tried some approaches, making mu a vector not worth it, too long with…

… the current method

demo/dryvr_demo/thermo_demo.py

@@ -36,6 +36,15 @@ class ThermoMode(Enum):
     # fig = go.Figure()
     # fig = simulation_tree(traces, None, fig, 2, 1, [2, 1], "lines", "trace")
 
+    # basis = np.array([[ 0.34474,-0.00154],[-0.00012,-0.02748]])
+    # center = np.array([-960.47483,6.7487])
+    # C = np.transpose(np.array([[1, 0, -1, 0], [0, 1, 0, -1]]))
+    # g = np.array([3030.84222,39.5072,-2989.43095,-35.91564,])
+    # StarSet(center, basis, C, g).plot()
+    # exit()
+    ### issue could be twofold as basestarsensor is also replace the bespoke therm sensor, which could be causing the errors
+    ### big issue: why are timers unsynched/not a multple of the ts? e.g., seeing this average time: [82.94445  1.15482  0.1    ]?
+    ### deepest issue though is why would post_cont_pca ever unsat for this?
     basis = np.array([[1, 0, 0], [0, 0, 0], [0, 0, 0]]) * np.diag([0.1, 0, 0]) # this doesn't actually make sense, but not sure how algorithm actually handles 1d polytopes
     center = np.array([75.5,0,0])
     C = np.transpose(np.array([[1,-1,0,0,0,0],[0,0,1,-1,0,0], [0,0,0,0,1,-1]]))

verse/stars/starset.py

@@ -616,15 +616,14 @@ def post_cont_pca(old_star: StarSet, derived_basis: np.ndarray,  points: np.ndar
         raise ValueError(f'Dimension of given basis does not match basis of original starset')
 
     center, basis, C, g = old_star.center, old_star.basis, old_star.C, old_star.g
-    
+
     old_star.print()
     print(derived_basis)
     print(np.average(points, axis=0))
 
     alpha = [RealVector(f'a_{i}', C.shape[1]) for i in range(points.shape[0])]
     u = Real('u')
     c = RealVector('i', old_star.dimension())
-    # u = RealVector('u', C.shape[0])
 
     o = Optimize()
     ### add equality constraints
@@ -644,15 +643,14 @@ def post_cont_pca(old_star: StarSet, derived_basis: np.ndarray,  points: np.ndar
             exp = 0 # there's probably a better way to do this, but this works
             for j in range(len(alpha[p])): # iterate over alphas
                 exp += C[i][j]*alpha[p][j]
-            # print(exp)
             o.add(exp <= u*g[i])
-            # o.add(exp <= u[i]*g[i])
 
     o.minimize(u)
 
     model = None
     new_basis = derived_basis
-    if o.check() == sat:
+
+    if o.check() == sat: 
         model = o.model()
         new_basis = derived_basis * float(model[u].as_fraction())
     else:
@@ -662,9 +660,7 @@ def post_cont_pca(old_star: StarSet, derived_basis: np.ndarray,  points: np.ndar
 
     # return StarSet(new_center, derived_basis, C, g * float(model[u].as_fraction()))
     new_center = np.array([float(model[c[i]].as_fraction()) for i in range(len(c))])
-
     print('---------\n---------')
-    StarSet(new_center, derived_basis, C, g * float(model[u].as_fraction())).print()
 
     return StarSet(new_center, derived_basis, C, g * float(model[u].as_fraction()))
     # return old_star.superposition(new_center, new_basis)