Closed JamesBremner closed 1 year ago
JamesBremner
commented
1 year ago @remidelattre Please add your description of this format here. I have a number of questions for you.
remidelattre
commented
1 year ago In my dataset, the first variable gives you the id of each polygon. de gives you the amount that they have. The rest is irrelevant to this task. I dont really know why verticles have three dimensions : there should only be an x and a y coordonate. Opening the shape with QGIS shows vector polygons. No 3D.
I have a question for you : in the example picture you provided, it seems like the groups are interconnected, or perhaps I am misunderstanding it. My idea was that when a group reaches -0,5 to 0,5, and if we have more than 5 entities, we create another group. What we have noticed is that most entities have a negative value in de, and its impossible to have every entity in a group because of that. Also, when we picked a random entity to start the group-making process, the results of every iteration would vary a lot, because depending on where the algorithm starts, its output will be different. Hence, we've decided to always start with the locality that has the highest value for "de". Then this locality tries to help its neighbors reaching 0, favouring neighbors that have reasonably low deficits to maximize the number of localities in a group. Thoughts on that method ?
remidelattre
commented
1 year ago also, here is an example of how this would work in practice : https://ibb.co/RQNyW38
remidelattre
commented
1 year ago By first listing all localities within 4 degrees of adjacency, then trying to maximize "de" using a contiguous path, then handing out the surpluses to the localities with the lowest deficits, we probably maximize group size : https://ibb.co/Kq1B4HY (this is me trying to manually form a group, "de" is probably not in fact comprised between -0,5 and 0,5 here.) Also there are mistakes in assessing adjacency degrees, hence why the need for an algorithm 🗡️
JamesBremner
commented
1 year ago In my dataset, de gives you the amount that they have.
Looking at your dataset ( I have posted it in the first post to this issue, above ) there is no 'de' anywhere that I can see.
JamesBremner
commented
1 year ago I dont really know why verticles have three dimensions : there should only be an x and a y coordonate.
Well, if you do not know, how can you expect me to understand and use your dataset?
JamesBremner
commented
1 year ago I would really like to demonstrate my method of assigning localities to groups using one of your datasets.
Please post a dataset that gives me the locality x and y coordinates, the locality values and the locality indices.
remidelattre
commented
1 year ago Using the dataset I provided on stack, the unique id is in the "insee" row (the first row of the .dbf file). The shapefile stores the x and y, but they are not explicit. I'll try to create dbf rows with the x and y's, but you dont need them if you read the shapefiles (I'm not sure you can do that with c++, does work with python though). By locality indices I assume you mean "de", which is another row in the .dbf.
JamesBremner
commented
1 year ago Thank you for explaining this.
Something must have gone wrong when I downloaded you file yesterday - there was no .dbf file.
Just now I tried again to download and this time got better results
remidelattre
commented
1 year ago Alright, sorry for the inconvenience. I just realized something : my data comes in the form of multipolygons. If i convert them to points and add x and y coords, you lose the knowledge of which ones are actually adjacent, because one set of x/y coordinates does not tell you the full geography of a polygon. I'm trying to create polylines between my points so you can have the coords while also having the polylines.
JamesBremner
commented
1 year ago It is not helpful to share data using binary file formats.
I have tried converting you dbf file to csv using https://www.dbf2002.com/dbf-converter/
However, that will only allow me to convert 50 records.
Please can you convert your data to a CSV text file and post that.
JamesBremner
commented
1 year ago Polygons are usually specified by listing the coords of their vertices in a defined order ( clockwise or anti-clockwise )
So a unit square might be "0,0,0,1,1,1,1,0"
Please post your locality polygons like this.
