sdt-python 20.1.4

Tools for fluorescence microscopy analysis

sdt-python is a collection of tools for analysis of fluorescence microscopy data.

It contains

• algorithms for localization of fluorescent features in images

• methods for evaluation of tracking data

• functions to evaluate brightness data

• as well as multi-color data

• support for automated determination and correction of chromatic aberrations

• methods for reading and writing single molecule data in various formats

• handling of ROIs (both rectangular and described by arbitrary paths)

• methods for simulation of fluorescence microscopy images

• much more.

A repository of tutorials is provided at https://github.com/schuetzgroup/sdt-python-tutorials. API documentation can be found at https://schuetzgroup.github.io/sdt-python.

If you use sdt-python in a project resulting in a scientific publication, please cite the software.

Installation

Using uv (recommended)

• Install uv according to the official instructions or using e.g. your Linux distribution’s package manager.

• Create a folder for your project.

• Inside this folder, run

  uv init

  in a console prompt to create a new project. See the official guide for more information.

• Add sdt-python and optional dependencies by running

  uv add sdt-python
  uv add opencv trackpy lmfit ipympl scikit-learn

• Start the python interpreter by executing

  uv run python

  or Jupyter Lab by executing

  uv run --with jupyter jupyter lab

  (see the official documentation for details).

Using conda-forge

Set up a conda forge-enabled installation by downloading and executing an installer from the web page.

Then open a Miniforge prompt and type

conda install sdt-python
conda install opencv trackpy lmfit ipympl scikit-learn

to install the sdt-python package and some optional, recommended packages.

Using pip

Install some Python distribution and run (possibly in a virtual environment)

pip install sdt-python

Updating

If using uv, execute

uv sync -P sdt-python

to update only sdt-python or

uv sync -U

to update everything.

If the conda-forge installation is used, type

conda update sdt-python

in a Miniforge prompt.

If pip is used, run

pip install --upgrade sdt-python

Requirements

• Python >= 3.10

• matplotlib

• numpy >= 2.1

• pandas >= 2.2.3

• imageio >= 2.29

• tifffile >= 0.7.0

• pyyaml

• lazy_loader

Recommended packages

• opencv

• trackpy

• lmfit

• ipympl

• scikit-learn

• pywavelets >= 0.3.0