Pymablock: quasi-degenerate perturbation theory in Python Pymablock (Python matrix block-diagonalization) is a Python package that constructs effective models using quasi-degenerate perturbation theory. It handles both numerical and symbolic inputs, and it efficiently block-diagonalizes Hamiltonians with multivariate perturbations to arbitrary order. Building an effective model using Pymablock is a three step process: Define a Hamiltonian Call pymablock.block_diagonalize Request the desired order of the effective Hamiltonian from pymablock import block_diagonalize # Define perturbation theory H_tilde, *_ = block_diagonalize([h_0, h_p], subspace_eigenvectors=[vecs_A, vecs_B]) # Request correction to the effective Hamiltonian H_AA_4 = H_tilde[0, 0, 4] Here is why you should use Pymablock: Do not reinvent the wheel Pymablock provides a tested reference implementation Apply to any problem Pymablock supports `numpy` arrays, `scipy` sparse arrays, `sympy` matrices and quantum operators Speed up your code Due to several optimizations, Pymablock can reliably handle both higher orders and large Hamiltonians For more details see the Pymablock documentation.