Mailman 3 October 2018 - yt-users

Error while deriving vorticity field
by Prateek Gupta 22 Oct '18

22 Oct '18

Dear yt-users I am creating a derived field for new_vorticity_magnitude. But when I take the SlicePlot of this derived field I am getting the following error: ############################################### slc = yt.SlicePlot(ds, 'z', "Vorticity_mag", center= center_old , width = (4,'Mpccm/h')) File "/home/dumbo/yt/yt/visualization/plot_window.py", line 1953, in SlicePlot return AxisAlignedSlicePlot(ds, normal, fields, *args, **kwargs) File "/home/dumbo/yt/yt/visualization/plot_window.py", line 1266, in __init__ slc.get_data(fields) File "/home/dumbo/yt/yt/data_objects/data_containers.py", line 1336, in get_data self._generate_fields(fields_to_generate) File "/home/dumbo/yt/yt/data_objects/data_containers.py", line 1356, in _generate_fields fd = self._generate_field(field) File "/home/dumbo/yt/yt/data_objects/data_containers.py", line 318, in _generate_field tr = self._generate_fluid_field(field) File "/home/dumbo/yt/yt/data_objects/data_containers.py", line 338, in _generate_fluid_field rv = finfo(gen_obj) File "/home/dumbo/yt/yt/fields/derived_field.py", line 237, in __call__ dd = self._function(self, data) File "vorticity_test.py", line 63, in _newvorticity_x_ f = (data[ftype, "zVel"][sl_center,sl_right,sl_center] - data[ftype, "zVel"][sl_center,sl_left,sl_center]) / (div_fac*just_one(data["index", "dy"])) File "/home/dumbo/yt/yt/units/yt_array.py", line 1036, in __getitem__ ret = super(YTArray, self).__getitem__(item) IndexError: too many indices for array ############################################### The current version of yt is: --- Version = 3.5.dev0 Changeset = 0cb7ad5befb2 --- Please let me know, why I am getting this error? Best regards -Prateek

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ISSUE WITH PLOTTING MULTIPLE PROFILES WITH RAMSES DATA
by Vadlamani Samhitha 17 Oct '18

17 Oct '18

Hi, I am trying to get multiple profile plots of density pdf for ramses data and I encounter the following error: > /home/samvad/yt-conda/lib/python3.6/site-packages/h5py/__init__.py:36: > FutureWarning: Conversion of the second argument of issubdtype from `float` > to `np.floating` is deprecated. In future, it will be treated as > `np.float64 == np.dtype(float).type`. > > from ._conv import register_converters as _register_converters > > Traceback (most recent call last): > > File "pdf.py", line 8, in <module> > > sim = yt.simulation(filename, "Ramses") > > File > "/home/samvad/yt-conda/lib/python3.6/site-packages/yt/convenience.py", line > 112, in simulation > > raise YTSimulationNotIdentified(simulation_type) > > yt.utilities.exceptions.YTSimulationNotIdentified: Simulation time-series > type ramses not defined. > and the following is the code I am using to do so: > import yt > > from yt.units import kpc > > import matplotlib.pyplot as plt > > import numpy as np > > import yt.visualization.eps_writer as eps > filename="/lunarc/nobackup/users/samvad/BIG-12-new/output/" > sim = yt.simulation(filename, "Ramses") > sim.get_time_series(time=[0,50,100,150,200,250,300]) > profiles=[] > labels=[] > plot_specs=[] > for ds in sim: > ds.define_unit("hpc", (1.674*10**(-24), "g/cm**3")) > ad=ds.all_data() > dens=ad.cut_region(["obj['density'].in_units('hpc') > 0"]) > profiles.append(yt.create_profile(dens, "density", "cell_mass", > weight_field=None, fractional=True)) > labels.append("time=%.2f" % ds.current_time.in_units('Myr')) > plot_specs.append(dict(linewidth=2, alpha=0.7)) > plot = yt.ProfilePlot.from_profiles(profiles, labels=labels, > plot_specs=plot_specs) > plot.set_log("density", True) > plot.set_unit("density", "hpc") > plot.set_xlim(1e-7, 1e7) plot.save(name='density-pdf', suffix='eps') could someone please let me know how I can get the simulation_type for ramses? Thanks.

