v0.5.0

First stable release. Identical to 0.5.0rc2 apart from the version number;
the changes since 0.5.0rc1 are listed under 0.5.0rc2 below.

What's Changed

• torchsky 0.5.0 by @sfabbro in #202

Full Changelog: v0.5.0rc2...v0.5.0

v0.5.0rc2

Second release candidate for 0.5.0. Changes since 0.5.0rc1:

Added

• cosmology.lookback_time and cosmology.age (also as FLRW methods), and
  HUBBLE_TIME_GYR.
• weighting="energy" for synthetic_photometry and
  synthetic_photometry_bands; vega_spectrum and vega_zero_point from the
  packaged CALSPEC alpha_lyr_stis_012 spectrum.
• G23, F19 and GCC09 extinction laws (law="g23", "f19", "gcc09"). They
  raise outside their calibrated wavelength and R_V ranges.
• sphere.pixwin computes the HEALPix pixel window (T and P) for any nside up
  to 8192 and l up to 4·nside, instead of shipping nside 8/16 tables.
• torchsky.wcs.WCSHeaderWarning for header interpretations that go beyond
  the literal CTYPE.
• API reference for gaussian_nll and poisson_gaussian_nll.
• Real-data examples: MaNGA IFU cube, Legacy Surveys multi-band field fit,
  Gaia forced photometry against Tractor; the distortions guide uses real
  HST SIP and DECam TPV headers.
• More real-data examples: JWST SMACS 0723 NIRCam coadd checked against the
  MAST mosaic; Legacy Surveys g/r/i/z on sparse HEALPix maps; a MaNGA cube
  rebuilt from fibre spectra; Gaia DR3 Pleiades proper motions in the
  Galactic frame; SFD dust at Gaia positions with G23; SDSS photometric
  redshifts with the experimental encoders; SDSS/GALEX/eFEDS footprint
  algebra with MOCs. TAP and SQL query snapshots are pinned by sha256.

Changed

• WCS: SIP coefficients without a -SIP CTYPE, and ambiguous TAN+PV
  headers, raise ValueError. TAN with SCAMP PVi_m (m ≥ 5) is read as TPV
  with a WCSHeaderWarning.
• observed_altaz always applies precession; TT−UTC defaults to the built-in
  leap-second table and raises outside it.
• coadd_healpix integrates the exact image overlap for HEALPix cells coarser
  than the kernel support, so cells larger than the image no longer drop out.
• HEALPix mapmaking applies the FK5→ICRS frame bias, with
  CelestialFrameBiasWarning, instead of treating FK5 as ICRS.
• Planck power-spectrum example: apodised Galactic mask, monopole and dipole
  removal, w₂ normalisation.

