Reading particles

Particle data source

If you want to look at the particles, you need to create a RamsesParticleSource. To do so, call the particle_source() method of your RamsesOutput object with a list of the different fields you might need in your analysis.

The available fields are :

• “vel” : the velocities of the particles
• “mass” : the mass of the particles
• “id” : the id number of the particles
• “level” : the AMR level of refinement of the cell each particle belongs to
• “epoch” : the birth time of the particles (0.0 for ic particles, >0.0 for star formation particles)
• “metal” : the metallicities of the particles

ro = pymses.RamsesOutput("/data/Aquarius/output", 193)
part = ro.particle_source(["vel", "mass"])

Warning

The data source you just created does not contain data. It is designed to provide an on-demand access to the data. To be memory-friendly, nothing is read from the disk yet at this point. All the part_00193.out_* files are only linked to the data source for further processing.

PointDataset

At the opposite, a PointDataset is an actual data container.

Single CPU particle dataset

If you want to read all the particles of the cpu number 3 (written in part_00193.out_00003), use the get_domain_dset() method :

dset3 = part.get_domain_dset(3)
Out:Reading particles : /data/Aquarius/output/output_00193/part_00193.out00003

Number of particles

Every PointDataset has a npoints int parameter which gives you the number of particles in this dataset :

print "CPU 3 has %i particles"%dset3.npoints
Out:CPU 3 has 157976 particles

Particle coordinates

The points parameter of the PointDataset contains the coordinates of the particles :

print dset3.points
Out:array([[ 0.49422911,  0.51383241,  0.50130034],
        [ 0.49423128,  0.51374527,  0.50136899],
        [ 0.49420231,  0.51378629,  0.50190981],
        ...,
        [ 0.49447162,  0.51394969,  0.50146777],
        [ 0.49422794,  0.51378071,  0.50176276],
        [ 0.4946566 ,  0.51491008,  0.50117673]])

Particle fields

You also have an easy access to the different fields :

print dset3["mass"]
Out:array([  4.69471978e-07,   4.69471978e-07,   9.38943957e-07, ...,
            4.69471978e-07,   4.69471978e-07,   4.69471978e-07])

Whole point data source concatenation

To read all the particles from all the ncpus part_00193.out* files and concatenate them into a single (but maybe not memory-friendly) dataset, call the flatten() method of your part object :

dset_all = part.flatten()
Out:Reading particles : /data/Aquarius/output/output_00193/part_00193.out00001
    Reading particles : /data/Aquarius/output/output_00193/part_00193.out00002
    Reading particles : /data/Aquarius/output/output_00193/part_00193.out00003
    Reading particles : /data/Aquarius/output/output_00193/part_00193.out00004

    [...]

    Reading particles : /data/Aquarius/output/output_00193/part_00193.out00062
    Reading particles : /data/Aquarius/output/output_00193/part_00193.out00063
    Reading particles : /data/Aquarius/output/output_00193/part_00193.out00064

print "Domain has %i particles"%dset_all.npoints
Out:Domain has 10000000 particles

CPU-by-CPU particles

In most cases, you won’t have enough memory to load all the particles of your simulation domain into a single dataset. You have two different options :

• Filter your particles (see Data filtering).
• Your analysis can be done on a cpu-by-cpu basis. The RamsesParticleSource provides a iter_dsets() iterator yielding cpu-by-cpu datasets :

for dset in part.iter_dsets():
    print dset.npoints

Out:Reading particles : /data/Aquarius/output/output_00193/part_00193.out00001
    254210
    Reading particles : /data/Aquarius/output/output_00193/part_00193.out00002
    214330
    Reading particles : /data/Aquarius/output/output_00193/part_00193.out00003
    359648
    [...]
    Reading particles : /data/Aquarius/output/output_00193/part_00193.out00064
    351203