transform op• Data kinds: image2d → table
• Call: import fullseye as fs; fs.ledger.dwt_transform(x, *, order: 'int' = 2, levels: 'int' = 1, axes=None) -> 'dict' (to call the implementation directly, import mathtransforms; mathtransforms.dwt_transform(x, *, order: 'int' = 2, levels: 'int' = 1, axes=None) -> 'dict'; from the registry, opsmath.get("dwt_transform"))
Multi-level, N-dimensional discrete wavelet transform with Daubechies dbN (exactly orthonormal with periodic boundaries).
Args:
x: real array (1-D signal, 2-D image, 3-D volume). The length of each transformed axis is a multiple of 2^levels.
order: the N of dbN (1 = Haar).
levels: number of levels. At each level the approximation (low-pass on all axes) is split further.
axes: axes to transform (all axes if omitted).
Returns:
approx the final approximation, details = a dict per level (keys are the sequence of 'a'/'d' per axis, e.g. for 2-D 'ad', 'da', 'dd'), energy_in, energy_out (equal by Parseval), order, levels, axes. Passing the whole result to dwt_inverse reconstructs the original.
Every mathops op validates its input before computing (nothing slips through silently):
• complex input raises ValueError — coercing to float64 silently discards the imaginary part (numpy only emits a ComplexWarning and returns a plausible-looking wrong real number). State .real/.imag/abs() explicitly, or use complexops, which handles complex data.
• masked arrays with masked elements raise ValueError — the implicit conversion that peels off the mask and uses the raw values underneath is refused. Say explicitly whether to fill or to drop.
• NaN/Inf raises ValueError on every input (refused with the count stated — it propagates through the whole result).
• Shapes are strict: 1-D and 2-D are never implicitly promoted or broadcast (a matrix in a vector slot, or a vector in a matrix slot, raises ValueError; reshape explicitly).
• Size cap: ops that take a matrix, and the stat_histogram bins, raise ValueError beyond mathops.MAX_ELEMENTS (2^26 ≈ 67 million elements).
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• transforms_tour — py -3.11 examples/transforms_tour.py
table as input)dynsys_poincare_section · dwt_inverse · chebyshev_eval_nd
transform)abel_transform · abel_inverse · abel_inverse_image · abel_revolve · hankel_transform · tf_poles_zeros · tf_freq_response · tf_impulse_response
*Provenance: mathtransforms.py — MATH operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.