRKgVB0KVWa+LoPE8nd2dQG1fWpMGCIAiCIAjVhUGNB6nvbQnbkJRVFMXWE7SqV6CJcyXNgJZnNqav/YfLBKEWU6s1cQxvzdDuzmDuIr0EsoIgCIIgCDWJvo36wsfogzxzHlYeW4kJrSZ4PM1A3JlMZOXmI8DXVG1yzwqCt1CrNXEMJ84Q0wyX7simTZswa9YsXHTRRVVSNkEQBEEQhMoi2DcYPev3rBS/uJYFQhxDERxOLI9fnCDUXmq1EPfMM8+oXEH0fXvooYeUTTiTHzNHGHMbMaLPY489VtXFFARBEARB8DgDG1tdTFYeX6k0cp4ipm4gfE2GCghuIgi1l1otxDFR7Pr163HhhReqBMKMTjl37lyVDJoJkZnkt27dulVdTEEQBEEQBI8zqInVLy41JxVb47d67Dw+JiOaR7rpFycIgh21WogjkZGR+PDDD1V0ylOnTikfuaSkJMycOVP5zAmCIAiCINQGmoc2R5OQJpViUqn5xYkQJwhlo9YLcbYwkWP9+vVhNEq1CIIgCIJQO1MNkOXHlnv0XC0LcsUdjBdzSkEoC7UqOuUXX3yhvmkqyY5K+780rr32Wg+XTBAEQRAEwTtMKuftnoe9Z/biZPpJNAhu4HFNHN1ZOC4TBMF1apUQd/3116tOgoFL/Pz81P+lwe1FiBMEQRAEoTbQq34vBJgCkJWfpbRxl7W9zCPnaVXPKsRl5OTj5NksNAwL9Mh5BKGmUqvsBg8dOoSDBw8qAU77v7QPtxeE6kDz5s1dmpjwRg4fPqwmTGbPno3awJNPPlnls848P8thC/u8cePGqYBO2v1YsmSJ+s3v2lhPglDbCPAJQJ+GfTzuF6eZUxIxqRQE96lVmrhmzZqV+L9QO/nyyy9VrsC7774btZXnn38eXbt2xcUXX+zR87z77rsICQmptsJmTWfatGnYsmWLEp4Y9Om8887D0aNHPXrOuLg4FUiKOTm7d+/u0XMJguB6qoGlcUux5sQa5OTnwM9knfyuSEIDfBEV4oeEtBx8uPQAYpMyMLFnE/iaapV+QRDKTK1+UhiJcvly5467XHfy5MlKLZNQNULcm2++Waur/oUXXsBPP/3k8fNQiNPTtnFCJTMzU/mrCpUD69s2D2Z+fj6WLVuGSZMm4a677lLfLVu2xKBBg9S2/PaUEPfUU09h8+bNxdaxfDy3IAiVixbcJDMvE+tPrvfIOXLyzMjOM6vfy/cl4KEftmHyrLXIzbcuEwShZGq1EHfffffhkUcecbr+8ccfxwMPPFCpZRJqDjL4dB2azAUEBMBkMqGqYKAjs1l/8LBnzx6sWrUKNQnWt49PkTEG06zk5eUhPDzcbjtG6+W2VRG1l+XjuQVBqFwahTRC6/DWHo1SOX9jHFKz7BOKrz6YiPkb4jxyPkGoadRqIW7p0qXK/8MZY8aMqRI/EKHiSEtLU8J6ixYt1GCQKSSGDx9eqIEdMmQI/vjjDxw5ckQJEtpH47XXXsOAAQNUzkDu36VLF8yaNUvXH01rL3379lXbvvTSS2rdxo0bVTtjCovAwECl3WCwHFshj1rh3bt3Izc3t9RrysnJwYMPPogGDRogODgYI0aMUPvqkZqaqiYieP3+/v5o2rQpHnroIWRnZxduw8E5///8888Lr5/14s4xNL7++mt1/SwXhQHWnabhYx1RGOJzp52Hy0ryidu+fTsuuOACdaygoCBl3sf7ZYvmszVv3jy8/vrr6pis/379+mHTpk1whfXr1ysTz1tuuaXYOrYN1jG1U67C402YMAERERGq3J07d1bazpJYuHCh2qdJkyaqnqmd5H12rGfms6TZY0xMjNquUaNGGD9+PHbs2FG4jSttztYnjt98Ngi1YrbPgTOfuL179+Kqq65CvXr1VH23adPGziSZ9XbbbbehQ4cOqg54D3l9tuXkMXmfyP/+97/C89qWS88njs8gzX95Xp6f947PkC1cxmtnfTGYVWhoqPL1u/HGG5GVlVXivRAEoUgb5ym/uKNJGbrLjzhZLghCLfaJc4R+UBxkOYMv/NOnT1dqmWoa2fnZOJ1xGvWC6sHf5F/p5+eg/Ntvv1WDyU6dOuHMmTNYs2aNMt0aOHAgHn30UbXs+PHjeOONN4rtT6Fg7NixuOyyy5SwQ4GEA2hqbG644Qa7bffv34+JEyeq9VOmTFHCTnx8PEaOHPn/9u4DvKmqjQP4m+5FSyeUvZcs2ThYIigKOBEFBGUIggv358KBe4LiQkDErQgKDgQUF0MBZe8N3aWle97v+Z9yQ5ImbdKmbdL8f89TGm5ubm5ObtL73vec96ggEIEQjqnjx4+rE/asrCx1kgkPP/ywCqJQWEIPbGy57bbbVLBz3XXXycCBA+Xvv/9Wz2F5Yor/4/6DBw+qE9eWLVuq143AFEHfsmXLjBmoiRMnquAL64F+Qm/vNmD27Nmq+1vPnj3l8ccfVyfumzdvVkHXyJEjVZfV22+/XcLCwlS7A8bH2YIg4cILL1SByj333KPWXbhwoQpOvvnmGxXcmcI+IeC54447VDD80ksvqXFWeF98fX3LbNMePXqo/UOghpN9PBbQnRoBHMpf4ziyx5o1a+SKK65QY8pmzJghDRs2VMEr3nMEv7YsWLBAZSLxGBwnGzZsUK8J3Q0//vhj43p437dv367WQ2CNYwyBMdoLx7i9x5ypa665RgV82CbGReL/ZUEghvdGPx5xXCAQ//zzz41dk3FcIkjD9nBMI8h69913VbdMPB4XIRDgIVDDD44vfCYBAZotzz//vPq8YDt4nzBmD910cWEGwSuOLx0+p/j8dunSRV1U2bRpk7z//vuqbTAOlIjKHhe3cMdCOZZxTI6eOSpNQ51bR6BJRJDV5U1tLCciC5oHa9GihTZp0iSb90+cOFFr0qSJ5s6OHz+u4W3Gb2sOHz6sfizlF+ZrR9KPVPpn9obZWsdFHdVvRx53OO2wdiD5gPqtL8M+Oapu3bra9OnTy1xn6NChWtOmTa3el5WVVWrZpZdeqrVq1cpsGR6Pdv7uu+/Mli9btkwt//vvv8vch/Hjx6v1rL0XprZt26bWu/XWW82WP/zww2o5tqN77rnntMDAQG3Xrl1m67711ltq3d9++039v7i4WPP39zd7rKPbOHDggObl5aVdfvnlWkFBgdm62L6ubdu2Wv/+/Us9D143trdw4ULjsmuvvVbz8fHRdu/ebVyWnp6uPpPNmjXTioqK1LJffvlFPRaf5+zsbOO633zzjVq+YsUKzV6zZs1Sj3n66ae1lJQUrVOnTlpUVFSp128L9gn7ERsbqyUmJpZqB/zk5+drjz/+uHqe8o612bNnawaDwfj5TUtLU4976aWXbO6Dvccc1nniiSeM/4+Liyu1zLR98VuH9xDHxb59+0q9/rJez8GDB9Wx9swzzxiXrV+/vtR7r8O+mLZTUlKSevyAAQPMjjP9NT/22GOlPlMPPPCA2TE4cuRI9Z56Glvf9c5SWFiojiH8pqpVXW2dX5Sv9f24r/obvnjnYqdvP6+gSLtm3h9a0wdXGH9ueHe9ll947nvEFbjjsV3euR/VDh6dicPVZlw1Rvc6dLcxhavuyIzgyrQnOpV1Sq785kqnbQ8Th+KnMlZcvcLhK4HIqiAjgCxAbGysw8+JbBIgs4Nuhbiyj8zUzz//LGfOnFHb16F7G7q1WT4//PDDD6rynukYJFPIrNlTXn/FihXqt2UlTWSqLLvrISuCbAkyLMg665BZgrVr1xozH7bYuw1kxtA2yKhYvsaKlIhHkY0ff/xRdb9r166dWXtOnTpVjWVFV0vTjA2yn6ZZpv79+6vfjkwT8sQTT6jMLMbDImOTlpamXiMyRvZAJgjPh6wP2syyHUpip7KPNbQjjjUcc+iOisdgu+hmie6DyCqiAAkyvpbj1xw55ipKz/why40ulKZMx83prweys7NVV07sW9u2bVWGtiJWr16tsq043k1fFzK92C4+H0899ZTZYyy7yOK4QEYdbVynTp0K7QeRJ/D18pW+DfrKqqOr5PcTv8u4Ds4tPOXn4yWfTekrXZ9cJVn5RTKyawN5+fourE5JZCePHhOHEzV0PxozZowa64TxHTfddJM6McRtvasPuS+cTG/btk2dAPfu3VudpNsaP2YNTvbQ1Q7BAbrH4cRcL4aDE3xT6NpmCWPLcLEA3QvRhQtdy1BOHd3aKkIfu9emTRuz5dgvdJszhS58OOnFfaY/ONkFe7oK27sNdLcEfI6cFSigjUwDOF2HDh3Ub3Q9NWXZDVVvj9TUVIeeG8Ewuvqhmx4u4nTv3t3ux1amHRCUoqsouo0iOEM764Gofqyhaym6E37//fdqLBiOLxzj6C5ZVcecJT0oLu81oisuunNizB7GSGJf8JrwebT87NgLXTbB2nGB72vLYwIBL7qzOuO4IPJE/RqVVKX9J+EfyS5w/lg1TCfQqVGYccoBTi9AZD+PzsThqvBff/0lL774onz99dfGAgwY34EToPvvv9/sarInaRDcQGW+KiozP1Mm/DhBcovOjdMK9AmUhUMXSoif7XFQOmQfigqLxNvH25jJwT45ChlWnAhjPNCqVavUuDeMhcHYKpRQL8sff/yhToCRicJYHmTyMFE8TqCxHctKhtbGGmHfcWxhLA6yBMjgYSzdM888o8bm6WPP7FVWJsfyPuzfoEGD1PghaxDYlsfebeC5q2tSZv11Wj6frcqWZbWZJVRnHDVqlDrBR8YV3w0Y44exdZXZt/Kkp6er50Owg+MT30E4nk6ePKkKdJgeazNnzlTHJb6vcDzhQtPTTz+t/o9eBc4+5ir6Gu+8805VgARjFPEZwlg1ZOqQRbZVBbSy+2W5T2VV1HTkuCDyVBc2LBn7WlBcICsOrZCRrUY6fXx723p1ZMOhVNmXkOHU7RLVdh4dxAGCNH1gPZ3j6+1bqUHMPx750RjAGcQgmmhqvpkTmSdkaLOh5T4eJ1g4oUaXqcoGBwi+UHwBP8gAoIAHKvDpQZyt7X/11Veq+xpOgk3LnP/yyy8O70OvXr3UD7p6oZsbMi7IjugFPuyFbBPaBkUsTDMhyFxZZjcQCKDLmN71sSy22sDebbRq1UrtF7JJCHocfR5LyNggoLGWNdWXlVcAxlEILFDBEe8PupGiix6CJVwI+O6771SxkPKgHQCFR1Ct1F44ptBdFcecnn0DHHvWIOuLYAg/yMKdf/75KkOHIM7Zx1xZr7Es6JKO9rScgxHdVZGV0zny+dbfcxwDltloLHP2MUHk6aICo6RjZEfZkbJDnt7wtCTnJMvtXW936nO0qV/SrRlBXHVeECRydx7dnZKqzuAmg2VSp0mqD/3YDmPV78mdJsslTc6dZFY1jKtChsMUuqnhBBgnkjoEC9a6d+kBpGnWAI9DFUF7YX3LK/7dunUz3ufoFAOoegiWJ8bWKmsi+MB4QGQhLWF8EqZfMG0D0/1xdBsIdpD1QHCMdjdl+vptPY8lZNUQBCGThIBVh4ASWVGcrKNsvzNh7BSmSECggyqjeP+//PJLVQIfmbg///yz3G3gvUU5/zfeeEPNu2Zv5kcf32W6Do47VEc1pY8tM4WxmOhaqbervcdcRSEAQ6CJMZx691Gd6fPiNVnuB6aBQCVYUzgm7N03XExAl1K0r+lxhiAbXX8tx6QSUeX1qt/LePvDnR/KmfwzTm3WNvVKgrjT2QWSlFl66hoiss6jMnG4Io2TclyJxgmn5QB4a7A+xs6RY3y8fOSubvbPq1UVcMKP8TA4IUeJcXSfxYk4CmZgygEdxryh+xnKy2PcHI4NBC84IUSJd2Rgxo0bp7rYodgFxkuh9Lw9UBznrbfeUkEOslo4AUdXTgQpKBWvs3eKAYzXxL4gkERhFX2KAWRsTLMbgPnxVq5cqcZHIeuIrAyKQuBkF1kSdAtFVhIw7gtj315++WXVRRJBAbpR2rsNvQsyMtroOoc2R5YbBTmQxUQb6G393nvvqWAPmRSM/0LxEmswZQFeFwqn4P3SpxjAWLWlS5c6dfJpzOuG4A3BMbov6rDvCBCQ4UI3RnRHLAv26Z133lHBNoqKoNgKjkFMc4BjD110rcH8dxhzOX78eNX9EGO5kJUzDbQBAS3eF0x5gfG8CGjwHuzevds4LYK9x1xlzJ07VxVdwfupTzGA9wVB8P79+9U6eF8xfQU+dwi4MTUFMpwIck2hOArWefvtt9V7jGIjWN9akI5jHMcYPi8I6HBcIhOJ/cF27733Xqe8PiI6J6fo3IWj7MJsWbJriVOzcW1izhUY2p+QKTF1zvV8IaIyaB4EpbpRBj0vL8/4//J+sL4nTjFQ0/RS7KalwR2F9/n+++/XunbtqoWGhmpBQUHaeeedp7388stm5ckzMzO1cePGaREREeo9N/1YfPjhh1q7du1UWfOWLVtqr7zyirZgwYJS0wFgigFMVWBpy5Yt2k033aTuxzaio6O1IUOGaOvWravQFAP667rvvvvUtvCaLrnkElWGH89hOU0AyryjTDtK+/v5+anX2KNHD+3JJ5/UTp8+rdZBG2/fvl0bOHCgFhwcrPbDdBoAe7Zh2l7du3fXAgIC1PQOF154obZ8+XLj/SjTPHz4cPV+4Hn0qR2sTTEA2K8rr7xSrY+S9n379tV++OEHqyXwP/3001JtZa1kvi1bt261eR+mGzh69Khmr7/++ksdD3Xq1FHvUceOHbXnn3++zCkGNmzYoNoL68fExGjTpk0zTimht0tycrI2Y8YMrX379lpISIjaPtr7gw8+cPiYq8wUA4BpFzANRHh4uHq/W7durd1zzz1m00FMnjxZvRa8pn79+mmbNm1Sx5blNBPffvutaiNfX1+zfbCcYkD3/vvvq/VxPEZGRmo333yzdurUKbN18FnA67f8HkFb2vtZq004xUDtUZ0l79Ny07ReS3qpaQb0n55LemrpeelOfZ7es1erKQY++P2Q5mo4xQC5KgP+KSvII/eGiYLR3QpXq60VstCrvbnaWBJnjokjtrWr4HHN9q4pVf1dj+6tGJuL8ay2igyR+7U1xrffv+7+Ustf7v+yXePb7TXug43y+/5kGd2zsTx/7bmpY1yBOx7b5Z37Ue3gUWPi0LXJtDsU5lrCB5OIiIiIbI9v71nvXNGqqADz7vvOqFAJrFBJZD+PCuIsB+LrkzYTERERkfXx7Q/0fEDeHfKutAgrGdP6yuZXpFgrroIKlZmc/oPITh4VxKHUvGnJcvYkJSIiIiqfr5ev3N+zpGvl9uTtsvLQSqdn4jLzCuVU+rn5ZYnINo+qTjlixAg14S3KlqPUvF4BD1XpbMF4rDVr1lTjXhIRERG5nosaXiQXN7xYfj/5u7y++XU1bVCQb1Clt9sqJsR4G10qG9YNrPQ2iWo7jwriMOdSgwYN1MS6CQkJKkDD/GCm84ARERERkXX39bxP1p9aL4k5ibJw50KZ3vXclD0VFezvI40jAuV4ao7si8+QgW1j2PxE5fCoIC4wMFDN+abP+4Y5nTC30k033VTTu0ZERETk8jAubnS70bJk9xJZuGOhXNPqGokNiXVKl0oEcXsTMpyyn0S1nUdXp0RGDhM5ExEREZF9pnaZKmH+YZJXlCevbXnNKc3WmhUqiRzi0dUpBw0axOqURERERA5AAHd7l9vV7R8O/yD/Jv7rtOImBxIzpaiYUxgTlcejgjhWpyQiIiKqvFFtR0nLsJbq9gubXqj0lANtzgZxuQXFcjw1m28RUTk8akwcq1MSEREROWcOOUw5MHX1VNmRskNNOTC85fAKb69FdLB4exlUFg7j4ppFBfNtIiqDRwVxrE5JRERE5BwXNrzQaVMOBPh6S9PIIDmUlCX7EzJk6Hn1+TYRlcGjgjhWpyQiIiKqmikHFuxYIDPOn1GpcXEI4vYmZPItIiqHR42Js6RXpzx+/Lh89NFH8sorr6jbUFRUJImJiVJYWFjTu0kuZtasWWqOQXd/DnKuX3/9Vb1n+O1sEyZMkGbNmklNOXLkiHptKA5lauvWrdKvXz8JCQkxvnasg9t4jDu0LRE5Z8oBWLRzkcRlxlV6XBzmiiOisnl0ENe/f3957rnnpEWLFjJ+/Hh54IEHZP/+/eq+rKwsadWqlcydO7emd5PIbf3zzz+qCixO8iMjI+Xmm29WF0fs8eabb6oAISYmRvz9/aV58+ZqmpCjR4+arXf48GHx9fWV9957T2644Qbp2rWr5OTkVNErIl1xcbFq7xMnTsjLL7+sLoS1b9/e4xsI7YGLMP/+W/lqfUSeNuWAHsQdSs6UgqLKFUohqu08OojDRN+vv/66zJw5U001oGnnStqGhobK1VdfLd98802N7iN5pkcffdTtA5Fdu3bJgAEDVND2wgsvqM/ZihUrVFBnz2vbvHmztG7dWh588EF5++23ZezYsfL9999L9+7d5eTJk2afY2SqLrnkEpVdX7p0qeo6Xd0QcOJ14Xdt07RpU/Xaxo0bZ1yGXgu46HXnnXfK1KlT1ftTr149tQ7WxWM8NYh78sknGcSRR0EAN73rdOOUA6uOrFIBnaPa1g9RvwuKNDmSnOX0/SSqTTxqTJyl999/X5144AQzJSWl1P2dOnWSn376qUb2jTybj4+P+nFFp0+fVpnqRo0albneww8/LEFBQfLbb79JRESEWnbhhRfKwIED5d1335W77767zMcvXLiw1LKrrrpKevToobrsPfLII5KQkCA7d+6UVatWyT333KOyQcis1wQvLy8JCAiQ2ghdGS1fG9oe6tata7bc29tb/ZBzZWdnq88Tkau6vs318vmez+Vg+kG5d929MrXzVJl+fklgZ6+mkcHi5+0l+UXFqkKlPgE4EZXm0Zm4Y8eOycUXX2zzfmTj0tLSqnWfagutsFASX31NEp573viD/2N5dfvjjz+kd+/e6iQU2YEXX3xRBQiW43a+/fZbGT58uApO0H0P6yILlJdX/tVEex6LjBS6Bl500UWqK5ru1KlTqqshghs9G2xrTNzatWtVJgvHZnBwsHrMX3/9ZbZOZmam3Hfffar7IV4zsiPIUv3+++8VbkPsF557zJgx0qBBA1m9enWZ62dkZMgPP/wgN910kzGAA2TmOnbsKJ9//nmF9kPP7uifS7y2devWqTafN2+eDB061Gz99PR02bNnj/pdHuxbu3btVAYRbYwTZrQhAkPYtGmTeu+wvGXLlrJ8+fJyx20h0Jk0aZI0btxY7WPDhg1VIIrA0xR6Aujva506dVS2cf78+WXuLwLZwYMHS/369dW227Rpoy5ImR5bcODAARk1apSaJxPHA/bl+uuvl7i4c+NW1qxZozKI4eHhqutr27Zt5Y477rA5Jg6ZT3ym4JZbblH36WP2bI2J27Jli/qM4DmQKe3Vq5fKzFrau3eveh/Rznht999/v+Tn54s9UlNT1fqdO3dW7YgfjHv+888/S637xRdfSM+ePVWbh4WFqYt2zzzzTLnPUVZb4b3v27evWbvgB59n2LZtm1qO4wfvRXR0tNx4443Gsdg6vQ2RWcbFDrQDPu+AcdrYT7zfaMeoqCh1cYS9RsgVphy4vWvJBOCwcOdCOZN/xqFt+Hp7qakGgOPiiMrmmpf6qwn++JmeyFjavn27OunyZPnlFCfwrltX/RjXP3FSpLBAMn/7TVLee69kIYKRs8GJb6OGEj5qlHH9goQE0ax0rUPQUFhUJFpQkPg1aFDh/f/vv/9kyJAhKkh67LHHxM/PT2Vg9RMiUwsWLFAZhBkzZqgTtA0bNqhiN+ge9fHHH5f5PPY8FgEcMlDXXHONGkOEMZiAk/yCggLjiZstCHwQFCFwe/rpp9XJOp4XJ/8IZPST6mnTpqkT1OnTp8t5552nMmcbN25U3bvKumhhDfYf+4XnwdgzvIbbb79dnRiXBZ8dvCacqFvCfn7yySdq/5G9Kk9ycrIqNISgAN3UoLzn1+HEFifNCNoReJQHwd7ll1+ugpxrr71WBVIYL4usKDJ9EydOVAHRnDlz1Hgw7BNOsG257rrrVFvguEBAiEAe79W+fftUMAsIEvEcOClHAILvJZzsr1y5Uh0bZY0ZRABx2WWXqYAHwcVDDz0kZ86ckdmzZ6t18B4gIEL3RhwPCOTwnffjjz+qiwf4P4LWK664QgUxCDawrYMHD5bZC+G2225TrwfrT5kyRR1XCGhswQUEfA7xmpFBRdCJ4xlzd3711VfqMwFoH4xVRtbp3nvvVZ/bxYsXqyDXHocOHZIvv/xStTvGNCPY/+CDD1Swi/GZepvjIsTo0aPVZwfjovHZRfBY3oWO8toKYwKx3LRdAEEl4HXs3r1b9QDBBR889p133pG///5bHSeW3YARHOL7BG2mX7jAZwDvL45FfL6QFUeBGXzGMQSAqCbtTyupKwDoTrlk1xKzwM7ecXF74jNkHytUEpVN82BTpkzRGjZsqCUlJWnJycmawWDQ1qxZo+7bsmWLFhgYqM2cOVNzZ8ePH0f0pH5bc/jwYfVjy6627cr8SXr7bbP19w8ZUub6J+4xb8+jU6aUuf7h0TdW6vWPGDFC8/f3144cOWJchvc7PDxctYvpa8/Kyir1+NmzZ6vjwrT9nnjiCfVYU/Y+FsaNG6f2afv27dq7776rtjV//nyzdSyfA9uPiIjQxo8fX+p5mzVrpg0aNMi4rG7dutr06dM1exUXF2v5+fnqN+D2V199pV1++eWal5eX5uvrq40cOVJbtmyZVlBQYNc2v/zyS7X/a9euLXXfY489pu7DZ648eD6sq/9ER0drc+bMsfu1LVy4UD0Ov8vTv39/te7ixYuNy3Dc4D3Ez6pVq4zLN27cqNZ9+eWXjct++eUXtQy/IS0tTf3/pZdestnW6enpWmhoqNalSxctMzPTbH/0dQDve9OmTcs95iZPnqyFhIRoubm56v///vuv2ge8H7a8/vrrah18LmzB58SyHdevX2+1bfU21z9beB3t2rXTBg4cqBUVFRnXw+3evXtrLVu2NC7D9y0e+9tvv5m9zlatWpm1rS143YWFhcb/47kTEhK0mJgYbdKkScbld999t2p303XtYU9b2WoX/bVY+vPPP9X6S5YsKdWGaB/Lz1zXrl21K664QnNUed/1lYW2jIuLc7hNqfa0dVpumtZrSS+t46KOxp/eS3pr6XnpDm3nzbX7taYPrtAGvlT2593T27sy535UO3h0d0pkM1DVDldVcQUbWRBkHHCFtk+fPupKKQpMkPMY/PyqrTmRvcGVb1ztNy2ygEwHMlqW9PEmyBAhI4MMELrPISuIrmBlceSxqHiKjBYyOcg24Mo+rqqXBa8DXcXQnRHb1n+QsUCWARkEfToMdA/Dlf2ysszW6NkPZJ+RyUAXLxQNQTZu2bJlMnLkSLvH6emFS5BxsaSPrbKnuAkyJHjtyEqhGyy6T6K7qL2QfcN7YE8WDpAJQRvrcNzgOZs0aWKW/cO4POwbMim24HXi+wVjAm11y8ZYPmTO/ve//5XKDpc3xYR+zOE4R7YVxwOyWGgfZJX0Y0HPPNlqb32d7777zqy4k7Mgq4gurcg+4RjWj13cHjZsmGpDveIould269bNLGOM14mslj1wvOnj8XJzc9VYZ7QPuk2iUI7pa0YGC9lLR1S2rUzHtOF9wv4hA4txhab7p8PrtvzMYR+QEUQ3WSJXsj5uvWQXZpstyyrMUnPIVaRC5ZGULMktKHLqPhLVJh7dnRIn0uhig64q6IKDP8ro5oU/kqiw9vzzz6uuLI5CFyYEiOiGhu5B+CONINFa4FDeWC79ZAYn5GV126oqLX/8ocz7TbtSQpMPFqjulFCUkSFHb75ZtJxcMQQGStOPFotvvXpm68c+9VSZ3Sl9KzGQH22PE1dUOLSE98TSjh07VBdHjGuxPOEtb2ykI4/F+BuM38L4INxG987y6Cfl6JJmC07kMcYG46LQhRAXIRBsoLsdxt1gvFd57fXqq6+q23fddZc8++yzFS6koAdq1sYT4uTadJ2yIJBBkAo44UdAjq5p+Iyie6CzIYC17OKJ9wjtagrroPsg2rysgALfITgu8F1zwQUXqPcC3THRFRH0IBAXkhyF7wcEf+hGZzlmTD/m8Dx4L9944w3VLRFjudCO+C7CxQzARSt0G8X0DfiewvhJHJsI5BGEVpZ+7JZ1oQLHHgJmBHN610pT6DZqD1xEQbCP6SbQ/deU3uamXY7R1RRjBHGMYawiXndZwXNl2wrHCx6HLqQIYsv7jjHdZx26U6LbJL7X8FnAdwIuCOGzTlSTBjcZLJM6TZL4rHhZcahkvOu1ra+VS5pc4tB22tQr6ZpdrIkcTMqU8xqEVcn+Erk7jw7iAGMuMCYBP0lJSeokACds9ozVsQVXT3HChJNMnJwhi4Gr+8iUYJ4se2A/MB4CV+dxxbim+Dk4wbBfI/MxhJG33CrJ8+ZJ5K23SuDZ8SimLIM60yDOq7Cwyio0Wl5FR/YMY83Q3gheUHgAWRmUskcWx7JYRGUfizFJegEQjKvC2KSy6NvAhQFb4zQRbOgnmsjIoNgKMj2vvfaa2i+MC0M2xBYEfR9++KEaQ4STfqyPsWF4DcgqOgLFT8BaNhBjsRDAmRY8sRdO5rt06SJLliypkiDOVlVFW8vLy8ZgWgWccKMICjKKOAFHUQp8JyBw0B/v6MTuGPuFzCBO5DFNCjKFCBqR9UVBHdNjDvdPnjzZeDxgn5566ik1Ng9jJnGsIpOL/6MYDcZ3ffrppyoLi4IglZ2uQd8XBLQo2GKNaZBmrS3szXrhOXBRDmMM0c76MYbXYpo1xQUxjJfFe4LPIn5wvGM8JLK+tt6PyrYVgi08HoWHzj//fFV4Bc+Fz6y17wlr20MBHrz/yAbi/cR+Y/wtPuMIEIlqsrDJXd3uksLiQllzbI3kFOZIl+guarkjGocHSYCvl+QWFMu+hAwGcUS21HR/ztoGY+nQrE899ZTZuIwBAwaocRl5eXl2bWfevHlaZGSkdtddd6ntoT92TYyJq6yi3Fwt79gx9dsRlmOHKgL91zGu8frrry91H8aMmY7b+eabb9T/f/31V7P1MA7KcnyL5Xg1Rx4Lq1evVmOs8N5irE/r1q1LjZWxfI4vvvhC/X/lypUOt8Pp06e1tm3bqueyt6337dunPfTQQ1psbKx6Xjz26aef1o4dO2bXc2KsF8bS4TVa6tixoxrrU1F4fPv27TVnw5g4tJMlLMN9lsLCwrQbbrjB5pg4a44ePao+14MHDzYbO4j3tyyWY+Jee+019TjTsZ7w3nvvlbsP//33n+bj42M2Rsza94/pOK3KjInbtGmT+v9bb72lladNmzZat27dSi1/8cUX7RoTh7GF+K61PLYvuOCCUmMKTWG9Bx98UD3HH3/8oTnCsq02bNhgtV1SU1PV8lmzZpktz8nJUWNPTce76m2INi5Pdna2Oj4xzrasMTscE1d7uMMYrfE/jFdj4p7669y5kCOGz/1djYt77vvdWk1zh/a2xDFxnsGjx8RVBXTRQRbPNEuAK62oTocuQygZXR50s0ElRVwtt5yDyd14+fuLX+PG6nd1Q/YE2QpkIPQxN4DxOOg2a0rP+Jle8ceVcb17YVkceSzGP6ErFjK06PaFzBoyBJhTrSzohodjAdkFa10UkUUGjP+xLKePx6FbVlld/ywhw4OqfRgXh/ZDxgZZJJSRRwYJlfTKgu6O6KqGdjZ9XnQ3RddTdCk0hTFTmPLDtMslspSW1q9fr6r72dt1zJEpBpwNYwwtu9ai6x4y/XqboCsc2gptjfXtzT5ZO+ZwXKBipeXxpo+V1KGCIjI8+j5YmyMT49LAkWPGFmTfcDyhIqu190E/dgHjQ5FNNK0SiXZB90h7oF0s2w3HDH5MWb5mfEcjM1bea7anrfSxjZbbsfaeATLlZWX6y9sHvJd4T/H+Wx5DRDWlU1RJF/HtyWX/rbCldUzJuLj9CaX/DhBRCY/vTulsGJyO7nSWXcX08u84QbGcy8oSiqmgex3KeGNsHVUcAg90eUJ3QIyDwbgVjEFDMIKTLL3bFMYroWstumGhGyvWw7gVe4poOPJYjE9CF0MERpjuAPM7oZgITnAxJgfdMq1BtyucyGJsG7oTonsuuoSh6AguDODEEd27EPiguyXK42M9BAjo5oXuYhXpfohAGGN+8IM5z9DdEsV/cJyXN44LgQmOe4zrRNvjBB6vEyecU6dONVsXy9AFVJ9jLT4+Xrp27aq6n+E+dL9EgQwEveg2ioscVTHFgDNhGgGUsEeXVATB6O74/fffq6ASATzg/UHXVQT2CEzxvuJYwjxy6I67dOlSq9tG8IftXXnllep7AifwmKrAshs45vbD+44xW+iyiAACpf1xnKALH+A7Bu2OAEr/XKB7OY4pvO+VhX3CMYPvvQ4dOqjXivFv+BwguMIFFn3ePHQFRVdZvK4777zTOMWAtSlBrMH+orw/uq3juMN7gM87ntf084ipG3AxB2PaEFijrREA43sXx6Et9rQVAla8r2+//bYaN4nPLqY2wA+6QuK9xxhGtAHGNaJrJl6nvfB5wNhGFGvBuEZ0C8U4PewTnovIFXSMKhk+se/0PsktzJUAn/LHQJtqW79kXBwm/CYi6xjEOZk+95KtMUK4vyz4g4yTdZyQ2xqHUxZcecePTh+ThAwNfizhpA6BTFVUpasM7I/+UxkIZBDEYf4tBHQIfDDPGa5eY24lnAjjOVDABpXxMFbliSeeUCdfKLCA4APbMN0Xy9/2PhbrIAjBiSAKEuiPR8YV7zdObvH+40TM8jkAJ+I4jhAc4eo9rrrjWEOghKIRWBevC68P1QgxDgtZGGThMGYHAaS19rS3rVGcA+2IH4zTLG99BC4IIjBOB8U90NYoqoF9wX7a2hfARRAENDjBxZgjZOYQnKIgBy5y4ATYnmPDtB3tPZZsrVfecsvnwhhD7C/aABlJfM5QUAdzBSKI0NdH8I/sHMZzYVwTPvdYD++j5XPq/0eggAAP47/QtjiZRzEmBAkIlvR9wHGGLC6CRwQzCIbxviC4RbVRrINiMciCIkBHVgwBBSasRqCMsXaWx76t11tWm+NiBQqw4NjH60dAj+MJgTqCLn09LEOQhGMVAT+CIYzjRNCK11He+4hjDccm2hvFqhA4IbjFRRUcS/pjcWwh8EEAhkAMFUhxbD7++OPq82vrOexpK1ycwXPivUEAjUJX2C7aHXNGYvJutAGWIxhDhUy98ml5bQtoG4yHw3GFTC+CUAS/+CnvGMf91v4OOAO2i4xiVW2f3Kutz4s4T/0u0opkZ/JO6Rrd1aHHtzo74feJ0zmSnp0nIf41d7rqDu1tyZ32lSrOgD6VlXg8WUAWDj8YcG4Jf9xxhRgnD7bgjzqCApyAA05wEHzYW51SX98SMid6IGkKJzA4ucaJgCvRTzZwQuto0Qd7oLgD3ge8/ooEy7VJVbc1sa1rCo/tc9A1GhnbilRctoc+vQoy5ZUpDEa1o63x2bvh1xvkdP5pmdp2qlzb7FqHHp+YkS8jPijpijn/hnbSMda+bLyntrclJAzQlR2fe1xQpNqJmTgnw5Vua2OW8CWAK69llVVHxmHDhg3GrkUVDU5wlV+H4K9Xr17qirFlmXTAlVycuFdVFciK0q8tYL8qG1jgNZpWecMVdFwRR8BsbR4zT+PMtia2tSvhsX0OTj7x98fa3wFnXvlHVtjTL4xVNXdp6y4xXeTXE7/KkdwjDh93UVGahPjvlsy8QknO96my47Y2tbcpa+ehVPu41pl7LYBsl2kRDZ3ejdJaNkyHbmoYP4MTaX0iV30uIZShxzgKdNkpC7of6RPSmsIXj7UvH/2k3RVP3rFP+k9loDshupqhixreB2TgMD4GXZxc8XW7c1sT29rV8Ng2b4uqPAlFoGjrbw15Xlt3iu6kgrjtKdsrtJ9t69eRzUdPy/6krBp/ne7Q3qbcZT+pchjEORkqlWGsAoIv0+ImGA+i328LBtdjLIdl5UTAuAsUJkBRBHIMCg6geyraF19sKCCBTBzG6RAREVHVFTc5mXlSUnNTJSLAsblB29QrCeIwVxwRlcYgzslQOh3Vx+bNm6cKMOhdelD5DN0B9OqDqIyGH2TWgoKC1DIMxLc2ZQGWoyhBeVk4sq6sMYhERERUdUEc7EjeIf0a9XPo8W3qlVSoZBBHZB2DOCfDQFJ03UOVQoy9Qhn2ZcuWqYprKLOtj8FCUIcCJCgPj4pyevVBS5hTC1By257CJkREREQ1LdQvVJqFNpMjZ46o+eIcDeLa1iuZMiPhTJ6kZedL3SC/KtpTIvfEIK6KMj8ogY5y8ihhjbFYKDmNUtmuBv28WYqWiKh2Q3EtVytgRbVf5+jOxiDOUW3qn5v3cF9CpvRq7lh3TKLazj1qpboZTCWA+ZD0ks7bt28vFcDpcyPpWThb9PWqKguHzCDm4GIgR0RUO2G+SPwtYjVeqqkulehO6eiMVlEh/hIZXJJ9Y5dKotIYxHk4vZIlqjYykCMiql3wvY6pZsBa5WKiqtQpqpP6nZ6XLsczjjv8+NYcF0dkE/tWeDjMnxYTEyOJiYmyb98+daXWVSazRPcfV9mX2o5tzbaurTz52MZr1+eLwve86XyZRNWhbXhb8fXylYLiAtWlskloE4fHxW04lCp741mhksiSZ/5lIzOYCLxZs2ZqSgRXGTOBbhc4+XC0+wWxrV0Zj2u2d3XC9zm+1/H9ju95ourm6+0r7SPaq9uVGReH7pQ8HyAy5xpn7FTjcIXWla7SogsQqntiWgZOWsm2ri14XLO9iTwNJv3elrytYkHc2QqVp7MLJDkzX6LrlFT4JiJm4oiIiIioioub7EnZIwVFBQ49tk2MaYVKdqkkMsXulERERERUJTpHdVa/84vzZd/pfQ49NizIV+qHBqjbHBdHZI5BHBERERFVicZ1GquJv6EiXSr1CpX7E5mJIzLFII6IiIiIqoTBYDBONVCRIA4VKoGZOCJzDOKIiIiIqEqLm1S2QuX+hExWqCQywSCOiIiIiKqMnok7nH5YMvIzKpSJy8grlLj03CrZPyJ3xCCOiIiIiKq8QiXsSN7h0GNbxZSMiYO9rFBJZMQgjoiIiIiqTERAhDQMaVihIC7Y30caR5TMY7svnsVNiHQM4oiIiIioWrpUYuLvChc3YSaOyIhBHBERERFVKWOFyqTtDhcoaV3vXHETIirBII6IiIiIqqVCZUpuiiRkJ1QoE4e54oqKHQsAiWorBnFEREREVKXaR7QXb4O3ur0tybEulW3OBnG5BcVyPDW7SvaPyN0wiCMiIiKiKhXgEyBtwttUqLhJi+hg8fYyqNv7OC6OSGEQR0RERETVNtWAo5N+B/h6S9PIIHWbQRxRCQZxRERERFRtxU12puyUouKiClaoZHETImAQR0RERETVFsTlFObIwfSDDj22ZXTJpN9/HkiWzzYdk4Ki4irZRyJ3wSCOiIiIiKpc87DmEuwbbJxqwF75hcXy0854dTs1K18eWrpdxn2wiYEceTQGcURERERU5by9vOW8yPMcHhf39ZYTsj/RvBvlhkMp8vXmE07fRyJ3wSCOiIiIiKq1S6UjFSqP2ZhW4CinGyAPxiCOiIiIiKo1iNuftl+yC+yb861JREllSktNbSwn8gQM4oiIiIioWqcZKNaKZXfqbrsec223RtKneYTZsj4tIuXa7o2qZB+J3AGDOCIiIiKqFvWC60lMUIxDXSr9fLzko0m9ZXjnWPX/Ov4+8tHEXuLrzdNY8lw8+omIiIio2rtUbkvaZvdjELCN6dNU3c7IK5QzOQVVtn9E7oBBHBERERG5dHETaF8/1Hh7d1yG0/eLyJ0wiCMiIiKiag/iTmWdkuScZLsfFxbkKw3rBqrbu+POVNn+EbkDBnFEREREVG06RHYQgxgqlo2LraN+M4gjT8cgjoiIiIiqTYhfiLSs29LhSb+hfWxJl8pdzMSRh2MQR0REREQ1MtXA9qSKBXEHEjMlr7CoSvaNyB0wiCMiIiKimilukrJDzRnnaBBXWKypQI7IUzGIIyIiIqIaCeIy8jNkY9xGySvKs+txTSOCJMjPW91mhUryZAziiIiIiKhatQpvJf7e/ur2lJ+nyAfbP7DrcV5eBmlbn8VNiBjEEREREVG18vXylTbhbYz/X7xzsZzJP+NQl0pWqCRPxiCOiIiIiKqdPs0AZBVmyZJdSxwO4jRNq7L9I3JlDOKIiIiIqFql56XL3tN7zZbZm43rcHauuNPZBZJwxr6xdES1DYM4IiIiIqpW6+PWlypmgmzc+lPry31s2/olmThgl0ryVAziiIiIiKhaDW4yWCZ1miTh/uHq/+0j2svkTpPlkiaXlPvYEH8faRoZpG5z0m/yVAziiIiIiKha+Xj5yF3d7pIrWlxhXHZntzvVcnu0P5uNYyaOPBWDOCIiIiKqET3r91S/96TuUePk7MUKleTpGMQRERERUY3oXq+7qlKpiSZbErbY/bj2Z4ubHE7OktyCoircQyLXxCCOiIiIiGpEmH+YtItop27/nfC3w5m4Yk1kb3xGle0fkatiEEdERERENaZH/R7q99/x9gdxjcIDpU5Ayfg5josjT8QgjoiIiIhqTM96JePi9qbutXtcnMFgYHET8mgM4oiIiIioxnSvf25c3OaEzQ6Pi9sdx+6U5HkYxBERERFRjQn1Cz03Ls6BLpXGCpXxZ0TTtCrbPyJXxCCOiIiIiFxiqoGKBHEZuYVy4nROle0bkStiEEdERERELhHE7Tu9z+5xcW3r1xEvQ8ltFjchT8MgjoiIiIhqVLd63cTL4KXGxf2T8I9djwnw9ZbmUcHqNsfFkadhEEdERERE7j0uLu5Mle0bkStiEEdERERELjPVQEWLmxB5EgZxRERERFTjesX2Mo6LS8tNs+sxHc4GcUdTsiUzr7BK94/IlTCIIyIiIqIad37M+WpcHNg7Lk7PxMFeN8/G5RcWy6ebjskLP+6RzzYdk4Ki4preJXJhPjW9A0REREREdfzqSPuI9rIzZafqUjm46eByG6VeqL+EB/nK6ewC2RWXId2bRrhtAHfzBxtlw+FU47Jl/56Sjyb2El9v5lyoNB4VRERERORa88Ul2DcuzmAw1IriJp9uOmoWwMGGQyny9eYTNbZP5NoYxBERERGRSwVx+0/vl9O5px0aF7frlPsEcZqmyf6EDHn/t0Mydv5GefK7XVbXO5qaXe37Ru6B3SmJiIiIyCV0iymZL65YK1bj4i5temm5j9EzcXvjM6SoWBNvfQZwF+km+fWWE3IsNVvq1fGX6DoB8seBJFm3N0lOpeeW+/imEUHVsp/kfhjEEREREZFLCPELkQ4RHWRHyg41Ls6RIC6noEiOpmRJi+gQcZUAbsz8DfL3EdsZxbpBvnJx62i5qFWkfP73cdly7FxVzj4tIuXa7o2qaW/J3TCIIyIiIiKX0TO2pzGIs0ermBDx9TZIQZEmu+MyXCaIQ6VJawFc4/BAuaZbIxnQNlo6N6przBxi2fSPt8iqXQkSFeLHoiZUJo6JIyIiIiKXm/T7QNoBSc01L/ZhjZ+Pl7Q8G7i5SnETzFn3zrqDVu+7sksDuefSNnJ+k3Czrp+oQjm6V2N1OyUrX2XyiGxhEEdERERELjVfnLfBW93+J/4fh4qbuEIQdya3QE0XEGdjzFtZ49z0rqGaJrInPqPK9pHcH4M4IiIiInKtcXGRHdRte7tUuso0A2nZ+arapD62rWmkecBW3ji3+qEBapycK7wWcm0cE0dERERELjfVwPbk7Q4Hcaj4iECqbpCfVLeUzDwZ+8EmY/D1zFUd5YaejdVcb5gqABk4BHBlTd6t5r2rHyrrD6UwiKMyMRNHRERERC45X9zB9IOSkpNS7vrtY+sYb6O4SXVLzMiV0e9tUIGXwSDy4nWdZWyfpmfHuTWRBy9rp36XFcC5WlaRXBuDuCpQUFAgjz/+uDRp0kQCAgKkc+fO8sknn5T7uD179sjDDz8s3bp1k7CwMAkJCZGLLrpIli1bVhW7SUREROT64+ISyh8XFxniLzF1/Gsk+IlPz5XR726Q/YmZgjolr43qKqN6lBQoqYgODUqCOIyJKy7WnLinVJswiKsCU6ZMkdmzZ8tVV10lc+fOlcaNG8uYMWNk8eLFZT5u/vz5Mm/ePOnSpYs8//zz8swzz0hOTo5cffXV6j4iIiIiTxDsGyznRZ7n8uPiTpzOllHvrpdDyVni42WQuTd2k6vOb1ipbepZxez8ItUNk8gaBnFOtnXrVlm0aJHMmjVL5syZI5MnT5YVK1bIgAED5P7775f8/Hybjx09erScOHFCFi5cKNOmTZO7775b1q9fL506dVIZuuJilpolIiIiz+pS6XAQF189QRwmFr/h3Q1yLDVbzVM3b0w3uaJzbKW3i3nvEBACu1SSLQzinOyLL74QLy8vmT59utkg1RkzZkhiYqL88ssvNh/bo0cPqVPnXJ9u8PPzk+HDh0tycrJ6PBEREZEnBXGH0g9Jck6y3RmsfQmZUlhUNRe+MXcbJvF+eOk2uXLuH3IyLUfNU/fezT1kyHn1nfIc/j7eKpADBnFkC6tTOtnmzZulZcuWEhERYba8d+/e6veWLVtk6NChDm3z1KlT4uPjo8bJlefMmTPqRxcXF6d+FxUVqR93gX1F5tGd9tldsa3Z1rUVj222dW3kScd158jO4mPwkUKtUDbFbZKhTcs+f2pbL8QYaO1POCNt6plfGK9se2O7Exb9LRsPnzbejyIm74w9X/q1inTqe9Kufh01Jm7XqXSHt+sJxwYxiHM6BFyxsaVT6Q0aNDDe74iDBw/KZ599JiNGjJDAwMBy13/11VflySefLLU8JSVF/P1LBvy6A3xhpqenq9vIbBLbujbgcc32rq14bLOtq0qbsDayK22X/H7kd+kW1K3MdUM0Tfy8DZJfpMmmfack3Mv8gnplj+1vd6aYBXD6pNz7TyRLh7riVI3rlJz77DyZLklJSQ49Fud8VPsxE+dkKERiLVhCIOLr66vut1d2draMGjVKbQ/BmT1mzpwpkyZNMsvE9erVSyIjIyU6OlrchX4VKSoqSry9S6pTEdva3fG4ZnvXVjy22dZVpU/DPiqI25m+067zGGTfdpw6IyezxCnnPabHdlpByQTelk7nezn9HKtnK4PI7yckPiNffIPDHJr3Li8vz6n7Qq6JQZyTYUoBax8eXMnB1AO43x5Y9/rrr5edO3fKypUrpWnTpnY9LjQ0VP1YQiDkbsEQAl933G93xLZmW9dWPLbZ1rWRJx3XvWN7y4KdC+TwmcNyOv+0RAVGlVueH0HcnoRMp7WP3t5No0q6a1pqFhVS5nPlFeVJYnaixATFiL+3fb2izmt4LrW3LzFb+rYsvzeWzhOOC2IQ53ToNnn06NFSy/VulHq3yrIg4Lv55pvlp59+ks8//1wuueQSHqtERETkcbrGdDWOi0OVysubX15j0wz0a1M6gOzTIlKu7d7I6vpaYaHEv/GarN7/o8RnxUnT0KbSIbaLxNx5l0TVqSdeBq8yAz7Me5eYkadeS9+WkU5/PeTemIlzMkzUvXbtWklNTTUrbrJx40bj/eWZOnWqGgf3/vvvy7XXXuvsXSQiIiJyC0G+QdIxqqP8m/SvQ0FcUkaeJGfmSVSI8+oBLP/nuIzf9b0EFhdK+wahEh7oJ20yIsVH62G14HvGzz9L2vsLpLtxyRExyBF5JPM72Xyev8QGx0psSKw0CG5g/L0pfpN8e/BbmdZlmrSP7SGJGUmsUElWMYhzMoxhe/HFF9Wk3Y8++qhapmmavPnmm6q/9MCBA9UyTBmAnyZNmkhQUJDx8ffdd58K3l544QWzsW1EREREnjrVgB7Elad9/XNDSpDBuri1c8aqFRVrsu+rb+W2fWtFQ1XKgwZV1QRlTgJbtpTAHt2l6PRpMfj4SED79uoxhp5dpcBbxKdIBLO+4XHZfiLbmhukoLhAjmUcUz/WLNq5SIbX7y3r9lXfvHfkXhjEOVn37t1l3Lhx8sQTT6hqQpioe9myZfLrr7/KggULjEVPENShiiTmjcNE4IDJwV955RU5//zzVbfLJUuWmG376quvluDgYGfvMhEREZHL6lG/h7y//X05cuaIJGUnSXSQ7cAsLMhXGtYNVPO3OTOI+31/sqwOaiY3e/tJQFF+SVnKs0499JDxdlDfPtJ04UJ1+5OT30rDaJGW8SX3IZD7ubefvDH8XUnPT5dTmafUT1xWnJzKOiVH0o+o4A5yCnMk2Wu1iHSUffGZUlBULL7erNZN5zCIqwLz589XhUgWLVok77zzjrRp00Y++ugjGTt2bJmPwxxysHXrVhUIWjp8+DCDOCIiIvIoXaO7io+XjxQWl4yLG9ZiWLmTfpcEcRlO24dPNx2XTL8gyQ4OlcAzticeLzpdUsEyvyhfFu9cLL26GKR5giZeZ2O+lkfyJSP5lAztfI3Z49Lz0mXIV0OMQRysT14q4tVS8osC5VBSlrStX/l576j2YEhfBfz8/OTpp5+W48ePq0qV27dvLxXAzZo1S3Wz1LNwgKAPy2z9NGvWrCp2l4iIiMilx8V1iuqkbv9y/BdV/KM6i5skZOTL2r2J6nb2qHMX2Q1+fhL77LPSZNEiaf7tcmn9+2/S/Msv1H0rDq2QMwVnZHU3b9kxrK3xMR2PizS/923J27/f7DnWx62X7MJss2U5RdkSEHrQqa+Fag8GcURERETk0nrUQ/EQkR+P/CgfbP/AriDuQGKm5BWWzPNWUfmHD8vKzSekWBMJDfCRi++cKHVHXa/ui5w8Wepec7UE9+ktAW3aiE90tBh8faWouEgW7ijpUnlJk0vk+mc/l5Y/r5J6jz0q4usrBcdPyJEbRkvGmjXG5xncZLBM6jRJrml1LkM3rPkwaR3SR91mEEeWGMQRERERkUvrENnBrOjHmXzbmalWMSXzuRUWa/LW2gNqPJmjtOJiSVm0SI5ee53U+XiBWoapBAL9vKXeI4+ooCxyymSrj0W2EOP34NaOt4qXv7/4NW4sEWPGSNNFC8U7IkKKs7Ml/dvvjI9Bd9G7ut0lT174pDQLLel5heqVHeqHq9u7mIkjCwziiIiIiMil7UzZabyNoh9LdpkXf9PlFxbLY8t2GP8/Z+0BGffBJocCufxjx+TozTdL4vMviJaXJ92ObZOQ/GwZ07uJul8PyvDbEoa/6JlCVNXsHN3Z7P6g7t2l+VdfSuiI4dLg2dlWn79PbB9jF0tMXg7MxJElFjYhIiIiokrBxNZJc+aqoEdn8PeX6DvvUGX3KwNFPz7Z/YnZMhQNGdthrIT6nZtSAL7eckI2Hk41W7bhUIp8vfmEjO7VpMz91kSTvD17JXvzZpHCQnX/zna95ckWw+S8tg2lVUz5hUVQeGVHSkkQObHjRKvr+DZoIA1ffNFsWc6//6rumL4NG0rfBn3ls72fye6U3XJbm5KKKMmZ+ZKYkSsxdQLK3QfyDAziiIiIiKhSMLF1ynvvlfzHUDKHGgR0aC+hl11WqW1bK/qRVZgl60+tl6HNhpotP5Zqvp7uqI3lZvttwis4WHwefkzu+6fkVPlGKwGgNR/sKMnCtYtoJxc0uMCux+SfOCnHpk4TLTdXQgb0l9bRETJ+ryb53kWS3mu7iHir9VBtk0Ec6RjEEREREVGlBF9wgRiCgkTLzj43h5qvr/jUr1/pltWLfuQV5smyg8skIz9D2oa3VUVDLDWJCLK6jbBA3/L3W+ftLc2+/kpe3Z0jIockPNBHhnSoV+5+7krZJX+d+ss4Fs6AYNYOBSeOi5aTo7KBGT/+pJZhEgU8+s9Vy6VR+Bg5cbpk3rv+bZwz7x25P46JIyIiIqIKwziwrPXrJWL8ePM7Cgrk6Ogb5fC110nq4o+kMCWlQtvXi3480OsBueW8W9SyE5knzOZU013brZH0aR5Ravma3QlShBKTFrzDwiRywgSzZZFTbxNp1Fi+/OeE+v+V50WKv0/5p8x6RcpGIY3k0qaX2v36gvv0kSYfLirJYJrI9hNZEXFM2p2dH27XKU4zQOcwiCMiIiKiCkGVxVP33isn775HZZKQ1VJ8fcUrsiSYyt25UxKefVb29+svx2+bKmd++EGKMjMl8dXXJOG5540/+D/GqJVleMvh4mXwkqyCLFl9dHWp+/18vOSjSb3l+Ws6ybQBLWV0z8Zq+d9HTsu7v5XMuVacny/J77wjhcklk3ZHjL/ZuN/oRhk5frz8uCNeUrPy1bKrOpWf/Tp25pisOrpK3b6l4y0q8HREUNeuEnFrSYAKCOdW9jLIweJ4aRSTpZaxuAmZYndKIiIiInJY/vHjcmL6DMnbt0/9v+DECZWNS3n7bYmaMkWipk1VGbr05d9KxurVasxX5rp16iewe3fJQQERB8fQ1Q+uL31j+8qfp/6U5QeXq6DOkq+3l1kRk7zCYvlm60l5ddU+uTj3pATNfUnyDx6UvH37peGrrxizccnz5knEhAniHRoqH28sqYZ5cesoaRhWugqlJUx7UKwVS2RApIxsNdLxxhRRbXb6oyWi5ZcEj5s7ByMElmJ/tG9DOZScJbkFRRLgWzJGjjwbgzgiIiIickjm73/Iyfvuk+L0dBWERd91l5o3TSsoUBNg+8TEqKqUIRdfrH6QectY9bOkf/utZG/cKOE3jpbc3bvNxtAhC4YxauW5qtVVKojbFLdJTmWekgYhDcpc/8mR58mOfSdlyF9fic/S9VISIol4hYWKVlQkBm9vibxtioRdfZXa7/0JGbLpbIXLm3qVZPLKkpyTLMsPLFe3UTHT37v8oM8aBJPh42+W1Pfnl7zO4/Xk1bCjEp+/TQVx6A66PyFTOjUKq9D2qXZhEEdEREREdo9/S5k/X5Jee12kuFi86tSRhi+/JCH9+xunFcAcapa8Q0JUcIefgrg48YmMlPxDh1X2S+cVEqK6Xgb37VvmPgxsMlDq+NVRBU6QjZvWZVqZUx3g+V7/Z7N4pZaMyUuLbihd3nhRgrp1O/fcJvv98cYD6ne9UH8Z1DZaTp99nC2Ysy6/OF+CfYNlVNtRUhnRM2ZIwak4yVi5UtqklUwnsD11s4T4D5PMPE11qWQQR8AgjoiIiIjsEve/RyT9m2/Ubb9WLaXxm2+KX7NmDrWeb2yscSxayqJFxsqQhQkJcuyWWyWod2+V2Qvqdr7VxyPTNaz5MPl87+cqA3Zb59vUOLmypjrAvcXePvJx60HyZetBMsc3Vi63su2c/CI11xyM7tlEfLzLLh+BQBL7AQjgLOetcxSCyZh77pao26aIfz0fkWUjJbMgU5o2SJGdhyNkVxyLm1AJFjYhIiIiolKQ1bIsPlKE7pMiUmfIEGn22ecOB3C2KkOGXnmF+Hdor26ju+XRm26SY7fdJjk7S8amWbq61dXq98nMk7I5YbMxS+jfocO54ir6VAcYa9epkzRb+rXsGTJKCrx95KGl2yUuHVMImPtu2ynJyC0UL4PIaDu6Un6570sVZPl6+cq49uPEGfwaNZKANm2keWhzqRdUMrVBcN1D6jeDONIxE0dEREREpdjKakXdPk2i7rjD7nnQymI6Fs3g56fGzSXNnSP5Bw5K1rrf1E/4LRPEy9fPrItktL+fnNeguWj7Dsr+N56X+qnRkvPvv+LbqJGxSIkuuH8/afz222Lw8pI3ohvJ5W/8Luk5BXLvF//Jkom9xQsR21kfbzymfg9qV09iwwKlqKjI5r7nFeXJR7s+UrdHtBwh0UHOncMN7du3QV9Ztv8byTDsFpEeqjslglVntD25NwZxRERERGR9Imx//5LgyaT4CCo4OiuIMB2LBqFDh0idwZfImZUrJenNt6Tg2DGRYu1cMGnicYOIQe3WLsk8uwwFVBq99aaxmyb2t+FLL6kADhpHBMnTV50n93z+n/x1MEXe//2Q3Na/pbpvx8l0+e94mro9ps+56pa2fHvwW1XUxCAGNa2As2Vt2CAj398tvj7FsrTfHhGvPMnIFTmZliONwq1Pak6eg90piYiIiMjKWaKXiI/59X69BH9VQrXIsBEjpOXKFWoKgKipt53rImm63tnekolhIun9Oku9Rx6RZp9/Lj7R0cZumtb296quDWVEl5KKli+v2quCN9MsXKPwQOnXuuysWlFxkSzasUjdHtx0sDQNbSrOlvnLrxK8YacM3qqJFBaKb3DJPHe74zKc/lzkfhjEEREREVEpiS++KFpW1rmTRmThxt9cbS1l8PWV0GHDxCc83BiU6QK6dJGGb7whHz57scy43UfmXe0vEePGSmDH84xTBrT8eZWa9qDUdg0GefqqjtKwbqAUFGly52dbJTEjV5b/e1Ldf2OvJuJt0sXSmtXHVsuxjJKgb2LHiVIV6t5wg/odniXSY78m4ZFH1f93nWJxE2IQR0REREQWMv/8U9K+/ErdDuzVs9qycLYgeNSzcQgmm7z/nup6OaTHaLVsa+JWOZJ+pFQ3Tfy2JizQV14f3VUVMDmUlCWXvfa7ZOcXqf9fc37DMvcltzBX3v73bXW7T2wfOS/qPKkK/i2aS1CfPur2pVs1MQSWTKqOcXFEzMQRERERkVFRZpbEPfaYuu3fvr00njfPZlaruphWsjQNJi9udLFEBESo25gzzhE9m0XItLPj4VKzS6YAL9ZE7vniPykoKrb5uCfXPykH00u6Nt7a8VapSuGjS7JxnY9oEpZySgw+6bI7nkEcMRNHRERERCYSX3lZCk/FqfFwDZ6drSbqLiurVV2sdZFEaf8rW1xpLDSCsWqOiK0bWGrZhkMp8vXmkrniTKXlpslbW9+SFYdWqP97iZd0iOwgVanOoEHiHRmpbg/+t1i8g/fL0ZRsycwrrNLnJdfHTBwRERERGSsipn36mbodNWWKBLQvmbvNFdjqIjmy1Uj1OzE7UdbHrXdom6j0aM3R1JIJyOHgmYMq+zb4q8HyzrZ3jMuLpVg+3v2xVCVMu1D3+uvU7QHbNAny36tu72U2zuMxiCMiIiIiJe/AQZWB82/TRlWFdAdtwtsYM2LLDixz6LFNIqyX6m8c7i+rjqySiasmytT1U+Wbg9+oeeEsLd65WM7kV233xvDrrxfNIFInV6RXAoK4YhY3Ic4TR0REREQlIsaOkaDu3dT0AsgCuYurWl0lu1J2ydpjayU9L13C/MPsety13RrJ8q0nZcORBDH4nBHRfKRF8z2y8NjrEr8n3rhebHCsdIvpJisPr1T/x9xwmmiSVZgl60+tl6HNhlbZa/Nt2FAM08bJE+mfyO7GeeJ1OF52xTWrsucj98DJvomIiIjIyJW6UNprWPNh8tLfL0lBcYF8f/h7ubHdjXY9zs/HSz6a1FsmrnhQtqb9KAbxkkQpFjnbm7JX/V4yrP4wGd5huHh5e0lsSKxZRi7AO0AuaXKJVLW2dzwkCV/8JJKbqsbF7Y6r2rF45PoYxBERERF5sOKcHDnz/fcSdvXVYsAE324ImbdBTQbJT0d+Ul0q7Q3i0BXypU0vqQAONClWgdnwlsPVNlqEtpCkpCTx9vJWP3d1u0tqgpfBS3rX7y0/HPlBfIIPyN74DCkq1sqdz45qL/f8pBIRERGRUyS9MUfiHnlUjk24RbRC9616iC6VgG6V+06XzKlmi6ZpaszbyGUjZdlB83F0CN4e7/u4tA5vLa6kb4O+0iJOk6m/7xPJSZejKecmYifPwyCOiIiIyENlb9kqqR9+qG4Hdu4kBh/37aTVN7avxATFqNvLD9ieMy4+K17uXHun3LvuXknOSS51/xd7v6jyYiUV0TuwvTyzuEiG/FskgxLWyC5O+u3RGMQREREReaDi3FyJe+QRpKXEr3lzibrjDnFn6O44ouUIdRtzuWF8nCnMIbdk1xIZsWyE/HriV7WsRVgL4/0oVgJ6sRJX06BRO9l5Xoi6PezgVtl9Kr2md4lqkPtebiEiIiKiCkuaO1fyDx8WMRgk9tnZNT6ZtzOMbDlS5m+fL6m5qfL7id/VODnYk7pHZv01S3am7FT/D/ULlft63KcmCp/337waKVZSEWeG9RHZvlpapGbKX1v+E7nM/YrQkHMwiCMiIiLyABjvljRnrmh5eVKYnCRnVn6vloePGydB558vtUGzsGZyfsz5sjVxq3y651N1e+GOhbJ412Ip0orUOpc3v1we6PmARAVGqf/XVLGSimg96Go59f5qaZAq0uyv70RkdE3vEtUQBnFEREREHiDj558l5b33Si0P7Hie1CYocIIgbkPcBrli6RWSUZChljcMaSiP9nlULmp4kbirnrG9ZPb53nLzmiLpefg/STmVJJENomt6t6gGcEwcERERkQcIvuACMQQFmS0zBARIyIABUpugiqMOAZyXeMmE8ybI0hFL3TqAgxC/EEno31HyvUX8i4rkyKdf1vQuUQ1hJl85WCMAACLRSURBVI6IiIjIA3iHhUnkhAmSPG+ecVnkxIniHRoqtcnS/UvN/n9N62vk3h73Sm3Rs01/Wd/uP+m/U8T344USn39GlWQx+PtL9J3uXZyG7MdMHBEREZGHiBh/szEb5xUcrP5fm6TnpcvinYvNlv1w+AeXnDKgMpnGUxEllTR9sjPl9OLFapoIdJXNWL26pnePqgmDOCIiIqJarjgnR5LfflsMgYEqGwcREybUuizc+rj1kl2Y7RZTBlRUx6iOsrZHsOT4nl2gacagHF1myTOwOyURERFRLaZpmsTPmiXpy7+VzF/XSeMP5kvY1VeJT0zJxNi1yeAmg2VSp0luM2VARfh4+UiDmPNlRa8/5Po/SwI4s6D8TO3JOpJtDOKIiIiIarG0zz5TARwEX3yxeIeEqJ/aCAGOO00ZUFEXNbpAPun5l1y5qUgCC0SKAgKlztixNb1bVI3YnZKIiIiolsr591+Jf/Y5dTu4fz+Jun1aTe8SOcGQ5v0kK9AgK3qVdBn9on0DmfDlHikoKmb7eghm4oiIiIhqocKUFDlx9z0iBQXi26iRNHzhBTF48fp9bbDloLcUF4TKsgvSZV0nkdSgODl95IR8vbmBXNSA77En4LtMREREVMtohYVycua9Uhgfr0rPN5rzhnjXrVvTu0VOcvx0jmhFwVLgY5DEcIMU+ueLX8QfcjS1pKgL1X4M4oiIiIhqmaQ35kj2xo3qdv0nnpCADh1qepfIiaLDisTLL0kvTKl+I4irF8bulJ6CQRwRERFRLRPUq5ea3LvuqFFS95qra3p3yMliYo6JwatQ3dYDOYN3nsTUO8a29hAcE0dERERUy4RcfJE0/2apeEdG1vSuUBW4rPmlsu/0RNkZlyJncgskNMBXOjaIkiHNBkv8qXi2uQdgEEdERERUC2j5+SK+vmIwlFQs9G3QoKZ3iapwKoWZPe5m+3owBnFEREREblzAJGnOXCnOy5Os335Ty0IGDJCYe2eKwYeneUS1FT/dRERERG4q4+efJeW998yWpR4+LIFdOkvoZZfV2H4RUdViYRMiIiIiNxV8wQVi8PMzW+YVHKyWE1HtxSCOiIiIyE2lffV1yVg4ExETJoh3aGiN7RMRVT12pyQiIiJyM1pxsSS89LKkLlpUssDLS6S4WGXhIsbfXNO7R0RVjJk4IiIiIjeT+PTTxgAuuN/FEjlxorrNLByRZ2AmjoiIiMjNhAwZIunfLJOwYcMk9pmnVWau7qjrxScmpqZ3jYiqAYM4IiIiIjcT3LevNP/8M/Fv317NC4eZ4fwaN67p3SKiasLulEREREQuLv/YMUl87XXRNM24LKBDB+PE3kTkWZiJIyIiInJhubt2ybHJU6QoJUW04iKRMWNqepeIqIYxiCMiIiJyIVphoSTNmStaXp4UxMVJxtq1IoWFYggMlMBevSS7pneQiGocgzgiIiIiF5Lx88+S8t57pZZHTZumJvHOTkqqkf0iItfBMXFERERELiSwWzcRH/Pr7MjChY++ocb2iYhcC4M4IiIiIheBwiUn77xLdZ80FXnrreIdGlpj+0VEroVBHBEREZGLQLXJ8DE34YYxG+cVHCwR42+u6V0jIhfCII6IiIiohmCS7tNffilFmZnGZaHDh0uLlSskasoU9f+ICROYhSMiMyxsQkRERFQDcvfulfjHn5Cc//6TvL37pP6jjxizcf4tWkjkbVMk7OqrxCcmhu8PEZlhEEdERERUTVMG6P/P2bFdcrfvECkuVssKU5JVVs7gda6TlJe/v/g1bsz3hohKYRBHREREVANTBqgTsQaxUv+xx6TOwIF8D4jIbgziiIiIiKoQ5nbDFAFaTo7Z8rpjx0i9mTPFKyiI7U9EDmFhEyIiIiInK87KksKzk3J7h4VJxC0TzO6ve8MNEvvoowzgiKhCGMRVgYKCAnn88celSZMmEhAQIJ07d5ZPPvnE7vlh3njjDWnTpo34+/ur33PmzFHLiYiIyDVgXFviq69JwnPPn/t58SVJ//4HOXH3PbLvwovUODhd5PjxYvDzM04ZEHPvzBrceyJyd+xOWQWmTJkiixcvlunTp0unTp1k2bJlMmbMGCksLJSbby57npennnpKZs2aJePGjZMHHnhA1q1bJ3fddZekpaWpwJCIiIhce5ybcZ01a6T+rCfE4O2tsnGRkyZJ8rx5nDKAiCrNoDHF41Rbt26Vbt26qWDsscceU8vQxIMGDZJdu3bJ8ePHxe/slThL8fHx0qxZMxXwffDBB8blEyZMkM8++0yOHDki9evXd2h/Tpw4IY0bN1bP26hRI3EXRUVFkpSUJNHR0eLt7S2eVrkMDP7+En3nHWI4