A collection of scripts for automating conversion to Photon-HDF5, archival and analysis of smFRET measurements using multiprocessing (each file is processed in a different CPU).
A brief description of each script follows.
Convert all files in a specified folder in batch.
If the --monitor argument is passed, monitors a given folder.
When a new YAML file appears in the same folder with the same name as
a data file, it starts these processing steps:
- copy the data to a temp folder
- convert data to Photon-HDF5 using the metadata from the YAML file
- copy all files to and archival folder
- optionally run smFRET analysis
Data files can be processed in parallel.
Type ./batch_convert.py -h for more info on how to use the script.
Analyze all the Photon-HDF5 files in a given folder using a default notebook or any other specified notebook. Multiple files can be processed in parallel. For optimal performances it is suggested to do not exceed the number of CPUs.
Type ./batch_analysis.py -h for more info on how to use the script.
Analyze a single Photon-HDF5 file using a the specified notebook.
Type ./analyze.py -h for more info on how to use the script.
Module used for processing a single file (copy, conversion, analysis).
The script monitor.py build a multiprocessing pool and calls functions
defined in transfer.py to process several files in parallel.
Download the repository and run the scripts directly from the repo folder (no installation).
- python 3.6 (older versions may work)
- jupyter notebook 5+
- jupyter nbconvert 5+
- pyyaml
- tqdm (progress bar)
- FRETBursts 0.6.3+
- phconvert 0.7.3+
- niconverter
See also conda_environment_linux.yml.
If you use this code for a publication, please cite as:
48-spot single-molecule FRET setup with periodic acceptor excitation
A. Ingargiola, M. Segal, A. Gulinatti, I. Rech, I. Labanca, P. Maccagnani, M. Ghioni, S. Weiss, X. Michalet
bioRxiv 156182; 2017. doi: https://doi.org/10.1101/156182
Copyright (C) 2017 The Regents of the University of California, Antonino Ingargiola and contributors.
This work was supported by NIH grants R01 GM069709 and R01 GM095904.