diff --git a/tools/stm/plot.py b/tools/stm/plot.py
index 50ef09a262..edeaa948dd 100644
--- a/tools/stm/plot.py
+++ b/tools/stm/plot.py
@@ -59,7 +59,7 @@
 start_points = [0, 0, 8]
 end_points = [2, 0, 8]
 distance = np.linalg.norm(np.array(end_points) - np.array(start_points))
-biases, points, dIdV_map = stm.line_sts(biasstart, biasend, biasstep, start_points, end_points, 50)
+biases, points, current, dIdV_map = stm.line_sts(biasstart, biasend, biasstep, start_points, end_points, 50)
 
 plt.figure(figsize=(10, 6), dpi=150)
 
diff --git a/tools/stm/stm.py b/tools/stm/stm.py
index edf7d8f3a7..a3a493367f 100644
--- a/tools/stm/stm.py
+++ b/tools/stm/stm.py
@@ -156,8 +156,6 @@ def linescan(self, bias, current, p1, p2, npoints=50, z0=None):
     def pointcurrent(self, bias, x, y, z):
         """Current for a single x, y, z position for a given bias."""
 
-        self.read_ldos(bias)
-
         nx = self.ldos.shape[0]
         ny = self.ldos.shape[1]
         nz = self.ldos.shape[2]
@@ -193,6 +191,7 @@ def sts(self, x, y, z, bias0, bias1, biasstep):
 
         for b in np.arange(len(biases)):
             print(b, biases[b])
+            self.read_ldos(biases[b])
             current[b] = self.pointcurrent(biases[b], x, y, z)
 
         dIdV = np.gradient(current, biasstep)
@@ -209,14 +208,21 @@ def line_sts(self, bias0, bias1, biasstep, p1, p2, npoints=50):
         d = p2 - p1
         s = np.dot(d, d)**0.5
         biases = np.arange(bias0, bias1 + biasstep, biasstep)
+        current = np.zeros((npoints, len(biases)))
 
-        dIdV = np.zeros((npoints, len(biases)))
+        for b in np.arange(len(biases)):
+            print(b, biases[b])
+            self.read_ldos(biases[b])
+
+            for i in range(npoints):
+                x, y, z = p1 + i * d / (npoints - 1)
+                current[i, b] = self.pointcurrent(biases[b], x, y, z)
 
+        dIdV = np.zeros((npoints, len(biases)))
         for i in range(npoints):
-            x, y, z = p1 + i * d / (npoints - 1)
-            biases, current, dIdV[i, :] = self.sts(x, y, z, bias0, bias1, biasstep)
+            dIdV[i, :] = np.gradient(current[i, :], biasstep)
 
-        return biases, np.linspace(0, s, npoints), dIdV
+        return biases, np.linspace(0, s, npoints), current, dIdV
 
 
     def find_current(self, ldos, z):