mesh_tools module

This file contains a selection of useful vector operations and mesh operations such as io, cleaning, converting etc. This file is imported by carto_mesh.

mesh_tools.calcAvNormal(facets: pandas.core.frame.DataFrame) → List

Calculates the average normal of a dataframe of triangles. Dataframe must contain the components of the normals of each triangle in columns named ‘NormalX’, ‘NormalY’, ‘NormalZ’ as is the case for Carto data

Args:

facets: A Pandas Dataframe of triangles. Must contain the following columns: ‘NormalX’, ‘NormalY’, ‘NormalZ’, ‘GroupID’

Returns:

List: An array containing the components of the normalized average normal of the given triangles.

mesh_tools.cleanMesh(pvmesh: pyvista.core.pointset.PolyData, tol: float, iterations: int = 10, print_si: bool = True) → pyvista.core.pointset.PolyData

Uses built-in PyVista and PyMesh methods to clean the mesh.

Args:

pvmesh: The mesh in PyVista’s PolyData format

tol: Absolute tolerance to use in PyMesh’s remove_duplicated_vertices() and PyVista’s clean() methods

iterations: Amount of iterations to try and remove self-intersections with PyMesh

print_si: Print progress of self-intersection cleanup

Returns:

PolyData: The cleaned mesh

mesh_tools.cleanMesh_Meshtool(meshname: str, threshold: float = 0.3, ifmt: str = 'carp_txt', ofmt: str = 'carp_txt') → None

Calls upon meshtool in a shell command to clean a mesh.

Args:

meshname: Name of the mesh to be cleaned threshold: Quality threshold. Cleaning will continue until this quality is reached, or the max amount of iterations has been reached

ifmt: The format of the input mesh. May be; carp_txt, carp_bin, vtk, vtk_bin, mmg, neu, purk, obj, off, gmsh, stellar, vcflow

ofmt: The format of the output mesh. May be: carp_txt, carp_bin, vtk, vtk_bin, vtk_polydata, mmg, neu, obj, off, stellar, vcflow, ensight_txt

Returns:

None: Nothing. Writes out the cleaned mesh in the desired format.

mesh_tools.colorFromCsv(meshdir: str = '') → pyvista.core.pointset.PolyData

Makes mesh from VerticesSection.csv and TrianglesSection.csv Assigns tags in .mesh file as scalar data on this mesh Returns mesh with this scalar data.

Args:

meshdir: Directory containing VerticesSection.csv and TrianglesSection.csv

Returns:

The mesh with scalar data applied in PyVista’s PolyData format

mesh_tools.convertMesh_Meshtool(meshname: str, ifmt: str = 'vtk', ofmt: str = 'carp_txt') → None

Calls upon meshtool in a shell command to convert a mesh.

Args:

meshname: Name of the mesh, excluding file extension.

ifmt: File format of the input mesh. Can be: carp_txt, carp_bin, vtk, vtk_bin, mmg, neu, purk, obj, off, gmsh, stellar, vcflow

ofmt: File format of the output mesh. Can be: carp_txt, carp_bin, vtk, vtk_bin, vtk_polydata, mmg, neu, obj, off, stellar, vcflow, ensight_txt

Returns:

None: Nothing. Writes out the mesh in the desired format.

mesh_tools.dist(co1: Union[numpy.ndarray, List, Tuple], co2: Union[numpy.ndarray, List, Tuple]) → float

Calculates the distance between two coordinates.

