Adds additional validators and "write_script" routine (#15)

* Adds code to remove layers for non-standard layers model types

* Adds code to ensure protected parameters cannot be removed

* Adds code to restore default domain ratios when switching calc_type

* Adds validators to "Data" model

* Adds "write_script" routine to "project.py"

* Addresses review comments

* Remove specific wrap data tests from "test_project", instead using new Data __eq__ method

* Adds test for Data.__eq__ to improve test coverage

* Adds fixture to setup and teardown temp directory

Author: DrPaulSharp

RAT/__init__.py

@@ -0,0 +1,2 @@
+from RAT.classlist import ClassList
+from RAT.project import Project

RAT/models.py

@@ -2,6 +2,7 @@
 
 import numpy as np
 from pydantic import BaseModel, Field, ValidationInfo, field_validator, model_validator
+from typing import Any
 
 try:
     from enum import StrEnum
@@ -43,7 +44,6 @@ class Languages(StrEnum):
 class Priors(StrEnum):
     Uniform = 'uniform'
     Gaussian = 'gaussian'
-    Jeffreys = 'jeffreys'
 
 
 class Types(StrEnum):
@@ -52,7 +52,19 @@ class Types(StrEnum):
     Function = 'function'
 
 
-class Background(BaseModel, validate_assignment=True, extra='forbid'):
+class RATModel(BaseModel):
+    """A BaseModel where enums are represented by their value."""
+    def __repr__(self):
+        fields_repr = (', '.join(repr(v) if a is None else
+                                 f'{a}={v.value!r}' if isinstance(v, StrEnum) else
+                                 f'{a}={v!r}'
+                                 for a, v in self.__repr_args__()
+                                 )
+                       )
+        return f'{self.__repr_name__()}({fields_repr})'
+
+
+class Background(RATModel, validate_assignment=True, extra='forbid'):
     """Defines the Backgrounds in RAT."""
     name: str = Field(default_factory=lambda: 'New Background ' + next(background_number), min_length=1)
     type: Types = Types.Constant
@@ -63,7 +75,7 @@ class Background(BaseModel, validate_assignment=True, extra='forbid'):
     value_5: str = ''
 
 
-class Contrast(BaseModel, validate_assignment=True, extra='forbid'):
+class Contrast(RATModel, validate_assignment=True, extra='forbid'):
     """Groups together all of the components of the model."""
     name: str = Field(default_factory=lambda: 'New Contrast ' + next(contrast_number), min_length=1)
     data: str = ''
@@ -76,7 +88,7 @@ class Contrast(BaseModel, validate_assignment=True, extra='forbid'):
     model: list[str] = []
 
 
-class ContrastWithRatio(BaseModel, validate_assignment=True, extra='forbid'):
+class ContrastWithRatio(RATModel, validate_assignment=True, extra='forbid'):
     """Groups together all of the components of the model including domain terms."""
     name: str = Field(default_factory=lambda: 'New Contrast ' + next(contrast_number), min_length=1)
     data: str = ''
@@ -90,20 +102,20 @@ class ContrastWithRatio(BaseModel, validate_assignment=True, extra='forbid'):
     model: list[str] = []
 
 
-class CustomFile(BaseModel, validate_assignment=True, extra='forbid'):
+class CustomFile(RATModel, validate_assignment=True, extra='forbid'):
     """Defines the files containing functions to run when using custom models."""
     name: str = Field(default_factory=lambda: 'New Custom File ' + next(custom_file_number), min_length=1)
     filename: str = ''
     language: Languages = Languages.Python
     path: str = 'pwd'  # Should later expand to find current file path
 
 
-class Data(BaseModel, validate_assignment=True, extra='forbid', arbitrary_types_allowed=True):
+class Data(RATModel, validate_assignment=True, extra='forbid', arbitrary_types_allowed=True):
     """Defines the dataset required for each contrast."""
     name: str = Field(default_factory=lambda: 'New Data ' + next(data_number), min_length=1)
-    data: np.ndarray[float] = np.empty([0, 3])
-    data_range: list[float] = []
-    simulation_range: list[float] = [0.005, 0.7]
+    data: np.ndarray[np.float64] = np.empty([0, 3])
+    data_range: list[float] = Field(default=[], min_length=2, max_length=2)
+    simulation_range: list[float] = Field(default=[], min_length=2, max_length=2)
 
     @field_validator('data')
     @classmethod
@@ -120,22 +132,79 @@ def check_data_dimension(cls, data: np.ndarray[float]) -> np.ndarray[float]:
 
     @field_validator('data_range', 'simulation_range')
     @classmethod
-    def check_list_elements(cls, limits: list[float], info: ValidationInfo) -> list[float]:
-        """The data range and simulation range must contain exactly two parameters."""
-        if len(limits) != 2:
-            raise ValueError(f'{info.field_name} must contain exactly two values')
+    def check_min_max(cls, limits: list[float], info: ValidationInfo) -> list[float]:
+        """The data range and simulation range maximum must be greater than the minimum."""
+        if limits[0] > limits[1]:
+            raise ValueError(f'{info.field_name} "min" value is greater than the "max" value')
         return limits
 
