def evaluate_at(self, coors, source_vals, mode='val', strategy='general', close_limit=0.1, get_cells_fun=None, cache=None, ret_cells=False, ret_status=False, ret_ref_coors=False, verbose=False): """ Evaluate source DOF values corresponding to the field in the given coordinates using the field interpolation. Parameters ---------- coors : array, shape ``(n_coor, dim)`` The coordinates the source values should be interpolated into. source_vals : array, shape ``(n_nod, n_components)`` The source DOF values corresponding to the field. mode : {'val', 'grad'}, optional The evaluation mode: the field value (default) or the field value gradient. strategy : {'general', 'convex'}, optional The strategy for finding the elements that contain the coordinates. For convex meshes, the 'convex' strategy might be faster than the 'general' one. close_limit : float, optional The maximum limit distance of a point from the closest element allowed for extrapolation. get_cells_fun : callable, optional If given, a function with signature ``get_cells_fun(coors, cmesh, **kwargs)`` returning cells and offsets that potentially contain points with the coordinates `coors`. Applicable only when `strategy` is 'general'. When not given, :func:`get_potential_cells() <sfepy.discrete.common.global_interp.get_potential_cells>` is used. cache : Struct, optional To speed up a sequence of evaluations, the field mesh and other data can be cached. Optionally, the cache can also contain the reference element coordinates as `cache.ref_coors`, `cache.cells` and `cache.status`, if the evaluation occurs in the same coordinates repeatedly. In that case the mesh related data are ignored. See :func:`Field.get_evaluate_cache() <sfepy.discrete.fem.fields_base.FEField.get_evaluate_cache()>`. ret_ref_coors : bool, optional If True, return also the found reference element coordinates. ret_status : bool, optional If True, return also the enclosing cell status for each point. ret_cells : bool, optional If True, return also the cell indices the coordinates are in. verbose : bool If False, reduce verbosity. Returns ------- vals : array The interpolated values with shape ``(n_coor, n_components)`` or gradients with shape ``(n_coor, n_components, dim)`` according to the `mode`. If `ret_status` is False, the values where the status is greater than one are set to ``numpy.nan``. ref_coors : array The found reference element coordinates, if `ret_ref_coors` is True. cells : array The cell indices, if `ret_ref_coors` or `ret_cells` or `ret_status` are True. status : array The status, if `ret_ref_coors` or `ret_status` are True, with the following meaning: 0 is success, 1 is extrapolation within `close_limit`, 2 is extrapolation outside `close_limit`, 3 is failure, 4 is failure due to non-convergence of the Newton iteration in tensor product cells. If close_limit is 0, then for the 'general' strategy the status 5 indicates points outside of the field domain that had no potential cells. """ from sfepy.discrete.common.global_interp import get_ref_coors from sfepy.discrete.common.extmods.crefcoors import evaluate_in_rc from sfepy.base.base import complex_types output('evaluating in %d points...' % coors.shape[0], verbose=verbose) ref_coors, cells, status = get_ref_coors(self, coors, strategy=strategy, close_limit=close_limit, get_cells_fun=get_cells_fun, cache=cache, verbose=verbose) timer = Timer(start=True) # Interpolate to the reference coordinates. source_dtype = nm.float64 if source_vals.dtype in complex_types\ else source_vals.dtype if mode == 'val': vals = nm.empty((coors.shape[0], source_vals.shape[1], 1), dtype=source_dtype) cmode = 0 elif mode == 'grad': vals = nm.empty( (coors.shape[0], source_vals.shape[1], coors.shape[1]), dtype=source_dtype) cmode = 1 ctx = self.create_basis_context() if source_vals.dtype in complex_types: valsi = vals.copy() evaluate_in_rc(vals, ref_coors, cells, status, nm.ascontiguousarray(source_vals.real), self.get_econn('volume', self.region), cmode, ctx) evaluate_in_rc(valsi, ref_coors, cells, status, nm.ascontiguousarray(source_vals.imag), self.get_econn('volume', self.region), cmode, ctx) vals = vals + valsi * 1j else: evaluate_in_rc(vals, ref_coors, cells, status, source_vals, self.get_econn('volume', self.region), cmode, ctx) output('interpolation: %f s' % timer.stop(), verbose=verbose) output('...done', verbose=verbose) if mode == 'val': vals.shape = (coors.shape[0], source_vals.shape[1]) if not ret_status: ii = nm.where(status > 1)[0] vals[ii] = nm.nan if ret_ref_coors: return vals, ref_coors, cells, status elif ret_status: return vals, cells, status elif ret_cells: return vals, cells else: return vals
