[yt-users] cloud in cell mesh construction for particle data
Matthew Turk
matthewturk at gmail.com
Fri Jun 6 16:26:52 PDT 2014
On Jun 6, 2014 4:54 PM, "Brendan Griffen" <brendan.f.griffen at gmail.com>
wrote:
>
> OK great. It is very low resolution but it worked. Thanks for all your
help. My higher resolution run 1024^3 in 100 Mpc seems to crash on 128GB
memory machine. I might have to look elsewhere.
>
If you are looking for low resolution extraction you can tune the memory
usage by changing the parameter n_ref to something higher.
Supporting extremely large datasets in a single mesh is on the roadmap for
the late summer or fall, after a 3.0 release goes out. For now you can also
extract before you load in; this is sort of how we are supporting an INCITE
project with very large particle counts.
> Also, I normally use Canopy distribution but I just use an alias to
loadyt which erases my PYTHONPATH and I can't access scipy and a few other
libraries any more. What is the best practice here? Should I just manually
export PYTHONPATH and point to the libraries need in canopy or can they
play nice together?
>
> Thanks.
>
> BG
>
>
> On Fri, Jun 6, 2014 at 2:54 PM, Nathan Goldbaum <nathan12343 at gmail.com>
wrote:
>>
>>
>>
>>
>> On Fri, Jun 6, 2014 at 11:48 AM, Brendan Griffen <
brendan.f.griffen at gmail.com> wrote:
>>>
>>> OK great. Thanks. I just wanted a homogeneous mesh. 512^3 with no
nesting of any kind. Though when I plot the image it looks like it is
assigning particles incorrectly (low resolution on the outside). This is
just a test image.
>>>
>>
>> The SlicePlot is visualizing the octree so there is less resolution
where there are fewer particles. If you want to visualize the covering grid
you're going to need to visualize that separately.
>>
>>>
>>> ds = yt.load_particles(data, length_unit=3.08e24,
mass_unit=1.9891e33,bbox=bbox)
>>>
>>> ad = ds.all_data()
>>> print ad['deposit', 'all_cic']
>>> slc = yt.SlicePlot(ds, 2, ('deposit', 'all_cic'))
>>> slc.set_figure_size(4)
>>> cg = ds.covering_grid(level=9, left_edge=[0,0,0],dims=[512,512,512])
>>>
>>
>> To actually produce the uniform resolution ndarray, you're going to need
to do something like:
>>
>> array = cg[('deposit', 'all_cic')]
>>
>> array will then be a 3D array you can do whatever you want with. By
default it has units, but to strip them off you'll just need to cast to
ndarray:
>>
>> array_without_units = array.v
>>
>>
>>>
>>> Also, is there a way to load multiple particle types?
>>>
>>> Do I just need to stack the particles into the array here?
>>>
>>> data = {'particle_position_x': pos[:,0],
>>> 'particle_position_y': pos[:,1],
>>> 'particle_position_z': pos[:,2],
>>> 'particle_mass': np.array([mpart]*npart)}
>>>
>>> Then feed it in as usual?
>>
>>
>> That's right, although if the particle masses are different for the
different particle types that code snippet will need to be generalized to
handle that.
>>
>> I think in principle it should be possible to make load_particles handle
different particle types just like an SPH dataset that contains multiple
particle types, but right now that hasn't been implemented yet.
>>
>>>
>>>
>>> Brendan
>>>
>>>
>>> On Thu, Jun 5, 2014 at 9:44 PM, Nathan Goldbaum <nathan12343 at gmail.com>
wrote:
>>>>
>>>> That's right, you can set that via the bbox keyword parameter for
load_particles. I'd urge you to take a look at the docstrings and source
code for load_particles.
>>>>
>>>>
>>>> On Thu, Jun 5, 2014 at 6:34 PM, Brendan Griffen <
brendan.f.griffen at gmail.com> wrote:
>>>>>
>>>>> Thanks very much Nathan. I tried to load in my own data but I think
there are too many particles or I have to specifically set the domain size.
