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triinterpolate.py
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""" Interpolation inside triangular grids. """ import numpy as np from matplotlib import cbook from matplotlib.tri import Triangulation from matplotlib.tri.trifinder import TriFinder from matplotlib.tri.tritools import TriAnalyzer __all__ = ('TriInterpolator', 'LinearTriInterpolator', 'CubicTriInterpolator') class TriInterpolator: """ Abstract base class for classes used to interpolate on a triangular grid. Derived classes implement the following methods: - ``__call__(x, y)``, where x, y are array-like point coordinates of the same shape, and that returns a masked array of the same shape containing the interpolated z-values. - ``gradient(x, y)``, where x, y are array-like point coordinates of the same shape, and that returns a list of 2 masked arrays of the same shape containing the 2 derivatives of the interpolator (derivatives of interpolated z values with respect to x and y). """ def __init__(self, triangulation, z, trifinder=None): cbook._check_isinstance(Triangulation, triangulation=triangulation) self._triangulation = triangulation self._z = np.asarray(z) if self._z.shape != self._triangulation.x.shape: raise ValueError("z array must have same length as triangulation x" " and y arrays") cbook._check_isinstance((TriFinder, None), trifinder=trifinder) self._trifinder = trifinder or self._triangulation.get_trifinder() # Default scaling factors : 1.0 (= no scaling) # Scaling may be used for interpolations for which the order of # magnitude of x, y has an impact on the interpolant definition. # Please refer to :meth:`_interpolate_multikeys` for details. self._unit_x = 1.0 self._unit_y = 1.0 # Default triangle renumbering: None (= no renumbering) # Renumbering may be used to avoid unnecessary computations # if complex calculations are done inside the Interpolator. # Please refer to :meth:`_interpolate_multikeys` for details. self._tri_renum = None # __call__ and gradient docstrings are shared by all subclasses # (except, if needed, relevant additions). # However these methods are only implemented in subclasses to avoid # confusion in the documentation. _docstring__call__ = """ Returns a masked array containing interpolated values at the specified (x, y) points. Parameters ---------- x, y : array-like x and y coordinates of the same shape and any number of dimensions. Returns ------- np.ma.array Masked array of the same shape as *x* and *y*; values corresponding to (*x*, *y*) points outside of the triangulation are masked out. """ _docstringgradient = r""" Returns a list of 2 masked arrays containing interpolated derivatives at the specified (x, y) points. Parameters ---------- x, y : array-like x and y coordinates of the same shape and any number of dimensions. Returns ------- dzdx, dzdy : np.ma.array 2 masked arrays of the same shape as *x* and *y*; values corresponding to (x, y) points outside of the triangulation are masked out. The first returned array contains the values of :math:`\frac{\partial z}{\partial x}` and the second those of :math:`\frac{\partial z}{\partial y}`. """ def _interpolate_multikeys(self, x, y, tri_index=None, return_keys=('z',)): """ Versatile (private) method defined for all TriInterpolators. :meth:`_interpolate_multikeys` is a wrapper around method :meth:`_interpolate_single_key` (to be defined in the child subclasses). :meth:`_interpolate_single_key actually performs the interpolation, but only for 1-dimensional inputs and at valid locations (inside unmasked triangles of the triangulation). The purpose of :meth:`_interpolate_multikeys` is to implement the following common tasks needed in all subclasses implementations: - calculation of containing triangles - dealing with more than one interpolation request at the same location (e.g., if the 2 derivatives are requested, it is unnecessary to compute the containing triangles twice) - scaling according to self._unit_x, self._unit_y - dealing with points outside of the grid (with fill value np.nan) - dealing with multi-dimensional *x*, *y* arrays: flattening for :meth:`_interpolate_params` call and final reshaping. (Note that np.vectorize could do most of those things very well for you, but it does it by function evaluations over successive tuples of the input arrays. Therefore, this tends to be more time consuming than using optimized numpy functions - e.g., np.dot - which can be used easily on the flattened inputs, in the child-subclass methods :meth:`_interpolate_single_key`.) It is guaranteed that the calls to :meth:`_interpolate_single_key` will be done with flattened (1-d) array-like input parameters *x*, *y* and with flattened, valid `tri_index` arrays (no -1 index allowed). Parameters ---------- x, y : array-like x and y coordinates where interpolated values are requested. tri_index : array-like of int, optional Array of the containing triangle indices, same shape as *x* and *y*. Defaults to None. If None, these indices will be computed by a TriFinder instance. (Note: For point outside the grid, tri_index[ipt] shall be -1). return_keys : tuple of keys from {'z', 'dzdx', 'dzdy'} Defines the interpolation arrays to return, and in which order. Returns ------- list of arrays Each array-like contains the expected interpolated values in the order defined by *return_keys* parameter. """ # Flattening and rescaling inputs arrays x, y # (initial shape is stored for output) x = np.asarray(x, dtype=np.float64) y = np.asarray(y, dtype=np.float64) sh_ret = x.shape if x.shape != y.shape: raise ValueError("x and y shall have same shapes." " Given: {0} and {1}".format(x.shape, y.shape)) x = np.ravel(x) y = np.ravel(y) x_scaled = x/self._unit_x y_scaled = y/self._unit_y size_ret = np.size(x_scaled) # Computes & ravels the element indexes, extract the valid ones. if tri_index is None: tri_index = self._trifinder(x, y) else: if tri_index.shape != sh_ret: raise ValueError( "tri_index array is provided and shall" " have same shape as x and y. Given: " "{0} and {1}".format(tri_index.shape, sh_ret)) tri_index = np.ravel(tri_index) mask_in = (tri_index != -1) if self._tri_renum is None: valid_tri_index = tri_index[mask_in] else: valid_tri_index = self._tri_renum[tri_index[mask_in]] valid_x = x_scaled[mask_in] valid_y = y_scaled[mask_in] ret = [] for return_key in return_keys: # Find the return index associated with the key. try: return_index = {'z': 0, 'dzdx': 1, 'dzdy': 