pymor.discretizers.builtin.grids.oned¶
Module Contents¶
Classes¶
One-dimensional |
- class pymor.discretizers.builtin.grids.oned.OnedGrid(domain=(0, 1), num_intervals=4, identify_left_right=False)[source]¶
Bases:
pymor.discretizers.builtin.grids.interfaces.GridWithOrthogonalCentersOne-dimensional
Gridon an interval.Parameters
- domain
Tuple
(left, right)containing the left and right boundary of the domain.- num_intervals
The number of codim-0 entities.
- subentities(self, codim, subentity_codim)[source]¶
retval[e,s]is the global index of thes-th codim-subentity_codimsubentity of the codim-codimentity with global indexe.The ordering of
subentities(0, subentity_codim)[e]has to correspond, w.r.t. the embedding ofe, to the local ordering inside the reference element.For
codim > 0, we provide a default implementation by calculating the subentities ofeas follows:Find the
codim-1parent entitye_0ofewith minimal global indexLookup the local indices of the subentities of
einsidee_0using the reference element.Map these local indices to global indices using
subentities(codim - 1, subentity_codim).
This procedures assures that
subentities(codim, subentity_codim)[e]has the right ordering w.r.t. the embedding determined bye_0, which agrees with what is returned byembeddings(codim)
- embeddings(self, codim)[source]¶
Returns tuple
(A, B)whereA[e]andB[e]are the linear part and the translation part of the map from the reference element ofetoe.For
codim > 0, we provide a default implementation by taking the embedding of the codim-1 parent entitye_0ofewith lowest global index and composing it with the subentity_embedding ofeintoe_0determined by the reference element.
- bounding_box(self)[source]¶
Returns a
(2, dim)-shaped array containing lower/upper bounding box coordinates.
- orthogonal_centers(self)[source]¶
retval[e]is a point inside the codim-0 entity with global indexesuch that the line segment fromretval[e]toretval[e2]is always orthogonal to the codim-1 entity shared by the codim-0 entities with global indexeande2.(This is mainly useful for gradient approximation in finite volume schemes.)
- visualize(self, U, codim=1, **kwargs)[source]¶
Visualize scalar data associated to the grid as a patch plot.
Parameters
- U
NumPy arrayof the data to visualize. IfU.dim == 2 and len(U) > 1, the data is visualized as a time series of plots. Alternatively, a tuple ofNumPy arrayscan be provided, in which case a subplot is created for each entry of the tuple. The lengths of all arrays have to agree.- codim
The codimension of the entities the data in
Uis attached to (either 0 or 1).- kwargs
See
visualize