| Name | schemantic JSON |
| Version |
1.0.0
JSON |
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| Summary | Manipulate model schemas utilizing homologous, grouped, or cultured paradigms |
| upload_time | 2023-10-02 15:34:11 |
| maintainer | |
| docs_url | None |
| author | caniko |
| requires_python | >=3.10,<4.0 |
| license | BSD-4 |
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No requirements were recorded.
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# Schemantic
Create schemas from models or classes with homologous, grouped, or cultured paradigms.
Best with `pydantic.BaseModel` instances, but works with any Python class and `dataclass`/`pydantic.dataclass`!
## Classes of schemas
### Homolog
```python
from pydantic import BaseModel
from ordered_set import OrderedSet
from schemantic.schema import HomologSchema
class Thief(BaseModel):
stolen_goods: int
steals_only: str
my_homolog = HomologSchema.from_model(Thief, instance_names=OrderedSet(["copycat", "pink_panther"]))
```
### Grouped
You can manage multiple schemas as a group:
```python
from pydantic import BaseModel
from schemantic.schema import GroupSchema
class Baker(BaseModel):
baked_goods: int
citizen_of: str
class Cop(BaseModel):
years_of_service: int
citizen_of: str
group_schema = GroupSchema.from_originating_types([Baker, Cop], )
```
### Culture
You can also manage multiple types of schemas under one culture:
```python
from ordered_set import OrderedSet
from schemantic.schema import CultureSchema
CultureSchema(source_schemas=OrderedSet([homolog_schema, group_schema]))
```
## Methods
`HomologSchema`, `GroupSchema`, and `CultureSchema` have the following methods.
### `.schema()`
Creates a dictionary, which represents the schema of the origin class/model.
```python
my_homolog.schema()
```
Output:
```yaml
"class_name": "Thief"
"common": {
steals_only: "jewelry"
}
"copycat": {}
"pink_panther": {}
"required": ["stolen_goods", "steals_only"]
"field_to_info": {"stolen_goods": "integer"}
```
### `.dump()`
Dump the dictionary from `.schema()` to a yaml or toml file.
```python
my_schema.dump("my/path/schema.yaml")
# There is also toml support
my_schema.dump("my/path/schema.toml")
```
### `.parse()`
```yaml
"class_name": "Thief"
"common": {
steals_only: "jewelry"
}
"copycat": {
stolen_goods: 10
}
"pink_panther": {
stolen_goods: 14
}
"required": ["stolen_goods", "steals_only"]
"field_to_info": {"stolen_goods": "integer"}
```
```python
parsed = my_homolog.parse_schema("my/path/schema.yaml")
# parsed["copycat"].stolen_goods == 10
# parsed["pink_panther"].stolen_goods == 14
# Both -> steals_only == "jewelry"
```
## Class configuration
Use `schemantic.project` module to control schemantic processing from the origin class/model side.
Classes and dataclasses
```python
from dataclasses import dataclass
from typing import Optional, Union
from schemantic.project import SchemanticProjectMixin
@dataclass
class TestDataclass(SchemanticProjectMixin):
must_be: int
we: str = "n"
n: None = None
age: Optional[int] = None
new_age: Union[int, str, None] = None
exclude_me: Optional[int] = None
_exclude_me_too: Optional[float] = None
@classmethod
@property
def fields_to_exclude_from_single_schema(cls) -> set[str]:
upstream = super().fields_to_exclude_from_single_schema
upstream.update(("exclude_me",))
return upstream
```
This will exclude `exclude_me` (defined in `fields_to_exclude_from_single_schema`) and `_exclude_me_too` (private).