2}[return_key] except KeyError as err: raise ValueError("return_keys items shall take values in" " {'z', 'dzdx', 'dzdy'}") from err # Sets the scale factor for f & df components scale = [1., 1./self._unit_x, 1./self._unit_y][return_index] # Computes the interpolation ret_loc = np.empty(size_ret, dtype=np.float64) ret_loc[~mask_in] = np.nan ret_loc[mask_in] = self._interpolate_single_key( return_key, valid_tri_index, valid_x, valid_y) * scale ret += [np.ma.masked_invalid(ret_loc.reshape(sh_ret), copy=False)] return ret def _interpolate_single_key(self, return_key, tri_index, x, y): """ Interpolate at points belonging to the triangulation (inside an unmasked triangles). Parameters ---------- return_index : {'z', 'dzdx', 'dzdy'} Identifies the requested values (z or its derivatives) tri_index : 1d int array Valid triangle index (-1 prohibited) x, y : 1d arrays, same shape as `tri_index` Valid locations where interpolation is requested. Returns ------- 1-d array Returned array of the same size as *tri_index* """ raise NotImplementedError("TriInterpolator subclasses" + "should implement _interpolate_single_key!") class LinearTriInterpolator(TriInterpolator): """ Linear interpolator on a triangular grid. Each triangle is represented by a plane so that an interpolated value at point (x, y) lies on the plane of the triangle containing (x, y). Interpolated values are therefore continuous across the triangulation, but their first derivatives are discontinuous at edges between triangles. Parameters ---------- triangulation : `~matplotlib.tri.Triangulation` The triangulation to interpolate over. z : array-like of shape (npoints,) Array of values, defined at grid points, to interpolate between. trifinder : `~matplotlib.tri.TriFinder`, optional If this is not specified, the Triangulation's default TriFinder will be used by calling `.Triangulation.get_trifinder`. Methods ------- `__call__` (x, y) : Returns interpolated values at (x, y) points. `gradient` (x, y) : Returns interpolated derivatives at (x, y) points. """ def __init__(self, triangulation, z, trifinder=None): TriInterpolator.__init__(self, triangulation, z, trifinder) # Store plane coefficients for fast interpolation calculations. self._plane_coefficients = \ self._triangulation.calculate_plane_coefficients(self._z) def __call__(self, x, y): return self._interpolate_multikeys(x, y, tri_index=None, return_keys=('z',))[0] __call__.__doc__ = TriInterpolator._docstring__call__ def gradient(self, x, y): return self._interpolate_multikeys(x, y, tri_index=None, return_keys=('dzdx', 'dzdy')) gradient.__doc__ = TriInterpolator._docstringgradient def _interpolate_single_key(self, return_key, tri_index, x, y): if return_key == 'z': return (self._plane_coefficients[tri_index, 0]*x + self._plane_coefficients[tri_index, 1]*y + self._plane_coefficients[tri_index, 2]) elif return_key == 'dzdx': return self._plane_coefficients[tri_index, 0] elif return_key == 'dzdy': return self._plane_coefficients[tri_index, 1] else: raise ValueError("Invalid return_key: " + return_key) class CubicTriInterpolator(TriInterpolator): r""" Cubic interpolator on a triangular grid. In one-dimension - on a segment - a cubic interpolating function is defined by the values of the function and its derivative at both ends. This is almost the same in 2-d inside a triangle, except that the values of the function and its 2 derivatives have to be defined at each triangle node. The CubicTriInterpolator takes the value of the function at each node - provided by the user - and internally computes the value of the derivatives, resulting in a smooth interpolation. (As a special feature, the user can also impose the value of the derivatives at each node, but this is not supposed to be the common usage.) Parameters ---------- triangulation : `~matplotlib.tri.Triangulation` The triangulation to interpolate over. z : array-like of shape (npoints,) Array of values, defined at grid points, to interpolate between. kind : {'min_E', 'geom', 'user'}, optional Choice of the smoothing algorithm, in order to compute the interpolant derivatives (defaults to 'min_E'): - if 'min_E': (default) The derivatives at each node is computed to minimize a bending energy. - if 'geom': The derivatives at each node is computed as a weighted average of relevant triangle normals. To be used for speed optimization (large grids). - if 'user': The user provides the argument *dz*, no computation is hence needed. trifinder : `~matplotlib.tri.TriFinder`, optional If not specified, the Triangulation's default TriFinder will be used by calling `.Triangulation.get_trifinder`. dz : tuple of array-likes (dzdx, dzdy), optional Used only if *kind* ='user'. In this case *dz* must be provided as (dzdx, dzdy) where dzdx, dzdy are arrays of the same shape as *z* and are the interpolant first derivatives at the *triangulation* points. Methods ------- `__call__` (x, y) : Returns interpolated values at (x, y) points. `gradient` (x, y) : Returns interpolated derivatives at (x, y) points. Notes ----- This note is a bit technical and details how the cubic interpolation is computed. The interpolation is based on a Clough-Tocher subdivision scheme of the *triangulation* mesh (to make it clearer, each triangle of the grid will be divided in 3 child-triangles, and on each child triangle the interpolated function is a cubic polynomial of the 2 coordinates). This technique originates from FEM (Finite Element Method) analysis; the element used is a reduced Hsieh-Clough-Tocher (HCT) element. Its shape functions are described in [1]_. The assembled function is guaranteed to be C1-smooth, i.e. it is continuous and its first derivatives are also continuous (this is easy to show inside the triangles but is also true when crossing the edges). In the default case (*kind* ='min_E'), the interpolant minimizes a curvature energy on the functional space generated by the HCT element shape functions - with imposed values but arbitrary derivatives at each node. The minimized functional is the integral of the so-called total curvature (implementation based on an algorithm from [2]_ - PCG sparse solver): .. math:: E(z) = \frac{1}{2} \int_{\Omega} \left( \left( \frac{\partial^2{z}}{\partial{x}^2} \right)^2 + \left( \frac{\partial^2{z}}{\partial{y}^2} \right)^2 + 2\left( \frac{\partial^2{z}}{\partial{y}\partial{x}} \right)^2 \right) dx\,dy If the case *kind* ='geom' is chosen by the user, a simple geometric approximation is used (weighted average of the triangle normal vectors), which could improve speed on very large grids. References ---------- .. [1] Michel Bernadou, Kamal Hassan, "Basis functions for general Hsieh-Clough-Tocher triangles, complete or reduced.", International Journal for Numerical Methods in Engineering, 17(5):784 - 789. 