diff --git a/cheetah/accelerator/cavity.py b/cheetah/accelerator/cavity.py
index c1b53b17..5c729f5c 100644
--- a/cheetah/accelerator/cavity.py
+++ b/cheetah/accelerator/cavity.py
@@ -346,7 +346,7 @@ def plot(self, ax: plt.Axes, s: float) -> None:
         height = 0.4
 
         patch = Rectangle(
-            (s, 0), self.length[0], height, color="gold", alpha=alpha, zorder=2
+            (s, 0), self.length, height, color="gold", alpha=alpha, zorder=2
         )
         ax.add_patch(patch)
 
diff --git a/cheetah/accelerator/custom_transfer_map.py b/cheetah/accelerator/custom_transfer_map.py
index 2f271af8..867cc2b1 100644
--- a/cheetah/accelerator/custom_transfer_map.py
+++ b/cheetah/accelerator/custom_transfer_map.py
@@ -105,5 +105,5 @@ def split(self, resolution: torch.Tensor) -> list[Element]:
     def plot(self, ax: plt.Axes, s: float) -> None:
         height = 0.4
 
-        patch = Rectangle((s, 0), self.length[0], height, color="tab:olive", zorder=2)
+        patch = Rectangle((s, 0), self.length, height, color="tab:olive", zorder=2)
         ax.add_patch(patch)
diff --git a/cheetah/accelerator/dipole.py b/cheetah/accelerator/dipole.py
index d6aab27c..2a2fec14 100644
--- a/cheetah/accelerator/dipole.py
+++ b/cheetah/accelerator/dipole.py
@@ -482,9 +482,9 @@ def defining_features(self) -> list[str]:
 
     def plot(self, ax: plt.Axes, s: float) -> None:
         alpha = 1 if self.is_active else 0.2
-        height = 0.8 * (np.sign(self.angle[0]) if self.is_active else 1)
+        height = 0.8 * (np.sign(self.angle) if self.is_active else 1)
 
         patch = Rectangle(
-            (s, 0), self.length[0], height, color="tab:green", alpha=alpha, zorder=2
+            (s, 0), self.length, height, color="tab:green", alpha=alpha, zorder=2
         )
         ax.add_patch(patch)
diff --git a/cheetah/accelerator/horizontal_corrector.py b/cheetah/accelerator/horizontal_corrector.py
index e17837d6..5b547351 100644
--- a/cheetah/accelerator/horizontal_corrector.py
+++ b/cheetah/accelerator/horizontal_corrector.py
@@ -82,10 +82,10 @@ def split(self, resolution: torch.Tensor) -> list[Element]:
 
     def plot(self, ax: plt.Axes, s: float) -> None:
         alpha = 1 if self.is_active else 0.2
-        height = 0.8 * (np.sign(self.angle[0]) if self.is_active else 1)
+        height = 0.8 * (np.sign(self.angle) if self.is_active else 1)
 
         patch = Rectangle(
-            (s, 0), self.length[0], height, color="tab:blue", alpha=alpha, zorder=2
+            (s, 0), self.length, height, color="tab:blue", alpha=alpha, zorder=2
         )
         ax.add_patch(patch)
 
diff --git a/cheetah/accelerator/quadrupole.py b/cheetah/accelerator/quadrupole.py
index 4123121c..6e4449e0 100644
--- a/cheetah/accelerator/quadrupole.py
+++ b/cheetah/accelerator/quadrupole.py
@@ -219,9 +219,9 @@ def split(self, resolution: torch.Tensor) -> list[Element]:
 
     def plot(self, ax: plt.Axes, s: float) -> None:
         alpha = 1 if self.is_active else 0.2
-        height = 0.8 * (np.sign(self.k1[0]) if self.is_active else 1)
+        height = 0.8 * (np.sign(self.k1) if self.is_active else 1)
         patch = Rectangle(
-            (s, 0), self.length[0], height, color="tab:red", alpha=alpha, zorder=2
+            (s, 0), self.length, height, color="tab:red", alpha=alpha, zorder=2
         )
         ax.add_patch(patch)
 
diff --git a/cheetah/accelerator/segment.py b/cheetah/accelerator/segment.py
index 99ba5583..bcb81edf 100644
--- a/cheetah/accelerator/segment.py
+++ b/cheetah/accelerator/segment.py
@@ -386,7 +386,7 @@ def split(self, resolution: torch.Tensor) -> list[Element]:
         ]
 
     def plot(self, ax: plt.Axes, s: float) -> None:
-        element_lengths = [element.length[0] for element in self.elements]
+        element_lengths = [element.length for element in self.elements]
         element_ss = [0] + [
             sum(element_lengths[: i + 1]) for i, _ in enumerate(element_lengths)
         ]
@@ -423,7 +423,7 @@ def plot_reference_particle_traces(
         reference_segment = deepcopy(self)
         splits = reference_segment.split(resolution=torch.tensor(resolution))
 
