Source code for sfepy.discrete.iga.mappings

Reference mappings for isogeometric analysis.
import numpy as nm

from sfepy.discrete.common.mappings import Mapping, PyCMapping
import sfepy.discrete.iga.extmods.igac as iga

[docs]class IGMapping(Mapping): """ Reference mapping for isogeometric analysis based on Bezier extraction. Parameters ---------- domain : IGDomain instance The mapping domain. cells : array The mapping region cells. (All domain cells required.) nurbs : NurbsPatch instance, optional If given, the `nurbs` is used instead of `domain.nurbs`. The `nurbs` has to be obtained by degree elevation of `domain.nurbs`. """ def __init__(self, domain, cells, nurbs=None): self.domain = domain self.cells = cells self.nurbs = domain.nurbs if nurbs is None else nurbs self.v_shape = (len(cells), -1, self.domain.shape.dim) self.s_shape = (len(cells), -1, 1)
[docs] def get_geometry(self): """ Return reference element geometry as a GeometryElement instance. """ return self.domain.gel
[docs] def get_physical_qps(self, qp_coors): """ Get physical quadrature points corresponding to given reference Bezier element quadrature points. Returns ------- qps : array The physical quadrature points ordered element by element, i.e. with shape (n_el, n_qp, dim). """ nurbs = self.nurbs variable = nm.ones((nurbs.weights.shape[0], 1), dtype=nm.float64) qps, _, _ = iga.eval_variable_in_qp(variable, qp_coors, nurbs.cps, nurbs.weights, nurbs.degrees, nurbs.cs, nurbs.conn, self.cells) qps = qps.reshape(self.v_shape) return qps
[docs] def get_mapping(self, qp_coors, weights): """ Get the mapping for given quadrature points and weights. Returns ------- cmap : CMapping instance The reference mapping. Notes ----- Does not set total volume of the C mapping structure! """ nurbs = self.nurbs bfs, bfgs, dets = iga.eval_mapping_data_in_qp(qp_coors, nurbs.cps, nurbs.weights, nurbs.degrees, nurbs.cs, nurbs.conn, self.cells) # Weight Jacobians by quadrature point weights. dets = nm.abs(dets) * weights[None, :, None, None] # Cell volumes. volumes = dets.sum(axis=1)[..., None] pycmap = PyCMapping(bfs, dets, volumes, bfgs, None, self.v_shape[2]) return pycmap