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Separate correlation means from the jackknife patch layout - #399
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A target b of 0.01 gives reporting measurements a common approximation policy while retaining bin_slop=1 on finer integration grids. Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
Tree approximations depend on the patch layout. A shared two-pass measurement keeps the full-sample means canonical while retaining patch results for covariance, including tomographic cross-pairs and aperture-mass integration. Patched text dumps cannot supply a covariance and are not reused. Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
…changes RhoStat and TauStat expose catalogue mappings and output tables. Measure the identical unpartitioned catalogues once, retain patched variances and joint covariance, and reuse the means across covariance draws. Record measurement settings so cached products cannot silently supply layout-dependent means. Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
…try points Reporting, fine-grid, and rho/tau jobs must use the configured target rather than silently falling back to a constructor default. Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
…atched covariances Synthetic auto/cross correlations and aperture-mass and rho/tau products retain identical single-thread means while their jackknife covariances differ. A patched-means mutation fails the layout test; fine grids retain bin_slop=1. Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
… covariance jobs Measurement calls retain the patch results needed for dense and derived covariances. Plotting explicitly reads the published columns and variances, at round-trip precision, so one-core figure jobs do not repeat the expensive measurement. A pinned root depth makes cached products reusable across thread counts. Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
A direct producer rerun must retain a full jackknife covariance and patch results; cached columns are not a substitute for covariance. Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
…he layout regression The unpatched pass has no shared jackknife centres. Verify that it occurs once, while all three covariance draws retain distinct shared layouts and fresh catalogues. Co-Authored-By: GPT-6.1 Sol <noreply@openai.com>
Keep b_target on the published-means pass and let TreeCorr choose its standard bin_slop for patched covariance, where the tolerance does not set the reported means. Record both configurations so caches from the shared-tolerance policy are rejected. Co-Authored-By: GPT-6 Luna <noreply@openai.com>
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Full-sample TreeCorr correlation means depend on the jackknife patch layout; seeded patches make a layout reproducible but do not remove that dependence.
correlation.measure_with_patchesruns the published-means pass without patches usingbin_slop=min(1, b_target/bin_size). The patched pass omitsbin_slop, so TreeCorr uses its standard default for covariance and resampling.shear_psf_leakagechanges. In Derive reporting xi means from an exactly nested fine grid #400, the fine-grid means pass retainsbin_slop=1;angle_slopstays at its default.Depends on #394 (
merge/develop-into-tomo); #400 derives reporting ξ± from a nested fine grid. The effect of changingbin_slopon jackknife covariance remains open in #401.— Luna on behalf of Cail