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Laboratory for Fluorescence Dynamics (LFD) file formats

Lfdfiles is a Python library and console script for reading, writing, converting, and viewing many of the proprietary file formats used to store experimental data and metadata at the Laboratory for Fluorescence Dynamics. For example:

  • SimFCS VPL, VPP, JRN, BIN, INT, CYL, REF, BH, BHZ, B64, I64, Z64, R64
  • CCP4 MAP
  • Vaa3D RAW
  • Bio-Rad(r) PIC
  • ISS Vista IFLI, IFI
  • FlimFast FLIF
Author:Christoph Gohlke
License:BSD 3-Clause


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

python -m pip install -U "lfdfiles[all]"

Print the console script usage:

python -m lfdfiles --help

The lfdfiles library is type annotated and documented via docstrings.

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):



  • Fix docstring examples not correctly rendered on GitHub.


  • Support NumPy 2.


  • Fix decoding 32-bit, 16 windows, 4 channels Spartan6 FBD files (#1).


  • Remove phasor and lifetime methods from VistaIfli (breaking).
  • Rename SimfcsFbd and SimfcsFbf to FlimboxFbd and FlimboxFbf (breaking).
  • Deprecate SimfcsFbd and SimfcsFbf.
  • Support int16 FLIMbox cross correlation phase indices (bins).
  • Add FlimboxFbs class for ISS VistaVision FLIMbox settings.
  • Add decoder for 32-bit, 16 windows, 4 channels FlimboxFbd (untested).


  • Rewrite VistaIfli based on file format specification (breaking).
  • Define positional and keyword parameters (breaking).
  • SimfcsFbd.asarray returns bins only (breaking).


Refer to the CHANGES file for older revisions.


The API is not stable yet and might change between revisions.

Python <= 3.8 is no longer supported. 32-bit versions are deprecated.

The latest Microsoft Visual C++ Redistributable for Visual Studio 2015-2022 is required on Windows.

Many of the LFD's file formats are not documented and might change arbitrarily. This implementation is mostly based on reverse engineering existing files. No guarantee can be made as to the correctness of code and documentation.

Experimental data are often stored in plain binary files with metadata available in separate, human readable journal files (.jrn).

Unless specified otherwise, data are stored in little-endian, C contiguous order.


The following software is referenced in this module:

  1. SimFCS, a.k.a. Globals for Images, is software for fluorescence image acquisition, analysis, and simulation, developed by Enrico Gratton at UCI.
  2. Globals, a.k.a. Globals for Spectroscopy, is software for the analysis of multiple files from fluorescence spectroscopy, developed by Enrico Gratton at UIUC and UCI.
  3. ImObj is software for image analysis, developed by LFD at UIUC. Implemented on Win16.
  4. FlimFast is software for frequency-domain, full-field, fluorescence lifetime imaging at video rate, developed by Christoph Gohlke at UIUC.
  5. FLImage is software for frequency-domain, full-field, fluorescence lifetime imaging, developed by Christoph Gohlke at UIUC. Implemented in LabVIEW.
  6. FLIez is software for frequency-domain, full-field, fluorescence lifetime imaging, developed by Glen Redford at UIUC.
  7. Flie is software for frequency-domain, full-field, fluorescence lifetime imaging, developed by Peter Schneider at MPIBPC. Implemented on a Sun UltraSPARC.
  8. FLOP is software for frequency-domain, cuvette, fluorescence lifetime measurements, developed by Christoph Gohlke at MPIBPC. Implemented in LabVIEW.
  9. VistaVision is commercial software for instrument control, data acquisition and data processing by ISS Inc (Champaign, IL).
  10. Vaa3D is software for multi-dimensional data visualization and analysis, developed by the Hanchuan Peng group at the Allen Institute.
  11. Voxx is a volume rendering program for 3D microscopy, developed by Jeff Clendenon et al. at the Indiana University.
  12. CCP4, the Collaborative Computational Project No. 4, is software for macromolecular X-Ray crystallography.


Create a Bio-Rad PIC file from a NumPy array:

>>> data = numpy.arange(1000000).reshape(100, 100, 100).astype('u1')
>>> bioradpic_write('_biorad.pic', data)

Read the volume data from the PIC file as NumPy array, and access metadata:

>>> with BioradPic('_biorad.pic') as f:
...     f.shape
...     f.spacing
...     data = f.asarray()
(100, 100, 100)
(1.0, 1.0, 1.0)

Convert the PIC file to a compressed TIFF file:

>>> with BioradPic('_biorad.pic') as f:
...     f.totiff('_biorad.tif', compression='zlib')