FLIMAnalyzer

Python package for analysis of Fluoresence Lifetime Imaging Microscopy (FLIM) data.

Requirements:

• Python 3.7 (or later)
• pandas
• numpy (1.18)
• matplotlib
• seaborn
• pytorch
• scikit-learn
• xlsxwriter
• wxpython (4.0.7)
• pypubsub
• python.app (for Mac OSX)
• setuptools_scm

Windows Installation:

• Install VS Code

Installation:

git clone https://github.com/uvaKCCI/flimanalyzer.git
cd flimanalyzer
conda env create -f environment.yml
conda activate flimenv
python setup.py install

This will create a Conda environment flimenv that contains all the Python packages required to run the FLIM Analyzer application.

MacOS X Installation

Activate the Conda environment, install the MacOS specific python.app package, and patch the shebang of the flimanalyzer console script.

conda activate flimenv
conda install python.app
sed -i '' -e "1s/.*/\#\!\/usr\/bin\/env pythonw/" $(which flimanalyzer)

Run the application

From the command line

On Windows, run this command

conda activate flimenv
set PREFECT__FLOWS__CHECKPOINTING=true 
flimanalyzer.exe

On Powershell, run this command

conda activate flimenv
$env:PREFECT__FLOWS__CHECKPOINTING='true'
flimanalyzer.exe

On Mac OSX and Linux, run this command

conda activate flimenv
export PREFECT__FLOWS__CHECKPOINTING=true 
flimanalyzer

Parallel execution

FLIMAnalyzer uses Prefect and Dask for parallel execution of tasks. For parallel execution on a single node, add these command line arguments:

export PREFECT__FLOWS__CHECKPOINTING=true 
flimanalyzer -e LocalDaskExecutor --execargs="scheduler=processes,num_workers=8" # or flimanalyzer.exe

As a general guideline, adjust num_workers to match the number of cpu cores in your system.