GPGI

A Generic Particle + Grid data Interface

This small Python library implements fundamental grid deposition algorithms to analyse (rectilinear) grid + particle datasets, with an emphasize on performance. Core algorithms are implemented as Cython extensions.

Installation

pip install gpgi

Usage

The API consists in a load function, which returns a Dataset object. Data can be 1D, 2D, or 3D and is validated at load time for consistency, in particular with the chosen geometry (e.g. cartesian, polar, cylindrical, spherical).

Load data

import numpy as np
import gpgi

nx = ny = 64
nparticles = 600_000

prng = np.random.RandomState(0)
ds = gpgi.load(
    geometry="cartesian",
    grid={
        "cell_edges": {
            "x": np.linspace(-1, 1, nx),
            "y": np.linspace(-1, 1, ny),
        },
    },
    particles={
        "coordinates": {
            "x": 2 * (prng.normal(0.5, 0.25, nparticles) % 1 - 0.5),
            "y": 2 * (prng.normal(0.5, 0.25, nparticles) % 1 - 0.5),
        },
        "fields": {
            "mass": np.ones(nparticles),
        },
    },
)

The Dataset object holds a grid and a particle attribute, which both hold a fields attribute for accessing their data. But more importantly, the Dataset has a deposit method to translate particle fields to the grid formalism.

Deposit Particle fields on the grid

particle_mass = ds.deposit("mass", method="particle_in_cell")  # or "pic" for shorts

Visualize In this example we'll use matplotlib for rendering, but note that matplotlib is not a dependency to gpgi

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.set(aspect=1, xlabel="x", ylabel="y")

im = ax.pcolormesh(
    "x",
    "y",
    particle_mass.T,
    data=ds.grid.cell_edges,
    cmap="viridis",
)
fig.colorbar(im, ax=ax)

The example script given here takes about a second (top to bottom).