Bruker TIFF data conversion

Install NeuroConv with the additional dependencies necessary for reading Bruker TIFF data.

pip install "neuroconv[brukertiff]"

Convert a Bruker session

A Prairie View session is a folder of OME-TIFF files together with the .xml configuration that describes them. Point BrukerTiffConverter at that folder.

>>> from zoneinfo import ZoneInfo
>>> from neuroconv.converters import BrukerTiffConverter
>>>
>>> # The 'folder_path' is the path to the folder containing the OME-TIF image files and the XML configuration file.
>>> folder_path = OPHYS_DATA_PATH / "imaging_datasets" / "BrukerTif" / "NCCR32_2023_02_20_Into_the_void_t_series_baseline-000"
>>> converter = BrukerTiffConverter(folder_path=folder_path)
>>>
>>> metadata = converter.get_metadata()
>>> # For data provenance we can add the time zone information to the conversion if missing
>>> session_start_time = metadata["NWBFile"]["session_start_time"]
>>> tzinfo = ZoneInfo("US/Pacific")
>>> metadata["NWBFile"].update(session_start_time=session_start_time.replace(tzinfo=tzinfo))
>>> # Add subject information (required for DANDI upload)
>>> metadata["Subject"] = dict(subject_id="subject1", species="Mus musculus", sex="M", age="P30D")
>>>
>>> nwbfile_path = f"{path_to_save_nwbfile}"
>>> converter.run_conversion(nwbfile_path=nwbfile_path, metadata=metadata, overwrite=True)

That call covers any Prairie View session, planar or volumetric, single-channel or multi-channel. Every channel in the session is written as its own ImagingPlane and TwoPhotonSeries, all referring to a single Device named BrukerFluorescenceMicroscope.

NeuroConv aims to automatically add all the metadata annotations that are present in the source format. It is often the case that crucial information is not available there, such as the anatomical location, the meaning of the values, or a semantically meaningful description of the data. Follow the ophys how-to for a modality-relevant guide to adding this extra metadata, which makes the data more useful for future users and for the community as a whole. Its section on templates starts from scratch, and the reference template lists every element the metadata accepts.

Writing a volumetric session

A volumetric session can be written two ways, chosen with plane_separation_type. The default, "contiguous", keeps the volume together: the extractor returns a 4D series (samples, height, width, planes) and it is written as a single TwoPhotonSeries per channel with the per-volume sampling rate.

Passing "disjoint" splits the volume instead, writing each z-plane as its own 2D TwoPhotonSeries and ImagingPlane, with each plane carrying its own focal depth. Prefer it when the planes are far enough apart to be read as separate fields of view rather than one volume. The setting has no effect on planar sessions.

>>> from zoneinfo import ZoneInfo
>>> from neuroconv.converters import BrukerTiffConverter
>>>
>>> folder_path = OPHYS_DATA_PATH / "imaging_datasets" / "BrukerTif" / "NCCR32_2022_11_03_IntoTheVoid_t_series-005"
>>> converter = BrukerTiffConverter(folder_path=folder_path, plane_separation_type="disjoint")
>>>
>>> metadata = converter.get_metadata()
>>> session_start_time = metadata["NWBFile"]["session_start_time"]
>>> tzinfo = ZoneInfo("US/Pacific")
>>> metadata["NWBFile"].update(session_start_time=session_start_time.replace(tzinfo=tzinfo))
>>> metadata["Subject"] = dict(subject_id="subject1", species="Mus musculus", sex="M", age="P30D")
>>>
>>> nwbfile_path = f"{path_to_save_nwbfile}"
>>> converter.run_conversion(nwbfile_path=nwbfile_path, metadata=metadata, overwrite=True)

Choosing what gets written

The converter writes the whole session. To write only part of it, or to combine it with interfaces from other modalities, build BrukerTiffImagingInterface yourself. channel_name selects one channel (BrukerTiffImagingInterface.get_available_channels(folder_path=...) lists what is available) and plane_index pins the interface to a single depth plane of a volumetric session.

>>> from zoneinfo import ZoneInfo
>>> from neuroconv.datainterfaces import BrukerTiffImagingInterface
>>>
>>> folder_path = OPHYS_DATA_PATH / "imaging_datasets" / "BrukerTif" / "NCCR62_2023_07_06_IntoTheVoid_t_series_Dual_color-000"
>>> interface = BrukerTiffImagingInterface(folder_path=folder_path, channel_name="Ch1")
>>>
>>> metadata = interface.get_metadata()
>>> session_start_time = metadata["NWBFile"]["session_start_time"]
>>> tzinfo = ZoneInfo("US/Pacific")
>>> metadata["NWBFile"].update(session_start_time=session_start_time.replace(tzinfo=tzinfo))
>>> metadata["Subject"] = dict(subject_id="subject1", species="Mus musculus", sex="M", age="P30D")
>>>
>>> nwbfile_path = f"{path_to_save_nwbfile}"
>>> interface.run_conversion(nwbfile_path=nwbfile_path, metadata=metadata, overwrite=True)

Several of them go together in a ConverterPipe, which is how you write an arbitrary set of channels or planes, or add interfaces from other modalities. Each one gets its own auto-suffixed metadata_key, so the imaging planes and series stay namespaced while the microscope stays shared.

>>> from zoneinfo import ZoneInfo
>>> from neuroconv import ConverterPipe
>>> from neuroconv.datainterfaces import BrukerTiffImagingInterface
>>>
>>> folder_path = OPHYS_DATA_PATH / "imaging_datasets" / "BrukerTif" / "NCCR62_2023_07_06_IntoTheVoid_t_series_Dual_color-000"
>>> interface_ch1 = BrukerTiffImagingInterface(folder_path=folder_path, channel_name="Ch1")
>>> interface_ch2 = BrukerTiffImagingInterface(folder_path=folder_path, channel_name="Ch2")
>>> converter = ConverterPipe(data_interfaces=dict(channel_1=interface_ch1, channel_2=interface_ch2))
>>>
>>> metadata = converter.get_metadata()
>>> session_start_time = metadata["NWBFile"]["session_start_time"]
>>> tzinfo = ZoneInfo("US/Pacific")
>>> metadata["NWBFile"].update(session_start_time=session_start_time.replace(tzinfo=tzinfo))
>>> metadata["Subject"] = dict(subject_id="subject1", species="Mus musculus", sex="M", age="P30D")
>>>
>>> nwbfile_path = f"{path_to_save_nwbfile}"
>>> converter.run_conversion(nwbfile_path=nwbfile_path, metadata=metadata, overwrite=True)