# numpydantic

A python package for array types in pydantic.

## Features:
- **Types** - Annotations (based on [npytyping](https://github.com/ramonhagenaars/nptyping))
  for specifying arrays in pydantic models
- **Validation** - Shape, dtype, and other array validations
- **Seralization** - JSON-Schema List-of-list schema generation
- **Interfaces** - Works with numpy, dask, HDF5, zarr, and a simple extension system to make it work with
  whatever else you want!

Coming soon:
- **Metadata** - This package was built to be used with [linkml arrays](https://linkml.io/linkml/schemas/arrays.html),
  so we will be extending it to include any metadata included in the type annotation object in the JSON schema representation.
- (see [todo](./todo.md))

## Usage

Specify an array using [nptyping syntax](https://github.com/ramonhagenaars/nptyping/blob/master/USERDOCS.md)
and use it with your favorite array library :)

```{todo}
We will be moving away from using nptyping in v2.0.0.

It was written for an older era in python before the dramatic changes in the Python
type system and is no longer actively maintained. We will be reimplementing a syntax
that extends its array specification syntax to include things like ranges and extensible
dtypes with varying precision (and is much less finnicky to deal with).
```

Use the {class}`~numpydantic.NDArray` class like you would any other python type,
combine it with {class}`typing.Union`, make it {class}`~typing.Optional`, etc.

```python
from typing import Union
from pydantic import BaseModel
import numpy as np

from numpydantic import NDArray, Shape

class Image(BaseModel):
    """
    Images: grayscale, RGB, RGBA, and videos too!
    """
    array: Union[
        NDArray[Shape["* x, * y"], np.uint8],
        NDArray[Shape["* x, * y, 3 rgb"], np.uint8],
        NDArray[Shape["* x, * y, 4 rgba"], np.uint8],
        NDArray[Shape["* t, * x, * y, 3 rgb"], np.uint8],
        NDArray[Shape["* t, * x, * y, 4 rgba"], np.float64]
    ]
```

And then use that as a transparent interface to your favorite array library!

### Numpy

The Coca-Cola of array libraries

```python
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, so..
# this works
float_video = Image(array=np.ones((100, 1280, 720, 4), dtype=float))
# this doesn't
wrong_shape_float_video = Image(array=np.ones((100, 1280, 720, 3), dtype=float))
```

### Dask

High performance chunked arrays! The backend for many new array libraries! 

Works exactly the same as numpy arrays

```python
import dask.array as da

# validate a huge video
video_array = da.zeros(shape=(1920,1080,1000000,3), dtype=np.uint8)

# this works
dask_video = Image(array=video_array)
```

### HDF5

Array work increasingly can't fit on memory, but dealing with arrays on disk 
can become a pain in concurrent applications. Numpydantic allows you to 
specify the location of an array within an hdf5 file on disk and use it just like
any other array!

eg. Make an array on disk...

```python
from pathlib import Path
import h5py
from numpydantic.interface.hdf5 import H5ArrayPath

h5f_file = Path('my_file.h5')
array_path = "/nested/array"

# make an HDF5 array
h5f = h5py.File(h5f_file, "w")
array = np.random.random((1920,1080,3)).astype(np.uint8)
h5f.create_dataset(array_path, data=array)
h5f.close()
```

Then use it in your model! numpydantic will only open the file as long as it's needed

```python
>>> h5f_image = Image(array=H5ArrayPath(file=h5f_file, path=array_path))
>>> h5f_image.array[0:5,0:5,0]
array([[0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]], dtype=uint8)
>>> h5f_image.array[0:2,0:2,0] = 1
>>> h5f_image.array[0:5,0:5,0]
array([[1, 1, 0, 0, 0],
       [1, 1, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]], dtype=uint8)
```

Numpydantic tries to be a smart but transparent proxy, exposing the methods and attributes
of the source type even when we aren't directly using them, like when dealing with on-disk HDF5 arrays.

If you want, you can take full control and directly interact with the underlying :class:`h5py.Dataset`
object and leave the file open between calls:

```python
>>> dataset = h5f_image.array.open()
>>> # do some stuff that requires the datset to be held open
>>> h5f_image.array.close()
```

### Zarr

Zarr works similarly!

Use it with any of Zarr's backends: Nested, Zipfile, S3, it's all the same!

```{todo}
Add the zarr examples!
```




```{toctree}
:maxdepth: 2
:caption: Contents
:hidden: true

overview
ndarray
hooks
todo
```

```{toctree}
:maxdepth: 2
:caption: API
:hidden: true

api/interface/index
api/index
api/dtype
api/ndarray
api/maps
api/monkeypatch
api/types

```