2.01. .. [2] C.T. Kelley, "Iterative Methods for Optimization". """ def __init__(self, triangulation, z, kind='min_E', trifinder=None, dz=None): TriInterpolator.__init__(self, triangulation, z, trifinder) # Loads the underlying c++ _triangulation. # (During loading, reordering of triangulation._triangles may occur so # that all final triangles are now anti-clockwise) self._triangulation.get_cpp_triangulation() # To build the stiffness matrix and avoid zero-energy spurious modes # we will only store internally the valid (unmasked) triangles and # the necessary (used) points coordinates. # 2 renumbering tables need to be computed and stored: # - a triangle renum table in order to translate the result from a # TriFinder instance into the internal stored triangle number. # - a node renum table to overwrite the self._z values into the new # (used) node numbering. tri_analyzer = TriAnalyzer(self._triangulation) (compressed_triangles, compressed_x, compressed_y, tri_renum, node_renum) = tri_analyzer._get_compressed_triangulation() self._triangles = compressed_triangles self._tri_renum = tri_renum # Taking into account the node renumbering in self._z: valid_node = (node_renum != -1) self._z[node_renum[valid_node]] = self._z[valid_node] # Computing scale factors self._unit_x = np.ptp(compressed_x) self._unit_y = np.ptp(compressed_y) self._pts = np.column_stack([compressed_x / self._unit_x, compressed_y / self._unit_y]) # Computing triangle points self._tris_pts = self._pts[self._triangles] # Computing eccentricities self._eccs = self._compute_tri_eccentricities(self._tris_pts) # Computing dof estimations for HCT triangle shape function self._dof = self._compute_dof(kind, dz=dz) # Loading HCT element self._ReferenceElement = _ReducedHCT_Element() def __call__(self, x, y): return self._interpolate_multikeys(x, y, tri_index=None, return_keys=('z',))[0] __call__.__doc__ = TriInterpolator._docstring__call__ def gradient(self, x, y): return self._interpolate_multikeys(x, y, tri_index=None, return_keys=('dzdx', 'dzdy')) gradient.__doc__ = TriInterpolator._docstringgradient def _interpolate_single_key(self, return_key, tri_index, x, y): tris_pts = self._tris_pts[tri_index] alpha = self._get_alpha_vec(x, y, tris_pts) ecc = self._eccs[tri_index] dof = np.expand_dims(self._dof[tri_index], axis=1) if return_key == 'z': return self._ReferenceElement.get_function_values( alpha, ecc, dof) elif return_key in ['dzdx', 'dzdy']: J = self._get_jacobian(tris_pts) dzdx = self._ReferenceElement.get_function_derivatives( alpha, J, ecc, dof) if return_key == 'dzdx': return dzdx[:, 0, 0] else: return dzdx[:, 1, 0] else: raise ValueError("Invalid return_key: " + return_key) def _compute_dof(self, kind, dz=None): """ Compute and return nodal dofs according to kind. Parameters ---------- kind : {'min_E', 'geom', 'user'} Choice of the _DOF_estimator subclass to estimate the gradient. dz : tuple of array-likes (dzdx, dzdy), optional Used only if *kind*=user; in this case passed to the :class:`_DOF_estimator_user`. Returns ------- array-like, shape (npts, 2) Estimation of the gradient at triangulation nodes (stored as degree of freedoms of reduced-HCT triangle elements). """ if kind == 'user': if dz is None: raise ValueError("For a CubicTriInterpolator with " "*kind*='user', a valid *dz* " "argument is expected.") TE = _DOF_estimator_user(self, dz=dz) elif kind == 'geom': TE = _DOF_estimator_geom(self) elif kind == 'min_E': TE = _DOF_estimator_min_E(self) else: cbook._check_in_list(['user', 'geom', 'min_E'], kind=kind) return TE.compute_dof_from_df() @staticmethod def _get_alpha_vec(x, y, tris_pts): """ Fast (vectorized) function to compute barycentric coordinates alpha. Parameters ---------- x, y : array-like of dim 1 (shape (nx,)) Coordinates of the points whose points barycentric coordinates are requested. tris_pts : array like of dim 3 (shape: (nx, 3, 2)) Coordinates of the containing triangles apexes. Returns ------- array of dim 2 (shape (nx, 3)) Barycentric coordinates of the points inside the containing triangles. """ ndim = tris_pts.ndim-2 a = tris_pts[:, 1, :] - tris_pts[:, 0, :] b = tris_pts[:, 2, :] - tris_pts[:, 0, :] abT = np.stack([a, b], axis=-1) ab = _transpose_vectorized(abT) OM = np.stack([x, y], axis=1) - tris_pts[:, 0, :] metric = _prod_vectorized(ab, abT) # Here we try to deal with the colinear cases. # metric_inv is in this case set to the Moore-Penrose pseudo-inverse # meaning that we will still return a set of valid barycentric # coordinates. metric_inv = _pseudo_inv22sym_vectorized(metric) Covar = _prod_vectorized(ab, _transpose_vectorized( np.expand_dims(OM, ndim))) ksi = _prod_vectorized(metric_inv, Covar) alpha = _to_matrix_vectorized([ [1-ksi[:, 0, 0]-ksi[:, 1, 0]], [ksi[:, 0, 0]], [ksi[:, 1, 0]]]) return alpha @staticmethod def _get_jacobian(tris_pts): """ Fast (vectorized) function to compute triangle jacobian matrix. Parameters ---------- tris_pts : array like of dim 3 (shape: (nx, 3, 2)) Coordinates of the containing triangles apexes. Returns ------- array of dim 3 (shape (nx, 2, 2)) Barycentric coordinates of the points inside the containing triangles. J[itri, :, :] is the jacobian matrix at apex 0 of the triangle itri, so that the following (matrix) relationship holds: [dz/dksi] = [J] x [dz/dx] with x: global coordinates ksi: element parametric coordinates in triangle first apex local basis. """ a = np.array(tris_pts[:, 1, :] - tris_pts[:, 0, :]) b = np.array(tris_pts[:, 2, :] - tris_pts[:, 0, :]) J = _to_matrix_vectorized([[a[:, 0], a[:, 1]], [b[:, 0], b[:, 1]]]) return J @staticmethod def _compute_tri_eccentricities(tris_pts): """ Compute triangle eccentricities. Parameters ---------- tris_pts : array like of dim 3 (shape: (nx, 3, 2)) Coordinates of the triangles apexes. Returns ------- array like of dim 2 (shape: (nx, 3)) The so-called eccentricity parameters [1] needed for HCT triangular element. """ a = np.expand_dims(tris_pts[:, 2, :] - tris_pts[:, 1, :], axis=2) b = np.expand_dims(tris_pts[:, 0, :] - tris_pts[:, 2, :], axis=2) c = np.expand_dims(tris_pts[:, 1, :] - tris_pts[:, 0, :], axis=2) # Do not use np.squeeze, this is dangerous if only one triangle # in the triangulation... dot_a = _prod_vectorized(_transpose_vectorized(a), a)[:, 0, 0] dot_b = _prod_vectorized(_transpose_vectorized(b), b)[:, 0, 0] dot_c = _prod_vectorized(_transpose_vectorized(c), c)[:, 0, 0] # Note that this line will raise a warning for dot_a, dot_b or dot_c # zeros, but we choose not to support triangles with duplicate points. return _to_matrix_vectorized([[(dot_c-dot_b) / dot_a], [(dot_a-dot_c) / dot_b], [(dot_b-dot_a) / dot_c]]) # FEM element used for interpolation and for solving minimisation # problem (Reduced HCT element) class _ReducedHCT_Element: """ Implementation of reduced HCT triangular element with explicit shape functions. Computes z, dz, d2z and the element stiffness matrix for bending energy: E(f) = integral( (d2z/dx2 + d2z/dy2)**2 dA) *** Reference for the shape functions: *** [1] Basis functions for general Hsieh-Clough-Tocher _triangles, complete or reduced. Michel Bernadou, Kamal Hassan International Journal for Numerical Methods in Engineering. 17(5):784 - 789. 2.01 *** Element description: *** 9 dofs: z and dz given at 3 apex C1 (conform) """ # 1) Loads matrices to generate shape functions as a function of # triangle eccentricities - based on [1] p.11 ''' M = np.array([ [ 0.00, 0.00, 0.00, 4.50, 4.50, 0.00, 0.00, 0.00, 0.00, 0.00], [-0.25, 0.00, 0.00, 0.50, 1.25, 0.00, 0.00, 0.00, 0.00, 0.00], [-0.25, 0.00, 0.00, 1.25, 0.50, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.50, 1.00, 0.00, -1.50, 0.00, 3.00, 3.00, 0.00, 0.00, 3.00], [ 0.00, 0.00, 0.00, -0.25, 0.25, 0.00, 1.00, 0.00, 0.00, 0.50], [ 0.25, 0.00, 0.00, -0.50, -0.25, 1.00, 0.00, 0.00, 0.00, 1.00], [ 0.50, 0.00, 1.00, 0.00, -1.50, 0.00, 0.00, 3.00, 3.00, 3.00], [ 0.25, 0.00, 0.00, -0.25, -0.50, 0.00, 0.00, 0.00, 1.00, 1.00], [ 0.00, 0.00, 0.00, 0.25, -0.25, 0.00, 0.00, 1.00, 0.00, 0.50]]) M0 = np.array([ [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [-1.00, 0.00, 0.00, 1.50, 1.50, 0.00, 0.00, 0.00, 0.00, -3.00], [-0.50, 0.00, 0.00, 0.75, 0.75, 0.00, 0.00, 0.00, 0.00, -1.50], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 1.00, 0.00, 0.00, -1.50, -1.50, 0.00, 0.00, 0.00, 0.00, 3.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.50, 0.00, 0.00, -0.75, -0.75, 0.00, 0.00, 0.00, 0.00, 1.50]]) M1 = np.array([ [-0.50, 0.00, 0.00, 1.50, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [-0.25, 0.00, 0.00, 0.75, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.50, 0.00, 0.00, -1.50, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.25, 0.00, 0.00, -0.75, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00]]) M2 = np.array([ [ 0.50, 0.00, 0.00, 0.00, -1.50, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.25, 0.00, 0.00, 0.00, -0.75, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [-0.50, 0.00, 0.00, 0.00, 1.50, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [-0.25, 0.00, 0.00, 0.00, 0.75, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00]]) # 2) Loads matrices to rotate components of gradient & Hessian # vectors in the reference basis of triangle first apex (a0) rotate_dV = np.array([[ 1., 0.], [ 0., 1.], [ 0., 1.], [-1., -1.], [-1., -1.], [ 1., 0.]]) rotate_d2V = np.array([[1., 0., 0.], [0., 1., 0.], [ 0., 0., 1.], [0., 1., 0.], [1., 1., 1.], [ 0., -2., -1.], [1., 1., 1.], [1., 0., 0.], [-2., 0., -1.]]) # 3) Loads Gauss points & weights on the 3 sub-_triangles for P2 # exact integral - 3 points on each subtriangles. # NOTE: as the 2nd derivative is discontinuous , we really need those 9 # points! n_gauss = 9 gauss_pts = np.array([[13./18., 4./18., 1./18.], [ 4./18., 13./18., 1./18.], [ 7./18., 7./18., 4./18.], [ 1./18., 13./18., 4./18.], [ 1./18., 4./18., 13./18.], [ 4./18., 7./18., 7./18.], [ 4./18., 1./18., 13./18.], [13./18., 1./18., 4./18.], [ 7./18., 4./18., 7./18.]], dtype=np.float64) gauss_w = np.ones([9], dtype=np.float64) / 9. # 4) Stiffness matrix for curvature energy E = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 2.]]) # 5) Loads the matrix to compute DOF_rot from tri_J at apex 0 J0_to_J1 = np.array([[-1., 1.], [-1., 0.]]) J0_to_J2 = np.array([[ 0., -1.], [ 1., -1.]]) def get_function_values(self, alpha, ecc, dofs): """ Parameters ---------- alpha : is a (N x 3 x 1) array (array of column-matrices) of barycentric coordinates, ecc : is a (N x 3 x 1) array (array of column-matrices) of triangle eccentricities, dofs : is a (N x 1 x 9) arrays (arrays of row-matrices) of computed degrees of freedom. Returns ------- Returns the N-array of interpolated function values. """ subtri = np.argmin(alpha, axis=1)[:, 0] ksi = _roll_vectorized(alpha, -subtri, axis=0) E = _roll_vectorized(ecc, -subtri, axis=0) x = ksi[:, 0, 0] y = ksi[:, 1, 0] z = ksi[:, 2, 0] x_sq = x*x y_sq = y*y z_sq = z*z V = _to_matrix_vectorized([ [x_sq*x], [y_sq*y], [z_sq*z], [x_sq*z], [x_sq*y], [y_sq*x], [y_sq*z], [z_sq*y], [z_sq*x], [x*y*z]]) prod = _prod_vectorized(self.M, V) prod += _scalar_vectorized(E[:, 0, 0], _prod_vectorized(self.M0, V)) prod += _scalar_vectorized(E[:, 1, 0], _prod_vectorized(self.M1, V)) prod += _scalar_vectorized(E[:, 2, 0], _prod_vectorized(self.M2, V)) s = _roll_vectorized(prod, 3*subtri, axis=0) return _prod_vectorized(dofs, s)[:, 0, 0] def get_function_derivatives(self, alpha, J, ecc, dofs): """ Parameters ---------- *alpha* is a (N x 3 x 1) array (array of column-matrices of barycentric coordinates) *J* is a (N x 2 x 2) array of jacobian matrices (jacobian matrix at triangle first apex) *ecc* is a (N x 3 x 1) array (array of column-matrices of triangle eccentricities) *dofs* is a (N x 1 x 9) arrays (arrays of row-matrices) of computed degrees of freedom. Returns ------- Returns the values of interpolated function derivatives [dz/dx, dz/dy] in global coordinates at locations alpha, as a column-matrices of shape (N x 2 x 1). """ subtri = np.argmin(alpha, axis=1)[:, 0] ksi = _roll_vectorized(alpha, -subtri, axis=0) E = _roll_vectorized(ecc, -subtri, axis=0) x = ksi[:, 0, 0] y = ksi[:, 1, 0] z = ksi[:, 2, 0] x_sq = x*x y_sq = y*y z_sq = z*z dV = _to_matrix_vectorized([ [ -3.*x_sq, -3.*x_sq], [ 3.*y_sq, 0.], [ 0., 3.*z_sq], [ -2.*x*z, -2.*x*z+x_sq], [-2.*x*y+x_sq, -2.*x*y], [ 2.*x*y-y_sq, -y_sq], [ 2.*y*z, y_sq], [ z_sq, 2.*y*z], [ -z_sq, 2.