sdt-python 19.0.2

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 anaconda (recommended)

Choose one of the three following options.

Install miniforge

Set up a minimal conda forge-enabled anaconda installation by downloading and executing a Miniforge3 installer from github.

Then open an Anaconda prompt and type

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

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

Convert a miniconda installation to conda forge

The following will most likely fail on a full Anaconda install, hence it is recommended to use miniconda (minimal Anaconda) First, install miniconda (Python 3.x version). Then open an Anaconda prompt and type

conda config --add channels conda-forge
conda config --set channel_priority strict
conda update --all
conda install sdt-python
conda install opencv trackpy lmfit ipympl scikit-learn pyqt

The last line installs optional, recommended packages.

Instead of converting the whole installation to conda-forge, it is possible to

Create a new environment using conda forge

This method works for Anaconda / miniconda installs.

conda create -n sdt_env -c conda-forge --strict-channel-priority sdt-python
conda install -n sdt_env -c conda-forge --strict-channel-priority opencv trackpy lmfit ipympl scikit-learn
conda activate sdt_env

The second line installs optional, recommended packages. sdt_env is the name of the new environment. For more information on conda environments, have a look here.

Using pip

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

pip install sdt-python

Updating

If the conda installation was converted to conda forge, type

conda update sdt-python

in an Anaconda prompt.

If a separate environment is used, type

conda activate sdt_env
conda update -c conda-forge --strict-channel-priority sdt-python

If you chose an environment name different from sdt_env when installing, adapt accordingly.

If pip is used, run

pip install --upgrade sdt-python

Requirements

• Python >= 3.9

• matplotlib

• numpy >= 1.10

• pandas

• imageio >= 2.29

• tifffile >= 0.7.0

• pyyaml

• lazy_loader

Recommended packages

• PyQt5 >= 5.12

• opencv

• trackpy

• lmfit

• ipympl

• scikit-learn

• pywavelets >= 0.3.0