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 <https://www.cgohlke.com>`_
:License: BSD 3-Clause
:Version: 2024.10.10
:DOI: `10.5281/zenodo.10120021 <https://doi.org/10.5281/zenodo.10120021>`_
Quickstart
----------
Install the ptufile package and all dependencies from the
`Python Package Index <https://pypi.org/project/ptufile/>`_::
python -m pip install -U "ptufile[all]"
See `Examples`_ for using the programming interface.
Source code and support are available on
`GitHub <https://github.com/cgohlke/ptufile>`_.
Requirements
------------
This revision was tested with the following requirements and dependencies
(other versions may work):
- `CPython <https://www.python.org>`_ 3.10.11, 3.11.9, 3.12.7, 3.13.0 64-bit
- `NumPy <https://pypi.org/project/numpy>`_ 2.1.2
- `Xarray <https://pypi.org/project/xarray>`_ 2024.9.0 (recommended)
- `Matplotlib <https://pypi.org/project/matplotlib/>`_ 3.9.2 (optional)
- `Tifffile <https://pypi.org/project/tifffile/>`_ 2024.9.20 (optional)
- `Numcodecs <https://pypi.org/project/numcodecs/>`_ 0.13.1 (optional)
- `Cython <https://pypi.org/project/cython/>`_ 3.0.11 (build)
Revisions
---------
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
- 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
<https://chanzuckerberg.com/eoss/proposals/phasorpy-a-python-library-for-phasor-analysis-of-flim-and-spectral-imaging>`_
financially supported the development of this library.
`PicoQuant GmbH <https://www.picoquant.com/>`_ is a manufacturer of photonic
components and instruments.
The PicoQuant unified file formats are documented at the
`PicoQuant-Time-Tagged-File-Format-Demos
<https://github.com/PicoQuant/PicoQuant-Time-Tagged-File-Format-Demos/tree/master/doc>`_.
The following features are currently not implemented: PT2 and PT3 files,
decoding images from T2 formats, bidirectional scanning, and deprecated
image reconstruction. Line-scanning is not tested.
Other Python or C/C++ modules for reading PicoQuant files are:
- `Read_PTU.py
<https://github.com/PicoQuant/PicoQuant-Time-Tagged-File-Format-Demos/blob/master/PTU/Python/Read_PTU.py>`_
- `readPTU_FLIM <https://github.com/SumeetRohilla/readPTU_FLIM>`_
- `PyPTU <https://gitlab.inria.fr/jrye/pyptu>`_
- `FlimReader <https://github.com/flimfit/FlimReader>`_
- `tttrlib <https://github.com/Fluorescence-Tools/tttrlib>`_
- `picoquantio <https://github.com/tsbischof/picoquantio>`_
- `ptuparser <https://pypi.org/project/ptuparser/>`_
- `phconvert <https://github.com/Photon-HDF5/phconvert/>`_
- `trattoria <https://pypi.org/project/trattoria/>`_
(wrapper of `trattoria-core <https://pypi.org/project/trattoria-core/>`_ and
`tttr-toolbox <https://github.com/GCBallesteros/tttr-toolbox/>`_)
- `napari-flim-phasor-plotter
<https://github.com/zoccoler/napari-flim-phasor-plotter/blob/0.0.6/src/napari_flim_phasor_plotter/_io/readPTU_FLIM.py>`_
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')
>>> 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
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
* C (C) uint8... 0
* H (H) float64... 0.0
Attributes...
frequency: 19999200.0
...