>>>>>
>>>>> In this area:
>>>>>
>>>>> data = {'particle_position_x': pos[:,0],
>>>>> 'particle_position_y': pos[:,1],
>>>>> 'particle_position_z': pos[:,2],
>>>>> 'particle_mass': np.array([mpart]*npart)}
>>>>>
>>>>> ds = yt.load_particles(data, length_unit=3.08e24, mass_unit=1.9891e36)
>>>>> ad = ds.all_data()
>>>>> print ad['deposit', 'all_cic']
>>>>>
>>>>> In [3]: run ytcic.py
>>>>> yt : [INFO ] 2014-06-05 21:29:06,183 Parameters: current_time
= 0.0
>>>>> yt : [INFO ] 2014-06-05 21:29:06,183 Parameters:
domain_dimensions = [2 2 2]
>>>>> yt : [INFO ] 2014-06-05 21:29:06,184 Parameters: domain_left_edge
= [ 0. 0. 0.]
>>>>> yt : [INFO ] 2014-06-05 21:29:06,185 Parameters:
domain_right_edge = [ 1. 1. 1.]
>>>>> yt : [INFO ] 2014-06-05 21:29:06,185 Parameters:
cosmological_simulation = 0.0
>>>>> yt : [INFO ] 2014-06-05 21:29:06,188 Allocating for 1.342e+08
particles
>>>>>
---------------------------------------------------------------------------
>>>>> YTDomainOverflow Traceback (most recent call
last)
>>>>>
/nfs/blank/h4231/bgriffen/data/lib/yt-x86_64/lib/python2.7/site-packages/IPython/utils/py3compat.pyc
in execfile(fname, *where)
>>>>> 202 else:
>>>>> 203 filename = fname
>>>>> --> 204 __builtin__.execfile(filename, *where)
>>>>>
>>>>>
/nfs/blank/h4231/bgriffen/work/projects/caterpillar/c2ray/cic/ytcic.py in
<module>()
>>>>> 52
>>>>> 53 ad = ds.all_data()
>>>>> ---> 54 print ad['deposit', 'all_cic']
>>>>> 55 slc = yt.SlicePlot(ds, 2, ('deposit', 'all_cic'))
>>>>> 56 slc.set_figure_size(4)
>>>>>
>>>>>
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in __getitem__(self, key)
>>>>> 205 Returns a single field. Will add if necessary.
>>>>> 206 """
>>>>> --> 207 f = self._determine_fields([key])[0]
>>>>> 208 if f not in self.field_data and key not in
self.field_data:
>>>>> 209 if f in self._container_fields:
>>>>>
>>>>>
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in _determine_fields(self, fields)
>>>>> 453 raise YTFieldNotParseable(field)
>>>>> 454 ftype, fname = field
>>>>> --> 455 finfo = self.pf._get_field_info(ftype, fname)
>>>>> 456 else:
>>>>> 457 fname = field
>>>>>
>>>>>
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/static_output.pyc
in _get_field_info(self, ftype, fname)
>>>>> 445 _last_finfo = None
>>>>> 446 def _get_field_info(self, ftype, fname = None):
>>>>> --> 447 self.index
>>>>> 448 if fname is None:
>>>>> 449 ftype, fname = "unknown", ftype
>>>>>
>>>>>
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/static_output.pyc
in index(self)
>>>>> 277 raise RuntimeError("You should not
instantiate Dataset.")
>>>>> 278 self._instantiated_index = self._index_class(
>>>>> --> 279 self, dataset_type=self.dataset_type)
>>>>> 280 # Now we do things that we need an instantiated
index for
>>>>> 281 # ...first off, we create our field_info now.
>>>>>
>>>>>
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/frontends/stream/data_structures.pyc
in __init__(self, pf, dataset_type)
>>>>> 942 def __init__(self, pf, dataset_type = None):
>>>>> 943 self.stream_handler = pf.stream_handler
>>>>> --> 944 super(StreamParticleIndex, self).__init__(pf,
dataset_type)
>>>>> 945
>>>>> 946 def _setup_data_io(self):
>>>>>
>>>>>
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/geometry/particle_geometry_handler.pyc
in __init__(self, pf, dataset_type)
>>>>> 48 self.directory = os.path.dirname(self.index_filename)
>>>>> 49 self.float_type = np.float64
>>>>> ---> 50 super(ParticleIndex, self).__init__(pf, dataset_type)
>>>>> 51
>>>>> 52 def _setup_geometry(self):
>>>>>
>>>>>
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/geometry/geometry_handler.pyc
in __init__(self, pf, dataset_type)
>>>>> 54
>>>>> 55 mylog.debug("Setting up domain geometry.")