Args:

co1: Array containing the components of the first coordinate

co2: Array containing the components of the second coordinate

Returns:

float: Distance between the two coordinates

mesh_tools.getEdgeLengths(mesh: pymesh.Mesh.Mesh) → List

Gets all edge lengths from a PyMesh mesh (used in homogenizeMesh())

Args:

mesh: The input mesh in PyMesh’s Mesh format

Returns:

List: A list containing all the lengths of the edges of the input mesh.

mesh_tools.getGroupIds(csvfile: str = 'TrianglesSection.csv', col_name: str = 'GroupID', skip: Tuple = (0, - 1000000)) → List

Extract the tags from TrianglesSection.csv as generated by __cartoToCsv()

Args:

csvfile: Name of the .csv file to read. Default = ‘TrianglesSection’. File must be .csv and contain a column <col_name>.

skip: Array of tags to ignore. These won’t be extracted. Default = [0, -1000000]

Returns:

List: a list containing the unique tags in the input file

mesh_tools.makeNonCollinear(pvmesh: pyvista.core.pointset.PolyData, edges: Union[numpy.ndarray, List, Tuple]) → pyvista.core.pointset.PolyData

Iterates mesh and replades points in the middle of two colinear edges. Edges must be 2D array

Args:

pvmesh: The input mesh containing colinearities in PyVista’s PolyData format

edges: A list of point index pairs. Each index pair defines a colinear edge.

Returns:

PolyData: The mesh with its colinear edges lifted.

mesh_tools.makePyMesh(pvmesh: pyvista.core.pointset.PolyData) → pymesh.Mesh.Mesh

Converts PyVista mesh to PyMesh mesh

Args:

pvmesh: The mesh in PyVista’s PolyData format

Returns:

Mesh: The mesh as a PyMesh Mesh object

mesh_tools.makePyVista(pmmesh: pymesh.Mesh.Mesh) → pyvista.core.pointset.PolyData

Converts PyMesh mesh to PyVista mesh (PolyData)

Args:

pmmesh: the mesh in PyMesh.Mesh format

Returns:

PolyData: The mesh in PyVista’s PolyData format

mesh_tools.normalize(vector: Union[numpy.ndarray, List, Tuple]) → List

Normalizes a vector such that its length equals 1.

Args:

vector: A 1xN array containing the vector components.

Returns:

List: the normalized vector

mesh_tools.pmToPvFaces(pmfaces: Union[numpy.ndarray, List, Tuple]) → numpy.ndarray

Given an array of pymesh triangles, where these triangles are arrays of the form [ind1, ind2, ind3], converts these faces to the PyVista representation of the form [3, ind1_0, ind2_0, ind3_0, 3, ind1_1, ind2_1, ind3_1, 3 …]

Args:

pmfaces: A Nx3 array, where N = n_faces.

Returns:

ndarray: A numpy ndarray containing the triangles in PyVista’s format

mesh_tools.ptsToParaview(filename: str, column_name: str = 'meshID') → None

Takes a pts file, adds point ID as extra data to each point, writes out to csv file. This function is useful to convert meshes and read them in in ParaView. ParaView does not remember original mesh indexing during mesh operations.

Args:

filename: Name of .pts file (including ‘.pts’ extension) to be converted to paraview-friendly format

column_name: Name of the column to contain the mesh indices. Default = “meshID”

Returns:

None: Nothing. Writes out the mesh as a .csv file containing the columns ‘X’, ‘Y’, ‘Z’ and <column_name>

mesh_tools.pvToPmCells(pyvistafaces: Union[numpy.ndarray, List, Tuple]) → numpy.ndarray

Given an array of pyvista faces, converts these faces to the Pymesh representation.

Args:

pyvistafaces: An array containing the faces in PyVista format

Returns:

ndarray: A numpy ndarray containing the triangles in PyMesh format

mesh_tools.pvToPmFaces(pyvistafaces: Union[numpy.ndarray, List, Tuple]) → numpy.ndarray

Given an array of pyvista triangles, converts these faces to the PyMesh representation.

Args:

pyvistafaces: An array containing the faces in PyVista format.

Returns:

ndarray: A numpy ndarray containing the triangles in PyMesh format

mesh_tools.writeToSmesh(mesh: pyvista.core.pointset.PolyData, name: str) → None

Writes out a .smesh file with a hole in the middle. This is used in reconstruct() to be used as input for TetGen.

Args:

mesh: The surface mesh, ready to be tetrahedralized, in PyVista’s PolyData format.

name: Name of the input mesh, and corresponding output .smesh file

Returns:

None: Nothing. Writes out a .smesh file.