swiftsimio.visualisation.volume_render module

Basic volume render for SPH data. This takes the 3D positions of the particles and projects them onto a grid.

swiftsimio.visualisation.volume_render.scatter(x: float64, y: float64, z: float64, m: float32, h: float32, res: int, box_x: float64 = 0.0, box_y: float64 = 0.0, box_z: float64 = 0.0) → ndarray[source]

Creates a weighted voxel grid

Computes contributions to a voxel grid from particles with positions (x,`y`,`z`) with smoothing lengths h weighted by quantities m. This includes periodic boundary effects.

Parameters:

  • x (np.array[float64]) – array of x-positions of the particles. Must be bounded by [0, 1].

  • y (np.array[float64]) – array of y-positions of the particles. Must be bounded by [0, 1].

  • z (np.array[float64]) – array of z-positions of the particles. Must be bounded by [0, 1].

  • m (np.array[float32]) – array of masses (or otherwise weights) of the particles

  • h (np.array[float32]) – array of smoothing lengths of the particles

  • res (int) – the number of voxels along one axis, i.e. this returns a cube of res * res * res.

  • box_x (float64) – box size in x, in the same rescaled length units as x, y and z. Used for periodic wrapping.

  • box_y (float64) – box size in y, in the same rescaled length units as x, y and z. Used for periodic wrapping.

  • box_z (float64) – box size in z, in the same rescaled length units as x, y and z. Used for periodic wrapping

Returns:

voxel grid of quantity

Return type:

np.array[float32, float32, float32]

See also

scatter_parallel

Parallel implementation of this function

slice_scatter

Create scatter plot of a slice of data

slice_scatter_parallel

Create scatter plot of a slice of data in parallel

Notes

Explicitly defining the types in this function allows for a 25-50% performance improvement. In our testing, using numpy floats and integers is also an improvement over using the numba ones.

swiftsimio.visualisation.volume_render.scatter_parallel(x: float64, y: float64, z: float64, m: float32, h: float32, res: int, box_x: float64 = 0.0, box_y: float64 = 0.0, box_z: float64 = 0.0) → ndarray[source]

Parallel implementation of scatter

Compute contributions to a voxel grid from particles with positions (x,`y`,`z`) with smoothing lengths h weighted by quantities m. This ignores boundary effects.

Parameters:

  • x (array of float64) – array of x-positions of the particles. Must be bounded by [0, 1].

  • y (array of float64) – array of y-positions of the particles. Must be bounded by [0, 1].

  • z (array of float64) – array of z-positions of the particles. Must be bounded by [0, 1].

  • m (array of float32) – array of masses (or otherwise weights) of the particles

  • h (array of float32) – array of smoothing lengths of the particles

  • res (int) – the number of voxels along one axis, i.e. this returns a cube of res * res * res.

  • box_x (float64) – box size in x, in the same rescaled length units as x, y and z. Used for periodic wrapping.

  • box_y (float64) – box size in y, in the same rescaled length units as x, y and z. Used for periodic wrapping.

  • box_z (float64) – box size in z, in the same rescaled length units as x, y and z. Used for periodic wrapping

Returns:

voxel grid of quantity

Return type:

ndarray of float32

See also

scatter

Create voxel grid of quantity

slice_scatter

Create scatter plot of a slice of data

slice_scatter_parallel

Create scatter plot of a slice of data in parallel

Notes

Explicitly defining the types in this function allows for a 25-50% performance improvement. In our testing, using numpy floats and integers is also an improvement over using the numba ones.

swiftsimio.visualisation.volume_render.render_gas_voxel_grid(data: SWIFTDataset, resolution: int, project: str | None = 'masses', parallel: bool = False, rotation_matrix: None | array = None, rotation_center: None | unyt_array = None, region: None | unyt_array = None, periodic: bool = True)[source]

Creates a 3D render of a SWIFT dataset, weighted by data field, in the form of a voxel grid.

Parameters:

  • data (SWIFTDataset) – Dataset from which slice is extracted

  • resolution (int) – Specifies size of return array

  • project (str, optional) – Data field to be projected. Default is mass. If None then simply count number of particles

  • parallel (bool) – used to determine if we will create the image in parallel. This defaults to False, but can speed up the creation of large images significantly at the cost of increased memory usage.

  • rotation_matrix (np.array, optional) – Rotation matrix (3x3) that describes the rotation of the box around rotation_center. In the default case, this provides a volume render viewed along the z axis.

  • rotation_center (np.array, optional) – Center of the rotation. If you are trying to rotate around a galaxy, this should be the most bound particle.

  • region (unyt_array, optional) –

    determines where the image will be created (this corresponds to the left and right-hand edges, and top and bottom edges, and front and back edges) if it is not None. It should have a length of six, and take the form:

    [x_min, x_max, y_min, y_max, z_min, z_max]

    Particles outside of this range are still considered if their smoothing lengths overlap with the range.

  • periodic (bool, optional) – Account for periodic boundaries for the simulation box? Default is True.

Returns:

Creates a resolution x resolution x resolution array and returns it, without appropriate units.

Return type:

ndarray of float32

See also

slice_gas_pixel_grid

Creates a 2D slice of a SWIFT dataset

swiftsimio.visualisation.volume_render.render_gas(data: SWIFTDataset, resolution: int, project: str | None = 'masses', parallel: bool = False, rotation_matrix: None | array = None, rotation_center: None | unyt_array = None, region: None | unyt_array = None, periodic: bool = True)[source]

Creates a 3D voxel grid of a SWIFT dataset, weighted by data field

Parameters:

  • data (SWIFTDataset) – Dataset from which slice is extracted

  • resolution (int) – Specifies size of return array

  • project (str, optional) – Data field to be projected. Default is mass. If None then simply count number of particles

  • parallel (bool) – used to determine if we will create the image in parallel. This defaults to False, but can speed up the creation of large images significantly at the cost of increased memory usage.

  • rotation_matrix (np.array, optional) – Rotation matrix (3x3) that describes the rotation of the box around rotation_center. In the default case, this provides a volume render viewed along the z axis.

  • rotation_center (np.array, optional) – Center of the rotation. If you are trying to rotate around a galaxy, this should be the most bound particle.

  • region (unyt_array, optional) – determines where the image will be created (this corresponds to the left and right-hand edges, and top and bottom edges, and front and back edges) if it is not None. It should have a length of six, and take the form: [x_min, x_max, y_min, y_max, z_min, z_max] Particles outside of this range are still considered if their smoothing lengths overlap with the range.

  • periodic (bool, optional) – Account for periodic boundaries for the simulation box? Default is True.

Returns:

a resolution x resolution x resolution array of the contribution of the projected data field to the voxel grid from all of the particles

Return type:

ndarray of float32

See also

slice_gas

Creates a 2D slice of a SWIFT dataset with appropriate units

render_gas_voxel_grid

Creates a 3D voxel grid of a SWIFT dataset

Notes

This is a wrapper function for slice_gas_pixel_grid ensuring that output units are appropriate