I used the locator pane to map a network, specifically the Maryland Power Grid. What I would like is to have some sort of numbering of the vertices on the unconnected network, how to make an adjacency matrix for the network to then use that to connect the network, and show either in color or shape that some vertices are different (i.e. loads and generators). It might be worthy to note that I first recorded the loads then the generators from left to right, consequently the last 13 vertices are generators. Here is the code I used for the locator pane, The Maryland Power Grid picture I imported to record vertices, and the resulting network. The locator pane code makes the x and y coordinates unitary.
Locator Pane Code
im= Import["http://esm.versar.com/pprp/ceir16/Images/Figure2_20.jpg"];
Module[{picturesize,i,bigger,imagesizes},
picturesize=675;
values={};
i=1;
imagesizes=ImageDimensions[im];bigger=1;
DynamicModule[{pt={0,0}},
If[imagesizes[[1]]<imagesizes[[2]], bigger=2];
TableForm@{
Button["Record!",AppendTo[values,{i,pt}];i++;],
LocatorPane[
Dynamic@pt,
Dynamic@Graphics[
Join[
{Inset[im,{0,0},{0,0},{1,1}]}
],
PlotRange->{{0,imagesizes[[1]]/imagesizes[[bigger]]},{0,imagesizes[[2]]/imagesizes[[bigger]]}},
ImageSize->picturesize]
],
Dynamic[N@pt],
Dynamic[MatrixForm@values]
}]
]
The Maryland Power Grid I used with the locator pane
The resulting unconnected network
The locator pane also returned an array of points in the form {n, {x,y}} where n is the vertex number recorded chronologically (I tried to do them from left to right) and the x and y are the x and y unitary coordinates.
({
{1, {0.0996, 0.4195}},
{2, {0.1046, 0.392}},
{3, {0.1144, 0.468}},
{4, {0.1164, 0.4415}},
{5, {0.1432, 0.4455}},
{6, {0.1442, 0.376}},
{7, {0.147, 0.5085}},
{8, {0.16, 0.4965}},
{9, {0.1628, 0.5305}},
{10, {0.1708, 0.484}},
{11, {0.1776, 0.379}},
{12, {0.1836, 0.4335}},
{13, {0.1856, 0.408}},
{14, {0.1936, 0.4495}},
{15, {0.2034, 0.5295}},
{16, {0.1974, 0.3595}},
{17, {0.2122, 0.4395}},
{18, {0.2152, 0.4465}},
{19, {0.2252, 0.3555}},
{20, {0.229, 0.4415}},
{21, {0.2716, 0.3535}},
{22, {0.2952, 0.313}},
{23, {0.3258, 0.4265}},
{24, {0.3446, 0.475}},
{25, {0.3476, 0.4315}},
{26, {0.3536, 0.408}},
{27, {0.3584, 0.3545}},
{28, {0.3694, 0.3665}},
{29, {0.3732, 0.384}},
{30, {0.393, 0.3535}},
{31, {0.395, 0.4205}},
{32, {0.4028, 0.3625}},
{33, {0.4028, 0.378}},
{34, {0.4088, 0.3525}},
{35, {0.4108, 0.4345}},
{36, {0.4286, 0.4505}},
{37, {0.4336, 0.405}},
{38, {0.4434, 0.247}},
{39, {0.4542, 0.303}},
{40, {0.4532, 0.4415}},
{41, {0.4612, 0.3515}},
{42, {0.469, 0.3455}},
{43, {0.47, 0.3685}},
{44, {0.477, 0.391}},
{45, {0.4888, 0.31}},
{46, {0.4948, 0.3315}},
{47, {0.4958, 0.371}},
{48, {0.4986, 0.217}},
{49, {0.4918, 0.4375}},
