Update readme for custom model

README.md

@@ -18,7 +18,8 @@ The modifier can be found under the label "Denoise particle positions" in the OV
 
 - "Number of denoising steps" | `steps`: Number of denoising interations taken. More iterations require more time. You can check the mean displacement per iteration graph to assess convergence.
 - "Nearest neighbor distance" | `scale` : Estimation of the nearest neighbor distance used to scale the coordinates before they are input into the model. If this is `None` OVITO will try to estimate the correct nearest neighbor distance. 
-- "Crystal structure / material system" | `structure` : Allows you to select one of: "FCC", "BCC", "HCP", or "SiO2", depending on your input structure. Note, that an SiO2 structure requires a type named "Si". 
+- "Crystal structure / material system" | `structure` : Allows you to select one of: "FCC", "BCC", "HCP", or "SiO2", depending on your input structure. Note, that an SiO2 structure requires a type named "Si" and "O". If you don't want to use any of the default material systems you can select "Custom". In that case OVITO will not estimate the "Nearest neighbor distance" and it has to be provided by the user.
+- "Model file path" | `model_path`: Allows you define a custom pytorch model. The model will be loaded from the path entered. If this is set to `None` the default models for "SiO2" or "Cu" shipped with [graphite](https://github.com/LLNL/graphite/) will be loaded.
 - "Device" | `device`: Allows you to select your computing device from: "cpu", "cuda", "mps". Only available devices will be shown. Please read the "Installation" section for additional information.
 - "Only selected" | `only_selected`: Apply the modifier only to the selected particles. Following the convention set by other modifiers, even atoms that are not selected will be used as neighbors.
 

src/scoreBasedDenoising/__init__.py

@@ -58,7 +58,7 @@ class ScoreBasedDenoising(ModifierInterface):
         label="Crystal structure / material system",
     )
 
-    modelPath = Union(None, Str, label="Model file path")
+    model_path = Union(None, Str, label="Model file path")
 
     if torch.cuda.is_available():
         device = Enum("cpu", "cuda", label="Device")
@@ -104,11 +104,11 @@ def denoise_snapshot(self, atoms, model, scale):
         return data.pos.to("cpu").numpy() / scale, convergence
 
     def getModelPath(self):
-        if self.modelPath is not None:
-            path = Path(self.modelPath)
+        if self.model_path is not None:
+            path = Path(self.model_path)
             if not path.exists():
                 raise FileNotFoundError(f"{path} does not exist.")
-            return Path(self.modelPath)
+            return Path(self.model_path)
         modelDir = impRes.files("graphite.pretrained_models.denoiser")
         match self.structure:
             case "SiO2":