1575 lines
61 KiB
Python
1575 lines
61 KiB
Python
"""
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Interpolation inside triangular grids.
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"""
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import numpy as np
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from matplotlib import _api
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from matplotlib.tri import Triangulation
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from matplotlib.tri._trifinder import TriFinder
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from matplotlib.tri._tritools import TriAnalyzer
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__all__ = ('TriInterpolator', 'LinearTriInterpolator', 'CubicTriInterpolator')
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class TriInterpolator:
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"""
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Abstract base class for classes used to interpolate on a triangular grid.
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Derived classes implement the following methods:
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- ``__call__(x, y)``,
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where x, y are array-like point coordinates of the same shape, and
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that returns a masked array of the same shape containing the
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interpolated z-values.
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- ``gradient(x, y)``,
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where x, y are array-like point coordinates of the same
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shape, and that returns a list of 2 masked arrays of the same shape
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containing the 2 derivatives of the interpolator (derivatives of
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interpolated z values with respect to x and y).
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"""
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def __init__(self, triangulation, z, trifinder=None):
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_api.check_isinstance(Triangulation, triangulation=triangulation)
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self._triangulation = triangulation
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self._z = np.asarray(z)
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if self._z.shape != self._triangulation.x.shape:
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raise ValueError("z array must have same length as triangulation x"
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" and y arrays")
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_api.check_isinstance((TriFinder, None), trifinder=trifinder)
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self._trifinder = trifinder or self._triangulation.get_trifinder()
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# Default scaling factors : 1.0 (= no scaling)
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# Scaling may be used for interpolations for which the order of
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# magnitude of x, y has an impact on the interpolant definition.
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# Please refer to :meth:`_interpolate_multikeys` for details.
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self._unit_x = 1.0
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self._unit_y = 1.0
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# Default triangle renumbering: None (= no renumbering)
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# Renumbering may be used to avoid unnecessary computations
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# if complex calculations are done inside the Interpolator.
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# Please refer to :meth:`_interpolate_multikeys` for details.
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self._tri_renum = None
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# __call__ and gradient docstrings are shared by all subclasses
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# (except, if needed, relevant additions).
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# However these methods are only implemented in subclasses to avoid
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# confusion in the documentation.
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_docstring__call__ = """
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Returns a masked array containing interpolated values at the specified
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(x, y) points.
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Parameters
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----------
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x, y : array-like
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x and y coordinates of the same shape and any number of
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dimensions.
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Returns
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-------
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np.ma.array
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Masked array of the same shape as *x* and *y*; values corresponding
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to (*x*, *y*) points outside of the triangulation are masked out.
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"""
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_docstringgradient = r"""
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Returns a list of 2 masked arrays containing interpolated derivatives
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at the specified (x, y) points.
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Parameters
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----------
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x, y : array-like
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x and y coordinates of the same shape and any number of
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dimensions.
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Returns
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-------
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dzdx, dzdy : np.ma.array
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2 masked arrays of the same shape as *x* and *y*; values
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corresponding to (x, y) points outside of the triangulation
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are masked out.
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The first returned array contains the values of
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:math:`\frac{\partial z}{\partial x}` and the second those of
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:math:`\frac{\partial z}{\partial y}`.
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"""
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def _interpolate_multikeys(self, x, y, tri_index=None,
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return_keys=('z',)):
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"""
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Versatile (private) method defined for all TriInterpolators.
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:meth:`_interpolate_multikeys` is a wrapper around method
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:meth:`_interpolate_single_key` (to be defined in the child
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subclasses).
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:meth:`_interpolate_single_key actually performs the interpolation,
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but only for 1-dimensional inputs and at valid locations (inside
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unmasked triangles of the triangulation).
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The purpose of :meth:`_interpolate_multikeys` is to implement the
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following common tasks needed in all subclasses implementations:
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- calculation of containing triangles
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- dealing with more than one interpolation request at the same
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location (e.g., if the 2 derivatives are requested, it is
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unnecessary to compute the containing triangles twice)
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- scaling according to self._unit_x, self._unit_y
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- dealing with points outside of the grid (with fill value np.nan)
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- dealing with multi-dimensional *x*, *y* arrays: flattening for
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:meth:`_interpolate_params` call and final reshaping.
