jftools 0.5.2

Collection of small useful helper tools for Python by Johannes Feist.

JF Tools

Collection of small useful helper tools for Python by Johannes Feist.

Short Iterative Lanczos Backend Selection

The jftools.short_iterative_lanczos solver provides two backends:

• numba for array-based propagation.
• python for the reference implementation.

Select backend behavior with the backend function argument:

• auto (default): select numba only when both the Hamiltonian and the input state are compatible with the Numba implementation; otherwise fall back to python.
• python: force the original Python implementation.
• numba: force the Numba backend.

Current Numba compatibility is intentionally narrower than the Python backend:

• States must be numpy.ndarray (converted to complex128) or qutip.Qobj states.
• Hamiltonians may be dense NumPy arrays, SciPy CSR matrices/arrays, callable Hfun(t, phi, Hphi) operators working on array states, or the specialized sum-operator form (H0, (H1, f1), ...).

The Python backend uses the same NumPy/Qobj-style state handling as the reference implementation.

Example:

prop = jftools.short_iterative_lanczos.lanczos_timeprop(H, maxsteps=14, target_convg=1e-12, backend="auto")

Installation

jftools is a pure Python package again. There is no compiled extension build step.

Recommended (uv-only) workflow:

uv sync --group dev
uv run python -m pip install -e .

For a non-editable local install through uv:

uv run python -m pip install .

QuTiP Compatibility

Short iterative Lanczos targets modern QuTiP (5.x):

• QuTiP Hamiltonians are converted through H.data.as_scipy() when available.
• QuTiP state outputs preserve dims and state shape.

Publishing

Before publishing a new version:

1. Update __version__ in jftools/init.py.
2. Commit the version bump.
3. Build locally.

For a local packaging sanity check on your current machine only:

uv build