>>> ptu.close()
Preview the image and metadata in a PTU file from the console::
python -m ptufile tests/FLIM.ptu
Raw data
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"description": "Read PicoQuant PTU and related files\n====================================\n\nPtufile is a Python library to read image and metadata from PicoQuant PTU\nand related files: PHU, PCK, PCO, PFS, PUS, and PQRES.\nPTU files contain time correlated single photon counting (TCSPC)\nmeasurement data and instrumentation parameters.\n\n:Author: `Christoph Gohlke <https://www.cgohlke.com>`_\n:License: BSD 3-Clause\n:Version: 2024.10.10\n:DOI: `10.5281/zenodo.10120021 <https://doi.org/10.5281/zenodo.10120021>`_\n\nQuickstart\n----------\n\nInstall the ptufile package and all dependencies from the\n`Python Package Index <https://pypi.org/project/ptufile/>`_::\n\n python -m pip install -U \"ptufile[all]\"\n\nSee `Examples`_ for using the programming interface.\n\nSource code and support are available on\n`GitHub <https://github.com/cgohlke/ptufile>`_.\n\nRequirements\n------------\n\nThis revision was tested with the following requirements and dependencies\n(other versions may work):\n\n- `CPython <https://www.python.org>`_ 3.10.11, 3.11.9, 3.12.7, 3.13.0 64-bit\n- `NumPy <https://pypi.org/project/numpy>`_ 2.1.2\n- `Xarray <https://pypi.org/project/xarray>`_ 2024.9.0 (recommended)\n- `Matplotlib <https://pypi.org/project/matplotlib/>`_ 3.9.2 (optional)\n- `Tifffile <https://pypi.org/project/tifffile/>`_ 2024.9.20 (optional)\n- `Numcodecs <https://pypi.org/project/numcodecs/>`_ 0.13.1 (optional)\n- `Cython <https://pypi.org/project/cython/>`_ 3.0.11 (build)\n\nRevisions\n---------\n\n2024.10.10\n\n- Also trim leading channels without photons (breaking).\n- Add property to identify channels with photons.\n\n2024.9.14\n\n- Improve typing.\n\n2024.7.13\n\n- Detect point scans in image mode.\n- Deprecate Python 3.9, support Python 3.13.\n\n2024.5.24\n\n- Fix docstring examples not correctly rendered on GitHub.\n\n2024.4.24\n\n- Build wheels with NumPy 2.\n\n2024.2.20\n\n- Change definition of PtuFile.frequency (breaking).\n- Add option to specify number of bins returned by decode_histogram.\n- Add option to return histograms of one period.\n\n2024.2.15\n\n- Add PtuFile.scanner property.\n- Add numcodecs compatible PTU codec.\n\n2024.2.8\n\n- Support sinusoidal scanning correction.\n\n2024.2.2\n\n- Change positive dtime parameter from index to size (breaking).\n- Fix segfault with ImgHdr_TimePerPixel = 0.\n- Rename MultiHarp to Generic conforming with changes in PicoQuant reference.\n\n2023.11.16\n\n- Fix empty line when first record is start marker.\n\n2023.11.13\n\n- Change image histogram dimension order to TYXCH (breaking).\n- Change frame start to start of first line in frame (breaking).\n- Improve trimming of incomplete frames (breaking).\n- Remove trim_dtime option (breaking).\n- Fix selection handling in PtuFile.decode_image.\n- Add option to trim T, C, and H axes of image histograms.\n- Add option to decode histograms to memory-mapped or user-provided arrays.\n- Add ``__getitem__`` interface to image histogram.\n\n2023.11.1\n\n- Initial alpha release.\n\nNotes\n-----\n\nThe `Chan Zuckerberg Initiative\n<https://chanzuckerberg.com/eoss/proposals/phasorpy-a-python-library-for-phasor-analysis-of-flim-and-spectral-imaging>`_\nfinancially supported the development of this library.\n\n`PicoQuant GmbH <https://www.picoquant.com/>`_ is a manufacturer of photonic\ncomponents and instruments.