distance grid export#135
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May 26, 2026
oshaughn
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- pseudo_pipe: enable extrinsic export
- demo/rift/ : demo file
Add RIFT.precision and route all extended-precision dtype use through it.
* RIFT.precision (new): RiftFloat resolves to numpy.longdouble whenever
the platform's long double has itemsize > 8 (e.g. Linux x86_64), and
otherwise falls back to numpy.float64. Also exports
RIFT_FLOAT_HIGH_PRECISION and RIFT_FLOAT_NAME. Eliminates the
import-time AttributeError when numpy.float128 is absent (macOS arm64,
Windows MSVC, non-x86 Linux, future numpy 2.x platforms).
* Integrator package: replace every numpy.float128 / np.float128 in
mcsampler.py, mcsamplerEnsemble.py, mcsamplerGPU.py,
mcsamplerAdaptiveVolume.py, mcsamplerNFlow.py, mcsamplerPortfolio.py,
statutils.py
with RiftFloat. The dtype-equality guards in mcsamplerGPU and
mcsamplerNFlow ("if weights_alt.dtype == numpy.float128: cast to
float64") degrade gracefully when RiftFloat == float64 (the
conditional astype becomes a no-op).
* likelihood/factored_likelihood.py and
interpolators/BayesianLeastSquares.py: same RiftFloat swap, so they
also import cleanly on platforms without np.float128.
* CI (.github/workflows/ci.yml): add rift_O4d_gmm_gpu to the trigger
branches; expand the install matrix to 3.9-3.13; convert
import-check and test-run into a two-lane matrix:
- legacy : python 3.9 + numpy==1.24.4 (historical green build)
- modern : python 3.12 + numpy>=2.0,<3.0 (forward-looking gate)
Numpy is pinned after requirements.txt, so the unpinned 'numpy' line
in requirements.txt is preserved. Test-log artifacts are named
per-lane so failures from each can be uploaded independently.
No behavioral change on the existing legacy CI lane: RiftFloat is
numpy.longdouble there, which is the exact 16-byte type previously
spelled numpy.float128. The modern CI lane is the new gate.
Implement a reusable distance-grid export helper for ILE, thread the export flag through pseudo_pipe, and add focused reconstruction tests. Add a zero-spin fake-data demo that builds a DAG with distance-grid export enabled, plus a small lalsimutils XML compatibility fix for current LAL bindings.
… 4.4.0 causes breaking changes)
Owner
Author
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Not happy with AI changes to the algorithm -- lots of testing needed. |
Provide a root pixi workspace that defaults local development to SWIG <4.4.0 while also defining a SWIG >=4.4.0 comparison environment. Add GitLab CI jobs for both pixi environments so deployment stability can be checked across the hidden SWIG binding change.
* RIFT/misc/distance_grid.py: build_distance_grid now divides out the distance sampling prior so the exported lnL is L_pure(d) = integral L(d,Omega) pi_Omega dOmega. New column ln_prior_d_sampling carries the per-bin sampling-prior factor so default reconstruction reproduces log_res exactly, while reconstruct_marginal_lnL(grid, ln_prior_d=...) re-marginalizes against any prior of choice. * integrate_likelihood_extrinsic_batchmode: handle mcsamplerEnsemble/GMM _rvs columns (raw integrand/joint_prior/joint_s_prior, not log_*); drop zero-weight samples cleanly instead of raising "missing type". Pass sampler.prior_pdf["distance"] values per-sample. * test/test_distance_grid.py: cover the pure-likelihood property (different priors yield correctly different marginals) and the round trip. * demo/rift/add_distance_grids/validate_distance_grid.py: new stress harness quantifies n_eff vs integral/shape error. * pixi.toml: pin lalsuite==7.25, lalmetaio<=4.0.5 to dodge the SWIG-4.4 cross-module SwigPyObject/LIGOTimeGPS regression (issue oshaughn#136). Verified end-to-end ILE run produces .dgrid whose reconstruction matches log_res to machine precision. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ILE batchmode now optionally emits a .dslice file per intrinsic point containing K independent extrinsic-marginalized likelihoods at K distance slice centers (quantile centers of the posterior in d). The estimator is importance-reweighting of the main run's Omega samples at each slice distance, re-using the cached likelihood machinery -- no waveform or PSD regeneration, no extra worker spin-up. With K~=10 the artifact stays within the user's <~10x .composite size budget. * RIFT/misc/distance_slices.py: importance_reweight_slices, quantile_slice_centers, table builder/loader, and reconstruct_marginal_lnL that takes an optional custom distance prior. Schema (DISTANCE_SLICE_FIELDS) deliberately mirrors .composite for downstream CIP integration. * integrate_likelihood_extrinsic_batchmode: new --export-distance-slices K and --distance-slice-method flags; threaded into analyze_event after the main integration. Reuses sampler._rvs and like_to_integrate. Emits a runtime warning when GMM + low main n_eff, since B2-reweight silently biases in that regime. * demo/rift/add_distance_grids/validate_distance_slices.py: synthetic stress harness with a known closed-form marginal and an adjustable d-Omega coupling. Confirms B2-reweight matches truth to <0.1 nat over a wide coupling range when main n_eff is healthy. * demo/rift/add_distance_grids/PLAN_B_DESIGN.md: design notes covering the math, the GMM-vs-AV finding, the (recommended) non-destructive workflow integration plan, and the deferred B2-fresh cross-check path. End-to-end check on the fake-data demo (AV sampler, main n_eff ~6): B2 marginal reconstructs the main log_res within sigmaL; per-slice n_eff 7-28; .dslice 10 rows x 19 columns per event. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Reweight alone breaks in the tails: Omega samples drawn during the main
run have no support at distances far from the posterior peak, so the
slice estimator silently biases or returns garbage there. Switch to a
hybrid scheme where core slices stay reweight (cheap, accurate inside
the posterior) and wing slices are fresh Omega-only AdaptiveVolume
integrations at the pinned distance (correct, expensive only on the few
points we need them).
* RIFT/misc/distance_slices.py:
- fresh_sample_slices builds a fresh AV sampler over Omega only, clones
the main sampler's per-param (pdf, prior, llim, rlim) config, wraps
like_to_integrate to pin distance and defensively clip Omega values
inside [llim, rlim] (avoids arccos NaN at boundary).
- pick_wing_centers places K_wing centers log-uniformly in
[d_min, d_core_lo] union [d_core_hi, d_max], evenly split.
- is_uninformative detects a flat-in-d core so wings are skipped on
events where the distance posterior carries no information.
- sigma_lnL conversion for fresh slices: AV returns log(rel_var) +
2*log_int; we report sqrt(rel_var) so the column is on the same
scale as the reweight branch and the main run's sigmaL_main.
* integrate_likelihood_extrinsic_batchmode: split --export-distance-slices
K into --n-distance-slice-core (reweight) + --n-distance-slice-wing
(fresh). Default 60/40 split. New flags --distance-slice-wing-nmax,
--distance-slice-wing-neff, --distance-slice-skip-threshold.
Per-row method column (reweight=0, fresh=1) marks which estimator
produced each slice.
* PLAN_B_DESIGN.md: documents the architecture and the empirical wing
reach on the demo event (~30 nats below peak with sigmaL ~0.1-0.2,
well past the ~7-nat-target for 10^{-3} prior weight outside).
End-to-end on the fake-data demo (AV, --n-distance-slice-core 6 --n-distance-slice-wing 4):
main log_res 59.09 +/- 0.30, n_eff 4.4
core slices: lnL 60.8-62.3 (peak-lnL 0-1.4 nat), sigmaL 0.19-0.55
wing slices at 23/376/613 Mpc: lnL 59/51/34 (peak-lnL 3/11/29 nat),
sigmaL 0.10-0.22, neff 9-24
far wing at 5 Mpc: lnL -100 (signal off), correctly flagged low neff
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two follow-ups left for the next session, called out as breadcrumbs
rather than implemented now:
1. Skip threshold should be an absolute lnL scale.
lnL is already a likelihood ratio with absolute meaning; the
current relative spread test will misfire on high-SNR events
whose distance posterior happens to be flat.
