Mailman 3 June 2020 - yt-users

Porting .ply files to Blender with colors?
by Jill P. Naiman 14 Aug '22

14 Aug '22

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 11 Dec '21

11 Dec '21

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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comoving scale annotation?
by Salome Mtchedlidze 05 Dec '20

05 Dec '20

Hello again, I am using yt slices and projections with scale annotation. I have my plots in comoving units ("Mpccm/h") but whenever I want to use the same units for annotation: slc.annotate_scale(corner='lower_right', coeff=10, units='Mpccm/h'), yt does not recognize these units. Am I missing something or only the physical scale annotation is possible ? Thanks, Salome

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Ortho Slice AMR issue
by Stephen Coleman 15 Jul '20

15 Jul '20

Hello, I am having an issue with a FLASH simulation and AMR, using the OrthoSlice method. I am aware of the note at https://yt-project.org/doc/reference/api/yt.data_objects.selection_data_con… but when I try to sort on the "t" field it is not found. Is that a derived field that should be constructed when yt loads the file, or is it the responsibility of the FLASH simulation to write the "t" field to file? Jupyter notebook: http://paste.yt-project.org/show/243/ Input file: http://use.yt/upload/cfef968c Thank you, -Stephen -- Stephen Coleman *Research Scientist* | RadiaSoft 720-502-3928 x 722 | coleman(a)radiasoft.net radiasoft.net | sirepo.com

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Question about Projection Plot Color Coding
by sflarkin＠ucsc.edu 12 Jul '20

12 Jul '20

Hello All, I have a question about the Projection plot function in yt. I have a script that plots the smallest mass darkmatter particles of a simulation in all 3 of the 2-d coordinate projections, and overplots the halos found by a halo finder. This script works, and currently plots the colorbar based on the mass in a given 2-d bin as done in some of the basic yt plots included in the website. I am wondering if it is possible to have the color scheme of the particles be based on the squared mass or squared density of the particles in any given 2-d bin, so that the higher density regions are highlighted compared to the background lower density regions. I have included the plotting part of my script below, in case it is helpful. I hope everyone is having a great New Year! ds = yt.load(VELA_snaps[position[0]]) domain_width = float(ds.domain_width.in_units('Mpc/h')[0]) ad = ds.all_data() masses = yt.np.unique(ad[('darkmatter', 'particle_mass')]) #filter out the darkmatter0 particles def mass_filter(pfilter, data): filter = data[(pfilter.filtered_type, 'particle_mass')] == masses[0] return filter yt.add_particle_filter('darkmatter0', function=mass_filter, filtered_type='darkmatter', requires=['particle_mass']) ds.add_particle_filter('darkmatter0') scale = float(ds.scale_factor) fig = plt.figure() grid = AxesGrid(fig, (0.075,0.075,10,5), nrows_ncols = (3, 1), axes_pad = 1.0, label_mode = "L", share_all = False, cbar_location="right", cbar_mode="each", cbar_size="3%", cbar_pad="0%") zoom = 10 x0 = float(consistent.halo_data_sorted[index][0][17]) * scale / domain_width y0 = float(consistent.halo_data_sorted[index][0][18]) * scale / domain_width z0 = float(consistent.halo_data_sorted[index][0][19]) * scale / domain_width center = [x0, y0, z0] a = yt.ParticlePlot(ds, ('darkmatter0', 'particle_position_x'), ('darkmatter0', 'particle_position_y'),\ ('darkmatter0', 'particle_mass'), center=center) a.set_unit(('darkmatter0','particle_mass'), 'Msun') a.zoom(zoom) b = yt.ParticlePlot(ds, ('darkmatter0', 'particle_position_y'), ('darkmatter0', 'particle_position_z'),\ ('darkmatter0', 'particle_mass'), center=center) b.set_unit(('darkmatter0','particle_mass'), 'Msun') b.zoom(zoom) c = yt.ParticlePlot(ds, ('darkmatter0', 'particle_position_z'), ('darkmatter0', 'particle_position_x'),\ ('darkmatter0', 'particle_mass'), center=center) c.set_unit(('darkmatter0','particle_mass'), 'Msun') c.zoom(zoom) for halos in consistent.halo_data_sorted[index]: x = float(halos[17]) * scale / domain_width y = float(halos[18]) * scale / domain_width z = float(halos[19]) * scale / domain_width r = float(halos[11]) * scale / .7 center = [x, y, z] a.annotate_sphere(center, radius=(r, 'kpc'), circle_args={'color':'red'}) b.annotate_sphere(center, radius=(r, 'kpc'), circle_args={'color':'red'}) c.annotate_sphere(center, radius=(r, 'kpc'), circle_args={'color':'red'}) index = 0 for letter in [a,b,c]: plot = letter.plots[('darkmatter0', 'particle_mass')] plot.figure = fig plot.axes = grid[index].axes plot.cax = grid.cbar_axes[index] letter._setup_plots() index = index + 1 plt.savefig('{}/CatalogProjectionYT_VELA{}_Scale{}.png'.format(out_dir, VELA_number, str(scale)[2:5]), bbox_inches='tight') plt.close()

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Data Loading with Particle Filters
by Andrew Emerick 25 Jun '20