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[ANN] yt_astro_analysis 1.0 released
by Britton Smith 16 Oct '18

16 Oct '18

Hi everyone, In conjunction with the release of yt 3.5, I'm pleased to announce the inaugural release of the yt_astro_analysis package. yt_astro_analysis is an extension of yt for astro-specific analysis modules. This functionality previously existed in yt as the analysis_modules submodule, but has been spun off into its own package to allow development to occur on its own timeline independent of yt. yt_astro_analysis includes: - halo finding - halo analysis - light cones - data export to RADMC-3D radiative transfer code Switching from yt's analysis_modules to yt_astro_analysis should be straightforward. yt_astro_analysis can be installed with pip: pip install yt_astro_analysis Then, analysis scripts need only change their import lines from "yt.analysis_modules" to "yt.extensions.astro_analysis". The yt_astro_analysis package lives at: https://github.com/yt-project/yt_astro_analysis Documentation can be found at: https://yt-astro-analysis.readthedocs.io Just like yt, contributions to yt_astro_analysis are highly encouraged! Regards, The yt development team yt_astro_analysis has received contributions from the following people: Pengfei Chen Bili Dong Hilary Egan Nathan Goldbaum Cameron Hummels Kacper Kowalik Matthew Krafczyk Michael Kuhlen Sam Leitner Joshua Moloney Christopher Moody Andrew Myers Mark Richardson Douglas Rudd Anthony Scopatz Sam Skillman Stephen Skory Britton Smith Geoffrey So Casey W. Stark Stephanie Tonnesen Matthew Turk Miguel de Val-Borro Mike Warren John Wise John ZuHone

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Homebrew clump finding script
by Sam Patrick 16 Oct '18

16 Oct '18

Hello yt users, I'm trying to write my own rudimentary clump finding script: the script will iteratively find the index of the densest unmasked cell, record its density and position and then update the mask as to exclude all cells within 0.2pc of this position. The script should then find the position and density of the next densest clump since all clumps with higher densities have already been masked. However, the densities in the output file aren't ordered from densest to least dense as expected, but instead the order appears completely random. Does anybody have any insight as to why this is? I've included the relevant part of my script below. I'm running Python version 2.7.12, numpy version 1.11.1 and am reading from an Enzo dataset. Any help is much appreciated. Many thanks, Sam Patrick import yt import sys import numpy as np from yt.units import Msun, Myr, pc from yt.units.yt_array import YTQuantity, YTArray yt.enable_parallelism() N_CLUMPS = 20 ds = yt.load("DD0306/output_0306") densities = [0 for i in range (N_CLUMPS)] val, loc = ds.find_max('density') sp = ds.sphere(loc, 4*pc) densities[0] = val far = sp['radius'] > 0.2*pc for i in range (1, N_CLUMPS, 1): max_index = np.argmax(sp['density'][far]) densities[i] = sp['density'][max_index] position = YTArray([sp[coord][max_index].in_units('pc').value for coord in ['x', 'y', 'z']], 'pc') distances = np.sqrt((sp['x'] - position[0])**2 + (sp['y'] - position[1])**2 + (sp['z'] - position[2])**2) far = far & (distances > 0.2*pc)

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[ANN] yt 3.5.0 released
by Nathan Goldbaum 16 Oct '18

