swiftsimio.visualisation.projection_backends.histogram module

Reference evaluation - returns a 2d histogram (i.e. no smoothing).

Uses double precision.

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

Create a weighted scatter plot.

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

Parameters:

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

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

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

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

  • res (int) – The number of pixels along one axis, i.e. this returns a square of res * res.

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

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

Returns:

Pixel grid of quantity.

Return type:

np.ndarray[np.float32, np.float32, np.float32]

See also

scatter_parallel

Parallel implementation of this function.

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.projection_backends.histogram.scatter_parallel(x: float64, y: float64, m: float32, h: float32, res: int, box_x: float64 = 0.0, box_y: float64 = 0.0) → ndarray[source]

Create a weighted scatter plot in parallel.

Creates a weighted scatter plot. Computes contributions from particles with positions (x,`y`) with smoothing lengths h weighted by quantities m. This includes periodic boundary effects.

Parameters:

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

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

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

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

  • res (int) – The number of pixels along one axis, i.e. this returns a square of res * res.

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

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

Returns:

Pixel grid of quantity.

Return type:

np.ndarray[np.float32, np.float32, np.float32]

See also

scatter

Creates 2D scatter plot from SWIFT data.

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.