Mailman 3 August 2018 - yt-users

Porting .ply files to Blender with colors?
by Jill P. Naiman Aug. 14, 2022

Aug. 14, 2022

Hi All, I was curious if anyone has had any luck porting .ply files generated with yt into Blender with colors? It seems to port in the surfaces just fine, but they are a uniform color. From what I can gather, Blender imports .ply files with face color instead of vertex so I tried the surf.export_ply routine with "face" instead of "vertex" for sample_type, but no luck ("vertex" doesn't work either). Let me know if I can provide any more useful info. Thanks for any help, -J

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Stars on volume rendering
by Elizabeth Harper-Clark Dec. 11, 2021

Dec. 11, 2021

Hi all, Thanks for all your help over the last couple of days. One more question: - Can I plot particles on a volume rendered image? I have stars and I want to show where they are! Thanks, Libby -- Elizabeth Harper-Clark MA MSci PhD Candidate, Canadian Institute for Theoretical Astrophysics, UofT Sciences and Engineering Coordinator, Teaching Assistants' Training Program, UofT www.astro.utoronto.ca/~h-clark <http://www.astro.utoronto.ca/%7Eh-clark> h-clark(a)cita.utoronto.ca Astronomy office phone: +1-416-978-5759

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Variance in phase/profiles
by david collins April 11, 2020

April 11, 2020

Hi, Everybody! Does anyone out there have a technique for getting the variance out of a profile object? A profile object is good at getting <X> vs. B, I'd then like to get < (X - <X>)^2 > vs B. Matt and I had spittballed the possibility some time ago, but I was wondering if anyone out there had successfully done it. Thanks, d. -- Sent from my computer.

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3-D Mass power spectra
by 梁家豪 June 3, 2019

June 3, 2019

Dear yt Can current yt calculate 3-D Mass power spectra? I checked the website but I didn't find any information. I think calculating 3-D Mass power spectra is a very useful for cosmological simulations. So I guess maybe yt supports this function now....? Thanks in advance

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Internal memory usage
by Nick Gnedin Sept. 7, 2018

Sept. 7, 2018

Folks, I would like to test the integrity of a large dataset, for example by finding the max value of some data field. The dataset is too large to be fully loaded into memory, so I would like to read it slice by slice and compute max in each slice, and then combine them together. To that end I wrote the following script: import yt, yt.funcs import sys import numpy as np f = sys.argv[1] ds = yt.load(f) n = ds.domain_dimensions[0] dmax = np.zeros((n,)) for i in range(n): d = ds.r[i:(i+1),:,:][('artio', 'HVAR_GAS_DENSITY')] dmax[i] = np.amax(d) print(i,dmax[i],yt.funcs.get_memory_usage()) del d print(np.amax(dmax)) However, when I run it, I get: yt : [INFO ] 2018-08-28 10:52:58,075 Created 4945 chunks for ARTIO 0 16945762.0 202.5546875 yt : [INFO ] 2018-08-28 10:52:59,388 Created 1232 chunks for ARTIO 1 2416576.25 221.36328125 yt : [INFO ] 2018-08-28 10:53:01,599 Created 9635 chunks for ARTIO 2 10419311.0 269.06640625 ... yt : [INFO ] 2018-08-28 10:53:23,397 Created 1474 chunks for ARTIO 13 2395590.5 698.91015625 yt : [INFO ] 2018-08-28 10:53:25,594 Created 9747 chunks for ARTIO 14 11139424.0 739.16015625 The number of chunks created varies in each iteration of the loop, but the total memory usage is still steadily climbing up. Could you advise what I am doing wrong? Many thanks, Nick

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Large Memory Usage
by Jared Coughlin Aug. 31, 2018

Aug. 31, 2018

Hello! I'm using the sph-viz branch of yt and I'm trying to make a density projection plot from a gadget-2 snapshot. This works like a charm when I use a snapshot with 428^3 particles in a 15 Mpc box. However, when I try to do it on a snapshot with 1024^3 particles in a 40 Mpc box, my job keeps getting killed because it is trying to exceed the available system memory. I'm submitting it to a node with 256 GB of RAM, and I'm requesting all of it, which means that more than this is trying to be used. The snapshot itself is ~ 40 GB. The memory usage appears to spike when allocating for the KDTree. Before that, it appears to generate the .ewah file without issue. I was just wondering if anyone had any thoughts on why this large spike in memory might be occurring and how I might go about fixing it? If not, no worries, I can try and investigate it myself, but it's a pain to do with pdb. My gadget simulation used a maximum of ~ 173 GB of RAM, so I feel like the kdtree shouldn't be using as much memory as it's trying to. Thanks, and sorry for the bother! Sincerely, -Jared

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clump finder
by Stephanie Tonnesen Aug. 30, 2018

