Fix doc2

README.md

@@ -4,6 +4,10 @@
 
 # elf
 
+**Important:** We have recently switched to using [bioimage-cpp](https://github.com/computational-cell-analytics/bioimage-cpp) as new library to implement computationally expensive functionality that was previously used from affogato, nifty, and vigra.
+This enables installing elf via pip, but may lead to some bugs and incompatibilities.
+If you encounter some problem let us know in an issue; you can also install `elf < 0.9` to restore the previous version.
+
 This repository implements common functionality for biomedical image analysis:
 - **evaluation**: evaluation of partitions via rand index and variation of information
 - **io**: common interface for different libraries / formats 
@@ -25,15 +29,17 @@ It is used by several down-stream dependencies:
 - [ilastik](https://github.com/ilastik/ilastik)
 - [mobie-python](https://github.com/mobie/mobie-utils-python)
 - [plantseg](https://github.com/hci-unihd/plant-seg)
+- [torch_em](https://github.com/constantinpape/torch-em)
+- [micro_sam](https://github.com/computational-cell-analytics/micro-sam)
 
 ## Installation
 
-Install the package from source and in development mode via
-```
-pip install -e .
-```
-or via conda
+Install the package from pip:
+```bash
+pip install python-elf
 ```
+or from conda:
+```bash
 conda install -c conda-forge python-elf
 ```
 

elf/__init__.py

@@ -10,12 +10,20 @@
 - `elf.tracking`: Graph-based tracking algorithms.
 - `elf.wrapper`: Wrapper for large microscopy data to enable on-the-fly processing.
 
+**Important:** We have recently switched to using [bioimage-cpp](https://github.com/computational-cell-analytics/bioimage-cpp) as new library to implement computationally expensive functionality that was previously used from affogato, nifty, and vigra.
+This enables installing elf via pip, but may lead to some bugs and incompatibilities.
+If you encounter some problem let us know in an issue; you can also install `elf < 0.9` to restore the previous version.
+
 # Installation
 