>>>>> ---> 56 self._setup_geometry()
>>>>> 57
>>>>> 58 mylog.debug("Initializing data grid data IO")
>>>>>
>>>>>
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/geometry/particle_geometry_handler.pyc
in _setup_geometry(self)
>>>>> 52 def _setup_geometry(self):
>>>>> 53 mylog.debug("Initializing Particle Geometry Handler.")
>>>>> ---> 54 self._initialize_particle_handler()
>>>>> 55
>>>>> 56
>>>>>
>>>>>
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/geometry/particle_geometry_handler.pyc
in _initialize_particle_handler(self)
>>>>> 87 pf.domain_left_edge, pf.domain_right_edge,
>>>>> 88 [N, N, N], len(self.data_files))
>>>>> ---> 89 self._initialize_indices()
>>>>> 90 self.oct_handler.finalize()
>>>>> 91 self.max_level = self.oct_handler.max_level
>>>>>
>>>>>
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/geometry/particle_geometry_handler.pyc
in _initialize_indices(self)
>>>>> 109 npart = sum(data_file.total_particles.values())
>>>>> 110 morton[ind:ind + npart] = \
>>>>> --> 111 self.io._initialize_index(data_file,
self.regions)
>>>>> 112 ind += npart
>>>>> 113 morton.sort()
>>>>>
>>>>>
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/frontends/stream/io.pyc
in _initialize_index(self, data_file, regions)
>>>>> 144 raise YTDomainOverflow(pos.min(axis=0),
pos.max(axis=0),
>>>>> 145
data_file.pf.domain_left_edge,
>>>>> --> 146
data_file.pf.domain_right_edge)
>>>>> 147 regions.add_data_file(pos, data_file.file_id)
>>>>> 148 morton.append(compute_morton(
>>>>>
>>>>> YTDomainOverflow: Particle bounds [ 0. 0. 0.] and [ 99.99999237
99.99999237 99.99999237] exceed domain bounds [ 0. 0. 0.] code_length
and [ 1. 1. 1.] code_length
>>>>>
>>>>>
>>>>>
>>>>> On Thu, Jun 5, 2014 at 8:22 PM, Nathan Goldbaum <nathan12343 at gmail.com>
wrote:
>>>>>>
>>>>>> Here's a worked out example that does what you're looking for using
a fake 1 million particle dataset:
>>>>>>
>>>>>> http://nbviewer.ipython.org/gist/ngoldbaum/546d37869aafe71cfe38
>>>>>>
>>>>>> In this notebook I make use of two key yt features:
`load_particles`, and `covering_grid`.
>>>>>>
>>>>>> load_particles creates a "stream" dataset based on in-memory data
fed in as a numpy array. This dataset acts just like an on-disk simulation
dataset, but doesn't come with the baggage of needing to write a custom
frontend to read a specific data format off disk.
>>>>>>
>>>>>> covering_grid is a way to generate uniform resolution data from an
AMR dataset. It acts like a python dictionary where the keys are field
names and returns 3D numpy arrays of whatever uniform resolution you
specify when you create the covering_grid.
>>>>>>
>>>>>> Note that if you're using load_particles all of your data needs to
live in memory. If your data is too big for that you'll need to write a
frontend for your data format or use a memmap to an on-disk file somehow.
I'm not an expert on that but others on the list should be able to help
out.
>>>>>>
>>>>>> Hope that gets you well on your way :)
>>>>>>
>>>>>> -Nathan
>>>>>>
>>>>>>
>>>>>> On Thu, Jun 5, 2014 at 5:04 PM, Desika Narayanan <
dnarayan at haverford.edu> wrote:
>>>>>>>
>>>>>>> Hey Brendan,
>>>>>>>
>>>>>>> A couple of extra tools you might find helpful in conjunction with
Nathan's example of depositing the particles onto an octree are at:
>>>>>>>
>>>>>>> http://paste.yt-project.org/show/4737/
>>>>>>>
>>>>>>> Where I load a gadget snapshot, and then recover the coordinates
and width of each cell.
