minor changes

.coveragerc

@@ -11,4 +11,5 @@ omit =
     syconn/analysis/syconn_knossos_viewer.py
     syconn/analysis/syconn_graphwalker.py
     syconn/analysis/server.py
+    syconn/version.py
     */__init__.py

README.md

@@ -25,7 +25,7 @@ We also present more general information about SyConn on our [Website](https://s
 
 Documentation
 -------------
-The documentation including API docs can be found as [readthedocs build](https://syconn.readthedocs.io/en/latest/) or partially as markdown [here](docs/doc.md).
+The documentation including installation procedure, requirements and API docs can be found as [readthedocs build](https://syconn.readthedocs.io/en/latest/) or partially as markdown [here](docs/doc.md).
 
 The Team
 --------

docs/conf.py

@@ -68,8 +68,8 @@ m2r2_disable_inline_math = False
 master_doc = 'index'
 
 # General information about the project.
-project = u'SyConn'
-copyright = u'2018, SyConn team'
+project = u'SyConn2'
+copyright = u'2021, SyConn team'
 author = u'Philipp Schubert, Jörgen Kornfeld'
 
 # The version info for the project you're documenting, acts as replacement for
@@ -77,9 +77,9 @@ author = u'Philipp Schubert, Jörgen Kornfeld'
 # built documents.
 #
 # The short X.Y version.
-version = u'0.3'
+version = u'2.0'
 # The full version, including alpha/beta/rc tags.
-release = u'0.3a'
+release = u'2.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/doc.md

@@ -38,15 +38,18 @@ If you encounter
 with open3d, you need to upgrade your system or downgrade open3d to `<=0.9`.
 
 ## Example run
-Place the example and model data (provided upon request) in `SyConn/examples/`,
+Place the example data and models (provided upon request) in `~/SyConnData/`,
 cd to `SyConn/examples/` and run
 
     python start.py [--working_dir=..]
 
-The example script analyzes the EM data based on KnossosDatasets (see `knossos_utils`) of the cell segmentation, 
+The example script analyzes the EM data together with the cell segmentation, 
 probability maps of sub-cellular structures (mitochondria, vesicle clouds and synaptic junctions) and synapse type
 (inhibitory, excitatory). For adding further cell organelles to this pipeline see [here](cellorganelle_integration.md).
 
+The data format for raw image and segmentation data is based on ``KnossosDataset`` 
+(see [knossos_utils](https://github.com/knossos-project/knossos_utils)).
+
 On a machine with 20 CPUs (Intel Xeon @ 2.60GHz) and 2 GPUs (NVidia Quadro RTX 5000) SyConn
 finished the following analysis steps for an example cube of shape \[1100 1100 600] (1.452e-06 mm^3; 0.726 GVx) after 00h:31min:46s.
 
@@ -73,11 +76,15 @@ finished the following analysis steps for an example cube of shape \[1100 1100 6
 \[11/11]   Matrix export                           0d:0h:0min:7s       0.4%
 
 
-
 ## Example scripts and API usage
 An introduction on how to use the example scripts can be found [here](examples.md)
 and API code examples [here](api.md).
 
+## Flowchart of SyConn
+
+<img src="https://docs.google.com/drawings/d/e/2PACX-1vSY7p2boPxb9OICxNhSrHQlvuHTBRbSMeIOgQ4_NV6pflxc0FKJvPBtskYMAgJsX_OP-6CNmb08tLC5/pub?w=2880&amp;h=1200">
+
+
 ## Package structure and data classes
 The basic data structures and initialization procedures are explained in the following sections:
 
@@ -90,7 +97,7 @@ handled by the `SegmentationDatasets`. For a more detailed description see [here
 (such as lustre, Google Cloud Filestore or AWS Elastic File System).
 
 * Agglomerated supervoxels (SVs) are implemented as SuperSegmentationObjects ([SSO](super_segmentation_objects.md)). The collection
- of super-SVs are usually defined in a region adjacency graph (RAG) which is used to initialize the SuperSegmentationDataset
+ of super-SVs are usually defined in a region supervoxel graph which is used to initialize the SuperSegmentationDataset
   ([SSD](super_segmentation_datasets.md)).
 
 * [Skeletons](skeletons.md) of (super-) supervoxels, usually computed from variants of the TEASAR algorithm (https://ieeexplore.ieee.org/document/883951)
@@ -98,15 +105,10 @@ handled by the `SegmentationDatasets`. For a more detailed description see [here
 
 * [Mesh](meshes.md) generation and representation of supervoxels
 
-* Multi-view representation of neurpn reconstructions for [glia](glia_removal.md) and
+* Multi-view representation of neuron reconstructions for [glia](glia_removal.md) and
  [neuron](neuron_analysis.md) analysis (published in [Nature Communications](https://www.nature.com/articles/s41467-019-10836-3))
 
