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Final version
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Patrick Nicodemus committed Jan 14, 2025
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10 changes: 6 additions & 4 deletions README.md
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Expand Up @@ -26,6 +26,7 @@ pip install git+https://github.com/CamaraLab/CAJAL.git

To build CAJAL from source, a C++ compiler is required for the Gromov-Wasserstein computation and may be required for the potpourri3d library if the precompiled binaries are not compatible with your system.
On Windows, we recommend Microsoft Visual C++ 14.0 or greater, which can be installed via the [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/). On Ubuntu, it requires g++ and may require the package python3.x-dev, which registers the Python header files with g++.
The Unbalanced Gromov-Wasserstein module requires a Gnu C compiler, such as is available through MinGW, and a library implementing pthreads on windows.

CAJAL contains numerous dependencies which are currently hosted only on PyPI; as such, it is not possible at this time to provide a CAJAL conda package. (conda packages require all their dependencies to also be conda packages.) However, it should be possible to install CAJAL in a conda is conscious of, using a conda-managed Python installation and calling pip from within a conda environment.

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```

## Docker image
We provide two Docker images which contain CAJAL and its dependencies, ```cajal:minimal``` and ```cajal:maximal```. ```cajal:minimal``` is built on top of the Jupyter notebook Docker image ```base-notebook``` and contains only CAJAL and its dependencies, ```cajal:maximal``` is built on top of the Docker image ```tensorflow-notebook``` and contains numerous data science tools for further analysis of the output of CAJAL. Running the following command will launch a Jupyter notebook server on localhost with CAJAL and its dependencies installed:
We provide a Docker image which contains CAJAL and its dependencies, ```cajal:latest``` is built on top of the Docker image ```tensorflow-notebook``` and contains numerous data science tools for further analysis of the output of CAJAL. Running the following command will launch a Jupyter notebook server on localhost with CAJAL and its dependencies installed:
```commandline
docker run -it -p 8888:8888 -v C:\Users\myusername\Documents\myfolder:/home/jovyan/work camaralab/cajal:maximal
docker run -it -p 8888:8888 -v C:\Users\myusername\Documents\myfolder:/home/jovyan/work camaralab/cajal:latest
```
The ```-p``` flag controls the port number on local host. For example, writing ```-p 4264:8888``` will let you access the Jupyter server from 127.0.0.1:4264. The ```-v``` "bind mount" flag allows one to mount a local directory on the host machine to a folder inside the container so that you can read and write files on the host machine from within the Docker image. Here one must mount the folder on the host machine as /home/jovyan/work or /home/jovyan/some_other_folder as the primary user "jovyan" in the Docker image only has access to that directory and to the /opt/conda folder. See the [Jupyter docker image documentation](https://jupyter-docker-stacks.readthedocs.io/en/latest/using/selecting.html) for more information.

## Documentation
Extensive documentation, including several tutorials, can be found in [CAJAL's readthedocs.io website](https://cajal.readthedocs.io/en/latest/index.html). This website is under development and will continue to be substantially updated during the coming months.

Extensive documentation, including several tutorials, can be found in [CAJAL's readthedocs.io website](https://cajal.readthedocs.io/en/latest/index.html).

## New in this release
Version 1.0.5 of CAJAL incorporates two significant new pieces of functionality for the analysis of neuron morphology, unbalanced Gromov-Wasserstein (a matching tool which allows for partial or incomplete matches) and fused Gromov-Wasserstein (a tool which allows the incorporation of additional feature data about neuron nodes, e.g., soma or dendrite.)
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],
"source": [
"fgw_results = cross_val_score(clf, X=fused_gw_dmat, y=RNA_family[hq],cv=cv)\n",
"print(\"Accuracy:\", fgw_results.sum()/fgw_results.shape[0])\n",
"cvp = cross_val_predict(clf, X=fused_gw_dmat, y=RNA_family[hq], cv=cv)\n",
"print(\"MCC: \", matthews_corrcoef(cvp, RNA_family[hq]))"
]
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