O9lrRdcvb13Ltnbmtl3lNbrKtosNhmppa3dtP2fvh+mx7aVpLvEa3an9HFm3Nn+PRN0xQ4rT0qTg1CkpSkuTkP791X1F6emy/+J+ouXnl3rewO7dJfTyyyV06BDxiY42Li/Oy5PCxEQ1ZQAqT1aEp/19rGnu2N7ueu5HjmEmzsm++OIL8fLyUlk4HeZ7mTFjhlx33XXyyy+/yNChQ60+dvny5ZKXlyd33HGH2XL8/8MPP1T333bbbc7eZXK1K7oGAyJ/dTOgQ3sJveyySq3vKtt2lf2oyW0HX3qpS+yHq27bVfbDXbftya/Rv01rqXPJJWqZV0CAcf30b7+1ur54eUlg507qd50BA0pv20TKggUihYUlmwgMlLZbNqu/68isBffrJ5mrVxvXDbrwQmkw+xnxtXHBlVMGEJGzMBPnZEOGDFEZs3379lm9KvLss8/Kww8/XGY3zOzsbBUI6oqLiyUwMFBl5N59990yn//MmTPqRxcXFye9evVS++ROV2Nw5Ss5OVmioqLc5spXZeCK7qHBl4qWnX1uocEgPrGxZnMGgW+zZhL74gt2ry/eXlKYlGxzXWSKi4qLxdvLS52YYJ6iotOnzauo2dg21i2Mizt3YoRVg4LEu25d+9YNDpYWq3+WE7fcKsWZmXavH//oo5K/d59d63qHhkri8y9I1i+/2L3+6SVLJO2jJTbXRXu0/OtPtS5krv1Fkl54ocz1myxdKr4tW6jjuk5qqiTcfY/NddHW9Z97VoJ69DAWSDhy1dU210VbR94+TUJHjjTedeTa6yR/716b64eOHCGRt99uPP4OXtwPHzyzdb3r1TO+j9gX7JNx/X79jSe2xvVjYlS3MfBt0EAaf7jI5rHtHR0tBi+cVJf0VsB+Nl32jRy+dIj5ujjxDQ8v2S5Ows8+Hsdq7Jw5cuKWW2xsu2S/9WMbbV2cnm6WZbFc15RaPy3NPMvi5SXeUVFWj+0iFLCwOJ6C+/aR3B07rW7b2vqhV18tWT//bDYGWisqkuKUFLPH68dqxg8/SMo775rvM9a1+Dy2WLNaHatZf/4p8Y88Wnp9020HBkqLtWvU+vlHj8rxCbfY3Dbar/7LL6lAS2UrEhIk66YxNrcNkTNnSuTEW43/P4rj9ORJ0TIySq0rAQESdtVVUu+xc/t8fOIkydmwQWwJ7NlDGi9aZPx//KOPyZlvvrG5vk/9+qp9jMd1/wEY2G5zfYOvr7RY96sK4CB37z45Pnq0Ok5Mv0Oqkqf9faxp7tjeOOdEzy5m4mo3ZuKc7NSpUxIbG1tqeYMGDYz3l/XYevXqmQVwgP9jeVmP1b366qvy5JNPllqekpKiCqW4CwSu6enp6rZle9RW/tdfJ7kfLj63QNOk0Mp7XuznJ6n5+XavL35+EnDj6HLXNTkdF//rrpO8r74qf9tW+A0fLnmff27Xunie1Lw82ydxNtbPRbemEyfsWleSkiQ7Lk4KHFg/Jz6+7PU1zbgu5CcmlLt+Wk62GJKT1XGNinU21z/b1mk4IT67fQ1V7qy1v8n7kh6fIHln14eChATzE26L9TPj4qTYZH1DRIRoJv/HukXx8cb/ZsfHqy5FOq9mTaX4wEHz9fGcZ2H6Yn19a8dqUWJiqZeDNi21LrZ1+rTVpkrPy7Vr2/qx7TtokBSsXVvufuh8enSXwn82n1vg71/m+pbHU+62bVJo0oblrZ+TmiKFJm1Y7ucgManc/fG75hrjsZqflFQSPJbB99JLjetj3TK3r2mSkZWljjt8Z5/Bd3Y528/OzDQ77goQKNv67OfmSs6ZdLPjrsD0woEVBbl5ZuubhOyleXlJMbrZmqzve8EFUrBunfH/Pt27if8VV4hXvfriVb+eGMLD1fev8XVGhIv/2e9X0++QquSJfx9rkju2N875qPZjJs7JWrZsqX5WrVpV6j6MhUNhk/nz51t97CWXXKKumlhm8QBVKlHtcrVJtw1rmIlzX6YZC1Qwi5gx3axbkM47rK6EXnmF3esbvLylzrDLz60bGCgR06Ya18UV88zMLAkJCTZmGEIGXSJHRowoWT8oSCKm3mZ121CckyOp77yrMne4Et1s5QrJtHL86+umvPmWutJtetU67YsvRSvIt3v9jB9/lEKLP1Jm6/r7S4tffzFeEUcGIv/IEbvXz9m2XXK3b7O+rmrrGWbZBGwbz2F7/elSd9QokaAgdUW3rsEg2T//bHNdtHXIwIEqm6Xeo/wCSfvyC5vr6pky/7ZtjfuQ/tVXUpiWZnN9/zZtJKhnz3Prf7NMEnAByOQ1Yl1kvAAZvJCBJd3OIHPNWom7776SDISfn0TeeafZGB+vkGAJHTFC3VbH6qBLRMvNLVn3jjvEKzBQBJvG9tVzGCTs+uuk+MwZs+M66r57S/ZZ00pi0pJ/1HbroKudppmtH3nXncbXWFysSWZmpoSEhIiXl0ECunaV4+NuLlk3OEhlIm0d1+DXqpWcnHb72fWDJXrmPeeygdaO1bPjqfRjNeefzVKYmGB7/blvmq2ft2+f5B86dHaNc89TnJsrKa+9Virbk7dnjzpWS2339deN70vztWvEJzxc3YcxXVl//lV6fX3bfn7S9Ntvxa9xI+P7lvHTKpvbjrznHqkzaKD4NmpUkolLTBT/3343fo9Ybjvq3pnqmDM9TtGFEe2L15j0/Asl62LM2iP/E++gILXtgE6djOvnbNki+SdOSOKsJ0vazt9f6j/3nHjVCVHfdV5hoRLQvr1x/YKzF2RO3ja15PgLDFQZX4xJQ1ZNP751Zt+rdmbWnDHOrbZnhtyZO7Y3M3EeAoVNyHk6dOig9evXr9TyoqIideYxffp0m48dNmyY1qRJE6v3NW7cWLviiisc3p/jx4+r58Vvd1JYWKjFxcWp354k8Y052q627bTEOXOdvr6tdW21tTO2Xdl1a9u2q7ut3XXbztoPa+3tKq+xKrfN75Hqbb/q5ql/H2uKO7a3u577kWOYiXOySy+9VI4ePWpzTNzs2bPlf//7n9XHTp48WT766CObY+LGjx8v75VTzri2VChyx2pQzuDoFV1H1re1rq22dsa2XeU1usq2q7ut3XXbztoPa+3tKq+xKrfN75Hqbb/q5ql/H2uKO7a3u577kWPco3OvG8H0AgcPHpTU1FSz5Rs3bjTeX9ZjUZ1y27Zz3bj0aQvy8/PLfCzVDnrlMntPHBxZ31W27Sr74a7bdpX9qMptu8p+uOu2XWU/qnLbrrIfREQ1hUGck40aNUplzubNm2dchkpjb775prqKM3DgQLUM/av37Nmjsm66kSNHqnFzWNfU3Llz1XLcT0REREREno3VKZ2se/fuMm7cOHniiSdU+r1Tp06ybNky+fXXX2XBggXGCpEI1FBFEvPGDTg7Rw0qWD744IPy9NNPS0FBgfTr10/WrVunulg+/vjjVqteEhERERGRZ2EQVwVQfbJp06ayaNEieeedd1RlSQRiY8eOLfexCOzCw8Plrbfeks8++0z1aca0AXfffXeF9qXwbDlmzBfnbn3QUSIX3UvdpQ+6u2Jbs61rKx7bbOvaiMc127s8+jmffg5ItRMLm9Ryf//9t5rsm4iIiIg8x6ZNm6SnyVQyVLswiKvlcnNzZfv27Wo8no+P+yRecRUJwSe+gNiNlG1dW/C4ZnvXVjy22da1lTse28jA6UN6AsqYC5Pcm/uc1VOF4MPrzldh8IXJ8rhs69qGxzXbu7bisc22rq3c7dhu1qxZTe8CVTFWpyQiIiIiInIjDOKIiIiIiIjcCIM4ckmhoaFqmgb8JrZ1bcHjmu1dW/HYZlvXVjy2yVWxsAkREREREZEbYSaOiIiIiIjIjTCIIyIiIiIiciMM4oiIiIiIiNwIgzgiIiIiIiI3wiCOiIiIiIjIjTCIoxqRmZmpphAYNmyYREdHi8FgkOeff97m+t9++630799flfqtU6eOnH/++fLRRx9V6z57QluvWrVKBgwYIFFRURIWFibdunWTd999V4qLi6t9v93R33//LXfeead06tRJQkJCVBsOGTJEfvvtt1LrJiQkyLhx4yQyMlKtO2jQINm8eXON7HdtbuulS5fKTTfdJC1btpSAgACpX7++avejR4/W2L7X5uPa1KRJk9T3zWWXXVZt++qJ7c2/j9XT1vz7SK6GUwxQjThy5Ig0b95cGjVqJO3bt5eff/5ZnnvuOXnooYdKrfviiy/Kgw8+KCNHjlRfrl5eXrJ3714VkPzvf/+rkf2vjW2Nk91rr71W+vTpo056vb29Zfny5eoP1wMPPCAvvPBCjb0Gd3HdddepP/74jQsNaWlp8t5778nhw4dlxYoVxpPZnJwc6dGjh8THx8vMmTOlbt26Mm/ePDl27Jhs3LhROnToUNMvpda0NS5IxMTEyNVXXy0tWrSQgwcPyltvvSWBgYEqaG7YsGFNv5Ra09am/vnnH+nbt6/4+PioC3A//vhjjex7bW9v/n2snrbm30dySRpRDcjNzdVOnjypbh8+fFjDofjcc8+VWu+ff/7RvLy8tBdffLEG9tKz2nrgwIFagwYN1Pq64uJirXv37lpMTEy17rO7+uOPP8zaD1JTU7X69etr3bp1My579dVX1fvw22+/GZclJydrkZGR2siRI6t1n2t7W69du7bUY9evX6/af+bMmdWyr57S1qbfG3369NEmTpyoNW3aVBs6dGg17q3ntDf/PlZfW/PvI7kidqekGuHv7y8NGjQod71XX31VXUVHtgIyMjKqYe88s63PnDkj4eHhan0dukLVq1dPgoKCqngva4cLL7zQrP0AbYqukjt37jQu++KLL6RLly5y8cUXG5ehW+WNN94oP/zwA49zJ7b1wIEDSz0W2WZ8JkzXo8q3tW7x4sVq+ezZs9msVdje/PtYfW3Nv4/kihjEkUtD17+ePXvKm2++qbpPYkwcukc9/vjjHKflZBgLhz9aDz/8sOzfv191J8FJwk8//cRuq5V06tQpddwCxhf++++/0qtXr1Lr9e7dW/Lz82XHjh2VfUqPZdrWtqA7K7pNlbceOd7WuNCGrtqPPPKIugBEVdfe/PtYfW3Nv4/kinxqegeIbMFJVlJSkqxfv17WrFkjjz32mLRu3Vr1TX/66aclKytLXnnlFTagk8yaNUsVe8DYN73wiZ+fn8yfP18mTJjAdq6gP//8U9atWyf33HOP+n9qaqrk5uZKbGxsqXX1jClOIKjybW3La6+9JtnZ2XLDDTewmZ3c1k8++aTK3N99991s2ypsb/59rN5jm38fyRUxiCOXrqoIycnJ8vHHH6tiG4DiG7jaO3fuXJU14tV050CXkrZt28qIESPUIG8EcJ999plMnjxZFYHgCa/jEhMT1XHbpEkTlT3Ws0B6e1tC9UTTdahybW3N77//rk7IrrrqKhk+fDib2IltjYJTc+bMkc8//9zq8U3Oa2/+fazeY5t/H8kVMYgjl6WfBKC6mWUAMWbMGPnuu+9UJb8rrriihvawdpk+fbq6Avnff/+pNodRo0bJ0KFD1X0I7hDMkX1woQHTOmAsBaqfoXS1aaCWl5dX6jHI0JmuQ5Vra0vopooqtx07dpQPP/yQzevktr7rrrvUGCNUAqWqbW/+fazeY5t/H8kVcUwcuSwUesDJLDJtKHdvSh9rgS4lVHkYh7Vw4UK58sorjQGcDie9KSkp6io72QeZNGR5du/eLStXrlRzEJke1zgBi4uLK/U4vRulPYVoqPy2NnXo0CE1RQnG1qLcPcbXkvPaevXq1Wr87IwZM+TAgQPGn8LCQtV1Fbf5fe289ubfx+pra/59JFfFII5cFuaDw7wtGBeHL1FTJ0+eVL9RuZIqD0EaTrbwY0lfZu0+Kq2goEB1R/3rr7/k66+/lgsuuKDUcY3KlJs2bSr1WGSWfX19bQYi5Fhbm35fXHLJJartMe8hvzec39YnTpxQv7EOxi7rP2h7dGHF7XfeeYeHsJPam38fq6+t+feRXBWDOHJpo0ePlqKiIpUl0mmaJgsWLJA6deqoUuFUeTipxYTT33zzjdl4LFRS/PTTT1VGlBNQlw/tha6+yPSgzLq1SZD1bqrotvrHH3+YnSigrfEYHNvknLbGRaDBgwerrlII4Jo2bcqmrYK2xlQOX375ZakfZD67du2qbrObpfPaG/j3sXramn8fyVVxTBzVGEwbgO41ehebX375xZjtueOOO1R/9ClTpqiADV109uzZo67mLl++XNauXStvvPEGT3ad2NYPPvigKhSD0veoRomMEAqbbNiwQQ3w5lxx5bvvvvvUyeqll16q2nfJkiVm948dO1b9njZtmqr6ia6q9957r2r/efPmqTFxzz77rP0fIg9mb1tjTCe+O+68807ZsmWL+jHtlo3HU+XbGsGxtQAZj0U7I9NBzj22+fexetoawzn495FcUk3PNk6eq2nTphoOQWs/hw8fNq6XnJysTZ48WYuOjtb8/Py0zp07a4sXL67Rfa+tbf3FF19offv21SIiIjR/f3/V1m+//XaN7rs76d+/v812tvy6jYuL08aMGaOFh4drQUFB2oABA7RNmzbV2L7X1rYuax1sg5zX1ra+e4YOHcpmroJjG/j3sframn8fydUY8E9NB5JERERERERkH46JIyIiIiIiciMM4oiIiIiIiNwIgzgiIiIiIiI3wiCOiIiIiIjIjTCIIyIiIiIiciMM4oiIiIiIiNwIgzgiIiIiIiI3wiCOiIiIiIjIjTCIIyIiIiIiciMM4oiIiIiIiNwIgzgiIg/wxx9/yKxZsyQtLU1c0aJFi8RgMFj9mTp1qnG9TZs2yfTp06Vnz57i7++v7o+Pj7e6zWbNmlnd3tixY0ute/LkSZk8ebI0b95cAgMDpUWLFnL77bfLqVOnqvR1ExERVYRPhR5FRERuF8Q9+eSTMmHCBKlbt664KgSaLVu2NFvWpk0b4+3vv/9e3nvvPenYsaNavmPHjjK317lzZ7n//vvNliFAM3XmzBnp06eP5OTkyLRp06Rp06aya9cueeedd+THH39UzxEUFOSU10dEROQMDOKIiKgUBDTISFW3oUOHqoDKFgRZDz74oNo3BHzlBXGxsbFWM2+mli1bJidOnJBvv/1Whg8fbpbJu+uuu2TdunVy+eWXV+DVEBERVQ12pyQiquUQ7Dz88MPqNroL6t0Kf/31V2Owctlll6n/I4AKCAiQF154Qd2H9fB4SwMGDFA/pgoKCmT27NnSrl071dWxfv36qiukM7tw1qtXz+HgEvuVlZVl835k4vSAz5T+//KycGg3tNOnn34qzz77rDRu3FhCQkJk5MiRkpycLIWFhar9sb3g4GAZPXq0ZGZmmm1DDyAbNWqk2g7ZQASreXl5VoPOTp06qfepbdu2snDhQvUeYR+IiMgzMBNHRFTLXXPNNbJnzx75/PPP5bXXXpOoqCi1vH379sZ1Dhw4INdee61MmjRJbr31VmnSpIlDz6Fpmnr8zz//rLaBIOPgwYPy5ptvypYtW+TPP/8UX1/fcreTnp6uAh9TkZGRFQ5QkEVDEIZACgHSHXfcIffdd594eZ27htmvXz+1fdz3yiuvGLtTPvLII+q+iy++2K7nevHFF8XPz0913zx27Ji8/vrrcsstt6jn3bt3rzz66KOybds21R0UAS7u1y1YsEC8vb1lxowZEh4eLhs2bFD7ggzhxx9/bFwP3TvRzgjennnmGZUx1QNEIiLyHAziiIhqOYwL69q1qwrirrrqKpV5s4SA67vvvpMrr7yyQs+Bba9YsULWrFkjAwcONC7v37+/XHHFFfLZZ5/JuHHjyt0OMoKWkpKSjIGno6/7wgsvVJnBlJQUWbJkicpuHT16VN566y2z9TD+DfdhfR3a6pNPPjEL+MqSm5srGzduVIEcnD59WgVnCAR/+eUX43aOHz+uliOg1oNTPI9pxu+2226T1q1bq8APWVEEgoB9jI6OlvXr10tYWJhaNmrUKDVGkIiIPAeDOCIiUl0AKxrA6UEcCo0gA2eaSevVq5fqWrh27Vq7grg5c+aYZQhBD1YchS6KppAVQxfHt99+W411My2Ygtffo0cPFUQieNq6dau89NJLcuONN8rSpUvtCuTGjx9vDOD0145gDc9r+vjevXvLDz/8oILTmJgYtUwP4IqLiyUjI0N1Ab3oootUhhOZTARxqJSJTB4yfaZtgqwcxhKuXLmyQu1ERETuh0EcERGpsXKVge6C+EGWyJrExES7toOpA8oqbFIZyHrNnDlTZRwRVOpB3O+//64CWFTw7Nu3r1o2YsQIdf9NN90kX3/9tVx//fXlbh+BoCk90LK1HJk6PYhDgZYHHnhAja9DF0lT+phCZBABQaYl7CuDOCIiz8EgjoiIHC4WUlRUpMZw6ZBB6tChg7zxxhtW169Id8iqoI/1S01NNS57//331f7pAZwOgRwCP4znsyeIM20Pe5Yjy6aPA0QXVBQ9QWEUTLGA9wNz12FKCLRtefRtERGRZ2AQR0TkASpaGARzylmrLnnkyBGz+dxwe/PmzTJo0CC7x5DVhEOHDqnfegYMEhISVOETa4EqgiNr9zkTxsuhC+pXX32lxhDqUCTGFAquwP79+0ttw9oyIiKqvVz3Ly0RETkNsjx6Fz5HtGrVyjgVgW758uWqaqIplM1HMIRqlJYQBDn6vJWFwNMyO4X9eO6551RmbPDgwWZjypCZ++mnn8zWRyEU6N69e5Xuq49PyfVU0/1F9u3VV181W69BgwaqCMtHH32ksnc6dGO13HciIqrdmIkjIvIAKNoB//vf/1SxDhTgQNbMNCNlDeZ5w5QBqNSICa8xVQHmQzPNwsGYMWNUARB9cmxklJD9w9QFyDChXD4CvcrCuDAEMfDbb7+p3yjVj+IpyFTpxVMwlxqeE5UxMd4PQSQqZP7333+qDUwrdGJqAcy1hqkYbr/9djXmDMVE5s+frwI8Z+x3WS644AI1jQIKo2BfMBUD2sxyLjlAEIrxe+j6OXHiRDV+DoEzqlP++++/VbqfRETkOhjEERF5ABQLwbxi7777rqqWiEwPuvGVF8Rh3cOHD6uABtkeVFzEXGV333232XroQonAY+7cubJo0SL5/vvvjZNWjx071u651sqDfXnsscfMlukTkyNw1IM4ZKxatGih5lhDURUEraiciewaAk5TetD2xBNPyJdffqmqQGKMHObLw+Tljo4XdFRERIQqSnLvvfeqfUBAirngpk2bpl6HqWHDhql2fvzxx1UwivZFYLdz504VYBMRkWcwaBwNTURE5NYwdQImKOfYOCIiz8AxcURERG4C88dZFlpBBg6ZT3SPJSIiz8BMHBERkZtAVVAEaxjXiK6U6F76zjvvqEqamKDccqwiERHVThwTR0RE5CbCw8PV+EaM9YuPj5eAgAA13hBj9xjAERF5DmbiiIiIiIiI3AjHxBEREREREbkRBnFERERERERuhEEcERERERGRG2EQR0RERERE5EYYxBEREREREbkRBnFERERERERuhEEcERERERGRG2EQR0RERERE5EYYxBEREREREbkRBnFERERERETiPv4PvqxtN3bXroYAAAAASUVORK5CYII=", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gal = (cat.loc[cat.matched & (cat.truth_gal_star == 0) & (cat[\"flags\"] < FLAG_CUT),\n", - " [\"truth_ind\", \"match_sep_arcsec\", \"class_star\", \"awin_world\", f\"truth_mag_{BAND}\"]]\n", - " .sort_values(\"match_sep_arcsec\").drop_duplicates(\"truth_ind\"))\n", - "gal[\"size_arcsec\"] = gal.awin_world * 3600.0\n", - "print(f\"measured size of true galaxies p10/50/90 = \"\n", - " f\"{np.percentile(gal.size_arcsec.dropna(), [10, 50, 90]).round(3)} arcsec\")\n", - "\n", - "small = gal[gal.size_arcsec < GAL_SIZE_MAX].dropna(subset=[f\"truth_mag_{BAND}\"])\n", - "mg = small[f\"truth_mag_{BAND}\"].values\n", - "fs = (small.class_star > CLASS_STAR_CUT).values\n", - "n_gal = np.histogram(mg, MAG_BINS)[0]\n", - "n_false = np.histogram(mg[fs], MAG_BINS)[0]\n", - "with np.errstate(invalid=\"ignore\"):\n", - " eff_false = np.where(n_gal >= 20, n_false / np.maximum(n_gal, 1), np.nan)\n", - "\n", - "fig, ax = plt.subplots(figsize=(7.5, 5))\n", - "ax.plot(MAG_MID, eff_det, \"o-\", ms=3, color=\"C0\", label=\"stars: detection\")\n", - "ax.plot(MAG_MID, eff_cls, \"s-\", ms=3, color=\"C1\", label=\"stars: classification (of detected)\")\n", - "ax.plot(MAG_MID, eff_both, \"^-\", ms=3, color=\"C2\", label=\"stars: detection × classification\")\n", - "ax.plot(MAG_MID, eff_false, \"v--\", ms=3, color=\"C3\",\n", - " label=f\"galaxies < {GAL_SIZE_MAX}″: misclassified as stars\")\n", - "ax.set(xlabel=\"true F158 mag\", ylabel=\"efficiency\", xlim=(15, 29), ylim=(-0.02, 1.05),\n", - " title=f\"True stars (N={ok.sum():,}) and detected small galaxies (N={len(small):,}), \"\n", - " f\"class_star > {CLASS_STAR_CUT}\")\n", - "ax.grid(alpha=0.3); ax.legend(loc=\"center left\")\n", - "fig.savefig(FIG_DIR / \"efficiency_f158.png\", dpi=130, bbox_inches=\"tight\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "758eac66", - "metadata": {}, - "source": [ - "## Magnitude distributions: true vs observed\n", - "\n", - "Number counts per band: **solid** = true mags of matched sources (deduplicated on\n", - "`truth_ind`, so one entry per true source), **dotted** = observed `mag_auto` of all\n", - "S/N>5 detections (matched + unmatched).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ff643fec", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-11T22:18:57.306449Z", - "iopub.status.busy": "2026-06-11T22:18:57.306281Z", - "iopub.status.idle": "2026-06-11T22:19:04.587899Z", - "shell.execute_reply": "2026-06-11T22:19:04.587015Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nbins = np.arange(15.0, 30.5, 0.2)\n", - "truth_uniq = cat.loc[cat.matched].drop_duplicates(\"truth_ind\")\n", - "\n", - "fig, axes = plt.subplots(2, 2, figsize=(12, 8), sharex=True, sharey=True)\n", - "for ax, b in zip(axes.ravel(), BANDS):\n", - " ax.hist(truth_uniq[f\"truth_mag_{b}\"].dropna(), bins=nbins, histtype=\"step\",\n", - " lw=1.8, color=\"C0\", label=\"true (matched, unique)\")\n", - " ax.hist(cat[f\"mag_auto_{b}\"].dropna(), bins=nbins, histtype=\"step\",\n", - " lw=1.8, ls=\":\", color=\"C3\", label=\"obs (all detections)\")\n", - " ax.set_yscale(\"log\")\n", - " ax.set_title(b)\n", - " ax.grid(alpha=0.3)\n", - "axes[0, 0].legend(loc=\"upper left\")\n", - "for ax in axes[1]:\n", - " ax.set_xlabel(\"mag\")\n", - "for ax in axes[:, 0]:\n", - " ax.set_ylabel(\"N / 0.2 mag\")\n", - "fig.tight_layout()\n", - "fig.savefig(FIG_DIR / \"mag_distributions.png\", dpi=130, bbox_inches=\"tight\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "9b97b3c0", - "metadata": {}, - "source": [ - "## 4. Depth maps at nside=1024 (desqr `depth.py` recipe)\n", - "\n", - "Per band, following [kadrlica/desqr depth.py](https://github.com/kadrlica/desqr/blob/main/desqr/depth.py):\n", - "\n", - "1. cut the bright end (peak of the mag histogram − 3) and mag < 30;\n", - "2. estimate the global slope of log10(magerr) vs mag from nearest-neighbour pairs\n", - " (KDE peak of the pairwise ratio, as in `depth.py`);\n", - "3. per object, extrapolate to the mag where S/N = 5\n", - " (`magerr = 2.5/ln(10)/5 ≈ 0.2171`);\n", - "4. take the **median per healpix pixel** (nside=1024, ring).\n", - "\n", - "Following the paper's selections, only **matched, `flags == 0`** detections are used;\n", - "set `STARS_ONLY=True` to further restrict to star-classified objects.