Aug. 30, 2018

Dear yt-users, I am having some trouble with the yt clump finder. Mostly, my issue is that I don't understand what I am getting when I annotate the projectionplot. Instead of the nice lines in the example, I get strange filled-in circles? My figure is attached, and here is my code: ################## ds = yt.load("/mnt/ceph/users/stonnesen/runs/JO204wind/DD"+loop[i]+"/JO204cw"+loop[i]) ad = ds.all_data() data_source = ad.cut_region(["obj['z'] > 0.27"]) #ds.disk([0.5, 0.5, 0.5], [0., 0., 1.],(80, 'kpc'), (145, 'kpc')) # the field to be used for contouring field = ("gas", "density") # This is the multiplicative interval between contours. step = 2.0 # Now we set some sane min/max values between which we want to find contours # This is how we tell the clump finder what to look for -- it won't look for # contours connected below or above these threshold values. c_min = 2e-25 #10**np.floor(np.log10(data_source[field]).min()+3 ) c_max = 10**np.floor(np.log10(data_source[field]).max()+1) # Now find get our 'base' clump -- this one just covers the whole domain. master_clump = Clump(data_source, 'density') # Add a "validator" to weed out clumps with less than 16 cells. # As many validators can be added as you want. master_clump.add_validator("min_cells", 16) # Calculate center of mass for all clumps. master_clump.add_info_item("center_of_mass") # Begin clump finding. find_clumps(master_clump, c_min, c_max, step) # Save the clump tree as a reloadable dataset fn = master_clump.save_as_dataset(fields=["density"]) # Traverse the clump hierarchy to get a list of all of the 'leaf' clumps leaf_clumps = get_lowest_clumps(master_clump) # To visualize these clumps, a list of clumps can be supplied to # the "clumps" callback on a plot. First, we create a projection plot: prj = yt.ProjectionPlot(ds, 0, 'density', center=[0.5,0.5,0.5],width=(160,'kpc')) prj.set_buff_size(4000) # Next we annotate the plot with contours on the borders of the clumps prj.annotate_clumps(leaf_clumps) # Save the plot to disk. prj.save('JO204cw'+loop[i]+'_clumps_16pix') ########################## Also, when I have > print (leaf_clumps[0]["gas","density"]) I get 22 outputs, which seems like a lot less than what is getting put on the figure? Anyway, I am lost and any help would be greatly appreciated. Best, Stephanie -- Dr. Stephanie Tonnesen Associate Research Scientist CCA, Flatiron Institute New York, NY stonnes(a)gmail.com

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set_buff_size
by Stephanie Tonnesen Aug. 29, 2018

Aug. 29, 2018

Hi yt-users! So I have a question about set_buff_size. I would like my pixels to be the same size as my more refined cell in a projectionplot, so have a couple lines of code like this: plot6z=yt.ProjectionPlot(ds,"x","density",center=[0.5,0.5,0.33],width=(76,'kpc')) plot6z.set_buff_size(2000) plot6z.save('JO201scc_76kpc_'+loop[i]+'t'+times[i]) When I take a look at the png file that is made, there are 800 pixels across instead of the 2000 that I expect. I just checked the docs here: http://yt-project.org/doc/visualizing/plots.html and based on the SlicePlot example I feel like it should work! Then again, it looks like in the example the number of pixels also doesn't change? I would appreciate any/all advice! Thanks!! Stephanie -- Dr. Stephanie Tonnesen Associate Research Scientist CCA, Flatiron Institute New York, NY stonnes(a)gmail.com

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ANN: ytree 2.2 released
by Britton Smith Aug. 28, 2018

Aug. 28, 2018

Hi everyone, ytree version 2.2 is released! ytree is an extension of yt for working with merger-tree data from multiple sources. This release adds a couple new features as well as improvements for i/o and memory usage. - New feature: vector fields return x/y/z components of a vector as a single array with automatically created magnitude fields. - New feature: a select_halos function to search all trees for halos matching specified criteria. - Enhancement: significant refactors of the field and i/o systems to reduce memory, speed up field access, and simplify implementing new frontends. - Enhancement: upgrades to the testing infrastructure. ytree can be installed with pip: pip install ytree or from source, by going to https://github.com/brittonsmith/ytree git clone https://github.com/brittonsmith/ytree cd ytree pip install -e . Full documentation can be found at http://ytree.readthedocs.io/. As always, ytree would be nothing without the wonderful yt community. Thanks to all those amazing people! Britton

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ARTIO question
by Nick Gnedin Aug. 27, 2018

Aug. 27, 2018

Folks, I need to make a small change in the ARTIO backend - I added an option to compress grid data, and that required a small change in the meaning of some disk variables. In order to test these changes, I made them inside yt/frontends/artio/artio_headers on my local anaconda install, but I do not know how to compile them into yt - sorry for a such a dummy question. Any advice? Nick Gnedin

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