| Name | pypermissive JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | Validation library |
| upload_time | 2024-08-14 15:54:09 |
| maintainer | None |
| docs_url | None |
| author | kaliv0 |
| requires_python | <4.0,>=3.12 |
| license | LICENSE |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
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<p align="center">
<img src="https://github.com/kaliv0/pypermissive/blob/main/permissive.jpg?raw=true" alt="Permissive Path">
</p>
---
# PyPermissive
[](https://github.com/kaliv0/pypermissive/actions/workflows/ci.yml)
[](https://pypi.org/project/pypermissive/)
[](https://github.com/kaliv0/pypermissive/blob/main/LICENSE)
Validation library in Python, modeled after Pydantic
## Example
Inherit from BaseModel and describe required types.<br>
<i>PyPermissive</i> supports validation for primitive types:
```python
class Employee(BaseModel):
employee_id: int
name: str
salary: float
elected_benefits: bool = False
employee = Employee(
employee_id=1,
name="Foo Bar",
salary=123_000.00,
elected_benefits=True,
)
```
collections:
```python
class Book(BaseModel):
characters: dict[str, str]
chapters: list[str]
book = Book(
characters={"Pelleas": "he", "Melisande": "she"},
chapters=["Beginning", "Middle", "End"]
)
```
unions, classes and fields.<br>
<br>Fields are similar to <i>pydantic</i> with one caveat: you need to give value type explicitly:
```python
class User(BaseModel):
name: Field(type=str, default="Jimmie", frozen=True)
age: Field(type=int, gt=18, lt=35)
id: Field(type=UUID, default_factory=uuid4)
email: Field(type=str, pattern=r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+[.][a-zA-Z0-9-.]+$")
nickname: Field(type=str, min_length=6, max_length=12)
PIN: Field(type=str, field_validator=lambda x: x.isdigit())
```
You can also use decorators:<br>
@ComputedField (invoke only from instances) and @ComputedClassField (invoke both on class and instance level)
```python
class Thesis:
BAZZ = ["1", "2", "3"]
def __init__(self):
self.fizz = [1, 2, 3, 4, 5]
self.buzz = [6, 7, 8, 9]
@ComputedField
def foo(self):
return [val for val in itertools.product(self.fizz, self.buzz)]
@ComputedClassField
def bar(self):
return list(itertools.permutations(self.BAZZ))
```
The library supports @validate_call that checks both argument and return types:
```python
@validate_call
def some_func(delimiter: str, count: int, numbers: list[int]) -> str:
return (delimiter * count).join([str(d) for d in numbers])
```
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"description": "<p align=\"center\">\n <img src=\"https://github.com/kaliv0/pypermissive/blob/main/permissive.jpg?raw=true\" alt=\"Permissive Path\">\n</p>\n\n---\n\n# PyPermissive\n\n[](https://github.com/kaliv0/pypermissive/actions/workflows/ci.yml)\n\n[](https://pypi.org/project/pypermissive/)\n[](https://github.com/kaliv0/pypermissive/blob/main/LICENSE)\n\nValidation library in Python, modeled after Pydantic\n\n## Example\n\nInherit from BaseModel and describe required types.<br>\n<i>PyPermissive</i> supports validation for primitive types:\n```python\nclass Employee(BaseModel):\n employee_id: int\n name: str\n salary: float\n elected_benefits: bool = False\n \nemployee = Employee(\n employee_id=1,\n name=\"Foo Bar\",\n salary=123_000.00,\n elected_benefits=True,\n)\n```\n\ncollections:\n```python\nclass Book(BaseModel):\n characters: dict[str, str]\n chapters: list[str]\n \nbook = Book(\n characters={\"Pelleas\": \"he\", \"Melisande\": \"she\"},\n chapters=[\"Beginning\", \"Middle\", \"End\"]\n)\n```\n\nunions, classes and fields.<br>\n<br>Fields are similar to <i>pydantic</i> with one caveat: you need to give value type explicitly:\n\n```python\nclass User(BaseModel):\n name: Field(type=str, default=\"Jimmie\", frozen=True)\n age: Field(type=int, gt=18, lt=35)\n id: Field(type=UUID, default_factory=uuid4)\n email: Field(type=str, pattern=r\"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+[.][a-zA-Z0-9-.]+$\")\n nickname: Field(type=str, min_length=6, max_length=12)\n PIN: Field(type=str, field_validator=lambda x: x.isdigit())\n\n```\n\nYou can also use decorators:<br>\n@ComputedField (invoke only from instances) and @ComputedClassField (invoke both on class and instance level)\n```python\nclass Thesis:\n BAZZ = [\"1\", \"2\", \"3\"]\n\n def __init__(self):\n self.fizz = [1, 2, 3, 4, 5]\n self.buzz = [6, 7, 8, 9]\n\n @ComputedField\n def foo(self):\n return [val for val in itertools.product(self.fizz, self.buzz)]\n\n @ComputedClassField\n def bar(self):\n return list(itertools.permutations(self.BAZZ))\n\n \n```\n\nThe library supports @validate_call that checks both argument and return types:\n```python\n@validate_call\ndef some_func(delimiter: str, count: int, numbers: list[int]) -> str:\n return (delimiter * count).join([str(d) for d in numbers])\n```\n",
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