 
-## Flowchart of SyConn
-
-<img src="https://docs.google.com/drawings/d/e/2PACX-1vSY7p2boPxb9OICxNhSrHQlvuHTBRbSMeIOgQ4_NV6pflxc0FKJvPBtskYMAgJsX_OP-6CNmb08tLC5/pub?w=2880&amp;h=1200">
-
-
 ## Analysis steps
 After initialization of the SDs (cell and sub-cellular structures, step 1 in the example run) and the SSD
 containing the agglomerated cell SVs (step 3), several analysis steps can be applied:

syconn/cnn/cnn_semseg_lcp.py

@@ -31,7 +31,7 @@ parser.add_argument('--sp', type=int, default=15000, help='Number of sample poin
 parser.add_argument('--scale_norm', type=int, default=5000, help='Scale factor for normalization')
 parser.add_argument('--co', action='store_true', help='Disable CUDA')
 parser.add_argument('--seed', default=0, help='Random seed', type=int)
-parser.add_argument('--ctx', default=15000, help='Context size in nm', type=float)
+parser.add_argument('--ctx', default=20000, help='Context size in nm', type=float)
 parser.add_argument(
     '-j', '--jit', metavar='MODE', default='disabled',  # TODO: does not work
     choices=['disabled', 'train', 'onsave'],
@@ -92,7 +92,7 @@ if cellshape_only:
 
 if name is None:
     name = f'semseg_pts_nb{npoints}_ctx{ctx}_{gt_type}_nclass' \
-           f'{num_classes[gt_type]}_lcp_GN_noKernelSep_AdamW_CE_large'
+           f'{num_classes[gt_type]}_lcp_GN_noKernelSep_AdamW_CE_large_v2'
     if not normalize_pts:
         name += '_NonormPts'
     if cellshape_only:
@@ -124,18 +124,18 @@ conv = dict(layer='ConvPoint', kernel_separation=False, normalize_pts=normalize_
 act = nn.ReLU
 # architecture = None
 architecture = [dict(ic=-1, oc=1, ks=48, nn=32, np=-1),
-                dict(ic=1, oc=1, ks=32, nn=32, np=2048),
+                dict(ic=1, oc=1, ks=48, nn=32, np=2048),
                 dict(ic=1, oc=1, ks=32, nn=16, np=1024),
                 dict(ic=1, oc=2, ks=32, nn=16, np=256),
-                dict(ic=2, oc=2, ks=16, nn=16, np=128),
-                dict(ic=2, oc=2, ks=16, nn=8, np=64),
-                dict(ic=2, oc=2, ks=16, nn=4, np=32),
+                dict(ic=2, oc=2, ks=32, nn=16, np=128),
+                dict(ic=2, oc=2, ks=16, nn=16, np=64),
+                dict(ic=2, oc=2, ks=16, nn=16, np=32),
                 dict(ic=2, oc=2, ks=16, nn=4, np='d'),
-                dict(ic=4, oc=2, ks=16, nn=8, np='d'),
-                dict(ic=4, oc=1, ks=16, nn=16, np='d'),
-                dict(ic=3, oc=1, ks=16, nn=16, np='d'),
-                dict(ic=2, oc=1, ks=16, nn=16, np='d'),
-                dict(ic=2, oc=1, ks=16, nn=16, np='d')]
+                dict(ic=4, oc=2, ks=16, nn=4, np='d'),
+                dict(ic=4, oc=1, ks=32, nn=4, np='d'),
+                dict(ic=3, oc=1, ks=32, nn=8, np='d'),
+                dict(ic=2, oc=1, ks=32, nn=8, np='d'),
+                dict(ic=2, oc=1, ks=48, nn=8, np='d')]
 model = ConvAdaptSeg(input_channels, num_classes[gt_type], get_conv(conv), get_search(search), kernel_num=64,
                      architecture=architecture, activation=act, norm='gn')
 
@@ -181,10 +181,10 @@ else:  # no additional validation data
 train_ds = CloudDataSemseg(npoints=npoints, transform=train_transform, use_subcell=use_subcell,
                            batch_size=batch_size, ctx_size=ctx, mask_borders_with_id=ignore_l,
                            source_dir=train_dir, remap_dict=remap_dicts[gt_type])
+valid_ds = None
 # valid_ds = CloudDataSemseg(npoints=npoints, transform=valid_transform, train=False, use_subcell=use_subcell,
 #                            batch_size=batch_size, ctx_size=ctx, mask_borders_with_id=ignore_l,
 #                            source_dir=valid_dir, remap_dict=remap_dicts[gt_type])
-valid_ds = None
 # PREPARE AND START TRAINING #
 
 # set up optimization

syconn/handler/prediction_pts.py

@@ -1822,7 +1822,7 @@ def infere_cell_morphology_ssd(ssv_params, mpath: Optional[str] = None, pred_key
                                postproc_kwargs=postproc_kwargs, postproc_func=pts_postproc_embedding,
                                show_progress=False, mpath=mpath, **loader_kwargs, **default_kwargs)
     if not np.all(list(out_dc.values())) or len(out_dc) != len(ssv_params):
-        raise ValueError('Invalid output during glia prediction.')
+        raise ValueError('Invalid output during cell morphology extraction.')
 
 
 def predict_celltype_ssd(ssd_kwargs, mpath: Optional[str] = None, ssv_ids: Optional[Iterable[int]] = None,

tests/full_run.ini

 [pytest]
 addopts = -v --cache-clear --tb=native --cov --cov-config=.coveragerc
-python_files = test_*.py
+python_files = tests/test_*.py
 norecursedirs = .git build
\ No newline at end of file

tests/test_run.py

@@ -25,19 +25,21 @@ def example_run():
     process = subprocess.Popen(
         ["python", startpy_fname, f"--working_dir={working_dir}"],
         stdout=subprocess.PIPE,
-        stderr=subprocess.STDOUT,
+        stderr=subprocess.PIPE,
         universal_newlines=True,
     )
-    out, _ = process.communicate()
+    out, err = process.communicate()
 
     if os.environ.get('syconn_wd') is not None:
         del os.environ['syconn_wd']
     shutil.rmtree(example_wd, ignore_errors=True)
-    return process.returncode
+    return process.returncode if process.returncode == 0 else err
 
 
 def test_example_run():
-    assert example_run() == 0
+    ret = example_run()
+    if ret != 0:
+        raise RuntimeError(ret)
 
 
 if __name__ == '__main__':