The sdt-python package
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
1 maintainer
Metadata
1 maintainer
Release files for sdt-python 20.1.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sdt_python-20.1.4.tar.gz | 2.2 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sdt_python-20.1.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 4.8 MB
Release files / sdt_python-20.1.4.tar.gz
| Download URL | sdt_python-20.1.4.tar.gz |
|---|---|
| Size | 2.2 MB |
| Tags | Source |
|
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No |
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Release files / sdt_python-20.1.4-py3-none-any.whl
| Download URL | sdt_python-20.1.4-py3-none-any.whl |
|---|---|
| Size | 2.7 MB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Uploaded via |
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