AbstractGrid¶

class plasmapy.plasma.grids.AbstractGrid(*seeds, num=100, **kwargs)¶

Bases: abc.ABC

Abstract grid represents a 3D grid of positions. The grid is stored as an np.ndarray, while the units associated with each dimension are stored separately.

Attributes Summary

`ax0`	First axis of the grid, only valid for uniform grids
`ax1`	Second axis of the grid, only valid for uniform grids
`ax2`	Third axis of the grid, only valid for uniform grids
`dax0`	Grid step size along axis `ax0`, only valid for uniform grids.
`dax1`	Grid step size along axis `ax1`, only valid for uniform grids.
`dax2`	Grid step size along axis `ax2`, only valid for uniform grids.
`grid`	A single grid of vertex positions of shape (N0, N1, N2, 3)
`grid_resolution`	A scalar estimate of the grid resolution.
`grids`	Three grids of vertex positions (in each coordinate), each having shape (N0, N1, N2)
`interpolator`	A nearest-neighbor interpolator that returns the nearest grid index to a position.
`is_uniform`	A boolean value reflecting whether or not the grid points are uniformly spaced.
`pts0`	Array of positions in dimension 1
`pts1`	Array of positions in dimension 2
`pts2`	Array of positions in dimension 3
`quantities`	A list of the keys corresponding to the quantities currently defined on the grid.
`recognized_quantities`	A dictionary of standard key names representing particular physical quantities.
`shape`	Shape of the grid
`unit`	The unit for the entire grid.
`unit0`	Unit of dimension 1
`unit1`	Unit of dimension 2
`unit2`	Unit of dimension 3
`units`	Returns a list of the units of each dimension

Methods Summary

`add_quantities`(**kwargs)	Adds a quantity to the dataset as a new DataArray
`interpolate_indices`(pos)	Interpolate the nearest grid indices to a position using a nearest-neighbor interpolator
`nearest_neighbor_interpolator`(pos, *args[, …])	Interpolate values on the grid using a nearest-neighbor scheme with no higher-order weighting.
`on_grid`(pos)	Given a list of positions, determines which are in the region bounded by the grid points.
`require_quantities`(req_quantities[, …])	Checks to make sure that a list of required quantities are present.
`vector_intersects`(p1, p2)	Returns True if the vector from p1 to p2 intersects the grid.
`volume_averaged_interpolator`(pos, *args[, …])	Interpolate values on the grid using a volume-averaged scheme with no higher-order weighting.

Attributes Documentation

ax0¶

First axis of the grid, only valid for uniform grids

Raises: ValueError – If grid is non-uniform.

ax1¶

Second axis of the grid, only valid for uniform grids

Raises: ValueError – If grid is non-uniform.

ax2¶

Third axis of the grid, only valid for uniform grids

Raises: ValueError – If grid is non-uniform.

dax0¶

Grid step size along axis ax0, only valid for uniform grids.

Raises: ValueError – If grid is non-uniform.

dax1¶

Grid step size along axis ax1, only valid for uniform grids.

Raises: ValueError – If grid is non-uniform.

dax2¶

Grid step size along axis ax2, only valid for uniform grids.

Raises: ValueError – If grid is non-uniform.

grid¶

A single grid of vertex positions of shape (N0, N1, N2, 3)

Only defined for grids for which the unit property is defined.

grid_resolution¶

A scalar estimate of the grid resolution.

For uniform grids, this is the minima of [dax0, dax1, dax2].

For non-uniform grids, it is the closest spacing between any two points.

grids¶: Three grids of vertex positions (in each coordinate), each having shape (N0, N1, N2)

interpolator¶: A nearest-neighbor interpolator that returns the nearest grid index to a position.

is_uniform¶: A boolean value reflecting whether or not the grid points are uniformly spaced.

pts0¶: Array of positions in dimension 1

pts1¶: Array of positions in dimension 2

pts2¶: Array of positions in dimension 3

quantities¶: A list of the keys corresponding to the quantities currently defined on the grid.

recognized_quantities¶: A dictionary of standard key names representing particular physical quantities. Using these keys allows these quantities to be recognized automatically by other PlasmaPy functions. Each entry contains a tuple containing a description and the unit associated with the quantity.

shape¶: Shape of the grid

unit¶

The unit for the entire grid. Only valid if all dimensions of the grid have the same units.

