Closed xrotwang closed 1 year ago
While Nexus might look like a candidate here, I'd be reluctant to even base a spec on it - because it would defeat the whole purpose of CLDF as well-defined, simple format.
So I guess, a phylogenetic component for CLDF woul rather look like a constrained version of Newick, including spec for
and considering that we don't support Nexus directly, a couple of tools or recipes to turn common Nexus variants into such a component.
@SimonGreenhill @LinguList @chrzyki Another question is what semantics we should attach to a Trees
component - or whether we can do that at all. E.g. can we consider a single tree to be a consensus tree from a bayesian analysis? But what about trees derived from distance matrices, then?
So maybe the question should be asked the other way round: Would a tree component without any semantics make sense - i.e. just having a way to throw a bunch of trees into a CLDF dataset?
If a tree component wouldn't have any semantics, would we need to allow for multiple such components, e.g. to add posterior tree sets and consensus tree?
Yes, I'm a little reluctant to create a new standard for trees after nexus, but can see the usefulness.
An easy way to handle this is as a csv file that has a column containing the tree in newick format (i.e. ((a,b),c)
), with one row per tree. The various bits of metadata we'd need or want are:
Name
: Nexus specifies a name for each tree tree name = ((a,b),c);
. This is often not used (so tree = ((a,b),c);
is valid), but this can be useful e.g. bayesian programs often use it to store the MCMC sample generation.
Units
: The branch length units e.g. none, time, years, millennia, changes, substitutions, etc. Would be good to have a validator on this to constrain the number of things that can be entered.
Method
: how the trees were built? (maybe not useful, and could be replaced by tree name?)
Type
(e.g "posterior", "consensus")
Rooting
- nexus can specify if a tree is rooted or unrooted: tree name = [&R] ((a,b),c);
or tree name = [&U] ((a,b),c);
. This would get lost if we're only storing the newick information and treename, so roundtripping wouldn't work unless we extracted rooted/unrooted/none.
Converting to/from common tree formats should be pretty easy -- to newick, it's just export the tree column, to nexus it's:
#nexus;
begin trees;
tree <name> = [&<rooting>] <newick>;
;
end;
Another thing worth thinking about is whether we want to extract taxa labels into their own file (e.g. taxa.csv
in DPLACE) as otherwise we don't know what's in the trees unless we parse the newick representation.
It might be nice to "compress" the tree format (i.e. remove labels from tree and replace with numbers), so ((a,b),c);
becomes ((1,2),3);
. This means that the trees take up (much) less space and is handled by nexus easily: https://github.com/D-PLACE/dplace-data/blob/master/phylogenies/birchall_et_al2016/summary.trees. Having a taxa.csv would make this pretty transparent as taxa.csv just becomes the translate
block.
Have a look at the things I've been collecting in phlorest for examples (this is private for the moment).
Yes! R.
@SimonGreenhill Regarding "compression" and tree labels: I'd say in the CLDF context labels in the Newick must be LanguageTable
ID
s only (well maybe with the exception of some comment format). Stuff like tree-specific original labels would then need to be added to LanguageTable
, but I wouldn't further specify this.
Regarding Method
and Type
: I'd make these informal, optional metadata - unless we'd have hopes to further constrain them. Maybe easier to specify - for dataset creators - and equally informative (if not more so) would be the tool used to create the tree. I'm thinking of BEAST's annotation format - if we now it's a BEAST tree, we'd know what to look for.
Yes to the above. One thing though, do we want to have one trees file or multiple? if it's one file then we need to be able to distinguish from a consensus/summary tree and the posterior (= Type
or Method
). If it's multiple files then we could could have one tree file for the summary tree, and one for the posterior.
I'd say just one tree table - and specification of type or method is left to the dataset creator, but unspecified (?). This would mirror the way we'd deal with different types in ParameterTable
at the moment. Or do you think we could come up with a fairly complete controlled vocabulary for Type
or Method
?
I think I'd go by the CLDF principle here again: Only things that have automated computational applications should be part of the CLDF spec. But maybe pypipeline
is already enough to roll the posterior
/ consensus
distinction into CLDF?
Hmm, I'm thinking of the types of analyses I'd want to do. These are things like (1) take out the summary tree and look at it, (2) take out all the trees in the posterior (BUT not the summary tree) and calculate some statistics on them.
Therefore being able to do that easily would be good, which means I think a non-optional restricted vocab column would be useful (although coming up with a good vocab might be hard), along with an optional one.
One issue is that there's not one type of 'summary' tree -- there's lots of methods. So it's not inconceivable that a dataset could have more than one summary.
Another issue is that the "posterior" is just one type of analysis, there are lots of older methods that give multiple trees e.g. this has a set of "equally most parsimonious trees".
So I think what this means is that the we should have a non-optional field Type
, with vocab {"summary", "sample"}
where:
pypipeline
probably just generates a lot of "samples" until we want to make a pretty figure for the paper.If the creator wants, they can further specify more details in the optional Method
field?
i.e. "summary" is defined as an overall summary tree (of whatever method, which can be specified in more detail in a Method
, while "sample" is a tree sampled from the analysis under which ever method, but is not an overall summary of the analysis.
(or is Description
a better name rather than Method
?)
Or TREX: http://dighl.github.io/TREX/
@SimonGreenhill yes, we also have Name
and Description
available, but I'd guess that Description
would be more comprehensive and more human readable. So here's the kind of logic I can see derived from Type
and Method
:
Type == 'summary'
: This tree can be used in isolation, e.g. to plot data against it. Method
can be used to disambiguate multiple summary trees in the same dataset, but so can Name
or Description
.Type == 'sample'
: Trees must be grouped (by Method
) to form a tree set that can be analyzed with methods that handle tree sets.Sounds good.
I suggested Name
above to store the information in name
in this tree <name> = [&<rooting>] <newick>;
, but given that name
here should be relatively slug-like, then this makes sense to use these as ID
(and vice versa)
@SimonGreenhill I think using the Name
column for whatever Nexus has a <name>
makes sense. Possibly the tool converting CLDF trees to Nexus might need to massage a Name
- if it didn't come from Nexus originally. ID
s should be assigned very consciously, I think. So any automation here might go against "explicit is better than implicit".
Ok, sounds good. I guess the next step is to throw some real data into it (DPLACE phylogenies?) and see if there's anything missing or uncomfortable?
Yes, we could actually do that - turn D-PLACE into an "artificial" CLDF dataset, with one parameter: "Society mapping", and a bunch of trees.
So example will go here https://github.com/cldf-datasets/dplace
renamed to dplacetrees: https://github.com/cldf-datasets/dplacetrees
@SimonGreenhill @LinguList @chrzyki so is this what we want: https://github.com/cldf-datasets/dplacetrees/tree/master/cldf It's sort of reasonable in terms of size - and D-PLACE is presumably the "worst" case when it comes to number and size of trees.
Looks good -- the trees from DPLACE come to 50mb, so trees.csv doesn't massively inflate that (55mb). The treelabels.csv is a bit annoyingly large, but I guess we need it?
Switching to integer tree IDs, treelabels.csv
clocks in at 7MB.
That's much nicer -- and surely we don't loose much with that, right?
No, I'd add the D-PLACE phylogeny ID in a separate column of trees.csv
.
perfect.
Very nice! Anything I can do to help testing this?
Possibly the next step would be basing the pypipeline code on this.
So, coming back to this for the refactoring of D-PLACE.
Here's the trees spec, I'd use:
args.writer.cldf.add_table(
'trees.csv',
{
'name': 'ID',
'propertyUrl': 'http://cf.clld.org/v1/terms.rdf#id',
},
{
'name': 'Name',
'propertyUrl': 'http://cf.clld.org/v1/terms.rdf#name',
},
{
'name': 'rooted', # bool or None
'datatype': 'boolean',
'dc:description': "Whether the tree is rooted (true) or unrooted (false) (or no "
"info is available (null))"
},
{
'name': 'type', # summary or sample
'datatype': {'base': 'string', 'format': 'summary|sample'},
'dc:description': "Whether the tree is a summary (or consensus) tree, i.e. can be "
"analysed in isolation, or whether it is a sample, resulting "
"from a method that creates multiple trees",
},
{
'name': 'method',
'dc:description': 'Specifies the method that was used to create the tree'
},
{
'name': 'newick',
'dc:format': 'text/plain+newick',
'dc:description': 'Newick representation of the tree, labeled with identifiers '
'as described in the LanguageTable',
},
{
'name': 'Source',
'separator': ';',
'propertyUrl': 'http://cf.clld.org/v1/terms.rdf#source',
},
)
The first issue I'm running in is labeled internal nodes (as in Atkinson 2006). I think internal node labels should
The second option would make the newick string smaller - which is probably a good thing, but would make round-tripping Nexus input impossible.
In any case, I'd make it illegal to have node names that don't appear in LanguageTable.
For atkinson2006
, it looks as follows ATM:
$ cldfbench makecldf cldfbench_atkinson2006.py
INFO running _cmd_makecldf on atkinson2006 ...
WARNING Summary tree references undefined inner node Huastecan
WARNING Summary tree references undefined inner node MayaProper
WARNING Summary tree references undefined inner node Kanjobalan
WARNING Summary tree references undefined inner node EasternMayan
WARNING Summary tree references undefined inner node Quichean
WARNING Summary tree references undefined inner node SipaSaca
WARNING Summary tree references undefined inner node CakTzut
WARNING Summary tree references undefined inner node Poco
WARNING Summary tree references undefined inner node Mamean
WARNING Summary tree references undefined inner node CholTzetYuc
WARNING Summary tree references undefined inner node CholYuc
WARNING Summary tree references undefined inner node Cholan
WARNING Summary tree references undefined inner node Yucatecan
WARNING Summary tree references undefined inner node Tzeltalan
INFO ... done atkinson2006 [9.7 secs]
Then I can run two quick csvkit commands:
$ csvgrep -c type -m"summary" cldf/trees.csv | csvcut -c newick
newick
"((Huastec:1.300817,Chicomuceltec:1.300817)Huastecan:5.253333...
and
$ csvcut -c ID,Latitude,Longitude cldf/languages.csv
ID,Latitude,Longitude
Acatec,15.5932,-91.8421
Achi,15.073,-90.3513
Aguacatec,15.3307,-91.3153
Chicomuceltec,15.6078,-91.2869
...
to get the input for https://camayal.info/wa/treetom/ to generate https://camayal.info/wa/treetom/?id=gg2UDldVB2iERBpHipNc
I think with a cldfbench
subcommand (defined in pyphlorest
?) to turn trees into nexus, we'd be good to go?
@SimonGreenhill @LinguList @chrzyki CLDF is here: https://github.com/D-PLACE/dplace-tree-atkinson2006/tree/main/cldf#table-treescsv
Hi Robert, some comments
re internal nodes, often these are just arbitrary labels so don't necessarily refer to e.g. a language group so 'defining' them is a bit difficult (e.g. something like CholTzetYuc, CholYuc, and Cholan are probably defined only so that their probability can be logged in the log file rather than "this maps to glottocode xyz".
another bit of metadata that would be useful is what units the branches are in. Phlorest has this in details.txt
I'm still not sure what the benefit of having these in a csv file is, when the nexus format is already defined (poorly, yes) as it means an extra step for people to use it (which then becomes "cldf does something weird with my trees").
I'm not against having nexus files in the cldf dir - in addition to the trees.csv. But trees.csv just makes describing the metadata and validating it a lot easier, e.g. linking the tree to a source. So basically, trees.csv will act as table of contents for the nexus files, even if it actually duplicates the content.
Simon J Greenhill @.***> schrieb am Do., 14. Okt. 2021, 03:21:
Hi Robert, some comments
-
re internal nodes, often these are just arbitrary labels so don't necessarily refer to e.g. a language group so 'defining' them is a bit difficult (e.g. something like CholTzetYuc, CholYuc, and Cholan are probably defined only so that their probability can be logged in the log file rather than "this maps to glottocode xyz".
another bit of metadata that would be useful is what units the branches are in. Phlorest has this in details.txt
I'm still not sure what the benefit of having these in a csv file is, when the nexus format is already defined (poorly, yes) as it means an extra step for people to use it (which then becomes "cldf does something weird with my trees").