2. Wing centers from a parabolic-in-1/dist fit of the core, solved
for the 1/dist values where lnL drops by ~7 nats from peak
(probability outside ~10^{-3}). Marginalized-lnL caveat (the
inclination-distance ridge can extend further toward small d
than a simple parabola predicts) documented inline.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Implements the two PLAN_B_DESIGN breadcrumbs: 1. is_uninformative now applies an absolute lnL detectability cut (peak core lnL < threshold) instead of a relative max-min spread test. lnL is a likelihood ratio vs noise, so this correctly skips undetected low-SNR events while keeping high-SNR events with a flat distance profile. ILE skip message and --distance-slice-skip-threshold help updated. 2. pick_wing_centers fits the core (lnL, 1/d) points to a parabola in 1/d (fit_lnL_parabola_in_inv_d) and spans each wing from the core edge out to where the model drops --distance-slice-wing-delta-lnL nats below peak (default 7), via _parabolic_wing_bounds. Bounds are clamped to the sampler's distance support; degenerate fits fall back to the original log-uniform full-range placement. New ILE flag --distance-slice-wing-delta-lnL threads the target. Adds a regression test (test_wing_placement_and_skip) to validate_distance_slices.py; verified end-to-end on the fake-data demo (AV sampler): wings concentrate near the core instead of the prior edges, and reconstruct_marginal_lnL matches log_res within sigmaL. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…ine builder Threads per-distance likelihood export from util_RIFT_pseudo_pipe.py through create_event_parameter_pipeline_* onto the ILE extrinsic stage (ILE_extr.sub), with an end-to-end pipeline-build test/demo. CEPP (Basic/Alternate/BasicMultiApprox): - New flags --last-iteration-export-distance-slices K plus -n-core/-n-wing/-wing-delta-lnL/-skip-threshold passthroughs. When set, the extrinsic stage gets --export-distance-slices K (+ the tunables + --internal-use-lnL) and --distance-marginalization is stripped, mirroring the existing grid export. - AlternateIteration and BasicMultiApproxIteration previously lacked the grid flag entirely; added both the grid and slice args + the ile_args_extr handling so the subdags/multi-approx CEPP variants accept the flags pseudo_pipe now routes to them. util_RIFT_pseudo_pipe.py: - New --export-distance-slices K (+ tunables), sibling to --export-marginal-distance-grid. - When either export is requested: force ILE lnL mode, disable distance marginalization (sane auto-config instead of erroring), and warn if --add-extrinsic is absent (the export is emitted there). - Fix: the --last-iteration-export-* flags are pipeline-builder flags, not ILE flags. They were being appended to args_ile.txt (the ILE argument string), where they would have been passed to the ILE executable and rejected. Move them to the CEPP command; keep only the ILE-side hygiene (lnL mode, no distance marginalization) in args_ile. Make the three create_event_parameter_pipeline_* scripts executable (100644 -> 100755), matching their sibling bin scripts: pseudo_pipe invokes them by bare name, so editable/source/pixi installs need +x. Validation: - New demo MonteCarloMarginalizeCode/Code/demo/pipeline (Makefile + README): builds baseline/grid/slices pipelines from the reference ini and asserts the flags land in ILE_extr.sub (and not in the intrinsic ILE.sub), with no distance marginalization. All pass. - Expanded .travis/test-build.sh with the same grid + slice build assertions (run in GitLab and GitHub CI). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