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(Note that np.vectorize could do most of those things very well for
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you, but it does it by function evaluations over successive tuples of
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the input arrays. Therefore, this tends to be more time-consuming than
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using optimized numpy functions - e.g., np.dot - which can be used
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easily on the flattened inputs, in the child-subclass methods
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:meth:`_interpolate_single_key`.)
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It is guaranteed that the calls to :meth:`_interpolate_single_key`
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will be done with flattened (1-d) array-like input parameters *x*, *y*
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and with flattened, valid `tri_index` arrays (no -1 index allowed).
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Parameters
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----------
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x, y : array-like
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x and y coordinates where interpolated values are requested.
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tri_index : array-like of int, optional
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Array of the containing triangle indices, same shape as
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*x* and *y*. Defaults to None. If None, these indices
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will be computed by a TriFinder instance.
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(Note: For point outside the grid, tri_index[ipt] shall be -1).
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return_keys : tuple of keys from {'z', 'dzdx', 'dzdy'}
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Defines the interpolation arrays to return, and in which order.
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Returns
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-------
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list of arrays
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Each array-like contains the expected interpolated values in the
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order defined by *return_keys* parameter.
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"""
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# Flattening and rescaling inputs arrays x, y
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# (initial shape is stored for output)
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x = np.asarray(x, dtype=np.float64)
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y = np.asarray(y, dtype=np.float64)
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sh_ret = x.shape
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if x.shape != y.shape:
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raise ValueError("x and y shall have same shapes."
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f" Given: {x.shape} and {y.shape}")
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x = np.ravel(x)
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y = np.ravel(y)
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x_scaled = x/self._unit_x
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y_scaled = y/self._unit_y
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size_ret = np.size(x_scaled)
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# Computes & ravels the element indexes, extract the valid ones.
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if tri_index is None:
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tri_index = self._trifinder(x, y)
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else:
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if tri_index.shape != sh_ret:
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raise ValueError(
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"tri_index array is provided and shall"
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" have same shape as x and y. Given: "
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f"{tri_index.shape} and {sh_ret}")
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tri_index = np.ravel(tri_index)
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mask_in = (tri_index != -1)
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if self._tri_renum is None:
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valid_tri_index = tri_index[mask_in]
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else:
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valid_tri_index = self._tri_renum[tri_index[mask_in]]
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valid_x = x_scaled[mask_in]
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valid_y = y_scaled[mask_in]
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ret = []
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for return_key in return_keys:
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# Find the return index associated with the key.
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try:
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return_index = {'z': 0, 'dzdx': 1, 'dzdy': 2}[return_key]
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except KeyError as err:
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raise ValueError("return_keys items shall take values in"
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" {'z', 'dzdx', 'dzdy'}") from err
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# Sets the scale factor for f & df components
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scale = [1., 1./self._unit_x, 1./self._unit_y][return_index]
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# Computes the interpolation
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ret_loc = np.empty(size_ret, dtype=np.float64)
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ret_loc[~mask_in] = np.nan
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ret_loc[mask_in] = self._interpolate_single_key(
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return_key, valid_tri_index, valid_x, valid_y) * scale
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ret += [np.ma.masked_invalid(ret_loc.reshape(sh_ret), copy=False)]
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return ret
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def _interpolate_single_key(self, return_key, tri_index, x, y):
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"""
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Interpolate at points belonging to the triangulation
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(inside an unmasked triangles).
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Parameters
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----------
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return_key : {'z', 'dzdx', 'dzdy'}
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The requested values (z or its derivatives).
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tri_index : 1D int array
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Valid triangle index (cannot be -1).
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x, y : 1D arrays, same shape as `tri_index`
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Valid locations where interpolation is requested.
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Returns
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-------
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1-d array
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Returned array of the same size as *tri_index*
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"""
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raise NotImplementedError("TriInterpolator subclasses" +
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"should implement _interpolate_single_key!")
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class LinearTriInterpolator(TriInterpolator):
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"""
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Linear interpolator on a triangular grid.
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Each triangle is represented by a plane so that an interpolated value at
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point (x, y) lies on the plane of the triangle containing (x, y).
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Interpolated values are therefore continuous across the triangulation, but
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their first derivatives are discontinuous at edges between triangles.
