Commit 7f7b622b authored by Philipp Schubert's avatar Philipp Schubert
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updated README and docs

parent 9933304c
# SyConn v2 # SyConn v2
Synaptic connectivity inference toolkit developed at Max-Planck-Institute of Neurobiology, Munich <br />
Authors: Philipp Schubert, Sven Dorkenwald, Joergen Kornfeld <br />
Refactored (still an early stage construction) version of SyConn for automated synaptic connectivity inference based on dense EM segmentation data. Refactored (still an early stage construction) version of SyConn for automated synaptic connectivity inference based on dense EM segmentation data.
For the first version see branch 'dorkenwald2017nm'. For v1 see the SyConn branch [dorkenwald2017nm](https://github.com/StructuralNeurobiologyLab/SyConn/tree/dorkenwald2017nm).
Version 2 currently features: Version 2 currently features:
- introduction of supervoxel and agglomerated supervoxel classes - introduction of supervoxel and agglomerated supervoxel classes
...@@ -12,13 +9,11 @@ Version 2 currently features: ...@@ -12,13 +9,11 @@ Version 2 currently features:
- [glia identification and splitting](https://www.biorxiv.org/content/early/2018/07/06/364034) - [glia identification and splitting](https://www.biorxiv.org/content/early/2018/07/06/364034)
- generation of connectivity matrix - generation of connectivity matrix
## System Requirements & Installation ## System Requirements & Installation
* Python 3.5 * Python 3.5
* The whole pipeline was designed and tested on Linux systems (CentOS, Arch) * The whole pipeline was designed and tested on Linux systems (CentOS, Arch)
* SyConn is based on the packages `elektronn <http://elektronn.org>`_, `knossos-utils <https://github.com/knossos-project/knossos_utils>`_ * SyConn is based on the packages [elektronn](http://elektronn.org)_, [knossos-utils](https://github.com/knossos-project/knossos_utils)
* `KNOSSOS <http://knossostool.org/>`_ is used for visualization and annotation of 3D EM data sets. is used for visualization and annotation of 3D EM data sets.
* [VIGRA](https://ukoethe.github.io/vigra/), e.g. ``conda install -c ukoethe vigra`` * [VIGRA](https://ukoethe.github.io/vigra/), e.g. ``conda install -c ukoethe vigra``
* osmesa, e.g.: ``conda install -c menpo osmesa`` * osmesa, e.g.: ``conda install -c menpo osmesa``
...@@ -29,6 +24,13 @@ You can install SyConn using ``git`` and ``pip``: ...@@ -29,6 +24,13 @@ You can install SyConn using ``git`` and ``pip``:
pip install -r requirements.txt pip install -r requirements.txt
pip install . pip install .
## Documentation ## Tutorials & Documentation
For tutorials see [here](docs/doc.md).
To build the documentation run `make html` in the `docs` folder.
# The Team
The Synaptic connectivity inference toolkit developed is developed at Max-Planck-Institute of Neurobiology, Munich.
For documentation see [here](docs/doc.md) Authors: Philipp Schubert, Sven Dorkenwald, Rangoli Saxena, Joergen Kornfeld
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# Analysis steps
_in progress_
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Next, we will introduce the important bits of SyConn. For an example full run see [here](example_full_run.md). # Tutorials
* [Working directory and config setup](config.md) * [Working directory and config setup](config.md)
...@@ -8,7 +8,7 @@ Next, we will introduce the important bits of SyConn. For an example full run se ...@@ -8,7 +8,7 @@ Next, we will introduce the important bits of SyConn. For an example full run se
* [Mapping cellular organelles](object_mapping.md) to SSVs * [Mapping cellular organelles](object_mapping.md) to SSVs
* [SSO](super_segmentation_objects.md) data class * Data class to store agglomerated super voxels [SSO](super_segmentation_objects.md)
* [Skeletons](skeletons.md) of (super) super voxel * [Skeletons](skeletons.md) of (super) super voxel
...@@ -23,4 +23,7 @@ Next, we will introduce the important bits of SyConn. For an example full run se ...@@ -23,4 +23,7 @@ Next, we will introduce the important bits of SyConn. For an example full run se
* [Synapse type](synapse_type.md) prediction * [Synapse type](synapse_type.md) prediction
For more detailed descriptions of parts of the analysis pipeline see [here](analysis_parts.md).
# Full-run example
_in progress_
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