Fixed

• Integer amin/amax scatters on MPS (MOC normalisation, one-to-one
  crossmatch, connected components, response bit planes) no longer need 64-bit
  Metal atomics, which some Apple GPUs lack ("Failed to create pipeline state
  object"); they reduce in int32 when values fit and on the CPU otherwise.
• Without the compiled extension (and on MPS), query_disc, RING
  query_polygon and SphericalPolygon.pixelize no longer scan every sky
  pixel: a torch quadtree descent selects the same pixels as the native path
  in O(boundary cells x depth + output). A 1.6-arcmin polygon at NSIDE 2^12
  took 38 s and now takes 0.02 s; NSIDE 2^16 takes 0.04 s. RING polygons whose
  centroid lies outside them also no longer scan the full sky with the
  extension.
• SparseHealpixMap with nside_coverage no longer allocates a dense
  lookup block of (nside / nside_coverage)**2 values per covered coverage
  cell when that exceeds eight slots per stored pixel; such maps look values
  up by binary search on the sorted pixels. A sparse NSIDE 2^19 coadd needed
  6 GiB per map and ran out of memory on a 10.75 GiB GPU.
• IFU drizzle with aperture_diameter_arcsec: spaxels that a fibre misses or
  only touches got rounding-noise weights (~1e-16) that differed between CPU
  and CUDA and switched whole spaxels on or off. Overlap fractions within the
  computation's rounding bound (64 eps) are now zero.
• MPS: moving an MPS tensor to CPU float64/complex128 in one .to(device, dtype), torch.as_tensor or .to(other) call returns zeros in PyTorch
  2.14. Every such conversion in torchsky now moves first and casts on the
  target device; a test keeps fused conversions out of src/.
• WCS: TPV PV1_3/PV1_4 are no longer read as LONPOLE/LATPOLE; DECam
  fields were mirrored about CRVAL.
• spectral_axis_from_header and SpectralReader.read_data_cube read
  CDi_i and PCi_i·CDELTi and apply -LOG, -F2W, -V2W and grism
  codes through the WCS; unsupported codes raise. MaNGA cubes were truncated
  at 8184 Å instead of 10354 Å.
• SkyConfig(mode="per_exposure") works on images no larger than the mesh.
• Passband.to("mps") holds the curve in float32 instead of raising.
• MOC.read_fits reads IVOA MOC files without EXTNAME='MOC' (the table in
  the first extension with ORDERING='NUNIQ', as CDS publishes them); time
  MOCs and non-ICRS coverage raise.
• build_ifu_cube and plan_ifu_cube_response are vectorised: the same plans
  as before, 780k MaNGA samples in about 10–80 s instead of 30–40 min.
• IFUObservation.aperture_diameter_arcsec gives fibre spectra exact circular
  footprints; drizzle without a footprint raises for samples with no
  neighbour in their own slice, instead of borrowing one from another slice.
• IFUSliceWCS.to works around a PyTorch 2.14 MPS bug where an MPS float32 to
  CPU float64 copy returns zeros.
• Scalar R_V and E(B-V) in extinction.a_lambda are built in the
  wavelength dtype, with no float32 round-trip.
• The Horsehead example draws the MOC boundary its caption describes.
• Coadd weights with interpolation kernels (bicubic, Lanczos, Catmull-Rom,
  Mitchell-Netravali) renormalise the kernel taps that fall off an input image,
  so a constant inverse-variance map keeps its value up to the edge. Lanczos3
  gave up to 1.2× the input inverse variance next to an edge (0.98 instead of
  0.81 in the maps overview), which also over-weighted that exposure in the
  mean. The fix covers coadd, coadd_cube, coadd_healpix and blot;
  inverse_variance stays the exact variance of the linear estimate.
• exact_intersection, exact_union and exact_difference on polygons, caps
  and multipolygons return a SphericalBooleanRegion instead of a
  NativeApproximateSphericalRegion whose area() raised.
  SphericalBooleanRegion.area() is the exact area of polygon combinations,
  clipped from the great-circle edges; caps, boxes and ellipses raise
  ValueError. exact_area() and native to_exact regions use it.
• SphericalMultiPolygon.area() counts overlapping members once; it summed
  them. SphericalCap.area() returns the analytic cap area.

What's Changed

• torchsky 0.5.0rc2 by @sfabbro in #201

Full Changelog: v0.5.0rc1...v0.5.0rc2

v0.5.0rc1

First public release of torchsky: a torch-native astronomy stack for sky
coordinates, geometry, maps, observation operators, scenes, and field models.
FITS file and table I/O is provided by the separate
torchfits package, a required
dependency.

Supported lanes

• Python 3.12, 3.13 and 3.14 (full CI suite on each, plus macOS for 3.14).
• PyTorch 2.14; torchfits 1.3.1 or newer.
• CPU, CUDA 13 and newer (PyTorch cu130, validated on an NVIDIA H100), and
  Apple MPS.
• Linux x86_64 and macOS 14+ arm64 wheels.

