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]

Creates 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.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].

  • 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 pixels along one axis, i.e. this returns a square of res * res.

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

  • box_y (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.array[float32, float32, 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]

Parallel implementation of scatter

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.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].

  • 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 pixels along one axis, i.e. this returns a square of res * res.

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

  • box_y (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.array[float32, float32, 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.