diff --git a/README.md b/README.md
index 44565ca..083e26b 100644
--- a/README.md
+++ b/README.md
@@ -77,4 +77,4 @@ coming soon.
 
 ### Geometry import example
 
-[![Open in Colab ](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/14L3WaV42PEu7NvAQdil7i8mEL4WTPF9h?usp=sharing)
\ No newline at end of file
+[![Open in Colab ](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/14L3WaV42PEu7NvAQdil7i8mEL4WTPF9h?usp=sharing)
diff --git a/pyproject.toml b/pyproject.toml
index 9461b96..c29c55c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pyvcell"
-version = "0.1.18"
+version = "0.1.19"
 description = "This is the python wrapper for vcell modeling and simulation"
 authors = ["Jim Schaff <schaff@uchc.edu>"]
 repository = "https://github.com/virtualcell/pyvcell"
diff --git a/pyvcell/sim_results/plotter.py b/pyvcell/sim_results/plotter.py
index e74642e..d464b34 100644
--- a/pyvcell/sim_results/plotter.py
+++ b/pyvcell/sim_results/plotter.py
@@ -63,7 +63,7 @@ def plot_concentrations(self) -> None:
         ax.plot(t, self.concentrations.T)
         ax.set(xlabel="time (s)", ylabel="concentration", title="Concentration over time")
 
-        y_labels = [c.label for c in self.channels if c.mean_values is not None]
+        y_labels = [c.label for c in self.channels if c.mean_values is not None and not c.label.startswith("Lumped")]
         ax.legend(y_labels)
         ax.grid()
         return plt.show()
diff --git a/pyvcell/sim_results/result.py b/pyvcell/sim_results/result.py
index ef3e923..00b0ee1 100644
--- a/pyvcell/sim_results/result.py
+++ b/pyvcell/sim_results/result.py
@@ -77,7 +77,9 @@ def post_processing(self) -> PostProcessing:
     @property
     def concentrations(self) -> NDArray2D:
         data: list[list[float]] = [
-            c.mean_values for c in self.channel_data if c.index > 0 and c.mean_values is not None
+            c.mean_values
+            for c in self.channel_data
+            if c.index > 0 and c.mean_values is not None and not c.label.startswith("Lumped")
         ]
         return np.array(dtype=np.float64, object=data)