Started refactoring #3

- moved load_skeleton and write_skeleton to knossos_utils (circular imports)
- unified .py headers

README.md

@@ -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
+ }
+  ```

requirements.txt

+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

scripts/rescue.py

+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

setup.py

@@ -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'

syconn/config/default_wd.py

-# 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/"