Hello,
I'm trying to create a radial profile of the gas density using the
create_profile function. Even with the extrema explicitly specified the
binning does not take these values. I was wondering whether there was a
simple way to work around this? My script and output are bellow:
ds = yt.load("DD0077/output_0077")
val, loc = ds.find_max('density')
sp = ds.sphere(loc, (10.0, 'pc'))
rp = yt.create_profile(sp, 'radius', ('gas', 'density'), n_bins= 10,
extrema= {'radius':((1e-2, 'pc'), (10.0, 'pc'))}, logs= {'radius':True})
rp.x.in_units('pc')
>>>YTArray([0.01497631, 0.02988167, 0.05962177, 0.11896107, 0.23735854,
0.47359256, 0.94494138, 1.88540592, 3.76187938,
7.50593617]) pc
(where 0.01 and 10 are expected to be the first and last values)
Many thanks,
Sam Patrick
Hi all,
Recently I have seen this error more often:
>> raise RuntimeError("YTQuantity values must be numeric”)
I have been able to solve most of the problems which were related with units (some packages behave differently if I do units*array or array*unit).
However, I’m getting an error when I’m trying to project a field, and I don’t know how to solve that
Code:
.
.
def _Disk_H(field, data):
center = data.get_field_parameter('center')
z = data["z"] - center[2]
return np.abs(z)
yt.add_field("Disk_H",
function=_Disk_H,
units="pc",
take_log=False,
validators=[ValidateParameter('center')])
.
.
.
disk_dd = dd.cut_region(["obj['Disk_H'].in_units('pc') < 1.0e3"])
proj = ds.proj('density', 2,data_source=disk_dd,weight_field='density’)
Saludos!
José Utreras
Error:
proj = ds.proj('density', 2,data_source=disk_dd,weight_field='density')
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/construction_data_containers.py", line 262, in __init__
self.get_data(field)
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/construction_data_containers.py", line 336, in get_data
self._handle_chunk(chunk, fields, tree)
File "/home/jutreras/miniconda3/lib/python3.5/contextlib.py", line 77, in __exit__
self.gen.throw(type, value, traceback)
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/data_containers.py", line 960, in _field_parameter_state
yield
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/construction_data_containers.py", line 330, in get_data
[], "io", local_only = True)):
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/utilities/parallel_tools/parallel_analysis_interface.py", line 508, in parallel_objects
for result_id, obj in oiter:
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/selection_data_containers.py", line 800, in chunks
yield self
File "/home/jutreras/miniconda3/lib/python3.5/contextlib.py", line 77, in __exit__
self.gen.throw(type, value, traceback)
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/data_containers.py", line 1265, in _chunked_read
yield
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/selection_data_containers.py", line 800, in chunks
yield self
File "/home/jutreras/miniconda3/lib/python3.5/contextlib.py", line 77, in __exit__
self.gen.throw(type, value, traceback)
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/data_containers.py", line 1265, in _chunked_read
yield
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/selection_data_containers.py", line 799, in chunks
self.get_data(fields)
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/selection_data_containers.py", line 805, in get_data
ind = self._cond_ind
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/selection_data_containers.py", line 837, in _cond_ind
np.logical_and(res, ind, ind)
File "/home/jutreras/miniconda3/lib/python3.5/contextlib.py", line 77, in __exit__
self.gen.throw(type, value, traceback)
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/data_containers.py", line 960, in _field_parameter_state
yield
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/data_objects/selection_data_containers.py", line 833, in _cond_ind
res = eval(cond)
File "<string>", line 1, in <module>
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/units/yt_array.py", line 1094, in __lt__
return super(YTArray, self).__lt__(oth)
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/units/yt_array.py", line 1215, in __array_wrap__
any_nonzero = [np.any(oper1), np.any(oper2)]
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/numpy/core/fromnumeric.py", line 2164, in any
return _wrapreduction(a, np.logical_or, 'any', axis, None, out, keepdims=keepdims)
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/numpy/core/fromnumeric.py", line 84, in _wrapreduction
return reduction(axis=axis, out=out, **passkwargs)
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/numpy/core/_methods.py", line 43, in _any
return umr_any(a, axis, dtype, out, keepdims)
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/units/yt_array.py", line 1175, in __array_wrap__
return ret[()]
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/units/yt_array.py", line 1165, in __getitem__
return YTQuantity(ret, self.units, bypass_validation=True)
File "/home/jutreras/miniconda3/lib/python3.5/site-packages/yt/units/yt_array.py", line 1353, in __new__
raise RuntimeError("YTQuantity values must be numeric")
RuntimeError: YTQuantity values must be numeric
Hi all,
I'm trying to write a simple piece of code which produces a phase plot
of the entire dataset. I'm currently using yt version 3.5.0 and Python
version 3.7.1. Essentially what I have is:
val, loc = ds.find_max('density')
ad = ds.all_data()
plot = yt.PhasePlot(ad, "density", "temperature", "cell_mass",
weight_field=None)
plot.set_unit('cell_mass', 'Msun')
filename = "phase_plot_z={:.2f}.png".format(ds.current_redshift)
plot.save(filename)
However when I try running the code I get the following error:
Traceback (most recent call last):
File "phase_plot.py", line 14, in <module>
plot = yt.PhasePlot(ad, "density", "temperature", "cell_mass",
weight_field=None)
File
"/opt/apps/yt/jan19/lib/python3.7/site-packages/yt/visualization/profile_plotter.py",
line 931, in __init__
fractional=fractional)
File
"/opt/apps/yt/jan19/lib/python3.7/site-packages/yt/data_objects/profiles.py",
line 1207, in create_profile
obj.add_fields([field for field in fields])
File
"/opt/apps/yt/jan19/lib/python3.7/site-packages/yt/data_objects/profiles.py",
line 129, in add_fields
self._bin_chunk(chunk, fields, temp_storage)
File
"/opt/apps/yt/jan19/lib/python3.7/site-packages/yt/data_objects/profiles.py",
line 627, in _bin_chunk
bin_ind_y = np.digitize(bf_y, self.y_bins) - 1
ValueError: bins must be monotonically increasing or decreasing
I'm not entirely sure what's happening since there's no part of the code
which is interfacing with binning directly. Any light shed on this issue
would be much appreciated.