*x*z-z_sq], [ x*z-y*z, x*y-y*z]]) # Puts back dV in first apex basis dV = _prod_vectorized(dV, _extract_submatrices( self.rotate_dV, subtri, block_size=2, axis=0)) prod = _prod_vectorized(self.M, dV) prod += _scalar_vectorized(E[:, 0, 0], _prod_vectorized(self.M0, dV)) prod += _scalar_vectorized(E[:, 1, 0], _prod_vectorized(self.M1, dV)) prod += _scalar_vectorized(E[:, 2, 0], _prod_vectorized(self.M2, dV)) dsdksi = _roll_vectorized(prod, 3*subtri, axis=0) dfdksi = _prod_vectorized(dofs, dsdksi) # In global coordinates: # Here we try to deal with the simplest colinear cases, returning a # null matrix. J_inv = _safe_inv22_vectorized(J) dfdx = _prod_vectorized(J_inv, _transpose_vectorized(dfdksi)) return dfdx def get_function_hessians(self, alpha, J, ecc, dofs): """ Parameters ---------- *alpha* is a (N x 3 x 1) array (array of column-matrices) of barycentric coordinates *J* is a (N x 2 x 2) array of jacobian matrices (jacobian matrix at triangle first apex) *ecc* is a (N x 3 x 1) array (array of column-matrices) of triangle eccentricities *dofs* is a (N x 1 x 9) arrays (arrays of row-matrices) of computed degrees of freedom. Returns ------- Returns the values of interpolated function 2nd-derivatives [d2z/dx2, d2z/dy2, d2z/dxdy] in global coordinates at locations alpha, as a column-matrices of shape (N x 3 x 1). """ d2sdksi2 = self.get_d2Sidksij2(alpha, ecc) d2fdksi2 = _prod_vectorized(dofs, d2sdksi2) H_rot = self.get_Hrot_from_J(J) d2fdx2 = _prod_vectorized(d2fdksi2, H_rot) return _transpose_vectorized(d2fdx2) def get_d2Sidksij2(self, alpha, ecc): """ Parameters ---------- *alpha* is a (N x 3 x 1) array (array of column-matrices) of barycentric coordinates *ecc* is a (N x 3 x 1) array (array of column-matrices) of triangle eccentricities Returns ------- Returns the arrays d2sdksi2 (N x 3 x 1) Hessian of shape functions expressed in covariant coordinates in first apex basis. """ subtri = np.argmin(alpha, axis=1)[:, 0] ksi = _roll_vectorized(alpha, -subtri, axis=0) E = _roll_vectorized(ecc, -subtri, axis=0) x = ksi[:, 0, 0] y = ksi[:, 1, 0] z = ksi[:, 2, 0] d2V = _to_matrix_vectorized([ [ 6.*x, 6.*x, 6.*x], [ 6.*y, 0., 0.], [ 0., 6.*z, 0.], [ 2.*z, 2.*z-4.*x, 2.*z-2.*x], [2.*y-4.*x, 2.*y, 2.*y-2.*x], [2.*x-4.*y, 0., -2.*y], [ 2.*z, 0., 2.*y], [ 0., 2.*y, 2.*z], [ 0., 2.*x-4.*z, -2.*z], [ -2.*z, -2.*y, x-y-z]]) # Puts back d2V in first apex basis d2V = _prod_vectorized(d2V, _extract_submatrices( self.rotate_d2V, subtri, block_size=3, axis=0)) prod = _prod_vectorized(self.M, d2V) prod += _scalar_vectorized(E[:, 0, 0], _prod_vectorized(self.M0, d2V)) prod += _scalar_vectorized(E[:, 1, 0], _prod_vectorized(self.M1, d2V)) prod += _scalar_vectorized(E[:, 2, 0], _prod_vectorized(self.M2, d2V)) d2sdksi2 = _roll_vectorized(prod, 3*subtri, axis=0) return d2sdksi2 def get_bending_matrices(self, J, ecc): """ Parameters ---------- *J* is a (N x 2 x 2) array of jacobian matrices (jacobian matrix at triangle first apex) *ecc* is a (N x 3 x 1) array (array of column-matrices) of triangle eccentricities Returns ------- Returns the element K matrices for bending energy expressed in GLOBAL nodal coordinates. K_ij = integral [ (d2zi/dx2 + d2zi/dy2) * (d2zj/dx2 + d2zj/dy2) dA] tri_J is needed to rotate dofs from local basis to global basis """ n = np.size(ecc, 0) # 1) matrix to rotate dofs in global coordinates J1 = _prod_vectorized(self.J0_to_J1, J) J2 = _prod_vectorized(self.J0_to_J2, J) DOF_rot = np.zeros([n, 9, 9], dtype=np.float64) DOF_rot[:, 0, 0] = 1 DOF_rot[:, 3, 3] = 1 DOF_rot[:, 6, 6] = 1 DOF_rot[:, 1:3, 1:3] = J DOF_rot[:, 4:6, 4:6] = J1 DOF_rot[:, 7:9, 7:9] = J2 # 2) matrix to rotate Hessian in global coordinates. H_rot, area = self.get_Hrot_from_J(J, return_area=True) # 3) Computes stiffness matrix # Gauss quadrature. K = np.zeros([n, 9, 9], dtype=np.float64) weights = self.gauss_w pts = self.gauss_pts for igauss in range(self.n_gauss): alpha = np.tile(pts[igauss, :], n).reshape(n, 3) alpha = np.expand_dims(alpha, 2) weight = weights[igauss] d2Skdksi2 = self.get_d2Sidksij2(alpha, ecc) d2Skdx2 = _prod_vectorized(d2Skdksi2, H_rot) K += weight * _prod_vectorized(_prod_vectorized(d2Skdx2, self.E), _transpose_vectorized(d2Skdx2)) # 4) With nodal (not elem) dofs K = _prod_vectorized(_prod_vectorized(_transpose_vectorized(DOF_rot), K), DOF_rot) # 5) Need the area to compute total element energy return _scalar_vectorized(area, K) def get_Hrot_from_J(self, J, return_area=False): """ Parameters ---------- *J* is a (N x 2 x 2) array of jacobian matrices (jacobian matrix at triangle first apex) Returns ------- Returns H_rot used to rotate Hessian from local basis of first apex, to global coordinates. if *return_area* is True, returns also the triangle area (0.5*det(J)) """ # Here we try to deal with the simplest colinear cases; a null # energy and area is imposed. J_inv = _safe_inv22_vectorized(J) Ji00 = J_inv[:, 0, 0] Ji11 = J_inv[:, 1, 1] Ji10 = J_inv[:, 1, 0] Ji01 = J_inv[:, 0, 1] H_rot = _to_matrix_vectorized([ [Ji00*Ji00, Ji10*Ji10, Ji00*Ji10], [Ji01*Ji01, Ji11*Ji11, Ji01*Ji11], [2*Ji00*Ji01, 2*Ji11*Ji10, Ji00*Ji11+Ji10*Ji01]]) if not return_area: return H_rot else: area = 0.5 * (J[:, 0, 0]*J[:, 1, 1] - J[:, 0, 1]*J[:, 1, 0]) return H_rot, area def get_Kff_and_Ff(self, J, ecc, triangles, Uc): """ Build K and F for the following elliptic formulation: minimization of curvature energy with value of function at node imposed and derivatives 'free'. Build the global Kff matrix in cco format. Build the full Ff vec Ff = - Kfc x Uc. Parameters ---------- *J* is a (N x 2 x 2) array of jacobian matrices (jacobian matrix at triangle first apex) *ecc* is a (N x 3 x 1) array (array of column-matrices) of triangle eccentricities *triangles* is a (N x 3) array of nodes indexes. *Uc* is (N x 3) array of imposed displacements at nodes Returns ------- (Kff_rows, Kff_cols, Kff_vals) Kff matrix in coo format - Duplicate (row, col) entries must be summed. Ff: force vector - dim npts * 3 """ ntri = np.size(ecc, 0) vec_range = np.arange(ntri, dtype=np.int32) c_indices = np.full(ntri, -1, dtype=np.int32) # for unused dofs, -1 f_dof = [1, 2, 4, 5, 7, 8] c_dof = [0, 3, 6] # vals, rows and cols indices in global dof numbering f_dof_indices = _to_matrix_vectorized([[ c_indices, triangles[:, 0]*2, triangles[:, 0]*2+1, c_indices, triangles[:, 1]*2, triangles[:, 1]*2+1, c_indices, triangles[:, 2]*2, triangles[:, 2]*2+1]]) expand_indices = np.ones([ntri, 9, 1], dtype=np.int32) f_row_indices = _prod_vectorized(_transpose_vectorized(f_dof_indices), _transpose_vectorized(expand_indices)) f_col_indices = _prod_vectorized(expand_indices, f_dof_indices) K_elem = self.get_bending_matrices(J, ecc) # Extracting sub-matrices # Explanation & notations: # * Subscript f denotes 