ome_types provides a set of python dataclasses and utility functions for
parsing the OME-XML
format into
fully-typed python objects for interactive or programmatic access in python. It
can also take these python objects and output them into valid OME-XML.
ome_types is a pure python library and does not require a Java virtual
machine.
Note: The generated python code can be seen in the
builtbranch. (Read the code generation section for details).
pip install ome-typesgit clone https://github.com/tlambert03/ome-types.git
cd ome-types
pip install -e .(The XML string/file will be validated against the ome.xsd schema)
from ome_types import from_xml
ome = from_xml('testing/data/hcs.ome.xml')from ome_types import from_tiff
ome2 = from_tiff('testing/data/ome.tiff')Both from_xml and from_tiff return an instance of ome_types.model.OME. All
classes in ome_types.model follow the naming conventions of the OME data
model,
but use snake_case attribute names instead of CamelCase, to be consistent
with the python ecosystem.
In [2]: ome = from_xml('testing/data/hcs.ome.xml')
In [3]: ome
Out[3]:
OME(
images=[<1 Images>],
plates=[<1 Plates>],
)
In [4]: ome.plates[0]
Out[4]:
Plate(
id='Plate:1',
name='Control Plate',
column_naming_convention='letter',
columns=12,
row_naming_convention='number',
rows=8,
wells=[<1 Wells>],
)
In [5]: ome.images[0]
Out[5]:
Image(
id='Image:0',
name='Series 1',
pixels=Pixels(
id='Pixels:0',
dimension_order='XYCZT',
size_c=3,
size_t=16,
size_x=1024,
size_y=1024,
size_z=1,
type='uint16',
bin_data=[<1 Bin_Data>],
channels=[<3 Channels>],
physical_size_x=0.207,
physical_size_y=0.207,
time_increment=120.1302,
),
acquisition_date=datetime.fromisoformat('2008-02-06T13:43:19'),
description='An example OME compliant file, based on Olympus.oib',
)In [6]: from ome_types.model.simple_types import UnitsLength
In [7]: from ome_types.model.channel import AcquisitionMode
In [8]: ome.images[0].description = "This is the new description."
In [9]: ome.images[0].pixels.physical_size_x = 350.0
In [10]: ome.images[0].pixels.physical_size_x_unit = UnitsLength.NANOMETER
In [11]: for c in ome.images[0].pixels.channels:
c.acquisition_mode = AcquisitionMode.SPINNING_DISK_CONFOCALIn [12]: from ome_types.model import Instrument, Microscope, Objective, InstrumentRef
In [13]: microscope_mk4 = Microscope(
manufacturer='OME Instruments',
model='Lab Mk4',
serial_number='L4-5678',
)
In [14]: objective_40x = Objective(
manufacturer='OME Objectives',
model='40xAir',
nominal_magnification=40.0,
)
In [15]: instrument = Instrument(
microscope=microscope_mk4,
objectives=[objective_40x],
)
In [16]: ome.instruments.append(instrument)
In [17]: ome.images[0].instrument_ref = InstrumentRef(instrument.id)
In [18]: ome.instruments
Out[18]:
[Instrument(
id='Instrument:1',
microscope=Microscope(
manufacturer='OME Instruments',
model='Lab Mk4',
serial_number='L4-5678',
),
objectives=[<1 Objectives>],
)]Finally, you can generate the OME-XML representation of the OME model object,
for writing to a standalone .ome.xml file or inserting into the header of an
OME-TIFF file:
In [19]: from ome_types import to_xml
In [20]: print(to_xml(ome))
<OME ...>
<Plate ColumnNamingConvention="letter" Columns="12" ID="Plate:1" ...>
...
</Plate>
<Instrument ID="Instrument:1">
<Microscope Manufacturer="OME Instruments" Model="Lab Mk4" SerialNumber="L4-5678" />
<Objective Manufacturer="OME Objectives" Model="40xAir" ID="Objective:1"
NominalMagnification="40.0" />
</Instrument>
<Image ID="Image:0" Name="Series 1">
<AcquisitionDate>2008-02-06T13:43:19</AcquisitionDate>
<Description>This is the new description.</Description>
<InstrumentRef ID="Instrument:1" />
<Pixels ... PhysicalSizeX="350.0" PhysicalSizeXUnit="nm" ...>
<Channel AcquisitionMode="SpinningDiskConfocal" ...>
...
</Pixels>
</Image>
</OME>The bulk of this library (namely, the ome_types.model module) is generated
at install time, and is therefore not checked into source (or visible in the master
branch of this repo).
You can see the code generated by the master branch in the built branch
The script at src/ome_autogen.py converts the ome.xsd
schema into valid
python code. To run the code generation script in a development environment,
clone this repository and run:
python src/ome_autogen.pyAs an example, the
OME/Image
model will be rendered as the following dataclass in ome_types/model/image.py
from dataclasses import field
from datetime import datetime
from typing import List, Optional
from ome_types.dataclasses import AUTO_SEQUENCE, ome_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
@ome_dataclass
class Image:
"""This element describes the actual image and its meta-data.
<docstring truncated for this example>
"""
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
id: ImageID = AUTO_SEQUENCE # type: ignore
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] = NoneThe documentation and types for the full model can be in the API Reference
To clone and install this repository locally (this will also build the model at
src/ome_types/model)
git clone https://github.com/tlambert03/ome-types.git
cd ome-types
pip install -e .[autogen]We use pre-commit to run various code-quality checks (isort, 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 should install
pre-commit after cloning this repository:
pip install pre-commit
pre-commit installIf you modify src/ome_autogen.py, you may need to regenerate the model with:
python src/ome_autogen.pyTo 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/ome_autogen.py.
Alternatively, you can install and run tox which will run tests and
code-quality checks in an isolated environment.
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