Ptufile is a Python library to
- read data and metadata from PicoQuant PTU and related files (PHU, PCK, PCO, PFS, PUS, and PQRES), and
- write TCSPC histograms to T3 image mode PTU files.
PTU files contain time correlated single photon counting (TCSPC) measurement data and instrumentation parameters.
| Author: | Christoph Gohlke |
|---|---|
| License: | BSD 3-Clause |
| Version: | 2024.12.28 |
| DOI: | 10.5281/zenodo.10120021 |
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.
This revision was tested with the following requirements and dependencies (other versions may work):
- CPython 3.10.11, 3.11.9, 3.12.8, 3.13.1 64-bit
- NumPy 2.1.3
- Xarray 2024.11.0 (recommended)
- Matplotlib 3.10.0 (optional)
- Tifffile 2024.12.12 (optional)
- Numcodecs 0.14.1 (optional)
- Python-dateutil 2.9.0 (optional)
- Cython 3.0.11 (build)
2024.12.28
- Add imwrite function to encode TCSPC image histogram in T3 PTU format.
- Add enums for more PTU tag values.
- Add PqFile.datetime property.
- Read TDateTime tag as datetime instead of struct_time (breaking).
- Rename PtuFile.type property to record_type (breaking).
- Fix reading PHU missing HistResDscr_HWBaseResolution tag.
- Warn if tags are not 8-byte aligned in file.
2024.12.20
- Support bi-directional sinusoidal scanning (WIP).
2024.11.26
- Support bi-directional scanning (FLIMbee scanner).
- Drop support for Python 3.9.
2024.10.10
- Also trim leading channels without photons (breaking).
- Add property to identify channels with photons.
2024.9.14
- Improve typing.
2024.7.13
- Detect point scans in image mode.
- Deprecate Python 3.9, support Python 3.13.
2024.5.24
- Fix docstring examples not correctly rendered on GitHub.
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
- …
Refer to the CHANGES file for older revisions.
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 due to the lack of test files or documentation: PT2 and PT3 files, decoding images from T2 formats, bidirectional per frame, and deprecated image reconstruction.
Compatibility of written PTU files with other software is limitedly tested, as are decoding line, bidirectional, and sinusoidal scanning.
Other modules for reading or writing PicoQuant files are Read_PTU.py, readPTU_FLIM, fastFLIM, PyPTU, PTU_Reader, PTU_Writer, FlimReader, tangy, tttrlib, picoquantio, ptuparser, phconvert, trattoria (wrapper of trattoria-core, tttr-toolbox), and napari-flim-phasor-plotter.
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.record_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')
>>> ptu.active_channels
(0, 1)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
into 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
* C (C) uint8... 0
* H (H) float64... 0.0
Attributes...
frequency: 19999200.0
...Write the TCSPC histogram and metadata to a PicoHarpT3 image mode PTU file:
>>> imwrite(
... '_test.ptu',
... ptu[:],
... ptu.global_resolution,
... ptu.tcspc_resolution,
... # optional metadata
... pixel_time=ptu.pixel_time,
... record_type=PtuRecordType.PicoHarpT3,
... comment='Written by ptufile.py',
... tags={'File_RawData_GUID': [ptu.guid]},
... )Read back the TCSPC histogram from the file:
>>> tcspc_histogram = imread('_test.ptu')
>>> import numpy
>>> numpy.array_equal(tcspc_histogram, ptu[:])
TrueClose the file handle:
>>> ptu.close()Preview the image and metadata in a PTU file from the console:
python -m ptufile tests/FLIM.ptu