-    # Also need model validators for data range compared to data etc -- need more details.
+    def model_post_init(self, __context: Any) -> None:
+        """If the "data_range" and "simulation_range" fields are not set, but "data" is supplied, the ranges should be
+        set to the min and max values of the first column (assumed to be q) of the supplied data.
+        """
+        if len(self.data[:, 0]) > 0:
+            data_min = np.min(self.data[:, 0])
+            data_max = np.max(self.data[:, 0])
+            for field in ["data_range", "simulation_range"]:
+                if field not in self.model_fields_set:
+                    getattr(self, field).extend([data_min, data_max])
+
+    @model_validator(mode='after')
+    def check_ranges(self) -> 'Data':
+        """The limits of the "data_range" field must lie within the range of the supplied data, whilst the limits
+        of the "simulation_range" field must lie outside of the range of the supplied data.
+        """
+        if len(self.data[:, 0]) > 0:
+            data_min = np.min(self.data[:, 0])
+            data_max = np.max(self.data[:, 0])
+            if "data_range" in self.model_fields_set and (self.data_range[0] < data_min or
+                                                          self.data_range[1] > data_max):
+                raise ValueError(f'The data_range value of: {self.data_range} must lie within the min/max values of '
+                                 f'the data: [{data_min}, {data_max}]')
+            if "simulation_range" in self.model_fields_set and (self.simulation_range[0] > data_min or
+                                                                self.simulation_range[1] < data_max):
+                raise ValueError(f'The simulation_range value of: {self.simulation_range} must lie outside of the '
+                                 f'min/max values of the data: [{data_min}, {data_max}]')
+        return self
+
+    def __eq__(self, other: Any) -> bool:
+        if isinstance(other, BaseModel):
+            # When comparing instances of generic types for equality, as long as all field values are equal,
+            # only require their generic origin types to be equal, rather than exact type equality.
+            # This prevents headaches like MyGeneric(x=1) != MyGeneric[Any](x=1).
+            self_type = self.__pydantic_generic_metadata__['origin'] or self.__class__
+            other_type = other.__pydantic_generic_metadata__['origin'] or other.__class__
+
+            return (
+                    self_type == other_type
+                    and self.name == other.name
+                    and (self.data == other.data).all()
+                    and self.data_range == other.data_range
+                    and self.simulation_range == other.simulation_range
+                    and self.__pydantic_private__ == other.__pydantic_private__
+                    and self.__pydantic_extra__ == other.__pydantic_extra__
+            )
+        else:
+            return NotImplemented  # delegate to the other item in the comparison
+
+    def __repr__(self):
+        """Only include the name if the data is empty."""
+        fields_repr = (f"name={self.name!r}" if self.data.size == 0 else
+                       ", ".join(repr(v) if a is None else
+                                 f"{a}={v!r}"
+                                 for a, v in self.__repr_args__()
+                                 )
+                       )
+        return f'{self.__repr_name__()}({fields_repr})'
 
 
-class DomainContrast(BaseModel, validate_assignment=True, extra='forbid'):
+class DomainContrast(RATModel, validate_assignment=True, extra='forbid'):
     """Groups together the layers required for each domain."""
     name: str = Field(default_factory=lambda: 'New Domain Contrast ' + next(domain_contrast_number), min_length=1)
     model: list[str] = []
 
 
-class Layer(BaseModel, validate_assignment=True, extra='forbid', populate_by_name=True):
+class Layer(RATModel, validate_assignment=True, extra='forbid', populate_by_name=True):
     """Combines parameters into defined layers."""
     name: str = Field(default_factory=lambda: 'New Layer ' + next(layer_number), min_length=1)
     thickness: str = ''
@@ -145,7 +214,7 @@ class Layer(BaseModel, validate_assignment=True, extra='forbid', populate_by_nam
     hydrate_with: Hydration = Hydration.BulkOut
 
 
-class AbsorptionLayer(BaseModel, validate_assignment=True, extra='forbid', populate_by_name=True):
+class AbsorptionLayer(RATModel, validate_assignment=True, extra='forbid', populate_by_name=True):
     """Combines parameters into defined layers including absorption terms."""
     name: str = Field(default_factory=lambda: 'New Layer ' + next(layer_number), min_length=1)
     thickness: str = ''
@@ -156,7 +225,7 @@ class AbsorptionLayer(BaseModel, validate_assignment=True, extra='forbid', popul
     hydrate_with: Hydration = Hydration.BulkOut
 
 
-class Parameter(BaseModel, validate_assignment=True, extra='forbid'):
+class Parameter(RATModel, validate_assignment=True, extra='forbid'):
     """Defines parameters needed to specify the model."""
     name: str = Field(default_factory=lambda: 'New Parameter ' + next(parameter_number), min_length=1)
     min: float = 0.0
@@ -180,7 +249,7 @@ class ProtectedParameter(Parameter, validate_assignment=True, extra='forbid'):
     name: str = Field(frozen=True, min_length=1)
 
 
-class Resolution(BaseModel, validate_assignment=True, extra='forbid'):
+class Resolution(RATModel, validate_assignment=True, extra='forbid'):
     """Defines Resolutions in RAT."""
     name: str = Field(default_factory=lambda: 'New Resolution ' + next(resolution_number), min_length=1)
     type: Types = Types.Constant