remidelattre
commented
1 year ago Here's a CSV of the same dataset. You can convert a .dbf to a .csv file using excel or calc. Encoding in UTF-8 is the best solution, though as you'll see, some localities names are wrongly processed (this can be fixed using QGIS). Your solution for listing the coords of the verticles is interesting, except my multipolygons have 4100 summits on average, making it highly unpractical. Will update the shapefile and the corresponding csv with coords once I find a solution. csv : https://file.io/mwdaTIDPcyZG to recap : insee is the id of every locality (their geometry is in the shapefile) de : value that we're trying to balance across groups
JamesBremner
commented
1 year ago Here we are:
insee,nom,wikipedia,surf_ha,de,INSEE_REG
2B222,Pie-d'Orezza,fr:Pie-d'Orezza,573.000000000000000,1.000000000000000,94
2B137,Lano,fr:Lano,824.000000000000000,1.000000000000000,94
2B051,Cambia,fr:Cambia,833.000000000000000,1.000000000000000,94
2B106,Érone,fr:Érone,393.000000000000000,1.000000000000000,94
2B185,Oletta,fr:Oletta,2674.000000000000000,-28.144484624975900,94
2B058,Canari,fr:Canari (Haute-Corse),1678.000000000000000,-0.594036587198047,94
2B188,Olmeta-di-Tuda,fr:Olmeta-di-Tuda,1753.000000000000000,-6.432021336520920,94
2B052,Campana,fr:Campana,236.000000000000000,1.000000000000000,94
2B063,Carcheto-Brustico,fr:Carcheto-Brustico,525.000000000000000,1.000000000000000,94
2B015,Ampriani,fr:Ampriani,230.000000000000000,1.027900000000000,94
2B213,Pianello,fr:Pianello,1677.000000000000000,1.000000000000000,94
2B364,Zuani,fr:Zuani,518.000000000000000,1.000000000000000,94
2B226,Pietraserena,fr:Pietraserena,678.000000000000000,1.090677029836480,94
2B221,Piedipartino,fr:Piedipartino,326.000000000000000,1.000000000000000,94
66113,Montbolo,fr:Montbolo,2230.000000000000000,2.136850000000000,76
66202,Targasonne,fr:Targasonne,787.000000000000000,1.130500000000000,76
66001,L'Albère,fr:L'Albère,1620.000000000000000,0.995370629370629,76
66117,Mont-Louis,fr:Mont-Louis (Pyrénées-Orientales),38.000000000000000,1.000000000000000,76
66072,Estavar,fr:Estavar,926.000000000000000,-19.660138808571400,76
66064,Égat,fr:Égat,451.000000000000000,1.008514114832530,76
66063,Les Cluses,fr:Les Cluses,879.000000000000000,1.556700000000000,76
66027,La Cabanasse,fr:La Cabanasse,317.000000000000000,2.040817051732790,76
66123,Nyer,fr:Nyer,3640.000000000000000,-2.107813205098700,76
66066,Enveitg,fr:Enveitg,3039.000000000000000,-3.095284413743880,76
2B229,Pietroso,fr:Pietroso,2586.000000000000000,0.114870862732372,94
2A269,Sari-Solenzara,fr:Sari-Solenzara,7448.000000000000000,-14.631546768086300,94
2A092,Conca,fr:Conca,7846.000000000000000,-11.859470906057300,94
2B121,Galéria,fr:Galéria,13592.000000000000000,0.973574701548631,94
2B095,Corscia,fr:Corscia,5876.000000000000000,1.295900000000000,94
2B153,Manso,fr:Manso,12103.000000000000000,0.262231356946367,94
66003,Amélie-les-Bains-Palalda,fr:Amélie-les-Bains-Palalda,2932.000000000000000,1.327592052849530,76
66106,Maureillas-las-Illas,fr:Maureillas-las-Illas,4235.000000000000000,2.232761507059700,76
66137,Le Perthus,fr:Le Perthus,423.000000000000000,0.980721794871795,76
66194,Serralongue,fr:Serralongue,2267.000000000000000,1.059850000000000,76
66179,Saint-Laurent-de-Cerdans,fr:Saint-Laurent-de-Cerdans,4482.000000000000000,1.118818093385210,76
66009,Arles-sur-Tech,fr:Arles-sur-Tech,2848.000000000000000,3.018029431474090,76
66060,Corsavy,fr:Corsavy,4765.000000000000000,1.000000000000000,76
66116,Montferrer,fr:Montferrer,2175.000000000000000,1.095200000000000,76
66206,Le Tech,fr:Le Tech,2613.000000000000000,1.000000000000000,76
66150,Prats-de-Mollo-la-Preste,fr:Prats-de-Mollo-la-Preste,11964.000000000000000,-4.967635443641400,76
66102,Mantet,fr:Mantet,3254.000000000000000,0.177247912482831,76
66155,Py,fr:Py (Pyrénées-Orientales),4997.000000000000000,1.025000000000000,76
66080,Fontpédrouse,fr:Fontpédrouse,6427.000000000000000,1.105651145311380,76
66142,Planès,fr:Planès,1441.000000000000000,0.795061976676205,76
66188,Saint-Pierre-dels-Forcats,fr:Saint-Pierre-dels-Forcats,1298.000000000000000,-0.543096279523108,76
66181,Sainte-Léocadie,fr:Sainte-Léocadie,884.000000000000000,-6.772902397415980,76
66025,Bourg-Madame,fr:Bourg-Madame,770.000000000000000,-6.007165231944400,76
66100,Llo,fr:Llo,2864.000000000000000,0.746754468180079,76
66075,Eyne,fr:Eyne,2044.000000000000000,0.976217181739371,76
66160,Reynès,fr:Reynès,2761.000000000000000,2.980374422060030,76So, now, all I need is the corresponding polygons
my multipolygons have 4100 summits on average,
What is a "summit"? Is it the same as a vertex?
JamesBremner
commented
1 year ago my data comes in the form of multipolygons.
I do not know what a "multipolygon" is.
JamesBremner
commented
1 year ago Perhaps you could post one locality?
I can take a look at it and come up with some preprocessing that removes redundant information.
