DECODE

Deep learning enables fast and dense single-molecule localization with high accuracy

This is the official implementation of the preprint (Link to ArXiV). The easiest way to try out the algorithm is to have a look at the Google Colab Notebooks we provide for training our algorithm and fitting experimental data. You can find these notebooks here:

• DECODE Training
• DECODE Fitting

You can find the installation instructions below, instructions and examples on how to use DECODE you can find in the documentation. The documentation can be found on the 'Wiki' button here in GitHub, or via this link: [LINK to Readthedocs].

Local Installation for Users (local machine)

Please refer to our docs.

Instructions for Developers

Travis CI:

0. Clone the repository

1. Install conda environment from file and activate it. Use the respective environment depending on whether you have a CUDA GPU or not. The cubic spline psf is pre-compiled in a different repo / as a separate package and will be installed automatically.

    # CUDA
    conda env create -f environment_cuda102_py38_pt15.yml
    conda activate decode_cuda_py38_pt15   
   
    # CPU / macOS
    conda env create -f environment_cpu_py38_pt15.yml
    conda activate deepsmlm_cpu
   

2. Test whether everything works as expected

        # assuming you are in the repo folder
        pytest decode/test
   
        # or if you fancy some nice figures, depending on your IDE
        # you might need to close popping up matplot figures
        pytest decode/test --plot  
   

3. The package can be used in python as

   import decode

Note

Simulation heavily relies on the Cubic Spline PSF Implementation (Li, Y. et al. Real-time 3D single-molecule localization using experimental point spread functions. Nature Methods 15, 367–369 (2018)). It has been factored out in a seperate Repo to make life easier (see https://github.com/Haydnspass/SplinePSF). It'll be auto-installed.

Building the Docs

For this we provide a conda environment for the sake of easy use.

conda env create -f environment_docs.yaml  # once
conda activate decode_docs

cd docs
make html

The docs can be found in the build folder.

Building and Deploy with Conda

# recommended: create a new conda build environment
conda create --name build_clean conda-build
conda activate build_clean

# navigate to [repo]/conda
cd conda
conda-build -c haydnspass -c pytorch -c conda-forge decode