use if False instead of commenting out code

meanas/fdfd/bloch.py

@@ -554,7 +554,7 @@ def eigsolve(
     prev_E = 0.0
     d_scale = 1.0
     prev_traceGtKG = 0.0
-    #prev_theta = 0.5
+    prev_theta = 0.5
     D = numpy.zeros(shape=y_shape, dtype=complex)
 
     Z: NDArray[numpy.complex128]
@@ -674,7 +674,7 @@ def eigsolve(
             trace = _rtrace_AtB(R, Qi)
             return numpy.abs(trace)
 
-        '''
+        if False:
             def trace_deriv(theta):
                 Qi = Qi_func(theta)
                 c2 = numpy.cos(2 * theta)
@@ -699,14 +699,24 @@ def eigsolve(
                    4 * _rtrace_AtB(U, symZtAD @ U_sZtD))
 
             # Newton-Raphson to find a root of the first derivative:
-        theta = -dE/d2E
+            theta = -dE / d2E
 
             if d2E < 0 or abs(theta) >= pi:
                 theta = -abs(prev_theta) * numpy.sign(dE)
 
             # theta, new_E, new_dE = linmin(theta, E, dE, 0.1, min(tolerance, 1e-6), 1e-14, 0, -numpy.sign(dE) * K_PI, trace_func)
-        theta, n, _, new_E, _, _new_dE = scipy.optimize.line_search(trace_func, trace_deriv, xk=theta, pk=numpy.ones((1,1)), gfk=dE, old_fval=E, c1=min(tolerance, 1e-6), c2=0.1, amax=pi)
-        '''
+            theta, n, _, new_E, _, _new_dE = scipy.optimize.line_search(
+                trace_func,
+                trace_deriv,
+                xk=theta,
+                pk=numpy.ones((1, 1)),
+                gfk=dE,
+                old_fval=E,
+                c1=min(tolerance, 1e-6),
+                c2=0.1,
+                amax=pi,
+                )
+
         result = scipy.optimize.minimize_scalar(trace_func, bounds=(0, pi), tol=tolerance)
         new_E = result.fun
         theta = result.x
@@ -716,7 +726,7 @@ def eigsolve(
         Z *= numpy.cos(theta)
         Z += D * numpy.sin(theta)
 
-        #prev_theta = theta
+        prev_theta = theta
         prev_E = E
 
         if callback: