Mailman 3 March 2019 - yt-users

2019 John Hunter Excellence in Plotting Contest
by Madicken Munk 18 Mar '19

18 Mar '19

In memory of John Hunter, we are pleased to be reviving the SciPy John Hunter Excellence in Plotting Competition for 2019. This open competition aims to highlight the importance of data visualization to scientific progress and showcase the capabilities of open source software. Participants are invited to submit scientific plots to be judged by a panel. The winning entries will be announced and displayed at the conference. John Hunter’s family and NumFocus are graciously sponsoring cash prizes for the winners in the following amounts: - 1st prize: $1000 - 2nd prize: $750 - 3rd prize: $500 - Entries must be submitted by June, 8th to the form at https://goo.gl/forms/cFTB3FUBrMPfQ7Vz1 - Winners will be announced at Scipy 2019 in Austin, TX. - Participants do not need to attend the Scipy conference. - Entries may take the definition of “visualization” rather broadly. Entries may be, for example, a traditional printed plot, an interactive visualization for the web, or an animation. - Source code for the plot must be provided, in the form of Python code and/or a Jupyter notebook, along with a rendering of the plot in a widely used format. This may be, for example, PDF for print, standalone HTML and Javascript for an interactive plot, or MPEG-4 for a video. If the original data can not be shared for reasons of size or licensing, "fake" data may be substituted, along with an image of the plot using real data. - Each entry must include a 300-500 word abstract describing the plot and its importance for a general scientific audience. - Entries will be judged on their clarity, innovation and aesthetics, but most importantly for their effectiveness in communicating a real-world problem. Entrants are encouraged to submit plots that were used during the course of research or work, rather than merely being hypothetical. - SciPy reserves the right to display any and all entries, whether prize-winning or not, at the conference, use in any materials or on its website, with attribution to the original author(s). SciPy John Hunter Excellence in Plotting Competition Co-Chairs Hannah Aizenman Thomas Caswell Madicken Munk Nelle Varoquaux

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yt streamlines and periodic boundaries
by Yann Pfau-Kempf 18 Mar '19

18 Mar '19

Hello, I am a new user of yt and I am specifically trying to use its streamline capabilities. My script essentially does the same as the example below, i.e. I load some data from numpy arrays and trace streamlines from a set of seeds. It turns out that despite being set to being fully periodic (the only option, otherwise yt tries to access data outside of the box) the streamlines stop at the box edges. Is there a trick to get actually periodic streamlines? Or should I work around, replicate my array N times in each direction and fold the coordinates to the original range afterwards by hand? Thanks in advance for your help, Best regards, Yann Pfau-Kempf University of Helsinki ------------------------------------------------------------------------ streamlength=30 Bx=np.ones((10,10,10)) By=np.ones((10,10,10)) Bz=np.ones((10,10,10)) data=dict(Bx=Bx,By=By,Bz=Bz) streamline_seeds = np.array([0,0,0]) yt_dataset = yt.frontends.stream.load_uniform_grid( data, [10, 10, 10], bbox=np.array([[0,10], [0, 10], [0, 10]]), periodicity=[True, True, True]) streamlines_pos = yt.visualization.api.Streamlines(yt_dataset, streamline_seeds, "Bx", "By", "Bz", length=streamlength, direction=1) streamlines_neg = yt.visualization.api.Streamlines(yt_dataset, streamline_seeds, "Bx", "By", "Bz", length=streamlength, direction=-1) print streamlines_pos.streamlines[0], streamlines_neg.streamlines[0] Output: [[ 0. 0. 0. ] [ 0.55872607 0.55872607 0.55872607] [ 1.11745213 1.11745213 1.11745213] [ 1.6761782 1.6761782 1.6761782 ] [ 2.23490427 2.23490427 2.23490427] [ 2.79363033 2.79363033 2.79363033] [ 3.3523564 3.3523564 3.3523564 ] [ 3.91108247 3.91108247 3.91108247] [ 4.46980854 4.46980854 4.46980854] [ 5.0285346 5.0285346 5.0285346 ] [ 5.58726067 5.58726067 5.58726067] [ 6.14598674 6.14598674 6.14598674] [ 6.7047128 6.7047128 6.7047128 ] [ 7.26343887 7.26343887 7.26343887] [ 7.82216494 7.82216494 7.82216494] [ 8.380891 8.380891 8.380891 ] [ 8.93961707 8.93961707 8.93961707] [ 9.49834314 9.49834314 9.49834314] [10.05706921 10.05706921 10.05706921] [ 0. 0. 0. ] [ 0. 0. 0. ] [ 0. 0. 0. ] [ 0. 0. 0. ] [ 0. 0. 0. ] [ 0. 0. 0. ] [ 0. 0. 0. ] [ 0. 0. 0. ] [ 0. 0. 0. ] [ 0. 0. 0. ] [ 0. 0. 0. ] [ 0. 0. 0. ]] code_length [[ 0. 0. 0. ]

