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readPTU_FLIM Library

For demo: Jupyter Notebook to work with PicoQuant PTU files

The library provides the capability to handle all PicoQuant's TCSPC Harps in T2 as well as T3 mode.

  • PicoQuant uses a bespoke file format called PTU to store data from time-tag-time-resolved (TTTR) measurements.
  • Current file format (.ptu) and is subsequent to the former .pt2 or .pt3 and can handle both T2 and T3 acquisition modes for a variety of TCSPC devices (MultiHarp, HydraHarp, TimeHarp, PicoHarp, etc.)
  • At the moment, the library was tested for FLIM data obtained using MultiHarp, HydraHarp and PicoHarp.

What is not available !

  • Option to save data after processing.

!!! Help is only an email away for MATLAB implementation of the same library.

Dependencies

readPTU_FLIM.py, numpy, matplotlib, numba,

from readPTU_FLIM import PTUreader 
import numpy as np from matplotlib 
import pyplot as plt

Use case

  • As an example, first we import the readPTU_FLIM library. Then we should open the PTU file using a PTUreader() object. By constructing a PTUreader() object, automatically opens the file, reads the PTU file header and TTTR data in raw format. PTU file header contains important measurement information (PQ TCSPC unit, laser/pulse sync rate, FLIM record type, scanner type, etc.)

  • Test_FLIM_image_daisyPollen_PicoHarp_2.ptu
    Download the test file here FLIM data was acquired using a PicoHarp (T3 mode)
    Exctiation Laser light: 485 nm
    Number of Detection Channels: 2

  • At the moment, PTUreader library contains implementation for reading imaging data from scanner types:
    • laser beam scanner (LSM)
    • Piezo scanner

Select a PTU file

ptu_file  = PTUreader('Test_FLIM_image_daisyPollen_PicoHarp_2.ptu', print_header_data = False)

How to access raw data from ptu_file object?

# # Raw data can be accessed using ptu_file object
# sync    = ptu_file.sync    # Macro photon arrival time
# tcspc   = ptu_file.tcspc   # Micro photon arrival time (tcspc time bin resoultion)
# channel = ptu_file.channel # Detection channel of tcspc unit (<=8 for PQ hardware in 2019)
# special = ptu_file.special # Special event markers, for e.g. Frame, LineStart, LineStop, etc.

Get flim_data_stack and intensity_image from raw TTTR data

Note: once NEXT CODE block is executed raw TTTR data variables (sync, tcspc, channel, special) are deleted

  • flim_data_stack: (pixX, pixY, spectral_detection_channel, tcspc_bins)
  • intensity_image: (pixX, pixY)

For example: How to get all the tcspc histogram data from all pixels from spectral detection channel 1? (hint below!)

flim_data_stack, intensity_image = ptu_file.get_flim_data_stack()
# channel_1_data = flim_data_stack[:,:,0,:]

Plot Intensity image

# %matplotlib inline
# #plot intensity image
# plt.imshow(intensity_image)
# plt.colorbar()

Plot & Play

  • Please see Jupyter notebook for implementation
  • Once the FLIM data stack is loaded, one could create custom gui for performing all sort of FLIM analysis routines as needed.
  • As an example a simple intensity image is plotted
  • User can investigate the fluorescence decays after drawing a rectangle on the intensity image
    Interactive Demo Snapshot

Updates

  • 27 Aug, 2019: Piezo Scanner data readability added to the library

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Python library to read PicoQuant PTU files (raw TTTR data) as well as build FLIM image

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