We only need to do preprocessing once ( I doubt the localities change shape very often ) so it doesn't matter much how long it takes.
remidelattre
commented
1 year ago Alright, I have the geometry figured out in ASCII representation. This is the closest thing to a polygon geometry. I have tried to convert the polygons to points : the ensuing file was 18 600 000 entries long. A layer is a set of points, so theres no single x,y,z coord, unless you take the center. Which you could do, but then you lose the notion of adjacency..the data with wkt is quite big, therefore i'll just send you a limited amount of entities.
Here is an intersting read on the different ESRI shapefile types https://gis.stackexchange.com/questions/225368/understanding-difference-between-polygon-and-multipolygon-for-shapefiles-in-qgis This is how I got the WKT coords https://gis.stackexchange.com/questions/8844/getting-list-of-coordinates-for-points-in-layer-using-qgis/8911#8911 This is the link to the file https://file.io/AsA8mDwxb2ZE
JamesBremner
commented
1 year ago Thank you.
I will convert this from excel binary to csv text and write some code to read it.
JamesBremner
commented
1 year ago Here are the results of the initial parsing of the data file
666027.14887595619075 6155106....75 6155106.7352640014141798
66113,Montbolo,fr:Montbolo,2230,2.13685,76The numbers in the first line specify the vertices of the locality 66113 is the locality index 2.13685 is the value of the locality
That seems fine for a start.
However many of the line in the file look like this:
1221914.16772219375707209 6160...9.41748260520398617, 1222562.7
,,,,,,That is, the details of locality index and value are empty.
What should I make of these lines? Ignore them?
remidelattre
commented
1 year ago I think whats happening is that the lines are so long they overlap and create bugs. Here is an example of a single cell :
_MultiPolygon (((1220731.03914475860074162 6163639.56559936609119177, 1220739.76265204604715109 6163644.28344513103365898, 1220755.39198782620951533 6163653.68498235382139683, 1220761.94441876676864922 6163659.67969244346022606, 1220774.77590833627618849 6163671.98204784467816353, 1220797.10135718109086156 6163694.86811114102602005, 1220812.99888525833375752 6163710.20687372982501984, 1220836.62846141401678324 6163732.30479677673429251, 1220872.70274045411497355 6163786.98816235270351171, 1220888.91903544683009386 6163810.72378411889076233, 1220889.34113059192895889 6163811.65056689642369747, 1220891.08347177132964134 6163832.21579181030392647, 1220894.94395803287625313 6163869.69311805535107851, 1220904.04230083082802594 6163922.32381283119320869, 1220908.84586746548302472 6163948.04569607134908438, 1220922.19430503714829683 6164002.35681462939828634, 1220927.44054448162205517 6164021.52899059467017651, 1220932.76559608406387269 6164030.43898897152394056, 1220940.2700562602840364 6164042.09101320896297693, 1220956.48745900625362992 6164066.83144382666796446, 1220965.20039287814870477 6164079.919852907769382, 1220969.80792649881914258 6164086.42840434238314629, 1220973.63784947083331645 6164092.31648165080696344, 1220997.705375284422189 6164132.64334410894662142, 1221002.58826722297817469 6164139.84370685089379549, 1221036.44879575143568218 6164203.50278901867568493, 1221055.42587067186832428 6164238.17543965484946966, 1221094.26373984618112445 6164306.81056968122720718, 1221107.18733397801406682 6164329.27795722614973783, 1221146.81667236378416419 6164401.43701763451099396, 1221174.23933748924173415 6164453.53024357371032238, 1221189.56755841872654855 6164482.10643344838172197, 1221197.0195834063924849 6164495.42879414651542902, 1221206.30332987289875746 6164437.50980930402874947, 1221221.46326803369447589 6164377.91963471099734306, 1221226.35804975358769298 6164368.89242135733366013, 1221266.2559926297981292 6164300.45093995984643698, 1221305.26737088593654335 6164232.65272753871977329, 1221351.45749624003656209 6164154.80546964984387159, 1221366.76099071837961674 6164103.02872530557215214, 1221373.38898291019722819 6164085.20042574312537909, 1221383.7005041460506618 6164061.17199473828077316, 1221405.59056217037141323 6164011.35294552147388458, 1221431.63263213215395808 6163949.29957478865981102, 1221471.79715194180607796 6163812.59396978188306093, 1221504.57252533128485084 6163700.00636865384876728, 1221563.18794950330629945 6163653.88562374375760555, 1221617.64678089297376573 6163611.51647269353270531, 1221695.47307543316856027 6163549.49477237649261951, 1221801.66343848733231425 6163466.41462834924459457, 1221841.53837576531805098 6163434.6280377758666873, 1221898.40236468845978379 6163390.07673998642712831, 1221904.02932778722606599 6163394.91560098994523287, 1221919.53404012997634709 6163410.08057297579944134, 1221971.66158801084384322 6163462.63473416958004236, 1222000.25261072674766183 6163491.1298656053841114, 1222026.84779369598254561 6163518.3038423964753747, 1222047.08049137657508254 6163538.15771725960075855, 1222065.40269891358911991 6163541.32765458151698112, 1222072.17076010536402464 6163541.74913960229605436, 1222076.30371858947910368 6163541.70200516097247601, 1222081.09285281482152641 6163540.90400428604334593, 1222090.91958873486146331 