Commit 2ace6a7a authored by Philipp Schubert's avatar Philipp Schubert
Browse files

Started refactoring #3

- moved load_skeleton and write_skeleton to knossos_utils (circular imports)
- unified .py headers
parent 33dd7189
......@@ -4,8 +4,31 @@ Synaptic connectivity inference toolkit developed at the Max-Planck-Institute fo
Max-Planck-Institute of Neurobiology, Munich <br />
Authors: Sven Dorkenwald, Philipp Schubert, Joergen Kornfeld <br />
If you use parts of this code base in your academic projects, please cite the corresponding publication appearing in Nature Methods at the end of February 2017. <br />
Documentation
--------------
To get started, please have a look at our [documentation](https://structuralneurobiologylab.github.io/SyConn/documentation/), with information on how to run our [example](https://github.com/StructuralNeurobiologyLab/SyConn/blob/master/examples/full_run_example.py). We will also present more general information about SyConn on our [Website](https://structuralneurobiologylab.github.io/SyConn/) soon.
To get started, please have a look at our [documentation](https://structuralneurobiologylab.github.io/SyConn/documentation/), with information on how to run our [example](https://github.com/StructuralNeurobiologyLab/SyConn/blob/master/examples/full_run_example.py). We also present more general information about SyConn on our [Website](https://structuralneurobiologylab.github.io/SyConn/).
Publication
-----------
SyConn was published in [Nature Methods](http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.4206.html) on February 27th 2017. If you use parts of this code base in your academic projects, please cite the corresponding publication. <br />
```
@ARTICLE{SyConn2017,
title = "Automated synaptic connectivity inference for volume electron
microscopy",
author = "Dorkenwald, Sven and Schubert, Philipp J and Killinger, Marius F
and Urban, Gregor and Mikula, Shawn and Svara, Fabian and
Kornfeld, Joergen",
abstract = "SyConn is a computational framework that infers the synaptic
wiring of neurons in volume electron microscopy data sets with
machine learning. It has been applied to zebra finch, mouse and
zebrafish neuronal tissue samples.",
journal = "Nat. Methods",
publisher = "Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.",
year = 2017,
month = Feb,
day = 27,
url = http://dx.doi.org/10.1038/nmeth.4206
}
```
h5py
matplotlib
scipy
networkx
lz4
configobj
flask
fasteners
cython
numpy
scikit-learn
cPickle
scipy
networkx
git+https://github.com/knossos-project/knossos_utils.git
git+https://github.com/ELEKTRONN/ELEKTRONN2.git
-e . --global-option="build_ext"
\ No newline at end of file
import syconnfs.representations.segmentation as segmentation
import syconnfs.representations.super_segmentation as ss
import syconnfs.representations.connectivity
working_dir = "/wholebrain/scratch/areaxfs/"
# ssd = ss.SuperSegmentationDataset(working_dir=working_dir)
#
# ssd.associate_objs_with_skel_nodes(("mi", "sj", "vc"), qsub_pe="openmp", stride=100)
#
# ssd.predict_axoness(qsub_pe="openmp", stride=100)
# ssd.predict_cell_types(qsub_pe="openmp", stride=100)
cm = syconnfs.representations.connectivity.ConnectivityMatrix(
working_dir=working_dir, version=2, sj_version=4, ssd_version=4, create=True)
cm.extract_connectivity(qsub_pe="openmp")
cm.get_sso_specific_info(qsub_pe="openmp")
\ No newline at end of file
......@@ -9,15 +9,17 @@ ext_modules = []
cmdclass = {}
config = {
'description': 'Classes for alternative dataset representations of'
'ultrastructure (instead of raw data or segmentations)',
'description': 'Analysis pipeline for EM raw data based on deep and '
'supervised learning to extract high level biological'
'features and connectivity. ',
'author': 'Sven Dorkenwald, Philipp Schubert, Joergen Kornfeld',
'url': 'syconn.org',
'download_url': 'https://github.com/StructuralNeurobiologyLab/SyConnFS.git',
'download_url': 'https://github.com/StructuralNeurobiologyLab/SyConn.git',
'author_email': '',
'version': '0.1',
'version': '0.2',
'install_requires': ['knossos_utils', 'matplotlib', 'numpy', 'scipy',
'lz4', 'h5py', 'networkx', 'configobj', 'fasteners'],
'lz4', 'h5py', 'networkx', 'configobj', 'fasteners',
'flask'],
'scripts': [], 'cmdclass': cmdclass, 'ext_modules': ext_modules,
'name': 'SyConn',
'dependency_links': ['https://github.com/knossos-project/knossos_utils'
......
# wd = "/wholebrain/scratch/areaxfs/"
wd = "/u/pschuber/areaxfs/"
import os
import socket
# -*- coding: utf-8 -*-
# SyConn - Synaptic connectivity inference toolkit
#
# Copyright (c) 2016 - now
# Max Planck Institute of Neurobiology, Martinsried, Germany
# Authors: Sven Dorkenwald, Philipp Schubert, Joergen Kornfeld
# # define global working directory
# # wd = "/u/pschuber/areaxfs/"
sn = socket.gethostname()
if sn == "L-N1-050" or sn == "fordprefect":
# wd = "/home/pschuber/mnt/external2/j0126_dense_syconn_v1/"
wd = "/home/pschuber/mnt/wb/wholebrain/u/pschuber/areaxfs/"
wd = os.path.expanduser(wd)
wd = "/wholebrain/scratch/areaxfs/"
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