{50, {0.5214, 0.376}},
{51, {0.5244, 0.3455}},
{52, {0.5284, 0.4315}},
{53, {0.5294, 0.457}},
{54, {0.5302, 0.471}},
{55, {0.5302, 0.2875}},
{56, {0.554, 0.243}},
{57, {0.554, 0.323}},
{58, {0.554, 0.299}},
{59, {0.559, 0.231}},
{60, {0.559, 0.2575}},
{61, {0.559, 0.2725}},
{62, {0.5628, 0.244}},
{63, {0.5668, 0.221}},
{64, {0.5668, 0.316}},
{65, {0.5708, 0.1755}},
{66, {0.5816, 0.2665}},
{67, {0.5836, 0.224}},
{68, {0.5916, 0.152}},
{69, {0.5806, 0.307}},
{70, {0.5826, 0.3365}},
{71, {0.5876, 0.32}},
{72, {0.5974, 0.242}},
{73, {0.5984, 0.2845}},
{74, {0.6044, 0.2865}},
{75, {0.5856, 0.397}},
{76, {0.5906, 0.3605}},
{77, {0.5916, 0.388}},
{78, {0.6014, 0.3385}},
{79, {0.6054, 0.388}},
{80, {0.6132, 0.299}},
{81, {0.6132, 0.2865}},
{82, {0.6142, 0.2745}},
{83, {0.6182, 0.375}},
{84, {0.6252, 0.245}},
{85, {0.6252, 0.3355}},
{86, {0.629, 0.3605}},
{87, {0.63, 0.371}},
{88, {0.63, 0.388}},
{89, {0.633, 0.474}},
{90, {0.635, 0.3535}},
{91, {0.641, 0.3525}},
{92, {0.639, 0.411}},
{93, {0.6438, 0.3585}},
{94, {0.6458, 0.4355}},
{95, {0.6488, 0.473}},
{96, {0.6548, 0.2695}},
{97, {0.6576, 0.38}},
{98, {0.6646, 0.4455}},
{99, {0.6736, 0.3685}},
{100, {0.6764, 0.4415}},
{101, {0.6774, 0.396}},
{102, {0.6902, 0.402}},
{103, {0.6932, 0.373}},
{104, {0.6962, 0.473}},
{105, {0.6972, 0.453}},
{106, {0.718, 0.389}},
{107, {0.7268, 0.292}},
{108, {0.7228, 0.4335}},
{109, {0.7288, 0.457}},
{110, {0.7426, 0.296}},
{111, {0.7406, 0.4235}},
{112, {0.7644, 0.295}},
{113, {0.7624, 0.457}},
{114, {0.7802, 0.4225}},
{115, {0.7802, 0.2855}},
{116, {0.7822, 0.3355}},
{117, {0.797, 0.4315}},
{118, {0.799, 0.4495}},
{119, {0.8028, 0.4415}},
{120, {0.8168, 0.1235}},
{121, {0.8088, 0.459}},
{122, {0.8226, 0.1045}},
{123, {0.8186, 0.3465}},
{124, {0.8168, 0.468}},
{125, {0.8286, 0.244}},
{126, {0.8256, 0.2825}},
{127, {0.8266, 0.329}},
{128, {0.8226, 0.38}},
{129, {0.8196, 0.4345}},
{130, {0.8394, 0.167}},
{131, {0.8532, 0.291}},
{132, {0.8602, 0.1245}},
{133, {0.8652, 0.1085}},
{134, {0.8898, 0.166}},
{135, {0.8938, 0.133}},
{136, {0.9164, 0.221}},
{137, {0.9244, 0.153}},
{138, {0.9284, 0.1845}},
{139, {0.1076, 0.4125}},
{140, {0.1224, 0.3445}},
{141, {0.15, 0.4205}},
{142, {0.4028, 0.4215}},
{143, {0.4642, 0.33}},
{144, {0.467, 0.3395}},
{145, {0.5362, 0.242}},
{146, {0.548, 0.2525}},
{147, {0.5638, 0.131}},
{148, {0.6102, 0.1775}},
{149, {0.629, 0.3435}},
{150, {0.64, 0.456}},
{151, {0.6458, 0.317}},
{152, {0.6488, 0.3535}},
{153, {0.6508, 0.385}},
{154, {0.6538, 0.3365}},
{155, {0.6636, 0.328}},
{156, {0.6626, 0.161}},
{157, {0.6764, 0.3555}},
{158, {0.6854, 0.456}},
{159, {0.7002, 0.465}},
{160, {0.708, 0.387}},
{161, {0.715, 0.4345}},
{162, {0.716, 0.2635}},
{163, {0.7812, 0.1815}},
{164, {0.8958, 0.1925}}
})
