Metadata-Version: 2.1
Name: fanc-fly
Version: 3.0.0
Summary: Tools for the Female Adult Nerve Cord Drosophila EM dataset
Home-page: https://github.com/htem/FANC_auto_recon
Author: Jasper Phelps
Author-email: jasper.s.phelps@gmail.com
License: UNKNOWN
Description: # FANC_auto_recon
        
        FANC (pronounced "fancy") is the Female Adult Nerve Cord, a GridTape-TEM dataset of an adult _Drosophila melanogaster_'s ventral nerve cord. The dataset was first published in [Phelps, Hildebrand, Graham et al. 2021 _Cell_](https://www.lee.hms.harvard.edu/phelps-hildebrand-graham-et-al-2021), after which we applied automated methods for reconstructing neurons, synapses, and nuclei to accelerate reconstruction of the ventral nerve cord connectome, as described in [Azevedo, Lesser, Mark, Phelps et al. 2022 _bioRxiv_](https://www.biorxiv.org/content/10.1101/2022.12.15.520299).
        
        This repository contains:
        - A python package for interacting with the connectome data (see the folder [`fanc/`](fanc), and installation instructions below)
        - Other files and information related to the automated reconstructions (e.g. [`nuclei_prediction/`](nuclei_prediction), [`synapse_prediction/`](synapse_prediction))
        - Information for the collaborative proofreading community (see the [wiki](https://github.com/htem/FANC_auto_recon/wiki)). 
        
        Have any questions? Please [open an issue](https://github.com/htem/FANC_auto_recon/issues/new) or contact [Jasper Phelps (jasper.s.phelps@gmail.com)](https://github.com/jasper-tms).
        
        ## Installing and configuring the `fanc` python package
        
        ### Before you start
        
        As is always the case in python, consider making a virtual environment (using your preference of virtualenv/virtualenvwrapper or conda) before installing.
        
        ### Installation option 1: pip install from PyPI
        
            pip install fanc-fly
        
        ### Installation option 2: pip install directly from GitHub
        The code on GitHub will sometimes be slightly more up to date than the version on PyPI
        
            pip install git+https://github.com/htem/FANC_auto_recon.git
        
        ### Installation option 3: Clone then install
        This is the best option if you want to make changes yourself to the code
        
            cd ~/repos  # or wherever you keep your repos
            git clone https://github.com/htem/FANC_auto_recon.git
            cd FANC_auto_recon
            pip install -e .
        
        ### Troubleshooting
        Depending on your Python 3 version and your operating system, you may need to battle some bugs in order to get the `pip install` commands above to succeed.
        
        If you get something that looks like
        
            .. ERROR:: Could not find a local HDF5 installation.
               You may need to explicitly state where your local HDF5 headers and
               library can be found by setting the ``HDF5_DIR`` environment
               variable or by using the ``--hdf5`` command-line option.
        
        and you're on a Mac, install `brew` (https://brew.sh) if you haven't yet, then use `brew` to install HDF5 with `brew install hdf5`, then put `HDF5_DIR=/opt/homebrew/opt/hdf5` in front of your `pip install` command (e.g. `HDF5_DIR=/opt/homebrew/opt/hdf5 pip install fanc-fly`).
        
        If you get an error that contains
        
            Error compiling Cython file:
            ...
            Cython.Compiler.Errors.CompileError: tables/utilsextension.pyx
        
        try to `pip install` the latest version of `tables` from GitHub by running `HDF5_DIR=/opt/homebrew/opt/hdf5 pip install git+https://github.com/PyTables/PyTables`, or alternatively, use conda to install it (`conda install tables`). After you get this package installed successfully, try installing `fanc-fly` again.
        
        ### Provide credentials
        
        Access to the latest reconstruction of FANC is restricted to authorized users. If you are a member of the FANC community (see [Collaborative community](../../wiki#collaborative-community) on this repo's wiki) and have been granted access, you can generate an API key by visiting [https://global.daf-apis.com/auth/api/v1/create_token](https://global.daf-apis.com/auth/api/v1/create_token) and logging in with your FANC-authorized google account. Copy the key that is displayed, then run the following commands in python to save your key to the appropriate file:
        ```python
        import fanc
        fanc.save_cave_credentials("THE API KEY YOU COPIED")
        ```
        
        Alternatively, you can manually do what the command above accomplishes, which is to create a text file at `~/.cloudvolume/secrets/cave-secret.json` with these contents:
        
            {
              "token": "THE API KEY YOU COPIED",
              "fanc_production_mar2021": "THE API KEY YOU COPIED"
            }
        
        You can verify that your API key has been saved successfully by running:
        ```python
        import fanc
        client = fanc.get_caveclient()
        ```
        
        ### Optional installation steps for additional functionality
        
        #### Install Elastix to transform neurons into alignment with the VNC template
        The mesh manipulation and coordinate transform code requires `pytransformix`, which is itself a Python wrapper for Elastix. Therefore, Elastix must be installed and its lib and bin paths must be appended to the `LD_LIBRARY_PATH` and `PATH` environment variables. See [`pytransformix` documentation](https://github.com/jasper-tms/pytransformix#installation) for specific instructions.
        
        #### Provide CATMAID credentials to pull data from CATMAID
        You can get your CATMAID API key by logging into https://radagast.hms.harvard.edu/catmaidvnc then hovering over "You are [Your Name]" in the top-right corner, then clicking "Get API token".
        
        Save your CATMAID API key by running:
        ```python
        import fanc
        fanc.catmaid.save_catmaid_credentials("YOUR CATMAID API KEY")
        ```
        
        You can verify that your API key has been saved successfully by running:
        ```python
        import fanc
        fanc.catmaid.connect()
        ```
        
        ## Documentation
        - First go through [`fanc_python_package_examples.ipynb`](https://github.com/htem/FANC_auto_recon/blob/main/example_notebooks/fanc_python_package_examples.ipynb)
        - Then check out other notebooks in [`example_notebooks/`](https://github.com/htem/FANC_auto_recon/tree/main/example_notebooks)
        - Finally you can [browse the code](https://github.com/htem/FANC_auto_recon/tree/main/fanc), check out modules that have names that interest you, and read the docstrings.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