'free' degrees of freedom (i.e. dz/dx, dz/dx) # * Subscript c denotes 'condensated' (imposed) degrees of freedom # (i.e. z at all nodes) # * F = [Ff, Fc] is the force vector # * U = [Uf, Uc] is the imposed dof vector # [ Kff Kfc ] # * K = [ ] is the laplacian stiffness matrix # [ Kcf Kff ] # * As F = K x U one gets straightforwardly: Ff = - Kfc x Uc # Computing Kff stiffness matrix in sparse coo format Kff_vals = np.ravel(K_elem[np.ix_(vec_range, f_dof, f_dof)]) Kff_rows = np.ravel(f_row_indices[np.ix_(vec_range, f_dof, f_dof)]) Kff_cols = np.ravel(f_col_indices[np.ix_(vec_range, f_dof, f_dof)]) # Computing Ff force vector in sparse coo format Kfc_elem = K_elem[np.ix_(vec_range, f_dof, c_dof)] Uc_elem = np.expand_dims(Uc, axis=2) Ff_elem = - _prod_vectorized(Kfc_elem, Uc_elem)[:, :, 0] Ff_indices = f_dof_indices[np.ix_(vec_range, [0], f_dof)][:, 0, :] # Extracting Ff force vector in dense format # We have to sum duplicate indices - using bincount Ff = np.bincount(np.ravel(Ff_indices), weights=np.ravel(Ff_elem)) return Kff_rows, Kff_cols, Kff_vals, Ff # :class:_DOF_estimator, _DOF_estimator_user, _DOF_estimator_geom, # _DOF_estimator_min_E # Private classes used to compute the degree of freedom of each triangular # element for the TriCubicInterpolator. class _DOF_estimator: """ Abstract base class for classes used to estimate a function's first derivatives, and deduce the dofs for a CubicTriInterpolator using a reduced HCT element formulation. Derived classes implement ``compute_df(self, **kwargs)``, returning ``np.vstack([dfx, dfy]).T`` where ``dfx, dfy`` are the estimation of the 2 gradient coordinates. """ def __init__(self, interpolator, **kwargs): cbook._check_isinstance( CubicTriInterpolator, interpolator=interpolator) self._pts = interpolator._pts self._tris_pts = interpolator._tris_pts self.z = interpolator._z self._triangles = interpolator._triangles (self._unit_x, self._unit_y) = (interpolator._unit_x, interpolator._unit_y) self.dz = self.compute_dz(**kwargs) self.compute_dof_from_df() def compute_dz(self, **kwargs): raise NotImplementedError def compute_dof_from_df(self): """ Compute reduced-HCT elements degrees of freedom, from the gradient. """ J = CubicTriInterpolator._get_jacobian(self._tris_pts) tri_z = self.z[self._triangles] tri_dz = self.dz[self._triangles] tri_dof = self.get_dof_vec(tri_z, tri_dz, J) return tri_dof @staticmethod def get_dof_vec(tri_z, tri_dz, J): """ Compute the dof vector of a triangle, from the value of f, df and of the local Jacobian at each node. Parameters ---------- tri_z : shape (3,) array f nodal values. tri_dz : shape (3, 2) array df/dx, df/dy nodal values. J Jacobian matrix in local basis of apex 0. Returns ------- dof : shape (9,) array For each apex ``iapex``:: dof[iapex*3+0] = f(Ai) dof[iapex*3+1] = df(Ai).(AiAi+) dof[iapex*3+2] = df(Ai).(AiAi-) """ npt = tri_z.shape[0] dof = np.zeros([npt, 9], dtype=np.float64) J1 = _prod_vectorized(_ReducedHCT_Element.J0_to_J1, J) J2 = _prod_vectorized(_ReducedHCT_Element.J0_to_J2, J) col0 = _prod_vectorized(J, np.expand_dims(tri_dz[:, 0, :], axis=2)) col1 = _prod_vectorized(J1, np.expand_dims(tri_dz[:, 1, :], axis=2)) col2 = _prod_vectorized(J2, np.expand_dims(tri_dz[:, 2, :], axis=2)) dfdksi = _to_matrix_vectorized([ [col0[:, 0, 0], col1[:, 0, 0], col2[:, 0, 0]], [col0[:, 1, 0], col1[:, 1, 0], col2[:, 1, 0]]]) dof[:, 0:7:3] = tri_z dof[:, 1:8:3] = dfdksi[:, 0] dof[:, 2:9:3] = dfdksi[:, 1] return dof class _DOF_estimator_user(_DOF_estimator): """dz is imposed by user; accounts for scaling if any.""" def compute_dz(self, dz): (dzdx, dzdy) = dz dzdx = dzdx * self._unit_x dzdy = dzdy * self._unit_y return np.vstack([dzdx, dzdy]).T class _DOF_estimator_geom(_DOF_estimator): """Fast 'geometric' approximation, recommended for large arrays.""" def compute_dz(self): """ self.df is computed as weighted average of _triangles sharing a common node. On each triangle itri f is first assumed linear (= ~f), which allows to compute d~f[itri] Then the following approximation of df nodal values is then proposed: f[ipt] = SUM ( w[itri] x d~f[itri] , for itri sharing apex ipt) The weighted coeff. w[itri] are proportional to the angle of the triangle itri at apex ipt """ el_geom_w = self.compute_geom_weights() el_geom_grad = self.compute_geom_grads() # Sum of weights coeffs w_node_sum = np.bincount(np.ravel(self._triangles), weights=np.ravel(el_geom_w)) # Sum of weighted df = (dfx, dfy) dfx_el_w = np.empty_like(el_geom_w) dfy_el_w = np.empty_like(el_geom_w) for iapex in range(3): dfx_el_w[:, iapex] = el_geom_w[:, iapex]*el_geom_grad[:, 0] dfy_el_w[:, iapex] = el_geom_w[:, iapex]*el_geom_grad[:, 1] dfx_node_sum = np.bincount(np.ravel(self._triangles), weights=np.ravel(dfx_el_w)) dfy_node_sum = np.bincount(np.ravel(self._triangles), weights=np.ravel(dfy_el_w)) # Estimation of df dfx_estim = dfx_node_sum/w_node_sum dfy_estim = dfy_node_sum/w_node_sum return np.vstack([dfx_estim, dfy_estim]).T def compute_geom_weights(self): """ Build the (nelems, 3) weights coeffs of _triangles angles, renormalized so that np.sum(weights, axis=1) == np.ones(nelems) """ weights = np.zeros([np.size(self._triangles, 0), 3]) tris_pts = self._tris_pts for ipt in range(3): p0 = tris_pts[:, ipt % 3, :] p1 = tris_pts[:, (ipt+1) % 3, :] p2 = tris_pts[:, (ipt-1) % 3, :] alpha1 = np.arctan2(p1[:, 1]-p0[:, 1], p1[:, 0]-p0[:, 0]) alpha2 = np.arctan2(p2[:, 1]-p0[:, 1], p2[:, 0]-p0[:, 0]) # In the below formula we could take modulo 2. but # modulo 1. is safer regarding round-off errors (flat triangles). angle = np.abs(((alpha2-alpha1) / np.pi) % 1) # Weight proportional to angle up np.pi/2; null weight for # degenerated cases 0 and np.pi (note that *angle* is normalized # by np.pi). weights[:, ipt] = 0.5 - np.abs(angle-0.5) return weights def compute_geom_grads(self): """ Compute the (global) gradient component of f assumed linear (~f). returns array df of shape (nelems, 2) df[ielem].dM[ielem] = dz[ielem] i.e. df = dz x dM = dM.T^-1 x dz """ tris_pts = self._tris_pts tris_f = self.z[self._triangles] dM1 = tris_pts[:, 1, :] - tris_pts[:, 0, :] dM2 = tris_pts[:, 2, :] - tris_pts[:, 0, :] dM = np.dstack([dM1, dM2]) # Here we try to deal with the simplest colinear cases: a null # gradient is assumed in this case. dM_inv = _safe_inv22_vectorized(dM) dZ1 = tris_f[:, 1] - tris_f[:, 0] dZ2 = tris_f[:, 2] - tris_f[:, 0] dZ = np.vstack([dZ1, dZ2]).T df = np.empty_like(dZ) # With np.einsum: could be ej,eji -> ej df[:, 0] = dZ[:, 0]*dM_inv[:, 0, 0] + dZ[:, 1]*dM_inv[:, 1, 0] df[:, 1] = dZ[:, 0]*dM_inv[:, 0, 1] + dZ[:, 1]*dM_inv[:, 1, 1] return df class _DOF_estimator_min_E(_DOF_estimator_geom): """ The 'smoothest' approximation, df is computed through global minimization of the bending energy: E(f) = integral[(d2z/dx2 + d2z/dy2 + 2 d2z/dxdy)**2 dA] """ def __init__(self, Interpolator): self._eccs = Interpolator._eccs _DOF_estimator_geom.