What torchsky provides

• WCS (torchsky.wcs): FITS WCS for celestial, spectral, and time axes with
  every standard projection, SIP, TPV, TNX/ZPX, paper-IV lookup tables, -TAB
  axes, exact Jacobians, footprints, and astrometric fitting; batched,
  differentiable, and device-aware.
• Coordinates (torchsky.coords): CatalogState with proper motion,
  parallax, radial velocity, and epochs; geometric frame conversion; observed
  place (ERFA Earth ephemeris, aberration, light deflection, refraction)
  through explicit EarthOrientation and Site.
• Sphere (torchsky.sphere): HEALPix pixelization (RING and NEST),
  queries that descend the quadtree, spherical regions, MOC and sparse maps,
  spherical harmonic transforms (scalar and spin) with lmax up to
  3*nside-1, and a healpy-shaped torchsky.sphere.compat namespace.
• Catalogs (torchsky.catalogs): sky-indexed cone search and crossmatch with
  best1, best2, mutual, symmetric, and all modes, per-row errors,
  join_rows, exact matching, self-matching groups, and multi-catalog
  matching, with exact pair-set parity with STILTS on Gaia DR3 x 2MASS x AllWISE.
• Mapmaking (torchsky.map): planned resampling and coaddition (Lanczos,
  bilinear, nearest, exact square drizzle), weights, masks, DQ and variance
  propagation, automatic memory-budget tiling, HEALPix maps, and IFU cubes
  (coadd_cube) with flux-conserving spectral rebinning.
• Spectra and photometry: spectral axes, relativistic Doppler shifts,
  passbands and synthetic photometry, dust extinction laws, and differentiable
  FLRW cosmology.
• Scenes and fields (torchsky.scene, torchsky.field): observation
  operators, source-catalog scenes, continuous field models, chromatic PSF
  fields, and fitting; the native response and likelihood layer is experimental.
• ML and tooling: torchsky.transforms and torchsky.encoders, the
  torchsky command line, and torchsky.viz all-sky figures
  (plot_healpix_sky, plot_sky_grid) on Mollweide, Aitoff, Hammer, and polar
  projections.
• The package ships py.typed.

Highlights

• 135 JWST/NIRCam exposures (566 Mpix) coadded onto a 5025 x 5025 grid: Lanczos-3
  in 221 s on CPU and 65 s on a GPU slice; exact drizzle in 149 s on CPU and
  148 s on GPU. Drizzle output matches STScI drizzle to better than 2e-6 sigma.
• A 280-CCD CFHT/MegaCam tile coadd onto 10k x 10k pixels in 711 s on CPU and
  660 s on a GPU slice with automatic tiling and no out-of-memory failures.
• SIP forward, inverse, and Jacobian use nested Horner evaluation (JWST order-5
  SIP, 4 M points: distort 15x and Newton inverse 4x faster than a power stack).
• Observed place agrees with erfa.atco13 to 0.012 mas.

Measurement environments and full tables are on the
Performance highlights page.

Known limitations

• The native response/likelihood layer and torchsky.map.solvers are
  experimental and do not establish calibrated real-sky response.
• On a 5025 x 5025 grid, CPU Lanczos-3 is slower than SWarp; the GPU run is
  faster.
• MPS float32 cannot reach half a pixel on very fine WCS grids; a
  PrecisionWarning is raised.
• Resampling between FK5 (J2000) and ICRS emits CelestialFrameBiasWarning
  (about 20-30 mas); set RADESYS = 'ICRS' on headers solved against Gaia.
• Irregular spectral axes in coadd_cube use index-space interpolation with a
  recorded bias envelope.

torchsky continues the sky-geometry part of torchfits ≤ 0.3.1; see the torchfits changelog for earlier history.