16 Oct '18

The yt community is proud to announce the release of yt 3.5.0. This is a major release and includes a number of new features, minor improvements, and bugfixes. yt (http://yt-project.org) is an open source, community developed toolkit for the analysis and visualization of volumetric data. If you are interested in contributing to yt, please see our repository on GitHub at http://github.com/yt-project/yt. Since yt 3.4.0 we have merged 328 pull requests from 41 contributors, including 23 new contributors. We would like to thank the following people who made code contributions to this release, new contributors are marked with an asterisk. Ashley Kelly * Fabian Koller Enrico Garaldi * Hugo Pfister * Josh Borrow * Kiran Eiden * Matthew Turk Prateek Gupta * Ricarda Beckmann Kacper Kowalik Andrew Myers Brandon Qiao * Britton Smith Christoph Behrens * Cameron Hummels Claire Kopenhafer * Clayton Strawn * Corentin Cadiou David Pérez-Suárez * Donald E Willcox * Hilary Egan Abhishek Singh * Hsi-Yu Scive Suoqing Ji Josh Soref * John ZuHone Max Gronke * Max Katz Miguel de Val-Borro Marianne Corvellec * Madicken Munk * Clément Robert * Nick Gnedin * Nathan Goldbaum Philip Grete * Bili Dong Sean Larkin * Stephanie Ho * Yi-Hao Chen Ying-Chao Lu * Mike Zingale We would also like to thank everyone who has contributed in other ways, including by reporting issues, participating on the mailing list, and evangelizing yt to your colleagues. Additionally, we would like to highlight the contributions of Ashley Kelly and Abhishek Singh, who contributed to this release in the context of Google Summery of Code projects. Both students passed their evaluations and made substantial improvements to yt. You can find out more about Ashley and Abhishek's projects on the Summer of Code website: https://summerofcode.withgoogle.com/projects/#5101882905722880 https://summerofcode.withgoogle.com/projects/#5325639024902144 Finally, we would also like to highlight the contributions of Corentin Cadiou, who has made substantial performance and functionality improvements to the RAMSES frontend as part of this release. Corentin's contributions came in on a volunteer basis and we thank him for his tireless efforts to improve yt's support for RAMSES data. Binaries for yt 3.5.0 are available via pip and conda. If you installed via the install script or use conda to manage your python installation, you can update yt via: $ conda update -c conda-forge yt Conda-forge package updates may not be up quite yet but should be uploaded by the end of the day. And if you manage your python installation with pip: $ pip install -U yt As always, if you have any questions, concerns, or run into any trouble updating please don’t hesitate to send a message to the mailing list or stop by our Slack or IRC channel. yt is the product of a large community of developers and users and we are extraordinarily grateful for and proud of their contributions. Please forward this announcement on to any interested parties. Best, The yt development team What's new? ----------- Please see the release notes for full details (http://yt-project.org/docs/3.5.0/reference/changelog.html#version-3-5-0), but we highlight a few major changes and improvements here. * yt.analysis_modules has been deprecated in favor of the new yt_astro_analysis package. New features and new astronomy-specific analysis modules will go into yt_astro_analysis and importing from yt.analysis_modules will raise a noisy warning. We will remove yt.analysis_modules in a future release * Vector fields and derived fields depending on vector fields have been systematically updated to account for a bulk correction field parameter. For example, for the velocity field, all derived fields that depend on velocity will now account for the "bulk_velocity" field parameter. In addition, we have defined "relative_velocity" and "relative_magnetic_field" fields that include the bulk correction. Both of these are vector fields, to access the components, use e.g. "relative_velocity_x". The "particle_position_relative" and "particle_velocity_relative" fields have been deprecated. * The RAMSES frontend has seen considerable improvements, with support for the new RAMSES self-describing particle output format, considerable speed improvements for I/O and initially loading data, and support for restricting the domain to a subset of the full simulation volume for imrpoved speed and reduced memory usage for large datasets. * Aliases to spatial fields with the "gas" field type will now be returned in the default unit system for the dataset. As an example the "x" field might resolve to the field tuples ("index", "x") or ("gas", "x"). Accessing the former will return data in code units while the latter will return data in whatever unit system the dataset is configured to use (CGS, by default). This means that to ensure the units of a spatial field will always be consistent, one must access the field as a tuple, explicitly specifying the field type. Accessing a spatial field using a string field name may return data in either code units or the dataset’s default unit system depending on the history of field accesses prior to accessing that field. In the future accessing fields using an ambiguous field name will raise an error. * The max_level and min_level attributes of yt data objects now correctly update the state of the underlying data objects when set. In addition we have added an example to the cookbook that shows how to downsample AMR data using this functionality. * It is now possible to customize the formatting of labels for ion species fields. Rather than using the default spectroscopic notation, one can call ds.set_field_label_format("ionization_label", "plus_minus") to use the more traditional notation where ionization state is indicated with + and - symbols.

