pydantic_numpy 4.1.3

Pydantic Model integration of the NumPy array

pydantic-numpy

Package that integrates NumPy Arrays into Pydantic!

• pydantic_numpy.typing provides many typings such as NpNDArrayFp64, Np3DArrayFp64 (float64 that must be 3D)! Works with both pydantic.BaseModel and pydantic.dataclass
• NumpyModel (derived from pydantic.BaseModel) make it possible to dump and load np.ndarray within model fields alongside other fields that are not instances of np.ndarray!

See the test.helper.groups to see types that are defined explicitly. Define your own NumPy types with pydantic_numpy.np_array_pydantic_annotated_typing.

Usage

For more examples see test_ndarray.py

import numpy as np
from pydantic import BaseModel

import pydantic_numpy.typing as pnd
from pydantic_numpy import np_array_pydantic_annotated_typing
from pydantic_numpy.model import NumpyModel, MultiArrayNumpyFile


class MyBaseModelDerivedModel(BaseModel):
    any_array_dtype_and_dimension: pnd.NpNDArray

    # Must be numpy float32 as dtype
    k: np_array_pydantic_annotated_typing(data_type=np.float32)
    shorthand_for_k: pnd.NpNDArrayFp32

    must_be_1d_np_array: np_array_pydantic_annotated_typing(dimensions=1)


class MyDemoNumpyModel(NumpyModel):
    k: np_array_pydantic_annotated_typing(data_type=np.float32)


# Instantiate from array
cfg = MyDemoModel(k=[1, 2])
# Instantiate from numpy file
cfg = MyDemoModel(k="path_to/array.npy")
# Instantiate from npz file with key
cfg = MyDemoModel(k=MultiArrayNumpyFile(path="path_to/array.npz", key="k"))

cfg.k   # np.ndarray[np.float32]

cfg.dump("path_to_dump_dir", "object_id")
cfg.load("path_to_dump_dir", "object_id")

NumpyModel.load requires the original model:

MyNumpyModel.load(<path>)

Use model_agnostic_load when you have several models that may be the correct model:

from pydantic_numpy.model import model_agnostic_load

cfg.dump("path_to_dump_dir", "object_id")
equals_cfg = model_agnostic_load("path_to_dump_dir", "object_id", models=[MyNumpyModel, MyDemoModel])

Install

pip install pydantic-numpy

Considerations

You can install from cheind's repository if you want Python 3.8 support, but this version only supports Pydantic V1 and will not work with V2.

Licensing notice

As of version 3.0.0 the license has moved over to BSD-4. The versions prior are under the MIT license.

History

The original idea originates from this discussion, and forked from cheind's repository.