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Regarding internal nodes: With the cldfbench setup, when nexus files in cldf/
are not just copied from raw/
, but re-written, we could keeping (and mapping) of internal nodes an editorial decision of whoever maintains the phylogeny.
@SimonGreenhill so: https://github.com/D-PLACE/dplace-tree-atkinson2006/tree/main/cldf#table-treescsv ?
I still think that the python-nexus
API to pull out the newick string is a bit awkward:
>>> import nexus
>>> nexus.NexusReader('cldf/summary.nex').trees.trees[0].newick_string
'((Huastec:1.300817,Chicomuceltec:1.300817)Huastecan:5.253333,(((((Acatec:0.507309,Kanjobal:0.507309):0.754114,Jacaltec:1.261423):0.728661,Motozintlec:1.990084)Kanjobalan:1.108511,((((((Achi:0.712835,(Sacapultec:0.426124,Sipacapa:0.426124)SipaSaca:0.286711):0.187372,(ColonialCakchiquel:0.353536,Tzutujil:0.656924)CakTzut:0.243284):0.085020,Quiche:0.985228):0.236714,Uspantec:1.221941):0.666708,(Kekchi:1.504348,(Pocomam:0.699146,Pocomchi:0.699146)Poco:0.805202):0.384302)Quichean:0.672105,((Aguacatec:1.664564,Ixil:1.664564):0.578361,(Mam:1.228384,Teco:1.228384):1.014542)Mamean:0.317830)EasternMayan:0.537841):1.072956,(((((Chol:1.615929,Chontal:1.615929):0.563078,Chorti:2.179007)Cholan:0.183311,((ColonialYucatec:0.790743,(Itza:0.815110,Mopan:0.815110):0.336037):0.211474,Lacandon:1.362621)Yucatecan:0.999697)CholYuc:0.142400,ClassicMaya:0.855427):0.618826,(Tzeltal:0.974543,Tzotzil:0.974543)Tzeltalan:2.149002)CholTzetYuc:1.048007)MayaProper:2.382599)root;'
But then, if most people will want the "full" Nexus anyway, and tools like phyltr
can get it out:
$ phyltr cat cldf/summary.nex
((Huastec:1.30082,Chicomuceltec:1.30082)1:5.25333,(((((Acatec:0.507309,Kanjobal:0.507309)1:0.754114,Jacaltec:1.26142)1:0.728661,Motozintlec:1.99008)1:1.10851,((((((Achi:0.712835,(Sacapultec:0.426124,Sipacapa:0.426124)1:0.286711)1:0.187372,(ColonialCakchiquel:0.353536,Tzutujil:0.656924)1:0.243284)1:0.08502,Quiche:0.985228)1:0.236714,Uspantec:1.22194)1:0.666708,(Kekchi:1.50435,(Pocomam:0.699146,Pocomchi:0.699146)1:0.805202)1:0.384302)1:0.672105,((Aguacatec:1.66456,Ixil:1.66456)1:0.578361,(Mam:1.22838,Teco:1.22838)1:1.01454)1:0.31783)1:0.537841)1:1.07296,(((((Chol:1.61593,Chontal:1.61593)1:0.563078,Chorti:2.17901)1:0.183311,((ColonialYucatec:0.790743,(Itza:0.81511,Mopan:0.81511)1:0.336037)1:0.211474,Lacandon:1.36262)1:0.999697)1:0.1424,ClassicMaya:0.855427)1:0.618826,(Tzeltal:0.974543,Tzotzil:0.974543)1:2.149)1:1.04801)1:2.3826)1:0;
all is good.
So basically, trees.csv will act as table of contents for the nexus files, even if it actually duplicates the content.
ok, just one concern here is that some of these trees get big, e.g. can be multiple megabytes in size (and I've stripped out a lot of data from the dplace trees but this is non-optimal, beast etc often adds useful information as annotations inside the newick string so the trees can be even bigger).
So I'd strongly push for keep data.nex, summary.trees, and posterior.trees as their own files
Ok, I agree with that - but would add some validation (see https://github.com/D-PLACE/dplace-tree-birchall_et_al2016/issues/1#issuecomment-944024882). With python-newick
having gained the functionality to read BEAST annotations, I'd also try to preserve these in posterior/summary (let's see if we hit the 100mb limit for a sample of 1000 trees with that).
Regarding file names, I'd lean towards *.nex
rather than *.trees
(based on what Wikipedia says about Nexus). Is the *.trees
variant much more common in our field?
Great, sounds good. Keen to keep the beast annotations if we can.
In my experience, .trees or .tre or .tree seems to be common for nexus-that-contain-trees, while .nex is used for data or data+trees. However, it doesn't matter, of course, but most tree viewing apps seem to default to looking for .tre or .trees.
Personally, I prefer .trees as it's transparent what the file contains and it's the beast default (and about 90% of the things in phlorest are from beast). Distinguishing .tree/.trees seems like overkill, and .tre seems like a painful DOS hangover
I think I like this https://github.com/D-PLACE/dplace-tree-birchall_et_al2016/tree/main/cldf as distribution format for phlorest.
Yes, this looks great!
and we could even think about some methods to cut down boiler plate in the cldfbench files e.g. https://github.com/D-PLACE/dplace-tree-birchall_et_al2016/blob/63e6fc0d407579bf17269751bff8a8dc575db2fe/cldfbench_birchall_et_al2016.py#L15-L20
with phlorest.NexusFile(self.cldf_dir / 'summary.trees') as nex:
f = nexus.NexusReader(self.raw_dir / 'relaxed-binary-simple.time.mcct.trees')
f.trees.detranslate()
assert len(f.trees.trees) == 1
self.add_tree(args, f.trees.trees[0], nex, 'summary', 'summary')
# or something like:
ds.add_trees('relaxed-binary-simple.time.mcct.trees', type='summary')
ds.add_trees('relaxed-binary-simple.time.trees.gz', type='summary')
I was thinking about this as well. There might be quite some variation, though: zipped/gzipped/not-zipped, detranslate, burn-in, and then trees coming from other sorts of analyses. So before rushing an all-encompassing API with lots of arguments, I'd go through a couple more phylogenies with the more fine-grained API for special tasks. And rather than having the slick API which might force us to pre-process the "raw" input, I'd have a bit more code in the benches.
Is this the time to also discuss how to add a distance matrix? E.g., based on speaker judgments? These are often only reported as such, may be useful for comparison of previous approaches, and I'd have some examples, where we could start for data on languages with some distance matrix / similarity matrix, based on some criterion.
@LinguList possibly. A distance matrix may be some way of creating a phylogeny, after all. We don't have any examples of this in phlorest, though, do we @SimonGreenhill ?
I could provide one on Chinese dialects later this week.
In fact, here's one example for some dialects from an article by Tang et al. 2009: https://github.com/digling/cddb/blob/master/datasets/Tang2009/raw/Tang2009-table-2.csv
Hm. What kind of distance matrix is this? It doesn't have zeros on the diagonal.
Looking around for distance matrices I have on my laptop:
#nexus
[!Data from Fitch and Margoliash, 1967, Table 3, bottom triangle]
BEGIN TAXA;
DIMENSIONS ntax=20;
TAXLABELS
[1] 'Man'
[2] 'Monkey'
[3] 'Dog'
[4] 'Horse'
[5] 'Donkey'
[6] 'Pig'
[7] 'Rabbit'
[8] 'Kangaroo'
[9] 'Pekin_duck'
[10] 'Pigeon'
[11] 'Chicken'
[12] 'King_penguin'
[13] 'Snapping_turtle'
[14] 'Rattlesnake'
[15] 'Tuna'
[16] 'Screwworm_fly'
[17] 'Moth'
[18] 'Neurospora'
[19] 'Saccharomyces'
[20] 'Candida'
;
END; [TAXA]
BEGIN DISTANCES;
DIMENSIONS ntax=20;
FORMAT labels=no no diagonal triangle=Lower;
MATRIX
1
13 12
17 16 10
16 15 8 1
13 12 4 5 4
12 11 6 11 10 6
12 13 7 11 12 7 7
17 16 12 16 15 13 10 14
16 15 12 16 15 13 8 14 3
18 17 14 16 15 13 11 15 3 4
18 17 14 17 16 14 11 13 3 4 2
19 18 13 16 15 13 11 14 7 8 8 8
20 21 30 32 31 30 25 30 24 24 28 28 31
32 29 27 26 25 26 27 26 27 26 27 27 38 33
32 24 24 25 26 23 26 25 26 26 28 30 40 34 16
36 35 28 33 32 31 29 31 29 30 31 30 33 41 41 16
63 62 64 64 64 64 62 66 61 59 61 62 65 61 72 58 59
56 57 61 60 59 59 59 58 62 62 62 61 64 61 66 63 60 57
66 65 66 68 67 67 67 68 66 66 66 65 67 69 69 65 61 61 41
;
END; [DISTANCES]
or:
#nexus
BEGIN Taxa;
DIMENSIONS ntax=28;
TAXLABELS
[1] 't0'
[2] 't1'
[3] 't2'
[4] 't3'
[5] 't4'
[6] 't5'
[7] 't6'
[8] 't7'
[9] 't8'
[10] 't9'
[11] 't10'
[12] 't11'
[13] 't12'
[14] 't13'
[15] 't14'
[16] 't15'
[17] 't16'
[18] 't17'
[19] 't18'
[20] 't19'
[21] 't20'
[22] 't21'
[23] 't22'
[24] 't23'
[25] 't24'
[26] 't25'
[27] 't26'
[28] 't27'
;
END; [Taxa]
BEGIN Distances;
DIMENSIONS ntax=28;
FORMAT labels=no diagonal triangle=both;
MATRIX
0.0 0.258843 0.409325 0.343611 0.386997 0.415368 0.467334 0.473925 0.408069 0.420966 0.43411 0.394818 0.389391 0.391833 0.378215 0.350729 0.345639 0.384907 0.508726 0.36965 0.401646 0.425956 0.475112 0.398581 0.446356 0.436988 0.369089 0.354247
