core/data_models/__init__.py

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+from __future__ import annotations
+
+from .angle import AngleData
+from .contact import ContactData
+from .distance import DistanceData
+from .framing import FramingData
+from .information_value import InformationValueData
+from .modality_color import ModalityColorData
+from .modality_depth import ModalityDepthData
+from .modality_lighting import ModalityLightingData
+from .point_of_view import PointOfViewData
+from .salience import SalienceData
+from .visual_syntax import VisualSyntaxData

core/data_models/angle.py

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+from __future__ import annotations
+
+from .data_model import DataModel
+
+
+class AngleData(DataModel):
+    high: float
+    eye_level: float
+    low: float

core/data_models/contact.py

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+from __future__ import annotations
+
+from .data_model import DataModel
+
+
+class ContactData(DataModel):
+    offer: float
+    demand: float

core/data_models/data_model.py

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+from __future__ import annotations
+
+import random
+
+from pydantic import BaseModel
+from pydantic import ValidationError
+
+
+class DataModel(BaseModel):
+
+    @classmethod
+    def classname(cls) -> str:
+        """Return classname."""
+        return cls.__name__
+
+    @classmethod
+    def list_fields(cls) -> list[str]:
+        """List options that are stored as attributes."""
+        return list(cls.model_fields.keys())
+
+    @classmethod
+    def from_random(cls):
+        """Instantiate with random numbers."""
+        kwargs = {field: random.random() for field in cls.list_fields()}
+        return cls(**kwargs)
+
+    @classmethod
+    def from_choice(cls, option: str):
+        """Instantiate from choice."""
+        if option is None:
+            raise ValidationError()
+        assert isinstance(option, str), 'option is not a string'
+        allowed_options_list = cls.list_fields()
+        assert option in allowed_options_list, \
+            f"{option} is not among allowed fields {allowed_options_list}"
+        kwargs = {field: 0 for field in cls.list_fields()}
+        kwargs[option] = 1
+        return cls(**kwargs)
+
+    def __repr__(self) -> str:
+        model_dict = self.model_dump()
+        model_repr_str = f"{self.classname()}("
+        model_repr_str += ', '.join([
+            f"{field}={value:.3f}"
+            for field, value
+            in model_dict.items()
+        ])
+        model_repr_str += ')'
+        return model_repr_str
+
+    def highest_score_field(self) -> str:
+        """Return name of field with highest score."""
+        model_dict = self.model_dump()
+        return max(model_dict, key=lambda k: model_dict[k])
+
+    def highest_score_value(self) -> float:
+        """Return value of field with highest score."""
+        model_dict = self.model_dump()
+        return max(model_dict.values())

core/data_models/distance.py

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+from __future__ import annotations
+
+from .data_model import DataModel
+
+
+class DistanceData(DataModel):
+    long: float
+    medium: float
+    close: float

core/data_models/framing.py

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+from __future__ import annotations
+
+from .data_model import DataModel
+
+
+class FramingData(DataModel):
+    frame_lines: float
+    empty_space: float
+    colour_contrast: float
+    form_contrast: float

core/data_models/information_value.py

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+from __future__ import annotations
+
+from .data_model import DataModel
+
+
+class InformationValueData(DataModel):
+    given_new: float
+    ideal_real: float
+    central_marginal: float

core/data_models/modality_color.py

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+from __future__ import annotations
+
+from .data_model import DataModel
+
+
+class ModalityColorData(DataModel):
+    high: float
+    medium: float
+    low: float

core/data_models/modality_depth.py

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+from __future__ import annotations
+
+from .data_model import DataModel
+
+
+class ModalityDepthData(DataModel):
+    high: float
+    medium: float
+    low: float

core/data_models/modality_lighting.py

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+from __future__ import annotations
+
+from .data_model import DataModel
+
+
+class ModalityLightingData(DataModel):
+    high: float
+    medium: float
+    low: float

core/data_models/point_of_view.py

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+from __future__ import annotations
+
+from .data_model import DataModel
+
+
+class PointOfViewData(DataModel):
+    frontal: float
+    oblique: float

core/data_models/salience.py

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+from __future__ import annotations
+
+from .data_model import DataModel
+
+
+class SalienceData(DataModel):
+    size: float
+    colour: float
+    tone: float
+    form: float
+    positioning: float

core/data_models/visual_syntax.py

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+from __future__ import annotations
+
+from .data_model import DataModel
+
+
+class VisualSyntaxData(DataModel):
+    non_transactional_action: float
+    non_transactional_reaction: float
+    unidirectional_transactional_action: float
+    unidirectional_transactional_reaction: float
+    bidirectional_transactional_action: float
+    bidirectional_transactional_reaction: float
+    conversion: float
+    speech_process: float
+    classification_overt_taxonomy: float
+    analytical_exhaustive: float
+    analytical_disarranged: float
+    analytical_temporal: float
+    analytical_distributed: float
+    analytical_topological: float
+    analytical_exploded: float
+    analytical_inclusive: float
+    symbolic_suggestive: float
+    symbolic_attributive: float