I was trying to plot validator results for data with 3 inputs (“x”, “y” and “t”) . The following variables were mapped to the corresponding columns of input csv file for validation.

mapping = {“time”: “t”, “x”: “x”, “r”: “y”, “Psi”: “Phi_2” }

csv_var = csv_to_dict(

to_absolute_path(“file.csv”), mapping

)

csv_invar_numpy = {

key: value for key, value in csv_var.items() if key in [“t”,“x”,“y”]

}

csv_outvar_numpy = {

key: value for key, value in csv_var.items() if key in [“Phi_2”]

}

```
```

Validator of the following form was defined. The default ValidatorPlotter() was used here.

csv_validator = PointwiseValidator(

nodes=nodes,

invar=csv_invar_numpy,

true_outvar=csv_outvar_numpy,

batch_size=16,

plotter=ValidatorPlotter(),

)

```
```

The default ValidatorPlotter class is capable of handling dimensions <= 2 only. The following message is displayed while training the model.

```
```

`[09:27:45] - [step: 0] record constraint batch time: 4.232e-01s`

Default plotter can only handle <=2 input dimensions, passing

[09:27:45] - [step: 0] record validators time: 2.474e-01s

Are there other validator plotters in modulus which can take care of such problems? Also, is there a way to freeze one of the dimensions to enforce variation along the remaining two dimensions only? I tried removing the “y” dimension, but that lead to graph unrolling error due to insufficient number of specified inputs.