interpcore 0.1.0

Interpolator of different kind of loads and BC to APDL format

InterpCore

A Python library for interpolating physical field data (electromagnetic forces, heat flux, etc.) between different mesh representations and exporting to ANSYS APDL format.

Features

• Multiple interpolation kernels: Distance-weighted, FEM-based, K-nearest neighbors, closest point
• Flexible query methods: K-nearest neighbors or radius-based search
• Support for multiple load types:
  • EM forces (3-component vector fields)
  • Heat flux (scalar fields)
• Export to ANSYS APDL: Direct export of interpolated results in APDL format
• Visualization: Built-in VTK export for ParaView or PyVista visualization
• Efficient: KDTree-based spatial queries for fast neighbor searches

Installation

pip install interpcore

Quick Start

from interpcore.interpolator import Interpolator
from interpcore.config import InterpolationConfig, QUERY_TYPE, INTERPOLATED_LOAD_TYPE
from interpcore.kernels import INTERPOLATION_KERNEL

# Configure interpolation
config = InterpolationConfig(
    method=QUERY_TYPE.K,
    param=5,
    max_distance=2.0,
    coincidence_tolerance=0.01,
    kernel=INTERPOLATION_KERNEL.DISTANCE_WEIGHTED,
    multithread=False,
    interpolated_load=INTERPOLATED_LOAD_TYPE.EM_FORCE
)

# Define file column indices
file_idx = {"ids": 0, "dest_x": 1, "src_x": 1, "val": 4}

# Create interpolator and run
interpolator = Interpolator(
    path_to_src_folder="source_data",
    path_to_dest_mesh="destination_mesh.txt",
    config=config,
    file_idx=file_idx
)

# Interpolate all source files
interpolator.interpolate_all()

# Export to ANSYS format
interpolator.export_to_ansys("output_directory")

# Optional: Build VTK for visualization
interpolator.build_vtk_output(outdir="vtk_output")

Examples

Complete working examples with sample data are available in the doc/ folder:

• Heat Flux Example: Scalar field interpolation using the AVERAGE kernel
• Heat Generation Example: Volumetric heat generation using the CLOSEST kernel
• EM Force Example: Vector field interpolation with glyph visualization

Each example includes:

• Sample mesh files
• Sample data files
• Jupyter notebook with full workflow
• Visualization with PyVista

Configuration Options

Query Methods

• QUERY_TYPE.K: K-nearest neighbors (param = number of neighbors)
• QUERY_TYPE.RADIUS: Radius-based search (param = radius in same unit as coordinates)

Interpolation Kernels

Source-to-target

Each source point is distributed to destination neighbours:

• DISTANCE_WEIGHTED: Weight by inverse distance
• FEM: FEM-based interpolation

Target-to-source

A value is assigned to each destination point based on source neighbours

• CLOSEST: Use closest source point value
• AVERAGE: Simple average of neighbors

Load Types

• EM_FORCE: 3-component vector fields (Fx, Fy, Fz). If "vol" column is provided the forces are interpreted as force densities and will be multiplied by the volume.
• HEAT_FLUX: Scalar fields for surface heat flux
• HEAT_GEN: Scalar fields for volumetric heat generation

File Format

Destination Mesh

Node_ID X Y Z
101 0.25 0.33 0.00
102 0.25 1.00 0.00
...

Source Data (EM Forces)

Node_ID X Y Z Fx Fy Fz
1 0.50 0.50 0.00 10.50 5.20 2.10
2 0.60 1.50 0.00 12.30 6.40 2.35
...

Source Data (Heat Flux)

Node_ID X Y Z HeatFlux
1 0.50 0.50 0.00 100.00
2 0.50 1.50 0.00 130.00
...

Source Data (Heat Generation)

Node_ID X Y Z HeatGen
1 0.45 0.70 0.00 500.00
2 0.45 1.50 0.00 550.00
...

Requirements

• Python ≥ 3.10
• scikit-learn
• pandas
• tqdm
• pyvista

License

Licensed under the European Union Public Licence (EUPL) 1.2

Authors

Developed by the F4E mechanical team