Constrained Arrays

Implementation details

**Docs:**

Describe implementation details!

Examples

Declaration

Type with a single {class}~numpydantic.NDArray class, or use a {class}~typing.Union to express more complex array constraints.

This package is effectively a Pydantic interface to nptyping, so any array syntax is valid there. (see TODO for caveats)

from typing import Union
from pydantic import BaseModel
from src.numpydantic import NDArray, Shape, UInt8, Float, Int


class Image(BaseModel):
    """
    Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.
    """
    array: Union[
        NDArray[Shape["* x, * y"], UInt8],
        NDArray[Shape["* x, * y, 3 rgb"], UInt8],
        NDArray[Shape["* x, * y, 4 rgba"], UInt8],
        NDArray[Shape["* t, * x, * y, 3 rgb"], UInt8],
        NDArray[Shape["* t, * x, * y, 4 rgba"], Float]
    ]

Validation:

import numpy as np
# works
frame_gray = Image(array=np.ones((1280, 720), dtype=np.uint8))
frame_rgb  = Image(array=np.ones((1280, 720, 3), dtype=np.uint8))
frame_rgba = Image(array=np.ones((1280, 720, 4), dtype=np.uint8))
video_rgb  = Image(array=np.ones((100, 1280, 720, 3), dtype=np.uint8))

# fails
wrong_n_dimensions = Image(array=np.ones((1280,), dtype=np.uint8))
wrong_shape = Image(array=np.ones((1280,720,10), dtype=np.uint8))
wrong_type = Image(array=np.ones((1280,720,3), dtype=np.float64))

# shapes and types are checked together
float_video = Image(array=np.ones((100, 1280, 720, 4),dtype=float))
wrong_shape_float_video = Image(array=np.ones((100, 1280, 720, 3),dtype=float))

JSON schema generation:

class MyArray(BaseModel):
  array: NDArray[Shape["2 x, * y, 4 z"], Float]

>>> print(json.dumps(MyArray.model_json_schema(), indent=2))

{
  "properties": {
    "array": {
      "items": {
        "items": {
          "items": {
            "type": "number"
          },
          "maxItems": 4,
          "minItems": 4,
          "type": "array"
        },
        "type": "array"
      },
      "maxItems": 2,
      "minItems": 2,
      "title": "Array",
      "type": "array"
    }
  },
  "required": [
    "array"
  ],
  "title": "MyArray",
  "type": "object"
}

Serialization

class SmolArray(BaseModel):
    array: NDArray[Shape["2 x, 2 y"], Int]

class BigArray(BaseModel):
    array: NDArray[Shape["1000 x, 1000 y"], Int]

Serialize small arrays as lists of lists, and big arrays as a b64-encoded blosc compressed string

>>> smol = SmolArray(array=np.array([[1,2],[3,4]], dtype=int))
>>> big = BigArray(array=np.random.randint(0,255,(1000,1000),int))

>>> print(smol.model_dump_json())
{"array":[[1,2],[3,4]]}
>>> print(big.model_dump_json())
{
  "array": "( long b64 encoded string )",
  "shape": [1000, 1000],
  "dtype": "int64",
  "unpack_fns": ["base64.b64decode", "blosc2.unpack_array2"],
}

TODO

Implement structured arrays

Implement pandas dataframe validation?

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