6163538.37925554253160954, 1222100.71882672375068069 6163534.85892386920750141, 1222105.31123340595513582 6163534.34648313373327255, 1222109.11839468427933753 6163534.44059234671294689, 1222118.21655331319198012 6163535.83083209488540888, 1222121.11645417008548975 6163535.7850131718441844, 1222127.6830825605429709 6163535.00722159817814827, 1222135.0489240875467658 6163533.75795557536184788, 1222144.89711274160072207 6163533.33339929115027189, 1222150.75993804121389985 6163532.76690443605184555, 1222153.26868768851272762 6163532.13155760895460844, 1222157.68731957860291004 6163530.07603126764297485, 1222166.77675042627379298 6163524.57890140824019909, 1222169.40696959290653467 6163523.46221781242638826, 1222173.02387602441012859 6163522.93833232019096613, 1222181.3620401811785996 6163522.87232580408453941, 1222186.3841678025200963 6163522.57309107482433319, 1222193.23354228027164936 6163521.57254963368177414, 1222199.73341480130329728 6163521.21360162552446127, 1222201.68503075605258346 6163521.5380074568092823, 1222203.60651070484891534 6163522.33994087018072605, 1222216.60115142585709691 6163530.84118714183568954, 1222220.72262926283292472 6163532.89161071926355362, 1222235.96303470502607524 6163537.95717141777276993, 1222250.75713463011197746 6163541.78140769060701132, 1222264.18219967954792082 6163544.7253901120275259, 1222285.66344496258534491 6163548.22822136990725994, 1222289.65581843024119735 6163548.69451012182980776, 1222321.16909940494224429 6163548.56220758054405451, 1222327.2869421630166471 6163549.04326192568987608, 1222333.00733896205201745 6163549.93881141766905785, 1222343.7918584025464952 6163553.40718147996813059, 1222364.8782057729549706 6163561.09761787950992584, 1222374.38617295422591269 6163562.84488434251397848, 1222380.49361476302146912 6163563.14656621962785721, 1222383.54558576690033078 6163562.85638237558305264, 1222387.10042377607896924 6163561.86999288480728865, 1222391.12894339067861438 6163559.72742129396647215, 1222396.49691211106255651 6163556.36439375579357147, 1222422.91480307001620531 6163543.86841483134776354, 1222457.48939618561416864 6163528.0107396524399519, 1222473.97987110097892582 6163520.83264089375734329, 1222484.8118378845974803 6163516.51420341152697802, 1222490.94072532560676336 6163514.59653408546000719, 1222496.31510495604015887 6163513.62269852124154568, 1222505.23931001173332334 6163512.85643586050719023, 1222526.23967741336673498 6163513.18492048047482967, 1222550.52316098310984671 6163515.18410532176494598, 1222552.63112335535697639 6163515.10821861494332552, 1222562.87133586336858571 6163512.28258978296071291, 1222572.30563581013120711 6163511.24494870472699404, 1222583.42962442734278738 6163511.62692915927618742, 1222585.83371465234085917 6163511.16190670523792505, 1222593.99835460842587054 6163508.62688074167817831, 1222595.45539126126095653 6163508.72184437606483698, 1222596.9396282930392772 6163509.19849605206400156, 1222615.61654827347956598 6163516.49489424470812082, 1222626.11480901227332652 6163519.30447754915803671, 1222634.01497142459265888 6163519.43818367831408978, 1222641.1751130660995841 6163520.95218587294220924, 1222647.39087150478735566 6163522.99290098063647747, 1222649.03254117979668081 6163523.1585494065657258, 1222651.21234162664040923 6163522.90989050082862377, 1222671.48367698560468853 6163518.72662735730409622, 1222680.21152047300711274 6163515.98033052496612072, 1222685.58221826213411987 6163512.99724169727414846, 1222689.38989439862780273 6163509.89947698637843132, 1222694.39940347848460078 6163504.72193995025008917, 1222698.31214897753670812 6163501.14152287412434816, 1222700.61897078435868025 6163499.9320362638682127, 1222703.55354877817444503 6163499.35350307915359735, 1222707.53266382566653192 6163499.05998228024691343, 1222721.68977469578385353 6163499.28458498883992434, 1222738.28961216262541711 6163499.18130243010818958, 1222745.05828529829159379 6163499.90490034781396389, 1222759.30033414671197534 6163503.59656181093305349, 1222774.09627645323053002 6163504.520099937915802, 1222777.41414261795580387 6163505.24490172974765301, 1222788.30997023964300752 6163509.69405466131865978, 1222788.35574349761009216 6163503.7820191215723753, 1222789.26787974429316819 6163498.62061479222029448, 1222789.79377052886411548 6163496.50874735601246357, 1222790.36742016207426786 6163495.03694495093077421, 1222791.03187855705618858 6163493.77336644381284714, 1222792.11617196211591363 6163492.33152301516383886, 1222793.12702319957315922 6163491.18513261340558529, 1222802.48326202831231058 6163482.89756080787628889, 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1221532.56206896086223423 6161380.38706582598388195, 1221530.42107085464522243 6161383.34041980933398008, 1221514.84324634238146245 6161401.0644089300185442, 1221489.30138188763521612 6161429.48489224165678024, 1221472.29890725505538285 6161447.42940821405500174, 1221457.47466718498617411 6161467.11150491889566183, 122)
This is probably not a viable method to deduce geometry and I think it creates overlaps. Besides, the cells look incomplete. I will try to create an adjacency matrix between all localities so you dont have to worry about geometry. I'm off for now though.