Raises: ValueError – If all grid dimensions do not have identical units.

unit0¶: Unit of dimension 1

unit1¶: Unit of dimension 2

unit2¶: Unit of dimension 3

units¶: Returns a list of the units of each dimension

Methods Documentation

add_quantities(**kwargs)¶

Adds a quantity to the dataset as a new DataArray

Parameters

key – The key will be used as the dataset key, while the array holds the quantity.
pairs as keyword arguments (array) – The key will be used as the dataset key, while the array holds the quantity.

Returns

Return type

None.

interpolate_indices(pos: Union[numpy.ndarray, astropy.units.quantity.Quantity])¶

Interpolate the nearest grid indices to a position using a nearest-neighbor interpolator

Parameters: pos (np.ndarray or u.Quantity array, shape (n,3)) – An array of positions in space, where the second dimension corresponds to the three dimensions of the grid. If an np.ndarray is provided, units will be assumed to match those of the grid.
Returns: i – An array of indices corresponding to the positions such that i[n,:] = ix,iy,iz such that grid[ix,iy,iz,:] ~ pos[n,:]
Return type: np.ndarray, shape (n,3)

nearest_neighbor_interpolator(pos: Union[numpy.ndarray, astropy.units.quantity.Quantity], *args, persistent=False)¶

Interpolate values on the grid using a nearest-neighbor scheme with no higher-order weighting.

Parameters

pos (np.ndarray or u.Quantity array, shape (n,3)) – An array of positions in space, where the second dimension corresponds to the three dimensions of the grid. If an np.ndarray is provided, units will be assumed to match those of the grid.
*args (str) – Strings that correspond to DataArrays in the dataset
persistent (bool) – If true, the interpolator will assume the grid and its contents have not changed since the last interpolation. This substantially speeds up the interpolation when many interpolations are performed on the same grid in a loop. persistent overrides to False if the arguments list has changed since the last call.

on_grid(pos)¶

Given a list of positions, determines which are in the region bounded by the grid points.

For non-uniform grids, “on grid” is defined as being bounded by grid points in all axes.

Parameters: pos (np.ndarray or u.Quantity array, shape (n,3)) – An array of positions in space, where the second dimension corresponds to the three dimensions of the grid.

require_quantities(req_quantities, replace_with_zeros=False)¶

Checks to make sure that a list of required quantities are present. Optionally, can create missing quantities and fill them with an array of zeros.

Parameters

req_quantities (list of str) – A list of quantity keys that are required
replace_with_zeros (boolean, optional) – If true, missing quantities will be replaced with an array of zeros. If false, an exception will be raised instead. The default is False.

Raises

KeyError – If replace_with_zeros is False and a required quantity is missing, raises a KeyError.
KeyError – If replace_with_zeros is True but the quantity is not in the list of recognized quantities, raises a KeyError. This is because in this case the units for the quantity are unknown, so an array of zeros cannot be constructed.

Returns

Return type

None.

vector_intersects(p1, p2)¶: Returns True if the vector from p1 to p2 intersects the grid. Otherwise, returns false. This is a standard ray-box intersection algorithm.

volume_averaged_interpolator(pos: Union[numpy.ndarray, astropy.units.quantity.Quantity], *args, persistent=False)¶

Interpolate values on the grid using a volume-averaged scheme with no higher-order weighting.

Parameters

pos (np.ndarray or u.Quantity array, shape (n,3)) – An array of positions in space, where the second dimension corresponds to the three dimensions of the grid. If an np.ndarray is provided, units will be assumed to match those of the grid.
*args (str) – Strings that correspond to DataArrays in the dataset
persistent (bool) – If true, the interpolator will assume the grid and its contents have not changed since the last interpolation. This substantially speeds up the interpolation when many interpolations are performed on the same grid in a loop. persistent overrides to False if the arguments list has changed since the last call.