Small patches

freegs4e/critical.py

@@ -410,9 +410,11 @@ def scan_for_crit(R, Z, psi):
                     )  # + 0.5*(fRR*delta_R**2 + fZZ*delta_Z**2 + fRZ*delta_R*delta_Z)
                     crpoint = (R0 + delta_R, Z0 + delta_Z, est_psi)
                     if det > 0.0:
-                        opoint = [crpoint] + opoint
+                        # opoint = [crpoint] + opoint
+                        opoint.append(crpoint)
                     else:
-                        xpoint = [crpoint] + xpoint
+                        # xpoint = [crpoint] + xpoint
+                        xpoint.append(crpoint)
 
     xpoint = np.array(xpoint)
     opoint = np.array(opoint)
@@ -859,6 +861,16 @@ def inside_mask(
     if use_geom:
         # cure flooding
         mask = mask * geom_inside_mask(R, Z, opoint, xpoint)
+        # apply geometric masking criterion to second Xpoint if close to double null
+        if len(xpoint > 1):
+            if (
+                np.abs(
+                    (xpoint[0, 2] - xpoint[1, 2])
+                    / (opoint[0, 2] - xpoint[0, 2])
+                )
+                < 0.1
+            ):
+                mask = mask * geom_inside_mask(R, Z, opoint, xpoint[1:])
     return mask
 
 

freegs4e/equilibrium.py

@@ -2272,7 +2272,7 @@ def normalised_total_Beta(self):
         """
         Calculates the total beta from the following definition:
 
-            normalised_total_Beta = ( (1 / poloidalBeta2) + (1/toroidalBeta) )^(-1).
+            normalised_total_Beta = ( (1 / poloidalBeta1) + (1/toroidalBeta1) )^(-1).
 
         Parameters
         ----------
@@ -2285,7 +2285,7 @@ def normalised_total_Beta(self):
         """
 
         return 1.0 / (
-            (1.0 / self.poloidalBeta()) + (1.0 / self.toroidalBeta())
+            (1.0 / self.poloidalBeta1()) + (1.0 / self.toroidalBeta1())
         )
 
     def strikepoints(

freegs4e/jtor.py

@@ -353,6 +353,8 @@ def Jtor_part2(self, R, Z, psi, psi_axis, psi_bndry, mask):
         IR = (
             np.sum(jtorshape * R / self.Raxis) * dR * dZ
         )  # romb(romb(jtorshape * R / self.Raxis)) * dR * dZ
+        if IR == 0:
+            raise ValueError("No core mask!")
         I_R = (
             np.sum(jtorshape * self.Raxis / R) * dR * dZ
         )  # romb(romb(jtorshape * self.Raxis / R)) * dR * dZ