[yt-users] yt - clump finding

[Nathan Goldbaum]

Hi Alex,

I think the issue is that you are using yt-3.0 to do the clump finding.  I
*believe* (Matt will correct me if I'm wrong) that clump finding does not
work correctly in 3.0.

Can you try again in 2.6dev?

-Nathan


On Thu, Nov 7, 2013 at 1:07 PM, Alex Bogert <bogart.alex at gmail.com> wrote:

>
> Hi Nathan & Matt,
>>
>> I am interested in using YT to find clumps in the Bolshoi grid particle
>> data. So, I tried to test it with Nathan's ENZO data he gave me (DD0025). I
>> ran into an errror.
>>
>> Thanks for your help!
>>
>> Alex
>>
>> Error:
>>
>>>  # set up our namespacefrom yt.mods import * from yt.analysis_modules.level_sets.api import *
>>> fn = "IsolatedGalaxy/galaxy0030/galaxy0030" # parameter file to loadfield = "Density" # this is the field we look for contours over -- we could do
>>>                   # this over anything.  Other common choices are 'AveragedDensity'
>>>                   # and 'Dark_Matter_Density'.step = 2.0 # This is the multiplicative interval between contours.
>>> pf = load(fn) # load data
>>> # We want to find clumps over the entire dataset, so we'll just grab the whole# thing!  This is a convenience parameter that prepares an object that covers# the whole domain.  Note, though, that it will load on demand and not before!data_source = pf.h.disk([0.5, 0.5, 0.5], [0., 0., 1.],
>>>                         8./pf.units['kpc'], 1./pf.units['kpc'])
>>> # Now we set some sane min/max values between which we want to find contours.# This is how we tell the clump finder what to look for -- it won't look for# contours connected below or above these threshold values.c_min = 10**na.floor(na.log10(data_source[field]).min()  )c_max = 10**na.floor(na.log10(data_source[field]).max()+1)
>>> # keep only clumps with at least 20 cellsfunction = 'self.data[\'%s\'].size > 20' % field
>>> # Now find get our 'base' clump -- this one just covers the whole domain.master_clump = Clump(data_source, None, field, function=function)
>>> # This next command accepts our base clump and we say the range between which# we want to contour.  It recursively finds clumps within the master clump, at# intervals defined by the step size we feed it.  The current value is# *multiplied* by step size, rather than added to it -- so this means if you# want to look in log10 space intervals, you would supply step = 10.0.find_clumps(master_clump, c_min, c_max, step)
>>> # As it goes, it appends the information about all the sub-clumps to the# master-clump.  Among different ways we can examine it, there's a convenience# function for outputting the full hierarchy to a file.f = open('%s_clump_hierarchy.txt' % pf,'w')amods.level_sets.write_clump_hierarchy(master_clump,0,f)f.close()
>>> # We can also output some handy information, as well.f = open('%s_clumps.txt' % pf,'w')amods.level_sets.write_clumps(master_clump,0,f)f.close()
>>> # We can traverse the clump hierarchy to get a list of all of the 'leaf' clumpsleaf_clumps = get_lowest_clumps(master_clump)
>>> # If you'd like to visualize these clumps, a list of clumps can be supplied to# the "clumps" callback on a plot.  First, we create a projection plot:prj = ProjectionPlot(pf, 2, field, center='c', width=(20,'kpc'))
>>> # Next we annotate the plot with contours on the borders of the clumpsprj.annotate_clumps(leaf_clumps)
>>> # Lastly, we write the plot to disk.prj.save('clumps')
>>> # We can also save the clump object to disk to read in later so we don't have# to spend a lot of time regenerating the clump objects.pf.h.save_object(master_clump, 'My_clumps')
>>> # Later, we can read in the clump object like so,master_clump = pf.h.load_object('My_clumps'
>>> )
>>>
>>>
>>> --------------------------------------------------------------------------------------------------------------------------------
>>>
>>>
>>>
>>> yt : [INFO     ] 2013-11-07 12:50:45,658 Parameters: current_time              = 0.02500008999
>>> yt : [INFO     ] 2013-11-07 12:50:45,659 Parameters: domain_dimensions         = [64 64 64]
>>> yt : [INFO     ] 2013-11-07 12:50:45,660 Parameters: domain_left_edge          = [ 0.  0.  0.]
>>> yt : [INFO     ] 2013-11-07 12:50:45,660 Parameters: domain_right_edge         = [ 1.  1.  1.]
>>> yt : [INFO     ] 2013-11-07 12:50:45,661 Parameters: cosmological_simulation   = 0.0
>>>
>>> ---------------------------------------------------------------------------TypeError                                 Traceback (most recent call last)<ipython-input-10-f290b737b0f4> in <module>()     34 # *multiplied* by step size, rather than added to it -- so this means if you     35 # want to look in log10 space intervals, you would supply step = 10.0.---> 36 find_clumps(master_clump, c_min, c_max, step)     37      38 # As it goes, it appends the information about all the sub-clumps to the
