Commit 7f7b622b authored by Philipp Schubert's avatar Philipp Schubert
Browse files

updated README and docs

parent 9933304c
# SyConn v2 # SyConn v2
Synaptic connectivity inference toolkit developed at Max-Planck-Institute of Neurobiology, Munich <br /> Refactored (still an early stage construction) version of SyConn for automated synaptic connectivity inference based on dense EM segmentation data.
Authors: Philipp Schubert, Sven Dorkenwald, Joergen Kornfeld <br /> For v1 see the SyConn branch [dorkenwald2017nm](https://github.com/StructuralNeurobiologyLab/SyConn/tree/dorkenwald2017nm).
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'.
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
\ No newline at end of file \ No newline at end of file
# Analysis steps
_in progress_
\ No newline at end of file
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_
\ No newline at end of file
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment