Source code for plugins.isosurface

#
##
##  This file is part of pyFormex 1.0.7  (Mon Jun 17 12:20:39 CEST 2019)
##  pyFormex is a tool for generating, manipulating and transforming 3D
##  geometrical models by sequences of mathematical operations.
##  Home page: http://pyformex.org
##  Project page:  http://savannah.nongnu.org/projects/pyformex/
##  Copyright 2004-2019 (C) Benedict Verhegghe (benedict.verhegghe@ugent.be)
##  Distributed under the GNU General Public License version 3 or later.
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##  GNU General Public License for more details.
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#
"""Isosurface: surface reconstruction algorithms

This module contains the marching cube algorithm.

Some of the code is based on the example by Paul Bourke from
http://paulbourke.net/geometry/polygonise/

"""
from __future__ import absolute_import, division, print_function
from pyformex import zip

import numpy as np
from pyformex.multi import multitask, cpu_count, splitar


[docs]def isosurface(data, level, nproc=-1, tet=0): """Create an isosurface through data at given level. - `data`: (nx,ny,nz) shaped array of data values at points with coordinates equal to their indices. This defines a 3D volume [0,nx-1], [0,ny-1], [0,nz-1] - `level`: data value at which the isosurface is to be constructed - `nproc`: number of parallel processes to use. On multiprocessor machines this may be used to speed up the processing. If <= 0 , the number of processes will be set equal to the number of processors, to achieve a maximal speedup. Returns an (ntr,3,3) array defining the triangles of the isosurface. The result may be empty (if level is outside the data range). """ if nproc is None: nproc = -1 if nproc < 1: nproc = cpu_count() if nproc == 1: # Perform single process isosurface (accelerated) from pyformex.lib import misc data = data.astype(np.float32) level = np.float32(level) tri = misc.isosurface(data, level, tet) else: # Perform parallel isosurface # 1. Split in blocks (and remember shift) datablocks = splitar(data, nproc, close=True) shift = (np.array([d.shape[0] for d in datablocks]) - 1).cumsum() # 2. Solve blocks independently tasks = [(isosurface, (d, level, 1, tet)) for d in datablocks] tri = multitask(tasks, nproc) # 3. Shift and merge blocks for t, s in zip(tri[1:], shift[:-1]): t[:, :, 2] += s tri = np.concatenate(tri, axis=0) return tri
[docs]def isoline(data, level, nproc=-1): """Create an isocontour through data at given level. - `data`: (nx,ny,nz) shaped array of data values at points with coordinates equal to their indices. This defines a 2D area [0,nx-1], [0,ny-1] - `level`: data value at which the isocontour is to be constructed - `nproc`: number of parallel processes to use. On multiprocessor machines this may be used to speed up the processing. If <= 0 , the number of processes will be set equal to the number of processors, to achieve a maximal speedup. Returns an (nseg,2,2) array defining the 2D coordinates of the segments of the isocontour. The result may be empty (if level is outside the data range). """ if nproc is None: nproc = -1 if nproc < 1: nproc = cpu_count() if nproc == 1: # Perform single process isoline (accelerated) from pyformex.lib import misc data = data.astype(np.float32) level = np.float32(level) seg = misc.isoline(data, level) else: # Perform parallel isoline # 1. Split in blocks (and remember shift) datablocks = splitar(data, nproc, close=True) shift = (np.array([d.shape[0] for d in datablocks]) - 1).cumsum() # 2. Solve blocks independently tasks = [(isoline, (d, level, 1)) for d in datablocks] seg = multitask(tasks, nproc) # 3. Shift and merge blocks for t, s in zip(seg[1:], shift[:-1]): t[:, :, 1] += s seg = np.concatenate(seg, axis=0) return seg
# End