I’m also throwing some ideas in the pot, but you may be interested in
using the particle trajectories functionality (although this isn’t
exactly covering what you’re interested into). Otherwise, I’ve had some
chance with a pseudo-code as follows (to be adapted to your use case)
|ds1 = yt.load("/path/to/output_78") ds2 =
yt.load("/path/to/other/output") old_ids = ds1.r["PartType0",
"particle_identifier"] old_order = np.argsort(old_ids) def
_old_density(field, data): ids = data["PartType0",
"particle_identifier"] # find ids in old_ids inds =
old_order[np.searchsorted(old_ids, ids, sorter=old_order)] # this is
just to make sure I haven't messed up the ids matching
np.testing.assert_allclose(ids, old_ids[inds]) # Return the value of the
density for those matched indices return ds1.r["density"][inds]
ds2.add_field(("PartType0", "old_density"), function=_old_density,
sampling_type="cell", units=…) |
On 04/04/2024 18:41, Jacob Morgan wrote:
Hey! Sorry I have been so long in replying. My current installation of yt is a
mess, I had to make another thread about some issues I have to fix before I can
test this:
https://mail.python.org/archives/list/[email protected]/thread/GM75POP42EHD6WLS7YRHPC3FPTNKCF63/
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