simesh: A Python Framework for Quantitative Analysis of Native AMR Data from MPI-AMRVAC

Adaptive mesh refinement (AMR) is widely used in large-scale astrophysical simulations, but the resulting multilevel data are challenging to analyze quantitatively. We present simesh, an independent Python framework for quantitative analysis of cell-centered fields on block-structured AMR meshes. The current implementation targets three-dimensional Cartesian datasets from MPI-AMRVAC and compatible AGILE snapshots, preserving the native AMR hierarchy and connecting complementary scientific diagnostics within a common numerical representation. The framework combines Python workflow interfaces with compiled Cython kernels, supporting efficient analysis on AMR meshes. We compare native AMR analysis with a VTK point-data conversion and uniform-grid resampling, which shows that changes in data association, interpolation support, and differentiation scale affect both sampled magnetic fields and derived current densities. Small differences in the sampled primary field can become more pronounced in derivative-based quantities, particularly near coarse-fine interfaces. The applications combine field diagnostics, tracing, regional statistics, and line-of-sight integration on the same native AMR representation. simesh makes these operations available through reusable Python interfaces for reproducible analysis of multiscale simulation data.