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Parameters
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----------
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triangulation : `~matplotlib.tri.Triangulation`
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The triangulation to interpolate over.
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z : (npoints,) array-like
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Array of values, defined at grid points, to interpolate between.
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trifinder : `~matplotlib.tri.TriFinder`, optional
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If this is not specified, the Triangulation's default TriFinder will
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be used by calling `.Triangulation.get_trifinder`.
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Methods
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-------
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`__call__` (x, y) : Returns interpolated values at (x, y) points.
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`gradient` (x, y) : Returns interpolated derivatives at (x, y) points.
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"""
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def __init__(self, triangulation, z, trifinder=None):
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super().__init__(triangulation, z, trifinder)
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# Store plane coefficients for fast interpolation calculations.
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self._plane_coefficients = \
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self._triangulation.calculate_plane_coefficients(self._z)
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def __call__(self, x, y):
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return self._interpolate_multikeys(x, y, tri_index=None,
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return_keys=('z',))[0]
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__call__.__doc__ = TriInterpolator._docstring__call__
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def gradient(self, x, y):
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return self._interpolate_multikeys(x, y, tri_index=None,
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return_keys=('dzdx', 'dzdy'))
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gradient.__doc__ = TriInterpolator._docstringgradient
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def _interpolate_single_key(self, return_key, tri_index, x, y):
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_api.check_in_list(['z', 'dzdx', 'dzdy'], return_key=return_key)
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if return_key == 'z':
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return (self._plane_coefficients[tri_index, 0]*x +
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self._plane_coefficients[tri_index, 1]*y +
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self._plane_coefficients[tri_index, 2])
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elif return_key == 'dzdx':
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return self._plane_coefficients[tri_index, 0]
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else: # 'dzdy'
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return self._plane_coefficients[tri_index, 1]
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class CubicTriInterpolator(TriInterpolator):
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r"""
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Cubic interpolator on a triangular grid.
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In one-dimension - on a segment - a cubic interpolating function is
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defined by the values of the function and its derivative at both ends.
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This is almost the same in 2D inside a triangle, except that the values
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of the function and its 2 derivatives have to be defined at each triangle
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node.
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The CubicTriInterpolator takes the value of the function at each node -
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provided by the user - and internally computes the value of the
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derivatives, resulting in a smooth interpolation.
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(As a special feature, the user can also impose the value of the
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derivatives at each node, but this is not supposed to be the common
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usage.)
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Parameters
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----------
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triangulation : `~matplotlib.tri.Triangulation`
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The triangulation to interpolate over.
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z : (npoints,) array-like
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Array of values, defined at grid points, to interpolate between.
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kind : {'min_E', 'geom', 'user'}, optional
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Choice of the smoothing algorithm, in order to compute
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the interpolant derivatives (defaults to 'min_E'):
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- if 'min_E': (default) The derivatives at each node is computed
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to minimize a bending energy.
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- if 'geom': The derivatives at each node is computed as a
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weighted average of relevant triangle normals. To be used for
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speed optimization (large grids).
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- if 'user': The user provides the argument *dz*, no computation
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is hence needed.
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trifinder : `~matplotlib.tri.TriFinder`, optional
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If not specified, the Triangulation's default TriFinder will
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be used by calling `.Triangulation.get_trifinder`.
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dz : tuple of array-likes (dzdx, dzdy), optional
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Used only if *kind* ='user'. In this case *dz* must be provided as
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(dzdx, dzdy) where dzdx, dzdy are arrays of the same shape as *z* and
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are the interpolant first derivatives at the *triangulation* points.
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Methods
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-------
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`__call__` (x, y) : Returns interpolated values at (x, y) points.
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`gradient` (x, y) : Returns interpolated derivatives at (x, y) points.
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Notes
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-----
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This note is a bit technical and details how the cubic interpolation is
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computed.
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The interpolation is based on a Clough-Tocher subdivision scheme of
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the *triangulation* mesh (to make it clearer, each triangle of the
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grid will be divided in 3 child-triangles, and on each child triangle
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the interpolated function is a cubic polynomial of the 2 coordinates).
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This technique originates from FEM (Finite Element Method) analysis;
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the element used is a reduced Hsieh-Clough-Tocher (HCT)
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element. Its shape functions are described in [1]_.
