%%time
import yt
ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
# 'deposit' means this is a deposited field - i.e. yt needs to construct it using a CIC operation.
# 'all' means that this field is based on all of the particles. This is only important if your dataset has multiple particle types (i.e. dark matter, stars, etc.)
# 'cic' means we are asking for a cloud-in-cell deposition. There's also 'density' (nearest-neighbot) 'mass' and 'count'.
field = ('deposit', 'all_cic')
ad = ds.all_data()
print ad[field]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 1.02107676e-24 3.16457065e-24 9.74194997e-24] g/cm**3 CPU times: user 5.12 s, sys: 615 ms, total: 5.74 s Wall time: 5.78 s
slc = yt.SlicePlot(ds, 2, field)
slc.show()
[field for field in ds.derived_field_list if field[0] == 'deposit']
[('deposit', 'all_cic'), ('deposit', 'all_count'), ('deposit', 'all_density'), ('deposit', 'all_mass'), ('deposit', 'io_cic'), ('deposit', 'io_count'), ('deposit', 'io_density'), ('deposit', 'io_mass')]
n_particles = len(ds.all_data()['all', 'particle_mass'])
print 'This dataset contains %s particles' % n_particles
This dataset contains 1124453 particles