25 Jun '20

Hi all, I'm using the particle filters to easily sub-select particular types of particles in an Enzo simulation. I know that when yt reads a particle field from file or generates a derived field then this is effectively cached in memory so it doesn't have to be re-constructed (e.g doing data[('all', 'example_field')] may take a while initially, but is effectively instantaneous in subsequent calls). However, I've noticed that when making new particle filters they don't seem to be taking advantage of this. For example if I have a new particle filter "all_stars" which is intended to be a subset of "all", filtering by "particle_type": @yt.particle_filter(requires=["particle_type"], filtered_type='all')def all_stars(pfilter, data): filter = data[(pfilter.filtered_type, "particle_type")] == 2 return filter I've noticed even when having already "loaded" the particle_type field and example field for all particles doing something like: all_pt = data[('all','particle_type')] all_ef = data[('all,'example_field')] the calls to get the above for the new particle type ( data[('all_stars','example_field')] ) still takes a while, as though it is completely re-loading the data. This is wouldn't be too much of a problem but for the fact that I have ~ a dozen different particle types and would like to loop over them, and re-loading takes quite a while. Is there a way I can construct the particle filters to take advantage of cached data a bit better? Or is this not currently set up this way? (if not I can just construct the filters myself outside the particle_filter framework). Best, Andew --- Pasadena Fellow in Theoretical Astrophysics Carnegie Observatories California Institute of Technology

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Data Loading with Particle Filters
by Andrew Emerick 24 Jun '20

24 Jun '20

Hi all, I'm using the particle filters to easily sub-select particular types of particles in an Enzo simulation. I know that when yt reads a particle field from file or generates a derived field then this is effectively cached in memory so it doesn't have to be re-constructed (e.g doing data[('all', 'example_field')] may take a while initially, but is effectively instantaneous in subsequent calls). However, I've noticed that when making new particle filters they don't seem to be taking advantage of this. For example if I have a new particle filter "all_stars" which is intended to be a subset of "all", filtering by "particle_type": @yt.particle_filter(requires=["particle_type"], filtered_type='all')def all_stars(pfilter, data): filter = data[(pfilter.filtered_type, "particle_type")] == 2 return filter I've noticed even when having already "loaded" the particle_type field and example field for all particles doing something like: all_pt = data[('all','particle_type')] all_ef = data[('all,'example_field')] the calls to get the above for the new particle type ( data[('all_stars','example_field')] ) still takes a while, as though it is completely re-loading the data. This is wouldn't be too much of a problem but for the fact that I have ~ a dozen different particle types and would like to loop over them, and re-loading takes quite a while. Is there a way I can construct the particle filters to take advantage of cached data a bit better? Or is this not currently set up this way? (if not I can just construct the filters myself outside the particle_filter framework). Best, Andew --- Pasadena Fellow in Theoretical Astrophysics Carnegie Observatories California Institute of Technology

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Undefined yt module
by Christopher Jessop 24 Jun '20

24 Jun '20

Hi, I'm trying to run the OneZoneFreefallTest in Enzo: /run/Cooling/OneZoneFreefallTest and I get the following error: Traceback (most recent call last): File "plot.py", line 6, in <module> import yt.extensions.EnzoSimulation as ES ImportError: No module named yt.extensions.EnzoSimulation I would have guessed that the plotting script has become a bit outdated and lagged behind yt updates, as from what I can see from github is that the last time it has been altered was 7 years ago. Is there instead a different module I should be using, or an alteration I can make to the plot.py script? Thank you, Chris Jessop

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KeyError("No field named...")
by clp3ef＠virginia.edu 23 Jun '20

23 Jun '20

Hi, I'm a new user of yt and have inherited some old code and data that was written in 2016 (original script and data: http://paste.yt-project.org/show/234/, http://use.yt/upload/6961a33c, http://use.yt/upload/b08c0345). The goal of the code was to produce projection plots of the density of tracer particles (notated by negative mass) which follow the ISM of simulated galaxies in groups. I believe it used to work, and produced a projection plot of the density of the tracer particles, called passivecic_field (https://i.imgur.com/woxJfm5.png). When I try to run the code now (using yt 3.60), I get the error KeyError: "No field named ('passive_part', 'passive_mass')". I attempted to fix the issue by changing 'passive_mass' to 'particle_mass' in the definition of passivecic_field, and changing the filter so it accepts positive rather than negative mass particles, since passive_mass was just a negative scalar multiple of particle mass (new script: http://paste.yt-project.org/show/235/). This allows the code to run, but outputs the plot shown in http://use.yt/upload/0ecc9669, which shows few of the details of the original plot (especially the galaxy tails, which are what I am interested in). Do you know what might be causing the KeyError when passive_mass is used, and why the plot for particle_mass would have so much less detail? Thanks, Camryn P.

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mainline development is moving to yt 4.0 shortly
by Matthew Turk 22 Jun '20

22 Jun '20

Hi folks, As a heads up, we're in the final stages of merging yt-4.0 into the branch that is presently called "master" on github. So if you are running off the mainline development, you will soon see this rather extensive change. You may need to install additional dependencies (notably unyt) but this should be handled by running "pip install -e ." in your yt directory. Once this is in, we'll work to update and edit PRs to retarget them. Most should not need much in the way of changes. Thanks to everybody who worked so hard to make this happen! -Matt

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