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The assembled function is guaranteed to be C1-smooth, i.e. it is
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continuous and its first derivatives are also continuous (this
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is easy to show inside the triangles but is also true when crossing the
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edges).
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In the default case (*kind* ='min_E'), the interpolant minimizes a
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curvature energy on the functional space generated by the HCT element
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shape functions - with imposed values but arbitrary derivatives at each
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node. The minimized functional is the integral of the so-called total
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curvature (implementation based on an algorithm from [2]_ - PCG sparse
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solver):
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.. math::
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E(z) = \frac{1}{2} \int_{\Omega} \left(
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\left( \frac{\partial^2{z}}{\partial{x}^2} \right)^2 +
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\left( \frac{\partial^2{z}}{\partial{y}^2} \right)^2 +
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2\left( \frac{\partial^2{z}}{\partial{y}\partial{x}} \right)^2
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\right) dx\,dy
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If the case *kind* ='geom' is chosen by the user, a simple geometric
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approximation is used (weighted average of the triangle normal
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vectors), which could improve speed on very large grids.
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References
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----------
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.. [1] Michel Bernadou, Kamal Hassan, "Basis functions for general
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Hsieh-Clough-Tocher triangles, complete or reduced.",
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International Journal for Numerical Methods in Engineering,
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17(5):784 - 789. 2.01.
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.. [2] C.T. Kelley, "Iterative Methods for Optimization".
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"""
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def __init__(self, triangulation, z, kind='min_E', trifinder=None,
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dz=None):
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super().__init__(triangulation, z, trifinder)
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# Loads the underlying c++ _triangulation.
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# (During loading, reordering of triangulation._triangles may occur so
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# that all final triangles are now anti-clockwise)
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self._triangulation.get_cpp_triangulation()
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# To build the stiffness matrix and avoid zero-energy spurious modes
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# we will only store internally the valid (unmasked) triangles and
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# the necessary (used) points coordinates.
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# 2 renumbering tables need to be computed and stored:
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# - a triangle renum table in order to translate the result from a
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# TriFinder instance into the internal stored triangle number.
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# - a node renum table to overwrite the self._z values into the new
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# (used) node numbering.
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tri_analyzer = TriAnalyzer(self._triangulation)
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(compressed_triangles, compressed_x, compressed_y, tri_renum,
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node_renum) = tri_analyzer._get_compressed_triangulation()
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self._triangles = compressed_triangles
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self._tri_renum = tri_renum
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# Taking into account the node renumbering in self._z:
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valid_node = (node_renum != -1)
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self._z[node_renum[valid_node]] = self._z[valid_node]
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# Computing scale factors
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self._unit_x = np.ptp(compressed_x)
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self._unit_y = np.ptp(compressed_y)
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self._pts = np.column_stack([compressed_x / self._unit_x,
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compressed_y / self._unit_y])
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# Computing triangle points
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self._tris_pts = self._pts[self._triangles]
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# Computing eccentricities
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self._eccs = self._compute_tri_eccentricities(self._tris_pts)
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# Computing dof estimations for HCT triangle shape function
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_api.check_in_list(['user', 'geom', 'min_E'], kind=kind)
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self._dof = self._compute_dof(kind, dz=dz)
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# Loading HCT element
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self._ReferenceElement = _ReducedHCT_Element()
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def __call__(self, x, y):
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return self._interpolate_multikeys(x, y, tri_index=None,
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return_keys=('z',))[0]
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__call__.__doc__ = TriInterpolator._docstring__call__
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def gradient(self, x, y):
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return self._interpolate_multikeys(x, y, tri_index=None,
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return_keys=('dzdx', 'dzdy'))
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gradient.__doc__ = TriInterpolator._docstringgradient
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def _interpolate_single_key(self, return_key, tri_index, x, y):
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_api.check_in_list(['z', 'dzdx', 'dzdy'], return_key=return_key)
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tris_pts = self._tris_pts[tri_index]
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alpha = self._get_alpha_vec(x, y, tris_pts)
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ecc = self._eccs[tri_index]
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dof = np.expand_dims(self._dof[tri_index], axis=1)
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if return_key == 'z':
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return self._ReferenceElement.get_function_values(
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alpha, ecc, dof)
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else: # 'dzdx', 'dzdy'
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J = self._get_jacobian(tris_pts)
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dzdx = self._ReferenceElement.get_function_derivatives(
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alpha, J, ecc, dof)
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if return_key == 'dzdx':
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return dzdx[:, 0, 0]
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else:
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return dzdx[:, 1, 0]
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def _compute_dof(self, kind, dz=None):
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"""
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Compute and return nodal dofs according to kind.