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Creating a 3D image of a cosmological box
by Andrew Cook 15 Mar '19

15 Mar '19

Hello, This is a slightly big ask but I'm trying to get an image of one of my simulations with the full box in view with all halos visible. I'm not sure if something needs to be specifically set up with Enzo or MUSIC but hopefully this is just a yt thing. Many thanks Andy

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Proper way to set units when using Scene() and VolumeSource()
by Steiner, Andrew (Andrew W. Steiner) 14 Mar '19

14 Mar '19

Hello all, I'm having trouble figuring out how to set the units with a generic data set. See the code below. It complains that: Traceback (most recent call last): File "post2.py", line 36, in <module> cam.width=1.5*ds.domain_width[0] ... more traceback info ... File "/usr/lib/python3/dist-packages/yt/units/unit_object.py", line 664, in _lookup_unit_symbol "Code units have not been defined. \n" yt.units.unit_registry.UnitParseError: Code units have not been defined. Try creating the array or quantity using ds.arr or ds.quan instead. Code for the simplified test case is given below. I'm just trying to find the simple addition which fixes my units to something so it will proceed with the rendering. Thank you, Andrew -- Andrew W. Steiner Joint Faculty Asst. Prof. at UTK/ORNL https://neutronstars.utk.edu/ --------------------------------------- import yt import numpy as np import h5py import math from yt.visualization.volume_rendering.api import Scene, \ VolumeSource, LineSource, PointSource from yt.visualization.volume_rendering.transfer_function_helper \ import TransferFunctionHelper # Generate the data, a sum of two Gaussians centered at (0.4,0.3,0.2) # and (0.4,0.3,0.8). grid=128 arr=np.zeros(shape=(grid,grid,grid)) width=0.3 for i in range(0,grid): x=i/127 for j in range(0,grid): y=j/127 for k in range(0,grid): z=k/127 arr[i][j][k]=(math.exp(-(x-0.4)**2/width**2-(y-0.3)**2/width**2- (z-0.2)**2/width**2)+ math.exp(-(x-0.4)**2/width**2-(y-0.3)**2/width**2- (z-0.8)**2/width**2)) # Load the data into yt ds=yt.load_uniform_grid(dict(density=arr),arr.shape,nprocs=4, bbox=np.array([[0.0,1.0],[0.0,1.0],[0.0,1.0]])) # Create the scene sc=Scene() # Create the camera cam=sc.add_camera() cam.width=1.5*ds.domain_width[0] cam.position=[1.5,0.6,0.7] cam.focus=ds.domain_center cam.north_vector=[0.0,0.0,1.0] cam.switch_orientation() # Create the volume source vol=VolumeSource(ds,field='density') # Setup the transfer function tfh=TransferFunctionHelper(ds) tfh.set_field('density') tfh.set_log(False) tfh.set_bounds() tfh.build_transfer_function() tfh.tf.add_layers(5) tfh.plot('tf.png') vol.set_transfer_function(tfh.tf) # Add the volume source to the scene sc.add_source(vol) sc.save('post2.png',sigma_clip=1.0)