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8f4f7263", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-11T22:19:04.591725Z", - "iopub.status.busy": "2026-06-11T22:19:04.591487Z", - "iopub.status.idle": "2026-06-11T22:19:16.179867Z", - "shell.execute_reply": "2026-06-11T22:19:16.177110Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Y106: slope=0.251 covered pixels=5,185 median maglim=26.77\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "J129: slope=0.238 covered pixels=5,185 median maglim=27.11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "H158: slope=0.240 covered pixels=5,185 median maglim=27.11\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "F184: slope=0.257 covered pixels=5,185 median maglim=26.67\n" - ] - } - ], - "source": [ - "STARS_ONLY = False\n", - "MIN_PER_PIX = 10\n", - "\n", - "def desqr_maglim(df, mag_col, magerr_col, nside=NSIDE, snr=SNR_DEPTH,\n", - " n_slope=300_000, seed=42):\n", - " d = df[[\"alphawin_j2000\", \"deltawin_j2000\", mag_col, magerr_col]].dropna()\n", - " m = d[mag_col].values\n", - " h, edges = np.histogram(m, bins=np.arange(17, 30, 0.1))\n", - " mag_bright_end = edges[np.argmax(h)] - 3.0\n", - " d = d[(m > mag_bright_end) & (m < 30.0)]\n", - " m, merr = d[mag_col].values, d[magerr_col].values\n", - " ra, dec = d[\"alphawin_j2000\"].values, d[\"deltawin_j2000\"].values\n", - "\n", - " # slope of log10(magerr) vs mag from NN pairs (on a subsample, for speed)\n", - " rng = np.random.default_rng(seed)\n", - " idx = rng.choice(len(d), size=min(n_slope, len(d)), replace=False)\n", - " xx = np.radians(ra[idx]) * np.cos(np.radians(dec[idx]))\n", - " yy = np.radians(dec[idx])\n", - " _, nn = cKDTree(np.c_[xx, yy]).query(np.c_[xx, yy], k=2, workers=-1)\n", - " j = nn[:, 1]\n", - " with np.errstate(divide=\"ignore\", invalid=\"ignore\"):\n", - " ratio = ((np.log10(merr[idx][j]) - np.log10(merr[idx]))\n", - " / (m[idx][j] - m[idx]))\n", - " ratio = ratio[np.isfinite(ratio)]\n", - " values = np.linspace(0.0, 1.0, 1000)\n", - " slope = values[np.argmax(gaussian_kde(ratio[::max(1, ratio.size // 100_000)])(values))]\n", - "\n", - " magerr_at_snr = (2.5 / np.log(10)) / snr\n", - " maglims = m - (np.log10(merr) - np.log10(magerr_at_snr)) / slope\n", - "\n", - " pix = hp.ang2pix(nside, ra, dec, lonlat=True)\n", - " g = pd.Series(maglims).groupby(pix)\n", - " med, cnt = g.median(), g.size()\n", - " med = med[cnt >= MIN_PER_PIX]\n", - " out = np.full(hp.nside2npix(nside), hp.UNSEEN)\n", - " out[med.index.values] = med.values\n", - " return out, slope\n", - "\n", - "depth_src = cat[(cat[\"flags\"] < FLAG_CUT) & cat.matched]\n", - "if STARS_ONLY:\n", - " depth_src = depth_src[depth_src.class_star > CLASS_STAR_CUT]\n", - "\n", - "maglim_maps, slopes = {}, {}\n", - "for b in BANDS:\n", - " maglim_maps[b], slopes[b] = desqr_maglim(depth_src, f\"mag_auto_{b}\", f\"magerr_auto_{b}\")\n", - " cov = maglim_maps[b] != hp.UNSEEN\n", - " print(f\"{b}: slope={slopes[b]:.3f} covered pixels={cov.sum():,} \"\n", - " f\"median maglim={np.median(maglim_maps[b][cov]):.2f}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "53a0e9b7", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-11T22:19:16.182742Z", - "iopub.status.busy": "2026-06-11T22:19:16.182254Z", - "iopub.status.idle": "2026-06-11T22:19:19.021096Z", - "shell.execute_reply": "2026-06-11T22:19:19.018962Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "wrote roman_dc2_maglim_f106_nside1024.fits.gz (median 26.77)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "wrote roman_dc2_maglim_f129_nside1024.fits.gz (median 27.11)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "wrote roman_dc2_maglim_f158_nside1024.fits.gz (median 27.11)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "wrote roman_dc2_maglim_f184_nside1024.fits.gz (median 26.67)\n" - ] - } - ], - "source": [ - "fig, axes = plt.subplots(2, 2, figsize=(13, 8))\n", - "for ax, b in zip(axes.ravel(), BANDS):\n", - " mlm = maglim_maps[b]\n", - " cov = mlm != hp.UNSEEN\n", - " vmin, vmax = np.percentile(mlm[cov], [2, 98])\n", - " plt.axes(ax)\n", - " hp.cartview(mlm, lonra=[50.5, 56.5], latra=[-42.3, -37.7], hold=True,\n", - " title=f\"{b} maglim (S/N={SNR_DEPTH})\", unit=\"mag\",\n", - " min=vmin, max=vmax, badcolor=\"0.9\")\n", - "fig.savefig(FIG_DIR / \"maglim_maps.png\", dpi=130, bbox_inches=\"tight\")\n", - "plt.show()\n", - "\n", - "fig, ax = plt.subplots(figsize=(7, 4.5))\n", - "for b in BANDS:\n", - " mlm = maglim_maps[b]\n", - " ax.hist(mlm[mlm != hp.UNSEEN], bins=80, histtype=\"step\", lw=1.5, label=b)\n", - "ax.set(xlabel=f\"maglim (S/N={SNR_DEPTH}) [mag]\", ylabel=\"N pixels (nside=1024)\")\n", - "ax.legend(); ax.grid(alpha=0.3)\n", - "plt.show()\n", - "\n", - "OUT_DIR.mkdir(parents=True, exist_ok=True)\n", - "for b in BANDS:\n", - " fb = \"f\" + b[1:]\n", - " mlm = maglim_maps[b]\n", - " cov = mlm != hp.UNSEEN\n", - " fname = OUT_DIR / f\"roman_dc2_maglim_{fb}_nside{NSIDE}.fits.gz\"\n", - " hp.write_map(fname, mlm, overwrite=True, dtype=np.float32)\n", - " print(f\"wrote {fname.name} (median {np.median(mlm[cov]):.2f})\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "20c12df0", - "metadata": {}, - "source": [ - "## 5. streamobs tables\n", - "\n", - "Same formats as the LSST files in `data/others/` (these are what\n", - "`streamobs.surveys.SurveyFactory.set_completeness` / `set_photo_error` read):\n", - "\n", - "- `roman_photoerror_f158.csv` — `# delta_mag,log_mag_err`, with\n", - " `delta_mag = true mag − maglim` at the object's nside=1024 pixel and\n", - " `log_mag_err` = log10 of the binned **truth-based scatter** ((p84−p16)/2 of\n", - " observed − true) of star-classified objects. The reported SExtractor magerr is\n", - " NOT used (it underestimates the true scatter ~2×); it is kept only as a\n", - " comparison curve in the figure.\n", - "- `roman_stellar_efficiency_cutf158.csv` —\n", - " `# mag_f158,delta_mag,detection_eff,classifiction_eff,classification_detection_eff`\n", - " (the `classifiction_eff` typo is intentional: `set_completeness` looks that name up).\n", - " `delta_mag` is keyed to the **median of the measured maglim map** (the maps and the\n", - " tables share one internal convention; no renormalization is applied anywhere).\n", - " Runtime maglim maps for other footprints (e.g. the HLWAS exptime-scaled map) should\n", - " be expressed in this same convention, i.e. scaled relative to the DC2 depth.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "882cdd12", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-11T22:19:19.025146Z", - "iopub.status.busy": "2026-06-11T22:19:19.024800Z", - "iopub.status.idle": "2026-06-11T22:19:19.914013Z", - "shell.execute_reply": "2026-06-11T22:19:19.913313Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "reference maglim = measured map median = 27.107\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "wrote data/surveys/roman_hlwas/roman_photoerror_f158.csv (148 rows, delta_mag -16.64 .. 1.36)\n", - "wrote data/surveys/roman_hlwas/roman_stellar_efficiency_cutf158.csv (56 rows)\n" - ] - }, - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mlm = maglim_maps[BAND]\n", - "covered = mlm != hp.UNSEEN\n", - "MAGLIM_REF = float(np.median(mlm[covered])) # tables keyed to the measured map median\n", - "print(f\"reference maglim = measured map median = {MAGLIM_REF:.3f}\")\n", - "\n", - "# --- photoerror table -------------------------------------------------------\n", - "sel = (cat.matched & (cat.class_star > CLASS_STAR_CUT) & (cat[\"flags\"] < FLAG_CUT))\n", - "pe = cat.loc[sel, [\"alphawin_j2000\", \"deltawin_j2000\", f\"truth_mag_{BAND}\",\n", - " f\"mag_auto_{BAND}\", f\"magerr_auto_{BAND}\"]].dropna()\n", - "pix = hp.ang2pix(NSIDE, pe.alphawin_j2000.values, pe.deltawin_j2000.values, lonlat=True)\n", - "ml_local = mlm[pix]\n", - "good = ml_local != hp.UNSEEN\n", - "delta = pe[f\"truth_mag_{BAND}\"].values[good] - ml_local[good]\n", - "# the error model is the TRUTH-BASED scatter of (observed - true): the reported\n", - "# SExtractor magerr underestimates it by ~2x (correlated noise in the coadds)\n", - "dm_obs = (pe[f\"mag_auto_{BAND}\"].values - pe[f\"truth_mag_{BAND}\"].values)[good]\n", - "logerr_reported = np.log10(pe[f\"magerr_auto_{BAND}\"].values[good])\n", - "\n", - "dbins = np.arange(np.floor(delta.min() * 10) / 10, 1.5 + 1e-6, 0.12)\n", - "dmid = 0.5 * (dbins[1:] + dbins[:-1])\n", - "log_scatter = np.full(dmid.size, np.nan)\n", - "med_logerr_rep = np.full(dmid.size, np.nan)\n", - "ib = np.digitize(delta, dbins) - 1\n", - "for i in range(dmid.size):\n", - " v = dm_obs[ib == i]\n", - " if v.size >= 20:\n", - " log_scatter[i] = np.log10((np.percentile(v, 84) - np.percentile(v, 16)) / 2)\n", - " med_logerr_rep[i] = np.median(logerr_reported[ib == i])\n", - "keep = np.isfinite(log_scatter)\n", - "\n", - "photoerr_tab = pd.DataFrame({\"delta_mag\": dmid[keep], \"log_mag_err\": log_scatter[keep]})\n", - "fpe = OUT_DIR / \"roman_photoerror_f158.csv\"\n", - "np.savetxt(fpe, photoerr_tab.values, delimiter=\",\", header=\"delta_mag,log_mag_err\", fmt=\"%.6f\")\n", - "print(f\"wrote {fpe.relative_to(REPO)} ({len(photoerr_tab)} rows, \"\n", - " f\"delta_mag {photoerr_tab.delta_mag.min():.2f} .. {photoerr_tab.delta_mag.max():.2f})\")\n", - "\n", - "# --- stellar efficiency table ------------------------------------------------\n", - "eff_tab = pd.DataFrame({\n", - " \"mag_f158\": MAG_MID,\n", - " \"delta_mag\": MAG_MID - MAGLIM_REF,\n", - " \"detection_eff\": eff_det,\n", - " \"classifiction_eff\": eff_cls,\n", - " \"classification_detection_eff\": eff_both,\n", - "})\n", - "eff_tab = eff_tab[n_all >= 20].fillna(0.0)\n", - "feff = OUT_DIR / \"roman_stellar_efficiency_cutf158.csv\"\n", - "np.savetxt(feff, eff_tab.values, delimiter=\",\",\n", - " header=\"mag_f158,delta_mag,detection_eff,classifiction_eff,classification_detection_eff\",\n", - " fmt=\"%.6f\")\n", - "print(f\"wrote {feff.relative_to(REPO)} ({len(eff_tab)} rows)\")\n", - "\n", - "# --- sanity overlay vs the LSST r-band tables --------------------------------\n", - "lsst_pe = np.genfromtxt(REPO / \"data/others/lsst_photoerror_r.csv\", delimiter=\",\", names=True)\n", - "lsst_eff = np.genfromtxt(REPO / \"data/others/lsst_stellar_efficiency_cutr.csv\", delimiter=\",\", names=True)\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))\n", - "axes[0].plot(lsst_pe[\"delta_mag\"], lsst_pe[\"log_mag_err\"], \"-\", color=\"0.6\", label=\"LSST r\")\n", - "axes[0].plot(photoerr_tab.delta_mag, photoerr_tab.log_mag_err, \"C3o-\", ms=3,\n", - " label=\"Roman F158 (truth-based scatter)\")\n", - "axes[0].plot(dmid[keep], med_logerr_rep[keep], \"C0--\", lw=1.5,\n", - " label=\"Roman F158 reported magerr (underestimated)\")\n", - "axes[0].set(xlabel=r\"$\\Delta$mag = mag $-$ maglim\", ylabel=r\"$\\log_{10}\\sigma$ [mag]\")\n", - "axes[0].legend(); axes[0].grid(alpha=0.3)\n", - "axes[1].plot(lsst_eff[\"delta_mag\"], lsst_eff[\"classification_detection_eff\"], \"-\", color=\"0.6\", label=\"LSST r\")\n", - "axes[1].plot(eff_tab.delta_mag, eff_tab.classification_detection_eff, \"C3o-\", ms=3, label=\"Roman F158\")\n", - "axes[1].set(xlabel=r\"$\\Delta$mag = mag $-$ maglim\", ylabel=\"classification_detection_eff\")\n", - "axes[1].legend(); axes[1].grid(alpha=0.3)\n", - "fig.savefig(FIG_DIR / \"lsst_comparison.png\", dpi=130, bbox_inches=\"tight\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "71329454", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "- Wire these into a `config/surveys/roman_hlwas.yaml` (mirroring `lsst_yr5.yaml`):\n", - " `completeness: roman_stellar_efficiency_cutf158.csv`, `completeness_band: f158`,\n", - " `log_photo_error: roman_photoerror_f158.csv`, `maglim_map_f158: roman_dc2_maglim_f158_nside1024.fits.gz`\n", - " (or, for the full HLWAS footprint, a maglim map scaled from the exposure-time map —\n", - " see `roman_hlwas_exptime_map.ipynb`; the DC2 map above characterizes the mock's depth).\n", - "- Caveats: the det→truth (detection-centric) match assigns a blended star to a single\n", - " truth source, so the detection efficiency here is slightly conservative for blends;\n", - " galaxy \"size\" is the measured `awin_world`, not a truth size.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (streamobs)", - "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": "3.14.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/build_roman_dc2_det_truth.py b/scripts/roman/build_roman_dc2_det_truth.py similarity index 100% rename from notebooks/build_roman_dc2_det_truth.py rename to scripts/roman/build_roman_dc2_det_truth.py diff --git a/scripts/roman/create_streamobs_files_hlwas.py b/scripts/roman/create_streamobs_files_hlwas.py new file mode 100644 index 0000000..4425b82 --- /dev/null +++ b/scripts/roman/create_streamobs_files_hlwas.py @@ -0,0 +1,691 @@ +#!/usr/bin/env python +# coding: utf-8 +"""Build the Roman HLWAS streamobs survey files from the Roman-Rubin DC2 mock. + +Run with the streamobs env: + /astro/store/shiren/conda-envs/stream_team/envs/streamobs/bin/python \ + notebooks/create_streamobs_files_hlwas.py + +Inputs : data/surveys/roman_dc2/roman_dc2_det_truth.parquet (+ per-tile truth indices + on first run, cached to roman_dc2_truth_stars.parquet) +Outputs: data/surveys/roman_hlwas/{roman_photoerror_f158.csv, + roman_stellar_efficiency_cutf158.csv, roman_dc2_maglim_f*_nside1024.fits.gz} + and the documentation figures in docs/source/_static/roman_dc2/. +""" + +# # Roman HLWAS streamobs survey files from the DC2 mock +# +# Build the survey characterization products streamobs needs for a Roman (HLWAS) survey, +# derived from the matched detection→truth catalog of the Roman–Rubin DC2 mock +# (`data/surveys/roman_dc2/roman_dc2_det_truth.parquet`, see its README; +# Troxel et al. 2023, [arXiv:2209.06829](https://arxiv.org/abs/2209.06829)). +# +# **Note on band naming:** the mock labels the Roman bands `Y106, J129, H158, F184`; +# `H158` is the Roman **F158** filter. Outputs are named `f158`. +# +# Products (mirroring the LSST files in `data/others/`): +# 1. **Photometric error vs true mag** in F158 for true stars passing the star classification. +# 2. **Detection & correct-classification efficiency** vs true F158 mag for *true* stars. +# 3. **False star-classification rate** vs true F158 mag for *true* galaxies with measured size < 0.3″. +# 4. **Depth (maglim) maps at nside=1024** following the recipe in +# [desqr/depth.py](https://github.com/kadrlica/desqr/blob/main/desqr/depth.py) (mag at S/N=5). +# 5. CSV tables in the format of `lsst_photoerror_r.csv` and `lsst_stellar_efficiency_cutr.csv`. +# + +# In[1]: + + +import gzip, io, glob, os +import multiprocessing as mp +from pathlib import Path +from concurrent.futures import ProcessPoolExecutor + +import numpy as np +import pandas as pd +import healpy as hp +import matplotlib.pyplot as plt +import pyarrow.parquet as pq +from astropy.io import fits +from astropy.table import Table +from scipy.spatial import cKDTree +from scipy.stats import gaussian_kde + +REPO = Path(__file__).resolve().parent.parent +DC2_DIR = REPO / "data/surveys/roman_dc2" +OUT_DIR = REPO / "data/surveys/roman_hlwas" +CAT_PATH = DC2_DIR / "roman_dc2_det_truth.parquet" +TRUTH_STARS_CACHE = DC2_DIR / "roman_dc2_truth_stars.parquet" + +BAND = "H158" # mock column name == Roman F158 +BANDS = ["Y106", "J129", "H158", "F184"] +CLASS_STAR_CUT = 0.5 # class_star > cut -> "classified as a star" +FLAG_CUT = 1 # flags < 1, i.e. flags == 0 (paper cut: removes 32% of detections) +NSIDE = 1024 +SNR_DEPTH = 5 # depth = mag at S/N = 5 (matches the catalog S/N>5 cut) +GAL_SIZE_MAX = 0.3 # arcsec; "small" galaxies = awin_world < this +MAG_BINS = np.arange(15.0, 29.0 + 1e-6, 0.25) +MAG_MID = 0.5 * (MAG_BINS[1:] + MAG_BINS[:-1]) + +plt.rcParams.update({"figure.dpi": 110, "font.size": 11}) +FIG_DIR = REPO / "docs/source/_static/roman_dc2" # figures embedded in the docs page +FIG_DIR.mkdir(parents=True, exist_ok=True) +print(f"repo: {REPO}") + + +# ## Load the matched det→truth catalog +# +# One row per S/N>5 detection; `matched` rows carry the truth payload. +# +# **Why ~29% of detections are unmatched (verified 2026-06-11):** each det coadd extends +# ~30″ beyond its truth tile's footprint, and the catalog was matched per tile — so +# detections in that margin (~21% of rows, >99% unmatched) could not see their truth +# object, which lives in the *neighboring* tile's index (they are also duplicates of +# detections in the neighboring tile's det file). Inside the truth footprint the unmatched +# rate is only ~0.1–5% per tile, consistent with the paper's 1.7%. So `matched=False` is +# mostly tile-edge geometry, **not** a spurious-detection rate; the matched-only cuts used +# below also conveniently drop the margin duplicates. We only pull the +# columns needed here (~19.2M