hyperion.grid.VoronoiGrid

class hyperion.grid.VoronoiGrid(*args, **kwargs)

A voronoi mesh.

The mesh can be initialized by passing the x, y, and z coordinates of the points used to compute the mesh:

>>> grid = Voronoi(x, y, z)

where x, y, and z are 1-d sequences of point positions.

VoronoiGrid objects may contain multiple quantities (e.g. density, specific energy). To access these, you can specify the name of the quantity as an item:

>>> grid['density']

which is no longer a VoronoiGrid object, but a VoronoiGridView object. When setting this for the first time, this can be set either to another VoronoiGridView object, an external h5py link, or an empty list. For example, the following should work:

>>> grid['density_new'] = grid['density']

VoronoiGridView objects allow the specific dust population to be selected as an index:

>>> grid['density'][0]

Which is also a VoronoiGridView object. The data can then be accessed with the array attribute:

>>> grid['density'][0].array

which is a 1-d array of the requested quantity.

Methods

read(group[, quantities]) Read the geometry and physical quantities from an voronoi grid
read_geometry(group) Read in geometry information from a cartesian grid
read_quantities(group[, quantities]) Read in physical quantities from a cartesian grid
write(group[, quantities, copy, …]) Write out the voronoi grid
write_single_array(group, name, array[, …]) Write out a single quantity, checking for consistency with geometry
add_derived_quantity(name, function)
evaluate_function_average(func[, n_samples, …]) Evaluate the average of a function inside each cell using randomly sampled points inside each cell.

Methods (detail)

read(group, quantities='all')

Read the geometry and physical quantities from an voronoi grid

Parameters:
group : h5py.Group

The HDF5 group to read the grid from. This group should contain groups named ‘Geometry’ and ‘Quantities’.

quantities : ‘all’ or list

Which physical quantities to read in. Use ‘all’ to read in all quantities or a list of strings to read only specific quantities.
read_geometry(group)

Read in geometry information from a cartesian grid

Parameters:
group : h5py.Group

The HDF5 group to read the grid geometry from.
read_quantities(group, quantities='all')

Read in physical quantities from a cartesian grid

Parameters:
group : h5py.Group

The HDF5 group to read the grid quantities from

quantities : ‘all’ or list

Which physical quantities to read in. Use ‘all’ to read in all quantities or a list of strings to read only specific quantities.
write(group, quantities='all', copy=True, absolute_paths=False, compression=True, wall_dtype=<class 'float'>, physics_dtype=<class 'float'>)

Write out the voronoi grid

Parameters:
group : h5py.Group

The HDF5 group to write the grid to

quantities : ‘all’ or list

Which physical quantities to write out. Use ‘all’ to write out all quantities or a list of strings to write only specific quantities.

copy : bool

Whether to copy external links, or leave them as links.

absolute_paths : bool

If copy is False, then this indicates whether to use absolute or relative paths for links.

compression : bool

Whether to compress the arrays in the HDF5 file

wall_dtype : type

The datatype to use to write the wall positions (ignored for this kind of grid)

physics_dtype : type

The datatype to use to write the physical quantities
write_single_array(group, name, array, copy=True, absolute_paths=False, compression=True, physics_dtype=<class 'float'>)

Write out a single quantity, checking for consistency with geometry

Parameters:
group : h5py.Group

The HDF5 group to write the grid to

name : str

The name of the array in the group

array : np.ndarray

The array to write out

copy : bool

Whether to copy external links, or leave them as links.

absolute_paths : bool

If copy is False, then this indicates whether to use absolute or relative paths for links.

compression : bool

Whether to compress the arrays in the HDF5 file

wall_dtype : type

The datatype to use to write the wall positions

physics_dtype : type

The datatype to use to write the physical quantities
add_derived_quantity(name, function)
evaluate_function_average(func, n_samples=None, min_cell_samples=None)

Evaluate the average of a function inside each cell using randomly sampled points inside each cell.

Note

This feature is still experimental, use with caution

Parameters:
func : function

The function to evaluate in each cell. This should take three 1-d arrays of x, y, and z and return a 1-d array of values.

n_samples : int

The total number of points to sample within the domain. Points will be uniformly sampled in each Voronoi cell so that at least n_samples total points will be produced. Each cell will contain a number of samples proportional to its volume (but at least min_cell_samples points will always be sampled in each cell).

min_cell_samples : int

The minimum number of samples per cell.
class hyperion.grid.VoronoiGridView(grid, quantity)

Methods

append(grid) Used to append quantities from another grid
add(grid) Used to add quantities from another grid

Methods (detail)

append(grid)

Used to append quantities from another grid

Parameters:
grid : 1D Numpy array or VoronoiGridView instance

The grid to copy the quantity from
add(grid)

Used to add quantities from another grid

Parameters:
grid : 1D Numpy array or VoronoiGridView instance

The grid to copy the quantity from