Merge pull request #38 from fusion-energy/adding_units_and_scale_to_plot

added axis type (log linear) and axis units (MeV, eV) to energy plot

src/openmc_source_plotter/core.py

@@ -37,7 +37,7 @@ def sample_initial_particles(self, n_samples: int = 1000, prn_seed: int = None):
             materials = openmc.Materials()
             model.materials = materials
 
-            sph = openmc.Sphere(r=9999999999, boundary_type="vacuum")
+            sph = openmc.Sphere(r=99999999999, boundary_type="vacuum")
             cell = openmc.Cell(region=-sph)
             geometry = openmc.Geometry([cell])
             model.geometry = geometry
@@ -72,6 +72,9 @@ def plot_source_energy(
     prn_seed: int = 1,
     energy_bins: typing.Union[str, np.array] = "auto",
     name: typing.Optional[str] = None,
+    yaxis_type: str = "linear",
+    xaxis_type: str = "linear",
+    xaxis_units: str = "MeV",
 ):
     """makes a plot of the initial creation positions of an OpenMC source
 
@@ -88,14 +91,22 @@ def plot_source_energy(
             Numpy bins can also be manually set by passing in a numpy array
             of bin edges.
         name: the legend name to use
+        yaxis_type: The type (scale) to use for the Y axis. Options are 'log'
+            or 'linear.
+        xaxis_type: The type (scale) to use for the Y axis. Options are 'log'
+            or 'linear.
+        xaxis_units: The units to use for the x axis. Options are 'eV' or 'MeV'.
     """
 
+    if xaxis_units not in ["eV", "MeV"]:
+        raise ValueError(f"xaxis_units must be either 'eV' or 'MeV' not {xaxis_units}")
+
     if figure is None:
         figure = plotly.graph_objects.Figure()
         figure.update_layout(
             title="Particle energy",
-            xaxis={"title": "Energy (eV)"},
-            yaxis={"title": "Probability"},
+            xaxis={"title": f"Energy [{xaxis_units}]", "type": xaxis_type},
+            yaxis={"title": "Probability", "type": yaxis_type},
             showlegend=True,
         )
 
@@ -109,11 +120,13 @@ def plot_source_energy(
     # scaling by strength
     if isinstance(self, openmc.SourceBase):
         probability = probability * self.strength
-
+    energy = bin_edges[:-1]
+    if xaxis_units == "MeV":
+        energy = energy / 1e6
     # Plot source energy histogram
     figure.add_trace(
         plotly.graph_objects.Scatter(
-            x=bin_edges[:-1],
+            x=energy,
             y=probability * np.diff(bin_edges),
             line={"shape": "hv"},
             hoverinfo="text",

tests/test_core_with_source.py

@@ -48,6 +48,17 @@ def test_energy_plot(test_source):
     assert len(plot.data[0]["x"]) == 1
 
 
+def test_energy_plot_axis(test_source):
+    plot = test_source.plot_source_energy(
+        n_samples=10, xaxis_type="log", yaxis_type="linear", xaxis_units="eV"
+    )
+    plot = test_source.plot_source_energy(
+        n_samples=10, xaxis_type="linear", yaxis_type="log", xaxis_units="MeV"
+    )
+    assert isinstance(plot, go.Figure)
+    assert len(plot.data[0]["x"]) == 1
+
+
 def test_position_plot(test_source):
     plot = test_source.plot_source_position(n_samples=10)
     assert isinstance(plot, go.Figure)