>>> /usr/local/yt-conda/src/yt-hg/yt/analysis_modules/level_sets/clump_handling.py in find_clumps(clump, min_val, max_val, d_clump)    168     print "Finding clumps: min: %e, max: %e, step: %f" % (min_val, max_val, d_clump)    169     if min_val >= max_val: return--> 170     clump.find_children(min_val)    171     172     if (len(clump.children) == 1):
>>> /usr/local/yt-conda/src/yt-hg/yt/analysis_modules/level_sets/clump_handling.py in find_children(self, min_val, max_val)    109         if max_val is None: max_val = self.max_val    110         contour_info = identify_contours(self.data, self.field, min_val, max_val,--> 111                                          self.cached_fields)    112         for cid in contour_info:    113             new_clump = self.data.extract_region(contour_info[cid])
>>> /usr/local/yt-conda/src/yt-hg/yt/analysis_modules/level_sets/contour_finder.py in identify_contours(data_source, field, min_val, max_val, cached_fields)     59 def identify_contours(data_source, field, min_val, max_val,     60                           cached_fields=None):---> 61     cur_max_id = np.sum([g.ActiveDimensions.prod() for g in data_source._grids])     62     pbar = get_pbar("First pass", len(data_source._grids))     63     grids = sorted(data_source._grids, key=lambda g: -g.Level)
>>> TypeError: 'NoneType' object is not iterable
>>>
>>>
>>> Finding clumps: min: 1.000000e-30, max: 1.000000e-21, step: 2.000000
>>>
>>>
>>>
>>>
>>> On Thu, Nov 7, 2013 at 12:49 PM, Alex Bogert <bogart.alex at gmail.com>wrote:
>>>
>>>>
>>>>
>>>> ---------- Forwarded message ----------
>>>> From: Alex Bogert <bogart.alex at gmail.com>
>>>> Date: Thu, Nov 7, 2013 at 12:46 PM
>>>> Subject: yt - clump finding
>>>> To: "Conor Kaminer (Google Drive)" <ckaminer at ucsc.edu>
>>>>
>>>>
>>>> # set up our namespacefrom yt.mods import * from yt.analysis_modules.level_sets.api import *
>>>> fn = "IsolatedGalaxy/galaxy0030/galaxy0030" # parameter file to loadfield = "Density" # this is the field we look for contours over -- we could do
>>>>                   # this over anything.  Other common choices are 'AveragedDensity'
>>>>                   # and 'Dark_Matter_Density'.step = 2.0 # This is the multiplicative interval between contours.
>>>> pf = load(fn) # load data
>>>> # We want to find clumps over the entire dataset, so we'll just grab the whole# thing!  This is a convenience parameter that prepares an object that covers# the whole domain.  Note, though, that it will load on demand and not before!data_source = pf.h.disk([0.5, 0.5, 0.5], [0., 0., 1.],
>>>>                         8./pf.units['kpc'], 1./pf.units['kpc'])
>>>> # Now we set some sane min/max values between which we want to find contours.# This is how we tell the clump finder what to look for -- it won't look for# contours connected below or above these threshold values.c_min = 10**na.floor(na.log10(data_source[field]).min()  )c_max = 10**na.floor(na.log10(data_source[field]).max()+1)
>>>> # keep only clumps with at least 20 cellsfunction = 'self.data[\'%s\'].size > 20' % field
>>>> # Now find get our 'base' clump -- this one just covers the whole domain.master_clump = Clump(data_source, None, field, function=function)
>>>> # This next command accepts our base clump and we say the range between which# we want to contour.  It recursively finds clumps within the master clump, at# intervals defined by the step size we feed it.  The current value is# *multiplied* by step size, rather than added to it -- so this means if you# want to look in log10 space intervals, you would supply step = 10.0.find_clumps(master_clump, c_min, c_max, step)
>>>> # As it goes, it appends the information about all the sub-clumps to the# master-clump.  Among different ways we can examine it, there's a convenience# function for outputting the full hierarchy to a file.f = open('%s_clump_hierarchy.txt' % pf,'w')amods.level_sets.write_clump_hierarchy(master_clump,0,f)f.close()
>>>> # We can also output some handy information, as well.f = open('%s_clumps.txt' % pf,'w')amods.level_sets.write_clumps(master_clump,0,f)f.close()
>>>> # We can traverse the clump hierarchy to get a list of all of the 'leaf' clumpsleaf_clumps = get_lowest_clumps(master_clump)
>>>> # If you'd like to visualize these clumps, a list of clumps can be supplied to# the "clumps" callback on a plot.  First, we create a projection plot:prj = ProjectionPlot(pf, 2, field, center='c', width=(20,'kpc'))
>>>> # Next we annotate the plot with contours on the borders of the clumpsprj.annotate_clumps(leaf_clumps)
>>>> # Lastly, we write the plot to disk.prj.save('clumps')
>>>> # We can also save the clump object to disk to read in later so we don't have# to spend a lot of time regenerating the clump objects.pf.h.save_object(master_clump, 'My_clumps')
>>>> # Later, we can read in the clump object like so,master_clump = pf.h.load_object('My_clumps')
>>>>
>>>>
>>>>
>>>
>>
>
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