モデルの測定から得られたポイントのリストがあります。行われる分析の重要な部分は、これらのポイントに沿って「候補パス」を見つけることであり、これはさらにトリミングおよび改良されます。
以下は、生データのプロットを示す画像と、検出されたパスがどのように見えるかを示す手書きの図です。これらのパスは連続的でほぼ垂直でなければならず、出力形式はポイントのリストのリストになります (ポイントの色はパス検索自体には関係ありません)。
これは総当りの徹底的な方法で解決できると思いますが、これにはよく知られたアルゴリズムが存在する可能性もあります。
私は Python を使用しているので、Numpy/Scipy の例は大歓迎です (scipy.spatialそのための完璧な候補のように聞こえます)。
編集: サンプル データセットを以下に示します ([x,y,weight] ポイントのリスト):
[[ -0.7898176 -3.35201728 4.36142086]
[ 2.99221402 -3.35201728 1.11907575]
[ 6.97475149 -3.35201728 2.4320322 ]
[ -4.82443609 -2.35201728 0.6479064 ]
[ -1.32418909 -2.35201728 1.88004944]
[ 0.07067882 -2.35201728 1.10982834]
[ 3.09169448 -2.35201728 1.8557436 ]
[ 7.10399403 -2.35201728 2.03906224]
[ -3.07207606 -1.35201728 0.35500973]
[ 2.63202993 -1.35201728 5.32397834]
[ 5.19884868 -1.35201728 1.63816326]
[ 7.65721835 -1.35201728 1.13843392]
[ 2.48172754 -0.35201728 6.65584512]
[ 6.0905911 -0.35201728 1.15552652]
[ 8.62497546 -0.35201728 0.30407144]
[ -4.7300089 0.64798272 0.31481496]
[ -3.03274093 0.64798272 0.95337568]
[ 2.19653614 0.64798272 10.3675204 ]
[ 6.20384058 0.64798272 1.42106077]
[ -4.08636605 1.64798272 0.28875288]
[ 2.03344989 1.64798272 13.04648211]
[ -4.11717795 2.64798272 0.39713141]
[ 1.93304283 2.64798272 10.41313242]
[ -4.37994815 3.64798272 0.84588643]
[ 1.66081408 3.64798272 14.96380955]
[ -4.19024027 4.64798272 0.73216113]
[ 1.60252433 4.64798272 14.72419286]
[ 6.77837359 4.64798272 0.6186005 ]
[ -4.14362668 5.64798272 0.93673165]
[ 1.55372968 5.64798272 12.9421123 ]
[ -4.62223541 6.64798272 0.6510101 ]
[ 1.527865 6.64798272 10.80209351]
[ 6.86820685 6.64798272 0.82550801]
[ -4.68259732 7.64798272 0.45321369]
[ 1.36167494 7.64798272 6.45338514]
[ -5.19205787 8.64798272 0.23935013]
[ 1.21003466 8.64798272 10.13528877]
[ 7.6689546 8.64798272 0.32421776]
[ -5.36436818 9.64798272 0.79809416]
[ 1.26248534 9.64798272 7.67036253]
[ 7.35472418 9.64798272 0.92555691]
[ -5.61723652 10.64798272 0.4741007 ]
[ 1.23101086 10.64798272 7.97064105]
[ -7.83024735 11.64798272 0.47557318]
[ 1.20348982 11.64798272 8.20694816]
[ 1.14422758 12.64798272 9.26244889]
[ 9.18164464 12.64798272 0.72428381]
[ 1.0827069 13.64798272 10.08599118]
[ 6.80116007 13.64798272 0.4571425 ]
[ 9.384236 13.64798272 0.42399893]
[ 1.04053491 14.64798272 10.48370805]
[ 9.16197972 14.64798272 0.39930227]
[ -9.85958581 15.64798272 0.39524976]
[ 0.9942501 15.64798272 8.39992264]
[ 8.07642416 15.64798272 0.61480371]
[ 9.55088151 15.64798272 0.54076473]
[ -7.13657331 16.64798272 0.32929172]
[ 0.92606211 16.64798272 7.83597033]
[ 8.74291069 16.64798272 0.74246827]
[ -7.20022443 17.64798272 0.52555351]
[ 0.81344517 17.64798272 6.81654834]
[ 8.52844624 17.64798272 0.70543711]
[ -6.97465178 18.64798272 1.04527813]
[ 0.61959631 18.64798272 10.33529022]
[ 5.733054 18.64798272 1.2309691 ]
[ 8.14818453 18.64798272 1.37532423]
[ -6.82823664 19.64798272 2.0314052 ]
[ 0.56391636 19.64798272 13.61447357]
[ 5.79971126 19.64798272 0.30148347]
[ 8.01499476 19.64798272 1.72465327]
[ -6.78504689 20.64798272 2.88657804]
[ -4.79580634 20.64798272 0.36201975]
[ 0.548376 20.64798272 7.8414544 ]
[ 7.62258506 20.64798272 1.52817905]
[-10.50328534 21.64798272 0.90358671]
[ -6.59976138 21.64798272 2.62980169]
[ -3.71180255 21.64798272 1.27094175]
[ 0.5060743 21.64798272 11.06117677]
[ 4.51983105 21.64798272 1.74626435]
[ 7.50948795 21.64798272 3.46497629]
[ 11.10199877 21.64798272 1.78047269]
[-10.15444935 22.64798272 1.47486166]
[ -6.26274479 22.64798272 4.73707852]
[ -3.45440904 22.64798272 1.72516012]
[ 0.52759064 22.64798272 12.58470433]
[ 4.22258017 22.64798272 2.63827535]
[ 7.03480033 22.64798272 3.506412 ]
[ 10.63560314 22.64798272 3.56076386]
[ -5.95693623 23.64798272 2.97403863]
[ -3.66261423 23.64798272 2.31667236]
[ 0.52051366 23.64798272 12.5526344 ]
[ 4.21083787 23.64798272 1.95794387]
[ 6.82438636 23.64798272 4.77995659]
[ 10.18138299 23.64798272 5.21836205]
[ -9.94629932 24.64798272 0.4074823 ]
[ -5.74101948 24.64798272 2.60992238]
[ 0.52987226 24.64798272 10.68846987]
[ 6.29981921 24.64798272 3.56204471]
[ 9.96431168 24.64798272 2.85079129]
[ -9.64229717 25.64798272 0.4503241 ]
[ -5.579063 25.64798272 0.64475469]
[ 0.52053534 25.64798272 10.05046667]
[ 5.79167815 25.64798272 0.92797027]
[ 10.05116919 25.64798272 2.52194933]
[ -8.55286247 26.64798272 0.94447148]
[ 0.45065604 26.64798272 10.97432823]
[ 5.50068393 26.64798272 2.39645232]
[ 10.08992273 26.64798272 2.77716257]
[-16.62381217 27.64798272 0.2021621 ]
[ -9.62146213 27.64798272 0.62245778]
[ -7.66905507 27.64798272 2.84466396]
[ 0.38656111 27.64798272 10.74369366]
[ 5.76925402 27.64798272 1.13362978]
[ 9.83525197 27.64798272 1.18241147]
[-15.64874512 28.64798272 0.18279302]
[ -7.52932494 28.64798272 2.94012191]
[ 0.32171219 28.64798272 10.73770466]
[ 9.4062684 28.64798272 1.41714298]
[-12.71287717 29.64798272 0.70268073]
[ -7.59473877 29.64798272 2.16183026]
[ 0.20748772 29.64798272 12.97312987]
[ 3.92952496 29.64798272 1.54987681]
[ 9.05148017 29.64798272 2.40563748]
[ 14.96021523 29.64798272 0.55258241]
[-12.14428813 30.64798272 0.36365363]
[ -7.12360666 30.64798272 2.54312163]
[ 0.40594038 30.64798272 12.64839117]
[ 4.59465757 30.64798272 1.23496581]
[ 8.54333134 30.64798272 2.18912857]
[-10.6296531 31.64798272 1.4839259 ]
[ -7.09532763 31.64798272 2.0113838 ]
[ 0.37037733 31.64798272 12.2071139 ]
[ 3.01253349 31.64798272 3.01591777]
[ 4.64523695 31.64798272 3.50267541]
[ 8.39369696 31.64798272 2.53195817]
[ -7.07947026 32.64798272 1.01324147]
[ 0.39269437 32.64798272 9.67368625]
[ 8.58669997 32.64798272 1.00475646]
[ 12.02329114 32.64798272 0.50782399]
[-10.13060786 33.64798272 0.31475653]
[ -7.30360407 33.64798272 0.35065243]
[ 0.49556923 33.64798272 9.66608818]
[ -5.37822311 34.64798272 0.38727401]
[ 0.4958055 34.64798272 7.5415026 ]
[ 6.07719006 34.64798272 0.63012453]
[ -4.64579055 35.64798272 0.39990249]
[ 0.46323666 35.64798272 4.60449213]
[ 4.72819312 35.64798272 0.98050594]
[ -4.62418372 36.64798272 0.64160709]
[ 0.48866236 36.64798272 4.29331656]
[ 5.06493722 36.64798272 0.59888608]
[ 0.49730481 37.64798272 1.32828464]
[ -1.31849217 38.64798272 0.70780886]
[ 1.70966455 38.64798272 0.88052135]
[ 0.06305774 39.64798272 0.47366487]
[ 2.13639356 39.64798272 0.67971461]
[ -0.84726354 40.64798272 0.63787522]
[ 0.55723562 40.64798272 0.62855097]
[ 2.22359779 40.64798272 0.33884894]
[ 0.77309816 41.64798272 0.4605534 ]
[ 0.56144565 42.64798272 0.43678788]]
助けてくれてありがとう！