0.258843 0.0 0.323278 0.239842 0.290738 0.303712 0.355727 0.361451 0.303126 0.328877 0.330235 0.305238 0.287997 0.28925 0.273233 0.243648 0.237544 0.285948 0.408866 0.273934 0.300483 0.316453 0.371314 0.289605 0.336424 0.335047 0.274434 0.251616
0.409325 0.323278 0.0 0.192319 0.275137 0.292431 0.35189 0.362158 0.296773 0.310806 0.326997 0.296513 0.272283 0.273158 0.265655 0.23153 0.229852 0.269492 0.391463 0.269358 0.28426 0.330917 0.349753 0.285794 0.307787 0.311625 0.257636 0.248276
0.343611 0.239842 0.192319 0.0 0.198776 0.213667 0.27468 0.280565 0.218724 0.241622 0.240754 0.218819 0.204383 0.202528 0.189195 0.15777 0.149061 0.201101 0.313442 0.193414 0.209843 0.237999 0.269041 0.214494 0.250222 0.253596 0.178383 0.171646
0.386997 0.290738 0.275137 0.198776 0.0 0.18134 0.319484 0.333494 0.263994 0.288442 0.294154 0.26471 0.243803 0.240583 0.227367 0.205057 0.198308 0.234238 0.355626 0.239961 0.25183 0.273172 0.3179 0.251589 0.285163 0.292887 0.221681 0.211134
0.415368 0.303712 0.292431 0.213667 0.18134 0.0 0.333731 0.340654 0.278111 0.300698 0.31011 0.28522 0.256139 0.258498 0.242481 0.221987 0.217595 0.24785 0.376793 0.252101 0.265266 0.293642 0.330253 0.272678 0.301029 0.302205 0.236378 0.229261
0.467334 0.355727 0.35189 0.27468 0.319484 0.333731 0.0 0.134287 0.168048 0.234852 0.24194 0.242607 0.30195 0.305477 0.288246 0.259075 0.256117 0.302991 0.41738 0.288657 0.317827 0.340062 0.383568 0.3144 0.350638 0.345894 0.292802 0.279407
0.473925 0.361451 0.362158 0.280565 0.333494 0.340654 0.134287 0.0 0.168357 0.231526 0.237933 0.239547 0.309204 0.307881 0.30116 0.27246 0.262342 0.317714 0.443878 0.299024 0.320617 0.343873 0.390457 0.316104 0.350009 0.345356 0.296305 0.28984
0.408069 0.303126 0.296773 0.218724 0.263994 0.278111 0.168048 0.168357 0.0 0.18008 0.181142 0.185641 0.244912 0.248834 0.236322 0.207572 0.201513 0.24871 0.372027 0.240296 0.259989 0.292156 0.320957 0.261104 0.294639 0.291276 0.240866 0.224824
0.420966 0.328877 0.310806 0.241622 0.288442 0.300698 0.234852 0.231526 0.18008 0.0 0.188549 0.209353 0.26783 0.265944 0.259007 0.231976 0.232078 0.276663 0.390486 0.266511 0.289918 0.309126 0.350459 0.278135 0.310626 0.303923 0.262268 0.254853
0.43411 0.330235 0.326997 0.240754 0.294154 0.31011 0.24194 0.237933 0.181142 0.188549 0.0 0.200935 0.276708 0.271141 0.260416 0.23615 0.238456 0.275479 0.390198 0.26919 0.289665 0.316638 0.352527 0.291514 0.320199 0.321656 0.269881 0.254806
0.394818 0.305238 0.296513 0.218819 0.26471 0.28522 0.242607 0.239547 0.185641 0.209353 0.200935 0.0 0.254629 0.248918 0.238901 0.214572 0.209137 0.247335 0.362256 0.244905 0.267859 0.289265 0.340281 0.26744 0.293766 0.290507 0.242726 0.227337
0.389391 0.287997 0.272283 0.204383 0.243803 0.256139 0.30195 0.309204 0.244912 0.26783 0.276708 0.254629 0.0 0.106404 0.215913 0.18846 0.188488 0.229021 0.350267 0.227728 0.243568 0.27675 0.298806 0.241919 0.279501 0.27594 0.228754 0.217302
0.391833 0.28925 0.273158 0.202528 0.240583 0.258498 0.305477 0.307881 0.248834 0.265944 0.271141 0.248918 0.106404 0.0 0.215622 0.188337 0.187822 0.231728 0.359961 0.226188 0.246065 0.271509 0.301748 0.242827 0.27931 0.272526 0.223715 0.214143
0.378215 0.273233 0.265655 0.189195 0.227367 0.242481 0.288246 0.30116 0.236322 0.259007 0.260416 0.238901 0.215913 0.215622 0.0 0.105689 0.170273 0.214167 0.335401 0.21004 0.227981 0.248821 0.290917 0.229404 0.267494 0.265412 0.20581 0.199473
0.350729 0.243648 0.23153 0.15777 0.205057 0.221987 0.259075 0.27246 0.207572 0.231976 0.23615 0.214572 0.18846 0.188337 0.105689 0.0 0.143812 0.184106 0.311927 0.183622 0.201611 0.220634 0.253471 0.201767 0.235578 0.234455 0.175537 0.16857
0.345639 0.237544 0.229852 0.149061 0.198308 0.217595 0.256117 0.262342 0.201513 0.232078 0.238456 0.209137 0.188488 0.187822 0.170273 0.143812 0.0 0.184446 0.309875 0.181246 0.200167 0.221079 0.254232 0.201279 0.233194 0.23272 0.174841 0.166217
0.384907 0.285948 0.269492 0.201101 0.234238 0.24785 0.302991 0.317714 0.24871 0.276663 0.275479 0.247335 0.229021 0.231728 0.214167 0.184106 0.184446 0.0 0.30631 0.205173 0.220753 0.263666 0.288162 0.231847 0.273379 0.270824 0.212613 0.200892
0.508726 0.408866 0.391463 0.313442 0.355626 0.376793 0.41738 0.443878 0.372027 0.390486 0.390198 0.362256 0.350267 0.359961 0.335401 0.311927 0.309875 0.30631 0.0 0.328767 0.345767 0.375018 0.413708 0.353073 0.377324 0.383577 0.326556 0.324104
0.36965 0.273934 0.269358 0.193414 0.239961 0.252101 0.288657 0.299024 0.240296 0.266511 0.26919 0.244905 0.227728 0.226188 0.21004 0.183622 0.181246 0.205173 0.328767 0.0 0.20445 0.244817 0.276658 0.231816 0.269496 0.272141 0.210992 0.20783
0.401646 0.300483 0.28426 0.209843 0.25183 0.265266 0.317827 0.320617 0.259989 0.289918 0.289665 0.267859 0.243568 0.246065 0.227981 0.201611 0.200167 0.220753 0.345767 0.20445 0.0 0.26742 0.303242 0.250707 0.278888 0.278873 0.227446 0.228395
0.425956 0.316453 0.330917 0.237999 0.273172 0.293642 0.340062 0.343873 0.292156 0.309126 0.316638 0.289265 0.27675 0.271509 0.248821 0.220634 0.221079 0.263666 0.375018 0.244817 0.26742 0.0 0.317486 0.270538 0.308484 0.306739 0.264346 0.244066
0.475112 0.371314 0.349753 0.269041 0.3179 0.330253 0.383568 0.390457 0.320957 0.350459 0.352527 0.340281 0.298806 0.301748 0.290917 0.253471 0.254232 0.288162 0.413708 0.276658 0.303242 0.317486 0.0 0.305015 0.339609 0.342366 0.282416 0.276724
0.398581 0.289605 0.285794 0.214494 0.251589 0.272678 0.3144 0.316104 0.261104 0.278135 0.291514 0.26744 0.241919 0.242827 0.229404 0.201767 0.201279 0.231847 0.353073 0.231816 0.250707 0.270538 0.305015 0.0 0.285912 0.284949 0.228325 0.220458
0.446356 0.336424 0.307787 0.250222 0.285163 0.301029 0.350638 0.350009 0.294639 0.310626 0.320199 0.293766 0.279501 0.27931 0.267494 0.235578 0.233194 0.273379 0.377324 0.269496 0.278888 0.308484 0.339609 0.285912 0.0 0.097874 0.268769 0.255562
0.436988 0.335047 0.311625 0.253596 0.292887 0.302205 0.345894 0.345356 0.291276 0.303923 0.321656 0.290507 0.27594 0.272526 0.265412 0.234455 0.23272 0.270824 0.383577 0.272141 0.278873 0.306739 0.342366 0.284949 0.097874 0.0 0.270444 0.256659
0.369089 0.274434 0.257636 0.178383 0.221681 0.236378 0.292802 0.296305 0.240866 0.262268 0.269881 0.242726 0.228754 0.223715 0.20581 0.175537 0.174841 0.212613 0.326556 0.210992 0.227446 0.264346 0.282416 0.228325 0.268769 0.270444 0.0 0.183681
0.354247 0.251616 0.248276 0.171646 0.211134 0.229261 0.279407 0.28984 0.224824 0.254853 0.254806 0.227337 0.217302 0.214143 0.199473 0.16857 0.166217 0.200892 0.324104 0.20783 0.228395 0.244066 0.276724 0.220458 0.255562 0.256659 0.183681 0.0
;
END; [Distances]
BEGIN Splits;
DIMENSIONS ntax=28 nsplits=90;
FORMAT labels=no weights=yes confidences=no intervals=no;
PROPERTIES fit=98.91263210217491 cyclic;
CYCLE 1 3 4 5 6 17 15 16 13 14 12 10 11 9 8 7 26 25 19 18 21 20 23 22 24 27 28 2;
MATRIX
[1, size=1] 0.1279713 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[2, size=2] 0.017143281 1 2 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[3, size=4] 0.0013902244 1 2 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[4, size=9] 1.5893235E-4 1 2 7 8 9 10 11 12 18 19 20 21 22 23 24 25 26 27 28,
[5, size=3] 0.002769119 1 2 28,
[6, size=2] 0.08403672 1 2,
[7, size=1] 0.17367192 1,
[8, size=1] 0.057841897 1 2 3 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[9, size=3] 1.7729466E-4 1 2 3 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[10, size=5] 3.662799E-4 1 2 3 7 8 9 10 11 12 13 14 16 18 19 20 21 22 23 24 25 26 27 28,
[11, size=2] 0.0060906718 1 3,
[12, size=1] 0.08134686 1 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[13, size=2] 0.038590923 1 2 3 4 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[14, size=4] 0.0018195357 1 2 3 4,
[15, size=1] 0.09856205 1 2 3 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[16, size=8] 4.167727E-4 1 2 3 4 5 24 27 28,
[17, size=4] 0.0012588556 1 3 4 5,
[18, size=1] 0.0688266 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 18 19 20 21 22 23 24 25 26 27 28,
[19, size=3] 8.21237E-4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 18 19 20 21 22 23 24 25 26 27 28,
[20, size=12] 8.992125E-4 1 2 3 4 5 6 18 19 20 21 22 23 24 25 27 28,
[21, size=8] 0.0022681346 1 2 3 4 5 6 27 28,
[22, size=1] 0.0665534 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20 21 22 23 24 25 26 27 28,
[23, size=2] 0.03262534 1 2 3 4 5 6 7 8 9 10 11 12 13 14 17 18 19 20 21 22 23 24 25 26 27 28,
[24, size=3] 4.7089203E-4 1 2 3 4 5 6 7 8 9 10 11 12 14 17 18 19 20 21 22 23 24 25 26 27 28,
[25, size=4] 4.685271E-4 1 2 3 4 5 6 7 8 9 10 11 12 17 18 19 20 21 22 23 24 25 26 27 28,
[26, size=9] 0.0010168224 1 2 3 4 5 6 17 27 28,
[27, size=1] 0.03859058 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17 18 19 20 21 22 23 24 25 26 27 28,
[28, size=3] 3.5754856E-4 1 2 3 4 5 6 7 8 9 10 11 12 15 17 18 19 20 21 22 23 24 25 26 27 28,
[29, size=1] 0.052748006 1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[30, size=2] 0.06064433 1 2 3 4 5 6 7 8 9 10 11 12 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[31, size=7] 0.0010652957 1 2 3 4 5 6 7 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[32, size=9] 7.895848E-4 1 2 3 4 5 6 15 16 17 18 19 20 21 22 23 24 25 27 28,
[33, size=1] 0.05142635 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[34, size=4] 0.0011806624 1 2 3 4 5 6 7 8 9 13 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[35, size=8] 1.787175E-4 1 2 3 4 5 6 13 15 16 17 18 19 20 21 22 23 24 25 27 28,
[36, size=9] 8.855418E-4 1 2 3 4 5 6 13 15 16 17 18 19 20 21 22 23 24 27 28,
[37, size=1] 0.0914247 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[38, size=5] 0.0012966593 1 2 3 4 5 6 7 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[39, size=6] 0.038550336 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[40, size=9] 0.001203692 1 2 3 4 5 6 13 14 15 16 17 18 20 21 22 23 24 27 28,
[41, size=1] 0.092642605 1 2 3 4 5 6 7 8 9 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[42, size=2] 0.0044335932 1 2 3 4 5 6 7 8 9 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[43, size=4] 0.0014246248 1 2 3 4 5 6 7 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[44, size=5] 0.012698617 1 2 3 4 5 6 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[45, size=7] 2.5534217E-4 1 2 3 4 5 6 12 13 14 15 16 17 18 19 20 21 22 23 24 27 28,
[46, size=1] 0.09702029 1 2 3 4 5 6 7 8 9 10 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[47, size=4] 0.0010406735 1 2 3 4 5 6 10 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[48, size=1] 0.05344081 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[49, size=3] 0.021949023 1 2 3 4 5 6 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[50, size=14] 3.9997647E-4 1 2 3 4 5 6 10 11 12 13 14 15 16 17,
[51, size=1] 0.06783958 1 2 3 4 5 6 7 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[52, size=2] 0.04591966 1 2 3 4 5 6 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[53, size=3] 2.8627258E-4 1 2 3 4 5 6 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 27 28,
[54, size=4] 4.0487162E-4 1 2 3 4 5 6 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 27 28,
[55, size=1] 0.064194575 1 2 3 4 5 6 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
[56, size=1] 0.047182973 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 27 28,
[57, size=2] 0.10500158 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 27 28,
[58, size=3] 0.0014762296 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 21 22 23 24 27 28,
[59, size=5] 0.001466786 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 20 22 23 24 27 28,
[60, size=8] 8.702291E-4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 24 27 28,
[61, size=9] 0.0010516174 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 27 28,
[62, size=1] 0.044313457 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 26 27 28,
[63, size=2] 0.004750093 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 21 22 23 24 26 27 28,
[64, size=6] 1.337294E-5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 22 24 26 27 28,
[65, size=1] 0.20566262 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 21 22 23 24 25 26 27 28,
[66, size=2] 0.013317523 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 20 21 22 23 24 25 26 27 28,
[67, size=4] 0.0021476466 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 22 23 24 25 26 27 28,
[68, size=8] 0.0015038507 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 25 26 28,
[69, size=1] 0.09029672 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 19 20 21 22 23 24 25 26 27 28,
[70, size=2] 1.5751319E-4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 19 20 22 23 24 25 26 27 28,
[71, size=4] 0.0019187863 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 19 22 24 25 26 27 28,
[72, size=1] 0.109228075 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23 24 25 26 27 28,
[73, size=2] 0.006326371 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 22 23 24 25 26 27 28,
[74, size=4] 0.0023116763 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 24 25 26 27 28,
[75, size=1] 0.089916565 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 21 22 23 24 25 26 27 28,
[76, size=2] 1.050739E-4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 21 22 24 25 26 27 28,
[77, size=3] 0.0016833246 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 21 24 25 26 27 28,
[78, size=1] 0.17474917 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 24 25 26 27 28,
[79, size=2] 0.0022174222 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 24 25 26 27 28,
[80, size=3] 0.001391344 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 25 26 27 28,
[81, size=5] 4.242624E-4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 25 26,
[82, size=1] 0.13961297 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 23 24 25 26 27 28,
[83, size=2] 0.0013124112 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 23 25 26 27 28,
[84, size=1] 0.1194786 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 25 26 27 28,
[85, size=2] 4.8682792E-4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 25 26 28,
[86, size=1] 0.09366718 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 28,
[87, size=2] 0.0045568696 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26,
[88, size=1] 0.08093566 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27,
[89, size=2] 0.0015117239 1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27,
[90, size=1] 0.076351464 1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28,
;
END; [Splits]
BEGIN Network;
DIMENSIONS ntax=28 nvertices=346 nedges=600;
DRAW to_scale;
TRANSLATE
2 't0',
3 't2',
4 't3',
5 't4',
6 't5',
7 't16',
8 't14',
9 't15',
10 't12',
11 't13',
12 't11',
13 't9',
14 't10',
15 't8',
16 't7',
17 't6',
18 't25',
19 't24',
20 't18',
21 't17',
22 't20',
23 't19',
24 't22',
25 't21',
26 't23',
27 't26',
28 't27',
29 't1',
;
VERTICES
1 -0.17367192 0.0 w=2 h=2 s=n,
2 0.0 0.0 w=2 h=2 s=n,
3 -0.11768977 0.045046512 w=2 h=2 s=n,
4 -0.19602421 0.041381847 w=2 h=2 s=n,
5 -0.17523988 0.08842624 w=2 h=2 s=n,
6 -0.17898935 0.1143971 w=2 h=2 s=n,
7 -0.24188966 0.07239327 w=2 h=2 s=n,
8 -0.2543338 0.10839781 w=2 h=2 s=n,
9 -0.26945123 0.082165845 w=2 h=2 s=n,
10 -0.30659822 0.12002788 w=2 h=2 s=n,
11 -0.31904063 0.11555596 w=2 h=2 s=n,
12 -0.3723963 0.09579757 w=2 h=2 s=n,
13 -0.40555912 0.08773562 w=2 h=2 s=n,
14 -0.4215746 0.07218595 w=2 h=2 s=n,
15 -0.40375552 0.03841092 w=2 h=2 s=n,
16 -0.46578634 0.021180429 w=2 h=2 s=n,
17 -0.457186 0.0051984675 w=2 h=2 s=n,
18 -0.4112275 -0.0713001 w=2 h=2 s=n,
19 -0.40493098 -0.082802355 w=2 h=2 s=n,
20 -0.41573197 -0.17496379 w=2 h=2 s=n,
21 -0.32123187 -0.093300976 w=2 h=2 s=n,
22 -0.29972327 -0.1157434 w=2 h=2 s=n,
23 -0.27406484 -0.09962054 w=2 h=2 s=n,
24 -0.23216128 -0.17543635 w=2 h=2 s=n,
25 -0.2095651 -0.12994294 w=2 h=2 s=n,
26 -0.19596717 -0.09110639 w=2 h=2 s=n,
27 -0.19119321 -0.05464892 w=2 h=2 s=n,
28 -0.1878907 -0.030641899 w=2 h=2 s=n,
29 -0.10504844 -0.018586801 w=2 h=2 s=n,
30 -0.25718024 0.009409128 w=2 h=2 s=n,
31 -0.26323262 0.008727189 w=2 h=2 s=n,
32 -0.17972429 -6.819389E-4 w=2 h=2 s=n,
33 -0.25987992 0.010025315 w=2 h=2 s=n,
34 -0.2659323 0.009343375 w=2 h=2 s=n,
35 -0.2677404 0.009139652 w=2 h=2 s=n,
36 -0.2650407 0.008523465 w=2 h=2 s=n,
37 -0.26892862 0.008723878 w=2 h=2 s=n,
38 -0.2662289 0.008107692 w=2 h=2 s=n,
39 -0.2644208 0.008311415 w=2 h=2 s=n,