__init__(self, Interpolator) def compute_dz(self): """ Elliptic solver for bending energy minimization. Uses a dedicated 'toy' sparse Jacobi PCG solver. """ # Initial guess for iterative PCG solver. dz_init = _DOF_estimator_geom.compute_dz(self) Uf0 = np.ravel(dz_init) reference_element = _ReducedHCT_Element() J = CubicTriInterpolator._get_jacobian(self._tris_pts) eccs = self._eccs triangles = self._triangles Uc = self.z[self._triangles] # Building stiffness matrix and force vector in coo format Kff_rows, Kff_cols, Kff_vals, Ff = reference_element.get_Kff_and_Ff( J, eccs, triangles, Uc) # Building sparse matrix and solving minimization problem # We could use scipy.sparse direct solver; however to avoid this # external dependency an implementation of a simple PCG solver with # a simple diagonal Jacobi preconditioner is implemented. tol = 1.e-10 n_dof = Ff.shape[0] Kff_coo = _Sparse_Matrix_coo(Kff_vals, Kff_rows, Kff_cols, shape=(n_dof, n_dof)) Kff_coo.compress_csc() Uf, err = _cg(A=Kff_coo, b=Ff, x0=Uf0, tol=tol) # If the PCG did not converge, we return the best guess between Uf0 # and Uf. err0 = np.linalg.norm(Kff_coo.dot(Uf0) - Ff) if err0 < err: # Maybe a good occasion to raise a warning here ? cbook._warn_external("In TriCubicInterpolator initialization, " "PCG sparse solver did not converge after " "1000 iterations. `geom` approximation is " "used instead of `min_E`") Uf = Uf0 # Building dz from Uf dz = np.empty([self._pts.shape[0], 2], dtype=np.float64) dz[:, 0] = Uf[::2] dz[:, 1] = Uf[1::2] return dz # The following private :class:_Sparse_Matrix_coo and :func:_cg provide # a PCG sparse solver for (symmetric) elliptic problems. class _Sparse_Matrix_coo: def __init__(self, vals, rows, cols, shape): """ Create a sparse matrix in coo format. *vals*: arrays of values of non-null entries of the matrix *rows*: int arrays of rows of non-null entries of the matrix *cols*: int arrays of cols of non-null entries of the matrix *shape*: 2-tuple (n, m) of matrix shape """ self.n, self.m = shape self.vals = np.asarray(vals, dtype=np.float64) self.rows = np.asarray(rows, dtype=np.int32) self.cols = np.asarray(cols, dtype=np.int32) def dot(self, V): """ Dot product of self by a vector *V* in sparse-dense to dense format *V* dense vector of shape (self.m,). """ assert V.shape == (self.m,) return np.bincount(self.rows, weights=self.vals*V[self.cols], minlength=self.m) def compress_csc(self): """ Compress rows, cols, vals / summing duplicates. Sort for csc format. """ _, unique, indices = np.unique( self.rows + self.n*self.cols, return_index=True, return_inverse=True) self.rows = self.rows[unique] self.cols = self.cols[unique] self.vals = np.bincount(indices, weights=self.vals) def compress_csr(self): """ Compress rows, cols, vals / summing duplicates. Sort for csr format. """ _, unique, indices = np.unique( self.m*self.rows + self.cols, return_index=True, return_inverse=True) self.rows = self.rows[unique] self.cols = self.cols[unique] self.vals = np.bincount(indices, weights=self.vals) def to_dense(self): """ Return a dense matrix representing self, mainly for debugging purposes. """ ret = np.zeros([self.n, self.m], dtype=np.float64) nvals = self.vals.size for i in range(nvals): ret[self.rows[i], self.cols[i]] += self.vals[i] return ret def __str__(self): return self.to_dense().__str__() @property def diag(self): """Return the (dense) vector of the diagonal elements.""" in_diag = (self.rows == self.cols) diag = np.zeros(min(self.n, self.n), dtype=np.float64) # default 0. diag[self.rows[in_diag]] = self.vals[in_diag] return diag def _cg(A, b, x0=None, tol=1.e-10, maxiter=1000): """ Use Preconditioned Conjugate Gradient iteration to solve A x = b A simple Jacobi (diagonal) preconditionner is used. Parameters ---------- A : _Sparse_Matrix_coo *A* must have been compressed before by compress_csc or compress_csr method. b : array Right hand side of the linear system. x0 : array, optional Starting guess for the solution. Defaults to the zero vector. tol : float, optional Tolerance to achieve. The algorithm terminates when the relative residual is below tol. Default is 1e-10. maxiter : int, optional Maximum number of iterations. Iteration will stop after *maxiter* steps even if the specified tolerance has not been achieved. Defaults to 1000. Returns ------- x : array The converged solution. err : float The absolute error np.linalg.norm(A.dot(x) - b) """ n = b.size assert A.n == n assert A.m == n b_norm = np.linalg.norm(b) # Jacobi pre-conditioner kvec = A.diag # For diag elem < 1e-6 we keep 1e-6. kvec = np.maximum(kvec, 1e-6) # Initial guess if x0 is None: x = np.zeros(n) else: x = x0 r = b - A.dot(x) w = r/kvec p = np.zeros(n) beta = 0.0 rho = np.dot(r, w) k = 0 # Following C. T. Kelley while (np.sqrt(abs(rho)) > tol*b_norm) and (k < maxiter): p = w + beta*p z = A.dot(p) alpha = rho/np.dot(p, z) r = r - alpha*z w = r/kvec rhoold = rho rho = np.dot(r, w) x = x + alpha*p beta = rho/rhoold #err = np.linalg.norm(A.dot(x) - b) # absolute accuracy - not used k += 1 err = np.linalg.norm(A.dot(x) - b) return x, err # The following private functions: # :func:`_safe_inv22_vectorized` # :func:`_pseudo_inv22sym_vectorized` # :func:`_prod_vectorized` # :func:`_scalar_vectorized` # :func:`_transpose_vectorized` # :func:`_roll_vectorized` # :func:`_to_matrix_vectorized` # :func:`_extract_submatrices` # provide fast numpy implementation of some standard operations on arrays of # matrices - stored as (:, n_rows, n_cols)-shaped np.arrays. # Development note: Dealing with pathologic 'flat' triangles in the # CubicTriInterpolator code and impact on (2, 2)-matrix inversion functions # :func:`_safe_inv22_vectorized` and :func:`_pseudo_inv22sym_vectorized`. # # Goals: # 1) The CubicTriInterpolator should be able to handle flat or almost flat # triangles without raising an error, # 2) These degenerated triangles should have no impact on the automatic dof # calculation (associated with null weight for the _DOF_estimator_geom and # with null energy for the _DOF_estimator_min_E), # 3) Linear patch