projection.md
In quollr: Visualising How Nonlinear Dimension Reduction Warps Your Data

gen_axes() works

Code
  gen_axes(proj = projection_df, axis_labels = paste0("x", 1:7))
Output
  $axes
             x1         y1         x2         y2    distance
  x1 -0.6666667 -0.6666667 -0.6955883 -0.6842517 0.033848117
  x2 -0.6666667 -0.6666667 -0.6715100 -0.6419517 0.025185097
  x3 -0.6666667 -0.6666667 -0.6335717 -0.6701050 0.033273130
  x4 -0.6666667 -0.6666667 -0.6666050 -0.6622367 0.004430429
  x5 -0.6666667 -0.6666667 -0.6664483 -0.6719167 0.005254538
  x6 -0.6666667 -0.6666667 -0.6750317 -0.6338367 0.033878933
  x7 -0.6666667 -0.6666667 -0.6610483 -0.6665933 0.005618812

  $circle
             c1         c2
  1  -0.5000000 -0.6666667
  2  -0.5013683 -0.6453538
  3  -0.5054509 -0.6243909
  4  -0.5121805 -0.6041222
  5  -0.5214469 -0.5848804
  6  -0.5330977 -0.5669816
  7  -0.5469418 -0.5507196
  8  -0.5627517 -0.5363614
  9  -0.5802679 -0.5241429
  10 -0.5992028 -0.5142646
  11 -0.6192454 -0.5068887
  12 -0.6400667 -0.5021364
  13 -0.6613247 -0.5000856
  14 -0.6826705 -0.5007701
  15 -0.7037535 -0.5041787
  16 -0.7242275 -0.5102553
  17 -0.7437564 -0.5189001
  18 -0.7620194 -0.5299713
  19 -0.7787168 -0.5432870
  20 -0.7935743 -0.5586286
  21 -0.8063480 -0.5757442
  22 -0.8168281 -0.5943527
  23 -0.8248426 -0.6141486
  24 -0.8302599 -0.6348069
  25 -0.8329909 -0.6559883
  26 -0.8329909 -0.6773450
  27 -0.8302599 -0.6985264
  28 -0.8248426 -0.7191847
  29 -0.8168281 -0.7389806
  30 -0.8063480 -0.7575892
  31 -0.7935743 -0.7747047
  32 -0.7787168 -0.7900463
  33 -0.7620194 -0.8033620
  34 -0.7437564 -0.8144332
  35 -0.7242275 -0.8230781
  36 -0.7037535 -0.8291547
  37 -0.6826705 -0.8325632
  38 -0.6613247 -0.8332477
  39 -0.6400667 -0.8311970
  40 -0.6192454 -0.8264446
  41 -0.5992028 -0.8190688
  42 -0.5802679 -0.8091905
  43 -0.5627517 -0.7969719
  44 -0.5469418 -0.7826138
  45 -0.5330977 -0.7663518
  46 -0.5214469 -0.7484529
  47 -0.5121805 -0.7292112
  48 -0.5054509 -0.7089424
  49 -0.5013683 -0.6879795
  50 -0.5000000 -0.6666667

Code
  get_projection(projection = projection_df, proj_scale = 1, highd_data = scurve,
    model_highd = df_bin, trimesh_data = edge_data, axis_param = list(limits = 1,
      axis_scaled = 3, axis_pos_x = -0.72, axis_pos_y = -0.72, threshold = 0.09))
Message
  New names:
  * `` -> `...1`
  * `` -> `...2`
  New names:
  * `` -> `...1`
  * `` -> `...2`
Output
  $projected_df
  # A tibble: 1,000 x 3
        proj1  proj2    ID
        <dbl>  <dbl> <int>
   1 -0.400   0.182      1
   2  0.00561 0.0247     2
   3  0.187   0.174      3
   4 -0.276   0.231      4
   5 -0.320   0.243      5
   6 -0.509   0.221      6
   7  0.0934  0.0246     7
   8 -0.0438  0.294      8
   9 -0.284   0.116      9
  10  0.147   0.289     10
  # i 990 more rows

  $model_df
  # A tibble: 10 x 12
      from    to x_from y_from  x_to  y_to from_count to_count proj1_from
     <int> <int>  <dbl>  <dbl> <dbl> <dbl>      <dbl>    <dbl>      <dbl>
   1     2     6  0.442 0.101  0.400 0.173         11       11     0.0869
   2     2     3  0.442 0.101  0.526 0.101         11       12     0.0869
   3     7     8  0.567 0.173  0.651 0.173         14       11     0.341 
   4     1     4  0.818 0.0288 0.776 0.101         12       12     0.401 
   5     4     5  0.776 0.101  0.859 0.101         12       12     0.427 
   6     9    10  0.776 0.245  0.734 0.318         11       12     0.440 
   7    11    12  0.400 0.751  0.442 0.823         11       12     0.0303
   8    13    14  0.109 1.11   0.192 1.11          13       14     0.248 
   9     3     7  0.526 0.101  0.567 0.173         12       14     0.389 
  10     1     5  0.818 0.0288 0.859 0.101         12       12     0.401 
  # i 3 more variables: proj2_from <dbl>, proj1_to <dbl>, proj2_to <dbl>

  $axes
        x1    y1        x2        y2   distance
  x1 -0.72 -0.72 -0.806765 -0.772755 0.10154435
  x3 -0.72 -0.72 -0.620715 -0.730315 0.09981939
  x6 -0.72 -0.72 -0.745095 -0.621510 0.10163680

  $circle
             c1         c2
  1  -0.5533333 -0.7200000
  2  -0.5547017 -0.6986871
  3  -0.5587842 -0.6777242
  4  -0.5655139 -0.6574555
  5  -0.5747802 -0.6382137
  6  -0.5864311 -0.6203149
  7  -0.6002751 -0.6040529
  8  -0.6160850 -0.5896948
  9  -0.6336012 -0.5774762
  10 -0.6525361 -0.5675979
  11 -0.6725787 -0.5602220
  12 -0.6934000 -0.5554697
  13 -0.7146581 -0.5534190
  14 -0.7360038 -0.5541035
  15 -0.7570868 -0.5575120
  16 -0.7775608 -0.5635886
  17 -0.7970897 -0.5722334
  18 -0.8153528 -0.5833046
  19 -0.8320501 -0.5966203
  20 -0.8469077 -0.6119619
  21 -0.8596814 -0.6290775
  22 -0.8701615 -0.6476860
  23 -0.8781760 -0.6674820
  24 -0.8835932 -0.6881402
  25 -0.8863242 -0.7093216
  26 -0.8863242 -0.7306784
  27 -0.8835932 -0.7518598
  28 -0.8781760 -0.7725180
  29 -0.8701615 -0.7923140
  30 -0.8596814 -0.8109225
  31 -0.8469077 -0.8280381
  32 -0.8320501 -0.8433797
  33 -0.8153528 -0.8566954
  34 -0.7970897 -0.8677666
  35 -0.7775608 -0.8764114
  36 -0.7570868 -0.8824880
  37 -0.7360038 -0.8858965
  38 -0.7146581 -0.8865810
  39 -0.6934000 -0.8845303
  40 -0.6725787 -0.8797780
  41 -0.6525361 -0.8724021
  42 -0.6336012 -0.8625238
  43 -0.6160850 -0.8503052
  44 -0.6002751 -0.8359471
  45 -0.5864311 -0.8196851
  46 -0.5747802 -0.8017863
  47 -0.5655139 -0.7825445
  48 -0.5587842 -0.7622758
  49 -0.5547017 -0.7413129
  50 -0.5533333 -0.7200000