JamesBremner
commented
1 year ago . I will try to create an adjacency matrix between all localities so you dont have to worry about geometry.
Well, that would be great!
remidelattre
commented
1 year ago alright, here's a csv with every adjacency. I used geopandas with py : avoids creating 348XX rows. insee = id for each locality insee_adjacent = adjacent localities (polygons touch each other) de = don't need to explain at this point lol if you have issues with the fact adjacencies are listed in the same row, please use column wizard with excel. You'll have empty cells, obviously, but one row will be one id. https://file.io/h1QHqEMJfRx6
JamesBremner
commented
1 year ago insee;insee_adjacent;de
01001;01146 01093 01188 01028 01412 01351;2,6759
01002;01056 01007 01363 01384 01199 01277;0,298986426
01004;01041 01089 01149 01384 01345 01421 01007;-11,28376052
01005;01446 01382 01207 01362 01261 01398 01318;0,58988963
...That looks perfect.
I will code a reader for this tommorrow.
JamesBremner
commented
1 year ago Guessing that
2,6759
means the number 2.6759 for de
remidelattre
commented
1 year ago yes, exactly, this is "de". my plan for later is to run the algorithm with these values, store the results, then edit values for "de", run it again, and compare the two iterations.
remidelattre
commented
1 year ago
adjacency_matrix2.csv
Added a row containing regional codes. This allows us to create .csv filles contaning a smaller amount of localities while running the tests within perimeters that are actually relevant to the problem the algorithm intends to solve. If you watched my video, you can see me reducing the sample at the start. This the same principle, except with actually relevant perimeters.
We may need to adress the fact that, using this method, we're asking the program to consider adjacencies that won't exist in the smaller files. Perhaps this would require ignoring localities that the program can't find or cleaning the .csv beforehand.
edit 21:25 : we could also run it on the whole dataset but i cant begin to fathom the computational work required if we consider 4 degrees of adjacency. besides i think it'd be difficult to actually visualize..
JamesBremner
commented
1 year ago Added a row containing regional codes. This allows us to create .csv filles contaning a smaller amount of localities
This is a good idea.
However, it seems that you have added a new column, not a row.
Also, you keep calling and labelling these files as CSV, which stands for Comma Separated Values. In reality, your files use a mixture of semicolons and spaces to separate values while commas are used to represent the decimal point in floating point numbers.
Oh, and one more small point - it is not an adjacency matrix ( which is rectangular ) but an adjacency list.
JamesBremner
commented
1 year ago We may need to address the fact that ...
OK. I will open a new issue for this.
