visualise individual voxels and contours passing through them

main.jl

@@ -18,11 +18,11 @@ tdfast = tempdecay(215, 25, 0.08)
 
 
 ### LOAD IN DATA
-obj = "/Users/jayant/phd/tempaware/" * "M1"
+obj = "/Users/jayant/phd/tempaware/models/" * "M5"
 contours = clean_contour.(contour.(JSON.parse(open(obj * "contours.json"))))
-cdata = contourdata(contours, 5, 5) # contour data
+cdata = contourdata(contours, 20, 5) # contour data
 @time vd = voxdata(obj * "_voxels.csv", cdata)
-stress_multiplier!(vd.voxels, 10)
+#stress_multiplier!(vd.voxels, 10)
 rl = random_rollout(cdata)
 
 ### CONSTRUCT COST FUNCTION GIVEN MODEL ADN MATERIAL
@@ -70,7 +70,7 @@ end
 # single local search
 rl = random_rollout(cdata)
 cost_f(rl)
-max_iterations = 10
+max_iterations = 50
 @time local_search!(rl, max_iterations)
 
 # run many times
@@ -96,3 +96,6 @@ data_strain = .√(vd.voxels.Txy.^2 + vd.voxels.Tyz.^2 + vd.voxels.Txz.^2)
 data = data_stress
 histogram(data) # vast majority of voxels near 0 stress - can ignore
 histogram!(sort(data, rev=true)[1:4000]) # vast majority of voxels near 0 stress - can ignore
+
+### Visualise voxmap
+plot(vd, 35, cdata)
\ No newline at end of file

utils.jl

@@ -7,6 +7,7 @@ using Statistics
 using BenchmarkTools
 using Plots
 using LinearAlgebra
+import Plots: plot, plot!
 
 
 struct material
@@ -571,3 +572,22 @@ function construct_cost(cdata::contourdata, vd::voxdata, mat::material, td::temp
     end
     return eval(a)
 end
+
+
+function plot(vd::voxdata, i::Int, cdata::contourdata)
+    vm = vd.maps[i]
+    loc = Array(vd.voxels[i, ["x", "y", "z"]])
+    w = vd.width/2
+
+    sq_x = [loc[1]-w, loc[1]-w, loc[1]+w, loc[1]+w, loc[1]-w]
+    sq_y = [loc[2]+w, loc[2]-w, loc[2]-w, loc[2]+w, loc[2]+w]
+
+    plt = plot(sq_x, sq_y, aspect_ratio=:equal, label="Voxel", lw=3, color="black")
+    for (c,l) in zip(vm.segcontours, vm.seglen)
+        plot!(plt,
+            cdata.contours[c].pos[:,1],
+            cdata.contours[c].pos[:,2],
+            label=l)
+    end
+    plt
+end
\ No newline at end of file

voxelise.py

@@ -110,13 +110,13 @@ def contour2dict(c):
 
 
 if __name__ == '__main__':
-    obj = 'models/M1'
+    obj = 'models/M5'
     #check_data(obj + '.stl', obj + '_raw.csv')
-    out = voxelised_csv(obj + '.stl', obj + '_raw.csv', voxels=obj+'_vox_centers.csv')
-    out.to_csv(obj + '_voxels.csv')
-    visualise_voxels(obj + '_voxels.csv')
-
-    #contours = gc.decode_gcode(obj + '.gcode')
-    #outfile = open(obj + 'contours.json','w')
-    #json.dump([contour2dict(c) for c in contours], outfile)
-    #outfile.close()
+    #out = voxelised_csv(obj + '.stl', obj + '_raw.csv', voxels=obj+'_vox_centers.csv')
+    #out.to_csv(obj + '_voxels.csv')
+    #visualise_voxels(obj + '_voxels.csv')
+
+    contours = gc.decode_gcode(obj + '.gcode')
+    outfile = open(obj + 'contours.json','w')
+    json.dump([contour2dict(c) for c in contours], outfile)
+    outfile.close()