# numpy-pydantic-types
**numpy-pydantic-types** is a Python library providing `numpy` scalar data types compatible with `pydantic`, allowing for easy validation and enforcement of numerical precision in `pydantic` models.
## Overview
`pydantic` models rely on Python's native types (e.g., `int`, `float`, `str`) for data validation, which can be limiting when working with scientific or numerical applications that require specific `numpy` types, like `float32`, `uint16`, or `int8`. This library bridges that gap by introducing numpy scalar types as valid pydantic field types, allowing you to define precise, controlled models that integrate with scientific workflows.
## Features
- **Numpy Scalar Compatibility**: Define `pydantic` models using `numpy` scalar types like `float32`, `uint32`, `int8`, etc.
- **Strict Type Enforcement**: Ensures model fields adhere to specific precision and range constraints.
- **Simple Integration**: Easily import and use `numpy` types in your `pydantic` models.
## Installation
```
pip install numpy_pydantic_types
```
# Usage
This library enables the use of `numpy` scalar types directly within `pydantic` models. Here’s how to set up a model using `numpy` types:
```
from pydantic import BaseModel, ValidationError
from numpy_pydantic_types import Float32, UInt32, UInt16
class ScientificModel(BaseModel):
precision_value: Float32 # Enforces a 32-bit floating point
sample_count: UInt32 # Enforces a 32-bit unsigned integer
sensor_id: UInt16 # Enforces a 16-bit unsigned integer
# Example data
data = {
"precision_value": 1.234567,
"sample_count": 4294967295, # Max value for UInt32
"sensor_id": 65535 # Max value for UInt16
}
# Instantiate the model with the specific numpy types
try:
model = ScientificModel(**data)
print("Model validated successfully:", model)
except ValidationError as e:
print("Validation error:", e)
```
## Supported Numpy Types
The library provides a range of `numpy` scalar types that can be used in `pydantic` models, including:
- **Float Types**: `Float32`, `Float64`
- **Signed Integer Types**: `Int8`, `Int16`, `Int32`, `Int64`
- **Unsigned Integer Types**: `UInt8`, `UInt16`, `UInt32`, `UInt64`
## Example Model
Using specific `numpy` scalar types can help enforce type constraints in applications where precision or memory footprint is crucial, such as scientific computations, data analysis, or embedded systems.
```
from numpy_pydantic_types import Int8, Float64
class DataProcessingModel(BaseModel):
temperature: Float64
adjustment_factor: Int8
```
## Why Use Numpy Scalar Types?
- **Precision**: Control over numerical precision, essential in scientific or numerical applications.
- **Range Enforcement**: Ensures values conform to specific data type ranges (e.g., `UInt8` ranges from 0 to 255).
- **Memory Efficiency**: Helps reduce memory consumption by enforcing smaller data types.
## License
MIT License
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