rows). SExtractor sentinel mags (99) are set to NaN. +# + +# In[2]: + + +usecols = (["alphawin_j2000", "deltawin_j2000", "mag_auto", "magerr_auto", + "flags", "class_star", "det_sn", "awin_world", + "matched", "match_sep_arcsec", "truth_ind", "truth_gal_star", + ] + + [f"truth_mag_{b}" for b in BANDS] + + [f"mag_auto_{b}" for b in BANDS] + [f"magerr_auto_{b}" for b in BANDS]) +cat = pq.read_table(CAT_PATH, columns=usecols).to_pandas() + +for b in BANDS: # SExtractor unmeasured -> 99 + bad = (cat[f"mag_auto_{b}"] > 90) | (cat[f"magerr_auto_{b}"] > 90) | (cat[f"magerr_auto_{b}"] <= 0) + cat.loc[bad, [f"mag_auto_{b}", f"magerr_auto_{b}"]] = np.nan + +print(f"{len(cat):,} detections, {int(cat.matched.sum()):,} matched " + f"({cat.matched.mean():.1%}); {int(((cat.truth_gal_star == 1) & cat.matched).sum()):,} matched-star rows") + + +# ## Truth-star denominator +# +# Detection efficiency needs *all* true stars, including undetected ones — that lives in the +# per-tile truth indices, not the detection-centric parquet. Build (once) a cached parquet of +# the true stars (`gal_star == 1`) from the 1039 real tiles, deduplicated on truth `ind` +# (tiles overlap at the edges). +# + +# In[3]: + + +def load_gz_fits(path): + with gzip.open(path, "rb") as f: + raw = f.read() + with fits.open(io.BytesIO(raw)) as h: + return Table(h[1].data) + +def stars_from_tile(path): + tile = Path(path).name.replace("dc2_index_", "").replace(".fits.gz", "") + t = load_gz_fits(path) + sel = np.asarray(t["gal_star"]) == 1 + if not sel.any(): + return None + df = pd.DataFrame({"ind": np.asarray(t["ind"], "i8")[sel], + "ra": np.asarray(t["ra"], "f8")[sel], + "dec": np.asarray(t["dec"], "f8")[sel]}) + for b in BANDS: + m = np.asarray(t[f"mag_{b}"], "f8")[sel] + m[m == 0.0] = np.nan # 0.0 = no flux in band + df[f"mag_{b}"] = m + df["tile"] = tile + return df + +if TRUTH_STARS_CACHE.exists(): + truth_stars = pd.read_parquet(TRUTH_STARS_CACHE) +else: + det_tiles = {Path(p).name.replace("dc2_det_", "").replace(".fits.gz", "") + for p in glob.glob(str(DC2_DIR / "det/dc2_det_*.fits.gz"))} + paths = [p for p in sorted(glob.glob(str(DC2_DIR / "truth/dc2_index_*.fits.gz"))) + if Path(p).name.replace("dc2_index_", "").replace(".fits.gz", "") in det_tiles + and os.path.getsize(p) > 1000] + print(f"reading {len(paths)} truth tiles ...") + # fork context: py3.14 defaults to forkserver, which can't pickle notebook-defined funcs + with ProcessPoolExecutor(32, mp_context=mp.get_context("fork")) as ex: + frames = [df for df in ex.map(stars_from_tile, paths, chunksize=8) if df is not None] + truth_stars = pd.concat(frames, ignore_index=True) + truth_stars.to_parquet(TRUTH_STARS_CACHE) + +n_rows = len(truth_stars) +truth_stars = truth_stars.drop_duplicates("ind").reset_index(drop=True) +print(f"{n_rows:,} truth-star rows -> {len(truth_stars):,} unique stars") + + +# ## 1. Photometric error vs true F158 magnitude (star-classified objects) +# +# Matched detections with `class_star > {cut}` and clean flags, observed F158 `magerr_auto` +# against the true F158 magnitude, with the binned median overlaid. The orange curve is the +# **truth-based scatter** of (observed − true): it runs a factor ~1.9–2.0 above the reported +# errors at all magnitudes — SExtractor underestimates the correlated noise of the resampled +# median coadds — so the error *model* (section 5) is built from the truth-based scatter. +# + +# In[4]: + + +sel = (cat.matched & (cat.class_star > CLASS_STAR_CUT) & (cat["flags"] < FLAG_CUT)) +pe = cat.loc[sel, [f"truth_mag_{BAND}", f"mag_auto_{BAND}", f"magerr_auto_{BAND}"]].dropna() +x, y = pe[f"truth_mag_{BAND}"].values, pe[f"magerr_auto_{BAND}"].values +print(f"{len(pe):,} star-classified matched detections with F158 photometry") + +dm = pe[f"mag_auto_{BAND}"].values - pe[f"truth_mag_{BAND}"].values +med, lo, hi, scat = (np.full(MAG_MID.size, np.nan) for _ in range(4)) +ib = np.digitize(x, MAG_BINS) - 1 +for i in range(MAG_MID.size): + yy = y[ib == i] + if yy.size >= 20: + lo[i], med[i], hi[i] = np.percentile(yy, [16, 50, 84]) + v = dm[ib == i] + scat[i] = (np.percentile(v, 84) - np.percentile(v, 16)) / 2 + +fig, ax = plt.subplots(figsize=(7, 5)) +hb = ax.hexbin(x, np.log10(y), gridsize=180, bins="log", mincnt=1, cmap="viridis") +ax.plot(MAG_MID, np.log10(med), "r-", lw=2, label="median reported magerr") +ax.plot(MAG_MID, np.log10(scat), "-", color="orange", lw=2, + label="truth-based scatter of (obs $-$ true)") +ax.plot(MAG_MID, np.log10(lo), "r--", lw=1, label="16/84%") +ax.plot(MAG_MID, np.log10(hi), "r--", lw=1) +ax.axhline(np.log10(2.5 / np.log(10) / SNR_DEPTH), color="w", ls=":", lw=1.5, + label=f"S/N = {SNR_DEPTH}") +ax.set(xlabel="true F158 mag", ylabel=r"$\log_{10}\,\sigma_{\rm F158}^{\rm obs}$ [mag]", + xlim=(15, 29), title=f"Observed F158 photo-error, class_star > {CLASS_STAR_CUT}, flags == 0") +fig.colorbar(hb, ax=ax, label="N (log)") +ax.legend(loc="upper left") +fig.savefig(FIG_DIR / "photoerror_f158.png", dpi=130, bbox_inches="tight") +plt.show() + + +# ### Same, restricted to *true* stars +# +# The plot above is selection-matched to observations (anything *classified* as a star), +# but at the faint end that sample is galaxy-dominated: true galaxies make up ~69% of +# star-classified objects at true F158 ≈ 25.5 and ~99% at 26.5, and they inflate the +# apparent scatter (0.33 vs 0.15 mag at 25.5). Below the same plot for **true stars** +# that pass the star classification — the population whose photometric response an +# injected stream star actually follows. +# + +# In[5]: + + +sel_ts = (cat.matched & (cat.truth_gal_star == 1) + & (cat.class_star > CLASS_STAR_CUT) & (cat["flags"] < FLAG_CUT)) +pe_ts = cat.loc[sel_ts, [f"truth_mag_{BAND}", f"mag_auto_{BAND}", f"magerr_auto_{BAND}"]].dropna() +x, y = pe_ts[f"truth_mag_{BAND}"].values, pe_ts[f"magerr_auto_{BAND}"].values +res = pe_ts[f"truth_mag_{BAND}"].values - pe_ts[f"mag_auto_{BAND}"].values # truth - obs +print(f"{len(pe_ts):,} star-classified matched TRUE stars with F158 photometry") + +med, lo, hi, scat, med_res = (np.full(MAG_MID.size, np.nan) for _ in range(5)) +ib = np.digitize(x, MAG_BINS) - 1 +for i in range(MAG_MID.size): + yy = y[ib == i] + if yy.size >= 20: + lo[i], med[i], hi[i] = np.percentile(yy, [16, 50, 84]) + v = res[ib == i] + med_res[i] = np.median(v) + scat[i] = (np.percentile(v, 84) - np.percentile(v, 16)) / 2 + +fig, (axl, axr) = plt.subplots(1, 2, figsize=(13.5, 5)) + +hb = axl.hexbin(x, np.log10(y), gridsize=180, bins="log", mincnt=1, cmap="viridis") +axl.plot(MAG_MID, np.log10(med), "r-", lw=2, label="median reported magerr") +axl.plot(MAG_MID, np.log10(scat), "-", color="orange", lw=2, + label="truth-based scatter") +axl.plot(MAG_MID, np.log10(lo), "r--", lw=1, label="16/84%") +axl.plot(MAG_MID, np.log10(hi), "r--", lw=1) +axl.axhline(np.log10(2.5 / np.log(10) / SNR_DEPTH), color="w", ls=":", lw=1.5, + label=f"S/N = {SNR_DEPTH}") +axl.set(xlabel="true F158 mag", ylabel=r"$\log_{10}\,\sigma_{\rm F158}^{\rm obs}$ [mag]", + xlim=(15, 29), title="reported errors") +fig.colorbar(hb, ax=axl, label="N (log)") +axl.legend(loc="upper left", fontsize=9) + +std_res = np.full(MAG_MID.size, np.nan) +for i in range(MAG_MID.size): + v = res[ib == i] + if v.size >= 20: + std_res[i] = np.std(v) + +axr.plot(MAG_MID, scat, "-", color="orange", lw=2, + label=r"$(p_{84}-p_{16})/2$ of (true $-$ obs)") +axr.plot(MAG_MID, std_res, "--", color="orange", lw=1.5, label="std of (true $-$ obs)") +axr.plot(MAG_MID, med, "r-", lw=2, label="median reported magerr") +axr.axhline(2.5 / np.log(10) / SNR_DEPTH, color="0.4", ls=":", lw=1.5, + label=f"S/N = {SNR_DEPTH}") +axr.set(xlabel="true F158 mag", ylabel=r"$\sigma$(true $-$ obs) [mag]", + xlim=(15, 29), ylim=(0, 0.7), title="truth-based scatter vs reported errors") +axr.grid(alpha=0.3) +axr.legend(loc="upper left", fontsize=9) + +fig.suptitle(f"True stars with class_star > {CLASS_STAR_CUT}, flags == 0", y=1.02) +fig.tight_layout() +fig.savefig(FIG_DIR / "photoerror_f158_truestars.png", dpi=130, bbox_inches="tight") +plt.show() + + +# ### Error validation: reported magerr vs truth +# +# Because we have the truth, the reported errors can be validated directly: for true stars, +# the scatter of (observed − true) magnitude should match the reported `magerr_auto`. It +# does not — the truth-based scatter runs a flat factor ~1.9–2.0 above the reported errors +# in every band, the signature of SExtractor underestimating the correlated noise of the +# resampled median coadds. Consequence: the photo-error *model* (section 5) is built from +# the truth-based scatter, and the reported-error depth maps are an internal reference, +# not a claim that the survey is genuinely deeper than the official depths. +# + +# In[6]: + + +vsel = (cat.matched & (cat.truth_gal_star == 1) & (cat.class_star > CLASS_STAR_CUT) + & (cat["flags"] < FLAG_CUT)) +fig, (axa, axb) = plt.subplots(2, 1, figsize=(7.5, 7.5), sharex=True, + gridspec_kw={"height_ratios": [2, 1]}) +for b, col in zip(BANDS, ["C0", "C1", "C2", "C3"]): + sub = cat.loc[vsel, [f"truth_mag_{b}", f"mag_auto_{b}", f"magerr_auto_{b}"]].dropna() + mt = sub[f"truth_mag_{b}"].values + dmv = sub[f"mag_auto_{b}"].values - mt + sg = sub[f"magerr_auto_{b}"].values + ib = np.digitize(mt, MAG_BINS) - 1 + scat_b, rep_b = np.full(MAG_MID.size, np.nan), np.full(MAG_MID.size, np.nan) + for i in range(MAG_MID.size): + v = dmv[ib == i] + if v.size >= 100: + scat_b[i] = (np.percentile(v, 84) - np.percentile(v, 16)) / 2 + rep_b[i] = np.median(sg[ib == i]) + axa.plot(MAG_MID, np.log10(scat_b), "-", color=col, lw=1.8, label=f"{b}") + axa.plot(MAG_MID, np.log10(rep_b), "--", color=col, lw=1.4, alpha=0.8) + axb.plot(MAG_MID, scat_b / rep_b, "-", color=col, lw=1.6, label=b) +axa.set(ylabel=r"$\log_{10}\,\sigma$ [mag]", + title="True stars: truth-based scatter (solid) vs reported magerr (dashed)") +axa.legend(title="band", fontsize=9); axa.grid(alpha=0.3) +axb.axhline(1.0, color="k", lw=0.8) +axb.axhline(2.0, color="0.5", lw=0.8, ls=":") +axb.set(xlabel="true mag", ylabel="scatter / reported", xlim=(20, 28), ylim=(0.5, 2.8)) +axb.grid(alpha=0.3) +fig.tight_layout() +fig.savefig(FIG_DIR / "error_validation.png", dpi=130, bbox_inches="tight") +plt.show() + + +# ## 2. Detection & classification efficiency for true stars +# +# Denominator: all unique true stars. A star counts as **detected** if any S/N>5 detection +# matched its `ind` **and passed the paper's `flags == 0` cut** (Troxel et al. selection 1 — +# a star whose only detection is flagged would not appear in the science catalog); for +# classification we take its nearest clean matched detection and ask whether `class_star > {cut}`. +# +# **Gotcha (found 2026-06-11):** ~26% of truth stars have a *duplicate* truth entry at the +# exact same position under a different `ind`, carrying only the J129 mag (other bands 0.0). +# The det→truth mag-tiebreak can assign the detection to the duplicate, which makes the real +# entry look undetected (a spurious, flat ~12% efficiency deficit). We therefore collapse +# truth stars to unique *positions* and count a star detected/classified if **any** member +# entry matched. Curves: +# +# - `detection_eff` = detected / all +# - `classification_eff` = correctly classified / detected +# - `classification_detection_eff` = product (detected **and** classified) +# + +# In[7]: + + +star_dets = (cat.loc[cat.matched & (cat.truth_gal_star == 1) & (cat["flags"] < FLAG_CUT), + ["truth_ind", "match_sep_arcsec", "class_star"]] + .sort_values("match_sep_arcsec").drop_duplicates("truth_ind")) +print(f"class_star of matched true stars: p10/50/90 = " + f"{np.percentile(star_dets.class_star, [10, 50, 90]).round(3)}") + +detected_ind = set(star_dets.truth_ind.astype("i8")) +classified_ind = set(star_dets.loc[star_dets.class_star > CLASS_STAR_CUT, "truth_ind"].astype("i8")) + +# collapse duplicate truth entries (same exact position, different ind, single-band payload): +# a position counts as detected/classified if ANY of its member inds matched +ts = truth_stars.copy() +ts["is_det"] = ts["ind"].isin(detected_ind) +ts["is_cls"] = ts["ind"].isin(classified_ind) +stars = (ts.groupby(["ra", "dec"], sort=False, as_index=False) + .agg(mag=(f"mag_{BAND}", "max"), is_det=("is_det", "any"), is_cls=("is_cls", "any"))) +print(f"{len(ts):,} unique-ind stars -> {len(stars):,} unique-position stars") + +mag = stars["mag"].values +ok = np.isfinite(mag) +is_det = stars["is_det"].values +is_cls = stars["is_cls"].values + +n_all = np.histogram(mag[ok], MAG_BINS)[0] +n_det = np.histogram(mag[ok & is_det], MAG_BINS)[0] +n_cls = np.histogram(mag[ok & is_cls], MAG_BINS)[0] +with np.errstate(invalid="ignore"): + eff_det = n_det / n_all + eff_cls = np.where(n_det > 0, n_cls / np.maximum(n_det, 1), np.nan) + eff_both = n_cls / n_all + +print(f"star sample for the efficiency curves: {ok.sum():,} " + "(combined plot with the galaxy misclassification below)") + + +# ## 3. False star-classification of small true galaxies (combined plot) +# +# True galaxies (`truth_gal_star == 0`) with measured semi-major axis +# `awin_world < 0.3″` — the truth index carries no size, so we use the SExtractor windowed +# size of the (nearest) matched detection. Plotted: fraction classified as a star +# (`class_star > {cut}`) among *detected* small galaxies, vs true F158 mag — on the same +# axes as the true-star detection/classification curves from section 2. +# + +# In[8]: + + +gal = (cat.loc[cat.matched & (cat.truth_gal_star == 0) & (cat["flags"] < FLAG_CUT), + ["truth_ind", "match_sep_arcsec", "class_star", "awin_world", f"truth_mag_{BAND}"]] + .sort_values("match_sep_arcsec").drop_duplicates("truth_ind")) +gal["size_arcsec"] = gal.awin_world * 3600.0 +print(f"measured size of true galaxies p10/50/90 = " + f"{np.percentile(gal.size_arcsec.dropna(), [10, 50, 90]).round(3)} arcsec") + +small = gal[gal.size_arcsec < GAL_SIZE_MAX].dropna(subset=[f"truth_mag_{BAND}"]) +mg = small[f"truth_mag_{BAND}"].values +fs = (small.class_star > CLASS_STAR_CUT).values +n_gal = np.histogram(mg, MAG_BINS)[0] +n_false = np.histogram(mg[fs], MAG_BINS)[0] +with np.errstate(invalid="ignore"): + eff_false = np.where(n_gal >= 20, n_false / np.maximum(n_gal, 1), np.nan) + +fig, ax = plt.subplots(figsize=(7.5, 5)) +ax.plot(MAG_MID, eff_det, "o-", ms=3, color="C0", label="stars: detection") +ax.plot(MAG_MID, eff_cls, "s-", ms=3, color="C1", label="stars: classification (of detected)") +ax.plot(MAG_MID, eff_both, "^-", ms=3, color="C2", label="stars: detection × classification") +ax.plot(MAG_MID, eff_false, "v--", ms=3, color="C3", + label=f"galaxies < {GAL_SIZE_MAX}″: misclassified as stars") +ax.set(xlabel="true F158 mag", ylabel="efficiency", xlim=(15, 29), ylim=(-0.02, 1.05), + title=f"True stars (N={ok.sum():,}) and detected small galaxies (N={len(small):,}), " + f"class_star > {CLASS_STAR_CUT}") +ax.grid(alpha=0.3); ax.legend(loc="center left") +fig.savefig(FIG_DIR / "efficiency_f158.png", dpi=130, bbox_inches="tight") +plt.show() + + +# ## Magnitude distributions: true vs observed +# +# Number counts per band: **solid** = true mags of matched sources (deduplicated on +# `truth_ind`, so one entry per true source), **dotted** = observed `mag_auto` of all +# S/N>5 detections (matched + unmatched). +# + +# In[9]: + + +nbins = np.arange(15.0, 30.5, 0.2) +truth_uniq = cat.loc[cat.matched].drop_duplicates("truth_ind") +colors = {"Y106": "C0", "J129": "C1", "H158": "C2", "F184": "C3"} + +fig, ax = plt.subplots(figsize=(8, 5.5)) +for b in BANDS: + ax.hist(truth_uniq[f"truth_mag_{b}"].dropna(), bins=nbins, histtype="step", + lw=1.8, ls="-", color=colors[b]) + ax.hist(cat[f"mag_auto_{b}"].dropna(), bins=nbins, histtype="step", + lw=1.6, ls=":", color=colors[b]) +ax.set_yscale("log") +ax.set(xlabel="mag", ylabel="N / 0.2 mag") +from matplotlib.lines import Line2D +handles = ([Line2D([], [], color=colors[b], lw=1.8) for b in BANDS] + + [Line2D([], [], color="k", ls="-", lw=1.8), + Line2D([], [], color="k", ls=":", lw=1.6)]) +ax.legend(handles, BANDS + ["true (matched, unique)", "obs (all detections)"], + ncol=2, fontsize=9) +ax.grid(alpha=0.3) +fig.tight_layout() +fig.savefig(FIG_DIR / "mag_distributions.png", dpi=130, bbox_inches="tight") +plt.show() + + +# ## 4. Depth maps at nside=1024 (desqr `depth.py` recipe) +# +# Per band, following [kadrlica/desqr depth.py](https://github.com/kadrlica/desqr/blob/main/desqr/depth.py): +# +# 1. cut the bright end (peak of the mag histogram − 3) and mag < 30; +# 2. estimate the global slope of log10(magerr) vs mag from nearest-neighbour pairs +# (KDE peak of the pairwise ratio, as in `depth.py`); +# 3. per object, extrapolate to the mag where S/N = 5 +# (`magerr = 2.5/ln(10)/5 ≈ 0.2171`); +# 4. take the **median per healpix pixel** (nside=1024, ring); +# 5. **truth-anchor the absolute scale**: the reported errors underestimate the real +# scatter ~2× (see the error validation above), so each band's map is shifted so its +# median equals the magnitude where the **truth-based scatter** of (obs − true) +# reaches S/N=5. The desqr machinery provides the (error-factor-immune) spatial +# structure; the truth provides the absolute depth. With this anchoring the +# photo-error model evaluates to σ = 0.217 at `delta_mag = 0` by construction. +# +# Following the true-star convention used for all streamobs products, the depth sample +# is **true stars passing the star classification** (matched, `flags == 0`) — the same +# sample as the photo-error model, so the maps and the error model describe one population. +# + +# In[10]: + + +MIN_PER_PIX = 5 # true stars are ~30 per nside-1024 pixel + +def desqr_maglim(df, mag_col, magerr_col, nside=NSIDE, snr=SNR_DEPTH, + n_slope=300_000, seed=42): + d = df[["alphawin_j2000", "deltawin_j2000", mag_col, magerr_col]].dropna() + m = d[mag_col].values + h, edges = np.histogram(m, bins=np.arange(17, 30, 0.1)) + mag_bright_end = edges[np.argmax(h)] - 3.0 + d = d[(m > mag_bright_end) & (m < 30.0)] + m, merr = d[mag_col].values, d[magerr_col].values + ra, dec = d["alphawin_j2000"].values, d["deltawin_j2000"].values + + # slope of log10(magerr) vs mag from NN pairs (on a subsample, for speed) + rng = np.random.default_rng(seed) + idx = rng.choice(len(d), size=min(n_slope, len(d)), replace=False) + xx = np.radians(ra[idx]) * np.cos(np.radians(dec[idx])) + yy = np.radians(dec[idx]) + _, nn = cKDTree(np.c_[xx, yy]).query(np.c_[xx, yy], k=2, workers=-1) + j = nn[:, 1] + with np.errstate(divide="ignore", invalid="ignore"): + ratio = ((np.log10(merr[idx][j]) - np.log10(merr[idx])) + / (m[idx][j] - m[idx])) + ratio = ratio[np.isfinite(ratio)] + values = np.linspace(0.0, 1.0, 1000) + slope = values[np.argmax(gaussian_kde(ratio[::max(1, ratio.size // 100_000)])(values))] + + magerr_at_snr = (2.5 / np.log(10)) / snr + maglims = m - (np.log10(merr) - np.log10(magerr_at_snr)) / slope + + pix = hp.ang2pix(nside, ra, dec, lonlat=True) + g = pd.Series(maglims).groupby(pix) + med, cnt = g.median(), g.size() + med = med[cnt >= MIN_PER_PIX] + out = np.full(hp.nside2npix(nside), hp.UNSEEN) + out[med.index.values] = med.values + return out, slope + +# true-star convention used for all streamobs products: same sample as the +# photo-error model, so the maps and the error model describe one population +depth_src = cat[(cat["flags"] < FLAG_CUT) & cat.matched + & (cat.truth_gal_star == 1) & (cat.class_star > CLASS_STAR_CUT)] + +def truth_anchor_m5(df, b, snr=SNR_DEPTH): + """Mag where the TRUTH-BASED scatter of (obs - true) reaches the S/N threshold. + The reported magerr underestimates the real errors ~2x, so the desqr maps + (built from reported errors) are anchored to this truth-validated depth.""" + sub = df[[f"truth_mag_{b}", f"mag_auto_{b}"]].dropna() + mt = sub[f"truth_mag_{b}"].values + dmv = sub[f"mag_auto_{b}"].values - mt + bins = np.arange(22.0, 28.2, 0.25) + mid = 0.5 * (bins[1:] + bins[:-1]) + scat = np.full(mid.size, np.nan) + ib = np.digitize(mt, bins) - 1 + for i in range(mid.size): + v = dmv[ib == i] + if v.size >= 100: + scat[i] = (np.percentile(v, 84) - np.percentile(v, 16)) / 2 + g = np.isfinite(scat) + err_at_snr = 2.5 / np.log(10) / snr + return float(np.interp(np.log10(err_at_snr), np.log10(scat[g]), mid[g])) + +maglim_maps, slopes = {}, {} +for b in BANDS: + maglim_maps[b], slopes[b] = desqr_maglim(depth_src, f"mag_auto_{b}", f"magerr_auto_{b}") + cov = maglim_maps[b] != hp.UNSEEN + raw_med = float(np.median(maglim_maps[b][cov])) + m5 = truth_anchor_m5(depth_src, b) + maglim_maps[b][cov] += m5 - raw_med # truth-anchor: median -> true S/N=5 depth + print(f"{b}: slope={slopes[b]:.3f} covered pixels={cov.sum():,} " + f"reported-error median={raw_med:.2f} -> truth-anchored maglim={m5:.2f}") + + +# In[11]: + + +fig, axes = plt.subplots(2, 2, figsize=(13, 8)) +for ax, b in zip(axes.ravel(), BANDS): + mlm = maglim_maps[b] + cov = mlm != hp.UNSEEN + vmin, vmax = np.percentile(mlm[cov], [2, 98]) + plt.axes(ax) + hp.cartview(mlm, lonra=[50.5, 56.5], latra=[-42.3, -37.7], hold=True, + title=f"{b} maglim (S/N={SNR_DEPTH})", unit="mag", + min=vmin, max=vmax, badcolor="0.9") +fig.savefig(FIG_DIR / "maglim_maps.png", dpi=130, bbox_inches="tight") +plt.show() + +fig, ax = plt.subplots(figsize=(7, 4.5)) +for b in BANDS: + mlm = maglim_maps[b] + ax.hist(mlm[mlm != hp.UNSEEN], bins=80, histtype="step", lw=1.5, label=b) +ax.set(xlabel=f"maglim (S/N={SNR_DEPTH}) [mag]", ylabel="N pixels (nside=1024)") +ax.legend(); ax.grid(alpha=0.3) +plt.show() + +OUT_DIR.mkdir(parents=True, exist_ok=True) +for b in BANDS: + fb = "f" + b[1:] + mlm = maglim_maps[b] + cov = mlm != hp.UNSEEN + fname = OUT_DIR / f"roman_dc2_maglim_{fb}_nside{NSIDE}.fits.gz" + hp.write_map(fname, mlm, overwrite=True, dtype=np.float32) + print(f"wrote {fname.name} (median {np.median(mlm[cov]):.2f})") + + +# ## 5. streamobs tables +# +# Same formats as the LSST files in `data/others/` (these are what +# `streamobs.surveys.SurveyFactory.set_completeness` / `set_photo_error` read): +# +# - `roman_photoerror_f158.csv` — `# delta_mag,log_mag_err`, with +# `delta_mag = true mag − maglim` at the object's nside=1024 pixel and +# `log_mag_err` = log10 of the binned **truth-based scatter** ((p84−p16)/2 of +# observed − true) of **true stars** passing the star classification — the +# observationally star-classified sample is galaxy-dominated faintward of ~25.5 +# and would inflate the faint-end errors. The reported SExtractor magerr is +# NOT used (it underestimates the true scatter ~2×); it is kept only as a +# comparison curve in the figure. +# - `roman_stellar_efficiency_cutf158.csv` — +# `# mag_f158,delta_mag,detection_eff,classifiction_eff,classification_detection_eff` +# (the `classifiction_eff` typo is intentional: `set_completeness` looks that name up). +# `delta_mag` is keyed to the **median of the measured maglim map** (the maps and the +# tables share one internal convention; no renormalization is applied anywhere). +# Runtime maglim maps for other footprints (e.g. the HLWAS exptime-scaled map) should +# be expressed in this same convention, i.e. scaled relative to the DC2 depth. +# + +# In[12]: + + +mlm = maglim_maps[BAND] +covered = mlm != hp.UNSEEN +MAGLIM_REF = float(np.median(mlm[covered])) # tables keyed to the measured map median +print(f"reference maglim = measured map median = {MAGLIM_REF:.3f}") + +# --- photoerror table ------------------------------------------------------- +# error model from TRUE stars passing the star classification: the observationally +# star-classified sample is galaxy-dominated faintward of ~25.5 and would inflate +# the faint-end errors (0.33 vs 0.15 mag at 25.5) +sel = (cat.matched & (cat.truth_gal_star == 1) + & (cat.class_star > CLASS_STAR_CUT) & (cat["flags"] < FLAG_CUT)) +pe = cat.loc[sel, ["alphawin_j2000", "deltawin_j2000", f"truth_mag_{BAND}", + f"mag_auto_{BAND}", f"magerr_auto_{BAND}"]].dropna() +pix = hp.ang2pix(NSIDE, pe.alphawin_j2000.values, pe.deltawin_j2000.values, lonlat=True) +ml_local = mlm[pix] +good = ml_local != hp.UNSEEN +delta = pe[f"truth_mag_{BAND}"].values[good] - ml_local[good] +# the error model is the TRUTH-BASED scatter of (observed - true): the reported +# SExtractor magerr underestimates it by ~2x (correlated noise in the coadds) +dm_obs = (pe[f"mag_auto_{BAND}"].values - pe[f"truth_mag_{BAND}"].values)[good] +logerr_reported = np.log10(pe[f"magerr_auto_{BAND}"].values[good]) + +dbins = np.arange(np.floor(delta.min() * 10) / 10, 1.5 + 1e-6, 0.12) +dmid = 0.5 * (dbins[1:] + dbins[:-1]) +log_scatter = np.full(dmid.size, np.nan) +med_logerr_rep = np.full(dmid.size, np.nan) +ib = np.digitize(delta, dbins) - 1 +for i in range(dmid.size): + v = dm_obs[ib == i] + if v.size >= 20: + log_scatter[i] = np.log10((np.percentile(v, 84) - np.percentile(v, 16)) / 2) + med_logerr_rep[i] = np.median(logerr_reported[ib == i]) +keep = np.isfinite(log_scatter) + +photoerr_tab = pd.DataFrame({"delta_mag": dmid[keep], "log_mag_err": log_scatter[keep]}) +fpe = OUT_DIR / "roman_photoerror_f158.csv" +np.savetxt(fpe, photoerr_tab.values, delimiter=",", header="delta_mag,log_mag_err", fmt="%.6f") +print(f"wrote {fpe.relative_to(REPO)} ({len(photoerr_tab)} rows, " + f"delta_mag {photoerr_tab.delta_mag.min():.2f} .. {photoerr_tab.delta_mag.max():.2f})") + +# --- stellar efficiency table ------------------------------------------------ +eff_tab = pd.DataFrame({ + "mag_f158": MAG_MID, + "delta_mag": MAG_MID - MAGLIM_REF, + "detection_eff": eff_det, + "classifiction_eff": eff_cls, + "classification_detection_eff": eff_both, +}) +eff_tab = eff_tab[n_all >= 20].fillna(0.0) +feff = OUT_DIR / "roman_stellar_efficiency_cutf158.csv" +np.savetxt(feff, eff_tab.values, delimiter=",", + header="mag_f158,delta_mag,detection_eff,classifiction_eff,classification_detection_eff", + fmt="%.6f") +print(f"wrote {feff.relative_to(REPO)} ({len(eff_tab)} rows)") + +# --- sanity overlay vs the LSST r-band tables -------------------------------- +lsst_pe = np.genfromtxt(REPO / "data/others/lsst_photoerror_r.csv", delimiter=",", names=True) +lsst_eff = np.genfromtxt(REPO / "data/others/lsst_stellar_efficiency_cutr.csv", delimiter=",", names=True) + +fig, axes = plt.subplots(1, 2, figsize=(12, 4.5)) +axes[0].plot(lsst_pe["delta_mag"], lsst_pe["log_mag_err"], "-", color="0.6", label="LSST r") +axes[0].plot(photoerr_tab.delta_mag, photoerr_tab.log_mag_err, "C3o-", ms=3, + label="Roman F158 (truth-based scatter)") +axes[0].plot(dmid[keep], med_logerr_rep[keep], "C0--", lw=1.5, + label="Roman F158 reported magerr (underestimated)") +axes[0].set(xlabel=r"$\Delta$mag = mag $-$ maglim", ylabel=r"$\log_{10}\sigma$ [mag]") +axes[0].legend(); axes[0].grid(alpha=0.3) +axes[1].plot(lsst_eff["delta_mag"], lsst_eff["classification_detection_eff"], "-", color="0.6", label="LSST r") +axes[1].plot(eff_tab.delta_mag, eff_tab.classification_detection_eff, "C3o-", ms=3, label="Roman F158") +axes[1].set(xlabel=r"$\Delta$mag = mag $-$ maglim", ylabel="classification_detection_eff") +axes[1].legend(); axes[1].grid(alpha=0.3) +fig.savefig(FIG_DIR / "lsst_comparison.png", dpi=130, bbox_inches="tight") +plt.show() + + +# ## Next steps +# +# - Wire these into a `config/surveys/roman_hlwas.yaml` (mirroring `lsst_yr5.yaml`): +# `completeness: roman_stellar_efficiency_cutf158.csv`, `completeness_band: f158`, +# `log_photo_error: roman_photoerror_f158.csv`, `maglim_map_f158: roman_dc2_maglim_f158_nside1024.fits.gz` +# (or, for the full HLWAS footprint, a maglim map scaled from the exposure-time map — +# see `roman_hlwas_exptime_map.ipynb`; the DC2 map above characterizes the mock's depth). +# - Caveats: the det→truth (detection-centric) match assigns a blended star to a single +# truth source, so the detection efficiency here is slightly conservative for blends; +# galaxy "size" is the measured `awin_world`, not a truth size. +# From 01afdf6381353812aef89858900ad7aea3b1d031 Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 11 Jun 2026 15:34:10 -0700 Subject: [PATCH 39/54] Add regenerated roman_dc2 figures, config + docs content updates Co-Authored-By: Claude Fable 5 --- config/surveys/roman_hlwas.yaml | 8 +- .../_static/roman_dc2/efficiency_f158.png | Bin 0 -> 87146 bytes .../_static/roman_dc2/error_validation.png | Bin 0 -> 112390 bytes .../_static/roman_dc2/lsst_comparison.png | Bin 0 -> 86104 bytes .../_static/roman_dc2/mag_distributions.png | Bin 0 -> 67396 bytes docs/source/_static/roman_dc2/maglim_maps.png | Bin 0 -> 396998 bytes .../_static/roman_dc2/photoerror_f158.png | Bin 0 -> 139505 bytes .../roman_dc2/photoerror_f158_truestars.png | Bin 0 -> 138829 bytes docs/source/index.rst | 2 +- docs/source/roman_dc2.md | 96 ++++++++++-------- scripts/roman/create_streamobs_files_hlwas.py | 4 +- 11 files changed, 64 insertions(+), 46 deletions(-) create mode 100644 docs/source/_static/roman_dc2/efficiency_f158.png create mode 100644 docs/source/_static/roman_dc2/error_validation.png create mode 100644 docs/source/_static/roman_dc2/lsst_comparison.png create mode 100644 docs/source/_static/roman_dc2/mag_distributions.png create mode 100644 docs/source/_static/roman_dc2/maglim_maps.png create mode 100644 docs/source/_static/roman_dc2/photoerror_f158.png create mode 100644 docs/source/_static/roman_dc2/photoerror_f158_truestars.png diff --git a/config/surveys/roman_hlwas.yaml b/config/surveys/roman_hlwas.yaml index 2e64aee..f3e13b5 100644 --- a/config/surveys/roman_hlwas.yaml +++ b/config/surveys/roman_hlwas.yaml @@ -11,8 +11,10 @@ survey_files: # Path to data files (leave empty for default location: data/surveys/roman_hlwas) file_path: '' - # Band-specific magnitude limit maps: the DC2-measured S/N=5 depth map - # (un-normalized). The delta_mag axes of the completeness and photo-error + # Band-specific magnitude limit maps: truth-anchored S/N=5 point-source + # depth (desqr spatial structure from the reported errors, absolute scale + # set by where the truth-based scatter of obs-true reaches S/N=5; F158 + # median 25.98). 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The turnover at ~26–26.5 reflects the survey depth.* @@ -78,7 +78,7 @@ that are detected and compact — measured semi-major axis `awin_world < 0.3″` truth catalog providing no intrinsic size — the fraction misclassified as stars is below 1% brighter than F158 ≈ 25.5 and rises to ~15–17% at the faint end. -![Star efficiency and galaxy misclassification vs true F158 mag](_static/roman_hlwas/efficiency_f158.png) +![Star efficiency and galaxy misclassification vs true F158 mag](_static/roman_dc2/efficiency_f158.png) *Detection and classification efficiency for true stars, with the misclassification rate of detected compact (<0.3″) true galaxies on the same axes.