swiftsimio.visualisation.projection_backends.renormalised module
Renormalised projection visualisation.
This version of the function is the same as fast but provides an explicit renormalisation of each kernel such that the mass is conserved up to floating point precision.
- swiftsimio.visualisation.projection_backends.renormalised.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.renormalised.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.