test should be passed exactly on degenerated meshes, # 4) Interpolation (with :meth:`_interpolate_single_key` or # :meth:`_interpolate_multi_key`) shall be correctly handled even *inside* # the pathologic triangles, to interact correctly with a TriRefiner class. # # Difficulties: # Flat triangles have rank-deficient *J* (so-called jacobian matrix) and # *metric* (the metric tensor = J x J.T). Computation of the local # tangent plane is also problematic. # # Implementation: # Most of the time, when computing the inverse of a rank-deficient matrix it # is safe to simply return the null matrix (which is the implementation in # :func:`_safe_inv22_vectorized`). This is because of point 2), itself # enforced by: # - null area hence null energy in :class:`_DOF_estimator_min_E` # - angles close or equal to 0 or np.pi hence null weight in # :class:`_DOF_estimator_geom`. # Note that the function angle -> weight is continuous and maximum for an # angle np.pi/2 (refer to :meth:`compute_geom_weights`) # The exception is the computation of barycentric coordinates, which is done # by inversion of the *metric* matrix. In this case, we need to compute a set # of valid coordinates (1 among numerous possibilities), to ensure point 4). # We benefit here from the symmetry of metric = J x J.T, which makes it easier # to compute a pseudo-inverse in :func:`_pseudo_inv22sym_vectorized` def _safe_inv22_vectorized(M): """ Inversion of arrays of (2, 2) matrices, returns 0 for rank-deficient matrices. *M* : array of (2, 2) matrices to inverse, shape (n, 2, 2) """ assert M.ndim == 3 assert M.shape[-2:] == (2, 2) M_inv = np.empty_like(M) prod1 = M[:, 0, 0]*M[:, 1, 1] delta = prod1 - M[:, 0, 1]*M[:, 1, 0] # We set delta_inv to 0. in case of a rank deficient matrix; a # rank-deficient input matrix *M* will lead to a null matrix in output rank2 = (np.abs(delta) > 1e-8*np.abs(prod1)) if np.all(rank2): # Normal 'optimized' flow. delta_inv = 1./delta else: # 'Pathologic' flow. delta_inv = np.zeros(M.shape[0]) delta_inv[rank2] = 1./delta[rank2] M_inv[:, 0, 0] = M[:, 1, 1]*delta_inv M_inv[:, 0, 1] = -M[:, 0, 1]*delta_inv M_inv[:, 1, 0] = -M[:, 1, 0]*delta_inv M_inv[:, 1, 1] = M[:, 0, 0]*delta_inv return M_inv def _pseudo_inv22sym_vectorized(M): """ Inversion of arrays of (2, 2) SYMMETRIC matrices; returns the (Moore-Penrose) pseudo-inverse for rank-deficient matrices. In case M is of rank 1, we have M = trace(M) x P where P is the orthogonal projection on Im(M), and we return trace(M)^-1 x P == M / trace(M)**2 In case M is of rank 0, we return the null matrix. *M* : array of (2, 2) matrices to inverse, shape (n, 2, 2) """ assert M.ndim == 3 assert M.shape[-2:] == (2, 2) M_inv = np.empty_like(M) prod1 = M[:, 0, 0]*M[:, 1, 1] delta = prod1 - M[:, 0, 1]*M[:, 1, 0] rank2 = (np.abs(delta) > 1e-8*np.abs(prod1)) if np.all(rank2): # Normal 'optimized' flow. M_inv[:, 0, 0] = M[:, 1, 1] / delta M_inv[:, 0, 1] = -M[:, 0, 1] / delta M_inv[:, 1, 0] = -M[:, 1, 0] / delta M_inv[:, 1, 1] = M[:, 0, 0] / delta else: # 'Pathologic' flow. # Here we have to deal with 2 sub-cases # 1) First sub-case: matrices of rank 2: delta = delta[rank2] M_inv[rank2, 0, 0] = M[rank2, 1, 1] / delta M_inv[rank2, 0, 1] = -M[rank2, 0, 1] / delta M_inv[rank2, 1, 0] = -M[rank2, 1, 0] / delta M_inv[rank2, 1, 1] = M[rank2, 0, 0] / delta # 2) Second sub-case: rank-deficient matrices of rank 0 and 1: rank01 = ~rank2 tr = M[rank01, 0, 0] + M[rank01, 1, 1] tr_zeros = (np.abs(tr) < 1.e-8) sq_tr_inv = (1.-tr_zeros) / (tr**2+tr_zeros) #sq_tr_inv = 1. / tr**2 M_inv[rank01, 0, 0] = M[rank01, 0, 0] * sq_tr_inv M_inv[rank01, 0, 1] = M[rank01, 0, 1] * sq_tr_inv M_inv[rank01, 1, 0] = M[rank01, 1, 0] * sq_tr_inv M_inv[rank01, 1, 1] = M[rank01, 1, 1] * sq_tr_inv return M_inv def _prod_vectorized(M1, M2): """ Matrix product between arrays of matrices, or a matrix and an array of matrices (*M1* and *M2*) """ sh1 = M1.shape sh2 = M2.shape assert len(sh1) >= 2 assert len(sh2) >= 2 assert sh1[-1] == sh2[-2] ndim1 = len(sh1) t1_index = [*range(ndim1-2), ndim1-1, ndim1-2] return np.sum(np.transpose(M1, t1_index)[..., np.newaxis] * M2[..., np.newaxis, :], -3) def _scalar_vectorized(scalar, M): """ Scalar product between scalars and matrices. """ return scalar[:, np.newaxis, np.newaxis]*M def _transpose_vectorized(M): """ Transposition of an array of matrices *M*. """ return np.transpose(M, [0, 2, 1]) def _roll_vectorized(M, roll_indices, axis): """ Roll an array of matrices along *axis* (0: rows, 1: columns) according to an array of indices *roll_indices*. """ assert axis in [0, 1] ndim = M.ndim assert ndim == 3 ndim_roll = roll_indices.ndim assert ndim_roll == 1 sh = M.shape r, c = sh[-2:] assert sh[0] == roll_indices.shape[0] vec_indices = np.arange(sh[0], dtype=np.int32) # Builds the rolled matrix M_roll = np.empty_like(M) if axis == 0: for ir in range(r): for ic in range(c): M_roll[:, ir, ic] = M[vec_indices, (-roll_indices+ir) % r, ic] elif axis == 1: for ir in range(r): for ic in range(c): M_roll[:, ir, ic] = M[vec_indices, ir, (-roll_indices+ic) % c] return M_roll def _to_matrix_vectorized(M): """ Build an array of matrices from individuals np.arrays of identical shapes. Parameters ---------- M ncols-list of nrows-lists of shape sh. Returns ------- M_res : np.array of shape (sh, nrow, ncols) *M_res* satisfies ``M_res[..., i, j] = M[i][j]``. """ assert isinstance(M, (tuple, list)) assert all(isinstance(item, (tuple, list)) for item in M) c_vec = np.asarray([len(item) for item in M]) assert np.all(c_vec-c_vec[0] == 0) r = len(M) c = c_vec[0] M00 = np.asarray(M[0][0]) dt = M00.dtype sh = [M00.shape[0], r, c] M_ret = np.empty(sh, dtype=dt) for irow in range(r): for icol in range(c): M_ret[:, irow, icol] = np.asarray(M[irow][icol]) return M_ret def _extract_submatrices(M, block_indices, block_size, axis): """ Extract selected blocks of a matrices *M* depending on parameters *block_indices* and *block_size*. Returns the array of extracted matrices *Mres* so that :: M_res[..., ir, :] = M[(block_indices*block_size+ir), :] """ assert block_indices.ndim == 1 assert axis in [0, 1] r, c = M.shape if axis == 0: sh = [block_indices.shape[0], block_size, c] elif axis == 1: sh = [block_indices.shape[0], r, block_size] dt = M.dtype M_res = np.empty(sh, dtype=dt) if axis == 0: for ir in range(block_size): M_res[:, ir, :] = M[(block_indices*block_size+ir), :] elif axis == 1: for ic in range(block_size): M_res[:, :, ic] = M[:, (block_indices*block_size+ic)] return M_res