JamesBremner
commented
1 year ago Here is the output from a run of the grouper app using your text file as input
For a quick look, here are the first few groups output
finished reading 34816 localities
============
01001 2.6759 01146 -1.502172811 01093 -7.803621135 01188 0.524909037 01028 2.06945 01412 1.755569314 01351 2.80125551 sum 0.52129
============
01002 0.298986426 01007 -4.306125486 01363 2.327430935 01199 1.232900641 sum -0.446807
============
01277 1.226157194 01012 -0.42692219 sum 0.799235
============
01004 -11.28376052 01041 1.3825 01089 6.279176383 01149 1.483930913 01345 1.113282445 01421 0.989867419 sum -0.0350034
============
01005 0.58988963 01362 -1.861710215 01261 2.3468 01318 -0.928684912 sum 0.146295
============
01446 0.326165044 01382 2.48955 01021 -1.949675381 sum 0.86604
============
Released v0.0.3
The input file will be placed in the correct folder when you pull the latest version of this repository
vertices = dict([('g0-v0', {'coords': (289505.29113884346, 3809582.8002458056, 3.4325371831655502)}), ('g0-v1', {'coords': (289505.89137526846, 3809584.4667251427, 3.4499223306775093)}), ('g0-v2', {'coords': (289504.4185388949, 3809584.992423441, 4.262407331727445)}), ('g0-v3', {'coords': (289502.92465452605, 3809585.5074766655, 3.3520891135558486)}), ('g0-v4', {'coords': (289502.1874815416, 3809584.046727399, 3.4383309045806527)}), ('g0-v5', {'coords': (289503.2098872459, 3809581.929851732, 4.259595467709005)}), ('g0-v7', {'coords': (289505.77128353063, 3809584.1539462083, 2.776375670917332)}), ('g0-v9', {'coords': (289502.6837983986, 3809585.4038596363, 2.815225132741034)}), ('g1-v0', {'coords': (289509.6128579881, 3809575.3195456313, 2.2534847604110837)}), ('g1-v1', {'coords': (289510.70581039955, 3809577.783933704, 3.5839293217286468)}), ('g1-v2', {'coords': (289511.4035359267, 3809579.731115867, 3.7048939522355795)}), ('g1-v3', {'coords': (289512.40618138015, 3809582.4432819095, 2.334692606702447)}), ('g1-v4', {'coords': (289501.51873695327, 3809586.4752561618, 2.51658580545336)}), ('g1-v5', {'coords': (289500.5641674058, 3809583.921679454, 3.831248564645648)}), ('g1-v6', {'coords': (289500.13612185075, 3809582.9820827004, 3.7128401873633265)}), ('g1-v7', {'coords': (289498.77303475, 3809579.366229753, 2.283148379996419)}), ('g1-v8', {'coords': (289500.88263875083, 3809578.604004119, 2.2721760366111994)}), ('g1-v9', {'coords': (289500.9781923641, 3809578.7744675647, 2.419397037476301)}), ('g1-v10', {'coords': (289500.0446753629, 3809579.125590793, 2.4324609553441405)}), ('g1-v11', {'coords': (289501.4418613444, 3809582.501332389, 4.319459212012589)}), ('g1-v12', {'coords': (289509.73036577494, 3809579.347299192, 4.246670736931264)}), ('g1-v13', {'coords': (289508.06265764986, 3809576.1304689297, 2.372382231988013)}), ('g1-v14', {'coords': (289507.5055342968, 3809576.3570000282, 2.2798113031312823)}), ('g1-v15', {'coords': (289507.40741197416, 3809576.0942560057, 2.224258623085916)}), ('g1-v18', {'coords': (289508.27952180186, 3809575.782694147, 3.432461282238364)}), ('g1-v19', {'coords': (289500.0481945323, 3809578.878560306, 3.4992404812946916)}), ('g1-v22', {'coords': (289499.70789473713, 3809581.873367534, 3.6957009537145495)}), ('g1-v25', {'coords': (289510.0618469392, 3809579.1657374133, 4.2692895056679845)}), ('g1-v28', {'coords': (289508.9101185086, 3809579.659428844, 4.253872682340443)}), ('g1-v29', {'coords': (289507.88647800055, 3809581.7532236963, 3.4469804102554917)}), ('g1-v30', {'coords': (289508.7267558488, 3809583.256266117, 2.686913950368762)}), ('g1-v31', {'coords': (289511.3417801809, 3809582.032448787, 2.765864943154156)}), ('g1-v32', {'coords': (289510.9850381132, 