semiflow-pde 0.10.2b0

Python bindings for SemiFlow: evolution-equation / PDE engines via Chernoff approximation of operator semigroups

semiflow-py

[![PyPI badge — pending

PyO3 Python bindings for semiflow — Chernoff approximations of operator semigroups (Remizov 2025, Theorem 6).

Built on the semiflow core crate (ADR-0154, 2026-06-10). The Python surface has parity with all core kernel families via ADR-0111 Waves P1–P7 plus the v9.0.0 addition of ReverseHeat1D (reverse-mode AD, math §51, ADR-0156): 26 binding classes + 1 free function. Pyright errors: 0. Complete __init__.pyi stubs; py.typed marker; GIL released in all evolve paths (ADR-0031).

TtChernoff / TtState and GridlessChernoff / ParticleReduction are Rust-only at v9.0.0 — not exposed via PyO3 (binding design deferred).

Installation

pip install semiflow-pde

Note: as of v6.0 the package is not yet published to PyPI. Wheels are distributed via GitHub releases. Download the semiflow-*.whl for your platform and install with pip install semiflow-*.whl.

Or build from source (requires Rust toolchain + maturin):

pip install maturin
maturin develop --profile release-ffi -m crates/semiflow-py/Cargo.toml

Array I/O conventions

• All real-valued state arrays are numpy.float64 (np.float64).
• Schrödinger and SchrodingerComplex1D state arrays are numpy.complex128.
• 2D state is flat float64 in row-major x-fastest order: index j*nx + i corresponds to u(x_i, y_j).
• 3D state is flat x-fastest: index k*nx*ny + j*nx + i.
• values() always returns a copy of the internal Rust state; mutations to the returned array do not affect the object.
• Inputs are validated for NaN/Inf at construction and before evolve; non-finite inputs raise SemiflowError(kind='NanInf').
• All finite-check and grid-size errors raise SemiflowError.

Error model

All semiflow-py operations raise a single exception type:

from semiflow import SemiflowError

The .kind attribute (a string) identifies the error category:

kind	When raised
GridMismatch	Invalid geometry, mismatched array lengths
NanInf	Input array contains NaN or Inf
OutOfDomain	Parameter out of valid range (e.g. t < 0, n < 4)
BoundaryFailure	Unrecognised boundary policy string
CflViolated	CFL-like stability constraint exceeded
ConvergenceFailed	Magnus / adaptive integration convergence check failed
Unsupported	Unrecognised string selector (e.g. subordinator=)
Panic	Unrecoverable internal Rust panic (should never occur)

Boundary policies

All 1D/2D/3D kernels accept a keyword argument boundary:

Value	Semantics
"reflect" (default)	Mirror / zero-flux Neumann at grid boundaries
"periodic"	Periodic wrap
"zero"	Dirichlet zero at grid boundaries
"linear"	Linear extrapolation

---

Usage examples

1. Unit-diffusion 1D heat

Solve ∂_t u = ∂²_x u on [-10, 10] with a Gaussian initial condition:

import numpy as np
import semiflow as rp

n = 1000
xs = np.linspace(-10.0, 10.0, n)
u0 = np.exp(-(xs - 0.5)**2 / 0.01)   # narrow Gaussian at x=0.5

state = rp.Heat1D(-10.0, 10.0, n, u0)
state.evolve(t=1.0, n_steps=100)

u = state.values()    # float64 ndarray, shape (n,)
print(f"n={len(state)}, max={u.max():.6f}")

The GIL is released during evolve (ADR-0031); concurrent Python threads make progress during long calls.