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Metallicity phase and slice plot
by up765045＠myport.ac.uk 13 Mar '19

13 Mar '19

Hi, I am unsure if this is a yt issue or an enzo issue but I am running into an issue following a supernova explosion where metallicity decreases relative to its surrounding area, where you'd instead expect an increase instead from the supernova ejecta. On both slice and phase plots created using yt of metallicity, for example, the centre of the expansion is displaying as 10^(-10) Z_solar, compared to 10^(-8) Z_solar in the outer regions of the expansion where a lower metallicity would be expected. A phase plot shows the same thing, where regions not yet affected by the supernova ejecta have a higher metallicty than the centre of the supernova itself. It is highly likely that I have made an error somewhere but I am unsure what, looking at the documentation here. I am mostly sure that it is a mistake that I have made with yt rather than enzo, and would appreciate any help with trying to correctly create these plots. Thanks Chris

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[Load] load a generic data in 3D spherical coordinate
by astrodoo＠gmail.com 08 Mar '19

08 Mar '19

Dear all, I have been in trouble for reading a generic data in 3D spherical coordinate, in which the radial grid is logarithmic (i.e. non-uniform). I first tried with the function: load_uniform_grid with the option "spherical" ##### data = dict(density = (ds.rho, "g/cm**3")) bbox = np.array([[1., 20.], [0., np.pi], [0., 2*np.pi]]) ds2 = yt.load_uniform_grid(data, ds.rho.shape, bbox=bbox, geometry="spherical") ##### this could read the data, but the grid information was not correct, because the r-coordinate in my data was in logarithmic. Then I also was thinking that transform the coordinates of each cell to Cartesian coordinates and use the function: load_hexahedral_mesh(). However, it also not worked as the coordinate in my data was irregular for every direction. I though that I might need to use the function: load_unstructured_mesh() for this case. I have been trying to figure out how to use it, but it was difficult for me to understand how to set up the coordinates and connectivity. It would be wonderful if you help me to clear this out. Thanks very much, Best wishes, Doosoo

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clump finding density threshold problem
by Stephanie Tonnesen 05 Mar '19

05 Mar '19

Hi all, I am trying to find clumps in a few wind-tunnel runs of a galaxy with ram pressure stripping, so that I can measure the mass, volume, etc. I want to make sure, then, that I know how clump finder is selecting its contours. Unfortunately, I am not getting the minimum densities that I am expecting--although not all the time! I have used IsolatedGalaxy and run the same clump finder routine, but with different maximum densities for the contours. To reproduce my error I had to make the step size smaller--here I am using step = 1.1. Basically, I am not always stepping up my minimum density contour, even when the maximum density in the clump makes it clear I *should* be. I am attaching the script where I have cmax = 9.0e-24, which shows the error. You can see that in the figure I attach, looking at the green triangles. The y-axis is the maximum density in a clump, the x-axis is the minimum density. To guide your eye I have drawn lines at each step. Basically, no point should be above a black line and to the left of the connected vertical line (because then the max value is above the next possible minimum in the steps) *unless* the cmax value was reached. To make this really clear if you look at the upper-left green triangle, the max density is ~7e-24 while the minimum density is ~3.6e-24--there are a lot of steps between those values--why is the minimum density not 6.68e-24? Strangely enough, this problem does *not* show up when I have cmax = 6.7e-24 (cyan circles). I write out the max and min values in a text file at the end of the script, so I also attach the little script I use to make the figure. Any help would be *much* appreciated! Thank you all, Stephanie -- Dr. Stephanie Tonnesen Associate Research Scientist CCA, Flatiron Institute New York, NY stonnes(a)gmail.com

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Quadruple Precision Enzo
by Al Zamora 04 Mar '19

04 Mar '19

Hi, I’m trying to access particle data in a dump from ENZO (compiled with particles-128), e.g. the particle_position_x field, but keep getting an error saying that the float128 type is not valid. Do I have to do something special to access float128’s? Is this a (known) bug? Is there a workaround? Best Al

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