3809580.6364425477, 3.4139089938253164)}), ('g1-v33', {'coords': (289511.58398212894, 3809582.2796163415, 3.448804317973554)}), ('g1-v34', {'coords': (289510.08774438093, 3809582.853807372, 4.274466275237501)}), ('g1-v35', {'coords': 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{'coords': (289486.4019410508, 3809576.48708044, 1.7745313439518213)}), ('g2-v17', {'coords': (289487.0800194229, 3809576.298943132, 1.4836841635406017)}), ('g2-v18', {'coords': (289490.87378455023, 3809577.949905314, 1.574909107759595)}), ('g2-v19', {'coords': (289490.8696355417, 3809578.1947361683, 1.7066296795383096)}), ('g2-v20', {'coords': (289491.51085372467, 3809576.809636472, 1.6801795084029436)}), ('g2-v21', {'coords': (289493.21269875986, 3809576.1465115435, 1.6421171138063073)}), ('g2-v22', {'coords': (289494.905870488, 3809576.9526914097, 1.698048540391028)}), ('g2-v23', {'coords': (289495.5369774403, 3809578.596833082, 1.746348861604929)}), ('g2-v24', {'coords': (289494.93250684463, 3809580.02379859, 1.7478158818557858)}), ('g2-v25', {'coords': (289495.1349019399, 3809579.8813362634, 1.6027319263666868)}), ('g2-v26', {'coords': (289496.1180691675, 3809580.3497205866, 1.6008373526856303)}), ('g2-v27', {'coords': (289499.3717700927, 3809579.1061575883, 1.5679800547659397)}), 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(289484.4860255022, 3809599.9076789366, 3.3046739120036364)}), ('g4-v27', {'coords': (289483.8230472922, 3809599.5909538963, 3.7548816334456205)}), ('g4-v28', {'coords': (289483.20636091067, 3809599.341622821, 3.745818270370364)}), ('g4-v29', {'coords': (289483.4985634192, 3809598.6911704177, 4.0851341392844915)}), ('g4-v30', {'coords': (289482.6250518036, 3809598.294584799, 4.030401179566979)}), ('g4-v31', {'coords': (289482.3724584045, 3809598.896244792, 3.734459549188614)}), ('g4-v32', {'coords': (289480.450362809, 3809598.0225363574, 3.709920003078878)}), ('g4-v33', {'coords': (289480.2048526807, 3809597.8760972507, 3.488342931494117)}), ('g4-v34', {'coords': (289481.28022064234, 3809595.4685293236, 3.461932540871203)}), ('g4-v35', {'coords': (289479.8172326384, 3809594.830899892, 2.693051228299737)}), ('g4-v37', {'coords': (289479.6995939894, 3809594.4092881894, 2.587766961194575)}), ('g4-v38', {'coords': (289479.7613328197, 3809594.289845235, 2.6287123765796423)}), ('g4-v39', {'coords': (289478.2051561482, 3809593.536503854, 2.582464028149843)}), ('g4-v40', {'coords': (289479.2954733621, 3809591.210564432, 3.866884051822126)}), ('g4-v42', {'coords': (289480.40739325905, 3809588.952592213, 2.568576207384467)}), ('g4-v43', {'coords': (289481.90547528723, 3809589.5936430045, 2.5431167567148805)}), ('g4-v44', {'coords': (289480.44008478365, 3809589.1119077955, 1.9394174329936504)}), ('g4-v46', {'coords': (289480.17402376037, 3809589.6812003707, 1.945013415068388)}), ('g4-v47', {'coords': (289479.74632796523, 3809589.463101419, 1.6615103986114264)}), ('g4-v48', {'coords': (289482.23655559774, 3809584.150537456, 1.5518957627937198)}), ('g4-v49', {'coords': (289482.79481579113, 3809584.355787751, 1.8986105006188154)}), ('g4-v50', {'coords': (289482.6484146575, 3809584.757735084, 1.9596313275396824)}), ('g4-v51', {'coords': (289485.1917839021, 3809585.764020973, 3.1308537758886814)}), ('g4-v52', {'coords': (289482.51720548247, 3809591.006972457, 3.1112184803932905)}), ('g4-v53', {'coords': (289483.4197953283, 3809591.392162719, 3.5575841683894396)}), ('g4-v55', {'coords': (289485.94348135585, 3809590.391592346, 4.488903337158263)}), ('g4-v59', {'coords': (289482.91145967756, 3809597.2189652026, 4.63420370221138)}), ('g4-v63', {'coords': (289484.813967597, 3809594.419773257, 4.238473925739527)}), ('g4-v66', {'coords': (289483.40592294093, 3809593.0233782884, 3.927971590310335)})])
