uniform_partition_fromintv¶

odl.discr.partition.uniform_partition_fromintv(intv_prod, shape, nodes_on_bdry=False)[source]¶

Return a partition of an interval product into equally sized cells.

Parameters

intv_prodIntervalProd

Interval product to be partitioned

shapeint or sequence of ints

Number of nodes per axis. For 1d intervals, a single integer can be specified.

nodes_on_bdrybool or sequence, optional

If a sequence is provided, it determines per axis whether to place the last grid point on the boundary (True) or shift it by half a cell size into the interior (False). In each axis, an entry may consist in a single bool or a 2-tuple of bool. In the latter case, the first tuple entry decides for the left, the second for the right boundary. The length of the sequence must be intv_prod.ndim.

A single boolean is interpreted as a global choice for all boundaries.

See also

uniform_partition_fromgrid

Examples

By default, no grid points are placed on the boundary:

>>> interval = odl.IntervalProd(0, 1)
>>> part = odl.uniform_partition_fromintv(interval, 4)
>>> part.cell_boundary_vecs
(array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ]),)
>>> part.grid.coord_vectors
(array([ 0.125,  0.375,  0.625,  0.875]),)

This can be changed with the nodes_on_bdry parameter:

>>> part = odl.uniform_partition_fromintv(interval, 3,
...                                       nodes_on_bdry=True)
>>> part.cell_boundary_vecs
(array([ 0.  ,  0.25,  0.75,  1.  ]),)
>>> part.grid.coord_vectors
(array([ 0. ,  0.5,  1. ]),)

We can specify this per axis, too. In this case we choose both in the first axis and only the rightmost in the second:

>>> rect = odl.IntervalProd([0, 0], [1, 1])
>>> part = odl.uniform_partition_fromintv(
...     rect, (3, 3), nodes_on_bdry=(True, (False, True)))
...
>>> part.cell_boundary_vecs[0]  # first axis, as above
array([ 0.  ,  0.25,  0.75,  1.  ])
>>> part.grid.coord_vectors[0]
array([ 0. ,  0.5,  1. ])
>>> part.cell_boundary_vecs[1]  # second, asymmetric axis
array([ 0. ,  0.4,  0.8,  1. ])
>>> part.grid.coord_vectors[1]
array([ 0.2,  0.6,  1. ])