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Parameters
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----------
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kind : {'min_E', 'geom', 'user'}
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Choice of the _DOF_estimator subclass to estimate the gradient.
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dz : tuple of array-likes (dzdx, dzdy), optional
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Used only if *kind*=user; in this case passed to the
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:class:`_DOF_estimator_user`.
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Returns
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-------
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array-like, shape (npts, 2)
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Estimation of the gradient at triangulation nodes (stored as
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degree of freedoms of reduced-HCT triangle elements).
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"""
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if kind == 'user':
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if dz is None:
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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)
|
|
else: # 'min_E', checked in __init__
|
|
TE = _DOF_estimator_min_E(self)
|
|
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 = 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 = ab @ _transpose_vectorized(np.expand_dims(OM, ndim))
|
|
ksi = 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 = (_transpose_vectorized(a) @ a)[:, 0, 0]
|
|
dot_b = (_transpose_vectorized(b) @ b)[:, 0, 0]
|
|
dot_c = (_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 = self.M @ V
|
|
prod += _scalar_vectorized(E[:, 0, 0], self.M0 @ V)
|
|
prod += _scalar_vectorized(E[:, 1, 0], self.M1 @ V)
|
|
prod += _scalar_vectorized(E[:, 2, 0], self.M2 @ V)
|
|
s = _roll_vectorized(prod, 3*subtri, axis=0)
|
|
return (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 = dV @ _extract_submatrices(
|
|
self.rotate_dV, subtri, block_size=2, axis=0)
|
|
|
|
prod = self.M @ dV
|
|
prod += _scalar_vectorized(E[:, 0, 0], self.M0 @ dV)
|
|
prod += _scalar_vectorized(E[:, 1, 0], self.M1 @ dV)
|
|
prod += _scalar_vectorized(E[:, 2, 0], self.M2 @ dV)
|
|
dsdksi = _roll_vectorized(prod, 3*subtri, axis=0)
|
|
dfdksi = 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 = 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 = dofs @ d2sdksi2
|
|
H_rot = self.get_Hrot_from_J(J)
|
|
d2fdx2 = 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 = d2V @ _extract_submatrices(
|
|
self.rotate_d2V, subtri, block_size=3, axis=0)
|
|
prod = self.M @ d2V
|
|
prod += _scalar_vectorized(E[:, 0, 0], self.M0 @ d2V)
|
|
prod += _scalar_vectorized(E[:, 1, 0], self.M1 @ d2V)
|
|
prod += _scalar_vectorized(E[:, 2, 0], 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 = self.J0_to_J1 @ J
|
|
J2 = 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 = d2Skdksi2 @ H_rot
|
|
K += weight * (d2Skdx2 @ self.E @ _transpose_vectorized(d2Skdx2))
|
|
|
|
# 4) With nodal (not elem) dofs
|
|
K = _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 = _transpose_vectorized(expand_indices @ f_dof_indices)
|
|
f_col_indices = 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 = -(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):
|
|
_api.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 = _ReducedHCT_Element.J0_to_J1 @ J
|
|
J2 = _ReducedHCT_Element.J0_to_J2 @ J
|
|
|
|
col0 = J @ np.expand_dims(tri_dz[:, 0, :], axis=2)
|
|
col1 = J1 @ np.expand_dims(tri_dz[:, 1, :], axis=2)
|
|
col2 = 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
|
|
super().__init__(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 = super().compute_dz()
|
|
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 ?
|
|
_api.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) preconditioner 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:`_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)
|
|
"""
|
|
_api.check_shape((None, 2, 2), M=M)
|
|
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)
|
|
"""
|
|
_api.check_shape((None, 2, 2), M=M)
|
|
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 _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]
|
|
else: # 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]
|
|
else: # 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), :]
|
|
else: # 1
|
|
for ic in range(block_size):
|
|
M_res[:, :, ic] = M[:, (block_indices*block_size+ic)]
|
|
|
|
return M_res
|