PTU FLIM Channel/Window Plotter

This repository contains a small utility script to quickly visualize which FLIM channels were active in a PicoQuant PTU file and to explore time-delay (dtime) windows. It produces:

• A log-scale histogram of the PTU counts summed over channels.
• A grid of images: rows = selected dtime windows, columns = detector channels.

Script: plot_ptu_channel_windows.py

Features

• Load a PTU file via a PtuFile interface.
• Compute simple, evenly spaced dtime windows (configurable count).
• Sum frames and dtime-bins into a 2D image for each channel and window.
• Save publication-ready PNGs.

Requirements

• Python 3.9+ (tested versions may vary)
• numpy
• matplotlib
• ptufile (provides PtuFile for PTU file handling)

Installation

This script is based on PicoQuant and related PTU files, therefore it is necessary to install the following dependencies:

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

Install all dependencies using pip:

pip install -r requirements.txt

Or install them individually:

pip install numpy matplotlib ptufile

Usage

1. Place or reference your PTU file (e.g., FLIM_20250715-170852/RawImage.ptu).
2. Open plot_ptu_channel_windows.py and adjust:
   • filename: path to the PTU file.
   • N_WINDOWS: number of evenly spaced dtime windows.
   • channel_ids: list of channel indices to visualize.
3. Run the script:

python plot_ptu_channel_windows.py

Output

• ptu_histogram_log.png: log-scale histogram of counts summed over channels.
• channels_overview_grid.png: grid of images with rows = windows and columns = channels.

Configuration Details

• Windowing: compute_windows_from_hist() currently splits the histogram range into N_WINDOWS equal-width windows. You can replace this with peak-based window detection if desired.
• Performance: The script decodes only the requested dtime window and integrates frames to keep memory usage manageable. Logging provides timing for decode and reduction steps.

Troubleshooting

• Import error for ptufile: Ensure you've installed all dependencies with pip install -r requirements.txt.
• No channels or unexpected shapes: The script logs ptu.shape, frames, channels, and bins to help verify file contents.
• Large files: Reduce N_WINDOWS, subset channel_ids, or run on a machine with more RAM.

Contributing

Issues and pull requests are welcome. Please include a short description, steps to reproduce (if applicable), and environment details.

License

Specify your preferred license (e.g., MIT, Apache-2.0). If you want, I can add a LICENSE file for you.