numpydantic/docs/serialization.md

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Serialization

Python

In most cases, dumping to python should work as expected.

When a given array framework doesn't provide a tidy means of interacting with it from python, we substitute a proxy class like {class}.hdf5.H5Proxy, but aside from that numpydantic {class}.NDArray annotations should be passthrough when using {func}~pydantic.BaseModel.model_dump .

JSON

JSON is the ~ ♥ fun one ♥ ~

There isn't necessarily a single optimal way to represent all possible arrays in JSON. The standard way that n-dimensional arrays are rendered in json is as a list-of-lists (or array of arrays, in JSON parlance), but that's almost never what is desirable, especially for large arrays.

Normal Style¹

Lists-of-lists are the standard, however, so it is the default behavior for all interfaces, and all interfaces must support it.

---
tags: [hide-cell]
---

from pathlib import Path
from pydantic import BaseModel
from numpydantic import NDArray, Shape
from numpydantic.interface.dask import DaskJsonDict
from numpydantic.interface.numpy import NumpyJsonDict
import numpy as np
import dask.array as da
import zarr
import json
from rich import print
from rich.console import Console

def print_json(string:str):
    data = json.loads(string)
    console = Console(width=74)
    console.print(data)

For our humble model:

class MyModel(BaseModel):
    array: NDArray

We should get the same thing for each interface:

model = MyModel(array=[[1,2],[3,4]])
print(model.model_dump_json())

model = MyModel(array=da.array([[1,2],[3,4]], dtype=int))
print(model.model_dump_json())

model = MyModel(array=zarr.array([[1,2],[3,4]], dtype=int))
print(model.model_dump_json())

model = MyModel(array="data/test.avi")
print(model.model_dump_json())

(ok maybe not that last one, since the video reader still incorrectly reads grayscale videos as BGR values for now, but you get the idea)

Since by default arrays are dumped into unadorned JSON arrays, when they are re-validated, they will always be handled by the {class}.NumpyInterface

dask_array = da.array([[1,2],[3,4]], dtype=int)
model = MyModel(array=dask_array)
type(model.array)

model_json = model.model_dump_json()
deserialized_model = MyModel.model_validate_json(model_json)
type(deserialized_model.array)

All information about dtype will be lost, and numbers will be either parsed as int ({class}numpy.int64) or float ({class}numpy.float64)

Roundtripping

To roundtrip make arrays round-trippable, use the round_trip argument to {func}~pydantic.BaseModel.model_dump_json.

All the following should return an equivalent array from the same file/etc. as the source array when using {func}~pydantic.BaseModel.model_validate_json .

print_json(model.model_dump_json(round_trip=True))

Each interface must implement a dataclass that describes a json-able roundtrip form (see {class}.interface.JsonDict).

That dataclass then has a {meth}JsonDict.is_valid method that checks whether an incoming dict matches its schema

roundtrip_json = json.loads(model.model_dump_json(round_trip=True))['array']
DaskJsonDict.is_valid(roundtrip_json)

NumpyJsonDict.is_valid(roundtrip_json)

Controlling paths

When possible, the full content of the array is omitted in favor of the path to the file that provided it.

model = MyModel(array="data/test.avi")
print_json(model.model_dump_json(round_trip=True))

model = MyModel(array=("data/test.h5", "/data"))
print_json(model.model_dump_json(round_trip=True))

You may notice the relative, rather than absolute paths.

We expect that when people are dumping data to json in this roundtripped form that they are either working locally (e.g. transmitting an array specification across a socket in multiprocessing or in a computing cluster), or exporting to some directory structure of data, where they are making an index file that refers to datasets in a directory as part of a data standard or vernacular format.

By default, numpydantic uses the current working directory as the root to find paths relative to, but this can be controlled by the relative_to context parameter:

For example if you're working on data in many subdirectories, you might want to serialize relative to each of them:

print_json(
  model.model_dump_json(
    round_trip=True, 
    context={"relative_to": Path('./data')}
  ))

Or in the other direction:

print_json(
  model.model_dump_json(
    round_trip=True, 
    context={"relative_to": Path('../')}
  ))

Or you might be working in some completely different place, numpydantic will try and find the way from here to there as long as it exists, even if it means traversing to the root of the readthedocs filesystem

print_json(
  model.model_dump_json(
    round_trip=True, 
    context={"relative_to": Path('/a/long/distance/directory')}
    ))

You can force absolute paths with the absolute_paths context parameter

print_json(
  model.model_dump_json(
    round_trip=True, 
    context={"absolute_paths": True}
    ))

Durable Interface Metadata

Numpydantic tries to be stable, but we're not perfect. To preserve the full information about the interface that's needed to load the data referred to by the value, use the mark_interface contest parameter:

print_json(
  model.model_dump_json(
    round_trip=True, 
    context={"mark_interface": True}
    ))

When an array marked with the interface is deserialized, it short-circuits the {meth}.Interface.match method, attempting to directly return the indicated interface as long as the array dumped in value still satisfies that interface's {meth}.Interface.check method. Arrays dumped without round_trip=True might not validate with the originating model, even when marked -- eg. an array dumped without round_trip will be revalidated as a numpy array for the same reasons it is everywhere else, since all connection to the source file is lost.

Currently, the version of the package the interface is from (usually `numpydantic`)
will be stored, but there is no means of resolving it on the fly. 
If there is a mismatch between the marked interface description and the interface
that was matched on revalidation, a warning is emitted, but validation
attempts to proceed as normal.

This feature is for extra-verbose provenance, rather than airtight serialization
and deserialization, but PRs welcome if you would like to make it be that way.

We will also add a separate `mark_version` parameter for marking
the specific version of the relevant interface package, like `zarr`, or `numpy`,
patience.

Context parameters

A reference listing of all the things that can be passed to {func}~pydantic.BaseModel.model_dump_json

mark_interface

Nest an additional layer of metadata for unambigous serialization that can be absolutely resolved across numpydantic versions (for now for downstream metadata purposes only, automatically resolving to a numpydantic version is not yet possible.)

Supported interfaces:

• (all)

model = MyModel(array=[[1,2],[3,4]])
data = model.model_dump_json(
  round_trip=True,
  context={"mark_interface": True}
)
print_json(data)

absolute_paths

Make all paths (that exist) absolute.

Supported interfaces:

• (all)

model = MyModel(array=("data/test.h5", "/data"))
data = model.model_dump_json(
    round_trip=True, 
    context={"absolute_paths": True}
    )
print_json(data)

relative_to

Make all paths (that exist) relative to the given path

Supported interfaces:

• (all)

model = MyModel(array=("data/test.h5", "/data"))
data = model.model_dump_json(
    round_trip=True, 
    context={"relative_to": Path('../')}
    )
print_json(data)

dump_array

Dump the raw array contents when serializing to json inside an array field

Supported interfaces:

• {class}.ZarrInterface

model = MyModel(array=("data/test.zarr",))
data = model.model_dump_json(
    round_trip=True, 
    context={"dump_array": True}
    )
print_json(data)

---

1. o ya we're posting JSON normal style ↩︎