Installation

Quick Install with Conda

Follow these steps to install WormLib using conda. This method is recommended for most users as it handles dependencies automatically.

1. Clone the repository:

git clone https://github.com/erinosb/WormLib.git
cd WormLib

2. Create the conda environment:

For CPU-based installation (recommended for most users):

conda env create -f installation/wormlib.yml
conda activate wormlib

For GPU acceleration (CUDA 11.8):

conda env create -f installation/wormlib_cuda.yml
conda activate wormlib

3. Verify installation:

import wormlib
print(f"WormLib version: {wormlib.__version__}")

If the import succeeds, you’re ready to go!

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Core Dependencies

Package | Version | Purpose |

|---------|———|---------| | [BigFISH](https://github.com/fish-quant/big-fish) | 0.6.2 | smFISH spot detection & analysis with LoG filtering and automated thresholding| | [Cellpose](https://github.com/MouseLand/cellpose) | 3.1.0 | Deep learning-based cell and embryo segmentation | | [scikit-image](https://scikit-image.org/) | 0.23.2 | Image processing & morphology | | [scikit-learn](https://scikit-learn.org/) | Conda-managed | Random Forest classifiers (transitive via joblib) | | [PyTorch](https://pytorch.org/) | 2.4.1 | GPU backend for Cellpose | | [OpenCV](https://opencv.org/) | 4.10.0.84 | Image manipulation without GUI, Contour & ellipse fitting | | [nd2](https://github.com/tlambert03/nd2) | 0.10.3 | Nikon ND2 file reader | | [tifffile](https://github.com/cgohlke/tifffile) | 2025.6.11 | TIFF file I/O | | [PyYAML](https://pyyaml.org/) | ≥ 6.0.1 | YAML configuration parsing | | [ReportLab](https://www.reportlab.com/) | ≥ 4.0.8 | PDF report generation | | [Pillow](https://python-pillow.org/) | ≥ 10.0 | Image handling for PDF reports | | [Python](https://www.python.org/) | 3.11+ | Core programming language |

Full dependency list is maintained in requirements.txt and environment files.

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Troubleshooting

Environment creation fails

If conda environment creation fails:

# Clear conda cache
conda clean --all

# Try creating environment again
conda env create -f installation/wormlib.yml --force-reinstall

Import errors after activation

Make sure you’ve activated the correct environment:

conda activate wormlib
python -c "import wormlib; print(wormlib.__version__)"

GPU support not working

If CUDA installation fails or torch doesn’t detect your GPU, use CPU I installation instead.

# Verify CUDA availability
python -c "import torch; print(torch.cuda.is_available())"

# If False, use CPU version instead
conda deactivate
conda env remove -n wormlib
conda env create -f installation/wormlib.yml

Missing data or models There are currently no example datasets included in the repository. Please download your own dv, nd2 or tiff files for analysis. The repository includes pre-trained classifiers in the models/ directory. Verify if files are missing:

# Verify model files exist
ls models/
# Expected output:
# 2-cell_classification_RFmodel.joblib
# 4-cell_classification_RFmodel.joblib
# ce-embryo/

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Next Steps

Once installed, explore the example notebook:

cd examples
jupyter notebook "1 - Single-cell spot detection.ipynb"

Or refer to Configuration (YAML Settings) to learn how to configure analysis pipelines with YAML.