graph-et
[grɑːf iː.tiː.]dask
and xarray
based library for loading particle-in-cell simulation data produced by a variety of different codes. The support for arbitrary codes is provided through the so-called plugins. Currently supported (and planned) codes include:
from graphet import Data
from graphet.plugins import TristanV2
# load the metadata using the TristanV2 plugin
d = Data(
TristanV2, # plugin
steps=range(150), # steps to load metadata for
path=f"output/", # path to the data
cfg_fname=f"input.cfg", # configuration file
params=True, # read configuration file
coord_transform={ # time/coordinate transformation
"t": lambda t, prm: t * prm["output:interval"] * prm["algorithm:c"] / prm["grid:my0"],
"x": lambda x, prm: (x - x.mean()) / prm["grid:my0"],
"z": lambda z, prm: (z - z.mean()) / prm["grid:my0"],
"y": lambda y, prm: (y - y.mean()) / prm["grid:my0"],
},
swapaxes=[(0, 1), (2, 1)], # axes swapping "zyx" -> "yxz"
)
# main containers are
d.fields # <- fields
d.particles # <- particles
d.spectra # <- spectra
## Examples of doing useful stuff
# plot averaged spectra of species #2 between 1.5 < t < 2.2
d.spectra.n2.sel(t=slice(1.5, 2.2)).mean("t").plot()
# plot the density of species #1 and #2 at time t = 2.5 and y = 0.1
(d.fields.dens1 + d.fields.dens2).sel(y=0.1, t=2.5, method="nearest").plot(cmap="turbo")
# compute the distribution function from the particle data for species #3 at 1.5 < t < 2.2
cnt, _ = np.histogram(
(np.sqrt(1 + d.particles[3].u ** 2 + d.particles[3].v ** 2 + d.particles[3].w ** 2) - 1)
.sel(t=slice(1.5, 2.2)).mean("t"),
bins=np.logspace(-1, 3, 100),
)
TristanV1
plugin