[yt-users] cloud in cell mesh construction for particle data
Brendan Griffen
brendan.f.griffen at gmail.com
Sun Jun 8 21:32:59 PDT 2014
This is the full error if it helps at all? It is indeed, loading in all of
the quantities.
Loading particles...
--> Loading particle type: 1
yt : [INFO ] 2014-06-08 15:12:05,540 Parameters: current_time
= 0.0
yt : [INFO ] 2014-06-08 15:12:05,540 Parameters: domain_dimensions
= [2 2 2]
yt : [INFO ] 2014-06-08 15:12:05,541 Parameters: domain_left_edge
= [ 0. 0. 0.]
yt : [INFO ] 2014-06-08 15:12:05,542 Parameters: domain_right_edge
= [ 100. 100. 100.]
yt : [INFO ] 2014-06-08 15:12:05,542 Parameters:
cosmological_simulation = 0.0
yt : [INFO ] 2014-06-08 15:12:05,548 Allocating for 1.074e+09 particles
yt : [INFO ] 2014-06-08 15:16:16,195 Identified 7.584e+07 octs
yt : [INFO ] 2014-06-08 15:16:16,299 Loading field plugins.
yt : [INFO ] 2014-06-08 15:16:16,299 Loaded angular_momentum (8 new
fields)
yt : [INFO ] 2014-06-08 15:16:16,299 Loaded astro (14 new fields)
yt : [INFO ] 2014-06-08 15:16:16,300 Loaded cosmology (20 new fields)
yt : [INFO ] 2014-06-08 15:16:16,300 Loaded fluid (56 new fields)
yt : [INFO ] 2014-06-08 15:16:16,301 Loaded fluid_vector (88 new fields)
yt : [INFO ] 2014-06-08 15:16:16,301 Loaded geometric (103 new fields)
yt : [INFO ] 2014-06-08 15:16:16,301 Loaded local (103 new fields)
yt : [INFO ] 2014-06-08 15:16:16,302 Loaded magnetic_field (109 new
fields)
yt : [INFO ] 2014-06-08 15:16:16,302 Loaded species (109 new fields)
---------------------------------------------------------------------------
MemoryError 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>()
99 slc.set_figure_size(4)
100 slc.save()
--> 101
102 for ndim in dimlist:
103 print
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in __getitem__(self, key)
218 return self.field_data[f]
219 else:
--> 220 self.get_data(f)
221 # fi.units is the unit expression string. We depend on the
registry
222 # hanging off the dataset to define this unit object.
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in get_data(self, fields)
627
628 fields_to_generate += gen_fluids + gen_particles
--> 629 self._generate_fields(fields_to_generate)
630
631 def _generate_fields(self, fields_to_generate):
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in _generate_fields(self, fields_to_generate)
644 fi = self.pf._get_field_info(*field)
645 try:
--> 646 fd = self._generate_field(field)
647 if type(fd) == np.ndarray:
648 fd = self.pf.arr(fd, fi.units)
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in _generate_field(self, field)
255 tr = self._generate_particle_field(field)
256 else:
--> 257 tr = self._generate_fluid_field(field)
258 if tr is None:
259 raise YTCouldNotGenerateField(field, self.pf)
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in _generate_fluid_field(self, field)
273 finfo.check_available(gen_obj)
274 except NeedsGridType as ngt_exception:
--> 275 rv = self._generate_spatial_fluid(field,
ngt_exception.ghost_zones)
276 else:
277 rv = finfo(gen_obj)
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in _generate_spatial_fluid(self, field, ngz)
289 o = self._current_chunk.objs[0]
290 with o._activate_cache():
--> 291 ind += o.select(self.selector, self[field],
rv, ind)
292 else:
293 chunks = self.index._chunk(self, "spatial", ngz = ngz)
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in __getitem__(self, key)
218 return self.field_data[f]
219 else:
--> 220 self.get_data(f)
221 # fi.units is the unit expression string. We depend on the
registry
222 # hanging off the dataset to define this unit object.
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in get_data(self, fields)
627
628 fields_to_generate += gen_fluids + gen_particles
--> 629 self._generate_fields(fields_to_generate)
630
631 def _generate_fields(self, fields_to_generate):
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in _generate_fields(self, fields_to_generate)
644 fi = self.pf._get_field_info(*field)
645 try:
--> 646 fd = self._generate_field(field)
647 if type(fd) == np.ndarray:
648 fd = self.pf.arr(fd, fi.units)
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in _generate_field(self, field)
255 tr = self._generate_particle_field(field)
256 else:
--> 257 tr = self._generate_fluid_field(field)
258 if tr is None:
259 raise YTCouldNotGenerateField(field, self.pf)
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/data_containers.pyc
in _generate_fluid_field(self, field)
275 rv = self._generate_spatial_fluid(field,
ngt_exception.ghost_zones)
276 else:
--> 277 rv = finfo(gen_obj)
278 return rv
279
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/fields/derived_field.pyc
in __call__(self, data)
176 "for %s" % (self.name,))
177 with self.unit_registry(data):
--> 178 dd = self._function(self, data)
179 for field_name in data.keys():
180 if field_name not in original_fields:
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/fields/particle_fields.pyc
in particle_cic(field, data)
113 def particle_cic(field, data):
114 pos = data[ptype, coord_name]
--> 115 d = data.deposit(pos, [data[ptype, mass_name]], method =
"cic")
116 d = data.apply_units(d, data[ptype, mass_name].units)
117 d /= data["index", "cell_volume"]
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/data_objects/octree_subset.pyc
in deposit(self, positions, fields, method)
167 mylog.debug("Depositing %s (%s^3) particles into %s Octs",
168 positions.shape[0], positions.shape[0]**0.3333333,
nvals[-1])
--> 169 pos = np.asarray(positions.convert_to_units("code_length"),
170 dtype="float64")
171 # We should not need the following if we know in advance
all our fields
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/units/yt_array.pyc in
convert_to_units(self, units)
366
367 self.units = new_units
--> 368 self *= conversion_factor
369 return self
370
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/units/yt_array.pyc in
__imul__(self, other)
667 """ See __mul__. """
668 oth = sanitize_units_mul(self, other)
--> 669 return np.multiply(self, oth, out=self)
670
671 def __div__(self, right_object):
/bigbang/data/bgriffen/lib/yt-x86_64/src/yt-hg/yt/units/yt_array.pyc in
__array_wrap__(self, out_arr, context)
966 # casting to YTArray avoids creating a YTQuantity
with size > 1
967 return YTArray(np.array(out_arr, unit))
--> 968 return ret_class(np.array(out_arr), unit)
969
970
MemoryError:
On Sun, Jun 8, 2014 at 10:50 PM, Matthew Turk <matthewturk at gmail.com> wrote:
> Hi Brendan,
>
> On Sun, Jun 8, 2014 at 9:21 PM, Brendan Griffen
> <brendan.f.griffen at gmail.com> wrote:
> > Hi Matt,
> >
> > Thanks for your detailed email. Forgive my naivety but why do you need
> the
> > oct-tree in the first place? I have a my own fortran code for
> constructing a
> > cloud in cell mesh and it uses very little overhead (just the n^3 grid
> and
> > the particle data itself). I then calculate the dx,dy,dzs to the nearest
> 8
> > grid points and distribute accordingly in a omp loop which is done in a
> > fraction of a second. Does the situation with yt come about (oct tree
> etc.)
> > necessarily because of the way it handles particle data? Is it
> essentially
> > used to map the particles to domains in the grid or something?
>
> That's not naive at all. There are two reasons --
>
> 1) The octree is used for indexing for neighbor lookups and
> early-termination of region selection for particles
> 2) The octree is used to estimate the "required resolution" for any
> operation that requires a space-filling value. (i.e., any time that a
> particle becomes a volume.)