… done in PLAN_B_DESIGN Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…ic stage Fix the per-distance export threading so distance marginalization is kept for the intrinsic ILE iterations (a large speedup) and removed ONLY on the final extrinsic stage that emits the per-distance output. Previously util_RIFT_pseudo_pipe.py set opts.internal_marginalize_distance = False and stripped --distance-marginalization from args_ile.txt, which disabled it for every ILE job in every iteration. Now pseudo_pipe only forces ILE lnL mode globally (clean lnL-scaled helper args) and leaves distance marginalization in place; create_event_parameter_pipeline_* already strips the standalone --distance-marginalization flag from the ILE_extr argument string, so the disable is confined to the export stage. Make util_InitMargTable executable (100644 -> 100755): the helper invokes it at build time to generate the distance-marginalization lookup table, which is now needed again because the intrinsic stage keeps distance marginalization. Validation updated to prove the last-stage-only invariant: demo/pipeline and .travis/test-build.sh now assert the standalone --distance-marginalization flag is present on the intrinsic ILE.sub / args_ile.txt but absent from ILE_extr.sub (matching the standalone flag via a trailing space, so the harmless leftover --distance-marginalization-lookup-table arg is not counted). All three demo targets pass. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…n is disabled only at the extrinsic stage Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…validation demo
Adds the consolidation step the previous threading work was missing, plus a
self-contained zero-spin IMRPhenomD demo that runs the whole chain (pipeline
build -> ILE_extr -> consolidate -> posterior) end-to-end without condor.
Pipeline:
- New util_ConsolidateDistanceGrids.py: concatenates per-event .dgrid/.dslice
files (header-checked) into a single net intrinsic+distance table.
- New write_consolidate_distance_grids_sub in RIFT.misc.dag_utils_generic:
mirrors write_cat_sub (extrinsic posterior samples) so the consolidation
plugs into the same post-extrinsic part of the DAG.
- create_event_parameter_pipeline_BasicIteration: when the last-iteration
per-distance export is on, emit consolidate_dgrid.sub /
consolidate_dslice.sub gated on the corresponding flag, build a DAG node,
and chain it as a child of every ILE_extr job (.dgrid / .dslice come
directly off ILE with no convert/resample step, so the consolidation node
parents the ILE_extr nodes, not the cat_node downstream).
Output: all_dgrid.dat / all_dslice.dat at the run root.
Demo + validation:
- demo/pipeline/Makefile: updated grid/slices assertions to require the
consolidation sub-file and DAG references.
- demo/pipeline/zero_spin_phenomD/: new end-to-end test. Uses the
.travis/ILE-GPU-Paper zero-noise BBH fake data, IMRPhenomD with
--assume-nospin, AV sampler. Steps:
build -> util_RIFT_pseudo_pipe.py constructs the pipeline and
asserts the extrinsic stage carries grid export + AV +
IMRPhenomD + lnL mode, distance marg only off at the
extrinsic stage, consolidate_dgrid in the DAG.
run-extr -> bypass condor; invoke ILE_extr directly on N_EVENTS
grid rows -> per-event .dgrid files.
consolidate -> util_ConsolidateDistanceGrids.py -> all_dgrid.dat.
posterior -> util_ConstructEOSPosterior.py with --parameter m1 -m2 -dist
reconstructs the joint (intrinsic+distance) posterior.
Whole chain in ~45 s on a laptop core. Ships a minimal zero_spin_phenomD.ini
whose [rift-pseudo-pipe] section deliberately omits approx /
ile-sampler-method so the CLI overrides win (the ini section parser
otherwise overrides the command line).
Drive-by fix needed for the demo:
- util_ConstructEOSPosterior.py had CRLF line endings, breaking its
/usr/bin/env shebang ("python\r" not found). Converted to LF.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
… in PLAN_B_DESIGN Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…--pipeline-builder hot-swap
The --use-subdags path (create_event_parameter_pipeline_AlternateIteration)
was broken for normal runs by a chain of issues, each masking the next:
- cip_args_list parsing crashed on the 'Z'/'G' prefixes emitted by
util_RIFT_pseudo_pipe.py (ValueError: invalid literal for int() ... 'Z').
Ported BasicIteration's tolerant prefix parsing.