def evaluate_at(self, coors, source_vals, mode='val', strategy='general', close_limit=0.1, get_cells_fun=None, cache=None, ret_cells=False, ret_status=False, ret_ref_coors=False, verbose=False): """ Evaluate source DOF values corresponding to the field in the given coordinates using the field interpolation. Parameters ---------- coors : array, shape ``(n_coor, dim)`` The coordinates the source values should be interpolated into. source_vals : array, shape ``(n_nod, n_components)`` The source DOF values corresponding to the field. mode : {'val', 'grad'}, optional The evaluation mode: the field value (default) or the field value gradient. strategy : {'general', 'convex'}, optional The strategy for finding the elements that contain the coordinates. For convex meshes, the 'convex' strategy might be faster than the 'general' one. close_limit : float, optional The maximum limit distance of a point from the closest element allowed for extrapolation. get_cells_fun : callable, optional If given, a function with signature ``get_cells_fun(coors, cmesh, **kwargs)`` returning cells and offsets that potentially contain points with the coordinates `coors`. Applicable only when `strategy` is 'general'. When not given, :func:`get_potential_cells() <sfepy.discrete.common.global_interp.get_potential_cells>` is used. cache : Struct, optional To speed up a sequence of evaluations, the field mesh and other data can be cached. Optionally, the cache can also contain the reference element coordinates as `cache.ref_coors`, `cache.cells` and `cache.status`, if the evaluation occurs in the same coordinates repeatedly. In that case the mesh related data are ignored. See :func:`Field.get_evaluate_cache() <sfepy.discrete.fem.fields_base.FEField.get_evaluate_cache()>`. ret_ref_coors : bool, optional If True, return also the found reference element coordinates. ret_status : bool, optional If True, return also the enclosing cell status for each point. ret_cells : bool, optional If True, return also the cell indices the coordinates are in. verbose : bool If False, reduce verbosity. Returns ------- vals : array The interpolated values with shape ``(n_coor, n_components)`` or gradients with shape ``(n_coor, n_components, dim)`` according to the `mode`. If `ret_status` is False, the values where the status is greater than one are set to ``numpy.nan``. ref_coors : array The found reference element coordinates, if `ret_ref_coors` is True. cells : array The cell indices, if `ret_ref_coors` or `ret_cells` or `ret_status` are True. status : array The status, if `ret_ref_coors` or `ret_status` are True, with the following meaning: 0 is success, 1 is extrapolation within `close_limit`, 2 is extrapolation outside `close_limit`, 3 is failure, 4 is failure due to non-convergence of the Newton iteration in tensor product cells. If close_limit is 0, then for the 'general' strategy the status 5 indicates points outside of the field domain that had no potential cells. """ from sfepy.discrete.common.global_interp import get_ref_coors from sfepy.discrete.common.extmods.crefcoors import evaluate_in_rc output('evaluating in %d points...' % coors.shape[0], verbose=verbose) ref_coors, cells, status = get_ref_coors(self, coors, strategy=strategy, close_limit=close_limit, get_cells_fun=get_cells_fun, cache=cache, verbose=verbose) tt = time.clock() # Interpolate to the reference coordinates. if mode == 'val': vals = nm.empty((coors.shape[0], source_vals.shape[1], 1), dtype=source_vals.dtype) cmode = 0 elif mode == 'grad': vals = nm.empty((coors.shape[0], source_vals.shape[1], coors.shape[1]), dtype=source_vals.dtype) cmode = 1 ctx = self.create_basis_context() evaluate_in_rc(vals, ref_coors, cells, status, source_vals, self.get_econn('volume', self.region), cmode, ctx) output('interpolation: %f s' % (time.clock()-tt),verbose=verbose) output('...done',verbose=verbose) if mode == 'val': vals.shape = (coors.shape[0], source_vals.shape[1]) if not ret_status: ii = nm.where(status > 1)[0] vals[ii] = nm.nan if ret_ref_coors: return vals, ref_coors, cells, status elif ret_status: return vals, cells, status elif ret_cells: return vals, cells else: return vals