Hi,
I am using arbitrary_grid to access my data without flattening:
> dd=ds.arbitrary_grid(left_edge=[(com_x_1.value-30), (com_y_1.value-30),
> (com_z_1.value-30)]*kpc, right_edge=[(com_x_1.value+30), (com_y_1.value+30),
> (com_z_1.value+30)]*kpc, dims=[128, 128, 128])
>
and as I attempt to get the gas fields for this object,
> print(dd['gas', 'velocity_x'])
>
but end up getting the error: (which isn't the case for deposit fields)
yt.units.unit_registry.UnitParseError: Code units have not been defined.
> Try creating the array or quantity using ds.arr or ds.quan instead.
If I'm thinking right, is it because the arbitrary_grid is only applied to
the particles. In that case, is there any equivalent for gas fields where I
can access my 3D data for the gas as it is , without flattening? Thanks
Hi Sam,
This is a new feature in yt 3.5.0, you’ll need to update to be able to use
it. Let us know if you have questions about updating.
Nathan
On Tue, Oct 30, 2018 at 2:16 PM Samantha M Benincasa <
smbenincasa(a)ucdavis.edu> wrote:
> Hi everyone,
>
> I'm trying to get a halo into a face-on and side-on configuration using
> sp.quantities.angular_momentum_vector.
>
> Is there a way to do this calculation with only the young stars in the
> sphere? The docs suggest that angular_momentum_vector will take a
> "particle_type" keyword, but my version does not recognize this.
>
> Cheers,
> Sam
> _______________________________________________
> yt-users mailing list -- yt-users(a)python.org
> To unsubscribe send an email to yt-users-leave(a)python.org
>
Hi everyone,
I'm trying to get a halo into a face-on and side-on configuration using sp.quantities.angular_momentum_vector.
Is there a way to do this calculation with only the young stars in the sphere? The docs suggest that angular_momentum_vector will take a "particle_type" keyword, but my version does not recognize this.
Cheers,
Sam
Hi,
I would like to access data from Ramses as it is because I would want to
divide my selected region into subgrids and compute some quantity locally
in those subgrids. However, using all_data or ds.r would rearrange the
information as it flattens the data (please correct me if I got this
wrong). Is there a way in yt, where I can simply access the 3D data as it
is and divide into subcubes? Thank you.
Hi yt-users,
We have an open postdoctoral research associate position in computational astrophysics/supernova theory at Michigan State. More info and application portal here: http://careers.msu.edu/cw/en-us/job/500160/research-associatefixed-term. Please pass along to anyone you think might be interested.
Apologies if you receive this announcement multiple times.
Thanks!
Sean
----------------------------------------------------------------------------------------------------------
Sean M. Couch, Ph.D.
Assistant Professor
Department of Physics and Astronomy
Department of Computational Mathematics, Science, and Engineering
National Superconducting Cyclotron Laboratory/Facility for Rare Isotope Beams
Michigan State University
567 Wilson Rd, 3250 BPS
East Lansing, MI 48824
(517) 884-5035 —— couch(a)pa.msu.edu<mailto:couch@pa.msu.edu> —— www.pa.msu.edu/~couch<http://www.pa.msu.edu/~couch>
Hi,
I am still a bit new to yt and am trying to compute local velocity
dispersions of the gas in my simulation by dividing the entire volume into
‘n’ number of sub grids. Does yt offer any functionality that could perform
this in a simple fashion? (I looked up the documentation and couldn’t find
any such function). Thank you.
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
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.
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
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)