\n\nThe PicoQuant unified file formats are documented at the\n`PicoQuant-Time-Tagged-File-Format-Demos\n<https://github.com/PicoQuant/PicoQuant-Time-Tagged-File-Format-Demos/tree/master/doc>`_.\n\nThe following features are currently not implemented: PT2 and PT3 files,\ndecoding images from T2 formats, bidirectional scanning, and deprecated\nimage reconstruction. Line-scanning is not tested.\n\nOther Python or C/C++ modules for reading PicoQuant files are:\n\n- `Read_PTU.py\n <https://github.com/PicoQuant/PicoQuant-Time-Tagged-File-Format-Demos/blob/master/PTU/Python/Read_PTU.py>`_\n- `readPTU_FLIM <https://github.com/SumeetRohilla/readPTU_FLIM>`_\n- `PyPTU <https://gitlab.inria.fr/jrye/pyptu>`_\n- `FlimReader <https://github.com/flimfit/FlimReader>`_\n- `tttrlib <https://github.com/Fluorescence-Tools/tttrlib>`_\n- `picoquantio <https://github.com/tsbischof/picoquantio>`_\n- `ptuparser <https://pypi.org/project/ptuparser/>`_\n- `phconvert <https://github.com/Photon-HDF5/phconvert/>`_\n- `trattoria <https://pypi.org/project/trattoria/>`_\n (wrapper of `trattoria-core <https://pypi.org/project/trattoria-core/>`_ and\n `tttr-toolbox <https://github.com/GCBallesteros/tttr-toolbox/>`_)\n- `napari-flim-phasor-plotter\n <https://github.com/zoccoler/napari-flim-phasor-plotter/blob/0.0.6/src/napari_flim_phasor_plotter/_io/readPTU_FLIM.py>`_\n\nExamples\n--------\n\nRead properties and tags from any type of PicoQuant unified tagged file:\n\n>>> pq = PqFile('tests/Settings.pfs')\n>>> pq.magic\n<PqFileMagic.PFS: ...>\n>>> pq.guid\nUUID('86d428e2-cb0b-4964-996c-04456ba6be7b')\n>>> pq.tags\n{...'CreatorSW_Name': 'SymPhoTime 64', 'CreatorSW_Version': '2.1'...}\n>>> pq.close()\n\nRead metadata from a PicoQuant PTU FLIM file:\n\n>>> ptu = PtuFile('tests/FLIM.ptu')\n>>> ptu.magic\n<PqFileMagic.PTU: ...>\n>>> ptu.type\n<PtuRecordType.PicoHarpT3: 66307>\n>>> ptu.measurement_mode\n<PtuMeasurementMode.T3: 3>\n>>> ptu.measurement_submode\n<PtuMeasurementSubMode.IMAGE: 3>\n\nDecode TTTR records from the PTU file to ``numpy.recarray``:\n\n>>> decoded = ptu.decode_records()\n\nGet global times of frame changes from markers:\n\n>>> decoded['time'][(decoded['marker'] & ptu.frame_change_mask) > 0]\narray([1571185680], dtype=uint64)\n\nDecode TTTR records to overall delay-time histograms per channel:\n\n>>> ptu.decode_histogram(dtype='uint8')\narray([[ 5, 7, 7, ..., 10, 9, 2]], dtype=uint8)\n\nGet information about the FLIM image histogram in the PTU file:\n\n>>> ptu.shape\n(1, 256, 256, 2, 3126)\n>>> ptu.dims\n('T', 'Y', 'X', 'C', 'H')\n>>> ptu.coords\n{'T': ..., 'Y': ..., 'X': ..., 'H': ...}\n>>> ptu.dtype\ndtype('uint16')\n>>> ptu.active_channels\n(0, 1)\n\nDecode parts of the image histogram to ``numpy.ndarray`` using slice notation.\nSlice step sizes define binning, -1 being used to integrate along axis:\n\n>>> ptu[:, ..., 0, ::-1]\narray([[[103, ..., 38],\n ...\n [ 47, ..., 30]]], dtype=uint16)\n\nAlternatively, decode the first channel and integrate all histogram bins\nto a ``xarray.DataArray``, keeping reduced axes:\n\n>>> ptu.decode_image(channel=0, dtime=-1, asxarray=True)\n<xarray.DataArray (T: 1, Y: 256, X: 256, C: 1, H: 1)> ...\narray([[[[[103]],\n ...\n [[ 30]]]]], dtype=uint16)\nCoordinates:\n * T (T) float64... 0.05625\n * Y (Y) float64... -0.0001304 ... 0.0001294\n * X (X) float64... -0.0001304 ... 0.0001294\n * C (C) uint8... 0\n * H (H) float64... 0.0\nAttributes...\n frequency: 19999200.0\n...\n>>> ptu.close()\n\nPreview the image and metadata in a PTU file from the console::\n\n python -m ptufile tests/FLIM.ptu\n",
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