dynsys_poincare_section — MATH dynsys op

• Data kinds: table → pairs

• Call: import fullseye as fs; fs.ledger.dynsys_poincare_section(states, axis=2, value=None, direction=1) (to call the implementation directly, import mathops; mathops.dynsys_poincare_section(states, axis=2, value=None, direction=1); from the registry, opsmath.get("dynsys_poincare_section"))

Usage

Where a trajectory crosses a plane — with the crossing point interpolated.

Takes the `x block of :func:ode_flow_states` and returns the points where

coordinate *axis* crosses *value* in the given *direction* (+1 upward, -1

downward, 0 either). The crossing is found by **linear interpolation between

the two straddling samples**, not by taking the nearer sample — otherwise the

section is quantised by the step size and a periodic orbit looks like a cloud.

★Why this earns its place: a periodic orbit must give one point (to

within the interpolation error), a period-2 orbit two, and a chaotic one a

fractal set. That is a check with a number in it, unlike "the picture looks

like a strange attractor".

Returns a `pairs` array of the remaining coordinates at each crossing.

Raises `ValueError`: states not (S, n) with S >= 2; axis out of range;

direction not in (-1, 0, 1); non-finite input; no crossing found (reported,

not returned as an empty array that a caller may read as "no orbit").

Family-wide input contract (fail-closed)

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).

Detailed usage guide

• math_metrology family guide

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• poc_what_a_picture_cannot_check — py -3.11 examples/poc_what_a_picture_cannot_check.py

Ops the type connects to (they accept pairs as input)

neighbour_index_gaps · curve_locality

Same category (dynsys)

ode_flow_states · ode_vector_field_grid · dynsys_lyapunov_spectrum · dynsys_bifurcation_map · dynsys_correlation_dimension


*Provenance: mathops.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.