# pydantic-numpy
[](https://python-poetry.org/)
## Usage
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.testing_groups`](https://github.com/caniko/pydantic-numpy/blob/trunk/tests/helper/testing_groups.py) to see types that are defined explicitly.
### Examples
For more examples see [test_ndarray.py](./tests/test_typing.py)
```python
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:
```python
MyNumpyModel.load(<path>)
```
Use `model_agnostic_load` when you have several models that may be the correct model:
```python
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])
```
### Custom type
There are two ways to define. Function derived types with `pydantic_numpy.helper.annotation.np_array_pydantic_annotated_typing`.
Function derived types don't work with static type checkers like Pyright and MyPy. In case you need the support,
just create the types yourself:
```python
NpStrict1DArrayInt64 = Annotated[
np.ndarray[tuple[int], np.dtype[np.int64]],
NpArrayPydanticAnnotation.factory(data_type=np.int64, dimensions=1, strict_data_typing=True),
]
```
#### Custom serialization
If the default serialization of NumpyDataDict, as outlined in [typing.py](https://github.com/caniko/pydantic-numpy/blob/trunk/pydantic_numpy/helper/typing.py), doesn't meet your requirements, you have the option to define a custom type with its own serializer. This can be achieved using the NpArrayPydanticAnnotation.factory method, which accepts a custom serialization function through its serialize_numpy_array_to_json parameter. This parameter expects a function of the form `Callable[[npt.ArrayLike], Iterable]`, allowing you to tailor the serialization process to your specific needs.
Example below illustrates definition of 1d-array of `float32` type that serializes to flat Python list (without nested dict as in default `NumpyDataDict` case):
```python
def _serialize_numpy_array_to_float_list(array_like: npt.ArrayLike) -> Iterable:
return np.array(array_like).astype(float).tolist()
Np1DArrayFp32 = Annotated[
np.ndarray[tuple[int], np.dtype[np.float32]],
NpArrayPydanticAnnotation.factory(
data_type=np.float32,
dimensions=1,
strict_data_typing=False,
serialize_numpy_array_to_json=_serialize_numpy_array_to_float_list,
),
]
```
### Install
```shell
pip install pydantic-numpy
```
### History
The original idea originates from [this discussion](https://gist.github.com/danielhfrank/00e6b8556eed73fb4053450e602d2434), and forked from [cheind's](https://github.com/cheind/pydantic-numpy) repository.
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"description": "# pydantic-numpy\n\n\n[](https://python-poetry.org/)\n\n\n\n\n\n## Usage\n\nPackage that integrates NumPy Arrays into Pydantic!\n\n- `pydantic_numpy.typing` provides many typings such as `NpNDArrayFp64`, `Np3DArrayFp64` (float64 that must be 3D)! Works with both `pydantic.BaseModel` and `pydantic.dataclass`\n- `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`!\n\nSee the [`test.helper.testing_groups`](https://github.com/caniko/pydantic-numpy/blob/trunk/tests/helper/testing_groups.py) to see types that are defined explicitly.\n\n### Examples\n\nFor more examples see [test_ndarray.py](./tests/test_typing.py)\n\n```python\nimport numpy as np\nfrom pydantic import BaseModel\n\nimport pydantic_numpy.typing as pnd\nfrom pydantic_numpy import np_array_pydantic_annotated_typing\nfrom pydantic_numpy.model import NumpyModel, MultiArrayNumpyFile\n\n\nclass MyBaseModelDerivedModel(BaseModel):\n any_array_dtype_and_dimension: pnd.NpNDArray\n\n # Must be numpy float32 as dtype\n k: np_array_pydantic_annotated_typing(data_type=np.float32)\n shorthand_for_k: pnd.NpNDArrayFp32\n\n must_be_1d_np_array: np_array_pydantic_annotated_typing(dimensions=1)\n\n\nclass MyDemoNumpyModel(NumpyModel):\n k: np_array_pydantic_annotated_typing(data_type=np.float32)\n\n\n# Instantiate from array\ncfg = MyDemoModel(k=[1, 2])\n# Instantiate from numpy file\ncfg = MyDemoModel(k=\"path_to/array.npy\")\n# Instantiate from npz file with key\ncfg = MyDemoModel(k=MultiArrayNumpyFile(path=\"path_to/array.npz\", key=\"k\"))\n\ncfg.k # np.ndarray[np.float32]\n\ncfg.dump(\"path_to_dump_dir\", \"object_id\")\ncfg.load(\"path_to_dump_dir\", \"object_id\")\n```\n\n`NumpyModel.load` requires the original model:\n```python\nMyNumpyModel.load(<path>)\n```\nUse `model_agnostic_load` when you have several models that may be the correct model:\n\n```python\nfrom pydantic_numpy.model import model_agnostic_load\n\ncfg.dump(\"path_to_dump_dir\", \"object_id\")\nequals_cfg = model_agnostic_load(\"path_to_dump_dir\", \"object_id\", models=[MyNumpyModel, MyDemoModel])\n```\n\n### Custom type\nThere are two ways to define. Function derived types with `pydantic_numpy.helper.annotation.np_array_pydantic_annotated_typing`.\n\nFunction derived types don't work with static type checkers like Pyright and MyPy. In case you need the support,\njust create the types yourself:\n \n```python\nNpStrict1DArrayInt64 = Annotated[\n np.ndarray[tuple[int], np.dtype[np.int64]],\n NpArrayPydanticAnnotation.factory(data_type=np.int64, dimensions=1, strict_data_typing=True),\n]\n```\n\n#### Custom serialization\n\nIf the default serialization of NumpyDataDict, as outlined in [typing.py](https://github.com/caniko/pydantic-numpy/blob/trunk/pydantic_numpy/helper/typing.py), doesn't meet your requirements, you have the option to define a custom type with its own serializer. This can be achieved using the NpArrayPydanticAnnotation.factory method, which accepts a custom serialization function through its serialize_numpy_array_to_json parameter. This parameter expects a function of the form `Callable[[npt.ArrayLike], Iterable]`, allowing you to tailor the serialization process to your specific needs.\n\nExample below illustrates definition of 1d-array of `float32` type that serializes to flat Python list (without nested dict as in default `NumpyDataDict` case):\n\n```python\ndef _serialize_numpy_array_to_float_list(array_like: npt.ArrayLike) -> Iterable:\n return np.array(array_like).astype(float).tolist()\n\n\nNp1DArrayFp32 = Annotated[\n np.ndarray[tuple[int], np.dtype[np.float32]],\n NpArrayPydanticAnnotation.factory(\n data_type=np.float32,\n dimensions=1,\n strict_data_typing=False,\n serialize_numpy_array_to_json=_serialize_numpy_array_to_float_list,\n ),\n]\n```\n\n### Install\n```shell\npip install pydantic-numpy\n```\n\n### History\nThe original idea originates from [this discussion](https://gist.github.com/danielhfrank/00e6b8556eed73fb4053450e602d2434), and forked from [cheind's](https://github.com/cheind/pydantic-numpy) repository.\n",
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