40 -0.18091251 -0.0010977125 w=2 h=2 s=n,
41 -0.26934275 0.008770542 w=2 h=2 s=n,
42 -0.27159664 0.008516592 w=2 h=2 s=n,
43 -0.27118248 0.008469928 w=2 h=2 s=n,
44 -0.27258795 0.008290328 w=2 h=2 s=n,
45 -0.27217382 0.008243664 w=2 h=2 s=n,
46 -0.27272007 0.007912797 w=2 h=2 s=n,
47 -0.2723059 0.007866133 w=2 h=2 s=n,
48 -0.2713146 0.008092397 w=2 h=2 s=n,
49 -0.2690607 0.008346348 w=2 h=2 s=n,
50 -0.26636103 0.007730161 w=2 h=2 s=n,
51 -0.27223247 0.009152431 w=2 h=2 s=n,
52 -0.2732238 0.008926167 w=2 h=2 s=n,
53 -0.2733559 0.008548636 w=2 h=2 s=n,
54 -0.25981095 0.010168508 w=2 h=2 s=n,
55 -0.26586333 0.009486569 w=2 h=2 s=n,
56 -0.26767144 0.009282845 w=2 h=2 s=n,
57 -0.26885965 0.008867072 w=2 h=2 s=n,
58 -0.2692738 0.008913736 w=2 h=2 s=n,
59 -0.27152768 0.008659785 w=2 h=2 s=n,
60 -0.2721635 0.009295625 w=2 h=2 s=n,
61 -0.27315485 0.00906936 w=2 h=2 s=n,
62 -0.27386624 0.009411948 w=2 h=2 s=n,
63 -0.2739352 0.009268755 w=2 h=2 s=n,
64 -0.27406728 0.008891224 w=2 h=2 s=n,
65 -0.27472958 0.009609 w=2 h=2 s=n,
66 -0.27479854 0.009465806 w=2 h=2 s=n,
67 -0.27493063 0.009088275 w=2 h=2 s=n,
68 -0.27421924 0.008745687 w=2 h=2 s=n,
69 -0.2735834 0.008109848 w=2 h=2 s=n,
70 -0.27600223 0.009465806 w=2 h=2 s=n,
71 -0.27613434 0.009088275 w=2 h=2 s=n,
72 -0.27542293 0.008745687 w=2 h=2 s=n,
73 -0.2747871 0.008109848 w=2 h=2 s=n,
74 -0.27392375 0.007912797 w=2 h=2 s=n,
75 -0.27636832 0.008063024 w=2 h=2 s=n,
76 -0.27565694 0.0077204364 w=2 h=2 s=n,
77 -0.2750211 0.007084597 w=2 h=2 s=n,
78 -0.27415776 0.0068875453 w=2 h=2 s=n,
79 -0.27295408 0.0068875453 w=2 h=2 s=n,
80 -0.2725399 0.0068408814 w=2 h=2 s=n,
81 -0.27489826 0.009668026 w=2 h=2 s=n,
82 -0.27496722 0.009524833 w=2 h=2 s=n,
83 -0.2761709 0.009524833 w=2 h=2 s=n,
84 -0.27630302 0.009147302 w=2 h=2 s=n,
85 -0.27653703 0.008122051 w=2 h=2 s=n,
86 -0.27682444 0.0073006563 w=2 h=2 s=n,
87 -0.27665576 0.0072416295 w=2 h=2 s=n,
88 -0.27594435 0.0068990416 w=2 h=2 s=n,
89 -0.27530852 0.006263202 w=2 h=2 s=n,
90 -0.27444518 0.0060661505 w=2 h=2 s=n,
91 -0.2732415 0.0060661505 w=2 h=2 s=n,
92 -0.2742768 0.004571756 w=2 h=2 s=n,
93 -0.2730731 0.004571756 w=2 h=2 s=n,
94 -0.2727857 0.0053931507 w=2 h=2 s=n,
95 -0.27237153 0.005346487 w=2 h=2 s=n,
96 -0.27213752 0.006371738 w=2 h=2 s=n,
97 -0.2711462 0.0065980023 w=2 h=2 s=n,
98 -0.26889235 0.006851953 w=2 h=2 s=n,
99 -0.27469912 0.010076149 w=2 h=2 s=n,
100 -0.27556247 0.0102732 w=2 h=2 s=n,
101 -0.27573115 0.010332228 w=2 h=2 s=n,
102 -0.2758001 0.010189034 w=2 h=2 s=n,
103 -0.2770038 0.010189034 w=2 h=2 s=n,
104 -0.2771359 0.009811503 w=2 h=2 s=n,
105 -0.27725914 0.010189034 w=2 h=2 s=n,
106 -0.27739123 0.009811503 w=2 h=2 s=n,
107 -0.27655837 0.009147302 w=2 h=2 s=n,
108 -0.27679238 0.008122051 w=2 h=2 s=n,
109 -0.2770798 0.0073006563 w=2 h=2 s=n,
110 -0.2769111 0.0072416295 w=2 h=2 s=n,
111 -0.2761997 0.0068990416 w=2 h=2 s=n,
112 -0.27556387 0.006263202 w=2 h=2 s=n,
113 -0.31339082 0.022921404 w=2 h=2 s=n,
114 -0.31364617 0.022921404 w=2 h=2 s=n,
115 -0.31377825 0.022543872 w=2 h=2 s=n,
116 -0.31294537 0.021879671 w=2 h=2 s=n,
117 -0.27556828 0.00625058 w=2 h=2 s=n,
118 -0.27531293 0.00625058 w=2 h=2 s=n,
119 -0.2744496 0.0060535283 w=2 h=2 s=n,
120 -0.27428123 0.004559133 w=2 h=2 s=n,
121 -0.27307752 0.004559133 w=2 h=2 s=n,
122 -0.26563498 0.009772938 w=2 h=2 s=n,
123 -0.26744306 0.009569215 w=2 h=2 s=n,
124 -0.26863128 0.009153441 w=2 h=2 s=n,
125 -0.26904544 0.0092001045 w=2 h=2 s=n,
126 -0.2712993 0.008946154 w=2 h=2 s=n,
127 -0.27193514 0.009581993 w=2 h=2 s=n,
128 -0.27292648 0.009355729 w=2 h=2 s=n,
129 -0.31455907 0.023484003 w=2 h=2 s=n,
130 -0.31481442 0.023484003 w=2 h=2 s=n,
131 -0.3149465 0.02310647 w=2 h=2 s=n,
132 -0.32719463 0.02630971 w=2 h=2 s=n,
133 -0.32732674 0.02593218 w=2 h=2 s=n,
134 -0.3261585 0.02536958 w=2 h=2 s=n,
135 -0.3253256 0.024705378 w=2 h=2 s=n,
136 -0.2779943 0.0061538764 w=2 h=2 s=n,
137 -0.27782562 0.00609485 w=2 h=2 s=n,
138 -0.27711424 0.005752262 w=2 h=2 s=n,
139 -0.27647838 0.0051164227 w=2 h=2 s=n,
140 -0.27648282 0.0051038004 w=2 h=2 s=n,
141 -0.27622747 0.0051038004 w=2 h=2 s=n,
142 -0.27536413 0.004906749 w=2 h=2 s=n,
143 -0.27519575 0.0034123538 w=2 h=2 s=n,
144 -0.27399206 0.0034123538 w=2 h=2 s=n,
145 -0.272813 0.0042274315 w=2 h=2 s=n,
146 -0.27280858 0.004240054 w=2 h=2 s=n,
147 -0.27252117 0.005061449 w=2 h=2 s=n,
148 -0.272107 0.005014785 w=2 h=2 s=n,
149 -0.271873 0.006040036 w=2 h=2 s=n,
150 -0.27088168 0.0062663006 w=2 h=2 s=n,
151 -0.26862782 0.0065202513 w=2 h=2 s=n,
152 -0.26879618 0.008014646 w=2 h=2 s=n,
153 -0.2660965 0.007398459 w=2 h=2 s=n,
154 -0.25863382 0.010908152 w=2 h=2 s=n,
155 -0.2646862 0.010226212 w=2 h=2 s=n,
156 -0.26445782 0.010512582 w=2 h=2 s=n,
157 -0.26626593 0.010308859 w=2 h=2 s=n,
158 -0.26745415 0.009893085 w=2 h=2 s=n,
159 -0.26786828 0.009939749 w=2 h=2 s=n,
160 -0.27297893 0.009821311 w=2 h=2 s=n,
161 -0.2732073 0.009534941 w=2 h=2 s=n,
162 -0.2739187 0.009877529 w=2 h=2 s=n,
163 -0.27475157 0.010541731 w=2 h=2 s=n,
164 -0.27561492 0.010738782 w=2 h=2 s=n,
165 -0.2757836 0.010797808 w=2 h=2 s=n,
166 -0.27661845 0.011632663 w=2 h=2 s=n,
167 -0.276566 0.011167082 w=2 h=2 s=n,
168 -0.27663496 0.011023888 w=2 h=2 s=n,
169 -0.27783865 0.011023888 w=2 h=2 s=n,
170 -0.31422567 0.023756258 w=2 h=2 s=n,
171 -0.31539392 0.024318857 w=2 h=2 s=n,
172 -0.31564927 0.024318857 w=2 h=2 s=n,
173 -0.32802948 0.027144564 w=2 h=2 s=n,
174 -0.32937416 0.027615089 w=2 h=2 s=n,
175 -0.3285393 0.026780235 w=2 h=2 s=n,
176 -0.32867143 0.026402703 w=2 h=2 s=n,
177 -0.3304083 0.027731607 w=2 h=2 s=n,
178 -0.32957345 0.026896752 w=2 h=2 s=n,
179 -0.32970557 0.026519222 w=2 h=2 s=n,
180 -0.3283609 0.026048698 w=2 h=2 s=n,
181 -0.32719263 0.025486099 w=2 h=2 s=n,
182 -0.32635975 0.024821898 w=2 h=2 s=n,
183 -0.3300877 0.026385501 w=2 h=2 s=n,
184 -0.32874304 0.025914976 w=2 h=2 s=n,
185 -0.3275748 0.025352377 w=2 h=2 s=n,
186 -0.3267419 0.024688177 w=2 h=2 s=n,
187 -0.32570776 0.024571659 w=2 h=2 s=n,
188 -0.31332752 0.02174595 w=2 h=2 s=n,
189 -0.2769405 0.009013581 w=2 h=2 s=n,
190 -0.2771745 0.00798833 w=2 h=2 s=n,
191 -0.27746195 0.0071669356 w=2 h=2 s=n,
192 -0.27837646 0.006020156 w=2 h=2 s=n,
193 -0.27820778 0.0059611294 w=2 h=2 s=n,
194 -0.27749637 0.0056185415 w=2 h=2 s=n,
195 -0.27686054 0.004982702 w=2 h=2 s=n,
196 -0.27686495 0.0049700798 w=2 h=2 s=n,
197 -0.27590507 0.001385226 w=2 h=2 s=n,
198 -0.2747014 0.001385226 w=2 h=2 s=n,
199 -0.27378684 0.0025320055 w=2 h=2 s=n,
200 -0.2749162 -5.2149536E-4 w=2 h=2 s=n,
201 -0.2740017 6.252841E-4 w=2 h=2 s=n,
202 -0.27329236 0.002652412 w=2 h=2 s=n,
203 -0.27302784 0.0023207103 w=2 h=2 s=n,
204 -0.2746574 -0.0028186364 w=2 h=2 s=n,
205 -0.27374285 -0.0016718569 w=2 h=2 s=n,
206 -0.27303353 3.55271E-4 w=2 h=2 s=n,
207 -0.272769 2.3569324E-5 w=2 h=2 s=n,
208 -0.2725542 0.0019302907 w=2 h=2 s=n,
209 -0.27254978 0.0019429132 w=2 h=2 s=n,
210 -0.2643192 0.0106231235 w=2 h=2 s=n,
211 -0.26612732 0.0104194 w=2 h=2 s=n,
212 -0.26731554 0.010003626 w=2 h=2 s=n,
213 -0.26772967 0.01005029 w=2 h=2 s=n,
214 -0.2717524 0.01038264 w=2 h=2 s=n,
215 -0.27274373 0.010156376 w=2 h=2 s=n,
216 -0.27279618 0.0106219575 w=2 h=2 s=n,
217 -0.27302456 0.010335588 w=2 h=2 s=n,
218 -0.27279618 0.011092849 w=2 h=2 s=n,
219 -0.27302456 0.01080648 w=2 h=2 s=n,
220 -0.2732073 0.010005834 w=2 h=2 s=n,
221 -0.2739187 0.0103484215 w=2 h=2 s=n,
222 -0.27475157 0.011012622 w=2 h=2 s=n,
223 -0.27310413 0.011155064 w=2 h=2 s=n,
224 -0.27328685 0.010354417 w=2 h=2 s=n,
225 -0.27399826 0.010697005 w=2 h=2 s=n,
226 -0.27483115 0.011361206 w=2 h=2 s=n,
227 -0.27483115 0.010890314 w=2 h=2 s=n,
228 -0.2756945 0.011087365 w=2 h=2 s=n,
229 -0.27586317 0.011146393 w=2 h=2 s=n,
230 -0.27669802 0.011981247 w=2 h=2 s=n,
231 -0.3516546 0.026519222 w=2 h=2 s=n,
232 -0.3520367 0.026385501 w=2 h=2 s=n,
233 -0.35069206 0.025914976 w=2 h=2 s=n,
234 -0.3495238 0.025352377 w=2 h=2 s=n,
235 -0.34869093 0.024688177 w=2 h=2 s=n,
236 -0.3523158 0.026321799 w=2 h=2 s=n,
237 -0.35097113 0.025851276 w=2 h=2 s=n,
238 -0.34980288 0.025288677 w=2 h=2 s=n,
239 -0.34897003 0.024624474 w=2 h=2 s=n,
240 -0.327021 0.024624474 w=2 h=2 s=n,
241 -0.32598686 0.024507957 w=2 h=2 s=n,
242 -0.31360662 0.02168225 w=2 h=2 s=n,
243 -0.2772196 0.00894988 w=2 h=2 s=n,
244 -0.2774536 0.007924628 w=2 h=2 s=n,
245 -0.27774104 0.007103234 w=2 h=2 s=n,
246 -0.27865556 0.005956454 w=2 h=2 s=n,
247 -0.2798097 0.00503604 w=2 h=2 s=n,
248 -0.2795306 0.0050997417 w=2 h=2 s=n,
249 -0.27936193 0.0050407154 w=2 h=2 s=n,
250 -0.27865055 0.004698127 w=2 h=2 s=n,
251 -0.27801472 0.004062288 w=2 h=2 s=n,
252 -0.27801913 0.0040496653 w=2 h=2 s=n,
253 -0.27763698 0.004183386 w=2 h=2 s=n,
254 -0.27738163 0.004183386 w=2 h=2 s=n,
255 -0.2765183 0.0039863344 w=2 h=2 s=n,
256 -0.2763499 0.0024919398 w=2 h=2 s=n,
257 -0.27705923 4.648119E-4 w=2 h=2 s=n,
258 -0.2733683 -0.003312977 w=2 h=2 s=n,
259 -0.27265897 -0.0012858491 w=2 h=2 s=n,
260 -0.27239445 -0.0016175507 w=2 h=2 s=n,
261 -0.2721796 2.891706E-4 w=2 h=2 s=n,
262 -0.2721752 3.017931E-4 w=2 h=2 s=n,
263 -0.27179077 -0.0028711085 w=2 h=2 s=n,
264 -0.27157593 -9.64387E-4 w=2 h=2 s=n,
265 -0.27157152 -9.517645E-4 w=2 h=2 s=n,
266 -0.27194607 6.893556E-4 w=2 h=2 s=n,
267 -0.2722049 0.0029864965 w=2 h=2 s=n,
268 -0.2719175 0.0038078912 w=2 h=2 s=n,
269 -0.27150333 0.0037612275 w=2 h=2 s=n,
270 -0.24245258 0.016570218 w=2 h=2 s=n,
271 -0.24850495 0.01588828 w=2 h=2 s=n,
272 -0.24827658 0.016174648 w=2 h=2 s=n,
273 -0.24813797 0.01628519 w=2 h=2 s=n,
274 -0.23883942 0.037707303 w=2 h=2 s=n,
275 -0.24002762 0.03729153 w=2 h=2 s=n,
276 -0.24044177 0.037338194 w=2 h=2 s=n,
277 -0.2691433 0.04351305 w=2 h=2 s=n,
278 -0.2693717 0.043226678 w=2 h=2 s=n,
279 -0.26945123 0.043575265 w=2 h=2 s=n,
280 -0.2948607 0.068602376 w=2 h=2 s=n,
281 -0.2948607 0.068131484 w=2 h=2 s=n,
282 -0.29572403 0.06832854 w=2 h=2 s=n,
283 -0.29589272 0.06838756 w=2 h=2 s=n,
284 -0.29672757 0.06922242 w=2 h=2 s=n,
285 -0.3331282 0.029973902 w=2 h=2 s=n,
286 -0.33416232 0.030090421 w=2 h=2 s=n,
287 -0.39794675 0.021180429 w=2 h=2 s=n,
288 -0.39660206 0.020709904 w=2 h=2 s=n,
289 -0.3954338 0.020147305 w=2 h=2 s=n,
290 -0.39460093 0.019483104 w=2 h=2 s=n,
291 -0.3687171 -0.05082817 w=2 h=2 s=n,
292 -0.368438 -0.050764468 w=2 h=2 s=n,
293 -0.36826932 -0.050823495 w=2 h=2 s=n,
294 -0.3675579 -0.051166084 w=2 h=2 s=n,
295 -0.36692208 -0.051801924 w=2 h=2 s=n,
296 -0.3669265 -0.051814545 w=2 h=2 s=n,
297 -0.37028533 -0.055173367 w=2 h=2 s=n,
298 -0.28137794 6.908425E-4 w=2 h=2 s=n,
299 -0.2809958 8.2456315E-4 w=2 h=2 s=n,
300 -0.28074044 8.2456315E-4 w=2 h=2 s=n,
301 -0.27987713 6.275116E-4 w=2 h=2 s=n,
302 -0.27970874 -8.668832E-4 w=2 h=2 s=n,
303 -0.28041807 -0.002894011 w=2 h=2 s=n,
304 -0.2875034 -0.01417028 w=2 h=2 s=n,
305 -0.28414458 -0.0108114565 w=2 h=2 s=n,
306 -0.28299043 -0.0098910425 w=2 h=2 s=n,
307 -0.28178674 -0.0098910425 w=2 h=2 s=n,
308 -0.28200155 -0.011797764 w=2 h=2 s=n,
309 -0.2820536 -0.011946438 w=2 h=2 s=n,
310 -0.27496824 -6.701694E-4 w=2 h=2 s=n,
311 -0.2747094 -0.0029673104 w=2 h=2 s=n,
312 -0.27541775 -0.009253902 w=2 h=2 s=n,
313 -0.2753657 -0.009105228 w=2 h=2 s=n,
314 -0.2744512 -0.0079584485 w=2 h=2 s=n,
315 -0.2740766 -0.009599569 w=2 h=2 s=n,
316 -0.27406484 -0.009703982 w=2 h=2 s=n,
317 -0.27335653 -0.0034173902 w=2 h=2 s=n,
318 -0.2726472 -0.0013902623 w=2 h=2 s=n,
319 -0.27238268 -0.001721964 w=2 h=2 s=n,
320 -0.271779 -0.0029755216 w=2 h=2 s=n,
321 -0.27104664 -0.0050685094 w=2 h=2 s=n,
322 -0.27105838 -0.0049640965 w=2 h=2 s=n,
323 -0.27084357 -0.0030573749 w=2 h=2 s=n,
324 -0.27083912 -0.0030447524 w=2 h=2 s=n,
325 -0.2701409 -0.004156003 w=2 h=2 s=n,
326 -0.27087325 -0.0020630148 w=2 h=2 s=n,
327 -0.27124783 -4.218947E-4 w=2 h=2 s=n,
328 -0.27150667 0.0018752463 w=2 h=2 s=n,
329 -0.27121925 0.002696641 w=2 h=2 s=n,
330 -0.2708051 0.0026499773 w=2 h=2 s=n,
331 -0.27046084 0.002305738 w=2 h=2 s=n,
332 -0.2711591 0.0034169883 w=2 h=2 s=n,
333 -0.2717628 0.004670546 w=2 h=2 s=n,
334 -0.27152878 0.005695797 w=2 h=2 s=n,
335 -0.27053744 0.0059220614 w=2 h=2 s=n,
336 -0.26828358 0.0061760116 w=2 h=2 s=n,
337 -0.26442516 0.003751611 w=2 h=2 s=n,
338 -0.2647694 0.0040958505 w=2 h=2 s=n,
339 -0.2649378 0.005590245 w=2 h=2 s=n,
340 -0.2622381 0.0049740584 w=2 h=2 s=n,
341 -0.26081118 0.0044747675 w=2 h=2 s=n,
342 -0.2646696 0.0068991682 w=2 h=2 s=n,
343 -0.26493412 0.00723087 w=2 h=2 s=n,
344 -0.26480204 0.007608401 w=2 h=2 s=n,
345 -0.26299393 0.007812124 w=2 h=2 s=n,
346 -0.17948562 -0.0015970032 w=2 h=2 s=n,
;
VLABELS
2 't0' x=12 y=6 f='Dialog-PLAIN-10',
3 't2' x=12 y=9,
4 't3' x=11 y=11,
5 't4' x=9 y=13,
6 't5' x=3 y=20,
7 't16' x=-3 y=22,
8 't14' x=-5 y=23,
9 't15' x=-8 y=23,
10 't12' x=-11 y=23,
11 't13' x=-13 y=17,
12 't11' x=-16 y=20,
13 't9' x=-19 y=13,
14 't10' x=-27 y=11,
15 't8' x=-22 y=9,
16 't7' x=-22 y=6,
17 't6' x=-22 y=3,
18 't25' x=-27 y=1,
19 't24' x=-25 y=-1,
20 't18' x=-15 y=-13,
21 't17' x=-13 y=-15,
22 't20' x=-11 y=-16,
23 't19' x=-8 y=-16,
24 't22' x=-5 y=-16,
25 't21' x=-3 y=-15,
26 't23' x=-1 y=-13,
27 't26' x=9 y=-1,
28 't27' x=11 y=1,
29 't1' x=12 y=3,
;
EDGES
1 1 2 s=7 w=0.17367192,
2 30 1 s=6 w=0.08403672,
3 31 30 s=11 w=0.0060906718,
4 32 1 s=11 w=0.0060906718,
5 32 31 s=6 w=0.08403672,
6 33 30 s=5 w=0.002769119,
7 34 31 s=5 w=0.002769119,
8 34 33 s=11 w=0.0060906718,
9 35 34 s=14 w=0.0018195357,
10 36 31 s=14 w=0.0018195357,
11 36 35 s=5 w=0.002769119,
12 37 35 s=17 w=0.0012588556,
13 38 36 s=17 w=0.0012588556,
14 38 37 s=5 w=0.002769119,
15 39 31 s=17 w=0.0012588556,
16 39 38 s=14 w=0.0018195357,
17 40 32 s=17 w=0.0012588556,
18 40 39 s=6 w=0.08403672,
19 41 37 s=16 w=4.167727E-4,
20 42 41 s=21 w=0.0022681346,
21 43 37 s=21 w=0.0022681346,
22 43 42 s=16 w=4.167727E-4,
23 44 42 s=26 w=0.0010168224,
24 45 43 s=26 w=0.0010168224,
25 45 44 s=16 w=4.167727E-4,
26 46 44 s=50 w=3.9997647E-4,
27 47 45 s=50 w=3.9997647E-4,
28 47 46 s=16 w=4.167727E-4,
29 48 43 s=50 w=3.9997647E-4,
30 48 47 s=26 w=0.0010168224,
31 49 37 s=50 w=3.9997647E-4,
32 49 48 s=21 w=0.0022681346,
33 50 38 s=50 w=3.9997647E-4,
34 50 49 s=5 w=0.002769119,
35 51 42 s=20 w=8.992125E-4,
36 52 44 s=20 w=8.992125E-4,
37 52 51 s=26 w=0.0010168224,
38 53 46 s=20 w=8.992125E-4,
39 53 52 s=50 w=3.9997647E-4,
40 54 33 s=4 w=1.5893235E-4,
41 55 34 s=4 w=1.5893235E-4,
42 55 54 s=11 w=0.0060906718,
43 56 35 s=4 w=1.5893235E-4,
44 56 55 s=14 w=0.0018195357,
45 57 37 s=4 w=1.5893235E-4,
46 57 56 s=17 w=0.0012588556,
47 58 41 s=4 w=1.5893235E-4,
48 58 57 s=16 w=4.167727E-4,
49 59 42 s=4 w=1.5893235E-4,
50 59 58 s=21 w=0.0022681346,
51 60 51 s=4 w=1.5893235E-4,
52 60 59 s=20 w=8.992125E-4,
53 61 52 s=4 w=1.5893235E-4,
54 61 60 s=26 w=0.0010168224,
55 62 61 s=32 w=7.895848E-4,
56 63 52 s=32 w=7.895848E-4,
57 63 62 s=4 w=1.5893235E-4,
58 64 53 s=32 w=7.895848E-4,
59 64 63 s=50 w=3.9997647E-4,
60 65 62 s=36 w=8.855418E-4,
61 66 63 s=36 w=8.855418E-4,
62 66 65 s=4 w=1.5893235E-4,
63 67 64 s=36 w=8.855418E-4,
64 67 66 s=50 w=3.9997647E-4,
65 68 53 s=36 w=8.855418E-4,
66 68 67 s=32 w=7.895848E-4,
67 69 46 s=36 w=8.855418E-4,
68 69 68 s=20 w=8.992125E-4,
69 70 66 s=40 w=0.001203692,
70 71 67 s=40 w=0.001203692,
71 71 70 s=50 w=3.9997647E-4,
72 72 68 s=40 w=0.001203692,
73 72 71 s=32 w=7.895848E-4,
74 73 69 s=40 w=0.001203692,
75 73 72 s=20 w=8.992125E-4,
76 74 46 s=40 w=0.001203692,
77 74 73 s=36 w=8.855418E-4,
78 75 71 s=61 w=0.0010516174,
79 76 72 s=61 w=0.0010516174,
80 76 75 s=32 w=7.895848E-4,
81 77 73 s=61 w=0.0010516174,
82 77 76 s=20 w=8.992125E-4,
83 78 74 s=61 w=0.0010516174,
84 78 77 s=36 w=8.855418E-4,
85 79 46 s=61 w=0.0010516174,
86 79 78 s=40 w=0.001203692,
87 80 47 s=61 w=0.0010516174,
88 80 79 s=16 w=4.167727E-4,
89 81 65 s=35 w=1.787175E-4,
90 82 66 s=35 w=1.787175E-4,
91 82 81 s=4 w=1.5893235E-4,
92 83 70 s=35 w=1.787175E-4,
93 83 82 s=40 w=0.001203692,
94 84 71 s=35 w=1.787175E-4,
95 84 83 s=50 w=3.9997647E-4,
96 85 75 s=35 w=1.787175E-4,
97 85 84 s=61 w=0.0010516174,
98 86 85 s=60 w=8.702291E-4,
99 87 75 s=60 w=8.702291E-4,
100 87 86 s=35 w=1.787175E-4,
101 88 76 s=60 w=8.702291E-4,
102 88 87 s=32 w=7.895848E-4,
103 89 77 s=60 w=8.702291E-4,
104 89 88 s=20 w=8.992125E-4,
105 90 78 s=60 w=8.702291E-4,
106 90 89 s=36 w=8.855418E-4,
107 91 79 s=60 w=8.702291E-4,
108 91 90 s=40 w=0.001203692,
109 92 90 s=68 w=0.0015038507,
110 93 91 s=68 w=0.0015038507,