- argparse rejected 8 options the helper passes (extrinsic samples-per-ile,
time-resampling, batched-convert, ile-request-disk, cip-explode-jobs-dag/-last,
n-iterations-subdag-max). Added them with BasicIteration's signatures; wired
the ones with a clean home, documented the rest as accepted-but-not-acted-on.
- completed the half-built extrinsic batched/time-resampling convert path
(batchConvertExtr_job was referenced but never defined) by porting
BasicIteration's 3-branch convert setup + node construction.
- fixed undefined unify_node_list used to attach SCRIPT POST composite checks.
AlternateIteration now builds a complete DAG end-to-end from a standard
pseudo_pipe invocation.
Apply the same int('Z') tolerant-parsing fix to BasicMultiApproxIteration,
which had the identical crash.
Thread AlternateIteration into util_RIFT_pseudo_pipe.py as a first-class
drop-in via a new --pipeline-builder {BasicIteration,AlternateIteration}
selector that overrides the implicit --use-subdags routing, enabling
side-by-side A/B testing of the two builders from an otherwise identical
command line. Warns if an explicit choice contradicts an AMR/subdag
requirement.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The mcsamplerEnsemble GPU port (and MonteCarloEnsemble / gaussian_mixture_model) had never been exercised with cupy installed and crashed in GPU mode. Validated end-to-end with .travis/test-integrate.sh on a Kepler/sm_30 card using a cupy 10.6 + cudatoolkit 10.2 environment (last CUDA supporting sm_30). mcsamplerEnsemble: * evaluate()/calc_pdf(): bridge the host integrand/prior -- convert samples to CPU, call the user function, push the result back to the active backend. * replace cupy-incompatible rot90([list]) with an order-preserving reshape. * build dim-group / bounds dict keys with host ints (range/np.arange); the self.xpy.arange variant produced unhashable 0-d cupy arrays on GPU. * return scalars and store _rvs on the host so downstream numpy code works. gaussian_mixture_model / MonteCarloEnsemble: * portable _xpy_logsumexp (cupyx.scipy.special.logsumexp is absent in the cupy CUDA 10.2 build needed for sm_30). * _near_psd: use Hermitian eigh/eigvalsh on GPU (cupy.linalg has no eig/eigvals); the matrices are symmetric. numpy path unchanged. * gpu_logpdf: cupy.linalg has no LinAlgError and cholesky returns NaN rather than raising; catch numpy's error type and treat a NaN factor as failure. All integrators: import cupyx.scipy.special explicitly (not auto-loaded by import cupyx in older cupy). mcsamplerGPU / mcsamplerAdaptiveVolume (so the full test passes in GPU mode): * use instance converters / self.xpy instead of module-level GPU converters, which were contaminating these otherwise-CPU samplers with cupy arrays; * bridge their CPU prior/integrand; ones_like to follow the data backend. No behavioral change without cupy: all GPU branches are guarded by cupy_ok. Note: the AC sampler's --as-test check is statistically flaky (no seed) on both CPU and GPU and can randomly fail; this is pre-existing and unrelated. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
… dep canary Three related pieces for container CI and flexible multi-architecture deployment: Feature C (core): container family manifest. New RIFT/misc/container_manifest.py parses a YAML manifest (advertising a family of images + GPU capability ranges) and builds HTCondor expressions. Wired into write_ILE_sub_simple and write_CIP_sub (dag_utils_generic.py, import-guarded): when SINGULARITY_RIFT_IMAGE points at a .yaml/.yml manifest, MY.SingularityImage becomes an expression-valued ifThenElse over the matched machine's GPU capability (default GPUs_Capability), selecting the right image per machine. Only the matched image is fetched (CVMFS images referenced in place / lazy-fetched; osdf images selectively transferred via a comma-free $$() ternary token), never the whole family. A require_gpus capability floor is &&-composed with any user RIFT_REQUIRE_GPUS. Plain .sif / osdf:// values keep byte-identical legacy behavior. Vanilla universe throughout. Feature B: multi-target build. New containers/ dir -- rift_container.def.in template + build_family.sh (build matrix; first entry keeps the current production base for broad compatibility), shared requirements-container.txt (single source of truth), example rift_container_family.yaml, and README. Feature A: CI dependency-resolution canary. New non-blocking container-dep-canary and container-swig-canary jobs in ci.yml, plus a weekly schedule, to catch upstream breakage (e.g. swig>=4.4.0, issue oshaughn#136) before a container rebuild. Tests: MonteCarloMarginalizeCode/Code/test/test_container_manifest.py (13 tests: parser, expression builders, integration via write_ILE_sub_simple condor_cmds, all-cvmfs, backward-compat). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Validated on a real HTCondor pool + GPU (cap-3.0 machine): GPUs_Capability /
