Points type

Points type

class tedeous.points_type.Points_type(grid)

Bases: object

Discretizing the grid and allocating subsets for Finite Difference method.

static shift_points(grid: Tensor, axis: int, shift: float) → Tensor

Shifts all values of an array ‘grid’ on a value ‘shift’ in a direction of axis ‘axis’, somewhat is equivalent to a np.roll. :param grid: array of a n-D points. :param axis: axis to which the shift is applied. :param shift: shift value.

Returns:

shifted array of a n-D points.

static in_hull(p: Tensor, hull: Tensor) → ndarray

Test if points in p are in hull p should be a NxK coordinates of N points in K dimensions hull is either a scipy.spatial.Delaunay object or the MxK array of the coordinates of M points in `K`dimensions for which Delaunay triangulation will be computed. :param p: shifted array of a n-D points. :param hull: initial array of a n-D points.

Returns:

array of a n-D boolean type points. True - if ‘p’ in ‘hull’, False - otherwise.

point_typization() → dict

Allocating subsets for FD (i.e., ‘f’, ‘b’, ‘central’). :param grid: array of a n-D points.

Returns:

type with a points in a ‘grid’ above. Type may be ‘central’ - inner point and string of ‘f’ and ‘b’, where the length of the string is a dimension n. ‘f’ means that if we add small number to a position of corresponding coordinate we stay in the ‘hull’. ‘b’ means that if we subtract small number from o a position of corresponding coordinate we stay in the ‘hull’.

grid_sort() → dict

Sorting grid points for each subset from result Points_type.point_typization. :param grid: array of a n-D points.

Returns:

sorted grid in each subset (see Points_type.point_typization).

bnd_sort(grid_dict: dict, b_coord: Union[Tensor, list])

Sorting boundary points :param grid_dict: array of a n-D points. :param b_coord: boundary points of grid. It will be list if periodic condition is.

Returns:

bnd_dict is similar to grid_dict but with b_coord values. It

will be list of ‘bnd_dict’s if ‘b_coord’ is list too.