Rename to onedm

.github/workflows/python-publish.yml

@@ -36,7 +36,7 @@ jobs:
     runs-on: ubuntu-latest
     environment:
       name: pypi
-      url: https://pypi.org/p/sdf-utils
+      url: https://pypi.org/p/onedm
     permissions:
       id-token: write  # IMPORTANT: mandatory for trusted publishing
 

README.md

@@ -1,10 +1,67 @@
-# sdf-utils
+# OneDM Python library
 
-[Semantic Definition Format (SDF) for Data and Interactions of Things](https://ietf-wg-asdf.github.io/SDF/sdf.html)
+This Python package is an early work-in-progress to simplify working with
+[One Data Model](https://onedm.org/) in Python.
 
-The Semantic Definition Format (SDF) is a format for domain experts to use in
-the creation and maintenance of data and interaction models that describe Things,
-i.e., physical objects that are available for interaction over a network.
-An SDF specification describes definitions of SDF Objects/SDF Things and their
-associated interactions (Events, Actions, Properties), as well as the Data types
-for the information exchanged in those interactions.
+Since OneDM is in early stages, this library intends to follow the progress
+as best as it can and should hence be considered unstable.
+
+
+## SDF
+
+Currently it supports limited loading and generation of
+[SDF](https://ietf-wg-asdf.github.io/SDF/sdf.html) documents.
+
+> The Semantic Definition Format (SDF) is a format for domain experts to use in
+> the creation and maintenance of data and interaction models that describe Things,
+> i.e., physical objects that are available for interaction over a network.
+> An SDF specification describes definitions of SDF Objects/SDF Things and their
+> associated interactions (Events, Actions, Properties), as well as the Data types
+> for the information exchanged in those interactions.
+
+This library uses [Pydantic](https://docs.pydantic.dev/) to parse, validate,
+and dump model descriptions. The Pydantic models enforce a stricter validation
+than the current SDF JSON schema where each data type has its own schema.
+
+
+## Examples
+
+Loading an existing SDF document:
+
+```
+>>> from onedm import sdf
+
+>>> loader = sdf.SDFLoader()
+>>> loader.load_file("tests/sdf/test.sdf.json")
+>>> doc = loader.to_sdf()
+
+>>> doc.info.title        
+'Example document for SDF (Semantic Definition Format)'
+
+>>> doc.properties["IntegerProperty"] 
+IntegerProperty(observable=True, readable=True, writable=True, label='Example integer', description=None, ref=None, required=[], type=<DataType.INTEGER: 'integer'>, sdf_type=None, nullable=True, const=2, unit=None, minimum=-2, maximum=2, exclusive_minimum=None, exclusive_maximum=None, multiple_of=2, format=None, choices=None, default=None)
+```
+
+Creating a new document:
+
+```
+>>> from onedm import sdf
+
+>>> doc = sdf.SDF()
+
+>>> doc.things["switch"] = sdf.Thing(label="Generic switch")
+>>> doc.things["switch"].actions["on"] = sdf.Action(label="Turn on")
+>>> print(doc.to_json())
+{
+  "sdfThing": {
+    "switch": {
+      "label": "Generic switch",
+      "sdfAction": {
+        "on": {
+          "label": "Turn on"
+        }
+      }
+    }
+  }
+}
+```

sdf/data.py → onedm/sdf/data.py

@@ -10,7 +10,7 @@
 from enum import Enum
 from typing import Annotated, Any, Literal, Union
 
-from pydantic import Field, NonNegativeInt
+from pydantic import Field, NonNegativeInt, model_validator
 from pydantic_core import SchemaValidator, core_schema
 
 from .common import CommonQualities
@@ -47,7 +47,7 @@ def validate(self, input: Any) -> Any:
 
 
 class NumberData(DataQualities):
-    type: Literal[DataType.NUMBER] = DataType.NUMBER
+    type: Literal[DataType.NUMBER]
     unit: str | None = None
     minimum: float | None = None
     maximum: float | None = None
@@ -58,6 +58,13 @@ class NumberData(DataQualities):
     const: float | None = None
     default: float | None = None
 
+    @model_validator(mode="before")
+    @classmethod
+    def set_default_type(cls, data: Any):
+        if isinstance(data, dict):
+            data.setdefault("type", DataType.NUMBER)
+        return data
+
     def get_pydantic_schema(self) -> core_schema.FloatSchema:
         return core_schema.float_schema(
             ge=self.minimum,
@@ -72,7 +79,7 @@ def validate(self, input: Any) -> int:
 
 
 class IntegerData(DataQualities):
-    type: Literal[DataType.INTEGER] = DataType.INTEGER
+    type: Literal[DataType.INTEGER]
     unit: str | None = None
     minimum: int | None = None
     maximum: int | None = None
@@ -84,6 +91,13 @@ class IntegerData(DataQualities):
     const: int | None = None
     default: int | None = None
 
