Mailman 3 March 2018 - yt-users

SlicePlot of dark matter density fields from an enzo sim with ParallelParticleIO=1.
by arrau08 . 09 Mar '18

09 Mar '18

Hi, Want to touch base in analyzing cosmological simulation results with ParallelParticleIO on. I'm in the middle of testing my own initial condition generator (named BCCOMICS). Starting from a unigrid IC from BCCOMICS, I ran two cosmological (with cooling on) simulations, one with AMR on and one with AMR off. For the AMR-on one, I turned on ParallelParticleIO and this figure ( https://www.chosun.ac.kr/user/kjahn/tmpFile/RD0001_Slice_y_Dark_Matter_Dens… ) is the plot I got by yting as follows: ------- import yt fname="RD0001/RD0001" ds=yt.load(fname) pc=yt.SlicePlot(ds,"y","Dark_Matter_Density") pc.annotate_quiver("all_cic_velocity_z","all_cic_velocity_x") pc.save() -------- The ring command I used is: ring -np 20 pv ParticlePositions ParticleVelocities So I see those rectangular artifacts in the plot. I don't see any artifact in the baryon density field (ParallelRootGridIO=1 too). Q1: I guess that enzo is internally getting the dark matter particle information and gravity correctly? In the AMR-off simulation I did not turn on ParallelParticleIO, and surely the DM field plotted by the same yt script looks fine. Q2: So, is this the right way you analyze the DM density field when ParallelParticleIO was on? Many thanks in advance. Kyungjin

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Error plotting radial velocity
by Joseph Smidt 08 Mar '18

08 Mar '18

Hey everyone, I am using Python 3.6.4 and yt 3.4.1 from conda-forge. I get errors when I try and save a slice plot of radial velocity. For example, if I download the tiny cosmology dataset [1], and run this script: -------- import yt # Load in tiny cosmology dump fn = "enzo_tiny_cosmology/DD0015/DD0015" ds = yt.load(fn) # load data # Make slice plot of radial_velocity p = yt.SlicePlot(ds, 'z', 'radial_velocity') p.save() ------- I get the following error [2]. This only happens when I save the plot for the radial_velocity field. Other fields like density and temperature work just fine. Let me know if anyone else gets this error and if there is an obvious fix. Thanks. [1] http://yt-project.org/data/enzo_tiny_cosmology.tar.gz [2] https://pastebin.com/EJkGxQXM -- ------------------------------------------------------------------------ Joseph Smidt <josephsmidt(a)gmail.com> Theoretical Division P.O. Box 1663, Mail Stop B283 Los Alamos, NM 87545 Office: 505-665-9752 Fax: 505-667-1931

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STAR FORMATION RATE FOR RAMSES DATASET
by Vadlamani Samhitha 05 Mar '18

05 Mar '18

Hi, I am trying to get a plot of Star formation rate in yt for RAMSES Data set and I am using the sample code given for Enzo instead > import yt > import numpy as np > from yt.data_objects.particle_filters import add_particle_filter > from matplotlib import pyplot as plt > def formed_star(pfilter, data): > filter = data["io", "creation_time"] > 0 > return filter > add_particle_filter("formed_star", function=formed_star, > filtered_type='io', > requires=["creation_time"]) > filename = "/Users/xxxxxx/Desktop/out/info_00014.txt" > ds = yt.load(filename) > ds.add_particle_filter('formed_star') > ad = ds.all_data() > masses = ad['formed_star', 'particle_mass'].in_units('Msun') > formation_time = ad['formed_star', 'creation_time'].in_units('yr') > time_range = [0, 5e9] # years > n_bins = 1000 > hist, bins = np.histogram(formation_time, bins=n_bins, range=time_range,) > inds = np.digitize(formation_time, bins=bins) time = (bins[:-1] + bins[1:])/2 > sfr = np.array([masses[inds == j+1].sum()/(bins[j+1]-bins[j]) > for j in range(len(time))]) > sfr[sfr == 0] = np.nan > plt.plot(time/1e6, sfr) > plt.xlabel('Time [Myr]') However, I am ending up with this error: > The fields > > ('io', 'creation_time'), > > required by the "formed_star" particle filter, are not defined for this > dataset. > Could anyone please suggest how I could access this "formed_star"/ an equivalent field for RAMSES. Thanks!

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Projection Plot on a Large Simulation
by Jared Coughlin 02 Mar '18

02 Mar '18

Hello! I have a large gadget simulation (1024^3 particles) that I am trying to make a projection plot of. I thought it would make sense to run in in parallel, so I'm currently running it on a node with 24 processors and 256GB of RAM. I know for a fact that this is enough RAM to hold the particle data because, well, the gadget simulation ran. However, the job keeps getting killed because it is using all of the system memory. Does yt try and give each processor a copy of the whole particle data set when run in parallel, or does it farm the data out more efficiently? I've tried lowering n_ref, but I can try lowering it more. Thanks! -Jared

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