edges = [('g0-v0', 'g0-v1'), ('g0-v0', 'g0-v5'), ('g0-v0', 'g0-v7'), ('g0-v1', 'g0-v2'), ('g0-v2', 'g0-v3'), ('g0-v2', 'g0-v5'), ('g0-v3', 'g0-v4'), ('g0-v4', 'g0-v5'), ('g0-v4', 'g0-v9'), ('g0-v7', 'g0-v9'), ('g1-v0', 'g1-v1'), ('g1-v0', 'g1-v15'), ('g1-v1', 'g1-v2'), ('g1-v1', 'g1-v25'), ('g1-v2', 'g1-v3'), ('g1-v2', 'g1-v25'), ('g1-v3', 'g1-v4'), ('g1-v4', 'g1-v5'), ('g1-v5', 'g1-v6'), ('g1-v5', 'g1-v36'), ('g1-v6', 'g1-v22'), ('g1-v7', 'g1-v8'), ('g1-v7', 'g1-v22'), ('g1-v8', 'g1-v9'), ('g1-v9', 'g1-v10'), ('g1-v10', 'g1-v11'), ('g1-v11', 'g1-v19'), ('g1-v11', 'g1-v36'), ('g1-v11', 'g1-v28'), ('g1-v12', 'g1-v13'), ('g1-v12', 'g1-v18'), ('g1-v12', 'g1-v25'), ('g1-v12', 'g1-v28'), ('g1-v13', 'g1-v14'), ('g1-v14', 'g1-v15'), ('g1-v18', 'g1-v19'), ('g1-v22', 'g1-v36'), ('g1-v28', 'g1-v29'), ('g1-v28', 'g1-v34'), ('g1-v28', 'g1-v32'), ('g1-v29', 'g1-v30'), ('g1-v29', 'g1-v35'), ('g1-v30', 'g1-v31'), ('g1-v31', 'g1-v32'), ('g1-v32', 'g1-v33'), ('g1-v33', 'g1-v34'), ('g1-v34', 'g1-v35'), ('g2-v0', 'g2-v1'), ('g2-v0', 'g2-v48'), ('g2-v1', 'g2-v2'), ('g2-v1', 'g2-v4'), ('g2-v2', 'g2-v3'), ('g2-v3', 'g2-v4'), ('g2-v4', 'g2-v6'), ('g2-v6', 'g2-v7'), ('g2-v7', 'g2-v8'), ('g2-v8', 'g2-v9'), ('g2-v9', 'g2-v12'), ('g2-v9', 'g2-v45'), ('g2-v12', 'g2-v14'), ('g2-v14', 'g2-v17'), ('g2-v17', 'g2-v18'), ('g2-v18', 'g2-v19'), ('g2-v19', 'g2-v20'), ('g2-v19', 'g2-v42'), ('g2-v20', 'g2-v21'), ('g2-v20', 'g2-v54'), ('g2-v21', 'g2-v22'), ('g2-v21', 'g2-v54'), ('g2-v22', 'g2-v23'), ('g2-v22', 'g2-v54'), ('g2-v23', 'g2-v24'), ('g2-v23', 'g2-v54'), ('g2-v24', 'g2-v25'), ('g2-v24', 'g2-v42'), ('g2-v25', 'g2-v26'), ('g2-v26', 'g2-v27'), ('g2-v26', 'g2-v37'), ('g2-v27', 'g2-v28'), ('g2-v28', 'g2-v29'), ('g2-v29', 'g2-v30'), ('g2-v30', 'g2-v31'), ('g2-v30', 'g2-v36'), ('g2-v31', 'g2-v35'), ('g2-v35', 'g2-v36'), ('g2-v35', 'g2-v64'), ('g2-v35', 'g2-v39'), ('g2-v36', 'g2-v37'), ('g2-v37', 'g2-v40'), ('g2-v39', 'g2-v64'), ('g2-v40', 'g2-v42'), ('g2-v40', 'g2-v64'), ('g2-v42', 'g2-v43'), ('g2-v42', 'g2-v54'), ('g2-v43', 'g2-v44'), ('g2-v44', 'g2-v45'), ('g2-v45', 'g2-v46'), ('g2-v46', 'g2-v47'), ('g2-v47', 'g2-v48'), ('g3-v0', 'g3-v1'), ('g3-v0', 'g3-v5'), ('g3-v1', 'g3-v2'), ('g3-v2', 'g3-v3'), ('g3-v3', 'g3-v4'), ('g3-v4', 'g3-v5'), ('g4-v0', 'g4-v1'), ('g4-v0', 'g4-v53'), ('g4-v1', 'g4-v2'), ('g4-v2', 'g4-v3'), ('g4-v3', 'g4-v4'), ('g4-v3', 'g4-v11'), ('g4-v4', 'g4-v5'), ('g4-v5', 'g4-v6'), ('g4-v6', 'g4-v8'), ('g4-v6', 'g4-v10'), ('g4-v8', 'g4-v9'), ('g4-v9', 'g4-v10'), ('g4-v10', 'g4-v11'), ('g4-v11', 'g4-v12'), ('g4-v12', 'g4-v13'), ('g4-v13', 'g4-v14'), ('g4-v14', 'g4-v15'), ('g4-v15', 'g4-v17'), ('g4-v15', 'g4-v55'), ('g4-v17', 'g4-v18'), ('g4-v18', 'g4-v19'), ('g4-v19', 'g4-v20'), ('g4-v19', 'g4-v63'), ('g4-v20', 'g4-v21'), ('g4-v21', 'g4-v22'), ('g4-v21', 'g4-v63'), ('g4-v22', 'g4-v23'), ('g4-v23', 'g4-v24'), ('g4-v23', 'g4-v63'), ('g4-v24', 'g4-v26'), ('g4-v26', 'g4-v27'), ('g4-v27', 'g4-v28'), ('g4-v27', 'g4-v59'), ('g4-v28', 'g4-v29'), ('g4-v29', 'g4-v30'), ('g4-v30', 'g4-v31'), ('g4-v31', 'g4-v32'), ('g4-v32', 'g4-v33'), ('g4-v32', 'g4-v59'), ('g4-v33', 'g4-v34'), ('g4-v34', 'g4-v35'), ('g4-v35', 'g4-v37'), ('g4-v37', 'g4-v38'), ('g4-v37', 'g4-v66'), ('g4-v38', 'g4-v39'), ('g4-v39', 'g4-v40'), ('g4-v40', 'g4-v42'), ('g4-v40', 'g4-v66'), ('g4-v42', 'g4-v43'), ('g4-v43', 'g4-v44'), ('g4-v43', 'g4-v66'), ('g4-v44', 'g4-v46'), ('g4-v46', 'g4-v47'), ('g4-v47', 'g4-v48'), ('g4-v48', 'g4-v49'), ('g4-v49', 'g4-v50'), ('g4-v50', 'g4-v51'), ('g4-v51', 'g4-v52'), ('g4-v52', 'g4-v53'), ('g4-v53', 'g4-v55'), ('g4-v55', 'g4-v59')]