+    @model_validator(mode="before")
+    @classmethod
+    def set_default_type(cls, data: Any):
+        if isinstance(data, dict):
+            data.setdefault("type", DataType.INTEGER)
+        return data
+
     def get_pydantic_schema(self) -> core_schema.IntSchema:
         return core_schema.int_schema(
             ge=self.minimum,
@@ -98,10 +112,17 @@ def validate(self, input: Any) -> int:
 
 
 class BooleanData(DataQualities):
-    type: Literal[DataType.BOOLEAN] = DataType.BOOLEAN
+    type: Literal[DataType.BOOLEAN]
     const: bool | None = None
     default: bool | None = None
 
+    @model_validator(mode="before")
+    @classmethod
+    def set_default_type(cls, data: Any):
+        if isinstance(data, dict):
+            data.setdefault("type", DataType.BOOLEAN)
+        return data
+
     def get_pydantic_schema(self) -> core_schema.BoolSchema:
         return core_schema.bool_schema()
 
@@ -110,7 +131,7 @@ def validate(self, input: Any) -> bool:
 
 
 class StringData(DataQualities):
-    type: Literal[DataType.STRING] = DataType.STRING
+    type: Literal[DataType.STRING]
     enum: list[str] | None = None
     min_length: NonNegativeInt = 0
     max_length: NonNegativeInt | None = None
@@ -121,6 +142,13 @@ class StringData(DataQualities):
     const: str | None = None
     default: str | None = None
 
+    @model_validator(mode="before")
+    @classmethod
+    def set_default_type(cls, data: Any):
+        if isinstance(data, dict):
+            data.setdefault("type", DataType.STRING)
+        return data
+
     def get_pydantic_schema(self) -> core_schema.StringSchema | core_schema.BytesSchema:
         if self.sdf_type == "byte-string":
             return core_schema.bytes_schema(
@@ -137,14 +165,21 @@ def validate(self, input: Any) -> str | bytes:
 
 
 class ArrayData(DataQualities):
-    type: Literal[DataType.ARRAY] = DataType.ARRAY
+    type: Literal[DataType.ARRAY]
     min_items: NonNegativeInt = 0
     max_items: NonNegativeInt | None = None
     unique_items: bool = False
     items: Data | None = None
     const: list | None = None
     default: list | None = None
 
+    @model_validator(mode="before")
+    @classmethod
+    def set_default_type(cls, data: Any):
+        if isinstance(data, dict):
+            data.setdefault("type", DataType.ARRAY)
+        return data
+
     def get_pydantic_schema(self) -> core_schema.ListSchema | core_schema.SetSchema:
         if self.unique_items:
             return core_schema.set_schema(
@@ -163,11 +198,18 @@ def validate(self, input: Any) -> list | set:
 
 
 class ObjectData(DataQualities):
-    type: Literal[DataType.OBJECT] = DataType.OBJECT
+    type: Literal[DataType.OBJECT]
     required: list[str] | None = None
     properties: dict[str, Data] | None = None
     const: dict[str, Any] | None = None
 
+    @model_validator(mode="before")
+    @classmethod
+    def set_default_type(cls, data: Any):
+        if isinstance(data, dict):
+            data.setdefault("type", DataType.OBJECT)
+        return data
+
     def get_pydantic_schema(self) -> core_schema.TypedDictSchema:
         required = self.required or []
         fields = {

sdf/document.py → onedm/sdf/document.py

@@ -25,4 +25,4 @@ class SDF(BaseModel):
     data: dict[str, Data] = Field(default_factory=dict, alias="sdfData")
 
     def to_json(self) -> str:
-        return self.model_dump_json(indent=2, exclude_unset=True, by_alias=True)
+        return self.model_dump_json(indent=2, exclude_defaults=True, by_alias=True)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
-name = "sdf-utils"
-description = "Semantic Definition Format (SDF) for Data and Interactions of Things"
+name = "onedm"
+description = "Common data model for IoT and IoT devices"
 keywords = ["iot", "data model"]
 authors = [
     {name = "Christian Sandberg", email = "christiansandberg@me.com"},

tests/conftest.py → tests/sdf/conftest.py

@@ -1,7 +1,7 @@
 from pathlib import Path
 import pytest
 
-import sdf
+from onedm import sdf
 
 
 @pytest.fixture(scope="session")

tests/test_datatypes.py → tests/sdf/test_datatypes.py

@@ -1,4 +1,4 @@
-import sdf
+from onedm import sdf
 
 
 def test_integer_data(test_model: sdf.SDF):

tests/test_playground_models.py → tests/sdf/test_playground_models.py

@@ -4,7 +4,7 @@
 import tempfile
 
 import pytest
-import sdf
+from onedm import sdf
 
 
 @pytest.fixture(scope="session")

tests/test_properties.py → tests/sdf/test_properties.py

@@ -1,4 +1,4 @@
-import sdf
+from onedm import sdf
 
 
 def test_integer_property(test_model: sdf.SDF):

tests/test_references.py → tests/sdf/test_references.py

@@ -1,4 +1,4 @@
-import sdf
+from onedm import sdf
 
 
 def test_reference(test_model: sdf.SDF):