-        split_lengths = [split.length[0] for split in splits]
+        split_lengths = [split.length for split in splits]
         ss = [0] + [sum(split_lengths[: i + 1]) for i, _ in enumerate(split_lengths)]
 
         references = []
@@ -464,7 +464,7 @@ def plot_reference_particle_traces(
 
         for particle_index in range(num_particles):
             xs = [
-                float(reference_beam.x[0, particle_index].cpu())
+                reference_beam.x[particle_index]
                 for reference_beam in references
                 if reference_beam is not Beam.empty
             ]
@@ -475,7 +475,7 @@ def plot_reference_particle_traces(
 
         for particle_index in range(num_particles):
             ys = [
-                float(reference_beam.ys[0, particle_index].cpu())
+                reference_beam.y[particle_index]
                 for reference_beam in references
                 if reference_beam is not Beam.empty
             ]
diff --git a/cheetah/accelerator/solenoid.py b/cheetah/accelerator/solenoid.py
index 2d89e208..d8b8afbe 100644
--- a/cheetah/accelerator/solenoid.py
+++ b/cheetah/accelerator/solenoid.py
@@ -121,7 +121,7 @@ def plot(self, ax: plt.Axes, s: float) -> None:
         height = 0.8
 
         patch = Rectangle(
-            (s, 0), self.length[0], height, color="tab:orange", alpha=alpha, zorder=2
+            (s, 0), self.length, height, color="tab:orange", alpha=alpha, zorder=2
         )
         ax.add_patch(patch)
 
diff --git a/cheetah/accelerator/transverse_deflecting_cavity.py b/cheetah/accelerator/transverse_deflecting_cavity.py
index 5ac9d6b3..2ccce890 100644
--- a/cheetah/accelerator/transverse_deflecting_cavity.py
+++ b/cheetah/accelerator/transverse_deflecting_cavity.py
@@ -223,7 +223,7 @@ def plot(self, ax: plt.Axes, s: float) -> None:
         height = 0.4
 
         patch = Rectangle(
-            (s, 0), self.length[0], height, color="olive", alpha=alpha, zorder=2
+            (s, 0), self.length, height, color="olive", alpha=alpha, zorder=2
         )
         ax.add_patch(patch)
 
diff --git a/cheetah/accelerator/undulator.py b/cheetah/accelerator/undulator.py
index c4c72e2c..50e3aa0f 100644
--- a/cheetah/accelerator/undulator.py
+++ b/cheetah/accelerator/undulator.py
@@ -69,7 +69,7 @@ def plot(self, ax: plt.Axes, s: float) -> None:
         height = 0.4
 
         patch = Rectangle(
-            (s, 0), self.length[0], height, color="tab:purple", alpha=alpha, zorder=2
+            (s, 0), self.length, height, color="tab:purple", alpha=alpha, zorder=2
         )
         ax.add_patch(patch)
 
diff --git a/cheetah/accelerator/vertical_corrector.py b/cheetah/accelerator/vertical_corrector.py
index bd78e367..b0f317f4 100644
--- a/cheetah/accelerator/vertical_corrector.py
+++ b/cheetah/accelerator/vertical_corrector.py
@@ -85,10 +85,10 @@ def split(self, resolution: torch.Tensor) -> list[Element]:
 
     def plot(self, ax: plt.Axes, s: float) -> None:
         alpha = 1 if self.is_active else 0.2
-        height = 0.8 * (np.sign(self.angle[0]) if self.is_active else 1)
+        height = 0.8 * (np.sign(self.angle) if self.is_active else 1)
 
         patch = Rectangle(
-            (s, 0), self.length[0], height, color="tab:cyan", alpha=alpha, zorder=2
+            (s, 0), self.length, height, color="tab:cyan", alpha=alpha, zorder=2
         )
         ax.add_patch(patch)
 
diff --git a/docs/examples/simple.ipynb b/docs/examples/simple.ipynb
index c0d7af7b..865cf2d3 100644
--- a/docs/examples/simple.ipynb
+++ b/docs/examples/simple.ipynb
@@ -119,22 +119,9 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "too many indices for tensor of dimension 1",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msegment\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_overview\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbeam\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mincoming_beam\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/DESY/cheetah/cheetah/accelerator/segment.py:510\u001b[0m, in \u001b[0;36mSegment.plot_overview\u001b[0;34m(self, fig, beam, n, resolution)\u001b[0m\n\u001b[1;32m    507\u001b[0m axs \u001b[38;5;241m=\u001b[39m gs\u001b[38;5;241m.\u001b[39msubplots(sharex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    509\u001b[0m axs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReference Particle Traces\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 510\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_reference_particle_traces\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbeam\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    512\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot(axs[\u001b[38;5;241m2\u001b[39m], \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    514\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n",
-      "File \u001b[0;32m~/Documents/DESY/cheetah/cheetah/accelerator/segment.py:467\u001b[0m, in \u001b[0;36mSegment.plot_reference_particle_traces\u001b[0;34m(self, axx, axy, beam, num_particles, resolution)\u001b[0m\n\u001b[1;32m    463\u001b[0m     references\u001b[38;5;241m.\u001b[39mappend(sample)\n\u001b[1;32m    465\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m particle_index \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_particles):\n\u001b[1;32m    466\u001b[0m     xs \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m--> 467\u001b[0m         \u001b[38;5;28mfloat\u001b[39m(\u001b[43mreference_beam\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparticle_index\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mcpu())\n\u001b[1;32m    468\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m reference_beam \u001b[38;5;129;01min\u001b[39;00m references\n\u001b[1;32m    469\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m reference_beam \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m Beam\u001b[38;5;241m.\u001b[39mempty\n\u001b[1;32m    470\u001b[0m     ]\n\u001b[1;32m    471\u001b[0m     axx\u001b[38;5;241m.\u001b[39mplot(ss[: \u001b[38;5;28mlen\u001b[39m(xs)], xs)\n\u001b[1;32m    472\u001b[0m axx\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ms (m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mIndexError\u001b[0m: too many indices for tensor of dimension 1"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]