2. SchrodingerComplex1D — native complex128 wavefunction

Solve i ψ_t = (−½∂²_x + V) ψ and verify unitarity:

import numpy as np
import semiflow as rp

n = 512
xs = np.linspace(-10.0, 10.0, n)
psi0 = np.exp(-xs**2 / 2.0).astype(np.complex128)  # normalised Gaussian
psi0 /= np.sqrt(np.trapz(np.abs(psi0)**2, xs))      # L2-normalise

sch = rp.SchrodingerComplex1D(-10.0, 10.0, n, psi0)
norm0 = sch.norm_squared()

sch.evolve(t=0.5, n_steps=200)

psi_t = sch.values()    # complex128 ndarray
assert abs(sch.norm_squared() / norm0 - 1.0) < 1e-12, "unitarity violated"
print(f"norm ratio = {sch.norm_squared() / norm0:.15f}")

3. Manifold2D — Riemannian manifold heat kernel

Solve ∂_t u = Δ_{S²} u on the 2-sphere via MMRS 2023 Chernoff formula:

import numpy as np
import semiflow as rp

nx, ny = 32, 64
u0 = np.zeros(nx * ny, dtype=np.float64)
u0[nx * (ny // 2) + nx // 2] = 1.0  # delta-like at chart centre

sphere = rp.Manifold2D(
    0.1, np.pi - 0.1, nx,    # theta axis
    0.0, 2 * np.pi,   ny,    # phi axis
    u0,
    manifold="sphere2",
    radius=1.0,
    curvature_correction=True,  # enables R/12 correction -> order 2
)
sphere.evolve(t=0.02, n_steps=50)

u_t = sphere.values()    # float64 ndarray, length nx*ny (row-major theta-fastest)
print(f"integral ≈ {u_t.sum() * (np.pi / nx) * (2 * np.pi / ny):.4f}")

Available manifolds: "torus" (flat T²), "sphere2" (S²(r)), "hyperbolic2" (Poincaré disk H²(s)). The radius parameter sets r or s.

---

Class reference

Classes are grouped by kernel family. All stateful classes expose at least evolve(t, n_steps=100) (mutates in-place, GIL released) and values() → NDArray[np.float64] (copy). See __init__.pyi for complete signatures.

1D diffusion family

Class	Kernel	Order	Notes
Heat1D	DiffusionChernoff	2	Unit or variable-a; .with_a_array / .with_a_function factories
Heat1D4th	Diffusion4thChernoff	4	4th-order temporal; .with_a_array
Heat1D6th	Diffusion6thChernoff	6	6th-order temporal; .with_a_array
Heat1DZeta4	Diffusion4thZeta4Chernoff	4	ζ⁴ kernel; .with_quintic_sampling() opt-in
Heat1DZeta6	Diffusion6thZeta6Chernoff	6	ζ⁶ kernel; Quintic spatial unconditional
Heat1DZeta8	Diffusion8thZeta8Chernoff	8	ζ⁸ kernel; Chebyshev sampling default
TruncatedExp1D	TruncatedExpChernoff	2	CFL-conditional truncated-exp
TruncatedExp4th1D	TruncatedExp4thChernoff	4	4th-order truncated-exp
DriftReaction1D	DriftReactionChernoff	2	b(x) ∂_x u + c(x) u; .with_arrays
Shift1D	ShiftChernoff1D	1	Universal a ∂² + b ∂ + c; .with_arrays
Strang1D	StrangSplit (diffusion + drift)	2	Advection-diffusion ∂²u + b ∂u; default b=0.5

Operator splitting — multi-dimensional

Class	Kernel	Order	Notes
Heat2D	Strang2D	2	Unit diffusion on 2D grid; flat x-fastest output
Heat3D	Strang3D	2	Unit diffusion on 3D grid; flat x-fastest output
Heat2DVarA	Strang2D + variable-a	2	a_x(x) u_xx + a_y(y) u_yy; pass a_x, a_y arrays
Heat3DVarA	Strang3D + variable-a	2	a_x u_xx + a_y u_yy + a_z u_zz; pass a_x, a_y, a_z arrays
NonSeparable2D	5-leg palindromic	2	∂²_x + ∂²_y + c·∂_x ∂_y; scalar or .with_beta_array
NonSeparable2DAniso	5-leg + position-dep. β	2	∂²_x + ∂²_y + β(x,y)·∂_x ∂_y; requires beta_values array

Schrödinger

Class	Kernel	Notes
Schrodinger1D	SchrodingerChernoff<f64>	Real-pair split; values() → complex128
SchrodingerComplex1D	SchrödingerChernoffComplex	Native complex128 state; exact unitary (ADR-0079 Option B)

Both support .with_potential(v_array) and .norm_squared().