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PARTICLE SPAWNING AND INDICES
by Vadlamani Samhitha 11 Oct '18

11 Oct '18

Hi, I have a query regarding how the newly formed particles are assigned the identifiers (particle_identifier). Say, I have particles from 1 to 10 initially, when the new particles are formed, are they assigned ids in between 1-10 changing all the existing particles’ ids or are they assigned ids starting from 11?

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How to adjust the number of ticks (tick labels) on slice plots
by Yuan Li 10 Oct '18

10 Oct '18

Hello yt users, Is there an easy way to change the number of ticks (or just tick labels) on a slice plot? When I increase the font size, the number of ticks does not change, and now the labels on my x axis are overlapping. I know I can save everything to fixed resolution buffer and make the plots outside of yt, but would like to be able to do it in the easier way :). Thank you! Yuan

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computing mass flux through a plane
by Yuan Li 10 Oct '18

10 Oct '18

Dear yt users, I am trying to compute the mass flux through a plane (parallel to the yz plane in my simulation). This is my script: surf_x=ds.surface(box,"x",(0.5,"code_length")) flux_x=surf_x.calculate_flux("x-velocity","y-velocity","z-velocity","density") Maybe I made an obvious mistake, but here is the error message: File "yt/utilities/lib/marching_cubes.pyx", line 351, in yt.utilities.lib.marching_cubes.march_cubes_grid_flux ZeroDivisionError: float division by zero I can compute flux through a sphere just fine. Does this method only work for a closed surface? Right now because this surface method does not work, I am using a rather clumsy way of calculating flux along my x-axis. I first get the cell-area weighted rho*v (defined field) profile along x. Then I multiply this rho*v by the total area. I think I am getting the right flux, but would like to verify using another method (the surface flux thing). Thank you! Yuan

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How to make the new drived-fields work?
by lianxiaoli87＠sjtu.edu.cn 10 Oct '18

10 Oct '18

Hi, I have written some new fields in yt/yt/fileds file named as XXX.py, but I have no idea how to make it work correctly like kinetic-energy, thermal-energy etc which can be projected and sliced. Please help me to work it out!!!

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ISSUE WITH CUT REGION PROJECTION PLOT
by Vadlamani Samhitha 09 Oct '18

09 Oct '18

Hi, I am trying to make a projection plot of a cut region with the following script: > import yt > > from yt.units import kpc > > import matplotlib.pyplot as plt > > import numpy as np > > import yt.visualization.eps_writer as eps > > > filename= > "/lunarc/nobackup/users/samvad/BIG-12-new/output/output_00044/info_00044.txt" > > ds = yt.load(filename) > > ds.define_unit("hpc", (1.674*10**(-24), "g/cm**3")) > > ad=ds.all_data() > > dens=ad.cut_region(["obj['density'].in_units('hpc') > 600"]) > > a=dens.quantities.extrema("density").in_units('hpc') > plot = yt.ProjectionPlot(ds, 'z', "density", weight_field="density", > data_source=dens) > plot.set_unit("density", "hpc") > plot.set_zlim("density", 1e-7, 1e8) plot.save("density.png") I find that my cut region is not empty and has some values, however, when I look at the plot, it is empty. I get the following error in the output: > /home/samvad/yt-conda/lib/python3.6/site-packages/yt/visualization/plot_window.py:785: > RuntimeWarning: All-NaN axis encountered > > elif np.nanmax(image) == np.nanmin(image): > > /home/samvad/yt-conda/lib/python3.6/site-packages/yt/visualization/plot_window.py:788: > RuntimeWarning: All-NaN axis encountered > > elif np.nanmax(image) <= 0: > > yt : [WARNING ] 2018-10-09 17:56:15,001 Plot image for field ('gas', > 'density') is filled with NaNs. > > yt : [WARNING ] 2018-10-09 17:56:15,001 Switching to linear colorbar > scaling. > > yt : [INFO ] 2018-10-09 17:56:15,513 Saving plot density.png >

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