What's Changed

• feat: allow schema override for integer types in to_tensor_frame by @sfabbro in #8
• ⚡ Bolt: [performance improvement] Vectorize contiguous range finding by @sfabbro in #10
• feat: handle FITS writing for torch_frame VLA, embeddings, and temporal stypes by @sfabbro in #13
• 🧪 Add map command tests by @sfabbro in #18
• 🧪 [testing improvement] Add comprehensive tests for sphere MOC by @sfabbro in #22
• 🧪 Add test suite for WCS cylindrical projections by @sfabbro in #9
• ⚡ Bolt: Vectorize fourier features generation loop by @sfabbro in #11
• ⚡ Bolt: Vectorize and Optimize Bandpass Features Generation by @sfabbro in #12
• 🧪 Add tests for datasets module base by @sfabbro in #14
• 🧪 Add tests for TableChunkDataset by @sfabbro in #15
• ⚡ Bolt: cache FITS filename extension check by @sfabbro in #16
• 🧪 Add test suite for healpix_image_plane map projections by @sfabbro in #17
• 🧹 [code health improvement] Analyze unused warnings import by @sfabbro in #19
• 🧹 [code health] Allow schema override for TensorFrame conversion by @sfabbro in #20
• 🧪 Add test coverage for FFT affine LDU module by @sfabbro in #21
• 🛡️ Sentinel: [HIGH] Fix insecure deserialization in torch.load by @sfabbro in #28
• ⚡ Bolt: Vectorize log spectral Fourier feature encoding by @sfabbro in #24
• 🧹 [code health improvement description] - False Positive: Unused re import in table.py by @sfabbro in #25
• 🧹 Fix bare pass in exception handler in torchsky.table by @sfabbro in #63
• 🛡️ Sentinel: [CRITICAL] Fix insecure deserialization by @sfabbro in #62
• 🧹 Move benchmark_header_parsing out of main source code by @sfabbro in #61
• 🧹 Remove unused _apply_cpu_scale function by @sfabbro in #57
• 🧪 [testing improvement] Add test for _can_use_mmap_row_path_for_full_read with exception by @sfabbro in #55
• 🧪 [testing improvement] Add unit tests for _finalize_crossmatch by @sfabbro in #54
• 🧪 Add test for is_compatible_arrow_table edge cases by @sfabbro in #53
• 🧹 [code health] Remove unused log_errors decorator by @sfabbro in #51
• 🧪 [testing improvement] Add test for create_inference_transform by @sfabbro in #50
• 🧪 [testing improvement] Add test for create_validation_transform by @sfabbro in #48
• 🛡️ Sentinel: [CRITICAL/HIGH] Fix Command Injection Vulnerability by @sfabbro in #45
• 🧹 [code health improvement] Remove unused import in fft_resample.py by @sfabbro in #44
• ⚡ Bolt: [performance improvement] Unroll fits metadata header loop by @sfabbro in #43
• 🛡️ Sentinel: [CRITICAL/HIGH] Fix CFITSIO Command Injection Vulnerability by @sfabbro in #41
• 🧪 [testing improvement] Add edge case test for _uniq_to_ranges with empty tensor by @sfabbro in #39
• 🧪 [testing improvement] Cover different types of empty tensors in MOC functions by @sfabbro in #37
• ⚡ Bolt: Replace torch.square with element-wise multiplication by @sfabbro in #36
• 🧪 [testing improvement] Add error handling tests for table reading heuristics by @sfabbro in #35
• 🎨 Palette: Add focus states and ARIA roles to viz UI by @sfabbro in #34
• 🧹 Remove unused annotations import in device.py by @sfabbro in #31
• 🛡️ Sentinel: [CRITICAL/HIGH] Fix XSS vulnerability in visualization frontend by @sfabbro in #83
• ⚡ Bolt: Optimize schema header parsing with fast TFIELDS loop by @sfabbro in #82
• 🎨 Palette: [UX improvement] Add focus rings and fix empty state direction by @sfabbro in #81
• ⚡ Bolt: [performance improvement] Optimize chunked loops in catalog crossmatching by @sfabbro in #80
• ⚡ Bolt: [performance improvement] Optimize nn.Linear initialization loops by @sfabbro in #79
• 🧹 Refactor read to reduce massive cyclomatic complexity by @sfabbro in #78
• 🧹 Code Health: Remove unused module_compute_device utility by @sfabbro in #77
• ⚡ Bolt: Optimize module type check in _reset_silu_mlp_linears by @sfabbro in #76
• 🧹 Refactor read_image to reduce cyclomatic complexity by @sfabbro in #75
• ⚡ Bolt: [performance improvement] by @sfabbro in...