* @@ -93,7 +93,7 @@ magnitude — exceeds the reported errors by a flat factor of ~1.9–2.0 at all magnitudes and in all four bands, the signature of SExtractor underestimating the correlated noise of the resampled, median-combined coadds. -![Reported errors vs truth-based scatter](_static/roman_hlwas/error_validation.png) +![Reported errors vs truth-based scatter](_static/roman_dc2/error_validation.png) *Truth-based scatter (solid) and median reported `magerr_auto` (dashed) for true stars per band, with their ratio in the lower panel: a constant factor ≈2 in every @@ -101,20 +101,24 @@ band.* The photometric error model `roman_photoerror_f158.csv` is therefore built from the **truth-based scatter**, not the reported errors: it tabulates the binned log10 -scatter of (observed − true) F158 magnitude for star-classified objects against -`delta_mag = m_true − maglim`, where the magnitude limit is evaluated at each -object's position from the nside=1024 depth map described below. Tabulating against -`delta_mag` rather than magnitude makes the model portable across regions (and -surveys) of different depth, under the assumption that the error profile depends on -magnitude only through the local depth. - -![Observed F158 photometric error vs true magnitude](_static/roman_hlwas/photoerror_f158.png) - -*Observed F158 `magerr_auto` against true F158 magnitude for star-classified -objects, with binned median and 16/84% curves of the reported errors and, in orange, -the truth-based scatter adopted for the error model. The reported error saturates -near the S/N≈5 floor at the faint end; the sparse cloud above the median relation is -blends.* +scatter of (observed − true) F158 magnitude against `delta_mag = m_true − maglim`, +where the magnitude limit is evaluated at each object's position from the nside=1024 +depth map described below. The sample is **true stars that pass the star +classification** — the population an injected stream star follows. (An +observationally star-classified sample would not do: it is galaxy-dominated +faintward of F158 ≈ 25.5, where misclassified compact galaxies roughly double the +apparent scatter.) Tabulating against `delta_mag` rather than magnitude makes the +model portable across regions (and surveys) of different depth, under the assumption +that the error profile depends on magnitude only through the local depth. + +![F158 photometric errors of true stars](_static/roman_dc2/photoerror_f158_truestars.png) + +*Left: reported F158 `magerr_auto` against true F158 magnitude for star-classified +true stars, with the binned median and 16/84% curves of the reported errors and, in +orange, the truth-based scatter adopted for the error model. Right: the binned +scatter of (true − observed) — robust (p84−p16)/2 and plain standard deviation — +against the median reported error: the reported errors under-cover the real scatter +by a factor ≈2 everywhere.* ## Zeropoints and extinction coefficients @@ -171,30 +175,38 @@ is estimated from nearest-neighbour pairs (the peak of a kernel density estimate the pairwise ratio); each object's magnitude is then extrapolated along that slope to the magnitude at which it would reach the threshold signal-to-noise, and the magnitude limit of a pixel is the median over its objects. We define depth at -**S/N = 5**, consistent with the catalog's detection threshold. The resulting -medians are 26.77/27.11/27.11/26.67 in F106/F129/F158/F184. - -These nominally exceed the official expected 5σ point-source depths of the simulated -survey (26.9 in F106/F129/F158, 26.2 in F184) by ~0.2–0.5 mag, but this is not real -extra depth: it follows from the same error underestimation established above (the -same excess is visible in Figure 5 of Troxel et al., most prominently in F184). The -maps are therefore an *internal* depth reference — their value is the spatial -structure and the `delta_mag` coordinate they define, not their absolute calibration. - -![Magnitude-limit maps per band at nside=1024](_static/roman_hlwas/maglim_maps.png) - -*Measured S/N=5 magnitude-limit maps over the DC2 footprint (RA 51–56, Dec −42 to -−38). The spatial structure traces the simulated dither/pass pattern.* +**S/N = 5**, consistent with the catalog's detection threshold, and use the same +sample as the photometric error model (true stars passing the star classification), +so the maps and the error model describe one population. + +Because the desqr extrapolation runs on the *reported* errors, which underestimate +the truth by a factor ≈2, the raw maps come out unphysically deep (medians +27.6–27.9). The spatial structure is unaffected by a global error factor, so we keep +it and **truth-anchor the absolute scale**: each band's map is shifted so that its +median equals the magnitude at which the truth-based scatter of (observed − true) +reaches S/N = 5. The anchored medians are **26.19 / 26.19 / 25.98 / 25.26** in +F106/F129/F158/F184. With this anchoring the photometric error model evaluates to +exactly σ = 0.217 (S/N = 5) at `delta_mag = 0` — "maglim" means S/N = 5 in the same +truth-based sense everywhere. These depths sit ~0.7–0.9 mag brighter than the +official expected point-source depths (26.9/26.2): the official values assume +optimal PSF photometry, while these describe what the catalog's `mag_auto` +photometry actually delivers in true-magnitude space. + +![Magnitude-limit maps per band at nside=1024](_static/roman_dc2/maglim_maps.png) + +*Truth-anchored S/N=5 magnitude-limit maps over the DC2 footprint (RA 51–56, Dec −42 +to −38). The spatial structure traces the simulated dither/pass pattern.* ### Depth convention -No renormalization is applied anywhere: the maps are released as measured +The maps are released truth-anchored (`roman_dc2_maglim_f*_nside1024.fits.gz`), and the `delta_mag` axes of the -completeness and photometric-error tables are keyed to the median of the F158 map, -so maps and tables share a single internal convention. A magnitude-limit map for a -different footprint (e.g. an exposure-time-scaled map of the real HLWAS) must be -expressed in this same convention — scaled relative to the DC2 depth — for the -tables to apply. For reference, the +completeness and photometric-error tables are keyed to the median of the F158 map +(25.98), so maps and tables share a single convention in which maglim is the +true-scatter S/N=5 depth. A magnitude-limit map for a different footprint (e.g. an +exposure-time-scaled map of the real HLWAS) must be expressed in this same +convention — scaled relative to the DC2 depth — for the tables to apply. For +reference, the [STScI community-defined HLWAS median 5σ point-source depths](https://roman-docs.stsci.edu/roman-community-defined-surveys/high-latitude-wide-area-survey) are: @@ -208,7 +220,7 @@ Substituting a shallower (e.g. wide-tier) map translates the completeness and er curves to the corresponding depths, under the assumption that the selection function depends on magnitude only through `mag − maglim`. -![Roman F158 vs LSST r selection-function tables](_static/roman_hlwas/lsst_comparison.png) +![Roman F158 vs LSST r selection-function tables](_static/roman_dc2/lsst_comparison.png) *The Roman F158 photometric-error and combined-efficiency tables compared with the LSST r-band tables in the shared `delta_mag = mag − maglim` convention.* @@ -227,6 +239,10 @@ completeness = survey.get_completeness("f158", mag, maglim) photo_error = survey.get_photo_error("f158", mag, maglim) ``` +All tables, maps, and the figures on this page are regenerated by +`scripts/roman/create_streamobs_files_hlwas.py` (the matched catalog itself by +`scripts/roman/build_roman_dc2_det_truth.py`). + ## Caveats - The simulation is the *reference* HLIS design (deeper than the current diff --git a/scripts/roman/create_streamobs_files_hlwas.py b/scripts/roman/create_streamobs_files_hlwas.py index 4425b82..d05e23f 100644 --- a/scripts/roman/create_streamobs_files_hlwas.py +++ b/scripts/roman/create_streamobs_files_hlwas.py @@ -4,7 +4,7 @@ Run with the streamobs env: /astro/store/shiren/conda-envs/stream_team/envs/streamobs/bin/python \ - notebooks/create_streamobs_files_hlwas.py + scripts/roman/create_streamobs_files_hlwas.py Inputs : data/surveys/roman_dc2/roman_dc2_det_truth.parquet (+ per-tile truth indices on first run, cached to roman_dc2_truth_stars.parquet) @@ -50,7 +50,7 @@ from scipy.spatial import cKDTree from scipy.stats import gaussian_kde -REPO = Path(__file__).resolve().parent.parent +REPO = Path(__file__).resolve().parent.parent.parent DC2_DIR = REPO / "data/surveys/roman_dc2" OUT_DIR = REPO / "data/surveys/roman_hlwas" CAT_PATH = DC2_DIR / "roman_dc2_det_truth.parquet" From 8395f5473224896af03a0cf70fec940cf0403e2f Mon Sep 17 00:00:00 2001 From: psferguson Date: Thu, 11 Jun 2026 15:46:35 -0700 Subject: [PATCH 40/54] Detection cut at true S/N>5 (corrected errors) for all products The catalog's S/N>5 selection uses the reported detection-image flux errors; truth-validating them (scatter of mag_auto - truth_bb_mag for clean stars, bright-end floor removed in quadrature) gives an error factor of 1.71, so a true S/N=5 selection is reported det_sn > 8.6 (keeps 81.6% of detections). Applied to every product selection; the error-validation cell intentionally keeps the raw catalog. Effect: detection efficiency now falls off at the true depth (0.82/0.59/0.18 at delta_mag = 0/+0.5/+1) instead of extending ~1 mag past it; the photo-error table ends at delta ~+0.9 and still evaluates to sigma = 0.217 at delta_mag = 0; anchored maglims unchanged. Combined 50% completeness now at F158 ~ 26.3. The notebook copy is regenerated from the script and rerun (both stay local/gitignored, including the converter helper). Co-Authored-By: Claude Fable 5 --- .gitignore | 1 + .../_static/roman_dc2/efficiency_f158.png | Bin 87146 -> 85201 bytes .../_static/roman_dc2/lsst_comparison.png | Bin 86104 -> 85225 bytes docs/source/_static/roman_dc2/maglim_maps.png | Bin 396998 -> 396873 bytes .../_static/roman_dc2/photoerror_f158.png | Bin 139505 -> 125492 bytes .../roman_dc2/photoerror_f158_truestars.png | Bin 138829 -> 135191 bytes docs/source/roman_dc2.md | 17 +++-- scripts/roman/create_streamobs_files_hlwas.py | 71 +++++++++++++++--- 8 files changed, 75 insertions(+), 14 deletions(-) diff --git a/.gitignore b/.gitignore index 3e43398..bade07c 100644 --- a/.gitignore +++ b/.gitignore @@ -141,3 +141,4 @@ docs/source/roman_multisurvey_plan.md # Local-only Roman HLWAS derivation notebook (kept un-tracked; script is the source) notebooks/create_streamobs_files_hlwas.ipynb +scripts/roman/script_to_notebook.py diff --git a/docs/source/_static/roman_dc2/efficiency_f158.png 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b/docs/source/roman_hlwas.md @@ -1,99 +1,149 @@ # Roman HLWAS Survey Files -This page documents how the Roman High Latitude Wide Area Survey (HLWAS) selection -function shipped with streamobs was derived. The full, executable derivation is in -[`notebooks/create_streamobs_files_hlwas.ipynb`](https://github.com/LSSTDESC/streamobs/blob/main/notebooks/create_streamobs_files_hlwas.ipynb); -the survey is configured by `config/surveys/roman_hlwas.yaml` and used like any other -survey: +This page describes the construction of the Roman High Latitude Wide Area Survey +(HLWAS) selection function shipped with streamobs: the stellar +detection-and-classification completeness, the photometric error model, and the +magnitude-limit (depth) maps, together with the conventions that tie them together. -```python -from streamobs.surveys import SurveyFactory -survey = SurveyFactory.create_survey("roman", release="hlwas") -``` - -## Input data +## The simulated survey -Everything is measured from the **Roman–Rubin DC2 synthetic survey** of +All quantities are measured from the Roman–Rubin DC2 synthetic survey of [Troxel et al. (2023)](https://arxiv.org/abs/2209.06829): ~20 deg² of image-level -simulations of the Roman reference High Latitude Imaging Survey at full depth, in the -four bands Y106/J129/H158/F184 (the mock's `H158` is the Roman **F158** filter). -Detection is SExtractor run on a median Y106+J129+H158+F184 coadd; stars come from -Galfast, galaxies from cosmoDC2. - -From the released per-tile detection and truth catalogs we built a single -**detection-centric matched catalog** (`roman_dc2_det_truth.parquet`, built by -`notebooks/build_roman_dc2_det_truth.py`): every S/N>5 detection is matched to a true -source within 1″, breaking ties among the up-to-3 nearest neighbours by taking the -closest in magnitude — the same recipe as the paper. - -## Selections (paper-exact) - -Following Troxel et al. §"object selection", all products apply: - -1. **`flags == 0`** — removes 32% of detections (we reproduce this fraction exactly); +simulations of the Roman High Latitude Imaging Survey reference design at full +depth, reaching a 5σ point-source depth of ~26.9 AB in the F106/F129/F158 bands and +26.2 AB in F184. Stars are drawn from a Galfast model of the Galaxy and galaxies +from the cosmoDC2 extragalactic catalog. Object detection and photometry are +performed with SExtractor on a median F106+F129+F158+F184 coadd detection image, +with forced photometry in each band, over 1039 coadd tiles. Each tile provides a +detection catalog and a truth index listing every simulated object in the tile with +its position, four-band magnitudes, and a star/galaxy label. + +## Matched detection–truth catalog + +We match every detection to a true source, following the recipe of Troxel et al.: +a detection is associated with the truth objects within 1″, and where several +qualify, with the closest in magnitude among the up to three nearest on the sky. +The magnitude comparison uses the detection's `mag_auto` (measured on the +median-combined detection image) against a truth broadband magnitude formed from +the mean flux across the four truth bands, which is the natural truth-side analog +of the detection image. The match is detection-centric: each detection is assigned +to its single dominant (typically brightest) true source, which keeps the +observed↔true magnitude relation clean in the presence of blending, at the cost of +assigning a blend to only one of its members. + +Following the paper, three selections define the analysis sample: + +1. **`flags == 0`** — objects with any SExtractor flag are removed (32% of + detections, matching the fraction quoted in the paper); 2. **S/N > 5** in the detection image; -3. **matched to a true object** within 1″. - -Two data subtleties discovered during the derivation, both handled in the notebook: - -- **Duplicate truth entries.** ~26% of truth stars have a second truth entry at the - *exact same position* under a different index, carrying only the J129 magnitude. - The match can assign the detection to the duplicate, which would mimic a flat ~12% - detection-efficiency deficit. Truth stars are therefore collapsed to unique - positions, and a star counts as detected if *any* of its entries matched. -- **Tile-margin geometry.