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).
đź“– Â Â documentation
pip install ome-typesWith all optional dependencies:
# lxml => if you ...
# - want to use lxml as the XML parser
# - want to validate XML against the ome.xsd schema
# - want to use XML documents older than the 2016-06 schema
# pint => if you want to use object.<field>_quantity properties
# xmlschema => if you want to validate XML but DON'T want lxml
pip install ome-types[lxml,pint]conda install -c conda-forge ome-typespip install git+https://github.com/tlambert03/ome-types.git(The XML string/file will be validated against the ome.xsd schema)
from ome_types import from_xml
ome = from_xml('tests/data/hcs.ome.xml')from ome_types import from_tiff
ome2 = from_tiff('tests/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('tests/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(id=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, modules inside ome_types._autogenerated) is
generated at install time, and is therefore not checked into source (or visible
in the main branch of this repo).
You can see the code generated by the main branch in the built branch
The package at src/ome_autogen 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 -m src.ome_autogenThe documentation and types for the full model can be in the API Reference
To clone and install this repository locally:
git clone https://github.com/tlambert03/ome-types.git
cd ome-types
pip install -e .[test,dev]We use pre-commit to run various code-quality checks 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:
pre-commit installIf you modify anything in src/ome_autogen, you may need to
regenerate the model with:
python -m src.ome_autogenTo run tests:
pytest