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Python dataclasses for the OME data model

Project description

ome-types: OME dataclasses for python

autogenerated dataclasses for a pythonic interface into the OME data model:

It converts the ome.xsd schema into a set of python dataclasses and types.

As an example, the OME/Image model will be rendered as the following dataclass in ome_types/model/

from dataclasses import field
from datetime import datetime
from typing import List, Optional

from pydantic.dataclasses import dataclass

from .annotation_ref import AnnotationRef
from .experiment_ref import ExperimentRef
from .experimenter_group_ref import ExperimenterGroupRef
from .experimenter_ref import ExperimenterRef
from .imaging_environment import ImagingEnvironment
from .instrument_ref import InstrumentRef
from .microbeam_manipulation_ref import MicrobeamManipulationRef
from .objective_settings import ObjectiveSettings
from .pixels import Pixels
from .roi_ref import ROIRef
from .simple_types import ImageID
from .stage_label import StageLabel

class Image:
    id: ImageID
    pixels: Pixels
    acquisition_date: Optional[datetime] = None
    annotation_ref: List[AnnotationRef] = field(default_factory=list)
    description: Optional[str] = None
    experiment_ref: Optional[ExperimentRef] = None
    experimenter_group_ref: Optional[ExperimenterGroupRef] = None
    experimenter_ref: Optional[ExperimenterRef] = None
    imaging_environment: Optional[ImagingEnvironment] = None
    instrument_ref: Optional[InstrumentRef] = None
    microbeam_manipulation_ref: List[MicrobeamManipulationRef] = field(default_factory=list)
    name: Optional[str] = None
    objective_settings: Optional[ObjectiveSettings] = None
    roi_ref: List[ROIRef] = field(default_factory=list)
    stage_label: Optional[StageLabel] = None

ome_autogen.convert_schema(url, target) is the main function. It accepts an xsd file path (only test on an ome.xsd), and a target directory, and writes a human-readable module, that will validate OME XML, provide pythonic method naming, and provides full typing support for IDEs, etc...


from pip

pip install ome-types

the autogenerated model is already included, but, to include dependencies required for re-generating the model, use the [autogen] extra

pip install ome-types[autogen]

or, to install from source

git clone
cd ome-types
pip install -e .


The model is not checked into source, but it is included when you pip install the package (and it will be built automatically at ome_types/model if it doesn't exist the first time you import the package.)

from ome_types import OME  # the root class

# or specific objects
from ome_types.model import Image, Pixels, Plate  # etc...

There is a convenience function that accepts xml, and outputs a validated OME model (if it fails validation, an exception is raised):

from ome_types import from_xml

metadata = from_xml(xml)

where xml in that example can be a path to a file, a URI of a resource, an opened file-like object, an Element instance, an ElementTree instance, or a literal string containing the XML data.

all attributes and variable names follow the OME data model, but camelCaseNames have been replaced with pythonic snake_case_names

Work in progress!

This is a work in progress and will absolutely need refining. Feel free to submit an issue or a PR if you try it out and have requests.


We use pre-commit to run various code-quality checks (black, mypy, flake8) during continuous integration. If you'd like to make sure that your code will pass these checks before you commit your code, you can install pre-commit after cloning this repository:

pip install pre-commit
pre-commit install

or, you can install and run tox which will run tests and code-quality checks in an isolated environment.


To run tests quickly, just install and run pytest. Note, however, that this requires that the ome_types.model module has already been built with python src/

Alternatively, you can install and run tox which will run tests and code-quality checks in an isolated environment.

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