Boundary-condition kernels

Class	Kernel	Order	Physics
Resolvent1D	LaplaceChernoffResolvent	—	(λI − ∂²)⁻¹ g via GL-32 quadrature; .eval(lambda_, g) + .residual(lambda_, g)
Killing1D	KillingChernoff	1	Absorbing (Dirichlet) BC via Feynman-Kac; lo/hi kwargs
Reflected1D	ReflectedHeatChernoff	2	Neumann (reflecting) BC via Walsh 1986 image method; origin kwarg
Robin1D	RobinHeatChernoff	1	Robin BC α u − β ∂_n u = 0; alpha, beta, origin kwargs

Time-dependent and subordinated

Class	Kernel	Notes
Howland1D	HowlandLift<DiffusionChernoff>	Nonautonomous lift (Howland 1974); n_t, t_horizon kwargs; .evolve() takes no args
Subordinated1D	SubordinatedChernoff	Bochner-Phillips subordination (Butko 2018); backends: "stable", "gamma", "inverse_gaussian"

Geometry and hypoelliptic operators

Class	Manifold / Group	Notes
Manifold2D	Torus / S²(r) / H²(s)	MMRS 2023 formula with optional R/12 correction; manifold=, radius=, curvature_correction= kwargs
HypoellipticChernoffKolmogorov	Kolmogorov phase space	∂_t p = v ∂_x p + ½ ∂²_v p; 2D state nx×nv
HypoellipticChernoffEngel	Engel step-3 Carnot (ℝ⁴)	n**4 flat state; n per-axis
HypoellipticChernoffHeisenberg	Heisenberg H₁	.kernel(h, x, y, tc) point evaluator; heisenberg_heat_kernel(h, x, y, tc) free function

Graph PDE

Class / Function	Role
Graph.path(n) / .cycle(n) / .from_edges(n, edges) / .erdos_renyi(n, p, seed)	Graph topology builders
GraphPath(n)	Legacy path builder (use Graph.path(n))
Laplacian.combinatorial(graph) / .normalized(graph)	Laplacian assembly
GraphHeat(graph=..., laplacian=..., rho_bar=...)	Order-1 static graph heat
GraphHeat4th(graph=..., laplacian=..., rho_bar=...)	Order-4 static
GraphHeat6(graph=..., laplacian=..., rho_bar=...)	Order-6 static
MagnusGraphHeat(graph, lap_at_t, rho_bar)	Magnus K=4 time-varying
MagnusGraphHeat6(graph=..., laplacian=..., lap_at_t=..., rho_bar_max=...)	Magnus K=6
VarCoefGraphHeat(graph, a, rho_bar)	Variable node-conductivity
VarCoefMagnusGraph(n_nodes, lap_at_t=..., a_at_t=..., rho_bar_max=..., a_sup_max=...)	Variable-coef Magnus K=4
QuantumGraph.path(n_edges) / .star(n_arms) / .from_edges(edges)	Metric graph (edge lengths)
QuantumGraphHeat(qgraph)	Kirchhoff-vertex heat Chernoff
GraphTraj(graph, t_horizon)	Fixed-topology graph trajectory
StrangGraph.from_path(graph) / .from_cycle(graph)	Palindromic Strang split on graph

Matrix and point-eval kernels

Class / Function	Role
MatrixDiffusion1D(xmin, xmax, n, u0, *, a_diag, c_coupling)	Coupled 2-component 1D diffusion; flat state length 2*n
PointEval(xmin, xmax, n)	Pointwise evaluation via Backend A; .eval_at(tau, u0, x, n_steps)
sample_gridfn2d(values, x0min, x0max, nx, x1min, x1max, ny, cx, cy)	Bilinear interpolation at chart point

Anisotropic multi-D

Class	Notes
AnisotropicShiftND2(nx, ny, xmin, xmax, ymin, ymax, a_values, *, b_values, c_values)	2D anisotropic shift; order 1 (ADR-0112); a_values is flat 2×2×nx×ny SPD tensor
AnisotropicShiftND3(nx, ny, nz, xmin, xmax, ymin, ymax, zmin, zmax, a_values, *, b_values, c_values)	3D variant

Adjoint and adaptive wrappers

Class	Notes
Adjoint(xmin, xmax, n, u0, *, kernel="heat2", self_adjoint=False, boundary="reflect")	Adjoint semigroup; kernel in "heat2", "heat4", "heat6", "drift", "shift"
AdaptivePI(xmin, xmax, n, u0, *, kernel="heat2", tol_abs=1e-6, tol_rel=1e-4, boundary="reflect")	PI-controller adaptive step

Reverse-mode AD (v9.0.0, ADR-0156)

Class	Notes
ReverseHeat1D(theta, xmin, xmax, n_grid, n_steps)	Reverse-mode AD for constant-a 1D heat (narrow scope: constant-a DiffusionChernoff only, §51.5); .value_and_grad(tau, u0, target) -> (float, float)

Constructor parameters:

Parameter	Type	Constraint
theta	float	Diffusivity θ > 0, finite
xmin	float	Left domain boundary
xmax	float	Right domain boundary (xmax > xmin)
n_grid	int	Grid nodes (>= 4)
n_steps	int	Chernoff steps per .value_and_grad call (>= 1)

.value_and_grad(tau, u0, target) -> (float, float):

Parameter	Type	Notes
tau	float	Per-step time increment (> 0, finite)
u0	numpy.ndarray[float64]	Initial condition, length n_grid
target	numpy.ndarray[float64]	Target state, length n_grid
returns value	float	L² loss ‖(F_θ(τ))ⁿ u₀ − target‖²
returns grad	float	∂J/∂θ (K=1 forward-mode Dual; 0-ULP vs core, §51.4)

import numpy as np
import semiflow as rp

n_grid = 24
xs = np.linspace(-4.0, 4.0, n_grid)

rc = rp.ReverseHeat1D(theta=0.4, xmin=-4.0, xmax=4.0, n_grid=n_grid, n_steps=8)
u0     = np.exp(-xs**2)
target = np.zeros(n_grid)

value, grad = rc.value_and_grad(tau=0.05, u0=u0, target=target)
print(f"loss={value:.6e}  ∂J/∂θ={grad:.6e}")

Raises SemiflowError with .kind in {'OutOfDomain', 'GridMismatch', 'NanInf'}.

NARROW scope (§51.5): constant-a DiffusionChernoff only; θ is the uniform diffusivity. Variable-coefficient and nonlinear kernels are out of scope at v9.0.0. TtChernoff and GridlessChernoff are not exposed in PyO3 (Rust-only at v9.0.0).

v3 Evolver surface

Class	Notes
EvolverHeat1DUnitV3(domain_lo, domain_hi, n_grid, u0, n_chernoff)	Zero-alloc apply_into hot path; .evolve_into(t, buf)
GrowthV3	Growth bound (multiplier, omega) returned by .growth()

---

Performance

GIL release follows the three-phase py.detach pattern (ADR-0031): acquire → snapshot inputs → detach → Rust compute → reacquire. Send + Sync is verified at compile time with static_assertions.

Indicative timings on i7-12700K (1000 nodes, 100 steps, Heat1D):

Metric	Value
Throughput (criterion)	~56.6 ms per call
p99.9 latency (HFT loop, N=1536)	45 ns/tick
Memory footprint	2.8 MB RSS

For large grids or many time steps, prefer .with_a_array over .with_a_function: the array path uses a pure-Rust Arc<Vec<f64>> Catmull-Rom interpolant and never re-acquires the GIL during evolve.

Type stubs

__init__.pyi and the py.typed marker ship with every wheel. Static type checkers (mypy, pyright, pylance) pick them up automatically. The pyrightconfig.json at the repo root adds crates/semiflow-py/python to extraPaths so local development also resolves the stubs correctly (0 reportAttributeAccessIssue errors).

Mathematical reference

I. D. Remizov, Vladikavkaz Math. J. 27(4) (2025) 124–135. DOI 10.46698/a3908-1212-5385-q

License

MIT OR Apache-2.0 — same as semiflow.