>>>>>>>
>>>>>>> In response to your last question - the particles are deposited
into an octree grid (so, you'll see that the cell sizes aren't all the same
size). I don't know if depositing onto a regular NxNxN mesh is possible,
though would be interested to hear if so.
>>>>>>>
>>>>>>> -d
>>>>>>>
>>>>>>>
>>>>>>> On Thu, Jun 5, 2014 at 7:58 PM, Brendan Griffen <
brendan.f.griffen at gmail.com> wrote:
>>>>>>>>
>>>>>>>> Thanks. I'll get the "bleeding edge" version first then try your
suggestions. Though I want to return the NxNxN array and be able to write
this mesh to a file. It is *only* using the cic part of yt and it should
return the mesh to be written? Just wanted to clarify?
>>>>>>>>
>>>>>>>> Thanks.
>>>>>>>> Brendan
>>>>>>>>
>>>>>>>>
>>>>>>>> On Thu, Jun 5, 2014 at 6:49 PM, Nathan Goldbaum <
nathan12343 at gmail.com> wrote:
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Thu, Jun 5, 2014 at 3:36 PM, John ZuHone <jzuhone at gmail.com>
wrote:
>>>>>>>>>>
>>>>>>>>>> Hi Brendan,
>>>>>>>>>>
>>>>>>>>>> Which version of yt are you using?
>>>>>>>>>>
>>>>>>>>>> If you're using 3.0, this is actually fairly easy. If you look
in yt.fields.particle_fields.py, around line 85, you can see how this is
done for the "particle_density" and "particle_mass" fields. Basically you
can call a "deposit" method which takes the particle field quantity you
want deposited and deposits it into cells. The underlying calculation is
done using Cython, so it's fast.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> And you shouldn't ever actually need to call these "deposit"
functions, since "deposit" is exposed as a field type for all datasets that
contain particles.
>>>>>>>>>
>>>>>>>>> Here is a notebook that does this for Enzo AMR data:
>>>>>>>>>
>>>>>>>>> http://nbviewer.ipython.org/gist/ngoldbaum/5e19e4e6cc2bf330149c
>>>>>>>>>
>>>>>>>>> This dataset contains about a million particles and generates a
CIC deposition for the whole domain in about 6 seconds from a cold start.
>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> If you're using 2.x, then you can do the same thing, but it's
not as straightforward. You can see how this works in
yt.data_objects.universal_fields.py, around line 986, where the
"particle_density" field is defined. Basically, it calls CICDeposit_3,
which is in yt.utilities.lib.CICDeposit.pyx.
>>>>>>>>>>
>>>>>>>>>> Let me know if you need any more clarification.
>>>>>>>>>>
>>>>>>>>>> Best,
>>>>>>>>>>
>>>>>>>>>> John Z
>>>>>>>>>>
>>>>>>>>>> On Jun 5, 2014, at 6:07 PM, Brendan Griffen <
brendan.f.griffen at gmail.com> wrote:
>>>>>>>>>>
>>>>>>>>>> > Hi,
>>>>>>>>>> >
>>>>>>>>>> > I was wondering if there were any Cython routines within yt
which takes particle data and converts it into a cloud-in-cell based mesh
which can be written to a file of my choosing.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> What sort of mesh were you looking for? yt will internally
construct an octree if it is fed particle data. I'm not sure whether this
octree can be saved to disk for later analysis.
>>>>>>>>>
>>>>>>>>> It's also possible to create a uniform resolution covering grid
containing field data for a deposited quantity, which can be quite easily
saved to disk in a number of ways.
>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> I heard a while ago there was some such functionality but it
could be too far down the yt rabbit hole to be used as a standalone? Is
this true? I have my own Python code for doing it but it just isn't fast
enough and thought I'd ask the yt community if there were any wrapper tools
available to boost the speed.
>>>>>>>>>> >
>>>>>>>>>> > Thanks.
>>>>>>>>>> > Brendan
>>>>>>>>>> > _______________________________________________
>>>>>>>>>> > yt-users mailing list
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