-`elf` is available on conda-forge. You can install it into an existing conda environment via:
-```
+`elf` is available on conda-forge and on PyPI. You can install it into an existing conda environment with conda via:
+```bash
 conda install -c conda-forge python-elf
 ```
+or install it via pip as follows:
+```bash
+pip install python-elf
+```
 
 We also provide a environment for a development environment. To set it up:
 1. Clone the elf repository:
@@ -45,15 +53,16 @@
 
 `elf` also provides command line functionality. Currently provided are the command line interfaces:
 - `view_container`: Visualize the content of any file supported by `elf.io` with napari.
-"""
+"""  # noqa
 
 import warnings
-from .__version__ import __version__
+from .__version__ import __version__  # noqa
 
 warnings.warn(
-    "elf will switch to from affogato, vigra, and nifty to"
-    "https://github.com/computational-cell-analytics/bioimage-cpp as new backed for custom functionality"
-    "implemented in C++, e.g. mutex watershed or multicut etc. This may lead to some changes in behavior and"
-    "interface. If you rely on elf you may want to consider pinning it to a version < 0.9",
-    FutureWarning, stacklevel=2
+    "elf has switched from the affogato, vigra, and nifty librares to "
+    "https://github.com/computational-cell-analytics/bioimage-cpp as new backed for custom functionality "
+    "implemented in C++, e.g. mutex watershed, multicut etc. This may lead to some changes in behavior and "
+    "interface. If you run into issues with the new version consider installing a version < 0.9. "
+    "Please also consider raising an issue on github so that we are aware of issues with the migration.",
+    UserWarning, stacklevel=2
 )

elf/__version__.py

@@ -1 +1 @@
-__version__ = "0.8.1"
+__version__ = "0.9.0"

test/io_tests/test_intern_wrapper.py

@@ -1,6 +1,7 @@
 import unittest
 
 import numpy as np
+import requests
 
 try:
     from intern import array
@@ -16,7 +17,10 @@ def test_can_access_dataset(self):
         from elf.io.intern_wrapper import InternDataset
 
         # Choosing a dataset at random to make sure we can access shape and dtype
-        ds = InternDataset("bossdb://witvliet2020/Dataset_1/em")
+        try:
+            ds = InternDataset("bossdb://witvliet2020/Dataset_1/em")
+        except (requests.exceptions.RequestException, OSError) as exc:
+            raise unittest.SkipTest(f"Could not access bossdb dataset: {exc}")
         self.assertEqual(ds.shape, (300, 26000, 22000))
         self.assertEqual(ds.dtype, np.uint8)
         self.assertEqual(ds.size, 300 * 26000 * 22000)
@@ -25,8 +29,11 @@ def test_can_access_dataset(self):
     def test_can_download_dataset(self):
         from elf.io.intern_wrapper import InternDataset
 
-        ds = InternDataset("bossdb://witvliet2020/Dataset_1/em")
-        cutout = ds[210:212, 7000:7064, 7000:7064]
+        try:
+            ds = InternDataset("bossdb://witvliet2020/Dataset_1/em")
+            cutout = ds[210:212, 7000:7064, 7000:7064]
+        except (requests.exceptions.RequestException, OSError) as exc:
+            raise unittest.SkipTest(f"Could not access bossdb dataset: {exc}")
         self.assertEqual(cutout.shape, (2, 64, 64))
         # Pick a few random points to verify. (This is a static dataset so
         # this won't fail unless the internet connection is broken.)
@@ -39,8 +46,11 @@ def test_can_download_dataset(self):
     def test_file(self):
         from elf.io.intern_wrapper import InternFile, InternDataset
 
-        f = InternFile("bossdb://witvliet2020/Dataset_1/em")
-        ds = f["data"]
+        try:
+            f = InternFile("bossdb://witvliet2020/Dataset_1/em")
+            ds = f["data"]
+        except (requests.exceptions.RequestException, OSError) as exc:
+            raise unittest.SkipTest(f"Could not access bossdb dataset: {exc}")
         self.assertIsInstance(ds, InternDataset)
 
 

test/parallel/test_seeded_watershed.py

@@ -14,7 +14,9 @@ class TestSeededWatershed(unittest.TestCase):
     def test_seeded_watershed(self):
         from elf.parallel import seeded_watershed
 
-        mask = binary_blobs(256)
+        # Seed binary_blobs so the test data is deterministic across runs; otherwise the
+        # blockwise/global discrepancy varies per run and occasionally exceeds the threshold.
+        mask = binary_blobs(256, rng=0)
         block_shape = (64, 64)
         halo = (16, 16)
 
@@ -29,6 +31,9 @@ def test_seeded_watershed(self):
         res = seeded_watershed(hmap, seeds, res, block_shape, halo, mask=mask)
         exp = bic.segmentation.watershed(hmap, seeds, mask=mask)
 
+        # The blockwise watershed is an approximation of the global one: a few pixels near
+        # block boundaries may be assigned to a different seed. The tolerance accounts for
+        # this expected, small discrepancy.
         vis, vim = variation_of_information(exp, res)
         self.assertLessEqual(vis + vim, 0.01)
 

test/segmentation/test_lifted_multicut.py

@@ -3,6 +3,7 @@
 import unittest
 import bioimage_cpp as bic
 import numpy as np
+import requests
 
 
 class TestLiftedMulticut(unittest.TestCase):
@@ -57,7 +58,10 @@ class TestLiftedMulticutRealProblem(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         from elf.segmentation.utils import load_multicut_problem
-        graph, costs = load_multicut_problem('A', 'small')
+        try:
+            graph, costs = load_multicut_problem('A', 'small')
+        except (requests.exceptions.RequestException, OSError) as exc:
+            raise unittest.SkipTest(f"Could not download multicut test problem: {exc}")
         cls.graph = graph
         cls.costs = costs.astype('float64')
 

test/segmentation/test_multicut.py

@@ -2,6 +2,7 @@
 
 import unittest
 import numpy as np
+import requests
 import bioimage_cpp as bic
 from elf.segmentation.utils import load_multicut_problem
 
@@ -26,7 +27,10 @@ class TestMulticut(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        load_multicut_problem('A', 'small')
+        try:
+            load_multicut_problem('A', 'small')
+        except (requests.exceptions.RequestException, OSError) as exc:
+            raise unittest.SkipTest(f"Could not download multicut test problem: {exc}")
 
     # https://github.com/constantinpape/graph/blob/master/src/andres/graph/unit-test/multicut/kernighan-lin.cxx
     def toy_problem(self):

test/transformation/test_ngff.py

@@ -26,15 +26,18 @@ class TestNgff(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         os.makedirs(cls.tmp_folder, exist_ok=True)
-        for version in cls.versions:
-            version_folder = os.path.join(cls.tmp_folder, version)
-            os.makedirs(version_folder, exist_ok=True)
-            examples = NGFF_EXAMPLES[version]
-            for name, example_url in examples.items():
-                url = os.path.join(example_url, ".zattrs")
-                out_path = os.path.join(version_folder, f"{name}.json")
-                with requests.get(url) as r, open(out_path, "w") as f:
-                    f.write(r.content.decode("utf8"))
+        try:
+            for version in cls.versions:
+                version_folder = os.path.join(cls.tmp_folder, version)
+                os.makedirs(version_folder, exist_ok=True)
+                examples = NGFF_EXAMPLES[version]
+                for name, example_url in examples.items():
+                    url = os.path.join(example_url, ".zattrs")
+                    out_path = os.path.join(version_folder, f"{name}.json")
+                    with requests.get(url, timeout=30) as r, open(out_path, "w") as f:
+                        f.write(r.content.decode("utf8"))
+        except requests.exceptions.RequestException as exc:
+            raise unittest.SkipTest(f"Could not download NGFF test data: {exc}")
 
     @classmethod
     def tearDownClass(cls):