Same for `pydantic.BaseModel`:
```python
from typing import Optional, Union
from pydantic import BaseModel, computed_field
from schemantic.project import SchemanticProjectModelMixin
class TestModel(SchemanticProjectModelMixin, BaseModel):
must_be: int
we: str = "n"
n: None = None
age: Optional[int] = None
new_age: Union[int, str, None] = None
exclude_me: Optional[int] = None
_exclude_me_too: Optional[float] = None
@classmethod # type: ignore[misc]
@computed_field(return_type=set[str])
@property
def fields_to_exclude_from_single_schema(cls) -> set[str]:
upstream = super().fields_to_exclude_from_single_schema
upstream.update(("exclude_me",))
return upstream
```
### Install
```shell
pip install schemantic
```
For `toml` or `yaml` dumping and parsing
```shell
pip install schemantic[toml]
pip install schemantic[yaml]
```
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"description": "# Schemantic\n\nCreate schemas from models or classes with homologous, grouped, or cultured paradigms.\n\nBest with `pydantic.BaseModel` instances, but works with any Python class and `dataclass`/`pydantic.dataclass`!\n\n## Classes of schemas\n\n### Homolog\n```python\nfrom pydantic import BaseModel\nfrom ordered_set import OrderedSet\nfrom schemantic.schema import HomologSchema\n\nclass Thief(BaseModel):\n stolen_goods: int\n steals_only: str\n\nmy_homolog = HomologSchema.from_model(Thief, instance_names=OrderedSet([\"copycat\", \"pink_panther\"]))\n```\n\n### Grouped\nYou can manage multiple schemas as a group:\n\n```python\nfrom pydantic import BaseModel\nfrom schemantic.schema import GroupSchema\n\nclass Baker(BaseModel):\n baked_goods: int\n citizen_of: str\n\nclass Cop(BaseModel):\n years_of_service: int\n citizen_of: str\n\ngroup_schema = GroupSchema.from_originating_types([Baker, Cop], )\n```\n\n### Culture\nYou can also manage multiple types of schemas under one culture:\n\n```python\nfrom ordered_set import OrderedSet\nfrom schemantic.schema import CultureSchema\n\nCultureSchema(source_schemas=OrderedSet([homolog_schema, group_schema]))\n```\n\n## Methods\n`HomologSchema`, `GroupSchema`, and `CultureSchema` have the following methods.\n\n### `.schema()`\nCreates a dictionary, which represents the schema of the origin class/model.\n\n```python\nmy_homolog.schema()\n```\nOutput:\n```yaml\n\"class_name\": \"Thief\"\n\"common\": {\n steals_only: \"jewelry\"\n}\n\"copycat\": {}\n\"pink_panther\": {}\n\"required\": [\"stolen_goods\", \"steals_only\"]\n\"field_to_info\": {\"stolen_goods\": \"integer\"}\n```\n\n### `.dump()`\nDump the dictionary from `.schema()` to a yaml or toml file.\n```python\nmy_schema.dump(\"my/path/schema.yaml\")\n# There is also toml support\nmy_schema.dump(\"my/path/schema.toml\")\n```\n\n### `.parse()`\n```yaml\n\"class_name\": \"Thief\"\n\"common\": {\n steals_only: \"jewelry\"\n}\n\"copycat\": {\n stolen_goods: 10\n}\n\"pink_panther\": {\n stolen_goods: 14\n}\n\"required\": [\"stolen_goods\", \"steals_only\"]\n\"field_to_info\": {\"stolen_goods\": \"integer\"}\n```\n```python\nparsed = my_homolog.parse_schema(\"my/path/schema.yaml\")\n\n# parsed[\"copycat\"].stolen_goods == 10\n# parsed[\"pink_panther\"].stolen_goods == 14\n\n# Both -> steals_only == \"jewelry\"\n```\n\n## Class configuration\nUse `schemantic.project` module to control schemantic processing from the origin class/model side.\n\nClasses and dataclasses\n```python\nfrom dataclasses import dataclass\nfrom typing import Optional, Union\n\nfrom schemantic.project import SchemanticProjectMixin\n\n\n@dataclass\nclass TestDataclass(SchemanticProjectMixin):\n must_be: int\n we: str = \"n\"\n\n n: None = None\n age: Optional[int] = None\n new_age: Union[int, str, None] = None\n\n exclude_me: Optional[int] = None\n _exclude_me_too: Optional[float] = None\n\n @classmethod\n @property\n def fields_to_exclude_from_single_schema(cls) -> set[str]:\n upstream = super().fields_to_exclude_from_single_schema\n upstream.update((\"exclude_me\",))\n return upstream\n```\n\nThis will exclude `exclude_me` (defined in `fields_to_exclude_from_single_schema`) and `_exclude_me_too` (private).\n\nSame for `pydantic.BaseModel`:\n```python\nfrom typing import Optional, Union\n\nfrom pydantic import BaseModel, computed_field\n\nfrom schemantic.project import SchemanticProjectModelMixin\n\n\nclass TestModel(SchemanticProjectModelMixin, BaseModel):\n must_be: int\n we: str = \"n\"\n\n n: None = None\n age: Optional[int] = None\n new_age: Union[int, str, None] = None\n\n exclude_me: Optional[int] = None\n _exclude_me_too: Optional[float] = None\n\n @classmethod # type: ignore[misc]\n @computed_field(return_type=set[str])\n @property\n def fields_to_exclude_from_single_schema(cls) -> set[str]:\n upstream = super().fields_to_exclude_from_single_schema\n upstream.update((\"exclude_me\",))\n return upstream\n```\n\n### Install\n```shell\npip install schemantic\n```\n\nFor `toml` or `yaml` dumping and parsing\n```shell\npip install schemantic[toml]\npip install schemantic[yaml]\n```\n",
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