** Each detection coadd extends ~30″ beyond its truth tile's - footprint, so per-tile matching leaves ~21% of detections (the margin band) - unmatched — they are duplicates of detections in the neighbouring tile. Inside the - truth footprint the unmatched rate is 0.1–5%, consistent with the paper's 1.7%. - The matched-only selection removes these margin duplicates. - -## Products - -All files live in `data/surveys/roman_hlwas/`. - -### Stellar completeness — `roman_stellar_efficiency_cutf158.csv` - -Columns `mag_f158, delta_mag, detection_eff, classifiction_eff, -classification_detection_eff` (same format as the LSST table, including the -`classifiction_eff` spelling that the loader expects). For true stars binned in true -F158 magnitude: - -- `detection_eff` — fraction with a clean (`flags==0`) S/N>5 detection matched to them; -- `classifiction_eff` — fraction of those whose detection has SExtractor - `class_star > 0.5`; -- `classification_detection_eff` — the product (detected *and* classified). - -The bright plateau is ~0.90–0.95 (not 1.0): blended stars whose only detection is -flagged are excluded by the paper's `flags==0` cut. The 50% point of the combined -efficiency is at F158 ≈ 27.2 in the simulation. - -### Photometric errors — `roman_photoerror_f158.csv` - -Columns `delta_mag, log_mag_err`: the binned median log10 of the observed F158 -`magerr_auto` of star-classified objects, against `delta_mag = true mag − maglim` at -each object's position. - -### Depth maps — `roman_dc2_maglim_f{106,129,158,184}_nside1024[_5sigps].fits.gz` - -HEALPix (nside=1024, ring) magnitude-limit maps over the DC2 footprint, computed with -the recipe of [desqr/depth.py](https://github.com/kadrlica/desqr/blob/main/desqr/depth.py): -estimate the slope of log(magerr) vs mag from nearest-neighbour pairs, extrapolate each -object to the magnitude where S/N=5, and take the median per pixel. - -Two conventions are provided: - -- the raw **measured** maps (median F158 depth 27.11 — the catalog's SExtractor errors - run ~0.2 mag past the official depths, plausibly because correlated noise in the - resampled median coadds is underestimated); -- the **`_5sigps`** maps, renormalized so the median equals the official 5σ - point-source depth of the simulated survey (26.9 in F106/F129/F158, 26.2 in F184). - -## The depth convention (important) - -The `delta_mag` axes of both tables are keyed to the **official 5σ point-source -depth** convention, *not* to the raw measured depth. This means the tables must be -paired with maglim maps in the same convention — the `_5sigps` maps above, or, for the -real HLWAS footprint, an exposure-time-scaled map normalized to the +3. **a positional match to a true object**, as defined above. + +The matched-only selection also serves a geometric purpose. Each detection coadd +extends ~30″ beyond the footprint of its tile's truth index, so detections in that +margin band (~21% of the total) have no truth object in their own tile — they are +duplicates of detections that appear, matched, in the neighbouring tile. Within the +truth footprint the unmatched fraction is 0.1–5% per tile, consistent with the 1.7% +quoted by Troxel et al.; requiring a match therefore removes the margin duplicates +along with the small residual of spurious and shredded detections. + +A further property of the truth catalog requires care: about a quarter of the true +stars carry a second truth entry at the same position under a different identifier, +populated in only one band. Because the matcher can assign a star's detection to +either entry, we collapse the truth catalog to unique positions (209,024 entries → +181,502 stars) and count a star as detected if *any* of its entries received a +match. Without this, the detection efficiency would show a spurious, flat ~12% +deficit at all magnitudes. + +![True vs observed magnitude distributions per band](_static/roman_hlwas/mag_distributions.png) + +*Number counts per band: solid — true magnitudes of matched sources; dotted — +observed `mag_auto` of all S/N>5 detections. The turnover at ~26–26.5 reflects the +survey depth.* + +## Stellar completeness + +The completeness table `roman_stellar_efficiency_cutf158.csv` gives, in bins of +true F158 magnitude for true stars: + +- `detection_eff` — the fraction with a clean (`flags==0`) S/N>5 detection matched + to them. The denominator is the full truth-star catalog, including undetected + stars, so this is a true completeness; +- `classifiction_eff` — among detected stars, the fraction whose detection is + classified as a point source, for which we adopt SExtractor `class_star > 0.5` + (the median `class_star` of detected true stars is 0.94); +- `classification_detection_eff` — the product: the probability that a true star + appears in the catalog *and* is classified as a star. + +The bright plateau sits at ~0.90–0.95 rather than unity: stars blended with +brighter neighbours either share a detection assigned to the neighbour or carry +blend flags and are removed by the `flags==0` selection. This is a property of the +adopted catalog cuts, not of the instrument. The combined efficiency crosses 50% at +F158 ≈ 27.2 in the simulated (reference-depth) survey. + +We characterize stellar contamination with the same machinery: for *true galaxies* +that are detected and compact — measured semi-major axis `awin_world < 0.3″`, the +truth catalog providing no intrinsic size — the fraction misclassified as stars is +below 1% brighter than F158 ≈ 25.5 and rises to ~15–17% at the faint end. + +![Star efficiency and galaxy misclassification vs true F158 mag](_static/roman_hlwas/efficiency_f158.png) + +*Detection and classification efficiency for true stars, with the misclassification +rate of detected compact (<0.3″) true galaxies on the same axes.* + +## Photometric errors + +The photometric error model `roman_photoerror_f158.csv` is the binned median of +log10 of the observed F158 `magerr_auto` for star-classified objects, tabulated +against `delta_mag = m_true − maglim`, where the magnitude limit is evaluated at +each object's position from the nside=1024 depth map described below. Tabulating +against `delta_mag` rather than magnitude makes the model portable across regions +(and surveys) of different depth, under the assumption that the error profile +depends on magnitude only through the local depth. + +![Observed F158 photometric error vs true magnitude](_static/roman_hlwas/photoerror_f158.png) + +*Observed F158 `magerr_auto` against true F158 magnitude for star-classified +objects, with binned median and 16/84% curves. The error saturates near the S/N≈5 +floor at the faint end; the sparse cloud above the median relation is blends.* + +## Survey depth + +Depth maps are computed per band on a HEALPix nside=1024 grid (ring ordering) with +the method of [desqr](https://github.com/kadrlica/desqr/blob/main/desqr/depth.py): +after removing the bright end, the global slope of log10(magerr) versus magnitude +is estimated from nearest-neighbour pairs (the peak of a kernel density estimate of +the pairwise ratio); each object's magnitude is then extrapolated along that slope +to the magnitude at which it would reach the threshold signal-to-noise, and the +magnitude limit of a pixel is the median over its objects. We define depth at +**S/N = 5**, consistent with the catalog's detection threshold. The resulting +medians are 26.77/27.11/27.11/26.67 in F106/F129/F158/F184. + +These measured depths run ~0.2 mag (F106–F158) to ~0.5 mag (F184) past the official +expected 5σ point-source depths of the simulated survey. The same excess is visible +in Figure 5 of Troxel et al., whose measured magnitude histograms extend beyond the +expected limiting-magnitude lines (most prominently in F184); the likely cause is +that SExtractor underestimates the correlated noise of the resampled, median-combined +coadds, inflating the apparent signal-to-noise. + +![Magnitude-limit maps per band at nside=1024](_static/roman_hlwas/maglim_maps.png) + +*Measured S/N=5 magnitude-limit maps over the DC2 footprint (RA 51–56, Dec −42 to +−38). The spatial structure traces the simulated dither/pass pattern.* + +### Depth convention and normalization + +Because of this offset, we adopt the **official 5σ point-source depth** as the +reference convention. Each band's map is provided in two forms: + +- the raw measured map (`roman_dc2_maglim_f*_nside1024.fits.gz`); +- a normalized map (`..._5sigps.fits.gz`) shifted so its median equals the official + depth of the simulated survey (26.9 in F106/F129/F158, 26.2 in F184), preserving + the measured spatial structure. + +The `delta_mag` axes of the completeness and photometric-error tables are keyed to +the official convention, so they must be paired with magnitude-limit maps in the +same convention — the `_5sigps` maps above or, for the real HLWAS footprint, an +exposure-time-scaled map normalized to the [STScI community-defined HLWAS median 5σ point-source depths](https://roman-docs.stsci.edu/roman-community-defined-surveys/high-latitude-wide-area-survey): | Tier | Area | 5σ point-source total depth (AB) | @@ -102,24 +152,41 @@ real HLWAS footprint, an exposure-time-scaled map normalized to the | Medium | ~2400 deg² | F106 26.5, F129 26.4, F158 26.4 | | Deep | ~19.2 deg² | F106 27.7, F129 27.6, F158 27.5, F184 27.0, F213 25.9 | -With this convention, swapping in a shallower (wide-tier) maglim map automatically -shifts the completeness and photo-error curves to the correct depths, under the +With this convention, substituting a shallower (e.g. wide-tier) map automatically +translates the completeness and error curves to the correct depths, under the assumption that the selection function depends on magnitude only through `mag − maglim`. +![Roman F158 vs LSST r selection-function tables](_static/roman_hlwas/lsst_comparison.png) + +*The Roman F158 photometric-error and combined-efficiency tables compared with the +LSST r-band tables in the shared `delta_mag = mag − maglim` convention.* + +## Using the survey in streamobs + +The survey is configured by `config/surveys/roman_hlwas.yaml`, with data files in +`data/surveys/roman_hlwas/`: + +```python +from streamobs.surveys import SurveyFactory +survey = SurveyFactory.create_survey("roman", release="hlwas") + +maglim = survey.get_maglim("f158", pixel=pix) +completeness = survey.get_completeness("f158", mag, maglim) +photo_error = survey.get_photo_error("f158", mag, maglim) +``` + ## Caveats - The simulation is the *reference* HLIS design (deeper than the current - community-defined wide tier); the wide-tier behaviour is obtained by translation in + community-defined wide tier); wide-tier behaviour is obtained by translation in `delta_mag`, not by an independent simulation. -- Star/galaxy classification is SExtractor `class_star` on the detection coadd; the - false-star rate for compact (<0.3″) galaxies is <1% brighter than F158 ≈ 25.5, - rising to ~15–17% at the faint end. - The detection-centric match assigns a blend to its dominant source, so the detection efficiency is slightly conservative for stars blended with brighter neighbours. - The truth catalog only extends to ~15 AB and the simulation does not model saturation; the configured `saturation: 15.0` marks the validity limit of the - table rather than the physical WFI saturation (~17–18 AB for point sources). -- Extinction coefficients are CCM89 (R_V=3.1) evaluated at the filter effective - wavelengths. + completeness table rather than the physical WFI saturation (~17–18 AB for point + sources). +- Extinction coefficients are CCM89 (R_V = 3.1) evaluated at the filter effective + wavelengths (1.14, 0.83, 0.60, 0.47 for F106/F129/F158/F184). diff --git a/notebooks/create_streamobs_files_hlwas.ipynb b/notebooks/create_streamobs_files_hlwas.ipynb index 06a0a66..9fed55e 100644 --- a/notebooks/create_streamobs_files_hlwas.ipynb +++ b/notebooks/create_streamobs_files_hlwas.ipynb @@ -30,10 +30,10 @@ "id": "8161d972", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:11:12.811144Z", - "iopub.status.busy": "2026-06-11T18:11:12.810468Z", - "iopub.status.idle": "2026-06-11T18:11:15.201089Z", - "shell.execute_reply": "2026-06-11T18:11:15.193304Z" + "iopub.execute_input": "2026-06-11T18:21:23.156071Z", + "iopub.status.busy": "2026-06-11T18:21:23.155960Z", + "iopub.status.idle": "2026-06-11T18:21:25.783765Z", + "shell.execute_reply": "2026-06-11T18:21:25.783070Z" } }, "outputs": [ @@ -90,6 +90,8 @@ "MAG_MID = 0.5 * (MAG_BINS[1:] + MAG_BINS[:-1])\n", "\n", "plt.rcParams.update({\"figure.dpi\": 110, \"font.size\": 11})\n", + "FIG_DIR = REPO / \"docs/source/_static/roman_hlwas\" # figures embedded in the docs page\n", + "FIG_DIR.mkdir(parents=True, exist_ok=True)\n", "print(f\"repo: {REPO}\")\n" ] }, @@ -119,10 +121,10 @@ "id": "ecced6fb", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:11:15.203924Z", - "iopub.status.busy": "2026-06-11T18:11:15.203518Z", - "iopub.status.idle": "2026-06-11T18:11:16.881573Z", - "shell.execute_reply": "2026-06-11T18:11:16.880971Z" + "iopub.execute_input": "2026-06-11T18:21:25.794023Z", + "iopub.status.busy": "2026-06-11T18:21:25.793441Z", + "iopub.status.idle": "2026-06-11T18:21:27.490576Z", + "shell.execute_reply": "2026-06-11T18:21:27.488598Z" } }, "outputs": [ @@ -170,10 +172,10 @@ "id": "24956ad3", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:11:16.889162Z", - "iopub.status.busy": "2026-06-11T18:11:16.888620Z", - "iopub.status.idle": "2026-06-11T18:11:17.282382Z", - "shell.execute_reply": "2026-06-11T18:11:17.281480Z" + "iopub.execute_input": "2026-06-11T18:21:27.494375Z", + "iopub.status.busy": "2026-06-11T18:21:27.493868Z", + "iopub.status.idle": "2026-06-11T18:21:27.903173Z", + "shell.execute_reply": "2026-06-11T18:21:27.902527Z" } }, "outputs": [ @@ -245,10 +247,10 @@ "id": "ae3481ab", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:11:17.284123Z", - "iopub.status.busy": "2026-06-11T18:11:17.283915Z", - "iopub.status.idle": "2026-06-11T18:11:17.647300Z", - "shell.execute_reply": "2026-06-11T18:11:17.646721Z" + "iopub.execute_input": "2026-06-11T18:21:27.904500Z", + "iopub.status.busy": "2026-06-11T18:21:27.904386Z", + "iopub.status.idle": "2026-06-11T18:21:28.817492Z", + "shell.execute_reply": "2026-06-11T18:21:28.816946Z" } }, "outputs": [ @@ -294,6 +296,7 @@ " xlim=(15, 29), title=f\"Observed F158 photo-error, class_star > {CLASS_STAR_CUT}, flags == 0\")\n", "fig.colorbar(hb, ax=ax, label=\"N (log)\")\n", "ax.legend(loc=\"upper left\")\n", + "fig.savefig(FIG_DIR / \"photoerror_f158.png\", dpi=130, bbox_inches=\"tight\")\n", "plt.show()\n" ] }, @@ -327,10 +330,10 @@ "id": "7a7ec629", "metadata": { "execution": { - "iopub.execute_input": "2026-06-11T18:11:17.648850Z", - "iopub.status.busy": "2026-06-11T18:11:17.648664Z", - "iopub.status.idle": "2026-06-11T18:11:17.916833Z", - "shell.execute_reply": "2026-06-11T18:11:17.916260Z" + "iopub.execute_input": "2026-06-11T18:21:28.819934Z", + "iopub.status.busy": "2026-06-11T18:21:28.819727Z", + "iopub.status.idle": "2026-06-11T18:21:29.085795Z", + "shell.execute_reply": "2026-06-11T18:21:29.085249Z" } }, "outputs": [ @@ -339,18 +342,9 @@ "output_type": "stream", "text": [ "class_star of matched true stars: p10/50/90 = [0.765 0.942 0.996]\n", - "209,024 unique-ind stars -> 181,502 unique-position stars\n" + "209,024 unique-ind stars -> 181,502 unique-position stars\n", + "star sample for the efficiency curves: 180,062 (combined plot with the galaxy misclassification below)\n" ] - }, - { - "data": { - "image/png": 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", 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