>
> Projections in yt are adaptive, in that they project down to the
> finest appropriate resolution. There's also the "arbitrary_grid"
> operation, which does precisely what you're describing, but as it
> stands right now the octree gets constructed at time of instantiation
> of the indexing system. Thinking it over, you may be able to avoid
> that completely by not using load_particles and instead using
> load_uniform_grid and supplying your desired dimensions. The field
> names should be the same.
>
> >
> > The machine I max memory on has 128GB and the snapshots are using 1024^3
> > particles. Do you have any idea of how much memory the oct-tree uses as a
> > function of particle/grid number? I am going to try on a 256GB machine
> > (though this is a bit of a hassle). I'll see how I go.
>
> I am disappointed that it's blowing out your RAM. This week I will
> try to get some memory profiling done. Could you file a bug to this
> effect, which will help me track it? Peak memory usage during
> indexing should only be 64 bits * Nparticles, unless you're using
> load_particles, in which case all the fields will *also* have to be in
> memory. It's about 8 gigabytes per field. So, I think there's
> something going wrong.
>
> >
> > Thanks.
> >
> > Brendan
> >
> >
> > On Sun, Jun 8, 2014 at 6:25 PM, Matthew Turk <matthewturk at gmail.com>
> wrote:
> >>
> >> Hi all,
> >>
> >> I feel like I owe a brief explanation of why things are tricky right
> >> now, what we're planning on doing, and how we're experimenting and
> >> developing.
> >>
> >> Presently, the particle geometry handlers build a single mesh from all
> >> particles in the dataset, along with a coarse bitmask that correlates
> >> files to regions in the domain. This requires the allocation of a
> >> single int64 array of size Nparticles, which is sorted in place and
> >> then fed into an octree construction algorithm that then spits back
> >> out the mesh. Each octree component contains 3 64-bit integers and
> >> eitehr a void pointer or a pointer to eight other octs. Increasing
> >> n_ref decreases the number of octs in this mesh; when smoothing
> >> operaitons are conducted, a second "index" mesh is created for looking
> >> up particles near mesh points. Mesh points are used for adaptive
> >> resolution smoothing and other "deposit particles on the grid somehow"
> >> operations (including SPH kernel).
> >>
> >> Anyway, because right now it requires a global mesh to be constructed,
> >> this is expensive and requires holding a 64-bit integer in memory for
> >> each particle. I think if you're loading the particles in differently
> >> there is some additional overhead as well, but I'm still a bit
> >> surprised you OOM on a 1024^3 dataset.
> >>
> >> In general, we don't *need* this global mesh; is can be constructed as
> >> required, which would speed up both the initial index phase as well as
> >> the final meshing process. I got about 50% of the way to implementing
> >> this last fall, but because of various concerns and deadlines I
> >> haven't finished it. I intend to get back to it probably in July,
> >> right after we put out a 3.0, so that we can have it in time for 3.1.
> >> In principle this will make the particle codes much more similar to
> >> ARTIO, in that the mesh will be constructed only as required and
> >> discarded when no longer required, which will make them much more
> >> memory efficient.
> >>
> >> But, getting a single mesh for extremely large data is a very high
> >> priority; right now for the 10240^3 run we've been loading up
> >> individual sub-chunks, which I want to stop doing.
> >>
> >> From the technical perspective, these are the things that need to
> >> happen on the yt side for particle datasets to move to this "lazy"
> >> mode of loading; most of this is based on things learned from 2HOT and
> >> ARTIO, and will involve converting to a forest-of-octrees.
> >>
> >> * Split into spatially-organized subchunks of ParticleOctreeSubset
> >> objects, such that these map 1:Nfiles, and that can be constructed on
> >> the fly.
> >> * Construct a dual-mesh of the bitmask "ParticleRegion" object that
> >> will help with identifying neighbors to a given oct cell, so that if
> >> we're inside one octree we know which neighbor octrees to grab if we
> >> need particles for smoothing things (fast boundary particle
> >> identification is later down the road)
> >> * Parallel sort of particles, or using the parallel ring function;
> >> may not be necessary after all
> >>
> >> All of this is doable, and I'd be happy to work with people if they'd
> >> like to take a shot at implementing it, but I've mostly put it on my
> >> list for post-3.0.
> >>
> >> -Matt
> >>
> >> On Sun, Jun 8, 2014 at 2:43 PM, Nathan Goldbaum <nathan12343 at gmail.com>
> >> wrote:
> >> >
> >> >
> >> >
> >> > On Sun, Jun 8, 2014 at 12:27 PM, Brendan Griffen
> >> > <brendan.f.griffen at gmail.com> wrote:
> >> >>
> >> >> Also, how do I construct just a zero filled yt array with dimensions
> >> >> (ndim,ndim,ndim)? Thanks
> >> >
> >> >
> >> >
> >> > from yt import YTArray
> >> > from numpy import np
> >> >
> >> > arr = YTArray(np.zeros([ndim, ndim, ndim]), input_units=units_string)
> >> >
> >> > or alternatively:
> >> >
> >> > from yt.units import kiloparsec
> >> >
> >> > arr = kiloparsec*np.zeros([ndim, ndim, ndim])
> >> >
> >> > it doesn't have to be kiloparsec - you can compose the units you want
> >> > out of
> >> > any of the unit symbols that live in yt.units.
> >> >
> >> > See this page for a ton more detail about yt's new unit system:
> >> > http://yt-project.org/docs/dev-3.0/analyzing/units/index.html
> >> >
> >> >>
> >> >>
> >> >> Brendan
> >> >>
> >> >>
> >> >> On Sun, Jun 8, 2014 at 3:26 PM, Brendan Griffen
> >> >> <brendan.f.griffen at gmail.com> wrote:
> >> >>>
> >> >>> Hi,
> >> >>>
> >> >>> Since I get memory errors. Could I not just read in the blocks of
> the
> >> >>> output individually then basically stack the mesh each time. That
> way
> >> >>> not
> >> >>> every single particle of the snapshot has to be loaded at the same
> >> >>> time.
> >> >>> Would that just be a case of doing
> >> >>>
> >> >>> level = int(math.log(ndim,2))
> >> >>> cg = ds.covering_grid(level=level,
> >> >>> left_edge=[0,0,0],dims=[ndim,ndim,ndim])
> >> >>> arr = cg['deposit', 'all_density']
> >> >>> arrall += arr
> >> >>>
> >> >>> in a loop over each HDF5 block?
> >> >
> >> >
> >> > It's likely that the memory use is dominated by the octree rather than
> >> > the
> >> > covering grid. This is with 1024^3 particles, correct?
> >> >
> >> > You can probably significantly reduce the memory used by the octree by
> >> > increasing n_ref in the call to load_particles.
> >> >
> >> > See this page for more detail about load_particles:
> >> >
> >> >
> http://yt-project.org/docs/dev-3.0/examining/loading_data.html#generic-particle-data
> >> >
> >> > Larger n_ref means fewer octree cells (lower resolution), but it also
> >> > means
> >> > lower poisson noise and lower memory use.
> >> >
> >> > Alternatively, as Matt suggested, you could break your 1024^3 ensemble
> >> > of
> >> > particles up into chunks, loop over the chunk, creating a particle
> >> > octree
> >> > and then a covering grid for each subset of the particles. Your final
> >> > covering grid is just the sub of the covering grids for each subset of
> >> > particles.
> >> >
> >> >>>
> >> >>>
> >> >>> Thanks.
