Fix bugs with dtype

optimas/core/base.py

@@ -2,8 +2,18 @@
 This module defines a base class for all classes that have a name attribute.
 Examples of these are the different optimization parameters and tasks.
 """
+import json
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Extra
+import numpy as np
+
+
+def json_dumps_dtype(v, *, default):
+    """Add support for dumping numpy dtype to json."""
+    for key, value in v.items():
+        if key == 'dtype':
+            v[key] = np.dtype(value).descr
+    return json.dumps(v)
 
 
 class NamedBase(BaseModel):
@@ -22,3 +32,7 @@ def __init__(
             **kwargs
     ) -> None:
         super().__init__(name=name, **kwargs)
+
+    class Config:
+        extra = Extra.ignore
+        json_dumps = json_dumps_dtype

optimas/core/parameter.py

@@ -2,6 +2,9 @@
 
 from typing import Optional, Any
 
+from pydantic import validator
+import numpy as np
+
 from .base import NamedBase
 
 
@@ -16,7 +19,7 @@ class Parameter(NamedBase):
         The data type of the parameter. Any object that can be converted to a
         numpy dtype.
     """
-    dtype: Optional[Any] = float
+    dtype: Optional[Any]
 
     def __init__(
         self,
@@ -26,6 +29,15 @@ def __init__(
     ) -> None:
         super().__init__(name=name, dtype=dtype, **kwargs)
 
+    @validator("dtype", pre=True)
+    def check_valid_out(cls, v):
+        try:
+            _ = np.dtype(v)
+        except TypeError:
+            raise ValueError(f"Unable to coerce '{v}' into a NumPy dtype.")
+        else:
+            return v
+
 
 class VaryingParameter(Parameter):
     """Defines an input parameter to be varied during optimization.