Crate tttr_toolbox[−][src]
Expand description
TTTR Toolbox
The fastest streaming algorithms for your TTTR data.
TTTR Toolbox can be used as a standalone Rust library. If you do most of your data analysis in Python you may prefer to check Trattoria, a wrapper library for this crate.
Project Goals
- Single threaded performance
- Ease of extensibility
Algorithms available
- second order autocorrelation Two versions of the algorithm are provided. A symmetric mode for increased performance and the asymmetric mode to capture both negative and positive delays.
- third order autocorrelation
- third order autocorrelation
- intensity time trace
- record number time trace
- zero delay finder
- lifetimes
Supported file and record formats
- PicoQuant PTU
- PHT2
- HHT2_HH1
- HHT2_HH2
- HHT3_HH2
If you want support for more record formats and file formats please ask for it. At the very least we will need the file format specification and a file with some discernible features to test the implementation.
Examples
ⓘ
pub fn main() { let filename = PathBuf::from("/Users/garfield/Downloads/20191205_Xminus_0p1Ve-6_CW_HBT.ptu"); let ptu_file = File::PTU(PTUFile::new(filename).unwrap()); // Unwrap the file so we can print the header let File::PTU(f) = &ptu_file; println!("{}", f); let params = G2Params { channel_1: 0, channel_2: 1, correlation_window: 50_000e-12, resolution: 600e-12, start_record: None, stop_record: None, }; let g2_histogram = g2(&ptu_file, ¶ms).unwrap(); println!("{:?}", g2_histogram.hist); }
Modules
Structs
Traits
The TTTRFile trait ensures that all files we support are aware of the time_resolution and the what type of records they contain.