> >> >>> Brendan
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>> On Fri, Jun 6, 2014 at 7:26 PM, Matthew Turk <matthewturk at gmail.com
> >
> >> >>> wrote:
> >> >>>>
> >> >>>>
> >> >>>> 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
> >> >>>> >>>>>>>>>> > yt-users at lists.spacepope.org
> >> >>>> >>>>>>>>>> >
> >> >>>> >>>>>>>>>> >
> >> >>>> >>>>>>>>>> >
> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>>> >>>>>>>>>>
> >> >>>> >>>>>>>>>> _______________________________________________
> >> >>>> >>>>>>>>>> yt-users mailing list
> >> >>>> >>>>>>>>>> yt-users at lists.spacepope.org
> >> >>>> >>>>>>>>>>
> >> >>>> >>>>>>>>>>
> >> >>>> >>>>>>>>>>
> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>>> >>>>>>>>>
> >> >>>> >>>>>>>>>
> >> >>>> >>>>>>>>>
> >> >>>> >>>>>>>>> _______________________________________________
> >> >>>> >>>>>>>>> yt-users mailing list
> >> >>>> >>>>>>>>> yt-users at lists.spacepope.org
> >> >>>> >>>>>>>>>
> >> >>>> >>>>>>>>>
> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>>> >>>>>>>>>
> >> >>>> >>>>>>>>
> >> >>>> >>>>>>>>
> >> >>>> >>>>>>>> _______________________________________________
> >> >>>> >>>>>>>> yt-users mailing list
> >> >>>> >>>>>>>> yt-users at lists.spacepope.org
> >> >>>> >>>>>>>>
> >> >>>> >>>>>>>>
> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>>> >>>>>>>>
> >> >>>> >>>>>>>
> >> >>>> >>>>>>>
> >> >>>> >>>>>>> _______________________________________________
> >> >>>> >>>>>>> yt-users mailing list
> >> >>>> >>>>>>> yt-users at lists.spacepope.org
> >> >>>> >>>>>>>
> >> >>>> >>>>>>>
> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>>> >>>>>>>
> >> >>>> >>>>>>
> >> >>>> >>>>>>
> >> >>>> >>>>>> _______________________________________________
> >> >>>> >>>>>> yt-users mailing list
> >> >>>> >>>>>> yt-users at lists.spacepope.org
> >> >>>> >>>>>>
> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>>> >>>>>>
> >> >>>> >>>>>
> >> >>>> >>>>>
> >> >>>> >>>>> _______________________________________________
> >> >>>> >>>>> yt-users mailing list
> >> >>>> >>>>> yt-users at lists.spacepope.org
> >> >>>> >>>>>
> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>>> >>>>>
> >> >>>> >>>>
> >> >>>> >>>>
> >> >>>> >>>> _______________________________________________
> >> >>>> >>>> yt-users mailing list
> >> >>>> >>>> yt-users at lists.spacepope.org
> >> >>>> >>>>
> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>>> >>>>
> >> >>>> >>>
> >> >>>> >>>
> >> >>>> >>> _______________________________________________
> >> >>>> >>> yt-users mailing list
> >> >>>> >>> yt-users at lists.spacepope.org
> >> >>>> >>> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>>> >>>
> >> >>>> >>
> >> >>>> >>
> >> >>>> >> _______________________________________________
> >> >>>> >> yt-users mailing list
> >> >>>> >> yt-users at lists.spacepope.org
> >> >>>> >> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>>> >>
> >> >>>> >
> >> >>>> >
> >> >>>> > _______________________________________________
> >> >>>> > yt-users mailing list
> >> >>>> > yt-users at lists.spacepope.org
> >> >>>> > http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>>> >
> >> >>>>
> >> >>>>
> >> >>>> _______________________________________________
> >> >>>> yt-users mailing list
> >> >>>> yt-users at lists.spacepope.org
> >> >>>> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>>>
> >> >>>
> >> >>
> >> >>
> >> >> _______________________________________________
> >> >> yt-users mailing list
> >> >> yt-users at lists.spacepope.org
> >> >> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >>
> >> >
> >> >
> >> > _______________________________________________
> >> > yt-users mailing list
> >> > yt-users at lists.spacepope.org
> >> > http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >> >
> >> _______________________________________________
> >> yt-users mailing list
> >> yt-users at lists.spacepope.org
> >> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >
> >
> >
> > _______________________________________________
> > yt-users mailing list
> > yt-users at lists.spacepope.org
> > http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
> >
> _______________________________________________
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> http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
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