Read PicoQuant PTU and related files

Ptufile is a Python library to read image and metadata from PicoQuant PTU and related files: PHU, PCK, PCO, PFS, PUS, and PQRES. PTU files contain time correlated single photon counting (TCSPC) measurement data and instrumentation parameters.

Author

Christoph Gohlke

License

BSD 3-Clause

Version

2024.4.24

DOI

10.5281/zenodo.10120021

Quickstart

Install the ptufile package and all dependencies from the Python Package Index:

python -m pip install -U ptufile[all]

See Examples for using the programming interface.

Source code and support are available on GitHub.

Requirements

This revision was tested with the following requirements and dependencies (other versions may work):

• CPython 3.9.13, 3.10.11, 3.11.9, 3.12.3 (64-bit)
• Numpy 1.26.4
• Xarray 2024.3.0 (recommended)
• Matplotlib 3.8.4 (optional)
• Tifffile 2024.4.24 (optional)
• Numcodecs 0.12.1 (optional)
• Cython 3.0.10 (build)

Revisions

2024.4.24

• Build wheels with numpy 2.

2024.2.20

• Change definition of PtuFile.frequency (breaking).
• Add option to specify number of bins returned by decode_histogram.
• Add option to return histograms of one period.

2024.2.15

• Add PtuFile.scanner property.
• Add numcodecs compatible PTU codec.

2024.2.8

• Support sinusoidal scanning correction.

2024.2.2

• Change positive dtime parameter from index to size (breaking).
• Fix segfault with ImgHdr_TimePerPixel = 0.
• Rename MultiHarp to Generic conforming with changes in PicoQuant reference.

2023.11.16

• Fix empty line when first record is start marker.

2023.11.13

• Change image histogram dimension order to TYXCH (breaking).
• Change frame start to start of first line in frame (breaking).
• Improve trimming of incomplete frames (breaking).
• Remove trim_dtime option (breaking).
• Fix selection handling in PtuFile.decode_image.
• Add option to trim T, C, and H axes of image histograms.
• Add option to decode histograms to memory-mapped or user-provided arrays.
• Add __getitem__ interface to image histogram.

2023.11.1

• Initial alpha release.

Notes

The Chan Zuckerberg Initiative financially supported the development of this library.

PicoQuant GmbH is a manufacturer of photonic components and instruments.

The PicoQuant unified file formats are documented at the PicoQuant-Time-Tagged-File-Format-Demos.

The following features are currently not implemented: PT2 and PT3 files, decoding images from T2 formats, bidirectional scanning, and deprecated image reconstruction.

Other Python modules for reading PicoQuant files are:

• Read_PTU.py
• readPTU_FLIM
• PyPTU
• tttrlib
• picoquantio
• ptuparser
• phconvert
• trattoria (wrapper of trattoria-core and tttr-toolbox)
• napari-flim-phasor-plotter

Examples

Read properties and tags from any type of PicoQuant unified tagged file:

>>> pq = PqFile('tests/Settings.pfs') >>> pq.magic <PqFileMagic.PFS: ...> >>> pq.guid UUID('86d428e2-cb0b-4964-996c-04456ba6be7b') >>> pq.tags {...'CreatorSW_Name': 'SymPhoTime 64', 'CreatorSW_Version': '2.1'...} >>> pq.close()

Read metadata from a PicoQuant PTU FLIM file:

>>> ptu = PtuFile('tests/FLIM.ptu') >>> ptu.magic <PqFileMagic.PTU: ...> >>> ptu.type <PtuRecordType.PicoHarpT3: 66307> >>> ptu.measurement_mode <PtuMeasurementMode.T3: 3> >>> ptu.measurement_submode <PtuMeasurementSubMode.IMAGE: 3>

Decode TTTR records from the PTU file to numpy.recarray.

>>> decoded = ptu.decode_records()

Get global times of frame changes from markers:

>>> decoded['time'][(decoded['marker'] & ptu.frame_change_mask) > 0] array([1571185680], dtype=uint64)

Decode TTTR records to overall delay-time histograms per channel:

>>> ptu.decode_histogram(dtype='uint8') array([[ 5, 7, 7, ..., 10, 9, 2]], dtype=uint8)

Get information about the FLIM image histogram in the PTU file:

>>> ptu.shape (1, 256, 256, 2, 3126) >>> ptu.dims ('T', 'Y', 'X', 'C', 'H') >>> ptu.coords {'T': ..., 'Y': ..., 'X': ..., 'H': ...} >>> ptu.dtype dtype('uint16')

Decode parts of the image histogram to numpy.ndarray using slice notation. Slice step sizes define binning, -1 being used to integrate along axis:

>>> ptu[:, ..., 0, ::-1] array([[[103, ..., 38], ... [ 47, ..., 30]]], dtype=uint16)

Alternatively, decode the first channel and integrate all histogram bins to a xarray.DataArray, keeping reduced axes:

>>> ptu.decode_image(channel=0, dtime=-1, asxarray=True) <xarray.DataArray (T: 1, Y: 256, X: 256, C: 1, H: 1)> ... array([[[[[103]], ... [[ 30]]]]], dtype=uint16) Coordinates: * T (T) float64... 0.05625 * Y (Y) float64... -0.0001304 ... 0.0001294 * X (X) float64... -0.0001304 ... 0.0001294 * H (H) float64... 0.0 Dimensions without coordinates: C Attributes... frequency: 19999200.0 ... >>> ptu.close()

Preview the image and metadata in a PTU file from the console:

python -m ptufile tests/FLIM.ptu