Capability attribute names, require_gpus floor matching+exclusion, $$()
match-time image selection, and tolerance of the empty-result ("") case for a
mixed CVMFS/osdf manifest. Mixed manifests are safe; uniform retrieval is not
required. Only the OSG/GWMS pilot evaluation of the expression-valued
MY.SingularityImage remains to be smoke-tested on a real glidein.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
New docs/source/containers.rst (wired into the toctree, cross-linked from osg.rst): how to point SINGULARITY_RIFT_IMAGE at a YAML container-family manifest, the manifest schema, what the pipeline generates (expression-valued MY.SingularityImage, selective $$() transfer, require_gpus floor), the GPU attribute names + verification command, building a family with containers/build_family.sh, and the CI dependency canary. Renders under the existing Sphinx 'docs' CI job. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
build_family.sh rendered defs ran `pip3 install -r containers/requirements-container.txt` from inside the cloned RIFT working dir, but the clone is upstream main, which does not carry that file yet -- so the build failed with "Could not open requirements file: containers/requirements-container.txt". Stage the host's copy into the image via an Apptainer %files section instead (@@REQFILE@@ -> absolute host path, substituted by build_family.sh), and install from /opt/requirements-container.txt. The build no longer depends on the cloned branch shipping the file. Comments/README updated to match (the top-level rift_container.def keeps its equivalent inline list for the default build). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…er builds Unprivileged apptainer builds on shared clusters (e.g. CIT) fall back to the proot engine, whose mksquashfs step is blocked by seccomp: proot error: ptrace(TRACEME): Operation not permitted mksquashfs command failed build_family.sh now exports PROOT_NO_SECCOMP=1 (overridable, harmless when proot is unused) -- the documented workaround. README gains a build-troubleshooting note covering this plus the better --fakeroot / userns alternatives and APPTAINER_TMPDIR for space. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…mksquashfs) PROOT_NO_SECCOMP=1 silenced the seccomp message but proot still cannot exec mksquashfs on locked-down clusters (CIT), so SIF creation kept failing. The actual fixes avoid the proot engine: - build_family.sh now accepts --fakeroot (pass-through to apptainer build; the IGWN/CIT-recommended path, needs subuid/subgid + userns), --sandbox (build a directory, skipping mksquashfs entirely), and passes any other --flag through. Empty-array expansion made safe for RHEL7-era bash under set -u. - README + docs troubleshooting rewritten to lead with --fakeroot / --sandbox / build-elsewhere; PROOT_NO_SECCOMP demoted to best-effort. Validated on cardassia: --fakeroot produces a .sif and --sandbox a directory with a tiny test image; flag/command construction verified via a stub apptainer. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…equirement A single SINGULARITY_BASE_EXE_DIR is applied to every image the selection expression can pick (ILE/CIP locate the executable as SINGULARITY_BASE_EXE_DIR + <exe name>, no per-image override). So all images in a manifest must install RIFT executables at the same in-container path / share a common layout -- don't mix apples and oranges. Documented in the prototype manifest, the generated-manifest stub, the README schema, and the docs page (also flags SINGULARITY_BASE_EXE_DIR_HYPERPIPE). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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