111 93 92 s=40 w=0.001203692,
112 94 79 s=68 w=0.0015038507,
113 94 93 s=60 w=8.702291E-4,
114 95 80 s=68 w=0.0015038507,
115 95 94 s=16 w=4.167727E-4,
116 96 47 s=68 w=0.0015038507,
117 96 95 s=61 w=0.0010516174,
118 97 48 s=68 w=0.0015038507,
119 97 96 s=26 w=0.0010168224,
120 98 49 s=68 w=0.0015038507,
121 98 97 s=21 w=0.0022681346,
122 99 62 s=31 w=0.0010652957,
123 100 65 s=31 w=0.0010652957,
124 100 99 s=36 w=8.855418E-4,
125 101 81 s=31 w=0.0010652957,
126 101 100 s=35 w=1.787175E-4,
127 102 82 s=31 w=0.0010652957,
128 102 101 s=4 w=1.5893235E-4,
129 103 83 s=31 w=0.0010652957,
130 103 102 s=40 w=0.001203692,
131 104 84 s=31 w=0.0010652957,
132 104 103 s=50 w=3.9997647E-4,
133 105 103 s=45 w=2.5534217E-4,
134 106 104 s=45 w=2.5534217E-4,
135 106 105 s=50 w=3.9997647E-4,
136 107 84 s=45 w=2.5534217E-4,
137 107 106 s=31 w=0.0010652957,
138 108 85 s=45 w=2.5534217E-4,
139 108 107 s=61 w=0.0010516174,
140 109 86 s=45 w=2.5534217E-4,
141 109 108 s=60 w=8.702291E-4,
142 110 87 s=45 w=2.5534217E-4,
143 110 109 s=35 w=1.787175E-4,
144 111 88 s=45 w=2.5534217E-4,
145 111 110 s=32 w=7.895848E-4,
146 112 89 s=45 w=2.5534217E-4,
147 112 111 s=20 w=8.992125E-4,
148 113 103 s=39 w=0.038550336,
149 114 105 s=39 w=0.038550336,
150 114 113 s=45 w=2.5534217E-4,
151 115 106 s=39 w=0.038550336,
152 115 114 s=50 w=3.9997647E-4,
153 116 107 s=39 w=0.038550336,
154 116 115 s=31 w=0.0010652957,
155 117 112 s=64 w=1.337294E-5,
156 118 89 s=64 w=1.337294E-5,
157 118 117 s=45 w=2.5534217E-4,
158 119 90 s=64 w=1.337294E-5,
159 119 118 s=36 w=8.855418E-4,
160 120 92 s=64 w=1.337294E-5,
161 120 119 s=68 w=0.0015038507,
162 121 93 s=64 w=1.337294E-5,
163 121 120 s=40 w=0.001203692,
164 122 55 s=10 w=3.662799E-4,
165 123 56 s=10 w=3.662799E-4,
166 123 122 s=14 w=0.0018195357,
167 124 57 s=10 w=3.662799E-4,
168 124 123 s=17 w=0.0012588556,
169 125 58 s=10 w=3.662799E-4,
170 125 124 s=16 w=4.167727E-4,
171 126 59 s=10 w=3.662799E-4,
172 126 125 s=21 w=0.0022681346,
173 127 60 s=10 w=3.662799E-4,
174 127 126 s=20 w=8.992125E-4,
175 128 61 s=10 w=3.662799E-4,
176 128 127 s=26 w=0.0010168224,
177 129 113 s=38 w=0.0012966593,
178 130 114 s=38 w=0.0012966593,
179 130 129 s=45 w=2.5534217E-4,
180 131 115 s=38 w=0.0012966593,
181 131 130 s=50 w=3.9997647E-4,
182 132 130 s=44 w=0.012698617,
183 133 131 s=44 w=0.012698617,
184 133 132 s=50 w=3.9997647E-4,
185 134 115 s=44 w=0.012698617,
186 134 133 s=38 w=0.0012966593,
187 135 116 s=44 w=0.012698617,
188 135 134 s=31 w=0.0010652957,
189 136 109 s=59 w=0.001466786,
190 137 110 s=59 w=0.001466786,
191 137 136 s=35 w=1.787175E-4,
192 138 111 s=59 w=0.001466786,
193 138 137 s=32 w=7.895848E-4,
194 139 112 s=59 w=0.001466786,
195 139 138 s=20 w=8.992125E-4,
196 140 117 s=59 w=0.001466786,
197 140 139 s=64 w=1.337294E-5,
198 141 118 s=59 w=0.001466786,
199 141 140 s=45 w=2.5534217E-4,
200 142 119 s=59 w=0.001466786,
201 142 141 s=36 w=8.855418E-4,
202 143 120 s=59 w=0.001466786,
203 143 142 s=68 w=0.0015038507,
204 144 121 s=59 w=0.001466786,
205 144 143 s=40 w=0.001203692,
206 145 121 s=81 w=4.242624E-4,
207 146 93 s=81 w=4.242624E-4,
208 146 145 s=64 w=1.337294E-5,
209 147 94 s=81 w=4.242624E-4,
210 147 146 s=60 w=8.702291E-4,
211 148 95 s=81 w=4.242624E-4,
212 148 147 s=16 w=4.167727E-4,
213 149 96 s=81 w=4.242624E-4,
214 149 148 s=61 w=0.0010516174,
215 150 97 s=81 w=4.242624E-4,
216 150 149 s=26 w=0.0010168224,
217 151 98 s=81 w=4.242624E-4,
218 151 150 s=21 w=0.0022681346,
219 152 49 s=81 w=4.242624E-4,
220 152 151 s=68 w=0.0015038507,
221 153 50 s=81 w=4.242624E-4,
222 153 152 s=5 w=0.002769119,
223 154 54 s=3 w=0.0013902244,
224 155 55 s=3 w=0.0013902244,
225 155 154 s=11 w=0.0060906718,
226 156 122 s=3 w=0.0013902244,
227 156 155 s=10 w=3.662799E-4,
228 157 123 s=3 w=0.0013902244,
229 157 156 s=14 w=0.0018195357,
230 158 124 s=3 w=0.0013902244,
231 158 157 s=17 w=0.0012588556,
232 159 125 s=3 w=0.0013902244,
233 159 158 s=16 w=4.167727E-4,
234 160 128 s=25 w=4.685271E-4,
235 161 61 s=25 w=4.685271E-4,
236 161 160 s=10 w=3.662799E-4,
237 162 62 s=25 w=4.685271E-4,
238 162 161 s=32 w=7.895848E-4,
239 163 99 s=25 w=4.685271E-4,
240 163 162 s=31 w=0.0010652957,
241 164 100 s=25 w=4.685271E-4,
242 164 163 s=36 w=8.855418E-4,
243 165 101 s=25 w=4.685271E-4,
244 165 164 s=35 w=1.787175E-4,
245 166 165 s=34 w=0.0011806624,
246 167 101 s=34 w=0.0011806624,
247 167 166 s=25 w=4.685271E-4,
248 168 102 s=34 w=0.0011806624,
249 168 167 s=4 w=1.5893235E-4,
250 169 103 s=34 w=0.0011806624,
251 169 168 s=40 w=0.001203692,
252 170 113 s=34 w=0.0011806624,
253 170 169 s=39 w=0.038550336,
254 171 129 s=34 w=0.0011806624,
255 171 170 s=38 w=0.0012966593,
256 171 12 s=37 w=0.0914247,
257 172 130 s=34 w=0.0011806624,
258 172 171 s=45 w=2.5534217E-4,
259 173 132 s=34 w=0.0011806624,
260 173 172 s=44 w=0.012698617,
261 174 173 s=43 w=0.0014246248,
262 175 132 s=43 w=0.0014246248,
263 175 174 s=34 w=0.0011806624,
264 176 133 s=43 w=0.0014246248,
265 176 175 s=50 w=3.9997647E-4,
266 177 174 s=47 w=0.0010406735,
267 178 175 s=47 w=0.0010406735,
268 178 177 s=34 w=0.0011806624,
269 179 176 s=47 w=0.0010406735,
270 179 178 s=50 w=3.9997647E-4,
271 180 133 s=47 w=0.0010406735,
272 180 179 s=43 w=0.0014246248,
273 181 134 s=47 w=0.0010406735,
274 181 180 s=38 w=0.0012966593,
275 182 135 s=47 w=0.0010406735,
276 182 181 s=31 w=0.0010652957,
277 183 179 s=54 w=4.0487162E-4,
278 184 180 s=54 w=4.0487162E-4,
279 184 183 s=43 w=0.0014246248,
280 185 181 s=54 w=4.0487162E-4,
281 185 184 s=38 w=0.0012966593,
282 186 182 s=54 w=4.0487162E-4,
283 186 185 s=31 w=0.0010652957,
284 187 135 s=54 w=4.0487162E-4,
285 187 186 s=47 w=0.0010406735,
286 188 116 s=54 w=4.0487162E-4,
287 188 187 s=44 w=0.012698617,
288 189 107 s=54 w=4.0487162E-4,
289 189 188 s=39 w=0.038550336,
290 190 108 s=54 w=4.0487162E-4,
291 190 189 s=61 w=0.0010516174,
292 191 109 s=54 w=4.0487162E-4,
293 191 190 s=60 w=8.702291E-4,
294 192 136 s=54 w=4.0487162E-4,
295 192 191 s=59 w=0.001466786,
296 193 137 s=54 w=4.0487162E-4,
297 193 192 s=35 w=1.787175E-4,
298 194 138 s=54 w=4.0487162E-4,
299 194 193 s=32 w=7.895848E-4,
300 195 139 s=54 w=4.0487162E-4,
301 195 194 s=20 w=8.992125E-4,
302 196 140 s=54 w=4.0487162E-4,
303 196 195 s=64 w=1.337294E-5,
304 197 143 s=67 w=0.0021476466,
305 198 144 s=67 w=0.0021476466,
306 198 197 s=40 w=0.001203692,
307 199 121 s=67 w=0.0021476466,
308 199 198 s=59 w=0.001466786,
309 200 198 s=71 w=0.0019187863,
310 201 199 s=71 w=0.0019187863,
311 201 200 s=59 w=0.001466786,
312 202 121 s=71 w=0.0019187863,
313 202 201 s=67 w=0.0021476466,
314 203 145 s=71 w=0.0019187863,
315 203 202 s=81 w=4.242624E-4,
316 204 200 s=74 w=0.0023116763,
317 205 201 s=74 w=0.0023116763,
318 205 204 s=59 w=0.001466786,
319 206 202 s=74 w=0.0023116763,
320 206 205 s=67 w=0.0021476466,
321 207 203 s=74 w=0.0023116763,
322 207 206 s=81 w=4.242624E-4,
323 208 145 s=74 w=0.0023116763,
324 208 207 s=71 w=0.0019187863,
325 209 146 s=74 w=0.0023116763,
326 209 208 s=64 w=1.337294E-5,
327 210 156 s=9 w=1.7729466E-4,
328 211 157 s=9 w=1.7729466E-4,
329 211 210 s=14 w=0.0018195357,
330 212 158 s=9 w=1.7729466E-4,
331 212 211 s=17 w=0.0012588556,
332 213 159 s=9 w=1.7729466E-4,
333 213 212 s=16 w=4.167727E-4,
334 214 127 s=19 w=8.21237E-4,
335 214 7 s=18 w=0.0688266,
336 215 128 s=19 w=8.21237E-4,
337 215 214 s=26 w=0.0010168224,
338 216 160 s=19 w=8.21237E-4,
339 216 215 s=25 w=4.685271E-4,
340 217 161 s=19 w=8.21237E-4,
341 217 216 s=10 w=3.662799E-4,
342 218 216 s=24 w=4.7089203E-4,
343 219 217 s=24 w=4.7089203E-4,
344 219 218 s=10 w=3.662799E-4,
345 220 161 s=24 w=4.7089203E-4,
346 220 219 s=19 w=8.21237E-4,
347 221 162 s=24 w=4.7089203E-4,
348 221 220 s=32 w=7.895848E-4,
349 222 163 s=24 w=4.7089203E-4,
350 222 221 s=31 w=0.0010652957,
351 223 219 s=28 w=3.5754856E-4,
352 224 220 s=28 w=3.5754856E-4,
353 224 223 s=19 w=8.21237E-4,
354 225 221 s=28 w=3.5754856E-4,
355 225 224 s=32 w=7.895848E-4,
356 226 222 s=28 w=3.5754856E-4,
357 226 225 s=31 w=0.0010652957,
358 227 163 s=28 w=3.5754856E-4,
359 227 226 s=24 w=4.7089203E-4,
360 228 164 s=28 w=3.5754856E-4,
361 228 227 s=36 w=8.855418E-4,
362 229 165 s=28 w=3.5754856E-4,
363 229 228 s=35 w=1.787175E-4,