Many thanks,
Sam Patrick
Hello,
I have recently subscribed to the yt mailing list. However, I am having
problems signing into my account. Below are my account details:
E-mail address: damle(a)uni-potsdam.de
Username: Mitali
I would be very helpful if you could kindly look into this matter.
Regards,
Mitali Damle,
MSc Astrophysics,
University of Potsdam
Hello,
I have an enzo cosmology simulation which consists only baryonic matter and no dark matter and I'm trying to find halos on this baryonic field. but the HaloCatalog requests particle_mass and probably all other particle fields. is there any way to run HaloCatalog on baryonic fields without defining particle fields?
if not, is there any well-defined way to turn gas fields in particle fields in yt?
Thanks.
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
Hi everybody, There are only a few days left to submit to the 2019 John
Hunter Excellence in Plotting Contest! If you're interested in
participating, note that you have until June 8th to prepare your
submission.
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
hi all
i am trying to plot the abundances of the species from the same dataset in the same panel.
isn't this script supposed to do that? https://justpaste.it/4x69h
thanks
cynthia
Hi, I couldn't find such a thread anywhere and I hope you are able to help me... I'm trying to visualize some data from Exodus II format using LinePlot.from_lines but I have no clue how to combine two different datasets in order to put everything into one plot. Basically I load one output file to one dataset and the second to another one, then I add lines using lines.append. How to plot things from two datasets in one graph?
My code is:
import yt
import numpy as np
from yt.units import K
home = "/home/peppe"
def _units(field, data):
return data[('all', 'temp')] * K
ds = yt.load(
home + '/projects/2out.e', step=-1)
ds.add_field(('all', 'temp_K'), units='K', function=_units, take_log=False)
es = yt.load(
home + '/projects/4out.e', step=-1)
es.add_field(('all', 'temp_K'), units='K', function=_units, take_log=False)
lines = []
lines.append(yt.LineBuffer(
es, (0, 0, 0), (0, 162.56, 0), 1000, label='fuel 4g'))
lines.append(yt.LineBuffer(es, (3, 0, 0), (3, 162.56, 0),
1000, label='mod 4g'))
lines.append(yt.LineBuffer(
ds, (0, 0, 0), (0, 162.56, 0), 1000, label='fuel 2g'))
lines.append(yt.LineBuffer(ds, (3, 0, 0), (3, 162.56, 0),
1000, label='mod 2g'))
plt = yt.LinePlot.from_lines(es, [('all', 'temp_K')], lines, field_labels={
('all', 'temp_K'): ''})
plt = yt.LinePlot.from_lines(ds, [('all', 'temp_K')], lines, field_labels={
('all', 'temp_K'): ''})
plt._setup_plots()
plt.annotate_legend(('all', 'temp_K'))
plt.set_x_unit('cm')
plt.set_xlabel('z (cm)')
plt.set_ylabel('Temperature (K)')
plt.save(home + '/projects/plot.jpg')
Dear yt-users,
I am using this script <http://paste.yt-project.org/show/135/> to run
Rockstar and it has worked great for me for years. Here is the situation
now: I ran rockstar a few weeks ago and since then have several additional
enzo data dump outputs I would like to add to my merger trees (done with
consistent-trees). To meet data quota requirements, I needed to move
several of my outputs to storage (already got the rockstar outputs on
these), which I believe is causing my rockstar restart to fail (see error
here <https://pastebin.com/ABMx3wzZ>).
In the script mentioned above, I tried removing find_outputs=True on line
51 and adding the keyword initial_redshift=11.749 to get_time_series() on
line 59. I keep getting the same error. I have also updated datasets.txt as
well as rockstar.cfg and restart.cfg to the desired RESTART_SNAP and
NUM_SNAPS.
What am I missing? Is there a way to run a restart on Rockstar with some of
the previous files (already processed by rockstar) missing? Thanks in
advance.
Kind regards,
--
Carla Bernhardt
PhD Student
Universität Heidelberg
ZAH Institut für Theoretische Astrophysik