364 230 166 s=28 w=3.5754856E-4,
365 230 229 s=34 w=0.0011806624,
366 231 179 s=49 w=0.021949023,
367 231 15 s=48 w=0.05344081,
368 232 183 s=49 w=0.021949023,
369 232 231 s=54 w=4.0487162E-4,
370 233 184 s=49 w=0.021949023,
371 233 232 s=43 w=0.0014246248,
372 234 185 s=49 w=0.021949023,
373 234 233 s=38 w=0.0012966593,
374 235 186 s=49 w=0.021949023,
375 235 234 s=31 w=0.0010652957,
376 236 232 s=53 w=2.8627258E-4,
377 237 233 s=53 w=2.8627258E-4,
378 237 236 s=43 w=0.0014246248,
379 238 234 s=53 w=2.8627258E-4,
380 238 237 s=38 w=0.0012966593,
381 239 235 s=53 w=2.8627258E-4,
382 239 238 s=31 w=0.0010652957,
383 240 186 s=53 w=2.8627258E-4,
384 240 239 s=49 w=0.021949023,
385 241 187 s=53 w=2.8627258E-4,
386 241 240 s=47 w=0.0010406735,
387 242 188 s=53 w=2.8627258E-4,
388 242 241 s=44 w=0.012698617,
389 243 189 s=53 w=2.8627258E-4,
390 243 242 s=39 w=0.038550336,
391 244 190 s=53 w=2.8627258E-4,
392 244 243 s=61 w=0.0010516174,
393 245 191 s=53 w=2.8627258E-4,
394 245 244 s=60 w=8.702291E-4,
395 246 192 s=53 w=2.8627258E-4,
396 246 245 s=59 w=0.001466786,
397 247 246 s=58 w=0.0014762296,
398 248 192 s=58 w=0.0014762296,
399 248 247 s=53 w=2.8627258E-4,
400 249 193 s=58 w=0.0014762296,
401 249 248 s=35 w=1.787175E-4,
402 250 194 s=58 w=0.0014762296,
403 250 249 s=32 w=7.895848E-4,
404 251 195 s=58 w=0.0014762296,
405 251 250 s=20 w=8.992125E-4,
406 252 196 s=58 w=0.0014762296,
407 252 251 s=64 w=1.337294E-5,
408 253 140 s=58 w=0.0014762296,
409 253 252 s=54 w=4.0487162E-4,
410 254 141 s=58 w=0.0014762296,
411 254 253 s=45 w=2.5534217E-4,
412 255 142 s=58 w=0.0014762296,
413 255 254 s=36 w=8.855418E-4,
414 256 143 s=58 w=0.0014762296,
415 256 255 s=68 w=0.0015038507,
416 257 197 s=58 w=0.0014762296,
417 257 256 s=67 w=0.0021476466,
418 258 205 s=77 w=0.0016833246,
419 259 206 s=77 w=0.0016833246,
420 259 258 s=67 w=0.0021476466,
421 260 207 s=77 w=0.0016833246,
422 260 259 s=81 w=4.242624E-4,
423 261 208 s=77 w=0.0016833246,
424 261 260 s=71 w=0.0019187863,
425 262 209 s=77 w=0.0016833246,
426 262 261 s=64 w=1.337294E-5,
427 263 260 s=80 w=0.001391344,
428 264 261 s=80 w=0.001391344,
429 264 263 s=71 w=0.0019187863,
430 265 262 s=80 w=0.001391344,
431 265 264 s=64 w=1.337294E-5,
432 266 209 s=80 w=0.001391344,
433 266 265 s=77 w=0.0016833246,
434 267 146 s=80 w=0.001391344,
435 267 266 s=74 w=0.0023116763,
436 268 147 s=80 w=0.001391344,
437 268 267 s=60 w=8.702291E-4,
438 269 148 s=80 w=0.001391344,
439 269 268 s=16 w=4.167727E-4,
440 270 154 s=2 w=0.017143281,
441 270 3 s=1 w=0.1279713,
442 271 155 s=2 w=0.017143281,
443 271 270 s=11 w=0.0060906718,
444 272 156 s=2 w=0.017143281,
445 272 271 s=10 w=3.662799E-4,
446 273 210 s=2 w=0.017143281,
447 273 272 s=9 w=1.7729466E-4,
448 273 4 s=8 w=0.057841897,
449 274 211 s=13 w=0.038590923,
450 274 5 s=12 w=0.08134686,
451 275 212 s=13 w=0.038590923,
452 275 274 s=17 w=0.0012588556,
453 276 213 s=13 w=0.038590923,
454 276 275 s=16 w=4.167727E-4,
455 276 6 s=15 w=0.09856205,
456 277 218 s=23 w=0.03262534,
457 277 8 s=22 w=0.0665534,
458 278 219 s=23 w=0.03262534,
459 278 277 s=10 w=3.662799E-4,
460 279 223 s=23 w=0.03262534,
461 279 278 s=28 w=3.5754856E-4,
462 279 9 s=27 w=0.03859058,
463 280 226 s=30 w=0.06064433,
464 280 10 s=29 w=0.052748006,
465 281 227 s=30 w=0.06064433,
466 281 280 s=24 w=4.7089203E-4,
467 282 228 s=30 w=0.06064433,
468 282 281 s=36 w=8.855418E-4,
469 283 229 s=30 w=0.06064433,
470 283 282 s=35 w=1.787175E-4,
471 284 230 s=30 w=0.06064433,
472 284 283 s=34 w=0.0011806624,
473 284 11 s=33 w=0.05142635,
474 285 174 s=42 w=0.0044335932,
475 285 13 s=41 w=0.092642605,
476 286 177 s=42 w=0.0044335932,
477 286 285 s=47 w=0.0010406735,
478 286 14 s=46 w=0.09702029,
479 287 236 s=52 w=0.04591966,
480 287 16 s=51 w=0.06783958,
481 288 237 s=52 w=0.04591966,
482 288 287 s=43 w=0.0014246248,
483 289 238 s=52 w=0.04591966,
484 289 288 s=38 w=0.0012966593,
485 290 239 s=52 w=0.04591966,
486 290 289 s=31 w=0.0010652957,
487 290 17 s=55 w=0.064194575,
488 291 247 s=57 w=0.10500158,
489 291 18 s=56 w=0.047182973,
490 292 248 s=57 w=0.10500158,
491 292 291 s=53 w=2.8627258E-4,
492 293 249 s=57 w=0.10500158,
493 293 292 s=35 w=1.787175E-4,
494 294 250 s=57 w=0.10500158,
495 294 293 s=32 w=7.895848E-4,
496 295 251 s=57 w=0.10500158,
497 295 294 s=20 w=8.992125E-4,
498 296 252 s=57 w=0.10500158,
499 296 295 s=64 w=1.337294E-5,
500 297 296 s=63 w=0.004750093,
501 297 19 s=62 w=0.044313457,
502 298 252 s=63 w=0.004750093,
503 298 297 s=57 w=0.10500158,
504 299 253 s=63 w=0.004750093,
505 299 298 s=54 w=4.0487162E-4,
506 300 254 s=63 w=0.004750093,
507 300 299 s=45 w=2.5534217E-4,
508 301 255 s=63 w=0.004750093,
509 301 300 s=36 w=8.855418E-4,
510 302 256 s=63 w=0.004750093,
511 302 301 s=68 w=0.0015038507,
512 303 257 s=63 w=0.004750093,
513 303 302 s=67 w=0.0021476466,
514 304 303 s=66 w=0.013317523,
515 304 20 s=65 w=0.20566262,
516 305 257 s=66 w=0.013317523,
517 305 304 s=63 w=0.004750093,
518 306 197 s=66 w=0.013317523,
519 306 305 s=58 w=0.0014762296,
520 307 198 s=66 w=0.013317523,
521 307 306 s=40 w=0.001203692,
522 308 200 s=66 w=0.013317523,
523 308 307 s=71 w=0.0019187863,
524 309 308 s=70 w=1.5751319E-4,
525 309 21 s=69 w=0.09029672,
526 310 200 s=70 w=1.5751319E-4,
527 310 309 s=66 w=0.013317523,
528 311 204 s=70 w=1.5751319E-4,
529 311 310 s=74 w=0.0023116763,
530 312 311 s=73 w=0.006326371,
531 312 22 s=72 w=0.109228075,
532 313 204 s=73 w=0.006326371,
533 313 312 s=70 w=1.5751319E-4,
534 314 205 s=73 w=0.006326371,
535 314 313 s=59 w=0.001466786,
536 315 258 s=73 w=0.006326371,
537 315 314 s=77 w=0.0016833246,
538 316 315 s=76 w=1.050739E-4,
539 316 23 s=75 w=0.089916565,
540 317 258 s=76 w=1.050739E-4,
541 317 316 s=73 w=0.006326371,
542 318 259 s=76 w=1.050739E-4,
543 318 317 s=67 w=0.0021476466,
544 319 260 s=76 w=1.050739E-4,
545 319 318 s=81 w=4.242624E-4,
546 320 263 s=76 w=1.050739E-4,
547 320 319 s=80 w=0.001391344,
548 321 320 s=79 w=0.0022174222,
549 321 24 s=78 w=0.17474917,
550 322 263 s=79 w=0.0022174222,
551 322 321 s=76 w=1.050739E-4,
552 323 264 s=79 w=0.0022174222,
553 323 322 s=71 w=0.0019187863,
554 324 265 s=79 w=0.0022174222,
555 324 323 s=64 w=1.337294E-5,
556 325 324 s=83 w=0.0013124112,
557 325 25 s=82 w=0.13961297,
558 326 265 s=83 w=0.0013124112,
559 326 325 s=79 w=0.0022174222,
560 327 266 s=83 w=0.0013124112,
561 327 326 s=77 w=0.0016833246,
562 328 267 s=83 w=0.0013124112,
563 328 327 s=74 w=0.0023116763,
564 329 268 s=83 w=0.0013124112,
565 329 328 s=60 w=8.702291E-4,
566 330 269 s=83 w=0.0013124112,
567 330 329 s=16 w=4.167727E-4,
568 331 330 s=85 w=4.8682792E-4,
569 331 26 s=84 w=0.1194786,
570 332 269 s=85 w=4.8682792E-4,
571 332 331 s=83 w=0.0013124112,
572 333 148 s=85 w=4.8682792E-4,
573 333 332 s=80 w=0.001391344,
574 334 149 s=85 w=4.8682792E-4,
575 334 333 s=61 w=0.0010516174,
576 335 150 s=85 w=4.8682792E-4,
577 335 334 s=26 w=0.0010168224,
578 336 151 s=85 w=4.8682792E-4,
579 336 335 s=21 w=0.0022681346,
580 337 336 s=87 w=0.0045568696,
581 337 27 s=86 w=0.09366718,
582 338 151 s=87 w=0.0045568696,
583 338 337 s=85 w=4.8682792E-4,
584 339 152 s=87 w=0.0045568696,
585 339 338 s=68 w=0.0015038507,
586 340 153 s=87 w=0.0045568696,
587 340 339 s=5 w=0.002769119,
588 341 340 s=89 w=0.0015117239,
589 341 28 s=88 w=0.08093566,
590 342 153 s=89 w=0.0015117239,
591 342 341 s=87 w=0.0045568696,
592 343 50 s=89 w=0.0015117239,
593 343 342 s=81 w=4.242624E-4,
594 344 38 s=89 w=0.0015117239,
595 344 343 s=50 w=3.9997647E-4,
596 345 39 s=89 w=0.0015117239,
597 345 344 s=14 w=0.0018195357,
598 346 40 s=89 w=0.0015117239,
599 346 345 s=6 w=0.08403672,
600 346 29 s=90 w=0.076351464,
;
END; [Network]
BEGIN st_Assumptions;
uptodate;
disttransform=NeighborNet;
splitstransform=EqualAngle;
SplitsPostProcess filter=dimension value=4;
exclude no missing;
autolayoutnodelabels;
END; [st_Assumptions]
So, we should implement a "DISTANCES" handler for python-nexus first - right?
yeah I guess so -- I've just never had a burning need for one :)
We should find a way to make phylogenetic data (a.k.a. trees) part of CLDF. With such a specification, we could generically support tools like https://camayal.info/wa/treetom/